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<title>bioRxiv Subject Collection: Systems Biology</title>
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<description>
This feed contains articles for bioRxiv Subject Collection "Systems Biology"
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<link>https://www.biorxiv.org</link>
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<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.18.744962v1?rss=1">
<title>
<![CDATA[
Supervised Learning of Phosphopeptide Sequence Constraints Enables Global Prediction of SH2 Domain Binding 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.18.744962v1?rss=1
</link>
<description><![CDATA[
Tyrosine kinase signaling for cell development and homeostasis in multicelluar organisms and a major biochemical contribution is by driving interactions between phosphorylated tyrosines (pY) and SH2 domain containing proteins. This assembly is so important to driving cell outcomes that a wide variety of experimental and computational approaches have been used to understand which SH2-pY interactions occur, which still remains a challenge given the immensity (more than 45,000 pY and 120 SH2 domains in the human proteome). Based on biophysical constraints suggested by comprehensive contact mapping, here, we ask whether an approach might consider first asking if pY sequences conform to the shared rules of SH2 domain recognition by developing a classification approach that combines diverse training data. A wide range of validation suggests this approach, SpY-C, can classify pY sites as having the potential, or not, to be involved in SH2 domain interactions. We find that a relatively small set of representative SH2 binders, integrated from different experimental techniques, provides good classification. We use this classifier to annotate the human phosphoproteome and individual experiments, to explore the consequences of using super-SH2 domain reagents for pY enrichment, and to analyze the effects of mutations in altering pY site function. SpY-C provides a helpful step to more rapidly annotating pY function and for possibly improving machine learning approaches focused on specific SH2-pY interactions downstream of a first pass classification approach.
]]></description>
<dc:creator><![CDATA[ Kandoor, A., Silva Oliveira, A. C., Machida, K., Blagoev, B., Naegle, K. M. ]]></dc:creator>
<dc:date>2026-08-19</dc:date>
<dc:identifier>doi:10.64898/2026.08.18.744962</dc:identifier>
<dc:title><![CDATA[Supervised Learning of Phosphopeptide Sequence Constraints Enables Global Prediction of SH2 Domain Binding]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.17.745376v1?rss=1">
<title>
<![CDATA[
Evidence-constrained mechanistic synthesis for drug discovery 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.17.745376v1?rss=1
</link>
<description><![CDATA[
Mechanistic drug-development programmes often have more biological evidence than they can safely quantify. We developed evidence-constrained mechanistic synthesis (ECMS), a framework that classifies what information each finding contains and converts only that information into restrictions on a family of mechanistic hypotheses. Evidence shifts the frequency of supported events in a reproducible ensemble rather than being converted into unsupported coefficients or probabilities of biological truth. In a chronic spontaneous urticaria (CSU) implementation, a representative, non-exhaustive corpus of 114 atomic findings from 53 sources and 13 public data resources compiled 18 relation/context constraints and a frozen 4,096-hypothesis ensemble. Regimen evaluation was formulated as continuous multi-node target matching: researchers specify desired changes and importance coefficients for modeled nodes, while package-declared controls vary continuously. A deterministic Sobol-to-block-refinement search, validated on all 4,096 hypotheses, reduced target-matching loss by 27.3% relative to the best of 44 deterministic anchors under a prespecified heuristic demonstration profile; changing the objective profile changed the selected control vector without changing the evidence ensemble. A complementary D-only reference analysis localized decision-relevant uncertainty around the mast-cell-to-disease relation, illustrating that mechanistic prioritization depends on the declared objective. ECMS is intended for the pre-calibration stage of drug development: it makes heterogeneous literature computable while keeping evidence, uncertainty and decision preferences distinct.
]]></description>
<dc:creator><![CDATA[ Sengupta, D., Panda, S. ]]></dc:creator>
<dc:date>2026-08-19</dc:date>
<dc:identifier>doi:10.64898/2026.08.17.745376</dc:identifier>
<dc:title><![CDATA[Evidence-constrained mechanistic synthesis for drug discovery]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.17.745343v1?rss=1">
<title>
<![CDATA[
Plantago lanceolata and Lolium perenne metabolite profiles, their impact on soil microbial community structures and soil biological nitrification inhibition. 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.17.745343v1?rss=1
</link>
<description><![CDATA[
Background and aims: Excess nitrate (NO3-), from fertilizer overuse and intensive agriculture, can pollute water and contribute to greenhouse gas production (nitrous oxide - N2O). Plant metabolites from pastural herbs such as Plantago lanceolata (plantain) can inhibit microbial nitrification of ammonium to NO3- (biological nitrification inhibition - BNI) and change soil nitrogen cycle dynamics (lower potential nitrification rate - PNR). The main aim was to investigate differential plant metabolite expression associated with BNI and lowered PNR in different soil types. Methods: Six plantain cultivars were tested for BNI potential and screened for metabolites that correlated with inhibition of the ammonia oxidising bacterium (AOB) Nitrosospira multiformis. PNR and microbiome change was then investigated in four different New Zealand soils under the plantain cultivar Agritonic and ryegrass cultivar One50. Results: PNR under plantain was 11 to 41% lower than fallow soil while PNR under ryegrass was 0 to 39% lower. In addition to verbascoside and aucubin, plantain metabolites associated with lower PNR included plantamajoside, riboflavin 3- and 5-sulfate, plantagoguanidinic acid. Chlorogenic acid was associated with lowered PNR under ryegrass. PNR reductions, microbiome structure and the ratio of ammonia oxidising archaea (AOA) relative to AOB was modulated by soil type. Conclusion: Plantain and ryegrass lowered the PNR in four different soils and was correlated with metabolites beyond just aucubin and verbascoside. Based on candidate BNI-associated metabolites identified, it was hypothesised that lowered PNR is likely indirect through mechanisms such as chelation and appears to be dependent on both plant physiology and soil physicochemistry.
]]></description>
<dc:creator><![CDATA[ Anderson, C. R., Peterson, M., Joyce, N., van Klink, J., Fraser, T., Panda, P. ]]></dc:creator>
<dc:date>2026-08-18</dc:date>
<dc:identifier>doi:10.64898/2026.08.17.745343</dc:identifier>
<dc:title><![CDATA[Plantago lanceolata and Lolium perenne metabolite profiles, their impact on soil microbial community structures and soil biological nitrification inhibition.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.17.745071v1?rss=1">
<title>
<![CDATA[
Circadian Oscillation Detection Analysis and Comparison (CODAC):a Multicriteria Method to Estimate and Compare Rhythmicity 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.17.745071v1?rss=1
</link>
<description><![CDATA[
Analysis of circadian patterns in time-series data requires computational methods that can accommodate several factors, including variable sampling resolution, replicate number, and missing values. Most existing tools simplify rhythmicity to a strict dichotomy based solely on a single p-value threshold. This leads to a level of uncertainty that affects many biological targets. We developed CODAC (Circadian Oscillation Detection Analysis and Comparison), a framework that integrates nonlinear constrained optimization with a multicriteria rhythmicity classification scheme to evaluate rhythmic patterns without relying on a single statistical cutoff. This approach allows CODAC to identify and exclude medium-confidence rhythms rather than force them into a rhythmic/arrhythmic dichotomy. CODAC comprises four modules: (i) CODAC_single estimates rhythmicity within a single group; (ii) CODAC_flex extends this to identify distinct waveform types within one group; (iii) CODAC_compare performs pairwise comparisons across two or more groups to detect rhythmic or arrhythmic changes; and (iv) CODAC_multi handles more complex designs involving multiple-group comparisons. Using in silico simulations and public transcriptomic datasets, we show that CODAC performs comparably to established methods while providing additional flexibility for rhythm classification and comparison. Taken together, CODAC provides a flexible and open-source package for circadian time-series analysis with automated visualization tools.
