Molecular Systems Biology最新文献

DNA bendability plays a critical role in stabilizing nucleosome assembly, yet its contribution to nucleosome dynamics in vivo remains poorly understood. Here, we applied chemical mapping to generate high-resolution nucleosome positioning maps at single-base-pair resolution from human interphase and metaphase chromosomes, revealing distinct patterns of nucleosome organization between the two states. Notably, we observed a unifying pattern of nucleosome positioning near euchromatic landmarks, including promoters, enhancers, and insulators, during mitosis. Interphase nucleosomes exhibited extensive repositioning, marked by increased nucleosome density, reduced spacing between nucleosomes, and the appearance of additional fragile nucleosomes compared to metaphase. Furthermore, our results show that metaphase nucleosomes display significantly higher DNA cyclizability around the dyad axis, whereas interphase nucleosomes, particularly those near regulatory regions, tend to position DNA with greater cyclizability at the edges of the nucleosome. Together, these findings highlight a dynamic interplay between DNA mechanics and nucleosome organization during the cell cycle.

Genome-wide association studies have identified numerous susceptibility loci in complex diseases, such as chronic immune-mediated inflammatory disorders (IMIDs), yet their impact on pathomechanisms remains poorly understood. Low effect sizes, polygenicity, and predominance within non-coding genomic regions remain major challenges to the functional interpretation of IMID-associated single-nucleotide polymorphisms (SNPs). To address this, we present a novel systems genomics approach which models the cumulative impact of non-coding SNPs on downstream cellular signalling and gene regulatory networks. Applying this to the prototypical chronic IMIDs of Crohn's disease (CD) and ulcerative colitis (UC), both forms of inflammatory bowel disease (IBD), we individually analysed 2,636 patient genomes. Signals from non-coding SNPs were found to propagate towards well-established and novel CD- and UC-associated pathogenic pathways through the signalling and gene regulatory layers. The SNP-propagated gene regulatory networks stratified CD and UC patients into distinct clusters corresponding to cell type-specific gene dysregulation and potential therapeutic response. This approach bridges the gap between genotype and phenotype, laying the foundations for accelerating precision medicine in complex diseases.

Tissue expansion, originally developed for super-resolution imaging, has become a foundation for expansion omics (ExO), a growing field that uses physical tissue expansion to enable spatially resolved omics profiling. In this perspective, we explore how ExO integrates multi-omics through chemical anchoring strategies that ensure selective retention of diverse molecular species, together with improved spatial resolution from the subcellular resolution for profiling to the sub-nanometer scale for imaging, allowing precise detection of biomolecules and their link with biological function. These capabilities have empowered tissue expansion to be successfully applied across multiple spatial omics modalities, including epigenomics, transcriptomics, proteomics, and lipidomics, enabling high-resolution mapping of chromatin states, gene expression, protein localization, and lipid distributions. Moreover, ExO supports spatial multi-omics approaches that jointly capture and correlate multiple biomolecular dimensions within the same tissue context. However, challenges remain in expansion resolution, molecular retention, hydrogel adaptability, data scalability, and AI-driven analysis. As tissue expansion evolves, its integration of super-resolution imaging and spatial omics establishes it as a core technology for whole-slide, single-cell multi-omics and the development of the Artificial Intelligence Virtual Cell, advancing spatial biology and medicine.

Tissue homeostasis requires a precise balance between stem cell self-renewal and differentiation. While fate decisions are known to be closely linked with cell cycle progression, the functional significance of this relationship is unclear. We propose a mechanistic framework to analyse cellular dynamics when cell fate is coupled to cell cycle duration. Our model highlights a unique aspect of cell cycle regulation where mitogens serve as control parameters for a bifurcation governing the G1-S transition. Under competitive feedback from cell-cell interactions, the cell cycle regulatory network fine-tunes near the critical point of this bifurcation. Critical positioning lengthens G1 while amplifying cell-to-cell variability in mitogenic signalling and biochemical states. Such regulation confers significant advantages for controlling cell population dynamics, with alternative topologies enabling rapid tissue growth and repair or efficient mutant rejection. Counter-intuitively, we propose that stem cells may couple prolonged G1 with increased self-renewal propensity to efficiently suppress mis-sensing mutants. Our theory provides a distinct explanation to dynamical and statistical patterns of G1 lengthening and predicts regulatory strategies across development, homeostasis, and ageing.

Caloric restriction (CR) without malnutrition delays aging in diverse species, including primates, with metabolic changes implicated in this process. To facilitate exploration of CR metabolism with aging, we developed a 15-minute LC-MS/MS metabolomics and lipidomics method, leveraging monophasic extractions and wide elution-strength solvents. We analyzed 494 plasma samples collected over 25 years from male and female rhesus monkeys (Macaca mulatta) on a Control or CR (30% restricted) diet. Quantitation of 359 biomolecules revealed that aging, followed by sex and diet, had the largest impact on metabolite abundances. In both sexes, aging was associated with significantly lower plasma levels of sphingomyelins (SMs) and higher levels of diglycerides (DGs) and triglycerides (TGs), each of which was opposed by CR. Sex dimorphism was evident by the increased abundance of phosphocholine (PC)-containing lipids in females. These results highlight the utility of a rapid metabolomics and lipidomics approach to elucidate complex biology in large-scale studies.

