{"gname":"University of California, Berkeley","grp_id":"13","rels":[{"rel_title":"Targeting COL4A1-Related Small Vessel Disease: Repurposed Pharmacotherapies for Genetic Vasculopathies","rel_doi":"10.64898\/2026.05.07.723462","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.07.723462","rel_abs":"COL4A1-related disorders cause early-onset stroke, intracerebral haemorrhage, visual impairment and kidney disease, often affecting children and young adults, yet no disease-modifying therapies exist. These disorders arise from pathogenic COL4A1 variants that disrupt type IV collagen and impair small-vessel integrity, leading to cerebral small-vessel disease and endothelial dysfunction. We performed a mechanism-guided screen in human brain endothelial cells using a CRISPR-engineered COL4A1 p.G755R line and patient-specific COL4A1 p.G773R iPSC-derived endothelial cells. Simvastatin, L-carnosine, and XPD-101 restored impaired endothelial proliferation, migration, and other markers of endothelial function, including transendothelial electrical resistance (TEER). In a Col4a1Svc\/+ mouse model, simvastatin increased pre-weaning survival, improved functional behaviour and reduced cerebral microhaemorrhage burden. These findings identify mechanism-informed candidates that rescue COL4A1-mutant endothelial dysfunction in vitro, with simvastatin demonstrating in vivo efficacy, supporting prioritisation for further preclinical development.","rel_num_authors":12,"rel_authors":[{"author_name":"Klaudia Kocsy","author_inst":"University of Sheffield"},{"author_name":"Harry Wilkinson","author_inst":"University of Sheffield"},{"author_name":"Zuzanna Sokolowska","author_inst":"University of Sheffield"},{"author_name":"Luke F Bolger","author_inst":"University of Sheffield"},{"author_name":"Vedanth Kumar","author_inst":"University of Sheffield"},{"author_name":"An Yan","author_inst":"University of Glasgow"},{"author_name":"Favour Felix-Ilemhenbhio","author_inst":"University of Sheffield"},{"author_name":"Alisdair McNeill","author_inst":"University of Sheffield"},{"author_name":"Sanjay Jain","author_inst":"Washington University School of Medicine"},{"author_name":"Tom Van Agtmael","author_inst":"University of Glasgow"},{"author_name":"Mimoun Azzouz","author_inst":"University of Sheffield"},{"author_name":"Arshad Majid","author_inst":"University of Sheffield"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"A Beta-Binomial Model for Estimating Zero- or One-inflated Pain Trajectories","rel_doi":"10.64898\/2026.05.07.721507","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.07.721507","rel_abs":"Chronic pain is a widespread public health issue that imposes substantial health, emotional, and economic burdens on individuals and communities. Because pain is subjective and lacks objective biomarkers, it is typically measured using patient-reported scores, often on a numerical scale from zero to ten. Increasingly, pain studies use ecological momentary assessment, with multiple daily assessments over days and across study phases (e.g., a series of baseline and post-intervention assessments). These data frequently show many ratings at the extremes (i.e., at minimum or maximum pain scores), commonly referred to as zero- and one-inflation in the statistical literature, along with considerable within-person variability both within and across days. These phenomena present challenges for statistical analyses, as they violate assumptions of most commonly used statistical techniques (e.g., the normality assumption of linear mixed models). We propose a Bayesian beta-binomial mixed-effects model for modeling potential zero- or one-inflated pain scores while accounting for variability using random effects on the mean and variance parameters across subjects. A simulation study demonstrates that the method accurately estimates model parameters across realistic sample sizes, time points, and zero- and one-inflation levels. An application to data from two longitudinal pain studies demonstrates that the model fits the data better and, when correctly specified, yields accurate uncertainty intervals for longitudinal changes in pain compared to existing models, especially for zero- and one-inflated outcomes. Additionally, the model directly estimates the probability of clinically meaningful pain events. The proposed method provides a powerful statistical framework for studying the patient-reported pain trajectories.","rel_num_authors":6,"rel_authors":[{"author_name":"Yanxi Liu","author_inst":"Johns Hopkins Bloomberg School of Public Health: Johns Hopkins University Bloomberg School of Public Health"},{"author_name":"Richard E. Harris","author_inst":"University of California Irvine"},{"author_name":"Daniel Clauw","author_inst":"University of Michigan Medical School"},{"author_name":"Emine Bayman","author_inst":"University of Iowa"},{"author_name":"Andrew Leroux","author_inst":"University of Colorado Anschutz Medical Campus School of Medicine"},{"author_name":"Martin A. Lindquist","author_inst":"Johns Hopkins Bloomberg School of Public Health: Johns Hopkins University Bloomberg School of Public Health"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"A Beta-Binomial Model for Estimating Zero- or One-inflated Pain Trajectories","rel_doi":"10.64898\/2026.05.07.721507","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.07.721507","rel_abs":"Chronic pain is a widespread public health issue that imposes substantial health, emotional, and economic burdens on individuals and communities. Because pain is subjective and lacks objective biomarkers, it is typically measured using patient-reported scores, often on a numerical scale from zero to ten. Increasingly, pain studies use ecological momentary assessment, with multiple daily assessments over days and across study phases (e.g., a series of baseline and post-intervention assessments). These data frequently show many ratings at the extremes (i.e., at minimum or maximum pain scores), commonly referred to as zero- and one-inflation in the statistical literature, along with considerable within-person variability both within and across days. These phenomena present challenges for statistical analyses, as they violate assumptions of most commonly used statistical techniques (e.g., the normality assumption of linear mixed models). We propose a Bayesian beta-binomial mixed-effects model for modeling potential zero- or one-inflated pain scores while accounting for variability using random effects on the mean and variance parameters across subjects. A simulation study demonstrates that the method accurately estimates model parameters across realistic sample sizes, time points, and zero- and one-inflation levels. An application to data from two longitudinal pain studies demonstrates that the model fits the data better and, when correctly specified, yields accurate uncertainty intervals for longitudinal changes in pain compared to existing models, especially for zero- and one-inflated outcomes. Additionally, the model directly estimates the probability of clinically meaningful pain events. The proposed method provides a powerful statistical framework for studying the patient-reported pain trajectories.","rel_num_authors":6,"rel_authors":[{"author_name":"Yanxi Liu","author_inst":"Johns Hopkins Bloomberg School of Public Health: Johns Hopkins University Bloomberg School of Public Health"},{"author_name":"Richard E. Harris","author_inst":"University of California Irvine"},{"author_name":"Daniel Clauw","author_inst":"University of Michigan Medical School"},{"author_name":"Emine Bayman","author_inst":"University of Iowa"},{"author_name":"Andrew Leroux","author_inst":"University of Colorado Anschutz Medical Campus School of Medicine"},{"author_name":"Martin A. Lindquist","author_inst":"Johns Hopkins Bloomberg School of Public Health: Johns Hopkins University Bloomberg School of Public Health"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"A fine-tuned genomic language model adds complementary nucleotide-context information to missense variant interpretation","rel_doi":"10.64898\/2026.05.06.723362","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.723362","rel_abs":"Missense variant interpretation remains a central challenge in clinical genomics. Missense pathogenicity predictors achieve strong performance, but many emphasize protein-level consequences or overlapping annotation priors. Whether genomic language models add non-redundant nucleotide-context signal to missense interpretation remains unclear. Here, we systematically adapted genomic language models to ClinVar missense pathogenicity prediction across backbone architectures, representation strategies, classifier heads, and adaptation regimes. In our analysis, variant-position embeddings consistently outperformed pooled sequence representations, multi-species pretraining provided the strongest backbone-level advantage, and low-rank adaptation generalized better than full fine-tuning. The resulting fine-tuned model, GLM-Missense, substantially outperformed zero-shot scoring from the same pretrained model. To test whether GLM-Missense contributes information beyond existing methods, we built MetaMissense, an XGBoost ensemble combining GLM-Missense with AlphaMissense, ESM1b, REVEL, CADD, SIFT, and PolyPhen-2. GLM-Missense showed the lowest concordance with other predictors, retained the strongest partial association with pathogenicity after controlling for the other predictors, and ranked as the most informative non-ensemble input to MetaMissense. MetaMissense achieved the best performance in both cross-validation and held-out testing. Analyses of variants correctly classified by GLM-Missense but misclassified by several established predictors suggested two patterns. First, part of the GLM-Missense signal may reflect splice-relevant exonic context. Second, GLM-Missense appears to add value in settings where other predictors may overweight allele frequency, gene-level constraint, or amino-acid-change severity. However, these features explained only about 10% of the distinction between the GLM-Missense-correct subset from the background. Together, our results demonstrate that fine-tuned genomic language models contribute complementary nucleotide-context information to missense variant interpretation.","rel_num_authors":2,"rel_authors":[{"author_name":"Yaqi Su","author_inst":"University of California, Berkeley"},{"author_name":"Yu-Jen Lin","author_inst":"University of California, Berkeley"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"G-SPRI: A Structure-Centric Graph Model for Comprehensive Prediction of Cancer Driver Events from Missense Mutations","rel_doi":"10.64898\/2026.05.06.723398","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.723398","rel_abs":"In silico approaches for predicting the functional impact of missense mutations are critical for interpreting personal genomes and identifying disease-related biomarkers. Existing methods largely rely on sequence-based information or intuitive structural features, but often overlook the complex biophysical patterns encoded in protein 3D structures. Here, we present G-SPRI, a multilevel framework built on a novel alpha shape protein graph that accurately captures residue connectivity from atomic-resolution geometry and enables precise message passing around mutation sites. Using this graph representation, G-SPRI integrates wild-type structural properties and mutation-specific perturbation signals derived from the Protein Data Bank (PDB) universe to support graph-based learning for distinguishing pathogenic from benign missense variants. G-SPRI performs strongly across multiple key tasks. On the binary prediction benchmark, G-SPRI delivers improved pathogenicity prediction for individual mutations. By integrating mutation recurrence across the pan-cancer cohort, G-SPRI recovers more known cancer driver genes than state-of-the-art methods from more than 2.3 million mutations. Furthermore, by jointly quantifying site-specific pathogenicity and co-clustering influence within higher order structural organization units, G-SPRI provides comprehensive evidence for pinpointing likely driver mutations and structurally susceptible regions within disease genes.","rel_num_authors":8,"rel_authors":[{"author_name":"Boshen Wang","author_inst":"UT Southwestern medical center"},{"author_name":"Bowei Ye","author_inst":"University of Illinois at Chicago"},{"author_name":"Ali Farhat","author_inst":"University of Illinois at Chicago"},{"author_name":"Jie Liang","author_inst":"University of Illinois at Chicago"},{"author_name":"Lei Yu","author_inst":"UT Southwestern Medical Center"},{"author_name":"Zeyu Lu","author_inst":"University of Texas at Arlington"},{"author_name":"Xinlei Wang","author_inst":"University of Texas at Arlington"},{"author_name":"Lin Xu","author_inst":"University of Texas Southwestern Medical Center at Dallas"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Goals as dynamical attractors: a momentum-based account of stable and flexible goal commitment","rel_doi":"10.64898\/2026.05.06.723407","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.723407","rel_abs":"Human goal pursuit is often marked by persistent activity toward achieving an objective, as well as flexibility in switching objectives based on environmental demands. How humans balance the stability and flexibility necessary for goal pursuit is the key question of this study. We propose that goal pursuit generates dynamic attractor modes in policy landscapes that produce stability in goal pursuit. The attractor properties are modulated through progress monitoring, allowing for the flexibility necessary to switch objectives in favor of alternative goals. Through simulations and behavioral cloning of human participants performing an extended goal selection task, we show how dynamic modes can develop in the latent spaces of recurrent neural networks trained with reinforcement learning. We develop metrics to quantitatively assess the attractor qualities of dynamic modes, validating them against synthetically generated dynamical systems, and use them to investigate the context modulation of attractor modes during goal pursuit. We then proceed to develop a circuit-level account of goal persistence incorporating self-excitation and cross-inhibition as motifs for fast, self-sustaining dynamics modulated by slow, progress-integrating momentum and context signals. Lastly, we show that the switching costs experienced while managing multiple goals are an emergent property of resistance to the intrinsic dynamics of goal pursuit, thereby contributing a fresh perspective on the dynamics of extended goal pursuit.","rel_num_authors":1,"rel_authors":[{"author_name":"Sneha Aenugu","author_inst":"California Institute of Technology"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Super-Resolution Macrophage Imaging via Ultrasound Localization Microscopy and Blinking Nanodroplets","rel_doi":"10.64898\/2026.05.07.723418","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.07.723418","rel_abs":"Tracking immune cells deep within living tissue remains a fundamental challenge due to the diffraction-limited resolution of ultrasound imaging and the inability to resolve dense cellular populations. Here, we introduce an intracellular super-resolution ultrasound imaging framework based on stochastic phase-changing nanodroplets (NDs) and ultrasound localization microscopy (ULM). We engineer ~170 nm perfluorocarbon NDs that undergo reversible, stochastic liquid-gas transitions under acoustic excitation, generating temporally sparse \"blinking\" signals. Leveraging the intrinsic endocytic activity of macrophages, these NDs are internalized, enabling intracellular contrast generation independent of vascular flow. We validate this approach across imaging scales, from controlled phantoms and in vitro systems to in vivo tumor models, demonstrating robust intracellular blinking, high cell viability, and consistent super-resolution reconstruction in dense cellular environments. The stochastic blinking of internalized NDs provides the temporal separation required to localize individual sources, overcoming a central limitation of conventional ULM. Following systemic administration, ND-labeled macrophages are tracked in vivo after homing to the liver, where super-resolution ULM resolves cellular distributions with a spatial resolution of 26.3 {+\/-} 3.2 m, corresponding to a 6.1-fold improvement over diffraction-limited imaging. This work establishes a previously unexplored paradigm for ultrasound-based intracellular super-resolution imaging, enabling non-invasive visualization of immune cell organization in deep tissue. By introducing spatiotemporally programmable intracellular contrast, this approach expands ultrasound beyond vascular imaging toward functional cellular imaging, with broad implications for immunology, diagnostics, and cell-based therapies.","rel_num_authors":4,"rel_authors":[{"author_name":"Saar Gotshal Zahavi","author_inst":"Tel Aviv University"},{"author_name":"Mike Bismuth","author_inst":"Tel Aviv University"},{"author_name":"Tiran Bercovici","author_inst":"Tel Aviv University"},{"author_name":"Tali Ilovitsh","author_inst":"Tel Aviv University"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"AI platform for CRISPR functional mapping and function-based drug design","rel_doi":"10.64898\/2026.05.06.722817","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.722817","rel_abs":"Conventional structure-based drug design has high clinical failure rates due to the disconnect where binding affinity does not guarantee safe functional modulation. To bridge this gap, we present CRISPRtile, a cloud-based platform for function-based drug design. By deriving library coverage optimization equations and leveraging AI to correct CRISPR guide biases, we generated toxicity and functional landscapes with over threefold error reduction compared to conventional methods. These maps bypass error and orders of magnitude higher computational cost in structure-based pipelines by enabling AI prediction of drug interaction directly from nontoxic functional sequences, while predicting brain penetration with benchmark leading performance. We demonstrate CRISPRtile by mapping the NLRP3 inflammasome and identifying FDA approved drugs with previously unrecognized ability to modulate it, revealing strategies to amplify or inhibit our immune response to homeostatic perturbations. These advances establish a generalizable strategy for the systematic discovery of safe functional modulators.","rel_num_authors":5,"rel_authors":[{"author_name":"Jason C. Ngo","author_inst":"Center for Translational and Computational Neuroimmunology,  Taub Institute for Research on Alzheimer's Disease and Aging Brain, Department of Neurology, Columb"},{"author_name":"Vivien A.C. Schoonenberg","author_inst":"Division of Hematology\/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA"},{"author_name":"Renu Nandakumar","author_inst":"Biomarkers Core, Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, NY USA"},{"author_name":"Xuebing Wu","author_inst":"Department of Medicine and Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA"},{"author_name":"Falak Sher","author_inst":"Center for Translational and Computational Neuroimmunology, Taub Institute for Research on Alzheimer's Disease and Aging Brain, Department of Neurology, Columbi"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Predictable adaptive evolution of a phage endolysin through substrate recognition optimization","rel_doi":"10.64898\/2026.05.06.723369","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.723369","rel_abs":"Bacteriophages release progeny by producing endolysins that degrade the bacterial cell wall. While the frequent horizontal transfer of endolysins suggests substantial evo-lutionary plasticity, the mechanisms by which these enzymes adapt to new phage-host contexts remain poorly understood. Here, we investigated the evolutionary dy-namics and structural mechanisms governing endolysin adaptation following experi-mental evolution of a chimeric phage generated via heterologous endolysin exchange between phages infecting different hosts. Using replicate experimental evolution and time-resolved PacBio sequencing, we iden-tified a dominant, highly reproducible adaptive trajectory characterized by the stepwise fixation of three key mutations. This constrained mutational order correlated with in-cremental gains in enzymatic activity, reflecting a rugged yet predictable fitness land-scape. High-resolution structural analyses revealed that these substitutions lie exclu-sively outside the catalytic site; instead, they enhance substrate recognition through electrostatic tuning, optimized hydrophobic packing, and local conformational refine-ment, resulting in significantly higher binding affinity. While the adaptive trajectory was largely conserved, one replicate followed an alterna-tive path, highlighting the interplay between selection and historical contingency. Adap-tation was further shaped by a functional trade-off, whereby increased lytic activity on the novel host was accompanied by reduced activity on the ancestral host, consistent with antagonistic pleiotropy. Genome-wide sequencing additionally identified a com-pensatory mutation in a lytic transglycosylase, suggesting coordinated evolution of the broader lysis machinery. Together, these results demonstrate that endolysins evolve through reproducible adaptive walks constrained by structure, selection, and trade-offs, providing a mechanistic framework for understanding enzyme evolution and in-forming rational protein engineering.","rel_num_authors":7,"rel_authors":[{"author_name":"Xiaojun Zhu","author_inst":"D\u00e9partement de biochimie, de microbiologie, et de bio-informatique, Facult\u00e9 des sciences et de g\u00e9nie, Universit\u00e9 Laval, Qu\u00e9bec City, Canada"},{"author_name":"Vincent Somerville","author_inst":"D\u00e9partement de biochimie, de microbiologie, et de bio-informatique, Facult\u00e9 des sciences et de g\u00e9nie, Universit\u00e9 Laval, Qu\u00e9bec City, Canada"},{"author_name":"Rong Shi","author_inst":"D\u00e9partement de biochimie, de microbiologie, et de bio-informatique, Facult\u00e9 des sciences et de g\u00e9nie, Universit\u00e9 Laval, Qu\u00e9bec City, Canada"},{"author_name":"Rojo V Rakotoharisoa","author_inst":"Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario, K1N 6N5, Canada"},{"author_name":"Roberto A Chica","author_inst":"Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario, K1N 6N5, Canada"},{"author_name":"Sylvain Moineau","author_inst":"D\u00e9partement de biochimie, de microbiologie, et de bio-informatique, Facult\u00e9 des sciences et de g\u00e9nie, Universit\u00e9 Laval, Qu\u00e9bec City, Canada"},{"author_name":"Frank Oechslin","author_inst":"D\u00e9partement de biochimie, de microbiologie, et de bio-informatique, Facult\u00e9 des sciences et de g\u00e9nie, Universit\u00e9 Laval, Qu\u00e9bec City, Canada"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Predictable adaptive evolution of a phage endolysin through substrate recognition optimization","rel_doi":"10.64898\/2026.05.06.723369","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.723369","rel_abs":"Bacteriophages release progeny by producing endolysins that degrade the bacterial cell wall. While the frequent horizontal transfer of endolysins suggests substantial evo-lutionary plasticity, the mechanisms by which these enzymes adapt to new phage-host contexts remain poorly understood. Here, we investigated the evolutionary dy-namics and structural mechanisms governing endolysin adaptation following experi-mental evolution of a chimeric phage generated via heterologous endolysin exchange between phages infecting different hosts. Using replicate experimental evolution and time-resolved PacBio sequencing, we iden-tified a dominant, highly reproducible adaptive trajectory characterized by the stepwise fixation of three key mutations. This constrained mutational order correlated with in-cremental gains in enzymatic activity, reflecting a rugged yet predictable fitness land-scape. High-resolution structural analyses revealed that these substitutions lie exclu-sively outside the catalytic site; instead, they enhance substrate recognition through electrostatic tuning, optimized hydrophobic packing, and local conformational refine-ment, resulting in significantly higher binding affinity. While the adaptive trajectory was largely conserved, one replicate followed an alterna-tive path, highlighting the interplay between selection and historical contingency. Adap-tation was further shaped by a functional trade-off, whereby increased lytic activity on the novel host was accompanied by reduced activity on the ancestral host, consistent with antagonistic pleiotropy. Genome-wide sequencing additionally identified a com-pensatory mutation in a lytic transglycosylase, suggesting coordinated evolution of the broader lysis machinery. Together, these results demonstrate that endolysins evolve through reproducible adaptive walks constrained by structure, selection, and trade-offs, providing a mechanistic framework for understanding enzyme evolution and in-forming rational protein engineering.","rel_num_authors":7,"rel_authors":[{"author_name":"Xiaojun Zhu","author_inst":"D\u00e9partement de biochimie, de microbiologie, et de bio-informatique, Facult\u00e9 des sciences et de g\u00e9nie, Universit\u00e9 Laval, Qu\u00e9bec City, Canada"},{"author_name":"Vincent Somerville","author_inst":"D\u00e9partement de biochimie, de microbiologie, et de bio-informatique, Facult\u00e9 des sciences et de g\u00e9nie, Universit\u00e9 Laval, Qu\u00e9bec City, Canada"},{"author_name":"Rong Shi","author_inst":"D\u00e9partement de biochimie, de microbiologie, et de bio-informatique, Facult\u00e9 des sciences et de g\u00e9nie, Universit\u00e9 Laval, Qu\u00e9bec City, Canada"},{"author_name":"Rojo V Rakotoharisoa","author_inst":"Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario, K1N 6N5, Canada"},{"author_name":"Roberto A Chica","author_inst":"Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario, K1N 6N5, Canada"},{"author_name":"Sylvain Moineau","author_inst":"D\u00e9partement de biochimie, de microbiologie, et de bio-informatique, Facult\u00e9 des sciences et de g\u00e9nie, Universit\u00e9 Laval, Qu\u00e9bec City, Canada"},{"author_name":"Frank Oechslin","author_inst":"D\u00e9partement de biochimie, de microbiologie, et de bio-informatique, Facult\u00e9 des sciences et de g\u00e9nie, Universit\u00e9 Laval, Qu\u00e9bec City, Canada"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"A Plasmodium falciparum PX1 haplotype is associated with reduced susceptibility to artemisinin and lumefantrine","rel_doi":"10.64898\/2026.05.05.722990","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.05.722990","rel_abs":"Effective control of falciparum malaria depends on the sustained efficacy of frontline antimalarial drugs, particularly artemether-lumefantrine (AL), the most widely used therapy in Africa. However, the emergence of artemisinin partial resistance and reduced lumefantrine susceptibility in eastern Africa threaten malaria control and elimination. Robust genetic markers of decreased susceptibility to lumefantrine remain elusive, and our understanding of artemisinin resistance is incomplete. We report results of a Plasmodium falciparum genetic cross between a drug-sensitive line and a Ugandan strain exhibiting reduced susceptibility to dihydroartemisinin and lumefantrine. Targeted deep sequencing of progeny pools and 460 recombinant progeny clones derived under drug pressures revealed distinct haplotypic signatures. Drug-selection experiments identified genetic polymorphisms in Plasmodium falciparum px1, encoding a phosphoinositide-binding protein, as the strongest correlates of reduced susceptibility to dihydroartemisinin and lumefantrine. The PX1 PIN haplotype (L1222P, M1701I, D1705N) recently discovered in Ugandan parasites was highly enriched following dihydroartemisinin or lumefantrine treatment of pooled mixtures of genetically diverse Ugandan clinical isolates. This haplotype was associated with reduced susceptibility to dihydroartemisinin and lumefantrine, compared to wild-type sequence, in culture-adapted Ugandan P. falciparum lines. These results confirm that PX1 mutations were selected across geographically distinct Ugandan parasite backgrounds. Long-term competitive fitness assays demonstrated that PX1 mutations confer asexual blood-stage parasites with a growth advantage, potentially explaining a rapid rise of PX1 PIN alleles over the last two decades in Uganda. Overall, our data suggest the PX1 PIN haplotype is a robust marker of reduced AL susceptibility in African P. falciparum, enabling surveillance of emerging drug resistance.","rel_num_authors":14,"rel_authors":[{"author_name":"Christopher Bower-Lepts","author_inst":"Columbia University Irving Medical Centre"},{"author_name":"Arvind Jangra","author_inst":"Columbia University Irving Medical Centre"},{"author_name":"Sachie Kanatani","author_inst":"Johns Hopkins School of Public Health"},{"author_name":"Abhai Tripathi","author_inst":"Johns Hopkins School of Public Health"},{"author_name":"Godfree Mlambo","author_inst":"Johns Hopkins School of Public Health"},{"author_name":"Alejandro McCotter-Gonzalez","author_inst":"Columbia University Irving Medical Centre"},{"author_name":"Linda Romano","author_inst":"Columbia University Irving Medical Centre"},{"author_name":"Stephen Orena","author_inst":"Infectious Diseases Research Collaboration"},{"author_name":"Martin Okitwi","author_inst":"Infectious Diseases Research Collaboration"},{"author_name":"Karamoko Niare","author_inst":"Brown University"},{"author_name":"Philip J Rosenthal","author_inst":"University of California San Francisco"},{"author_name":"Melissa Conrad","author_inst":"Johns Hopkins School of Public Health"},{"author_name":"Photini Sinnis","author_inst":"Johns Hopkins School of Public Health"},{"author_name":"Sachel Mok","author_inst":"Columbia University Irving Medical Centre"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"A Plasmodium falciparum PX1 haplotype is associated with reduced susceptibility to artemisinin and lumefantrine","rel_doi":"10.64898\/2026.05.05.722990","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.05.722990","rel_abs":"Effective control of falciparum malaria depends on the sustained efficacy of frontline antimalarial drugs, particularly artemether-lumefantrine (AL), the most widely used therapy in Africa. However, the emergence of artemisinin partial resistance and reduced lumefantrine susceptibility in eastern Africa threaten malaria control and elimination. Robust genetic markers of decreased susceptibility to lumefantrine remain elusive, and our understanding of artemisinin resistance is incomplete. We report results of a Plasmodium falciparum genetic cross between a drug-sensitive line and a Ugandan strain exhibiting reduced susceptibility to dihydroartemisinin and lumefantrine. Targeted deep sequencing of progeny pools and 460 recombinant progeny clones derived under drug pressures revealed distinct haplotypic signatures. Drug-selection experiments identified genetic polymorphisms in Plasmodium falciparum px1, encoding a phosphoinositide-binding protein, as the strongest correlates of reduced susceptibility to dihydroartemisinin and lumefantrine. The PX1 PIN haplotype (L1222P, M1701I, D1705N) recently discovered in Ugandan parasites was highly enriched following dihydroartemisinin or lumefantrine treatment of pooled mixtures of genetically diverse Ugandan clinical isolates. This haplotype was associated with reduced susceptibility to dihydroartemisinin and lumefantrine, compared to wild-type sequence, in culture-adapted Ugandan P. falciparum lines. These results confirm that PX1 mutations were selected across geographically distinct Ugandan parasite backgrounds. Long-term competitive fitness assays demonstrated that PX1 mutations confer asexual blood-stage parasites with a growth advantage, potentially explaining a rapid rise of PX1 PIN alleles over the last two decades in Uganda. Overall, our data suggest the PX1 PIN haplotype is a robust marker of reduced AL susceptibility in African P. falciparum, enabling surveillance of emerging drug resistance.","rel_num_authors":14,"rel_authors":[{"author_name":"Christopher Bower-Lepts","author_inst":"Columbia University Irving Medical Centre"},{"author_name":"Arvind Jangra","author_inst":"Columbia University Irving Medical Centre"},{"author_name":"Sachie Kanatani","author_inst":"Johns Hopkins School of Public Health"},{"author_name":"Abhai Tripathi","author_inst":"Johns Hopkins School of Public Health"},{"author_name":"Godfree Mlambo","author_inst":"Johns Hopkins School of Public Health"},{"author_name":"Alejandro McCotter-Gonzalez","author_inst":"Columbia University Irving Medical Centre"},{"author_name":"Linda Romano","author_inst":"Columbia University Irving Medical Centre"},{"author_name":"Stephen Orena","author_inst":"Infectious Diseases Research Collaboration"},{"author_name":"Martin Okitwi","author_inst":"Infectious Diseases Research Collaboration"},{"author_name":"Karamoko Niare","author_inst":"Brown University"},{"author_name":"Philip J Rosenthal","author_inst":"University of California San Francisco"},{"author_name":"Melissa Conrad","author_inst":"Johns Hopkins School of Public Health"},{"author_name":"Photini Sinnis","author_inst":"Johns Hopkins School of Public Health"},{"author_name":"Sachel Mok","author_inst":"Columbia University Irving Medical Centre"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"A Plasmodium falciparum PX1 haplotype is associated with reduced susceptibility to artemisinin and lumefantrine","rel_doi":"10.64898\/2026.05.05.722990","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.05.722990","rel_abs":"Effective control of falciparum malaria depends on the sustained efficacy of frontline antimalarial drugs, particularly artemether-lumefantrine (AL), the most widely used therapy in Africa. However, the emergence of artemisinin partial resistance and reduced lumefantrine susceptibility in eastern Africa threaten malaria control and elimination. Robust genetic markers of decreased susceptibility to lumefantrine remain elusive, and our understanding of artemisinin resistance is incomplete. We report results of a Plasmodium falciparum genetic cross between a drug-sensitive line and a Ugandan strain exhibiting reduced susceptibility to dihydroartemisinin and lumefantrine. Targeted deep sequencing of progeny pools and 460 recombinant progeny clones derived under drug pressures revealed distinct haplotypic signatures. Drug-selection experiments identified genetic polymorphisms in Plasmodium falciparum px1, encoding a phosphoinositide-binding protein, as the strongest correlates of reduced susceptibility to dihydroartemisinin and lumefantrine. The PX1 PIN haplotype (L1222P, M1701I, D1705N) recently discovered in Ugandan parasites was highly enriched following dihydroartemisinin or lumefantrine treatment of pooled mixtures of genetically diverse Ugandan clinical isolates. This haplotype was associated with reduced susceptibility to dihydroartemisinin and lumefantrine, compared to wild-type sequence, in culture-adapted Ugandan P. falciparum lines. These results confirm that PX1 mutations were selected across geographically distinct Ugandan parasite backgrounds. Long-term competitive fitness assays demonstrated that PX1 mutations confer asexual blood-stage parasites with a growth advantage, potentially explaining a rapid rise of PX1 PIN alleles over the last two decades in Uganda. Overall, our data suggest the PX1 PIN haplotype is a robust marker of reduced AL susceptibility in African P. falciparum, enabling surveillance of emerging drug resistance.","rel_num_authors":14,"rel_authors":[{"author_name":"Christopher Bower-Lepts","author_inst":"Columbia University Irving Medical Centre"},{"author_name":"Arvind Jangra","author_inst":"Columbia University Irving Medical Centre"},{"author_name":"Sachie Kanatani","author_inst":"Johns Hopkins School of Public Health"},{"author_name":"Abhai Tripathi","author_inst":"Johns Hopkins School of Public Health"},{"author_name":"Godfree Mlambo","author_inst":"Johns Hopkins School of Public Health"},{"author_name":"Alejandro McCotter-Gonzalez","author_inst":"Columbia University Irving Medical Centre"},{"author_name":"Linda Romano","author_inst":"Columbia University Irving Medical Centre"},{"author_name":"Stephen Orena","author_inst":"Infectious Diseases Research Collaboration"},{"author_name":"Martin Okitwi","author_inst":"Infectious Diseases Research Collaboration"},{"author_name":"Karamoko Niare","author_inst":"Brown University"},{"author_name":"Philip J Rosenthal","author_inst":"University of California San Francisco"},{"author_name":"Melissa Conrad","author_inst":"Johns Hopkins School of Public Health"},{"author_name":"Photini Sinnis","author_inst":"Johns Hopkins School of Public Health"},{"author_name":"Sachel Mok","author_inst":"Columbia University Irving Medical Centre"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"A Plasmodium falciparum PX1 haplotype is associated with reduced susceptibility to artemisinin and lumefantrine","rel_doi":"10.64898\/2026.05.05.722990","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.05.722990","rel_abs":"Effective control of falciparum malaria depends on the sustained efficacy of frontline antimalarial drugs, particularly artemether-lumefantrine (AL), the most widely used therapy in Africa. However, the emergence of artemisinin partial resistance and reduced lumefantrine susceptibility in eastern Africa threaten malaria control and elimination. Robust genetic markers of decreased susceptibility to lumefantrine remain elusive, and our understanding of artemisinin resistance is incomplete. We report results of a Plasmodium falciparum genetic cross between a drug-sensitive line and a Ugandan strain exhibiting reduced susceptibility to dihydroartemisinin and lumefantrine. Targeted deep sequencing of progeny pools and 460 recombinant progeny clones derived under drug pressures revealed distinct haplotypic signatures. Drug-selection experiments identified genetic polymorphisms in Plasmodium falciparum px1, encoding a phosphoinositide-binding protein, as the strongest correlates of reduced susceptibility to dihydroartemisinin and lumefantrine. The PX1 PIN haplotype (L1222P, M1701I, D1705N) recently discovered in Ugandan parasites was highly enriched following dihydroartemisinin or lumefantrine treatment of pooled mixtures of genetically diverse Ugandan clinical isolates. This haplotype was associated with reduced susceptibility to dihydroartemisinin and lumefantrine, compared to wild-type sequence, in culture-adapted Ugandan P. falciparum lines. These results confirm that PX1 mutations were selected across geographically distinct Ugandan parasite backgrounds. Long-term competitive fitness assays demonstrated that PX1 mutations confer asexual blood-stage parasites with a growth advantage, potentially explaining a rapid rise of PX1 PIN alleles over the last two decades in Uganda. Overall, our data suggest the PX1 PIN haplotype is a robust marker of reduced AL susceptibility in African P. falciparum, enabling surveillance of emerging drug resistance.","rel_num_authors":14,"rel_authors":[{"author_name":"Christopher Bower-Lepts","author_inst":"Columbia University Irving Medical Centre"},{"author_name":"Arvind Jangra","author_inst":"Columbia University Irving Medical Centre"},{"author_name":"Sachie Kanatani","author_inst":"Johns Hopkins School of Public Health"},{"author_name":"Abhai Tripathi","author_inst":"Johns Hopkins School of Public Health"},{"author_name":"Godfree Mlambo","author_inst":"Johns Hopkins School of Public Health"},{"author_name":"Alejandro McCotter-Gonzalez","author_inst":"Columbia University Irving Medical Centre"},{"author_name":"Linda Romano","author_inst":"Columbia University Irving Medical Centre"},{"author_name":"Stephen Orena","author_inst":"Infectious Diseases Research Collaboration"},{"author_name":"Martin Okitwi","author_inst":"Infectious Diseases Research Collaboration"},{"author_name":"Karamoko Niare","author_inst":"Brown University"},{"author_name":"Philip J Rosenthal","author_inst":"University of California San Francisco"},{"author_name":"Melissa Conrad","author_inst":"Johns Hopkins School of Public Health"},{"author_name":"Photini Sinnis","author_inst":"Johns Hopkins School of Public Health"},{"author_name":"Sachel Mok","author_inst":"Columbia University Irving Medical Centre"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"EVd3x: a source-attributed multi-omic platform for mapping extracellular vesicle cargo evidence","rel_doi":"10.64898\/2026.05.06.723262","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.723262","rel_abs":"Extracellular vesicle (EV) studies increasingly generate mixed cargo lists that include genes, proteins, miRNAs, biofluids, cell contexts, disease labels, pathways, and interaction networks. The central interpretive challenge is determining which source supports each record and what level of biological claim that source can justify. We developed EVd3x, a source-attributed multi-omic platform that integrates 28 public resources into 17 canonical Apache Parquet analysis tables and converts molecule, disease, or natural-language queries into a reusable analysis state. The same state can be inspected across linked evidence layers for EV cargo, disease aggregation, pathway enrichment, cell context, ligand receptor evidence, miRNA target support, STRING protein protein interactions, and exportable source rows. We evaluated EVd3x using the disease-first query: early onset Alzheimers disease with behavioral disturbance. The query resolved a PSEN1-centered state with 5 seeds, 109 nodes, and 197 edges, and exported 647 EV evidence rows, 4,053 disease rows, 2,204 pathway rows, 3,555 cell-context\/communication\/ligand receptor rows, and 4,032 bridge rows. EVd3x recovered familial Alzheimer disease type 3, gamma-secretase and Notch context, nervous-system pathway terms, oligodendrocyte to astrocyte communication hypotheses, and PSEN1 bridges in which six queried miRNAs, including hsa-miR-107, target PSEN1 directly. These outputs are reported as separable evidence layers rather than as a composite proof score. A table-backed research assistant fine-tuned from Qwen2.5-1.5B-Instruct with QLoRA routes natural-language requests through deterministic retrieval before optional synthesis. EVd3x supports transparent EV hypothesis generation by preserving source attribution from query to export.","rel_num_authors":2,"rel_authors":[{"author_name":"Karima Ait Ouares","author_inst":"Yale University"},{"author_name":"Jonathan S. Weerakkody","author_inst":"Yale University"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"NO modulates human airway smooth muscle function by altering glucose-6-phosphate dehydrogenase effects on sGC function in asthma","rel_doi":"10.64898\/2026.05.06.723287","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.723287","rel_abs":"Since NO can modulate mesenchymal cell function, we posit that NO can modulate gene expression associated with excitation-contraction coupling. Our study shows that treating asthma-derived HASMCs with a low dose of NO plus sGC stimulator BAY-41, in most cases sensitized smooth muscle sGC towards activation via an elevated sGC heterodimer and in some cases also improved sGC{beta}1, catalase, Cyb5r3 or Trx1 expression (n=24 non-asthma and n=25 asthma). Interestingly we found that majority of asthma HASMCs showed a marked downregulation of G6PD expression inducing a low GSH\/GSSG ratio in asthma, and these findings were replicated in murine lungs of allergic asthma (OVA and CFA\/HDM). Studies with HEK\/COS-7 cells showed G6PD synergizing with hsp90 in enabling sGC heme-maturation. G6PD overexpression in HASMCs enhanced the sGC heterodimerization while silencing of endogenous G6PD abrogated it. Complementation of these cellular results with whole animal models of G6PD deficiency or overexpression provided verification to our findings. Mouse lung tissue from the humanized variant of G6PD deficiency, V68M (G6PD A- deficiency) showed significant downregulation in the sGC heterodimer, with a concomitant reduction in its NO heme-dependent activity, thereby showing that G6PD deficiency lowers sGC heme. Conversely, G6PD overexpressing mouse lung tissue displayed an elevated sGC heterodimer and also showed a robust G6PD-sGC{beta}1 interaction, suggesting G6PD to be involved in the heme-maturation of sGC{beta}1. While G6PD maintains the cell redox by generating NADPH, its new role in regulating sGC maturation links sGC dysfunction in asthma to G6PD deficiency and may potentially uncover new targets for asthma treatment.","rel_num_authors":9,"rel_authors":[{"author_name":"Arnab Ghosh","author_inst":"The Cleveland Clinic"},{"author_name":"Mamta P. Sumi","author_inst":"The Cleveland Clinic"},{"author_name":"Cynthia Koziol-White","author_inst":"Rutgers Institute for Translational Medicine and Science, Rutgers University"},{"author_name":"Blair Tupta","author_inst":"The Cleveland Clinic"},{"author_name":"Ling Wang","author_inst":"The University of Iowa"},{"author_name":"Chaitali Ghosh","author_inst":"The Cleveland Clinic"},{"author_name":"William F. Jester","author_inst":"Rutgers Institute for Translational Medicine and Science, Rutgers University"},{"author_name":"Reynold A. Panettieri Jr.","author_inst":"Rutgers Institute for Translational Medicine and Science, Rutgers University"},{"author_name":"Dennis J. Stuehr","author_inst":"The Cleveland Clinic"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"NO modulates human airway smooth muscle function by altering glucose-6-phosphate dehydrogenase effects on sGC function in asthma","rel_doi":"10.64898\/2026.05.06.723287","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.723287","rel_abs":"Since NO can modulate mesenchymal cell function, we posit that NO can modulate gene expression associated with excitation-contraction coupling. Our study shows that treating asthma-derived HASMCs with a low dose of NO plus sGC stimulator BAY-41, in most cases sensitized smooth muscle sGC towards activation via an elevated sGC heterodimer and in some cases also improved sGC{beta}1, catalase, Cyb5r3 or Trx1 expression (n=24 non-asthma and n=25 asthma). Interestingly we found that majority of asthma HASMCs showed a marked downregulation of G6PD expression inducing a low GSH\/GSSG ratio in asthma, and these findings were replicated in murine lungs of allergic asthma (OVA and CFA\/HDM). Studies with HEK\/COS-7 cells showed G6PD synergizing with hsp90 in enabling sGC heme-maturation. G6PD overexpression in HASMCs enhanced the sGC heterodimerization while silencing of endogenous G6PD abrogated it. Complementation of these cellular results with whole animal models of G6PD deficiency or overexpression provided verification to our findings. Mouse lung tissue from the humanized variant of G6PD deficiency, V68M (G6PD A- deficiency) showed significant downregulation in the sGC heterodimer, with a concomitant reduction in its NO heme-dependent activity, thereby showing that G6PD deficiency lowers sGC heme. Conversely, G6PD overexpressing mouse lung tissue displayed an elevated sGC heterodimer and also showed a robust G6PD-sGC{beta}1 interaction, suggesting G6PD to be involved in the heme-maturation of sGC{beta}1. While G6PD maintains the cell redox by generating NADPH, its new role in regulating sGC maturation links sGC dysfunction in asthma to G6PD deficiency and may potentially uncover new targets for asthma treatment.","rel_num_authors":9,"rel_authors":[{"author_name":"Arnab Ghosh","author_inst":"The Cleveland Clinic"},{"author_name":"Mamta P. Sumi","author_inst":"The Cleveland Clinic"},{"author_name":"Cynthia Koziol-White","author_inst":"Rutgers Institute for Translational Medicine and Science, Rutgers University"},{"author_name":"Blair Tupta","author_inst":"The Cleveland Clinic"},{"author_name":"Ling Wang","author_inst":"The University of Iowa"},{"author_name":"Chaitali Ghosh","author_inst":"The Cleveland Clinic"},{"author_name":"William F. Jester","author_inst":"Rutgers Institute for Translational Medicine and Science, Rutgers University"},{"author_name":"Reynold A. Panettieri Jr.","author_inst":"Rutgers Institute for Translational Medicine and Science, Rutgers University"},{"author_name":"Dennis J. Stuehr","author_inst":"The Cleveland Clinic"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Kappa opioid receptors regulate cocaine effects on nucleus accumbens dopamine through phosphorylation of dopamine transporter at the threonine 53 site","rel_doi":"10.64898\/2026.05.06.722744","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.722744","rel_abs":"The endogenous peptide dynorphin (Dyn) and its target the kappa opioid receptor (KOR) play a crucial role in regulating factors related to stress and reward. The KOR is expressed in multiple cell types in the nucleus accumbens (NAc), including presynaptic dopamine (DA) terminals, where it inhibits DA release modulates the function of the DA transporter (DAT). The Dyn\/KOR system is upregulated by exposure to drugs of abuse including the DAT inhibitor, cocaine, and their activity is integrally involved in negative affective states associated with withdrawal from substance abuse. We aimed to better understand the impact of the Dyn\/KOR system on presynaptic DA terminals and potential effects on DAT interactions with cocaine by measuring the impact of the KOR agonist U50,488 on electrically-evoked DA release and subsequent reuptake in NAc slices from C57BL6\/J mice. We showed that superfusion of U50,488 inhibited DA release and markedly reduced cocaine-induced inhibition of DA reuptake, indicating tolerance to cocaine effects. We replicated this finding in the NAc of rhesus macaques using the DAT\/NET inhibitor nomifensine, demonstrating that these mechanisms are conserved across DAT inhibitors and in non-human primates. KOR activation results in phosphorylation of the Threonine-53 site on the DAT, a process thought to mediate its impact on DAT function. We tested whether this phosphorylation site is required for the KOR-mediated reduction cocaine effects. To tackle this question, we employed a knock-in mouse line with an Alanine-53 on the DAT (DAT-T53A), rendering that residue insensitive to phosphorylation. We show that DAT-T53A mice have enhanced DA release and uptake, and U50,488 has a reduced inhibitory effect on peak DA release. Remarkably, U50,488 no longer modified the effect of cocaine on uptake in these mice, demonstrating the dependence of this effect on phosphorylated Threonine-53 and highlighting a potential mechanism underlying cocaine tolerance.","rel_num_authors":11,"rel_authors":[{"author_name":"Emanuel F Lopes","author_inst":"Wake Forest University School of Medicine"},{"author_name":"Paige M Estave","author_inst":"Wake Forest University School of Medicine"},{"author_name":"Alyson M Curry","author_inst":"Wake Forest University School of Medicine"},{"author_name":"Kathryn R Beard","author_inst":"Wake Forest University School of Medicine"},{"author_name":"Monica H Dawes","author_inst":"Wake Forest University School of Medicine"},{"author_name":"Jonathan H Sciortino","author_inst":"Wake Forest University School of Medicine"},{"author_name":"Katherine M Holleran","author_inst":"Wake Forest University School of Medicine"},{"author_name":"Kathleen M Grant","author_inst":"Oregon Health & Science University"},{"author_name":"Lankupalle D Jayanthi","author_inst":"Virginia Commonwealth University"},{"author_name":"Sammanda Ramamoorthy","author_inst":"Virginia Commonwealth University"},{"author_name":"Sara R Jones","author_inst":"Wake Forest University School of Medicine"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Epithelial mesenchymal transition initiates precancer states in BRCA1 mutation carriers","rel_doi":"10.64898\/2026.05.06.723061","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.723061","rel_abs":"Epithelial-to-mesenchymal transition (EMT) is activated to equip cells with the capacity to adapt to and escape hostile conditions. While EMT is required for cancer progression, its role in breast cancer initiation remains elusive. Given the basal-like phenotype of breast cancers arising in female carriers of germline BRCA1 pathogenic variants (BRCA1 carriers), we hypothesized that enhanced EMT susceptibility underlies precancerous initiation in mammary epithelium. Perturbation of patient-derived normal mammary organoids from BRCA1 carriers and non-carriers with inflammatory cytokines induced copy number variations (CNV) and the acquisition of oncogenic mutations in both groups. However, in organoids derived from BRCA1 carriers, cytokine exposure induced morphological, transcriptomic, and functional EMT, accompanied by a transition to basal-like phenotype. Concomitant DNA damage accumulation in organoids from BRCA1-carriers demonstrated PARP inhibitor sensitivity. EMT-primed states were identified in a subpopulation of normal mammary epithelium from BRCA1 carriers. We demonstrate the utility of patient-derived normal BRCA1 heterozygous mammary organoids to reveal a plastic, high-risk epithelial state that is associated with a transient, targetable vulnerability.","rel_num_authors":20,"rel_authors":[{"author_name":"Neta Bar-Hai","author_inst":"Department of Oncology, Sheba Medical Center, Ramat Gan, Israel and The Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv, Israel"},{"author_name":"Rakefet Ben-Yishay","author_inst":"Department of Oncology, Sheba Medical Center, Ramat Gan, Israel"},{"author_name":"Sheli Arbili-Yarhi","author_inst":"Department of Oncology, Sheba Medical Center, Ramat Gan, Israel"},{"author_name":"Rinat Bernstein-Molho","author_inst":"Department of Oncology, Sheba Medical Center, Ramat Gan, Israel  and The Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv, Israel"},{"author_name":"Gil Goldinger","author_inst":"Department of Pathology, Sheba Medical Center, Ramat Gan, Israel"},{"author_name":"Nora Balint-Lahat","author_inst":"Department of Pathology, Sheba Medical Center, Ramat Gan, Israel"},{"author_name":"Tehilla Menes","author_inst":"Department of General Surgery, Sheba Medical Center, Ramat-Gan, Israel"},{"author_name":"Naama Herman","author_inst":"Department of General Surgery, Sheba Medical Center, Ramat-Gan, Israel"},{"author_name":"Vered Noy","author_inst":"Department of General Surgery, Sheba Medical Center, Ramat-Gan, Israel"},{"author_name":"Aiham Mansour","author_inst":"Department of General Surgery, Sheba Medical Center, Ramat-Gan, Israel"},{"author_name":"Opher Globus","author_inst":"Department of Oncology, Sheba Medical Center, Ramat Gan, Israel"},{"author_name":"Pnina Hilman","author_inst":"Department of Pathology, Shamir (Assaf Harofeh) Medical Center, Israel"},{"author_name":"Yonathan Zehavi","author_inst":"Department of Pathology, Shamir (Assaf Harofeh) Medical Center, Israel"},{"author_name":"Inbal Eizenberg-Magar","author_inst":"Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel"},{"author_name":"Elmir Mahammadov","author_inst":"Systems Biology of Gene Regulatory Elements, Max-Delbrueck-Center for Molecular Medicine, Berlin, Germany"},{"author_name":"Thomas Conrad","author_inst":"Genomics Technology Platform, Max Delbrueck Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany"},{"author_name":"Nikolaus Rajewsky","author_inst":"Systems Biology of Gene Regulatory Elements, Max-Delbrueck-Center for Molecular Medicine, Berlin, Germany"},{"author_name":"Yaron E. Antebi","author_inst":"Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel"},{"author_name":"Raanan Berger","author_inst":"Department of Oncology, Sheba Medical Center, Ramat Gan, Israel  and The Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv, Israel"},{"author_name":"Dana Ishay Ronen","author_inst":"Department of Oncology, Sheba Medical Center, Ramat Gan, Israel"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Epithelial mesenchymal transition initiates precancer states in BRCA1 mutation carriers","rel_doi":"10.64898\/2026.05.06.723061","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.723061","rel_abs":"Epithelial-to-mesenchymal transition (EMT) is activated to equip cells with the capacity to adapt to and escape hostile conditions. While EMT is required for cancer progression, its role in breast cancer initiation remains elusive. Given the basal-like phenotype of breast cancers arising in female carriers of germline BRCA1 pathogenic variants (BRCA1 carriers), we hypothesized that enhanced EMT susceptibility underlies precancerous initiation in mammary epithelium. Perturbation of patient-derived normal mammary organoids from BRCA1 carriers and non-carriers with inflammatory cytokines induced copy number variations (CNV) and the acquisition of oncogenic mutations in both groups. However, in organoids derived from BRCA1 carriers, cytokine exposure induced morphological, transcriptomic, and functional EMT, accompanied by a transition to basal-like phenotype. Concomitant DNA damage accumulation in organoids from BRCA1-carriers demonstrated PARP inhibitor sensitivity. EMT-primed states were identified in a subpopulation of normal mammary epithelium from BRCA1 carriers. We demonstrate the utility of patient-derived normal BRCA1 heterozygous mammary organoids to reveal a plastic, high-risk epithelial state that is associated with a transient, targetable vulnerability.","rel_num_authors":20,"rel_authors":[{"author_name":"Neta Bar-Hai","author_inst":"Department of Oncology, Sheba Medical Center, Ramat Gan, Israel and The Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv, Israel"},{"author_name":"Rakefet Ben-Yishay","author_inst":"Department of Oncology, Sheba Medical Center, Ramat Gan, Israel"},{"author_name":"Sheli Arbili-Yarhi","author_inst":"Department of Oncology, Sheba Medical Center, Ramat Gan, Israel"},{"author_name":"Rinat Bernstein-Molho","author_inst":"Department of Oncology, Sheba Medical Center, Ramat Gan, Israel  and The Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv, Israel"},{"author_name":"Gil Goldinger","author_inst":"Department of Pathology, Sheba Medical Center, Ramat Gan, Israel"},{"author_name":"Nora Balint-Lahat","author_inst":"Department of Pathology, Sheba Medical Center, Ramat Gan, Israel"},{"author_name":"Tehilla Menes","author_inst":"Department of General Surgery, Sheba Medical Center, Ramat-Gan, Israel"},{"author_name":"Naama Herman","author_inst":"Department of General Surgery, Sheba Medical Center, Ramat-Gan, Israel"},{"author_name":"Vered Noy","author_inst":"Department of General Surgery, Sheba Medical Center, Ramat-Gan, Israel"},{"author_name":"Aiham Mansour","author_inst":"Department of General Surgery, Sheba Medical Center, Ramat-Gan, Israel"},{"author_name":"Opher Globus","author_inst":"Department of Oncology, Sheba Medical Center, Ramat Gan, Israel"},{"author_name":"Pnina Hilman","author_inst":"Department of Pathology, Shamir (Assaf Harofeh) Medical Center, Israel"},{"author_name":"Yonathan Zehavi","author_inst":"Department