Molecular Systems Biology最新文献

{"title":"Decoding and steering spatially resolved cellular dynamics.","authors":"Jiahao Wu, Suoqin Jin","doi":"10.1038/s44320-026-00216-7","DOIUrl":"10.1038/s44320-026-00216-7","url":null,"abstract":"","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1037-1039"},"PeriodicalIF":6.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13328731/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147949038","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":"Dissecting and steering cell dynamics using spatially-informed RNA velocity with veloAgent.","authors":"Vishvak Raghavan, Brent Yoon, Gregory J Fonseca, Yue Li, Jun Ding","doi":"10.1038/s44320-026-00213-w","DOIUrl":"10.1038/s44320-026-00213-w","url":null,"abstract":"<p><p>RNA velocity enables inference of cell state transitions from single-cell transcriptomics by modeling transcriptional dynamics from spliced and unspliced mRNA. However, existing methods overlook spatial context and struggle to scale to large datasets, limiting insights into tissue organization and dynamic processes. We introduce veloAgent, a deep generative and agent-based framework that estimates gene- and cell-specific transcriptional kinetics while integrating spatial information through agent-based simulations of local microenvironments. By leveraging both molecular and spatial cues, veloAgent improves velocity accuracy and achieves sublinear memory scaling, enabling efficient analysis of large and multi-batch spatial datasets. A distinctive feature of veloAgent is its in silico perturbation module, which allows targeted manipulation of spatial velocity vectors to simulate regulatory interventions and predict their impact on cell fate dynamics. These capabilities position veloAgent as a scalable and versatile framework for dissecting spatially resolved cellular dynamics and guiding cell fate manipulation across diverse biological processes.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1180-1200"},"PeriodicalIF":6.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13328566/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147840591","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":"Interpretable deep generative ensemble learning for single-cell omics with Hydra.","authors":"Manoj M Wagle, Chunlei Liu, Zunpeng Liu, Yongheng Wang, Manolis Kellis, Ellis Patrick, Pengyi Yang","doi":"10.1038/s44320-026-00208-7","DOIUrl":"10.1038/s44320-026-00208-7","url":null,"abstract":"<p><p>Single-cell omics enable the dissection of cellular heterogeneity, yet the high dimensionality, inherent noise, and sparsity present significant challenges. These challenges are amplified for rare cell populations, which are often difficult to annotate reliably but can be central to development and disease. As single-cell assays increasingly capture multiple molecular layers, the integrative analysis of such multimodal data further increases complexity. Here, we propose Hydra, a deep generative framework based on an ensemble of variational autoencoders for effective learning of unimodal and multimodal single-cell omics data. Hydra implements interpretable modules for capturing cell-type-specific molecular signatures. The ensemble of such interpretable modules enables reproducible feature selection and robust cell-type annotation, with particular effectiveness for rare populations. We benchmarked Hydra on a repertoire of 21 datasets, including unimodal and multimodal single-cell omics data. Our results demonstrate that Hydra offers comparable to superior performance to several state-of-the-art methods. Finally, we highlight the utility of Hydra in robustly annotating brain cellular subtypes and preserving disease-relevant signatures using our previously published dataset that profiles Alzheimer's disease.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1161-1179"},"PeriodicalIF":6.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13328726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147662952","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":"Neighborhood-informed positional information for precise cell identity specification.","authors":"Michal Erez, Roy Friedman, Mor Nitzan","doi":"10.1038/s44320-026-00211-y","DOIUrl":"10.1038/s44320-026-00211-y","url":null,"abstract":"<p><p>During development, cells reliably establish their identities, a process that is enabled in part by positional information encoded in gene expression patterns. Previous works showed that cells in Drosophila embryos can utilize this information to decode their position along the anterior-posterior axis with a 1% embryo-length positional precision. However, this precision is insufficient to uniquely determine position, leading to a positional information gap. Here, we propose a neighborhood-informed information-theoretic framework which allows to quantitatively