Molecular Systems Biology分子系统生物学
Molecular Systems Biology(英文缩写 MOL SYST BIOL),ISSN 1744-4292,eISSN 1744-4292,中文译名:分子系统生物学 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-09-28 至 2026-09-28，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 12.744 | Q1 |
| 2022 | 9.900 | Q1 |
| 2023 | 8.500 | Q1 |
| 2024 | 7.700 | Q1 |
| 2025 | 6.700 | Q1 |
Molecular Systems Biology 最新收录文献
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1. Engineering a compact CgCas12n platform for efficient genome and base editing in mammalian cells.
PMID:日期:2026-09-26Type V CRISPR-Cas12 systems evolved from transposon-encoded TnpB proteins, with type V-U4 nucleases (Cas12n) as evolutionary intermediates linking TnpB to larger type V effectors. Despite their compact size, most Cas12n orthologs, including Corynebacterium glutamicum Cas12n (CgCas12n), exhibit low genome-editing activity in mammalian cells. Here, we engineered CgCas12n using an arginine enrichment strategy, generating a variant with approximately 60-fold enhanced editing efficiency. Concurrently, we optimized the sgRNA scaffold to reduce its size without compromising activity. These improvements were combined to establish an enhanced CgCas12n system (eCgCas12n) that enables efficient mammalian genome editing. We further adapted this system for base editing by constructing a cytosine base editor (eCgCas12n-CBE) that mediates efficient C-to-T conversion. As proof of concept, eCgCas12n-CBE introduced a premature stop codon into the Dmd gene, reducing dystrophin expression and impairing myogenic differentiation. AAV9-mediated delivery of eCgCas12n-CBE into mouse skeletal muscle demonstrated its in vivo editing capability. Collectively, this work establishes a robust engineering strategy to enhance compact Cas12n nucleases and provides an efficient genome- and base-editing platform for functional genetic studies.
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2. An integrated spatially defined roadmap of normal and cancer-associated colon fibroblasts.
PMID:日期:2026-09-23Cancer-associated fibroblasts (CAFs) promote tumorigenesis and represent potential therapeutic targets, highlighting the need for precise understanding of CAF heterogeneity. In colorectal cancer (CRC), CAF subsets and nomenclature vary widely, and many studies overlook the diversity of normal colon fibroblasts that shapes tumor mesenchyme. Here, we combine large-scale single-cell RNA-sequencing analysis with spatial validation and functional assays to construct a comprehensive reference map of human normal colon fibroblasts and mesenchymal subsets, guided by the well-characterized mouse colon. We define three major fibroblast populations, subepithelial myofibroblasts (SEMFs), mucosa-associated fibroblasts (MAFs), and submucosa-associated fibroblasts (SAFs), and characterize a previously underexplored muscle-embedded interstitial fibroblast (MIF) population. This reference enabled mapping of CRC-specific changes, revealing four cancer-specific CAF subsets, including inflammatory CAFs (iCAFs), matrix CAFs (mCAFs), and two precursor populations (pre-CAFs). We also identify robust CRC CAF markers including CTHRC1, infer transcriptional regulators, and define distinct developmental trajectories driving iCAF and mCAF activation. Together, our study provides a spatially and transcriptionally resolved reference map of fibroblasts in the normal colon and CRC, enabling mechanistic studies and informing CAF-targeted therapies.
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3. Cell cycle-dependent protein dynamics in budding yeast resolved by deconvolution of bulk proteomics.
PMID:日期:2026-09-14The cell division cycle is characterised by oscillatory dynamics in regulatory mechanisms and biosynthesis, coordinated with genome replication and segregation. To understand these dynamics, quantitative cell cycle-dependent protein concentration data are essential. Unfortunately, accurately resolving cell cycle-dependent protein dynamics is challenging because single-cell proteomics is currently infeasible and bulk proteomics requires - inherently imperfect - cell synchronisation. Here, we developed a computational method to deconvolve cell cycle-dependent protein concentration dynamics and applied it to new budding yeast bulk proteome data. Key to this method was a yeast population model, parameterised with experimental cell cycle progression and volume growth data, for quantifying the desynchronisation in sampled populations. We performed deconvolution on 3272 proteins, using cross-validation to determine regularisation parameters, and identified 539 proteins with cell cycle-dependent dynamics. Many of these dynamics were consistent with known yeast biology and dynamic proteins were enriched for several metabolic process, extending previous observations and supporting the emerging picture of metabolic activity as varying substantially over cell cycle phases. We consider the generated cell cycle-resolved budding yeast proteome data a key resource.
