{"gname":"University of Geneva","grp_id":"26","rels":[{"rel_title":"Estimating Hepatitis C Virus Prevalence in US States and the District of Columbia, 2017-2020","rel_doi":"10.64898\/2026.09.29.26364309","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.29.26364309","rel_abs":"Background State-level estimates of hepatitis C virus (HCV) prevalence are needed to guide resource allocation and to provide a baseline against which to measure progress toward elimination goals. Population prevalence is not directly observable, requiring estimation from indirect data sources. Methods We adapted a Bayesian spatial integrated abundance model to estimate state-level HCV prevalence during 2017-2020 across 48 states and the District of Columbia. The model integrated six HCV-related outcomes: acute and chronic surveillance cases, HCV-related deaths, observations of HCV in [MarketScan] administrative claims data, diagnoses of HCV in Medicaid recipients, and treatment with direct-acting antivirals in Medicaid recipients. Estimates were anchored to a national prevalence estimate, and the model accounted for data source-specific selection, heterogeneity in HCV risk factors across states, and geospatial correlation. Results Estimated average prevalence was 1.32% (95% credible interval [CrI]: 0.94%-1.80%), corresponding to 3.33 million (95% CrI: 2.37-4.65 million) adults with HCV infection across 48 states and DC. State estimates ranged from 0.74% in North Dakota to 2.22% in Oklahoma (median state-specific prevalence, 1.27%), with higher prevalence concentrated in South Central states, Appalachia, and the West, and the lower prevalence in the upper Midwest, Southeast, and New England. Ten states accounted for 56% of estimated infections. Estimates were stable in sensitivity analyses (most differences <1 percentage point). Conclusions These estimates quantify state-level HCV burden prior to the 2021 federal expansion of HCV surveillance funding, providing a baseline for monitoring elimination progress. Geographic variation indicates that resource needs will differ by jurisdiction.","rel_num_authors":6,"rel_authors":[{"author_name":"Heather Bradley","author_inst":"Emory University"},{"author_name":"Ya-Hui Yu","author_inst":"Emory University"},{"author_name":"Lanxin Li","author_inst":"University of Edinburgh"},{"author_name":"Shashi N Kapadia","author_inst":"Weill Cornell Medicine"},{"author_name":"Patrick S Sullivan","author_inst":"Emory University"},{"author_name":"Eric William Hall","author_inst":"Oregon Health & Science University"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Measuring enteric pathogen force of infection through antibody responses in children","rel_doi":"10.64898\/2026.09.29.26364344","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.29.26364344","rel_abs":"Enteric pathogens account for a substantial global disease burden, yet population-based surveillance remains limited by the transient nature of pathogen shedding, which constrains the sensitivity of stool-based molecular testing to a narrow detection window. Detection of IgG responses in serological surveys could enable new insights into enteric pathogen transmission, but benchmarking serological measures relative to stool-based measures of infection remains a critical evidence gap. We compared measures of disease transmission in longitudinal birth cohort samples using multiplex IgG (1,601 dried blood spots, 370 children) and PCR assays (2,231 stool samples, 370 children) that overlapped for nine pathogens (norovirus GI, GII, Shigella\/enteroinvasive Escherichia coli (EIEC), Campylobacter spp., enterotoxigenic Escherichia coli (LT-ETEC), Salmonella enterica, Giardia spp., Cryptosporidium spp., Entamoeba histolytica). LT-ETEC and Campylobacter ranked highest while E. histolytica and S. enterica ranked lowest by both IgG and PCR measures of infection. Measures of infection were less aligned for norovirus GI, norovirus GII, Cryptosporidium, Shigella\/EIEC, and Giardia. Despite this, for most pathogens, and across both measures, force of infection was lowest in the urban city of Esmeraldas and substantially higher in more rural populations, with relative risks in assay measures (seroconversion rate and PCR detected prevalence) over 2.0 for pathogens with the largest differences (Shigella\/EIEC and norovirus GI). Together, our results suggest that although absolute levels of infection differed between assays, IgG and PCR captured consistent relative risk of infection across geographic strata, suggesting either method can identify high- versus low-transmission settings.","rel_num_authors":25,"rel_authors":[{"author_name":"Nikolina Walas","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"},{"author_name":"Lesly Simba\u00f1a Vivanco","author_inst":"Institute of Microbiology, Colegio de Ciencias Biol\u00f3gicas y Ambientales, Universidad San Francisco de Quito, Quito, Ecuador"},{"author_name":"Stuart Torres Ayala","author_inst":"Institute of Microbiology, Colegio de Ciencias Biol\u00f3gicas y Ambientales, Universidad San Francisco de Quito, Quito, Ecuador"},{"author_name":"Chabier Coleman","author_inst":"Independent consultant"},{"author_name":"E. Brook Goodhew","author_inst":"Independent consultant"},{"author_name":"Kelsey J. Jesser","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Nicolette A. Zhou","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Christine S. Fagnant-Sperati","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Jesse Contreras","author_inst":"Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Caitlin Hemlock","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Jeffrey W. Priest","author_inst":"Retired"},{"author_name":"Richelle C. Charles","author_inst":"Massachusetts General Hospital, Harvard Medical School, Harvard T.H. Chan School of Public Health, Boston, MA, USA"},{"author_name":"Edward T. Ryan","author_inst":"Massachusetts General Hospital, Harvard Medical School, Harvard T.H. Chan School of Public Health, Boston, MA, USA"},{"author_name":"Claire Munroe","author_inst":"Massachusetts General Hospital, Harvard Medical School, Harvard T.H. Chan School of Public Health, Boston, MA, USA"},{"author_name":"Robert L. Atmar","author_inst":"Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Julianna Colado","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"},{"author_name":"Hadley Burroughs","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"},{"author_name":"Manuel Calvopi\u00f1a","author_inst":"Universidad de las Am\u00e9ricas, Facultad de Medicina, Carrera de Medicina, Quito, Ecuador"},{"author_name":"William Cevallos","author_inst":"Universidad Central del Ecuador, Instituto de Biomedicina, Quito, Ecuador"},{"author_name":"Josefina Coloma","author_inst":"School of Public Health, University of California, Berkeley, CA, USA"},{"author_name":"Gwenyth O. Lee","author_inst":"Rutgers Global Health Institute and Department of Biostatistics and Epidemiology, School of Public Health, Rutgers University, New Brunswick, NJ, USA"},{"author_name":"Gabriel Trueba","author_inst":"Institute of Microbiology, Colegio de Ciencias Biol\u00f3gicas y Ambientales, Universidad San Francisco de Quito, Quito, Ecuador"},{"author_name":"Joseph N.S. Eisenberg","author_inst":"Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Karen Levy","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Benjamin F. Arnold","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Measuring enteric pathogen force of infection through antibody responses in children","rel_doi":"10.64898\/2026.09.29.26364344","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.29.26364344","rel_abs":"Enteric pathogens account for a substantial global disease burden, yet population-based surveillance remains limited by the transient nature of pathogen shedding, which constrains the sensitivity of stool-based molecular testing to a narrow detection window. Detection of IgG responses in serological surveys could enable new insights into enteric pathogen transmission, but benchmarking serological measures relative to stool-based measures of infection remains a critical evidence gap. We compared measures of disease transmission in longitudinal birth cohort samples using multiplex IgG (1,601 dried blood spots, 370 children) and PCR assays (2,231 stool samples, 370 children) that overlapped for nine pathogens (norovirus GI, GII, Shigella\/enteroinvasive Escherichia coli (EIEC), Campylobacter spp., enterotoxigenic Escherichia coli (LT-ETEC), Salmonella enterica, Giardia spp., Cryptosporidium spp., Entamoeba histolytica). LT-ETEC and Campylobacter ranked highest while E. histolytica and S. enterica ranked lowest by both IgG and PCR measures of infection. Measures of infection were less aligned for norovirus GI, norovirus GII, Cryptosporidium, Shigella\/EIEC, and Giardia. Despite this, for most pathogens, and across both measures, force of infection was lowest in the urban city of Esmeraldas and substantially higher in more rural populations, with relative risks in assay measures (seroconversion rate and PCR detected prevalence) over 2.0 for pathogens with the largest differences (Shigella\/EIEC and norovirus GI). Together, our results suggest that although absolute levels of infection differed between assays, IgG and PCR captured consistent relative risk of infection across geographic strata, suggesting either method can identify high- versus low-transmission settings.","rel_num_authors":25,"rel_authors":[{"author_name":"Nikolina Walas","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"},{"author_name":"Lesly Simba\u00f1a Vivanco","author_inst":"Institute of Microbiology, Colegio de Ciencias Biol\u00f3gicas y Ambientales, Universidad San Francisco de Quito, Quito, Ecuador"},{"author_name":"Stuart Torres Ayala","author_inst":"Institute of Microbiology, Colegio de Ciencias Biol\u00f3gicas y Ambientales, Universidad San Francisco de Quito, Quito, Ecuador"},{"author_name":"Chabier Coleman","author_inst":"Independent consultant"},{"author_name":"E. Brook Goodhew","author_inst":"Independent consultant"},{"author_name":"Kelsey J. Jesser","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Nicolette A. Zhou","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Christine S. Fagnant-Sperati","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Jesse Contreras","author_inst":"Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Caitlin Hemlock","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Jeffrey W. Priest","author_inst":"Retired"},{"author_name":"Richelle C. Charles","author_inst":"Massachusetts General Hospital, Harvard Medical School, Harvard T.H. Chan School of Public Health, Boston, MA, USA"},{"author_name":"Edward T. Ryan","author_inst":"Massachusetts General Hospital, Harvard Medical School, Harvard T.H. Chan School of Public Health, Boston, MA, USA"},{"author_name":"Claire Munroe","author_inst":"Massachusetts General Hospital, Harvard Medical School, Harvard T.H. Chan School of Public Health, Boston, MA, USA"},{"author_name":"Robert L. Atmar","author_inst":"Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Julianna Colado","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"},{"author_name":"Hadley Burroughs","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"},{"author_name":"Manuel Calvopi\u00f1a","author_inst":"Universidad de las Am\u00e9ricas, Facultad de Medicina, Carrera de Medicina, Quito, Ecuador"},{"author_name":"William Cevallos","author_inst":"Universidad Central del Ecuador, Instituto de Biomedicina, Quito, Ecuador"},{"author_name":"Josefina Coloma","author_inst":"School of Public Health, University of California, Berkeley, CA, USA"},{"author_name":"Gwenyth O. Lee","author_inst":"Rutgers Global Health Institute and Department of Biostatistics and Epidemiology, School of Public Health, Rutgers University, New Brunswick, NJ, USA"},{"author_name":"Gabriel Trueba","author_inst":"Institute of Microbiology, Colegio de Ciencias Biol\u00f3gicas y Ambientales, Universidad San Francisco de Quito, Quito, Ecuador"},{"author_name":"Joseph N.S. Eisenberg","author_inst":"Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Karen Levy","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Benjamin F. Arnold","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Measuring enteric pathogen force of infection through antibody responses in children","rel_doi":"10.64898\/2026.09.29.26364344","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.29.26364344","rel_abs":"Enteric pathogens account for a substantial global disease burden, yet population-based surveillance remains limited by the transient nature of pathogen shedding, which constrains the sensitivity of stool-based molecular testing to a narrow detection window. Detection of IgG responses in serological surveys could enable new insights into enteric pathogen transmission, but benchmarking serological measures relative to stool-based measures of infection remains a critical evidence gap. We compared measures of disease transmission in longitudinal birth cohort samples using multiplex IgG (1,601 dried blood spots, 370 children) and PCR assays (2,231 stool samples, 370 children) that overlapped for nine pathogens (norovirus GI, GII, Shigella\/enteroinvasive Escherichia coli (EIEC), Campylobacter spp., enterotoxigenic Escherichia coli (LT-ETEC), Salmonella enterica, Giardia spp., Cryptosporidium spp., Entamoeba histolytica). LT-ETEC and Campylobacter ranked highest while E. histolytica and S. enterica ranked lowest by both IgG and PCR measures of infection. Measures of infection were less aligned for norovirus GI, norovirus GII, Cryptosporidium, Shigella\/EIEC, and Giardia. Despite this, for most pathogens, and across both measures, force of infection was lowest in the urban city of Esmeraldas and substantially higher in more rural populations, with relative risks in assay measures (seroconversion rate and PCR detected prevalence) over 2.0 for pathogens with the largest differences (Shigella\/EIEC and norovirus GI). Together, our results suggest that although absolute levels of infection differed between assays, IgG and PCR captured consistent relative risk of infection across geographic strata, suggesting either method can identify high- versus low-transmission settings.","rel_num_authors":25,"rel_authors":[{"author_name":"Nikolina Walas","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"},{"author_name":"Lesly Simba\u00f1a Vivanco","author_inst":"Institute of Microbiology, Colegio de Ciencias Biol\u00f3gicas y Ambientales, Universidad San Francisco de Quito, Quito, Ecuador"},{"author_name":"Stuart Torres Ayala","author_inst":"Institute of Microbiology, Colegio de Ciencias Biol\u00f3gicas y Ambientales, Universidad San Francisco de Quito, Quito, Ecuador"},{"author_name":"Chabier Coleman","author_inst":"Independent consultant"},{"author_name":"E. Brook Goodhew","author_inst":"Independent consultant"},{"author_name":"Kelsey J. Jesser","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Nicolette A. Zhou","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Christine S. Fagnant-Sperati","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Jesse Contreras","author_inst":"Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Caitlin Hemlock","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Jeffrey W. Priest","author_inst":"Retired"},{"author_name":"Richelle C. Charles","author_inst":"Massachusetts General Hospital, Harvard Medical School, Harvard T.H. Chan School of Public Health, Boston, MA, USA"},{"author_name":"Edward T. Ryan","author_inst":"Massachusetts General Hospital, Harvard Medical School, Harvard T.H. Chan School of Public Health, Boston, MA, USA"},{"author_name":"Claire Munroe","author_inst":"Massachusetts General Hospital, Harvard Medical School, Harvard T.H. Chan School of Public Health, Boston, MA, USA"},{"author_name":"Robert L. Atmar","author_inst":"Baylor College of Medicine, Houston, TX, USA"},{"author_name":"Julianna Colado","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"},{"author_name":"Hadley Burroughs","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"},{"author_name":"Manuel Calvopi\u00f1a","author_inst":"Universidad de las Am\u00e9ricas, Facultad de Medicina, Carrera de Medicina, Quito, Ecuador"},{"author_name":"William Cevallos","author_inst":"Universidad Central del Ecuador, Instituto de Biomedicina, Quito, Ecuador"},{"author_name":"Josefina Coloma","author_inst":"School of Public Health, University of California, Berkeley, CA, USA"},{"author_name":"Gwenyth O. Lee","author_inst":"Rutgers Global Health Institute and Department of Biostatistics and Epidemiology, School of Public Health, Rutgers University, New Brunswick, NJ, USA"},{"author_name":"Gabriel Trueba","author_inst":"Institute of Microbiology, Colegio de Ciencias Biol\u00f3gicas y Ambientales, Universidad San Francisco de Quito, Quito, Ecuador"},{"author_name":"Joseph N.S. Eisenberg","author_inst":"Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA"},{"author_name":"Karen Levy","author_inst":"Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA"},{"author_name":"Benjamin F. Arnold","author_inst":"Francis I. Proctor Foundation, University of California, San Francisco, CA, USA"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Escaping the Negative Attentional Bias in Depression with Real-Time Neurofeedback","rel_doi":"10.64898\/2026.09.30.26363896","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26363896","rel_abs":"Individuals with major depressive disorder (MDD) show an attentional bias toward negatively valenced stimuli and thoughts. In this study, we applied a closed-loop neurofeedback procedure designed to reduce this bias. Participants were shown composite negative faces and neutral scenes with variable opacity and were instructed to attend to the neutral scene while ignoring the negative face. Internal attentional states were decoded in real time from functional magnetic resonance imaging (fMRI) data. When a participant's decoded attentional state indicated a failure to ignore the negative faces, the faces became more visible (higher opacity), thus externalizing the brain's attentional lapse in that moment. Forty-eight individuals with MDD were randomly assigned to a real neurofeedback training group (N = 24) or a sham control group (N = 24); the control group received feedback yoked to a participant in the real group. All participants completed three fMRI neurofeedback sessions. The main outcome quantified the extent to which participants got ``stuck'' in the most negative attentional state of focusing strongly on the negative faces. We hypothesized that the real neurofeedback group would learn to escape that state by the end of training, relative to the sham control group. Consistent with our hypothesis, neurofeedback training reduced the probability of getting stuck in the most negative attentional state. In offline analyses, training reduced fMRI activity in the precuneus\/posterior cingulate and the medial prefrontal cortex when participants successfully attended to scenes and ignored faces. These results demonstrate the efficacy of remediating the negative attentional bias in depression with closed-loop neurofeedback from real-time fMRI.","rel_num_authors":9,"rel_authors":[{"author_name":"Nitzan Lubianiker","author_inst":"Department of Psychology, Yale University, New Haven, CT, USA"},{"author_name":"Brendan Woods","author_inst":"Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"},{"author_name":"Frederick Nitchie","author_inst":"Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"},{"author_name":"Alexandra Batzdorf","author_inst":"Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"},{"author_name":"Anne Mennen","author_inst":"Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA"},{"author_name":"Qi Lin","author_inst":"Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea"},{"author_name":"Kenneth A. Norman","author_inst":"Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA"},{"author_name":"Nicholas B. Turk-Browne","author_inst":"Department of Psychology, Yale University, New Haven, CT, USA"},{"author_name":"Yvette I. Sheline","author_inst":"Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Metabolic Signatures of Resistance to Mycobacterium Tuberculosis Infection: Insights from a Multi-Country Plasma Metabolomics Study","rel_doi":"10.64898\/2026.09.29.26364337","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.29.26364337","rel_abs":"Background: Tuberculosis (TB) remains the leading infectious disease cause of mortality worldwide. A subset of individuals exposed to Mycobacterium tuberculosis (Mtb) remain TST\/IGRA-negative despite sustained exposure, termed \"TB resisters\". The biological mechanisms underlying this resistance remain poorly understood. We applied untargeted high-resolution metabolomics to determine whether systemic metabolic profiles differ between TB resisters and matched Mtb-infected close contacts. Methods: We compared untargeted high-resolution plasma metabolomics using liquid chromatography mass spectrometry between 269 TB resisters and 269 matched Mtb-infected close contacts, enrolled across Brazil, India, and South Africa. TB resisters were defined as highly exposed close contacts who slept in the same room or spent at least 5 hours indoors per day with an infectious pulmonary TB index patient, but remained TST\/IGRA-negative. Mtb-infected close contacts were those who were TST\/IGRA positive. Metabolome-wide association studies (MWAS) were conducted using paired t-tests. Results: There were 1,787 features with nominal differences between TB resisters and matched Mtb-infected contacts (p < 0.05). Pathway enrichment identified fructose and mannose metabolism and bile acid biosynthesis in the overall cohort, while lipid-related pathways were enriched in Brazil. Among metabolites with confirmed chemical identities, glyceric acid concentrations were higher in TB resisters by 0.53 uM, whereas hydroxyproline was lower by 0.90 uM, butyrobetaine was lower by 0.06 uM, and homogentisate was lower by 0.0036 uM at false discovery rate of 0.2. Conclusions: Plasma metabolomic profiles differed between TB resisters and matched Mtb-infected close contacts. These findings indicate that systemic metabolic differences are associated with resistance to Mtb infection.","rel_num_authors":22,"rel_authors":[{"author_name":"Chang Liu","author_inst":"Emory University School of Public Health"},{"author_name":"Jeffrey  M. Collins","author_inst":"Emory University School of Medicine"},{"author_name":"Matheus Fernandes Gyorfy","author_inst":"Emory University"},{"author_name":"Mariana Araujo Pereira","author_inst":"Fundacao Oswaldo Cruz"},{"author_name":"Vidya Mave","author_inst":"Johns Hopkins University"},{"author_name":"Senbagavalli Prakash","author_inst":"Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India"},{"author_name":"Kamakshi Prudhula Devalraju","author_inst":"Bhagwan Mahavir Medical Research Centre, Hyderabad, India"},{"author_name":"Neil A. Martinson","author_inst":"Perinatal HIV Research Unit, University of the Witwatersrand, Johannesburg, South Africa"},{"author_name":"Fay Willis","author_inst":"Emory University"},{"author_name":"Marina  C Figueiredo","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Marcelo Cordeiro-Santos","author_inst":"Universidade do Estado do Amazonas, Manaus, Brazil"},{"author_name":"Artur Trancoso Lopo de Queiroz","author_inst":"Laboratorio de Pesquisa Clinica e Translacional, Instituto Goncalo Moniz, Fundacao Oswaldo Cruz, Salvador, Brazil"},{"author_name":"Venkata Sanjeev Kumar Neela","author_inst":"Bhagwan Mahavir Medical Research Centre, Hyderabad, India"},{"author_name":"Rajesh Karyakarte","author_inst":"BJ Government Medical College, Pune"},{"author_name":"Timothy R Sterling","author_inst":"Vanderbilt University"},{"author_name":"Jerrold J. Ellner","author_inst":"Department of Medicine, Division of Infectious Diseases, Rutgers New Jersey Medical School, Rutgers Health, Newark, New Jersey, USA"},{"author_name":"James C.M. Brust","author_inst":"Division of General Internal Medicine, Albert Einstein College of Medicine, Bronx, NY, USA"},{"author_name":"Amita Gupta","author_inst":"Johns Hopkins School of Medicine"},{"author_name":"Bruno  B Andrade","author_inst":"FIOCRUZ Bahia: Instituto Goncalo Moniz"},{"author_name":"Yan V. Sun","author_inst":"Emory University"},{"author_name":"Neel  R. Gandhi","author_inst":"Emory University School of Public Health"},{"author_name":"- TB GWAS collaboration","author_inst":""}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Metabolic Signatures of Resistance to Mycobacterium Tuberculosis Infection: Insights from a Multi-Country Plasma Metabolomics Study","rel_doi":"10.64898\/2026.09.29.26364337","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.29.26364337","rel_abs":"Background: Tuberculosis (TB) remains the leading infectious disease cause of mortality worldwide. A subset of individuals exposed to Mycobacterium tuberculosis (Mtb) remain TST\/IGRA-negative despite sustained exposure, termed \"TB resisters\". The biological mechanisms underlying this resistance remain poorly understood. We applied untargeted high-resolution metabolomics to determine whether systemic metabolic profiles differ between TB resisters and matched Mtb-infected close contacts. Methods: We compared untargeted high-resolution plasma metabolomics using liquid chromatography mass spectrometry between 269 TB resisters and 269 matched Mtb-infected close contacts, enrolled across Brazil, India, and South Africa. TB resisters were defined as highly exposed close contacts who slept in the same room or spent at least 5 hours indoors per day with an infectious pulmonary TB index patient, but remained TST\/IGRA-negative. Mtb-infected close contacts were those who were TST\/IGRA positive. Metabolome-wide association studies (MWAS) were conducted using paired t-tests. Results: There were 1,787 features with nominal differences between TB resisters and matched Mtb-infected contacts (p < 0.05). Pathway enrichment identified fructose and mannose metabolism and bile acid biosynthesis in the overall cohort, while lipid-related pathways were enriched in Brazil. Among metabolites with confirmed chemical identities, glyceric acid concentrations were higher in TB resisters by 0.53 uM, whereas hydroxyproline was lower by 0.90 uM, butyrobetaine was lower by 0.06 uM, and homogentisate was lower by 0.0036 uM at false discovery rate of 0.2. Conclusions: Plasma metabolomic profiles differed between TB resisters and matched Mtb-infected close contacts. These findings indicate that systemic metabolic differences are associated with resistance to Mtb infection.","rel_num_authors":22,"rel_authors":[{"author_name":"Chang Liu","author_inst":"Emory University School of Public Health"},{"author_name":"Jeffrey  M. Collins","author_inst":"Emory University School of Medicine"},{"author_name":"Matheus Fernandes Gyorfy","author_inst":"Emory University"},{"author_name":"Mariana Araujo Pereira","author_inst":"Fundacao Oswaldo Cruz"},{"author_name":"Vidya Mave","author_inst":"Johns Hopkins University"},{"author_name":"Senbagavalli Prakash","author_inst":"Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India"},{"author_name":"Kamakshi Prudhula Devalraju","author_inst":"Bhagwan Mahavir Medical Research Centre, Hyderabad, India"},{"author_name":"Neil A. Martinson","author_inst":"Perinatal HIV Research Unit, University of the Witwatersrand, Johannesburg, South Africa"},{"author_name":"Fay Willis","author_inst":"Emory University"},{"author_name":"Marina  C Figueiredo","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Marcelo Cordeiro-Santos","author_inst":"Universidade do Estado do Amazonas, Manaus, Brazil"},{"author_name":"Artur Trancoso Lopo de Queiroz","author_inst":"Laboratorio de Pesquisa Clinica e Translacional, Instituto Goncalo Moniz, Fundacao Oswaldo Cruz, Salvador, Brazil"},{"author_name":"Venkata Sanjeev Kumar Neela","author_inst":"Bhagwan Mahavir Medical Research Centre, Hyderabad, India"},{"author_name":"Rajesh Karyakarte","author_inst":"BJ Government Medical College, Pune"},{"author_name":"Timothy R Sterling","author_inst":"Vanderbilt University"},{"author_name":"Jerrold J. Ellner","author_inst":"Department of Medicine, Division of Infectious Diseases, Rutgers New Jersey Medical School, Rutgers Health, Newark, New Jersey, USA"},{"author_name":"James C.M. Brust","author_inst":"Division of General Internal Medicine, Albert Einstein College of Medicine, Bronx, NY, USA"},{"author_name":"Amita Gupta","author_inst":"Johns Hopkins School of Medicine"},{"author_name":"Bruno  B Andrade","author_inst":"FIOCRUZ Bahia: Instituto Goncalo Moniz"},{"author_name":"Yan V. Sun","author_inst":"Emory University"},{"author_name":"Neel  R. Gandhi","author_inst":"Emory University School of Public Health"},{"author_name":"- TB GWAS collaboration","author_inst":""}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Metabolic Signatures of Resistance to Mycobacterium Tuberculosis Infection: Insights from a Multi-Country Plasma Metabolomics Study","rel_doi":"10.64898\/2026.09.29.26364337","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.29.26364337","rel_abs":"Background: Tuberculosis (TB) remains the leading infectious disease cause of mortality worldwide. A subset of individuals exposed to Mycobacterium tuberculosis (Mtb) remain TST\/IGRA-negative despite sustained exposure, termed \"TB resisters\". The biological mechanisms underlying this resistance remain poorly understood. We applied untargeted high-resolution metabolomics to determine whether systemic metabolic profiles differ between TB resisters and matched Mtb-infected close contacts. Methods: We compared untargeted high-resolution plasma metabolomics using liquid chromatography mass spectrometry between 269 TB resisters and 269 matched Mtb-infected close contacts, enrolled across Brazil, India, and South Africa. TB resisters were defined as highly exposed close contacts who slept in the same room or spent at least 5 hours indoors per day with an infectious pulmonary TB index patient, but remained TST\/IGRA-negative. Mtb-infected close contacts were those who were TST\/IGRA positive. Metabolome-wide association studies (MWAS) were conducted using paired t-tests. Results: There were 1,787 features with nominal differences between TB resisters and matched Mtb-infected contacts (p < 0.05). Pathway enrichment identified fructose and mannose metabolism and bile acid biosynthesis in the overall cohort, while lipid-related pathways were enriched in Brazil. Among metabolites with confirmed chemical identities, glyceric acid concentrations were higher in TB resisters by 0.53 uM, whereas hydroxyproline was lower by 0.90 uM, butyrobetaine was lower by 0.06 uM, and homogentisate was lower by 0.0036 uM at false discovery rate of 0.2. Conclusions: Plasma metabolomic profiles differed between TB resisters and matched Mtb-infected close contacts. These findings indicate that systemic metabolic differences are associated with resistance to Mtb infection.","rel_num_authors":22,"rel_authors":[{"author_name":"Chang Liu","author_inst":"Emory University School of Public Health"},{"author_name":"Jeffrey  M. Collins","author_inst":"Emory University School of Medicine"},{"author_name":"Matheus Fernandes Gyorfy","author_inst":"Emory University"},{"author_name":"Mariana Araujo Pereira","author_inst":"Fundacao Oswaldo Cruz"},{"author_name":"Vidya Mave","author_inst":"Johns Hopkins University"},{"author_name":"Senbagavalli Prakash","author_inst":"Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India"},{"author_name":"Kamakshi Prudhula Devalraju","author_inst":"Bhagwan Mahavir Medical Research Centre, Hyderabad, India"},{"author_name":"Neil A. Martinson","author_inst":"Perinatal HIV Research Unit, University of the Witwatersrand, Johannesburg, South Africa"},{"author_name":"Fay Willis","author_inst":"Emory University"},{"author_name":"Marina  C Figueiredo","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Marcelo Cordeiro-Santos","author_inst":"Universidade do Estado do Amazonas, Manaus, Brazil"},{"author_name":"Artur Trancoso Lopo de Queiroz","author_inst":"Laboratorio de Pesquisa Clinica e Translacional, Instituto Goncalo Moniz, Fundacao Oswaldo Cruz, Salvador, Brazil"},{"author_name":"Venkata Sanjeev Kumar Neela","author_inst":"Bhagwan Mahavir Medical Research Centre, Hyderabad, India"},{"author_name":"Rajesh Karyakarte","author_inst":"BJ Government Medical College, Pune"},{"author_name":"Timothy R Sterling","author_inst":"Vanderbilt University"},{"author_name":"Jerrold J. Ellner","author_inst":"Department of Medicine, Division of Infectious Diseases, Rutgers New Jersey Medical School, Rutgers Health, Newark, New Jersey, USA"},{"author_name":"James C.M. Brust","author_inst":"Division of General Internal Medicine, Albert Einstein College of Medicine, Bronx, NY, USA"},{"author_name":"Amita Gupta","author_inst":"Johns Hopkins School of Medicine"},{"author_name":"Bruno  B Andrade","author_inst":"FIOCRUZ Bahia: Instituto Goncalo Moniz"},{"author_name":"Yan V. Sun","author_inst":"Emory University"},{"author_name":"Neel  R. Gandhi","author_inst":"Emory University School of Public Health"},{"author_name":"- TB GWAS collaboration","author_inst":""}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Metabolic Signatures of Resistance to Mycobacterium Tuberculosis Infection: Insights from a Multi-Country Plasma Metabolomics Study","rel_doi":"10.64898\/2026.09.29.26364337","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.29.26364337","rel_abs":"Background: Tuberculosis (TB) remains the leading infectious disease cause of mortality worldwide. A subset of individuals exposed to Mycobacterium tuberculosis (Mtb) remain TST\/IGRA-negative despite sustained exposure, termed \"TB resisters\". The biological mechanisms underlying this resistance remain poorly understood. We applied untargeted high-resolution metabolomics to determine whether systemic metabolic profiles differ between TB resisters and matched Mtb-infected close contacts. Methods: We compared untargeted high-resolution plasma metabolomics using liquid chromatography mass spectrometry between 269 TB resisters and 269 matched Mtb-infected close contacts, enrolled across Brazil, India, and South Africa. TB resisters were defined as highly exposed close contacts who slept in the same room or spent at least 5 hours indoors per day with an infectious pulmonary TB index patient, but remained TST\/IGRA-negative. Mtb-infected close contacts were those who were TST\/IGRA positive. Metabolome-wide association studies (MWAS) were conducted using paired t-tests. Results: There were 1,787 features with nominal differences between TB resisters and matched Mtb-infected contacts (p < 0.05). Pathway enrichment identified fructose and mannose metabolism and bile acid biosynthesis in the overall cohort, while lipid-related pathways were enriched in Brazil. Among metabolites with confirmed chemical identities, glyceric acid concentrations were higher in TB resisters by 0.53 uM, whereas hydroxyproline was lower by 0.90 uM, butyrobetaine was lower by 0.06 uM, and homogentisate was lower by 0.0036 uM at false discovery rate of 0.2. Conclusions: Plasma metabolomic profiles differed between TB resisters and matched Mtb-infected close contacts. These findings indicate that systemic metabolic differences are associated with resistance to Mtb infection.","rel_num_authors":22,"rel_authors":[{"author_name":"Chang Liu","author_inst":"Emory University School of Public Health"},{"author_name":"Jeffrey  M. Collins","author_inst":"Emory University School of Medicine"},{"author_name":"Matheus Fernandes Gyorfy","author_inst":"Emory University"},{"author_name":"Mariana Araujo Pereira","author_inst":"Fundacao Oswaldo Cruz"},{"author_name":"Vidya Mave","author_inst":"Johns Hopkins University"},{"author_name":"Senbagavalli Prakash","author_inst":"Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India"},{"author_name":"Kamakshi Prudhula Devalraju","author_inst":"Bhagwan Mahavir Medical Research Centre, Hyderabad, India"},{"author_name":"Neil A. Martinson","author_inst":"Perinatal HIV Research Unit, University of the Witwatersrand, Johannesburg, South Africa"},{"author_name":"Fay Willis","author_inst":"Emory University"},{"author_name":"Marina  C Figueiredo","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Marcelo Cordeiro-Santos","author_inst":"Universidade do Estado do Amazonas, Manaus, Brazil"},{"author_name":"Artur Trancoso Lopo de Queiroz","author_inst":"Laboratorio de Pesquisa Clinica e Translacional, Instituto Goncalo Moniz, Fundacao Oswaldo Cruz, Salvador, Brazil"},{"author_name":"Venkata Sanjeev Kumar Neela","author_inst":"Bhagwan Mahavir Medical Research Centre, Hyderabad, India"},{"author_name":"Rajesh Karyakarte","author_inst":"BJ Government Medical College, Pune"},{"author_name":"Timothy R Sterling","author_inst":"Vanderbilt University"},{"author_name":"Jerrold J. Ellner","author_inst":"Department of Medicine, Division of Infectious Diseases, Rutgers New Jersey Medical School, Rutgers Health, Newark, New Jersey, USA"},{"author_name":"James C.M. Brust","author_inst":"Division of General Internal Medicine, Albert Einstein College of Medicine, Bronx, NY, USA"},{"author_name":"Amita Gupta","author_inst":"Johns Hopkins School of Medicine"},{"author_name":"Bruno  B Andrade","author_inst":"FIOCRUZ Bahia: Instituto Goncalo Moniz"},{"author_name":"Yan V. Sun","author_inst":"Emory University"},{"author_name":"Neel  R. Gandhi","author_inst":"Emory University School of Public Health"},{"author_name":"- TB GWAS collaboration","author_inst":""}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"A Framework to Monitor Editing of Artificial Intelligence-Generated Medical Documentation","rel_doi":"10.64898\/2026.09.30.26364427","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26364427","rel_abs":"Objective: To develop and evaluate a framework for characterizing clinician editing of Artificial Intelligence (AI)-generated[KS1.1] documentation and assess its feasibility for health system-level monitoring of AI scribes. Materials and Methods: We analyzed outpatient encounters in which an AI scribe was used at a single academic health system, examining the History of Present Illness (HPI) and Assessment and Plan (A&P) sections of notes. We characterized edits on three dimensions[KS2.1][AG2.2]: lexical edit intensity based on Levenshtein distance, embedding edit intensity BERT[KS3.1]Score, and clinical edit intensity based on removed and added UMLS[KS4.1][AG4.2] concepts.[KS5.1][AG5.2] We analyzed the Positive Predictive Value (PPV) of clinical edit intensity as a measure of clinically meaningful editing using clinicians as the gold standard, examined correlations among dimensions, and designed exponentially weighted moving-average control charts to monitor longitudinal changes in clinician editing behavior.[KS6.1][AG6.2] Results: 268,379 encounters were included (267,654 HPI, 267,594 A&P). Clinical edit intensity [&ge;]1 had an 88.9% PPV for clinically meaningful editing. Lexical and embedding edit intensity were highly correlated (Spearman {rho} 0.95), while clinical edit intensity was less strongly correlated with both ({rho} 0.77-0.81). Longitudinal monitoring detected changes coinciding with system-wide rollout.