estimate the amount of information regarding position which exists in the microenvironment of each cell. We formulate how much additional information exists in neighboring cells as a function of spatial variation in gene expression. We show that the additional information encoded by local neighborhoods is sufficient to uniquely specify cell identities, closing the information gap on average across major patterning axes in Drosophila embryos, gastruloids, and the developing neural tube. Furthermore, neighborhood-informed decoders predict cell positions and downstream gene expression patterns more accurately than cell-independent decoders, resulting in lower decoding variability, which is maintained in mutant Drosophila embryos. Our results provide a basis for the analysis of cellular decision-making in the context of their microenvironments.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1118-1131"},"PeriodicalIF":6.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13328375/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147840622","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":"VEFill: accurate and generalizable deep mutational scanning score imputation across protein domains.","authors":"Polina V Polunina, Wolfgang Maier, Alan F Rubin","doi":"10.1038/s44320-026-00203-y","DOIUrl":"10.1038/s44320-026-00203-y","url":null,"abstract":"<p><p>Deep Mutational Scanning (DMS) assays can systematically assess the effects of amino acid substitutions on protein function, but many datasets have incomplete variant coverage due to technical constraints. We developed VEFill (Variant Effect Fill), a gradient boosting model for imputing missing DMS scores across protein domains. Trained on the Human Domainome 1, VEFill integrates ESM-1v sequence embeddings, evolutionary conservation (EVE scores), amino acid substitution matrices, and physicochemical descriptors. The model achieved robust predictive performance (Pearson r = 0.80) and generalized reliably to unseen proteins in stability-based datasets, while showing weaker performance on activity-based assays. Per-protein models confirmed VEFill's effectiveness under limited-data conditions and a reduced two-feature version performed comparably to the full model, suggesting an efficient alternative. Across multiple benchmarking settings, VEFill consistently outperformed baselines once ≥20% of experimental measurements were available. However, true zero-shot prediction without positional context remains challenging, particularly for functionally complex proteins. Overall, VEFill offers an interpretable, scalable framework for DMS score imputation, and enables systematic mutation prioritization including the design of sparse experimental libraries for variant effect studies.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"979-1002"},"PeriodicalIF":6.7,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13230771/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147491478","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":"A universal surface functionalization technique to chemically enhance live microbial cells.","authors":"Gabriel T Vercelli, Xingcheng Zhou, Stefany Moreno-Gámez, Rashi R Jeeda, Rachel Gregor, Jonasz Słomka, Akorfa Dagadu, Ariel L Furst, Otto X Cordero","doi":"10.1038/s44320-026-00202-z","DOIUrl":"10.1038/s44320-026-00202-z","url":null,"abstract":"<p><p>Microbial surface functionalization is a powerful strategy for endowing microbes with novel, non-genetic functions. However, existing methods are often species-specific, limited in scope, and compromise cell viability. Here, we present a universal and modular platform for high-density, reproducible surface functionalization across diverse microbial species-including Gram-positive, Gram-negative, aerobic, and anaerobic bacteria-using multiple molecular classes such as fluorophores, enzymes, and nucleic acids. Our method preserves cell viability and achieves 50× higher functionalization efficiency than previous methods with a standardized protocol applicable to any azide-containing molecule. Applications of the method show reproducible and tunable phenotypic outcomes at the single-cell level: fluorophore labeling yielded adjustable fluorescence, β-lactamase conferred scalable antibiotic resistance, and DNA coatings modulated adhesion and aggregation. This platform provides quantitative, non-genetic control over microbial phenotypes and complements genetic engineering approaches. It enables new possibilities for microbial design in biotechnology, medicine, and environmental applications where genetic modification is impractical or undesirable.