of Pathology, Shamir (Assaf Harofeh) Medical Center, Israel"},{"author_name":"Inbal Eizenberg-Magar","author_inst":"Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel"},{"author_name":"Elmir Mahammadov","author_inst":"Systems Biology of Gene Regulatory Elements, Max-Delbrueck-Center for Molecular Medicine, Berlin, Germany"},{"author_name":"Thomas Conrad","author_inst":"Genomics Technology Platform, Max Delbrueck Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany"},{"author_name":"Nikolaus Rajewsky","author_inst":"Systems Biology of Gene Regulatory Elements, Max-Delbrueck-Center for Molecular Medicine, Berlin, Germany"},{"author_name":"Yaron E. Antebi","author_inst":"Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel"},{"author_name":"Raanan Berger","author_inst":"Department of Oncology, Sheba Medical Center, Ramat Gan, Israel  and The Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv, Israel"},{"author_name":"Dana Ishay Ronen","author_inst":"Department of Oncology, Sheba Medical Center, Ramat Gan, Israel"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"On the state of protein function prediction: a report on the fourth CAFA challenge","rel_doi":"10.64898\/2026.05.06.722942","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.722942","rel_abs":"Background: The Critical Assessment of Functional Annotation (CAFA) is a community effort held to understand the field of computational protein function prediction. Every three years, since 2010, the organizers initiate an experiment to collect function predictions on a large set of proteins and then evaluate the performance of predicting methods on a subset of proteins that have accumulated experimental annotations between the submission deadline and the evaluation time. CAFA provides an independent and rigorous assessment of the current state of the art, thus leveling the playing field, highlighting successes, revealing bottlenecks, and offering a forum for the exchange of ideas in protein science. Here, we report the results of the fourth CAFA experiment (CAFA4). Results: CAFA4 featured the participation of 148 methods from 70 research groups on a total of 46,205 unique proteins over a 5-year annotation accumulation phase, the longest in any CAFA. In a comparison across CAFA2-CAFA4 methods, the prediction of Gene Ontology (GO) terms has clearly improved across all three GO aspects and traditional evaluation settings. While not achieving the first rank, several CAFA2 and CAFA3 methods featured in the top ten methods in many evaluations, suggesting that earlier methods still hold relevance. The performance is weaker in the newly introduced \"partial knowledge\" evaluation category (proteins with experimental annotations before submission deadline that gained additional annotations in the same GO aspect during the annotation accumulation phase), highlighting the need for a new class of methods. The rankings of the methods were stable over the years in traditional evaluation settings, but less so in the new partial knowledge evaluation. Overall, the field continues to progress with some influx of new participants. Sustained efforts will be necessary to substantially advance it.","rel_num_authors":154,"rel_authors":[{"author_name":"Rashika Ramola","author_inst":"Northeastern University"},{"author_name":"M. Clara De Paolis Klauza","author_inst":"Northeastern University"},{"author_name":"Damiano Piovesan","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Yisu Peng","author_inst":"Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA"},{"author_name":"Parnal Joshi","author_inst":"Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, USA; Department of Veterinary Microbiology & Preventive Medicine, Iowa State "},{"author_name":"Mahta Mehdiabadi","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Federica Quaglia","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Rita Pancsa","author_inst":"HUN-REN Research Centre for Natural Sciences, Institute of Molecular Life Sciences, Budapest, Hungary"},{"author_name":"Lucia B. Chemes","author_inst":"Instituto de Investigaciones Biotecnologicas, Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET), Escuela de Bio y Nanotecnologias (EByN), Uni"},{"author_name":"Meisam Ahmadi","author_inst":"Computer Engineering Department, Iran University of Science And Technology, Tehran, Iran"},{"author_name":"Hongryul Ahn","author_inst":"Division of Data Science, The University of Suwon, Gyeonggi-do, South Korea"},{"author_name":"Adrian M. Altenhoff","author_inst":"Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland; Department of Computer Science, ETH Zurich, Zurich, Switzerland"},{"author_name":"Ehsaneddin Asgari","author_inst":"Qatar Computing Research Institute, HBKU, Doha, Qatar"},{"author_name":"Maria Cristina Aspromonte","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Volkan Atalay","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Giulia Babbi","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy"},{"author_name":"Davide Baldazzi","author_inst":"Oncogenetics and Functional Oncogenomics, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy"},{"author_name":"Meet M. Barot","author_inst":"Center for Data Science, New York University, New York, NY, USA"},{"author_name":"Asa Ben-Hur","author_inst":"Department of Computer Science, Colorado State University, Fort Collins, CO, USA"},{"author_name":"Alfredo Benso","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Daniel Berenberg","author_inst":"Department of Computer Science, New York University, New York, NY, USA"},{"author_name":"Jari Bjorne","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Florian Boecker","author_inst":"INRES Crop Bioinformatics, University of Bonn, Bonn, Germany"},{"author_name":"Paolo Boldi","author_inst":"Dipartimento di Informatica Giovanni Degli Antoni, University of Milan, Milano, Italy"},{"author_name":"Joseph Bonello","author_inst":"Computer Information Systems, University of Malta, Msida, Malta; Structural and Molecular Biology, University College London, London, England"},{"author_name":"Nicola Bordin","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Piyush Borole","author_inst":"College of Science and Technology, Temple University, Philadelphia, PA, USA"},{"author_name":"Ali Ebrahimpour Boroojeny","author_inst":"Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA"},{"author_name":"Renzhi Cao","author_inst":"Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA"},{"author_name":"Stefano Di Carlo","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Rita Casadio","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy"},{"author_name":"Elena Casiraghi","author_inst":"Department of Computer Science, University of Milan, Milano, Italy; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, "},{"author_name":"Jia-Ming Chang","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Chen Chen","author_inst":"Department of Electrical Engineering and Computer Science, University of Missouri Columbia, Columbia, MO, USA"},{"author_name":"Tse-Ming Chen","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Jianlin Cheng","author_inst":"Department of Electrical Engineering and Computer Science, University of Missouri Columbia, Columbia, MO, USA; NextGen Precision Health, University of Missouri "},{"author_name":"Ssu Chiu","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Alperen Dalkiran","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Radoslav S. Davidovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Christophe Dessimoz","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switz"},{"author_name":"Rucheng Diao","author_inst":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Warith Eddine Djeddi","author_inst":"FST Manar, University of Tunis El Manar, Tunis, Tunisia; University of Jendouba, Jendouba, Tunisia"},{"author_name":"Tunca Dogan","author_inst":"Department of Computer Engineering, Hacettepe University, Ankara, Turkey; Department of Bioinformatics, Hacettepe University, Ankara, Turkey"},{"author_name":"Sean T. Flannery","author_inst":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"Paolo Fontana","author_inst":"Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, Italy"},{"author_name":"Marco Frasca","author_inst":"Dipartimento di Informatica Giovanni Degli Antoni, University of Milan, Milano, Italy"},{"author_name":"Lydia Freddolino","author_inst":"Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA; Department of Computational Medicine and Bioinformatics, University of Michigan,"},{"author_name":"Branislava Gemovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Jesse Gillis","author_inst":"Department of Physiology, University of Toronto, Toronto, ON, Canada; Donnelly Centre, University of Toronto, Toronto, ON, Canada; Cold Spring Harbor Laboratory"},{"author_name":"Filip Ginter","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Vladimir Gligorijevic","author_inst":"Prescient Design, Genentech, New York, NY, USA"},{"author_name":"Giuliano Grossi","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Michael Heinzinger","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany"},{"author_name":"Kyle Hippe","author_inst":"Department of Computer Science, University of Chicago, Chicago, IL, USA; Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA"},{"author_name":"Robert Hoehndorf","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; KAUST Center of Excell"},{"author_name":"Liisa Holm","author_inst":"HiLIFE, Institute of Biotechnology, University of Helsinki, Helsinki, Finland; Faculty of Biological and Environmental Sciences, Organismal and Evolutionary Bio"},{"author_name":"Jie Hou","author_inst":"Department of Computer Science, Saint Louis University,  St. Louis, MO, USA"},{"author_name":"John R. Hover","author_inst":"MAP\/BARseq Core Facility, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Yen-Ting Huang","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Emilio Ispano","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Suraiya Jabin","author_inst":"Department of Computer Science, Jamia Millia Islamia, New Delhi, India; Faculty of Sciences, Jamia Millia Islamia, New Delhi, India"},{"author_name":"Aashish Jain","author_inst":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"David T. Jones","author_inst":"Department of Computer Science, University College London, London, UK"},{"author_name":"Suwisa Kaewphan","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Yuki Kagaya","author_inst":"Department of Biological Sciences, Purdue University, West Lafayette, IN, USA"},{"author_name":"Jenna Kanerva","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Daisuke Kihara","author_inst":"Department of Biological Sciences, Purdue University, West Lafayette, IN, USA; Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"Maxat Kulmanov","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; KAUST Center of Excell"},{"author_name":"Sunil Kumar","author_inst":"Farming Solutions and Digital, Corteva Agriscience, Hyderabad, India"},{"author_name":"Lukasz Kurgan","author_inst":"Department of Computer Science, Virginia Commonwealth University, Richmond, VA,USA"},{"author_name":"Enrico Lavezzo","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Jon Lees","author_inst":"Faculty of Health and Life Sciences, University of Bristol, Bristol, UK"},{"author_name":"Wen-Hung Liao","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Han Lin","author_inst":"Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, New Taipei City, Taiwan"},{"author_name":"Michal Linial","author_inst":"Department of Biological Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Maria Littmann","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany"},{"author_name":"Lizhi Liu","author_inst":"School of Computer Science, Fudan University, Shanghai, China"},{"author_name":"Tong Liu","author_inst":"Department of Computer Science, University of Miami, Coral Gables, FL, USA"},{"author_name":"Yi Wei Liu","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Stavros Makrodimitris","author_inst":"Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands; Intelligent Systems, Delft University of Technology,  Delft, Netherlands"},{"author_name":"Laura Manuto","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Pier Luigi Martelli","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy; Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy"},{"author_name":"Alice Carolyn Mchardy","author_inst":"Computational Biology for Infection Research, Helmholtz Centre for Infection Research, Brunswick, Germany"},{"author_name":"Gabriela A. Merino","author_inst":"European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK"},{"author_name":"Diego H. Milone","author_inst":"Department of Informatics, FICH-UNL, Research institute for Signals, Systems and Computational intelligence, sinc(i), CONICET\/UNL, Santa Fe, Argentina"},{"author_name":"Sarthak Mishra","author_inst":"Department of Computer Science, Jamia Millia Islamia, New Delhi, India"},{"author_name":"Mohammad R. K. Mofrad","author_inst":"Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California Berkeley, Berkeley, CA, USA"},{"author_name":"David Moi","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switz"},{"author_name":"Tsukasa Nakamura","author_inst":"Graduate School of Information Sciences, Tohoku University, Sendai, Japan"},{"author_name":"Vijay Kumar Narsapuram","author_inst":"Genomics Molecular and Data Science, Corteva Agriscience, IN, USA"},{"author_name":"Maria Victoria Nugnes","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Takeshi Obayashi","author_inst":"Graduate School of Information Sciences, Tohoku University, Sendai, Japan; WPI-AIMEC, Tohoku University, Sendai, Japan"},{"author_name":"Dan Ofer","author_inst":"Department of Biology, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Alberto Paccanaro","author_inst":"Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Surrey, UK; School of Applied Mathematics, Funda"},{"author_name":"Vladimir R. Perovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Alessandro Petrini","author_inst":"Huawei Galois Lab, Huawei Technologies France SASU, Boulogne-Billancourt, France; Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Gianfranco Politano","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Daniele Raimondi","author_inst":"ESAT-STADIUS, KU Leuven, Leuven, Belgium"},{"author_name":"Nadav Rappoport","author_inst":"Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel"},{"author_name":"Hafeez Ur Rehman","author_inst":"Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan; School of Computing and Data Science, Oryx Universal"},{"author_name":"Maarten J. M. F. Reijnders","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland"},{"author_name":"Marcel J. T. Reinders","author_inst":"Intelligent Systems, Delft University of Technology,  Delft, Netherlands"},{"author_name":"P. Douglas Renfrew","author_inst":"Center for Computational Biology, Flatiron Institute, New York, NY, USA"},{"author_name":"Ahmet S. Rifaioglu","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Alfonso E. Romero","author_inst":"Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Surrey, UK"},{"author_name":"Abhiman Saraswathi","author_inst":"Farming Solutions and Digital, Corteva Agriscience, IA, USA"},{"author_name":"Castrense Savojardo","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy; Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy"},{"author_name":"Harry M. Scholes","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Heiko Schoof","author_inst":"INRES Crop Bioinformatics, University of Bonn, Bonn, Germany"},{"author_name":"Yang Shen","author_inst":"Department of Electrical and Computer Engineering, Texas A&M University, College Station, USA"},{"author_name":"Ian Sillitoe","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Georgina Stegmayer","author_inst":"FICH, Research Institute for Signals, Systems and Computational Intelligence (sinc(i)); Universidad Nacional del Litoral (UNL), CONICET"},{"author_name":"Amos Stern","author_inst":"The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Henri Tiittanen","author_inst":"Institute of Biotechnology, University of Helsinki, Helsinki, Finland"},{"author_name":"Sumyyah Toonsi","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia"},{"author_name":"Stefano Toppo","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Petri Toronen","author_inst":"Institute of Biotechnology, University of Helsinki, Helsinki, Finland"},{"author_name":"Mateo Torres","author_inst":"School of Applied Mathematics, Fundacao Getulio Vargas, Rio de Janeiro, Brazil;"},{"author_name":"Gabriella Trucco","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Giorgio Valentini","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Nevena Veljkovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Alex Warwick Vesztrocy","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland"},{"author_name":"Vedrana Vidulin","author_inst":"Pink Data Analytics, Croatia"},{"author_name":"Amelia Villegas-Morcillo","author_inst":"Intelligent Systems, Delft University of Technology,  Delft, Netherlands; Signal Theory, Telematics and Communications, University of Granada, Granada, Spain"},{"author_name":"Antti Virtanen","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Wim Vranken","author_inst":"Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium"},{"author_name":"Slobodan Vucetic","author_inst":"Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA"},{"author_name":"Cen Wan","author_inst":"School of Computing and Mathematical Sciences, Birkbeck, University of London, London, UK"},{"author_name":"Zheng Wang","author_inst":"Department of Computer Science, University of Miami, Coral Gables, FL, USA; Department of Biology, University of Miami, Coral Gables, FL, USA; Sylvester Compreh"},{"author_name":"Mark N. Wass","author_inst":"School of Natural Sciences, University of Kent, Canterbury, Kent, UK"},{"author_name":"Robert M. Waterhouse","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; Environmental Bioinformatics, SIB Swiss Institute of Bioinformatics, Lausann"},{"author_name":"Sadok Ben Yahia","author_inst":"The Faculty of Engineering, University of Technology Tallinn, Tallinn, Estonia; University of Southern Denmark, Denmark"},{"author_name":"Haixuan Yang","author_inst":"School of Mathematics and Statistical Sciences, National University of Ireland Galway, Galway, Ireland"},{"author_name":"Shuwei Yao","author_inst":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China"},{"author_name":"Ronghui You","author_inst":"School of Statistics and Data Science, Nankai University, Tianjin, China"},{"author_name":"Jeffrey Yunes","author_inst":"Yunes Foundation for Research on Aging, San Francisco, CA, USA"},{"author_name":"Chengxin Zhang","author_inst":"CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of "},{"author_name":"Yang Zhang","author_inst":"Department of Computer Science, School of Computing, National University of Singapore, Singapore; Department of Biochemistry, Yong Loo Lin School of Medicine, N"},{"author_name":"Chenguang Zhao","author_inst":"Computer and Information Sciences Department, St. Ambrose University, Davenport, IA, USA"},{"author_name":"Xiaogen Zhou","author_inst":"College of Information Engineering, Zhejiang University of Technology, Zhejiang, China"},{"author_name":"Yi-Heng Zhu","author_inst":"College of Artificial Intelligence, Nanjing Agricultural University, Jiangsu, China"},{"author_name":"Shanfeng Zhu","author_inst":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China"},{"author_name":"Hao Zhu","author_inst":"Department of Computer Science, Florida Memorial University, Miami Gardens, FL, USA"},{"author_name":"Gokhan Ozsari","author_inst":"Chalmers E-commons, Chalmers University of Technology, Goteborg, Sweden; Department of Computer Engineering, Middle East Technical University, Ankara, Turkey;"},{"author_name":"Burkhard Rost","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany; Chair for Bioinformatics, Technical University of Muni"},{"author_name":"Christine Orengo","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Marc Robinson-Rechavi","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland"},{"author_name":"Dannie Durand","author_inst":"Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Computational Biology, Carnegie Mellon University, Pittsburgh,"},{"author_name":"Steven E. Brenner","author_inst":"Department of Plant and Microbial Biology and Center for Computational Biology, University of California, Berkeley, CA, USA"},{"author_name":"Casey S. Greene","author_inst":"Department of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA"},{"author_name":"Sean D. Mooney","author_inst":"Center for Information Technology, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Silvio C. E. Tosatto","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Iddo Friedberg","author_inst":"Department of Veterinary Microbiology & Preventive Medicine, Iowa State University, Ames, IA, USA"},{"author_name":"Predrag Radivojac","author_inst":"Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"On the state of protein function prediction: a report on the fourth CAFA challenge","rel_doi":"10.64898\/2026.05.06.722942","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.722942","rel_abs":"Background: The Critical Assessment of Functional Annotation (CAFA) is a community effort held to understand the field of computational protein function prediction. Every three years, since 2010, the organizers initiate an experiment to collect function predictions on a large set of proteins and then evaluate the performance of predicting methods on a subset of proteins that have accumulated experimental annotations between the submission deadline and the evaluation time. CAFA provides an independent and rigorous assessment of the current state of the art, thus leveling the playing field, highlighting successes, revealing bottlenecks, and offering a forum for the exchange of ideas in protein science. Here, we report the results of the fourth CAFA experiment (CAFA4). Results: CAFA4 featured the participation of 148 methods from 70 research groups on a total of 46,205 unique proteins over a 5-year annotation accumulation phase, the longest in any CAFA. In a comparison across CAFA2-CAFA4 methods, the prediction of Gene Ontology (GO) terms has clearly improved across all three GO aspects and traditional evaluation settings. While not achieving the first rank, several CAFA2 and CAFA3 methods featured in the top ten methods in many evaluations, suggesting that earlier methods still hold relevance. The performance is weaker in the newly introduced \"partial knowledge\" evaluation category (proteins with experimental annotations before submission deadline that gained additional annotations in the same GO aspect during the annotation accumulation phase), highlighting the need for a new class of methods. The rankings of the methods were stable over the years in traditional evaluation settings, but less so in the new partial knowledge evaluation. Overall, the field continues to progress with some influx of new participants. Sustained efforts will be necessary to substantially advance it.","rel_num_authors":154,"rel_authors":[{"author_name":"Rashika Ramola","author_inst":"Northeastern University"},{"author_name":"M. Clara De Paolis Klauza","author_inst":"Northeastern University"},{"author_name":"Damiano Piovesan","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Yisu Peng","author_inst":"Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA"},{"author_name":"Parnal Joshi","author_inst":"Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, USA; Department of Veterinary Microbiology & Preventive Medicine, Iowa State "},{"author_name":"Mahta Mehdiabadi","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Federica Quaglia","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Rita Pancsa","author_inst":"HUN-REN Research Centre for Natural Sciences, Institute of Molecular Life Sciences, Budapest, Hungary"},{"author_name":"Lucia B. Chemes","author_inst":"Instituto de Investigaciones Biotecnologicas, Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET), Escuela de Bio y Nanotecnologias (EByN), Uni"},{"author_name":"Meisam Ahmadi","author_inst":"Computer Engineering Department, Iran University of Science And Technology, Tehran, Iran"},{"author_name":"Hongryul Ahn","author_inst":"Division of Data Science, The University of Suwon, Gyeonggi-do, South Korea"},{"author_name":"Adrian M. Altenhoff","author_inst":"Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland; Department of Computer Science, ETH Zurich, Zurich, Switzerland"},{"author_name":"Ehsaneddin Asgari","author_inst":"Qatar Computing Research Institute, HBKU, Doha, Qatar"},{"author_name":"Maria Cristina Aspromonte","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Volkan Atalay","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Giulia Babbi","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy"},{"author_name":"Davide Baldazzi","author_inst":"Oncogenetics and Functional Oncogenomics, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy"},{"author_name":"Meet M. Barot","author_inst":"Center for Data Science, New York University, New York, NY, USA"},{"author_name":"Asa Ben-Hur","author_inst":"Department of Computer Science, Colorado State University, Fort Collins, CO, USA"},{"author_name":"Alfredo Benso","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Daniel Berenberg","author_inst":"Department of Computer Science, New York University, New York, NY, USA"},{"author_name":"Jari Bjorne","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Florian Boecker","author_inst":"INRES Crop Bioinformatics, University of Bonn, Bonn, Germany"},{"author_name":"Paolo Boldi","author_inst":"Dipartimento di Informatica Giovanni Degli Antoni, University of Milan, Milano, Italy"},{"author_name":"Joseph Bonello","author_inst":"Computer Information Systems, University of Malta, Msida, Malta; Structural and Molecular Biology, University College London, London, England"},{"author_name":"Nicola Bordin","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Piyush Borole","author_inst":"College of Science and Technology, Temple University, Philadelphia, PA, USA"},{"author_name":"Ali Ebrahimpour Boroojeny","author_inst":"Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA"},{"author_name":"Renzhi Cao","author_inst":"Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA"},{"author_name":"Stefano Di Carlo","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Rita Casadio","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy"},{"author_name":"Elena Casiraghi","author_inst":"Department of Computer Science, University of Milan, Milano, Italy; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, "},{"author_name":"Jia-Ming Chang","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Chen Chen","author_inst":"Department of Electrical Engineering and Computer Science, University of Missouri Columbia, Columbia, MO, USA"},{"author_name":"Tse-Ming Chen","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Jianlin Cheng","author_inst":"Department of Electrical Engineering and Computer Science, University of Missouri Columbia, Columbia, MO, USA; NextGen Precision Health, University of Missouri "},{"author_name":"Ssu Chiu","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Alperen Dalkiran","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Radoslav S. Davidovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Christophe Dessimoz","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switz"},{"author_name":"Rucheng Diao","author_inst":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Warith Eddine Djeddi","author_inst":"FST Manar, University of Tunis El Manar, Tunis, Tunisia; University of Jendouba, Jendouba, Tunisia"},{"author_name":"Tunca Dogan","author_inst":"Department of Computer Engineering, Hacettepe University, Ankara, Turkey; Department of Bioinformatics, Hacettepe University, Ankara, Turkey"},{"author_name":"Sean T. Flannery","author_inst":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"Paolo Fontana","author_inst":"Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, Italy"},{"author_name":"Marco Frasca","author_inst":"Dipartimento di Informatica Giovanni Degli Antoni, University of Milan, Milano, Italy"},{"author_name":"Lydia Freddolino","author_inst":"Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA; Department of Computational Medicine and Bioinformatics, University of Michigan,"},{"author_name":"Branislava Gemovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Jesse Gillis","author_inst":"Department of Physiology, University of Toronto, Toronto, ON, Canada; Donnelly Centre, University of Toronto, Toronto, ON, Canada; Cold Spring Harbor Laboratory"},{"author_name":"Filip Ginter","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Vladimir Gligorijevic","author_inst":"Prescient Design, Genentech, New York, NY, USA"},{"author_name":"Giuliano Grossi","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Michael Heinzinger","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany"},{"author_name":"Kyle Hippe","author_inst":"Department of Computer Science, University of Chicago, Chicago, IL, USA; Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA"},{"author_name":"Robert Hoehndorf","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; KAUST Center of Excell"},{"author_name":"Liisa Holm","author_inst":"HiLIFE, Institute of Biotechnology, University of Helsinki, Helsinki, Finland; Faculty of Biological and Environmental Sciences, Organismal and Evolutionary Bio"},{"author_name":"Jie Hou","author_inst":"Department of Computer Science, Saint Louis University,  St. Louis, MO, USA"},{"author_name":"John R. Hover","author_inst":"MAP\/BARseq Core Facility, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Yen-Ting Huang","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Emilio Ispano","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Suraiya Jabin","author_inst":"Department of Computer Science, Jamia Millia Islamia, New Delhi, India; Faculty of Sciences, Jamia Millia Islamia, New Delhi, India"},{"author_name":"Aashish Jain","author_inst":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"David T. Jones","author_inst":"Department of Computer Science, University College London, London, UK"},{"author_name":"Suwisa Kaewphan","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Yuki Kagaya","author_inst":"Department of Biological Sciences, Purdue University, West Lafayette, IN, USA"},{"author_name":"Jenna Kanerva","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Daisuke Kihara","author_inst":"Department of Biological Sciences, Purdue University, West Lafayette, IN, USA; Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"Maxat Kulmanov","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; KAUST Center of Excell"},{"author_name":"Sunil Kumar","author_inst":"Farming Solutions and Digital, Corteva Agriscience, Hyderabad, India"},{"author_name":"Lukasz Kurgan","author_inst":"Department of Computer Science, Virginia Commonwealth University, Richmond, VA,USA"},{"author_name":"Enrico Lavezzo","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Jon Lees","author_inst":"Faculty of Health and Life Sciences, University of Bristol, Bristol, UK"},{"author_name":"Wen-Hung Liao","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Han Lin","author_inst":"Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, New Taipei City, Taiwan"},{"author_name":"Michal Linial","author_inst":"Department of Biological Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Maria Littmann","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany"},{"author_name":"Lizhi Liu","author_inst":"School of Computer Science, Fudan University, Shanghai, China"},{"author_name":"Tong Liu","author_inst":"Department of Computer Science, University of Miami, Coral Gables, FL, USA"},{"author_name":"Yi Wei Liu","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Stavros Makrodimitris","author_inst":"Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands; Intelligent Systems, Delft University of Technology,  Delft, Netherlands"},{"author_name":"Laura Manuto","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Pier Luigi Martelli","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy; Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy"},{"author_name":"Alice Carolyn Mchardy","author_inst":"Computational Biology for Infection Research, Helmholtz Centre for Infection Research, Brunswick, Germany"},{"author_name":"Gabriela A. Merino","author_inst":"European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK"},{"author_name":"Diego H. Milone","author_inst":"Department of Informatics, FICH-UNL, Research institute for Signals, Systems and Computational intelligence, sinc(i), CONICET\/UNL, Santa Fe, Argentina"},{"author_name":"Sarthak Mishra","author_inst":"Department of Computer Science, Jamia Millia Islamia, New Delhi, India"},{"author_name":"Mohammad R. K. Mofrad","author_inst":"Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California Berkeley, Berkeley, CA, USA"},{"author_name":"David Moi","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switz"},{"author_name":"Tsukasa Nakamura","author_inst":"Graduate School of Information Sciences, Tohoku University, Sendai, Japan"},{"author_name":"Vijay Kumar Narsapuram","author_inst":"Genomics Molecular and Data Science, Corteva Agriscience, IN, USA"},{"author_name":"Maria Victoria Nugnes","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Takeshi Obayashi","author_inst":"Graduate School of Information Sciences, Tohoku University, Sendai, Japan; WPI-AIMEC, Tohoku University, Sendai, Japan"},{"author_name":"Dan Ofer","author_inst":"Department of Biology, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Alberto Paccanaro","author_inst":"Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Surrey, UK; School of Applied Mathematics, Funda"},{"author_name":"Vladimir R. Perovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Alessandro Petrini","author_inst":"Huawei Galois Lab, Huawei Technologies France SASU, Boulogne-Billancourt, France; Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Gianfranco Politano","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Daniele Raimondi","author_inst":"ESAT-STADIUS, KU Leuven, Leuven, Belgium"},{"author_name":"Nadav Rappoport","author_inst":"Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel"},{"author_name":"Hafeez Ur Rehman","author_inst":"Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan; School of Computing and Data Science, Oryx Universal"},{"author_name":"Maarten J. M. F. Reijnders","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland"},{"author_name":"Marcel J. T. Reinders","author_inst":"Intelligent Systems, Delft University of Technology,  Delft, Netherlands"},{"author_name":"P. Douglas Renfrew","author_inst":"Center for Computational Biology, Flatiron Institute, New York, NY, USA"},{"author_name":"Ahmet S. Rifaioglu","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Alfonso E. Romero","author_inst":"Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Surrey, UK"},{"author_name":"Abhiman Saraswathi","author_inst":"Farming Solutions and Digital, Corteva Agriscience, IA, USA"},{"author_name":"Castrense Savojardo","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy; Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy"},{"author_name":"Harry M. Scholes","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Heiko Schoof","author_inst":"INRES Crop Bioinformatics, University of Bonn, Bonn, Germany"},{"author_name":"Yang Shen","author_inst":"Department of Electrical and Computer Engineering, Texas A&M University, College Station, USA"},{"author_name":"Ian Sillitoe","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Georgina Stegmayer","author_inst":"FICH, Research Institute for Signals, Systems and Computational Intelligence (sinc(i)); Universidad Nacional del Litoral (UNL), CONICET"},{"author_name":"Amos Stern","author_inst":"The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Henri Tiittanen","author_inst":"Institute of Biotechnology, University of Helsinki, Helsinki, Finland"},{"author_name":"Sumyyah Toonsi","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia"},{"author_name":"Stefano Toppo","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Petri Toronen","author_inst":"Institute of Biotechnology, University of Helsinki, Helsinki, Finland"},{"author_name":"Mateo Torres","author_inst":"School of Applied Mathematics, Fundacao Getulio Vargas, Rio de Janeiro, Brazil;"},{"author_name":"Gabriella Trucco","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Giorgio Valentini","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Nevena Veljkovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Alex Warwick Vesztrocy","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland"},{"author_name":"Vedrana Vidulin","author_inst":"Pink Data Analytics, Croatia"},{"author_name":"Amelia Villegas-Morcillo","author_inst":"Intelligent Systems, Delft University of Technology,  Delft, Netherlands; Signal Theory, Telematics and Communications, University of Granada, Granada, Spain"},{"author_name":"Antti Virtanen","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Wim Vranken","author_inst":"Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium"},{"author_name":"Slobodan Vucetic","author_inst":"Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA"},{"author_name":"Cen Wan","author_inst":"School of Computing and Mathematical Sciences, Birkbeck, University of London, London, UK"},{"author_name":"Zheng Wang","author_inst":"Department of Computer Science, University of Miami, Coral Gables, FL, USA; Department of Biology, University of Miami, Coral Gables, FL, USA; Sylvester Compreh"},{"author_name":"Mark N. Wass","author_inst":"School of Natural Sciences, University of Kent, Canterbury, Kent, UK"},{"author_name":"Robert M. Waterhouse","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; Environmental Bioinformatics, SIB Swiss Institute of Bioinformatics, Lausann"},{"author_name":"Sadok Ben Yahia","author_inst":"The Faculty of Engineering, University of Technology Tallinn, Tallinn, Estonia; University of Southern Denmark, Denmark"},{"author_name":"Haixuan Yang","author_inst":"School of Mathematics and Statistical Sciences, National University of Ireland Galway, Galway, Ireland"},{"author_name":"Shuwei Yao","author_inst":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China"},{"author_name":"Ronghui You","author_inst":"School of Statistics and Data Science, Nankai University, Tianjin, China"},{"author_name":"Jeffrey Yunes","author_inst":"Yunes Foundation for Research on Aging, San Francisco, CA, USA"},{"author_name":"Chengxin Zhang","author_inst":"CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of "},{"author_name":"Yang Zhang","author_inst":"Department of Computer Science, School of Computing, National University of Singapore, Singapore; Department of Biochemistry, Yong Loo Lin School of Medicine, N"},{"author_name":"Chenguang Zhao","author_inst":"Computer and Information Sciences Department, St. Ambrose University, Davenport, IA, USA"},{"author_name":"Xiaogen Zhou","author_inst":"College of Information Engineering, Zhejiang University of Technology, Zhejiang, China"},{"author_name":"Yi-Heng Zhu","author_inst":"College of Artificial Intelligence, Nanjing Agricultural University, Jiangsu, China"},{"author_name":"Shanfeng Zhu","author_inst":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China"},{"author_name":"Hao Zhu","author_inst":"Department of Computer Science, Florida Memorial University, Miami Gardens, FL, USA"},{"author_name":"Gokhan Ozsari","author_inst":"Chalmers E-commons, Chalmers University of Technology, Goteborg, Sweden; Department of Computer Engineering, Middle East Technical University, Ankara, Turkey;"},{"author_name":"Burkhard Rost","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany; Chair for Bioinformatics, Technical University of Muni"},{"author_name":"Christine Orengo","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Marc Robinson-Rechavi","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland"},{"author_name":"Dannie Durand","author_inst":"Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Computational Biology, Carnegie Mellon University, Pittsburgh,"},{"author_name":"Steven E. Brenner","author_inst":"Department of Plant and Microbial Biology and Center for Computational Biology, University of California, Berkeley, CA, USA"},{"author_name":"Casey S. Greene","author_inst":"Department of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA"},{"author_name":"Sean D. Mooney","author_inst":"Center for Information Technology, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Silvio C. E. Tosatto","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Iddo Friedberg","author_inst":"Department of Veterinary Microbiology & Preventive Medicine, Iowa State University, Ames, IA, USA"},{"author_name":"Predrag Radivojac","author_inst":"Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"On the state of protein function prediction: a report on the fourth CAFA challenge","rel_doi":"10.64898\/2026.05.06.722942","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.722942","rel_abs":"Background: The Critical Assessment of Functional Annotation (CAFA) is a community effort held to understand the field of computational protein function prediction. Every three years, since 2010, the organizers initiate an experiment to collect function predictions on a large set of proteins and then evaluate the performance of predicting methods on a subset of proteins that have accumulated experimental annotations between the submission deadline and the evaluation time. CAFA provides an independent and rigorous assessment of the current state of the art, thus leveling the playing field, highlighting successes, revealing bottlenecks, and offering a forum for the exchange of ideas in protein science. Here, we report the results of the fourth CAFA experiment (CAFA4). Results: CAFA4 featured the participation of 148 methods from 70 research groups on a total of 46,205 unique proteins over a 5-year annotation accumulation phase, the longest in any CAFA. In a comparison across CAFA2-CAFA4 methods, the prediction of Gene Ontology (GO) terms has clearly improved across all three GO aspects and traditional evaluation settings. While not achieving the first rank, several CAFA2 and CAFA3 methods featured in the top ten methods in many evaluations, suggesting that earlier methods still hold relevance. The performance is weaker in the newly introduced \"partial knowledge\" evaluation category (proteins with experimental annotations before submission deadline that gained additional annotations in the same GO aspect during the annotation accumulation phase), highlighting the need for a new class of methods. The rankings of the methods were stable over the years in traditional evaluation settings, but less so in the new partial knowledge evaluation. Overall, the field continues to progress with some influx of new participants. Sustained efforts will be necessary to substantially advance it.","rel_num_authors":154,"rel_authors":[{"author_name":"Rashika Ramola","author_inst":"Northeastern University"},{"author_name":"M. Clara De Paolis Klauza","author_inst":"Northeastern University"},{"author_name":"Damiano Piovesan","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Yisu Peng","author_inst":"Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA"},{"author_name":"Parnal Joshi","author_inst":"Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, USA; Department of Veterinary Microbiology & Preventive Medicine, Iowa State "},{"author_name":"Mahta Mehdiabadi","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Federica Quaglia","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Rita Pancsa","author_inst":"HUN-REN Research Centre for Natural Sciences, Institute of Molecular Life Sciences, Budapest, Hungary"},{"author_name":"Lucia B. Chemes","author_inst":"Instituto de Investigaciones Biotecnologicas, Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET), Escuela de Bio y Nanotecnologias (EByN), Uni"},{"author_name":"Meisam Ahmadi","author_inst":"Computer Engineering Department, Iran University of Science And Technology, Tehran, Iran"},{"author_name":"Hongryul Ahn","author_inst":"Division of Data Science, The University of Suwon, Gyeonggi-do, South Korea"},{"author_name":"Adrian M. Altenhoff","author_inst":"Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland; Department of Computer Science, ETH Zurich, Zurich, Switzerland"},{"author_name":"Ehsaneddin Asgari","author_inst":"Qatar Computing Research Institute, HBKU, Doha, Qatar"},{"author_name":"Maria Cristina Aspromonte","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Volkan Atalay","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Giulia Babbi","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy"},{"author_name":"Davide Baldazzi","author_inst":"Oncogenetics and Functional Oncogenomics, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy"},{"author_name":"Meet M. Barot","author_inst":"Center for Data Science, New York University, New York, NY, USA"},{"author_name":"Asa Ben-Hur","author_inst":"Department of Computer Science, Colorado State University, Fort Collins, CO, USA"},{"author_name":"Alfredo Benso","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Daniel Berenberg","author_inst":"Department of Computer Science, New York University, New York, NY, USA"},{"author_name":"Jari Bjorne","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Florian Boecker","author_inst":"INRES Crop Bioinformatics, University of Bonn, Bonn, Germany"},{"author_name":"Paolo Boldi","author_inst":"Dipartimento di Informatica Giovanni Degli Antoni, University of Milan, Milano, Italy"},{"author_name":"Joseph Bonello","author_inst":"Computer Information Systems, University of Malta, Msida, Malta; Structural and Molecular Biology, University College London, London, England"},{"author_name":"Nicola Bordin","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Piyush Borole","author_inst":"College of Science and Technology, Temple University, Philadelphia, PA, USA"},{"author_name":"Ali Ebrahimpour Boroojeny","author_inst":"Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA"},{"author_name":"Renzhi Cao","author_inst":"Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA"},{"author_name":"Stefano Di Carlo","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Rita Casadio","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy"},{"author_name":"Elena Casiraghi","author_inst":"Department of Computer Science, University of Milan, Milano, Italy; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, "},{"author_name":"Jia-Ming Chang","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Chen Chen","author_inst":"Department of Electrical Engineering and Computer Science, University of Missouri Columbia, Columbia, MO, USA"},{"author_name":"Tse-Ming Chen","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Jianlin Cheng","author_inst":"Department of Electrical Engineering and Computer Science, University of Missouri Columbia, Columbia, MO, USA; NextGen Precision Health, University of Missouri "},{"author_name":"Ssu Chiu","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Alperen Dalkiran","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Radoslav S. Davidovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Christophe Dessimoz","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switz"},{"author_name":"Rucheng Diao","author_inst":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Warith Eddine Djeddi","author_inst":"FST Manar, University of Tunis El Manar, Tunis, Tunisia; University of Jendouba, Jendouba, Tunisia"},{"author_name":"Tunca Dogan","author_inst":"Department of Computer Engineering, Hacettepe University, Ankara, Turkey; Department of Bioinformatics, Hacettepe University, Ankara, Turkey"},{"author_name":"Sean T. Flannery","author_inst":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"Paolo Fontana","author_inst":"Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, Italy"},{"author_name":"Marco Frasca","author_inst":"Dipartimento di Informatica Giovanni Degli Antoni, University of Milan, Milano, Italy"},{"author_name":"Lydia Freddolino","author_inst":"Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA; Department of Computational Medicine and Bioinformatics, University of Michigan,"},{"author_name":"Branislava Gemovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Jesse Gillis","author_inst":"Department of Physiology, University of Toronto, Toronto, ON, Canada; Donnelly Centre, University of Toronto, Toronto, ON, Canada; Cold Spring Harbor Laboratory"},{"author_name":"Filip Ginter","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Vladimir Gligorijevic","author_inst":"Prescient Design, Genentech, New York, NY, USA"},{"author_name":"Giuliano Grossi","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Michael Heinzinger","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany"},{"author_name":"Kyle Hippe","author_inst":"Department of Computer Science, University of Chicago, Chicago, IL, USA; Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA"},{"author_name":"Robert Hoehndorf","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; KAUST Center of Excell"},{"author_name":"Liisa Holm","author_inst":"HiLIFE, Institute of Biotechnology, University of Helsinki, Helsinki, Finland; Faculty of Biological and Environmental Sciences, Organismal and Evolutionary Bio"},{"author_name":"Jie Hou","author_inst":"Department of Computer Science, Saint Louis University,  St. Louis, MO, USA"},{"author_name":"John R. Hover","author_inst":"MAP\/BARseq Core Facility, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Yen-Ting Huang","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Emilio Ispano","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Suraiya Jabin","author_inst":"Department of Computer Science, Jamia Millia Islamia, New Delhi, India; Faculty of Sciences, Jamia Millia Islamia, New Delhi, India"},{"author_name":"Aashish Jain","author_inst":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"David T. Jones","author_inst":"Department of Computer Science, University College London, London, UK"},{"author_name":"Suwisa Kaewphan","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Yuki Kagaya","author_inst":"Department of Biological Sciences, Purdue University, West Lafayette, IN, USA"},{"author_name":"Jenna Kanerva","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Daisuke Kihara","author_inst":"Department of Biological Sciences, Purdue University, West Lafayette, IN, USA; Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"Maxat Kulmanov","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; KAUST Center of Excell"},{"author_name":"Sunil Kumar","author_inst":"Farming Solutions and Digital, Corteva Agriscience, Hyderabad, India"},{"author_name":"Lukasz Kurgan","author_inst":"Department of Computer Science, Virginia Commonwealth University, Richmond, VA,USA"},{"author_name":"Enrico Lavezzo","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Jon Lees","author_inst":"Faculty of Health and Life Sciences, University of Bristol, Bristol, UK"},{"author_name":"Wen-Hung Liao","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Han Lin","author_inst":"Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, New Taipei City, Taiwan"},{"author_name":"Michal Linial","author_inst":"Department of Biological Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Maria Littmann","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany"},{"author_name":"Lizhi Liu","author_inst":"School of Computer Science, Fudan University, Shanghai, China"},{"author_name":"Tong Liu","author_inst":"Department of Computer Science, University of Miami, Coral Gables, FL, USA"},{"author_name":"Yi Wei Liu","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Stavros Makrodimitris","author_inst":"Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands; Intelligent Systems, Delft University of Technology,  Delft, Netherlands"},{"author_name":"Laura Manuto","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Pier Luigi Martelli","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy; Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy"},{"author_name":"Alice Carolyn Mchardy","author_inst":"Computational Biology for Infection Research, Helmholtz Centre for Infection Research, Brunswick, Germany"},{"author_name":"Gabriela A. Merino","author_inst":"European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK"},{"author_name":"Diego H. Milone","author_inst":"Department of Informatics, FICH-UNL, Research institute for Signals, Systems and Computational intelligence, sinc(i), CONICET\/UNL, Santa Fe, Argentina"},{"author_name":"Sarthak Mishra","author_inst":"Department of Computer Science, Jamia Millia Islamia, New Delhi, India"},{"author_name":"Mohammad R. K. Mofrad","author_inst":"Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California Berkeley, Berkeley, CA, USA"},{"author_name":"David Moi","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switz"},{"author_name":"Tsukasa Nakamura","author_inst":"Graduate School of Information Sciences, Tohoku University, Sendai, Japan"},{"author_name":"Vijay Kumar Narsapuram","author_inst":"Genomics Molecular and Data Science, Corteva Agriscience, IN, USA"},{"author_name":"Maria Victoria Nugnes","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Takeshi Obayashi","author_inst":"Graduate School of Information Sciences, Tohoku University, Sendai, Japan; WPI-AIMEC, Tohoku University, Sendai, Japan"},{"author_name":"Dan Ofer","author_inst":"Department of Biology, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Alberto Paccanaro","author_inst":"Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Surrey, UK; School of Applied Mathematics, Funda"},{"author_name":"Vladimir R. Perovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Alessandro Petrini","author_inst":"Huawei Galois Lab, Huawei Technologies France SASU, Boulogne-Billancourt, France; Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Gianfranco Politano","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Daniele Raimondi","author_inst":"ESAT-STADIUS, KU Leuven, Leuven, Belgium"},{"author_name":"Nadav Rappoport","author_inst":"Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel"},{"author_name":"Hafeez Ur Rehman","author_inst":"Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan; School of Computing and Data Science, Oryx Universal"},{"author_name":"Maarten J. M. F. Reijnders","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland"},{"author_name":"Marcel J. T. Reinders","author_inst":"Intelligent Systems, Delft University of Technology,  Delft, Netherlands"},{"author_name":"P. Douglas Renfrew","author_inst":"Center for Computational Biology, Flatiron Institute, New York, NY, USA"},{"author_name":"Ahmet S. Rifaioglu","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Alfonso E. Romero","author_inst":"Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Surrey, UK"},{"author_name":"Abhiman Saraswathi","author_inst":"Farming Solutions and Digital, Corteva Agriscience, IA, USA"},{"author_name":"Castrense Savojardo","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy; Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy"},{"author_name":"Harry M. Scholes","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Heiko Schoof","author_inst":"INRES Crop Bioinformatics, University of Bonn, Bonn, Germany"},{"author_name":"Yang Shen","author_inst":"Department of Electrical and Computer Engineering, Texas A&M University, College Station, USA"},{"author_name":"Ian Sillitoe","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Georgina Stegmayer","author_inst":"FICH, Research Institute for Signals, Systems and Computational Intelligence (sinc(i)); Universidad Nacional del Litoral (UNL), CONICET"},{"author_name":"Amos Stern","author_inst":"The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Henri Tiittanen","author_inst":"Institute of Biotechnology, University of Helsinki, Helsinki, Finland"},{"author_name":"Sumyyah Toonsi","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia"},{"author_name":"Stefano Toppo","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Petri Toronen","author_inst":"Institute of Biotechnology, University of Helsinki, Helsinki, Finland"},{"author_name":"Mateo Torres","author_inst":"School of Applied Mathematics, Fundacao Getulio Vargas, Rio de Janeiro, Brazil;"},{"author_name":"Gabriella Trucco","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Giorgio Valentini","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Nevena Veljkovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Alex Warwick Vesztrocy","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland"},{"author_name":"Vedrana Vidulin","author_inst":"Pink Data Analytics, Croatia"},{"author_name":"Amelia Villegas-Morcillo","author_inst":"Intelligent Systems, Delft University of Technology,  Delft, Netherlands; Signal Theory, Telematics and Communications, University of Granada, Granada, Spain"},{"author_name":"Antti Virtanen","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Wim Vranken","author_inst":"Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium"},{"author_name":"Slobodan Vucetic","author_inst":"Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA"},{"author_name":"Cen Wan","author_inst":"School of Computing and Mathematical Sciences, Birkbeck, University of London, London, UK"},{"author_name":"Zheng Wang","author_inst":"Department of Computer Science, University of Miami, Coral Gables, FL, USA; Department of Biology, University of Miami, Coral Gables, FL, USA; Sylvester Compreh"},{"author_name":"Mark N. Wass","author_inst":"School of Natural Sciences, University of Kent, Canterbury, Kent, UK"},{"author_name":"Robert M. Waterhouse","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; Environmental Bioinformatics, SIB Swiss Institute of Bioinformatics, Lausann"},{"author_name":"Sadok Ben Yahia","author_inst":"The Faculty of Engineering, University of Technology Tallinn, Tallinn, Estonia; University of Southern Denmark, Denmark"},{"author_name":"Haixuan Yang","author_inst":"School of Mathematics and Statistical Sciences, National University of Ireland Galway, Galway, Ireland"},{"author_name":"Shuwei Yao","author_inst":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China"},{"author_name":"Ronghui You","author_inst":"School of Statistics and Data Science, Nankai University, Tianjin, China"},{"author_name":"Jeffrey Yunes","author_inst":"Yunes Foundation for Research on Aging, San Francisco, CA, USA"},{"author_name":"Chengxin Zhang","author_inst":"CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of "},{"author_name":"Yang Zhang","author_inst":"Department of Computer Science, School of Computing, National University of Singapore, Singapore; Department of Biochemistry, Yong Loo Lin School of Medicine, N"},{"author_name":"Chenguang