Molecular Systems Biology最新文献

{"title":"Author Correction: Fast analysis and engineering of protein function by microbe-independent deep assembly and screening.","authors":"Yan Wu, Pengli Wang, Lan Xiang Liu, Daesun Song, Qin Qin, Chao Gao, Matt Hageman, Thomas A Kirkland, Yichi Su, Michael Z Lin","doi":"10.1038/s44320-026-00233-6","DOIUrl":"10.1038/s44320-026-00233-6","url":null,"abstract":"","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1501"},"PeriodicalIF":6.7,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13538345/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148471810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two essential criteria for transcription factor footprint discovery using V-plot.","authors":"Qifan Zhang, Chenhuan Xu","doi":"10.1038/s44320-026-00242-5","DOIUrl":"https://doi.org/10.1038/s44320-026-00242-5","url":null,"abstract":"<p><p>A recent study reported sequence biases in transcription factor (TF) footprinting data, and expressed concern about the accuracy in TF footprint discovery using V-plot analysis. Here we define two graphical parameters of V-plot as important criteria to implement into future analysis. Guaranteed by the criteria, V-plot still serves as a powerful and straightforward tool for TF footprint discovery from various chromatin accessibility sequencing datasets.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148819066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint-RPCA: domain-aware multi-omics integration for systems microbiology.","authors":"Bianca Cordazzo Vargas, Cameron Martino, Amanda Hazel Dilmore, Jessica L Metcalf, Zachary M Burcham, Leo Lahti, Aituar Bektanov, Tuomas Borman, Veikko Salomaa, Teemu Niiranen, Aki S Havulinna, Rachel Gregor, Stav Eyal, Michael M Meijler, Itzhak Mizrahi, Se Jin Song, Andrew Bartko, Pieter C Dorrestein, James T Morton, Daniel McDonald, Rob Knight, Liat Shenhav","doi":"10.1038/s44320-026-00236-3","DOIUrl":"https://doi.org/10.1038/s44320-026-00236-3","url":null,"abstract":"<p><p>Integrating multi-omics data is essential for microbiome research, as microbial communities are shaped by and respond to interdependent processes, including taxonomic composition, metabolite production and utilization, and gene expression. However, accurately capturing ecosystem-wide patterns across these modalities is statistically challenging due to differences in scale, sparsity, and compositionality. While a growing number of multi-omics methods have emerged, they differ in their mathematical objectives and modeling assumptions, which in turn shape how biological patterns are represented and interpreted. This underscores the need for tools that explicitly account for the statistical properties of microbial ecosystems. Here, we present Joint Robust Principal Component Analysis (Joint-RPCA), a method designed with these statistical properties in mind and broadly applicable to multi-omics settings with similar challenges. Built on the OptSpace matrix completion framework, Joint-RPCA assumes an underlying shared low-rank structured component across modalities to identify shared variation and cross-modal associations from matched samples. Within this setting and under these statistical assumptions, Joint-RPCA showed stronger performance than the benchmarked general-purpose methods in phenotype separation and feature association tasks, achieving up to sixfold improvement in classification accuracy and over 100-fold faster runtimes. Applied to real-world datasets, including the Integrative Human Microbiome Project (iHMP), mammalian gut microbiomes, and decomposition studies, Joint-RPCA reveals replicable and interpretable multi-omic patterns, offering a scalable and domain-aware solution for systems-level microbiome analysis. Joint-RPCA is available in both Python ( https://github.com/biocore/gemelli ) and R ( https://bioconductor.org/packages/mia ).</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148795762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic comparison of estimates of transcription factor activity by ATAC-seq and multiplexed reporter assays.","authors":"Max Trauernicht, Vinícius H Franceschini-Santos, Hatice Yücel, Teodora Filipovska, Bas van Steensel","doi":"10.1038/s44320-026-00240-7","DOIUrl":"https://doi.org/10.1038/s44320-026-00240-7","url":null,"abstract":"<p><p>Transcription factors (TFs) are central to gene regulation and play critical roles in development, cellular homeostasis and disease. The ability to accurately measure TF activity is essential to understanding how TFs respond to signals and regulate target genes. In one commonly used approach, activities of TFs are computationally inferred from genome-wide chromatin accessibility data (ATAC-seq). However, it has remained unclear how well these inferences reflect actual regulatory activity of TFs. An alternative approach employs a collection of synthetic reporters that are designed to each probe the regulatory activity of a single TF. In this study, we systematically compared TF activities as inferred by ATAC-seq with those measured by multiplexed reporters, across diverse perturbations known to alter specific TF activities. We observed considerable overlap between the two methods, but also notable discrepancies. Our findings suggest that reporter assays and chromatin-based inference capture distinct aspects of TF function: reporter assays are more sensitive to signal-responsive TFs, while ATAC-seq better detects chromatin-modifying TFs.