[KS7.1][AG7.2] Discussion:[KS8.1][AG8.2] Clinicians edited A&Ps more heavily than HPIs, potentially reflecting greater attention to content involving clinical decision-making. Over one-third of sections involved clinical concept changes, and clinical edit intensity identified clinically meaningful edits while providing information complementary to lexical editing measures. Conclusion: Clinician editing can be characterized at scale using complementary editing dimensions, providing a scalable signal for post-deployment surveillance of the human-AI documentation process.","rel_num_authors":17,"rel_authors":[{"author_name":"Augusto Garcia-Agundez","author_inst":"University of California San Francisco"},{"author_name":"Siyu Zhou","author_inst":"University of California San Francisco"},{"author_name":"Jessica Pourian","author_inst":"University of California San Francisco"},{"author_name":"Catherine Blebea","author_inst":"University of California San Francisco"},{"author_name":"Parnaz Daneshpajouhnejad","author_inst":"University of California San Francisco"},{"author_name":"Elizabeth Dente","author_inst":"University of California San Francisco"},{"author_name":"Kevin Shi","author_inst":"University of California San Francisco"},{"author_name":"Sarah Pollet","author_inst":"University of California San Francisco"},{"author_name":"Fan Xia","author_inst":"University of California San Francisco"},{"author_name":"Xu Shi","author_inst":"University of Michigan"},{"author_name":"Robert Thombley","author_inst":"University of California San Francisco"},{"author_name":"Cynthia Fenton","author_inst":"University of California San Francisco"},{"author_name":"Sara G Murray","author_inst":"University of California San Francisco"},{"author_name":"Julia Adler-Milstein","author_inst":"University of California San Francisco"},{"author_name":"Gabriela Schmajuk","author_inst":"University of California San Francisco"},{"author_name":"Jean Feng","author_inst":"University of California San Francisco"},{"author_name":"Jinoos Yazdany","author_inst":"University of California San Francisco"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Cohort-scale Spatial Host-Microbiome Predicts Post-Resection Recurrence in Colorectal Cancer","rel_doi":"10.64898\/2026.09.29.26363734","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.29.26363734","rel_abs":"The tumor microenvironment in colorectal cancer (CRC) is a heterogeneous ecosystem in which host cells and microbial communities interact dynamically, influencing disease progression. However, the clinical utility is limited by the lack of a scalable spatial host-microbiome technique and by insufficient integration of artificial intelligence for the interpretation of high-dimensional multi-omics data. To overcome the barriers, we present AlphaFISH, a platform technology integrating both technical and computational innovations for multi-omics spatial analysis of clinical biopsies at subcellular resolution. The system uses a sequencing-free, high-throughput, spatial profiling technique to construct, to date, the largest clinical spatial transcriptomics and spatial microbiome datasets acquired from 149 colorectal biopsies from 68 human subjects, supported by a comprehensive scRNA-seq atlas covering 4.27 million cells across 650 patients for robust cell annotation. Deep learning of the cohort-scale dual-omics data, consisting of more than 10 million subcellular sampling vectors, enables the development of a transformer model with joint embeddings of gene expression, spatial architecture, and the microbial microenvironment in colon tissues, achieving nearly 90% accuracy in predicting CRC-associated pathological features using unseen spatial omics inputs. The AI interrogation further predicts tumour recurrence at 81% accuracy in 28 patients followed within 1 year post tumor resection period. AlphaFISH reveals that spatial interactions between Fusobacterium and cellular niche consisting of tumor and T cells serve as key markers of CRC malignancy, progression, and recurrence, indicating the critical role of spatial bacterial-immune crosstalks.","rel_num_authors":13,"rel_authors":[{"author_name":"Feng Guo","author_inst":"City University of Hong Kong"},{"author_name":"Hailiang Sun","author_inst":"City University of Hong Kong"},{"author_name":"Chenxi Hu","author_inst":"Tsinghua University; Institute for AI Industry Research, Tsinghua University"},{"author_name":"Minsheng Hao","author_inst":"Tsinghua University"},{"author_name":"Youyang Wan","author_inst":"City University of Hong Kong"},{"author_name":"Chuxiao Xiong","author_inst":"City University of Hong Kong"},{"author_name":"Feng Gao","author_inst":"The Sixth Affiliated Hospital, Sun Yat-sen University"},{"author_name":"Lung-Yi Mak","author_inst":"University of Hong Kong"},{"author_name":"Xin Deng","author_inst":"City University of Hong Kong"},{"author_name":"Ajay Goel","author_inst":"City of Hope Comprehensive Cancer Center"},{"author_name":"Jia Ke","author_inst":"The Sixth Affiliated Hospital, Sun Yat-sen University"},{"author_name":"Jianzhu Ma","author_inst":"Institute for AI Industry Research, Tsinghua University; Department of Electronic Engineering, Tsinghua University"},{"author_name":"Peng Shi","author_inst":"City University of Hong Kong; Hong Kong Centre for Cerebro-Cardiovascular Health Engineering; COSDAF, City University of Hong Kong; Shenzhen Research Institute,"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Functional near infrared spectroscopy-based dual-stream adaptive language mapping in adults with and without post-stroke aphasia","rel_doi":"10.64898\/2026.09.28.26364106","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.28.26364106","rel_abs":"In aphasia, variability in lesion location and post-stroke network reorganization complicates efforts to identify the regions that support semantic versus phonological processing. In prior work, researchers have often examined these domains in separate patient cohorts, further limiting direct comparison of their neural substrates within aphasia. To address this gap, we investigated the degree of overlap and specialization within semantic and phonological networks in adults with and without aphasia and examined how neural activity relates to task performance and lesion profiles. Sixteen neurologically healthy adults and 15 participants with aphasia following left hemisphere stroke completed adaptive Semantic Matching and Rhyme Judgment tasks during functional near-infrared spectroscopy (fNIRS) recording. Changes in oxyhemoglobin and deoxyhemoglobin were measured within regions of interest, and activation was related to standardized task performance and damage to left ventral and dorsal language pathways in participants with aphasia. Controls showed largely distinct activation patterns consistent with prior fMRI findings: Semantic Matching preferentially recruited ventral stream regions, particularly left temporal cortex, whereas Rhyme Judgment more strongly recruited dorsal regions, including left inferior frontal and inferior parietal cortex. Participants with aphasia showed greater inter-individual variability and no clear group-level segregation of dorsal and ventral activity. Better Semantic Matching performance was associated with less left ventral stream damage and greater left dorsal activation, whereas better Rhyme Judgment performance was associated with lower right hemisphere ventral and dorsal activation. These findings suggest that post-stroke language performance reflects residual specialization, lesion-dependent constraints, and flexible recruitment of surviving tissue. Overall, fNIRS shows promise as an alternative to fMRI for language mapping in post-stroke aphasia, although replication in larger samples with more extensive cortical coverage is warranted.","rel_num_authors":8,"rel_authors":[{"author_name":"Erin L. Meier","author_inst":"Northeastern University"},{"author_name":"Veronica Fletcher","author_inst":"Northeastern University"},{"author_name":"Caela Hung","author_inst":"Northeastern University"},{"author_name":"Esprit Ange Andraos","author_inst":"Northeastern University"},{"author_name":"Leanna Ugent","author_inst":"Northeastern University"},{"author_name":"Gengchen Wei","author_inst":"Northeastern University"},{"author_name":"David J. Lin","author_inst":"Massachusetts General Hospital"},{"author_name":"Meryem A. Y\u00fccel","author_inst":"Boston University"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Diurnal Heart Rate Range and Heart Rate Excursions: Novel Characterizations of Wearable Heart Rate Variability With an Application to Aging","rel_doi":"10.64898\/2026.09.29.26364190","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.29.26364190","rel_abs":"Background Wearable heart rate (HR) monitors collect minute-level HR continuously across days, yet these data are commonly reduced to scalars such as resting HR (RHR) and heart rate reserve (HRR), defined as the difference between maximum HR and RHR. This discards two dimensions of within-day HR dynamics: how HR range varies across the day and the magnitude and duration of individual HR rises and declines. We introduce diurnal heart rate range (dHRR), a time-of-day-specific measure of HR range across repeated days, and heart rate excursions (HREs), individual HR rises and declines characterized by amplitude and duration. Methods We analyzed minute-level HR from 750 participants in the Baltimore Longitudinal Study of Aging (mean [SD] age, 66 [12] years). We estimated the 5th, 50th, and 95th HR percentiles at each minute of the 24-hour cycle across repeated days, defining diurnal HR (dHR) as the median curve and dHRR as the 95th minus 5th percentile curve. A moving-average algorithm segmented HR into HREs. Function-on-scalar regression modeled dHR and dHRR by age, sex,and BMI across the day; local polynomial regression examined age and sex differences in HRR and HREs. Results Conventional HRR decreased with age, indicating a smaller overall observed HR range. Age-related differences in dHR varied substantially across the day: between ages 40 and 80, fitted dHR was approximately 2 bpm lower at 3:00 AM but 8 bpm lower at 6:00 PM. dHRR showed that this contraction was not uniform across the day: the evening peak near 6:00 PM at younger ages was largely absent by age 70, while the morning peak shifted from approximately 9:00 AM toward noon. At the event level, HREs showed two parallel age-related changes: between ages 40 and 80, mean excursion amplitude decreased from approximately 40 to 32 bpm, while mean excursion duration increased from approximately 90 to 104 minutes. Conclusions Aging was characterized by a smaller global HR range, compression and reorganization of HR range across the day, and smaller, longer-lasting HREs. dHRR and HREs provide complementary views of the diurnal organization and event-level dynamics of wearable HR and are implemented in the open-source ihr R package.","rel_num_authors":8,"rel_authors":[{"author_name":"Samuel D Fansler","author_inst":"Johns Hopkins University Bloomberg School of Public Health"},{"author_name":"Owen Yoo","author_inst":"University of Michigan College of Literature, Science, and the Arts"},{"author_name":"Shuiqing Han","author_inst":"University of Michigan College of Literature, Science, and the Arts"},{"author_name":"Luigi Ferrucci","author_inst":"NIH-NIA"},{"author_name":"Eleanor Simonsick","author_inst":"NIH-NIA"},{"author_name":"Jennifer A Schrack","author_inst":"Johns Hopkins University Bloomberg School of Public Health"},{"author_name":"Irina Gaynanova","author_inst":"University of Michigan School of Public Health"},{"author_name":"Vadim Zipunnikov","author_inst":"Johns Hopkins University Bloomberg School of Public Health"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Rare coding variation implicates thirteen genes in bipolar disorder across 232,536 individuals from global populations","rel_doi":"10.64898\/2026.09.30.26364416","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26364416","rel_abs":"Bipolar disorder (BD) is highly heritable, yet the contribution of rare coding variation remains incompletely characterized. We analyzed sequencing data from 64,435 individuals with BD and 168,101 controls spanning multiple ancestries and 22 countries, representing the largest and most global sequencing resource with a 6.7-fold increase in effective sample size over the previous study iteration. We observe enrichment of protein-truncating and damaging missense variants in constrained genes and curated neuropsychiatric gene sets, with no enrichment of synonymous variation. These enrichment signals were consistent across ancestry groups, suggesting that genetic risk factors for BD are consistent worldwide. Gene-level analyses identified 13 exome-wide significant genes and an additional 20 genes at FDR < 0.05. These genes showed convergence with common and rare variant risk across other neuropsychiatric disorders. Expression analyses also showed preferential brain expression and increased developmental expression during early childhood. Modelling 3D protein structures further highlighted clustering of ultra-rare missense variants at a predicted interaction interfaces in two Bonferroni-significant genes, DOP1A and ATP9A; a potential mechanism linking membrane trafficking to BD risk. Together, these results implicate a constellation of rare variants that map onto neuronal biology and demonstrate that diverse global populations converge on shared genetic signals underlying BD risk.","rel_num_authors":88,"rel_authors":[{"author_name":"Calwing Liao","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "},{"author_name":"Robert Ye","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Julia M Sealock","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Toni Boltz","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Franjo Ivankovic","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Hilary Finucane","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Daniel Howrigan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Yijia Christiana Liu","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"F Kyle Satterstrom","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Arsalan Hassan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Melkam Alemayehu","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Stella Gichuru","author_inst":"Aga Khan University Medical College, East Africa, Nairobi, Kenya"},{"author_name":"Rehema Mwende","author_inst":"Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Charles Newton","author_inst":"Kenya Medical Research Institute (KEMRI), Nairobi, Kenya"},{"author_name":"Nastassja Koen","author_inst":"Dept of Psychiatry and Neuroscience Institute, University of Cape Town, South Africa"},{"author_name":"Zukiswa Zingela","author_inst":"Nelson Mandela University, Gqeberha, South Africa"},{"author_name":"Ana M Diaz-Zuluaga","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Ana M Ramirez-Diaz","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Victor I Reus","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Medicine, University of California, San Francisco, San Francisco, California, USA; Laboratory of Neu"},{"author_name":"Terri Teshiba","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Aarno Palotie","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Eija Hamalainen","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland"},{"author_name":"Olli Pietilainen","author_inst":"Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland"},{"author_name":"Penelope A Lind","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Dan J Siskind","author_inst":"Addiction and Mental Health Service, Metro South Health, Brisbane, Qld, Australia; Faculty of Health, Medicine and Behavioural Sciences, University of Queenslan"},{"author_name":"Ian B Hickie","author_inst":"Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia"},{"author_name":"Pamela Morales-Cedillo","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Joanna Jimenez-Pavon","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico"},{"author_name":"Marco Antonio Sanabrais-Jimenez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlo Esteban Sotelo-Ramirez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Eric Hahn","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Van Phi Nguyen","author_inst":"Hanoi Medical University, Vietnam, National Geriatric Hospital"},{"author_name":"Elizabeth Karlson","author_inst":"Harvard Medical School, Mass General Brigham (MGB), Brigham and Women's Hospital, Boston, Massachusetts, USA"},{"author_name":"Chiao-Erh Chang","author_inst":"Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan; Stanley Center for Psychiatric Research, Broad Institute, Cambrid"},{"author_name":"Hsi-Chung Chen","author_inst":"School of Medicine, National Taiwan University College of Medicine, Taipei, Taiwan; Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwa"},{"author_name":"Martin Alda","author_inst":"Dalhousie University"},{"author_name":"Mikael Landen","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Jordan W Smoller","author_inst":"Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA; Department of Psychiatry, Harvard Medical School, "},{"author_name":"Nicholas Craddock","author_inst":"Cardiff University, Cardiff, Wales, UK"},{"author_name":"Marquis P Vawter","author_inst":"University of California, Irvine, Irvine, California, USA"},{"author_name":"David Curtis","author_inst":"UCL Genetics Institute, University College London, London, UK"},{"author_name":"Andrew McQuillin","author_inst":"University College London, London, UK"},{"author_name":"Rene S Kahn","author_inst":"Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA"},{"author_name":"Roel A Ophoff","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Annabel Vreeker","author_inst":"Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, Netherlands; "},{"author_name":"Christina Hultman","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Patrick F Sullivan","author_inst":"Karolinska Institutet, Stockholm, Sweden; University of North Carolina, Chapel Hill, North Carolina, USA"},{"author_name":"Michael E Talkowski","author_inst":"Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA"},{"author_name":"Douglas H Blackwood","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Andrew McIntosh","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Ann E Pulver","author_inst":"School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Bruce Cohen","author_inst":"McLean Hospital, Harvard Medical School, Belmont, Massachusetts, USA"},{"author_name":"Rolf Adolfsson","author_inst":"Department of Clinical Sciences, Psychiatry, Umea University, Umea, Sweden"},{"author_name":"Andreas Reif","author_inst":"Department of Psychiatry, Universitatsklinikum Frankfurt, Frankfurt, Germany"},{"author_name":"Fernando Goes","author_inst":"Johns Hopkins University"},{"author_name":"Robert Yolken","author_inst":"Stanley Division of Developmental Neurovirology, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Aiden P Corvin","author_inst":"Trinity College Dublin, Dublin, Ireland"},{"author_name":"Derek W Morris","author_inst":"University of Galway, Galway, Ireland"},{"author_name":"- BIPEX Collection Scientists","author_inst":""},{"author_name":"Felecia Cerrato","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Sinead B Chapman","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Caroline Cusick","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Zhenglin Guo","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Ana Maria Olivares","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Guy A Rouleau","author_inst":"McGill University, Montreal, Quebec, Canada"},{"author_name":"Biju Viswanath","author_inst":"National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India"},{"author_name":"Po-Hsiu Kuo","author_inst":"Department of Public Health and Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan"},{"author_name":"Van Tuan Nguyen","author_inst":"Hanoi Medical University"},{"author_name":"Thi Minh Tam Ta","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Beatriz Camarena","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlos N Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Michele T Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Sarah E Medland","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Nelson Freimer","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Loes Olde Loohuis","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Carlos Lopez-Jaramillo","author_inst":"Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Antioquia, Colombia; Research Group in Psychiatry, Department of Psychiatry, S"},{"author_name":"Rocky Stroud II","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Lukoye Atwoli","author_inst":"Department of Medicine, Aga Khan University Medical College East Africa, Nairobi, Kenya; Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Akena Dickens","author_inst":"Makerere University, Kampala, Uganda"},{"author_name":"Karestan C Koenen","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Symon M Kariuki","author_inst":"African Population and Health Research Center, Nairobi, Kenya"},{"author_name":"Solomon Teferra","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Dan J Stein","author_inst":"South African Medical Research Council (SAMRC) Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University "},{"author_name":"Muhammad Ayub","author_inst":"Division of Psychiatry, University College London, London, UK"},{"author_name":"James Knowles","author_inst":"Rutgers University, New Brunswick, New Jersey, USA"},{"author_name":"Mark J Daly","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Hailiang Huang","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Benjamin M Neale","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Rare coding variation implicates thirteen genes in bipolar disorder across 232,536 individuals from global populations","rel_doi":"10.64898\/2026.09.30.26364416","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26364416","rel_abs":"Bipolar disorder (BD) is highly heritable, yet the contribution of rare coding variation remains incompletely characterized. We analyzed sequencing data from 64,435 individuals with BD and 168,101 controls spanning multiple ancestries and 22 countries, representing the largest and most global sequencing resource with a 6.7-fold increase in effective sample size over the previous study iteration. We observe enrichment of protein-truncating and damaging missense variants in constrained genes and curated neuropsychiatric gene sets, with no enrichment of synonymous variation. These enrichment signals were consistent across ancestry groups, suggesting that genetic risk factors for BD are consistent worldwide. Gene-level analyses identified 13 exome-wide significant genes and an additional 20 genes at FDR < 0.05. These genes showed convergence with common and rare variant risk across other neuropsychiatric disorders. Expression analyses also showed preferential brain expression and increased developmental expression during early childhood. Modelling 3D protein structures further highlighted clustering of ultra-rare missense variants at a predicted interaction interfaces in two Bonferroni-significant genes, DOP1A and ATP9A; a potential mechanism linking membrane trafficking to BD risk. Together, these results implicate a constellation of rare variants that map onto neuronal biology and demonstrate that diverse global populations converge on shared genetic signals underlying BD risk.","rel_num_authors":88,"rel_authors":[{"author_name":"Calwing Liao","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "},{"author_name":"Robert Ye","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Julia M Sealock","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Toni Boltz","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Franjo Ivankovic","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Hilary Finucane","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Daniel Howrigan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Yijia Christiana Liu","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"F Kyle Satterstrom","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Arsalan Hassan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Melkam Alemayehu","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Stella Gichuru","author_inst":"Aga Khan University Medical College, East Africa, Nairobi, Kenya"},{"author_name":"Rehema Mwende","author_inst":"Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Charles Newton","author_inst":"Kenya Medical Research Institute (KEMRI), Nairobi, Kenya"},{"author_name":"Nastassja Koen","author_inst":"Dept of Psychiatry and Neuroscience Institute, University of Cape Town, South Africa"},{"author_name":"Zukiswa Zingela","author_inst":"Nelson Mandela University, Gqeberha, South Africa"},{"author_name":"Ana M Diaz-Zuluaga","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Ana M Ramirez-Diaz","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Victor I Reus","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Medicine, University of California, San Francisco, San Francisco, California, USA; Laboratory of Neu"},{"author_name":"Terri Teshiba","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Aarno Palotie","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Eija Hamalainen","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland"},{"author_name":"Olli Pietilainen","author_inst":"Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland"},{"author_name":"Penelope A Lind","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Dan J Siskind","author_inst":"Addiction and Mental Health Service, Metro South Health, Brisbane, Qld, Australia; Faculty of Health, Medicine and Behavioural Sciences, University of Queenslan"},{"author_name":"Ian B Hickie","author_inst":"Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia"},{"author_name":"Pamela Morales-Cedillo","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Joanna Jimenez-Pavon","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico"},{"author_name":"Marco Antonio Sanabrais-Jimenez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlo Esteban Sotelo-Ramirez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Eric Hahn","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Van Phi Nguyen","author_inst":"Hanoi Medical University, Vietnam, National Geriatric Hospital"},{"author_name":"Elizabeth Karlson","author_inst":"Harvard Medical School, Mass General Brigham (MGB), Brigham and Women's Hospital, Boston, Massachusetts, USA"},{"author_name":"Chiao-Erh Chang","author_inst":"Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan; Stanley Center for Psychiatric Research, Broad Institute, Cambrid"},{"author_name":"Hsi-Chung Chen","author_inst":"School of Medicine, National Taiwan University College of Medicine, Taipei, Taiwan; Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwa"},{"author_name":"Martin Alda","author_inst":"Dalhousie University"},{"author_name":"Mikael Landen","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Jordan W Smoller","author_inst":"Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA; Department of Psychiatry, Harvard Medical School, "},{"author_name":"Nicholas Craddock","author_inst":"Cardiff University, Cardiff, Wales, UK"},{"author_name":"Marquis P Vawter","author_inst":"University of California, Irvine, Irvine, California, USA"},{"author_name":"David Curtis","author_inst":"UCL Genetics Institute, University College London, London, UK"},{"author_name":"Andrew McQuillin","author_inst":"University College London, London, UK"},{"author_name":"Rene S Kahn","author_inst":"Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA"},{"author_name":"Roel A Ophoff","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Annabel Vreeker","author_inst":"Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, Netherlands; "},{"author_name":"Christina Hultman","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Patrick F Sullivan","author_inst":"Karolinska Institutet, Stockholm, Sweden; University of North Carolina, Chapel Hill, North Carolina, USA"},{"author_name":"Michael E Talkowski","author_inst":"Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA"},{"author_name":"Douglas H Blackwood","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Andrew McIntosh","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Ann E Pulver","author_inst":"School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Bruce Cohen","author_inst":"McLean Hospital, Harvard Medical School, Belmont, Massachusetts, USA"},{"author_name":"Rolf Adolfsson","author_inst":"Department of Clinical Sciences, Psychiatry, Umea University, Umea, Sweden"},{"author_name":"Andreas Reif","author_inst":"Department of Psychiatry, Universitatsklinikum Frankfurt, Frankfurt, Germany"},{"author_name":"Fernando Goes","author_inst":"Johns Hopkins University"},{"author_name":"Robert Yolken","author_inst":"Stanley Division of Developmental Neurovirology, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Aiden P Corvin","author_inst":"Trinity College Dublin, Dublin, Ireland"},{"author_name":"Derek W Morris","author_inst":"University of Galway, Galway, Ireland"},{"author_name":"- BIPEX Collection Scientists","author_inst":""},{"author_name":"Felecia Cerrato","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Sinead B Chapman","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Caroline Cusick","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Zhenglin Guo","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Ana Maria Olivares","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Guy A Rouleau","author_inst":"McGill University, Montreal, Quebec, Canada"},{"author_name":"Biju Viswanath","author_inst":"National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India"},{"author_name":"Po-Hsiu Kuo","author_inst":"Department of Public Health and Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan"},{"author_name":"Van Tuan Nguyen","author_inst":"Hanoi Medical University"},{"author_name":"Thi Minh Tam Ta","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Beatriz Camarena","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlos N Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Michele T Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Sarah E Medland","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Nelson Freimer","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Loes Olde Loohuis","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Carlos Lopez-Jaramillo","author_inst":"Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Antioquia, Colombia; Research Group in Psychiatry, Department of Psychiatry, S"},{"author_name":"Rocky Stroud II","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Lukoye Atwoli","author_inst":"Department of Medicine, Aga Khan University Medical College East Africa, Nairobi, Kenya; Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Akena Dickens","author_inst":"Makerere University, Kampala, Uganda"},{"author_name":"Karestan C Koenen","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Symon M Kariuki","author_inst":"African Population and Health Research Center, Nairobi, Kenya"},{"author_name":"Solomon Teferra","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Dan J Stein","author_inst":"South African Medical Research Council (SAMRC) Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University "},{"author_name":"Muhammad Ayub","author_inst":"Division of Psychiatry, University College London, London, UK"},{"author_name":"James Knowles","author_inst":"Rutgers University, New Brunswick, New Jersey, USA"},{"author_name":"Mark J Daly","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Hailiang Huang","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Benjamin M Neale","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Rare coding variation implicates thirteen genes in bipolar disorder across 232,536 individuals from global populations","rel_doi":"10.64898\/2026.09.30.26364416","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26364416","rel_abs":"Bipolar disorder (BD) is highly heritable, yet the contribution of rare coding variation remains incompletely characterized. We analyzed sequencing data from 64,435 individuals with BD and 168,101 controls spanning multiple ancestries and 22 countries, representing the largest and most global sequencing resource with a 6.7-fold increase in effective sample size over the previous study iteration. We observe enrichment of protein-truncating and damaging missense variants in constrained genes and curated neuropsychiatric gene sets, with no enrichment of synonymous variation. These enrichment signals were consistent across ancestry groups, suggesting that genetic risk factors for BD are consistent worldwide. Gene-level analyses identified 13 exome-wide significant genes and an additional 20 genes at FDR < 0.05. These genes showed convergence with common and rare variant risk across other neuropsychiatric disorders. Expression analyses also showed preferential brain expression and increased developmental expression during early childhood. Modelling 3D protein structures further highlighted clustering of ultra-rare missense variants at a predicted interaction interfaces in two Bonferroni-significant genes, DOP1A and ATP9A; a potential mechanism linking membrane trafficking to BD risk. Together, these results implicate a constellation of rare variants that map onto neuronal biology and demonstrate that diverse global populations converge on shared genetic signals underlying BD risk.","rel_num_authors":88,"rel_authors":[{"author_name":"Calwing Liao","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "},{"author_name":"Robert Ye","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Julia M Sealock","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Toni Boltz","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Franjo Ivankovic","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Hilary Finucane","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Daniel Howrigan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Yijia Christiana Liu","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"F Kyle Satterstrom","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Arsalan Hassan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Melkam Alemayehu","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Stella Gichuru","author_inst":"Aga Khan University Medical College, East Africa, Nairobi, Kenya"},{"author_name":"Rehema Mwende","author_inst":"Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Charles Newton","author_inst":"Kenya Medical Research Institute (KEMRI), Nairobi, Kenya"},{"author_name":"Nastassja Koen","author_inst":"Dept of Psychiatry and Neuroscience Institute, University of Cape Town, South Africa"},{"author_name":"Zukiswa Zingela","author_inst":"Nelson Mandela University, Gqeberha, South Africa"},{"author_name":"Ana M Diaz-Zuluaga","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Ana M Ramirez-Diaz","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Victor I Reus","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Medicine, University of California, San Francisco, San Francisco, California, USA; Laboratory of Neu"},{"author_name":"Terri Teshiba","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Aarno Palotie","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Eija Hamalainen","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland"},{"author_name":"Olli Pietilainen","author_inst":"Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland"},{"author_name":"Penelope A Lind","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Dan J Siskind","author_inst":"Addiction and Mental Health Service, Metro South Health, Brisbane, Qld, Australia; Faculty of Health, Medicine and Behavioural Sciences, University of Queenslan"},{"author_name":"Ian B Hickie","author_inst":"Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia"},{"author_name":"Pamela Morales-Cedillo","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Joanna Jimenez-Pavon","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico"},{"author_name":"Marco Antonio Sanabrais-Jimenez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlo Esteban Sotelo-Ramirez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Eric Hahn","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Van Phi Nguyen","author_inst":"Hanoi Medical University, Vietnam, National Geriatric Hospital"},{"author_name":"Elizabeth Karlson","author_inst":"Harvard Medical School, Mass General Brigham (MGB), Brigham and Women's Hospital, Boston, Massachusetts, USA"},{"author_name":"Chiao-Erh Chang","author_inst":"Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan; Stanley Center for Psychiatric Research, Broad Institute, Cambrid"},{"author_name":"Hsi-Chung Chen","author_inst":"School of Medicine, National Taiwan University College of Medicine, Taipei, Taiwan; Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwa"},{"author_name":"Martin Alda","author_inst":"Dalhousie University"},{"author_name":"Mikael Landen","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Jordan W Smoller","author_inst":"Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA; Department of Psychiatry, Harvard Medical School, "},{"author_name":"Nicholas Craddock","author_inst":"Cardiff University, Cardiff, Wales, UK"},{"author_name":"Marquis P Vawter","author_inst":"University of California, Irvine, Irvine, California, USA"},{"author_name":"David Curtis","author_inst":"UCL Genetics Institute, University College London, London, UK"},{"author_name":"Andrew McQuillin","author_inst":"University College London, London, UK"},{"author_name":"Rene S Kahn","author_inst":"Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA"},{"author_name":"Roel A Ophoff","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Annabel Vreeker","author_inst":"Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, Netherlands; "},{"author_name":"Christina Hultman","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Patrick F Sullivan","author_inst":"Karolinska Institutet, Stockholm, Sweden; University of North Carolina, Chapel Hill, North Carolina, USA"},{"author_name":"Michael E Talkowski","author_inst":"Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA"},{"author_name":"Douglas H Blackwood","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Andrew McIntosh","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Ann E Pulver","author_inst":"School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Bruce Cohen","author_inst":"McLean Hospital, Harvard Medical School, Belmont, Massachusetts, USA"},{"author_name":"Rolf Adolfsson","author_inst":"Department of Clinical Sciences, Psychiatry, Umea University, Umea, Sweden"},{"author_name":"Andreas Reif","author_inst":"Department of Psychiatry, Universitatsklinikum Frankfurt, Frankfurt, Germany"},{"author_name":"Fernando Goes","author_inst":"Johns Hopkins University"},{"author_name":"Robert Yolken","author_inst":"Stanley Division of Developmental Neurovirology, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Aiden P Corvin","author_inst":"Trinity College Dublin, Dublin, Ireland"},{"author_name":"Derek W Morris","author_inst":"University of Galway, Galway, Ireland"},{"author_name":"- BIPEX Collection Scientists","author_inst":""},{"author_name":"Felecia Cerrato","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Sinead B Chapman","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Caroline Cusick","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Zhenglin Guo","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Ana Maria Olivares","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Guy A Rouleau","author_inst":"McGill University, Montreal, Quebec, Canada"},{"author_name":"Biju Viswanath","author_inst":"National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India"},{"author_name":"Po-Hsiu Kuo","author_inst":"Department of Public Health and Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan"},{"author_name":"Van Tuan Nguyen","author_inst":"Hanoi Medical University"},{"author_name":"Thi Minh Tam Ta","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Beatriz Camarena","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlos N Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Michele T Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Sarah E Medland","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Nelson Freimer","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Loes Olde Loohuis","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Carlos Lopez-Jaramillo","author_inst":"Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Antioquia, Colombia; Research Group in Psychiatry, Department of Psychiatry, S"},{"author_name":"Rocky Stroud II","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Lukoye Atwoli","author_inst":"Department of Medicine, Aga Khan University Medical College East Africa, Nairobi, Kenya; Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Akena Dickens","author_inst":"Makerere University, Kampala, Uganda"},{"author_name":"Karestan C Koenen","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Symon M Kariuki","author_inst":"African Population and Health Research Center, Nairobi, Kenya"},{"author_name":"Solomon Teferra","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Dan J Stein","author_inst":"South African Medical Research Council (SAMRC) Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University "},{"author_name":"Muhammad Ayub","author_inst":"Division of Psychiatry, University College London, London, UK"},{"author_name":"James Knowles","author_inst":"Rutgers University, New Brunswick, New Jersey, USA"},{"author_name":"Mark J Daly","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Hailiang Huang","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Benjamin M Neale","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Rare coding variation implicates thirteen genes in bipolar disorder across 232,536 individuals from global populations","rel_doi":"10.64898\/2026.09.30.26364416","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26364416","rel_abs":"Bipolar disorder (BD) is highly heritable, yet the contribution of rare coding variation remains incompletely characterized. We analyzed sequencing data from 64,435 individuals with BD and 168,101 controls spanning multiple ancestries and 22 countries, representing the largest and most global sequencing resource with a 6.7-fold increase in effective sample size over the previous study iteration. We observe enrichment of protein-truncating and damaging missense variants in constrained genes and curated neuropsychiatric gene sets, with no enrichment of synonymous variation. These enrichment signals were consistent across ancestry groups, suggesting that genetic risk factors for BD are consistent worldwide. Gene-level analyses identified 13 exome-wide significant genes and an additional 20 genes at FDR < 0.05. These genes showed convergence with common and rare variant risk across other neuropsychiatric disorders. Expression analyses also showed preferential brain expression and increased developmental expression during early childhood. Modelling 3D protein structures further highlighted clustering of ultra-rare missense variants at a predicted interaction interfaces in two Bonferroni-significant genes, DOP1A and ATP9A; a potential mechanism linking membrane trafficking to BD risk. Together, these results implicate a constellation of rare variants that map onto neuronal biology and demonstrate that diverse global populations converge on shared genetic signals underlying BD risk.","rel_num_authors":88,"rel_authors":[{"author_name":"Calwing Liao","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "},{"author_name":"Robert Ye","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Julia M Sealock","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Toni Boltz","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Franjo Ivankovic","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Hilary Finucane","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Daniel Howrigan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Yijia Christiana Liu","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"F Kyle Satterstrom","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Arsalan Hassan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Melkam Alemayehu","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Stella Gichuru","author_inst":"Aga Khan University Medical College, East Africa, Nairobi, Kenya"},{"author_name":"Rehema Mwende","author_inst":"Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Charles Newton","author_inst":"Kenya Medical Research Institute (KEMRI), Nairobi, Kenya"},{"author_name":"Nastassja Koen","author_inst":"Dept of Psychiatry and Neuroscience Institute, University of Cape Town, South Africa"},{"author_name":"Zukiswa Zingela","author_inst":"Nelson Mandela University, Gqeberha, South Africa"},{"author_name":"Ana M Diaz-Zuluaga","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Ana M Ramirez-Diaz","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Victor I Reus","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Medicine, University of California, San Francisco, San Francisco, California, USA; Laboratory of Neu"},{"author_name":"Terri Teshiba","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Aarno Palotie","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Eija Hamalainen","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland"},{"author_name":"Olli Pietilainen","author_inst":"Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland"},{"author_name":"Penelope A Lind","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Dan J Siskind","author_inst":"Addiction and Mental Health Service, Metro South Health, Brisbane, Qld, Australia; Faculty of Health, Medicine and Behavioural Sciences, University of Queenslan"},{"author_name":"Ian B Hickie","author_inst":"Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia"},{"author_name":"Pamela Morales-Cedillo","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Joanna Jimenez-Pavon","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico"},{"author_name":"Marco Antonio Sanabrais-Jimenez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlo Esteban Sotelo-Ramirez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Eric Hahn","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Van Phi Nguyen","author_inst":"Hanoi Medical University, Vietnam, National Geriatric Hospital"},{"author_name":"Elizabeth Karlson","author_inst":"Harvard Medical School, Mass General Brigham (MGB), Brigham and Women's Hospital, Boston, Massachusetts, USA"},{"author_name":"Chiao-Erh Chang","author_inst":"Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan; Stanley Center for Psychiatric Research, Broad Institute, Cambrid"},{"author_name":"Hsi-Chung Chen","author_inst":"School of Medicine, National Taiwan University College of Medicine, Taipei, Taiwan; Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwa"},{"author_name":"Martin Alda","author_inst":"Dalhousie University"},{"author_name":"Mikael Landen","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Jordan W Smoller","author_inst":"Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA; Department of Psychiatry, Harvard Medical School, "},{"author_name":"Nicholas Craddock","author_inst":"Cardiff University, Cardiff, Wales, UK"},{"author_name":"Marquis P Vawter","author_inst":"University of California, Irvine, Irvine, California, USA"},{"author_name":"David Curtis","author_inst":"UCL Genetics Institute, University College London, London, UK"},{"author_name":"Andrew McQuillin","author_inst":"University College London, London, UK"},{"author_name":"Rene S Kahn","author_inst":"Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA"},{"author_name":"Roel A Ophoff","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Annabel Vreeker","author_inst":"Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, Netherlands; "},{"author_name":"Christina Hultman","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Patrick F Sullivan","author_inst":"Karolinska Institutet, Stockholm, Sweden; University of North Carolina, Chapel Hill, North Carolina, USA"},{"author_name":"Michael E Talkowski","author_inst":"Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA"},{"author_name":"Douglas H Blackwood","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Andrew McIntosh","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Ann E Pulver","author_inst":"School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Bruce Cohen","author_inst":"McLean Hospital, Harvard Medical School, Belmont, Massachusetts, USA"},{"author_name":"Rolf Adolfsson","author_inst":"Department of Clinical Sciences, Psychiatry, Umea University, Umea, Sweden"},{"author_name":"Andreas Reif","author_inst":"Department of Psychiatry, Universitatsklinikum Frankfurt, Frankfurt, Germany"},{"author_name":"Fernando Goes","author_inst":"Johns Hopkins University"},{"author_name":"Robert Yolken","author_inst":"Stanley Division of Developmental Neurovirology, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Aiden P Corvin","author_inst":"Trinity College Dublin, Dublin, Ireland"},{"author_name":"Derek W Morris","author_inst":"University of Galway, Galway, Ireland"},{"author_name":"- BIPEX Collection Scientists","author_inst":""},{"author_name":"Felecia Cerrato","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Sinead B Chapman","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Caroline Cusick","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Zhenglin Guo","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Ana Maria Olivares","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Guy A Rouleau","author_inst":"McGill University, Montreal, Quebec, Canada"},{"author_name":"Biju Viswanath","author_inst":"National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India"},{"author_name":"Po-Hsiu Kuo","author_inst":"Department of Public Health and Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan"},{"author_name":"Van Tuan Nguyen","author_inst":"Hanoi Medical University"},{"author_name":"Thi Minh Tam Ta","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Beatriz Camarena","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlos N Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Michele T Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Sarah E Medland","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Nelson Freimer","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Loes Olde Loohuis","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Carlos Lopez-Jaramillo","author_inst":"Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Antioquia, Colombia; Research Group in Psychiatry, Department of Psychiatry, S"},{"author_name":"Rocky Stroud II","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Lukoye Atwoli","author_inst":"Department of Medicine, Aga Khan University Medical College East Africa, Nairobi, Kenya; Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Akena Dickens","author_inst":"Makerere University, Kampala, Uganda"},{"author_name":"Karestan