Zhao","author_inst":"Computer and Information Sciences Department, St. Ambrose University, Davenport, IA, USA"},{"author_name":"Xiaogen Zhou","author_inst":"College of Information Engineering, Zhejiang University of Technology, Zhejiang, China"},{"author_name":"Yi-Heng Zhu","author_inst":"College of Artificial Intelligence, Nanjing Agricultural University, Jiangsu, China"},{"author_name":"Shanfeng Zhu","author_inst":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China"},{"author_name":"Hao Zhu","author_inst":"Department of Computer Science, Florida Memorial University, Miami Gardens, FL, USA"},{"author_name":"Gokhan Ozsari","author_inst":"Chalmers E-commons, Chalmers University of Technology, Goteborg, Sweden; Department of Computer Engineering, Middle East Technical University, Ankara, Turkey;"},{"author_name":"Burkhard Rost","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany; Chair for Bioinformatics, Technical University of Muni"},{"author_name":"Christine Orengo","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Marc Robinson-Rechavi","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland"},{"author_name":"Dannie Durand","author_inst":"Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Computational Biology, Carnegie Mellon University, Pittsburgh,"},{"author_name":"Steven E. Brenner","author_inst":"Department of Plant and Microbial Biology and Center for Computational Biology, University of California, Berkeley, CA, USA"},{"author_name":"Casey S. Greene","author_inst":"Department of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA"},{"author_name":"Sean D. Mooney","author_inst":"Center for Information Technology, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Silvio C. E. Tosatto","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Iddo Friedberg","author_inst":"Department of Veterinary Microbiology & Preventive Medicine, Iowa State University, Ames, IA, USA"},{"author_name":"Predrag Radivojac","author_inst":"Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"On the state of protein function prediction: a report on the fourth CAFA challenge","rel_doi":"10.64898\/2026.05.06.722942","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.722942","rel_abs":"Background: The Critical Assessment of Functional Annotation (CAFA) is a community effort held to understand the field of computational protein function prediction. Every three years, since 2010, the organizers initiate an experiment to collect function predictions on a large set of proteins and then evaluate the performance of predicting methods on a subset of proteins that have accumulated experimental annotations between the submission deadline and the evaluation time. CAFA provides an independent and rigorous assessment of the current state of the art, thus leveling the playing field, highlighting successes, revealing bottlenecks, and offering a forum for the exchange of ideas in protein science. Here, we report the results of the fourth CAFA experiment (CAFA4). Results: CAFA4 featured the participation of 148 methods from 70 research groups on a total of 46,205 unique proteins over a 5-year annotation accumulation phase, the longest in any CAFA. In a comparison across CAFA2-CAFA4 methods, the prediction of Gene Ontology (GO) terms has clearly improved across all three GO aspects and traditional evaluation settings. While not achieving the first rank, several CAFA2 and CAFA3 methods featured in the top ten methods in many evaluations, suggesting that earlier methods still hold relevance. The performance is weaker in the newly introduced \"partial knowledge\" evaluation category (proteins with experimental annotations before submission deadline that gained additional annotations in the same GO aspect during the annotation accumulation phase), highlighting the need for a new class of methods. The rankings of the methods were stable over the years in traditional evaluation settings, but less so in the new partial knowledge evaluation. Overall, the field continues to progress with some influx of new participants. Sustained efforts will be necessary to substantially advance it.","rel_num_authors":154,"rel_authors":[{"author_name":"Rashika Ramola","author_inst":"Northeastern University"},{"author_name":"M. Clara De Paolis Klauza","author_inst":"Northeastern University"},{"author_name":"Damiano Piovesan","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Yisu Peng","author_inst":"Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA"},{"author_name":"Parnal Joshi","author_inst":"Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, USA; Department of Veterinary Microbiology & Preventive Medicine, Iowa State "},{"author_name":"Mahta Mehdiabadi","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Federica Quaglia","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Rita Pancsa","author_inst":"HUN-REN Research Centre for Natural Sciences, Institute of Molecular Life Sciences, Budapest, Hungary"},{"author_name":"Lucia B. Chemes","author_inst":"Instituto de Investigaciones Biotecnologicas, Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET), Escuela de Bio y Nanotecnologias (EByN), Uni"},{"author_name":"Meisam Ahmadi","author_inst":"Computer Engineering Department, Iran University of Science And Technology, Tehran, Iran"},{"author_name":"Hongryul Ahn","author_inst":"Division of Data Science, The University of Suwon, Gyeonggi-do, South Korea"},{"author_name":"Adrian M. Altenhoff","author_inst":"Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland; Department of Computer Science, ETH Zurich, Zurich, Switzerland"},{"author_name":"Ehsaneddin Asgari","author_inst":"Qatar Computing Research Institute, HBKU, Doha, Qatar"},{"author_name":"Maria Cristina Aspromonte","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Volkan Atalay","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Giulia Babbi","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy"},{"author_name":"Davide Baldazzi","author_inst":"Oncogenetics and Functional Oncogenomics, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy"},{"author_name":"Meet M. Barot","author_inst":"Center for Data Science, New York University, New York, NY, USA"},{"author_name":"Asa Ben-Hur","author_inst":"Department of Computer Science, Colorado State University, Fort Collins, CO, USA"},{"author_name":"Alfredo Benso","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Daniel Berenberg","author_inst":"Department of Computer Science, New York University, New York, NY, USA"},{"author_name":"Jari Bjorne","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Florian Boecker","author_inst":"INRES Crop Bioinformatics, University of Bonn, Bonn, Germany"},{"author_name":"Paolo Boldi","author_inst":"Dipartimento di Informatica Giovanni Degli Antoni, University of Milan, Milano, Italy"},{"author_name":"Joseph Bonello","author_inst":"Computer Information Systems, University of Malta, Msida, Malta; Structural and Molecular Biology, University College London, London, England"},{"author_name":"Nicola Bordin","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Piyush Borole","author_inst":"College of Science and Technology, Temple University, Philadelphia, PA, USA"},{"author_name":"Ali Ebrahimpour Boroojeny","author_inst":"Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA"},{"author_name":"Renzhi Cao","author_inst":"Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA"},{"author_name":"Stefano Di Carlo","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Rita Casadio","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy"},{"author_name":"Elena Casiraghi","author_inst":"Department of Computer Science, University of Milan, Milano, Italy; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, "},{"author_name":"Jia-Ming Chang","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Chen Chen","author_inst":"Department of Electrical Engineering and Computer Science, University of Missouri Columbia, Columbia, MO, USA"},{"author_name":"Tse-Ming Chen","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Jianlin Cheng","author_inst":"Department of Electrical Engineering and Computer Science, University of Missouri Columbia, Columbia, MO, USA; NextGen Precision Health, University of Missouri "},{"author_name":"Ssu Chiu","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Alperen Dalkiran","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Radoslav S. Davidovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Christophe Dessimoz","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switz"},{"author_name":"Rucheng Diao","author_inst":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Warith Eddine Djeddi","author_inst":"FST Manar, University of Tunis El Manar, Tunis, Tunisia; University of Jendouba, Jendouba, Tunisia"},{"author_name":"Tunca Dogan","author_inst":"Department of Computer Engineering, Hacettepe University, Ankara, Turkey; Department of Bioinformatics, Hacettepe University, Ankara, Turkey"},{"author_name":"Sean T. Flannery","author_inst":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"Paolo Fontana","author_inst":"Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, Italy"},{"author_name":"Marco Frasca","author_inst":"Dipartimento di Informatica Giovanni Degli Antoni, University of Milan, Milano, Italy"},{"author_name":"Lydia Freddolino","author_inst":"Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA; Department of Computational Medicine and Bioinformatics, University of Michigan,"},{"author_name":"Branislava Gemovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Jesse Gillis","author_inst":"Department of Physiology, University of Toronto, Toronto, ON, Canada; Donnelly Centre, University of Toronto, Toronto, ON, Canada; Cold Spring Harbor Laboratory"},{"author_name":"Filip Ginter","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Vladimir Gligorijevic","author_inst":"Prescient Design, Genentech, New York, NY, USA"},{"author_name":"Giuliano Grossi","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Michael Heinzinger","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany"},{"author_name":"Kyle Hippe","author_inst":"Department of Computer Science, University of Chicago, Chicago, IL, USA; Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA"},{"author_name":"Robert Hoehndorf","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; KAUST Center of Excell"},{"author_name":"Liisa Holm","author_inst":"HiLIFE, Institute of Biotechnology, University of Helsinki, Helsinki, Finland; Faculty of Biological and Environmental Sciences, Organismal and Evolutionary Bio"},{"author_name":"Jie Hou","author_inst":"Department of Computer Science, Saint Louis University,  St. Louis, MO, USA"},{"author_name":"John R. Hover","author_inst":"MAP\/BARseq Core Facility, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Yen-Ting Huang","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Emilio Ispano","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Suraiya Jabin","author_inst":"Department of Computer Science, Jamia Millia Islamia, New Delhi, India; Faculty of Sciences, Jamia Millia Islamia, New Delhi, India"},{"author_name":"Aashish Jain","author_inst":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"David T. Jones","author_inst":"Department of Computer Science, University College London, London, UK"},{"author_name":"Suwisa Kaewphan","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Yuki Kagaya","author_inst":"Department of Biological Sciences, Purdue University, West Lafayette, IN, USA"},{"author_name":"Jenna Kanerva","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Daisuke Kihara","author_inst":"Department of Biological Sciences, Purdue University, West Lafayette, IN, USA; Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"Maxat Kulmanov","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; KAUST Center of Excell"},{"author_name":"Sunil Kumar","author_inst":"Farming Solutions and Digital, Corteva Agriscience, Hyderabad, India"},{"author_name":"Lukasz Kurgan","author_inst":"Department of Computer Science, Virginia Commonwealth University, Richmond, VA,USA"},{"author_name":"Enrico Lavezzo","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Jon Lees","author_inst":"Faculty of Health and Life Sciences, University of Bristol, Bristol, UK"},{"author_name":"Wen-Hung Liao","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Han Lin","author_inst":"Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, New Taipei City, Taiwan"},{"author_name":"Michal Linial","author_inst":"Department of Biological Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Maria Littmann","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany"},{"author_name":"Lizhi Liu","author_inst":"School of Computer Science, Fudan University, Shanghai, China"},{"author_name":"Tong Liu","author_inst":"Department of Computer Science, University of Miami, Coral Gables, FL, USA"},{"author_name":"Yi Wei Liu","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Stavros Makrodimitris","author_inst":"Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands; Intelligent Systems, Delft University of Technology,  Delft, Netherlands"},{"author_name":"Laura Manuto","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Pier Luigi Martelli","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy; Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy"},{"author_name":"Alice Carolyn Mchardy","author_inst":"Computational Biology for Infection Research, Helmholtz Centre for Infection Research, Brunswick, Germany"},{"author_name":"Gabriela A. Merino","author_inst":"European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK"},{"author_name":"Diego H. Milone","author_inst":"Department of Informatics, FICH-UNL, Research institute for Signals, Systems and Computational intelligence, sinc(i), CONICET\/UNL, Santa Fe, Argentina"},{"author_name":"Sarthak Mishra","author_inst":"Department of Computer Science, Jamia Millia Islamia, New Delhi, India"},{"author_name":"Mohammad R. K. Mofrad","author_inst":"Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California Berkeley, Berkeley, CA, USA"},{"author_name":"David Moi","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switz"},{"author_name":"Tsukasa Nakamura","author_inst":"Graduate School of Information Sciences, Tohoku University, Sendai, Japan"},{"author_name":"Vijay Kumar Narsapuram","author_inst":"Genomics Molecular and Data Science, Corteva Agriscience, IN, USA"},{"author_name":"Maria Victoria Nugnes","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Takeshi Obayashi","author_inst":"Graduate School of Information Sciences, Tohoku University, Sendai, Japan; WPI-AIMEC, Tohoku University, Sendai, Japan"},{"author_name":"Dan Ofer","author_inst":"Department of Biology, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Alberto Paccanaro","author_inst":"Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Surrey, UK; School of Applied Mathematics, Funda"},{"author_name":"Vladimir R. Perovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Alessandro Petrini","author_inst":"Huawei Galois Lab, Huawei Technologies France SASU, Boulogne-Billancourt, France; Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Gianfranco Politano","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Daniele Raimondi","author_inst":"ESAT-STADIUS, KU Leuven, Leuven, Belgium"},{"author_name":"Nadav Rappoport","author_inst":"Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel"},{"author_name":"Hafeez Ur Rehman","author_inst":"Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan; School of Computing and Data Science, Oryx Universal"},{"author_name":"Maarten J. M. F. Reijnders","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland"},{"author_name":"Marcel J. T. Reinders","author_inst":"Intelligent Systems, Delft University of Technology,  Delft, Netherlands"},{"author_name":"P. Douglas Renfrew","author_inst":"Center for Computational Biology, Flatiron Institute, New York, NY, USA"},{"author_name":"Ahmet S. Rifaioglu","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Alfonso E. Romero","author_inst":"Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Surrey, UK"},{"author_name":"Abhiman Saraswathi","author_inst":"Farming Solutions and Digital, Corteva Agriscience, IA, USA"},{"author_name":"Castrense Savojardo","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy; Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy"},{"author_name":"Harry M. Scholes","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Heiko Schoof","author_inst":"INRES Crop Bioinformatics, University of Bonn, Bonn, Germany"},{"author_name":"Yang Shen","author_inst":"Department of Electrical and Computer Engineering, Texas A&M University, College Station, USA"},{"author_name":"Ian Sillitoe","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Georgina Stegmayer","author_inst":"FICH, Research Institute for Signals, Systems and Computational Intelligence (sinc(i)); Universidad Nacional del Litoral (UNL), CONICET"},{"author_name":"Amos Stern","author_inst":"The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Henri Tiittanen","author_inst":"Institute of Biotechnology, University of Helsinki, Helsinki, Finland"},{"author_name":"Sumyyah Toonsi","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia"},{"author_name":"Stefano Toppo","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Petri Toronen","author_inst":"Institute of Biotechnology, University of Helsinki, Helsinki, Finland"},{"author_name":"Mateo Torres","author_inst":"School of Applied Mathematics, Fundacao Getulio Vargas, Rio de Janeiro, Brazil;"},{"author_name":"Gabriella Trucco","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Giorgio Valentini","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Nevena Veljkovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Alex Warwick Vesztrocy","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland"},{"author_name":"Vedrana Vidulin","author_inst":"Pink Data Analytics, Croatia"},{"author_name":"Amelia Villegas-Morcillo","author_inst":"Intelligent Systems, Delft University of Technology,  Delft, Netherlands; Signal Theory, Telematics and Communications, University of Granada, Granada, Spain"},{"author_name":"Antti Virtanen","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Wim Vranken","author_inst":"Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium"},{"author_name":"Slobodan Vucetic","author_inst":"Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA"},{"author_name":"Cen Wan","author_inst":"School of Computing and Mathematical Sciences, Birkbeck, University of London, London, UK"},{"author_name":"Zheng Wang","author_inst":"Department of Computer Science, University of Miami, Coral Gables, FL, USA; Department of Biology, University of Miami, Coral Gables, FL, USA; Sylvester Compreh"},{"author_name":"Mark N. Wass","author_inst":"School of Natural Sciences, University of Kent, Canterbury, Kent, UK"},{"author_name":"Robert M. Waterhouse","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; Environmental Bioinformatics, SIB Swiss Institute of Bioinformatics, Lausann"},{"author_name":"Sadok Ben Yahia","author_inst":"The Faculty of Engineering, University of Technology Tallinn, Tallinn, Estonia; University of Southern Denmark, Denmark"},{"author_name":"Haixuan Yang","author_inst":"School of Mathematics and Statistical Sciences, National University of Ireland Galway, Galway, Ireland"},{"author_name":"Shuwei Yao","author_inst":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China"},{"author_name":"Ronghui You","author_inst":"School of Statistics and Data Science, Nankai University, Tianjin, China"},{"author_name":"Jeffrey Yunes","author_inst":"Yunes Foundation for Research on Aging, San Francisco, CA, USA"},{"author_name":"Chengxin Zhang","author_inst":"CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of "},{"author_name":"Yang Zhang","author_inst":"Department of Computer Science, School of Computing, National University of Singapore, Singapore; Department of Biochemistry, Yong Loo Lin School of Medicine, N"},{"author_name":"Chenguang Zhao","author_inst":"Computer and Information Sciences Department, St. Ambrose University, Davenport, IA, USA"},{"author_name":"Xiaogen Zhou","author_inst":"College of Information Engineering, Zhejiang University of Technology, Zhejiang, China"},{"author_name":"Yi-Heng Zhu","author_inst":"College of Artificial Intelligence, Nanjing Agricultural University, Jiangsu, China"},{"author_name":"Shanfeng Zhu","author_inst":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China"},{"author_name":"Hao Zhu","author_inst":"Department of Computer Science, Florida Memorial University, Miami Gardens, FL, USA"},{"author_name":"Gokhan Ozsari","author_inst":"Chalmers E-commons, Chalmers University of Technology, Goteborg, Sweden; Department of Computer Engineering, Middle East Technical University, Ankara, Turkey;"},{"author_name":"Burkhard Rost","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany; Chair for Bioinformatics, Technical University of Muni"},{"author_name":"Christine Orengo","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Marc Robinson-Rechavi","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland"},{"author_name":"Dannie Durand","author_inst":"Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Computational Biology, Carnegie Mellon University, Pittsburgh,"},{"author_name":"Steven E. Brenner","author_inst":"Department of Plant and Microbial Biology and Center for Computational Biology, University of California, Berkeley, CA, USA"},{"author_name":"Casey S. Greene","author_inst":"Department of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA"},{"author_name":"Sean D. Mooney","author_inst":"Center for Information Technology, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Silvio C. E. Tosatto","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Iddo Friedberg","author_inst":"Department of Veterinary Microbiology & Preventive Medicine, Iowa State University, Ames, IA, USA"},{"author_name":"Predrag Radivojac","author_inst":"Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"On the state of protein function prediction: a report on the fourth CAFA challenge","rel_doi":"10.64898\/2026.05.06.722942","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.722942","rel_abs":"Background: The Critical Assessment of Functional Annotation (CAFA) is a community effort held to understand the field of computational protein function prediction. Every three years, since 2010, the organizers initiate an experiment to collect function predictions on a large set of proteins and then evaluate the performance of predicting methods on a subset of proteins that have accumulated experimental annotations between the submission deadline and the evaluation time. CAFA provides an independent and rigorous assessment of the current state of the art, thus leveling the playing field, highlighting successes, revealing bottlenecks, and offering a forum for the exchange of ideas in protein science. Here, we report the results of the fourth CAFA experiment (CAFA4). Results: CAFA4 featured the participation of 148 methods from 70 research groups on a total of 46,205 unique proteins over a 5-year annotation accumulation phase, the longest in any CAFA. In a comparison across CAFA2-CAFA4 methods, the prediction of Gene Ontology (GO) terms has clearly improved across all three GO aspects and traditional evaluation settings. While not achieving the first rank, several CAFA2 and CAFA3 methods featured in the top ten methods in many evaluations, suggesting that earlier methods still hold relevance. The performance is weaker in the newly introduced \"partial knowledge\" evaluation category (proteins with experimental annotations before submission deadline that gained additional annotations in the same GO aspect during the annotation accumulation phase), highlighting the need for a new class of methods. The rankings of the methods were stable over the years in traditional evaluation settings, but less so in the new partial knowledge evaluation. Overall, the field continues to progress with some influx of new participants. Sustained efforts will be necessary to substantially advance it.","rel_num_authors":154,"rel_authors":[{"author_name":"Rashika Ramola","author_inst":"Northeastern University"},{"author_name":"M. Clara De Paolis Klauza","author_inst":"Northeastern University"},{"author_name":"Damiano Piovesan","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Yisu Peng","author_inst":"Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA"},{"author_name":"Parnal Joshi","author_inst":"Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, USA; Department of Veterinary Microbiology & Preventive Medicine, Iowa State "},{"author_name":"Mahta Mehdiabadi","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Federica Quaglia","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Rita Pancsa","author_inst":"HUN-REN Research Centre for Natural Sciences, Institute of Molecular Life Sciences, Budapest, Hungary"},{"author_name":"Lucia B. Chemes","author_inst":"Instituto de Investigaciones Biotecnologicas, Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET), Escuela de Bio y Nanotecnologias (EByN), Uni"},{"author_name":"Meisam Ahmadi","author_inst":"Computer Engineering Department, Iran University of Science And Technology, Tehran, Iran"},{"author_name":"Hongryul Ahn","author_inst":"Division of Data Science, The University of Suwon, Gyeonggi-do, South Korea"},{"author_name":"Adrian M. Altenhoff","author_inst":"Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland; Department of Computer Science, ETH Zurich, Zurich, Switzerland"},{"author_name":"Ehsaneddin Asgari","author_inst":"Qatar Computing Research Institute, HBKU, Doha, Qatar"},{"author_name":"Maria Cristina Aspromonte","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Volkan Atalay","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Giulia Babbi","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy"},{"author_name":"Davide Baldazzi","author_inst":"Oncogenetics and Functional Oncogenomics, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy"},{"author_name":"Meet M. Barot","author_inst":"Center for Data Science, New York University, New York, NY, USA"},{"author_name":"Asa Ben-Hur","author_inst":"Department of Computer Science, Colorado State University, Fort Collins, CO, USA"},{"author_name":"Alfredo Benso","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Daniel Berenberg","author_inst":"Department of Computer Science, New York University, New York, NY, USA"},{"author_name":"Jari Bjorne","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Florian Boecker","author_inst":"INRES Crop Bioinformatics, University of Bonn, Bonn, Germany"},{"author_name":"Paolo Boldi","author_inst":"Dipartimento di Informatica Giovanni Degli Antoni, University of Milan, Milano, Italy"},{"author_name":"Joseph Bonello","author_inst":"Computer Information Systems, University of Malta, Msida, Malta; Structural and Molecular Biology, University College London, London, England"},{"author_name":"Nicola Bordin","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Piyush Borole","author_inst":"College of Science and Technology, Temple University, Philadelphia, PA, USA"},{"author_name":"Ali Ebrahimpour Boroojeny","author_inst":"Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA"},{"author_name":"Renzhi Cao","author_inst":"Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA"},{"author_name":"Stefano Di Carlo","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Rita Casadio","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy"},{"author_name":"Elena Casiraghi","author_inst":"Department of Computer Science, University of Milan, Milano, Italy; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, "},{"author_name":"Jia-Ming Chang","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Chen Chen","author_inst":"Department of Electrical Engineering and Computer Science, University of Missouri Columbia, Columbia, MO, USA"},{"author_name":"Tse-Ming Chen","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Jianlin Cheng","author_inst":"Department of Electrical Engineering and Computer Science, University of Missouri Columbia, Columbia, MO, USA; NextGen Precision Health, University of Missouri "},{"author_name":"Ssu Chiu","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Alperen Dalkiran","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Radoslav S. Davidovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Christophe Dessimoz","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switz"},{"author_name":"Rucheng Diao","author_inst":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Warith Eddine Djeddi","author_inst":"FST Manar, University of Tunis El Manar, Tunis, Tunisia; University of Jendouba, Jendouba, Tunisia"},{"author_name":"Tunca Dogan","author_inst":"Department of Computer Engineering, Hacettepe University, Ankara, Turkey; Department of Bioinformatics, Hacettepe University, Ankara, Turkey"},{"author_name":"Sean T. Flannery","author_inst":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"Paolo Fontana","author_inst":"Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, Italy"},{"author_name":"Marco Frasca","author_inst":"Dipartimento di Informatica Giovanni Degli Antoni, University of Milan, Milano, Italy"},{"author_name":"Lydia Freddolino","author_inst":"Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA; Department of Computational Medicine and Bioinformatics, University of Michigan,"},{"author_name":"Branislava Gemovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Jesse Gillis","author_inst":"Department of Physiology, University of Toronto, Toronto, ON, Canada; Donnelly Centre, University of Toronto, Toronto, ON, Canada; Cold Spring Harbor Laboratory"},{"author_name":"Filip Ginter","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Vladimir Gligorijevic","author_inst":"Prescient Design, Genentech, New York, NY, USA"},{"author_name":"Giuliano Grossi","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Michael Heinzinger","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany"},{"author_name":"Kyle Hippe","author_inst":"Department of Computer Science, University of Chicago, Chicago, IL, USA; Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA"},{"author_name":"Robert Hoehndorf","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; KAUST Center of Excell"},{"author_name":"Liisa Holm","author_inst":"HiLIFE, Institute of Biotechnology, University of Helsinki, Helsinki, Finland; Faculty of Biological and Environmental Sciences, Organismal and Evolutionary Bio"},{"author_name":"Jie Hou","author_inst":"Department of Computer Science, Saint Louis University,  St. Louis, MO, USA"},{"author_name":"John R. Hover","author_inst":"MAP\/BARseq Core Facility, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Yen-Ting Huang","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Emilio Ispano","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Suraiya Jabin","author_inst":"Department of Computer Science, Jamia Millia Islamia, New Delhi, India; Faculty of Sciences, Jamia Millia Islamia, New Delhi, India"},{"author_name":"Aashish Jain","author_inst":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"David T. Jones","author_inst":"Department of Computer Science, University College London, London, UK"},{"author_name":"Suwisa Kaewphan","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Yuki Kagaya","author_inst":"Department of Biological Sciences, Purdue University, West Lafayette, IN, USA"},{"author_name":"Jenna Kanerva","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Daisuke Kihara","author_inst":"Department of Biological Sciences, Purdue University, West Lafayette, IN, USA; Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"Maxat Kulmanov","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; KAUST Center of Excell"},{"author_name":"Sunil Kumar","author_inst":"Farming Solutions and Digital, Corteva Agriscience, Hyderabad, India"},{"author_name":"Lukasz Kurgan","author_inst":"Department of Computer Science, Virginia Commonwealth University, Richmond, VA,USA"},{"author_name":"Enrico Lavezzo","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Jon Lees","author_inst":"Faculty of Health and Life Sciences, University of Bristol, Bristol, UK"},{"author_name":"Wen-Hung Liao","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Han Lin","author_inst":"Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, New Taipei City, Taiwan"},{"author_name":"Michal Linial","author_inst":"Department of Biological Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Maria Littmann","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany"},{"author_name":"Lizhi Liu","author_inst":"School of Computer Science, Fudan University, Shanghai, China"},{"author_name":"Tong Liu","author_inst":"Department of Computer Science, University of Miami, Coral Gables, FL, USA"},{"author_name":"Yi Wei Liu","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Stavros Makrodimitris","author_inst":"Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands; Intelligent Systems, Delft University of Technology,  Delft, Netherlands"},{"author_name":"Laura Manuto","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Pier Luigi Martelli","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy; Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy"},{"author_name":"Alice Carolyn Mchardy","author_inst":"Computational Biology for Infection Research, Helmholtz Centre for Infection Research, Brunswick, Germany"},{"author_name":"Gabriela A. Merino","author_inst":"European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK"},{"author_name":"Diego H. Milone","author_inst":"Department of Informatics, FICH-UNL, Research institute for Signals, Systems and Computational intelligence, sinc(i), CONICET\/UNL, Santa Fe, Argentina"},{"author_name":"Sarthak Mishra","author_inst":"Department of Computer Science, Jamia Millia Islamia, New Delhi, India"},{"author_name":"Mohammad R. K. Mofrad","author_inst":"Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California Berkeley, Berkeley, CA, USA"},{"author_name":"David Moi","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switz"},{"author_name":"Tsukasa Nakamura","author_inst":"Graduate School of Information Sciences, Tohoku University, Sendai, Japan"},{"author_name":"Vijay Kumar Narsapuram","author_inst":"Genomics Molecular and Data Science, Corteva Agriscience, IN, USA"},{"author_name":"Maria Victoria Nugnes","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Takeshi Obayashi","author_inst":"Graduate School of Information Sciences, Tohoku University, Sendai, Japan; WPI-AIMEC, Tohoku University, Sendai, Japan"},{"author_name":"Dan Ofer","author_inst":"Department of Biology, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Alberto Paccanaro","author_inst":"Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Surrey, UK; School of Applied Mathematics, Funda"},{"author_name":"Vladimir R. Perovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Alessandro Petrini","author_inst":"Huawei Galois Lab, Huawei Technologies France SASU, Boulogne-Billancourt, France; Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Gianfranco Politano","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Daniele Raimondi","author_inst":"ESAT-STADIUS, KU Leuven, Leuven, Belgium"},{"author_name":"Nadav Rappoport","author_inst":"Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel"},{"author_name":"Hafeez Ur Rehman","author_inst":"Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan; School of Computing and Data Science, Oryx Universal"},{"author_name":"Maarten J. M. F. Reijnders","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland"},{"author_name":"Marcel J. T. Reinders","author_inst":"Intelligent Systems, Delft University of Technology,  Delft, Netherlands"},{"author_name":"P. Douglas Renfrew","author_inst":"Center for Computational Biology, Flatiron Institute, New York, NY, USA"},{"author_name":"Ahmet S. Rifaioglu","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Alfonso E. Romero","author_inst":"Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Surrey, UK"},{"author_name":"Abhiman Saraswathi","author_inst":"Farming Solutions and Digital, Corteva Agriscience, IA, USA"},{"author_name":"Castrense Savojardo","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy; Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy"},{"author_name":"Harry M. Scholes","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Heiko Schoof","author_inst":"INRES Crop Bioinformatics, University of Bonn, Bonn, Germany"},{"author_name":"Yang Shen","author_inst":"Department of Electrical and Computer Engineering, Texas A&M University, College Station, USA"},{"author_name":"Ian Sillitoe","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Georgina Stegmayer","author_inst":"FICH, Research Institute for Signals, Systems and Computational Intelligence (sinc(i)); Universidad Nacional del Litoral (UNL), CONICET"},{"author_name":"Amos Stern","author_inst":"The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Henri Tiittanen","author_inst":"Institute of Biotechnology, University of Helsinki, Helsinki, Finland"},{"author_name":"Sumyyah Toonsi","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia"},{"author_name":"Stefano Toppo","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Petri Toronen","author_inst":"Institute of Biotechnology, University of Helsinki, Helsinki, Finland"},{"author_name":"Mateo Torres","author_inst":"School of Applied Mathematics, Fundacao Getulio Vargas, Rio de Janeiro, Brazil;"},{"author_name":"Gabriella Trucco","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Giorgio Valentini","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Nevena Veljkovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Alex Warwick Vesztrocy","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland"},{"author_name":"Vedrana Vidulin","author_inst":"Pink Data Analytics, Croatia"},{"author_name":"Amelia Villegas-Morcillo","author_inst":"Intelligent Systems, Delft University of Technology,  Delft, Netherlands; Signal Theory, Telematics and Communications, University of Granada, Granada, Spain"},{"author_name":"Antti Virtanen","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Wim Vranken","author_inst":"Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium"},{"author_name":"Slobodan Vucetic","author_inst":"Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA"},{"author_name":"Cen Wan","author_inst":"School of Computing and Mathematical Sciences, Birkbeck, University of London, London, UK"},{"author_name":"Zheng Wang","author_inst":"Department of Computer Science, University of Miami, Coral Gables, FL, USA; Department of Biology, University of Miami, Coral Gables, FL, USA; Sylvester Compreh"},{"author_name":"Mark N. Wass","author_inst":"School of Natural Sciences, University of Kent, Canterbury, Kent, UK"},{"author_name":"Robert M. Waterhouse","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; Environmental Bioinformatics, SIB Swiss Institute of Bioinformatics, Lausann"},{"author_name":"Sadok Ben Yahia","author_inst":"The Faculty of Engineering, University of Technology Tallinn, Tallinn, Estonia; University of Southern Denmark, Denmark"},{"author_name":"Haixuan Yang","author_inst":"School of Mathematics and Statistical Sciences, National University of Ireland Galway, Galway, Ireland"},{"author_name":"Shuwei Yao","author_inst":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China"},{"author_name":"Ronghui You","author_inst":"School of Statistics and Data Science, Nankai University, Tianjin, China"},{"author_name":"Jeffrey Yunes","author_inst":"Yunes Foundation for Research on Aging, San Francisco, CA, USA"},{"author_name":"Chengxin Zhang","author_inst":"CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of "},{"author_name":"Yang Zhang","author_inst":"Department of Computer Science, School of Computing, National University of Singapore, Singapore; Department of Biochemistry, Yong Loo Lin School of Medicine, N"},{"author_name":"Chenguang Zhao","author_inst":"Computer and Information Sciences Department, St. Ambrose University, Davenport, IA, USA"},{"author_name":"Xiaogen Zhou","author_inst":"College of Information Engineering, Zhejiang University of Technology, Zhejiang, China"},{"author_name":"Yi-Heng Zhu","author_inst":"College of Artificial Intelligence, Nanjing Agricultural University, Jiangsu, China"},{"author_name":"Shanfeng Zhu","author_inst":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China"},{"author_name":"Hao Zhu","author_inst":"Department of Computer Science, Florida Memorial University, Miami Gardens, FL, USA"},{"author_name":"Gokhan Ozsari","author_inst":"Chalmers E-commons, Chalmers University of Technology, Goteborg, Sweden; Department of Computer Engineering, Middle East Technical University, Ankara, Turkey;"},{"author_name":"Burkhard Rost","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany; Chair for Bioinformatics, Technical University of Muni"},{"author_name":"Christine Orengo","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Marc Robinson-Rechavi","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland"},{"author_name":"Dannie Durand","author_inst":"Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Computational Biology, Carnegie Mellon University, Pittsburgh,"},{"author_name":"Steven E. Brenner","author_inst":"Department of Plant and Microbial Biology and Center for Computational Biology, University of California, Berkeley, CA, USA"},{"author_name":"Casey S. Greene","author_inst":"Department of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA"},{"author_name":"Sean D. Mooney","author_inst":"Center for Information Technology, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Silvio C. E. Tosatto","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Iddo Friedberg","author_inst":"Department of Veterinary Microbiology & Preventive Medicine, Iowa State University, Ames, IA, USA"},{"author_name":"Predrag Radivojac","author_inst":"Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"On the state of protein function prediction: a report on the fourth CAFA challenge","rel_doi":"10.64898\/2026.05.06.722942","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.722942","rel_abs":"Background: The Critical Assessment of Functional Annotation (CAFA) is a community effort held to understand the field of computational protein function prediction. Every three years, since 2010, the organizers initiate an experiment to collect function predictions on a large set of proteins and then evaluate the performance of predicting methods on a subset of proteins that have accumulated experimental annotations between the submission deadline and the evaluation time. CAFA provides an independent and rigorous assessment of the current state of the art, thus leveling the playing field, highlighting successes, revealing bottlenecks, and offering a forum for the exchange of ideas in protein science. Here, we report the results of the fourth CAFA experiment (CAFA4). Results: CAFA4 featured the participation of 148 methods from 70 research groups on a total of 46,205 unique proteins over a 5-year annotation accumulation phase, the longest in any CAFA. In a comparison across CAFA2-CAFA4 methods, the prediction of Gene Ontology (GO) terms has clearly improved across all three GO aspects and traditional evaluation settings. While not achieving the first rank, several CAFA2 and CAFA3 methods featured in the top ten methods in many evaluations, suggesting that earlier methods still hold relevance. The performance is weaker in the newly introduced \"partial knowledge\" evaluation category (proteins with experimental annotations before submission deadline that gained additional annotations in the same GO aspect during the annotation accumulation phase), highlighting the need for a new class of methods. The rankings of the methods were stable over the years in traditional evaluation settings, but less so in the new partial knowledge evaluation. Overall, the field continues to progress with some influx of new participants. Sustained efforts will be necessary to substantially advance it.","rel_num_authors":154,"rel_authors":[{"author_name":"Rashika Ramola","author_inst":"Northeastern University"},{"author_name":"M. Clara De Paolis Klauza","author_inst":"Northeastern University"},{"author_name":"Damiano Piovesan","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Yisu Peng","author_inst":"Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA"},{"author_name":"Parnal Joshi","author_inst":"Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, USA; Department of Veterinary Microbiology & Preventive Medicine, Iowa State "},{"author_name":"Mahta Mehdiabadi","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Federica Quaglia","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Rita Pancsa","author_inst":"HUN-REN Research Centre for Natural Sciences, Institute of Molecular Life Sciences, Budapest, Hungary"},{"author_name":"Lucia B. Chemes","author_inst":"Instituto de Investigaciones Biotecnologicas, Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET), Escuela de Bio y Nanotecnologias (EByN), Uni"},{"author_name":"Meisam Ahmadi","author_inst":"Computer Engineering Department, Iran University of Science And Technology, Tehran, Iran"},{"author_name":"Hongryul Ahn","author_inst":"Division of Data Science, The University of Suwon, Gyeonggi-do, South Korea"},{"author_name":"Adrian M. Altenhoff","author_inst":"Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland; Department of Computer Science, ETH Zurich, Zurich, Switzerland"},{"author_name":"Ehsaneddin Asgari","author_inst":"Qatar Computing Research Institute, HBKU, Doha, Qatar"},{"author_name":"Maria Cristina Aspromonte","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Volkan Atalay","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Giulia Babbi","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy"},{"author_name":"Davide Baldazzi","author_inst":"Oncogenetics and Functional Oncogenomics, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy"},{"author_name":"Meet M. Barot","author_inst":"Center for Data Science, New York University, New York, NY, USA"},{"author_name":"Asa Ben-Hur","author_inst":"Department of Computer Science, Colorado State University, Fort Collins, CO, USA"},{"author_name":"Alfredo Benso","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Daniel Berenberg","author_inst":"Department of Computer Science, New York University, New York, NY, USA"},{"author_name":"Jari Bjorne","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Florian Boecker","author_inst":"INRES Crop Bioinformatics, University of Bonn, Bonn, Germany"},{"author_name":"Paolo Boldi","author_inst":"Dipartimento di Informatica Giovanni Degli Antoni, University of Milan, Milano, Italy"},{"author_name":"Joseph Bonello","author_inst":"Computer Information Systems, University of Malta, Msida, Malta; Structural and Molecular Biology, University College London, London, England"},{"author_name":"Nicola Bordin","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Piyush Borole","author_inst":"College of Science and Technology, Temple University, Philadelphia, PA, USA"},{"author_name":"Ali Ebrahimpour Boroojeny","author_inst":"Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA"},{"author_name":"Renzhi Cao","author_inst":"Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA"},{"author_name":"Stefano Di Carlo","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Rita Casadio","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy"},{"author_name":"Elena Casiraghi","author_inst":"Department of Computer Science, University of Milan, Milano, Italy; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, "},{"author_name":"Jia-Ming Chang","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Chen Chen","author_inst":"Department of Electrical Engineering and Computer Science, University of Missouri Columbia, Columbia, MO, USA"},{"author_name":"Tse-Ming Chen","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Jianlin Cheng","author_inst":"Department of Electrical Engineering and Computer Science, University of Missouri Columbia, Columbia, MO, USA; NextGen Precision Health, University of Missouri "},{"author_name":"Ssu Chiu","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Alperen Dalkiran","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Radoslav S. Davidovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Christophe Dessimoz","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switz"},{"author_name":"Rucheng Diao","author_inst":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Warith Eddine Djeddi","author_inst":"FST Manar, University of Tunis El Manar, Tunis, Tunisia; University of Jendouba, Jendouba, Tunisia"},{"author_name":"Tunca Dogan","author_inst":"Department of Computer Engineering, Hacettepe University, Ankara, Turkey; Department of Bioinformatics, Hacettepe University, Ankara, Turkey"},{"author_name":"Sean T. Flannery","author_inst":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"Paolo Fontana","author_inst":"Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, Italy"},{"author_name":"Marco Frasca","author_inst":"Dipartimento di Informatica Giovanni Degli Antoni, University of Milan, Milano, Italy"},{"author_name":"Lydia Freddolino","author_inst":"Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA; Department of Computational Medicine and Bioinformatics, University of Michigan,"},{"author_name":"Branislava Gemovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Jesse Gillis","author_inst":"Department of Physiology, University of Toronto, Toronto, ON, Canada; Donnelly Centre, University of Toronto, Toronto, ON, Canada; Cold Spring Harbor Laboratory"},{"author_name":"Filip Ginter","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Vladimir Gligorijevic","author_inst":"Prescient Design, Genentech, New York, NY, USA"},{"author_name":"Giuliano Grossi","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Michael Heinzinger","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany"},{"author_name":"Kyle Hippe","author_inst":"Department of Computer Science, University of Chicago, Chicago, IL, USA; Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA"},{"author_name":"Robert Hoehndorf","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; KAUST Center of Excell"},{"author_name":"Liisa Holm","author_inst":"HiLIFE, Institute of Biotechnology, University of Helsinki, Helsinki, Finland; Faculty of Biological and Environmental Sciences, Organismal and Evolutionary Bio"},{"author_name":"Jie Hou","author_inst":"Department of Computer Science, Saint Louis University,  St. Louis, MO, USA"},{"author_name":"John R. Hover","author_inst":"MAP\/BARseq Core Facility, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Yen-Ting Huang","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Emilio Ispano","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Suraiya Jabin","author_inst":"Department of Computer Science, Jamia Millia Islamia, New Delhi, India; Faculty of Sciences, Jamia Millia Islamia, New Delhi, India"},{"author_name":"Aashish Jain","author_inst":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"David T. Jones","author_inst":"Department of Computer Science, University College London, London, UK"},{"author_name":"Suwisa Kaewphan","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Yuki Kagaya","author_inst":"Department of Biological Sciences, Purdue University, West Lafayette, IN, USA"},{"author_name":"Jenna Kanerva","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Daisuke Kihara","author_inst":"Department of Biological Sciences, Purdue University, West Lafayette, IN, USA; Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"Maxat Kulmanov","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; KAUST Center of Excell"},{"author_name":"Sunil Kumar","author_inst":"Farming Solutions and Digital, Corteva Agriscience, Hyderabad, India"},{"author_name":"Lukasz Kurgan","author_inst":"Department of Computer Science, Virginia Commonwealth University, Richmond, VA,USA"},{"author_name":"Enrico Lavezzo","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Jon Lees","author_inst":"Faculty of Health and Life Sciences, University of Bristol, Bristol, UK"},{"author_name":"Wen-Hung Liao","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Han Lin","author_inst":"Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, New Taipei City, Taiwan"},{"author_name":"Michal Linial","author_inst":"Department of Biological Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Maria Littmann","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany"},{"author_name":"Lizhi Liu","author_inst":"School of Computer Science, Fudan University, Shanghai, China"},{"author_name":"Tong Liu","author_inst":"Department of Computer Science, University of Miami, Coral Gables, FL, USA"},{"author_name":"Yi Wei Liu","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Stavros Makrodimitris","author_inst":"Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands; Intelligent Systems, Delft University of Technology,  Delft, Netherlands"},{"author_name":"Laura Manuto","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Pier Luigi Martelli","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy; Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy"},{"author_name":"Alice Carolyn Mchardy","author_inst":"Computational Biology for Infection Research, Helmholtz Centre for Infection Research, Brunswick, Germany"},{"author_name":"Gabriela A. Merino","author_inst":"European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK"},{"author_name":"Diego H. Milone","author_inst":"Department of Informatics, FICH-UNL, Research institute for Signals, Systems and Computational intelligence, sinc(i), CONICET\/UNL, Santa Fe, Argentina"},{"author_name":"Sarthak Mishra","author_inst":"Department of Computer Science, Jamia Millia Islamia, New Delhi, India"},{"author_name":"Mohammad R. K. Mofrad","author_inst":"Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California Berkeley, Berkeley, CA, USA"},{"author_name":"David Moi","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switz"},{"author_name":"Tsukasa Nakamura","author_inst":"Graduate School of Information Sciences, Tohoku University, Sendai, Japan"},{"author_name":"Vijay Kumar Narsapuram","author_inst":"Genomics Molecular and Data Science, Corteva Agriscience, IN, USA"},{"author_name":"Maria Victoria Nugnes","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Takeshi Obayashi","author_inst":"Graduate School of Information Sciences, Tohoku University, Sendai, Japan; WPI-AIMEC, Tohoku University, Sendai, Japan"},{"author_name":"Dan Ofer","author_inst":"Department of Biology, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Alberto Paccanaro","author_inst":"Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Surrey, UK; School of Applied Mathematics, Funda"},{"author_name":"Vladimir R. Perovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Alessandro Petrini","author_inst":"Huawei Galois Lab, Huawei Technologies France SASU, Boulogne-Billancourt, France; Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Gianfranco Politano","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Daniele Raimondi","author_inst":"ESAT-STADIUS, KU Leuven, Leuven, Belgium"},{"author_name":"Nadav Rappoport","author_inst":"Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel"},{"author_name":"Hafeez Ur Rehman","author_inst":"Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan; School of Computing and Data Science, Oryx Universal"},{"author_name":"Maarten J. M. F. Reijnders","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland"},{"author_name":"Marcel J. T. Reinders","author_inst":"Intelligent Systems, Delft University of Technology,  Delft, Netherlands"},{"author_name":"P. Douglas Renfrew","author_inst":"Center for Computational Biology, Flatiron Institute, New York, NY, USA"},{"author_name":"Ahmet S. Rifaioglu","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Alfonso E. Romero","author_inst":"Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Surrey, UK"},{"author_name":"Abhiman Saraswathi","author_inst":"Farming Solutions and Digital, Corteva Agriscience, IA, USA"},{"author_name":"Castrense Savojardo","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy; Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy"},{"author_name":"Harry M. Scholes","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Heiko Schoof","author_inst":"INRES Crop Bioinformatics, University of Bonn, Bonn, Germany"},{"author_name":"Yang Shen","author_inst":"Department of Electrical and Computer Engineering, Texas A&M University, College Station, USA"},{"author_name":"Ian Sillitoe","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Georgina Stegmayer","author_inst":"FICH, Research Institute for Signals, Systems and Computational Intelligence (sinc(i)); Universidad Nacional del Litoral (UNL), CONICET"},{"author_name":"Amos Stern","author_inst":"The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Henri Tiittanen","author_inst":"Institute of Biotechnology, University of Helsinki, Helsinki, Finland"},{"author_name":"Sumyyah Toonsi","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia"},{"author_name":"Stefano Toppo","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Petri Toronen","author_inst":"Institute of Biotechnology, University of Helsinki, Helsinki, Finland"},{"author_name":"Mateo Torres","author_inst":"School of Applied Mathematics, Fundacao Getulio Vargas, Rio de Janeiro, Brazil;"},{"author_name":"Gabriella Trucco","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Giorgio Valentini","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Nevena Veljkovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Alex Warwick Vesztrocy","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland"},{"author_name":"Vedrana Vidulin","author_inst":"Pink Data Analytics, Croatia"},{"author_name":"Amelia Villegas-Morcillo","author_inst":"Intelligent Systems, Delft University of Technology,  Delft, Netherlands; Signal Theory, Telematics and Communications, University of Granada, Granada, Spain"},{"author_name":"Antti Virtanen","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Wim Vranken","author_inst":"Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium"},{"author_name":"Slobodan Vucetic","author_inst":"Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA"},{"author_name":"Cen Wan","author_inst":"School of Computing and Mathematical Sciences, Birkbeck, University of London, London, UK"},{"author_name":"Zheng Wang","author_inst":"Department of Computer Science, University of Miami, Coral Gables, FL, USA; Department of Biology, University of Miami, Coral Gables, FL, USA; Sylvester Compreh"},{"author_name":"Mark N. Wass","author_inst":"School of Natural Sciences, University of Kent, Canterbury, Kent, UK"},{"author_name":"Robert M. Waterhouse","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; Environmental Bioinformatics, SIB Swiss Institute of Bioinformatics, Lausann"},{"author_name":"Sadok Ben