Successful computational modelling of complex biological phenomena will depend on the seamless sharing of models and hypotheses between researchers of all backgrounds-experimental and theoretical. LimbNET, a new online tool for modelling, simulating and visualising spatiotemporal patterning in limb development, aims to facilitate this process within the limb development community. LimbNET enables remote users to define and simulate arbitrary gene regulatory network (GRN) models of 2D spatiotemporal developmental patterning processes. Researchers can test and compare each others' hypotheses within a common framework. A database of previously created models empowers users to simulate, explore, and extend each others' work. Spatiotemporally varying gene expression intensities, derived from image-based data, are mapped into a standardised computational description of limb growth, integrated within our modelling framework. This enables direct comparison not only between datasets but between data and simulation outputs, closing the feedback loop between experiments and simulation via parameter optimisation. All functionality is accessible through a web browser ( https://limbnet.embl.es ), requiring no special software, and opening the field of image-driven modelling to the full scientific community.

Functional genomics screens in human induced pluripotent stem cells (hiPSCs) remain challenging despite their transformative potential. We developed iPS2-seq: an inducible, clone-aware screening platform that enables phenotype-agnostic, single-cell resolved dissection of loss-of-function effects in hiPSC derivatives, including complex multicellular models such as organoids. iPS2-seq distinguishes true perturbation effects from genetic and epigenetic variability. It supports pooled and arrayed formats, integrates with microfluidic or split-pool single-cell RNA sequencing, and extends to multi-omic profiling of chromatin and proteins. A dedicated pipeline, catcheR, streamlines design and analysis. The platform enables stage-specific follow-up dissection of screen hits. We demonstrate this by targeting congenital heart disease-associated genes in monolayer cardiomyocytes and organoids. This reveals that epigenetic neuroectodermal priming interferes with germ layer differentiation in specific clones. Accounting for this bias, we show that SMAD2 controls cardiac progenitor specification, with knockdown redirecting cells toward fibroblast and epicardial fates. iPS2-seq unlocks rigorous functional genomics in hiPSC-based models.

Pathogenic mutations in Leucine-rich repeat kinase 2 (LRRK2) are the predominant genetic cause of Parkinson's disease (PD) and often increase kinase activity, making LRRK2 inhibitors promising treatment options. Although LRRK2 kinase inhibitors are advancing clinically, non-invasive readouts of LRRK2-linked pathway modulation remain limited. Profiling urinary proteomes from 1215 individuals across three cohorts and integrating whole-genome sequencing from >500 participants to map genotype-proteome associations, we identified 177 urinary proteins associated with pathogenic LRRK2, enriched for lysosomal/glycosphingolipid, immune, and membrane-trafficking pathways. Machine learning narrowed the features to a cohort-agnostic 30-protein panel that classified G2019S carriers with a mean ROC AUC of 0.91 across independent tests. To evaluate translation, we performed multi-organ and urinary proteomics in rat gain- and loss-of-function models (BAC-LRRK2^G2019S and Lrrk2^KO) and after Lrrk2 inhibition (MLi-2 and PF-475), revealing tissue-specific responses-strongest in kidney-and cross-species overlap, including 24 brain proteins detectable in human urine. Rat-derived perturbations predicted LRRK2 mutation status in patients (AUC 0.75) and reversed with Lrrk2 inhibition, supporting their pharmacodynamic utility. Together, our findings establish urine as a scalable, non-invasive matrix that captures systemic and brain-relevant consequences of LRRK2 dysfunction and nominate candidate pharmacodynamic markers set to support LRRK2-directed trials.

Aneuploidy, a hallmark of cancer, alters chromosome copy numbers and with that the abundance of hundreds of proteins. Evidence suggests that levels of proteins encoded on affected chromosomes are often buffered toward their abundances observed in diploids. Despite its prevalence, the molecular mechanisms driving this protein dosage compensation remain largely unknown. It is unclear whether all proteins are buffered similarly, what factors determine buffering, and whether dosage compensation varies across different cell lines or tumor types. Moreover, its potential adaptive advantage and therapeutic relevance remain unexplored. We established a novel approach to quantify protein dosage buffering in a gene copy number-dependent manner, showing that dosage compensation is widespread but variable in cancer samples. By developing multifactorial machine learning models, we identify gene dependency, protein complex participation, haploinsufficiency, and mRNA decay as key predictors of buffering. We show that dosage compensation affects oncogenic potential and that higher buffering correlates with reduced proteotoxic stress and increased drug resistance. These findings highlight protein dosage compensation as a crucial regulatory mechanism with therapeutic potential in aneuploid cancers.