Molecular Systems Biology最新文献

{"title":"Cell painting and thermal proteome profiling for inference of drug targets and mechanism of action.","authors":"Camilla Johansson, Martin Johansson, Phanindra Babu Kasi, Mårten Larsson, Per-Johan Jakobsson, Ulf Göransson, Jordi Carreras Puigvert, Ola Spjuth, Erik T Jansson","doi":"10.1038/s44320-026-00214-9","DOIUrl":"10.1038/s44320-026-00214-9","url":null,"abstract":"<p><p>Understanding the mechanism of action (MoA) of bioactive compounds is a central challenge in drug discovery and chemical biology. We propose a strategy for integrating morphological data with proteomics to provide deeper insights into the MoA of compounds. We combine the rich phenotypic profiles of Cell Painting (CP) with the unbiased protein target detection of Thermal Proteome Profiling (TPP), and construct protein-protein interaction networks based on potential targets identified using both assays. We validated our method with public TPP datasets for the five compounds (+)-JQ1, I-BET151, Vemurafenib, Crizotinib and Panobinostat, and public Proteome Integral Solubility Alteration (PISA) data for 49 compounds, together with CP data for 5259 drugs on U2OS cells. We show that the combined approach could accurately identify known MoAs for four out of five validation compounds and known targets for 29 out of 49 compounds. Finally, we deployed our method to characterize sinomenine, a compound with elusive knowledge of MoA. Our findings revealed novel facets of sinomenine's biological activity and highlight the value of multimodal profiling for chemical biology.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1270-1291"},"PeriodicalIF":6.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13434728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147948889","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":"Burst of aneuploidy: a cost of adaptation driven by breakdown of cell cycle control.","authors":"Adrian Pirog, Hanna Tutaj, Katarzyna Tomala, Ryszard Korona","doi":"10.1038/s44320-026-00217-6","DOIUrl":"10.1038/s44320-026-00217-6","url":null,"abstract":"<p><p>Complex genome rearrangements are intriguing because it remains unclear whether they occurred gradually or suddenly, and what promoted or impeded their development. We started by asking whether double loss of heterozygosity (dLOH) involving two different chromosomes is simply the result of two independent single LOH events. We examined hundreds of vegetatively growing diploid yeast strains with gene deletions known to stimulate LOH through different mechanisms. In dozens of these strains, dLOH mutants were overrepresented by tens or even hundreds of times. Interestingly, the (deleted) genes that caused the greatest instability were functionally diverse yet apparently affected the same biological process: cell cycle control. Furthermore, mutants were dominated by aneuploidy, which often involved multiple non-target chromosomes, with virtually no segmental loss, non-homologous recombination, or other small-scale changes. We conclude that the supply of separate, local rearrangements affecting only the required regions is insufficient, even in mutator strains. Consequently, double-pointed selection tends to reveal extensive reassortments resulting from systemic failures in mitotic cell division. Therefore, overcoming multiple adaptation barriers tends to produce aneuploidy, which is often reversible.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1292-1311"},"PeriodicalIF":6.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13434691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147988432","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":"Immunoglobulin sub-class levels define inter-donor plasma variability: a longitudinal dual-lab study.","authors":"Stephan Michalik, Sofia Kalaidopoulou Nteak, Nicolas Drouin, Manuela Gesell Salazar, Elke Hammer, Vishnu Mukund Dhople, Silva Holtfreter, Stefan Weiss, Henk W P van den Toorn, Barbara M Bröker, Grażyna Domańska, Uwe Völker, Albert J R Heck","doi":"10.1038/s44320-026-00218-5","DOIUrl":"10.1038/s44320-026-00218-5","url":null,"abstract":"<p><p>Advances in mass spectrometry have transformed plasma proteomics, allowing high-throughput analysis of large cohorts. This study utilized the TIMES (Tracking Individuals Monthly for Evaluating Stability) cohort, consisting of 51 healthy participants monitored monthly over 12 months, to evaluate intra- and inter-individual variability in the plasma proteome. Approximately 600 samples were analyzed in two laboratories independently, revealing strong correlations despite methodological differences. The study revealed high stability of the plasma proteome within donors over a year, with larger differences observed between donors. A support vector machine model achieved a 98% median classification accuracy, confirming stable, donor-specific proteomic profiles. Immunoglobulins, often overlooked in plasma proteomics, were found to contribute significantly to donor-specific signatures, showing pronounced inter-individual variability but remarkable intra-donor stability. In contrast, C-reactive protein and other inflammation markers exhibited significant temporal fluctuations from donor-specific baseline levels. This inter-laboratory study highlights the importance of longitudinal sampling for biomarker discovery, the robustness of MS-based proteomic workflows, and provides new insights into immunoglobulin dynamics and variability in human plasma.