Yahia","author_inst":"The Faculty of Engineering, University of Technology Tallinn, Tallinn, Estonia; University of Southern Denmark, Denmark"},{"author_name":"Haixuan Yang","author_inst":"School of Mathematics and Statistical Sciences, National University of Ireland Galway, Galway, Ireland"},{"author_name":"Shuwei Yao","author_inst":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China"},{"author_name":"Ronghui You","author_inst":"School of Statistics and Data Science, Nankai University, Tianjin, China"},{"author_name":"Jeffrey Yunes","author_inst":"Yunes Foundation for Research on Aging, San Francisco, CA, USA"},{"author_name":"Chengxin Zhang","author_inst":"CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of "},{"author_name":"Yang Zhang","author_inst":"Department of Computer Science, School of Computing, National University of Singapore, Singapore; Department of Biochemistry, Yong Loo Lin School of Medicine, N"},{"author_name":"Chenguang Zhao","author_inst":"Computer and Information Sciences Department, St. Ambrose University, Davenport, IA, USA"},{"author_name":"Xiaogen Zhou","author_inst":"College of Information Engineering, Zhejiang University of Technology, Zhejiang, China"},{"author_name":"Yi-Heng Zhu","author_inst":"College of Artificial Intelligence, Nanjing Agricultural University, Jiangsu, China"},{"author_name":"Shanfeng Zhu","author_inst":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China"},{"author_name":"Hao Zhu","author_inst":"Department of Computer Science, Florida Memorial University, Miami Gardens, FL, USA"},{"author_name":"Gokhan Ozsari","author_inst":"Chalmers E-commons, Chalmers University of Technology, Goteborg, Sweden; Department of Computer Engineering, Middle East Technical University, Ankara, Turkey;"},{"author_name":"Burkhard Rost","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany; Chair for Bioinformatics, Technical University of Muni"},{"author_name":"Christine Orengo","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Marc Robinson-Rechavi","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland"},{"author_name":"Dannie Durand","author_inst":"Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Computational Biology, Carnegie Mellon University, Pittsburgh,"},{"author_name":"Steven E. Brenner","author_inst":"Department of Plant and Microbial Biology and Center for Computational Biology, University of California, Berkeley, CA, USA"},{"author_name":"Casey S. Greene","author_inst":"Department of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA"},{"author_name":"Sean D. Mooney","author_inst":"Center for Information Technology, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Silvio C. E. Tosatto","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Iddo Friedberg","author_inst":"Department of Veterinary Microbiology & Preventive Medicine, Iowa State University, Ames, IA, USA"},{"author_name":"Predrag Radivojac","author_inst":"Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"On the state of protein function prediction: a report on the fourth CAFA challenge","rel_doi":"10.64898\/2026.05.06.722942","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.722942","rel_abs":"Background: The Critical Assessment of Functional Annotation (CAFA) is a community effort held to understand the field of computational protein function prediction. Every three years, since 2010, the organizers initiate an experiment to collect function predictions on a large set of proteins and then evaluate the performance of predicting methods on a subset of proteins that have accumulated experimental annotations between the submission deadline and the evaluation time. CAFA provides an independent and rigorous assessment of the current state of the art, thus leveling the playing field, highlighting successes, revealing bottlenecks, and offering a forum for the exchange of ideas in protein science. Here, we report the results of the fourth CAFA experiment (CAFA4). Results: CAFA4 featured the participation of 148 methods from 70 research groups on a total of 46,205 unique proteins over a 5-year annotation accumulation phase, the longest in any CAFA. In a comparison across CAFA2-CAFA4 methods, the prediction of Gene Ontology (GO) terms has clearly improved across all three GO aspects and traditional evaluation settings. While not achieving the first rank, several CAFA2 and CAFA3 methods featured in the top ten methods in many evaluations, suggesting that earlier methods still hold relevance. The performance is weaker in the newly introduced \"partial knowledge\" evaluation category (proteins with experimental annotations before submission deadline that gained additional annotations in the same GO aspect during the annotation accumulation phase), highlighting the need for a new class of methods. The rankings of the methods were stable over the years in traditional evaluation settings, but less so in the new partial knowledge evaluation. Overall, the field continues to progress with some influx of new participants. Sustained efforts will be necessary to substantially advance it.","rel_num_authors":154,"rel_authors":[{"author_name":"Rashika Ramola","author_inst":"Northeastern University"},{"author_name":"M. Clara De Paolis Klauza","author_inst":"Northeastern University"},{"author_name":"Damiano Piovesan","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Yisu Peng","author_inst":"Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA"},{"author_name":"Parnal Joshi","author_inst":"Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, USA; Department of Veterinary Microbiology & Preventive Medicine, Iowa State "},{"author_name":"Mahta Mehdiabadi","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Federica Quaglia","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Rita Pancsa","author_inst":"HUN-REN Research Centre for Natural Sciences, Institute of Molecular Life Sciences, Budapest, Hungary"},{"author_name":"Lucia B. Chemes","author_inst":"Instituto de Investigaciones Biotecnologicas, Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET), Escuela de Bio y Nanotecnologias (EByN), Uni"},{"author_name":"Meisam Ahmadi","author_inst":"Computer Engineering Department, Iran University of Science And Technology, Tehran, Iran"},{"author_name":"Hongryul Ahn","author_inst":"Division of Data Science, The University of Suwon, Gyeonggi-do, South Korea"},{"author_name":"Adrian M. Altenhoff","author_inst":"Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland; Department of Computer Science, ETH Zurich, Zurich, Switzerland"},{"author_name":"Ehsaneddin Asgari","author_inst":"Qatar Computing Research Institute, HBKU, Doha, Qatar"},{"author_name":"Maria Cristina Aspromonte","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Volkan Atalay","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Giulia Babbi","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy"},{"author_name":"Davide Baldazzi","author_inst":"Oncogenetics and Functional Oncogenomics, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy"},{"author_name":"Meet M. Barot","author_inst":"Center for Data Science, New York University, New York, NY, USA"},{"author_name":"Asa Ben-Hur","author_inst":"Department of Computer Science, Colorado State University, Fort Collins, CO, USA"},{"author_name":"Alfredo Benso","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Daniel Berenberg","author_inst":"Department of Computer Science, New York University, New York, NY, USA"},{"author_name":"Jari Bjorne","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Florian Boecker","author_inst":"INRES Crop Bioinformatics, University of Bonn, Bonn, Germany"},{"author_name":"Paolo Boldi","author_inst":"Dipartimento di Informatica Giovanni Degli Antoni, University of Milan, Milano, Italy"},{"author_name":"Joseph Bonello","author_inst":"Computer Information Systems, University of Malta, Msida, Malta; Structural and Molecular Biology, University College London, London, England"},{"author_name":"Nicola Bordin","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Piyush Borole","author_inst":"College of Science and Technology, Temple University, Philadelphia, PA, USA"},{"author_name":"Ali Ebrahimpour Boroojeny","author_inst":"Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA"},{"author_name":"Renzhi Cao","author_inst":"Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA"},{"author_name":"Stefano Di Carlo","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Rita Casadio","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy"},{"author_name":"Elena Casiraghi","author_inst":"Department of Computer Science, University of Milan, Milano, Italy; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, "},{"author_name":"Jia-Ming Chang","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Chen Chen","author_inst":"Department of Electrical Engineering and Computer Science, University of Missouri Columbia, Columbia, MO, USA"},{"author_name":"Tse-Ming Chen","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Jianlin Cheng","author_inst":"Department of Electrical Engineering and Computer Science, University of Missouri Columbia, Columbia, MO, USA; NextGen Precision Health, University of Missouri "},{"author_name":"Ssu Chiu","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Alperen Dalkiran","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Radoslav S. Davidovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Christophe Dessimoz","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switz"},{"author_name":"Rucheng Diao","author_inst":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Warith Eddine Djeddi","author_inst":"FST Manar, University of Tunis El Manar, Tunis, Tunisia; University of Jendouba, Jendouba, Tunisia"},{"author_name":"Tunca Dogan","author_inst":"Department of Computer Engineering, Hacettepe University, Ankara, Turkey; Department of Bioinformatics, Hacettepe University, Ankara, Turkey"},{"author_name":"Sean T. Flannery","author_inst":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"Paolo Fontana","author_inst":"Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, Italy"},{"author_name":"Marco Frasca","author_inst":"Dipartimento di Informatica Giovanni Degli Antoni, University of Milan, Milano, Italy"},{"author_name":"Lydia Freddolino","author_inst":"Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA; Department of Computational Medicine and Bioinformatics, University of Michigan,"},{"author_name":"Branislava Gemovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Jesse Gillis","author_inst":"Department of Physiology, University of Toronto, Toronto, ON, Canada; Donnelly Centre, University of Toronto, Toronto, ON, Canada; Cold Spring Harbor Laboratory"},{"author_name":"Filip Ginter","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Vladimir Gligorijevic","author_inst":"Prescient Design, Genentech, New York, NY, USA"},{"author_name":"Giuliano Grossi","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Michael Heinzinger","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany"},{"author_name":"Kyle Hippe","author_inst":"Department of Computer Science, University of Chicago, Chicago, IL, USA; Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA"},{"author_name":"Robert Hoehndorf","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; KAUST Center of Excell"},{"author_name":"Liisa Holm","author_inst":"HiLIFE, Institute of Biotechnology, University of Helsinki, Helsinki, Finland; Faculty of Biological and Environmental Sciences, Organismal and Evolutionary Bio"},{"author_name":"Jie Hou","author_inst":"Department of Computer Science, Saint Louis University,  St. Louis, MO, USA"},{"author_name":"John R. Hover","author_inst":"MAP\/BARseq Core Facility, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Yen-Ting Huang","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Emilio Ispano","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Suraiya Jabin","author_inst":"Department of Computer Science, Jamia Millia Islamia, New Delhi, India; Faculty of Sciences, Jamia Millia Islamia, New Delhi, India"},{"author_name":"Aashish Jain","author_inst":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"David T. Jones","author_inst":"Department of Computer Science, University College London, London, UK"},{"author_name":"Suwisa Kaewphan","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Yuki Kagaya","author_inst":"Department of Biological Sciences, Purdue University, West Lafayette, IN, USA"},{"author_name":"Jenna Kanerva","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Daisuke Kihara","author_inst":"Department of Biological Sciences, Purdue University, West Lafayette, IN, USA; Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"Maxat Kulmanov","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; KAUST Center of Excell"},{"author_name":"Sunil Kumar","author_inst":"Farming Solutions and Digital, Corteva Agriscience, Hyderabad, India"},{"author_name":"Lukasz Kurgan","author_inst":"Department of Computer Science, Virginia Commonwealth University, Richmond, VA,USA"},{"author_name":"Enrico Lavezzo","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Jon Lees","author_inst":"Faculty of Health and Life Sciences, University of Bristol, Bristol, UK"},{"author_name":"Wen-Hung Liao","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Han Lin","author_inst":"Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, New Taipei City, Taiwan"},{"author_name":"Michal Linial","author_inst":"Department of Biological Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Maria Littmann","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany"},{"author_name":"Lizhi Liu","author_inst":"School of Computer Science, Fudan University, Shanghai, China"},{"author_name":"Tong Liu","author_inst":"Department of Computer Science, University of Miami, Coral Gables, FL, USA"},{"author_name":"Yi Wei Liu","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Stavros Makrodimitris","author_inst":"Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands; Intelligent Systems, Delft University of Technology,  Delft, Netherlands"},{"author_name":"Laura Manuto","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Pier Luigi Martelli","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy; Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy"},{"author_name":"Alice Carolyn Mchardy","author_inst":"Computational Biology for Infection Research, Helmholtz Centre for Infection Research, Brunswick, Germany"},{"author_name":"Gabriela A. Merino","author_inst":"European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK"},{"author_name":"Diego H. Milone","author_inst":"Department of Informatics, FICH-UNL, Research institute for Signals, Systems and Computational intelligence, sinc(i), CONICET\/UNL, Santa Fe, Argentina"},{"author_name":"Sarthak Mishra","author_inst":"Department of Computer Science, Jamia Millia Islamia, New Delhi, India"},{"author_name":"Mohammad R. K. Mofrad","author_inst":"Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California Berkeley, Berkeley, CA, USA"},{"author_name":"David Moi","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switz"},{"author_name":"Tsukasa Nakamura","author_inst":"Graduate School of Information Sciences, Tohoku University, Sendai, Japan"},{"author_name":"Vijay Kumar Narsapuram","author_inst":"Genomics Molecular and Data Science, Corteva Agriscience, IN, USA"},{"author_name":"Maria Victoria Nugnes","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Takeshi Obayashi","author_inst":"Graduate School of Information Sciences, Tohoku University, Sendai, Japan; WPI-AIMEC, Tohoku University, Sendai, Japan"},{"author_name":"Dan Ofer","author_inst":"Department of Biology, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Alberto Paccanaro","author_inst":"Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Surrey, UK; School of Applied Mathematics, Funda"},{"author_name":"Vladimir R. Perovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Alessandro Petrini","author_inst":"Huawei Galois Lab, Huawei Technologies France SASU, Boulogne-Billancourt, France; Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Gianfranco Politano","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Daniele Raimondi","author_inst":"ESAT-STADIUS, KU Leuven, Leuven, Belgium"},{"author_name":"Nadav Rappoport","author_inst":"Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel"},{"author_name":"Hafeez Ur Rehman","author_inst":"Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan; School of Computing and Data Science, Oryx Universal"},{"author_name":"Maarten J. M. F. Reijnders","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland"},{"author_name":"Marcel J. T. Reinders","author_inst":"Intelligent Systems, Delft University of Technology,  Delft, Netherlands"},{"author_name":"P. Douglas Renfrew","author_inst":"Center for Computational Biology, Flatiron Institute, New York, NY, USA"},{"author_name":"Ahmet S. Rifaioglu","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Alfonso E. Romero","author_inst":"Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Surrey, UK"},{"author_name":"Abhiman Saraswathi","author_inst":"Farming Solutions and Digital, Corteva Agriscience, IA, USA"},{"author_name":"Castrense Savojardo","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy; Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy"},{"author_name":"Harry M. Scholes","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Heiko Schoof","author_inst":"INRES Crop Bioinformatics, University of Bonn, Bonn, Germany"},{"author_name":"Yang Shen","author_inst":"Department of Electrical and Computer Engineering, Texas A&M University, College Station, USA"},{"author_name":"Ian Sillitoe","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Georgina Stegmayer","author_inst":"FICH, Research Institute for Signals, Systems and Computational Intelligence (sinc(i)); Universidad Nacional del Litoral (UNL), CONICET"},{"author_name":"Amos Stern","author_inst":"The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Henri Tiittanen","author_inst":"Institute of Biotechnology, University of Helsinki, Helsinki, Finland"},{"author_name":"Sumyyah Toonsi","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia"},{"author_name":"Stefano Toppo","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Petri Toronen","author_inst":"Institute of Biotechnology, University of Helsinki, Helsinki, Finland"},{"author_name":"Mateo Torres","author_inst":"School of Applied Mathematics, Fundacao Getulio Vargas, Rio de Janeiro, Brazil;"},{"author_name":"Gabriella Trucco","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Giorgio Valentini","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Nevena Veljkovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Alex Warwick Vesztrocy","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland"},{"author_name":"Vedrana Vidulin","author_inst":"Pink Data Analytics, Croatia"},{"author_name":"Amelia Villegas-Morcillo","author_inst":"Intelligent Systems, Delft University of Technology,  Delft, Netherlands; Signal Theory, Telematics and Communications, University of Granada, Granada, Spain"},{"author_name":"Antti Virtanen","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Wim Vranken","author_inst":"Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium"},{"author_name":"Slobodan Vucetic","author_inst":"Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA"},{"author_name":"Cen Wan","author_inst":"School of Computing and Mathematical Sciences, Birkbeck, University of London, London, UK"},{"author_name":"Zheng Wang","author_inst":"Department of Computer Science, University of Miami, Coral Gables, FL, USA; Department of Biology, University of Miami, Coral Gables, FL, USA; Sylvester Compreh"},{"author_name":"Mark N. Wass","author_inst":"School of Natural Sciences, University of Kent, Canterbury, Kent, UK"},{"author_name":"Robert M. Waterhouse","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; Environmental Bioinformatics, SIB Swiss Institute of Bioinformatics, Lausann"},{"author_name":"Sadok Ben Yahia","author_inst":"The Faculty of Engineering, University of Technology Tallinn, Tallinn, Estonia; University of Southern Denmark, Denmark"},{"author_name":"Haixuan Yang","author_inst":"School of Mathematics and Statistical Sciences, National University of Ireland Galway, Galway, Ireland"},{"author_name":"Shuwei Yao","author_inst":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China"},{"author_name":"Ronghui You","author_inst":"School of Statistics and Data Science, Nankai University, Tianjin, China"},{"author_name":"Jeffrey Yunes","author_inst":"Yunes Foundation for Research on Aging, San Francisco, CA, USA"},{"author_name":"Chengxin Zhang","author_inst":"CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of "},{"author_name":"Yang Zhang","author_inst":"Department of Computer Science, School of Computing, National University of Singapore, Singapore; Department of Biochemistry, Yong Loo Lin School of Medicine, N"},{"author_name":"Chenguang Zhao","author_inst":"Computer and Information Sciences Department, St. Ambrose University, Davenport, IA, USA"},{"author_name":"Xiaogen Zhou","author_inst":"College of Information Engineering, Zhejiang University of Technology, Zhejiang, China"},{"author_name":"Yi-Heng Zhu","author_inst":"College of Artificial Intelligence, Nanjing Agricultural University, Jiangsu, China"},{"author_name":"Shanfeng Zhu","author_inst":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China"},{"author_name":"Hao Zhu","author_inst":"Department of Computer Science, Florida Memorial University, Miami Gardens, FL, USA"},{"author_name":"Gokhan Ozsari","author_inst":"Chalmers E-commons, Chalmers University of Technology, Goteborg, Sweden; Department of Computer Engineering, Middle East Technical University, Ankara, Turkey;"},{"author_name":"Burkhard Rost","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany; Chair for Bioinformatics, Technical University of Muni"},{"author_name":"Christine Orengo","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Marc Robinson-Rechavi","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland"},{"author_name":"Dannie Durand","author_inst":"Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Computational Biology, Carnegie Mellon University, Pittsburgh,"},{"author_name":"Steven E. Brenner","author_inst":"Department of Plant and Microbial Biology and Center for Computational Biology, University of California, Berkeley, CA, USA"},{"author_name":"Casey S. Greene","author_inst":"Department of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA"},{"author_name":"Sean D. Mooney","author_inst":"Center for Information Technology, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Silvio C. E. Tosatto","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Iddo Friedberg","author_inst":"Department of Veterinary Microbiology & Preventive Medicine, Iowa State University, Ames, IA, USA"},{"author_name":"Predrag Radivojac","author_inst":"Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"On the state of protein function prediction: a report on the fourth CAFA challenge","rel_doi":"10.64898\/2026.05.06.722942","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.722942","rel_abs":"Background: The Critical Assessment of Functional Annotation (CAFA) is a community effort held to understand the field of computational protein function prediction. Every three years, since 2010, the organizers initiate an experiment to collect function predictions on a large set of proteins and then evaluate the performance of predicting methods on a subset of proteins that have accumulated experimental annotations between the submission deadline and the evaluation time. CAFA provides an independent and rigorous assessment of the current state of the art, thus leveling the playing field, highlighting successes, revealing bottlenecks, and offering a forum for the exchange of ideas in protein science. Here, we report the results of the fourth CAFA experiment (CAFA4). Results: CAFA4 featured the participation of 148 methods from 70 research groups on a total of 46,205 unique proteins over a 5-year annotation accumulation phase, the longest in any CAFA. In a comparison across CAFA2-CAFA4 methods, the prediction of Gene Ontology (GO) terms has clearly improved across all three GO aspects and traditional evaluation settings. While not achieving the first rank, several CAFA2 and CAFA3 methods featured in the top ten methods in many evaluations, suggesting that earlier methods still hold relevance. The performance is weaker in the newly introduced \"partial knowledge\" evaluation category (proteins with experimental annotations before submission deadline that gained additional annotations in the same GO aspect during the annotation accumulation phase), highlighting the need for a new class of methods. The rankings of the methods were stable over the years in traditional evaluation settings, but less so in the new partial knowledge evaluation. Overall, the field continues to progress with some influx of new participants. Sustained efforts will be necessary to substantially advance it.","rel_num_authors":154,"rel_authors":[{"author_name":"Rashika Ramola","author_inst":"Northeastern University"},{"author_name":"M. Clara De Paolis Klauza","author_inst":"Northeastern University"},{"author_name":"Damiano Piovesan","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Yisu Peng","author_inst":"Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA"},{"author_name":"Parnal Joshi","author_inst":"Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, USA; Department of Veterinary Microbiology & Preventive Medicine, Iowa State "},{"author_name":"Mahta Mehdiabadi","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Federica Quaglia","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Rita Pancsa","author_inst":"HUN-REN Research Centre for Natural Sciences, Institute of Molecular Life Sciences, Budapest, Hungary"},{"author_name":"Lucia B. Chemes","author_inst":"Instituto de Investigaciones Biotecnologicas, Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET), Escuela de Bio y Nanotecnologias (EByN), Uni"},{"author_name":"Meisam Ahmadi","author_inst":"Computer Engineering Department, Iran University of Science And Technology, Tehran, Iran"},{"author_name":"Hongryul Ahn","author_inst":"Division of Data Science, The University of Suwon, Gyeonggi-do, South Korea"},{"author_name":"Adrian M. Altenhoff","author_inst":"Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland; Department of Computer Science, ETH Zurich, Zurich, Switzerland"},{"author_name":"Ehsaneddin Asgari","author_inst":"Qatar Computing Research Institute, HBKU, Doha, Qatar"},{"author_name":"Maria Cristina Aspromonte","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Volkan Atalay","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Giulia Babbi","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy"},{"author_name":"Davide Baldazzi","author_inst":"Oncogenetics and Functional Oncogenomics, CRO Aviano, National Cancer Institute, IRCCS, Aviano, Italy"},{"author_name":"Meet M. 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Davidovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Christophe Dessimoz","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switz"},{"author_name":"Rucheng Diao","author_inst":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Warith Eddine Djeddi","author_inst":"FST Manar, University of Tunis El Manar, Tunis, Tunisia; University of Jendouba, Jendouba, Tunisia"},{"author_name":"Tunca Dogan","author_inst":"Department of Computer Engineering, Hacettepe University, Ankara, Turkey; Department of Bioinformatics, Hacettepe University, Ankara, Turkey"},{"author_name":"Sean T. Flannery","author_inst":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"Paolo Fontana","author_inst":"Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, Italy"},{"author_name":"Marco Frasca","author_inst":"Dipartimento di Informatica Giovanni Degli Antoni, University of Milan, Milano, Italy"},{"author_name":"Lydia Freddolino","author_inst":"Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA; Department of Computational Medicine and Bioinformatics, University of Michigan,"},{"author_name":"Branislava Gemovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Jesse Gillis","author_inst":"Department of Physiology, University of Toronto, Toronto, ON, Canada; Donnelly Centre, University of Toronto, Toronto, ON, Canada; Cold Spring Harbor Laboratory"},{"author_name":"Filip Ginter","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Vladimir Gligorijevic","author_inst":"Prescient Design, Genentech, New York, NY, USA"},{"author_name":"Giuliano Grossi","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Michael Heinzinger","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany"},{"author_name":"Kyle Hippe","author_inst":"Department of Computer Science, University of Chicago, Chicago, IL, USA; Department of Computer Science, Pacific Lutheran University, Tacoma, WA, USA"},{"author_name":"Robert Hoehndorf","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; KAUST Center of Excell"},{"author_name":"Liisa Holm","author_inst":"HiLIFE, Institute of Biotechnology, University of Helsinki, Helsinki, Finland; Faculty of Biological and Environmental Sciences, Organismal and Evolutionary Bio"},{"author_name":"Jie Hou","author_inst":"Department of Computer Science, Saint Louis University,  St. Louis, MO, USA"},{"author_name":"John R. Hover","author_inst":"MAP\/BARseq Core Facility, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA"},{"author_name":"Yen-Ting Huang","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Emilio Ispano","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Suraiya Jabin","author_inst":"Department of Computer Science, Jamia Millia Islamia, New Delhi, India; Faculty of Sciences, Jamia Millia Islamia, New Delhi, India"},{"author_name":"Aashish Jain","author_inst":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"David T. Jones","author_inst":"Department of Computer Science, University College London, London, UK"},{"author_name":"Suwisa Kaewphan","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Yuki Kagaya","author_inst":"Department of Biological Sciences, Purdue University, West Lafayette, IN, USA"},{"author_name":"Jenna Kanerva","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Daisuke Kihara","author_inst":"Department of Biological Sciences, Purdue University, West Lafayette, IN, USA; Department of Computer Science, Purdue University, West Lafayette, IN, USA"},{"author_name":"Maxat Kulmanov","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; KAUST Center of Excell"},{"author_name":"Sunil Kumar","author_inst":"Farming Solutions and Digital, Corteva Agriscience, Hyderabad, India"},{"author_name":"Lukasz Kurgan","author_inst":"Department of Computer Science, Virginia Commonwealth University, Richmond, VA,USA"},{"author_name":"Enrico Lavezzo","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Jon Lees","author_inst":"Faculty of Health and Life Sciences, University of Bristol, Bristol, UK"},{"author_name":"Wen-Hung Liao","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Han Lin","author_inst":"Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, New Taipei City, Taiwan"},{"author_name":"Michal Linial","author_inst":"Department of Biological Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Maria Littmann","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany"},{"author_name":"Lizhi Liu","author_inst":"School of Computer Science, Fudan University, Shanghai, China"},{"author_name":"Tong Liu","author_inst":"Department of Computer Science, University of Miami, Coral Gables, FL, USA"},{"author_name":"Yi Wei Liu","author_inst":"Department of Computer Science, National Chengchi University, Taipei City, Taiwan"},{"author_name":"Stavros Makrodimitris","author_inst":"Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands; Intelligent Systems, Delft University of Technology,  Delft, Netherlands"},{"author_name":"Laura Manuto","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Pier Luigi Martelli","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy; Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy"},{"author_name":"Alice Carolyn Mchardy","author_inst":"Computational Biology for Infection Research, Helmholtz Centre for Infection Research, Brunswick, Germany"},{"author_name":"Gabriela A. Merino","author_inst":"European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK"},{"author_name":"Diego H. Milone","author_inst":"Department of Informatics, FICH-UNL, Research institute for Signals, Systems and Computational intelligence, sinc(i), CONICET\/UNL, Santa Fe, Argentina"},{"author_name":"Sarthak Mishra","author_inst":"Department of Computer Science, Jamia Millia Islamia, New Delhi, India"},{"author_name":"Mohammad R. K. Mofrad","author_inst":"Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California Berkeley, Berkeley, CA, USA"},{"author_name":"David Moi","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Comparative Genomics, SIB Swiss Institute of Bioinformatics, Lausanne, Switz"},{"author_name":"Tsukasa Nakamura","author_inst":"Graduate School of Information Sciences, Tohoku University, Sendai, Japan"},{"author_name":"Vijay Kumar Narsapuram","author_inst":"Genomics Molecular and Data Science, Corteva Agriscience, IN, USA"},{"author_name":"Maria Victoria Nugnes","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Takeshi Obayashi","author_inst":"Graduate School of Information Sciences, Tohoku University, Sendai, Japan; WPI-AIMEC, Tohoku University, Sendai, Japan"},{"author_name":"Dan Ofer","author_inst":"Department of Biology, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Alberto Paccanaro","author_inst":"Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Surrey, UK; School of Applied Mathematics, Funda"},{"author_name":"Vladimir R. Perovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Alessandro Petrini","author_inst":"Huawei Galois Lab, Huawei Technologies France SASU, Boulogne-Billancourt, France; Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Gianfranco Politano","author_inst":"Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy"},{"author_name":"Daniele Raimondi","author_inst":"ESAT-STADIUS, KU Leuven, Leuven, Belgium"},{"author_name":"Nadav Rappoport","author_inst":"Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel"},{"author_name":"Hafeez Ur Rehman","author_inst":"Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan; School of Computing and Data Science, Oryx Universal"},{"author_name":"Maarten J. M. F. Reijnders","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland"},{"author_name":"Marcel J. T. Reinders","author_inst":"Intelligent Systems, Delft University of Technology,  Delft, Netherlands"},{"author_name":"P. Douglas Renfrew","author_inst":"Center for Computational Biology, Flatiron Institute, New York, NY, USA"},{"author_name":"Ahmet S. Rifaioglu","author_inst":"Department of Computer Engineering, Middle East Technical University, Ankara, Turkey"},{"author_name":"Alfonso E. Romero","author_inst":"Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Surrey, UK"},{"author_name":"Abhiman Saraswathi","author_inst":"Farming Solutions and Digital, Corteva Agriscience, IA, USA"},{"author_name":"Castrense Savojardo","author_inst":"Bologna Biocomputing Group, University of Bologna, Bologna, Italy; Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy"},{"author_name":"Harry M. Scholes","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Heiko Schoof","author_inst":"INRES Crop Bioinformatics, University of Bonn, Bonn, Germany"},{"author_name":"Yang Shen","author_inst":"Department of Electrical and Computer Engineering, Texas A&M University, College Station, USA"},{"author_name":"Ian Sillitoe","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Georgina Stegmayer","author_inst":"FICH, Research Institute for Signals, Systems and Computational Intelligence (sinc(i)); Universidad Nacional del Litoral (UNL), CONICET"},{"author_name":"Amos Stern","author_inst":"The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel"},{"author_name":"Henri Tiittanen","author_inst":"Institute of Biotechnology, University of Helsinki, Helsinki, Finland"},{"author_name":"Sumyyah Toonsi","author_inst":"Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia"},{"author_name":"Stefano Toppo","author_inst":"Department of Molecular Medicine, University of Padova, Padova, Italy"},{"author_name":"Petri Toronen","author_inst":"Institute of Biotechnology, University of Helsinki, Helsinki, Finland"},{"author_name":"Mateo Torres","author_inst":"School of Applied Mathematics, Fundacao Getulio Vargas, Rio de Janeiro, Brazil;"},{"author_name":"Gabriella Trucco","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Giorgio Valentini","author_inst":"Department of Computer Science, University of Milan, Milano, Italy"},{"author_name":"Nevena Veljkovic","author_inst":"Laboratory for Bioinformatics and Computational Chemistry, Institute of Nuclear Sciences Vinca, National Institute of the Republic of Serbia, University of Belg"},{"author_name":"Alex Warwick Vesztrocy","author_inst":"Department of Computational Biology, University of Lausanne, Lausanne, Switzerland"},{"author_name":"Vedrana Vidulin","author_inst":"Pink Data Analytics, Croatia"},{"author_name":"Amelia Villegas-Morcillo","author_inst":"Intelligent Systems, Delft University of Technology,  Delft, Netherlands; Signal Theory, Telematics and Communications, University of Granada, Granada, Spain"},{"author_name":"Antti Virtanen","author_inst":"Department of Computing, University of Turku, Turku, Finland"},{"author_name":"Wim Vranken","author_inst":"Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium"},{"author_name":"Slobodan Vucetic","author_inst":"Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA"},{"author_name":"Cen Wan","author_inst":"School of Computing and Mathematical Sciences, Birkbeck, University of London, London, UK"},{"author_name":"Zheng Wang","author_inst":"Department of Computer Science, University of Miami, Coral Gables, FL, USA; Department of Biology, University of Miami, Coral Gables, FL, USA; Sylvester Compreh"},{"author_name":"Mark N. Wass","author_inst":"School of Natural Sciences, University of Kent, Canterbury, Kent, UK"},{"author_name":"Robert M. Waterhouse","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; Environmental Bioinformatics, SIB Swiss Institute of Bioinformatics, Lausann"},{"author_name":"Sadok Ben Yahia","author_inst":"The Faculty of Engineering, University of Technology Tallinn, Tallinn, Estonia; University of Southern Denmark, Denmark"},{"author_name":"Haixuan Yang","author_inst":"School of Mathematics and Statistical Sciences, National University of Ireland Galway, Galway, Ireland"},{"author_name":"Shuwei Yao","author_inst":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China"},{"author_name":"Ronghui You","author_inst":"School of Statistics and Data Science, Nankai University, Tianjin, China"},{"author_name":"Jeffrey Yunes","author_inst":"Yunes Foundation for Research on Aging, San Francisco, CA, USA"},{"author_name":"Chengxin Zhang","author_inst":"CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of "},{"author_name":"Yang Zhang","author_inst":"Department of Computer Science, School of Computing, National University of Singapore, Singapore; Department of Biochemistry, Yong Loo Lin School of Medicine, N"},{"author_name":"Chenguang Zhao","author_inst":"Computer and Information Sciences Department, St. Ambrose University, Davenport, IA, USA"},{"author_name":"Xiaogen Zhou","author_inst":"College of Information Engineering, Zhejiang University of Technology, Zhejiang, China"},{"author_name":"Yi-Heng Zhu","author_inst":"College of Artificial Intelligence, Nanjing Agricultural University, Jiangsu, China"},{"author_name":"Shanfeng Zhu","author_inst":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China"},{"author_name":"Hao Zhu","author_inst":"Department of Computer Science, Florida Memorial University, Miami Gardens, FL, USA"},{"author_name":"Gokhan Ozsari","author_inst":"Chalmers E-commons, Chalmers University of Technology, Goteborg, Sweden; Department of Computer Engineering, Middle East Technical University, Ankara, Turkey;"},{"author_name":"Burkhard Rost","author_inst":"School of Computation, Information and Technology (CIT), Technical University of Munich, Munich, Germany; Chair for Bioinformatics, Technical University of Muni"},{"author_name":"Christine Orengo","author_inst":"Institute of Structural and Molecular Biology, University College London, London, UK"},{"author_name":"Marc Robinson-Rechavi","author_inst":"Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland"},{"author_name":"Dannie Durand","author_inst":"Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Computational Biology, Carnegie Mellon University, Pittsburgh,"},{"author_name":"Steven E. Brenner","author_inst":"Department of Plant and Microbial Biology and Center for Computational Biology, University of California, Berkeley, CA, USA"},{"author_name":"Casey S. Greene","author_inst":"Department of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA"},{"author_name":"Sean D. Mooney","author_inst":"Center for Information Technology, National Institutes of Health, Bethesda, MD, USA"},{"author_name":"Silvio C. E. Tosatto","author_inst":"Department of Biomedical Sciences, University of Padova, Padova, Italy"},{"author_name":"Iddo Friedberg","author_inst":"Department of Veterinary Microbiology & Preventive Medicine, Iowa State University, Ames, IA, USA"},{"author_name":"Predrag Radivojac","author_inst":"Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"AEGIS reveals epitope- and clone-resolved convergence of CNS B and T cell autoreactivity in ROHHAD","rel_doi":"10.64898\/2026.05.06.722823","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.722823","rel_abs":"Autoimmune diseases arise when B and T lymphocytes lose tolerance to self. Yet in most disorders, the underlying molecular determinants, including autoantibodies, epitopes and lymphocyte clones that drive tissue injury remain undefined. Rapid-onset obesity with hypothalamic dysfunction, hypoventilation and autonomic dysregulation (ROHHAD) is a rare and often fatal pediatric neuroendocrine syndrome with strong evidence of antigen-driven paraneoplastic autoimmunity, including association with the intracellular autoantigen ZSCAN1. However, the effector immune circuit and the epitope-level determinants operating within the hypothalamus and brainstem have remained unknown. To address this challenge in ROHHAD and more broadly in autoimmune disease, we developed the Autoimmune Epitope and immunoGlobulin\/Immune-receptor identification System (AEGIS), an integrated framework that links immune repertoires to their cognate self-epitopes. AEGIS combines B cell and T cell receptor profiling from sites of tissue injury with high-resolution epitope mapping, direct sequencing of antigen-specific autoantibodies, in silico antibody-antigen folding, selection, and T cell antigen discovery. Applied to a deeply phenotyped child with ROHHAD, AEGIS revealed a compartmentalized, clonally restricted immune response in which brain-deposited IgG and expanded cerebrospinal fluid B cell and CD4 T cell clonotypes converged on shared ZSCAN1 epitopes, resolved to minimal determinants and peptide-MHC ligands. These findings provide a clone- and epitope-linked mechanistic map of ROHHAD autoimmunity and establish a generalizable framework for identifying candidate pathogenic clones and antigens across diverse autoimmune diseases.","rel_num_authors":49,"rel_authors":[{"author_name":"Aaron Bodansky","author_inst":"University of California San Francisco"},{"author_name":"Vida Ahyong","author_inst":"University of California San Francisco"},{"author_name":"Monica Dayao","author_inst":"University of California San Francisco"},{"author_name":"James Asaki","author_inst":"University of California San Francisco"},{"author_name":"ShiLu Vanasupa","author_inst":"University of California San Francisco"},{"author_name":"Ravi Dandekar","author_inst":"University of California San Francisco"},{"author_name":"Shini Chen","author_inst":"University of California San Francisco"},{"author_name":"Joe Sabatino","author_inst":"University of California San Francisco"},{"author_name":"Sam Klauer","author_inst":"University of California San Francisco"},{"author_name":"Giselle Knudsen","author_inst":"University of California San Francisco"},{"author_name":"Carlos Lizama Valenzuela","author_inst":"University of California San Francisco"},{"author_name":"Krista McCutcheon","author_inst":"University of California San Francisco"},{"author_name":"Kelsey Zorn","author_inst":"University of California San Francisco"},{"author_name":"Sukhman Sidhu","author_inst":"University of California San Francisco"},{"author_name":"David Yu","author_inst":"University of California San Francisco"},{"author_name":"Samantha Garcia","author_inst":"University of California San Francisco"},{"author_name":"Zoe Quandt","author_inst":"University of California San Francisco"},{"author_name":"Chung-Yu Wang","author_inst":"University of California San Francisco"},{"author_name":"Bryan Castillo Rojas","author_inst":"University of California San Francisco"},{"author_name":"Iris Tilton","author_inst":"University of California San Francisco"},{"author_name":"Y. Rose Citron","author_inst":"University of California San Francisco"},{"author_name":"Akshay Sharathchandra","author_inst":"University of California San Francisco"},{"author_name":"Chloe Gerungan","author_inst":"University of California San Francisco"},{"author_name":"Stuart Tomko","author_inst":"Washington University in St. Louis"},{"author_name":"Nathaniel M. Robbins","author_inst":"Harvard Medical School"},{"author_name":"Andrew McKeon","author_inst":"Mayo Clinic"},{"author_name":"Tiffany Cooper","author_inst":"University of California San Francisco"},{"author_name":"Meagan Harms","author_inst":"University of California San Francisco"},{"author_name":"Refujia Gomez","author_inst":"University of California San Francisco"},{"author_name":"Stephen P J Fancy","author_inst":"University of California San Francisco"},{"author_name":"Ari J Green","author_inst":"University of California San Francisco"},{"author_name":"Ilay Caliskan","author_inst":"University of California San Francisco"},{"author_name":"Cathryn R. Cadwell","author_inst":"University of California San Francisco"},{"author_name":"Bridget EL Ostrem","author_inst":"University of California San Francisco"},{"author_name":"Mary Karalius","author_inst":"University of California San Francisco"},{"author_name":"Lindsay Braun","author_inst":"University of California San Francisco"},{"author_name":"Sasha Gupta","author_inst":"University of California San Francisco"},{"author_name":"Carla Francisco","author_inst":"University of California San Francisco"},{"author_name":"Greta Peng","author_inst":"University of California San Francisco"},{"author_name":"Alyssa T. Reddy","author_inst":"University of California San Francisco"},{"author_name":"Kendall Nash","author_inst":"University of California San Francisco"},{"author_name":"Samuel J. Pleasure","author_inst":"University of California San Francisco"},{"author_name":"Caleigh Mandel-Brehm","author_inst":"Yale School of Medicine"},{"author_name":"Thomas D. Arnold","author_inst":"University of California San Francisco"},{"author_name":"- Consortium","author_inst":""},{"author_name":"Mark S. Anderson","author_inst":"University of California San Francisco"},{"author_name":"Josiah Gerdts","author_inst":"University of California San Francisco"},{"author_name":"Michael R. Wilson","author_inst":"University of California San Francisco"},{"author_name":"Joseph L. DeRisi","author_inst":"University of California San Francisco"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"AEGIS reveals epitope- and clone-resolved convergence of CNS B and T cell autoreactivity in ROHHAD","rel_doi":"10.64898\/2026.05.06.722823","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.722823","rel_abs":"Autoimmune diseases arise when B and T lymphocytes lose tolerance to self. Yet in most disorders, the underlying molecular determinants, including autoantibodies, epitopes and lymphocyte clones that drive tissue injury remain undefined. Rapid-onset obesity with hypothalamic dysfunction, hypoventilation and autonomic dysregulation (ROHHAD) is a rare and often fatal pediatric neuroendocrine syndrome with strong evidence of antigen-driven paraneoplastic autoimmunity, including association with the intracellular autoantigen ZSCAN1. However, the effector immune circuit and the epitope-level determinants operating within the hypothalamus and brainstem have remained unknown. To address this challenge in ROHHAD and more broadly in autoimmune disease, we developed the Autoimmune Epitope and immunoGlobulin\/Immune-receptor identification System (AEGIS), an integrated framework that links immune repertoires to their cognate self-epitopes. AEGIS combines B cell and T cell receptor profiling from sites of tissue injury with high-resolution epitope mapping, direct sequencing of antigen-specific autoantibodies, in silico antibody-antigen folding, selection, and T cell antigen discovery. Applied to a deeply phenotyped child with ROHHAD, AEGIS revealed a compartmentalized, clonally restricted immune response in which brain-deposited IgG and expanded cerebrospinal fluid B cell and CD4 T cell clonotypes converged on shared ZSCAN1 epitopes, resolved to minimal determinants and peptide-MHC ligands. These findings provide a clone- and epitope-linked mechanistic map of ROHHAD autoimmunity and establish a generalizable framework for identifying candidate pathogenic clones and antigens across diverse autoimmune diseases.","rel_num_authors":49,"rel_authors":[{"author_name":"Aaron Bodansky","author_inst":"University of California San Francisco"},{"author_name":"Vida Ahyong","author_inst":"University of California San Francisco"},{"author_name":"Monica Dayao","author_inst":"University of California San Francisco"},{"author_name":"James Asaki","author_inst":"University of California San Francisco"},{"author_name":"ShiLu Vanasupa","author_inst":"University of California San Francisco"},{"author_name":"Ravi Dandekar","author_inst":"University of California San Francisco"},{"author_name":"Shini Chen","author_inst":"University of California San Francisco"},{"author_name":"Joe Sabatino","author_inst":"University of California San Francisco"},{"author_name":"Sam Klauer","author_inst":"University of California San Francisco"},{"author_name":"Giselle Knudsen","author_inst":"University of California San Francisco"},{"author_name":"Carlos Lizama Valenzuela","author_inst":"University of California San Francisco"},{"author_name":"Krista McCutcheon","author_inst":"University of California San Francisco"},{"author_name":"Kelsey Zorn","author_inst":"University of California San Francisco"},{"author_name":"Sukhman Sidhu","author_inst":"University of California San Francisco"},{"author_name":"David Yu","author_inst":"University of California San Francisco"},{"author_name":"Samantha Garcia","author_inst":"University of California San Francisco"},{"author_name":"Zoe Quandt","author_inst":"University of California San Francisco"},{"author_name":"Chung-Yu Wang","author_inst":"University of California San Francisco"},{"author_name":"Bryan Castillo Rojas","author_inst":"University of California San Francisco"},{"author_name":"Iris Tilton","author_inst":"University of California San Francisco"},{"author_name":"Y. Rose Citron","author_inst":"University of California San Francisco"},{"author_name":"Akshay Sharathchandra","author_inst":"University of California San Francisco"},{"author_name":"Chloe Gerungan","author_inst":"University of California San Francisco"},{"author_name":"Stuart Tomko","author_inst":"Washington University in St. Louis"},{"author_name":"Nathaniel M. Robbins","author_inst":"Harvard Medical School"},{"author_name":"Andrew McKeon","author_inst":"Mayo Clinic"},{"author_name":"Tiffany Cooper","author_inst":"University of California San Francisco"},{"author_name":"Meagan Harms","author_inst":"University of California San Francisco"},{"author_name":"Refujia Gomez","author_inst":"University of California San Francisco"},{"author_name":"Stephen P J Fancy","author_inst":"University of California San Francisco"},{"author_name":"Ari J Green","author_inst":"University of California San Francisco"},{"author_name":"Ilay Caliskan","author_inst":"University of California San Francisco"},{"author_name":"Cathryn R. Cadwell","author_inst":"University of California San Francisco"},{"author_name":"Bridget EL Ostrem","author_inst":"University of California San Francisco"},{"author_name":"Mary Karalius","author_inst":"University of California San Francisco"},{"author_name":"Lindsay Braun","author_inst":"University of California San Francisco"},{"author_name":"Sasha Gupta","author_inst":"University of California San Francisco"},{"author_name":"Carla Francisco","author_inst":"University of California San Francisco"},{"author_name":"Greta Peng","author_inst":"University of California San Francisco"},{"author_name":"Alyssa T. Reddy","author_inst":"University of California San Francisco"},{"author_name":"Kendall Nash","author_inst":"University of California San Francisco"},{"author_name":"Samuel J. Pleasure","author_inst":"University of California San Francisco"},{"author_name":"Caleigh Mandel-Brehm","author_inst":"Yale School of Medicine"},{"author_name":"Thomas D. Arnold","author_inst":"University of California San Francisco"},{"author_name":"- Consortium","author_inst":""},{"author_name":"Mark S. Anderson","author_inst":"University of California San Francisco"},{"author_name":"Josiah Gerdts","author_inst":"University of California San Francisco"},{"author_name":"Michael R. Wilson","author_inst":"University of California San Francisco"},{"author_name":"Joseph L. DeRisi","author_inst":"University of California San Francisco"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"3D cortical microtissue with innate microglia for studying real-time cell behavior across maturation and inflammatory response","rel_doi":"10.64898\/2026.05.06.723271","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.723271","rel_abs":"Microglia represent the immune component of the central nervous system (CNS) that displays dynamic responses to injury and disease. Across the developing and mature CNS, microglia emerge as immunocompetent cells that continuously survey their surroundings to maintain tissue homeostasis and respond to threats. There remains a gap in 3D in vitro models that contain microglia and can provide both developmental and mature functional hallmarks. Using a 3D neural multicellular model, cortical microtissues, derived from primary rat cortical cells, we conducted live imaging to monitor microglia dynamics from early, middle, and late stage microtissue maturation. We optimized a within-micromold imaging approach that allows for live microglia imaging without removing microtissues from their culturing environment. We confirm that microglia exhibit baseline surveillance characterized by relatively stationary somas and highly dynamic cell processes that continuously extend and retract. Following proinflammatory challenges, microglia engulf lipopolysaccharide particles, accompanied by dynamic shifts in motility patterns; and rapidly respond to laser-induced tissue damage through process extension, whole-cell displacement, and local recruitment. Lastly, we show that microtissue age in culture strongly influences both baseline and directed motility profiles. Collectively, these studies demonstrate that within a 3D microenvironment, microglia exhibit pronounced changes in morphology, surveillance area, motility, and injury response across microtissue maturation. Microtissues can serve as a valuable in vitro platform for both microglia developmental studies and investigations of brain inflammation related to CNS injuries, infections, and diseases.","rel_num_authors":6,"rel_authors":[{"author_name":"Alexander Del Toro","author_inst":"Department of Neuroscience, Brown University, Providence, RI, USA,  Carney Institute for Brain Science, Brown University, Providence, RI, USA"},{"author_name":"Kaylen Aguilar","author_inst":"Department of Neuroscience, Brown University, Providence, RI, USA"},{"author_name":"Angelina Clark","author_inst":"Institute for Biology, Engineering and Medicine, Brown University, Providence, RI, USA"},{"author_name":"Alexander Bautista","author_inst":"Department of Neuroscience, Brown University, Providence, RI, USA"},{"author_name":"Nathan Ashby","author_inst":"Department of Neuroscience, Brown University, Providence, RI, USA"},{"author_name":"Diane Hoffman-Kim","author_inst":"Department of Neuroscience, Brown University, Providence, RI, USA, Carney Institute for Brain Science, Brown University, Providence, RI, USA  3 Institute for Bi"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Illusory path configurations reveal age-related differences in egocentric pointing variability","rel_doi":"10.64898\/2026.05.06.722714","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.722714","rel_abs":"A consistent finding across studies with older adults is that they typically perform worse at spatial memory tasks, particularly those conducted in virtual reality and involving novel environments, compared to young adults. While the underlying reasons for this difference remain unclear, some proposed hypotheses include differences in sensory cue integration and cue conflict resolution. Here, we tested older (n = 29) and young adults (n = 28) in immersive and walkable virtual reality using both correctly rendered and illusory hallways to test how visual cues (i.e., an intersection) and self-motion cues are integrated. In the illusory or false-intersection condition, we hypothesized that participants who walked an uncrossed path would merge two disconnected intersections, creating the illusion of a crossed path. The overall accuracy and pointing patterns were similar between young and older adults in both true- and false-intersection conditions. We did find, however, a significant age by condition interaction effect in egocentric pointing variability where older adults showed lower variability in the illusory condition and higher variability in the control condition. At the same time, older adults also drew worse maps for the control condition compared to young adults. However, the pointing error correlated with the accuracy of maps drawn regardless of age, suggesting that the pointing patterns shown by both age groups related to their underlying representations of the paths. Our findings are inconsistent with a global deficit in allocentric navigation or path integration and instead suggest that more subtle differences in strategy use might manifest with age.","rel_num_authors":6,"rel_authors":[{"author_name":"Abhilasha Vishwanath","author_inst":"The University of Arizona"},{"author_name":"Matthew Frederick Watson","author_inst":"The University of Arizona"},{"author_name":"Melanie K Gin","author_inst":"The