]]></description>
<dc:creator><![CDATA[ da Silveira, T. P., Nguyen, T., Lincoln, K., de Assis, L. V. ]]></dc:creator>
<dc:date>2026-08-18</dc:date>
<dc:identifier>doi:10.64898/2026.08.17.745071</dc:identifier>
<dc:title><![CDATA[Circadian Oscillation Detection Analysis and Comparison (CODAC):a Multicriteria Method to Estimate and Compare Rhythmicity]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.17.745217v1?rss=1">
<title>
<![CDATA[
Small intestinal microbiota of undernourished women perturbs placentaldevelopment in mice 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.17.745217v1?rss=1
</link>
<description><![CDATA[
Children of undernourished women have impaired pre- and postnatal growth. Undernourished women and children have a high incidence of environmental enteric dysfunction (EED), an enteropathy characterized by gut barrier dysfunction and systemic inflammation. Here, we employ gnotobiotic mice to compare the effects of bacterial consortia cultured from the duodenal microbiota of Bangladeshi women with EED and their healthy counterparts. Female mice harboring the EED-derived consortium exhibited fetal and placental growth restriction. Transcriptomic and proteomic analyses disclosed pronounced effects of the EED-derived consortium on the decidual component of the maternal-fetal interface involving tissue-resident uterine natural killer (uNK) cells and disruption of TGF-{beta} signaling between uNK and decidual stromal cells. Co-housing mice with EED and healthy consortia ameliorated these effects, disclosing bacterial targets to improve prenatal development.
]]></description>
<dc:creator><![CDATA[ Coskun, R., Chang, Z. L., Pruss, K. M., Liu, H., Marcial Rodriguez, A., Lee, E., Diamond, M. S., Ahmed, T., Barratt, M. J., Gordon, J. ]]></dc:creator>
<dc:date>2026-08-18</dc:date>
<dc:identifier>doi:10.64898/2026.08.17.745217</dc:identifier>
<dc:title><![CDATA[Small intestinal microbiota of undernourished women perturbs placentaldevelopment in mice]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.17.745297v1?rss=1">
<title>
<![CDATA[
Proteoform Barcode: An Intuitive Visualization Framework for Top-Down Proteomics 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.17.745297v1?rss=1
</link>
<description><![CDATA[
Top-down proteomics (TDP) advances biomedical research by providing a birds-eye view of proteoforms in cells, tissues, and biofluids. Thousands of proteoforms can be characterized using well-established TDP technologies, and potential proteoform biomarkers of diseases have been discovered. However, there is a lack of an easy and biologically informative approach to present the quantitative global TDP data. Here, we present proteoform barcode as a straightforward visualization approach that simultaneously displays proteoform abundance and their associated Gene Ontology (GO) biological processes, converting a list of proteoforms to a biologically informative image. The proteoform barcode allows 1) a global view of proteoforms (i.e., relative abundance and functional information) in complex biological systems (i.e., bacteria, yeast, human cells, and human plasma) and 2) the accurate distinction of samples in diverse biological conditions (i.e., control and disease) assisted by machine learning approaches. The proteoform barcode, assisted by the random forest model, accurately separated the human plasma samples of healthy controls and early-stage breast cancer. The data demonstrates the high potential of the proteoform barcode-based approach for early diagnosis of diseases in an easy and biologically informative manner.
]]></description>
<dc:creator><![CDATA[ Yue, Y., Gao, G., Fang, F., Zhu, G., Sadeghi, S. A., Nimavard, R. T., Sun, L. ]]></dc:creator>
<dc:date>2026-08-18</dc:date>
<dc:identifier>doi:10.64898/2026.08.17.745297</dc:identifier>
<dc:title><![CDATA[Proteoform Barcode: An Intuitive Visualization Framework for Top-Down Proteomics]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.16.744343v1?rss=1">
<title>
<![CDATA[
Port of Protein-Protein Interactomes: An experiment-based protein-protein interactome database for rice 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.16.744343v1?rss=1
</link>
<description><![CDATA[
Protein protein interactions (PPIs) play a crucial role in enabling proteins to carry out their functions within various biological processes. Rice, as a key model organism in plant biological studies, has been at the forefront of PPI research. However, most PPI datasets in rice stem from computational predictions, while experiment-based rice PPI datasets are fragmented due to the lack of systematic profiling at the rice PPIome level, which largely hinders information sharing in the rice research community. In previous research, we constructed the Port of Protein-Protein Interactomes (POPPIN; https://riceome.hzau.edu.cn/poppin/), an integrated database dedicated to sharing experimentally verified 150,451 PPIs and functional clues in rice. Empowered by high-throughput PPIome profiling technologies and text mining assisted by a large language model. Additionally, POPPIN provides detailed protein information, including GO annotations, subcellular localizations, domains, trait ontology (TO) information, and hyperlinks to external biological databases. Through offering a user-friendly web interface for search and dynamic network visualization, POPPIN serves as the first large-scale, experiment-based database for searchable PPIs in rice.
]]></description>
<dc:creator><![CDATA[ Liu, X., Lu, J., Jia, L., Xia, D., Huang, J., Chen, Y., Li, M., Chen, Y., Liu, X., Li, G., Liu, W., Li, J., Ying, J., Wang, Y., Li, Z., Tong, X., Hou, Y., E, Z., Zhang, J., Zhang, J. ]]></dc:creator>
<dc:date>2026-08-17</dc:date>
<dc:identifier>doi:10.64898/2026.08.16.744343</dc:identifier>
<dc:title><![CDATA[Port of Protein-Protein Interactomes: An experiment-based protein-protein interactome database for rice]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.14.744825v1?rss=1">
<title>
<![CDATA[
A Structural Design Principle for Temperature Robustness in Biomolecular Circuits 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.14.744825v1?rss=1
</link>
<description><![CDATA[
The dominant paradigm for temperature robustness in biomolecular circuits is for the parameters to be tuned to have matching temperature dependencies so that their overall effect cancels out. This contrasts with the robustness due to circuit structure, typically operative in circuits where robustness to a single input parameter is desired. The importance of the circuit structure in temperature robustness is generally unclear. We addressed this issue in a benchmark negative feedback circuit using a combination of theoretical modelling and experimental measurements. We found that the response to a temperature perturbation in a model of negative feedback was qualitatively different from the response in a model without feedback. We experimentally measured the response of the negative feedback circuit to a temperature perturbation and found that it was smaller than that of the circuit without feedback, in line with the theoretical finding. We confirmed this theoretical prediction experimentally. The initial response of the negative feedback circuit, paradoxically, was larger than the circuit without feedback. The resolution of this paradox was in accounting for the faster dynamics in the negative feedback circuit. These results show a simple design principle of temperature robustness that can operate in a widespread circuit motif and may also apply to other perturbations which, like temperature, affect multiple parameters simultaneously.
]]></description>
<dc:creator><![CDATA[ Chorasiya, G., Sen, S. ]]></dc:creator>
<dc:date>2026-08-17</dc:date>
<dc:identifier>doi:10.64898/2026.08.14.744825</dc:identifier>
<dc:title><![CDATA[A Structural Design Principle for Temperature Robustness in Biomolecular Circuits]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.13.744720v1?rss=1">
<title>
<![CDATA[
Integrated Clinical and Proteomic Precision Subgrouping for Severe Dengue Endotype Signature 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.13.744720v1?rss=1
</link>
<description><![CDATA[
Background: Severe dengue remains difficult to predict because patients with different clinical trajectories may present with overlapping features, and conventional severity classifications may not fully capture underlying biological heterogeneity. In this study, we applied an integrated clinical and proteomic endotyping approach to dissect dengue disease heterogeneity and identify molecular signatures associated with severity. Methods: Plasma proteomic profiles were analyzed together with detailed clinical, biochemical, hematological, coagulation, and immunological parameters from healthy controls and dengue patients classified according to WHO 2009 severity criteria. High-throughput proteomic analysis, unsupervised clustering, pathway enrichment, and machine-learning-based classification were used to identify dengue endotypes and define molecular features associated with predicted severe disease. Results: Increasing dengue severity was associated with progressive abnormalities in liver function, coagulation parameters, hematological indices, and inflammatory mediators, including IL-6, IL-15, HGF, and MUC-16. However, proteomic profiling revealed substantial overlap across conventional severity categories, indicating that clinical classification alone does not fully resolve dengue host-response heterogeneity. Integrated clinical-proteomic clustering identified distinct dengue endotypes, including a predicted severe endotype enriched for inflammatory, antiviral, and cytotoxic lymphocyte-associated pathways. This high-risk endotype was characterized by elevated IL-15, IFN-{gamma}, and granzymes, consistent with coordinated activation of cytotoxic lymphocyte-associated antiviral responses. Machine-learning analysis further showed that proteomic features were strong discriminators of this endotype, supporting their potential utility as biomarkers of severe host-response states. Conclusion: Integrated clinical-proteomic endotyping provides molecular resolution beyond conventional severity grading and identifies immune pathways associated with severe dengue. This framework may improve biological understanding of dengue progression and support future risk stratification and biomarker development.