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1312-1332"},"PeriodicalIF":6.7,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13434392/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148032981","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-genus analysis reveals architecturally programmed sgRNA synthesis patterns in coronaviruses.","authors":"Zi Wen, Lei Chen, Dehua Luo, Ju Sun, Liangrong Guo, Yingxiang Deng, Zhiyuan Huang, Yuxiang Wang, Ke Pan, Fan Wang, Shaobo Xiao, Li Li, Dengguo Wei","doi":"10.1038/s44320-026-00239-0","DOIUrl":"https://doi.org/10.1038/s44320-026-00239-0","url":null,"abstract":"<p><p>Coronaviruses employ discontinuous transcription to produce canonical subgenomic RNAs (sgRNAs) essential for gene expression. Although TRS-dependent template switching mechanism has been proposed, its structural basis remains poorly defined, and the functional significance of abundant non-canonical sgRNAs persists as a critical gap since the discovery of discontinuous RNA synthesis. Here, we help bridge this gap through the first cross-genus integrated analysis of coronavirus transcriptomes and RNA interactomes. We show that canonical sgRNA formation is associated with same-direction RNA-RNA interactions. In contrast, non-canonical sgRNAs form through distinct architectural mechanisms: short-range junctions mediated by stem-loop structures overlapping genomic deletion hotspots, and conserved long-range ORF1a-N interactions generating sgRNAs encoding immune-modulatory ORFs - a function not previously attributed to non-canonical transcription. These findings suggest architecturally programmed discontinuous RNA synthesis and highlight a potential link between non-canonical sgRNAs, genomic plasticity, and immune modulation, which may have implications for coronavirus adaptation.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148630976","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":"xDecoder unlocks the potential of genomic foundation models for few-shot personal gene expression prediction.","authors":"Shumin Li, Ruibang Luo, Yuanhua Huang","doi":"10.1038/s44320-026-00238-1","DOIUrl":"https://doi.org/10.1038/s44320-026-00238-1","url":null,"abstract":"<p><p>Large-scale genomic language models (gLMs) hold promise for modeling gene regulation, yet their ability to capture personal gene expression variations remains unresolved. We developed xDecoder, a unified decoding framework that utilizes gLMs and sequence-to-function (S2F) embeddings to learn how personal genetic variation shapes gene expression from paired genome-transcriptome data. Compared to the pretrained genomic models, xDecoder with personalized DNA-RNA training makes cross-individual prediction tractable for seen genes in a few-shot setting. However, zero-shot prediction at unseen loci remains unreliable and gene-dependent, revealing a cross-locus transfer bottleneck of current sequence models. Experiments incorporating individual-level chromatin accessibility suggested that regulatory-state information important for unseen-locus prediction is not fully captured by current DNA-only models. Overall, these results highlight the potential utility of the few-shot setting, the limitations of DNA-only models, and point toward multi-omic, variant-aware frameworks as a promising direction for building personalized regulatory models.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148605482","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":"Extracellular matrix phenotyping by imaging mass cytometry defines distinct cellular matrix environments associated with allergic airway inflammation.","authors":"James E Parkinson, Morgan Bryant, Mohamed Ghafoor, Rebecca J Dodd, Hannah E Tompkins, Martin Fergie, Matthew O Burgess, Magnus Rattray, Tara E Sutherland","doi":"10.1038/s44320-026-00234-5","DOIUrl":"https://doi.org/10.1038/s44320-026-00234-5","url":null,"abstract":"<p><p>The extracellular matrix (ECM) forms the scaffold in which cells reside and interact. The composition of this scaffold guides the development of local immune responses and tissue function. With the advent of multiplexed spatial imaging methodologies, investigating the intricacies of cellular spatial organisation is more accessible than ever. However, the