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148795816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering global transcriptional dynamics coordinated by gene-gene regulatory interactions using single-cell data.","authors":"Liying Zhou, Songhao Luo, Zhiwei Huang, Zhenquan Zhang, Zihao Wang, Jiajun Zhang","doi":"10.1038/s44320-026-00235-4","DOIUrl":"https://doi.org/10.1038/s44320-026-00235-4","url":null,"abstract":"<p><p>Transcription is an inherently dynamic and stochastic process that often occurs in bursts, governed by gene-gene regulatory interactions and thereby driving cell-to-cell heterogeneity. However, a genome-wide, mechanistic understanding of how regulatory networks globally shape transcriptional bursting dynamics remains lacking. Here, we present BurstLink, an interpretable and tractable statistical-mechanistic framework that simultaneously infers coupled regulatory interactions and transcriptional bursting kinetics at the genome-wide scale from single-cell data. BurstLink introduces reweighted mutual information to quantify regulatory strength as network edge weights, while jointly inferring regulatory directionality and interaction type for each gene pair within a unified mechanistic model of transcriptional bursting. Applied to mouse embryonic fibroblasts data, BurstLink reveals several genome-wide regulatory mechanisms on transcriptional bursting: downstream target genes exhibit higher burst frequency and gene-expression variability than upstream transcription factor genes; stronger transcription factor binding affinity is associated with lower burst frequency and higher burst size of target genes. Notably, positive regulation primarily enhances the burst frequency and gene-expression variability in target genes, in contrast to negative regulation. In summary, BurstLink deciphers multiple general principles of global transcriptional dynamics, providing novel biological insights into cell fate decisions.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148670302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward trustworthy healthcare AI: designing academic research for translation readiness.","authors":"Achim Hekler, Florian Buettner","doi":"10.1038/s44320-026-00219-4","DOIUrl":"10.1038/s44320-026-00219-4","url":null,"abstract":"<p><p>In controlled proof-of-concept studies, healthcare artificial intelligence (AI) systems now routinely match or exceed specialist-level performance. Despite these impressive technical achievements, most systems remain confined to research settings, highlighting an opportunity to better prepare academic AI research for subsequent clinical translation. Successful translation requires attention to technical, regulatory, organizational, and infrastructural factors, ideally from the earliest stages of research. This Perspective focuses on trustworthiness as a key factor that academic researchers can address proactively. First, we outline how trustworthiness requirements differ across three stakeholder groups-patients, clinicians, and regulatory bodies-and how academic research can better align with these expectations. We then discuss key dimensions of trustworthy AI-explainability, uncertainty quantification, and evaluation-and how researchers can address them during study design. We also consider foundation models, which require adapted validation approaches. Based on this analysis, we propose a stakeholder-centered framework that emphasizes proactive integration of trust requirements during early development phases rather than post hoc considerations.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1201-1213"},"PeriodicalIF":6.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13434723/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148043032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Active learning-guided mechanistic modeling reveals context-specific regulators of CXCL9 expression in pancreatic cancer cells.","authors":"Bi-Rong Wang, Maaruthy Yelleswarapu, Lucie Descamps, Federica Eduati","doi":"10.1038/s44320-026-00221-w","DOIUrl":"10.1038/s44320-026-00221-w","url":null,"abstract":"<p><p>Cold tumors like pancreatic cancer suffer from poor immune infiltration, limiting effective anti-tumor responses. The chemokine CXCL9 promotes immune cell recruitment, but the signaling mechanisms regulating its expression in tumor cells remain poorly understood and underexplored as targets for modulation. We present a framework that integrates active learning with mechanistic logic-ODE models to guide perturbation screenings and uncover regulators of CXCL9 in pancreatic cancer cells. Using perturbation-response data and curated prior knowledge, we trained interpretable models to identify signaling mechanisms that enhance CXCL9 expression and prioritize drug combinations. Active learning enabled data-efficient model refinement and guided informative experiments under resource constraints. Benchmarking on synthetic data and experimental validation confirmed the performance of different acquisition strategies and its applicability to feasible iterative wet lab experiments. Our results demonstrate how combining active learning with mechanistic modeling supports rational, targeted experimental design.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1360-1375"},"PeriodicalIF":6.