C Koenen","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Symon M Kariuki","author_inst":"African Population and Health Research Center, Nairobi, Kenya"},{"author_name":"Solomon Teferra","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Dan J Stein","author_inst":"South African Medical Research Council (SAMRC) Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University "},{"author_name":"Muhammad Ayub","author_inst":"Division of Psychiatry, University College London, London, UK"},{"author_name":"James Knowles","author_inst":"Rutgers University, New Brunswick, New Jersey, USA"},{"author_name":"Mark J Daly","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Hailiang Huang","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Benjamin M Neale","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Rare coding variation implicates thirteen genes in bipolar disorder across 232,536 individuals from global populations","rel_doi":"10.64898\/2026.09.30.26364416","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26364416","rel_abs":"Bipolar disorder (BD) is highly heritable, yet the contribution of rare coding variation remains incompletely characterized. We analyzed sequencing data from 64,435 individuals with BD and 168,101 controls spanning multiple ancestries and 22 countries, representing the largest and most global sequencing resource with a 6.7-fold increase in effective sample size over the previous study iteration. We observe enrichment of protein-truncating and damaging missense variants in constrained genes and curated neuropsychiatric gene sets, with no enrichment of synonymous variation. These enrichment signals were consistent across ancestry groups, suggesting that genetic risk factors for BD are consistent worldwide. Gene-level analyses identified 13 exome-wide significant genes and an additional 20 genes at FDR < 0.05. These genes showed convergence with common and rare variant risk across other neuropsychiatric disorders. Expression analyses also showed preferential brain expression and increased developmental expression during early childhood. Modelling 3D protein structures further highlighted clustering of ultra-rare missense variants at a predicted interaction interfaces in two Bonferroni-significant genes, DOP1A and ATP9A; a potential mechanism linking membrane trafficking to BD risk. Together, these results implicate a constellation of rare variants that map onto neuronal biology and demonstrate that diverse global populations converge on shared genetic signals underlying BD risk.","rel_num_authors":88,"rel_authors":[{"author_name":"Calwing Liao","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "},{"author_name":"Robert Ye","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Julia M Sealock","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Toni Boltz","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Franjo Ivankovic","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Hilary Finucane","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Daniel Howrigan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Yijia Christiana Liu","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"F Kyle Satterstrom","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Arsalan Hassan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Melkam Alemayehu","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Stella Gichuru","author_inst":"Aga Khan University Medical College, East Africa, Nairobi, Kenya"},{"author_name":"Rehema Mwende","author_inst":"Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Charles Newton","author_inst":"Kenya Medical Research Institute (KEMRI), Nairobi, Kenya"},{"author_name":"Nastassja Koen","author_inst":"Dept of Psychiatry and Neuroscience Institute, University of Cape Town, South Africa"},{"author_name":"Zukiswa Zingela","author_inst":"Nelson Mandela University, Gqeberha, South Africa"},{"author_name":"Ana M Diaz-Zuluaga","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Ana M Ramirez-Diaz","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Victor I Reus","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Medicine, University of California, San Francisco, San Francisco, California, USA; Laboratory of Neu"},{"author_name":"Terri Teshiba","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Aarno Palotie","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Eija Hamalainen","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland"},{"author_name":"Olli Pietilainen","author_inst":"Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland"},{"author_name":"Penelope A Lind","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Dan J Siskind","author_inst":"Addiction and Mental Health Service, Metro South Health, Brisbane, Qld, Australia; Faculty of Health, Medicine and Behavioural Sciences, University of Queenslan"},{"author_name":"Ian B Hickie","author_inst":"Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia"},{"author_name":"Pamela Morales-Cedillo","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Joanna Jimenez-Pavon","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico"},{"author_name":"Marco Antonio Sanabrais-Jimenez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlo Esteban Sotelo-Ramirez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Eric Hahn","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Van Phi Nguyen","author_inst":"Hanoi Medical University, Vietnam, National Geriatric Hospital"},{"author_name":"Elizabeth Karlson","author_inst":"Harvard Medical School, Mass General Brigham (MGB), Brigham and Women's Hospital, Boston, Massachusetts, USA"},{"author_name":"Chiao-Erh Chang","author_inst":"Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan; Stanley Center for Psychiatric Research, Broad Institute, Cambrid"},{"author_name":"Hsi-Chung Chen","author_inst":"School of Medicine, National Taiwan University College of Medicine, Taipei, Taiwan; Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwa"},{"author_name":"Martin Alda","author_inst":"Dalhousie University"},{"author_name":"Mikael Landen","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Jordan W Smoller","author_inst":"Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA; Department of Psychiatry, Harvard Medical School, "},{"author_name":"Nicholas Craddock","author_inst":"Cardiff University, Cardiff, Wales, UK"},{"author_name":"Marquis P Vawter","author_inst":"University of California, Irvine, Irvine, California, USA"},{"author_name":"David Curtis","author_inst":"UCL Genetics Institute, University College London, London, UK"},{"author_name":"Andrew McQuillin","author_inst":"University College London, London, UK"},{"author_name":"Rene S Kahn","author_inst":"Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA"},{"author_name":"Roel A Ophoff","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Annabel Vreeker","author_inst":"Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, Netherlands; "},{"author_name":"Christina Hultman","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Patrick F Sullivan","author_inst":"Karolinska Institutet, Stockholm, Sweden; University of North Carolina, Chapel Hill, North Carolina, USA"},{"author_name":"Michael E Talkowski","author_inst":"Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA"},{"author_name":"Douglas H Blackwood","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Andrew McIntosh","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Ann E Pulver","author_inst":"School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Bruce Cohen","author_inst":"McLean Hospital, Harvard Medical School, Belmont, Massachusetts, USA"},{"author_name":"Rolf Adolfsson","author_inst":"Department of Clinical Sciences, Psychiatry, Umea University, Umea, Sweden"},{"author_name":"Andreas Reif","author_inst":"Department of Psychiatry, Universitatsklinikum Frankfurt, Frankfurt, Germany"},{"author_name":"Fernando Goes","author_inst":"Johns Hopkins University"},{"author_name":"Robert Yolken","author_inst":"Stanley Division of Developmental Neurovirology, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Aiden P Corvin","author_inst":"Trinity College Dublin, Dublin, Ireland"},{"author_name":"Derek W Morris","author_inst":"University of Galway, Galway, Ireland"},{"author_name":"- BIPEX Collection Scientists","author_inst":""},{"author_name":"Felecia Cerrato","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Sinead B Chapman","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Caroline Cusick","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Zhenglin Guo","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Ana Maria Olivares","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Guy A Rouleau","author_inst":"McGill University, Montreal, Quebec, Canada"},{"author_name":"Biju Viswanath","author_inst":"National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India"},{"author_name":"Po-Hsiu Kuo","author_inst":"Department of Public Health and Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan"},{"author_name":"Van Tuan Nguyen","author_inst":"Hanoi Medical University"},{"author_name":"Thi Minh Tam Ta","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Beatriz Camarena","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlos N Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Michele T Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Sarah E Medland","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Nelson Freimer","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Loes Olde Loohuis","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Carlos Lopez-Jaramillo","author_inst":"Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Antioquia, Colombia; Research Group in Psychiatry, Department of Psychiatry, S"},{"author_name":"Rocky Stroud II","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Lukoye Atwoli","author_inst":"Department of Medicine, Aga Khan University Medical College East Africa, Nairobi, Kenya; Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Akena Dickens","author_inst":"Makerere University, Kampala, Uganda"},{"author_name":"Karestan C Koenen","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Symon M Kariuki","author_inst":"African Population and Health Research Center, Nairobi, Kenya"},{"author_name":"Solomon Teferra","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Dan J Stein","author_inst":"South African Medical Research Council (SAMRC) Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University "},{"author_name":"Muhammad Ayub","author_inst":"Division of Psychiatry, University College London, London, UK"},{"author_name":"James Knowles","author_inst":"Rutgers University, New Brunswick, New Jersey, USA"},{"author_name":"Mark J Daly","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Hailiang Huang","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Benjamin M Neale","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Rare coding variation implicates thirteen genes in bipolar disorder across 232,536 individuals from global populations","rel_doi":"10.64898\/2026.09.30.26364416","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26364416","rel_abs":"Bipolar disorder (BD) is highly heritable, yet the contribution of rare coding variation remains incompletely characterized. We analyzed sequencing data from 64,435 individuals with BD and 168,101 controls spanning multiple ancestries and 22 countries, representing the largest and most global sequencing resource with a 6.7-fold increase in effective sample size over the previous study iteration. We observe enrichment of protein-truncating and damaging missense variants in constrained genes and curated neuropsychiatric gene sets, with no enrichment of synonymous variation. These enrichment signals were consistent across ancestry groups, suggesting that genetic risk factors for BD are consistent worldwide. Gene-level analyses identified 13 exome-wide significant genes and an additional 20 genes at FDR < 0.05. These genes showed convergence with common and rare variant risk across other neuropsychiatric disorders. Expression analyses also showed preferential brain expression and increased developmental expression during early childhood. Modelling 3D protein structures further highlighted clustering of ultra-rare missense variants at a predicted interaction interfaces in two Bonferroni-significant genes, DOP1A and ATP9A; a potential mechanism linking membrane trafficking to BD risk. Together, these results implicate a constellation of rare variants that map onto neuronal biology and demonstrate that diverse global populations converge on shared genetic signals underlying BD risk.","rel_num_authors":88,"rel_authors":[{"author_name":"Calwing Liao","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "},{"author_name":"Robert Ye","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Julia M Sealock","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Toni Boltz","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Franjo Ivankovic","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Hilary Finucane","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Daniel Howrigan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Yijia Christiana Liu","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"F Kyle Satterstrom","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Arsalan Hassan","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Melkam Alemayehu","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Stella Gichuru","author_inst":"Aga Khan University Medical College, East Africa, Nairobi, Kenya"},{"author_name":"Rehema Mwende","author_inst":"Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Charles Newton","author_inst":"Kenya Medical Research Institute (KEMRI), Nairobi, Kenya"},{"author_name":"Nastassja Koen","author_inst":"Dept of Psychiatry and Neuroscience Institute, University of Cape Town, South Africa"},{"author_name":"Zukiswa Zingela","author_inst":"Nelson Mandela University, Gqeberha, South Africa"},{"author_name":"Ana M Diaz-Zuluaga","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Ana M Ramirez-Diaz","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Victor I Reus","author_inst":"Department of Psychiatry and Behavioral Sciences, School of Medicine, University of California, San Francisco, San Francisco, California, USA; Laboratory of Neu"},{"author_name":"Terri Teshiba","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Aarno Palotie","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Eija Hamalainen","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland"},{"author_name":"Olli Pietilainen","author_inst":"Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland"},{"author_name":"Penelope A Lind","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Dan J Siskind","author_inst":"Addiction and Mental Health Service, Metro South Health, Brisbane, Qld, Australia; Faculty of Health, Medicine and Behavioural Sciences, University of Queenslan"},{"author_name":"Ian B Hickie","author_inst":"Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia"},{"author_name":"Pamela Morales-Cedillo","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Joanna Jimenez-Pavon","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico"},{"author_name":"Marco Antonio Sanabrais-Jimenez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlo Esteban Sotelo-Ramirez","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Eric Hahn","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Van Phi Nguyen","author_inst":"Hanoi Medical University, Vietnam, National Geriatric Hospital"},{"author_name":"Elizabeth Karlson","author_inst":"Harvard Medical School, Mass General Brigham (MGB), Brigham and Women's Hospital, Boston, Massachusetts, USA"},{"author_name":"Chiao-Erh Chang","author_inst":"Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan; Stanley Center for Psychiatric Research, Broad Institute, Cambrid"},{"author_name":"Hsi-Chung Chen","author_inst":"School of Medicine, National Taiwan University College of Medicine, Taipei, Taiwan; Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwa"},{"author_name":"Martin Alda","author_inst":"Dalhousie University"},{"author_name":"Mikael Landen","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Jordan W Smoller","author_inst":"Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA; Department of Psychiatry, Harvard Medical School, "},{"author_name":"Nicholas Craddock","author_inst":"Cardiff University, Cardiff, Wales, UK"},{"author_name":"Marquis P Vawter","author_inst":"University of California, Irvine, Irvine, California, USA"},{"author_name":"David Curtis","author_inst":"UCL Genetics Institute, University College London, London, UK"},{"author_name":"Andrew McQuillin","author_inst":"University College London, London, UK"},{"author_name":"Rene S Kahn","author_inst":"Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA"},{"author_name":"Roel A Ophoff","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Annabel Vreeker","author_inst":"Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, Netherlands; "},{"author_name":"Christina Hultman","author_inst":"Karolinska Institutet, Stockholm, Sweden"},{"author_name":"Patrick F Sullivan","author_inst":"Karolinska Institutet, Stockholm, Sweden; University of North Carolina, Chapel Hill, North Carolina, USA"},{"author_name":"Michael E Talkowski","author_inst":"Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA"},{"author_name":"Douglas H Blackwood","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Andrew McIntosh","author_inst":"University of Edinburgh, Edinburgh, UK"},{"author_name":"Ann E Pulver","author_inst":"School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Bruce Cohen","author_inst":"McLean Hospital, Harvard Medical School, Belmont, Massachusetts, USA"},{"author_name":"Rolf Adolfsson","author_inst":"Department of Clinical Sciences, Psychiatry, Umea University, Umea, Sweden"},{"author_name":"Andreas Reif","author_inst":"Department of Psychiatry, Universitatsklinikum Frankfurt, Frankfurt, Germany"},{"author_name":"Fernando Goes","author_inst":"Johns Hopkins University"},{"author_name":"Robert Yolken","author_inst":"Stanley Division of Developmental Neurovirology, Johns Hopkins University, Baltimore, Maryland, USA"},{"author_name":"Aiden P Corvin","author_inst":"Trinity College Dublin, Dublin, Ireland"},{"author_name":"Derek W Morris","author_inst":"University of Galway, Galway, Ireland"},{"author_name":"- BIPEX Collection Scientists","author_inst":""},{"author_name":"Felecia Cerrato","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Sinead B Chapman","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Analytic and Translational Genetics Unit, Depart"},{"author_name":"Caroline Cusick","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Zhenglin Guo","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Ana Maria Olivares","author_inst":"Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"author_name":"Guy A Rouleau","author_inst":"McGill University, Montreal, Quebec, Canada"},{"author_name":"Biju Viswanath","author_inst":"National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India"},{"author_name":"Po-Hsiu Kuo","author_inst":"Department of Public Health and Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan"},{"author_name":"Van Tuan Nguyen","author_inst":"Hanoi Medical University"},{"author_name":"Thi Minh Tam Ta","author_inst":"Charite - Universitatsmedizin Berlin, Berlin, Germany; Hanoi Medical University, Hanoi, Vietnam"},{"author_name":"Beatriz Camarena","author_inst":"Instituto Nacional de Psiquiatria Ramon de la Fuente Muniz, Mexico City, Mexico"},{"author_name":"Carlos N Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Michele T Pato","author_inst":"Rutgers University, New Brunswick, New Jersey, USA; BD2: Breakthrough Discoveries for Thriving with Bipolar Disorder, Santa Monica, California, USA"},{"author_name":"Sarah E Medland","author_inst":"QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia"},{"author_name":"Nelson Freimer","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Loes Olde Loohuis","author_inst":"Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles,"},{"author_name":"Carlos Lopez-Jaramillo","author_inst":"Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Antioquia, Colombia; Research Group in Psychiatry, Department of Psychiatry, S"},{"author_name":"Rocky Stroud II","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Lukoye Atwoli","author_inst":"Department of Medicine, Aga Khan University Medical College East Africa, Nairobi, Kenya; Brain and Mind Institute, Aga Khan University, Nairobi, Kenya"},{"author_name":"Akena Dickens","author_inst":"Makerere University, Kampala, Uganda"},{"author_name":"Karestan C Koenen","author_inst":"Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA; Stanley Center for Psychiatric Research, The Broad Institute"},{"author_name":"Symon M Kariuki","author_inst":"African Population and Health Research Center, Nairobi, Kenya"},{"author_name":"Solomon Teferra","author_inst":"Addis Ababa University, Addis Ababa, Ethiopia"},{"author_name":"Dan J Stein","author_inst":"South African Medical Research Council (SAMRC) Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University "},{"author_name":"Muhammad Ayub","author_inst":"Division of Psychiatry, University College London, London, UK"},{"author_name":"James Knowles","author_inst":"Rutgers University, New Brunswick, New Jersey, USA"},{"author_name":"Mark J Daly","author_inst":"Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Department of Medic"},{"author_name":"Hailiang Huang","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Stanley Center for Psychiatric Res"},{"author_name":"Benjamin M Neale","author_inst":"Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Program in Medical and Population "}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Release-aware, SAS-equivalent Elixhauser comorbidity scoring in R: cross-implementation validation and application to Texas inpatient discharges","rel_doi":"10.64898\/2026.10.01.26364551","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.01.26364551","rel_abs":"The Elixhauser comorbidity measures are among the most widely used risk adjusters in administrative health data. Their reference implementation, published by the Agency for Healthcare Research and Quality (AHRQ) as SAS programs, has eleven annual releases in two families, one screening pre-existing conditions on the present-on-admission (POA) indicator and the earlier one on Medicare Severity Diagnosis-Related Group, and no implementation of every release existed outside SAS. We developed `ecsr10`, an open-source R package covering all eleven releases of both families, with a browser-based application over the same functions, and validated it against AHRQ's own SAS programs over 40,027,114 value-level comparisons with zero disagreements. Two existing open-source reimplementations scored on the same dataset showed reproducible defects. We then used the package to quantify two choices AHRQ's software leaves to the analyst and studies seldom report, the release and the handling of diagnoses not present on admission, on the Texas Inpatient Public Use Data File, 2016 Q1-2019 Q4 (8,585,244 adult discharges from 726 hospitals), scoring one predefined cohort under every release and pre-existing-condition setting. POA handling dominated: disabling POA changed the comorbidity profile of 56.7% of admissions, and a POA-naive mortality index scored higher on discrimination (area under the curve 0.807 versus 0.789; difference 0.0183, 95% confidence interval 0.0161-0.0204) by counting in-hospital complications as pre-existing disease. POA reporting was bimodal across hospitals, so pooled hospital comparisons on such a file partly compare documentation practice. Because the cohort predates every release compared, the release contrast is a lower bound: the release chosen between v2022.1 and v2026.1 changed at least one comorbidity flag for 0.0036% of admissions, yet a single revision of ten mortality weights changed the comorbidity index for 42.4% of them. An exactly validated implementation makes such choices measurable; release, POA handling and the implementation used should be reported as study characteristics.","rel_num_authors":2,"rel_authors":[{"author_name":"Minh Tran","author_inst":"University of Kansas Medical Center"},{"author_name":"Dong Pei","author_inst":"University of Kansas Medical Center"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Safety & Effectiveness of Mifepristone for Medication Abortion in a National Claims Cohort, 2017-2023","rel_doi":"10.64898\/2026.10.02.26364611","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.02.26364611","rel_abs":"BACKGROUND Extensive research has documented medication abortion safety. Large-scale insurance claims data offer an important complement to clinical studies by assessing real-world outcomes in routine practice. In claims data, researchers must infer mifepristone use, clinical outcomes, and event severity from diagnosis, treatment, and utilization codes. Understanding how these analytic choices influence safety and effectiveness estimates is important for interpreting evidence used to inform care delivery and medication policy. METHODS We conducted a retrospective cohort study of mifepristone use with national multipayer claims data, both medical and pharmacy, from 2017 through 2023. We identified both a broad cohort and a more specific medication-abortion cohort after excluding evidence of other indications. We classified potential safety events, effectiveness outcomes, and health care utilization within 45 days of mifepristone use. Serious adverse events required claims-observable consequences consistent with FDA seriousness criteria. RESULTS The broad cohort included 869,409 mifepristone uses among 698,780 unique patients for medication abortion or other indications. After restriction to uses with additional evidence of medication abortion, 775,931 uses among 626,443 unique patients remained. In the medication-abortion cohort, 3,306 uses were followed by at least one serious adverse event, resulting in an absolute 45-day risk of 0.43%. Serious bleeding or hemorrhage occurred after 0.24% of uses, serious infection or sepsis after 0.05%, serious hypersensitivity or anaphylaxis after 0.01%, and serious ectopic-pregnancy outcomes after 0.05%. CONCLUSIONS Serious adverse events after mifepristone use were rare in this national study. Applying established, clinically grounded criteria markedly reduced the number of events classified as serious.","rel_num_authors":6,"rel_authors":[{"author_name":"Liana R Woskie","author_inst":"Department of Community Health, Tufts University, Medford, Massachusetts, USA"},{"author_name":"Julia Strasser","author_inst":"Fitzhugh Mullan Institute for Health Workforce Equity, The George Washington University, Washington, DC, USA"},{"author_name":"Risa Griffin","author_inst":"Fitzhugh Mullan Institute for Health Workforce Equity, The George Washington University, Washington, DC, USA"},{"author_name":"Qian (Eric) Luo","author_inst":"Fitzhugh Mullan Institute for Health Workforce Equity, The George Washington University, Washington, DC, USA"},{"author_name":"Maria I. Rodriguez","author_inst":"Department of Obstetrics and Gynecology, Oregon Health & Science University, Portland, Oregon, USA"},{"author_name":"Ushma Upadhyay","author_inst":"Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, San Francisco, California, USA"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Safety & Effectiveness of Mifepristone for Medication Abortion in a National Claims Cohort, 2017-2023","rel_doi":"10.64898\/2026.10.02.26364611","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.02.26364611","rel_abs":"BACKGROUND Extensive research has documented medication abortion safety. Large-scale insurance claims data offer an important complement to clinical studies by assessing real-world outcomes in routine practice. In claims data, researchers must infer mifepristone use, clinical outcomes, and event severity from diagnosis, treatment, and utilization codes. Understanding how these analytic choices influence safety and effectiveness estimates is important for interpreting evidence used to inform care delivery and medication policy. METHODS We conducted a retrospective cohort study of mifepristone use with national multipayer claims data, both medical and pharmacy, from 2017 through 2023. We identified both a broad cohort and a more specific medication-abortion cohort after excluding evidence of other indications. We classified potential safety events, effectiveness outcomes, and health care utilization within 45 days of mifepristone use. Serious adverse events required claims-observable consequences consistent with FDA seriousness criteria. RESULTS The broad cohort included 869,409 mifepristone uses among 698,780 unique patients for medication abortion or other indications. After restriction to uses with additional evidence of medication abortion, 775,931 uses among 626,443 unique patients remained. In the medication-abortion cohort, 3,306 uses were followed by at least one serious adverse event, resulting in an absolute 45-day risk of 0.43%. Serious bleeding or hemorrhage occurred after 0.24% of uses, serious infection or sepsis after 0.05%, serious hypersensitivity or anaphylaxis after 0.01%, and serious ectopic-pregnancy outcomes after 0.05%. CONCLUSIONS Serious adverse events after mifepristone use were rare in this national study. Applying established, clinically grounded criteria markedly reduced the number of events classified as serious.","rel_num_authors":6,"rel_authors":[{"author_name":"Liana R Woskie","author_inst":"Department of Community Health, Tufts University, Medford, Massachusetts, USA"},{"author_name":"Julia Strasser","author_inst":"Fitzhugh Mullan Institute for Health Workforce Equity, The George Washington University, Washington, DC, USA"},{"author_name":"Risa Griffin","author_inst":"Fitzhugh Mullan Institute for Health Workforce Equity, The George Washington University, Washington, DC, USA"},{"author_name":"Qian (Eric) Luo","author_inst":"Fitzhugh Mullan Institute for Health Workforce Equity, The George Washington University, Washington, DC, USA"},{"author_name":"Maria I. Rodriguez","author_inst":"Department of Obstetrics and Gynecology, Oregon Health & Science University, Portland, Oregon, USA"},{"author_name":"Ushma Upadhyay","author_inst":"Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, San Francisco, California, USA"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Three Cerebellar Imaging Subtypes in Schizophrenia with Distinct Spatiotemporal Trajectories and Biological Characteristics","rel_doi":"10.64898\/2026.10.03.26364634","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.03.26364634","rel_abs":"The potential role of the cerebellum in the pathophysiology of schizophrenia (SCZ) has received relatively insufficient attention. Prior studies characterized cerebellar abnormalities mainly at the group level, without fully considering the heterogeneity of the disease. Here, we applied a machine learning approach (Subtype and Stage Inference, SuStaIn) to cross-sectional 3D volumetric MRIs of the cerebellum, including 1,588 individuals with SCZ (638 females; mean age: 31.9 +\/- 12.0 years) and 2,341 healthy controls (HC) (1,041 females; mean age: 33.3 +\/- 13.6 years), from 17 sites worldwide. SuStaIn identified three distinct spatiotemporal trajectories in cerebellar gray matter volume (GMV) reduction, respectively originating in lobule X (subtype 1), lobule III (subtype 2) and lobule VIIb (subtype 3), with subtypes 1 and 3 corresponding to the posterior lobe and subtype 2 to the anterior lobe. These cross-sectionally inferred trajectories were replicated in two independent samples of 1,334 and 530 patients, respectively. Multimodal analyses using neuroimaging, transcriptomic and behavioral data revealed subtype-specific biological characteristics in brain morphological patterns, cerebellar-cortical connectivity, gene expression and clinical symptoms. Specifically, subtype-related genes were enriched in metabolism-related processes and immunity-related processes, respectively, in subtype 1 and subtype 2; subtype 3 showed more severe brain abnormalities and worse cognitive symptoms. Treatment data from 381 patients, with up to 12 months of follow-up, revealed poorer response to antipsychotic medications (APM) in subtype 2 and subtype 3, but better response to transcranial magnetic stimulation (TMS) in subtype 1, which also had worse emotion-related symptoms. Together, our findings offer a comprehensive characterization of the heterogeneity of cerebellar pathophysiological progresses in SCZ, which may help in developing future clinical stratification and targeted intervention strategies.","rel_num_authors":69,"rel_authors":[{"author_name":"Zhaoyun Liu","author_inst":"School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, PR China"},{"author_name":"Zhenyu Huang","author_inst":"School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, PR China"},{"author_name":"Xinjia Lin","author_inst":"School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, PR China"},{"author_name":"Jingyu Zhou","author_inst":"School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, PR China"},{"author_name":"Hao Hu","author_inst":"Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, PR China"},{"author_name":"Qian Guo","author_inst":"Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, PR China"},{"author_name":"Yingying Tang","author_inst":"Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, PR China"},{"author_name":"Tianhong Zhang","author_inst":"Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, PR China"},{"author_name":"Jijun Wang","author_inst":"Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, PR China"},{"author_name":"Weihua Yue","author_inst":"Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, PR China."},{"author_name":"Yuyanan Zhang","author_inst":"Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, PR China."},{"author_name":"Xin Yu","author_inst":"Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, PR China."},{"author_name":"Long-Biao Cui","author_inst":"Schizophrenia Imaging Lab, Xijing 986 Hospital, Fourth Military Medical University, Xi'an, PR China"},{"author_name":"Xiao Chang","author_inst":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China."},{"author_name":"Huan Huang","author_inst":"huan_huang17@163.com"},{"author_name":"Cheng Luo","author_inst":"The Clinical Hospital of Chengdu Brain Science Institute, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chen"},{"author_name":"Dezhong Yao","author_inst":"The Clinical Hospital of Chengdu Brain Science Institute, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chen"},{"author_name":"Ryota Hashimoto","author_inst":"Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan."},{"author_name":"Junya Matsumoto","author_inst":"Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan."},{"author_name":"Kiyotaka Nemoto","author_inst":"Department of Medical Informatics and Management and Psychiatry, Institute of Medicine, University of Tsukuba, Tsukuba, Japan."},{"author_name":"Tilo Kircher","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany."},{"author_name":"Florian Thomas-Odenthal","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany"},{"author_name":"Paula Usemann","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany."},{"author_name":"Lea Teutenberg","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany."},{"author_name":"Benjamin Straube","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany."},{"author_name":"Igor Nenadi\u0107","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany."},{"author_name":"Frederike Stein","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany"},{"author_name":"Nina Alexander","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany."},{"author_name":"Andreas Jansen","author_inst":"Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany."},{"author_name":"Hamidreza Jamalabadi","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany."},{"author_name":"Nooshin Javaheripour","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany."},{"author_name":"Jannik Lepper","author_inst":"Department of Psychiatry and Psychotherapy, Marburg University, Marburg, Germany."},{"author_name":"Udo Dannlowski","author_inst":"Department of Psychiatry, Medical School and University Medical Center OWL, Protestant Hospital of the Bethel Foundation, Bielefeld University."},{"author_name":"Dominik Grotegerd","author_inst":"Institute for Translational Psychiatry, University of Munster, Munster, Germany."},{"author_name":"Susanne Meinert","author_inst":"Institute for Translational Psychiatry, University of Munster, Munster, Germany."},{"author_name":"Kira Flinkenflugel","author_inst":"Institute for Translational Psychiatry, University of Munster, Munster, Germany"},{"author_name":"Rebekka Lencer","author_inst":"Institute for Translational Psychiatry, University of Munster, Munster, Germany"},{"author_name":"Michael Ziller","author_inst":"Department of Psychiatry, University of Munster, 48149 Munster, Germany."},{"author_name":"Ali Saffet Gonul","author_inst":"Ege University school of medicine SoCAT Lab, Izmir, Turkey."},{"author_name":"Asli Ceren Hinc","author_inst":"Ege University school of medicine SoCAT Lab, Izmir, Turkey."},{"author_name":"Kang Sim","author_inst":"West Region, Institute of Mental Health, Singapore, Singapore."},{"author_name":"Qian Hui Chew","author_inst":"West Region, Institute of Mental Health, Singapore, Singapore."},{"author_name":"Yann Quid\u00e9","author_inst":"NeuroRecovery Research Hub, School of Psychology, The Unversity of New South Wales (UNSW) Sydney, Sydney, NSW, Australia; Centre for Pain IMPACT, Neuroscience R"},{"author_name":"Melissa J. Green","author_inst":"School of Clinical Medicine, Discipline of Psychiatry and Mental Health, The University of New South Wales (UNSW) Sydney, Sydney, NSW, Australia."},{"author_name":"Young-Chul chung","author_inst":"Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Republic of Korea."},{"author_name":"Woo-Sung Kim","author_inst":"Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Republic of Korea."},{"author_name":"Soyolsaikhan Odkhuu","author_inst":"Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Republic of Korea."},{"author_name":"Felice Iasevoli","author_inst":"Section of Psychiatry - Department of Neuroscience and Reproductive Science and Dentistry - University \"Federico II\", Naples, Italy."},{"author_name":"Giuseppe Pontillo","author_inst":"Department of Advanced Biomedical Sciences - University \"Federico II\", Naples, Italy."},{"author_name":"Andrea de Bartolomeis","author_inst":"Section of Psychiatry - Department of Neuroscience and Reproductive Science and Dentistry - University \"Federico II\", Naples, Italy."},{"author_name":"Sirio Cocozza","author_inst":"Department of Advanced Biomedical Sciences - University \"Federico II\", Naples, Italy"},{"author_name":"Annarita Barone","author_inst":"Section of Psychiatry - Department of Neuroscience and Reproductive Science and Dentistry - University \"Federico II\", Naples, Italy."},{"author_name":"Arturo Brunetti","author_inst":"Department of Advanced Biomedical Sciences - University \"Federico II\", Naples, Italy"},{"author_name":"Mariateresa Ciccarelli","author_inst":"Section of Psychiatry - Department of Neuroscience and Reproductive Science and Dentistry - University \"Federico II\", Naples, Italy."},{"author_name":"Mario Tranfa","author_inst":"Department of Advanced Biomedical Sciences - University \"Federico II\", Naples, Italy"},{"author_name":"Tamsyn E.Van Rheenen","author_inst":"Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, MEL, Australia."},{"author_name":"Susan L Rossell","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne Australia."},{"author_name":"Matthew Hughes","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne Australia."},{"author_name":"Will Woods","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne Australia."},{"author_name":"Sean Carruthers","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne Australia."},{"author_name":"Philip J. Sumner","author_inst":"Centre for Mental Health and Brain Sciences, Swinburne University, Melbourne Australia."},{"author_name":"Elysha Ringin","author_inst":"Department of Psychiatry, University of Melbourne, Parkville, Australia."},{"author_name":"Georgia Caruana","author_inst":"Department of Psychiatry, University of Melbourne, Parkville, Australia."},{"author_name":"Jessica A. Turner","author_inst":"Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH, United States."},{"author_name":"Theo G.M. van Erp","author_inst":"Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine Hall, room 109, Irvine, CA,"},{"author_name":"Lena Palaniyappan","author_inst":"Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Canada."},{"author_name":"Paul M. Thompson","author_inst":"Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA"},{"author_name":"Jianfeng Feng","author_inst":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China."