relationship between cellular organisation and ECM composition has been broadly overlooked. Using imaging mass cytometry, we investigated the association between cellular niches and their surrounding matrix environment during allergic airway inflammation in two commonly used in-bred mouse strains. We have integrated both intracellular and extracellular analysis by first classifying cells according to their canonical marker expression and then utilising a novel pipeline to independently characterise their ECM environment. Applying this methodology across three distinct lung tissue regions we reveal region-specific and spatially constrained allergic inflammatory responses. In our model of allergic airway inflammation, recruited neutrophils were dispersed within the alveolar parenchyma, alongside a loss of alveolar type-I cells and an expansion of alveolar type-II cells. This activated parenchyma was associated with increased proximity to hyaluronan and chondroitin sulphate. In contrast, infiltrating CD11b⁺ and MHCII⁺ cells accumulated in the adventitial cuff specifically in BALB/c mice. This region was around the airway-adjacent pulmonary artery and aligned with an expansion of the subepithelial airway region. This expanded subepithelial region was enriched for closely interacting stromal and CD11b⁺ immune cells which overlaid regions enriched for type-I and type-III collagen. The detailed map of cell and matrix interactions generated here provides a critical resource for understanding ECM contribution to specific lung niches during allergic disease. It reveals strain-dependent differences in inflammation and remodelling and generates novel hypotheses for targeting aspects of remodelling during allergic airway pathology.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148536078","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":"Competing gene regulatory networks drive naive and memory B cell differentiation.","authors":"Pietro Demela, Laura Esposito, Pietro Marchesan, Leonardo Nossa, Davide Bolognini, Edoardo Giacopuzzi, Eugenia Ricciardelli, Paolo Ferrari, Silvia Bombelli, Clelia Peano, Daniele Prati, Luca Valenti, Blagoje Soskic","doi":"10.1038/s44320-026-00207-8","DOIUrl":"10.1038/s44320-026-00207-8","url":null,"abstract":"<p><p>Elucidating the gene regulatory networks (GRNs) that control human B cell differentiation is crucial for understanding immune responses to infection, vaccination, and autoimmunity. Here, we map the GRNs guiding naive and memory B cell differentiation. Early in activation, both cell types engage highly similar GRNs. However, at later stages, naive B cells diverge into two opposing GRNs that promote differentiation into either plasma cells or germinal center (GC) cells. In contrast, memory B cells predominantly activate the GRNs associated with plasma cell differentiation, such as IRF4. CRISPR perturbations of IRF4 and its downstream effector PRDM1 rewired the GRNs, blocking plasma cell programs and promoting GC fate. Finally, we trained machine learning algorithms to predict sister cells, solely based on the transcriptome. This showed that plasma cell gene expression is tightly correlated among sister cells, revealing heritable transcriptional programs. Collectively, our findings reveal distinct regulatory trajectories in naive and memory B cells and uncover heritable transcriptional states that shape human B cell fate decisions.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1097-1117"},"PeriodicalIF":6.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13328623/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147699255","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":"Single-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation.","authors":"Mari Tolonen, Ziwei Xu, Ozgur Beker, Varun Kapoor, Bianca Dumitrascu, Jakub Sedzinski","doi":"10.1038/s44320-026-00212-x","DOIUrl":"10.1038/s44320-026-00212-x","url":null,"abstract":"<p><p>Cell state transitions underlie the emergence of diverse cell types and are traditionally defined by changes in gene expression. Yet these transitions also involve coordinated shifts in cell morphology and behavior, which remain poorly characterized in densely packed epithelia. We developed a quantitative live-imaging and computational framework to track thousands of individual cells over time in the rapidly differentiating Xenopus mucociliary epithelium (MCE). From segmentations and trajectories, we extracted dynamic features-cell and nuclear shape, movement, and position-to create a time-resolved morphodynamic dataset spanning the full course of differentiation. While single features showed high noise and low separability of ground-truth cell types, supervised machine learning revealed that integrating time-resolved features improves the prediction of final cell fate. Gradient-boosted trees and multinomial logistic regression achieved moderate but consistent accuracy, especially for abundant epithelial lineages. Key discriminants included normalized Z position, membrane-nucleus offset, and absolute experimental time, whereas movement contributed minimally to the results. Our data show that morphodynamic signatures encode predictive information about cell identity and provide a framework linking cellular dynamics with molecular state.