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4. Decoding spatiotemporal fibrotic and cellular immunosuppression of therapeutic T cells in live pancreatic ductal adenocarcinoma.
PMID:日期:2026-09-10Pancreatic ductal adenocarcinoma (PDA) is profoundly immunosuppressive. To help define this behavior, we present integrated experimental and computational frameworks to elucidate therapeutic T cell dynamics. Through the development of TME-CARTographer (TME-CART), a computational pipeline integrating high-dimensional data, graph theory, behavior analysis, and deep learning (DL), we present quantitative insights on 4D T cell-TME interactions in live PDA tumors. Mapping physical immunosuppression demonstrates that collagen fiber architectures direct migration while concomitantly limiting off-axis movement, creating immune exclusion zones. Expanding these findings, we establish that the collagen matrix harbors and spatially organizes immunosuppressive myeloid cells to serve as cooperative co-modulators of T cell behaviors, including migration, sampling, repulsion, and sequestration. Consistent with these findings, DL defines both linear and nonlinear collagen matrix and cellular neighborhood interactions as drivers of T cell behavior. The TME-CART DL framework also accurately predicts shifts in immunosuppression following depletion of myeloid cells. Overall, we identify synergistic barriers impeding anti-tumor T cell behaviors and present TME-CART as a discovery platform for interpreting complex 4D data to enhance the understanding and design of immunotherapies.
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5. Engineered E. coli swarming for binary and analog input recording.
PMID:日期:2026-09-01Many bacterial species form self-organized macroscale patterns through swarming. Despite its extensive genetic tractability, Escherichia coli remains underexplored for robust, applied control of swarming. Here we develop a set of E. coli strains that generate centimeter-scale swarming patterns to spatially record environmental inputs. Specifically, we modulate the expression of swarming-related genes in response to chemical and optical signals, reshaping baseline swarm patterns in analog or binary-like fashions. To decode bacterial patterns across space and time, we develop scalable computational methods incorporating feature extraction, regression, and deep-learning models. Time-lapse imaging reveals that colonies record inputs dynamically, enabling early-stage classification. This work establishes a strategy for spatial information recording in E. coli and expands the toolkit for programming emergent microbial behaviors at macroscopic scales.
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6. Paradoxical non-catalytic kinase functions are driven by inhibitor-induced displacement of autoinhibitory domains.
PMID:日期:2026-09-01ATP-competitive kinase inhibitors represent one of the largest classes of targeted anti-cancer drugs. While their primary mechanism is to block catalytic activity, they can also trigger paradoxical phenotypic effects that cannot be explained by catalytic inhibition alone. These observations point to a hidden layer of drug action that modulates non-catalytic kinase functions via changes in kinase conformation and protein-protein interactions (PPIs). Here, we developed a multimodal proteomics approach combining limited proteolysis coupled mass spectrometry on affinity-purified samples (AP-LiP-MS), AP-MS, and proximity labeling-MS to map inhibitor-induced conformation and PPI changes. We show that inhibitor binding causes structural rearrangements in the autoinhibitory domains (AIDs) of all tested kinases, consistent with a transition to an open, active-like kinase conformation. These structural shifts drive distinct kinase-protein interaction changes that control non-catalytic functions: sequestration of AMPK by inhibited CAMKK2 blocks phosphorylation by other kinases, CHEK1 inhibition causes dissociation from the mitochondrial protein CLPB and leads to mitochondrial fragmentation, and structural changes in inhibited PRKCA trigger rapid relocalization to cell junctions. Thus, we identify the ATP-binding site as a major organizing center of kinase conformation and interaction. Our work suggests that these on-target, off-mechanism effects are likely to occur in other kinases as well, and provides the analytical framework to systematically characterize a frequently overlooked phenomenon highly relevant for understanding drug side effects to guide the development of novel therapeutics.
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7. Common xenobiotics modulate gut microbial responses to low‑calorie sweeteners in vitro.