]]></description>
<dc:creator><![CDATA[ Kadni, T. S., Ambikan, A., Filipovic, I., Varma, M., Dutta, D., Mukhopadhyay, C., Gupta, S., Mudgal, P. P., Neogi, U. ]]></dc:creator>
<dc:date>2026-08-17</dc:date>
<dc:identifier>doi:10.64898/2026.08.13.744720</dc:identifier>
<dc:title><![CDATA[Integrated Clinical and Proteomic Precision Subgrouping for Severe Dengue Endotype Signature]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.14.744901v1?rss=1">
<title>
<![CDATA[
HI-JEPA: A World Model of Molecular Organization Learned from Measured Proximity 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.14.744901v1?rss=1
</link>
<description><![CDATA[
Proteins act through the company they keep. Which molecules occupy the same nanoscale neighborhood in intact tissue determines what can physically interact, and disease rearranges those neighborhoods before it changes anything a sequence records. That quantity (measured proximity between molecular species in unperturbed tissue) has never been acquired broadly enough to train on. Published colocalization arrives study by study and never accumulates into a graph. The measurement has to be made rather than collected. We built ASCEND, a spatial computing platform that measures pairwise molecular proximity from expansion microscopy at molecular resolution in intact tissue, and applied it to 164 proteins across 37 imaged regions in five studies, spanning cultured neurons, isolated synapses and mouse cortex in disease and control. HI-JEPA is a representation trained on those measurements. Each protein is one embedding, trained to predict the embeddings of its measured neighbors in latent space; it never reconstructs its input and generates no negatives. A set of proteins measured in one neighborhood forms a configuration, which is the object the model perturbs and plans over. The representation performs operations a sequence model cannot. It names a protein from the bare geometry of a microscopy point cloud, matched against 234,048 deposited structures, at top-1 accuracy 0.748 against a chance rate of 1.0 x 10^-5. It predicts physical interaction between sequence-dissimilar proteins that were both withheld from training at AUC 0.908, where ESM-C 6B reaches 0.514 against partner-count-matched negatives. It recovers a held-out complex member in the top 100 of 13,447 candidates at recall 0.954, against 0.514 for a ranking built from complex frequency alone. Asked which partners a knockout disrupts, it recovers the experimentally observed ones at recall@100 0.640; asked the same question about a different protein, with the ranking rule and denominators unchanged, it recovers 0.028, so the answer follows the action. Given 5xFAD mouse cortex with no disease label, no reward and no indication that amyloid is relevant, ranking 1,574 measured assemblies by their departure from wild type returns amyloid-beta bound to AMPA receptor subunits in nine of the top ten. Planning over the same configurations independently selects the same subunits (GluA2, GluA3, GluA4) and predicts that disrupting the PSD-95 scaffold worsens the configuration, both agreeing in sign with experiments the model never saw. Ablating the measured-proximity channel at training time degrades cross-scale partner recovery from median rank 14 to 68 while leaving navigation and within-scale dynamics intact; ablating the perturbation channel does the reverse. The cross-scale capability therefore comes from the measurement and not from having seen more data. The intended application is target nomination in diseases where sequence and structure supply no starting point. Note: This is a capability report. The architecture, the training procedure and the acquisition protocol are proprietary and are not described. Section 4.2 gives the evaluation protocol behind every number reported.
]]></description>
<dc:creator><![CDATA[ Shihabi, R., Karmali, S., Vaughan, B., Taraman, S., Kellis, M. ]]></dc:creator>
<dc:date>2026-08-17</dc:date>
<dc:identifier>doi:10.64898/2026.08.14.744901</dc:identifier>
<dc:title><![CDATA[HI-JEPA: A World Model of Molecular Organization Learned from Measured Proximity]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.13.744710v1?rss=1">
<title>
<![CDATA[
Potential benefit of loss-of-function on bacterial fitness 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.13.744710v1?rss=1
</link>
<description><![CDATA[
Escherichia coli is a well-studied organism with extensive genomic and proteomic data. This study examines how gene loss reallocates cellular resources and impacts fitness. Genes were classified based on fitness measurements as essential, important, mean-effect, or fitness-enhancing. Using proteomic data, we analyzed the relationship between protein production cost and fitness, finding that genes with a high proteomic mass fraction are more likely to affect fitness, while fitness-enhancing deletions rarely improve fitness by reducing proteomic burden. We calculated the cumulative of proteome fractions encoded by genes classified as mean-effect and compared it with the results from the ME-model simulations. The mean-effect category constitutes 31-75% of the proteome, with the highest proportion LB, while enrichment analysis of core mean-effect genes highlighted transmembrane transport as the main functional category. Furthermore, we identified a subset of genes whose deletion increased fitness compared to the mean; they generally have low expression, and many have unknown functions. AI-assisted structural analyses identified domains and conserved features compatible with DNA-binding proteins, suggesting that some may represent putative transcriptional regulators requiring further validation. RpoS, stress sigma factor controlling up to 15% of the proteome is one of the transcriptional regulators in the fitness-enhancing category. Our findings suggest that the cost of being a generalist is linked to transcriptional regulation, while molecular transport represents a high burden for nutrient readiness.

ImportanceThis study provides new insights into how gene loss benefits bacteria by identifying gene categories and their associated protein fractions whose disruption does not impose large fitness penalties. Additionally, it uncovers specific fitness-enhancing genes and generates hypotheses based on structural analyses for previously uncharacterized ones. Our findings suggest that several of these genes may encode putative transcriptional regulators, highlighting a potential role for regulatory complexity in cellular efficiency. By revealing how certain gene deletions enhance fitness and which gene categories are nonessential, this work advances our understanding of bacterial adaptation and genome streamlining. These insights have broad implications for evolutionary biology, metabolic engineering, and biotechnology, offering strategies to optimize microbial function by selectively reducing genetic and regulatory burden.
]]></description>
<dc:creator><![CDATA[ Hidalgo, D., Soto-Avila, L., Aguilar-Vera, O. A., Ledezma-Tejeida, D., Farias-Rico, J. A., Utrilla, J. ]]></dc:creator>
<dc:date>2026-08-15</dc:date>
<dc:identifier>doi:10.64898/2026.08.13.744710</dc:identifier>
<dc:title><![CDATA[Potential benefit of loss-of-function on bacterial fitness]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.12.744385v1?rss=1">
<title>
<![CDATA[
LIPOGRID: A HIGH-THROUGHPUT MULTI-OMICS PERTURBATION SCREEN DISSECTS THE GENETIC ARCHITECTURE OF LIPID METABOLISM 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.12.744385v1?rss=1
</link>
<description><![CDATA[
Lipids constitute one of the largest and most diverse classes of cellular molecules, sustaining membrane architecture, energy storage, and signaling. Consequently, their dysregulation underlies a broad spectrum of human disease. However, the genetic mechanisms governing lipid homeostasis have remained largely inaccessible, owing to the lack of approaches capable of systematically linking defined genetic perturbations to large-scale changes in cellular lipidome composition. Here we introduce LipoGrid, a spatial mass spectrometry platform that resolves the genetic architecture of lipid metabolism at single-cell resolution. LipoGrid arrays CRISPR/Cas9-perturbed cells on a micropatterned grid and sequentially captures lipidomic and gRNA identity from the same cells, complemented by single-cell RNA sequencing of matched cell populations subjected to the same perturbations. Using this approach, we quantified the relative abundance of 158 distinct lipid species across 143 target genes in a rigorously controlled experimental framework. We find that most gene knockouts produced measurable alterations in lipid composition, often affecting specific lipid classes and molecular subspecies. The screen accurately recapitulated established gene-lipid relationships, including enzyme-substrate specificities, lipid pathway regulators, and disease-associated loss-of-function phenotypes, thereby demonstrating the sensitivity and accuracy of LipoGrid. By jointly profiling transcriptomic and lipidomic responses, we further uncover compensatory feedback mechanisms that buffer the impact of genetic perturbations on the cellular lipidome. Collectively, these findings establish LipoGrid as a scalable multimodal platform for systematically mapping gene-lipid interactions and reveal the regulatory networks linking gene perturbation, transcriptional adaptation, and lipidome remodeling.