University of Arizona"},{"author_name":"Yu Karen Du","author_inst":"University of Western Ontario"},{"author_name":"Robert C Wilson","author_inst":"Georgia Institute of Technology"},{"author_name":"Arne Ekstrom","author_inst":"The University of Arizona"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"LSD persistently disrupts affective pain processing","rel_doi":"10.64898\/2026.05.06.723205","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.723205","rel_abs":"Psychedelics produce long-lasting effects, but their circuit mechanisms remain unclear. Here we show that, in rats, a single dose of lysergic acid diethylamide (LSD) persistently reduces pain affect. This effect is recapitulated by local administration in the anterior cingulate cortex (ACC), but not primary somatosensory cortex. Neuropixels recordings reveal that LSD suppresses stimulus-evoked nociceptive responses in the ACC, reducing the encoding of aversive value. Despite increasing intrinsic excitability ex vivo, LSD reduces the maximum stimulus-evoked firing of ACC neurons in vivo, indicating a dissociation between excitability and sensory encoding. Together, these findings show that psychedelics disrupt the cortical transformation of nociceptive input into aversive representations.","rel_num_authors":10,"rel_authors":[{"author_name":"Jared Plotkin","author_inst":"Department of Psychiatry and Neuroscience, New York University Grossman School of Medicine, New York, NY 10016, USA"},{"author_name":"Elaine Zhu","author_inst":"Department of Anesthesiology, New York University Grossman School of Medicine, New York, NY 10016, USA"},{"author_name":"Melanie Druart","author_inst":"Department of Psychiatry and Neuroscience, New York University Grossman School of Medicine, New York, NY 10016, USA"},{"author_name":"Qiaosheng Zhang","author_inst":"Department of Anesthesiology, New York University Grossman School of Medicine, New York, NY 10016, USA"},{"author_name":"Eric Hu","author_inst":"Department of Anesthesiology, New York University Grossman School of Medicine, New York, NY 10016, USA"},{"author_name":"Deven Cathcart","author_inst":"Department of Anesthesiology, New York University Grossman School of Medicine, New York, NY 10016, USA"},{"author_name":"Nellie Jun","author_inst":"Department of Anesthesiology, New York University Grossman School of Medicine, New York, NY 10016, USA"},{"author_name":"Leo Kwok","author_inst":"Department of Anesthesiology, New York University Grossman School of Medicine, New York, NY 10016, USA"},{"author_name":"Tanya Sippy","author_inst":"Department of Psychiatry and Neuroscience, New York University Grossman School of Medicine, New York, NY 10016, USA"},{"author_name":"Jing Wang","author_inst":"Department of Anesthesiology, New York University Grossman School of Medicine, New York, NY 10016, USA"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Learning activator-inhibitor dynamics at the cell cortex with neural likelihood ratio estimation","rel_doi":"10.64898\/2026.05.06.722433","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.722433","rel_abs":"A key question in cell biology is how cell-scale organization emerges from a given set of molecular players and rules of interaction. Given its multiscale nature, addressing this question requires a combination of experimental perturbation, mathematical modeling, and parameter inference. We leverage recent advances in each of these fields, focusing in particular on neural-network methods for simulation-based inference, to study how cell-scale patterns of Rho GTPase activity are defined by molecular-scale activator-inhibitor interactions with filamentous actin. We show that variations in F-actin assembly dynamics can be inferred directly from experimental data by combining a mathematical model with a neural network trained to associate parameter sets with data. Our neural approach differentiates data sets more precisely than traditional summary statistics, and yields a complete and robust likelihood function for each data set. Utilizing the trained network, we demonstrate how RhoGAP tunes RhoA waves via interaction with F-actin. After showing that the known functions of RhoGAP are insufficient to explain experimentally-observed dynamics, we use neural methods to infer that RhoGAP must, at a minimum, also decrease filament nucleation rates to sustain waves. Our work yields specific, experimentally-testable predictions and illustrates how a combination of traditional forward models and modern inference tools can aid in unraveling mechanisms of self-organization.","rel_num_authors":3,"rel_authors":[{"author_name":"Ondrej Maxian","author_inst":"University of Notre Dame"},{"author_name":"Edwin Munro","author_inst":"University of Chicago"},{"author_name":"Aaron Dinner","author_inst":"University of Chicago"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Pan-cancer analysis of single-cell RNA sequencing data from 304 human tumors sheds light on the aneuploidy paradox","rel_doi":"10.64898\/2026.05.06.723237","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.723237","rel_abs":"Aneuploidy poses a central paradox in cancer biology: it impairs cellular fitness in normal cells but drives cancer progression. To resolve this, we analyzed single cell transcriptomes from >665,000 cells - including ~288,000 malignant cells - across 304 tumors and 15 cancer types. Integrating transcriptomics with inferred aneuploidy profiles, we characterized cell-intrinsic programs and interactions with the tumor microenvironment. Unexpectedly, highly aneuploid single cells exhibited reduced proliferation and metabolism, contrasting sharply with tumor-bulk profiles. We show this divergence is driven by karyotypic heterogeneity: in highly heterogeneous tumors, aneuploid cells display signatures of acute stress and negative selection. Conversely, in clonally aneuploid tumors, these detrimental signatures are lost and replaced by signatures of increased proliferation and enhanced metabolism, reflecting adaptation. Additionally, we identified consistent transcriptional programs driven by recurrent chromosome-arm alterations across both single cells and bulk tumors. These findings illuminate the selective forces shaping tumor evolution and the aneuploidy paradox.","rel_num_authors":7,"rel_authors":[{"author_name":"Guy Wolf-Dankovich","author_inst":"Tel Aviv University"},{"author_name":"Tomer Mashiah","author_inst":"Tel Aviv University"},{"author_name":"Ron Saad","author_inst":"Tel Aviv University"},{"author_name":"Einav Somech","author_inst":"Weizmann Insitute of Science"},{"author_name":"Haia Khoury","author_inst":"Tel Aviv University"},{"author_name":"Itay Tirosh","author_inst":"Weizmann Institute of Science"},{"author_name":"Uri Ben-David","author_inst":"Tel Aviv University"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Pan-cancer analysis of single-cell RNA sequencing data from 304 human tumors sheds light on the aneuploidy paradox","rel_doi":"10.64898\/2026.05.06.723237","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.723237","rel_abs":"Aneuploidy poses a central paradox in cancer biology: it impairs cellular fitness in normal cells but drives cancer progression. To resolve this, we analyzed single cell transcriptomes from >665,000 cells - including ~288,000 malignant cells - across 304 tumors and 15 cancer types. Integrating transcriptomics with inferred aneuploidy profiles, we characterized cell-intrinsic programs and interactions with the tumor microenvironment. Unexpectedly, highly aneuploid single cells exhibited reduced proliferation and metabolism, contrasting sharply with tumor-bulk profiles. We show this divergence is driven by karyotypic heterogeneity: in highly heterogeneous tumors, aneuploid cells display signatures of acute stress and negative selection. Conversely, in clonally aneuploid tumors, these detrimental signatures are lost and replaced by signatures of increased proliferation and enhanced metabolism, reflecting adaptation. Additionally, we identified consistent transcriptional programs driven by recurrent chromosome-arm alterations across both single cells and bulk tumors. These findings illuminate the selective forces shaping tumor evolution and the aneuploidy paradox.","rel_num_authors":7,"rel_authors":[{"author_name":"Guy Wolf-Dankovich","author_inst":"Tel Aviv University"},{"author_name":"Tomer Mashiah","author_inst":"Tel Aviv University"},{"author_name":"Ron Saad","author_inst":"Tel Aviv University"},{"author_name":"Einav Somech","author_inst":"Weizmann Insitute of Science"},{"author_name":"Haia Khoury","author_inst":"Tel Aviv University"},{"author_name":"Itay Tirosh","author_inst":"Weizmann Institute of Science"},{"author_name":"Uri Ben-David","author_inst":"Tel Aviv University"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Substrate-dependent crosslinking by the cytochrome P450 from aminopyruvatide biosynthesis","rel_doi":"10.64898\/2026.05.07.723658","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.07.723658","rel_abs":"Cytochrome P450s catalyze a diverse array of reactions including crosslinking of aromatic side chains in the biosynthesis of ribosomally synthesized and post-translationally modified peptides (RiPPs). ApyO is a cytochrome P450 enzyme that forms a C-C bond between two tyrosines in a YLY motif in the substrate ApyA, the precursor peptide of the RiPP aminopyruvatide. We utilized cell-free translation to generate ApyA variants and probe the substrate tolerance of ApyO. Through Alphafold-based modelling and in vitro assays, we show that ApyO accepts the 10 C-terminal residues of ApyA and requires a conserved Arg\/Lys in the substrate peptide. Inspired by substrate sequences found in orthologous biosynthetic gene clusters, we substituted one of the tyrosine residues with a tryptophan and observed that ApyO catalyzed the formation of an N-C bond between the indole of Trp and the C{epsilon}2 of Tyr. ApyO unexpectedly catalyzed formation of a C-O bond between the two tyrosine residues when we substituted the leucine residue in the YLY motif with tyrosine and tryptophan. We also show that a peptide containing a biaryl linkage and the C-terminal aminopyruvate displayed sub-nanomolar inhibitory activity against selected proteases. Overall, this study demonstrates plasticity in the manner of macrocyclization catalyzed by the P450 ApyO and provides a starting point for chemoenzymatic approaches towards producing diverse macrocyclic scaffolds.","rel_num_authors":7,"rel_authors":[{"author_name":"Chandrashekhar Padhi","author_inst":"University of Illinois at Urbana-Champaign"},{"author_name":"Dinh T Nguyen","author_inst":"University of Illinois at Urbana-Champaign"},{"author_name":"Lingyang Zhu","author_inst":"University of Illinois at Urbana-Champaign"},{"author_name":"Lide Cha","author_inst":"University of Illinois at Urbana-Champaign"},{"author_name":"Jesse W Wald","author_inst":"University of Illinois at Urbana-Champaign"},{"author_name":"Douglas A Mitchell","author_inst":"Vanderbilt University"},{"author_name":"Wilfred van der Donk","author_inst":"University of Illinois at Urbana-Champaign"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Redesign of energetically frustrated regions rescues function in defective T4 clamp loaders","rel_doi":"10.64898\/2026.05.08.723874","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.08.723874","rel_abs":"DNA polymerase clamp loaders are AAA+ ATPases that load sliding clamps on DNA for high-speed replication. Using a platform for high-throughput mutagenesis of replication proteins in T4 bacteriophage, we carried out saturation mutagenesis of the AAA+ ATPase module of the T4 clamp loader bearing a mutation, Gln 118 to Asn (Q118N), that reduces fitness. We identified residues for which different mutations improve the fitness of the Q118N variant but are neutral in the wild-type background. These conditionally neutral rescue hotspots overlap with those identified earlier in another defective variant (D110C). These rescue hotspots localize to regions where the sequence is not optimal for the structure, as determined by energetic frustration analysis. We designed new sequences for three of these regions, using the protein-design algorithm ProteinMPNN. In two helical regions, several designed sequences increased the fitness of both wild-type and mutant proteins, likely due to enhanced stability. An inter-domain hinge in AAA+ module changes conformation during activation, and designs for the hinge lead to loss of fitness in the wild-type background. However, when using the active conformation as the template, designs for the hinge increase the fitness of defective variants. In contrast designs templated on the inactive conformation led to loss of fitness, suggesting that a proper conformational balance is crucial. Thus, adaptive capacity in the clamp loader resides in a network of conditionally neutral sites that enable functional tuning through shifts in stability and conformational equilibria.","rel_num_authors":6,"rel_authors":[{"author_name":"Siddharth Nimkar","author_inst":"Vanderbilt University"},{"author_name":"Thu Nguyen","author_inst":"Vanderbilt University"},{"author_name":"Deepti Karandur","author_inst":"Vanderbilt University"},{"author_name":"Subu Subramanian","author_inst":"Vanderbilt University"},{"author_name":"Michael E O'Donnell","author_inst":"The Rockefeller University"},{"author_name":"John Kuriyan","author_inst":"Vanderbilt University"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Redesign of energetically frustrated regions rescues function in defective T4 clamp loaders","rel_doi":"10.64898\/2026.05.08.723874","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.08.723874","rel_abs":"DNA polymerase clamp loaders are AAA+ ATPases that load sliding clamps on DNA for high-speed replication. Using a platform for high-throughput mutagenesis of replication proteins in T4 bacteriophage, we carried out saturation mutagenesis of the AAA+ ATPase module of the T4 clamp loader bearing a mutation, Gln 118 to Asn (Q118N), that reduces fitness. We identified residues for which different mutations improve the fitness of the Q118N variant but are neutral in the wild-type background. These conditionally neutral rescue hotspots overlap with those identified earlier in another defective variant (D110C). These rescue hotspots localize to regions where the sequence is not optimal for the structure, as determined by energetic frustration analysis. We designed new sequences for three of these regions, using the protein-design algorithm ProteinMPNN. In two helical regions, several designed sequences increased the fitness of both wild-type and mutant proteins, likely due to enhanced stability. An inter-domain hinge in AAA+ module changes conformation during activation, and designs for the hinge lead to loss of fitness in the wild-type background. However, when using the active conformation as the template, designs for the hinge increase the fitness of defective variants. In contrast designs templated on the inactive conformation led to loss of fitness, suggesting that a proper conformational balance is crucial. Thus, adaptive capacity in the clamp loader resides in a network of conditionally neutral sites that enable functional tuning through shifts in stability and conformational equilibria.","rel_num_authors":6,"rel_authors":[{"author_name":"Siddharth Nimkar","author_inst":"Vanderbilt University"},{"author_name":"Thu Nguyen","author_inst":"Vanderbilt University"},{"author_name":"Deepti Karandur","author_inst":"Vanderbilt University"},{"author_name":"Subu Subramanian","author_inst":"Vanderbilt University"},{"author_name":"Michael E O'Donnell","author_inst":"The Rockefeller University"},{"author_name":"John Kuriyan","author_inst":"Vanderbilt University"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Adaptation of \u03b1-synuclein fibrils following multiple system atrophy transmission to mice","rel_doi":"10.64898\/2026.05.06.723086","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.723086","rel_abs":"Synucleinopathies are a group of neurodegenerative diseases characterized by the presence of misfolded -synuclein inclusions which cause progressive disease by spreading throughout the brain in a prion-like manner. Throughout the neurodegenerative disease field, the ability of a single protein to give rise to multiple distinct clinical disorders is explained by the strain hypothesis, or the idea that the misfolded protein conformation determines the resulting disease. This was initially shown using transmission studies in cell lines and mouse models; more recently cryo-electron microscopy (cryo-EM) validated this idea by identifying distinct -synuclein filament folds in brain tissues from patients with Parkinsons disease, multiple system atrophy (MSA), and juvenile-onset synucleinopathy. However, very little is known about the -synuclein filament structures that form in animal models of these disorders, and thus their relevance to human disease and suitability as models for therapeutic development remains a question. Here we report the first atomic resolution cryo-EM structures of -synuclein fibrils from an MSA patient sample before and after transmission to a transgenic mouse model of disease. Our findings indicate that while distinct adaptations occur during fibril replication in the mouse host, key structural facets are maintained, validating the merits of this transmission model for supporting preclinical research on MSA.","rel_num_authors":11,"rel_authors":[{"author_name":"Mylan Mayer","author_inst":"University of California, San Francisco"},{"author_name":"Chase R. Khedmatgozar","author_inst":"Colorado State University"},{"author_name":"Gianna Zinnen","author_inst":"Colorado State University"},{"author_name":"Matthew P. Frost","author_inst":"University of Connecticut Health Center: UConn Health"},{"author_name":"Patricia M. Reis","author_inst":"University of Massachusetts Amherst"},{"author_name":"Sara A. M. Holec","author_inst":"SiVEC Biotechnologies"},{"author_name":"Melissa Dexter","author_inst":"Colorado State University"},{"author_name":"Arthur A. Melo","author_inst":"University of California San Francisco"},{"author_name":"Eric Tse","author_inst":"University of California San Francisco"},{"author_name":"Gregory E Merz","author_inst":"University of California San Francisco"},{"author_name":"Amanda L. Woerman","author_inst":"Colorado State University"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Adaptation of \u03b1-synuclein fibrils following multiple system atrophy transmission to mice","rel_doi":"10.64898\/2026.05.06.723086","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.723086","rel_abs":"Synucleinopathies are a group of neurodegenerative diseases characterized by the presence of misfolded -synuclein inclusions which cause progressive disease by spreading throughout the brain in a prion-like manner. Throughout the neurodegenerative disease field, the ability of a single protein to give rise to multiple distinct clinical disorders is explained by the strain hypothesis, or the idea that the misfolded protein conformation determines the resulting disease. This was initially shown using transmission studies in cell lines and mouse models; more recently cryo-electron microscopy (cryo-EM) validated this idea by identifying distinct -synuclein filament folds in brain tissues from patients with Parkinsons disease, multiple system atrophy (MSA), and juvenile-onset synucleinopathy. However, very little is known about the -synuclein filament structures that form in animal models of these disorders, and thus their relevance to human disease and suitability as models for therapeutic development remains a question. Here we report the first atomic resolution cryo-EM structures of -synuclein fibrils from an MSA patient sample before and after transmission to a transgenic mouse model of disease. Our findings indicate that while distinct adaptations occur during fibril replication in the mouse host, key structural facets are maintained, validating the merits of this transmission model for supporting preclinical research on MSA.","rel_num_authors":11,"rel_authors":[{"author_name":"Mylan Mayer","author_inst":"University of California, San Francisco"},{"author_name":"Chase R. Khedmatgozar","author_inst":"Colorado State University"},{"author_name":"Gianna Zinnen","author_inst":"Colorado State University"},{"author_name":"Matthew P. Frost","author_inst":"University of Connecticut Health Center: UConn Health"},{"author_name":"Patricia M. Reis","author_inst":"University of Massachusetts Amherst"},{"author_name":"Sara A. M. Holec","author_inst":"SiVEC Biotechnologies"},{"author_name":"Melissa Dexter","author_inst":"Colorado State University"},{"author_name":"Arthur A. Melo","author_inst":"University of California San Francisco"},{"author_name":"Eric Tse","author_inst":"University of California San Francisco"},{"author_name":"Gregory E Merz","author_inst":"University of California San Francisco"},{"author_name":"Amanda L. Woerman","author_inst":"Colorado State University"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Heterogeneous reconstruction algorithms for cryoEM achieve limited particle classification accuracy on real benchmark datasets","rel_doi":"10.64898\/2026.05.08.722747","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.08.722747","rel_abs":"The emergence of single-particle cryoEM as a powerful method for structure determination has in large part been fueled by its ability to resolve both single static structures and complex conformational landscapes. Indeed, modern approaches to the heterogeneous reconstruction task can resolve 100s-1,000s of different maps from a single cryoEM dataset. How accurate these algorithms are, however, has proven difficult to rigorously assess, due to a lack of suitable benchmark datasets containing both realistic noise features and ground-truth labels. To address this obstacle, we recently developed a series of benchmark datasets that leverage the targeting power of Cas9 and the programmable heterogeneity of DNA to newly offer access to ground-truth per-particle structural labels in real data. Here, we challenged two popular heterogeneous reconstruction algorithms with mixed particle stacks resampled in silico from these datasets, finding that existing approaches resolve the encoded heterogeneity with limited accuracy. In particular, in realistic particle stacks with complex, multi-scale, and multi-axis heterogeneity, we observed that reconstruction of encoded heterogeneity depended strongly on the application of prior information about where heterogeneity was expected, and that individual particle assignments were made with significant error even when the correct structural states were reconstructed. Both molecular breathing motions and data collection features, such as defocus and projection angle, contributed to the observed particle assignment error. These results highlight important shortcomings of existing heterogeneous reconstruction methods and suggest new avenues for method development in both data collection strategies and in heterogeneous classification and reconstruction algorithms.","rel_num_authors":4,"rel_authors":[{"author_name":"Laurel F. Kinman","author_inst":"Massachusetts Institute of Technology, University of California at San Francisco"},{"author_name":"Andrew V. Grassetti","author_inst":"Massachusetts Institute of Technology"},{"author_name":"Maria V. Carreira","author_inst":"Massachusetts Institute of Technology"},{"author_name":"Joseph H. Davis","author_inst":"Massachusetts Institute of Technology"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"A multi-species toolkit of TOP2 hypercleavage mutants for studying topoisomerase II-mediated DNA damage","rel_doi":"10.64898\/2026.05.09.723998","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.09.723998","rel_abs":"DNA topoisomerase II (TOP2) generates transient DNA double-strand breaks that are trapped as TOP2-DNA covalent complexes (TOP2cc) by antibiotic and chemotherapy drugs. Here, we characterize tools for study of cellular responses to TOP2cc, exploiting a Saccharomyces cerevisiae TOP2 mutant (TOP2-F1025Y,R1128G) that generates spontaneous and inhibitor-induced covalent complexes at elevated frequencies. This Top2-hc (for \"hypercleavage\") mutant protein inhibits yeast cell growth when expressed alone or with endogenous Top2, and growth defects are exacerbated in DNA-repair-deficient genetic backgrounds and\/or in the presence of low doses of the Top2 poison mAMSA. We generated analogous mutations in human and mouse TOP2A and TOP2B that gave increased TOP2cc, hypersensitization to topoisomerase poisons, increased DNA damage, and decreased cell survival in cultured cells. We further established knock-in mouse models with inducible, tissue-specific expression of each TOP2-hc isoform, demonstrating overt organismal toxicity and cellular markers of DNA damage responses. To illustrate the potential of these genetic tools, we carried out proof-of-principle screens in yeast and cultured human cells for sensitivity to TOP2-hc. The yeast screen revealed strong requirements for homologous recombination, moderate roles for sister chromatid cohesion and kinetochore function, and dependencies on vesicle and vacuolar functions. The pilot shRNA screen in human cells revealed shared requirements for resistance to expression of either TOP2A-hc or TOP2B-hc as well as examples of isoform specificity. These findings establish hypercleavage mutant proteins as effective tools for studying topoisomerase isoform-specific DNA damage and offer a foundation for exploring TOP2cc toxicity and tolerance in vivo.","rel_num_authors":8,"rel_authors":[{"author_name":"David Ontoso","author_inst":"Memorial Sloan Kettering Cancer Center"},{"author_name":"Monika Mehta","author_inst":"Memorial Sloan Kettering Cancer Center"},{"author_name":"Aidin Shabro","author_inst":"Memorial Sloan Kettering Cancer Center"},{"author_name":"John Dittmar","author_inst":"Columbia University Irving Medical Center"},{"author_name":"Robert J. D. Reid","author_inst":"Columbia University Medical Center"},{"author_name":"Rodney Rothstein","author_inst":"Columbia University Irving Medical Center"},{"author_name":"John L Nitiss","author_inst":"Retzky College of Pharmacy, University of Illinois Chicago,"},{"author_name":"Scott Keeney","author_inst":"Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"A multi-species toolkit of TOP2 hypercleavage mutants for studying topoisomerase II-mediated DNA damage","rel_doi":"10.64898\/2026.05.09.723998","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.09.723998","rel_abs":"DNA topoisomerase II (TOP2) generates transient DNA double-strand breaks that are trapped as TOP2-DNA covalent complexes (TOP2cc) by antibiotic and chemotherapy drugs. Here, we characterize tools for study of cellular responses to TOP2cc, exploiting a Saccharomyces cerevisiae TOP2 mutant (TOP2-F1025Y,R1128G) that generates spontaneous and inhibitor-induced covalent complexes at elevated frequencies. This Top2-hc (for \"hypercleavage\") mutant protein inhibits yeast cell growth when expressed alone or with endogenous Top2, and growth defects are exacerbated in DNA-repair-deficient genetic backgrounds and\/or in the presence of low doses of the Top2 poison mAMSA. We generated analogous mutations in human and mouse TOP2A and TOP2B that gave increased TOP2cc, hypersensitization to topoisomerase poisons, increased DNA damage, and decreased cell survival in cultured cells. We further established knock-in mouse models with inducible, tissue-specific expression of each TOP2-hc isoform, demonstrating overt organismal toxicity and cellular markers of DNA damage responses. To illustrate the potential of these genetic tools, we carried out proof-of-principle screens in yeast and cultured human cells for sensitivity to TOP2-hc. The yeast screen revealed strong requirements for homologous recombination, moderate roles for sister chromatid cohesion and kinetochore function, and dependencies on vesicle and vacuolar functions. The pilot shRNA screen in human cells revealed shared requirements for resistance to expression of either TOP2A-hc or TOP2B-hc as well as examples of isoform specificity. These findings establish hypercleavage mutant proteins as effective tools for studying topoisomerase isoform-specific DNA damage and offer a foundation for exploring TOP2cc toxicity and tolerance in vivo.","rel_num_authors":8,"rel_authors":[{"author_name":"David Ontoso","author_inst":"Memorial Sloan Kettering Cancer Center"},{"author_name":"Monika Mehta","author_inst":"Memorial Sloan Kettering Cancer Center"},{"author_name":"Aidin Shabro","author_inst":"Memorial Sloan Kettering Cancer Center"},{"author_name":"John Dittmar","author_inst":"Columbia University Irving Medical Center"},{"author_name":"Robert J. D. Reid","author_inst":"Columbia University Medical Center"},{"author_name":"Rodney Rothstein","author_inst":"Columbia University Irving Medical Center"},{"author_name":"John L Nitiss","author_inst":"Retzky College of Pharmacy, University of Illinois Chicago,"},{"author_name":"Scott Keeney","author_inst":"Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Compartmentalized glycolysis powers ATP production in primary cilia and engages mitochondria via the phosphoenolpyruvate cycle","rel_doi":"10.64898\/2026.05.06.723011","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.723011","rel_abs":"Primary cilia are antenna-like sensory and signaling organelles present on most mammalian cells, including glucose-sensing pancreatic {beta}-cells. Here, we show that the local energetic demands of primary cilia require the ATP-producing enzyme pyruvate kinase. Loss of PKm1, but not PKm2, impairs ciliary glycolytic flux. While the entire glycolytic machinery localizes to cilia, our data indicate that mitochondria are a critical source of phosphoenolpyruvate (PEP), the high-energy glycolytic intermediate that drives the pyruvate kinase reaction. Abolishing PCK2, the mitochondrial enzyme that generates PEP, prevents cilia from sensing not only glucose but also the amino acids glutamine and leucine. Finally, by experimentally mislocalizing glycolysis, we demonstrate that primary cilia can utilize ATP generated within the cell body when glucose is limited. These findings indicate that primary cilia possess the capacity for local ATP generation, and when necessary, leverage a ciliary-mitochondrial signaling axis to meet their bioenergetic needs.","rel_num_authors":9,"rel_authors":[{"author_name":"Shih Ming Huang","author_inst":"Yale School of Medicine"},{"author_name":"Hannah R Foster","author_inst":"Yale School of Medicine"},{"author_name":"Eun Young Lee","author_inst":"Washington University School of Medicine"},{"author_name":"Jeong Hun Jo","author_inst":"Yale School of Medicine"},{"author_name":"Xinhang Dong","author_inst":"Washington University School of Medicine"},{"author_name":"Byoung-Kyu Cho","author_inst":"Washington University School of Medicine"},{"author_name":"Young Ah Goo","author_inst":"Washington University School of Medicine"},{"author_name":"Jing W Hughes","author_inst":"Yale School of Medicine"},{"author_name":"Matthew J Merrins","author_inst":"Yale School of Medicine"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Compartmentalized glycolysis powers ATP production in primary cilia and engages mitochondria via the phosphoenolpyruvate cycle","rel_doi":"10.64898\/2026.05.06.723011","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.06.723011","rel_abs":"Primary cilia are antenna-like sensory and signaling organelles present on most mammalian cells, including glucose-sensing pancreatic {beta}-cells. Here, we show that the local energetic demands of primary cilia require the ATP-producing enzyme pyruvate kinase. Loss of PKm1, but not PKm2, impairs ciliary glycolytic flux. While the entire glycolytic machinery localizes to cilia, our data indicate that mitochondria are a critical source of phosphoenolpyruvate (PEP), the high-energy glycolytic intermediate that drives the pyruvate kinase reaction. Abolishing PCK2, the mitochondrial enzyme that generates PEP, prevents cilia from sensing not only glucose but also the amino acids glutamine and leucine. Finally, by experimentally mislocalizing glycolysis, we demonstrate that primary cilia can utilize ATP generated within the cell body when glucose is limited. These findings indicate that primary cilia possess the capacity for local ATP generation, and when necessary, leverage a ciliary-mitochondrial signaling axis to meet their bioenergetic needs.","rel_num_authors":9,"rel_authors":[{"author_name":"Shih Ming Huang","author_inst":"Yale School of Medicine"},{"author_name":"Hannah R Foster","author_inst":"Yale School of Medicine"},{"author_name":"Eun Young Lee","author_inst":"Washington University School of Medicine"},{"author_name":"Jeong Hun Jo","author_inst":"Yale School of Medicine"},{"author_name":"Xinhang Dong","author_inst":"Washington University School of Medicine"},{"author_name":"Byoung-Kyu Cho","author_inst":"Washington University School of Medicine"},{"author_name":"Young Ah Goo","author_inst":"Washington University School of Medicine"},{"author_name":"Jing W Hughes","author_inst":"Yale School of Medicine"},{"author_name":"Matthew J Merrins","author_inst":"Yale School of Medicine"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"A cohesion optimum underlies chromosome segregation fidelity in oocytes","rel_doi":"10.64898\/2026.05.08.723885","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.08.723885","rel_abs":"Chromosome segregation is compromised in eggs from women of both early and advanced reproductive ages. Deteriorating cohesion causes premature separation of sister-chromatids in eggs from older females. We show that the converse is true for oocytes of adolescents, with excessive cohesion impeding segregation. Oocytes from juvenile mice show severe chromosome lagging in anaphase I, leading to nondisjunction or, in extreme cases, failure of the first meiotic division. These defects are suppressed by experimentally weakening cohesion or enhancing its resolution during anaphase I. By contrast, lagging and nondisjunction are rare in the oocytes of young adults because cohesion is inherently weaker. Thus, relative cohesion strength underlies both the frequency and type of segregation errors observed in eggs throughout the female reproductive lifespan.","rel_num_authors":8,"rel_authors":[{"author_name":"Yan Yun","author_inst":"Shantou Clinical Medical College of Jinan University"},{"author_name":"Kanako Ikami","author_inst":"University of California Davis"},{"author_name":"Duy Do","author_inst":"University of California Davis"},{"author_name":"Ziyang Guo","author_inst":"Shantou Clinical Medical College of Jinan University"},{"author_name":"Jiyeon Leem","author_inst":"Yale University"},{"author_name":"Hilina Bekele","author_inst":"Yale University"},{"author_name":"Binyam Mogessie","author_inst":"Yale University"},{"author_name":"Neil Hunter","author_inst":"University of California, Davis"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Glutamine codon-driven translational readthrough reveals context-dependent stop codon decoding fidelity","rel_doi":"10.64898\/2026.05.08.723929","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.08.723929","rel_abs":"Deviations from the canonical genetic code include reassignment of UAA\/UAG stop codons to glutamine in divergent eukaryotes, and tRNAGln has been shown to mediate near-cognate stop codon readthrough in canonical-code organisms. However, the sequence determinants and mechanistic basis of this decoding event remain poorly understood. Using ribosome profiling, quantitative immunoblotting, and mass spectrometry in Saccharomyces cerevisiae, we demonstrate that premature stop codon readthrough efficiency is governed by both local glutamine codon context and the global glutamine codon content of the mRNA. A QXQ motif flanking the stop codon promotes baseline readthrough, which is amplified in proportion to total transcript glutamine codon abundance. Mass spectrometry confirms that glutamine is specifically inserted at the premature stop, with no flanking miscoding, implicating tRNAGln competition with the release factor as the mechanistic basis of readthrough. Consistent with this model, yeast proteins terminating in short C-terminal glutamine repeats are evolutionarily enriched for strong stop codon contexts, suggesting selective pressure to reinforce termination fidelity at readthrough-prone loci.","rel_num_authors":5,"rel_authors":[{"author_name":"Jessica M Leslie","author_inst":"University of California Berkeley"},{"author_name":"Kaitlin Morse","author_inst":"University of California Berkeley"},{"author_name":"Sarah Swerdlow","author_inst":"University of California Berkeley"},{"author_name":"Gloria A Brar","author_inst":"University of California Berkeley"},{"author_name":"Elcin Unal","author_inst":"University of California Berkeley"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Mitochondrial respiration modulates Hsf1 activation and the heat shock response.","rel_doi":"10.64898\/2026.05.07.723568","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.07.723568","rel_abs":"Cells employ a bevy of transcriptional and post-translational stress responses to tolerate the burden of misfolded proteins induced by stress. In particular, the heat shock response facilitates the upregulation of molecular chaperones and protein remodeling factors that mediate proteostasis in response to accumulated misfolded proteins in the nucleus and cytosol. However, in response to stress neurons struggle to induce a canonical heat shock response, highlighting our poor understanding of how neurons maintain proteostasis. Specifically, the ability of post-mitotic respiring cells to regulate the heat shock response in comparison to their rapidly dividing, predominantly glycolytic counterparts has been under-studied. In this study, we employ yeast models that are easily manipulated to generate energy via glycolysis or mitochondrial respiration by changing the carbon source in the media. Using this model, we demonstrate that Hsf1 activity, the heat shock response and proteostasis are impaired in respiring cells. Interestingly, our data show that reduced Hsf1 activity regulates viability of respiring cells, with respiring cells poorly tolerating constitutively activated Hsf1. Finally, we describe alternative post-translational programming of the molecular chaperones Hsp70 and Hsp104 that plausibly enables respiring cells to mediate proteostasis despite a dampened heat shock response. Our findings offer new insights into possible proteostatic strategies employed by cells in different metabolic conditions.","rel_num_authors":7,"rel_authors":[{"author_name":"Donovan W McDonald","author_inst":"Department of Biology, The University of Western Ontario, London, ON, N6A 3K7, Canada"},{"author_name":"Annisa Dea","author_inst":"Department of Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL, United States"},{"author_name":"Rares Sava","author_inst":"Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, N6A 3K7, Canada"},{"author_name":"Young Joon Kim","author_inst":"Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, N6A 3K7, Canada"},{"author_name":"Laura Joos","author_inst":"Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, N6A 3K7, Canada"},{"author_name":"David Pincus","author_inst":"Department of Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL, United States"},{"author_name":"Martin L Duennwald","author_inst":"Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, N6A 3K7, Canada"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"Mitochondrial respiration modulates Hsf1 activation and the heat shock response.","rel_doi":"10.64898\/2026.05.07.723568","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.05.07.723568","rel_abs":"Cells employ a bevy of transcriptional and post-translational stress responses to tolerate the burden of misfolded proteins induced by stress. In particular, the heat shock response facilitates the upregulation of molecular chaperones and protein remodeling factors that mediate proteostasis in response to accumulated misfolded proteins in the nucleus and cytosol. However, in response to stress neurons struggle to induce a canonical heat shock response, highlighting our poor understanding of how neurons maintain proteostasis. Specifically, the ability of post-mitotic respiring cells to regulate the heat shock response in comparison to their rapidly dividing, predominantly glycolytic counterparts has been under-studied. In this study, we employ yeast models that are easily manipulated to generate energy via glycolysis or mitochondrial respiration by changing the carbon source in the media. Using this model, we demonstrate that Hsf1 activity, the heat shock response and proteostasis are impaired in respiring cells. Interestingly, our data show that reduced Hsf1 activity regulates viability of respiring cells, with respiring cells poorly tolerating constitutively activated Hsf1. Finally, we describe alternative post-translational programming of the molecular chaperones Hsp70 and Hsp104 that plausibly enables respiring cells to mediate proteostasis despite a dampened heat shock response. Our findings offer new insights into possible proteostatic strategies employed by cells in different metabolic conditions.","rel_num_authors":7,"rel_authors":[{"author_name":"Donovan W McDonald","author_inst":"Department of Biology, The University of Western Ontario, London, ON, N6A 3K7, Canada"},{"author_name":"Annisa Dea","author_inst":"Department of Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL, United States"},{"author_name":"Rares Sava","author_inst":"Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, N6A 3K7, Canada"},{"author_name":"Young Joon Kim","author_inst":"Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, N6A 3K7, Canada"},{"author_name":"Laura Joos","author_inst":"Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, N6A 3K7, Canada"},{"author_name":"David Pincus","author_inst":"Department of Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL, United States"},{"author_name":"Martin L Duennwald","author_inst":"Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, N6A 3K7, Canada"}],"rel_date":"2026-05-11","rel_site":"biorxiv"},{"rel_title":"The TBVaxRepository: A living database of projects supporting the preparedness for adult and adolescent TB vaccine rollout","rel_doi":"10.64898\/2026.05.06.26352615","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.06.26352615","rel_abs":"Background Tuberculosis (TB) continues to cause substantial morbidity and mortality, with adults and adolescents carrying the largest burden of disease. Multiple promising novel vaccine candidates are in clinical trials, and their eventual impact will depend on effective implementation strategies. Information on TB vaccine preparedness efforts that could inform coordination remains fragmented. Methods We developed the first living and interactive online repository (https:\/\/tbvaxrepository.org\/) collating completed, ongoing, and planned adult and adolescent TB vaccine preparedness initiatives. Data were obtained through a prior scoping review, direct stakeholder engagement, international conferences, and open calls via social media and partner networks between March 2023-November 2024. Projects were categorized using the World Health Organizations (WHO) framework for TB vaccine preparedness across three thematic areas: availability, accessibility, and acceptability. Findings By December 2024, the repository included 90 projects from 119 countries. Most projects focused on health- (47%) and economic modelling (21%), demand and acceptability studies (19%) or implementation feasibility (14%). Most of the projects were situated in India (n=36), South Africa (n=34), China (n=19), Indonesia, (n=17), Kenya (n=17), Brazil (n=14), and Pakistan (n=14). Few initiatives targeted key populations such as people living with HIV, pregnant or lactating individuals, or socially marginalized and occupational high-risk groups. Research on communication strategies for facilitating uptake as part of rollout were absent. Conclusions The repository reveals both progress and gaps in global TB vaccine preparedness across WHOs three thematic areas, with particular attention to geographic coverage, and the inclusion of key populations. As novel vaccines for adults and adolescents approach potential licensure, coordinated and inclusive preparedness efforts will be critical to ensure equitable and effective rollout. This repository offers a transparent platform to strengthen collaboration, reduce duplication, and guide strategic planning in a historically underfunded field.","rel_num_authors":4,"rel_authors":[{"author_name":"Joeri  Sumina Buis","author_inst":"KNCV Tuberculosis Foundation: KNCV Tuberculosefonds"},{"author_name":"Andrew  D Kerkhoff","author_inst":"UCSF: University of California San Francisco"},{"author_name":"Christiaan Mulder","author_inst":"KNCV Tuberculosis Foundation: KNCV Tuberculosefonds"},{"author_name":"Degu Jerene","author_inst":"KNCV Tuberculosis Foundation: KNCV Tuberculosefonds"}],"rel_date":"2026-05-10","rel_site":"medrxiv"},{"rel_title":"Germline polygenic score for prostate cancer aggressiveness","rel_doi":"10.64898\/2026.05.07.26352488","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352488","rel_abs":"Background Risk stratification for prostate cancer (PCa) progression or aggressiveness is often based on clinicopathologic features, some of which may be influenced by genetic factors. We developed a novel, germline polygenic risk score (PRSagg) to predict likelihood of developing aggressive PCa. Methods PRSagg was developed using data from 38,688 patients with PCa (case-only analysis) from the Million Veteran Program (MVP) through a genome-wide search for variants associated with PCa grade group at diagnosis. We tested associations of PRSagg with grade group using the entire MVP dataset using the .632 bootstrap method. In an MVP cohort with localized PCa that was initially monitored without treatment, we tested PRSagg for association with unfavorable outcomes (subsequent development of grade group 4-5, metastasis, and\/or biochemical recurrence after definitive treatment). We performed external validation in data from patients in the PRACTICAL Consortium (n=45,214) and from participants in the ProtecT randomized trial who underwent active monitoring (n=316). Odds ratios (ORs) were calculated per standard deviation (SD) increase with 95% confidence intervals, while adjusting for age, genetic ancestry, a previously developed polygenic score for risk of PCa (PHS601), and a polygenic score for benign elevated prostate-specific antigen (PRSPSA). For the outcome of metastasis, we additionally adjusted for PSA at diagnosis. Results In the MVP training dataset, PRSagg (172 variants) was associated with higher grade group at diagnosis (OR = 1.53 [1.51-1.56]) and with increased risk of unfavorable outcomes during monitoring (OR = 1.13 [1.09-1.18]). These findings were confirmed in the external datasets. PRSagg was associated with greater odds of higher grade group at diagnosis (OR = 1.09 [1.06-1.11]). Among ProtecT participants undergoing active monitoring, PRSagg was associated with higher risk of metastasis (OR = 2.15 [1.02-3.88]). Among MVP participants with high polygenic risk of developing any PCa, the risk of aggressive disease was highest in men with high PRSagg and low genetic risk of PSA elevation. Conclusions Among men who develop PCa, a weighted sum of common germline variants (PRSagg) is independently associated with PCa aggressiveness. These findings may inform future study of germline influence on tumor evolution and risk-stratified intensity of active surveillance.","rel_num_authors":82,"rel_authors":[{"author_name":"George Jiajie Xu","author_inst":"VA San Diego Healthcare System"},{"author_name":"Roshan Karunamuni","author_inst":"VA San Diego Healthcare System"},{"author_name":"Anna M Dornisch","author_inst":"University of California San Diego"},{"author_name":"Charles A Brunette","author_inst":"VA Boston Healthcare System"},{"author_name":"Morgan E Danowski","author_inst":"VA Boston Healthcare System"},{"author_name":"Heena Desai","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Daniel Dochtermann","author_inst":"VA Boston Healthcare System"},{"author_name":"Isla P Garraway","author_inst":"VA Greater Los Angeles Healthcare System"},{"author_name":"Richard L Hauger","author_inst":"VA San Diego Healthcare System"},{"author_name":"Adam S Kibel","author_inst":"Harvard Medical School"},{"author_name":"Julie A Lynch","author_inst":"VA Salt Lake City Healthcare System"},{"author_name":"Saiju Pyarajan","author_inst":"VA Boston Healthcare System"},{"author_name":"Brent S Rose","author_inst":"VA San Diego Healthcare System"},{"author_name":"Craig C Teerlink","author_inst":"VA Salt Lake City Healthcare System"},{"author_name":"Ole A Andreassen","author_inst":"Oslo University Hospital and University of Oslo"},{"author_name":"Anders M Dale","author_inst":"University of California San Diego"},{"author_name":"Jenny L Donovan","author_inst":"University of Bristol"},{"author_name":"Freddie Hamdy","author_inst":"University of Oxford"},{"author_name":"Linda Kachuri","author_inst":"Stanford University"},{"author_name":"Athene Lane","author_inst":"University of Bristol"},{"author_name":"Richard M Martin","author_inst":"University of Bristol"},{"author_name":"Ian G Mills","author_inst":"University of Oxford"},{"author_name":"David E Neal","author_inst":"University of Oxford"},{"author_name":"Emma L Turner","author_inst":"University of Bristol"},{"author_name":"John S Witte","author_inst":"Stanford University"},{"author_name":"Johanna Schleutker","author_inst":"University of Turku"},{"author_name":"Nora Pashayan","author_inst":"University of Cambridge"},{"author_name":"Jyotsna Batra","author_inst":"Bond University"},{"author_name":"- Australian Prostate Cancer BioResource (APCB)","author_inst":"-"},{"author_name":"B\u00f8rge G Nordestgaard","author_inst":"University of Copenhagen"},{"author_name":"Robert J Hamilton","author_inst":"Princess Margaret Cancer Centre"},{"author_name":"Alicja Wolk","author_inst":"Karolinska Institutet"},{"author_name":"Demetrius Albanes","author_inst":"National Cancer Institute"},{"author_name":"Joshua Atkins","author_inst":"University of Oxford"},{"author_name":"William J Blot","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Lorelei A Mucci","author_inst":"Harvard T.H. Chan School of Public Health"},{"author_name":"Sune F Nielsen","author_inst":"Copenhagen University Hospital"},{"author_name":"Olivier Cussenot","author_inst":"Sorbonne Universite"},{"author_name":"Sonja I Berndt","author_inst":"National Cancer Institute"},{"author_name":"Stella Koutros","author_inst":"National Cancer Institute"},{"author_name":"Karina Dalsgaard S\u00f8rensen","author_inst":"Aarhus University Hospital"},{"author_name":"Cezary Cybulski","author_inst":"Pomeranian Medical University"},{"author_name":"Florence Menegaux","author_inst":"Universit\u00e9 Paris-Saclay"},{"author_name":"Jong Y Park","author_inst":"Moffitt Cancer Center"},{"author_name":"Robert J MacInnis","author_inst":"Cancer Council Victoria"},{"author_name":"Barry S Rosenstein","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Yong-Jie Lu","author_inst":"Queen Mary University of London"},{"author_name":"Stephen Watya","author_inst":"Uro Care"},{"author_name":"Ana Vega","author_inst":"Santiago de Compostela"},{"author_name":"- NC-LA PCaP Investigators","author_inst":"-"},{"author_name":"- The IMPACT Study Steering Committee and Collaborators","author_inst":"-"},{"author_name":"Manolis Kogevinas","author_inst":"ISGLOBAL: Instituto de Salud Global de Barcelona"},{"author_name":"Fredrik Wiklund","author_inst":"Karolinska Institutet"},{"author_name":"Anna Plym","author_inst":"Karolinska Institutet"},{"author_name":"Manuel R Teixeira","author_inst":"Porto Comprehensive Cancer Center"},{"author_name":"Luc Multigner","author_inst":"Institut de recherche en sant\u00e9, environnement et travail"},{"author_name":"Robin J Leach","author_inst":"University of Texas Health Science Center at San Antonio"},{"author_name":"Hermann Brenner","author_inst":"German Cancer Research Centre: Deutsches Krebsforschungszentrum"},{"author_name":"Esther M John","author_inst":"Stanford University"},{"author_name":"Radka Kaneva","author_inst":"Medical University of Sofia"},{"author_name":"Christopher J Logothetis","author_inst":"The University of Texas M. D. Anderson Cancer Center"},{"author_name":"Susan L Neuhausen","author_inst":"Beckman Research Institute of City of Hope"},{"author_name":"Piet Ost","author_inst":"Ghent University"},{"author_name":"Azad Razack","author_inst":"University of Malaya"},{"author_name":"Jay H Fowke","author_inst":"University of Tennessee Health Science Center"},{"author_name":"Marija Gamulin","author_inst":"University of Zagreb School of Medicine"},{"author_name":"Nawaid Usmani","author_inst":"University of Alberta"},{"author_name":"Frank Claessens","author_inst":"KU Leuven"},{"author_name":"Jose Esteban Castelao","author_inst":"Instituto de Investigaci\u00f3n Biom\u00e9dica Galicia Sur"},{"author_name":"Gyorgy Petrovics","author_inst":"Uniformed Services University"},{"author_name":"Marie-\u00c9lise Parent","author_inst":"Institut national de la recherche scientifique"},{"author_name":"Jennifer J Hu","author_inst":"The University of Miami School of Medicine"},{"author_name":"Wei Zheng","author_inst":"Vanderbilt University Medical Center"},{"author_name":"- The Profile Study Steering Committee","author_inst":"-"},{"author_name":"- UKGPCS collaborators","author_inst":"-"},{"author_name":"Zsofia Kote-Jarai","author_inst":"The Institute of Cancer Research"},{"author_name":"Rosalind A Eeles","author_inst":"The Institute of Cancer Research"},{"author_name":"- The PRACTICAL Consortium","author_inst":"-"},{"author_name":"- VA Million Veteran Program","author_inst":"-"},{"author_name":"Kara N Maxwell","author_inst":"Corporal Michael Crescenz Veterans Affairs Medical Center"},{"author_name":"Jason L Vassy","author_inst":"VA Boston Healthcare System"},{"author_name":"Tyler M Seibert","author_inst":"VA San Diego Healthcare System"}],"rel_date":"2026-05-10","rel_site":"medrxiv"},{"rel_title":"Germline polygenic score for prostate cancer aggressiveness","rel_doi":"10.64898\/2026.05.07.26352488","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352488","rel_abs":"Background Risk stratification for prostate cancer (PCa) progression or aggressiveness is often based on clinicopathologic features, some of which may be influenced by genetic factors. We developed a novel, germline polygenic risk score (PRSagg) to predict likelihood of developing aggressive PCa. Methods PRSagg was developed using data from 38,688 patients with PCa (case-only analysis) from the Million Veteran Program (MVP) through a genome-wide search for variants associated with PCa grade group at diagnosis. We tested associations of PRSagg with grade group using the entire MVP dataset using the .632 bootstrap method. In an MVP cohort with localized PCa that was initially monitored without treatment, we tested PRSagg for association with unfavorable outcomes (subsequent development of grade group 4-5, metastasis, and\/or biochemical recurrence after definitive treatment). We performed external validation in data from patients in the PRACTICAL Consortium (n=45,214) and from participants in the ProtecT randomized trial who underwent active monitoring (n=316). Odds ratios (ORs) were calculated per standard deviation (SD) increase with 95% confidence intervals, while adjusting for age, genetic ancestry, a previously developed polygenic score for risk of PCa (PHS601), and a polygenic score for benign elevated prostate-specific antigen (PRSPSA). For the outcome of metastasis, we additionally adjusted for PSA at diagnosis. Results In the MVP training dataset, PRSagg (172 variants) was associated with higher grade group at diagnosis (OR = 1.53 [1.51-1.56]) and with increased risk of unfavorable outcomes during monitoring (OR = 1.13 [1.09-1.18]). These findings were confirmed in the external datasets. PRSagg was associated with greater odds of higher grade group at diagnosis (OR = 1.09 [1.06-1.11]). Among ProtecT participants undergoing active monitoring, PRSagg was associated with higher risk of metastasis (OR = 2.15 [1.02-3.88]). Among MVP participants with high polygenic risk of developing any PCa, the risk of aggressive disease was highest in men with high PRSagg and low genetic risk of PSA elevation. Conclusions Among men who develop PCa, a weighted sum of common germline variants (PRSagg) is independently associated with PCa aggressiveness. These findings may inform future study of germline influence on tumor evolution and risk-stratified intensity of active surveillance.","rel_num_authors":82,"rel_authors":[{"author_name":"George Jiajie Xu","author_inst":"VA San Diego Healthcare System"},{"author_name":"Roshan Karunamuni","author_inst":"VA San Diego Healthcare System"},{"author_name":"Anna M Dornisch","author_inst":"University of California San Diego"},{"author_name":"Charles A Brunette","author_inst":"VA Boston Healthcare System"},{"author_name":"Morgan E Danowski","author_inst":"VA Boston Healthcare System"},{"author_name":"Heena Desai","author_inst":"University of Pennsylvania Perelman School of Medicine"},{"author_name":"Daniel Dochtermann","author_inst":"VA Boston Healthcare System"},{"author_name":"Isla P Garraway","author_inst":"VA Greater Los Angeles Healthcare System"},{"author_name":"Richard L Hauger","author_inst":"VA San Diego Healthcare System"},{"author_name":"Adam S Kibel","author_inst":"Harvard Medical School"},{"author_name":"Julie A Lynch","author_inst":"VA Salt Lake City Healthcare System"},{"author_name":"Saiju Pyarajan","author_inst":"VA Boston Healthcare System"},{"author_name":"Brent S Rose","author_inst":"VA San Diego Healthcare System"},{"author_name":"Craig C Teerlink","author_inst":"VA Salt Lake City Healthcare System"},{"author_name":"Ole A Andreassen","author_inst":"Oslo University Hospital and University of Oslo"},{"author_name":"Anders M Dale","author_inst":"University of California San Diego"},{"author_name":"Jenny L Donovan","author_inst":"University of Bristol"},{"author_name":"Freddie Hamdy","author_inst":"University of Oxford"},{"author_name":"Linda Kachuri","author_inst":"Stanford University"},{"author_name":"Athene Lane","author_inst":"University of Bristol"},{"author_name":"Richard M Martin","author_inst":"University of Bristol"},{"author_name":"Ian G Mills","author_inst":"University of Oxford"},{"author_name":"David E Neal","author_inst":"University of Oxford"},{"author_name":"Emma L Turner","author_inst":"University of Bristol"},{"author_name":"John S Witte","author_inst":"Stanford University"},{"author_name":"Johanna Schleutker","author_inst":"University of Turku"},{"author_name":"Nora Pashayan","author_inst":"University of Cambridge"},{"author_name":"Jyotsna Batra","author_inst":"Bond University"},{"author_name":"- Australian