},{"author_name":"Yuchao Jiang","author_inst":"School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, PR China"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"An Open-Label Multidose Psilocybin Intervention for Obsessive-Compulsive Disorder.","rel_doi":"10.64898\/2026.10.01.26364511","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.01.26364511","rel_abs":"Importance: Obsessive-Compulsive Disorder (OCD) has a 2% global prevalence, but only about 50% of patients respond to conventional treatment, underscoring the need for additional effective interventions. Objective: To assess the feasibility, safety, and preliminary evidence for efficacy of repeated psilocybin administration in OCD patients who had previously attempted psychotherapy or pharmacotherapy. Design: Waitlist-controlled open label randomized clinical trial. Setting: Data were collected at the Johns Hopkins University School of Medicine. Participants: Data were analyzed from 30 participants (total randomized N=37, by age, sex and OCD severity) with a failed previous attempt at either pharmacotherapy or evidence-based psychotherapies such as exposure response prevention (ERP). Intervention: Participants received two psilocybin doses over two weeks (20 mg followed by 30 mg, if well-tolerated) under supportive conditions. The immediate-treatment arm received the drug within a month following enrollment, and the waitlist-control arm received identical support and dosing after an 8-week waiting period. Main Outcomes: Clinician-administered Y-BOCS scores was collected one week post each drug administration and one month post session 2. Self-assessed reports were collected for acute subjective effects (Mystical Experience Questionnaire (MEQ), Challenging Experience Questionnaire (CEQ), and 11-Dimensional Altered States of Consciousness (11D-ASC)), State-Trait Anxiety Inventory (STAI), Beck Depression Inventory-II (BDI-II), and Quality of Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q). Results: A total of 37 participants were enrolled in the study (age[SD] years:38.4[11.6] years; 21 [56.75%] female), with 30 [81.08%] completing the 1-month follow-up. We show that two successive doses of psilocybin over two weeks were well-tolerated, leading to improvements in OCD symptoms. Compared with the waitlist group (N=14), the immediate-treatment group (N=16) showed a greater reduction in the Y-BOCS scores (F(2,56)=16.17, p<0.001). After crossover, this improvement persisted one month after the second psilocybin dose for both groups (F(3,84)=29.91, p<0.001). Reductions in Y-BOCS score post sessions were correlated with several acute subjective effects measures, including total scores on MEQ, and individual factors on CEQ and 11D-ASC. Conclusions and Relevance: Repeated psilocybin administration is tolerable and potentially efficacious in reducing OCD symptoms. These results support further investigation of multidose psilocybin protocols as a viable therapeutic approach. Trial Registration: ClinicalTrials.gov Identifier NCT05546658","rel_num_authors":12,"rel_authors":[{"author_name":"Praachi Tiwari","author_inst":"Johns Hopkins Medicine"},{"author_name":"Sandeep M Nayak","author_inst":"Johns Hopkins Medicine"},{"author_name":"Nathan D Sepeda","author_inst":"Johns Hopkins Medicine"},{"author_name":"Rebecca Ehrenkranz","author_inst":"Johns Hopkins Medicine"},{"author_name":"Michael A Levine","author_inst":"Johns Hopkins Medicine"},{"author_name":"Julia S Rohde","author_inst":"Johns Hopkins Medicine"},{"author_name":"Eliza Miller","author_inst":"Johns Hopkins Medicine"},{"author_name":"Carina M Beritela","author_inst":"Johns Hopkins Hospital"},{"author_name":"Jeremy V Scott","author_inst":"Johns Hopkins Medicine"},{"author_name":"Gerald Nestadt","author_inst":"Johns Hopkins Medicine"},{"author_name":"Frederick S Barrett","author_inst":"Johns Hopkins Medicine"},{"author_name":"David B Yaden","author_inst":"Johns Hopkins Medicine"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Chest Pain Hospitalizations: How Varying Emergency Clinician Tendencies Impact Care, Outcomes, and Costs","rel_doi":"10.64898\/2026.10.02.26364626","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.02.26364626","rel_abs":"Background: The practice of hospitalizing emergency department (ED) patients with chest pain after ruling out acute myocardial infarction (AMI) varies widely. The long-term outcome and cost implications of this variation are uncertain. Methods: Analyzing 2007-2021 claims from a national commercial insurer, we identified 223,569 adults (18 years or older) ED visits with a principal diagnosis of chest pain without major secondary cardiopulmonary diagnoses, seen by 19,001 clinicians in 2,273 EDs. Within each ED, clinicians with the highest and lowest terciles of risk-adjusted hospitalization rates were classified as high- versus low-admitting. Primary outcomes were 30- and 180-day subsequent AMI hospitalizations. Secondary outcomes included 7-day cardiac testing, 30-day coronary intervention, and 30-day total and out-of-pocket costs. We estimated adjusted rate ratios (aRRs) and relative cost differences using generalized estimating equations, adjusting for patient and visit characteristics. Results: Patients had a mean age of 48.6 years, and 53.9% were female; measured characteristics were similar between groups. Compared with low-admitting clinicians, visits to high-admitting clinicians had higher 7-day cardiac testing (13.1% vs 10.8%; aRR 1.22, 95% CI 1.17-1.27) and 30-day coronary interventions (4.3% vs 3.8%; aRR 1.16, 95% CI 1.09-1.23). AMI hospitalization did not differ at 30 days (0.60% vs 0.60%; aRR 1.00, 95% CI 0.84-1.15) or 180 days (0.85% vs 0.87%; aRR 0.97, 95% CI 0.85-1.10). Thirty-day total costs were higher after visits to high-admitting clinicians (relative change 11.3%, 95% CI 8.8-13.8), and out-of-pocket costs $1,000 or more were more common (32.0% vs 29.6%; relative change 8.2%, 95% CI 6.1-10.3). Conclusions: Higher clinician hospitalizing tendency for ED chest pain is associated with greater downstream testing, more coronary interventions, and higher costs without lower short- or intermediate-term AMI risk. Reducing marginal admissions among high-admitting clinicians may decrease spending and practice variation with minimal impact on AMI outcomes.","rel_num_authors":4,"rel_authors":[{"author_name":"Shih-Chuan Chou","author_inst":"UCSF"},{"author_name":"Renee Y Hsia","author_inst":"UCSF"},{"author_name":"Fang Zhang","author_inst":"Harvard Pilgrim Health Care Institute"},{"author_name":"J. Frank Wharam","author_inst":"Duke University"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Cell type specific enrichment of substance use and substance use disorder heritability","rel_doi":"10.64898\/2026.10.02.26364595","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.02.26364595","rel_abs":"Genome-wide association studies (GWAS) have identified numerous loci associated with substance use disorders (SUDs). To connect common genetic variation to cell type- and region-specific gene expression and chromatin accessibility, we examined the enrichment of genetic liability for several SUDs (alcohol, cannabis, tobacco, and opioid use disorders) and substance consumption phenotypes (drinks per week, cannabis ever-use, and cigarettes per day) in available single-nucleus RNAseq data spanning 10 brain regions and snATACseq data spanning 42 brain regions. Significant cell-type enrichment after multiple testing corrections was observed only for drinks per week and tobacco use disorder in the snRNAseq data and for cannabis use disorder and problematic alcohol use in snATACseq data. Across both transcriptomic and chromatin accessibility annotations, enrichment patterns were concentrated in excitatory neuronal populations, particularly upper layer intratelencephalic neurons, amygdala excitatory neurons, and regionally resolved striatal medium spiny neuron subtypes. Drinks per week, problematic alcohol use, and cannabis use disorder showed enrichment values that were significantly correlated within cell-type across snRNAseq and snATACseq, suggesting convergent biological signals across independent single-nucleus modalities. These findings implicate chromatin-mediated expression differences in specific excitatory neuronal populations as potential mediators of SUD genetic liability and highlight the value of integrating transcriptomic and chromatin accessibility data for characterising the neurobiology of addiction.","rel_num_authors":7,"rel_authors":[{"author_name":"Nithya Sarabudla","author_inst":"Washington University School of Medicine"},{"author_name":"Pamela N Romero Villela","author_inst":"Washington University School of Medicine"},{"author_name":"Ronald P. Hart","author_inst":"Rutgers University"},{"author_name":"Yang E. Li","author_inst":"Washington University School of Medicine"},{"author_name":"Zhiping P. Pang","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Arpana Agrawal","author_inst":"Washington University School of Medicine"},{"author_name":"Emma C Johnson","author_inst":"Washington University School of Medicine"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Cell type specific enrichment of substance use and substance use disorder heritability","rel_doi":"10.64898\/2026.10.02.26364595","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.02.26364595","rel_abs":"Genome-wide association studies (GWAS) have identified numerous loci associated with substance use disorders (SUDs). To connect common genetic variation to cell type- and region-specific gene expression and chromatin accessibility, we examined the enrichment of genetic liability for several SUDs (alcohol, cannabis, tobacco, and opioid use disorders) and substance consumption phenotypes (drinks per week, cannabis ever-use, and cigarettes per day) in available single-nucleus RNAseq data spanning 10 brain regions and snATACseq data spanning 42 brain regions. Significant cell-type enrichment after multiple testing corrections was observed only for drinks per week and tobacco use disorder in the snRNAseq data and for cannabis use disorder and problematic alcohol use in snATACseq data. Across both transcriptomic and chromatin accessibility annotations, enrichment patterns were concentrated in excitatory neuronal populations, particularly upper layer intratelencephalic neurons, amygdala excitatory neurons, and regionally resolved striatal medium spiny neuron subtypes. Drinks per week, problematic alcohol use, and cannabis use disorder showed enrichment values that were significantly correlated within cell-type across snRNAseq and snATACseq, suggesting convergent biological signals across independent single-nucleus modalities. These findings implicate chromatin-mediated expression differences in specific excitatory neuronal populations as potential mediators of SUD genetic liability and highlight the value of integrating transcriptomic and chromatin accessibility data for characterising the neurobiology of addiction.","rel_num_authors":7,"rel_authors":[{"author_name":"Nithya Sarabudla","author_inst":"Washington University School of Medicine"},{"author_name":"Pamela N Romero Villela","author_inst":"Washington University School of Medicine"},{"author_name":"Ronald P. Hart","author_inst":"Rutgers University"},{"author_name":"Yang E. Li","author_inst":"Washington University School of Medicine"},{"author_name":"Zhiping P. Pang","author_inst":"Rutgers Robert Wood Johnson Medical School"},{"author_name":"Arpana Agrawal","author_inst":"Washington University School of Medicine"},{"author_name":"Emma C Johnson","author_inst":"Washington University School of Medicine"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Predicting the efficacy of Ervebo against Bundibugyo virus disease","rel_doi":"10.64898\/2026.10.02.26364627","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.02.26364627","rel_abs":"There is currently no clinical data for the effectiveness of the only licensed Ebola virus disease vaccine (Ervebo) against Bundibugyo virus disease. A number of studies have shown immune cross-reactivity with Bundibugyo virus after Ervebo vaccination. However, antibody recognition of Bundibugyo is around 2.8-fold lower than recognition of Ebola virus. Here, we aimed to infer how a 2.8-fold drop in immune recognition may affect Ervebo protection against Bundibugyo virus disease based on analysis of data on Ervebo immunogenicity and protection from Ebola virus disease. This work has three main components. Firstly, we analysed the timing of vaccine protection in the Ervebo pivotal ring vaccination clinical trial. Secondly, we performed a systematic review and meta-analysis of antibody responses to Ebola virus after Ervebo vaccination over time. Thirdly, we combined these data with previously reported data on the cross-reactivity to Bundibugyo virus to infer protection of Ervebo against Bundibugyo virus disease. We find that the clinical trial data for Ervebo supports vaccine protection beginning earlier than 10 days after vaccination. Secondly, binding antibody levels against Ebola virus on day 28 post vaccination (peak responses) were on average 12.9-fold (95% confidence interval, CI: 6.4 - 26.0) higher than on day 7, 6.3-fold (95% CI: 3.7 -10.8) higher than on day 10, and 2.5-fold (95% CI: 1.4 - 4.4) higher than on day 14 (neutralising antibodies showed similar results). Finally, we consider the scenarios where antibody levels to Ebola virus at day 7 or 10 are putative protective thresholds against Ebola virus disease, and we assume these thresholds also apply for Bundibugyo virus disease. Then, with a 2.8-fold reduction of responses to Bundibugyo compared to Ebola virus, we predict antibody responses to Bundibugyo virus after Ervebo vaccination are likely above these putative protective thresholds.","rel_num_authors":13,"rel_authors":[{"author_name":"Karen M Elias","author_inst":"Kirby Institute, UNSW Sydney"},{"author_name":"Ece Egilmezer","author_inst":"Kirby Institute, UNSW Sydney"},{"author_name":"Shanchita R Khan","author_inst":"Kirby Institute, UNSW Sydney"},{"author_name":"Ainslie Mitchell","author_inst":"Kirby Institute, UNSW Sydney"},{"author_name":"Arnold Reynaldi","author_inst":"Kirby Institute, UNSW Sydney"},{"author_name":"Matthew T Berry","author_inst":"Kirby Institute, UNSW Sydney"},{"author_name":"Timothy E Schlub","author_inst":"Faculty of Medicine and Health, Sydney School of Public Health, University of Sydney"},{"author_name":"Bronwyn A Bailey","author_inst":"Kirby Institute, UNSW Sydney"},{"author_name":"Deborah Cromer","author_inst":"Kirby Institute, UNSW Sydney"},{"author_name":"Tari Turner","author_inst":"School of Public Health and Preventive Medicine, Monash University"},{"author_name":"Eva Stadler","author_inst":"Kirby Institute, UNSW Sydney"},{"author_name":"Miles Philip Davenport","author_inst":"Kirby Institute, UNSW Sydney"},{"author_name":"David S Khoury","author_inst":"Kirby Institute, UNSW Sydney"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Predicting the efficacy of Ervebo against Bundibugyo virus disease","rel_doi":"10.64898\/2026.10.02.26364627","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.02.26364627","rel_abs":"There is currently no clinical data for the effectiveness of the only licensed Ebola virus disease vaccine (Ervebo) against Bundibugyo virus disease. A number of studies have shown immune cross-reactivity with Bundibugyo virus after Ervebo vaccination. However, antibody recognition of Bundibugyo is around 2.8-fold lower than recognition of Ebola virus. Here, we aimed to infer how a 2.8-fold drop in immune recognition may affect Ervebo protection against Bundibugyo virus disease based on analysis of data on Ervebo immunogenicity and protection from Ebola virus disease. This work has three main components. Firstly, we analysed the timing of vaccine protection in the Ervebo pivotal ring vaccination clinical trial. Secondly, we performed a systematic review and meta-analysis of antibody responses to Ebola virus after Ervebo vaccination over time. Thirdly, we combined these data with previously reported data on the cross-reactivity to Bundibugyo virus to infer protection of Ervebo against Bundibugyo virus disease. We find that the clinical trial data for Ervebo supports vaccine protection beginning earlier than 10 days after vaccination. Secondly, binding antibody levels against Ebola virus on day 28 post vaccination (peak responses) were on average 12.9-fold (95% confidence interval, CI: 6.4 - 26.0) higher than on day 7, 6.3-fold (95% CI: 3.7 -10.8) higher than on day 10, and 2.5-fold (95% CI: 1.4 - 4.4) higher than on day 14 (neutralising antibodies showed similar results). Finally, we consider the scenarios where antibody levels to Ebola virus at day 7 or 10 are putative protective thresholds against Ebola virus disease, and we assume these thresholds also apply for Bundibugyo virus disease. Then, with a 2.8-fold reduction of responses to Bundibugyo compared to Ebola virus, we predict antibody responses to Bundibugyo virus after Ervebo vaccination are likely above these putative protective thresholds.","rel_num_authors":13,"rel_authors":[{"author_name":"Karen M Elias","author_inst":"Kirby Institute, UNSW Sydney"},{"author_name":"Ece Egilmezer","author_inst":"Kirby Institute, UNSW Sydney"},{"author_name":"Shanchita R Khan","author_inst":"Kirby Institute, UNSW Sydney"},{"author_name":"Ainslie Mitchell","author_inst":"Kirby Institute, UNSW Sydney"},{"author_name":"Arnold Reynaldi","author_inst":"Kirby Institute, UNSW Sydney"},{"author_name":"Matthew T Berry","author_inst":"Kirby Institute, UNSW Sydney"},{"author_name":"Timothy E Schlub","author_inst":"Faculty of Medicine and Health, Sydney School of Public Health, University of Sydney"},{"author_name":"Bronwyn A Bailey","author_inst":"Kirby Institute, UNSW Sydney"},{"author_name":"Deborah Cromer","author_inst":"Kirby Institute, UNSW Sydney"},{"author_name":"Tari Turner","author_inst":"School of Public Health and Preventive Medicine, Monash University"},{"author_name":"Eva Stadler","author_inst":"Kirby Institute, UNSW Sydney"},{"author_name":"Miles Philip Davenport","author_inst":"Kirby Institute, UNSW Sydney"},{"author_name":"David S Khoury","author_inst":"Kirby Institute, UNSW Sydney"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Predictive markers of response and toxicity to tarlatamab in patients with extensive stage small cell lung cancer: a multi-institutional real-world analysis","rel_doi":"10.64898\/2026.10.02.26364624","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.02.26364624","rel_abs":"Purpose Tarlatamab, a bispecific T-cell engager, has generated favorable response and survival outcomes in relapsed small cell lung cancer (SCLC). Factors influencing tarlatamab-associated treatment outcomes and toxicity have yet to be established. We performed a multivariate analysis to identify biomarkers associated with cytokine release syndrome (CRS) and immune effector cell associated neurotoxicity syndrome (ICANS) and integrated these variables into a novel risk stratification tool to identify patients at high risk of clinically significant toxicity. Patients and Methods Logistic and ordinal regression analyses evaluated the predictive value of variables contributing to the presence and severity, respectively, of CRS and ICANS in 115 patients with ES-SCLC receiving tarlatamab. Selected variables were then tuned using 5-fold cross-validation and incorporated into novel elastic net-based prediction models. Results Rates of observed CRS, ICANS, and dysgeusia were 46%, 28%, and 47%, respectively. ORR (overall response rate) and DCR (disease control rate) were 44% and 58.2%, while 6-month PFS (progression free survival) and OS (overall survival) rates were 30.4% and 54.9%, respectively. Excluding patients on tarlatamab <30 days, presence of dysgeusia was associated with a lower rate of disease progression (HR 0.44, p=0.00691) and death (HR 0.34, p=0.0145). The presence of, increased size and number of extracranial metastases as well as elevated baseline LDH were significantly associated with increased likelihood of CRS. Increased ECOG performance status, increased volume of brain metastases, development of CRS, and elevated baseline LDH were all associated with increased risk of developing ICANS. Our cross-validated risk model successfully predicted CRS grade [&ge;]2 and ICANS events with 90.9% and 75% sensitivity, respectively. Conclusions This exploratory analysis identified several predictive biomarkers of toxicity that successfully stratified low and high-risk populations as part of an internally validated novel risk scoring tool.","rel_num_authors":14,"rel_authors":[{"author_name":"Graeme Fenton","author_inst":"University of Maryland Medical Center"},{"author_name":"Wanru Guo","author_inst":"University of Maryland Greenebaum Comprehensive Cancer Center"},{"author_name":"Daniel L. Hess","author_inst":"Duke Department of Medicine, Duke University School of Medicine"},{"author_name":"Annie L. Zhang","author_inst":"Case Western Reserve University"},{"author_name":"Curtis Tatsuoka","author_inst":"University of Maryland Baltimore"},{"author_name":"Alexis L. Green","author_inst":"Duke Department of Medicine, Duke University School of Medicine"},{"author_name":"Michelle Sittig","author_inst":"University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center"},{"author_name":"Ranee Mehra","author_inst":"University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center"},{"author_name":"Alexandra Simms","author_inst":"University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center"},{"author_name":"Afshin Dowlati","author_inst":"University Hospitals Seidman Cancer Center and Case Western Reserve University"},{"author_name":"Taofeek K. Owonikoko","author_inst":"University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center"},{"author_name":"Melinda Hsu","author_inst":"University Hospitals Seidman Cancer Center and Case Western Reserve University"},{"author_name":"Laura Alder","author_inst":"Duke Cancer Institute"},{"author_name":"Samuel Rosner","author_inst":"University of Maryland Greenebaum Comprehensive Cancer Center"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Resection of highly functionally connected glioma regions predicts long-term cognitive preservation","rel_doi":"10.64898\/2026.09.28.26363885","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.28.26363885","rel_abs":"Background. Glioma surgery requires balancing maximal tumor resection against preservation of neurological and cognitive function. Intraoperative direct electrical stimulation guides this balance but is invasive and not always feasible. Preoperative magnetoencephalography (MEG) can identify tumor regions with high functional connectivity (HFC) to the rest of the brain. These HFC areas contain more malignant glioma cells and relate to poorer short-term outcome when resected. We aimed to determine whether HFC voxel resection relates to postoperative neurological and cognitive outcomes. Methods. In this preregistered analysis, 54 adults with diffuse glioma underwent resting-state MEG before resection. Imaginary coherence identified voxels with significantly higher connectivity against their contralateral controls, and these HFC voxels within the resection cavity were counted. Neurological status was assessed at short-term (~1 week) and long-term (~1 year) follow-up, and neuropsychological assessment took place at baseline and long-term follow-up. Logistic regressions related resected HFC voxels to decline at each timepoint, adjusting for Karnofsky performance status, sex and resectability index. Results. Resected HFC voxels were not associated with neurological decline at short-term (48% declined; odds ratio [OR] 0.98, P = .50) or long-term follow-up (27% declined; OR 0.99). For cognition, more resected HFC voxels were associated with less long-term decline (48% declined; OR 0.85, P = .02); a binarized analysis was consistent (OR 0.076, P = .01), indicating that tumor volume did not drive this result. Conclusions. Resecting more HFC voxels associates with preserved long-term cognition, without apparent neurological cost. Non-invasive MEG connectivity mapping may help optimize the onco-functional balance in glioma surgery.","rel_num_authors":9,"rel_authors":[{"author_name":"Marike Roos van Lingen","author_inst":"Amsterdam UMC, Vrije Universiteit Amsterdam"},{"author_name":"Amit Jaiswal","author_inst":"MEGIN"},{"author_name":"Srikantan Nagarajan","author_inst":"University of California, San Francisco"},{"author_name":"Velmurugan Jayabal","author_inst":"University of California, San Francisco"},{"author_name":"Martin Klein","author_inst":"Amsterdam UMC, Vrije Universiteit Amsterdam"},{"author_name":"Niels Verburg","author_inst":"AmsterdamUMC, Vrije Universiteit Amsterdam"},{"author_name":"Philip de Witt Hamer","author_inst":"AmsterdamUMC, Vrije Universiteit Amsterdam"},{"author_name":"Arjan Hillebrand","author_inst":"AmsterdamUMC, Vrije Universiteit Amsterdam"},{"author_name":"Linda Douw","author_inst":"AmsterdamUMC, Vrije Universiteit Amsterdam"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Predicting One-Year CPAP Adherence and Use Trajectories Utilizing Baseline and Early Use Data","rel_doi":"10.64898\/2026.10.01.26363483","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.01.26363483","rel_abs":"Introduction Maintaining long-term positive airway pressure (CPAP) adherence is a major challenge when treating obstructive sleep apnea (OSA). We evaluated factors associated with one-year CPAP adherence and developed models predicting Month-12 adherence and CPAP-use trajectories. Methods CPAP-naive adults with OSA (apnea-hypopnea index[&ge;]5) at Kaiser Permanente Southern California with 364-day observation period were analyzed (development\/internal-validation cohort, n=14,906; temporal holdout, n=1,676). Logistic regression evaluated baseline associations with Month-12 adherence (mean[&ge;]4 hours\/night during days 331-360). XGBoost models utilized baseline features (clinical, sleep-study, questionnaire data), early CPAP data (days 1-7), or combined to predict long-term CPAP use. A latent-class model identified trajectories over days 8-364. Predictive models were evaluated in both random and temporal holdouts. Results About one-third (34.6%) of the primary cohort (49.7{+\/-}13.8 years, 63.2% male) were adherent during Month-12. Higher AHI and longer self-reported sleep duration favored adherence while greater comorbidity burden, depression, and substance-use disorder were associated with lower adherence. In held-out testing, adherence prediction using baseline features yielded an AUROC of 0.678. Using CPAP-only data, AUROCs were higher from 0.787 with seven days of usage data to 0.816 and 0.854 with 14 and 30 days. Models combining CPAP and baseline features increased Day-7 AUROC to 0.810. We identified four one-year trajectories: Early-Decliners (42.9%), Gradual-Decliners (17.4%), Stable-Moderate (19.0%), Stable-High (20.7%). Models predicting patient trajectory revealed macro-average AUROCs of 0.619, 0.756, and 0.770 for baseline, CPAP-only, and combined models. Conclusion Baseline predictors may identify strategies for adherence support, while early CPAP data substantially improved predictive performance. These models may guide personalized follow-up but should not restrict CPAP access. External and implementation validation are needed.","rel_num_authors":16,"rel_authors":[{"author_name":"Nathanael Hayashi Hwang","author_inst":"Beth Israel Deaconess Medical Center"},{"author_name":"Soniya Mishra","author_inst":"University of Kansas Medical Center"},{"author_name":"M Brandon Westover","author_inst":"Stanford University"},{"author_name":"Robert J Thomas","author_inst":"Beth Israel Deaconess Medical Center"},{"author_name":"Emmanuel Mignot","author_inst":"Stanford University"},{"author_name":"Umakanth Katwa","author_inst":"Boston Children's Hospital"},{"author_name":"Jiaxiao M Shi","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Rui Yan","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Kendra A Becker","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Dennis Hwang","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Matthew T Klimper","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Jessica Jara","author_inst":"Kaiser Permanente Southern California"},{"author_name":"Damien R Stevens","author_inst":"University of Kansas Medical Center"},{"author_name":"Matthew K.P. Gratton","author_inst":"University of Kansas Medical Center"},{"author_name":"Diego R Mazzotti","author_inst":"University of Kansas Medical Center"},{"author_name":"Haoqi Sun","author_inst":"Beth Israel Deaconess Medical Center"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Planner-Executor Style Multimodal Agentic System to Answer Patient Questions in Lung Cancer Screening CT","rel_doi":"10.64898\/2026.10.02.26364620","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.02.26364620","rel_abs":"Background: Accurate patient understanding of lung cancer screening (LCS) results is critical for engagement and follow-up adherence, given the currently low screening uptake. While artificial intelligence (AI) systems show promise in facilitating patient communication, its effective use in clinical settings requires appropriate invocation of imaging tools and self-regulation by deferring certain questions to physicians. To this end, we developed and evaluated a planner-executor style multimodal agentic system to answer simulated patient questions about LCS CT results. Methods: This retrospective study utilized 116 LCS CT reports and images collected from a tertiary academic hospital. The system employed a multi-agent architecture (planner and executor) to mimic clinical reasoning. We developed a large language model (LLM)-based question generation framework to prepare a comprehensive set of 699 simulated patient questions, categorized as answerable and defer-to-doctor to assess self-regulation. Performance was evaluated on tool-calling, self-regulation accuracy (ability to correctly defer out-of-scope questions to a human provider) assessed by LLM-as-a-judge, and clinical quality assessed by a reader performance study with four physicians evaluating a subset of 100 responses. Results: The system demonstrated high overall tool-calling accuracy of 93.1% (646\/694; 95% CI: 91.2%, 95.0%) and self-regulation accuracy of 92.6% (462\/499; 90.2%, 94.8%). For answerable questions, the percentage of responses receiving perfect 5.0 scores across all readers included 78.9% (180\/228) for clinical accuracy (inter-rater agreement: Gwet's AC2=0.92), and 73.2% (167\/228) for patient understandability (0.86). For defer-to-doctor questions, the percentage of responses included 77.3% (133\/172) for clinical accuracy (0.80) and 70.3% (121\/172) for patient understandability (0.78). Conclusions: The planner-executor style multi-modal agentic system reliably answers patient-specific questions about LCS. Its high tool-calling and self-regulation performance demonstrates its potential as a safe and effective digital communication facilitator in LCS programs.","rel_num_authors":13,"rel_authors":[{"author_name":"Koharu Sakiyama","author_inst":"University of California, San Francisco"},{"author_name":"Adrian Serapio","author_inst":"University of California, San Francisco"},{"author_name":"Meng Ye","author_inst":"SRI International"},{"author_name":"Ali Nowroozi","author_inst":"University of California, San Francisco"},{"author_name":"Masha Bondarenko","author_inst":"University of California, San Francisco"},{"author_name":"Yufu Wu","author_inst":"University of California, San Francisco"},{"author_name":"Kang Qi","author_inst":"University of California, San Francisco"},{"author_name":"Jonathan Liu","author_inst":"University of California, San Francisco"},{"author_name":"Maya Vella","author_inst":"University of California, San Francisco"},{"author_name":"Yannan Yu","author_inst":"University of California, San Francisco"},{"author_name":"Tician Schnitzler","author_inst":"Cantonal Hospital Aarau"},{"author_name":"Alison S Rustagi","author_inst":"University of California, San Francisco"},{"author_name":"Jae Ho Sohn","author_inst":"University of California, San Francisco"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Planner-Executor Style Multimodal Agentic System to Answer Patient Questions in Lung Cancer Screening CT","rel_doi":"10.64898\/2026.10.02.26364620","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.02.26364620","rel_abs":"Background: Accurate patient understanding of lung cancer screening (LCS) results is critical for engagement and follow-up adherence, given the currently low screening uptake. While artificial intelligence (AI) systems show promise in facilitating patient communication, its effective use in clinical settings requires appropriate invocation of imaging tools and self-regulation by deferring certain questions to physicians. To this end, we developed and evaluated a planner-executor style multimodal agentic system to answer simulated patient questions about LCS CT results. Methods: This retrospective study utilized 116 LCS CT reports and images collected from a tertiary academic hospital. The system employed a multi-agent architecture (planner and executor) to mimic clinical reasoning. We developed a large language model (LLM)-based question generation framework to prepare a comprehensive set of 699 simulated patient questions, categorized as answerable and defer-to-doctor to assess self-regulation. Performance was evaluated on tool-calling, self-regulation accuracy (ability to correctly defer out-of-scope questions to a human provider) assessed by LLM-as-a-judge, and clinical quality assessed by a reader performance study with four physicians evaluating a subset of 100 responses. Results: The system demonstrated high overall tool-calling accuracy of 93.1% (646\/694; 95% CI: 91.2%, 95.0%) and self-regulation accuracy of 92.6% (462\/499; 90.2%, 94.8%). For answerable questions, the percentage of responses receiving perfect 5.0 scores across all readers included 78.9% (180\/228) for clinical accuracy (inter-rater agreement: Gwet's AC2=0.92), and 73.2% (167\/228) for patient understandability (0.86). For defer-to-doctor questions, the percentage of responses included 77.3% (133\/172) for clinical accuracy (0.80) and 70.3% (121\/172) for patient understandability (0.78). Conclusions: The planner-executor style multi-modal agentic system reliably answers patient-specific questions about LCS. Its high tool-calling and self-regulation performance demonstrates its potential as a safe and effective digital communication facilitator in LCS programs.","rel_num_authors":13,"rel_authors":[{"author_name":"Koharu Sakiyama","author_inst":"University of California, San Francisco"},{"author_name":"Adrian Serapio","author_inst":"University of California, San Francisco"},{"author_name":"Meng Ye","author_inst":"SRI International"},{"author_name":"Ali Nowroozi","author_inst":"University of California, San Francisco"},{"author_name":"Masha Bondarenko","author_inst":"University of California, San Francisco"},{"author_name":"Yufu Wu","author_inst":"University of California, San Francisco"},{"author_name":"Kang Qi","author_inst":"University of California, San Francisco"},{"author_name":"Jonathan Liu","author_inst":"University of California, San Francisco"},{"author_name":"Maya Vella","author_inst":"University of California, San Francisco"},{"author_name":"Yannan Yu","author_inst":"University of California, San Francisco"},{"author_name":"Tician Schnitzler","author_inst":"Cantonal Hospital Aarau"},{"author_name":"Alison S Rustagi","author_inst":"University of California, San Francisco"},{"author_name":"Jae Ho Sohn","author_inst":"University of California, San Francisco"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Virtual Noncontrast Imaging with Deep-Silicon Photon-Counting CT: Matched Phantom and Initial Human Evaluation","rel_doi":"10.64898\/2026.10.02.26364524","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.02.26364524","rel_abs":"To compare virtual noncontrast (VNC) error between deep-silicon photon-counting CT (PCCT) and dual-energy energy-integrating detector CT (EID-CT) in a matched phantom and characterize tissue-specific error in humans. This prospective exploratory study included a matched phantom experiment and pilot human evaluation. Prototype projection-domain water-density maps served as the VNC surrogate. Twenty-four vials containing 0.1--20 mg I\/mL in deionized water or simulated blood were scanned with PCCT and EID-CT. Three men underwent true noncontrast and contrast-enhanced PCCT; 80 matched measurements were analyzed. Paired t tests and mixed-effects models assessed system differences and iodine associations. Three participants (mean age, 67 {+\/-} 10 years; all men) were included. Mean VNC error was 29.1 HU lower with PCCT than EID-CT (95% CI, 15.5--42.7 HU; {rho} < 0.001). Error was not associated with iodine concentration for PCCT (slope, 0.03 HU per mg I\/mL; 95% CI, -0.92 to 0.98; {rho} = 0.95) but increased for EID-CT (slope, 3.51 HU per mg I\/mL; 95% CI, 2.56--4.46 HU; {rho} < 0.001). The slopes differed by 3.48 HU per mg I\/mL ({rho} < 0.001). In humans, errors in five non-blood-pool tissues were within 5 HU. Error was not associated with iodine concentration among 61 non-blood-pool measurements (adjusted slope, -0.17 HU per mg I\/mL; 95% CI, -1.22 to 0.88; {rho} = 0.75). PCCT provided VNC measurements with errors closer to zero and less dependent on iodine than EID-CT in the phantom. In humans, error was generally small in non-blood-pool tissues and was not associated with iodine concentration.","rel_num_authors":9,"rel_authors":[{"author_name":"Navid Azimi","author_inst":"Emory University"},{"author_name":"Arnaud Brian-Choux","author_inst":"Emory University"},{"author_name":"Samuel Feemster","author_inst":"Georgia Institute of Technology"},{"author_name":"Alireza Rahbar","author_inst":"Emory University"},{"author_name":"Hugo Linder","author_inst":"GE HealthCare"},{"author_name":"Dominic Crotty","author_inst":"GE HealthCare"},{"author_name":"Zhye Yin","author_inst":"GE HealthCare"},{"author_name":"Patricia Balthazar","author_inst":"Emory University"},{"author_name":"Amir Pourmorteza","author_inst":"Emory University"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Nominal and real cigarette Price trends from a global dataset, 2008-2024 and implications for the World Health Organization's 3 by 35 initiative: a longitudinal study and forecasting analysis","rel_doi":"10.64898\/2026.09.28.26364093","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.28.26364093","rel_abs":"Introduction In July 2025, WHO launched the 3 by 35 Initiative calling on countries to increase real prices of tobacco, alcohol, and sugary drinks by at least 50% by 2035 to secure an additional $ 1 trillion in public revenue. While the effectiveness of excise taxes and prices in reducing tobacco use is well-documented, the feasibility of this call to action has not been evaluated empirically across the world. Methods Trend changes in countries nominal and inflation-adjusted retail prices were investigated using the most-sold brand of cigarettes over windows ranging from 2 to 16 years over the period 2008-2024, derived from nine editions of the World Health Organization Report on the Global Tobacco Epidemic. Statistical models of the association of cigarette excise taxes and prices from the panel of 195 countries were estimated. The frequencies of cigarette prices rising by at least 20% and 50% over preceding periods was estimated, and the length of time countries would need to reach these targets based on past trends were predicted. Results Over one- fifth of nominal cigarettes prices (and up to 40% in WHOs African region) were sticky across successive periods, even in countries with high overall consumer price inflation. Between 2008 and 2024, inflation-adjusted international dollar cigarette prices rose at a least squares annual trend growth rate of 2.9% (95% UI 2.8-3.0%) Excise tax increases strongly predict price increases. One-third of countries saw real prices at least 50% higher than 10 years prior. Conclusion Many of the instances of real price increases of 50% or more over 10-year periods occurred without countries implementing a conscious strategy to raise taxes. It is feasible to achieve and even surpass WHOs 3 by 35 call to action for tobacco if countries strategize and accelerate tobacco tax increases.","rel_num_authors":5,"rel_authors":[{"author_name":"Rajeev Cherukupalli","author_inst":"Johns Hopkins University"},{"author_name":"Guillermo A Sandoval","author_inst":"World Health Organization"},{"author_name":"Anne-Marie Perucic","author_inst":"World Health Organization"},{"author_name":"Mark Goodchild","author_inst":"World Health Organization"},{"author_name":"Jeremias Paul Jr.","author_inst":"paulj@who.int"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Extracellular matrix stiffness in oral squamous cell carcinoma: a systematic review and meta-analysis of associations with tumor progression and prognosis","rel_doi":"10.64898\/2026.09.28.26363990","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.28.26363990","rel_abs":"Objective: To critically evaluate the influence of extracellular matrix stiffness on tumor progression, prognosis, and clinical implications in patients with oral squamous cell carcinoma. Design: This systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, and the protocol was registered in PROSPERO (CRD420251156429). Five databases (PubMed, Scopus, Embase, Web of Science, and OATD) were searched without restrictions on publication year or language. Human studies that evaluated extracellular matrix stiffness in oral squamous cell carcinoma, either as an assessment method or as a prognostic factor, were included; in vitro and animal studies were excluded. Results: Eight human studies involving 436 patients were included. Elastography was the most frequent measurement method, employed in four studies. Mean tumor thickness was reported in three studies and ranged from 3.0 to 4.4 mm. Extracellular matrix stiffness was expressed in various units (e.g., 37.7 gf, 23.5 kPa) and was consistently higher in tumor tissue than in normal mucosa in all studies. Three studies demonstrated a significant positive correlation between stiffness and tumor thickness. One study identified predictive cut-off values for lymph node metastasis and reported that high matrix stiffness was an independent predictor of worse overall survival (HR = 4.022; 95% CI: 1.294-12.495; p = 0.016) and recurrence-free survival (HR = 5.680; 95% CI: 1.146-28.143; p = 0.033). A meta-analysis of five studies (307 patients) showed a significantly higher standardized stiffness in oral squamous cell carcinoma compared with normal oral mucosa (standardized mean difference = 5.28; 95% CI: 2.61-7.94; p < 0.001), with substantial heterogeneity (I2= 98.6%). Conclusions: Extracellular matrix stiffness is elevated in oral squamous cell carcinoma and is associated with greater tumor thickness, lymph node metastasis and, in one study, worse survival. These findings support extracellular matrix stiffness as a potential biomechanical marker, pending further validation in standardized prospective studies.","rel_num_authors":5,"rel_authors":[{"author_name":"Igor Felipe Pereira Lima","author_inst":"Federal University of Rio Grande do Sul"},{"author_name":"Ana Carolina Toebe Silva","author_inst":"Federal University of Rio Grande do Sul"},{"author_name":"Adam Jeffrey Engler","author_inst":"UC San Diego"},{"author_name":"Leonardo da Silva Bittencourt","author_inst":"Hospital de Clinicas de Porto Alegre"},{"author_name":"Marcelo Lazzaron Lamers","author_inst":"Federal University of Rio Grande do Sul"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Patient Trajectories from Electronic Health Records Suggest Specialty Specific Warning Signs for Common Variable Immunodeficiency","rel_doi":"10.64898\/2026.10.01.26364525","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.01.26364525","rel_abs":"Background: Common variable immunodeficiency is frequently underrecognized due to its heterogeneous clinical presentation, resulting in diagnostic delays that increase disease-related complications, with each year of delay increasing mortality risk by 4% for the most common inborn error of immunity. Objective: In individuals with established common variable immunodeficiency, we sought to recognize patterns in patient diagnostic trajectories to identify opportunities for interventions to reduce time to diagnosis. Methods: We analyzed retrospective electronic health records data from 139 individuals with physician-confirmed common variable immunodeficiency across five academic health systems representing >10million patients. We evaluated diagnosing specialties and international classification of disease code trajectories in the two years prior to and at common variable immunodeficiency confirmation. Additionally, we construct specialty-specific prediction on a 1:50 case: control cohort using a gradient boosting algorithm. Results: The leading diagnoses at common variable immunodeficiency determination were respiratory (35% of individuals), and individuals with respiratory symptoms were more likely to be diagnosed by Allergy \/ Immunology than by other specialties (odds ratio 2.9, standard error 0.35, p-value 1.9x10-3) and had a higher frequency of visits to pulmonary disease in the two years prior to diagnosis. Individuals with neoplasms were 50% more likely than other patients (OR 2.0, SE 0.29, p-value 0.02) to receive a diagnosis at each visit, reducing their diagnostic delay by up to a year. We find that a larger ratio of number of specialties visited to the total number of visits is significantly correlated with an increased time to diagnosis. A trajectory-informed specialty-specific prediction improved detection with an area under the precision-recall curve of fifty times the value expected under the null. Conclusions: Individuals referred to Allergy \/ Immunology have shorter time to diagnosis, and a specialty-specific prediction could improve detection of common variable immunodeficiency. Clinical Implications: Overall, we find that phenotype-specialty interactions may inform targeted interventions to improve common variable immunodeficiency recognition across specialties, reducing diagnostic odyssey and disease-related complications.","rel_num_authors":13,"rel_authors":[{"author_name":"Rachel Mester","author_inst":"University of California, Los Angeles"},{"author_name":"Aaron T Chin","author_inst":"UCLA Health Information Technology, UCLA Health, University of California, Los Angeles"},{"author_name":"Veronica Tozzo","author_inst":"University of California, Los Angeles"},{"author_name":"Alexis V Stephens","author_inst":"University of California, Los Angeles"},{"author_name":"Hal M Hoffman","author_inst":"University of California, San Diego"},{"author_name":"Marc A Riedl","author_inst":"University of California, San Diego"},{"author_name":"Morna J Dorsey","author_inst":"University of California, San Francisco"},{"author_name":"Sudhir Gupta","author_inst":"University of California, Irvine"},{"author_name":"Yesim Y Demirdag","author_inst":"University of California, Irvine"},{"author_name":"Sriram Sankararaman","author_inst":"UCLA"},{"author_name":"Lisa Bastarache","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Bogdan Pasaniuc","author_inst":"University of Pennsylvania"},{"author_name":"Manish J Butte","author_inst":"UCLA"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Patient Trajectories from Electronic Health Records Suggest Specialty Specific Warning Signs for Common Variable Immunodeficiency","rel_doi":"10.64898\/2026.10.01.26364525","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.01.26364525","rel_abs":"Background: Common variable immunodeficiency is frequently underrecognized due to its heterogeneous clinical presentation, resulting in diagnostic delays that increase disease-related complications, with each year of delay increasing mortality risk by 4% for the most common inborn error of immunity. Objective: In individuals with established common variable immunodeficiency, we sought to recognize patterns in patient diagnostic trajectories to identify opportunities for interventions to reduce time to diagnosis. Methods: We analyzed retrospective electronic health records data from 139 individuals with physician-confirmed common variable immunodeficiency across five academic health systems representing >10million patients. We evaluated diagnosing specialties and international classification of disease code trajectories in the two years prior to and at common variable immunodeficiency confirmation. Additionally, we construct specialty-specific prediction on a 1:50 case: control cohort using a gradient boosting algorithm. Results: The leading diagnoses at common variable immunodeficiency determination were respiratory (35% of individuals), and individuals with respiratory symptoms were more likely to be diagnosed by Allergy \/ Immunology than by other specialties (odds ratio 2.9, standard error 0.35, p-value 1.9x10-3) and had a higher frequency of visits to pulmonary disease in the two years prior to diagnosis. Individuals with neoplasms were 50% more likely than other patients (OR 2.0, SE 0.29, p-value 0.02) to receive a diagnosis