HighlightsO_LIMicropatterned single-cell growth enables spatial lipidomic perturbation screens
C_LIO_LILipoGrid maps 143 gene knockouts to 158 lipid species and transcriptomic states
C_LIO_LIPerturbed lipidomes reveal compensatory feedback and lipid-class-specific uptake
C_LIO_LIRecovers enzyme substrate specificities and disease-linked lipid signatures
C_LI
]]></description>
<dc:creator><![CDATA[ Jacobs, J., Van Minsel, P., Ravoet, N., De Rieck, E., Vandermeulen, N., Venturelli, L., Vandereyken, K., Ven, K., Breukers, J., Wouters, D., Voet, T., Lammertyn, J., Swinnen, J., Thienpont, B., Sifrim, A. ]]></dc:creator>
<dc:date>2026-08-13</dc:date>
<dc:identifier>doi:10.64898/2026.08.12.744385</dc:identifier>
<dc:title><![CDATA[LIPOGRID: A HIGH-THROUGHPUT MULTI-OMICS PERTURBATION SCREEN DISSECTS THE GENETIC ARCHITECTURE OF LIPID METABOLISM]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.12.744566v1?rss=1">
<title>
<![CDATA[
Structural proteomics reveals a coagulation-complement accessibility signature of macrovascular invasion in hepatocellular carcinoma 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.12.744566v1?rss=1
</link>
<description><![CDATA[
Macrovascular invasion (MVI) and extrahepatic spread (EHS) define the most aggressive, treatment-refractory hepatocellular carcinoma (HCC), yet blood-based markers that report the underlying protein-network biology are lacking. Conventional proteomics measures protein abundance but not the conformational and protein-protein-interaction (PPI) states that govern function. We applied covalent proteome painting (CPP)--a dimethylation-based accessibility assay that reads out binding-site openness--to matched tumor and serum, reasoning that intravascular tumor dissemination remodels plasma protein complexes in a manner detectable as changes in accessibility.

Eight treatment-native HCC patients were profiled by CPP using matched FFPE tumor and top-14- depleted serum on a Q Exactive Orbitrap HF. The 85 tumor-serum common proteins defined an 81-protein targeted panel, validated by multiple-reaction-monitoring (MRM) mass spectrometry with heavy stable-isotope-standard peptides (296 peptides; 3,717 light/heavy transition pairs) in 22 FFPE tumors and 22 matched sera. Accessibility was the light/heavy ratio (high, open; low, closed). We assessed differential accessibility, serum-tissue translatability, pathway enrichment, and biomarker/survival performance.

Aggressive disease showed broadly decreased protein accessibility. MVI-associated changes were directionally concordant between tumor and serum (Spearman {rho}=0.21; 59% concordant), driven by coagulation and complement proteins (FGG, CTSD, LBP, C4BPA); the EHS axis did not translate. Decreased-accessibility proteins were enriched for complement-coagulation cascades and IGF/IGFBP transport. A six-protein serum accessibility signature discriminated MVI (leave-one-out cross-validated AUC 0.80; best single markers ceruloplasmin 0.83 and haemoglobin- 0.77), and MVI status trended with shorter overall survival (log-rank p=0.06).

Accessibility-based serum proteomics captures MVI-associated protein-complex remodeling that abundance assays miss, nominating a coagulation/complement-anchored serum signature for vascular-invasive HCC that warrants prospective validation.
]]></description>
<dc:creator><![CDATA[ Son, A., Hur, M. H., Cho, E. J., Ji, J., Han, E., Choi, Y., Park, J., Lee, H., Park, S., Yu, S. J., Kim, H. ]]></dc:creator>
<dc:date>2026-08-13</dc:date>
<dc:identifier>doi:10.64898/2026.08.12.744566</dc:identifier>
<dc:title><![CDATA[Structural proteomics reveals a coagulation-complement accessibility signature of macrovascular invasion in hepatocellular carcinoma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.12.744295v1?rss=1">
<title>
<![CDATA[
Metabolomic, lipidomic, and N-glycomic analyses of a human cell model of Krabbe disease reveal treatable deficits in glycosylation and serine-ceramide metabolism 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.12.744295v1?rss=1
</link>
<description><![CDATA[
Krabbe disease is a rare autosomal recessive lysosomal disease caused by deficiency of galactocerebrosidase (GALC), leading to accumulation of galactosylceramide and formation of the toxic metabolite galactosylsphingosine (psychosine). While psychosine accumulation is well-established as a primary pathogenic mechanism, the broader metabolic consequences of GALC deficiency remain incompletely understood. In this study, we used stable isotope tracing to comprehensively characterize metabolic perturbations in a human oligodendrocellular Krabbe disease model. This approach revealed elevated de novo ceramide synthesis in GALC knock-out cells, characterized by increased incorporation of glucose-derived serine into ceramide biosynthetic pathways. This enhanced ceramide production was amenable to pharmacological intervention by tezacaftor, an inhibitor of sphingolipid {Delta}4-desaturate (DEGS); tezacaftor administration also normalized psychosine levels, raising the possibility of its use as substrate reduction therapy. Additionally, we identified significant disruption of UDP-hexose metabolism, manifesting as an overabundance of truncated and hypogalactosylated glycans. These findings suggest impaired protein glycosylation as a previously unrecognized pathogenic mechanism in Krabbe disease. Our findings reveal novel metabolic dysregulation in Krabbe disease extending beyond established psychosine toxicity. The identification of enhanced de novo ceramide synthesis presents a new therapeutic target, while the discovery of galactose-deficient glycosylation defects supports galactose supplementation as a potential therapeutic intervention. These metabolic insights provide new mechanistic understanding and therapeutic opportunities for this devastating neurodegenerative disorder.
]]></description>
<dc:creator><![CDATA[ Starosta, R., Saeger, H., ten Hoeve, J., Kim, S., Van Hove, J. L. K., Jiang, X., He, M., Bennett, N. K. ]]></dc:creator>
<dc:date>2026-08-13</dc:date>
<dc:identifier>doi:10.64898/2026.08.12.744295</dc:identifier>
<dc:title><![CDATA[Metabolomic, lipidomic, and N-glycomic analyses of a human cell model of Krabbe disease reveal treatable deficits in glycosylation and serine-ceramide metabolism]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.07.743459v1?rss=1">
<title>
<![CDATA[
Differential routing of spectral light inputs separates circadian timing from energetic responsiveness 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.07.743459v1?rss=1
</link>
<description><![CDATA[
Light simultaneously provides phototrophic organisms with energy and with information about environmental time. These two functions need not impose the same response to fluctuations in irradiance: photosynthetic outputs should remain amplitude-sensitive, whereas circadian phase should reject changes that do not alter dawn, dusk, or photoperiod. We formulate this problem for two spectral inputs by decomposing their logarithmic intensities into a common-irradiance coordinate a and a spectral-contrast coordinate r. The contribution of channel i to phase is Qi = ZiGi, where the non-negative gate Gi determines when the pathway is active and the signed phase-response projection Zi determines whether this activity advances or delays the oscillator. For a locked oscillator, robustness to common irradiance together with retained contrast sensitivity requires two non-zero cycle-averaged contributions of opposite sign, A1 [~=] -A2 = 0. Energetic responsiveness is preserved only when the physiological projection of the same inputs is not proportional to their phase projection. A canonical repressilator provides an explicit nonlinear realization of these conditions. Positive gates placed on opposite lobes of its infinitesimal phase-response curve strongly attenuate common-mode phase shifts while preserving contrast sensitivity. A minimal photosynthetic-capacity model then shows how this organization protects temporal alignment under day-to-day irradiance fluctuations. At the largest variability tested, differential routing reduced the mean phase displacement by more than one half and the associated alignment loss by approximately 82%, whereas the resulting production advantage remained small, approximately 0.1%. Thus, multichannel light sensing can stabilize circadian timing without suppressing the energetic response to irradiance.