PMID:日期:2026-09-01The gut microbiota is implicated in adverse effects associated with low-calorie sweeteners. Yet, the direct impact of sweeteners on gut bacteria remains largely uncharacterized. Here, we report interactions between 25 phylogenetically diverse gut bacterial strains and 39 commercially used sweeteners. We tested these sweeteners individually and in combination with four commonly co-consumed compounds, viz., advantame, caffeine, vanillin, and duloxetine. Three-quarters of the tested sweeteners individually impacted the growth of at least one tested bacterial strain. Further, over 100 interactions were found between sweeteners and the four co-consumed compounds. Isosteviol, a commonly used sweetener-component, and duloxetine, an antidepressant, synergistically inhibited Roseburia intestinalis, a bacterium previously linked to glucose homeostasis, and Parabacteroides merdae, a prevalent commensal linked to healthy microbiota. Proteomic, metabolomic, and genetic analyses indicate altered small molecule transport underpinning this sweetener-drug synergy. The isosteviol-duloxetine combination also modulated metabolism of a synthetic gut bacterial community, leading to increased toxicity to HeLa cells and altered secretion of inflammation-modulatory cytokines IL-6 and IL-8 by Caco-2 cells. Our data warrant further studies on interactions between low-calorie sweeteners and common xenobiotics.
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8. Quantitative interactome mapping of skeletal muscle insulin resistance.
PMID:日期:2026-09-01Protein-protein interactions (PPIs) are dynamic and critical to adaptive homeostasis. While there have been massive efforts to catalogue proteome-wide PPIs, global quantification of changes remains a challenge. Here, we integrate dynamic protein correlation profiling - mass spectrometry (PCP-MS) and quantitative cross linking-mass spectrometry (qXL-MS) using multiplexed stable isotope labelling to characterise global PPI remodelling following the development of chronic skeletal muscle insulin resistance (IR) with or without acute insulin stimulation. We quantify >7,000 unique PPIs amongst 5,346 proteins and show changes in the interactome network dominate the proteome response. Our data show the dysregulation of protein processing in the endoplasmic/sarcoplasmic reticulum involving changes in PPIs with protein chaperones and disulfide isomerases is a major hallmark of skeletal muscle IR. Mechanistically, we show the dysregulation of PPIs with Protein-Disulfide Isomerase 6 (PDIA6) regulates cysteine oxidation and insulin sensitivity. Taken together, we show in vivo quantitative interactome mapping is a powerful approach to understand disease mechanisms and provide new insights into protein network re-organisations with IR.
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9. Interpretable multi-omics integration across mixed-order tensors with MANTRA.
PMID:日期:2026-09-01The integration of multi-modal molecular data is crucial for understanding complex diseases, but existing methods struggle with modern experimental designs that generate datasets with mixed-order tensors-for example, a third-order drug-response tensor alongside a second-order transcriptomics matrix. Here, we present MANTRA (Multi-view ANalysis with Tensor and matRix Alignment), a probabilistic framework that integrates collections of tensors of different orders, combining the strengths of group factor analysis and tensor decomposition. MANTRA learns interpretable latent factors and naturally handles missing data through a Bayesian approach with structured sparsity priors. On a Chronic Lymphocytic Leukemia (CLL) dataset, the joint analysis of a third-order drug-response tensor and a second-order RNA-seq matrix with MANTRA revealed clinically relevant patient subgroups that were missed by single-view or matrix-based analyses. In a single-cell multi-omics study of Acute Lymphoblastic Leukemia (ALL), MANTRA identified a novel patient subgroup defined by a distinct molecular program in plasmacytoid dendritic cells (pDCs), linking disease heterogeneity to a specific cell type. By explicitly modeling higher- order data structures, MANTRA provides an interpretable tool to uncover hidden biological variation from complex experimental data.
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10. To cleave or not to cleave: a systemic evaluation of DSS versus DSSO for cross-linking mass spectrometry analysis.
PMID:日期:2026-09-01Cross-linking mass spectrometry is a powerful method for structural analysis, but choosing between cleavable and non-cleavable cross-linkers remains challenging. We rigorously compared non-cleavable DSS with cleavable DSSO and found that DSS consistently yields more cross-link identifications from isolated protein complexes to bacterial lysates. The advantage of DSS diminishes as sample complexity increases. At the highest complexity tested-human cell lysate-the trend reverses, with DSSO outperforming DSS. The superior performance of DSS in less complex samples is likely explained by its longer and more flexible spacer arm, which interrogates a spatial volume >40% larger than that of DSSO. For both cross-linkers, the number of identified cross-links decreases as the search space expands, but more steeply for DSS. This sharper decline arises from DSS cross-links producing slightly lower fragment ion coverage, not from the absence of signature ions that could reduce search space. Fragment ion coverage is key to interactome mapping: when coverage reaches 85% or above, identification sensitivity hardly decreases as the search space expands, regardless of the cross-linker used. In summary, we recommend DSS for samples no more complex than bacterial lysates. For interactome mapping of mammalian cells, although DSSO outperforms DSS, neither achieves deep interactome coverage.