at each visit, reducing their diagnostic delay by up to a year. We find that a larger ratio of number of specialties visited to the total number of visits is significantly correlated with an increased time to diagnosis. A trajectory-informed specialty-specific prediction improved detection with an area under the precision-recall curve of fifty times the value expected under the null. Conclusions: Individuals referred to Allergy \/ Immunology have shorter time to diagnosis, and a specialty-specific prediction could improve detection of common variable immunodeficiency. Clinical Implications: Overall, we find that phenotype-specialty interactions may inform targeted interventions to improve common variable immunodeficiency recognition across specialties, reducing diagnostic odyssey and disease-related complications.","rel_num_authors":13,"rel_authors":[{"author_name":"Rachel Mester","author_inst":"University of California, Los Angeles"},{"author_name":"Aaron T Chin","author_inst":"UCLA Health Information Technology, UCLA Health, University of California, Los Angeles"},{"author_name":"Veronica Tozzo","author_inst":"University of California, Los Angeles"},{"author_name":"Alexis V Stephens","author_inst":"University of California, Los Angeles"},{"author_name":"Hal M Hoffman","author_inst":"University of California, San Diego"},{"author_name":"Marc A Riedl","author_inst":"University of California, San Diego"},{"author_name":"Morna J Dorsey","author_inst":"University of California, San Francisco"},{"author_name":"Sudhir Gupta","author_inst":"University of California, Irvine"},{"author_name":"Yesim Y Demirdag","author_inst":"University of California, Irvine"},{"author_name":"Sriram Sankararaman","author_inst":"UCLA"},{"author_name":"Lisa Bastarache","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Bogdan Pasaniuc","author_inst":"University of Pennsylvania"},{"author_name":"Manish J Butte","author_inst":"UCLA"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Patient Trajectories from Electronic Health Records Suggest Specialty Specific Warning Signs for Common Variable Immunodeficiency","rel_doi":"10.64898\/2026.10.01.26364525","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.01.26364525","rel_abs":"Background: Common variable immunodeficiency is frequently underrecognized due to its heterogeneous clinical presentation, resulting in diagnostic delays that increase disease-related complications, with each year of delay increasing mortality risk by 4% for the most common inborn error of immunity. Objective: In individuals with established common variable immunodeficiency, we sought to recognize patterns in patient diagnostic trajectories to identify opportunities for interventions to reduce time to diagnosis. Methods: We analyzed retrospective electronic health records data from 139 individuals with physician-confirmed common variable immunodeficiency across five academic health systems representing >10million patients. We evaluated diagnosing specialties and international classification of disease code trajectories in the two years prior to and at common variable immunodeficiency confirmation. Additionally, we construct specialty-specific prediction on a 1:50 case: control cohort using a gradient boosting algorithm. Results: The leading diagnoses at common variable immunodeficiency determination were respiratory (35% of individuals), and individuals with respiratory symptoms were more likely to be diagnosed by Allergy \/ Immunology than by other specialties (odds ratio 2.9, standard error 0.35, p-value 1.9x10-3) and had a higher frequency of visits to pulmonary disease in the two years prior to diagnosis. Individuals with neoplasms were 50% more likely than other patients (OR 2.0, SE 0.29, p-value 0.02) to receive a diagnosis at each visit, reducing their diagnostic delay by up to a year. We find that a larger ratio of number of specialties visited to the total number of visits is significantly correlated with an increased time to diagnosis. A trajectory-informed specialty-specific prediction improved detection with an area under the precision-recall curve of fifty times the value expected under the null. Conclusions: Individuals referred to Allergy \/ Immunology have shorter time to diagnosis, and a specialty-specific prediction could improve detection of common variable immunodeficiency. Clinical Implications: Overall, we find that phenotype-specialty interactions may inform targeted interventions to improve common variable immunodeficiency recognition across specialties, reducing diagnostic odyssey and disease-related complications.","rel_num_authors":13,"rel_authors":[{"author_name":"Rachel Mester","author_inst":"University of California, Los Angeles"},{"author_name":"Aaron T Chin","author_inst":"UCLA Health Information Technology, UCLA Health, University of California, Los Angeles"},{"author_name":"Veronica Tozzo","author_inst":"University of California, Los Angeles"},{"author_name":"Alexis V Stephens","author_inst":"University of California, Los Angeles"},{"author_name":"Hal M Hoffman","author_inst":"University of California, San Diego"},{"author_name":"Marc A Riedl","author_inst":"University of California, San Diego"},{"author_name":"Morna J Dorsey","author_inst":"University of California, San Francisco"},{"author_name":"Sudhir Gupta","author_inst":"University of California, Irvine"},{"author_name":"Yesim Y Demirdag","author_inst":"University of California, Irvine"},{"author_name":"Sriram Sankararaman","author_inst":"UCLA"},{"author_name":"Lisa Bastarache","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Bogdan Pasaniuc","author_inst":"University of Pennsylvania"},{"author_name":"Manish J Butte","author_inst":"UCLA"}],"rel_date":"2026-10-05","rel_site":"medrxiv"},{"rel_title":"Haplotype-phased, chromosome-level, annotated genome assembly of the soybean rust pathogen Phakopsora pachyrhizi","rel_doi":"10.64898\/2026.09.29.755439","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.29.755439","rel_abs":"Phakopsora pachyrhizi, the causal agent of soybean rust, is among the most aggressive and economically impactful pathogens affecting soybean (Glycine max) production worldwide. As a dikaryotic rust fungus, P. pachyrhizi maintains its genetic information as two discrete haplotypes that are sequestered within separate nuclei for most of its life cycle as it reproduces asexually via the production of infectious urediniospores. Previous genomes of three P. pachyrhizi isolates collected in South America revealed a large genome with a high transposable element content, measuring up to 1.25 Gb and 93%, respectively. Although these efforts provided valuable foundational resources, the complexity of the genome resulted in highly fragmented assemblies. Here, we generated a highly contiguous, substantially improved, annotated genome resource from the P. pachyrhizi isolate LA04-1, collected during the initial outbreak of soybean rust in the continental United States in 2004. The assembly is haplotype-phased, with each haplotype assembled into 18 chromosomes totaling 572 Mb and 569 Mb in size. The isolate contains unique mating-type genes and does not share haplotype genomes with any available isolates with whole-genome sequencing datasets. This resource will be valuable to future investigations into the underlying mechanisms of virulence in this important plant pathogen.","rel_num_authors":8,"rel_authors":[{"author_name":"Nicholas F Greatens","author_inst":"Oak Ridge Institute for Science and Education"},{"author_name":"Manjula G Elmore","author_inst":"Iowa State University"},{"author_name":"Sowmya R Ramachandran","author_inst":"Oak Ridge Institute for Science and Education"},{"author_name":"Amy Wright","author_inst":"USDA Agricultural Research Service"},{"author_name":"Luke J. Tallon","author_inst":"University of Maryland"},{"author_name":"Steven A Whitham","author_inst":"Iowa State University"},{"author_name":"Rachel A. Koch Bach","author_inst":"USDA Agricultural Research Service"},{"author_name":"Kerry F. Pedley","author_inst":"USDA Agricultural Research Service Northeast Area"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"An Expanded Lpt Pathway Couples Surface Lipoprotein Export to Outer Membrane Vesicle Biogenesis in Bacteroides","rel_doi":"10.64898\/2026.10.02.756314","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.02.756314","rel_abs":"The ecological dominance of Bacteroidota in the human gut microbiota derives from their exceptional capacity to metabolize diverse dietary and host-derived glycans. This ability relies on surface-exposed lipoproteins (SLPs), which are displayed on the bacterial surface and selectively packaged into outer membrane vesicles (OMVs). Although SLP export and OMV biogenesis are tightly coupled, the mechanism linking these processes has remained unknown. Here, we identify LptZ, a previously uncharacterized protein, as a critical link between SLP surface translocation and OMV biogenesis. Cryo-electron tomography of the lptZ mutant revealed chains of vesicles tethered to the outer membrane, offering a rare glimpse into an arrested stage of OMV formation in which vesicle fission is impaired. Proteomic analysis further showed that, in the absence of LptZ, a subset of SLPs fails to reach the cell surface and instead accumulates at the inner membrane. Unexpectedly, LptZ associates with the lipopolysaccharide transport (Lpt) machinery and repurposes it for SLP translocation. Crosslinking experiments revealed selective associations between multiple SLPs and Lpt proteins, providing mechanistic evidence for the direct involvement of the Lpt machinery in SLP trafficking. We further show that the Bacteroides Lpt machinery contains previously unrecognized components, revealing an unanticipated level of specialization relative to canonical Lpt systems. Together, our findings uncover a previously unknown protein export pathway, expand the functional role of the Lpt machinery, and establish a direct mechanistic link between SLP translocation and OMV biogenesis in Bacteroidota.","rel_num_authors":12,"rel_authors":[{"author_name":"Biswanath Jana","author_inst":"Washington University School of Medicine"},{"author_name":"Evan J. Pardue","author_inst":"Washington University School of Medicine"},{"author_name":"Tengfei Zhong","author_inst":"University of Chicago"},{"author_name":"Mariana G. Sartorio","author_inst":"Washington University School of Medicine"},{"author_name":"Manon Janet-Maitre","author_inst":"Washington University School of Medicine"},{"author_name":"Juan C. Ortiz-Marquez","author_inst":"Boston Childrens Hospital"},{"author_name":"Marta Nieckarz","author_inst":"Umea University"},{"author_name":"Tim Van Opijnen","author_inst":"Boston Childrens Hospital"},{"author_name":"Felipe Cava","author_inst":"Umea University"},{"author_name":"Nichollas E. Scott","author_inst":"University of Melbourne"},{"author_name":"Mohammed Kaplan","author_inst":"University of Chicago"},{"author_name":"Mario F. Feldman","author_inst":"Washington University School of Medicine"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"An Expanded Lpt Pathway Couples Surface Lipoprotein Export to Outer Membrane Vesicle Biogenesis in Bacteroides","rel_doi":"10.64898\/2026.10.02.756314","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.02.756314","rel_abs":"The ecological dominance of Bacteroidota in the human gut microbiota derives from their exceptional capacity to metabolize diverse dietary and host-derived glycans. This ability relies on surface-exposed lipoproteins (SLPs), which are displayed on the bacterial surface and selectively packaged into outer membrane vesicles (OMVs). Although SLP export and OMV biogenesis are tightly coupled, the mechanism linking these processes has remained unknown. Here, we identify LptZ, a previously uncharacterized protein, as a critical link between SLP surface translocation and OMV biogenesis. Cryo-electron tomography of the lptZ mutant revealed chains of vesicles tethered to the outer membrane, offering a rare glimpse into an arrested stage of OMV formation in which vesicle fission is impaired. Proteomic analysis further showed that, in the absence of LptZ, a subset of SLPs fails to reach the cell surface and instead accumulates at the inner membrane. Unexpectedly, LptZ associates with the lipopolysaccharide transport (Lpt) machinery and repurposes it for SLP translocation. Crosslinking experiments revealed selective associations between multiple SLPs and Lpt proteins, providing mechanistic evidence for the direct involvement of the Lpt machinery in SLP trafficking. We further show that the Bacteroides Lpt machinery contains previously unrecognized components, revealing an unanticipated level of specialization relative to canonical Lpt systems. Together, our findings uncover a previously unknown protein export pathway, expand the functional role of the Lpt machinery, and establish a direct mechanistic link between SLP translocation and OMV biogenesis in Bacteroidota.","rel_num_authors":12,"rel_authors":[{"author_name":"Biswanath Jana","author_inst":"Washington University School of Medicine"},{"author_name":"Evan J. Pardue","author_inst":"Washington University School of Medicine"},{"author_name":"Tengfei Zhong","author_inst":"University of Chicago"},{"author_name":"Mariana G. Sartorio","author_inst":"Washington University School of Medicine"},{"author_name":"Manon Janet-Maitre","author_inst":"Washington University School of Medicine"},{"author_name":"Juan C. Ortiz-Marquez","author_inst":"Boston Childrens Hospital"},{"author_name":"Marta Nieckarz","author_inst":"Umea University"},{"author_name":"Tim Van Opijnen","author_inst":"Boston Childrens Hospital"},{"author_name":"Felipe Cava","author_inst":"Umea University"},{"author_name":"Nichollas E. Scott","author_inst":"University of Melbourne"},{"author_name":"Mohammed Kaplan","author_inst":"University of Chicago"},{"author_name":"Mario F. Feldman","author_inst":"Washington University School of Medicine"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"Mycobacterium tuberculosis inactivates host oxysterols to impair macrophage cholesterol homeostasis","rel_doi":"10.64898\/2026.10.04.756586","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.04.756586","rel_abs":"Host oxysterols coordinate macrophage cholesterol homeostasis and antimicrobial defense, but whether pathogens directly target oxysterol signaling is unknown. Here, we identify pathogen-mediated enzymatic oxysterol inactivation as a mechanism by which Mycobacterium tuberculosis (Mtb) subverts host cholesterol metabolism. Mtb oxidizes the endogenous oxysterols 27-hydroxycholesterol (27-HC) and 3{beta}-hydroxycholest-5-enoic acid (3{beta}-HCA) into 3-oxo-{Delta}4 metabolites. Sputum lipidomics across TB cohorts on three continents revealed an active-disease signature characterized by 27-HC depletion and accumulation of 27-hydroxycholest-4-en-3-one (27-HCO) and 3-oxocholest-4-enoic acid (3O-CA), which normalized with treatment. In infected human macrophages, Mtb 3{beta}-hydroxysteroid dehydrogenase (3{beta}-Hsd) generated 27-HCO. Unlike their parent oxysterols, 27-HCO and 3O-CA did not activate liver X receptor (LXR), and 3O-CA failed to suppress sterol regulatory element-binding protein 2 (SREBP2) target genes. Accordingly, 3{beta}-Hsd-deficient Mtb did not induce macrophage cholesterol retention, a phenotype restored by LXR antagonism. Thus, Mtb enzymatically inactivates host oxysterols, disrupting macrophage cholesterol homeostasis and generating treatment-responsive metabolic signatures in human TB.","rel_num_authors":16,"rel_authors":[{"author_name":"Andrew Roth","author_inst":"Washington University in St. Louis School of Medicine"},{"author_name":"Mohsen Ali Asgari","author_inst":"Swansea University Medical School"},{"author_name":"Pallavi Chandra","author_inst":"Washington University in St. Louis School of Medicine"},{"author_name":"Jalalah Muhammad","author_inst":"Washington University in St. Louis School of Medicine"},{"author_name":"Amy Lopez","author_inst":"Swansea University School of Medicine"},{"author_name":"Sourav Bhattacharya","author_inst":"Washington University in St. Louis School of Medicine"},{"author_name":"Isabelle A. Williams","author_inst":"Washington University in St. Louis School of Medicine"},{"author_name":"Brian Edelson","author_inst":"Washington University in St. Louis School of Medicine"},{"author_name":"Charles W Goss","author_inst":"Washington University in St. Louis School of Medicine"},{"author_name":"Dmitri I Kotov","author_inst":"Washington University in St. Louis School of Medicine"},{"author_name":"Mingxing Qian","author_inst":"Washington University School of Medicine in St. Louis"},{"author_name":"Douglas F Covey","author_inst":"Washington University in St. Louis School of Medicine"},{"author_name":"Xuntian Jiang","author_inst":"Washington University in St. Louis School of Medicine"},{"author_name":"William J. Griffiths","author_inst":"Cardiff University"},{"author_name":"Yuqin Wang","author_inst":"Swansea University School of Medicine"},{"author_name":"Jennifer A. Philips","author_inst":"Washington University in St. Louis School of Medicine"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"Differential PCDH1 usage by Laguna Negra and Black Creek Canal hantaviruses","rel_doi":"10.64898\/2026.10.05.756634","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.05.756634","rel_abs":"Laguna Negra virus and Black Creek Canal virus are hantaviruses associated with hantavirus cardiopulmonary syndrome (HCPS), but their cellular entry receptors have not been defined. We used replication-competent recombinant vesicular stomatitis viruses (rVSVs) bearing hantavirus Gn\/Gc glycoprotein complexes to compare entry by these viruses, the Old World rodent-borne Dabieshan virus and Tigray virus, and the shrew-borne Cao Bang virus and Amga virus. All six Gn\/Gc complexes supported infection of primary human pulmonary microvascular endothelial cells, with virus-specific efficiencies and kinetics. Genetic ablation of protocadherin-1 (PCDH1) reduced infection mediated by Laguna Negra virus and Black Creek Canal virus Gn\/Gc in human osteosarcoma and endothelial cells, whereas PCDH1 re-expression restored susceptibility. Both viruses were captured by a soluble PCDH1 protein comprising the first two extracellular cadherin domains. Infection was also inhibited by a soluble decoy containing the first cadherin domain, and by an antibody specific to that domain, indicating that the first PCDH1 cadherin domain mediates viral engagement. Black Creek Canal Gn\/Gc-bearing rVSVs incorporated less Gn\/Gc and showed lower apparent capture by PCDH1 than Laguna Negra virus and Andes virus Gn\/Gc-bearing particles, even after normalization for glycoprotein abundance. In contrast, PCDH1 depletion did not detectably alter infection mediated by the other four hantavirus Gn\/Gc complexes. Thus, Laguna Negra virus and Black Creek Canal virus use PCDH1 for efficient entry but differ in apparent receptor engagement and responses to PCDH1-directed inhibitors, whereas all six viruses are potently neutralized by the broadly reactive Gn\/Gc-directed antibody ADI-42898.","rel_num_authors":8,"rel_authors":[{"author_name":"Nahomi Guerra-Pilaquinga","author_inst":"LSU Health Shreveport"},{"author_name":"Cierra Word","author_inst":"LSU Health Shreveport"},{"author_name":"Upendra P Lambe","author_inst":"LSU Health Shreveport"},{"author_name":"Lohit Khera","author_inst":"LSU Health Shreveport"},{"author_name":"Ridwan Arinola","author_inst":"LSU Health Shreveport"},{"author_name":"Ramandeep Kaur","author_inst":"LSU Health Shreveport"},{"author_name":"M Eugenia Dieterle","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Rohit K Jangra","author_inst":"Louisiana State University Health Sciences Center Shreveport School of Medicine"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"Targeting catecholate siderophore systems enables precision depletion of gut enterobacteria during colitis","rel_doi":"10.64898\/2026.10.02.756324","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.02.756324","rel_abs":"Adherent-invasive Escherichia coli (AIEC) is frequently isolated from the dysbiotic gut microbiome in inflammatory bowel disease and is implicated in disease progression, highlighting its potential as a therapeutic target. Here we show that iron acquisition is essential for AIEC colonization of the inflamed gut. Mechanistically, four catecholate siderophore receptors act redundantly to support AIEC growth under iron-limited conditions and during colitis. Immunization of colitis-prone Il10-\/- mice against catecholate siderophores reduced intestinal AIEC colonization, particularly its association with the mucosa, while largely preserving overall gut microbiome composition. Longitudinal metagenomic profiling identified immunization-associated shifts in Akkermansiaceae and Lactobacillaceae, and experiments in gnotobiotic Il10-\/- mice demonstrated that Limosilactobacillus reuteri, a member of the Lactobacillaceae, limited AIEC colonization, whereas Akkermansiaceae exacerbated disease. Our findings establish catecholate siderophore-mediated iron acquisition as a therapeutic target in AIEC and provide proof-of-principle that virulence-targeted immunization can selectively antagonize a disease-associated bacterium while preserving the gut microbiota","rel_num_authors":17,"rel_authors":[{"author_name":"Grant J. Norton","author_inst":"University of California, San Diego"},{"author_name":"Anna M. Dudek","author_inst":"University of California, San Diego"},{"author_name":"Romana R. Gerner","author_inst":"University of California, San Diego"},{"author_name":"Chuchu Guo","author_inst":"Massachusetts Institute of Technology"},{"author_name":"Vanessa Castillo","author_inst":"University of California, San Diego"},{"author_name":"Felix A. Argueta","author_inst":"University of California, San Diego"},{"author_name":"Connor J. Beebout","author_inst":"University of California, San Diego"},{"author_name":"Ghazal Kanyabi","author_inst":"Massachusetts Institute of Technology"},{"author_name":"Julia Drushell","author_inst":"University of California, San Diego"},{"author_name":"Gregory T. Walker","author_inst":"University of California, San Diego"},{"author_name":"Chia-Yun Hsu","author_inst":"University of California, San Diego"},{"author_name":"Shingo Bessho","author_inst":"University of California, San Diego"},{"author_name":"Sean-Paul Nuccio","author_inst":"University of California, San Diego"},{"author_name":"Hiutung Chu","author_inst":"University of California, San Diego"},{"author_name":"Karsten Zengler","author_inst":"University of California, San Diego"},{"author_name":"Elizabeth M. Nolan","author_inst":"Massachusetts Institute of Technology"},{"author_name":"Manuela Raffatellu","author_inst":"University of California, San Diego"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"Decoupled recovery of soil physicochemistry, microbiome and methane sink function during secondary forest succession in the Eastern Himalayas","rel_doi":"10.64898\/2026.10.04.753464","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.04.753464","rel_abs":"Secondary forests are increasingly important for biodiversity recovery and ecosystem functioning after disturbance. As vegetation regenerates, accompanying shifts in soil conditions and microbial communities regulate climate-relevant processes such as atmospheric methane (CH$_4$) uptake. Whether these belowground dimensions recover synchronously, however, remains unclear. Here we show that belowground recovery followed asynchronous trajectories across a forest chronosequence in Metok, Eastern Himalayas. Soil inorganic properties and microbial community composition converged progressively on the primary-forest state as soils acidified. By contrast, soil organic carbon, microbial diversity and abundance appeared to recover by 25 years but diverged again at 40 years. Co-occurrence network topology resembled the primary forest by 40 years, yet the keystone taxa underlying it were entirely stage specific. Atmospheric CH$_4$ uptake fluctuated: uptake recovered to primary-forest levels in 25-year stands but weakened in 40-year stands to rates comparable with the recently disturbed 5-year forest. Rather than canonical methane-cycling taxa, four amplicon sequence variants---three assigned to \\textit{Mycobacterium}---tracked CH$_4$ flux closely while acting as hubs or connectors in the co-occurrence networks, warranting genomic and experimental work to resolve their function. Apparent recovery within the first 25 years therefore did not persist, cautioning against inferring restoration success from early succession alone and highlighting the need for longer chronosequences and repeated observation to resolve the dynamics of secondary-forest recovery.","rel_num_authors":7,"rel_authors":[{"author_name":"Mianhao Zhang","author_inst":"Peking University College of Urban and Environmental Sciences"},{"author_name":"Xiaohui Zhao","author_inst":"Peking University College of Urban and Environmental Sciences"},{"author_name":"Yuting Zhao","author_inst":"Peking University College of Urban and Environmental Sciences"},{"author_name":"Dandan Shi","author_inst":"Peking University College of Urban and Environmental Sciences"},{"author_name":"Wenzheng Fu","author_inst":"Peking University College of Urban and Environmental Sciences"},{"author_name":"Cuiyun Yang","author_inst":"Peking University College of Urban and Environmental Sciences"},{"author_name":"Hang Yu","author_inst":"Peking University College of Urban and Environmental Sciences"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"Upper respiratory tract bacterial microbiome features associated with Streptococcus pneumoniae colonisation, environmental and demographic factors among older children in a rural setting in The Gambia","rel_doi":"10.64898\/2026.10.04.756419","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.04.756419","rel_abs":"Abstract The upper respiratory tract (URT) microbiome plays a vital role in respiratory health, with older children (5-14 years old) serving as key reservoirs for the transmission of bacteria to younger individuals (< 5 years old). However, the URT microbiome in this age group remains poorly characterised, particularly in relation to colonisation by the pathobiont Streptococcus pneumoniae. To address this gap, we analysed URT samples collected over eight visits from 134 healthy children aged 5-14 years in The Gambia, using culture-dependent methods to detect S. pneumoniae and culture-independent 16S rRNA sequencing to characterise the microbiome in parallel. 16S rRNA sequencing identified Streptococcus and Prevotella as the dominant microbiome constituents. When integrating longitudinal sampling data, we observed temporal instability with frequent switches in microbiome profiles over time. URT samples colonised with S. pneumoniae were associated with increased abundance of Moraxella, Streptococcus and Haemophilus, whereas non-colonised URT samples were enriched with Granulicatella and Filifactor. S. pneumoniae colonisation was also associated with lower Shannon diversity compared with non-colonised samples. Age and season were the strongest determinants of microbiome composition; increasing age was associated with decreasing prevalence and abundance of taxa including Haemophilus and Moraxella, while gram-positive taxa were predominantly enriched in the dry season. These findings provide new insight into the URT microbiome of older children in The Gambia and highlight Granulicatella and Filifactor as potential contributors to colonisation resistance against S. pneumoniae.","rel_num_authors":15,"rel_authors":[{"author_name":"Dam Khan","author_inst":"Medical Research Council Unit The Gambia At The London School Of Hygiene And Tropical Medicine"},{"author_name":"Brenda Kwambana-Adams","author_inst":"Malawi Liverpool Wellcome Programme, Blantyre, Malawi."},{"author_name":"Peggy Estelle Tientcheu","author_inst":"Medical Research Council Unit The Gambia At The London School Of Hygiene And Tropical Medicine"},{"author_name":"Shola Able Thomas","author_inst":"Medical Research Council Unit The Gambia At The London School Of Hygiene And Tropical Medicine"},{"author_name":"Josh L Espinoza","author_inst":"J. Craig Venter Institute"},{"author_name":"Gavin John","author_inst":"J. Craig Venter Institute"},{"author_name":"Tiana Mamaghani","author_inst":"J. Craig Venter Institute"},{"author_name":"Peter Sylvanus Ndow","author_inst":"Medical Research Council Unit The Gambia At The London School Of Hygiene And Tropical Medicine"},{"author_name":"Ma-Ansu Kinteh","author_inst":"Medical Research Council Unit The Gambia At The London School Of Hygiene And Tropical Medicine"},{"author_name":"Archibald Worwui","author_inst":"Medical Research Council Unit The Gambia At The London School Of Hygiene And Tropical Medicine"},{"author_name":"Isaac Darko Otchere","author_inst":"Department of Bacteriology, Noguchi Memorial Institute for Medical Research, Legon, University of Ghana, Accra, Ghana"},{"author_name":"Nuredin Ibrahim Mohammed","author_inst":"Medical Research Council Unit The Gambia At The London School Of Hygiene And Tropical Medicine"},{"author_name":"Mark P Nicol","author_inst":"Marshall Centre, Division of Infection and Immunity, School of Biomedical Sciences, University of Western Australia, Perth, Australia."},{"author_name":"Christopher L. Dupont","author_inst":"J. Craig Venter Institute"},{"author_name":"Martin Antonio","author_inst":"Medical Research Council Unit The Gambia At The London School Of Hygiene And Tropical Medicine"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"Conserved mycobacteriophage protein uses structural mimicry to displace host initiation factors","rel_doi":"10.64898\/2026.10.05.756390","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.05.756390","rel_abs":"Compact viral genomes encode sophisticated strategies to reprogram host gene expression, often by co-opting rather than replacing the host RNA polymerase. How bacteriophages achieve such control in Mycobacteria remains largely unknown. Here, time-resolved interaction proteomics of mycobacteriophage D29 infection identifies a conserved two-protein module, gp53-gp52, that binds the host RNA polymerase. Affinity purification confirmed association of both proteins with the polymerase, while native mass spectrometry demonstrated that gp53 displaces the host sigma factor. Structural modelling places gp53 at the sigma-binding surface of RNA polymerase, consistent with molecular mimicry of sigma-factor engagement. The module is conserved throughout Cluster A mycobacteriophages and occurs in fused or split genomic architectures. These findings suggest sigma-factor displacement as a mechanism of transcriptional takeover in viruses of mycobacteria and show how a compact viral module can repurpose an essential host molecular machine","rel_num_authors":12,"rel_authors":[{"author_name":"David R. Parker","author_inst":"Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Solna, Sweden; Science for Life Laboratory, Karolinska Institutet, Solna, Swede"},{"author_name":"Shrestha Ghosh","author_inst":"Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Solna, Sweden; Science for Life Laboratory, Karolinska Institutet, Solna, Swede"},{"author_name":"Adela Dujiskova","author_inst":"Laboratory of Molecular Pathogenesis, The Rockefeller University, New York, NY, USA"},{"author_name":"Paul Dominic B. Olinares","author_inst":"Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY, USA"},{"author_name":"Sarah Narrowe Danielsson","author_inst":"Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden"},{"author_name":"Grisna Isabel Prensa","author_inst":"Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Solna, Sweden; Science for Life Laboratory, Karolinska Institutet, Solna, Swede"},{"author_name":"Doyeon Kim","author_inst":"Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Solna, Sweden; Science for Life Laboratory, Karolinska Institutet, Solna, Swede"},{"author_name":"Florian Andreas Rosenberger","author_inst":"Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany; Division of Molecular Metabolism, Department of Me"},{"author_name":"Arne Elofsson","author_inst":"Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden"},{"author_name":"Matthias Mann","author_inst":"Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany; NNF Center for Protein Research, Faculty of Health"},{"author_name":"Elizabeth Campbell","author_inst":"Laboratory of Molecular Pathogenesis, The Rockefeller University, New York, NY, USA"},{"author_name":"Andrea Fossati","author_inst":"Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Solna, Sweden; Science for Life Laboratory, Karolinska Institutet, Solna, Swede"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"Diffusion MRI of cortical organoids reveals protocol-associated spatial reproducibility and increasing diffusivity with diffusion time","rel_doi":"10.64898\/2026.09.29.755111","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.29.755111","rel_abs":"Early human cortical development involves rapid changes in cytoarchitecture that remain difficult to interrogate non-invasively. Biophysical diffusion MRI models provide a potential window onto this microstructure, including water exchange across cell membranes, but their assumptions are difficult to validate directly in developing human tissue. Human cortical organoids provide a tractable biological model in which diffusion measurements can be related to tissue architecture within the same specimen. We scanned six fixed cortical organoids at 9.4~T, three grown under a directed and three under an undirected differentiation protocol, at three diffusion times and b-values up to 7000~s\/mm$^2$. Center-to-periphery profiles of DKI-derived diffusion metrics were reproducible across the directed organoids but more variable across the undirected organoids, while nuclear staining revealed greater variability in the internal architecture of the undirected organoids. Unexpectedly, mean diffusivity increased with diffusion time in more than 80% of tissue voxels in both batches, opposite to previous organoid measurements. Across 12,348 simulated two-compartment substrates, this increase was reproduced only with permeable membranes and an extracellular diffusivity substantially exceeding the apparent intracellular diffusivity, a regime that challenges assumptions commonly used in current gray-matter exchange models. These results support cortical organoids as biologically realistic platforms for probing early cortical microstructure and testing diffusion models under controlled conditions, while remaining amenable to post-MRI histological validation.","rel_num_authors":13,"rel_authors":[{"author_name":"Andr\u00e9s le Boeuf Fl\u00f3","author_inst":"Ecole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL)"},{"author_name":"Ekin Taskin","author_inst":"Ecole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL)"},{"author_name":"Oriana Lavielle","author_inst":"NeuroNA Human Cellular Neuroscience Platform (HCNP), Fondation Campus Biotech"},{"author_name":"Athanasios Grigoriou","author_inst":"Ecole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL)"},{"author_name":"Katarzyna Pierzchala","author_inst":"Ecole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL)"},{"author_name":"Thanh Phong L\u00ea","author_inst":"Ecole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL)"},{"author_name":"Ileana Jelescu","author_inst":"Lausanne University Hospital (CHUV)"},{"author_name":"Zeljka Krsnik","author_inst":"Croatian Institute for Brain Research, University of Zagreb"},{"author_name":"Th\u00e9o Ribierre","author_inst":"NeuroNA Human Cellular Neuroscience Platform (HCNP), Fondation Campus Biotech"},{"author_name":"Jean-Philippe Thiran","author_inst":"Ecole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL)"},{"author_name":"Jonathan Rafael Pati\u00f1o","author_inst":"Ecole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL)"},{"author_name":"Erick Jorge Canales Rodr\u00edguez","author_inst":"Universidad de Granada"},{"author_name":"Elda Fischi G\u00f3mez","author_inst":"Ecole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL)"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"Learning continuous updating and control of neural population activities from sparse time samples","rel_doi":"10.64898\/2026.09.28.754781","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.28.754781","rel_abs":"The brain frequently updates or controls its internal representations of sensory or motor variables over time. An example is transsaccadic updating of stimuli's retinotopic positions: When the eye moves in one direction, the positions are updated continuously in the opposite direction, and the process is controlled by the corollary discharge (CD) of the saccade motor command. We previously trained neural networks to perform such updating using the desired time courses of stimuli's positions at every time step. However, this may not be biologically plausible because, before a system learns to perform the task, it may not have access to complete time courses and the actual visual inputs are delayed and thus incorrect. Here we show that by adding the continuity equation to the loss, we can train neural networks with only stimuli's pre- and post-saccadic retinotopic positions (without specifying the intermediate transitions), and with heavily down-sampled time courses. Moreover, both the connectivity patterns and the CD control signal can be learned simultaneously. Since the continuity equation can be applied to waves of neural population activities that are distributed representations of variables, our work also suggests an advantage of distributed representations which are ubiquitous in the brain.","rel_num_authors":3,"rel_authors":[{"author_name":"Zezhao Wu","author_inst":"School of Life Sciences, Tsinghua University, Beijing, China"},{"author_name":"Mingsha Zhang","author_inst":"State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China"},{"author_name":"Ning Qian","author_inst":"Department of Neuroscience and Zuckerman Institute, Columbia University, New York, NY, USA"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"Imputation-Based Harmonization Mitigates Site Effects Without Data Leakage in Machine Learning Studies","rel_doi":"10.64898\/2026.09.28.755204","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.28.755204","rel_abs":"Neuroimaging studies that pool data across clinical sites often suffer from site effects --- variability in imaging measures that arises from technical heterogeneity across sites as opposed to true biological signal. While numerous harmonization methods have been proposed to remove site effects from imaging features, less attention has been placed on how to incorporate harmonization models into common machine-learning pipelines. We demonstrate that current approaches for integration either suffer from data leakage, fail to fully remove site effects from the target data, or attenuate true biological associations during the harmonization process. To address these issues, we propose Multiple Imputation for Removing Technical Heterogeneity (MIRTH), which uses imputed outcomes to harmonize the test data. We benchmark MIRTH's performance using both simulated and real-world volumetric data from the Alzheimer's Disease Neuroimaging Initiative and the Baltimore Longitudinal Study of Aging, demonstrating that MIRTH can achieve high predictive accuracy while avoiding inflated performance when the outcome is imbalanced across clinical sites.","rel_num_authors":10,"rel_authors":[{"author_name":"Noah Hillman","author_inst":"University of Pennsylvania"},{"author_name":"Andrew Chen","author_inst":"Medical University of South Carolina"},{"author_name":"Fengling Hu","author_inst":"University of Pennsylvania"},{"author_name":"Simon Vandekar","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Randa Melhem","author_inst":"University of Pennsylvania"},{"author_name":"Lori Beason-Held","author_inst":"National Institutes of Health"},{"author_name":"Theodore Satterthwaite","author_inst":"University of Pennsylvania"},{"author_name":"Christos Davatzikos","author_inst":"University of Pennsylvania"},{"author_name":"Haochang Shou","author_inst":"University of Pennsylvania"},{"author_name":"Russell Shinohara","author_inst":"University of Pennsylvania"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"Communication as a Two-Person Neural Process: fNIRS Hyperscanning in Adults with and without Post-Stroke Aphasia","rel_doi":"10.64898\/2026.09.28.755138","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.28.755138","rel_abs":"Communication is inherently interactive, yet most neuroimaging studies examine individuals in isolation. Here, we used functional near-infrared spectroscopy (fNIRS) hyperscanning to investigate interbrain synchrony (IBS) during naturalistic communication. In Study 1, 13 healthy familiar dyads completed Joint Singing, Joint Reading, and Picture Guessing tasks. IBS was quantified using wavelet transform coherence across bilateral frontal and temporoparietal regions. Joint Singing produced higher IBS than the other tasks in several regions, including the dorsolateral prefrontal cortex and temporoparietal junction, and selected effects were greater in true than pseudo-dyads. In Study 2, we applied the same paradigm to five individuals with chronic post-stroke aphasia interacting with trained speech-language pathologists. IBS could be estimated in all dyads after lesion-informed channel exclusion, although regional coverage varied with lesion location. These findings demonstrate that fNIRS hyperscanning can capture task-dependent interpersonal neural coupling during spoken interaction and can be extended to communication involving individuals with aphasia. This approach provides a foundation for studying how neural coordination between communication partners relates to functional communication and partner-supported interaction.","rel_num_authors":10,"rel_authors":[{"author_name":"Grace Magee","author_inst":"Boston University"},{"author_name":"James Colwell","author_inst":"Boston University"},{"author_name":"Shannon Kelley","author_inst":"Boston University"},{"author_name":"Yasaa Mohammad","author_inst":"Boston University"},{"author_name":"Yiwen Zhang","author_inst":"Boston University"},{"author_name":"David  A. A Boas","author_inst":"Boston University"},{"author_name":"Swathi Kiran","author_inst":"Boston University"},{"author_name":"Maria Varkanitsa","author_inst":"Boston University"},{"author_name":"Erin L Meier","author_inst":"Northeastern University"},{"author_name":"Meryem A Yucel","author_inst":"Boston University"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"Notch1 and Notch2 receptors mediate the prosensory functions of the Notch ligand Jagged1 in the mammalian cochlea","rel_doi":"10.64898\/2026.10.03.756416","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.03.756416","rel_abs":"Mechanosensory hair cells in the cochlea of the inner ear are essential for sound detection. Previous studies have shown that loss of the Notch ligand Jagged1 (JAG1) disrupts maintenance of prosensory progenitors, from which hair cells and their surrounding supporting cells derive. However, the molecular and cellular mechanisms by which JAG1 regulates prosensory progenitor maintenance are poorly understood, and the Notch receptors that mediate this function in the developing cochlea remain unknown. To address this, we generated Jag1 conditional knockout mice and characterized their prosensory deficits using transcriptomic and phenotypic analyses. We show that JAG1 loss abolishes prosensory expression of CDKN1B (p27Kip1) and reduces expression of essential neurotrophic factors, thereby disrupting the stereotyped pattern of cell-cycle exit and innervation within the prosensory domain. Furthermore, conditional knockout of Notch1 and Notch2 (Notch1\/2) receptors in supporting cells phenocopied the Jag1 deletion phenotype, showing both a severe reduction in outer hair cell formation and a disrupted pattern of cell-cycle exit and innervation, thereby confirming their essential roles in mediating the prosensory functions of JAG1.","rel_num_authors":4,"rel_authors":[{"author_name":"He Huang","author_inst":"Johns Hopkins University"},{"author_name":"Xiaojun Li","author_inst":"Xian Jiaotong University"},{"author_name":"Elannah Venhaus","author_inst":"Johns Hopkins University"},{"author_name":"Angelika Doetzlhofer","author_inst":"Johns Hopkins University"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"DLGAP5 promotes vascular cell proliferation and remodeling in pulmonary arterial hypertension","rel_doi":"10.64898\/2026.09.29.755207","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.29.755207","rel_abs":"Background: Pulmonary arterial hypertension (PAH) manifests by pulmonary vascular remodeling, caused by hyper-proliferation and apoptosis resistance of resident PA cells, elevated right ventricular (RV) afterload, and death of RV failure. The goal of this study is to determine the role and mechanisms of action of mitotic spindle-associated microtubule regulator DLG associated protein 5 (DLGAP5) in pulmonary vascular remodeling in PAH. Methods: DLGAP5 expression was assessed in human PAH transcriptomic