HighlightsO_LIAnalytical routing conditions separate common irradiance from spectral contrast.
C_LIO_LIPositive temporal gates can generate opposite signed phase contributions.
C_LIO_LIPhase robustness requires a projection distinct from the energetic projection.
C_LIO_LIA canonical oscillator provides a constructive illustration of the mechanism.
C_LIO_LIThe functional benefit is improved temporal alignment rather than a large growth gain.
C_LI
]]></description>
<dc:creator><![CDATA[ Thommen, Q. ]]></dc:creator>
<dc:date>2026-08-13</dc:date>
<dc:identifier>doi:10.64898/2026.08.07.743459</dc:identifier>
<dc:title><![CDATA[Differential routing of spectral light inputs separates circadian timing from energetic responsiveness]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.11.744139v1?rss=1">
<title>
<![CDATA[
A dysregulated stromal remodelling programme characterises prior anti-TNF failure in ulcerative colitis 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.11.744139v1?rss=1
</link>
<description><![CDATA[
Prior anti-tumour necrosis factor (TNF) failure is associated with reduced efficacy of subsequent advanced therapies in ulcerative colitis (UC), but the biological basis of this treatment-refractory state remains unclear. We integrated clinical outcomes and baseline colonic transcriptomic data from UC patients in the UNIFI phase III trial programme with regulatory and signalling network inference, connectivity mapping, and single-cell-resolution spatial transcriptomics. Colonic transcriptomic analyses identified coordinated enrichment of extracellular matrix organisation, collagen remodelling and integrin-associated programmes, increased stromal cell representation and elevated inferred MAPK/EGFR activity in UC patients with prior anti-TNF failure. Causal network inference prioritised MAPK3 as a candidate regulator of this state, while connectivity mapping identified MEK/EGFR inhibitors as candidate perturbagens. MEK inhibition suppressed stromal pathways and reduced inferred MAPK/EGFR activity ex vivo. Spatial profiling of active UC and non-IBD colonic tissues localised these programmes to UC-enriched stromal niches. Ligand-receptor inference further identified reciprocal stromal-myeloid communication within these niches. Collectively, these findings define a stromal remodelling programme associated with prior anti-TNF failure and nominate MAPK/EGFR signalling as a potentially tractable component of treatment-refractory UC.
]]></description>
<dc:creator><![CDATA[ Thomas, J. P., Wooldridge, T., Cozzetto, D., Lambie, N., Kudo, H., Saifuddin, A., Gul, L., Modos, D., Goldin, R., Matthews, N., Korcsmaros, T., Powell, N. ]]></dc:creator>
<dc:date>2026-08-12</dc:date>
<dc:identifier>doi:10.64898/2026.08.11.744139</dc:identifier>
<dc:title><![CDATA[A dysregulated stromal remodelling programme characterises prior anti-TNF failure in ulcerative colitis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.11.744197v1?rss=1">
<title>
<![CDATA[
Decoding the role of microbial interspecies interactions on nitrogen fixation 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.11.744197v1?rss=1
</link>
<description><![CDATA[
Nitrogen fixation performed by rhizosphere bacteria has the potential to improve the sustainability of cereal crop cultivation. Deciphering the role of interspecies interactions on nitrogen fixation is crucial for devising strategies to enhance this process. To unravel the contributions of interspecies interactions, we constructed synthetic microbial communities from the bottom-up that contain diazotrophic bacteria that fix nitrogen and maize rhizosphere bacteria that do not have this capability. Interactions that impacted nitrogenase activity via growth-independent mechanisms were prevalent in the system. Nitrogenase activity increased and eventually saturated as a function of the number of inoculated diazotrophs. Using a tailored machine learning model for microbiome dynamics and explainable artificial intelligence, we deciphered species contributions on nitrogenase activity and diazotroph growth. We identified a community containing Klebsiella variicola, Herbaspirillum seropedicae, and Stutzerimonas stutzeri as a starting point for developing microbial inoculants for cereal crops. Taken together, these results provide insights into the role of interspecies interactions on nitrogenase activity.
]]></description>
<dc:creator><![CDATA[ Palmer, C. M., Thompson, J., Hwang, J. H., Ranger, W., Ane, J.-M., Venturelli, O. S. ]]></dc:creator>
<dc:date>2026-08-12</dc:date>
<dc:identifier>doi:10.64898/2026.08.11.744197</dc:identifier>
<dc:title><![CDATA[Decoding the role of microbial interspecies interactions on nitrogen fixation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.11.744137v1?rss=1">
<title>
<![CDATA[
Cytokine interaction networks, not individual cytokines, drive anti-TNF response in Crohn's disease 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.11.744137v1?rss=1
</link>
<description><![CDATA[
Crohns disease (CD) is a chronic inflammatory condition of the gastrointestinal tract for which anti-tumour necrosis factor (anti-TNF) agents remain a first-line biologic therapy. However, remission rates are modest, and the mechanistic basis of non-response is poorly characterised. A common resistance mechanism is thought to emerge when alternative inflammatory cascades compensate for TNF inhibition, but the interactions underlying this rewiring have not been systematically characterised. We applied CytokineLink, our previously developed systems immunology framework, to single-cell RNA sequencing data from CD patients sampled before and after anti-TNF therapy. We reconstructed networks of interacting cytokines across samples stratified by treatment phase, response, and inflammation status, and identified condition-specific cytokine interactions and feedback loops, statistically validated against degree-matched random networks. We clustered the generated networks based on their inflammation, response, and treatment status. The pre-treatment inflamed non-responder network contained the largest set of unique interactions, organised around a connected module driven by IL17C targeting downstream TNF, IL6, IL1B, CXCL1/2/3/8, and CCL20. IL17C was produced by a population of non-ileal enteroendocrine cells, differentially abundant at baseline in non-responders. Gene set variation analysis in an independent cohort confirmed elevated non-responder module activity in colonic tissues of non-responders. Feedback loop analysis revealed that responder networks were characterised by persistent IL10 circuits sustained by macrophage populations and acquired tissue-remodelling interactions after therapy, whereas non-responders lost IL10 feedback loops post-treatment and gained TNF-containing motifs, including circuits signalling through the upstream activator TL1A. Our findings characterise the mechanism of anti-TNF non-response as a cytokine network, in which pre-existing epithelial-driven inflammatory modules and the failure to preserve regulatory feedback sustain TNF-independent inflammation in CD. By characterising cytokine interactions at the systems level, our approach moves beyond single-cytokine models of anti-TNF resistance to provide a mechanistic framework for understanding the biological basis of treatment failure in immune mediated diseases.
]]></description>
<dc:creator><![CDATA[ Olbei, M., Thomas, J. P., Liu, Y., Malas, S., Modos, D., Powell, N., Korcsmaros, T. ]]></dc:creator>
<dc:date>2026-08-11</dc:date>
<dc:identifier>doi:10.64898/2026.08.11.744137</dc:identifier>
<dc:title><![CDATA[Cytokine interaction networks, not individual cytokines, drive anti-TNF response in Crohn's disease]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.09.743783v1?rss=1">
<title>
<![CDATA[
Solving High-Dimensional Population Balance Equations via Dynamics-Preserving Autoencoders 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.09.743783v1?rss=1
</link>
<description><![CDATA[
High-dimensional population balance equations (PBEs) provide a natural framework for modeling heterogeneous cell populations, but their direct numerical solution becomes computationally prohibitive when the internal state space contains many molecular variables. We propose a hybrid mechanistic-machine learning framework for reducing and simulating PBEs defined over high-dimensional intracellular coordinates. The cell population is described by a number density n(x, t), where x [isin] [R]N represents gene and protein states associated with macrophage activation. A dynamics-preserving autoencoder maps this state space to a low-dimensional latent coordinate z [isin] [R]d, with d << N, while retaining key qualitative features of the underlying gene regulatory network, including attractor structure and multistability. Mechanistic information from the original regulatory dynamics is used to construct interpretable drift and diffusion terms for the reduced latent-space PBE. The reduced PBE is solved using a stochastic Lagrangian particle representation, in which particles evolve according to stochastic differential equations (SDEs) corresponding to the latent drift and diffusion fields. The resulting latent-space solution is subsequently decoded and propagated back into the original state space to recover physically interpretable cellular dynamics. We demonstrate the framework on macrophage polarization under cytokine-dependent regulation, including gene knockout perturbations. Overall, the proposed framework provides a computationally tractable and mechanistically interpretable route for integrating single-cell genomic data with population balance models of cell-state dynamics.