Prostate Cancer BioResource (APCB)","author_inst":"-"},{"author_name":"B\u00f8rge G Nordestgaard","author_inst":"University of Copenhagen"},{"author_name":"Robert J Hamilton","author_inst":"Princess Margaret Cancer Centre"},{"author_name":"Alicja Wolk","author_inst":"Karolinska Institutet"},{"author_name":"Demetrius Albanes","author_inst":"National Cancer Institute"},{"author_name":"Joshua Atkins","author_inst":"University of Oxford"},{"author_name":"William J Blot","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Lorelei A Mucci","author_inst":"Harvard T.H. Chan School of Public Health"},{"author_name":"Sune F Nielsen","author_inst":"Copenhagen University Hospital"},{"author_name":"Olivier Cussenot","author_inst":"Sorbonne Universite"},{"author_name":"Sonja I Berndt","author_inst":"National Cancer Institute"},{"author_name":"Stella Koutros","author_inst":"National Cancer Institute"},{"author_name":"Karina Dalsgaard S\u00f8rensen","author_inst":"Aarhus University Hospital"},{"author_name":"Cezary Cybulski","author_inst":"Pomeranian Medical University"},{"author_name":"Florence Menegaux","author_inst":"Universit\u00e9 Paris-Saclay"},{"author_name":"Jong Y Park","author_inst":"Moffitt Cancer Center"},{"author_name":"Robert J MacInnis","author_inst":"Cancer Council Victoria"},{"author_name":"Barry S Rosenstein","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Yong-Jie Lu","author_inst":"Queen Mary University of London"},{"author_name":"Stephen Watya","author_inst":"Uro Care"},{"author_name":"Ana Vega","author_inst":"Santiago de Compostela"},{"author_name":"- NC-LA PCaP Investigators","author_inst":"-"},{"author_name":"- The IMPACT Study Steering Committee and Collaborators","author_inst":"-"},{"author_name":"Manolis Kogevinas","author_inst":"ISGLOBAL: Instituto de Salud Global de Barcelona"},{"author_name":"Fredrik Wiklund","author_inst":"Karolinska Institutet"},{"author_name":"Anna Plym","author_inst":"Karolinska Institutet"},{"author_name":"Manuel R Teixeira","author_inst":"Porto Comprehensive Cancer Center"},{"author_name":"Luc Multigner","author_inst":"Institut de recherche en sant\u00e9, environnement et travail"},{"author_name":"Robin J Leach","author_inst":"University of Texas Health Science Center at San Antonio"},{"author_name":"Hermann Brenner","author_inst":"German Cancer Research Centre: Deutsches Krebsforschungszentrum"},{"author_name":"Esther M John","author_inst":"Stanford University"},{"author_name":"Radka Kaneva","author_inst":"Medical University of Sofia"},{"author_name":"Christopher J Logothetis","author_inst":"The University of Texas M. D. Anderson Cancer Center"},{"author_name":"Susan L Neuhausen","author_inst":"Beckman Research Institute of City of Hope"},{"author_name":"Piet Ost","author_inst":"Ghent University"},{"author_name":"Azad Razack","author_inst":"University of Malaya"},{"author_name":"Jay H Fowke","author_inst":"University of Tennessee Health Science Center"},{"author_name":"Marija Gamulin","author_inst":"University of Zagreb School of Medicine"},{"author_name":"Nawaid Usmani","author_inst":"University of Alberta"},{"author_name":"Frank Claessens","author_inst":"KU Leuven"},{"author_name":"Jose Esteban Castelao","author_inst":"Instituto de Investigaci\u00f3n Biom\u00e9dica Galicia Sur"},{"author_name":"Gyorgy Petrovics","author_inst":"Uniformed Services University"},{"author_name":"Marie-\u00c9lise Parent","author_inst":"Institut national de la recherche scientifique"},{"author_name":"Jennifer J Hu","author_inst":"The University of Miami School of Medicine"},{"author_name":"Wei Zheng","author_inst":"Vanderbilt University Medical Center"},{"author_name":"- The Profile Study Steering Committee","author_inst":"-"},{"author_name":"- UKGPCS collaborators","author_inst":"-"},{"author_name":"Zsofia Kote-Jarai","author_inst":"The Institute of Cancer Research"},{"author_name":"Rosalind A Eeles","author_inst":"The Institute of Cancer Research"},{"author_name":"- The PRACTICAL Consortium","author_inst":"-"},{"author_name":"- VA Million Veteran Program","author_inst":"-"},{"author_name":"Kara N Maxwell","author_inst":"Corporal Michael Crescenz Veterans Affairs Medical Center"},{"author_name":"Jason L Vassy","author_inst":"VA Boston Healthcare System"},{"author_name":"Tyler M Seibert","author_inst":"VA San Diego Healthcare System"}],"rel_date":"2026-05-10","rel_site":"medrxiv"},{"rel_title":"Psychosocial mediators for the impact of personal genomic risk information on melanoma prevention and early detection behaviors","rel_doi":"10.64898\/2026.05.07.26352695","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352695","rel_abs":"Background: In the Melanoma Genomics Managing Your Risk Study, access to personal genomic risk testing led to improvements in some melanoma prevention and early detection behaviors. Purpose: We aimed to examine the hypothesized psychosocial mediators of the effects observed in the trial. Methods: Australians of European ancestry without melanoma and aged 18-69 years were recruited via the national Medicare database and randomized to receive personal genomic risk information or usual care (N=1,025). Questionnaires were administered at baseline, 1-month post-intervention, and 12-months post-baseline to assess self-reported prevention and early detection behaviors and psychosocial measures. To identify potential mediators, we first evaluated the intervention's effect on psychosocial measures and the associations between psychosocial measures and behavioral outcomes. We then estimated the natural indirect effects (NIEs) and their 95% confidence intervals (CIs) to quantify the effects mediated by potential mediators identified. Results: Among participants with high traditional melanoma risk, the intervention's effect on increased sun protection at 1-month was partially mediated by changes in perceived importance [NIE mean difference (95% CI): 0.02 (0.00, 0.04)] and perceived effectiveness [0.01 (0.00, 0.03)] of sun protection strategies. Among women, the intervention's effect on increased whole-body skin examinations at 1-month was partially mediated by perceived capability to engage in skin examinations [NIE odds ratio (95% CI): 1.08 (1.00, 1.29)] and perceived control over detecting a future melanoma [1.13 (1.03, 1.32)]. Conclusions: The effectiveness of precision prevention and early detection interventions may be enhanced by targeting key psychosocial mediators through tailored communication of personal melanoma risk.","rel_num_authors":5,"rel_authors":[{"author_name":"Sabrina E Wang","author_inst":"The Daffodil Centre, The University of Sydney, and Cancer Council NSW"},{"author_name":"David Espinoza","author_inst":"NHMRC Clinical Trials Centre, The University of Sydney"},{"author_name":"Serigne Lo","author_inst":"Melanoma Institute Australia, The University of Sydney"},{"author_name":"Amelia K Smit","author_inst":"The Daffodil Centre, The University of Sydney, and Cancer Council NSW"},{"author_name":"Anne  E. Cust","author_inst":"The Daffodil Centre, The University of Sydney, and Cancer Council NSW"}],"rel_date":"2026-05-10","rel_site":"medrxiv"},{"rel_title":"Decoding the diet-gut-liver axis: links between dietary pattern adherence, gut microbiome, and hepatic health","rel_doi":"10.64898\/2026.05.04.26352208","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.04.26352208","rel_abs":"Metabolic dysfunction-associated steatotic liver disease (MASLD) is rapidly becoming the leading cause of chronic liver disease and confers substantial cardiometabolic burden. Diet quality and gut microbiota composition have been implicated in MASLD development; however, the interplay among diet, gut microbiota, and hepatic health remains insufficiently characterized. Here, in 9,616 deeply phenotyped middle-aged participants (mean age 52 years) from the Human Phenotype Project, we investigated how five dietary quality indices capturing complementary dimensions of healthy eating, including plant-based (hPDI), Mediterranean-style (AMED), anti-inflammatory (rDII), anti-hyperinsulinemic (rEDIH), and overall quality (AHEI), relate to gut microbial composition and liver steatosis. Dietary pattern scores were derived from two-week continuous diet logs, gut microbiota was characterized by shotgun metagenomic sequencing, and hepatic health was assessed by both ultrasound-derived metrics and prevalent MASLD status. Adherence to each of the five healthy dietary patterns was inversely associated with MASLD prevalence and positively associated with liver speed of sound (SoS), an ultrasound-derived metric that correlates inversely with hepatic fat content. Across all five dietary patterns, greater adherence was consistently associated with 138 gut microbial species, including inverse associations with Flavonifractor plautii, Dysosmobacter welbionis, Ruthenibacterium lactatiformans, Bilophila wadsworthia, and Phocea massiliensis. These five species were also associated with lower liver SoS and higher odds of prevalent MASLD, emerging as potential mediators of the diet-liver relationship in cross-sectional mediation analyses after adjustment for body mass index (BMI). This study identifies candidate microbial targets for future interventional studies investigating dietary strategies for MASLD prevention.","rel_num_authors":10,"rel_authors":[{"author_name":"Keyong Deng","author_inst":"Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands"},{"author_name":"Quinten R. Ducarmon","author_inst":"Leiden University Center for Infectious Diseases (LUCID), Leiden University Medical Center, Leiden, the Netherlands"},{"author_name":"Anastasia Godneva","author_inst":"Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel. Department of Molecular Cell Biology, Weizmann Institute"},{"author_name":"Zheqing Zhang","author_inst":"Department of Nutrition and Food Hygiene, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University"},{"author_name":"Astrid van Hylckama Vlieg","author_inst":"Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands"},{"author_name":"Frits R. Rosendaal","author_inst":"Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands"},{"author_name":"Georg Zeller","author_inst":"Leiden University Center for Infectious Diseases (LUCID), Leiden University Medical Center, Leiden, the Netherlands"},{"author_name":"Eran Segal","author_inst":"Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel; Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE"},{"author_name":"Ruifang Li-Gao","author_inst":"Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands"},{"author_name":"- DIYUFOOD consortium","author_inst":""}],"rel_date":"2026-05-10","rel_site":"medrxiv"},{"rel_title":"Scalable deep-learning-based inference of time-varying transmission dynamics from outbreak phylogenies","rel_doi":"10.64898\/2026.05.07.26352673","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352673","rel_abs":"Infectious disease dynamics can be inferred from pathogen genomic data using phylodynamic methods, but the applicability of many such approaches to large data sets is constrained by computational cost. Recent deep-learning approaches to phylodynamics have improved scalability, yet challenges remain when genetic divergence is limited during fast spreading outbreaks. To address this, we use pathogen-specific models to show that deep-learning models trained on outbreak-like phylogenies can accurately estimate the reproductive number (R) when both the birth-death model and the expected phylogenetic resolution are matched to the target pathogen, highlighting the importance of realistic training conditions. Focusing on three major respiratory pathogens of public health importance (SARS-CoV-2, seasonal human influenza virus, and respiratory syncytial virus (RSV)), we introduce PhyloRt, a scalable framework for estimating the time-varying reproductive number (Rt) from large outbreak phylogenies. PhyloRt decomposes large trees into overlapping subtrees and applies a hierarchical deep-learning-based inference strategy to classify subtrees as exhibiting constant or time-varying reproduction numbers, enabling identifiable and computationally efficient estimation of Rt as a piecewise-constant trajectory through time. Applications to SARS-CoV-2 and influenza outbreaks show that PhyloRt recovers transmission dynamics consistent with estimates derived from mathematical epidemiological and Bayesian phylodynamic analyses. Our work enables scalable and rapid estimation of time-varying transmission dynamics from very large-scale outbreak genomic data sets, supporting real-time genomic epidemiology of emerging pathogens.","rel_num_authors":11,"rel_authors":[{"author_name":"RUOPENG XIE","author_inst":"University of Oxford"},{"author_name":"Anna Zhukova","author_inst":"Institut Pasteur"},{"author_name":"Pablo G. Pena","author_inst":"Real Jardin Botanico CSIC"},{"author_name":"Guillermo Iglesias","author_inst":"ETSI de Sistemas Informaticos"},{"author_name":"Shu Hu","author_inst":"Fudan University"},{"author_name":"Jiawei Wang","author_inst":"University of Bath"},{"author_name":"Tim K Tsang","author_inst":"University of Hong Kong"},{"author_name":"Vijaykrishna Dhanasekaran","author_inst":"Univesity of Hong Kong"},{"author_name":"Moritz U. G. Kraemer","author_inst":"University of Oxford"},{"author_name":"Oliver G. Pybus","author_inst":"University of Oxford"},{"author_name":"Olivier Gascuel","author_inst":"Museum National dHistoire Naturelle"}],"rel_date":"2026-05-10","rel_site":"medrxiv"},{"rel_title":"Patterns of emergency department use among young people with bipolar disorder: A data linkage cohort study","rel_doi":"10.64898\/2026.05.07.26352617","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352617","rel_abs":"Objective: To charactertise emergency department (ED) use among young people with bipolar disorder (BD) and compare patterns to those observed in anxiety, depressive, and psychotic disorders. Design, setting and participants: Data linkage study using administrative ED presentation records (January 2020 to October 2020) and a transdiagnostic youth mental health cohort of 2243 individuals aged 12-30 years in New South Wales, Australia. Main outcome measures: ED presentation patterns (any presentation, frequency, and rates) and reasons for presentation (mental health-related and non-mental health-related). Results: Of the 354 young people with BD, 309 (87.3%) presented to an ED at least once. ED presentation rates were higher for BD than for anxiety (incidence rate ratio [IRR]=1.82, p<.001) and depressive disorders (IRR=1.32, p<.001), but similar to psychotic disorders (IRR=0.91, p=.379). Differences were primarily driven by mental health-related presentations. Recurrent mental health presentations were associated with illness progression (clinical stage and functional impairment) rather than diagnosis. However, the likelihood of mental health-related presentations remained higher in BD compared with anxiety and depressive disorders after adjustment. Conclusions: Young people with BD have high rates of ED use, comparable to those with psychotic disorders. Although mental health-related presentations are more common in BD than in anxiety and depressive disorders, recurrence is largely explained by markers of illness progression. These findings highlight the need for community-based services that provide continuous and coordinated care for young people with complex mental health needs.","rel_num_authors":12,"rel_authors":[{"author_name":"Ashlee Turner Dr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Ian B Hickie Prof","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Mathew Varidel Dr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Nicholas Ho Mr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Catherine M McHugh Dr","author_inst":"Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney NSW Australia"},{"author_name":"Jacob J Crouse Dr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Joanne S Carpenter Dr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Alissa Nichles Ms","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Natalia Zmicerevska Ms","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Yun Ju (Christine) Song Dr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Elizabeth M Scott A\/Prof","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Frank Iorfino A\/Prof","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"}],"rel_date":"2026-05-10","rel_site":"medrxiv"},{"rel_title":"Patterns of emergency department use among young people with bipolar disorder: A data linkage cohort study","rel_doi":"10.64898\/2026.05.07.26352617","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352617","rel_abs":"Objective: To charactertise emergency department (ED) use among young people with bipolar disorder (BD) and compare patterns to those observed in anxiety, depressive, and psychotic disorders. Design, setting and participants: Data linkage study using administrative ED presentation records (January 2020 to October 2020) and a transdiagnostic youth mental health cohort of 2243 individuals aged 12-30 years in New South Wales, Australia. Main outcome measures: ED presentation patterns (any presentation, frequency, and rates) and reasons for presentation (mental health-related and non-mental health-related). Results: Of the 354 young people with BD, 309 (87.3%) presented to an ED at least once. ED presentation rates were higher for BD than for anxiety (incidence rate ratio [IRR]=1.82, p<.001) and depressive disorders (IRR=1.32, p<.001), but similar to psychotic disorders (IRR=0.91, p=.379). Differences were primarily driven by mental health-related presentations. Recurrent mental health presentations were associated with illness progression (clinical stage and functional impairment) rather than diagnosis. However, the likelihood of mental health-related presentations remained higher in BD compared with anxiety and depressive disorders after adjustment. Conclusions: Young people with BD have high rates of ED use, comparable to those with psychotic disorders. Although mental health-related presentations are more common in BD than in anxiety and depressive disorders, recurrence is largely explained by markers of illness progression. These findings highlight the need for community-based services that provide continuous and coordinated care for young people with complex mental health needs.","rel_num_authors":12,"rel_authors":[{"author_name":"Ashlee Turner Dr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Ian B Hickie Prof","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Mathew Varidel Dr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Nicholas Ho Mr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Catherine M McHugh Dr","author_inst":"Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney NSW Australia"},{"author_name":"Jacob J Crouse Dr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Joanne S Carpenter Dr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Alissa Nichles Ms","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Natalia Zmicerevska Ms","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Yun Ju (Christine) Song Dr","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Elizabeth M Scott A\/Prof","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"},{"author_name":"Frank Iorfino A\/Prof","author_inst":"Brain and Mind Centre, The University of Sydney, Sydney NSW Australia"}],"rel_date":"2026-05-10","rel_site":"medrxiv"},{"rel_title":"Self and Caregiver Reported Choice Making in Autistic Adults: Development and Validation of the AASPIRE Choices and Decisions Scale","rel_doi":"10.64898\/2026.05.07.26352693","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352693","rel_abs":"Self-determination has been assessed as an internal psychological construct. External factors may also affect self-determination, but opportunities to make choices and decisions remain understudied. We developed and evaluated the AASPIRE Choices and Decisions Scale (AASPIRE CDS), a new measure of autistic adults opportunities to make choices and decisions, using a community based participatory approach. We created and refined the AASPIRE CDS through an iterative process. Data, from the AASPIRE Outcomes Project, included 839 autistic adults participating through direct report, supported direct report, and caregiver report (CR). Exploratory and confirmatory analyses supported a unidimensional structure. Measurement invariance analyses supported configural, metric, and partial scalar invariance across report type without CR, and across living status, with and without CR. The AASPIRE CDS showed high internal consistency, test-retest reliability, and responsiveness to change over time. Convergent validity analyses showed that higher AASPIRE CDS scores were associated with greater self determination and communication fluency, more independent living, and fewer support needs. The AASPIRE-CDS showed weaker (albeit significant) associations with quality of life, overall health, and employment satisfaction than the self-determination measure showed with these variables. This pattern suggests that opportunities for choice-making are related to, but distinct from, commonly used measures of self-determination. Findings support the AASPIRE CDS as a valid and reliable measure of choice making opportunities in autistic adults across support needs but suggest caution interpreting CR. They underscore the importance of supporting autistic adults choice making and evaluating opportunities for choice alongside internal self determination. Future research should use larger CR samples to examine the validity of caregiver reported choice making opportunities.","rel_num_authors":15,"rel_authors":[{"author_name":"So Yoon Kim","author_inst":"Korea University"},{"author_name":"Kristen Gillespie-Lynch","author_inst":"City University of New York"},{"author_name":"Steven Kapp","author_inst":"University of Portsmouth"},{"author_name":"Liu-Qin Yang","author_inst":"Portland State University"},{"author_name":"Anna Furra Wallington","author_inst":"Academic Autism Spectrum Partnership in Research and Education"},{"author_name":"Dora Raymaker","author_inst":"Portland State University"},{"author_name":"Ian Moura","author_inst":"Brandeis University"},{"author_name":"Katherine McDonald","author_inst":"Syracuse University"},{"author_name":"Joelle Maslak","author_inst":"Academic Autism Spectrum Partnership in Research and Education"},{"author_name":"Rachel Kripke-Ludwig","author_inst":"Academic Autism Spectrum Partnership in Research and Education"},{"author_name":"Andrea Joyce","author_inst":"Academic Autism Spectrum Partnership in Research and Education"},{"author_name":"Willi Horner-Johnson","author_inst":"Oregon Health & Science University"},{"author_name":"Emanuel Frowner","author_inst":"Academic Autism Spectrum Partnership in Research and Education"},{"author_name":"Mary Baker-Ericzen","author_inst":"San Diego State University"},{"author_name":"Christina Nicolaidis","author_inst":"Portland State University"}],"rel_date":"2026-05-10","rel_site":"medrxiv"},{"rel_title":"CT Attenuation Map Derived Body Composition Is Associated with Cardiorespiratory Fitness in Multicenter External Validation","rel_doi":"10.64898\/2026.05.07.26352573","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352573","rel_abs":"AimsExercise capacity is a powerful predictor of cardiovascular risk. In patients unable to exercise, body composition analysis can potentially be used to estimate cardiorespiratory fitness. We developed a body composition \"fitness\" score, then validated its utility in two external populations.\n\nMethods and ResultsWe included patients from four sites undergoing single photon emission computed tomography (SPECT) and twelve sites undergoing positron emission tomography (PET). We quantified body composition using deep learning. We evaluated associations between body composition and good exercise capacity (defined as completing [&ge;]7 minutes on a Bruce protocol) then developed a body composition \"fitness\" score. We then assessed the associations of \"fitness\" score with exercise capacity and all-cause mortality in external populations. In total, 36471 patients were included with median age 67 (interquartile range 58 - 74). Median skeletal muscle density was higher among patients with good exercise capacity. In the external SPECT population, the body composition \"fitness\" score had higher prediction performance for good exercise capacity (AUC 0.771, 95% CI 0.752 - 0.789) than age (AUC 0.717, p<0.01). In the external PET population, high body composition \"fitness\" score was associated with lower cardiovascular death (adjusted hazard ratio 0.70, 95% CI 0.62 - 0.79, p<0.001).\n\nConclusionsWe demonstrated that a comprehensive body composition \"fitness\" score could identify patients with good cardiorespiratory fitness. This approach transforms routinely acquired CT data into a surrogate marker of fitness which can be applied in patients undergoing PET, or other CT imaging, where exercise testing is not performed.\n\nGraphical AbstractOverview of study design. The overall population (n=36471) was split as outlined above. Body composition was analyzed from available computed tomography imaging. The distribution of body composition measures were analyzed in the combined single photon emission computed tomography (SPECT) populations. A body composition \"fitness\" score was derived to predict good exercise capacity in the internal population, with associations assessed in the two external testing populations.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26352573v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (50K):\norg.highwire.dtl.DTLVardef@943b29org.highwire.dtl.DTLVardef@1b80e60org.highwire.dtl.DTLVardef@b7ff0eorg.highwire.dtl.DTLVardef@1ca01f0_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":28,"rel_authors":[{"author_name":"Robert JH Miller","author_inst":"University of Calgary"},{"author_name":"Jirong Yi","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aakash Shanbhag","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Krishna K Patel","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Terrence D Ruddy","author_inst":"University of Ottawa Heart Institute"},{"author_name":"Andrew J Einstein","author_inst":"Columbia University Irving Medical Center and New York-Presbyterian Hospital"},{"author_name":"Attila Feher","author_inst":"Yale University School of Medicine"},{"author_name":"Edward J Miller","author_inst":"Yale School of Medicine"},{"author_name":"Joanna X Liang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Giselle Ramirez","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Leandro Slipczuk","author_inst":"Montefiore Health System\/Albert Einstein College of Medicine"},{"author_name":"Mark I Travin","author_inst":"Montefiore Medical Center and Albert Einstein College of Medicine"},{"author_name":"Erick Alexanderson","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Isabel Carvajal-Juarez","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Rene R.S. Packard","author_inst":"David Geffen School of Medicine, University of California Los Angeles"},{"author_name":"Mouaz Al-Mallah","author_inst":"Houston Methodist Academic Institute"},{"author_name":"Wanda Acampa","author_inst":"University of Naples Federico II"},{"author_name":"Stacey Knight","author_inst":"Intermountain Healthcare"},{"author_name":"Viet T Le","author_inst":"Intermountain Healthcare"},{"author_name":"Steve Mason","author_inst":"Intermountain Healthcare"},{"author_name":"Samuel Wopperer","author_inst":"Mayo Clinic"},{"author_name":"Panithaya Chareonthaitawee","author_inst":"Mayo Clinic"},{"author_name":"Ronny R. Buechel","author_inst":"University Hospital Zurich"},{"author_name":"Thomas L. Rosamond","author_inst":"University of Kansas Medical Center"},{"author_name":"Daniel S. Berman","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Damini Dey","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Marcelo F. Di Carli","author_inst":"Brigham and Women's Hospital"},{"author_name":"Piotr Slomka","author_inst":"Cedars-Sinai Medical Center"}],"rel_date":"2026-05-08","rel_site":"medrxiv"},{"rel_title":"CT Attenuation Map Derived Body Composition Is Associated with Cardiorespiratory Fitness in Multicenter External Validation","rel_doi":"10.64898\/2026.05.07.26352573","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352573","rel_abs":"AimsExercise capacity is a powerful predictor of cardiovascular risk. In patients unable to exercise, body composition analysis can potentially be used to estimate cardiorespiratory fitness. We developed a body composition \"fitness\" score, then validated its utility in two external populations.\n\nMethods and ResultsWe included patients from four sites undergoing single photon emission computed tomography (SPECT) and twelve sites undergoing positron emission tomography (PET). We quantified body composition using deep learning. We evaluated associations between body composition and good exercise capacity (defined as completing [&ge;]7 minutes on a Bruce protocol) then developed a body composition \"fitness\" score. We then assessed the associations of \"fitness\" score with exercise capacity and all-cause mortality in external populations. In total, 36471 patients were included with median age 67 (interquartile range 58 - 74). Median skeletal muscle density was higher among patients with good exercise capacity. In the external SPECT population, the body composition \"fitness\" score had higher prediction performance for good exercise capacity (AUC 0.771, 95% CI 0.752 - 0.789) than age (AUC 0.717, p<0.01). In the external PET population, high body composition \"fitness\" score was associated with lower cardiovascular death (adjusted hazard ratio 0.70, 95% CI 0.62 - 0.79, p<0.001).\n\nConclusionsWe demonstrated that a comprehensive body composition \"fitness\" score could identify patients with good cardiorespiratory fitness. This approach transforms routinely acquired CT data into a surrogate marker of fitness which can be applied in patients undergoing PET, or other CT imaging, where exercise testing is not performed.\n\nGraphical AbstractOverview of study design. The overall population (n=36471) was split as outlined above. Body composition was analyzed from available computed tomography imaging. The distribution of body composition measures were analyzed in the combined single photon emission computed tomography (SPECT) populations. A body composition \"fitness\" score was derived to predict good exercise capacity in the internal population, with associations assessed in the two external testing populations.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26352573v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (50K):\norg.highwire.dtl.DTLVardef@943b29org.highwire.dtl.DTLVardef@1b80e60org.highwire.dtl.DTLVardef@b7ff0eorg.highwire.dtl.DTLVardef@1ca01f0_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":28,"rel_authors":[{"author_name":"Robert JH Miller","author_inst":"University of Calgary"},{"author_name":"Jirong Yi","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aakash Shanbhag","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Krishna K Patel","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Terrence D Ruddy","author_inst":"University of Ottawa Heart Institute"},{"author_name":"Andrew J Einstein","author_inst":"Columbia University Irving Medical Center and New York-Presbyterian Hospital"},{"author_name":"Attila Feher","author_inst":"Yale University School of Medicine"},{"author_name":"Edward J Miller","author_inst":"Yale School of Medicine"},{"author_name":"Joanna X Liang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Giselle Ramirez","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Leandro Slipczuk","author_inst":"Montefiore Health System\/Albert Einstein College of Medicine"},{"author_name":"Mark I Travin","author_inst":"Montefiore Medical Center and Albert Einstein College of Medicine"},{"author_name":"Erick Alexanderson","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Isabel Carvajal-Juarez","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Rene R.S. Packard","author_inst":"David Geffen School of Medicine, University of California Los Angeles"},{"author_name":"Mouaz Al-Mallah","author_inst":"Houston Methodist Academic Institute"},{"author_name":"Wanda Acampa","author_inst":"University of Naples Federico II"},{"author_name":"Stacey Knight","author_inst":"Intermountain Healthcare"},{"author_name":"Viet T Le","author_inst":"Intermountain Healthcare"},{"author_name":"Steve Mason","author_inst":"Intermountain Healthcare"},{"author_name":"Samuel Wopperer","author_inst":"Mayo Clinic"},{"author_name":"Panithaya Chareonthaitawee","author_inst":"Mayo Clinic"},{"author_name":"Ronny R. Buechel","author_inst":"University Hospital Zurich"},{"author_name":"Thomas L. Rosamond","author_inst":"University of Kansas Medical Center"},{"author_name":"Daniel S. Berman","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Damini Dey","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Marcelo F. Di Carli","author_inst":"Brigham and Women's Hospital"},{"author_name":"Piotr Slomka","author_inst":"Cedars-Sinai Medical Center"}],"rel_date":"2026-05-08","rel_site":"medrxiv"},{"rel_title":"CT Attenuation Map Derived Body Composition Is Associated with Cardiorespiratory Fitness in Multicenter External Validation","rel_doi":"10.64898\/2026.05.07.26352573","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352573","rel_abs":"AimsExercise capacity is a powerful predictor of cardiovascular risk. In patients unable to exercise, body composition analysis can potentially be used to estimate cardiorespiratory fitness. We developed a body composition \"fitness\" score, then validated its utility in two external populations.\n\nMethods and ResultsWe included patients from four sites undergoing single photon emission computed tomography (SPECT) and twelve sites undergoing positron emission tomography (PET). We quantified body composition using deep learning. We evaluated associations between body composition and good exercise capacity (defined as completing [&ge;]7 minutes on a Bruce protocol) then developed a body composition \"fitness\" score. We then assessed the associations of \"fitness\" score with exercise capacity and all-cause mortality in external populations. In total, 36471 patients were included with median age 67 (interquartile range 58 - 74). Median skeletal muscle density was higher among patients with good exercise capacity. In the external SPECT population, the body composition \"fitness\" score had higher prediction performance for good exercise capacity (AUC 0.771, 95% CI 0.752 - 0.789) than age (AUC 0.717, p<0.01). In the external PET population, high body composition \"fitness\" score was associated with lower cardiovascular death (adjusted hazard ratio 0.70, 95% CI 0.62 - 0.79, p<0.001).\n\nConclusionsWe demonstrated that a comprehensive body composition \"fitness\" score could identify patients with good cardiorespiratory fitness. This approach transforms routinely acquired CT data into a surrogate marker of fitness which can be applied in patients undergoing PET, or other CT imaging, where exercise testing is not performed.\n\nGraphical AbstractOverview of study design. The overall population (n=36471) was split as outlined above. Body composition was analyzed from available computed tomography imaging. The distribution of body composition measures were analyzed in the combined single photon emission computed tomography (SPECT) populations. A body composition \"fitness\" score was derived to predict good exercise capacity in the internal population, with associations assessed in the two external testing populations.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26352573v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (50K):\norg.highwire.dtl.DTLVardef@943b29org.highwire.dtl.DTLVardef@1b80e60org.highwire.dtl.DTLVardef@b7ff0eorg.highwire.dtl.DTLVardef@1ca01f0_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":28,"rel_authors":[{"author_name":"Robert JH Miller","author_inst":"University of Calgary"},{"author_name":"Jirong Yi","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aakash Shanbhag","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Krishna K Patel","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Terrence D Ruddy","author_inst":"University of Ottawa Heart Institute"},{"author_name":"Andrew J Einstein","author_inst":"Columbia University Irving Medical Center and New York-Presbyterian Hospital"},{"author_name":"Attila Feher","author_inst":"Yale University School of Medicine"},{"author_name":"Edward J Miller","author_inst":"Yale School of Medicine"},{"author_name":"Joanna X Liang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Giselle Ramirez","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Leandro Slipczuk","author_inst":"Montefiore Health System\/Albert Einstein College of Medicine"},{"author_name":"Mark I Travin","author_inst":"Montefiore Medical Center and Albert Einstein College of Medicine"},{"author_name":"Erick Alexanderson","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Isabel Carvajal-Juarez","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Rene R.S. Packard","author_inst":"David Geffen School of Medicine, University of California Los Angeles"},{"author_name":"Mouaz Al-Mallah","author_inst":"Houston Methodist Academic Institute"},{"author_name":"Wanda Acampa","author_inst":"University of Naples Federico II"},{"author_name":"Stacey Knight","author_inst":"Intermountain Healthcare"},{"author_name":"Viet T Le","author_inst":"Intermountain Healthcare"},{"author_name":"Steve Mason","author_inst":"Intermountain Healthcare"},{"author_name":"Samuel Wopperer","author_inst":"Mayo Clinic"},{"author_name":"Panithaya Chareonthaitawee","author_inst":"Mayo Clinic"},{"author_name":"Ronny R. Buechel","author_inst":"University Hospital Zurich"},{"author_name":"Thomas L. Rosamond","author_inst":"University of Kansas Medical Center"},{"author_name":"Daniel S. Berman","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Damini Dey","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Marcelo F. Di Carli","author_inst":"Brigham and Women's Hospital"},{"author_name":"Piotr Slomka","author_inst":"Cedars-Sinai Medical Center"}],"rel_date":"2026-05-08","rel_site":"medrxiv"},{"rel_title":"CT Attenuation Map Derived Body Composition Is Associated with Cardiorespiratory Fitness in Multicenter External Validation","rel_doi":"10.64898\/2026.05.07.26352573","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352573","rel_abs":"AimsExercise capacity is a powerful predictor of cardiovascular risk. In patients unable to exercise, body composition analysis can potentially be used to estimate cardiorespiratory fitness. We developed a body composition \"fitness\" score, then validated its utility in two external populations.\n\nMethods and ResultsWe included patients from four sites undergoing single photon emission computed tomography (SPECT) and twelve sites undergoing positron emission tomography (PET). We quantified body composition using deep learning. We evaluated associations between body composition and good exercise capacity (defined as completing [&ge;]7 minutes on a Bruce protocol) then developed a body composition \"fitness\" score. We then assessed the associations of \"fitness\" score with exercise capacity and all-cause mortality in external populations. In total, 36471 patients were included with median age 67 (interquartile range 58 - 74). Median skeletal muscle density was higher among patients with good exercise capacity. In the external SPECT population, the body composition \"fitness\" score had higher prediction performance for good exercise capacity (AUC 0.771, 95% CI 0.752 - 0.789) than age (AUC 0.717, p<0.01). In the external PET population, high body composition \"fitness\" score was associated with lower cardiovascular death (adjusted hazard ratio 0.70, 95% CI 0.62 - 0.79, p<0.001).\n\nConclusionsWe demonstrated that a comprehensive body composition \"fitness\" score could identify patients with good cardiorespiratory fitness. This approach transforms routinely acquired CT data into a surrogate marker of fitness which can be applied in patients undergoing PET, or other CT imaging, where exercise testing is not performed.\n\nGraphical AbstractOverview of study design. The overall population (n=36471) was split as outlined above. Body composition was analyzed from available computed tomography imaging. The distribution of body composition measures were analyzed in the combined single photon emission computed tomography (SPECT) populations. A body composition \"fitness\" score was derived to predict good exercise capacity in the internal population, with associations assessed in the two external testing populations.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26352573v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (50K):\norg.highwire.dtl.DTLVardef@943b29org.highwire.dtl.DTLVardef@1b80e60org.highwire.dtl.DTLVardef@b7ff0eorg.highwire.dtl.DTLVardef@1ca01f0_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":28,"rel_authors":[{"author_name":"Robert JH Miller","author_inst":"University of Calgary"},{"author_name":"Jirong Yi","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aakash Shanbhag","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Krishna K Patel","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Terrence D Ruddy","author_inst":"University of Ottawa Heart Institute"},{"author_name":"Andrew J Einstein","author_inst":"Columbia University Irving Medical Center and New York-Presbyterian Hospital"},{"author_name":"Attila Feher","author_inst":"Yale University School of Medicine"},{"author_name":"Edward J Miller","author_inst":"Yale School of Medicine"},{"author_name":"Joanna X Liang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Giselle Ramirez","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Leandro Slipczuk","author_inst":"Montefiore Health System\/Albert Einstein College of Medicine"},{"author_name":"Mark I Travin","author_inst":"Montefiore Medical Center and Albert Einstein College of Medicine"},{"author_name":"Erick Alexanderson","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Isabel Carvajal-Juarez","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Rene R.S. Packard","author_inst":"David Geffen School of Medicine, University of California Los Angeles"},{"author_name":"Mouaz Al-Mallah","author_inst":"Houston Methodist Academic Institute"},{"author_name":"Wanda Acampa","author_inst":"University of Naples Federico II"},{"author_name":"Stacey Knight","author_inst":"Intermountain Healthcare"},{"author_name":"Viet T Le","author_inst":"Intermountain Healthcare"},{"author_name":"Steve Mason","author_inst":"Intermountain Healthcare"},{"author_name":"Samuel Wopperer","author_inst":"Mayo Clinic"},{"author_name":"Panithaya Chareonthaitawee","author_inst":"Mayo Clinic"},{"author_name":"Ronny R. Buechel","author_inst":"University Hospital Zurich"},{"author_name":"Thomas L. Rosamond","author_inst":"University of Kansas Medical Center"},{"author_name":"Daniel S. Berman","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Damini Dey","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Marcelo F. Di Carli","author_inst":"Brigham and Women's Hospital"},{"author_name":"Piotr Slomka","author_inst":"Cedars-Sinai Medical Center"}],"rel_date":"2026-05-08","rel_site":"medrxiv"},{"rel_title":"CT Attenuation Map Derived Body Composition Is Associated with Cardiorespiratory Fitness in Multicenter External Validation","rel_doi":"10.64898\/2026.05.07.26352573","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352573","rel_abs":"AimsExercise capacity is a powerful predictor of cardiovascular risk. In patients unable to exercise, body composition analysis can potentially be used to estimate cardiorespiratory fitness. We developed a body composition \"fitness\" score, then validated its utility in two external populations.\n\nMethods and ResultsWe included patients from four sites undergoing single photon emission computed tomography (SPECT) and twelve sites undergoing positron emission tomography (PET). We quantified body composition using deep learning. We evaluated associations between body composition and good exercise capacity (defined as completing [&ge;]7 minutes on a Bruce protocol) then developed a body composition \"fitness\" score. We then assessed the associations of \"fitness\" score with exercise capacity and all-cause mortality in external populations. In total, 36471 patients were included with median age 67 (interquartile range 58 - 74). Median skeletal muscle density was higher among patients with good exercise capacity. In the external SPECT population, the body composition \"fitness\" score had higher prediction performance for good exercise capacity (AUC 0.771, 95% CI 0.752 - 0.789) than age (AUC 0.717, p<0.01). In the external PET population, high body composition \"fitness\" score was associated with lower cardiovascular death (adjusted hazard ratio 0.70, 95% CI 0.62 - 0.79, p<0.001).\n\nConclusionsWe demonstrated that a comprehensive body composition \"fitness\" score could identify patients with good cardiorespiratory fitness. This approach transforms routinely acquired CT data into a surrogate marker of fitness which can be applied in patients undergoing PET, or other CT imaging, where exercise testing is not performed.\n\nGraphical AbstractOverview of study design. The overall population (n=36471) was split as outlined above. Body composition was analyzed from available computed tomography imaging. The distribution of body composition measures were analyzed in the combined single photon emission computed tomography (SPECT) populations. A body composition \"fitness\" score was derived to predict good exercise capacity in the internal population, with associations assessed in the two external testing populations.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26352573v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (50K):\norg.highwire.dtl.DTLVardef@943b29org.highwire.dtl.DTLVardef@1b80e60org.highwire.dtl.DTLVardef@b7ff0eorg.highwire.dtl.DTLVardef@1ca01f0_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":28,"rel_authors":[{"author_name":"Robert JH Miller","author_inst":"University of Calgary"},{"author_name":"Jirong Yi","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aakash Shanbhag","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Krishna K Patel","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Terrence D Ruddy","author_inst":"University of Ottawa Heart Institute"},{"author_name":"Andrew J Einstein","author_inst":"Columbia University Irving Medical Center and New York-Presbyterian Hospital"},{"author_name":"Attila Feher","author_inst":"Yale University School of Medicine"},{"author_name":"Edward J Miller","author_inst":"Yale School of Medicine"},{"author_name":"Joanna X Liang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Giselle Ramirez","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Leandro Slipczuk","author_inst":"Montefiore Health System\/Albert Einstein College of Medicine"},{"author_name":"Mark I Travin","author_inst":"Montefiore Medical Center and Albert Einstein College of Medicine"},{"author_name":"Erick Alexanderson","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Isabel Carvajal-Juarez","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Rene R.S. Packard","author_inst":"David Geffen School of Medicine, University of California Los Angeles"},{"author_name":"Mouaz Al-Mallah","author_inst":"Houston Methodist Academic Institute"},{"author_name":"Wanda Acampa","author_inst":"University of Naples Federico II"},{"author_name":"Stacey Knight","author_inst":"Intermountain Healthcare"},{"author_name":"Viet T Le","author_inst":"Intermountain Healthcare"},{"author_name":"Steve Mason","author_inst":"Intermountain Healthcare"},{"author_name":"Samuel Wopperer","author_inst":"Mayo Clinic"},{"author_name":"Panithaya Chareonthaitawee","author_inst":"Mayo Clinic"},{"author_name":"Ronny R. Buechel","author_inst":"University Hospital Zurich"},{"author_name":"Thomas L. Rosamond","author_inst":"University of Kansas Medical Center"},{"author_name":"Daniel S. Berman","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Damini Dey","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Marcelo F. Di Carli","author_inst":"Brigham and Women's Hospital"},{"author_name":"Piotr Slomka","author_inst":"Cedars-Sinai Medical Center"}],"rel_date":"2026-05-08","rel_site":"medrxiv"},{"rel_title":"CT Attenuation Map Derived Body Composition Is Associated with Cardiorespiratory Fitness in Multicenter External Validation","rel_doi":"10.64898\/2026.05.07.26352573","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352573","rel_abs":"AimsExercise capacity is a powerful predictor of cardiovascular risk. In patients unable to exercise, body composition analysis can potentially be used to estimate cardiorespiratory fitness. We developed a body composition \"fitness\" score, then validated its utility in two external populations.\n\nMethods and ResultsWe included patients from four sites undergoing single photon emission computed tomography (SPECT) and twelve sites undergoing positron emission tomography (PET). We quantified body composition using deep learning. We evaluated associations between body composition and good exercise capacity (defined as completing [&ge;]7 minutes on a Bruce protocol) then developed a body composition \"fitness\" score. We then assessed the associations of \"fitness\" score with exercise capacity and all-cause mortality in external populations. In total, 36471 patients were included with median age 67 (interquartile range 58 - 74). Median skeletal muscle density was higher among patients with good exercise capacity. In the external SPECT population, the body composition \"fitness\" score had higher prediction performance for good exercise capacity (AUC 0.771, 95% CI 0.752 - 0.789) than age (AUC 0.717, p<0.01). In the external PET population, high body composition \"fitness\" score was associated with lower cardiovascular death (adjusted hazard ratio 0.70, 95% CI 0.62 - 0.79, p<0.001).\n\nConclusionsWe demonstrated that a comprehensive body composition \"fitness\" score could identify patients with good cardiorespiratory fitness. This approach transforms routinely acquired CT data into a surrogate marker of fitness which can be applied in patients undergoing PET, or other CT imaging, where exercise testing is not performed.\n\nGraphical AbstractOverview of study design. The overall population (n=36471) was split as outlined above. Body composition was analyzed from available computed tomography imaging. The distribution of body composition measures were analyzed in the combined single photon emission computed tomography (SPECT) populations. A body composition \"fitness\" score was derived to predict good exercise capacity in the internal population, with associations assessed in the two external testing populations.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR\/small\/26352573v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (50K):\norg.highwire.dtl.DTLVardef@943b29org.highwire.dtl.DTLVardef@1b80e60org.highwire.dtl.DTLVardef@b7ff0eorg.highwire.dtl.DTLVardef@1ca01f0_HPS_FORMAT_FIGEXP  M_FIG C_FIG","rel_num_authors":28,"rel_authors":[{"author_name":"Robert JH Miller","author_inst":"University of Calgary"},{"author_name":"Jirong Yi","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Aakash Shanbhag","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Krishna K Patel","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Terrence D Ruddy","author_inst":"University of Ottawa Heart Institute"},{"author_name":"Andrew J Einstein","author_inst":"Columbia University Irving Medical Center and New York-Presbyterian Hospital"},{"author_name":"Attila Feher","author_inst":"Yale University School of Medicine"},{"author_name":"Edward J Miller","author_inst":"Yale School of Medicine"},{"author_name":"Joanna X Liang","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Giselle Ramirez","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Leandro Slipczuk","author_inst":"Montefiore Health System\/Albert Einstein College of Medicine"},{"author_name":"Mark I Travin","author_inst":"Montefiore Medical Center and Albert Einstein College of Medicine"},{"author_name":"Erick Alexanderson","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Isabel Carvajal-Juarez","author_inst":"Ignacio Chavez National Institute of Cardiology"},{"author_name":"Rene R.S. Packard","author_inst":"David Geffen School of Medicine, University of California Los Angeles"},{"author_name":"Mouaz Al-Mallah","author_inst":"Houston Methodist Academic Institute"},{"author_name":"Wanda Acampa","author_inst":"University of Naples Federico II"},{"author_name":"Stacey Knight","author_inst":"Intermountain Healthcare"},{"author_name":"Viet T Le","author_inst":"Intermountain Healthcare"},{"author_name":"Steve Mason","author_inst":"Intermountain Healthcare"},{"author_name":"Samuel Wopperer","author_inst":"Mayo Clinic"},{"author_name":"Panithaya Chareonthaitawee","author_inst":"Mayo Clinic"},{"author_name":"Ronny R. Buechel","author_inst":"University Hospital Zurich"},{"author_name":"Thomas L. Rosamond","author_inst":"University of Kansas Medical Center"},{"author_name":"Daniel S. Berman","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Damini Dey","author_inst":"Cedars-Sinai Medical Center"},{"author_name":"Marcelo F. Di Carli","author_inst":"Brigham and Women's Hospital"},{"author_name":"Piotr Slomka","author_inst":"Cedars-Sinai Medical Center"}],"rel_date":"2026-05-08","rel_site":"medrxiv"},{"rel_title":"Adoption and Real-World Effectiveness of Adjunctive Azithromycin for Unscheduled Cesarean Delivery: A National Difference-in-Differences Analysis","rel_doi":"10.64898\/2026.05.07.26352377","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352377","rel_abs":"ImportanceCesarean delivery is the most common surgery in the US with more than 1 million performed each year; it is also the most significant risk factor for postpartum infection. The Cesarean Section Optimal Antibiotic Prophylaxis trial demonstrated that the addition of azithromycin at the time of cesarean birth performed in labor reduces postpartum infection.\n\nObjectiveTo determine the real-world adoption and effect of this trial on clinical practice and postpartum infections among U.S. pregnant persons undergoing cesarean delivery in labor.\n\nDesignDifference-in-differences analysis from 2013-2024.\n\nSettingPopulation-based, patient-level analysis using Epic Cosmos, a large longitudinal national electronic health record database of patients seen in health systems using Epic.\n\nParticipantsPregnant individuals who received outpatient prenatal care in the system, who labored and gave birth to a liveborn singleton infant at 24-43 weeks of gestation were included. Exclusion criteria included unknown mode of delivery and intraamniotic infection.\n\nExposuresThe treatment group included those delivered by cesarean and the control group included those who delivered vaginally. The pre-period was defined as 2013-2016, excluding a washout period from trial publication until December 31, 2016, and the post-period was defined from 2017-2024.\n\nMain Outcomes and MeasuresThe primary outcomes were perioperative azithromycin administration and postpartum infection within 6 weeks of delivery.\n\nResults1,663,341 participants were included in the final analysis. In the pre- and post-periods, azithromycin was administered in 0.01% and 0.04% of vaginal births and in 2.2% and 39.6% of cesarean births, respectively. In the pre- and post-periods, postpartum infection occurred in 2.0% and 2.7% of vaginal births and 9.2% and 8.0% of cesarean births. In the adjusted difference-in-difference analysis, the trial resulted in an absolute increase in azithromycin use by 37.6 percentage points (pp) (95% CI: 33.1 to 42.2 pp); postpartum infection decreased by 2.0 pp (95% CI: -2.5 to -1.4 pp), a relative decrease of 20%.\n\nConclusions and RelevanceOutside the clinical trial setting, this study provides evidence that azithromycin significantly reduces postpartum infection among pregnant persons undergoing a cesarean delivery in labor.\n\nKey PointsO_ST_ABSQuestionC_ST_ABSDid evidence from the Cesarean Section Optimal Antibiotic Prophylaxis (C\/SOAP) trial change real-world clinical practice and decrease postpartum infections among U.S. pregnant persons who underwent a cesarean delivery in labor?\n\nFindingsIn this difference-in-differences analysis of 1.6 million births, azithromycin use increased 37.6 percentage points and postpartum infections decreased by 2.0 percentage points following the C\/SOAP trial.\n\nMeaningOutside the clinical trial setting, this study provides evidence that azithromycin significantly reduces postpartum infection among individuals having a cesarean delivery in labor.","rel_num_authors":6,"rel_authors":[{"author_name":"Taylor S Freret","author_inst":"Beth Israel Deaconess Medical Center"},{"author_name":"Ethan Litman","author_inst":"Beth Israel Deaconess Medical Center"},{"author_name":"Timothy Wen","author_inst":"University of California, San Diego"},{"author_name":"Jeanne-Marie Guise","author_inst":"Beth Israel Deaconess Medical Center"},{"author_name":"Sarah E Little","author_inst":"Beth Israel Deaconess Medical Center"},{"author_name":"Mark Allen Clapp","author_inst":"Massachusetts General Hospital"}],"rel_date":"2026-05-08","rel_site":"medrxiv"},{"rel_title":"Automated Brain and CSF Volume Assessment in Infant Hydrocephalus Using Deep Learning","rel_doi":"10.64898\/2026.05.07.26352592","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352592","rel_abs":"Accurate brain and cerebrospinal fluid (CSF) volume assessment is essential for pediatric hydrocephalus management. Current clinical practice relies on linear measurements that fail to capture complex three-dimensional ventricular morphology, while quantitative volumetric assessment remains limited by laborious processing and lack of clinically optimized automated tools. This study developed a rapid, automated AI-based intracranial segmentation model suitable for clinical workflows. We retrospectively analyzed 167 T2-weighted MRI scans from infants with hydrocephalus, randomly split into training (60%), validation (20%), and hold-out test (20%) sets. All scans were manually segmented into CSF, brain parenchyma, and background. Our model integrates DenseNet and U-Net architectures with feature smoothness regularization to enhance generalizability. Performance was evaluated using Dice scores and absolute relative volume error (ARVE) compared with state-of-the-art methods. The AI model achieved Dice scores of 95.7% for CSF and 96.4% for brain parenchyma on the hold-out test set, significantly outperforming FSL FAST (85.0% and 77.9%) and contemporary deep learning approaches (90.4% and 89.7%). Processing time was 0.8 seconds per scan using GPU acceleration. The model demonstrated consistent performance across different hydrocephalus etiologies and effectively handled challenging scenarios including noise, artifacts, and variable resolution. This study successfully developed a robust MRI segmentation model demonstrating superior accuracy and efficiency compared to existing methods. By incorporating domain-specific enhancements, the model enables rapid, clinically viable brain and CSF volume estimation for pediatric hydrocephalus care.","rel_num_authors":9,"rel_authors":[{"author_name":"Mingzhao Yu","author_inst":"Boston Children's Hospital"},{"author_name":"Marcia H Yoshikawa","author_inst":"Boston Children's Hospital"},{"author_name":"Ariadna S Luviano","author_inst":"Boston Children's Hospital"},{"author_name":"Steven J Schiff","author_inst":"Yale University"},{"author_name":"Vishal Monga","author_inst":"Pennsylvania State University"},{"author_name":"Benjamin C Warf","author_inst":"Boston Children's Hospital"},{"author_name":"P. Ellen Grant","author_inst":"Boston Children's Hospital"},{"author_name":"Jason Sutin","author_inst":"Boston Children's Hospital"},{"author_name":"Pei-Yi Lin","author_inst":"Boston Children's Hospital"}],"rel_date":"2026-05-08","rel_site":"medrxiv"},{"rel_title":"Simpler is not always better: Phylodynamic misspecification and deep-learning corrections","rel_doi":"10.64898\/2026.05.07.26352661","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.07.26352661","rel_abs":"Phylodynamics bridges the gap between epidemiology and pathogen genetic data by estimating epidemiological parameters from time-scaled pathogen phylogenies. Multi-type birth-death (MTBD) models are phylodynamic analogies of compartmental models in classical epidemiology. They serve to infer the average number of secondary infections R and the infection duration d. Moreover, more complex MTBD models add extra parameters, such as the average length of the incubation period or the proportion of superspreaders in the infected population.