datasets, PAH patients' lung tissues, pulmonary artery smooth muscle cells (PASMCs), and pulmonary artery adventitial fibroblasts (PAAFs). Loss-of-function, molecular, functional, RNAseq, and network analyses were performed. Smooth muscle (SM)-specific DLGAP5 knockdown using adeno-associated virus serotype 6 (AAV6) in mice with SU5416\/hypoxia-induced PH was performed. Results: DLGAP5 was overaccumulated in small muscular PAs, PASMCs, and PAAFs from PAH lungs. siRNA DLGAP5 down-regulated cyclin A2, suppressed hyper-proliferation, increased pro-apoptotic BIM, and induced apoptosis in human PAH PASMCs and PAAFs. DEP domain containing 1 (DEPDC1) was identified and validated as a downstream effector of DLGAP5 in PAH PASMCs responsible for hyper-proliferation and apoptosis resistance of human PAH PASMCs. SM-specific DLGAP5 knockdown with AAV6-enTagln-shDLGAP5 reversed DEPDC1 overaccumulation in small muscular PAs, reduced PA medial thickness, RV systolic pressure, mean PA pressure, and RV hypertrophy in mice with SuHx-induced PH. Conclusions: DLGAP5 supports proliferation and survival of human PAH PASMCs and PAAFs through up-regulation of cyclin A2, DEPDC1, and suppression of BIM, and promotes PA remodeling and PH in mice. DLGAP5 could serve as a potential molecular target to attenuate pulmonary vascular remodeling in PAH.","rel_num_authors":9,"rel_authors":[{"author_name":"Tapan Dey","author_inst":"University of California Davis School of Medicine"},{"author_name":"Iryna Zhyvylo","author_inst":"University of California Davis"},{"author_name":"Dmitry Goncharov","author_inst":"University of California Davis School of Medicine"},{"author_name":"Derek Lin","author_inst":"University of California Davis School of Medicine"},{"author_name":"John R Greenland","author_inst":"University of California San Francisco"},{"author_name":"Paul J Wolters","author_inst":"University of California San Francisco"},{"author_name":"Delphine Gomez","author_inst":"University of Pittsburgh"},{"author_name":"Lifeng Jiang","author_inst":"University of California Davis"},{"author_name":"Elena A. Goncharova","author_inst":"University of California Davis School of Medicine"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"Calcium-independent biosensors for multiplexed in vivo imaging of extracellular lactate dynamics","rel_doi":"10.64898\/2026.10.02.756141","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.02.756141","rel_abs":"Extracellular L-lactate reports cellular metabolism, but resolving its dynamics alongside intracellular metabolism and neuronal activity remains challenging. Here we develop eLACCO3 and R-eLACCO3, green and red cell-surface lactate biosensors with Ca2+-independent responses and faster response and recovery kinetics in cultured cells. Selected variants provide larger fluorescence responses than their predecessors in brain cells, slices, and living mice and improve detection of glucose-evoked extracellular lactate oscillations. Combining R-eLACCO3 with GCaMP6f enabled imaging of extracellular L-lactate simultaneously with odor-evoked activity in anesthetized mice and cortical activity in awake mice. These biosensors enable multiplexed measurements of extracellular lactate dynamics in relation to intracellular metabolism and neuronal activity.","rel_num_authors":18,"rel_authors":[{"author_name":"Jia-Jin Liu","author_inst":"Academia Sinica"},{"author_name":"Hidenobu Mizuno","author_inst":"Kumamoto University"},{"author_name":"Emmanuel Than-Trong","author_inst":"Institut de la Vision"},{"author_name":"Philipp Machler","author_inst":"University of California San Diego"},{"author_name":"Hideaki Kubotera","author_inst":"RIKEN"},{"author_name":"Tony Barbay","author_inst":"University of Oxford"},{"author_name":"Ching-Yao Wang","author_inst":"Academia Sinica"},{"author_name":"Qin-Ling To","author_inst":"Academia Sinica"},{"author_name":"Yuki Kamijo","author_inst":"The University of Tokyo"},{"author_name":"Taki Nishimura","author_inst":"The University of Osaka"},{"author_name":"Marc Boisvert","author_inst":"Laval University"},{"author_name":"Marie-Eve Paquet","author_inst":"Laval University"},{"author_name":"Mikhail Drobizhev","author_inst":"Montana State University"},{"author_name":"Stephanie J Cragg","author_inst":"University of Oxford"},{"author_name":"Jun Nagai","author_inst":"RIKEN"},{"author_name":"David Kleinfeld","author_inst":"University of California San Diego"},{"author_name":"Serge Charpak","author_inst":"Institut de la Vision"},{"author_name":"Yusuke NASU","author_inst":"Academia Sinica"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"Calcium-independent biosensors for multiplexed in vivo imaging of extracellular lactate dynamics","rel_doi":"10.64898\/2026.10.02.756141","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.02.756141","rel_abs":"Extracellular L-lactate reports cellular metabolism, but resolving its dynamics alongside intracellular metabolism and neuronal activity remains challenging. Here we develop eLACCO3 and R-eLACCO3, green and red cell-surface lactate biosensors with Ca2+-independent responses and faster response and recovery kinetics in cultured cells. Selected variants provide larger fluorescence responses than their predecessors in brain cells, slices, and living mice and improve detection of glucose-evoked extracellular lactate oscillations. Combining R-eLACCO3 with GCaMP6f enabled imaging of extracellular L-lactate simultaneously with odor-evoked activity in anesthetized mice and cortical activity in awake mice. These biosensors enable multiplexed measurements of extracellular lactate dynamics in relation to intracellular metabolism and neuronal activity.","rel_num_authors":18,"rel_authors":[{"author_name":"Jia-Jin Liu","author_inst":"Academia Sinica"},{"author_name":"Hidenobu Mizuno","author_inst":"Kumamoto University"},{"author_name":"Emmanuel Than-Trong","author_inst":"Institut de la Vision"},{"author_name":"Philipp Machler","author_inst":"University of California San Diego"},{"author_name":"Hideaki Kubotera","author_inst":"RIKEN"},{"author_name":"Tony Barbay","author_inst":"University of Oxford"},{"author_name":"Ching-Yao Wang","author_inst":"Academia Sinica"},{"author_name":"Qin-Ling To","author_inst":"Academia Sinica"},{"author_name":"Yuki Kamijo","author_inst":"The University of Tokyo"},{"author_name":"Taki Nishimura","author_inst":"The University of Osaka"},{"author_name":"Marc Boisvert","author_inst":"Laval University"},{"author_name":"Marie-Eve Paquet","author_inst":"Laval University"},{"author_name":"Mikhail Drobizhev","author_inst":"Montana State University"},{"author_name":"Stephanie J Cragg","author_inst":"University of Oxford"},{"author_name":"Jun Nagai","author_inst":"RIKEN"},{"author_name":"David Kleinfeld","author_inst":"University of California San Diego"},{"author_name":"Serge Charpak","author_inst":"Institut de la Vision"},{"author_name":"Yusuke NASU","author_inst":"Academia Sinica"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"A synthetic PGC1\u03b1 co-activator reprograms microglial immunometabolism to preserve cone function in retinal degeneration","rel_doi":"10.64898\/2026.09.29.753881","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.29.753881","rel_abs":"Retinitis pigmentosa (RP) is genetically heterogeneous, yet initiating mutations converge on primary rod degeneration followed by cone dysfunction and loss. Activated microglia are implicated in this secondary phase, but whether selective immunometabolic reprogramming of microglia preserves retinal function remains unknown. In human iPSC-derived microglia and retinal CD11b cells, PGC1 induction suppressed LPS-driven interferon signaling and cytokine release, yet Seahorse analysis and isotopologue tracing showed that energy metabolism remained altered. We therefore fused PGC1 to a multivalent p65-HSF1 module, yielding ~2.5-fold higher nuclear receptor output than native PGC1 and coordinated engagement of oxidative and anti-inflammatory programs in mouse retinal CD11b cells. Microglia-restricted expression in humanized RHOP347L mice preserved photopic responses through six months, increased retinal thickness and promoted ramified subretinal IBA1 morphology. Microglial immunometabolism thus represents a potentially genotype-independent neuroprotective target in RP, and engineered PGC1 co-activators provide a means to sustain oxidative metabolism under inflammatory pressure.","rel_num_authors":20,"rel_authors":[{"author_name":"Thomas Winogrodzki","author_inst":"Department of Ophthalmology, Columbia University, New York, NY, United States"},{"author_name":"Jayvin Hernandez-Reyes","author_inst":"Department of Ophthalmology, Columbia University, New York, NY, United States"},{"author_name":"Rajvir Solanky","author_inst":"Department of Ophthalmology, Columbia University, New York, NY, United States"},{"author_name":"Richard Lowery","author_inst":"Department of Biochemistry, University of Washington, Seattle, WA, United States"},{"author_name":"Georgy Komissarov","author_inst":"Department of Ophthalmology, Columbia University, New York, NY, United States"},{"author_name":"Wen Huang","author_inst":"Department of Ophthalmology, Columbia University, New York, NY, United States"},{"author_name":"Boyuan Wang","author_inst":"Department of Ophthalmology, Columbia University, New York, NY, United States; Department of Biomedical Engineering, Columbia University, New York, NY, United S"},{"author_name":"Neoklis Makrides","author_inst":"Department of Ophthalmology, Columbia University, New York, NY, United States"},{"author_name":"Sharifa Davis","author_inst":"Department of Ophthalmology, Columbia University, New York, NY, United States; Department of Biomedical Engineering, Columbia University, New York, NY, United S"},{"author_name":"Siyuan Liu","author_inst":"Department of Ophthalmology, Columbia University, New York, NY, United States; Department of Biomedical Engineering, Columbia University, New York, NY, United S"},{"author_name":"Yong-Shi Li","author_inst":"Department of Ophthalmology, Columbia University, New York, NY, United States"},{"author_name":"Wen-Hsuan Wu","author_inst":"Department of Ophthalmology, Columbia University, New York, NY, United States"},{"author_name":"Nicholas Nolan","author_inst":"Department of Ophthalmology, Columbia University, New York, NY, United States"},{"author_name":"Robert van de Werken","author_inst":"Department of Ophthalmology, Columbia University, New York, NY, United States; Department of Biomedical Engineering, Columbia University, New York, NY, United S"},{"author_name":"Aykut Demirkol","author_inst":"Department of Ophthalmology, Columbia University, New York, NY, United States"},{"author_name":"Anders Knudsen","author_inst":"Department of Ophthalmology, Columbia University, New York, NY, United States; Department of Biomedical Engineering, Columbia University, New York, NY, United S"},{"author_name":"Nuntachai Surawatsatien","author_inst":"Department of Ophthalmology, Columbia University, New York, NY, United States; Center of Excellence in Retina, Department of Ophthalmology, Faculty of Medicine,"},{"author_name":"Yao Li","author_inst":"Department of Ophthalmology, Columbia University, New York, NY, United States"},{"author_name":"James Hurley","author_inst":"Department of Biochemistry, University of Washington, Seattle, WA, United States"},{"author_name":"Stephen H Tsang","author_inst":"Department of Ophthalmology, Columbia University, New York, NY, United States"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"Designing enzymes for new-to-nature chemistry and non-natural substrates with AlphaProtein Novo","rel_doi":"10.64898\/2026.10.01.756017","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.01.756017","rel_abs":"Creating highly active enzymes for arbitrary reactions is a transformative goal for the molecular sciences (1). Traditional protein engineering (2,3) is constrained by a reliance on existing natural starting points, which are often challenging to discover. De novo design offers the potential to overcome this by creating enzymes from first principles (4), but has yet to achieve practically relevant catalytic properties. Here we present AlphaProtein Novo (AP Novo), a machine-learning pipeline that demonstrates, for the first time, that de novo enzyme design can outperform natural sequence mining in addressing challenging chemistry. We used AP Novo to design new-to-nature nitrene transferases to synthesise the pharmacophore piperidine with unprecedented product selectivity, and to create enzymes that degrade the environmental toxin DEHP under conditions that denature natural enzymes. We also obtained state-of-the-art catalytic efficiencies on two well-studied model reactions, and through iterative design and analysis found that key enablers of success were mechanism-inspired metrics based on AlphaFold 3 predictions and sequence-ensemble-based scoring of designed backbones. Our results show that de novo design has become a powerful complement to natural enzyme diversity for discovering catalysts for a range of applications.","rel_num_authors":37,"rel_authors":[{"author_name":"Zachary Wu","author_inst":"Google DeepMind"},{"author_name":"Joshua Abramson","author_inst":"Google DeepMind"},{"author_name":"Thomas Frerix","author_inst":"Google DeepMind"},{"author_name":"Alexander E Chu","author_inst":"Google DeepMind"},{"author_name":"Ruijie K Zhang","author_inst":"Google DeepMind"},{"author_name":"Luca Schulz","author_inst":"Google DeepMind"},{"author_name":"Amy E Danson","author_inst":"Google DeepMind"},{"author_name":"Tristan O. C. Kwan","author_inst":"Google DeepMind"},{"author_name":"Wenliang K Li","author_inst":"Google DeepMind"},{"author_name":"Jacob Kelly","author_inst":"Google DeepMind"},{"author_name":"Zi-Qi Li","author_inst":"California Institute of Technology"},{"author_name":"Rosalia G Schneider","author_inst":"Google DeepMind"},{"author_name":"Ashok Thillaisundaram","author_inst":"Google DeepMind"},{"author_name":"Harshnira Patani","author_inst":"Google DeepMind"},{"author_name":"Vinicius F Zambaldi","author_inst":"Google DeepMind"},{"author_name":"Sukhdeep Singh","author_inst":"Google DeepMind"},{"author_name":"David La","author_inst":"Google DeepMind"},{"author_name":"Masy Domecillo","author_inst":"California Institute of Technology"},{"author_name":"Ariane N. Mora","author_inst":"work while done at California Institute of Technology"},{"author_name":"Julia C. Reisenbauer","author_inst":"work while done at California Institute of Technology"},{"author_name":"Yu Zhang","author_inst":"California Institute of Technology"},{"author_name":"Eliseo Papa","author_inst":"Google DeepMind"},{"author_name":"Akvile Zemgulyte","author_inst":"Google DeepMind"},{"author_name":"Yu-Han Wu","author_inst":"Google DeepMind"},{"author_name":"Augustin Zidek","author_inst":"Google DeepMind"},{"author_name":"Jiaxin Shi","author_inst":"Work done while at Google DeepMind"},{"author_name":"Grace Margand","author_inst":"Google DeepMind"},{"author_name":"Naila Assem","author_inst":"Google DeepMind"},{"author_name":"Kate Stephen","author_inst":"Google DeepMind"},{"author_name":"Charlie Emrich","author_inst":"Google DeepMind"},{"author_name":"Peng Liu","author_inst":"University of Pittsburgh"},{"author_name":"Colwell Lucy","author_inst":"Google DeepMind"},{"author_name":"Demis Hassabis","author_inst":"Google DeepMind"},{"author_name":"Rob Fergus","author_inst":"Work done while at Google DeepMind"},{"author_name":"Frances H. Arnold","author_inst":"California Institute of Technology"},{"author_name":"Pushmeet Kohli","author_inst":"Google DeepMind"},{"author_name":"Jue Wang","author_inst":"Google DeepMind"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"Effector-host networks in the barley powdery mildew pathosystem reveal significant targeting of ubiquitin-associated proteins and transcriptional regulators","rel_doi":"10.64898\/2026.10.01.756047","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.01.756047","rel_abs":"- The ascomycete fungal pathogen Blumeria hordei (Bh) is predicted to secrete over 530 effector proteins to infect barley and cause powdery mildew disease, yet their individual and collective functions are largely unexplored. We used yeast two-hybrid next-generation interaction screening (Y2H-NGIS) to probe the host targets of 48 Bh candidate secreted effectors (CSEPs), including multiple AVRA effectors that are recognized by MLA nucleotide-binding leucine-rich repeat (NLR) receptors. - Y2H-NGIS and subsequent binary retesting identified 356 high-confidence protein-protein interactions (PPI) among Bh CSEPs, MLA NLR fragments, and barley proteins. We uncovered unique and shared host targets, including numerous proteins involved in ubiquitin-associated processes, gene expression, immune and kinase signaling, metabolism, and vesicle trafficking. - We identified a host protein (HvCAP2) that interacts with MLA and at least five Bh effectors, as well as additional effector-host PPI that correlate with loss of recognition of AVRA13 and AVRA6 by MLA13 and MLA6, respectively. - Results were integrated with an interolog-based barley interactome (HvInt) to assemble a host-pathogen network of 1248 proteins and 1701 interactions. This PPI resource can serve as a platform for functional investigations into the molecular basis of powdery mildew pathogenesis and host recognition.","rel_num_authors":9,"rel_authors":[{"author_name":"J. Mitch Elmore","author_inst":"USDA-Agricultural Research Service, Cereal Disease Laboratory, 1551 Lindig Street, St. Paul, MN 55108"},{"author_name":"Valeria Velasquez-Zapata","author_inst":"Department of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA 50011"},{"author_name":"Gregory Fuerst","author_inst":"USDA-Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011"},{"author_name":"Schuyler D Smith","author_inst":"Department of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA 50011"},{"author_name":"Megan Mueller","author_inst":"Department of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA 50011"},{"author_name":"Zoi Goebel","author_inst":"Department of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA 50011"},{"author_name":"Ashton Byal","author_inst":"Department of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA 50011"},{"author_name":"Matthew James Moscou","author_inst":"USDA-Agricultural Research Service, Cereal Disease Laboratory, 1551 Lindig Street, St. Paul, MN 55108"},{"author_name":"Roger P. Wise","author_inst":"USDA-Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"PWK genetic background reprograms heterochromatin and malignant epithelial states in Neu-driven mammary tumors","rel_doi":"10.64898\/2026.10.01.755936","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.01.755936","rel_abs":"Genetic background can alter the latency and behavior of cancers initiated by the same oncogenic driver, but how inherited variation reshapes the regulatory landscape that permits tumor development remains poorly understood. The PWK genetic background accelerates mammary tumor onset in the NeuT model. We compared PWK NeuT and BALB NeuT tumors using tumor-level RNA-seq, histone ChIP-seq, and single-cell multiome profiling. PWK tumors exhibited broad transcriptional reprogramming characterized by reduced immune-associated expression and increased epithelial differentiation and neuronal-like programs. Chromatin remodeling was spatially redistributed rather than uniformly increased or decreased. PWK tumors showed expansion of H3K9me3- and H4K20me3-associated chromatin, while both marks were depleted across many expressed genes and concentrated within selected intergenic and repeat-rich territories, revealing coordinated redistribution of constitutive heterochromatin. Single-cell multiome profiling showed that broad epithelial representation remained similar between strains, yet specific malignant states were strongly strain biased. Integrated RNA and accessibility analyses identified concordant strain-selective regulatory programs and candidate loci, while trajectory analysis revealed distinct epithelial state transitions between PWK NeuT and BALB NeuT tumors. Together, these findings indicate that inherited genetic background reshapes transcriptional programs, heterochromatin organization, and malignant epithelial state trajectories in NeuT mammary tumors, providing a regulatory framework for understanding strain-dependent differences in tumor onset.","rel_num_authors":4,"rel_authors":[{"author_name":"Rui Geng","author_inst":"Wayne State University School of Medicine"},{"author_name":"Ryann R Ray","author_inst":"Michigan State University"},{"author_name":"Jennifer B Jacob","author_inst":"Michigan State University"},{"author_name":"Benjamin L Kidder","author_inst":"Wayne State University School of Medicine"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"DE-SWAN analysis of biomarker trajectories over the life course is highly sensitive to age distribution and model parameterization","rel_doi":"10.64898\/2026.09.29.754760","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.29.754760","rel_abs":"DE-SWAN is a commonly used method to study how large ensembles of omics biomarkers change across the life course. It has been used to demonstrate apparent repeated accelerations and decelerations of aging rates across the life course, including 2-3 peaks of aging-related changes. This result contradicts what is expected based on the demography of aging and known biological mechanisms. Here, we use simulations to study the robustness of DE-SWAN to the age distribution of the study sample and to choice of window size. We show that under many realistic scenarios, DE-SWAN is highly sensitive to factors unrelated to the underlying rates of change in aging biology. This calls into question the results of previous analyses that have attributed DE-SWAN peaks to acceleration and deceleration of aging. We conclude that the method is unreliable for studying rates of change across the life course under most circumstances.","rel_num_authors":4,"rel_authors":[{"author_name":"Anchen Che","author_inst":"Shanghai Pinghe School"},{"author_name":"Prisha Shah","author_inst":"Woodbridge Academy Magnet School"},{"author_name":"Kamaryn T Tanner","author_inst":"Robert N. Butler Columbia Aging Center, Mailman School of Public Health, Columbia University"},{"author_name":"Alan A Cohen","author_inst":"Robert N. Butler Columbia Aging Center, Mailman School of Public Health, Columbia University"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"DE-SWAN analysis of biomarker trajectories over the life course is highly sensitive to age distribution and model parameterization","rel_doi":"10.64898\/2026.09.29.754760","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.29.754760","rel_abs":"DE-SWAN is a commonly used method to study how large ensembles of omics biomarkers change across the life course. It has been used to demonstrate apparent repeated accelerations and decelerations of aging rates across the life course, including 2-3 peaks of aging-related changes. This result contradicts what is expected based on the demography of aging and known biological mechanisms. Here, we use simulations to study the robustness of DE-SWAN to the age distribution of the study sample and to choice of window size. We show that under many realistic scenarios, DE-SWAN is highly sensitive to factors unrelated to the underlying rates of change in aging biology. This calls into question the results of previous analyses that have attributed DE-SWAN peaks to acceleration and deceleration of aging. We conclude that the method is unreliable for studying rates of change across the life course under most circumstances.","rel_num_authors":4,"rel_authors":[{"author_name":"Anchen Che","author_inst":"Shanghai Pinghe School"},{"author_name":"Prisha Shah","author_inst":"Woodbridge Academy Magnet School"},{"author_name":"Kamaryn T Tanner","author_inst":"Robert N. Butler Columbia Aging Center, Mailman School of Public Health, Columbia University"},{"author_name":"Alan A Cohen","author_inst":"Robert N. Butler Columbia Aging Center, Mailman School of Public Health, Columbia University"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"A 4D GelMA-Cad\/Gelatin Hydrogel for High-Resolution DMD Bioprinting of Vascularized Soft Tissue Models","rel_doi":"10.64898\/2026.10.01.756011","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.01.756011","rel_abs":"Digital micromirror device (DMD)-based bioprinting enables high-resolution fabrication of hydrogel-based tissue constructs, but print fidelity and hydrogel mechanics are typically coupled through polymer concentration, light exposure, and crosslinking density. This creates a materials constraint for vascular bioprinting, where constructs must preserve microscale architecture during fabrication while supporting multicellular remodeling, lumen formation, and long-term tissue organization. To meet these requirements, we developed a 4D GelMA-Cad\/gelatin hydrogel with time-dependent mechanics that maintains print fidelity while providing a soft, cell-permissive matrix. GelMA-Cad provides a covalently photocrosslinked, bioactive network containing an N-cadherin-mimetic peptide, while uncrosslinked gelatin serves as a transient matrix-modifying component that diffuses from the hydrogel after fabrication, producing further softening without bulk degradation of the GelMA-Cad network. We characterized hydrogel chemistry with NMR, quantified time-dependent changes in stiffness, visualized pore morphology with cryo-SEM, and evaluated the platform in retinal and pulmonary constructs. The 4D hydrogel supports 3D-patterned accuracy, cell viability, inner blood-retinal barrier vasculature, and vascularized lung-alveolar tissue models. Single-cell RNA-sequencing of the pulmonary vascular-alveolar model demonstrated compartment-specific transcriptional responses, including enrichment of lung capillary-associated endothelial features and AT1-like epithelial states. Overall, this platform decouples DMD print fidelity from post-print matrix mechanics for vascularized soft tissue bioprinting and tissue modeling.","rel_num_authors":10,"rel_authors":[{"author_name":"Jason Wang","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Claire McClain","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Evan Krystofiak","author_inst":"Vanderbilt University"},{"author_name":"Nicole Marguerite","author_inst":"Vanderbilt University"},{"author_name":"William Hoskins","author_inst":"Vanderbilt University"},{"author_name":"Haley Sutphen","author_inst":"Vanderbilt University"},{"author_name":"Jonathan A Kropski","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Holly David","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Abraham Scott McCall","author_inst":"Vanderbilt University Medical Center"},{"author_name":"Brian O'Grady","author_inst":"Vanderbilt University Medical Center"}],"rel_date":"2026-10-05","rel_site":"biorxiv"},{"rel_title":"Validation of Machine Learning Models for classification of bladder dysfunction and risk of hydronephrosis in patients with Spina Bifida using videourodynamic data","rel_doi":"10.64898\/2026.09.30.26364423","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26364423","rel_abs":"Purpose: Videourodynamics (VUDS) are used to assess bladder dysfunction and risk of upper urinary tract deterioration in patients with spina bifida (SB), but interpretation is subject to interrater variability. Machine learning models have previously classified bladder dysfunction severity and predicted incident hydronephrosis using VUDS data. This study evaluated the performance of adapted versions of these models in an independent external cohort. Materials and Methods: VUDS data were collected from SB patients who underwent VUDS at a single institution between 2016 and 2025. Previously developed machine learning frameworks were adapted for implementation in the independent dataset. Three models were utilized: a deep learning convolutional neural network model using pressure-volume data, a deep learning imaging model using fluoroscopic imaging data, and an ensemble model that averaged the risk data from the pressure-volume and fluoroscopic data. The models were evaluated for prediction of incident hydronephrosis and classification of bladder dysfunction severity compared to expert pediatric urologist reviewers. Results: 70 patients were included in the hydronephrosis cohort, of whom 13 (18%) developed incident hydronephrosis. The ensemble model outperformed individual modalities, with a concordance index of 0.77 (pressure-volume only: 0.70, imaging only: 0.72) and an overall AUROC of 0.75 (pressure-volume: 0.67, imaging: 0.74). For bladder dysfunction classification in 95 VUDS studies, the ensemble model achieved 71% accuracy compared to 60% for pressure-volume data alone and 66% for imaging alone, with no substantial disagreements with expert reviewers. Conclusions: The machine learning models that were developed to predict incident hydronephrosis and classify the severity of bladder dysfunction using VUDS data were able to be adapted to an external, independent dataset and perform with similar discrimination and accuracy as previously reported.","rel_num_authors":7,"rel_authors":[{"author_name":"John K Weaver","author_inst":"Cleveland Clinic Foundation"},{"author_name":"Joseph Logan","author_inst":"Children's Hospital of Philadelphia"},{"author_name":"Julie A Klock","author_inst":"Cleveland Clinic Foundation"},{"author_name":"Kristina Dortche","author_inst":"Cleveland Clinic Foundation"},{"author_name":"Hassaan A Bukhari","author_inst":"University of Massachusetts Dartmouth"},{"author_name":"David S. Buchinsky","author_inst":"Wright State University Boonshoft School of Medicine"},{"author_name":"Madison Burns","author_inst":"Case Western Reserve University School of Medicine"}],"rel_date":"2026-10-03","rel_site":"medrxiv"},{"rel_title":"REM sleep EEG slowing signals basal forebrain integrity in older adults with subjective and mild cognitive impairment","rel_doi":"10.64898\/2026.09.30.26363850","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26363850","rel_abs":"The basal forebrain is an early site of pathology in Alzheimers disease (AD). Within this region, the nucleus basalis of Meynert (NbM) provides the primary cholinergic input to the neocortex and supports cortical activation during rapid eye movement (REM) sleep. REM electroencephalography (EEG) slowing is observed in older adults with AD and amnestic mild cognitive impairment (MCI). This study examined the relationship between REM EEG slowing and NbM volume, and whether it differed by cognitive status, sex or sleep apnea severity.\n\nParticipants aged [&ge;]60 years with subjective cognitive impairment or MCI underwent neuropsychological and medical assessments, polysomnography and magnetic resonance imaging (MRI). REM EEG slowing was quantified as the ratio of delta and theta (<8Hz) to alpha, sigma and beta (8-32Hz) power. Bilateral NbM and Ch1-3 volume were derived from T1-weighted MRI images. Multiple linear regression and moderation analyses examined associations between REM EEG slowing and NbM volume, adjusting for age, sex, and sleep apnoea severity. To assess anatomical specificity, primary analyses were repeated using Ch1- 3 volume and non-REM EEG slowing. Exploratory analyses examined associations between NbM volume, sleep macroarchitecture, and memory.\n\nThe sample comprised 116 participants (59.5% female; mean age = 70.5 years, 64.7% MCI). Greater REM EEG slowing was associated with smaller NbM volume (all p < 0.05), whereas no significant associations were observed with Ch1-3 volume and non-REM EEG slowing (all p > 0.05). Although moderation effects were not significant after FDR correction, within-group analyses suggested a stronger relationship in MCI. Smaller NbM volume was associated with longer sleep latency and poorer episodic memory (all p < 0.05).\n\nREM EEG slowing may reflect early NbM degeneration and provide a non-invasive biomarker of basal forebrain integrity in older adults with cognitive concerns, with findings showing anatomical specificity to the NbM and sleep-stage specificity to REM sleep. Longitudinal studies are required to establish its prognostic value.","rel_num_authors":12,"rel_authors":[{"author_name":"Aaron Lam","author_inst":"Healthy Brain Ageing Program, The Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia.; School of Psychology, Faculty of Science, University"},{"author_name":"Nicole Espinosa","author_inst":"Healthy Brain Ageing Program, The Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia.; School of Psychology, Faculty of Science, University"},{"author_name":"Elie Matar","author_inst":"Woolcock Institute of Medical Research, Macquarie University, Macquarie Park, NSW, Australia.; NHMRC Synergise, Integrate and Enhance Sleep Research to transfor"},{"author_name":"Nathan Cross","author_inst":"Healthy Brain Ageing Program, The Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia.; School of Psychology, Faculty of Science, University"},{"author_name":"Jurgen Fripp","author_inst":"NHMRC Synergise, Integrate and Enhance Sleep Research to transform Brain Ageing (SIESTA) Program; The Australian e-Health Research Centre, CSIRO Healthy and Bio"},{"author_name":"Ying Xia","author_inst":"The Australian e-Health Research Centre, CSIRO Healthy and Biosecurity, Herston, Queensland, Australia."},{"author_name":"Claire Andr\u00e9","author_inst":"Normandie Univ, UNICAEN, INSERM, UA20, NEUROPRESAGE, GIP Cyceron, 14000 Caen, France"},{"author_name":"Elizabeth Coulson","author_inst":"School of Anatomy and Physiology, Faculty of Medicine, Dentistry and Health Science, University of Melbourne, Melbourne, VIC, Australia"},{"author_name":"Renata Taranto","author_inst":"Woolcock Institute of Medical Research, Macquarie University, Macquarie Park, NSW, Australia."},{"author_name":"Ronald Grunstein","author_inst":"NHMRC Synergise, Integrate and Enhance Sleep Research to transform Brain Ageing (SIESTA) Program; Woolcock Institute of Medical Research, Macquarie University, "},{"author_name":"Angela L D'Rozario","author_inst":"NHMRC Synergise, Integrate and Enhance Sleep Research to transform Brain Ageing (SIESTA) Program; Woolcock Institute of Medical Research, Macquarie University, "},{"author_name":"Sharon L Naismith","author_inst":"NHMRC Synergise, Integrate and Enhance Sleep Research to transform Brain Ageing (SIESTA) Program; Healthy Brain Ageing Program, The Brain and Mind Centre, Unive"}],"rel_date":"2026-10-02","rel_site":"medrxiv"},{"rel_title":"AI-assisted nurse-led skin cancer screening in a teledermoscopy framework: a multi-site evaluation with one million lesions","rel_doi":"10.64898\/2026.10.01.26364543","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.01.26364543","rel_abs":"Nurse-led skin cancer screening extends specialist reach but depends on the nurse selecting which lesions to forward for diagnosis, and performance varies with experience. We evaluated whether real-time decision support using artificial intelligence (AI) improves malignancy detection in routine nurse-led teledermoscopy screening across MoleMap clinics in New Zealand and Australia (January 2024-July 2025). In this real-world study, clinics using AI decision support were compared with standard clinics. Nurses examined patients, captured dermoscopic images, and forwarded selected lesions to teledermatologists, who provided the reference diagnosis. The analytic cohort comprised 1,102,382 lesions from 98,422 patients across 577 sites. AI-assisted screening was associated with a higher malignancy detection rate than standard screening (25.5 vs 15.7 malignancies per 1,000 lesions; odds ratio adjusted for nurse experience 1.73, 95% CI 1.68-1.77), a finding consistent across all nurse-experience tiers and the three major malignant subtypes. AI assistance was also associated with a shift in recommended management: 21 additional intervention recommendations and 13 additional safety-netting recommendations (self-monitoring, short-term follow-up, or specialist referral) per 1,000 lesions, approximately balanced by 34 fewer no-action recommendations. The reference standard was teledermatologist diagnosis and allocation was not randomised; findings are therefore associational and require prospective, outcome-based confirmation.","rel_num_authors":8,"rel_authors":[{"author_name":"Noor E Karishma Shaik","author_inst":"The University of Melbourne"},{"author_name":"Nandakishor Desai","author_inst":"The University of Melbourne"},{"author_name":"Kyle Wang","author_inst":"MoleMap New Zealand"},{"author_name":"Lara Wild","author_inst":"MoleMap New Zealand"},{"author_name":"Adam G Dunn","author_inst":"The University of Sydney"},{"author_name":"Amanda Oakley","author_inst":"University of Auckland"},{"author_name":"Marimuthu Palaniswami","author_inst":"The University of Melbourne"},{"author_name":"Johan Vendrig","author_inst":"MoleMap New Zealand"}],"rel_date":"2026-10-02","rel_site":"medrxiv"},{"rel_title":"Subclinical hepatorenal biomarker alterations and urogenital indicators in Schistosoma haematobium-infected School-Aged Children in Sene West District, Ghana","rel_doi":"10.64898\/2026.09.30.26364430","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26364430","rel_abs":"Urogenital schistosomiasis, caused by Schistosoma haematobium, remains a major public health challenge among school-aged children in rural Ghana. Although urogenital indicators are well-established, systemic hepatic biomarker alterations in post-mass drug administration pediatric settings remain poorly characterized. This cross-sectional study evaluated point-of-care urinalysis profiles alongside a panel of serum hepatic and renal biomarkers in 174 children (aged 3 to 16 years) from the Sene West District, Ghana. Active infection (urine microscopy egg detection) was identified in 84 participants (48.3%, median egg count = 4.50 eggs\/10 mL, IQR: 1.00 - 9.25). Active infection was strongly associated with microhematuria (57.1% vs. 11.1%, p < 0.001), proteinuria (42.9% vs. 22.2%, p = 0.004), and leukocyturia (27.4% vs. 4.4%, p < 0.001). Children with active infection also demonstrated a higher frequency of composite categorical hepatic biomarker alterations (60.7% vs. 37.8%, p = 0.002), whereas categorical renal alterations showed no significant difference (p = 0.332). Controlling for age and sex, multivariable logistic regression confirmed active infection as a significant independent predictor of composite hepatic biomarker alterations (aOR = 2.54, 95% CI: 1.37 - 4.69, p = 0.003), but not renal alterations (aOR = 1.40, p = 0.359). Continuous medians for individual serum biomarkers showed no significant differences between groups. Combining composite hepatic biomarker thresholds with routine dipstick screening improves the detection of subclinical morbidity in pediatric field settings.","rel_num_authors":9,"rel_authors":[{"author_name":"Dennis Kyei Ofori","author_inst":"UENR: University of Energy and Natural Resources"},{"author_name":"Anabel Acheampong","author_inst":"UENR: University of Energy and Natural Resources"},{"author_name":"Prince-Charles Kudzordzi","author_inst":"UENR: University of Energy and Natural Resources"},{"author_name":"Prince Nyarko","author_inst":"UENR: University of Energy and Natural Resources"},{"author_name":"Claudia Wubuareyasa Nseide","author_inst":"University of Energy and Natural Resources"},{"author_name":"Emmanuel Ansah Boateng","author_inst":"University of Energy and Natural Resources"},{"author_name":"Yahuza Sabit Tanko","author_inst":"Sene West District Health Directorate"},{"author_name":"Conor R. Caffrey","author_inst":"University of California San Diego"},{"author_name":"Kenneth Bentum Otabil","author_inst":"University of Energy and Natural Resources"}],"rel_date":"2026-10-02","rel_site":"medrxiv"},{"rel_title":"Safety-Relevant Biomedical Machine Learning Should Adopt Stability-First Reporting","rel_doi":"10.64898\/2026.09.30.26364447","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26364447","rel_abs":"Biomedical Machine Learning (ML) is increasingly evaluated through Independent and Identically Distributed (IID) benchmarks, even when deployment involves irregular observation, missingness, distribution shift, and high failure costs. This position paper argues for a stability-oriented reporting standard for safety-relevant biomedical ML. The central requirement is not a specific model class or optimizer, but auditable evidence: performance should be reported under clinically plausible perturbations such as thinning, timestamp jitter, bursty missingness, channel dropout, and Signal-to-Noise Ratio (SNR) shift. When authors invoke training-stability diagnostics, those diagnostics should be hazard-linked: their assumptions, operational meaning, and relationship to stress-test degradation should be stated explicitly. We use controlled mechanism probes to illustrate two hazards that IID scores can hide: observation-process dependence under irregular sampling, and perturbation sensitivity associated with high-curvature training regimes such as Edge of Stability (EoS). We do not propose Neural ODEs, continuous-time models, Sharpness-Aware Minimization (SAM), or EoS metrics as default prescriptions. Instead, we propose a minimum reporting standard that makes robustness claims testable and supports escalation to more complex modelling or optimization only when stress tests justify the cost.","rel_num_authors":5,"rel_authors":[{"author_name":"Zayn Andre Zainal","author_inst":"The Universtiy of Sydney"},{"author_name":"Omid Kavehei","author_inst":"The University of Sydney"},{"author_name":"Isabelle Aguilar","author_inst":"The University of Sydney"},{"author_name":"Luis Fernando Herbozo Contreras","author_inst":"The University of Sydney"},{"author_name":"Zhaojing Huang","author_inst":"The University of Sydney"}],"rel_date":"2026-10-02","rel_site":"medrxiv"},{"rel_title":"Lanreotide for Advanced Pheochromocytoma and Paraganglioma: Results of a Multicenter Phase II Trial","rel_doi":"10.64898\/2026.10.01.26364504","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.01.26364504","rel_abs":"Background: Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine neoplasms originating from chromaffin cells that express somatostatin receptors (SSTRs) and may therefore be susceptible to somatostatin analogue therapy. Despite this biological rationale, prospective evidence supporting the antiproliferative activity of somatostatin analogues in PPGL is limited. We conducted a multicenter phase 2 clinical trial to assess the efficacy and safety of lanreotide in patients with advanced or metastatic PPGL. [LAMPARA, NCT03946527] Methods: Patients with advanced or metastatic PPGL and evidence of recent disease progression received lanreotide depot\/autogel 120 mg subcutaneously every 4 weeks. Treatment was planned for 52 weeks with an option to continue for an additional 52 weeks. Endpoints included overall survival (OS), progression-free survival (PFS), and response according to RECIST. Serum chromogranin A (CgA) was evaluated as an exploratory biomarker. A tumor growth rate analysis was planned to allow comparison with data from the CLARINET trial in gastroenteropancreatic NETs. Results: Eighteen patients (median age 42 years; range 28-77) across three centers participated; 78% carried SDHx mutations. Lanreotide was well tolerated with predominantly grade 1-2 adverse events and no treatment discontinuations due to toxicity. The most common treatment-emergent adverse events were diarrhea (67%), injection-site reaction (44%), constipation (39%), fatigue (39%), and abdominal pain (39%). Serious adverse events were reported in four patients and were assessed as not related or unlikely related to lanreotide. All 18 patients had at least one recorded post-baseline tumor assessment. Two partial responses were recorded during extended follow-up. At approximately 1 year, disease stabilization was observed in 11 of 13 patients (85%) with an available assessment, while two patients (15%) had progressive disease. Biomarker analysis showed substantial variability in serum CgA values. Tumor growth rate analysis revealed a median growth rate of 0.00124\/day (tumor doubling time 559 days), comparable in order of magnitude to the rate of 0.00046\/day observed in 83 lanreotide-treated patients in CLARINET, and consistent with a median PFS [&ge;]2 years. Conclusions: Lanreotide exhibits promising antiproliferative activity in PPGL, with a favorable safety profile and extended periods of disease stabilization. These findings support the consideration of SSAs as an option in managing SSTR-positive PPGLs, delaying more aggressive treatments until disease progression mandates escalation.","rel_num_authors":6,"rel_authors":[{"author_name":"Jaydira Del Rivero","author_inst":"National Cancer Institute, National Institutes of Health"},{"author_name":"Bahar Laderian","author_inst":"Montefiore\/Albert Einstein College of Medicine"},{"author_name":"Mengxi Zhou","author_inst":"Memorial Sloan Kettering Cancer Institute, Department of Radiology."},{"author_name":"Lyndon Luk","author_inst":"Columbia University Department of Radiology."},{"author_name":"Susan E Bates","author_inst":"Columbia University Department of Medicine."},{"author_name":"Tito Fojo","author_inst":"Columbia University Department of Medicine."