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13434641/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148163598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ParTIpy: a scalable framework for archetypal analysis and Pareto task inference.","authors":"Philipp Sven Lars Schäfer, Leoni Zimmermann, Paul L Burmedi, Avia Walfisch, Noa Goldenberg, Shira Yonassi, Einat Shaer Tamar, Miri Adler, Jovan Tanevski, Ricardo O Ramirez Flores, Julio Saez-Rodriguez","doi":"10.1038/s44320-026-00209-6","DOIUrl":"10.1038/s44320-026-00209-6","url":null,"abstract":"<p><p>Trade-offs between different tasks are pervasive across scales in biological systems. For example, cells cannot perform all possible functions simultaneously; instead they allocate limited resources to specialize in subsets of tasks by activating specific gene expression programs. Pareto Task Inference (ParTI) is a framework for analyzing biological trade-offs grounded in multi-objective optimality. However, existing software for ParTI neither scales to large datasets nor integrates well with standard data analysis workflows. To address this gap, we developed ParTIpy ( https://pypi.org/project/partipy ), an open-source Python package that leverages optimization and coreset methods to scale archetypal analysis, the core algorithm underlying ParTI, to millions of cells. By providing tools to characterize archetypes and comprehensive documentation ( https://partipy.readthedocs.io ), ParTIpy integrates seamlessly into existing analysis workflows, especially for single-cell data. We demonstrate how ParTIpy can be used to study intra-cell-type gene expression variability through the lens of task allocation, offering a principled alternative to methods that impose discrete cell state classifications on inherently continuous variation.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1252-1269"},"PeriodicalIF":6.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13433804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148331395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-species insights from ART-D to uncover evolutionarily conserved oncogenic mechanisms.","authors":"Yifan Guo, Jiadong Zheng, Yixin Wu, Kun Zhao, Dian Lv, Wenhan Liu, Mengling Wang, Jin-Yu Lu, Wenyan Xu, Xianping Wang, Xianjue Ma","doi":"10.1038/s44320-026-00215-8","DOIUrl":"10.1038/s44320-026-00215-8","url":null,"abstract":"<p><p>Cancer arises from oncogenic clones, yet the dynamic mechanisms driving their stepwise evolution toward malignancy remain incompletely understood. Here, we establish the Atlas of Ras-driven Tumors in Drosophila (ART-D), a systematic, cross-species platform that dissects the molecular and phenotypic trajectories of tumorigenesis across ten genetically defined Ras^V12-driven models. By integrating longitudinal phenotypic profiling, we define three conserved stages of tumor development-initiation, promotion, and progression-distinguished by distinct shifts in tumor burden and tumor-induced cachexia. Transcriptomic analysis reveals stage-specific signaling rewiring: early tumorigenesis is characterized by co-activation of JAK/STAT, NF-κB/Toll, and MAPK pathways, whereas malignant progression is driven by Notch hyperactivation and Hippo pathway inactivation. Through integrative multi-omics and machine learning, we uncover an evolutionarily conserved pathogenic network coordinating JNK, NF-κB/Toll, Notch, and Hippo signaling, which we functionally validate across species. ART-D serves as a transformative resource bridging Drosophila genetics and human cancer biology, offering a robust framework for decoding conserved oncogenic principles and identifying of stage-specific vulnerabilities in RAS-driven cancers.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1214-1251"},"PeriodicalIF":6.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13433787/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147941941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiscale learning of gene network-driven phenotypic dynamics of single cells.","authors":"Dongyan Zhang, Jinan Li, Qing Nie, Xiaoqiang Sun","doi":"10.1038/s44320-026-00220-x","DOIUrl":"10.1038/s44320-026-00220-x","url":null,"abstract":"<p><p>Understanding how gene regulatory networks (GRNs) dynamically orchestrate cell fate emergence remains a fundamental challenge. Here, we present GRNvelo, a computational framework that reconstructs multiscale cell fate dynamics by integrating GRNs with phenotypic dynamics from temporal single-cell RNA-seq data. GRNvelo establishes a biologically interpretable and mathematically rigorous multiscale model that couples GRN-driven single-cell velocity with nonlocal cell growth-mediated population dynamics. To operationalize this model, GRNvelo devises a two-phase cooperative optimization algorithm based on physics-informed neural networks (PINNs): TC-PINN for jointly inferring GRN velocity and latent time, and MP-PINN for refining GRN velocity within the context of cell population dynamics. In benchmark evaluations, GRNvelo demonstrates superior performance across two synthetic datasets and four real datasets, including branching development and diverse perturbation-response scenarios. Collectively, GRNvelo not only accurately infers GRN-driven cell fate dynamics but also predicts altered cell fates in response to diverse genetic perturbations, including dynamic and combined ones, thus establishing a new computational paradigm for predicting and modulating cell fate outcomes.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1333-1359"},"PeriodicalIF":6.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13434742/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148176282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}