\n\nHowever, these additional parameters come at an important computational cost: Apart from the simplest, BD, model, MTBD models do not have a closed-form solution and require numerical methods for their likelihood computation. This leads to increased computational times and potential numerical errors. Therefore, the BD model remains the favorite researchers choice for real dataset analyses, and is often applied even in cases where more complex epidemiological aspects are present.\n\nWe investigated, using simulations, how model misspecification influences inference of R and d in the phylodynamic framework. We showed that the use of models not accounting for various epidemiological aspects leads to bias. In particular the simplest, BD, estimator tends to underestimate R in the presence of super-spreading or incubation, which might be dangerous from the public health prospective. However, deep-learning-based estimators for complex models, which account for multiple epidemiological factors, perform well both on the data where those factors are present and where they are absent. This advocates for the use of complex epidemiologically realistic estimators, whose design has recently become possible thanks to deep learning.","rel_num_authors":3,"rel_authors":[{"author_name":"RUOPENG XIE","author_inst":"University of Oxford"},{"author_name":"Olivier Gascuel","author_inst":"CNRS"},{"author_name":"ANNA ZHUKOVA","author_inst":"Institut Pasteur & IBENS"}],"rel_date":"2026-05-08","rel_site":"medrxiv"},{"rel_title":"Prompt-engineering improves clinical safety of large language models for opioid equipotency conversion","rel_doi":"10.64898\/2026.05.06.26352590","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.06.26352590","rel_abs":"Background: Large language models (LLMs) are increasingly used in medical education and clinical decision-making, but their reliability in high-risk medication dosing remains unclear. Opioid rotation is a common task requiring precise calculations where errors may result in overdose or inadequate pain relief. Methods: Thirteen LLMs were tested using an API-based framework to ensure independent queries across trials. First, fictional clinical scenarios were tested to simulate real-world clinical situations involving opioid rotation; to test the effects of changes in wording, scenarios were revised into 4 vignettes showing the same clinical situation. Next, opioid pairs were tested with a random-dose paradigm across a clinically-pertinent range (5-120 mg daily morphine equivalents). LLM outputs were compared with expected values derived from reference standards. Accuracy was assessed using predefined safety thresholds: tight accuracy (0.85-1.15x expected dose) and broad accuracy (0.6-1.7x). We tested models naively and with prompts augmented with reference tables and unit explanations. Results: Naive models generally exhibited low tight-range accuracy across opioid pairs. For any given opioid pair, each model would consistently produce similar incorrect conversion ratios despite wide variability across opioid pairs and language models. Vignette wording changes accounted for 76% of within-scenario response variance. Reference-based prompt augmentation significantly improved performance, with over half of models achieving high proportions of conversions within tight accuracy for morphine equivalent conversions. Conclusions: While commercial LLMs demonstrated variable accuracy in the native state, prompt augmentation significantly improved their performance.","rel_num_authors":5,"rel_authors":[{"author_name":"Tanya Marton","author_inst":"Google"},{"author_name":"David Corpman","author_inst":"University of Washington"},{"author_name":"Lytia Lai","author_inst":"University of Washington"},{"author_name":"Rodney A Gabriel","author_inst":"University of California-San Diego"},{"author_name":"Yian Chen","author_inst":"University of Washington"}],"rel_date":"2026-05-08","rel_site":"medrxiv"},{"rel_title":"Geospatial Impact Indexing of Agricultural Incidents: A Multi-Criteria Risk Assessment in the U.S. Midwest","rel_doi":"10.64898\/2026.05.06.26352581","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.06.26352581","rel_abs":"Traditional agricultural safety assessments often rely on raw incident counts that emphasize exposure but underrepresent outcome severity. This study presents a multi-criteria impact framework to distinguish frequency-driven activity patterns from severity-driven risk across the U.S. Midwest. Agricultural incident records from 2012 to 2023 across seven states were analyzed using descriptive statistics, county-level mapping, and quartic kernel density estimation. Comparative impact indices were constructed using Analytic Hierarchy Process (AHP) and Geometric-Fuzzy AHP weighting schemes to integrate incident frequency, outcome severity, and post-incident survivability. Results indicate that while overall incident frequency is strongly concentrated in northwestern Iowa, reflecting intensive agricultural activity, fatal outcomes exhibit a broader spatial footprint extending across central and northern Iowa and into central-southern Minnesota. Severity-weighted mapping further consolidates northwestern Iowa and the Minnesota-Iowa corridor as dominant high-impact zones. At the regional scale, Geometric-Fuzzy AHP produced consistently lower mean scores and reduced dispersion than AHP, yielding smoother spatial gradients while preserving the primary hotspot structure. These findings demonstrate that frequency-based mapping alone fails to capture the multi-dimensional nature of agricultural risk. By explicitly linking incident locations with survival infrastructure, this research provides an evidence-based framework for targeting safety interventions and improving rural emergency medical service planning.","rel_num_authors":3,"rel_authors":[{"author_name":"Ege Duran","author_inst":"University of Iowa"},{"author_name":"Omer Mermer","author_inst":"Tulane University"},{"author_name":"Ibrahim Demir","author_inst":"Tulane University"}],"rel_date":"2026-05-08","rel_site":"medrxiv"},{"rel_title":"One-Year Brain Structural Changes Are Associated with Postoperative Delirium and Delayed Resolution of Interleukin-6","rel_doi":"10.64898\/2026.05.03.26352074","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.03.26352074","rel_abs":"BackgroundPostoperative delirium is a common complication in older adults and is associated with neuroinflammation and cognitive decline. Previous studies have shown that the number of surgical procedures is associated with hippocampal volume loss in older adults in a large-scale UK Biobank study. However, it remains unclear whether hippocampal volume loss within one year after surgery is associated with postoperative delirium.\n\nMethodsLongitudinal structural MRI data and blood biomarkers were collected before surgery and one year postoperatively from 62 participants (>65 years, 27 females) undergoing major non-intracranial surgery. Hippocampal and other subcortical volumes were quantified using FreeSurfer. Cortical thickness was measured for cortical regions defined by the Desikan-Killiany (DK) atlas. One-year structural changes were examined in relation to peak Delirium Rating Scale (DRS) scores and one-year changes in plasma interleukin (IL)-6 levels.\n\nResultsOne-year volume loss in the right hippocampus was significantly correlated with postoperative peak DRS scores and the one-year change in IL-6. Additional gray matter reductions were observed in the right putamen and the right superior parietal cortex. Right putamen volume loss was also associated with the one-year change in IL-6, while cortical thinning in the right superior parietal cortex was associated with peak DRS scores.\n\nConclusionsPostoperative delirium is associated with longitudinal gray matter loss following surgery. Delayed resolution of inflammation may also contribute to postoperative brain structural changes.\n\nClinical trial registrationNCT01980511 and NCT03124303.","rel_num_authors":12,"rel_authors":[{"author_name":"Jinglei Lv","author_inst":"1.\tCentral Clinical School, Faculty of Medicine & Health, The University of Sydney, Camperdown, NSW, Australia. 2.\tSchool of Biomedical Engineering, Faculty of "},{"author_name":"Jennifer Taylor","author_inst":"1. Central Clinical School, Faculty of Medicine & Health, The University of Sydney, Camperdown, NSW, Australia."},{"author_name":"Samantha Curtis","author_inst":"4. Royal Prince Alfred Hospital, Camperdown, NSW, Australia."},{"author_name":"Kaitlin Kramer","author_inst":"4. Royal Prince Alfred Hospital, Camperdown, NSW, Australia."},{"author_name":"David Kunkel","author_inst":"5. Department of Anaesthesiology, University of Wisconsin, Madison, WI, USA."},{"author_name":"Shalini Thakur","author_inst":"Department of Anaesthesiology, University of Wisconsin, Madison, WI, USA."},{"author_name":"Veena Nair","author_inst":"6. Department of Radiology, University of Wisconsin, Madison, WI, USA."},{"author_name":"Matthew I. Banks","author_inst":"5. Department of Anaesthesiology, University of Wisconsin, Madison, WI, USA."},{"author_name":"Robert A. Pearce","author_inst":"5. Department of Anaesthesiology, University of Wisconsin, Madison, WI, USA."},{"author_name":"Vivek Prabhakaran","author_inst":"6. Department of Radiology, University of Wisconsin, Madison, WI, USA."},{"author_name":"Richard Lennertz","author_inst":"5. Department of Anaesthesiology, University of Wisconsin, Madison, WI, USA."},{"author_name":"Robert D. Sanders","author_inst":"1.\tCentral Clinical School, Faculty of Medicine & Health, The University of Sydney, Camperdown, NSW, Australia. 3.\tBrain and Mind Centre, The University of Sydn"}],"rel_date":"2026-05-08","rel_site":"medrxiv"},{"rel_title":"Serum IgG antibodies induced by the synthetic carbohydrate-based conjugate vaccine candidate SF2a-TT15 against Shigella flexneri 2a cross-react with the heterologous lipopolysaccharide of Shigella flexneri 6","rel_doi":"10.64898\/2026.05.05.26352385","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.05.26352385","rel_abs":"BackgroundShigella flexneri 2a (SF2a) and 6 (SF6) are two of the most common S. flexneri serotypes. They have distant O-specific polysaccharide (O-SP) structures. Previous studies showed no cross-reactivity or cross-protection between the two serotypes in a guinea pig model of infection. However, partial cross-reactivity and cross-protection were reported in humans immunized with a SF2a lattice-type conjugate vaccine candidate comprising the chemically detoxified lipopolysaccharide (LPS) attached to recombinant Pseudomonas aeruginosa Exoprotein A (rEPA).\n\nObjectivesThis study aimed at deciphering the possible cross-reactivity with heterologous SF6 strains of antibodies induced in humans by SF2a-TT15, a sun-type SF2a conjugate vaccine candidate featuring a non-O-acetylated synthetic oligosaccharide (OS) as surrogate of the detoxified LPS. Special focus was on the impact of the O-SP non-stoichiometric O-acetylation on cross-reactivity.\n\nMethodsSerum IgG antibody titers to LPSs from SF6 strains harboring different degrees of O-SP O-acetylation, and from Escherichia coli O147 (EC147) which shares an identical but non-O-acetylated O-SP with SF6, were measured by ELISA in 63 serum samples of volunteers receiving 2 {micro}g and 10 {micro}g OS doses of SF2a-TT15 or placebo in the frame of a phase I clinical study. Antibody in-lymphocyte-supernatants (ALS), avidity, and serum bactericidal activity (SBA) were measured in a subset of volunteers.\n\nResultsSF2a-TT15 induced cross-reacting IgG antibodies to all SF6 LPSs and EC147 LPS. A [&ge;]4-fold rise in anti-SF6 IgG titers was more frequent with the 10 {micro}g dose than with 2 {micro}g (50% vs 22%, p=0.045). Cross-reactivity rate was higher with the low O-acetylated SF6 O-SP than with the high O-acetylated one (50% versus 21%, p<0.05). Anti-SF6 responses correlated with homologous anti-SF2a LPS responses. Similar cross-reactivity was detected in ALS samples at day 7 after vaccination. Cross-reacting antibodies were partially functional against the heterologous SF6 parental strains, as shown by bactericidal activity and increased avidity.\n\nConclusionsSF2a-TT15 induces stronger SF6 cross-reactive IgG responses than the previously tested detoxified O-acetylated SF2a LPS-rEPA conjugate. While both serotypes are included in most multivalent Shigella vaccine candidates, cross-reactivity and cross-protection between SF2a and SF6 could enhance the immunogenicity and efficacy of a Shigella multivalent vaccine candidate, particularly in infants in low- and middle-income countries, the primary target population for a Shigella vaccine.","rel_num_authors":10,"rel_authors":[{"author_name":"Valeria Asato","author_inst":"Department of Epidemiology and Preventive Medicine, School of Public Health, Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 69978, Isr"},{"author_name":"Shiri Meron-Sudai","author_inst":"Department of Epidemiology and Preventive Medicine, School of Public Health, Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 69978, Isr"},{"author_name":"Anya Bialik","author_inst":"Department of Epidemiology and Preventive Medicine, School of Public Health, Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 69978, Isr"},{"author_name":"Sophy Goren","author_inst":"Department of Epidemiology and Preventive Medicine, School of Public Health, Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 69978, Isr"},{"author_name":"Shubham Mathur","author_inst":"Department of Epidemiology and Preventive Medicine, School of Public Health, Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 69978, Isr"},{"author_name":"Jonas St\u00e5hle","author_inst":"Department of Organic Chemistry, Arrhenius Laboratory, Stockholm University, S-106 91 Stockholm, Sweden"},{"author_name":"G\u00f6ran Widmalm","author_inst":"Department of Organic Chemistry, Arrhenius Laboratory, Stockholm University, S-106 91 Stockholm, Sweden"},{"author_name":"Armelle Phalipon","author_inst":"Institut Pasteur, Universit\u00e9 Paris Cit\u00e9, Medical Direction, F-75015 Paris, France"},{"author_name":"Laurence A. Mulard","author_inst":"Institut Pasteur, Universit\u00e9 Paris Cit\u00e9, CNRS UMR3523, Unit\u00e9 Chimie des Biomol\u00e9cules, 28 rue du Dr Roux, F-75015 Paris, France"},{"author_name":"Dani Cohen","author_inst":"Department of Epidemiology and Preventive Medicine, School of Public Health, Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 69978, Isr"}],"rel_date":"2026-05-07","rel_site":"medrxiv"},{"rel_title":"Serum IgG antibodies induced by the synthetic carbohydrate-based conjugate vaccine candidate SF2a-TT15 against Shigella flexneri 2a cross-react with the heterologous lipopolysaccharide of Shigella flexneri 6","rel_doi":"10.64898\/2026.05.05.26352385","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.05.26352385","rel_abs":"BackgroundShigella flexneri 2a (SF2a) and 6 (SF6) are two of the most common S. flexneri serotypes. They have distant O-specific polysaccharide (O-SP) structures. Previous studies showed no cross-reactivity or cross-protection between the two serotypes in a guinea pig model of infection. However, partial cross-reactivity and cross-protection were reported in humans immunized with a SF2a lattice-type conjugate vaccine candidate comprising the chemically detoxified lipopolysaccharide (LPS) attached to recombinant Pseudomonas aeruginosa Exoprotein A (rEPA).\n\nObjectivesThis study aimed at deciphering the possible cross-reactivity with heterologous SF6 strains of antibodies induced in humans by SF2a-TT15, a sun-type SF2a conjugate vaccine candidate featuring a non-O-acetylated synthetic oligosaccharide (OS) as surrogate of the detoxified LPS. Special focus was on the impact of the O-SP non-stoichiometric O-acetylation on cross-reactivity.\n\nMethodsSerum IgG antibody titers to LPSs from SF6 strains harboring different degrees of O-SP O-acetylation, and from Escherichia coli O147 (EC147) which shares an identical but non-O-acetylated O-SP with SF6, were measured by ELISA in 63 serum samples of volunteers receiving 2 {micro}g and 10 {micro}g OS doses of SF2a-TT15 or placebo in the frame of a phase I clinical study. Antibody in-lymphocyte-supernatants (ALS), avidity, and serum bactericidal activity (SBA) were measured in a subset of volunteers.\n\nResultsSF2a-TT15 induced cross-reacting IgG antibodies to all SF6 LPSs and EC147 LPS. A [&ge;]4-fold rise in anti-SF6 IgG titers was more frequent with the 10 {micro}g dose than with 2 {micro}g (50% vs 22%, p=0.045). Cross-reactivity rate was higher with the low O-acetylated SF6 O-SP than with the high O-acetylated one (50% versus 21%, p<0.05). Anti-SF6 responses correlated with homologous anti-SF2a LPS responses. Similar cross-reactivity was detected in ALS samples at day 7 after vaccination. Cross-reacting antibodies were partially functional against the heterologous SF6 parental strains, as shown by bactericidal activity and increased avidity.\n\nConclusionsSF2a-TT15 induces stronger SF6 cross-reactive IgG responses than the previously tested detoxified O-acetylated SF2a LPS-rEPA conjugate. While both serotypes are included in most multivalent Shigella vaccine candidates, cross-reactivity and cross-protection between SF2a and SF6 could enhance the immunogenicity and efficacy of a Shigella multivalent vaccine candidate, particularly in infants in low- and middle-income countries, the primary target population for a Shigella vaccine.","rel_num_authors":10,"rel_authors":[{"author_name":"Valeria Asato","author_inst":"Department of Epidemiology and Preventive Medicine, School of Public Health, Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 69978, Isr"},{"author_name":"Shiri Meron-Sudai","author_inst":"Department of Epidemiology and Preventive Medicine, School of Public Health, Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 69978, Isr"},{"author_name":"Anya Bialik","author_inst":"Department of Epidemiology and Preventive Medicine, School of Public Health, Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 69978, Isr"},{"author_name":"Sophy Goren","author_inst":"Department of Epidemiology and Preventive Medicine, School of Public Health, Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 69978, Isr"},{"author_name":"Shubham Mathur","author_inst":"Department of Epidemiology and Preventive Medicine, School of Public Health, Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 69978, Isr"},{"author_name":"Jonas St\u00e5hle","author_inst":"Department of Organic Chemistry, Arrhenius Laboratory, Stockholm University, S-106 91 Stockholm, Sweden"},{"author_name":"G\u00f6ran Widmalm","author_inst":"Department of Organic Chemistry, Arrhenius Laboratory, Stockholm University, S-106 91 Stockholm, Sweden"},{"author_name":"Armelle Phalipon","author_inst":"Institut Pasteur, Universit\u00e9 Paris Cit\u00e9, Medical Direction, F-75015 Paris, France"},{"author_name":"Laurence A. Mulard","author_inst":"Institut Pasteur, Universit\u00e9 Paris Cit\u00e9, CNRS UMR3523, Unit\u00e9 Chimie des Biomol\u00e9cules, 28 rue du Dr Roux, F-75015 Paris, France"},{"author_name":"Dani Cohen","author_inst":"Department of Epidemiology and Preventive Medicine, School of Public Health, Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 69978, Isr"}],"rel_date":"2026-05-07","rel_site":"medrxiv"},{"rel_title":"The Effect of Legalizing Online Sports Gambling on Population Mental Health","rel_doi":"10.64898\/2026.05.06.26352568","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.06.26352568","rel_abs":"ImportanceThe rapid rise of online sports gambling in the U.S. has been associated with financial harms, raising concern that it may adversely affect population mental health.\n\nObjectiveTo estimate the causal effect of state legalization of online sports gambling on population mental health, including a range of self-reported and registry-based outcomes.\n\nDesign, Setting, and ParticipantsRepeated cross-sectional study using nationally representative Behavioral Risk Factor Surveillance System (BRFSS) data from 2014-2025 and registry-based mortality records from 2012-2024. We leveraged state-level variation in the legalization of online sports gambling and applied a stacked difference-in-differences with event study design. The analytic sample included 4,660,948 BRFSS respondents and mortality records for virtually all state-years. We estimated effects on all adults and several higher-risk subgroups, including men, young men, and men with lower educational attainment.\n\nExposureState legalization of online sports gambling.\n\nMain Outcomes and MeasuresSelf-reported outcomes included poor mental health days, depressive disorder diagnoses, ever binge drinking, number of binge drinking episodes, and marijuana use. Registry-based outcomes included suicide mortality and alcohol-induced mortality per 100,000.\n\nResultsAmong 4,660,948 BRFSS respondents, 48.7% were men, 40.2% had no more than a high school education, and the mean age was 47.6 years. Legalization of online sports gambling had no discernible effect on poor mental health days of all U.S. adults (-0.01 days; 95% CI, -0.16 to 0.14; P=0.88), depressive disorder diagnoses (0.1 percentage points; 95% CI, -0.7 to 0.9; P=0.84), binge drinking, binge drinking episodes, or marijuana use. Meanwhile, mean suicide mortality was 14.1 per 100,000 and mean alcohol-induced mortality was 12.2 per 100,000. Legalization did not affect adult suicides (0.13 deaths per 100,000; 95% CI, -0.71 to 0.97; P=0.76) or alcohol-induced mortality (1.08 deaths per 100,000; 95% CI, -0.58 to 2.73; P=0.21). Results were null among men and higher-risk subgroups of men.\n\nConclusions and RelevanceThe legalization of online sports gambling has not produce detectable population-level changes in a range of mental health outcomes, including reported symptoms, diagnoses, substance use, and registry-based mortality due to suicide or alcohol, in up to 3 years of follow-up. These findings suggest that although online sports gambling may cause financial harm and severe distress for some individuals, legalization has not produced measurable average changes in population mental health over the observed follow-up period.\n\nKey pointsO_ST_ABSQuestionC_ST_ABSHas the legalization of online sports gambling affected population-level mental health, including symptoms, diagnoses, substance use, suicides, and alcohol-induced mortality?\n\nFindingsIn this repeated cross-sectional study that applied a difference-in-differences design to more than 4.6 million individual-level survey responses and mortality records, the legalization of online sports gambling from 2018-2024 did not affect reported poor mental health days, depressive disorders, binge drinking, marijuana use, suicide mortality, or alcohol-induced mortality. Results were similar among men and higher-risk subgroups of men.\n\nMeaningThe legalization of online sports gambling has not produced detectable population-level changes in a broad range of mental health outcomes in up to 3 years of follow-up.","rel_num_authors":4,"rel_authors":[{"author_name":"Nolan M. Kavanagh","author_inst":"Harvard University"},{"author_name":"Jacob C. Jameson","author_inst":"Harvard University"},{"author_name":"Harold A. Pollack","author_inst":"University of Chicago"},{"author_name":"Nathaniel J. Glasser","author_inst":"University of Chicago"}],"rel_date":"2026-05-07","rel_site":"medrxiv"},{"rel_title":"Comparative Evaluation of Wearable Sensor Form Factors for Physiological Monitoring in Youth with Autism Spectrum Disorder","rel_doi":"10.64898\/2026.05.06.26352564","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.06.26352564","rel_abs":"Sudden behavioral outbursts in youth with autism spectrum disorder (ASD) are difficult to predict and create substantial caregiving burdens. Wearable physiological monitoring might enable prediction, but sustained use may be limited by tolerability. We evaluated adherence and data completeness in 40 youth with ASD over a two-week period across four device types (wristband, headband, adhesive chest patch, and finger ring) alongside caregiver-reported useability and comfort. Data completeness varied markedly by device, with the patch achieving the highest completeness ([~]80%), followed by the wristband ([~]60%), headband ([~]50%), and ring ([~]20%). In multivariate analyses, adherence was driven by the device form factor rather than participant-level clinical characteristics. Devices rated as more comfortable did not yield higher completeness, revealing a divergence between reported preference and actual use. These findings suggest that device choice is a key consideration for studies in ASD youths, highlighting the need for research into model stability across sensor types in neurodivergent populations.","rel_num_authors":9,"rel_authors":[{"author_name":"Caden Stewart","author_inst":"Halicioglu Data Science Institute, University of California San Diego"},{"author_name":"Abigail Albertazzi","author_inst":"Rady Childrens Hospital of San Diego"},{"author_name":"Jacqueline Tasarz","author_inst":"Rady Childrens Hospital of San Diego"},{"author_name":"Kristen Kim","author_inst":"Division of Child & Adolescent Psychiatry, Department of Psychiatry, University of California San Diego"},{"author_name":"Veronica Gandara","author_inst":"St. Josephs Medical Center Stockton"},{"author_name":"Corrine Blucher","author_inst":"Rady Childrens Institute for Genomic Medicine"},{"author_name":"Camilla C. Reyes-Martinez","author_inst":"Division of Child & Adolescent Psychiatry, Department of Psychiatry, University of California San Diego"},{"author_name":"Benjamin Smarr","author_inst":"Shiu Chen - Gene Lay Department of Bioengineering, University of California San Diego"},{"author_name":"Aaron D. Besterman","author_inst":"Division of Child & Adolescent Psychiatry, Department of Psychiatry, University of California San Diego"}],"rel_date":"2026-05-07","rel_site":"medrxiv"},{"rel_title":"Conserved neuroectodermal aging encodes primate health and longevity","rel_doi":"10.64898\/2026.05.05.26352498","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.05.26352498","rel_abs":"Neuroectoderm-derived tissues are highly metabolically active and exhibit minimal regenerative turnover, rendering them uniquely vulnerable to age-related stress while preserving undiluted degenerative signals. Yet aging dynamics in these tissues remain elusive in living primates. Here, we introduce an in vivo neuroectodermal aging clock and trace its trajectory in 66,602 human adults and six rhesus macaques across nine health and disease cohorts using an in situ optical biopsy. Through a digital histology atlas integrated with artificial intelligence, we resolve tissue representations of neuroectodermal aging within the human retina, predominantly localized to the metabolically active ganglion and bipolar cell populations and the photoreceptor complex, while demonstrating their evolutionary conservation across primate species. Neuroectodermal aging predicts health and longevity, scales across space and time, and captures preclinical aging signals within and beyond the neuroectodermal compartment. This framework is further validated in a diabetic population, where robust prognostic and dynamic sensitivity are preserved across physiological and perturbed states. Our work establishes a scalable framework for resolving neuroectodermal aging in living primates and linking tissue-level vulnerability to systemic health trajectories.","rel_num_authors":3,"rel_authors":[{"author_name":"Shaopeng Yang","author_inst":"Zhongshan Ophthalmic Center, Sun Yat-sen University"},{"author_name":"Zhuoyao Xin","author_inst":"Department of Biomedical Engineering, Johns Hopkins University"},{"author_name":"Wei Wang","author_inst":"Zhongshan Ophthalmic Center, Sun Yat-sen university"}],"rel_date":"2026-05-07","rel_site":"medrxiv"},{"rel_title":"Modeling rare coding variation on chromosome X provides insight into the genetics and differential sex prevalence of autism spectrum disorder","rel_doi":"10.64898\/2026.05.04.26352380","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.04.26352380","rel_abs":"Autism spectrum disorder (ASD) is estimated to be up to four times as common in males as in females, yet the causes of this prevalence difference are not well established. One possible driver is genetic variation on the X chromosome, as it contains genes capable of contributing to ASD (e.g., PTCHD1, MECP2) and is known to play a role in genetic disorders with differential sex prevalence (e.g., color blindness). However, a lack of power compared to the autosomes combined with the complexities of modeling its biology have led to the X being largely overlooked in sequencing studies. Here, we develop quantitative X-linked TADA, a new model designed specifically for application to this chromosome, and use it to analyze rare variation from 50,663 individuals with ASD (and 136,670 individuals total). We find 9 genes on the X associated with ASD at a false discovery rate (FDR) < 0.05 and an additional 9 genes at FDR < 0.2, with many of these previously identified as involved in specific neurodevelopmental disorders. Point estimates of the liability conferred by de novo variants on the X are similar in females and males, with both sexes estimates elevated >20% above the corresponding autosomal values. We also develop a general theory of how X-linked variation of any additive or non-additive effect influences liability and describe its implications for prevalence. Using this theory and our empirical results, we show how genetic variation on the X could contribute to the sex-differential prevalence of ASD.","rel_num_authors":15,"rel_authors":[{"author_name":"F. Kyle Satterstrom","author_inst":"Broad Institute of MIT and Harvard"},{"author_name":"Kiana Jodeiry","author_inst":"Emory University School of Medicine"},{"author_name":"Behrang Mahjani","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Gad Hatem","author_inst":"Emory University School of Medicine"},{"author_name":"Se Jun Park","author_inst":"Emory University School of Medicine"},{"author_name":"Lambertus Klei","author_inst":"University of Pittsburgh"},{"author_name":"Jack M. Fu","author_inst":"Broad Institute of MIT and Harvard"},{"author_name":"Emilie M. Wigdor","author_inst":"University of Oxford"},{"author_name":"- the Autism Sequencing Consortium","author_inst":""},{"author_name":"Catalina Betancur","author_inst":"Sorbonne Universite"},{"author_name":"Mark J. Daly","author_inst":"Broad Institute of MIT and Harvard"},{"author_name":"Kathryn Roeder","author_inst":"Carnegie Mellon University"},{"author_name":"Bernie Devlin","author_inst":"University of Pittsburgh"},{"author_name":"Joseph D. Buxbaum","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"David J. Cutler","author_inst":"Emory University School of Medicine"}],"rel_date":"2026-05-07","rel_site":"medrxiv"},{"rel_title":"A tool for assessing changes in food preferences and health perceptions during nutritional interventions","rel_doi":"10.64898\/2026.05.06.26352307","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.06.26352307","rel_abs":"Understanding how nutritional interventions alter food evaluations may help clarify mechanisms of dietary behavior change; however, most studies focus on intake outcomes and rarely assess within-person changes in subjective food evaluation. We developed a brief, image-based rating tool that measures two core dimensions of food evaluation, liking and perceived healthiness, using standardized food images.\n\nThe tool was piloted in adults with type 2 diabetes participating in a medically supervised intervention that included structured glucose monitoring and professional dietary guidance. Ratings were collected at baseline, post-monitoring, and follow-up. In line with the methodological aim of this study, we examined whether the tool demonstrates internal coherence, sensitivity to change, and external validity against expert ratings and physiological measures, and whether it can capture item-level patterns relevant to eating behavior.\n\nResults provide preliminary evidence that the tool is feasible, it is low-burden, and capable of detecting coherent relationships between food liking and health perceptions, including coordinated within-person changes over time and meaningful associations with external benchmarks. To support scalability and self-administration, we also developed an online smartphone-based demonstration version to exemplify the task structure and user experience. Overall, this pilot study suggests that a short, flexible rating task can serve as a practical measurement tool for tracking intervention-relevant changes in food evaluation and for informing the design of future nutritional interventions.","rel_num_authors":5,"rel_authors":[{"author_name":"Maya Bar Or","author_inst":"Tel Aviv University"},{"author_name":"Nuphar Vinegrad","author_inst":"Diabetes clinic, Soroka university Medical Center & Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel"},{"author_name":"Sara Menashe Auman","author_inst":"Diabetes clinic, Soroka university Medical Center"},{"author_name":"Idit F Liberty","author_inst":"Diabetes clinic, Soroka university Medical Center & Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel"},{"author_name":"Tom Schonberg","author_inst":"Tel Aviv University"}],"rel_date":"2026-05-07","rel_site":"medrxiv"},{"rel_title":"Generating synthetic tau-PET scans in Alzheimer's disease from MRI, blood biomarkers and demographics with deep learning","rel_doi":"10.64898\/2026.05.06.26352540","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.06.26352540","rel_abs":"Tau protein aggregation in the brain is a hallmark of Alzheimers disease (AD). Positron emission tomography (PET) is the only in vivo method to visualize tau pathology and estimate both its burden and regional distribution, but the use of tau-PET is constrained by high cost and limited accessibility. Here, we develop a deep learning model to synthesize tau-PET scans from more accessible data: structural magnetic resonance imaging (MRI), demographics, and when available, blood biomarkers. We included 5,191 participants across the AD continuum or with another neurological disorder from 13 cohorts (mean age 70 years, 51% female) and optimized a 3D U-Net neural network with residual and attention units for this task. In held-out test data, synthetic tau-PET reliably modeled tau burden, with correlations of R=0.77-0.86 with true tau-PET across individuals in common AD regions of interest. Spatial similarity between synthetic and true tau-PET was likewise high, with mean regional correlation of R=0.75. Synthetic scans also captured clinically meaningful prognostic information comparable to true tau-PET, including distinction between early (HR=12, p<0.001) and late (HR=45, p<0.001) stages of tau accumulation. These findings demonstrate that clinically informative synthetic tau-PET scans can be generated from widely available modalities using deep learning, potentially offering a scalable and cost-effective approach for estimating tau AD pathology in the brain.","rel_num_authors":23,"rel_authors":[{"author_name":"Linda Karlsson","author_inst":"Clinical Memory Research Unit, Department of Clinical Sciences, Lund University"},{"author_name":"Olof Strandberg","author_inst":"Clinical Memory Research Unit, Department of Clinical Sciences, Lund University"},{"author_name":"Ruben Smith","author_inst":"Clinical Memory Research Unit, Department of Clinical Sciences, Lund University"},{"author_name":"Weizhong Tang","author_inst":"Clinical Memory Research Unit, Department of Clinical Sciences, Lund University"},{"author_name":"Ida Arvidsson","author_inst":"Centre for Mathematical Sciences, Lund University, Lund, Sweden"},{"author_name":"Kalle Astrom","author_inst":"Centre for Mathematical Sciences, Lund University, Lund, Sweden"},{"author_name":"Kevin Oliviera Hauer","author_inst":"Clinical Memory Research Unit, Department of Clinical Sciences, Lund University"},{"author_name":"Shorena Janelidze","author_inst":"Clinical Memory Research Unit, Department of Clinical Sciences, Lund University"},{"author_name":"Erik Stomrud","author_inst":"Clinical Memory Research Unit, Department of Clinical Sciences, Lund University"},{"author_name":"Sebastian Palmqvist","author_inst":"Clinical Memory Research Unit, Department of Clinical Sciences, Lund University"},{"author_name":"Philip B Verghese","author_inst":"C2N Diagnostics LLC, St Louis, MO, USA"},{"author_name":"Joel B Braunstein","author_inst":"C2N Diagnostics LLC, St Louis, MO, USA"},{"author_name":"- Alzheimer's Disease Neuroimaging Initiative","author_inst":""},{"author_name":"- PREVENT-AD Research Group","author_inst":""},{"author_name":"Gregory Klein","author_inst":"Pharma Research and Early Development, F Hoffmann-La Roche Ltd., Basel, Switzerland"},{"author_name":"Sergey Shcherbinin","author_inst":"Eli Lilly and Company, Indianapolis, Indiana, USA"},{"author_name":"William J Jagust","author_inst":"Department of Neuroscience, University of California, Berkeley, California, USA"},{"author_name":"Sylvia Villeneuve","author_inst":"Centre for Studies in the Prevention of Alzheimer's Disease, Douglas Mental Health Institute, McGill University, Montreal, QC, Canada."},{"author_name":"Renaud La Joie","author_inst":"Department of Neurology, Edward and Pearl Fein Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, California, "},{"author_name":"Gil D Rabinovici","author_inst":"Department of Neurology, Edward and Pearl Fein Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, California, "},{"author_name":"Niklas Mattsson-Carlgren","author_inst":"Clinical Memory Research Unit, Department of Clinical Sciences, Lund University"},{"author_name":"Jacob W Vogel","author_inst":"Clinical Memory Research Unit, SciLifeLab, Department of Clinical Sciences in Malmo, Lund University, Lund, Sweden"},{"author_name":"Oskar Hansson","author_inst":"Clinical Memory Research Unit, Department of Clinical Sciences, Lund University"}],"rel_date":"2026-05-07","rel_site":"medrxiv"},{"rel_title":"Generating synthetic tau-PET scans in Alzheimer's disease from MRI, blood biomarkers and demographics with deep learning","rel_doi":"10.64898\/2026.05.06.26352540","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.06.26352540","rel_abs":"Tau protein aggregation in the brain is a hallmark of Alzheimers disease (AD). Positron emission tomography (PET) is the only in vivo method to visualize tau pathology and estimate both its burden and regional distribution, but the use of tau-PET is constrained by high cost and limited accessibility. Here, we develop a deep learning model to synthesize tau-PET scans from more accessible data: structural magnetic resonance imaging (MRI), demographics, and when available, blood biomarkers. We included 5,191 participants across the AD continuum or with another neurological disorder from 13 cohorts (mean age 70 years, 51% female) and optimized a 3D U-Net neural network with residual and attention units for this task. In held-out test data, synthetic tau-PET reliably modeled tau burden, with correlations of R=0.77-0.86 with true tau-PET across individuals in common AD regions of interest. Spatial similarity between synthetic and true tau-PET was likewise high, with mean regional correlation of R=0.75. Synthetic scans also captured clinically meaningful prognostic information comparable to true tau-PET, including distinction between early (HR=12, p<0.001) and late (HR=45, p<0.001) stages of tau accumulation. These findings demonstrate that clinically informative synthetic tau-PET scans can be generated from widely available modalities using deep learning, potentially offering a scalable and cost-effective approach for estimating tau AD pathology in the brain.","rel_num_authors":23,"rel_authors":[{"author_name":"Linda Karlsson","author_inst":"Clinical Memory Research Unit, Department of Clinical Sciences, Lund University"},{"author_name":"Olof Strandberg","author_inst":"Clinical Memory Research Unit, Department of Clinical Sciences, Lund University"},{"author_name":"Ruben Smith","author_inst":"Clinical Memory Research Unit, Department of Clinical Sciences, Lund University"},{"author_name":"Weizhong Tang","author_inst":"Clinical Memory Research Unit, Department of Clinical Sciences, Lund University"},{"author_name":"Ida Arvidsson","author_inst":"Centre for Mathematical Sciences, Lund University, Lund, Sweden"},{"author_name":"Kalle Astrom","author_inst":"Centre for Mathematical Sciences, Lund University, Lund, Sweden"},{"author_name":"Kevin Oliviera Hauer","author_inst":"Clinical Memory Research Unit, Department of Clinical Sciences, Lund University"},{"author_name":"Shorena Janelidze","author_inst":"Clinical Memory Research Unit, Department of Clinical Sciences, Lund University"},{"author_name":"Erik Stomrud","author_inst":"Clinical Memory Research Unit, Department of Clinical Sciences, Lund University"},{"author_name":"Sebastian Palmqvist","author_inst":"Clinical Memory Research Unit, Department of Clinical Sciences, Lund University"},{"author_name":"Philip B Verghese","author_inst":"C2N Diagnostics LLC, St Louis, MO, USA"},{"author_name":"Joel B Braunstein","author_inst":"C2N Diagnostics LLC, St Louis, MO, USA"},{"author_name":"- Alzheimer's Disease Neuroimaging Initiative","author_inst":""},{"author_name":"- PREVENT-AD Research Group","author_inst":""},{"author_name":"Gregory Klein","author_inst":"Pharma Research and Early Development, F Hoffmann-La Roche Ltd., Basel, Switzerland"},{"author_name":"Sergey Shcherbinin","author_inst":"Eli Lilly and Company, Indianapolis, Indiana, USA"},{"author_name":"William J Jagust","author_inst":"Department of Neuroscience, University of California, Berkeley, California, USA"},{"author_name":"Sylvia Villeneuve","author_inst":"Centre for Studies in the Prevention of Alzheimer's Disease, Douglas Mental Health Institute, McGill University, Montreal, QC, Canada."},{"author_name":"Renaud La Joie","author_inst":"Department of Neurology, Edward and Pearl Fein Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, California, "},{"author_name":"Gil D Rabinovici","author_inst":"Department of Neurology, Edward and Pearl Fein Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, California, "},{"author_name":"Niklas Mattsson-Carlgren","author_inst":"Clinical Memory Research Unit, Department of Clinical Sciences, Lund University"},{"author_name":"Jacob W Vogel","author_inst":"Clinical Memory Research Unit, SciLifeLab, Department of Clinical Sciences in Malmo, Lund University, Lund, Sweden"},{"author_name":"Oskar Hansson","author_inst":"Clinical Memory Research Unit, Department of Clinical Sciences, Lund University"}],"rel_date":"2026-05-07","rel_site":"medrxiv"},{"rel_title":"Regulatory architecture underlying immune dysregulation reconstructed by single-cell multi-omics in lupus nephritis","rel_doi":"10.64898\/2026.05.06.26352515","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.06.26352515","rel_abs":"ObjectivesLupus nephritis (LN) is a severe complication of systemic lupus erythematosus with heterogeneous clinical outcomes and limited therapeutic options. Although immune dysregulation is central to LN pathogenesis, the underlying cell-type-specific regulatory mechanisms and their genetic determinants remain poorly characterized.\n\nMethodsWe generated a single-cell multi-omics atlas of peripheral blood mononuclear cells (PBMCs) from newly diagnosed, minimally treated LN patients by integrating single-cell RNA-seq (scRNA-seq) and single-nucleus ATAC-seq (snATAC-seq) profiles. To elucidate genetically driven regulatory programs in a broaden LN population, we generated a blood expression quantitative trait loci (eQTL) atlas from 99 Chinese LN patients and performed Bayesian colocalization analysis to systematically prioritize putative causal genes for LN. Finally, we investigated how fine-mapped SNPs associated with LN phenotypic manifestations exert regulatory effects within distinct single-cell chromation contexts by leveraging peak-to-gene linkages at single-cell resolution.\n\nResultsOur single-cell multi-omic dataset and orthogonal analytical approaches revealed extensive immune remodeling in LN, characterized by amplified innate immune activation and impaired adaptive immune responses, and identified transcription factors (TFs) orchestrating immune regulatory circuits. Bayesian colocalization analysis nominated 14 high-fidelity causal genes for kidney function and 23 for SLE. Integration with fine-mapped GWAS variants highlighted critical cell type convergence across autoimmune disorders and immune-mediated nephropathies, particularly within B cell subsets, where TF-driven programs delineated stage-specific differentiation networks.\n\nConclusionsTogether, these analyses reconstruct the regulatory architecture underlying immune dysregulation in LN and connect genetic variation to cell-type-specific regulation, guiding genetically informed therapeutic development.","rel_num_authors":16,"rel_authors":[{"author_name":"Huanhuan Zhao","author_inst":"Zhejiang University"},{"author_name":"Fan Yang","author_inst":"National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University of Medicine"},{"author_name":"Tingyu Chen","author_inst":"National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University of Medicine"},{"author_name":"Jian Zhang","author_inst":"Liangzhu Laboratory, Zhejiang University"},{"author_name":"Jinsong Shi","author_inst":"National Clinical Research Center of Kidney Diseases, Jinling"},{"author_name":"Xiaoyang Liu","author_inst":"Liangzhu Laboratory, Zhejiang University"},{"author_name":"Siyi Chen","author_inst":"chensyi@126.com"},{"author_name":"Ziyuan Ma","author_inst":"ziyuan.ma@rutgers.edu"},{"author_name":"Shuai Liu","author_inst":"shuailiu@zju.edu.cn"},{"author_name":"Xudong Fu","author_inst":"xudongfu@zju.edu.cn"},{"author_name":"Na Kong","author_inst":"Liangzhu Laboratory, Zhejiang University"},{"author_name":"Jin Zhang","author_inst":"Liangzhu Laboratory, Zhejiang University"},{"author_name":"Xiaomin Yu","author_inst":"Liangzhu Laboratory, Zhejiang University"},{"author_name":"Katalin Susztak","author_inst":"Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania"},{"author_name":"Xin Sheng","author_inst":"Zhejiang University"},{"author_name":"Zhihong Liu","author_inst":"Liangzhu Laboratory, Zhejiang University"}],"rel_date":"2026-05-07","rel_site":"medrxiv"},{"rel_title":"NeuroDev: etiology and experience of neurodevelopmental disorders in Kenya and South Africa","rel_doi":"10.64898\/2026.04.30.26351947","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.30.26351947","rel_abs":"The NeuroDev study, conducted in Kenya and South Africa, is a large-scale clinical, genetic, and epidemiologic characterization of neurodevelopmental disorders (NDDs) on the African continent. NeuroDev assessments capture birth, demographic, and developmental history; cognitive and behavioral outcomes; and physical health variables. DNA samples are collected for exome sequencing and clinical genetic analysis. This paper presents novel data from 521 children with NDDs, 739 of those childrens parents, and 255 unrelated, typically-developing children. The analyses offer unique genetic and phenotypic characterizations of NDDs in two African countries and underscore the importance of including underrepresented populations in NDD research. Ultimately, 107 children with NDDs from the NeuroDev cohort (22.1%) had likely pathogenic or pathogenic variants in established NDD genes. High rates of genetic diagnosis were associated with high rates of environmental risk factors for NDDs. All data, materials, and measures generated from this study are publicly available through the US National Institute of Mental Health.","rel_num_authors":44,"rel_authors":[{"author_name":"Patricia Kipkemoi","author_inst":"Neuroscience Unit, KEMRI-Wellcome Trust, Center for Geographic Medicine Research Coast, Kilifi, Kenya"},{"author_name":"Emily O Heir","author_inst":"The Broad Institute of MIT and Harvard, Cambridge MA, USA"},{"author_name":"Mutaz Amin","author_inst":"The Broad Institute of MIT and Harvard, Cambridge MA, USA"},{"author_name":"Sarah L Stenton","author_inst":"The Broad Institute of MIT and Harvard, Cambridge MA, USA"},{"author_name":"William Baddoo","author_inst":"The Broad Institute of MIT and Harvard, Cambridge MA, USA"},{"author_name":"Harrison Brand","author_inst":"Center for Genomic Medicine, Massachusetts General Hospital, Boston MA, USA"},{"author_name":"Zandre Bruwer","author_inst":"Department of Paediatrics and Child Health, Red Cross War Memorial Childrens Hospital, University of Cape Town, Rondebosch, South Africa"},{"author_name":"Sam Bryant","author_inst":"The Broad Institute of MIT and Harvard, Cambridge MA, USA"},{"author_name":"Eunice Chepkemoi","author_inst":"Neuroscience Unit, KEMRI-Wellcome Trust, Center for Geographic Medicine Research Coast, Kilifi, Kenya"},{"author_name":"Bjorn Christ","author_inst":"Department of Paediatrics and Child Health, Red Cross War Memorial Childrens Hospital, University of Cape Town, Rondebosch, South Africa"},{"author_name":"Emma Eastman","author_inst":"Department of Paediatrics and Child Health, Red Cross War Memorial Childrens Hospital, University of Cape Town, Rondebosch, South Africa"},{"author_name":"Claire Fourie","author_inst":"Department of Paediatrics and Child Health, Red Cross War Memorial Childrens Hospital, University of Cape Town, Rondebosch, South Africa"},{"author_name":"Jack M Fu","author_inst":"Center for Genomic Medicine, Massachusetts General Hospital, Boston MA, USA"},{"author_name":"Alice Galvin","author_inst":"The Broad Institute of MIT and Harvard, Cambridge MA, USA"},{"author_name":"Stacey Hall","author_inst":"The Broad Institute of MIT and Harvard, Cambridge MA, USA"},{"author_name":"Fatima Khan","author_inst":"Department of Paediatrics and Child Health, Red Cross War Memorial Childrens Hospital, University of Cape Town, Rondebosch, South Africa"},{"author_name":"Heesu Ally Kim","author_inst":"The Broad Institute of MIT and Harvard, Cambridge MA, USA"},{"author_name":"Collins Kipkoech","author_inst":"Neuroscience Unit, KEMRI-Wellcome Trust, Center for Geographic Medicine Research Coast, Kilifi, Kenya"},{"author_name":"Martha Kombe","author_inst":"Neuroscience Unit, KEMRI-Wellcome Trust, Center for Geographic Medicine Research Coast, Kilifi, Kenya"},{"author_name":"Racheal Mapenzi","author_inst":"Neuroscience Unit, KEMRI-Wellcome Trust, Center for Geographic Medicine Research Coast, Kilifi, Kenya"},{"author_name":"Brigitte Melly","author_inst":"Department of Paediatrics and Child Health, Red Cross War Memorial Childrens Hospital, University of Cape Town, Rondebosch, South Africa"},{"author_name":"Celia van der Merwe","author_inst":"The Broad Institute of MIT and Harvard, Cambridge MA, USA"},{"author_name":"Beatrice Mkubwa","author_inst":"Neuroscience Unit, KEMRI-Wellcome Trust, Center for Geographic Medicine Research Coast, Kilifi, Kenya"},{"author_name":"Serini Murugasen","author_inst":"Department of Paediatrics and Child Health, Red Cross War Memorial Childrens Hospital, University of Cape Town, Rondebosch, South Africa"},{"author_name":"Katini Mwangasha","author_inst":"Neuroscience Unit, KEMRI-Wellcome Trust, Center for Geographic Medicine Research Coast, Kilifi, Kenya"},{"author_name":"Paul Mwangi","author_inst":"Neuroscience Unit, KEMRI-Wellcome Trust, Center for Geographic Medicine Research Coast, Kilifi, Kenya"},{"author_name":"Samuel Mwasambu","author_inst":"Neuroscience Unit, KEMRI-Wellcome Trust, Center for Geographic Medicine Research Coast, Kilifi, Kenya"},{"author_name":"Alfred Ngombo","author_inst":"Neuroscience Unit, KEMRI-Wellcome Trust, Center for Geographic Medicine Research Coast, Kilifi, Kenya"},{"author_name":"Javan Nyale","author_inst":"Neuroscience Unit, KEMRI-Wellcome Trust, Center for Geographic Medicine Research Coast, Kilifi, Kenya"},{"author_name":"Grace E VanNoy","author_inst":"The Broad Institute of MIT and Harvard, Cambridge MA, USA"},{"author_name":"Ikeoluwa Osei-Owusu","author_inst":"The Broad Institute of MIT and Harvard, Cambridge MA, USA"},{"author_name":"Jessica E Ringshaw","author_inst":"Department of Paediatrics and Child Health, Red Cross War Memorial Childrens Hospital, University of Cape Town, Rondebosch, South Africa"},{"author_name":"Kathryn A Russell","author_inst":"The Broad Institute of MIT and Harvard, Cambridge MA, USA"},{"author_name":"Kaitlin E Samocha","author_inst":"The Broad Institute of MIT and Harvard, Cambridge MA, USA"},{"author_name":"Alba Sanchis-Juan","author_inst":"Center for Genomic Medicine, Massachusetts General Hospital, Boston MA, USA"},{"author_name":"Moriel Singer-Berk","author_inst":"The Broad Institute of MIT and Harvard, Cambridge MA, USA"},{"author_name":"Michal Zieff","author_inst":"Department of Paediatrics and Child Health, Red Cross War Memorial Childrens Hospital, University of Cape Town, Rondebosch, South Africa"},{"author_name":"Michael E Talkowski","author_inst":"Center for Genomic Medicine, Massachusetts General Hospital, Boston MA, USA"},{"author_name":"Anne O Donnell-Luria","author_inst":"The Broad Institute of MIT and Harvard, Cambridge MA, USA"},{"author_name":"Christina Austin-Tse","author_inst":"The Broad Institute of MIT and Harvard, Cambridge MA, USA"},{"author_name":"Charles Newton","author_inst":"Neuroscience Unit, KEMRI-Wellcome Trust, Center for Geographic Medicine Research Coast, Kilifi, Kenya"},{"author_name":"Amina Abubakar","author_inst":"Neuroscience Unit, KEMRI-Wellcome Trust, Center for Geographic Medicine Research Coast, Kilifi, Kenya"},{"author_name":"Kirsten A Donald","author_inst":"Department of Paediatrics and Child Health, Red Cross War Memorial Childrens Hospital, University of Cape Town, Rondebosch, South Africa"},{"author_name":"Elise B Robinson","author_inst":"Center for Genomic Medicine, Massachusetts General Hospital, Boston MA, USA"}],"rel_date":"2026-05-06","rel_site":"medrxiv"},{"rel_title":"The dynamic motor control index as a measure of post-stroke impairments in neuromotor control","rel_doi":"10.64898\/2026.04.30.26351964","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.04.30.26351964","rel_abs":"Measuring neuromotor control after stroke is crucial for identifying the mechanisms underlying asymmetrical walking and guiding rehabilitation. The lower extremity portion of the Fugl-Meyer (FM-LE) and the number of muscle synergies are commonly used measures, but have important limitations. The dynamic motor control index has emerged as a complementary metric, yet its relationship to established clinical measures (i.e., FM-LE), muscle synergy number, and gait biomechanics remains unclear. This study evaluated the ability of the dynamic motor control index to quantify post-stroke neuromotor impairment relative to FM-LE and muscle synergy number and examined its relationship with propulsion asymmetry. Electromyography data from 22 individuals post-stroke and 31 neurotypical controls were analyzed using non-negative matrix factorization. The dynamic motor control index and not the muscle synergy number differentiated paretic, non-paretic, and neurotypical limbs ({chi}2(2) = 27.57, p < .001). It also differed significantly between less and more impaired individuals classified by FM-LE (p = .05) and demonstrated good discriminative performance between these groups (AUC: 0.777, p = .017). The index also moderated the relationship between FM-LE and propulsion asymmetry ({Delta}R2 = 0.223, p = .007). These findings support the dynamic motor control index as a clinically relevant msarker of post-stroke neuromotor impairment and recovery.","rel_num_authors":7,"rel_authors":[{"author_name":"Ashley N Collimore-Doherty","author_inst":"Department of Physical Therapy, Boston University, Boston, MA, USA"},{"author_name":"Ruoxi Wang","author_inst":"Department of Physical Therapy, Boston University, Boston, MA, USA"},{"author_name":"David A Sherman","author_inst":"Department of Physical Therapy, Movement & Rehabilitation Sciences, Northeastern University, Boston, MA"},{"author_name":"Conor J Walsh","author_inst":"Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA"},{"author_name":"Paolo Bonato","author_inst":"Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA, USA"},{"author_name":"Terry Ellis","author_inst":"Department of Physical Therapy, Boston University, Boston, MA, USA"},{"author_name":"Louis N Awad","author_inst":"Department of Physical Therapy, Boston University, Boston, MA, USA"}],"rel_date":"2026-05-06","rel_site":"medrxiv"},{"rel_title":"Exposome contribution to the brain metabolome: importance of body brain connection.","rel_doi":"10.64898\/2026.05.05.26352469","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.05.26352469","rel_abs":"INTRODUCTIONMounting evidence support exposome influences on brain function and health, complementing genome influences. Understanding the molecular imprint of exposome on brain metabolism and the biochemical communication between the body and brain can impact our fundamental understanding and treatment of neuropsychiatric diseases.\n\nMETHODSLeveraging two complementary metabolomics platforms, we classified 1400 features in 514 brains from the ROSMAP collection. We evaluated the origin of these compounds using literature and databases. We correlated those metabolites with cognitive function using linear models.