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"962-978"},"PeriodicalIF":6.7,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13230988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147468848","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":"Structure-function relationship of alpha-synuclein fibrillar polymorphs derived from distinct synucleinopathies.","authors":"Tetiana Serdiuk, Virginie Redeker, Jimmy Savistchenko, Sandesh Neupane, Walther Haenseler, Yanick Fleischmann, Viviane Reber, Sabrina Keller, Cinzia Tiberi, Ruxandra Bachmann-Gagescu, Matthias Gstaiger, Thomas Braun, Roland Riek, Steve Gentleman, Adriano Aguzzi, Natalie de Souza, Ronald Melki, Paola Picotti","doi":"10.1038/s44320-026-00199-5","DOIUrl":"10.1038/s44320-026-00199-5","url":null,"abstract":"<p><p>The aggregation of the protein alpha-synuclein (αSyn) is a common feature of multiple neurodegenerative diseases collectively called synucleinopathies, for which the pathobiology is not well understood. The different phenotypic characteristics of the synucleinopathies Parkinson's disease (PD), Dementia with Lewy Bodies (DLB) and Multiple System Atrophy (MSA) have been proposed to originate from the distinct structures adopted by αSyn in its amyloid forms. Here, using covalent labeling and limited proteolysis coupled to mass spectrometry (LiP-MS) in vitro and in situ within neuronal cells and directly in native patient brain homogenates, we show that pathogenic αSyn from distinct synucleinopathies (PD, DLB and MSA) are structurally different. Further, we found that fibril structural differences are associated with different putative fibril interactomes and neuronal responses. We discovered disease-specific ubiquitination patterns and turnover profiles for pathogenic αSyn species, detected molecular pathways responding specifically to the uptake of different αSyn fibrillar polymorphs, and identified a subset of the involved proteins as putative interactors of αSyn. In particular, components of the ubiquitin-proteasomal System (UPS), including E3 ubiquitin ligases, chaperones, and deubiquitinating proteins, showed disease/polymorph-specific putative interaction patterns, possibly accounting for different resistance of patient-derived αSyn fibrils to degradation. Genetic modulation with CRISPR-based tools showed that members of the UPS degradation pathway (three E3 ligases: UBE3A, TRIM25, HUWE1 and the AAA+ ATPase VCP) reduced αSyn inclusions, in a strain-specific manner. LiP-MS also identified sets of proteins with altered protease susceptibility in postmortem brain homogenates of PD, DLB, and MSA patients. These sets were largely disease-specific and included proteins altered in cells treated with fibrils derived from patients with the matching disease. Our findings provide insight into cellular processes involved in the accumulation and turnover of αSyn pathogenic aggregates in PD, DLB and MSA in a disease/specific manner and constitutes a resource of potential novel drug targets in these synucleinopathies.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"868-901"},"PeriodicalIF":6.7,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13230553/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147434406","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":"Identifying clinically relevant cell state interactions in the tumor microenvironment of IDH-mutant gliomas using CSI-TME.","authors":"Arashdeep Singh, Bharati Mehani, Vishaka Gopalan, Sushant Puri, Kenneth Aldape, Sridhar Hannenhalli","doi":"10.1038/s44320-026-00201-0","DOIUrl":"10.1038/s44320-026-00201-0","url":null,"abstract":"<p><p>Tumor microenvironment (TME) is characterized by a milieu of distinct cell types that exist in heterogenous transcriptional states across tumors. Functional interactions among these cell states drive tumor progression and therapy response. Systematic characterization of functional cell-state interactions (CSIs) remains challenging due to the paucity of scRNA-seq cohorts with clinical information on one hand, and the lack of cellular context in bulk RNA-seq cohorts on the other. We present CSI-TME, a computational pipeline that extends the concept of gene interactions, such as synthetic lethality, to cell states, to infer prognostic CSIs by directly leveraging large cohorts of bulk transcriptomic datasets. Applied CSI-TME to IDH-mutant gliomas, we identified a highly reproducible cell-state interaction network (CSIN) that is predominantly pro-tumor and differentially activated in IDH-mut astrocytoma versus oligodendroglioma. Malignant cell states within the CSIN resemble multiple neuronal lineages, including astrocyte-like and oligodendrocyte-progenitor-like programs, and reveal key interactions between glioma stem cells and T cells. CSIN stratifies patient response to immune-checkpoint blockade therapy. Roughly 20% of CSIs involve direct ligand-receptor communication, and co-localize in spatial-transcriptomic datasets, most notably for a pro-tumorigenic interaction between tip-like endothelial cells and hypoxic malignant cells supported by multiple ligand-receptor interactions. Interestingly, anti-tumor CSIs correlated with oncogenic mutations are preferentially active in early stages of cancer, hinting at tissue homeostatic response. Overall, CSI-TME is a novel approach that, leveraging clinical bulk transcriptomic data, identifies prognostic CSIs and therapeutic ligand-receptor targets, while providing novel insight into how interactions among the cell states shape the TME in IDH-mutant glioma.