]]></description>
<dc:creator><![CDATA[ Gupta, P., Verma, S., Grama, A., Ramkrishna, D. ]]></dc:creator>
<dc:date>2026-08-11</dc:date>
<dc:identifier>doi:10.64898/2026.08.09.743783</dc:identifier>
<dc:title><![CDATA[Solving High-Dimensional Population Balance Equations via Dynamics-Preserving Autoencoders]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.09.743801v1?rss=1">
<title>
<![CDATA[
Glucose repression of HXK1 is glucose flux-dependent via non-canonical regulation of Mig1 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.09.743801v1?rss=1
</link>
<description><![CDATA[
Glucose is the preferred carbon source for budding yeast. Glucose sensing is achieved through multiple pathways, and the regulation of glucose-responsive genes has been reported to depend on both glucose concentration and glucose flux. However, the extent to which either of these mechanisms is used, and how cells sense glucose metabolic flux and couple it to transcriptional repression, remains unclear. Using tunable control of hexose transporters and hexokinases together with an optimized intracellular glucose sensor, we decoupled glucose uptake, phosphorylation, and intracellular glucose levels. We found that regulation of a Mig1-dependent reporter gene correlates with glucose flux rather than glucose concentration. Deletion of all known plasma membrane glucose sensors or replacement of yeast hexokinase with a bacterial glucokinase did not disrupt flux-correlated repression. Systematic mutational analysis of glucose signaling pathways showed that this Mig1-dependent response is mediated by the Snf1/AMPK pathway, but only at low glucose concentrations. At high glucose concentrations, Mig1 activity is controlled by an unknown, non-canonical mechanism. While consistent with much of the extensive literature on glucose regulation in S. cerevisiae, this work shows that careful quantitative analysis can uncover previously unrecognized modes of regulation.
]]></description>
<dc:creator><![CDATA[ Li, A., Springer, M. ]]></dc:creator>
<dc:date>2026-08-11</dc:date>
<dc:identifier>doi:10.64898/2026.08.09.743801</dc:identifier>
<dc:title><![CDATA[Glucose repression of HXK1 is glucose flux-dependent via non-canonical regulation of Mig1]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.05.743148v1?rss=1">
<title>
<![CDATA[
Projection criteria and information risks forzero-dimensional biological dynamics across molecular,epidemic, and ecological systems 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.05.743148v1?rss=1
</link>
<description><![CDATA[
Zero-dimensional chemical master equations, ordinary differential equations, and compartmental population models replace spatial stochastic biological systems by vectors of total counts or densities. This study asks when that projection is exact and whether information retained in spatial correlations can diagnose its practical failure. Exact Markov closure is characterized by an aggregate-rate lumpability condition: for every retained transition, the sum of microscopic transition rates must be constant over all spatial configurations with the same counts. Violations are connected to BBGKY-type correlation hierarchies and to mean-field, pair, and triplet closures. Conditional rate, finite-time predictive, memory, path-space, and correlation Kullback-Leibler risks quantify distinct losses. An exactly solvable two-compartment reaction separates structural non-closure from recovery of a well-mixed law under fast hidden mixing. Copy number and a spatial mixing-interaction ratio connect concentration, volume, diffusion, and reaction parameters to practical screening, including an Escherichia coli-scale example. The same projection logic is evaluated in controlled spatial susceptible-infectious-removed and predator-prey benchmarks. Across mixed and segregated initial conditions and four mobility regimes, pair-correlation risk was strongly associated with the error of the corresponding zero-dimensional ordinary differential equations (Spearman correlations 0.95 and 1.00; pooled 0.99). A nearest-neighbour exchange sensitivity analysis preserved the positive risk-error ranking. These benchmarks do not establish a universal threshold, but support correlation information as a transferable diagnostic for selecting among count, pair, higher-order, and explicit spatial descriptions.
]]></description>
<dc:creator><![CDATA[ Oosawa, C. ]]></dc:creator>
<dc:date>2026-08-10</dc:date>
<dc:identifier>doi:10.64898/2026.08.05.743148</dc:identifier>
<dc:title><![CDATA[Projection criteria and information risks forzero-dimensional biological dynamics across molecular,epidemic, and ecological systems]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.09.743737v1?rss=1">
<title>
<![CDATA[
Sample-specific protein-protein interaction networks inferred from transcriptomics and proteomics show high similarities 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.09.743737v1?rss=1
</link>
<description><![CDATA[
Contextualized protein-protein interaction networks provide crucial insight into diseases and other biological processes, but for a profound understanding of such processes and their distinct effects on individuals, the protein-protein interactions within individual samples must be investigated. A straightforward approach to estimate the PPI network of a sample is to restrict a general network of known PPIs to the proteins that are found in the sample. Although proteomics methods are becoming more accessible and precise, large-scale and single-cell studies still mainly target characterizing the transcriptomics profile of the samples, which is then often used as an approximation of the protein activities. The correlation of gene expression and protein abundance has been addressed in the past, but information about the deviations of the different omics-based estimates of the PPI networks is still lacking. In this study, we performed a comparative analysis of transcriptomic-based and proteomic-based sample-specific PPI network estimates to fill this gap. We created a framework for a comprehensive and transparent comparison of the two omics levels in two independent datasets, with a special focus on time-related network dynamics. We found that the size-adjusted characteristics of the different omics-based networks are very similar; the overall trend of how they change with time is also often the same, but the rate of the changes typically differs. The characteristics of the nodes present in both types of networks also show high similarity and often different time-related rates of change, but this varies among metrics. These results shed light on the properties of PPI network estimations and advise caution in interpreting them appropriately.
]]></description>
<dc:creator><![CDATA[ Zakar-Polyak, E., Kerepesi, C. ]]></dc:creator>
<dc:date>2026-08-10</dc:date>
<dc:identifier>doi:10.64898/2026.08.09.743737</dc:identifier>
<dc:title><![CDATA[Sample-specific protein-protein interaction networks inferred from transcriptomics and proteomics show high similarities]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.08.743701v1?rss=1">
<title>
<![CDATA[
Double Machine Learning with Multi-Gene Shared Backgroundfor Causal Inference in Single-Cell Data: Grouping Deviation Follows a Random Walk and the Accuracy-Compute Trade-Off 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.08.743701v1?rss=1
</link>
<description><![CDATA[
In high-throughput single-cell transcriptomics (p {approx} 20,000 genes), performing double machine learning (DML) causal inference on q {approx} 5,000 target genes requires nuisance function fits that grow linearly with the number of targets (Kf cross-fitting folds, Kf = 5 or 10), far exceeding feasible computational budgets, especially with deep learning. We propose a Randomized Partition Strategy (RPS): randomly divide target genes into groups, share one background compression per group, reducing deep learning model training to q/m runs (m = group size) --- a factor of m savings. The cost of grouping is accuracy loss --- we prove that the cumulative deviation of the estimator follows a one-dimensional drift-free symmetric random walk, with diffusion variance growing linearly with group size and mean squared displacement equaling the mean squared error, so accuracy loss is predictable: m = 1 is always optimal, accuracy cost is monotonically increasing, and a small accuracy sacrifice yields m-fold compute savings. On GSE189050 SLE single-cell data (Memory B cells, n = 2120), both PCA and DL methods converge to the same conclusion, confirming the random walk mechanism is method-independent; an unexpected finding is that DL diffusion growth is only 16%, far slower than PCAs 7.4 times. This work provides a quantifiable theoretical foundation for compute strategy selection in single-cell high-dimensional causal inference.