}],"rel_date":"2026-10-02","rel_site":"medrxiv"},{"rel_title":"Lanreotide for Advanced Pheochromocytoma and Paraganglioma: Results of a Multicenter Phase II Trial","rel_doi":"10.64898\/2026.10.01.26364504","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.01.26364504","rel_abs":"Background: Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine neoplasms originating from chromaffin cells that express somatostatin receptors (SSTRs) and may therefore be susceptible to somatostatin analogue therapy. Despite this biological rationale, prospective evidence supporting the antiproliferative activity of somatostatin analogues in PPGL is limited. We conducted a multicenter phase 2 clinical trial to assess the efficacy and safety of lanreotide in patients with advanced or metastatic PPGL. [LAMPARA, NCT03946527] Methods: Patients with advanced or metastatic PPGL and evidence of recent disease progression received lanreotide depot\/autogel 120 mg subcutaneously every 4 weeks. Treatment was planned for 52 weeks with an option to continue for an additional 52 weeks. Endpoints included overall survival (OS), progression-free survival (PFS), and response according to RECIST. Serum chromogranin A (CgA) was evaluated as an exploratory biomarker. A tumor growth rate analysis was planned to allow comparison with data from the CLARINET trial in gastroenteropancreatic NETs. Results: Eighteen patients (median age 42 years; range 28-77) across three centers participated; 78% carried SDHx mutations. Lanreotide was well tolerated with predominantly grade 1-2 adverse events and no treatment discontinuations due to toxicity. The most common treatment-emergent adverse events were diarrhea (67%), injection-site reaction (44%), constipation (39%), fatigue (39%), and abdominal pain (39%). Serious adverse events were reported in four patients and were assessed as not related or unlikely related to lanreotide. All 18 patients had at least one recorded post-baseline tumor assessment. Two partial responses were recorded during extended follow-up. At approximately 1 year, disease stabilization was observed in 11 of 13 patients (85%) with an available assessment, while two patients (15%) had progressive disease. Biomarker analysis showed substantial variability in serum CgA values. Tumor growth rate analysis revealed a median growth rate of 0.00124\/day (tumor doubling time 559 days), comparable in order of magnitude to the rate of 0.00046\/day observed in 83 lanreotide-treated patients in CLARINET, and consistent with a median PFS [&ge;]2 years. Conclusions: Lanreotide exhibits promising antiproliferative activity in PPGL, with a favorable safety profile and extended periods of disease stabilization. These findings support the consideration of SSAs as an option in managing SSTR-positive PPGLs, delaying more aggressive treatments until disease progression mandates escalation.","rel_num_authors":6,"rel_authors":[{"author_name":"Jaydira Del Rivero","author_inst":"National Cancer Institute, National Institutes of Health"},{"author_name":"Bahar Laderian","author_inst":"Montefiore\/Albert Einstein College of Medicine"},{"author_name":"Mengxi Zhou","author_inst":"Memorial Sloan Kettering Cancer Institute, Department of Radiology."},{"author_name":"Lyndon Luk","author_inst":"Columbia University Department of Radiology."},{"author_name":"Susan E Bates","author_inst":"Columbia University Department of Medicine."},{"author_name":"Tito Fojo","author_inst":"Columbia University Department of Medicine."}],"rel_date":"2026-10-02","rel_site":"medrxiv"},{"rel_title":"Weakly Supervised Multiple-Instance Learning for Seizure Onset Zone Identification","rel_doi":"10.64898\/2026.09.30.26364032","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.09.30.26364032","rel_abs":"Accurate localization of the seizure onset zone (SOZ) is essential for planning resection or ablation in patients with focal drug-resistant epilepsy (DRE). However, the true SOZ cannot be directly observed during clinical evaluation, so precise electrode-level annotations are rarely available. To address this limitation, we developed a weakly supervised multiple-instance learning (MIL) framework in which labels were constructed from electrode resection status and postsurgical outcome. A shared gated recurrent unit (GRU) with temporal attention encoded each electrodes peri-onset neural fragility sequence. And attention-based pooling integrated information across variable numbers of electrodes to estimate whether each electrode group contained SOZ-related evidence. The cohort comprised 42 patients with DRE from three datasets, including 29 with successful and 13 with failed postsurgical outcomes. These patients contributed 136 seizure epochs. The cohort was divided at the patient level into training, validation and held-out test sets containing 25, 8 and 9 patients, respectively, with all data from each patient retained in the same subset. Predictions from non-resected regions were evaluated at the seizure-epoch and patient levels to assess postsurgical outcome discrimination for individual seizures and after aggregation across seizures from the same patient, respectively. At the seizure-epoch level, the proposed framework achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.828, with a 95% confidence interval (CI) of 0.511-1.000, and performed comparably to Manifold Oblique Random Forests (MORF). At the patient level, the framework achieved an AUC of 0.944, with a 95% CI of 0.677-1.000, and yielded a numerically higher AUC than MORF. However, DeLongs test did not show a statistically significant difference between the methods, with a P value of 0.398. These findings support the feasibility of extracting clinically meaningful SOZ-related information from electrode-wise neural fragility sequences without precise electrode-level labels. Because the held-out test set included only nine patients and the CIs were wide, these preliminary findings require validation in larger independent cohorts.","rel_num_authors":7,"rel_authors":[{"author_name":"Junan Mao","author_inst":"University of Illinois Chicago"},{"author_name":"ANNE-CECILE LESAGE","author_inst":"University of Texas Medical Branch"},{"author_name":"Liliana Camarillo-Rodriguez","author_inst":"University of Texas Medical Branch"},{"author_name":"Diosely C Silveira","author_inst":"University of Texas Medical Branch"},{"author_name":"Yuanyi Zhang","author_inst":"University of Texas Medical Branch"},{"author_name":"Patrick J Karas","author_inst":"University of Texas Medical Branch"},{"author_name":"Jiefei Wang","author_inst":"University of Texas Medical Branch"}],"rel_date":"2026-10-02","rel_site":"medrxiv"},{"rel_title":"A vulnerable space: Health professions education scholars' reflections on AI-use disclosure","rel_doi":"10.64898\/2026.10.01.26364502","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.01.26364502","rel_abs":"Background AI use is growing by HPE scholars, and disclosure of that use is required by scientific ethics and journal policy. In spite of growing unease about the meaning, and meaningfulness, of AI-use disclosures in published manuscripts, little is known about how authors represent their AI use in published disclosure statements. Without such understanding, we are ill-equipped to redirect disclosure practices for increased sufficiency and sincerity, undermining efforts towards transparency in the era of AI-supported scholarship Methods In this descriptive qualitative study, 18 HPE researchers with experience disclosing generative AI use in publications from 2025-2026 were interviewed using Zoom. Data collection and thematic analysis proceeded iteratively, exploring participant experiences with AI use, their disclosure decisions, and evolving disclosure practices. Results Participants reported a wide range of experience writing AI-use disclosures (from 1-16 disclosures\/participant); in total 31 published disclosures were shared by participants and discussed during the interviews. Our analysis identified four themes: A spectrum of AI uses; Multi-factorial disclosure decisions; Discrepancies between actual and disclosed use; and Emotions around disclosure. Discussion AI-disclosure takes place at the intersection of shared ethical principles and situated pragmatic concerns. Disclosure is both an intellectual and an emotional practice, changing over time as AI use evolves and journal expectations develop. More generative uses may provoke divisive responses and are related to uncertainty about how to disclose, concern about disclosure penalties, and periodic use-disclosure discrepancies. Understanding how researchers are navigating these complexities may help the HPE community articulate clearer guidance for transparent and meaningful disclosure of AI-use in scholarly work. Reflective methodologies can help bring these issues to the surface of ongoing conversations about AI use.","rel_num_authors":4,"rel_authors":[{"author_name":"Lorelei A Lingard","author_inst":"Western University"},{"author_name":"Lauren A Maggio","author_inst":"University of Chicago at Illinois"},{"author_name":"Muhammad Ans","author_inst":"Western University"},{"author_name":"Erik Driessen","author_inst":"Maastricht University"}],"rel_date":"2026-10-02","rel_site":"medrxiv"},{"rel_title":"Comparative immunogenicity and relative vaccine efficacy of recombinant and cell culture-based influenza vaccines: Results from a randomized trial of adults in the United States, 2024-2025","rel_doi":"10.64898\/2026.10.01.26364413","rel_link":"http:\/\/medrxiv.org\/content\/10.64898\/2026.10.01.26364413","rel_abs":"Background: Recombinant (RIV) and cell culture-based inactivated (ccIIV) influenza vaccines could improve seasonal influenza prevention; however, evidence is limited in how protection may differ among non-egg-based vaccines. Methods: We conducted a randomized trial of RIV versus ccIIV to compare vaccine immunogenicity in adults aged 18 to 64 years during the 2024-25 influenza season (ClinicalTrials.gov, NCT06518577). Active surveillance for influenza-like illness was conducted for six months post-vaccination and cumulative incidence of influenza by vaccine type was compared to estimate relative vaccine efficacy. Participants were tested for influenza by reverse-transcription polymerase chain reaction or rapid antigen tests. Immunogenicity was evaluated by hemagglutination inhibition (HAI), virus microneutralization (MN), and neuraminidase inhibition (NAI) assays in a subset of blood specimens collected pre-vaccination and one-month post-vaccination. Results: Among the 591 participants, 25 cases of influenza A were detected; 2.4% cumulative incidence among RIV recipients versus 6.1% among ccIIV recipients. The probability of influenza was 61% lower with RIV than ccIIV (95% confidence interval: 7 to 84). Among 151 participants, post-vaccination HAI and MN titers vaccine components were higher among RIV recipients while NAI titers were higher among ccIIV recipients. Conclusions: RIV provided better protection than ccIIV against influenza A illness in adults during the U.S. 2024-2025 season.","rel_num_authors":26,"rel_authors":[{"author_name":"Kelsey M Sumner","author_inst":"Centers for Disease Control and Prevention"},{"author_name":"Emma K Noble","author_inst":"Centers for Disease Control and Prevention"},{"author_name":"Lauren B Grant","author_inst":"Centers for Disease Control and Prevention"},{"author_name":"Jessica R Meeker","author_inst":"Centers for Disease Control and Prevention"},{"author_name":"Vel Murugan","author_inst":"Arizona State University"},{"author_name":"Lora Nordstrom","author_inst":"Valleywise Health"},{"author_name":"Stacey L House","author_inst":"Washington University School of Medicine, Department of Emergency Medicine"},{"author_name":"Rachel Presti","author_inst":"Wash U"},{"author_name":"Emmanuel B Walter","author_inst":"Duke University School of Medicine, Department of Pediatrics"},{"author_name":"Olivia L Williams","author_inst":"Duke University School of Medicine, Duke Human Vaccine Institute"},{"author_name":"Elie A Saade","author_inst":"University Hospitals Cleveland Medical Center"},{"author_name":"David H. Canaday","author_inst":"Case Western Reserve University"},{"author_name":"Katherine V Williams","author_inst":"University of Pittsburgh School of Medicine"},{"author_name":"Richard Zimmerman","author_inst":"University of Pittsburgh"},{"author_name":"F Liaini Gross","author_inst":"Centers for Disease Control and Prevention"},{"author_name":"Sara Valencia","author_inst":"Centers for Disease Control and Prevention"},{"author_name":"Brian M. Gurbaxani","author_inst":"Centers for Disease Control and Prevention"},{"author_name":"Nathaniel M Lewis","author_inst":"Centers for Disease Control and Prevention"},{"author_name":"Zhu-Nan Li","author_inst":"Centers for Disease Control and Prevention"},{"author_name":"Vasiliy Mishin","author_inst":"Centers for Disease Control and Prevention"},{"author_name":"Larisa Gubareva","author_inst":"Centers for Disease Control and Prevention"},{"author_name":"Bin Zhou","author_inst":"Centers for Disease Control and Prevention"},{"author_name":"Min Z Levine","author_inst":"Centers for Disease Control and Prevention"},{"author_name":"Brendan M Flannery","author_inst":"CDC"},{"author_name":"Sascha Ellington","author_inst":"Centers for Disease Control and Prevention"},{"author_name":"- US Flu VE Network Collaborators","author_inst":"-"}],"rel_date":"2026-10-02","rel_site":"medrxiv"},{"rel_title":"Spontaneous behavior predicts colony membership, individual identity, and rank in naked mole-rat societies","rel_doi":"10.64898\/2026.09.27.754730","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.27.754730","rel_abs":"Animal societies are organized across scales, from moment-to-moment actions of individuals to dynamic group structure. A central challenge in sociobiology is to understand how behaviors of individuals shape group structure. Here, using machine vision to map recurrent fine-scale behavioral modules in the eusocial naked mole-rat (Heterocephalus glaber), we show that spontaneous behavior in isolation predicts group-level social organization, including colony membership and individual identity. We determined dominance relationships and found highly linear hierarchies, with ranks stable over months to years. Remarkably, fine-scale behavioral profiles in isolation correctly predicted pairwise dominance relationships, allowing reconstruction of rank order within each colony. Models trained within one colony predicted rank poorly in others, suggesting that naked mole-rats express social position through behavioral patterns specific to each colony. Together, our results connect fine-scale behavior to social structure maintained over years and show that, even when an animal is alone, its behavior carries signatures of its identity, colony, and place within that society.","rel_num_authors":14,"rel_authors":[{"author_name":"Yuki Haba","author_inst":"Zuckerman Mind Brain Behavior Institute; Department of Biological Sciences and Howard Hughes Medical Institute, Columbia University"},{"author_name":"Ryan Schwark","author_inst":"Zuckerman Mind Brain Behavior Institute; Department of Biological Sciences and Howard Hughes Medical Institute, Columbia University"},{"author_name":"Caleb Weinreb","author_inst":"Department of Neurobiology, Harvard Medical School"},{"author_name":"Simon Ogundare","author_inst":"Zuckerman Mind Brain Behavior Institute; Department of Biological Sciences and Howard Hughes Medical Institute, Columbia University"},{"author_name":"William Foster","author_inst":"Zuckerman Mind Brain Behavior Institute; Department of Biological Sciences and Howard Hughes Medical Institute, Columbia University"},{"author_name":"Yu-Young Wesley Tsai","author_inst":"Zuckerman Mind Brain Behavior Institute; Department of Biological Sciences and Howard Hughes Medical Institute, Columbia University"},{"author_name":"Jerry Lyu","author_inst":"Zuckerman Mind Brain Behavior Institute; Department of Biological Sciences and Howard Hughes Medical Institute, Columbia University"},{"author_name":"Mayssam Mohamed","author_inst":"Zuckerman Mind Brain Behavior Institute; Department of Biological Sciences and Howard Hughes Medical Institute, Columbia University"},{"author_name":"Phalaen Chang","author_inst":"Zuckerman Mind Brain Behavior Institute; Department of Biological Sciences and Howard Hughes Medical Institute, Columbia University"},{"author_name":"Amanda Arnold","author_inst":"Zuckerman Mind Brain Behavior Institute; Department of Biological Sciences and Howard Hughes Medical Institute, Columbia University"},{"author_name":"Evan Schaffer","author_inst":"Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai"},{"author_name":"Kanaka Rajan","author_inst":"Department of Neurobiology, Harvard Medical School; Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University"},{"author_name":"Sandeep Robert Datta","author_inst":"Department of Neurobiology, Harvard Medical School"},{"author_name":"Ishmail Abdus-Saboor","author_inst":"Zuckerman Mind Brain Behavior Institute; Department of Biological Sciences and Howard Hughes Medical Institute, Columbia University"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"ALS\/FTD-linked TDP-43 alterations prevent HSV-1 infection by disrupting cell-adhesion pathways","rel_doi":"10.64898\/2026.09.28.755069","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.28.755069","rel_abs":"TAR DNA-binding protein-43 (TDP-43) alterations are a key hallmark of amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and other neurodegenerative disorders. Increasing evidence links these conditions to viral infections, but it is not known if and how viral pathogens may be affected by disease-associated TDP-43. We found that ALS\/FTD-linked forms of human TDP-43 prevent infection by herpes simplex virus type-1 (HSV-1), a common neurotropic virus. This cell-autonomous protective effect was conserved across different TDP-43 mutations, sporadic ALS, and C9ORF72-ALS, in neural and non-neural cell types, and in patient-derived cells and animal models. In addition to restricting infection, TDP-43 mutation prevented inflammatory and cell stress-related responses to HSV-1 exposure. TDP-43 knockdown but not overexpression was sufficient to protect cells from HSV-1 infection, implicating a loss-of-function mechanism. Neurons with TDP-43 mutation had disruptions in adhesion-related pathways and, accordingly, had reduced early-stage viral binding and entry. Further analyses revealed alternative RNA splicing and impairment of focal adhesion kinase (FAK\/PTK2), a key mediator of cell-adhesion pathways. Inhibition of FAK was sufficient to decrease HSV-1 infection, and genetic enhancement of functional but not inactive FAK promoted infectivity in cells with TDP-43 mutation. Together, these data indicate that TDP-43 affects virus-host interactions by influencing adhesion pathways required for viral infection.","rel_num_authors":12,"rel_authors":[{"author_name":"Stephanie Jackvony Infurna","author_inst":"Weill Cornell Medicine"},{"author_name":"Laraib Ijaz","author_inst":"Weill Cornell Medicine"},{"author_name":"Evelyn J Hardin","author_inst":"Weill Cornell Medicine"},{"author_name":"Abulimiti Aikedan","author_inst":"Weill Cornell Medicine"},{"author_name":"Constance Zhou","author_inst":"Weill Cornell Medicine"},{"author_name":"Tejabhiram Yadavalli","author_inst":"University of Illinois Chicago"},{"author_name":"Minwoo Wendy Jang","author_inst":"Weill Cornell Medicine"},{"author_name":"Matthew Mahoney","author_inst":"Weill Cornell Medicine"},{"author_name":"Li Gan","author_inst":"Weill Cornell Cornell"},{"author_name":"Deepak Shukla","author_inst":"University of Illinois Chicago"},{"author_name":"Adam L Orr","author_inst":"Weill Cornell Medicine"},{"author_name":"Anna G Orr","author_inst":"Weill Cornell Medicine"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"Efficacy profiling of structurally diverse cannabinoid receptor ligands across transducer-coupling assays","rel_doi":"10.64898\/2026.09.27.754774","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.27.754774","rel_abs":"The full efficacy range of ligands at CB1 and CB2 (i.e., inverse agonists, neutral agonists, partial agonists, full agonists, and high-efficacy agonists) has been reported collectively across numerous laboratories and assay platforms. Canonical Gi\/o and {beta}-arrestin signaling have each been suggested to shape physiological effects differently via different classes of cannabinoid ligands. These prior efficacy classifications, however, were obtained in multiple assay systems, often using indirect, amplified readouts and different reference agonists, making them difficult to compare across ligands. Here we profiled a structurally diverse panel of 22 cannabinoid ligands: synthetic cannabinoid receptor agonists (SCRAs), {Delta}-tetrahydrocannabinol ({Delta}-THC) analogues, endocannabinoid analogues, and antagonists\/inverse agonists at CB1 and CB2 receptors using bioluminescence resonance energy transfer (BRET) assays, measuring Gi1 engagement and {beta}-arrestin 2 recruitment and normalizing every response to the reference full agonist CP55,940. At CB1, SCRAs and {Delta}-THC analogues acted as high-efficacy agonists. At CB1, these high-efficacy agonists were also highly efficacious in {beta}-arrestin 2 recruitment. Across the panel, the most consistent trend was a loss of agonist efficacy from CB1 to CB2. Among antagonists, inverse agonists suppressed constitutive CB1 activity more than neutral antagonists. Together, these CP55,940-normalized measurements provide a common efficacy scale and a framework for interpreting pharmacodynamics of CB1 and CB2 cannabinoid receptors.","rel_num_authors":12,"rel_authors":[{"author_name":"Soo Jung Oh","author_inst":"Northeastern University"},{"author_name":"Christopher Lucaj","author_inst":"Northeastern University"},{"author_name":"Kiera Truong","author_inst":"Northeastern University"},{"author_name":"Ruiru Guo","author_inst":"Northeastern University"},{"author_name":"Hayato Umaoka","author_inst":"Northeastern University"},{"author_name":"Christos Iliopoulos-Tsoutsouvas","author_inst":"Northeastern University"},{"author_name":"Maria Gerasi","author_inst":"Northeastern University"},{"author_name":"Markos-Orestis Georgiadis","author_inst":"Northeastern University"},{"author_name":"Lipin Ji","author_inst":"Northeastern University"},{"author_name":"Spyros P Nikas","author_inst":"Northeastern University"},{"author_name":"Alexandros Makriyannis","author_inst":"Northeastern University"},{"author_name":"Hideaki Yano","author_inst":"Northeastern University"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"GGE: General-purpose deep meta-learning for classification of human transcriptomes with limited data","rel_doi":"10.64898\/2026.09.27.754054","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.27.754054","rel_abs":"Transcriptomic classification is often hindered by the small number of samples relative to the high dimensionality of gene expression data. We introduce General Gene Expression (GGE), a deep meta-learning framework designed to support robust classification in this limited-sample setting. By training across 5,220 distinct biomedical prediction objectives drawn from 1,779 different human datasets, GGE learns a model initialization that captures biological patterns shared across heterogeneous classification tasks. This learned initialization has two key advantages. First, it enables improved performance to new datasets using only a small number of labeled samples. Second, because it is learned across diverse prediction objectives, it can be applied to a broad range of biomedical problems. We show that GGE outperforms established classifiers in data-limited settings across a wide range of applications, including datasets generated using different RNA-seq platforms and preprocessing pipelines. In addition, attention-based analysis identifies recurrent genes that contribute to performance across multiple biological objectives, providing insight into shared determinants of human biological states. Together, these results establish GGE is a general-purpose framework for human transcriptome-based classification in biomedical settings where labeled data are scarce.","rel_num_authors":2,"rel_authors":[{"author_name":"Gal Yankovitz","author_inst":"Tel-Aviv University"},{"author_name":"Irit Gat-Viks","author_inst":"Tel-Aviv University"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"X-Y divergence of house fly (Musca domestica) proto-sex chromosomes follows distinct evolutionary trajectories despite residing in the same genome","rel_doi":"10.64898\/2026.09.26.754726","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.26.754726","rel_abs":"Sex determination systems and sex chromosomes frequently differ between species. One cause of these differences is new sex determining genes that drive evolutionary turnover of sex chromosomes. At the earliest stages of this turnover, X and Y (or Z and W) chromosomes start out as nearly identical homologs, and they can diverge via chromosomal rearrangements (e.g., inversions) that suppress X-Y recombination. However, multiple examples from across animals and plants provide exceptions to this canonical model of sex chromosome evolution. For example, some X-Y pairs remain undifferentiated for long evolutionary time periods, while other sex chromosomes become differentiated without chromosomal rearrangements. How or why these non-canonical trajectories occur remains elusive, despite increasing evidence of their pervasiveness. The house fly, Musca domestica, is a well-suited system to address this gap because all six chromosomes can be a Y, providing multiple replicates of a natural experiment within a single genomic environment. To test for canonical and non-canonical evolutionary trajectories, we generated haplotype-resolved chromosome-level assemblies from five strains of the house fly, each of which carries a different Y chromosome (IM, IIM, IIIM, VM, and YM). We identified an inversion on only one of the sex chromosomes (IIM), which was associated with elevated X-Y divergence but did not capture the male-determining locus. In contrast, there was X-Y divergence across almost the entire length of the IM, IIIM, and VM sex chromosomes, despite no detectable inversions. YM was the only sex chromosome to contain substantial Y-specific sequences, which were limited to a segment on one end of the chromosome containing the male-determining gene. This YM chromosome, and its corresponding X, were highly diverged from the X chromosome found in many other flies (Muller element F), despite a strong cytological resemblance. This study highlights how multiple different canonical and non-canonical modes of sex chromosome evolution can co-exist within a single genome.","rel_num_authors":10,"rel_authors":[{"author_name":"Jae Hak Son","author_inst":"Department of Genetics, Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ, USA"},{"author_name":"David Luecke","author_inst":"USDA Agricultural Research Service, Veterinary Pest Genetics Research Unit, Kerrville, TX, USA"},{"author_name":"Basanta Bista","author_inst":"Department of Biology and Biochemistry, University of Houston, Houston, TX, USA"},{"author_name":"Yesbol Manat","author_inst":"Department of Biology and Biochemistry, University of Houston, Houston, TX, USA"},{"author_name":"Weihuan Cao","author_inst":"Department of Genetics, Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ, USA"},{"author_name":"Leo Beukeboom","author_inst":"Groningen Institute for Evolutionary Life Sciences, University of Groningen, The Netherlands"},{"author_name":"Daniel Bopp","author_inst":"Institute of Molecular Life Sciences, University of Zurich, Switzerland"},{"author_name":"Christopher E Ellison","author_inst":"Department of Genetics, Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ, USA"},{"author_name":"Perot Saelao","author_inst":"USDA Agricultural Research Service, Veterinary Pest Genetics Research Unit, Kerrville, TX, USA"},{"author_name":"Richard P Meisel","author_inst":"Department of Biology and Biochemistry, University of Houston, Houston, TX, USA"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"Method-dependent biases in cell type detection between single-cell and single-nucleus RNA sequencing in the photosymbiotic acoel Praesagittifera naikaiensis","rel_doi":"10.64898\/2026.09.27.754747","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.27.754747","rel_abs":"Background Comparisons of single-cell and single-nucleus RNA sequencing (scRNA-seq and snRNA-seq) data have been described in some mammalian tissues and, subsequently, in Drosophila, but remain unexplored in most invertebrate lineages. The xenacoelomorphs occupy key phylogenetic positions, yet they differ anatomically from mammals. They have a reduced extracellular matrix, high-salt body fluid, and no circulatory system. Despite these differences, they possess a well-developed nervous system. One of the xenacoelomorphs, the photosymbiotic acoel (Praesagittifera naikaiensis) also harbours symbiotic Tetraselmis algae, whose RNA can be co-captured with host RNA. Results We compared scRNA-seq and snRNA-seq data from whole P. naikaiensis specimens. Both methods yielded high-quality data with comparable gene detection but a larger share of scRNA-seq reads derived from symbiotic algae. Gene-level analyses revealed that neural genes were enriched in snRNA-seq relative to non-neural genes. Cross-method label transfer and integration-based validation identified six snRNA-seq clusters, including some neural populations, that lacked a clear scRNA-seq counterpart. In contrast, three cell populations, including muscle and metabolically active clusters, were reciprocally validated as captured by both methods. Conclusions Our results show that key snRNA-seq advantages, particularly the enhanced recovery of neural transcripts, are recapitulated in our dataset, which is consistent with previous reports in mammals. Furthermore, snRNA-seq reduces symbiont-derived reads and recovers several cell populations underrepresented in scRNA-seq. These findings provide practical guidance for cell atlas construction in non-model, symbiotic invertebrates.","rel_num_authors":10,"rel_authors":[{"author_name":"Ryo Nakamura","author_inst":"Okayama University"},{"author_name":"Mayuko Hamada","author_inst":"Okayama University"},{"author_name":"Kenji Kobayashi","author_inst":"Kyoto University"},{"author_name":"Satoshi Ansai","author_inst":"Okayama University"},{"author_name":"Mirco Dindo","author_inst":"University of Perugia"},{"author_name":"Tosuke Sakagami","author_inst":"Academia Sinica"},{"author_name":"Yi-Jyun Luo","author_inst":"Academia Sinica"},{"author_name":"Yutaka Satou","author_inst":"Kyoto University"},{"author_name":"Toshio Sekiguchi","author_inst":"Kanazawa University"},{"author_name":"Tatsuya Sakamoto","author_inst":"Okayama University"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"Folate metabolism in tumor-associated macrophages drives immunosuppressive function to promote tumor growth","rel_doi":"10.64898\/2026.09.29.755523","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.29.755523","rel_abs":"Tumor-associated macrophages (TAMs) promote tumor growth, inhibit effector lymphocytes, and induce resistance to immune checkpoint inhibitors (ICI). Therapies designed to deplete TAMs have had little success in the clinic, and strategies aimed to repolarize immunosuppressive TAMs to pro-inflammatory states have struggled with systemic toxicity. We have identified that the combination of low folate in the tumor microenvironment and TAM dependency on the high affinity folate receptor beta (FR{beta}) for folate uptake results in a unique metabolic dependency of TAMs necessary for their immunosuppressive function. FR{beta} (encoded by Folr2) is uniquely expressed on myeloid cells and upregulated on TAMs. FR{beta}+ TAMs exhibit an immunosuppressive phenotype in melanoma and are associated with worse clinical outcomes and resistance to ICI in melanoma patients. We generated a novel Folr2-\/- mouse model to study why TAMs express a unique folate receptor and showed that tumor growth was slowed in the absence of Folr2 in a T-cell dependent manner. We observed a dramatic repolarization of Folr2-\/- TAMs in vivo to pro-inflammatory states, resulting in increased cytotoxic T cell and NK cell infiltration into tumors. Importantly, we show that folate is low in the tumor microenvironment by performing metabolomics on melanoma tumors and adjacent normal tissue from warm autopsy patient specimens, and that we could increase tumor growth in Folr2-\/- mice by increasing serum folate to supraphysiologic concentrations with a high folate diet. Integrated metabolomics and transcriptomic analysis demonstrated that Folr2-\/- macrophages have impaired folate uptake and 1C metabolism-based reduction of oxidized glutathione in low folate conditions, resulting in increased mitochondrial reactive oxygen species (ROS) in Folr2-\/- cells. Excess ROS results in leakage of mitochondrial DNA into the cytoplasm, activating cGAS-STING signaling and promoting pro-inflammatory macrophage polarization through TBK1 and NF-kB. Together, our data demonstrate that TAM expression of FR{beta} promotes their immunosuppressive functions by maintaining folate uptake in the low folate tumor microenvironment, suggesting that FR{beta} is a novel metabolic checkpoint on TAMs.","rel_num_authors":31,"rel_authors":[{"author_name":"Matthew P Zimmerman","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Vasyl Zhabotynsky","author_inst":"University of North Carolina"},{"author_name":"Haiyang Wang","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Emily K Cox","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Wan Lin Chong","author_inst":"UNC Chapel Hill"},{"author_name":"Alexander G. Bastian","author_inst":"High Point University"},{"author_name":"Andrew S. Kennedy","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Brian P. Fay","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Amy G. Reynolds","author_inst":"University of North Carolina at Chapel Hil"},{"author_name":"Anna Ebacher","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Jeremy A. Meier","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Katherine Vietor","author_inst":"Carbone Cancer Center, University of Wisconsin at Madison, Madison, WI and Cancer Biology Graduate Program, University of Wisconsin at Madison, Madison, WI, USA"},{"author_name":"Khalilah E. Taylor","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Pristine C. Onuoha","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Siddharth Maruvada","author_inst":"North Carolina State University"},{"author_name":"Katie E. Hurst","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Christopher Wong","author_inst":"University of California, Los Angeles"},{"author_name":"Carlton W. Anderson","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Nancy E. Thomas","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Rihe Liu","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Kirsten L. Bryant","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Albert S. Baldwin","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"David W. Ollila","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Jenny P-Y Ting","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Blake R. Rushing","author_inst":"University of North Carolina at Chapel Hill Gillings School of Global Public Health"},{"author_name":"Susan J. Sumner","author_inst":"University of North Carolina at Chapel Hill Gillings School of Global Public Health"},{"author_name":"Sergey A. Krupenko","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"WILLY HUGO","author_inst":"UCLA"},{"author_name":"Stergios J. Moschos","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Jessica E. Thaxton","author_inst":"University of North Carolina at Chapel Hill"},{"author_name":"Brian C Miller","author_inst":"University of North Carolina at Chapel Hill"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"A kinetic-aware approach to infer metabolic variations and flux using transcriptomics and metabolomics data","rel_doi":"10.64898\/2026.09.27.754711","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.27.754711","rel_abs":"Assessing metabolic variations and flux quantities enable systematic understandings of metabolic shifts, reprogramming, adaptation and interactions in human diseases. However, omics-based estimation of metabolic flux and its variation remains challenging due to several fundamental limitations: the need for disease and tissue context specific metabolic model; nonlinear enzyme kinetic model that links enzyme and substrate changes to reaction flux; partial, unpaired, and snap-shot measurements across omics modalities; and uncertainty in computational prediction. Here, we present Michaelis-Menten model-based Flux Estimation Analysis (mmFEA), a Monte Carlo framework for estimating condition-specific flux changes by integrating paired or unpaired metabolomics and transcriptomics (or proteomics) data. mmFEA separates each reaction-rate change into enzyme- and substrate-associated components and assess reaction rate using Michaelis-Menten kinetics equation. A baseline metabolite saturation rate is introduced by integrating protein language model predicted kinetic parameters and human baseline level metabolic concentration to enable kinetic-aware integration of unpaired substrate and enzyme level measurements. Distribution of metabolic flux and variations between conditions are further computed using MCMC sampling by treating Michaelis-Menten-derived marginal flux distribution as prior and coherency in flux balance as likelihood. To benchmark mmFEA, we generated an in-house multi-omics data set including transcriptomics, metabolomics, metabolic activity functional assay, and CRISPR screening data using pancreatic cancer cell line system treated by APEX1 inhibitors. We demonstrated that mmFEA could accurately capture experimentally observed metabolic changes and achieved a better performance than all baseline methods. Our analysis revealed the necessity in using both substrate and enzyme modality and kinetic aware model in metabolic flux assessment. Further analysis using independent pancreatic cancer cohorts further validated the robustness of mmFEA, supporting integration of condition-linked unpaired data. Pan-cancer and spatial multi-omic applications demonstrated the use of mmFEA for resolving context-dependent metabolic variation when direct flux measurements are unavailable. Together, mmFEA provides a mechanistically grounded framework for estimating relative metabolic flux changes and their uncertainty from heterogeneous omics data.","rel_num_authors":17,"rel_authors":[{"author_name":"Haiqi Zhu","author_inst":"Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN 47408, USA"},{"author_name":"Changlin Wan","author_inst":"Department of Biomedical Engineering, Center for Biomedical Data Sciences, Knight Cancer Institute, and Brenden-Colson Center for Pancreatic Care, Oregon Health"},{"author_name":"Min Yang","author_inst":"Department of Biomedical Engineering, Center for Biomedical Data Sciences, Knight Cancer Institute, and Brenden-Colson Center for Pancreatic Care, Oregon Health"},{"author_name":"Yue Fang","author_inst":"Department of Biomedical Engineering, Center for Biomedical Data Sciences, Knight Cancer Institute, and Brenden-Colson Center for Pancreatic Care, Oregon Health"},{"author_name":"Zheng An","author_inst":"Department of Biomedical Engineering, Center for Biomedical Data Sciences, Knight Cancer Institute, and Brenden-Colson Center for Pancreatic Care, Oregon Health"},{"author_name":"Paveethran Swaminathan","author_inst":"Department of Biomedical Engineering, Center for Biomedical Data Sciences, Knight Cancer Institute, and Brenden-Colson Center for Pancreatic Care, Oregon Health"},{"author_name":"Pengtao Dang","author_inst":"Department of Biomedical Engineering, Center for Biomedical Data Sciences, Knight Cancer Institute, and Brenden-Colson Center for Pancreatic Care, Oregon Health"},{"author_name":"Zhi Li","author_inst":"Department of Biomedical Engineering, Center for Biomedical Data Sciences, Knight Cancer Institute, and Brenden-Colson Center for Pancreatic Care, Oregon Health"},{"author_name":"Jia Wang","author_inst":"Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN 47408, USA"},{"author_name":"Yijie Wang","author_inst":"Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN 47408, USA"},{"author_name":"Yabing Chen","author_inst":"Department of Pathology and Laboratory Medicine, School of Medicine, Oregon Health & Science University, Portland, OR 97239, USA"},{"author_name":"Anjun Ma","author_inst":"Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA"},{"author_name":"Qin Ma","author_inst":"Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA"},{"author_name":"Mark R. Kelley","author_inst":"Department of Pediatrics and Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Indiana Universit"},{"author_name":"Sha Cao","author_inst":"Department of Biomedical Engineering, Center for Biomedical Data Sciences, Knight Cancer Institute, and Brenden-Colson Center for Pancreatic Care, Oregon Health"},{"author_name":"Melissa L. Fishel","author_inst":"Department of Pediatrics and Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Indiana Universit"},{"author_name":"Chi Zhang","author_inst":"Department of Biomedical Engineering, Center for Biomedical Data Sciences, Knight Cancer Institute, and Brenden-Colson Center for Pancreatic Care, Oregon Health"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"A Natural qE- and qZ-Deficient Alga Retains Functional Plant-Like and Chlorophycean Violaxanthin De-Epoxidases","rel_doi":"10.64898\/2026.09.26.754548","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.26.754548","rel_abs":"Photosynthetic organisms protect themselves from excess light through non-photochemical quenching (NPQ), a process that safely dissipates excess light energy as heat. Current understanding of NPQ is largely drawn from a few model systems, leaving it unclear how universal NPQ mechanisms are across the green lineage. We characterize the slow, sustained NPQ of the green alga, Auxenochlorella protothecoides x symbiontica (UTEX 250-A). The Auxenochlorella genome lacks Light-Harvesting Complex Stress Related (LHCSR), one of the two proteins required for the fast energy-dependent quenching (qE) component of NPQ. Mutants lacking the Photosystem II Subunit S (PSBS)-like gene (psbsl1) in Auxenochlorella had no effect on NPQ, indicating this alga lacks canonical qE. We identified two candidate enzymes associated with zeaxanthin-dependent quenching (qZ), which has slower kinetics: a plant-type Violaxanthin De-Epoxidase (VDE) and an algal-type Chlorophycean Violaxanthin De-Epoxidase (CVDE). High light did not trigger the expected conversion of violaxanthin to zeaxanthin, a pigment change that is a hallmark of qZ. Mutants lacking VDE (vde1), CVDE (cvde1), or both enzymes (vde1 cvde1) still displayed slow, reversible NPQ. However, when the Auxenochlorella VDE1 and CVDE1 were expressed in a VDE-deficient mutant of Nicotiana benthamiana, both enzymes restored zeaxanthin production and NPQ, confirming they are catalytically functional de-epoxidases. Together, these results reveal an alga that retains two functional, evolutionarily distinct xanthophyll-cycle enzymes yet does not rely on typical xanthophyll pigment dynamics for photoprotection. This points to a biological function that is decoupled from de-epoxidase activity, highlighting unexplored diversity in green algal photoprotection.","rel_num_authors":11,"rel_authors":[{"author_name":"Karina W Cunningham","author_inst":"University of California, Berkeley"},{"author_name":"Setsuko Wakao","author_inst":"Lawrence Berkeley National Laboratory"},{"author_name":"Johanna L Hall","author_inst":"University of California, Berkeley"},{"author_name":"Crysten Blaby-Haas","author_inst":"Lawrence Berkeley National Laboratory"},{"author_name":"Rituparna Ivatury","author_inst":"University of California, Berkeley"},{"author_name":"Lucy Gleeson","author_inst":"University of California, Berkeley"},{"author_name":"Derrick Chuang","author_inst":"University of California, Berkeley"},{"author_name":"Jane J Kim","author_inst":"University of California, Berkeley"},{"author_name":"Jeffrey L Moseley","author_inst":"University of California, Berkeley"},{"author_name":"Graham R Fleming","author_inst":"University of California, Berkeley"},{"author_name":"Krishna K. Niyogi","author_inst":"HHMI, University of California"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"Corallimorpharian genome supports the monophyly of Scleractinia and illuminates the cellular evolution of coral calcification","rel_doi":"10.64898\/2026.09.25.754439","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.25.754439","rel_abs":"The production of calcified skeletons by stony corals is the cornerstone of reef ecosystems and the persistence of marine biodiversity. Corallimorpharians, sometimes described as naked corals, resemble stony corals in many respects but lack hardened skeletons. However, whether they are stony corals that secondarily lost their skeletons has been a subject of much debate. Here, we present a chromosome-level genome of the corallimorpharian Ricordea yuma and show that macrosynteny and microsynteny analyses provide strong support for corallimorpharians as the sister group to stony corals. This phylogenetic placement indicates that corallimorpharians were not ancestrally skeletonized and therefore occupy a critical outgroup position for reconstructing the origin of coral calcification. Guided by this phylogenetic context, we sequenced whole-polyp single-cell transcriptomes from R. yuma and the complex coral Galaxea fascicularis and found that the scleractinian skeleton evolved through the emergence of a derived calicoblast cell state in stony corals. A combination of comparative genomics and regeneration experiments in G. fascicularis show that the emergence of the calicoblast cell state was accompanied by the evolution of a biomineralization toolkit assembled from lineage-specific innovations and co-opted ancestral components. Although corallimorpharians retain some ancestral genes later incorporated into coral biomineralization, they lack a detectable calicoblast-like transcriptional state and many core calcification genes. Together, our findings indicate that coral calcification arose through coordinated changes in genome architecture, gene repertoire, and cell identity, highlighting this process as a major evolutionary transition driven by the integration of genomic and cellular innovations.","rel_num_authors":15,"rel_authors":[{"author_name":"Thomas D. Lewin","author_inst":"Academia Sinica"},{"author_name":"Tosuke Sakagami","author_inst":"Academia Sinica"},{"author_name":"Victor M. Pinon-Gonzalez","author_inst":"Academia Sinica"},{"author_name":"Yuki Yoshioka","author_inst":"Academia Sinica"},{"author_name":"Li-Jung Kao","author_inst":"Academia Sinica"},{"author_name":"Yi-Ling Chiu","author_inst":"Academia Sinica"},{"author_name":"Kohsei Sasaki","author_inst":"National Taiwan Ocean University"},{"author_name":"Yi-Hua Chen","author_inst":"Academia Sinica"},{"author_name":"Jeng-Yi Li","author_inst":"Academia Sinica"},{"author_name":"Kai-Xuan Tin","author_inst":"Academia Sinica"},{"author_name":"Mei-Yeh Jade Lu","author_inst":"Academia Sinica"},{"author_name":"David J. Miller","author_inst":"James Cook University"},{"author_name":"Shinya Shikina","author_inst":"National Taiwan Ocean University"},{"author_name":"Mei-Fang Lin","author_inst":"National Sun Yat-sen University"},{"author_name":"Yi-Jyun Luo","author_inst":"Academia Sinica"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"Engineered CTLA-4 targeting chimeras for targeted protein degradation and immune synapse reprogramming","rel_doi":"10.64898\/2026.09.29.755403","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.29.755403","rel_abs":"Despite the success of immune checkpoint blockade in cancer therapy, overcoming the immunosuppressive tumor microenvironment remains a major barrier to durable responses. Here, we introduce cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4)-targeting chimeras (cTACs), a class of synthetic molecules that harness CTLA-4-mediated trans-endocytosis (TE) for targeted protein degradation across cellular boundaries. The lead candidate, cTAC4.0, is composed of the extracellular domain of CD80 fused to an anti-programmed death-ligand 1 (PD-L1) fragment via a human immunoglobulin G4 (IgG4) heavy chain. Unlike existing protein degraders that function within cancer cells via house-keeping pathways, cTAC4.0 operates in trans between immune and tumor cells. It redirects the natural CD80-CTLA-4 internalization to capture and degrade PD-L1 from tumor cells into T cells, while simultaneously converting PD-L1-mediated suppression into CD28-driven co-stimulation, depending on the relative availability of CTLA-4 or CD28. Mechanistically, we demonstrate that cTAC4.0 promotes CTLA-4-dependent internalization and lysosomal degradation of both membrane-bound and soluble PD-L1, reduces surface PD-L1 levels on target cells, and activates T cells in a CD28-dependent manner. Functionally, cTAC4.0 enhances the cytotoxicity of both bispecific T cell engagers and T cell receptor (TCR)-engineered T cells in vitro, and when combined with these engineered T cells, it suppresses tumor growth in vivo in a glioblastoma xenograft model. By coupling immune checkpoint degradation with T cell activation in an immune synapse-enriched manner, cTACs establish a paradigm for intercellular protein degradation systems that simultaneously deplete suppressive ligands and enhance T cell function to overcome resistance in cancer immunotherapy.","rel_num_authors":13,"rel_authors":[{"author_name":"Ping Ren","author_inst":"1.