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1040-1069"},"PeriodicalIF":6.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13328729/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147874995","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":"Spatially-guided metabolomics profiling of metabolic regions in human tumor tissues.","authors":"Jia-Ying Joey Lee, Jingtao Zhang, Sin-Chi Chew, Shihleone Loong, Liang Xu, Jia-Wen Carmen Kong, Fedor Grigoryev, Alexander Yaw-Fui Chung, Jin-Yao Teo, Peng-Chung Cheow, Glenn Bonney, Brian K P Goh, Wei-Qiang Leow, Yulan Wang, Lit-Hsin Loo, Pierce Kah-Hoe Chow","doi":"10.1038/s44320-026-00205-w","DOIUrl":"10.1038/s44320-026-00205-w","url":null,"abstract":"<p><p>Bulk high-resolution mass spectrometry provides sensitive and global snapshots of metabolites involved in cancer metabolism. However, intratumoral heterogeneity obscures the cellular origins of detected metabolites, making it difficult to identify reproducible and predictive metabolic markers. Here, we present \"Spatially guided MEtabolomics (SgME) profiling\", a multi-modal metabolomics data analysis approach that delineates and maps metabolic regions (MERs), including overlapping regions, within tumor tissues. We applied SgME profiling to human hepatocellular carcinoma (HCC) tumors and refined potential RNA markers that were also found in previous transcriptomics or bioinformatics studies to those specifically associated with malignant regions. We further estimated that more than 50% of the highly abundant metabolites detected in bulk tumors originated from the non-malignant MERs and are therefore unlikely to be predictive and/or reproducible markers. Importantly, SgME profiling also revealed new potential metabolic markers that were not apparent in bulk analysis because they increased in low-grade tumor regions but declined sharply in necrotic regions. Together, these findings show that SgME profiling overcomes key limitations of conventional metabolomics profiling by enabling more granular, spatially resolved metabolic characterization.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1132-1160"},"PeriodicalIF":6.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13328631/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147593331","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 novel combination therapy for ER+ breast cancer suppresses drug resistance via an evolutionary double-bind.","authors":"Rena Emond, Jeffrey West, Vince K Grolmusz, Patrick A Cosgrove, Aritro Nath, Alexander R A Anderson, Andrea H Bild","doi":"10.1038/s44320-026-00191-z","DOIUrl":"10.1038/s44320-026-00191-z","url":null,"abstract":"<p><p>Chemotherapy remains a commonly used and important treatment option for metastatic breast cancer. A majority of Estrogen Receptor-positive (ER + ) metastatic breast cancer patients ultimately develop resistance to chemotherapy, resulting in disease progression. We hypothesized that an \"evolutionary double-bind\", where adapting to one treatment inadvertently makes cancer cells more susceptible to another treatment, would improve the effectiveness and durability of response to chemotherapy. This approach exploits vulnerabilities in acquired resistance mechanisms. Evolutionary models can be used to identify alternative treatment strategies that capitalize on such vulnerabilities in refractory cancers, leading to improved outcomes. To develop and test these models, ER+ breast cancer cell lineages sensitive and resistant to chemotherapy were grown in spheroids with varied initial population frequencies to measure cross-sensitivity and efficacy of chemotherapy and add-on treatments, such as disulfiram. Different treatment schedules were evaluated to identify the most effective strategy for reducing the selection of resistant populations, thereby preventing their proliferation and dominance. We developed a game-theoretic mathematical model, parameterized from this in vitro experimental data, and used it to predict the existence of a double-bind, where selection for resistance to chemotherapy induces sensitivity to disulfiram. The model predicts a dose-dependent re-sensitization to chemotherapy for monotherapy disulfiram.</p>","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1070-1096"},"PeriodicalIF":6.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13328575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147521328","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}