]]></description>
<dc:creator><![CDATA[ Ye, W., Jiang, X., Shen, F. ]]></dc:creator>
<dc:date>2026-08-10</dc:date>
<dc:identifier>doi:10.64898/2026.08.08.743701</dc:identifier>
<dc:title><![CDATA[Double Machine Learning with Multi-Gene Shared Backgroundfor Causal Inference in Single-Cell Data: Grouping Deviation Follows a Random Walk and the Accuracy-Compute Trade-Off]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.07.743628v1?rss=1">
<title>
<![CDATA[
LiverDCP: A Disease-Cell-Protein Framework for Multi-scale Modeling of Disease Biology 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.07.743628v1?rss=1
</link>
<description><![CDATA[
Understanding how molecular interactions give rise to disease phenotypes across cellular contexts remains a central challenge in biomedical research. Here, we introduce a Disease-Cell-Protein (DCP) paradigm for modeling multi-scale disease biology, which jointly represents disease states, cellular composition, and protein interaction networks within a unified graph architecture. We instantiate this paradigm in the liver as LiverDCP by integrating a large-scale liver single-cell atlas (LiverHomo) with proteome-wide predicted protein-protein interactions to construct over 280 context-specific interactomes across diverse liver disease and cellular conditions. LiverDCP employs a multi-context representation learning strategy that enables joint training across hundreds of disease-cell environments, capturing shared interaction principles while preserving context-specific variation. LiverDCP incorporates pretrained protein sequence-derived features through a geometry-aware two-phase training scheme that preserves embedding structure while improving predictive performance. The resulting protein embeddings encode context-specific functional states and reveal extensive rewiring of protein roles across diseases. They provide a context-resolved representation of protein function, enabling interpretation of GWAS risk genes and prioritization of therapeutic targets, including recovery of known targets and nomination of candidate repurposed and novel targets for MASH. Overall, this work establishes a generalizable framework for linking molecular interactions to disease phenotypes and enabling mechanistic understanding and target discovery across complex diseases.
]]></description>
<dc:creator><![CDATA[ Shi, Z., Song, Z., Steveson-Lerner, H., Dong, B., Zhao, H. ]]></dc:creator>
<dc:date>2026-08-10</dc:date>
<dc:identifier>doi:10.64898/2026.08.07.743628</dc:identifier>
<dc:title><![CDATA[LiverDCP: A Disease-Cell-Protein Framework for Multi-scale Modeling of Disease Biology]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.07.743516v1?rss=1">
<title>
<![CDATA[
scROMA: batch-aware pathway-activity inference and a ground-truth simulation framework for single-cell transcriptomics 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.07.743516v1?rss=1
</link>
<description><![CDATA[
BackgroundPathway-activity analysis summarizes gene-level single-cell measurements into interpretable functional modules, but widely used methods lack an integrated significance framework, do not account for the batch effects that pervade multi-sample studies, and are not natively interoperable with Python-based workflows. The field also lacks simulation resources with ground-truth pathway activity for quantitative benchmarking.

ResultsWe present scROMA, a singular-value-decomposition-based method that quantifies pathway activity as coordinated variation, with per-cell scores, per-gene contributions, and permutation-based significance, natively integrated with the Scanpy/AnnData ecosystem. Its batch-aware extension is, to our knowledge, the first to correct batch effects within the gene-set subspace rather than across the full transcriptome, isolating technical variation at the pathway level while preserving signal in other genes. We also release a generative simulation framework producing synthetic data with fully specified ground-truth activities. On simulated benchmarks scROMA is competitive across tasks, and under batch effects its batch-aware mode recovers ordinal pathway structure that full-transcriptome integration misses. Across cystic fibrosis airway, intestinal-organoid, breast cancer, and lung cancer datasets it recovers established biology while separating it from technical and inter-donor variation; in the intestinal-organoid atlas it reproducibly recovers an inflammatory program across donors, separates its sustained from transient components, and resolves cell-type-specific niche-factor targets.

ConclusionsscROMA is open-source and released with the simulation framework and pre-generated benchmark datasets as a community resource, providing a scalable, statistically grounded, and batch-aware approach to pathway-level analysis in single-cell transcriptomics.
]]></description>
<dc:creator><![CDATA[ Zhubanchaliyev, A., Najm, M., Laigle, V., Bonnet, E., Martignetti, L. ]]></dc:creator>
<dc:date>2026-08-10</dc:date>
<dc:identifier>doi:10.64898/2026.08.07.743516</dc:identifier>
<dc:title><![CDATA[scROMA: batch-aware pathway-activity inference and a ground-truth simulation framework for single-cell transcriptomics]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.07.742769v1?rss=1">
<title>
<![CDATA[
Improved Metabolic Flux Estimations through Compositional Data Analysis 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.07.742769v1?rss=1
</link>
<description><![CDATA[
Isotopic Metabolic Flux Analysis (I-MFA) is a standard approach for estimating intracellular metabolic fluxes. I-MFA infers fluxes by comparing simulated and measured metabolite isotopologue distributions (MIDs) of metabolites from isotope labeling experiments. MIDs represent fractional abundances that strictly sum to one for any given metabolite, thus they are inherently compositional data. However, state-of-the-art estimation approaches rely on calculating standard Euclidean distances between MIDs in a non-compositional paradigm, introducing a systemic bias. To resolve this, our study proposes compositional I-MFA. We demonstrate how to construct a meaningful orthonormal basis for MIDs via ordered sequential binary partitioning, which can be used to perform isometric log-ratio (ILR) transformation. As a minimal change to existing I-MFA workflows, we suggest estimating fluxes by minimizing Euclidean distances between ILR-transformed MIDs. We validated this framework against traditional methods using both a toy model and a biologically realistic model, evaluating point estimates, sensitivity across varied true fluxes, and confidence intervals. In the two examples, compositional I-MFA consistently outperformed traditional approaches, reducing mean squared error of flux point estimates by an average of 42.6% and substantially narrowing confidence intervals. We conclude that compositional data analysis significantly improves I-MFA and can be implemented as a simple drop-in replacement for current pipelines.

Graphical Abstract

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=156 SRC="FIGDIR/small/742769v1_ufig1.gif" ALT="Figure 1">
View larger version (34K):
org.highwire.dtl.DTLVardef@f8f1aborg.highwire.dtl.DTLVardef@1c22fccorg.highwire.dtl.DTLVardef@1d0118corg.highwire.dtl.DTLVardef@13012bf_HPS_FORMAT_FIGEXP  M_FIG C_FIG HighlightsO_LINew compositional data approach improves metabolic flux estimation.
C_LIO_LIThis data transformation requires minimal changes to existing workflows.
C_LIO_LIThe new method reduced MSE of flux estimates by 42.6% in two examples tested.
C_LIO_LIThe confidence intervals of the estimated fluxes were substantially narrowed.
C_LIO_LIEstimation accuracy remained robust across a wide range of metabolic fluxes.
C_LI
]]></description>
<dc:creator><![CDATA[ Carlsen, A. S., Chen, T., Cowie, N. L., Brinch, C., Groves, T., Nielsen, L. K. ]]></dc:creator>
<dc:date>2026-08-10</dc:date>
<dc:identifier>doi:10.64898/2026.08.07.742769</dc:identifier>
<dc:title><![CDATA[Improved Metabolic Flux Estimations through Compositional Data Analysis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.03.740213v1?rss=1">
<title>
<![CDATA[
VariantFlux: A genotype-first modelling workflow for predicting the impact of genetic variations on human metabolism 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.03.740213v1?rss=1
</link>
<description><![CDATA[
Human genetic variation is a major determinant of organ metabolism, yet how naturally occurring variants shape quantitative metabolic phenotypes remains unclear. We present VariantFlux, a workflow that integrates ancestry-aware variant interpretation into genome-scale metabolic modelling to generate personalised, variant-constrained kidney reconstructions. Using the Human1 v1.19 model, we built a kidney-specific baseline model constrained by 482 metabolites and analysed 2,547 individuals from the 1000 Genomes Project, in whom [~]50% of metabolic genes were predicted damaging by at least three computational tools. These variants, whose burden differed subtly across ancestries, were translated into gene-dosage-anchored flux constraints for homozygous knockouts and graded heterozygous knockdowns. Despite widespread perturbation, >97% of models preserved baseline growth, indicating strong metabolic robustness. Yet individual genomes exhibited distinct flux-rewiring patterns, with frequent individual-specific gain-of-flux events and fewer shared loss-of-flux reactions. Limited ancestry clustering suggests metabolic responses are driven mainly by unique variant combinations. VariantFlux links human genomes to organ-level flux phenotypes, enabling precision medicine, pharmacogenomics, and disease risk prediction.