\tDepartment of Genetics, Yale University School of Medicine, New Haven, CT, USA 2.\tSystem Biology Institute, Yale University, West Haven, CT, USA 3.\tCenter fo"},{"author_name":"Shao-Yu Fang","author_inst":"1.\tDepartment of Genetics, Yale University School of Medicine, New Haven, CT, USA 2.\tSystem Biology Institute, Yale University, West Haven, CT, USA 3.\tCenter fo"},{"author_name":"Shushu Zhao","author_inst":"11.\tVaccine and Immunotherapy Center, The Wistar Institute, Philadelphia, PA, USA 12.\tEllen and Ronald Caplan Cancer Center, The Wistar Institute, Philadelphia,"},{"author_name":"Hanbing Cao","author_inst":"1.\tDepartment of Genetics, Yale University School of Medicine, New Haven, CT, USA 2.\tSystem Biology Institute, Yale University, West Haven, CT, USA 3.\tCenter fo"},{"author_name":"Kaiyuan Tang","author_inst":"1.\tDepartment of Genetics, Yale University School of Medicine, New Haven, CT, USA 2.\tSystem Biology Institute, Yale University, West Haven, CT, USA 3.\tCenter fo"},{"author_name":"Xingbo Shang","author_inst":"2.\tSystem Biology Institute, Yale University, West Haven, CT, USA 3.\tCenter for Cancer Systems Biology, Yale University, West Haven, CT, USA 7.\tDepartment of Bi"},{"author_name":"Cole W. Christopher","author_inst":"11.\tVaccine and Immunotherapy Center, The Wistar Institute, Philadelphia, PA, USA 12.\tEllen and Ronald Caplan Cancer Center, The Wistar Institute, Philadelphia,"},{"author_name":"Yunfei Jiao","author_inst":"11.\tVaccine and Immunotherapy Center, The Wistar Institute, Philadelphia, PA, USA 12.\tEllen and Ronald Caplan Cancer Center, The Wistar Institute, Philadelphia,"},{"author_name":"Medha Majety","author_inst":"1.\tDepartment of Genetics, Yale University School of Medicine, New Haven, CT, USA 2.\tSystem Biology Institute, Yale University, West Haven, CT, USA 3.\tCenter fo"},{"author_name":"Andre Levchenko","author_inst":"2.\tSystem Biology Institute, Yale University, West Haven, CT, USA 3.\tCenter for Cancer Systems Biology, Yale University, West Haven, CT, USA 7.\tDepartment of Bi"},{"author_name":"David B. Weiner","author_inst":"11.\tVaccine and Immunotherapy Center, The Wistar Institute, Philadelphia, PA, USA 12.\tEllen and Ronald Caplan Cancer Center, The Wistar Institute, Philadelphia,"},{"author_name":"Sidi Chen","author_inst":"1.\tDepartment of Genetics, Yale University School of Medicine, New Haven, CT, USA 2.\tSystem Biology Institute, Yale University, West Haven, CT, USA 3.\tCenter fo"},{"author_name":"Xiaoyu Zhou","author_inst":"11.\tVaccine and Immunotherapy Center, The Wistar Institute, Philadelphia, PA, USA 12.\tEllen and Ronald Caplan Cancer Center, The Wistar Institute, Philadelphia,"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"Evidence Accumulation in Synaptic Efficacy: Activity-Silent Decision-Making with Short-Term Plasticity","rel_doi":"10.64898\/2026.09.27.754756","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.27.754756","rel_abs":"Decision-making requires accumulation of evidence over time, yet exactly how neural circuits implement this computation remains debated. Classical models posit that evidence accumulation is realized by ramping neural activity, whereas recent experimental findings suggest an alternative mechanism in which neural activity remains largely silent before abruptly transitioning into an active decision state. This mechanism leaves a fundamental question unanswered: how is evidence accumulated during the neural activity-silent period? To address this question, we trained excitatory-inhibitory recurrent neural networks (RNNs) with short-term synaptic plasticity (STP) on a reaction-time decision task. We found that after training, the networks learned to leverage STP to make decisions in an activity-silent manner. Choice-tuning analysis revealed a clear functional organization of the network, and connection-pruning analysis uncovered the coupling between choice-selective excitatory and inhibitory neural groups. Guided by the architecture of trained RNNs, we constructed a minimal neural circuit model, in which evidence accumulates in facilitated synaptic efficacy that progressively reshapes the circuit's phase portrait, eventually driving the network state across the stability boundary and triggering a rapid transition to a choice state. We further compared activity before a choice between the theoretical STP and Static models to examine the potential energy benefit of activity-silent decision-making. Together, our results suggest STP as a mechanism for evidence accumulation in activity-silent decision-making and offer insights into the design of energy-efficient artificial neural networks.","rel_num_authors":5,"rel_authors":[{"author_name":"Tianhao Chu","author_inst":"School of Psychological and Cognitive Sciences, Peking University, China."},{"author_name":"Yuling Wu","author_inst":"Peking University"},{"author_name":"Chenhao Zhang","author_inst":"Peking University"},{"author_name":"Fang Fang","author_inst":"Peking University"},{"author_name":"Si Wu","author_inst":"Peking University"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"Distinct population dimensions separate sensory adaptation and expectation-related signals in visual cortex","rel_doi":"10.64898\/2026.09.30.755465","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.30.755465","rel_abs":"Mismatch negativity is conventionally quantified as a scalar response-amplitude difference between rare deviant and repeated standard stimuli, leaving its organization at the neuronal population level unresolved. We recorded population activity in mouse primary visual cortex (V1) during visual oddball and control paradigms while testing contributions from anterior cingulate cortex (ACC) input using optogenetic inhibition. Mismatch modulated firing rates bidirectionally and these changes largely canceled out on average, yet produced robustly separable population activity patterns. On the stimulus-encoding manifold, repeated standard exposure produced standard-specific response attenuation and a shared rotation of standard and deviant representations, revealing a component not isolated by conventional stimulus-specific adaptation and deviance-detection contrasts. Off the manifold, standards and deviants occupied opposing positions along an ACC-modulated axis consistent with expectation confirmation and violation. Thus, visual mismatch responses engage separable transformations of stimulus representations and expectation-related population activity, with ACC input selectively contributing to the latter while not significantly affecting stimulus encoding or on-manifold adaptation.","rel_num_authors":4,"rel_authors":[{"author_name":"Soyoun Kim","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Arenski Vazquez","author_inst":"Albert Einstein College of Medicine"},{"author_name":"Renata Batista-Brito","author_inst":"Icahn School of Medicine at Mount Sinai"},{"author_name":"Lucas Sjulson","author_inst":"Albert Einstein College of Medicine"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"Programming primary human T cells to deliver gene editing machinery","rel_doi":"10.64898\/2026.09.30.753584","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.30.753584","rel_abs":"Clinical applications of gene editors including CRISPR-Cas9 are limited by the cell-type specificity and tissue penetrance of current in vivo delivery options. Cell-mediated gene editor delivery could overcome these obstacles. To date, systems utilizing immortalized cell lines have demonstrated gene editor delivery in vitro. Because T cells traffic throughout the organism and engage in intercellular communication with individual target cells, we wondered whether they could be engineered to deliver gene editing machinery. Here, we demonstrate that primary human T cells can produce retroviral-based enveloped delivery vehicles (EDVs) packaging functional Cas9 ribonucleoproteins. When paired with fusogenic VSV-G, T cell-generated Cas9-EDVs edited a diverse array of target cells. We developed a miniaturized Moloney murine leukemia virus-based system that proved superior to existing architectures in this context and optimized both lentiviral and site-specific knock-in approaches for T cell engineering. These Enveloped Particle Information Courier- (EPIC-) T cells offer key advantages as delivery systems.","rel_num_authors":5,"rel_authors":[{"author_name":"James Meixiong","author_inst":"University of California, San Francisco"},{"author_name":"Oleksandr Zginnyk","author_inst":"University of California, San Francisco"},{"author_name":"Lauren Chow","author_inst":"University of California, San Francisco"},{"author_name":"Brian R Shy","author_inst":"University of California, San Francisco"},{"author_name":"Matthew H. Spitzer","author_inst":"University of California, San Francisco"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"Hyperexcitable but seizure-free: hippocampal network dysfunction in a Wolfram syndrome model","rel_doi":"10.64898\/2026.09.27.754840","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.27.754840","rel_abs":"Wolfram syndrome (WS) is a rare, life-limiting progressive neurodegenerative disorder caused by pathogenic variants in the WFS1 gene. It is a prototype endoplasmic reticulum (ER) stress disorder, and the mechanisms underlying neurological phenotypes in WS remain incompletely understood. Although WFS1 protein is abundant in the hippocampus, the role of this region in WS remains relatively understudied. Here we characterize hippocampal function across scales in mice carrying a patient orthologous Wfs1 variant (p.W542*) on both alleles. Male mutants lose excitatory synapses, and dissociated hippocampal neurons are markedly hyperexcitable yet generate no increase in network bursting and no shift in network criticality, indicating that their additional firing is not recruited into coordinated population activity. This finding translates in vivo wherein intracranial electroencephalogram (EEG) recordings show no epileptiform activity or seizures in either sex, with no hippocampal astrogliosis, alongside a male-specific reduction in hippocampal low-gamma power. Additionally, EEG demonstrates a sex-specific disruption of sleep, which parallels a sex-divergent synaptic phenotype. Mutant male mice show impaired hippocampus-dependent fear memory and reduced voluntary motor activity. Taken together, we propose that cellular hyperexcitability in the WS hippocampus does not translate into increased network bursting, potentially due to disproportionate loss of excitatory synapses. This dissociation may provide a functional substrate for the neurological features of WS.","rel_num_authors":18,"rel_authors":[{"author_name":"Saumel Ahmadi","author_inst":"Washington University"},{"author_name":"Juan Gallardo Pinera","author_inst":"Washington University"},{"author_name":"Sophie Dokholyan","author_inst":"Washington University"},{"author_name":"Andrew D Sauerbeck","author_inst":"Washington University"},{"author_name":"Hong Nhung Phan","author_inst":"Washington University"},{"author_name":"Brianna Carman","author_inst":"Washington University"},{"author_name":"Avishek Debnath","author_inst":"Washington University"},{"author_name":"Samuel Brunwasser","author_inst":"University of California San Francisco"},{"author_name":"Gabriel Skinner","author_inst":"Washington University"},{"author_name":"Nicholas Rensing","author_inst":"Washington University School of Medicine"},{"author_name":"Michael Wong","author_inst":"Washington University School of Medicine"},{"author_name":"Cory Cearlock","author_inst":"Washington University"},{"author_name":"Susan E Maloney","author_inst":"Washington University in St. Louis School of Medicine"},{"author_name":"Terrance T Kummer","author_inst":"Washington University School of Medicine in St. Louis"},{"author_name":"Srikanth Singamaneni","author_inst":"Washington University in Saint Louis"},{"author_name":"John Cirrito","author_inst":"Washington University"},{"author_name":"Thomas Jonathan Foutz","author_inst":"Washingotn University in St. Louis"},{"author_name":"Fumihiko Urano","author_inst":"Washington University School of Medicine"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"Hyperexcitable but seizure-free: hippocampal network dysfunction in a Wolfram syndrome model","rel_doi":"10.64898\/2026.09.27.754840","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.27.754840","rel_abs":"Wolfram syndrome (WS) is a rare, life-limiting progressive neurodegenerative disorder caused by pathogenic variants in the WFS1 gene. It is a prototype endoplasmic reticulum (ER) stress disorder, and the mechanisms underlying neurological phenotypes in WS remain incompletely understood. Although WFS1 protein is abundant in the hippocampus, the role of this region in WS remains relatively understudied. Here we characterize hippocampal function across scales in mice carrying a patient orthologous Wfs1 variant (p.W542*) on both alleles. Male mutants lose excitatory synapses, and dissociated hippocampal neurons are markedly hyperexcitable yet generate no increase in network bursting and no shift in network criticality, indicating that their additional firing is not recruited into coordinated population activity. This finding translates in vivo wherein intracranial electroencephalogram (EEG) recordings show no epileptiform activity or seizures in either sex, with no hippocampal astrogliosis, alongside a male-specific reduction in hippocampal low-gamma power. Additionally, EEG demonstrates a sex-specific disruption of sleep, which parallels a sex-divergent synaptic phenotype. Mutant male mice show impaired hippocampus-dependent fear memory and reduced voluntary motor activity. Taken together, we propose that cellular hyperexcitability in the WS hippocampus does not translate into increased network bursting, potentially due to disproportionate loss of excitatory synapses. This dissociation may provide a functional substrate for the neurological features of WS.","rel_num_authors":18,"rel_authors":[{"author_name":"Saumel Ahmadi","author_inst":"Washington University"},{"author_name":"Juan Gallardo Pinera","author_inst":"Washington University"},{"author_name":"Sophie Dokholyan","author_inst":"Washington University"},{"author_name":"Andrew D Sauerbeck","author_inst":"Washington University"},{"author_name":"Hong Nhung Phan","author_inst":"Washington University"},{"author_name":"Brianna Carman","author_inst":"Washington University"},{"author_name":"Avishek Debnath","author_inst":"Washington University"},{"author_name":"Samuel Brunwasser","author_inst":"University of California San Francisco"},{"author_name":"Gabriel Skinner","author_inst":"Washington University"},{"author_name":"Nicholas Rensing","author_inst":"Washington University School of Medicine"},{"author_name":"Michael Wong","author_inst":"Washington University School of Medicine"},{"author_name":"Cory Cearlock","author_inst":"Washington University"},{"author_name":"Susan E Maloney","author_inst":"Washington University in St. Louis School of Medicine"},{"author_name":"Terrance T Kummer","author_inst":"Washington University School of Medicine in St. Louis"},{"author_name":"Srikanth Singamaneni","author_inst":"Washington University in Saint Louis"},{"author_name":"John Cirrito","author_inst":"Washington University"},{"author_name":"Thomas Jonathan Foutz","author_inst":"Washingotn University in St. Louis"},{"author_name":"Fumihiko Urano","author_inst":"Washington University School of Medicine"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"Structural streamlining of axonemal doublet microtubules in Plasmodium male gametes","rel_doi":"10.64898\/2026.10.01.755548","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.01.755548","rel_abs":"Male gametogenesis in malaria parasites produces up to eight flagellated gametes within approximately 15 min, requiring de novo construction of motile axonemes on an unusually short timescale. Here we combine cellular cryo-electron tomography, high-resolution cryo-EM, proteomics, and parasite genetics to define the molecular architecture and assembly states of the Plasmodium male gamete axoneme. The doublet microtubules retain the principal surface-associated motility modules but contain a markedly reduced luminal microtubule inner protein network. Their inner junction lacks PACRG and is instead organized by CFAP20 and CFAP52, whose combined loss disrupts doublet integrity and exflagellation. We further identify a calmodulin protein at the outer dynein arm docking region and show that outer dynein arms accumulate progressively during gametogenesis. These findings establish distinct form of axonemal remodeling in Plasmodium and reveal the ordered assembly of structural and motor modules during rapid male gamete formation.","rel_num_authors":12,"rel_authors":[{"author_name":"Zhixun Li","author_inst":"Peking University"},{"author_name":"Shuzhen Yang","author_inst":"Peking University"},{"author_name":"Yujin Huang","author_inst":"Xiamen University"},{"author_name":"Shanshan Ma","author_inst":"Peking University"},{"author_name":"Liping Luo","author_inst":"Peking University"},{"author_name":"Xiaoyang Hou","author_inst":"Xiamen University"},{"author_name":"Kunyue Deng","author_inst":"Peking University"},{"author_name":"Jinhua Wang","author_inst":"Capital Normal University"},{"author_name":"Guanbo Wang","author_inst":"Peking University"},{"author_name":"Miao Gui","author_inst":"Zhejiang University"},{"author_name":"Jing Yuan","author_inst":"Xiamen University"},{"author_name":"Qiang Guo","author_inst":"Peking University"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"The superior temporal asymmetrical pit remains asymmetric in autism spectrum disorder","rel_doi":"10.64898\/2026.10.01.755796","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.01.755796","rel_abs":"Neuroimaging studies identify the superior temporal sulcus (STS) as an anatomical and functional target in autism spectrum disorder (ASD). Yet, the STS is a heterogeneous structure containing unique subregions, such as the superior temporal asymmetrical pit (STAP), which is a recently identified human-specific landmark. To date, no study has compared the STAP between ASD individuals and neurotypical controls (NTs) in a large sample. Here, we analyzed the mean intra-hemispheric sulcal depth and inter-hemispheric laterality of the STAP, as well as the anterior and posterior portions of the STS (aSTS, pSTS), in 100 NTs and 100 ASD male individuals (ages 5-18) using a discovery-replication design. Across samples, the STAP and pSTS showed rightward asymmetry, while the aSTS showed leftward asymmetry within groups. All features of the STAP, aSTS, or pSTS did not differ significantly between groups. However, pSTS laterality related to individual differences in social cognition, which was further supported by a meta-analysis across over 200 studies showing that neurocognitive functional differences associated with social cognition and ASD are restricted to portions of the STS outside of the STAP. Together, these findings provide fine-grained insight into prior studies showing coarse morphological differences of the STS between individuals with ASD and NTs, as well as open up new questions and new neuroanatomical targets of the heterogeneous STS in future studies of brain structure, brain function, and cognition in ASD, as well as other neurodevelopmental disorders targeting the superior temporal cortex.","rel_num_authors":3,"rel_authors":[{"author_name":"Jake William Wirtjes","author_inst":"University of California, Berkeley"},{"author_name":"Ethan H Willbrand","author_inst":"University of Wisconsin School of Medicine and Public Health, Madison"},{"author_name":"Kevin S Weiner","author_inst":"University of California, Berkeley"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"Multi-omic spatial mapping of the human habenula defines the molecular and anatomical organization of medial and lateral subregions","rel_doi":"10.64898\/2026.09.30.755667","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.30.755667","rel_abs":"The habenula (Hb) is a small epithalamic midline structure critical for reward processing and mood regulation that is implicated in many complex brain disorders, including depression and addiction. The molecular anatomy of the Hb has been well-characterized in model organisms, but little is known about its organization in the human brain, including the transcriptomic and epigenetic signatures of spatially-organized lateral (LHb) and medial (MHb) cell types. Here, we generated a high resolution spatiomolecular map of the human Hb using both multiomic single nucleus sequencing (snMultiome) and spatially-resolved transcriptomics. We measured gene expression and chromatin accessibility across 4 MHb and 5 LHb neuronal cell types as defined by transcriptomic conservation with rodents and zebrafish. We identified cell type-specific molecular specializations related to GABAergic and glutamatergic signaling as well as regulatory relationships between open chromatin regions, transcription factors, and target genes associated with depression and addiction. High resolution spatial transcriptomics identified the anatomical location of heterogeneous LHb subpopulations, including two putative inhibitory populations in the LHb. We further characterized cell-type relationships between Hb neurons and glial subtypes and predicted ligand-receptor interactions between LHb and MHb neurons. Finally, we delineated Hb cell types and spatial domains enriched in genes associated with genetic risk for psychiatric disorders, identifying the strongest enrichment in the MHb. Collectively, the generated data and analyses resolve the spatiomolecular organization of the human Hb and identify molecular-genetic associations with neuropsychiatric disease.","rel_num_authors":19,"rel_authors":[{"author_name":"Kelsey D. Montgomery","author_inst":"Lieber Institute for Brain Development"},{"author_name":"Jonathan M. Werner","author_inst":"Lieber Institute for Brain Development"},{"author_name":"Nicholas J. Eagles","author_inst":"Lieber Institute for Brain Development"},{"author_name":"Chunyu Liu","author_inst":"Johns Hopkins Bloomerg School of Public Health"},{"author_name":"Cynthia S. Cardinault","author_inst":"Lieber Institute for Brain Development"},{"author_name":"Svitlana V. Bach","author_inst":"Lieber Institute for Brain Development"},{"author_name":"Atharv Chandra","author_inst":"Lieber Institute for Brain Development"},{"author_name":"Ruth Zhang","author_inst":"Lieber Institute for Brain Development"},{"author_name":"Sarah E. Maguire","author_inst":"Lieber Institute for Brain Development"},{"author_name":"Heena R. Divecha","author_inst":"Lieber Institute for Brain Development"},{"author_name":"Amy Deep-Soboslay","author_inst":"Lieber Institute for Brain Development"},{"author_name":"Ryan A. Miller","author_inst":"Lieber Institute for Brain Development"},{"author_name":"Louise A. Huuki-Myers","author_inst":"Lieber Institute for Brain Development"},{"author_name":"Rahul A. Bharadwaj","author_inst":"Lieber Institute for Brain Development"},{"author_name":"Joel E. Kleinman","author_inst":"Lieber Institute for Brain Development"},{"author_name":"Thomas M. Hyde","author_inst":"Lieber Institute for Brain Development"},{"author_name":"Brion S. Maher","author_inst":"Johns Hopkins Bloomerg School of Public Health"},{"author_name":"Leonardo Collado-Torres","author_inst":"Lieber Institute for Brain Development"},{"author_name":"Kristen R. Maynard","author_inst":"Lieber Institute for Brain Development"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"Integrating CSF and EV Proteomes Enhances Biomarker Discovery by Capturing Shared Variation Across Compartments","rel_doi":"10.64898\/2026.09.30.754912","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.30.754912","rel_abs":"Cerebrospinal fluid (CSF) contains brain derived proteins that can be leveraged as biomarkers for Alzheimer's disease (AD) and other neurodegenerative diseases. However, converting protein intensities to a disease signal remains a challenge because physiology driven changes to the localization of proteins across CSF subcompartments are obscured when CSF is analyzed alone. Where subcompartment data, such as that from extracellular vesicles (EVs), are also measured, separate but parallel analyses of EVs and CSF can obscure their shared disease signal. Here, we present an integrated matrix factorization approach that combines proteomic signals from matched CSF and phosphatidylserine exposed EVs and nanoparticles (EVps). In a ROS\/MAP cohort of 48 participants, integrating CSF and EVps signals improved biological concordance across compartments and increased the number of candidate proteins distinguishing participants with AD from controls. Notably, the joint signal identified as many brain related biomarker candidates as studies using substantially larger cohorts. By demonstrating that integration of CSF and EVps proteomes maximizes information from limited samples, this framework offers a path toward more efficient biomarker discovery across two compartments increasingly leveraged in neurodegenerative disease research.","rel_num_authors":5,"rel_authors":[{"author_name":"Bianca R. P. Brown","author_inst":"Yale University"},{"author_name":"Xiaoting Li","author_inst":"University of Cambridge"},{"author_name":"Monica R Grasty","author_inst":"Yale University"},{"author_name":"Andrew D Miranker","author_inst":"Yale University"},{"author_name":"Gamze Gursoy","author_inst":"University of Cambridge"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"Divergence in wilt tolerance across the range of Mimulus guttatus in the absence of stomatal regulation","rel_doi":"10.64898\/2026.09.30.755782","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.30.755782","rel_abs":"Whether phenotypes and environments predict performance under stress is central to ecology and evolution and can reveal species' vulnerability to global change. We sampled Mimulus guttatus range-wide to test whether reproductive, morphological, and physiological traits predict drought responses. Surprisingly, we find that stomatal response, a key mediator of water balance, did not change as drought intensified. Climate, however, predicted survival after turgor loss: plants from regions with large daily relative to seasonal temperature ranges survived wilting longer. Stress responses also depended on life history: where summers are dry, perennials withstood lower water potentials, whereas annuals increased leaf cooling as they wilted. Known trait associations were not fixed: although many baseline traits tracked climate, none predicted drought responses. Drought performance therefore cannot be inferred from traits measured under well-watered conditions. Lacking stomatal regulation, riparian drought escapers like M. guttatus may be vulnerable if shorter growing seasons bring drought before reproduction.","rel_num_authors":14,"rel_authors":[{"author_name":"Haley A Branch","author_inst":"Connecticut College"},{"author_name":"Pauline Raimondeau","author_inst":"Yale University"},{"author_name":"Vanessa Tonet","author_inst":"Yale University"},{"author_name":"Henry Arenas Castro","author_inst":"Yale University"},{"author_name":"Andrew Fairclough","author_inst":"Michigan State University"},{"author_name":"Lily Hyde","author_inst":"Yale University"},{"author_name":"Dristen Lakes","author_inst":"North Carolina Agricultural and Technical State University"},{"author_name":"Alyssa Singletary","author_inst":"North Carolina Agricultural and Technical State University"},{"author_name":"Benjamin K Blackman","author_inst":"University of California, Berkeley"},{"author_name":"Katherine Toll","author_inst":"University of South Carolina"},{"author_name":"Mario Vallejo-Marin","author_inst":"Uppsala University"},{"author_name":"Craig R Brodersen","author_inst":"Yale University School of the Environment"},{"author_name":"Erika Edwards","author_inst":"Yale University"},{"author_name":"Jennifer M Coughlan","author_inst":"Yale University"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"Divergence in wilt tolerance across the range of Mimulus guttatus in the absence of stomatal regulation","rel_doi":"10.64898\/2026.09.30.755782","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.30.755782","rel_abs":"Whether phenotypes and environments predict performance under stress is central to ecology and evolution and can reveal species' vulnerability to global change. We sampled Mimulus guttatus range-wide to test whether reproductive, morphological, and physiological traits predict drought responses. Surprisingly, we find that stomatal response, a key mediator of water balance, did not change as drought intensified. Climate, however, predicted survival after turgor loss: plants from regions with large daily relative to seasonal temperature ranges survived wilting longer. Stress responses also depended on life history: where summers are dry, perennials withstood lower water potentials, whereas annuals increased leaf cooling as they wilted. Known trait associations were not fixed: although many baseline traits tracked climate, none predicted drought responses. Drought performance therefore cannot be inferred from traits measured under well-watered conditions. Lacking stomatal regulation, riparian drought escapers like M. guttatus may be vulnerable if shorter growing seasons bring drought before reproduction.","rel_num_authors":14,"rel_authors":[{"author_name":"Haley A Branch","author_inst":"Connecticut College"},{"author_name":"Pauline Raimondeau","author_inst":"Yale University"},{"author_name":"Vanessa Tonet","author_inst":"Yale University"},{"author_name":"Henry Arenas Castro","author_inst":"Yale University"},{"author_name":"Andrew Fairclough","author_inst":"Michigan State University"},{"author_name":"Lily Hyde","author_inst":"Yale University"},{"author_name":"Dristen Lakes","author_inst":"North Carolina Agricultural and Technical State University"},{"author_name":"Alyssa Singletary","author_inst":"North Carolina Agricultural and Technical State University"},{"author_name":"Benjamin K Blackman","author_inst":"University of California, Berkeley"},{"author_name":"Katherine Toll","author_inst":"University of South Carolina"},{"author_name":"Mario Vallejo-Marin","author_inst":"Uppsala University"},{"author_name":"Craig R Brodersen","author_inst":"Yale University School of the Environment"},{"author_name":"Erika Edwards","author_inst":"Yale University"},{"author_name":"Jennifer M Coughlan","author_inst":"Yale University"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"Divergence in wilt tolerance across the range of Mimulus guttatus in the absence of stomatal regulation","rel_doi":"10.64898\/2026.09.30.755782","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.30.755782","rel_abs":"Whether phenotypes and environments predict performance under stress is central to ecology and evolution and can reveal species' vulnerability to global change. We sampled Mimulus guttatus range-wide to test whether reproductive, morphological, and physiological traits predict drought responses. Surprisingly, we find that stomatal response, a key mediator of water balance, did not change as drought intensified. Climate, however, predicted survival after turgor loss: plants from regions with large daily relative to seasonal temperature ranges survived wilting longer. Stress responses also depended on life history: where summers are dry, perennials withstood lower water potentials, whereas annuals increased leaf cooling as they wilted. Known trait associations were not fixed: although many baseline traits tracked climate, none predicted drought responses. Drought performance therefore cannot be inferred from traits measured under well-watered conditions. Lacking stomatal regulation, riparian drought escapers like M. guttatus may be vulnerable if shorter growing seasons bring drought before reproduction.","rel_num_authors":14,"rel_authors":[{"author_name":"Haley A Branch","author_inst":"Connecticut College"},{"author_name":"Pauline Raimondeau","author_inst":"Yale University"},{"author_name":"Vanessa Tonet","author_inst":"Yale University"},{"author_name":"Henry Arenas Castro","author_inst":"Yale University"},{"author_name":"Andrew Fairclough","author_inst":"Michigan State University"},{"author_name":"Lily Hyde","author_inst":"Yale University"},{"author_name":"Dristen Lakes","author_inst":"North Carolina Agricultural and Technical State University"},{"author_name":"Alyssa Singletary","author_inst":"North Carolina Agricultural and Technical State University"},{"author_name":"Benjamin K Blackman","author_inst":"University of California, Berkeley"},{"author_name":"Katherine Toll","author_inst":"University of South Carolina"},{"author_name":"Mario Vallejo-Marin","author_inst":"Uppsala University"},{"author_name":"Craig R Brodersen","author_inst":"Yale University School of the Environment"},{"author_name":"Erika Edwards","author_inst":"Yale University"},{"author_name":"Jennifer M Coughlan","author_inst":"Yale University"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"Divergence in wilt tolerance across the range of Mimulus guttatus in the absence of stomatal regulation","rel_doi":"10.64898\/2026.09.30.755782","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.30.755782","rel_abs":"Whether phenotypes and environments predict performance under stress is central to ecology and evolution and can reveal species' vulnerability to global change. We sampled Mimulus guttatus range-wide to test whether reproductive, morphological, and physiological traits predict drought responses. Surprisingly, we find that stomatal response, a key mediator of water balance, did not change as drought intensified. Climate, however, predicted survival after turgor loss: plants from regions with large daily relative to seasonal temperature ranges survived wilting longer. Stress responses also depended on life history: where summers are dry, perennials withstood lower water potentials, whereas annuals increased leaf cooling as they wilted. Known trait associations were not fixed: although many baseline traits tracked climate, none predicted drought responses. Drought performance therefore cannot be inferred from traits measured under well-watered conditions. Lacking stomatal regulation, riparian drought escapers like M. guttatus may be vulnerable if shorter growing seasons bring drought before reproduction.","rel_num_authors":14,"rel_authors":[{"author_name":"Haley A Branch","author_inst":"Connecticut College"},{"author_name":"Pauline Raimondeau","author_inst":"Yale University"},{"author_name":"Vanessa Tonet","author_inst":"Yale University"},{"author_name":"Henry Arenas Castro","author_inst":"Yale University"},{"author_name":"Andrew Fairclough","author_inst":"Michigan State University"},{"author_name":"Lily Hyde","author_inst":"Yale University"},{"author_name":"Dristen Lakes","author_inst":"North Carolina Agricultural and Technical State University"},{"author_name":"Alyssa Singletary","author_inst":"North Carolina Agricultural and Technical State University"},{"author_name":"Benjamin K Blackman","author_inst":"University of California, Berkeley"},{"author_name":"Katherine Toll","author_inst":"University of South Carolina"},{"author_name":"Mario Vallejo-Marin","author_inst":"Uppsala University"},{"author_name":"Craig R Brodersen","author_inst":"Yale University School of the Environment"},{"author_name":"Erika Edwards","author_inst":"Yale University"},{"author_name":"Jennifer M Coughlan","author_inst":"Yale University"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"Jasmonate-induced expansion of the MYC2 cistrome occurs within a preconfigured chromatin landscape","rel_doi":"10.64898\/2026.10.01.755910","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.10.01.755910","rel_abs":"The transcription factor (TF) MYC2 is a central regulator of jasmonate (JA) signaling, yet its genome-wide occupancy has primarily been characterized using transgenic lines expressing tagged MYC2 proteins, leaving the dynamics of the native MYC2 cistrome unresolved. Here, using a MYC2-specific antibody, we define the wildtype MYC2 cistrome in Arabidopsis thaliana under basal and JA-induced conditions. Native MYC2 showed minimal genome-wide occupancy under control conditions but underwent a pronounced expansion following JA treatment, resulting in more than 3,000 binding sites enriched at core components of the JA signaling network. Cistrome expansion coincided with increased MYC2 protein abundance, while elevated basal MYC2 levels in a transgenic line were associated with extensive MYC2 occupancy even in the absence of JA, supporting a dosage-dependent relationship between MYC2 abundance and genome-wide binding. Despite this extensive cistrome expansion and transcriptional reprogramming, ATAC-seq revealed remarkably limited remodeling of chromatin accessibility following JA treatment. MYC2 recruitment occurred predominantly within pre-existing accessible chromatin regions that remained largely intact in myc2 myc3 myc4 mutants. Together, our findings reveal that JA-dependent expansion of the native MYC2 cistrome occurs within a largely pre-established accessome and identify MYC2 dosage as an important determinant of the extent of genome-wide occupancy.","rel_num_authors":7,"rel_authors":[{"author_name":"Linkan Dash","author_inst":"Waksman Institute of Microbiology, Department of Plant Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA"},{"author_name":"Moonia Ammari","author_inst":"Waksman Institute of Microbiology, Department of Plant Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA"},{"author_name":"Aanchal Choudhary","author_inst":"Department of Immunobiology, University of Lausanne, Epalinges, Switzerland"},{"author_name":"Kashif Maseh","author_inst":"Waksman Institute of Microbiology, Department of Plant Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA"},{"author_name":"Moorthy Gnanarajah","author_inst":"Waksman Institute of Microbiology, Department of Plant Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA"},{"author_name":"Jeevika Gupta","author_inst":"Waksman Institute of Microbiology, Department of Plant Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA"},{"author_name":"Mark Zander","author_inst":"Waksman Institute of Microbiology, Department of Plant Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA"}],"rel_date":"2026-10-02","rel_site":"biorxiv"},{"rel_title":"Transcriptomic analysis reveals differential regulation of synaptic components in pediatric and adult brain tumors","rel_doi":"10.64898\/2026.09.30.755821","rel_link":"http:\/\/biorxiv.org\/content\/10.64898\/2026.09.30.755821","rel_abs":"Neuronal-cancer cell interactions are a key component of brain tumor pathophysiology, yet many fundamental questions remain unanswered, with even more remaining unasked. Here, we present a comprehensive brain tumor reference map generated from bulk RNA-seq data of ~4,300 adult and pediatric brain tumors, including various glioma subtypes, medulloblastomas, ependymomas, and meningiomas, alongside ~1,400 healthy brain samples. By analyzing synaptic processes and synaptic gene expression across tumor types, we reveal the complexity of neuron-tumor crosstalk and its potential impact on patient survival. Our findings identify key synaptic signaling pathways and dysregulated patterns in brain tumors, including HNRNPH2 upregulation, a key factor in RNA processing and metabolism, in gliomas and medulloblastomas. Additionally, we highlight the role of cholinergic signaling, particularly CHRNA9, as a driver of glioma progression, which is also upregulated in WNT and Group 4 medulloblastomas, ependymomas, and meningiomas. Furthermore, P2 purinergic signaling emerges as a key player in glioma progression. These insights lay the groundwork for future studies exploring whether targeting these pathways could reprogram the tumor microenvironment toward an anti-tumor state, ultimately leading to novel therapeutic strategies and improved patient outcomes.","rel_num_authors":7,"rel_authors":[{"author_name":"Leyre Merino-Galan","author_inst":"Seattle Children's Research Institute"},{"author_name":"Sonali Arora","author_inst":"FHCRC"},{"author_name":"Greg Glatzer","author_inst":"FHCRH"},{"author_name":"Matt Jensen","author_inst":"FHCRH"},{"author_name":"Ashmitha Rajendran","author_inst":"Seattle Children's Research Institute"},{"author_name":"Eric C Holland","author_inst":"FHCRH"},{"author_name":"Siobhan S Pattwell","author_inst":"Seattle Children's; University of Washington School of Medicine; Fred Hutchinson Cancer Center"}],"rel_date":"2026-10-02","rel_site":"biorxiv"}]}