\n\nRESULTSWe identified over 230 non-endogenous compounds in the brain, including 103 drugs and metabolites, 120 dietary and microbial products and possibly 15 compounds from environmental exposures. Over 20 dietary and gut microbial compounds showed associations with cognition.\n\nDISCUSSIONComprehensive profiling of chemicals in the brain and the link to cognitive function provides foundational work to connect body and brain in the study of AD and related dementias.","rel_num_authors":15,"rel_authors":[{"author_name":"Naama Karu","author_inst":"Tasmanian Independent Metabolomics and Analytical Chemistry Solutions"},{"author_name":"Haoqi Nina Zhao","author_inst":"UC San Diego"},{"author_name":"Richa Batra","author_inst":"Weill Cornell Medical College of Cornell University"},{"author_name":"Matthias Arnold","author_inst":"Helmholtz Zentrum Munchen - German Research Center for Environmental Health"},{"author_name":"Jan Krumsiek","author_inst":"Weill Cornell Medicine"},{"author_name":"Lurian Caetano David","author_inst":"UC San Diego"},{"author_name":"Dinesh Barupal","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Leyla Schimmel","author_inst":"Duke University"},{"author_name":"Alexandra Kueider-Paisley","author_inst":"Duke University"},{"author_name":"Colette Blach","author_inst":"Duke University"},{"author_name":"Kamil Borkowski","author_inst":"University of California Davis"},{"author_name":"Pieter Dorrestein","author_inst":"UC San Diego"},{"author_name":"David  A Bennett","author_inst":"Rush University Medical Center"},{"author_name":"Rima Kaddurah-Daouk","author_inst":"Duke University"},{"author_name":"- Alzheimer's Disease Metabolomics Consortium","author_inst":"-"}],"rel_date":"2026-05-06","rel_site":"medrxiv"},{"rel_title":"Soluble Glycoprotein 120 is associated with Coronary Artery Inflammation Measured by Pericoronary Fat Attenuation Index in People Living with HIV","rel_doi":"10.64898\/2026.05.05.26352462","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.05.26352462","rel_abs":"Persistence of HIV antigens may drive chronic inflammation, leading to early-onset comorbidity among people living with HIV. We found that the presence of soluble glycoprotein 120 in plasma is associated with increased coronary inflammation, as measured by the pericoronary fat attenuation index (PFAI), a predictor of overt cardiovascular disease.","rel_num_authors":16,"rel_authors":[{"author_name":"Pengd-Wende Habib Bousse Traore","author_inst":"Centre de recherche du CHUM, Montreal, Canada"},{"author_name":"Kevin Boczar","author_inst":"University of Ottawa Heart Institute, Ottawa, Canada"},{"author_name":"Mehdi Benlarbi","author_inst":"Centre de recherche du CHUM, and Departement of microbiology, infectiology and immunology, University of Montreal, Canada"},{"author_name":"Victoria Devine-Ducharme","author_inst":"University of Ottawa Heart Institute, Ottawa, Canada"},{"author_name":"Valerie Shirobokov","author_inst":"University of Ottawa Heart Institute, Ottawa, Canada"},{"author_name":"Bethlehem Mengesha","author_inst":"University of Ottawa Heart Institute, Ottawa, Canada"},{"author_name":"Jonathan Richard","author_inst":"Centre de recherche du CHUM and Departement of microbiology, infectiology and immunology, University of Montreal, Canada"},{"author_name":"Nicolas Chomont","author_inst":"University de Montreal"},{"author_name":"Marc Messier-Peet","author_inst":"Centre de recherche du CHUM, Montreal, Canada"},{"author_name":"Annie Chamberland","author_inst":"Centre de recherche du CHUM, Montreal, Canada"},{"author_name":"Branka Vulesevic","author_inst":"Centre de recherche du CHUM, Montreal, Canada"},{"author_name":"Mohamed El-Far","author_inst":"Centre de recherche du CHUM, Montreal, Canada"},{"author_name":"Cecile Tremblay","author_inst":"Centre de recherche du CHUM, Montreal, Canada"},{"author_name":"Carl Chartrand-Lefebvre","author_inst":"Department of Radiology, Radiation Oncology and Nuclear Medicine, Centre Hospitalier de L'Universite de Montreal, Montreal, Canada"},{"author_name":"Andres Finzi","author_inst":"University of Montreal"},{"author_name":"Madeleine Durand","author_inst":"CHUM: Centre Hospitalier de L'Universite de Montreal"}],"rel_date":"2026-05-06","rel_site":"medrxiv"},{"rel_title":"Soluble Glycoprotein 120 is associated with Coronary Artery Inflammation Measured by Pericoronary Fat Attenuation Index in People Living with HIV","rel_doi":"10.64898\/2026.05.05.26352462","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.05.26352462","rel_abs":"Persistence of HIV antigens may drive chronic inflammation, leading to early-onset comorbidity among people living with HIV. We found that the presence of soluble glycoprotein 120 in plasma is associated with increased coronary inflammation, as measured by the pericoronary fat attenuation index (PFAI), a predictor of overt cardiovascular disease.","rel_num_authors":16,"rel_authors":[{"author_name":"Pengd-Wende Habib Bousse Traore","author_inst":"Centre de recherche du CHUM, Montreal, Canada"},{"author_name":"Kevin Boczar","author_inst":"University of Ottawa Heart Institute, Ottawa, Canada"},{"author_name":"Mehdi Benlarbi","author_inst":"Centre de recherche du CHUM, and Departement of microbiology, infectiology and immunology, University of Montreal, Canada"},{"author_name":"Victoria Devine-Ducharme","author_inst":"University of Ottawa Heart Institute, Ottawa, Canada"},{"author_name":"Valerie Shirobokov","author_inst":"University of Ottawa Heart Institute, Ottawa, Canada"},{"author_name":"Bethlehem Mengesha","author_inst":"University of Ottawa Heart Institute, Ottawa, Canada"},{"author_name":"Jonathan Richard","author_inst":"Centre de recherche du CHUM and Departement of microbiology, infectiology and immunology, University of Montreal, Canada"},{"author_name":"Nicolas Chomont","author_inst":"University de Montreal"},{"author_name":"Marc Messier-Peet","author_inst":"Centre de recherche du CHUM, Montreal, Canada"},{"author_name":"Annie Chamberland","author_inst":"Centre de recherche du CHUM, Montreal, Canada"},{"author_name":"Branka Vulesevic","author_inst":"Centre de recherche du CHUM, Montreal, Canada"},{"author_name":"Mohamed El-Far","author_inst":"Centre de recherche du CHUM, Montreal, Canada"},{"author_name":"Cecile Tremblay","author_inst":"Centre de recherche du CHUM, Montreal, Canada"},{"author_name":"Carl Chartrand-Lefebvre","author_inst":"Department of Radiology, Radiation Oncology and Nuclear Medicine, Centre Hospitalier de L'Universite de Montreal, Montreal, Canada"},{"author_name":"Andres Finzi","author_inst":"University of Montreal"},{"author_name":"Madeleine Durand","author_inst":"CHUM: Centre Hospitalier de L'Universite de Montreal"}],"rel_date":"2026-05-06","rel_site":"medrxiv"},{"rel_title":"Enhancing dengue diagnosis and surveillance by integrating machine learning technologies with the NS1 rapid test kit","rel_doi":"10.64898\/2026.05.05.26352445","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.05.26352445","rel_abs":"BackgroundDengue has been a major health threat globally in recent years. In particular, dengue incidences continue to increase annually and the epidemic area has expanded primarily due to global warming. Therefore, effective case detection and surveillance strategies are crucial to tackle this global health challenge. In clinical practice, the rapid test kit detecting dengue non-structural protein 1 antigen and commonly referred as NS1, is widely employed for early diagnosis. However, real-world studies revealed that the sensitivity of the NS1 test kit ranged from approximately 61% to 95%. Since early diagnosis is really critical for disease surveillance in the early stage of a dengue epidemic, scientists have been working hard to develop novel diagnosis methods that can provide higher sensitivity levels.\n\nMethodology\/Principal FindingsIn response to this challenge, in this study, we have developed a novel diagnosis procedure that integrates machine learning technologies with the NS1 test kit. Our experimental results revealed that we would be able to raise the sensitivity of the dengue diagnosis procedure to higher than 99% by incorporating machine learning based prediction models to screen the suspected patients with a negative NS1 result. Furthermore, the relative risks between the suspected patients who were predicted to be positive and those who were predicted to be negative exceeded 4.8.\n\nConclusions\/SignificanceThese results illustrate that the proposed approach provides an effective and efficient diagnosis procedure to address the global health challenge caused by spread of dengue.\n\nAuthor SummaryThis study has aimed to enhance surveillance of the dengue disease by integrating machine learning technologies with the rapid test kit commonly employed in early diagnosis. In clinical practice, the NS1 rapid test kit is widely employed for early diagnosis. However, real-world studies revealed that a certain percentage of the patients with a negative NS1 test result, ranging from 5% to 39%, were actually infected by dengue. Since early diagnosis is critical for disease control in the early stage of a dengue epidemic, scientists have been working hard to tackle this challenge. Based on this observation, this study was launched to investigate the effects of incorporating machine learning based prediction models to further screen those patients with a negative NS1 test result. The experimental results revealed that the proposed approach was able to identify over 99% of the patients who were infected by the dengue disease. Furthermore, the risk of the suspected patients who were predicted to be positive was 4.8 times higher than the risk of those who were predicted to be negative. The experimental results illustrate that the proposed approach provides an effective and efficient diagnosis procedure to enhance surveillance of the dengue disease.","rel_num_authors":5,"rel_authors":[{"author_name":"Chun-Kai Hwang","author_inst":"National Taiwan University"},{"author_name":"Ying-Wen Chen","author_inst":"National Cheng Kung University Hospital"},{"author_name":"YU-TSENG WANG","author_inst":"National Taiwan University"},{"author_name":"Tzong-Shiann Ho","author_inst":"National Cheng Kung University Hospital"},{"author_name":"Yen-Jen Oyang","author_inst":"National Taiwan University"}],"rel_date":"2026-05-06","rel_site":"medrxiv"},{"rel_title":"Enhancing dengue diagnosis and surveillance by integrating machine learning technologies with the NS1 rapid test kit","rel_doi":"10.64898\/2026.05.05.26352445","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.05.26352445","rel_abs":"BackgroundDengue has been a major health threat globally in recent years. In particular, dengue incidences continue to increase annually and the epidemic area has expanded primarily due to global warming. Therefore, effective case detection and surveillance strategies are crucial to tackle this global health challenge. In clinical practice, the rapid test kit detecting dengue non-structural protein 1 antigen and commonly referred as NS1, is widely employed for early diagnosis. However, real-world studies revealed that the sensitivity of the NS1 test kit ranged from approximately 61% to 95%. Since early diagnosis is really critical for disease surveillance in the early stage of a dengue epidemic, scientists have been working hard to develop novel diagnosis methods that can provide higher sensitivity levels.\n\nMethodology\/Principal FindingsIn response to this challenge, in this study, we have developed a novel diagnosis procedure that integrates machine learning technologies with the NS1 test kit. Our experimental results revealed that we would be able to raise the sensitivity of the dengue diagnosis procedure to higher than 99% by incorporating machine learning based prediction models to screen the suspected patients with a negative NS1 result. Furthermore, the relative risks between the suspected patients who were predicted to be positive and those who were predicted to be negative exceeded 4.8.\n\nConclusions\/SignificanceThese results illustrate that the proposed approach provides an effective and efficient diagnosis procedure to address the global health challenge caused by spread of dengue.\n\nAuthor SummaryThis study has aimed to enhance surveillance of the dengue disease by integrating machine learning technologies with the rapid test kit commonly employed in early diagnosis. In clinical practice, the NS1 rapid test kit is widely employed for early diagnosis. However, real-world studies revealed that a certain percentage of the patients with a negative NS1 test result, ranging from 5% to 39%, were actually infected by dengue. Since early diagnosis is critical for disease control in the early stage of a dengue epidemic, scientists have been working hard to tackle this challenge. Based on this observation, this study was launched to investigate the effects of incorporating machine learning based prediction models to further screen those patients with a negative NS1 test result. The experimental results revealed that the proposed approach was able to identify over 99% of the patients who were infected by the dengue disease. Furthermore, the risk of the suspected patients who were predicted to be positive was 4.8 times higher than the risk of those who were predicted to be negative. The experimental results illustrate that the proposed approach provides an effective and efficient diagnosis procedure to enhance surveillance of the dengue disease.","rel_num_authors":5,"rel_authors":[{"author_name":"Chun-Kai Hwang","author_inst":"National Taiwan University"},{"author_name":"Ying-Wen Chen","author_inst":"National Cheng Kung University Hospital"},{"author_name":"YU-TSENG WANG","author_inst":"National Taiwan University"},{"author_name":"Tzong-Shiann Ho","author_inst":"National Cheng Kung University Hospital"},{"author_name":"Yen-Jen Oyang","author_inst":"National Taiwan University"}],"rel_date":"2026-05-06","rel_site":"medrxiv"},{"rel_title":"Effect of ancestry and shared genetic architecture of serious mental illness on symptoms and cognition in an admixed Latin American population","rel_doi":"10.64898\/2026.05.05.26351986","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.05.26351986","rel_abs":"Most genome-wide association studies (GWAS) of serious mental illness (SMI) have been conducted for categorical diagnoses in samples of primarily European ancestry. The portability of findings to non-Europeans, and to SMI-related symptoms\/dimensional traits remains uncertain. In a sample of 8,666 SMI cases and controls from the Paisa region of Colombia we show that a primarily European schizophrenia GWAS polygenic risk score (PRS) predicted all SMI diagnoses in this sample, as well as symptoms (assessed in cases only) and traits assessed agnostic to SMI diagnosis: a one SD unit (SDU) increase in this PRS was associated to decreased risk in cases of suicidal thoughts (OR=0.89, 95% confidence interval 0.84-0.94), depressed mood (OR=0.90, 95% confidence interval 0.85-0.95), and increased risk of delusions (OR=1.12, 95% confidence interval 1.06-1.18) and to decreased cognition (in cases and controls) across five distinct domains (average decrease in cognition of 0.065 SDU, p<7e-05). We show that a published European GWAS of cognition predicted levels of executive function (average decrease in cognition of 0.06 SDU per unit increase in PRS, p<2e-04), but not diagnosis or symptoms. Specific loci identified in the SMI GWAS also showed association to multiple diagnoses, symptoms, and cognitive traits in Paisa. The most noteworthy result was for a locus on chromosome 7p22.3, associated in multiple SMI GWAS, that showed association in Paisa to increased risk of bipolar disorder, and to reduced complex cognition and social cognition. Our findings demonstrate wide portability from European GWAS to an admixed American sample, with associations to multiple transdiagnostic phenotypes.","rel_num_authors":22,"rel_authors":[{"author_name":"Esteban A Lopera Maya","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Susan K Service","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Ana M Diaz-Zuluaga","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Mauricio Castano Ramirez","author_inst":"Department of Mental Health and Human Behavior, University of Caldas, Manizales, Colombia"},{"author_name":"Juan Camilo Mejia","author_inst":"Research Group in Psychiatry GIPSI, Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Colombia"},{"author_name":"Johanna Valencia","author_inst":"Research Group in Psychiatry GIPSI, Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Colombia"},{"author_name":"Terri Teshiba","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Ana M Ramirez-Diaz","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Juan F De la Hoz","author_inst":"Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA"},{"author_name":"Jonathan Valdez","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Marfred Munoz Umanes","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Tyler M Moore","author_inst":"Department of Psychiatry, University of Pennsylvania School of Medicine; Philadelphia, USA"},{"author_name":"Sinead Chapman","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA"},{"author_name":"Benjamin M Neale","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA"},{"author_name":"Carrie E Bearden","author_inst":"Department of Psychology, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Javier I Escobar","author_inst":"Department of Psychiatry, Rutgers Robert Wood Johnson Medical School, New Brunswick, USA"},{"author_name":"Ruben C Gur","author_inst":"Department of Psychiatry, University of Pennsylvania School of Medicine; Philadelphia, USA"},{"author_name":"Victor I Reus","author_inst":"Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, USA"},{"author_name":"Chiara Sabatti","author_inst":"Departments of Biomedical Data Science and Statistics, Stanford University, Stanford, USA"},{"author_name":"Loes Olde Loohuis","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Carlos Lopez Jaramillo","author_inst":"Department of Psychiatry, University of Antioquia, Medellin, Colombia"},{"author_name":"Nelson B. Freimer","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"}],"rel_date":"2026-05-06","rel_site":"medrxiv"},{"rel_title":"Effect of ancestry and shared genetic architecture of serious mental illness on symptoms and cognition in an admixed Latin American population","rel_doi":"10.64898\/2026.05.05.26351986","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.05.26351986","rel_abs":"Most genome-wide association studies (GWAS) of serious mental illness (SMI) have been conducted for categorical diagnoses in samples of primarily European ancestry. The portability of findings to non-Europeans, and to SMI-related symptoms\/dimensional traits remains uncertain. In a sample of 8,666 SMI cases and controls from the Paisa region of Colombia we show that a primarily European schizophrenia GWAS polygenic risk score (PRS) predicted all SMI diagnoses in this sample, as well as symptoms (assessed in cases only) and traits assessed agnostic to SMI diagnosis: a one SD unit (SDU) increase in this PRS was associated to decreased risk in cases of suicidal thoughts (OR=0.89, 95% confidence interval 0.84-0.94), depressed mood (OR=0.90, 95% confidence interval 0.85-0.95), and increased risk of delusions (OR=1.12, 95% confidence interval 1.06-1.18) and to decreased cognition (in cases and controls) across five distinct domains (average decrease in cognition of 0.065 SDU, p<7e-05). We show that a published European GWAS of cognition predicted levels of executive function (average decrease in cognition of 0.06 SDU per unit increase in PRS, p<2e-04), but not diagnosis or symptoms. Specific loci identified in the SMI GWAS also showed association to multiple diagnoses, symptoms, and cognitive traits in Paisa. The most noteworthy result was for a locus on chromosome 7p22.3, associated in multiple SMI GWAS, that showed association in Paisa to increased risk of bipolar disorder, and to reduced complex cognition and social cognition. Our findings demonstrate wide portability from European GWAS to an admixed American sample, with associations to multiple transdiagnostic phenotypes.","rel_num_authors":22,"rel_authors":[{"author_name":"Esteban A Lopera Maya","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Susan K Service","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Ana M Diaz-Zuluaga","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Mauricio Castano Ramirez","author_inst":"Department of Mental Health and Human Behavior, University of Caldas, Manizales, Colombia"},{"author_name":"Juan Camilo Mejia","author_inst":"Research Group in Psychiatry GIPSI, Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Colombia"},{"author_name":"Johanna Valencia","author_inst":"Research Group in Psychiatry GIPSI, Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Colombia"},{"author_name":"Terri Teshiba","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Ana M Ramirez-Diaz","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Juan F De la Hoz","author_inst":"Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA"},{"author_name":"Jonathan Valdez","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Marfred Munoz Umanes","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Tyler M Moore","author_inst":"Department of Psychiatry, University of Pennsylvania School of Medicine; Philadelphia, USA"},{"author_name":"Sinead Chapman","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA"},{"author_name":"Benjamin M Neale","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA"},{"author_name":"Carrie E Bearden","author_inst":"Department of Psychology, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Javier I Escobar","author_inst":"Department of Psychiatry, Rutgers Robert Wood Johnson Medical School, New Brunswick, USA"},{"author_name":"Ruben C Gur","author_inst":"Department of Psychiatry, University of Pennsylvania School of Medicine; Philadelphia, USA"},{"author_name":"Victor I Reus","author_inst":"Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, USA"},{"author_name":"Chiara Sabatti","author_inst":"Departments of Biomedical Data Science and Statistics, Stanford University, Stanford, USA"},{"author_name":"Loes Olde Loohuis","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Carlos Lopez Jaramillo","author_inst":"Department of Psychiatry, University of Antioquia, Medellin, Colombia"},{"author_name":"Nelson B. Freimer","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"}],"rel_date":"2026-05-06","rel_site":"medrxiv"},{"rel_title":"Effect of ancestry and shared genetic architecture of serious mental illness on symptoms and cognition in an admixed Latin American population","rel_doi":"10.64898\/2026.05.05.26351986","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.05.26351986","rel_abs":"Most genome-wide association studies (GWAS) of serious mental illness (SMI) have been conducted for categorical diagnoses in samples of primarily European ancestry. The portability of findings to non-Europeans, and to SMI-related symptoms\/dimensional traits remains uncertain. In a sample of 8,666 SMI cases and controls from the Paisa region of Colombia we show that a primarily European schizophrenia GWAS polygenic risk score (PRS) predicted all SMI diagnoses in this sample, as well as symptoms (assessed in cases only) and traits assessed agnostic to SMI diagnosis: a one SD unit (SDU) increase in this PRS was associated to decreased risk in cases of suicidal thoughts (OR=0.89, 95% confidence interval 0.84-0.94), depressed mood (OR=0.90, 95% confidence interval 0.85-0.95), and increased risk of delusions (OR=1.12, 95% confidence interval 1.06-1.18) and to decreased cognition (in cases and controls) across five distinct domains (average decrease in cognition of 0.065 SDU, p<7e-05). We show that a published European GWAS of cognition predicted levels of executive function (average decrease in cognition of 0.06 SDU per unit increase in PRS, p<2e-04), but not diagnosis or symptoms. Specific loci identified in the SMI GWAS also showed association to multiple diagnoses, symptoms, and cognitive traits in Paisa. The most noteworthy result was for a locus on chromosome 7p22.3, associated in multiple SMI GWAS, that showed association in Paisa to increased risk of bipolar disorder, and to reduced complex cognition and social cognition. Our findings demonstrate wide portability from European GWAS to an admixed American sample, with associations to multiple transdiagnostic phenotypes.","rel_num_authors":22,"rel_authors":[{"author_name":"Esteban A Lopera Maya","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Susan K Service","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Ana M Diaz-Zuluaga","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Mauricio Castano Ramirez","author_inst":"Department of Mental Health and Human Behavior, University of Caldas, Manizales, Colombia"},{"author_name":"Juan Camilo Mejia","author_inst":"Research Group in Psychiatry GIPSI, Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Colombia"},{"author_name":"Johanna Valencia","author_inst":"Research Group in Psychiatry GIPSI, Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Colombia"},{"author_name":"Terri Teshiba","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Ana M Ramirez-Diaz","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Juan F De la Hoz","author_inst":"Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA"},{"author_name":"Jonathan Valdez","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Marfred Munoz Umanes","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Tyler M Moore","author_inst":"Department of Psychiatry, University of Pennsylvania School of Medicine; Philadelphia, USA"},{"author_name":"Sinead Chapman","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA"},{"author_name":"Benjamin M Neale","author_inst":"Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA"},{"author_name":"Carrie E Bearden","author_inst":"Department of Psychology, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Javier I Escobar","author_inst":"Department of Psychiatry, Rutgers Robert Wood Johnson Medical School, New Brunswick, USA"},{"author_name":"Ruben C Gur","author_inst":"Department of Psychiatry, University of Pennsylvania School of Medicine; Philadelphia, USA"},{"author_name":"Victor I Reus","author_inst":"Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, USA"},{"author_name":"Chiara Sabatti","author_inst":"Departments of Biomedical Data Science and Statistics, Stanford University, Stanford, USA"},{"author_name":"Loes Olde Loohuis","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"},{"author_name":"Carlos Lopez Jaramillo","author_inst":"Department of Psychiatry, University of Antioquia, Medellin, Colombia"},{"author_name":"Nelson B. Freimer","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA"}],"rel_date":"2026-05-06","rel_site":"medrxiv"},{"rel_title":"MAP3K7 novel variants in syndromic 46,XY DSD","rel_doi":"10.64898\/2026.05.05.26352427","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.05.26352427","rel_abs":"Mutations in MAP3K7 are responsible for two distinct syndromes Cardiospondylocarpofacial (CSCF) and Frontometaphyseal dysplasia 2 (FMD2). Both are characterized by skeletal malformations, facial dysmorphisms, hearing loss, and mild intellectual disability. While cardiac defects are predominant in CSCF, keloid scar is a distinct feature in FMD2. Problem with gonadal development and disorders of sexual development (DSD) have not been previously chracterized.\n\nHere we report three syndromic cases of 46,XY DSD with CSCF or FMD2, each carrying a novel heterozygous missense variants in MAP3K7 (NM_145331.3:c.250G>A; p.V84M, NM_145331.3:c.195A>G; p.I65M, and NM_145331.3: c.574A>G; p.S192G). The DSD phenotypes include cryptorchidism, micropenis, small testis, and hypospadias. In silico tools predict all three variants are deleterious. All three MAP3K7 variants occur in the kinase domain at highly conservative positions among mammals. MAP3K7 is highly expressed in human fetal Sertoli cells. MAP3K7 knock-out in HEK293T cells led to downregulation of GATA4 and FOG2 expression by RNA-Seq. Like MAP3K1, MAP3K7 phosphorylated p38 while all three MAP3K7 variants did not alter phosphorylated p38 compared to wildtype in HEK293TMAP3K7-\/- cells. Two MAP3K7 missense mutants (p.V84M and p.I65M) ectopically activate ovarian beta catenin\/ Wnt signalling in TOPFLASH assays. Our data suggest that MAP3K7 contributes to male sex differentiation by increasing expression of pro-testis genes GATA4 and FOG2 in HEK293TMAP3K7-\/- cells and antagonizing pro-ovarian beta-catenin signalling, and that one or more of these activities were likely affected in 3 cases of 46,XY DSD with CSCF\/FMD2 during sex development.","rel_num_authors":8,"rel_authors":[{"author_name":"Thanh Nha Uyen Le","author_inst":"Hudson Institute of Medical Research"},{"author_name":"Shirin M Moradifard","author_inst":"Monash University"},{"author_name":"Alejandra P Reyes","author_inst":"Genetics Department, Children Hospital of Mexico Federico Gomez, Mexico City, Mexico"},{"author_name":"Thi Bich Ngoc Can","author_inst":"Vietnam National Children Hospital, Hanoi, Vietnam"},{"author_name":"Adriana Tavares Gomes","author_inst":"Department of Pediatrics and Rady Children Hospital San Diego, University of California, San Diego, La Jolla, California, USA"},{"author_name":"Marilyn C. Jones","author_inst":"Department of Pediatrics and Rady Children Hospital San Diego, University of California, San Diego, La Jolla, California, USA"},{"author_name":"Dung Vu Chi","author_inst":"Vietnam National Children Hospital, Hanoi, Vietnam"},{"author_name":"Vincent Harley","author_inst":"Hudson Institute of Medical Research"}],"rel_date":"2026-05-06","rel_site":"medrxiv"},{"rel_title":"An APOE*4-Informed Genomic Atlas of the X Chromosome in Alzheimer's Disease","rel_doi":"10.64898\/2026.05.05.26352461","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.05.26352461","rel_abs":"The genetic contributions of the X chromosome to Alzheimers disease (AD) remain poorly understood yet are expected to importantly shape sex differences in AD. We therefore performed large-scale X-chromosome-wide association studies (N=1,240,451), evaluating differential risk due to sex, APOE*4, and escape from X-chromosome inactivation, finding most X-linked loci appear relevant to female-biased AD etiology. In evaluating genetic pleiotropy with hormonal, lipid, and brain imaging traits, we discovered X-linked AD loci converged on white matter traits, particularly in the anterior corona radiata and splenium of the corpus callosum. Through brain-centric functional genomics analyses, we then nominated candidate causal genes, including 5 that appeared highly robust. Notably, we found the escape gene RBBP7 decreases AD risk in APOE*4 carriers likely through higher expression in excitatory neurons to counter tau-related neurodegeneration. Altogether, we provide an atlas of sex and APOE*4-informed candidate X-linked AD risk loci, genes, and mechanisms that will guide future studies.","rel_num_authors":28,"rel_authors":[{"author_name":"Noah Cook","author_inst":"Washington University in St. Louis"},{"author_name":"Youjie Zeng","author_inst":"Washington University in St. Louis"},{"author_name":"Chenyu Yang","author_inst":"Washington University in St. Louis"},{"author_name":"Zhiwen Jiang","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Ting-Chen Wang","author_inst":"Vanderbilt University"},{"author_name":"Yann Le Guen","author_inst":"Stanford University"},{"author_name":"Karly Cody","author_inst":"Stanford University"},{"author_name":"Matthew Johnson","author_inst":"Washington University in St. Louis"},{"author_name":"Rui Zhang","author_inst":"VA Boston Healthcare System"},{"author_name":"Victoria C. Merritt","author_inst":"University of California San Diego"},{"author_name":"Richard L. Hauger","author_inst":"University of California San Diego"},{"author_name":"- The VA Million Veteran Program","author_inst":""},{"author_name":"- FinnGen","author_inst":""},{"author_name":"Mary Ellen Koran","author_inst":"Mayo Clinic"},{"author_name":"Elizabeth C. Mormino","author_inst":"Stanford University"},{"author_name":"Brian Gordon","author_inst":"Washington University in St. Louis"},{"author_name":"Alex DeCasien","author_inst":"National Institute on Aging"},{"author_name":"Shea J. Andrews","author_inst":"University of California San Francisco"},{"author_name":"Logan Dumitrescu","author_inst":"Vanderbilt University"},{"author_name":"Derek B Archer","author_inst":"Vanderbilt University"},{"author_name":"Timothy J. Hohman","author_inst":"Vanderbilt University"},{"author_name":"Cyril Pottier","author_inst":"Washington University in St. Louis"},{"author_name":"Carlos Cruchaga","author_inst":"Washington University St. Louis"},{"author_name":"Richard Sherva","author_inst":"Boston University School of Medicine"},{"author_name":"Mark Logue","author_inst":"Boston University School of Medicine"},{"author_name":"Valerio Napolioni","author_inst":"University of Camerino"},{"author_name":"Michael D. Greicius","author_inst":"Stanford University"},{"author_name":"Michael E. Belloy","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-05-06","rel_site":"medrxiv"},{"rel_title":"An APOE*4-Informed Genomic Atlas of the X Chromosome in Alzheimer's Disease","rel_doi":"10.64898\/2026.05.05.26352461","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.05.26352461","rel_abs":"The genetic contributions of the X chromosome to Alzheimers disease (AD) remain poorly understood yet are expected to importantly shape sex differences in AD. We therefore performed large-scale X-chromosome-wide association studies (N=1,240,451), evaluating differential risk due to sex, APOE*4, and escape from X-chromosome inactivation, finding most X-linked loci appear relevant to female-biased AD etiology. In evaluating genetic pleiotropy with hormonal, lipid, and brain imaging traits, we discovered X-linked AD loci converged on white matter traits, particularly in the anterior corona radiata and splenium of the corpus callosum. Through brain-centric functional genomics analyses, we then nominated candidate causal genes, including 5 that appeared highly robust. Notably, we found the escape gene RBBP7 decreases AD risk in APOE*4 carriers likely through higher expression in excitatory neurons to counter tau-related neurodegeneration. Altogether, we provide an atlas of sex and APOE*4-informed candidate X-linked AD risk loci, genes, and mechanisms that will guide future studies.","rel_num_authors":28,"rel_authors":[{"author_name":"Noah Cook","author_inst":"Washington University in St. Louis"},{"author_name":"Youjie Zeng","author_inst":"Washington University in St. Louis"},{"author_name":"Chenyu Yang","author_inst":"Washington University in St. Louis"},{"author_name":"Zhiwen Jiang","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Ting-Chen Wang","author_inst":"Vanderbilt University"},{"author_name":"Yann Le Guen","author_inst":"Stanford University"},{"author_name":"Karly Cody","author_inst":"Stanford University"},{"author_name":"Matthew Johnson","author_inst":"Washington University in St. Louis"},{"author_name":"Rui Zhang","author_inst":"VA Boston Healthcare System"},{"author_name":"Victoria C. Merritt","author_inst":"University of California San Diego"},{"author_name":"Richard L. Hauger","author_inst":"University of California San Diego"},{"author_name":"- The VA Million Veteran Program","author_inst":""},{"author_name":"- FinnGen","author_inst":""},{"author_name":"Mary Ellen Koran","author_inst":"Mayo Clinic"},{"author_name":"Elizabeth C. Mormino","author_inst":"Stanford University"},{"author_name":"Brian Gordon","author_inst":"Washington University in St. Louis"},{"author_name":"Alex DeCasien","author_inst":"National Institute on Aging"},{"author_name":"Shea J. Andrews","author_inst":"University of California San Francisco"},{"author_name":"Logan Dumitrescu","author_inst":"Vanderbilt University"},{"author_name":"Derek B Archer","author_inst":"Vanderbilt University"},{"author_name":"Timothy J. Hohman","author_inst":"Vanderbilt University"},{"author_name":"Cyril Pottier","author_inst":"Washington University in St. Louis"},{"author_name":"Carlos Cruchaga","author_inst":"Washington University St. Louis"},{"author_name":"Richard Sherva","author_inst":"Boston University School of Medicine"},{"author_name":"Mark Logue","author_inst":"Boston University School of Medicine"},{"author_name":"Valerio Napolioni","author_inst":"University of Camerino"},{"author_name":"Michael D. Greicius","author_inst":"Stanford University"},{"author_name":"Michael E. Belloy","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-05-06","rel_site":"medrxiv"},{"rel_title":"An APOE*4-Informed Genomic Atlas of the X Chromosome in Alzheimer's Disease","rel_doi":"10.64898\/2026.05.05.26352461","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.05.26352461","rel_abs":"The genetic contributions of the X chromosome to Alzheimers disease (AD) remain poorly understood yet are expected to importantly shape sex differences in AD. We therefore performed large-scale X-chromosome-wide association studies (N=1,240,451), evaluating differential risk due to sex, APOE*4, and escape from X-chromosome inactivation, finding most X-linked loci appear relevant to female-biased AD etiology. In evaluating genetic pleiotropy with hormonal, lipid, and brain imaging traits, we discovered X-linked AD loci converged on white matter traits, particularly in the anterior corona radiata and splenium of the corpus callosum. Through brain-centric functional genomics analyses, we then nominated candidate causal genes, including 5 that appeared highly robust. Notably, we found the escape gene RBBP7 decreases AD risk in APOE*4 carriers likely through higher expression in excitatory neurons to counter tau-related neurodegeneration. Altogether, we provide an atlas of sex and APOE*4-informed candidate X-linked AD risk loci, genes, and mechanisms that will guide future studies.","rel_num_authors":28,"rel_authors":[{"author_name":"Noah Cook","author_inst":"Washington University in St. Louis"},{"author_name":"Youjie Zeng","author_inst":"Washington University in St. Louis"},{"author_name":"Chenyu Yang","author_inst":"Washington University in St. Louis"},{"author_name":"Zhiwen Jiang","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Ting-Chen Wang","author_inst":"Vanderbilt University"},{"author_name":"Yann Le Guen","author_inst":"Stanford University"},{"author_name":"Karly Cody","author_inst":"Stanford University"},{"author_name":"Matthew Johnson","author_inst":"Washington University in St. Louis"},{"author_name":"Rui Zhang","author_inst":"VA Boston Healthcare System"},{"author_name":"Victoria C. Merritt","author_inst":"University of California San Diego"},{"author_name":"Richard L. Hauger","author_inst":"University of California San Diego"},{"author_name":"- The VA Million Veteran Program","author_inst":""},{"author_name":"- FinnGen","author_inst":""},{"author_name":"Mary Ellen Koran","author_inst":"Mayo Clinic"},{"author_name":"Elizabeth C. Mormino","author_inst":"Stanford University"},{"author_name":"Brian Gordon","author_inst":"Washington University in St. Louis"},{"author_name":"Alex DeCasien","author_inst":"National Institute on Aging"},{"author_name":"Shea J. Andrews","author_inst":"University of California San Francisco"},{"author_name":"Logan Dumitrescu","author_inst":"Vanderbilt University"},{"author_name":"Derek B Archer","author_inst":"Vanderbilt University"},{"author_name":"Timothy J. Hohman","author_inst":"Vanderbilt University"},{"author_name":"Cyril Pottier","author_inst":"Washington University in St. Louis"},{"author_name":"Carlos Cruchaga","author_inst":"Washington University St. Louis"},{"author_name":"Richard Sherva","author_inst":"Boston University School of Medicine"},{"author_name":"Mark Logue","author_inst":"Boston University School of Medicine"},{"author_name":"Valerio Napolioni","author_inst":"University of Camerino"},{"author_name":"Michael D. Greicius","author_inst":"Stanford University"},{"author_name":"Michael E. Belloy","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-05-06","rel_site":"medrxiv"},{"rel_title":"An APOE*4-Informed Genomic Atlas of the X Chromosome in Alzheimer's Disease","rel_doi":"10.64898\/2026.05.05.26352461","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.05.26352461","rel_abs":"The genetic contributions of the X chromosome to Alzheimers disease (AD) remain poorly understood yet are expected to importantly shape sex differences in AD. We therefore performed large-scale X-chromosome-wide association studies (N=1,240,451), evaluating differential risk due to sex, APOE*4, and escape from X-chromosome inactivation, finding most X-linked loci appear relevant to female-biased AD etiology. In evaluating genetic pleiotropy with hormonal, lipid, and brain imaging traits, we discovered X-linked AD loci converged on white matter traits, particularly in the anterior corona radiata and splenium of the corpus callosum. Through brain-centric functional genomics analyses, we then nominated candidate causal genes, including 5 that appeared highly robust. Notably, we found the escape gene RBBP7 decreases AD risk in APOE*4 carriers likely through higher expression in excitatory neurons to counter tau-related neurodegeneration. Altogether, we provide an atlas of sex and APOE*4-informed candidate X-linked AD risk loci, genes, and mechanisms that will guide future studies.","rel_num_authors":28,"rel_authors":[{"author_name":"Noah Cook","author_inst":"Washington University in St. Louis"},{"author_name":"Youjie Zeng","author_inst":"Washington University in St. Louis"},{"author_name":"Chenyu Yang","author_inst":"Washington University in St. Louis"},{"author_name":"Zhiwen Jiang","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Ting-Chen Wang","author_inst":"Vanderbilt University"},{"author_name":"Yann Le Guen","author_inst":"Stanford University"},{"author_name":"Karly Cody","author_inst":"Stanford University"},{"author_name":"Matthew Johnson","author_inst":"Washington University in St. Louis"},{"author_name":"Rui Zhang","author_inst":"VA Boston Healthcare System"},{"author_name":"Victoria C. Merritt","author_inst":"University of California San Diego"},{"author_name":"Richard L. Hauger","author_inst":"University of California San Diego"},{"author_name":"- The VA Million Veteran Program","author_inst":""},{"author_name":"- FinnGen","author_inst":""},{"author_name":"Mary Ellen Koran","author_inst":"Mayo Clinic"},{"author_name":"Elizabeth C. Mormino","author_inst":"Stanford University"},{"author_name":"Brian Gordon","author_inst":"Washington University in St. Louis"},{"author_name":"Alex DeCasien","author_inst":"National Institute on Aging"},{"author_name":"Shea J. Andrews","author_inst":"University of California San Francisco"},{"author_name":"Logan Dumitrescu","author_inst":"Vanderbilt University"},{"author_name":"Derek B Archer","author_inst":"Vanderbilt University"},{"author_name":"Timothy J. Hohman","author_inst":"Vanderbilt University"},{"author_name":"Cyril Pottier","author_inst":"Washington University in St. Louis"},{"author_name":"Carlos Cruchaga","author_inst":"Washington University St. Louis"},{"author_name":"Richard Sherva","author_inst":"Boston University School of Medicine"},{"author_name":"Mark Logue","author_inst":"Boston University School of Medicine"},{"author_name":"Valerio Napolioni","author_inst":"University of Camerino"},{"author_name":"Michael D. Greicius","author_inst":"Stanford University"},{"author_name":"Michael E. Belloy","author_inst":"Washington University in St. Louis"}],"rel_date":"2026-05-06","rel_site":"medrxiv"},{"rel_title":"Impact of a Social Media Campaign on HIV-Related Stigma among Young Adults Living with HIV in Lima, Peru: A Sequential Explanatory Mixed Methods Study","rel_doi":"10.64898\/2026.05.04.26352384","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.04.26352384","rel_abs":"BackgroundStigma remains a pervasive barrier to curbing the spread of human immunodeficiency virus (HIV) among adolescents and young adults in Lima, Peru. Social media offers a promising avenue for scalable, youth-centered stigma reduction, but few interventions have been rigorously evaluated in this context.\n\nObjectiveWe evaluated the potential of a social media campaign to reduce perceived HIV-related stigma among young adults living with HIV. This involved a sequential explanatory mixed-methods study, including a randomized evaluation, followed by focus groups to understand the findings.\n\nMethods150 young adults (aged 18-29 years) living with HIV (YLWH) were randomized to receive information on social media from one of the following: (1) the control account; (2) the control account and the social media campaign accounts (Instagram and TikTok); or (3) the control account, the campaign accounts, and the accounts of participating influencers. Perceived stigma was measured via pre- and post-campaign surveys using Spanish versions of the abridged Berger HIV Stigma Scale and the Stigma Stress Scale. Focus groups and interviews were conducted with a purposive sample of participants to contextualize quantitative results. Qualitative data were analyzed using Framework Analysis.\n\nResultsMean changes in HIV Stigma and Stigma Stress scores were small and not statistically significant. Post-hoc as-treated analyses supported these findings. Fidelity to intervention allocation was low to moderate, depending on the metric considered. Qualitative data suggested that the campaign positively impacted participants perceived stigma and that personal circumstances, crossover, frequency of exposure to content, and issues related to completing study questionnaires contributed to the lack of meaningful change in stigma scores.\n\nConclusionsWhile quantitative data did not support that exposure to a social media campaign led to meaningful reductions in HIV-related stigma, qualitative data suggested that the campaign had a positive impact and that limitations in the study design, together with external factors, may have obscured benefits in quantitative analyses.","rel_num_authors":12,"rel_authors":[{"author_name":"Samara Ruberg","author_inst":"Boston University School of Public Health"},{"author_name":"Alyson Nunez","author_inst":"Harvard Medical School"},{"author_name":"Milagros Wong","author_inst":"Socios En Salud Peru"},{"author_name":"Marguerite Curtis","author_inst":"Harvard Medical School"},{"author_name":"Yihan Shi","author_inst":"Harvard Medical School"},{"author_name":"Hugo Sanchez","author_inst":"Epicentro Peru"},{"author_name":"Eduardo Matos","author_inst":"Hospital Nacional Arzobispo Loayza"},{"author_name":"Frine Samalvides","author_inst":"Universidad Peruana Cayetano Heredia"},{"author_name":"Kristin Kosyluk","author_inst":"University of South Florida"},{"author_name":"Jerome T Galea","author_inst":"University of South Florida"},{"author_name":"Renato Errea","author_inst":"Socios En Salud Peru"},{"author_name":"Molly  F Franke","author_inst":"Harvard Medical School"}],"rel_date":"2026-05-06","rel_site":"medrxiv"},{"rel_title":"Choice of estimands and estimators affected the interpretation of results for some outcomes in a cluster-randomised trial (RESTORE) due to informative cluster size","rel_doi":"10.64898\/2026.05.05.26352371","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.05.26352371","rel_abs":"Background and objectiveIn cluster-randomised trials (CRTs), different estimands can be targeted, such as the individual-or cluster-average effect. These two estimands can differ in magnitude when outcomes or treatment effects vary with cluster size (termed informative cluster size). When informative cluster size is present, commonly used estimators for CRTs, such as mixed-effects model and generalised estimating equations with an exchangeable correlation structure (termed GEEs(exch)), can be biased for both these estimands. With little documented evaluation of When informative cluster size, it is currently unknown how commonly it occurs in practice. The aim of this work was to explore whether informative cluster size is present in a published CRT and to investigate its impact on trial results.\n\nMethodsWe re-analysed the RESTORE CRT, which compared protocolised sedation with usual care for critically ill children. For each outcome, we first modelled the association between cluster size and outcome\/treatment effect; next, we assessed the impact of informative cluster size by comparing differences between (i) individual-vs. cluster-average estimates and (ii) estimates from mixed-effects models and GEEs(exch) (which can be affected by informative cluster size) to those from IEEs (which are robust to informative cluster size).\n\nResultsWe found evidence of an association between cluster size and either outcomes or treatment effects for 16\/33 outcomes (48%). This led to statistically significant differences between the individual- and cluster-average treatment effects for 5 of 33 outcomes (15%). There were >10% differences between (i) individual- and cluster-average treatment effect estimates for 17 outcomes (52%) and (ii) estimates from mixed-effects models\/GEEs(exch) and estimates from unweighted IEEs for 13 outcomes (39%). For some outcomes, differences in the choice of estimator or estimand led to differences in the interpretation of results. For example, for the outcome postextubation stridor, the individual-average estimate showed a significant harmful effect (OR=1.65, 95% CI 1.02 to 2.67), unlike the cluster-average (OR=1.38, 95% CI 0.87 to 2.19) or GEEs(exch) estimate (OR=1.57, 95% CI 0.98, 2.50).\n\nDiscussioninformative cluster size can occur in CRTs, and the use of estimators that are not clearly aligned to the target estimand can affect the interpretation of some results.\n\nWhat is new?O_ST_ABSKey findingsC_ST_ABSO_LIThis re-analysis of the RESTORE cluster randomised trial found that choice of estimand and estimator could affect the interpretation of results for some outcomes\nC_LI\n\nWhat this adds to what is knownO_LIThis work provides empirical evidence that informative cluster size can occur in cluster randomised trials, and can affect results based on the choice of estimand or estimator\nC_LI\n\nWhat is the implication and what should we change nowO_LITrialists should clearly define their target estimand and choose an estimator that is aligned to that estimand\nC_LIO_LICareful consideration of the plausibility of assumptions underpinning each estimator, including the likelihood of informative cluster size, can help ensure appropriate analysis methods are used\nC_LIO_LIWhen mixed-effects models or GEEs with an exchangeable correlation structure are used, sensitivity analyses using independence estimating equations or other appropriate methods should be used to evaluate the robustness of results to informative cluster size\nC_LI","rel_num_authors":5,"rel_authors":[{"author_name":"Dongquan Bi","author_inst":"UCL Innovative Clinical Trials Unit, London, UK"},{"author_name":"Andrew Copas","author_inst":"Medical Research Council Centre of Research Excellence in Clinical Trial Innovation (CCTI), London UK"},{"author_name":"Fan Li","author_inst":"Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA"},{"author_name":"Michael O Harhay","author_inst":"Department of Biostatistics, Epidemiology and Informatics and Palliative and Advanced Illness Research Center, Department of Medicine, University of Pennsylvani"},{"author_name":"Brennan C Kahan","author_inst":"Medical Research Council Centre of Research Excellence in Clinical Trial Innovation (CCTI), London UK"}],"rel_date":"2026-05-06","rel_site":"medrxiv"},{"rel_title":"Heterogeneous Treatment Effect for Targeted Temperature Management After Cardiac Arrest: A Causal Machine Learning Analysis","rel_doi":"10.64898\/2026.05.04.26352388","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.04.26352388","rel_abs":"ObjectivesTo determine whether heterogeneous treatment effects (HTE) explain the inconclusive results of targeted temperature management (TTM) trials after cardiac arrest, using causal machine learning across four datasets.\n\nDesignSecondary analysis of one multicenter RCT and three observational ICU cohorts using S-learner and forest-based R-learner models to estimate conditional average treatment effects (CATE).\n\nSettingTwenty-six French ICUs (HYPERION), approximately 200 U.S. ICUs (eICU-CRD), Johns Hopkins Hospital (PMAP), and Beth Israel Deaconess Medical Center (MIMIC-IV).\n\nPatientsAdults ([&ge;]18 years) with cardiac arrest; 4,507 patients across the four datasets, of whom 1,814 (40.2%) received TTM.\n\nInterventionsTTM as administered clinically or per HYPERION protocol. Ascertainment: randomization (HYPERION), treatment documentation (eICU-CRD), sustained hypothermia <36{degrees}C for >12 hours (PMAP), or documented cooling device use [&ge;]12 hours (MIMIC-IV).\n\nMeasurements and Main ResultsThe primary outcome was hospital mortality; the secondary outcome was favorable neurologic function (Cerebral Performance Category 1-2 at 90 days for HYPERION; last motor Glasgow Coma Scale = 6 for observational cohorts). Three S-learner models (XGBoost, neural network, Bayesian Additive Regression Trees) and one forest-based R-learner (CausalForestDML) estimated CATE. HTE was assessed by likelihood-ratio tests for CATExtreatment interaction, CausalForestDML 95% confidence intervals, Group Average Treatment Effects (GATES) across CATE quintiles, and SHAP feature importance. S-learner discrimination was adequate (AUROC 0.72-0.82). No model showed a significant CATExTTM interaction in any dataset (all p > 0.05). Individual CATE confidence intervals uniformly crossed zero, and GATES showed no monotonic gradient of benefit across quintiles in any dataset.\n\nConclusionsAcross four diverse datasets and multiple causal machine-learning approaches, we found no evidence of heterogeneous treatment effects for TTM after cardiac arrest. The inconclusive findings of TTM trials are unlikely explained by differential effects in identifiable subgroups defined by routinely available clinical features.\n\nKEY POINTSQuestion: Do identifiable patient subgroups derive differential benefit from targeted temperature management (TTM) after cardiac arrest?\n\nFindings: In a causal machine-learning analysis of 4,507 patients across one randomized trial and three observational ICU cohorts, no model detected significant heterogeneous TTM effects on mortality or neurologic outcome.\n\nMeaning: Conflicting TTM trial results are unlikely explained by differential effects in identifiable subgroups, weakening the rationale for personalized TTM strategies based on routinely available clinical features.","rel_num_authors":6,"rel_authors":[{"author_name":"Michel Brandao Raskin","author_inst":"Johns Hopkins University"},{"author_name":"Isalis Karhu-Leperd","author_inst":"Federal Institute of Technology Lausanne (EPFL)"},{"author_name":"Carl W Harris","author_inst":"Johns Hopkins University"},{"author_name":"Romain Pirrachio","author_inst":"University of California San Francisco"},{"author_name":"Jean Baptiste Lascarrou","author_inst":"CHU Nantes"},{"author_name":"Robert D Stevens","author_inst":"Johns Hopkins University"}],"rel_date":"2026-05-06","rel_site":"medrxiv"},{"rel_title":"Heterogeneous Treatment Effect for Targeted Temperature Management After Cardiac Arrest: A Causal Machine Learning Analysis","rel_doi":"10.64898\/2026.05.04.26352388","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.05.04.26352388","rel_abs":"ObjectivesTo determine whether heterogeneous treatment effects (HTE) explain the inconclusive results of targeted temperature management (TTM) trials after cardiac arrest, using causal machine learning across four datasets.\n\nDesignSecondary analysis of one multicenter RCT and three observational ICU cohorts using S-learner and forest-based R-learner models to estimate conditional average treatment effects (CATE).\n\nSettingTwenty-six French ICUs (HYPERION), approximately 200 U.S. ICUs (eICU-CRD), Johns Hopkins Hospital (PMAP), and Beth Israel Deaconess Medical Center (MIMIC-IV).\n\nPatientsAdults ([&ge;]18 years) with cardiac arrest; 4,507 patients across the four datasets, of whom 1,814 (40.2%) received TTM.\n\nInterventionsTTM as administered clinically or per HYPERION protocol. Ascertainment: randomization (HYPERION), treatment documentation (eICU-CRD), sustained hypothermia <36{degrees}C for >12 hours (PMAP), or documented cooling device use [&ge;]12 hours (MIMIC-IV).\n\nMeasurements and Main ResultsThe primary outcome was hospital mortality; the secondary outcome was favorable neurologic function (Cerebral Performance Category 1-2 at 90 days for HYPERION; last motor Glasgow Coma Scale = 6 for observational cohorts). Three S-learner models (XGBoost, neural network, Bayesian Additive Regression Trees) and one forest-based R-learner (CausalForestDML) estimated CATE. HTE was assessed by likelihood-ratio tests for CATExtreatment interaction, CausalForestDML 95% confidence intervals, Group Average Treatment Effects (GATES) across CATE quintiles, and SHAP feature importance. S-learner discrimination was adequate (AUROC 0.72-0.82). No model showed a significant CATExTTM interaction in any dataset (all p > 0.05). Individual CATE confidence intervals uniformly crossed zero, and GATES showed no monotonic gradient of benefit across quintiles in any dataset.\n\nConclusionsAcross four diverse datasets and multiple causal machine-learning approaches, we found no evidence of heterogeneous treatment effects for TTM after cardiac arrest. The inconclusive findings of TTM trials are unlikely explained by differential effects in identifiable subgroups defined by routinely available clinical features.\n\nKEY POINTSQuestion: Do identifiable patient subgroups derive differential benefit from targeted temperature management (TTM) after cardiac arrest?\n\nFindings: In a causal machine-learning analysis of 4,507 patients across one randomized trial and three observational ICU cohorts, no model detected significant heterogeneous TTM effects on mortality or neurologic outcome.\n\nMeaning: Conflicting TTM trial results are unlikely explained by differential effects in identifiable subgroups, weakening the rationale for personalized TTM strategies based on routinely available clinical features.","rel_num_authors":6,"rel_authors":[{"author_name":"Michel Brandao Raskin","author_inst":"Johns Hopkins University"},{"author_name":"Isalis Karhu-Leperd","author_inst":"Federal Institute of Technology Lausanne (EPFL)"},{"author_name":"Carl W Harris","author_inst":"Johns Hopkins University"},{"author_name":"Romain Pirrachio","author_inst":"University of California San Francisco"},{"author_name":"Jean Baptiste Lascarrou","author_inst":"CHU Nantes"},{"author_name":"Robert D Stevens","author_inst":"Johns Hopkins University"}],"rel_date":"2026-05-06","rel_site":"medrxiv"}]}