Conceptual advanceWe present VariantFlux, a genotype-first framework that integrates predicted variant effects directly into genome-scale metabolic networks to generate personalised, organ-specific flux phenotypes. Unlike association-based metabolomics studies, this bottom-up approach enables exploratory, mechanistic prediction of how naturally occurring genetic variation reshapes human metabolism.
]]></description>
<dc:creator><![CDATA[ Nazem-Bokaee, H. ]]></dc:creator>
<dc:date>2026-08-09</dc:date>
<dc:identifier>doi:10.64898/2026.08.03.740213</dc:identifier>
<dc:title><![CDATA[VariantFlux: A genotype-first modelling workflow for predicting the impact of genetic variations on human metabolism]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.06.743074v1?rss=1">
<title>
<![CDATA[
17α-Estradiol Confers Limited Protection Against APOE4 Phenotypes in Middle-Aged Female Mice 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.06.743074v1?rss=1
</link>
<description><![CDATA[
Longevity-promoting interventions represent a promising strategy to mitigate brain aging and reduce Alzheimers disease (AD) risk. The NIA Interventions Testing Program identified the weak estrogen 17-estradiol (17E2) as a compound that extends healthspan and lifespan in mice, with effects observed primarily in males. Our recent work demonstrated that 17E2 healthspan benefits were modulated by human apolipoprotein E (APOE) genotype such that aging phenotypes were improved more strongly in middle-aged male mice with targeted-replacement of the AD-associated APOE4 allele compared to APOE3, the risk neutral and most common APOE allele. Here, we tested whether APOE-dependent, AD-relevant benefits of 17E2 observed in males extend to females. Specifically, we treated 12-month-old APOE3 and APOE4 targeted-replacement female mice for 6 months with chow containing 0 or 14.4ppm 17E2. We find that relative to APOE3, APOE4 genotype largely exhibits more robust systemic phenotypes associated with aging, including increased adiposity, impaired glucose tolerance, and reduced energy expenditure. Further, we observe that treatment with 17E2 yields modest improvements in some outcomes, including decreased adiposity and increased lean mass, glucose tolerance, and energy expenditure, though significant benefits are found only in APOE4 females. In the CNS, we observed mixed effects of APOE genotype on behavioral performance and indices of brain aging, with APOE4 females performing worse in the Barnes Maze and having higher levels of the AD-related peptide soluble {beta}-amyloid, but no APOE genotype differences in cortical lipid raft oxidative damage. In contrast to its systemic effects, 17E2 did not significantly improve neural outcomes in APOE3 or APOE4 females. These findings address the impact of biological sex on established protective effects of a longevity-promoting intervention against APOE4 phenotypes, which have significant relevance to the prevention of age-related conditions including metabolic dysfunction, cognitive impairment and vulnerability to AD.
]]></description>
<dc:creator><![CDATA[ McGill, C. J., Christensen, A., Namvari, S., Thorwald, M. A., Anson, H., Vermulst, M., Finch, C. E., Benayoun, B. A., Pike, C. J. ]]></dc:creator>
<dc:date>2026-08-07</dc:date>
<dc:identifier>doi:10.64898/2026.08.06.743074</dc:identifier>
<dc:title><![CDATA[17α-Estradiol Confers Limited Protection Against APOE4 Phenotypes in Middle-Aged Female Mice]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.03.742469v1?rss=1">
<title>
<![CDATA[
A Spatiotemporal Atlas of Extranuclear Androgen Receptor Proximal Interaction Networks 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.03.742469v1?rss=1
</link>
<description><![CDATA[
Androgen receptor-interacting proteins (AR-IPs) comprise nearly 1,000 proteins, yet their organization across subcellular space and time remains uncharted. Proximity labeling captures direct binding partners along with neighboring proteins that populate a receptors local environment, thereby broadening AR-IPs into a broader population of AR-proximal interacting proteins (AR-PIPs). Here, we apply proximity labeling quantitative mass spectrometry (PL-qMS) to construct a spatiotemporal atlas of the extranuclear AR-proximal interactome in LNCaP prostate tumor cells. PL-qMS recovered 82.2% of the known AR-IPs and identified 3,947 AR-PIPs across cytosolic and membrane compartments, revealing dynamic remodeling across an androgen time course. Functional enrichment and network analyses identified the retromer complex as an androgen-sensitive AR-proximal interaction, which was verified by proximity ligation assays. Partial genetic disruption of VPS26A attenuated transcription of canonical androgen-regulated genes by mislocalizing the AR coactivator TMF1, establishing the retromer-AR-TMF1 axis as a functionally validated AR-proximal interaction network (AR-PIN). This work establishes subcellular proximal proteomes as a spatiotemporal framework for probing AR function and its dysregulation in disease.

SynopsisProximity labeling constructs a spatiotemporal atlas of the extranuclear AR-proximal interactome and identifies the retromer complex as an androgen-sensitive regulator of AR transcription. O_LIPL-qMS constructs a spatiotemporal atlas of the extranuclear AR-proximal interactome
C_LIO_LIAR-PIPs recover 3,947 proximal interactors across cytosolic and membrane fractions
C_LIO_LIRetromer complex is an androgen-sensitive AR-proximal interaction
C_LIO_LIPartial VPS26A disruption attenuates AR-dependent gene transcription via TMF1 mislocalization
C_LI
]]></description>
<dc:creator><![CDATA[ Ptak, C. C., O'Rourke, C., Eng, J., Radoshevich, L., Wright, M. E. ]]></dc:creator>
<dc:date>2026-08-06</dc:date>
<dc:identifier>doi:10.64898/2026.08.03.742469</dc:identifier>
<dc:title><![CDATA[A Spatiotemporal Atlas of Extranuclear Androgen Receptor Proximal Interaction Networks]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.08.03.742470v1?rss=1">
<title>
<![CDATA[
A Temporal Atlas of the Nuclear Androgen Receptor Proximal Interactome 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.08.03.742470v1?rss=1
</link>
<description><![CDATA[
Androgen receptor-interacting proteins (AR-IPs) comprise nearly 1,000 cataloged partners, yet how AR engages this interactome inside the nucleus, in what temporal order, and through what molecular handoffs, remains uncharted. Here, we construct a minute-scale temporal atlas of the nuclear AR proximal interactome by proximity-labeling quantitative mass spectrometry (PL-qMS) in androgen-treated LNCaP prostate cancer cells, capturing 84.2% of the known AR-interactome and resolving 3,378 nuclear AR-proximal interacting proteins (AR-PIPs) across six time points. The atlas recapitulates the cyclic sequential recruitment model established at AR-regulated loci by classical ChIP and recovers 100% of previously known AR interactors from the Launonen 2021 ChIP-SICAP chromatome. Embedded within this canonical chromatin signature, we uncover a time-resolved translation-to-transcription handoff in which cap-binding eIF4G and 4E-BP1 are transiently AR-proximal at the earliest time points, verified by PLA. The atlas converts the AR coregulator catalog into a temporally resolved quantitative framework for AR-dependent transcription.

SynopsisA minute-scale temporal atlas of the nuclear AR-proximal interactome resolves a translation-to-transcription handoff during the androgen response. O_LITemporal nuclear AR-proximal interactome atlas in androgen-treated LNCaP cells
C_LIO_LI3,378 nuclear AR-PIPs recovered, 100% of previously known Launonen AR partners
C_LIO_LICap-binding eIF4G and 4E-BP1 are transiently AR-proximal at early time points
C_LIO_LIPLA verifies AR-eIF4G and AR-4E-BP1 proximal interactions pointing to a translation-to-transcription handoff
C_LI
]]></description>
<dc:creator><![CDATA[ Ptak, C. C., Eng, J., Radoshevich, L., Wright, M. E. ]]></dc:creator>
<dc:date>2026-08-06</dc:date>
<dc:identifier>doi:10.64898/2026.08.03.742470</dc:identifier>
<dc:title><![CDATA[A Temporal Atlas of the Nuclear Androgen Receptor Proximal Interactome]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-08-06</prism:publicationDate>
<prism:section></prism:section>
</item>
</rdf:RDF>