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"928-961"},"PeriodicalIF":6.7,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13230996/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147434422","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":"The evolutionary dynamics between viral mimics and host proteins.","authors":"Rotem Fuchs, Ofir Schor, Bar Naim, Dafna Tussia-Cohen, Alessandra Mozzi, Diego Forni, Sivan Friedman, Zohar Haggai, Manuela Sironi, Tzachi Hagai","doi":"10.1038/s44320-026-00200-1","DOIUrl":"10.1038/s44320-026-00200-1","url":null,"abstract":"<p><p>Viral proteins interact with host proteins to hijack cellular pathways important for viral replication. Viral mimics are proteins whose structural similarity to host-mimicked proteins allows them to interact with mutual host targets. This mimicry poses a challenge for the host-how to avoid mimics without compromising essential interactions with host-mimicked proteins. Despite the prevalence of mimicry, the evolutionary dynamics between host and viral mimics remain largely unknown. We address this by integrating structural modeling, host-virus interaction networks, and comprehensive evolutionary analyses of host and viral proteins. We show that host proteins targeted by mimics and host-mimicked proteins are highly conserved, and that this is related to functional constraints imposed on host proteins. Host interface residues that interact with both mimics and host-mimicked proteins evolve slowly, while residues that exclusively interact with mimics evolve significantly faster. Surprisingly, viral mimics do not evolve rapidly, instead displaying complex evolutionary patterns. Our analysis reveals host's limited capacity to escape mimicry and viral evolution to exploit this, and highlights how constraints lead to unexpectedly slow evolution of host-virus interaction networks.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"902-927"},"PeriodicalIF":6.7,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13230584/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147491472","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":"SyntheMol-RL: a flexible reinforcement learning framework for designing easily synthesizable antibiotics.","authors":"Kyle Swanson, Gary Liu, Denise B Catacutan, Stewart McLellan, Autumn Arnold, Megan M Tu, Eric D Brown, James Zou, Jonathan M Stokes","doi":"10.1038/s44320-026-00206-9","DOIUrl":"10.1038/s44320-026-00206-9","url":null,"abstract":"<p><p>The rise of antibiotic-resistant pathogens such as Staphylococcus aureus has created an urgent need for new antibiotics. Generative artificial intelligence (AI) has shown promise in drug discovery, but existing models often fail to propose compounds that are both effective and synthetically tractable. To address these challenges, we introduce SyntheMol-RL, a reinforcement learning-based generative model that can rapidly design synthetically accessible small-molecule drug candidates from a massive chemical space of 46 billion compounds. SyntheMol-RL improves upon our prior Monte Carlo tree search (MCTS)-based SyntheMol model by generalizing across chemically similar building blocks and enabling multi-parameter optimization. We applied SyntheMol-RL to generate candidate antibiotics against S. aureus by optimizing for both antibacterial activity and aqueous solubility, and we found that SyntheMol-RL generated molecules with improved predicted properties compared to both the previous MCTS version of SyntheMol as well as an AI-based virtual screening baseline. We synthesized 79 SyntheMol-RL compounds that were unique relative to the training dataset and found that 13 showed potent in vitro activity, of which seven passed our structural novelty filters that compared them to known antibiotics. Furthermore, one hit compound, synthecin, demonstrated efficacy in a murine wound infection model of methicillin-resistant S. aureus (MRSA). These results validate SyntheMol-RL's ability to generate synthetically accessible candidate antibiotics and position SyntheMol-RL as a powerful tool for drug design across therapeutic domains.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"833-867"},"PeriodicalIF":6.7,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13230741/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147777079","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}