Molecular Systems Biology最新文献

High-grade serous ovarian cancer (HGSOC) is often detected at an advanced stage, where curative treatment options are limited. Recent advances in ultrasensitive mass spectrometry-based spatial proteomics have provided a unique opportunity to uncover molecular drivers of early tumorigenesis and novel therapeutic targets. Here, we present a comprehensive proteomic analysis of serous tubal intraepithelial carcinoma (STIC), the HGSOC precursor lesion, and concurrent invasive carcinoma, covering more than 10,000 proteins from ultra-low input archival tissue. STIC and HGSOC showed highly similar proteomes, clustering into two subtypes with distinct tumor-immune microenvironments and common remodeling of the extracellular matrix. We discovered cell-of-origin signatures from secretory fallopian tube epithelial cells in STICs and identified early dysregulated pathways of therapeutic relevance. Targeting cholesterol biosynthesis by inhibiting the terminal steps via DHCR7 showed therapeutic effects in ovarian cancer cell lines and synergized with standard-of-care carboplatin treatment. This study demonstrates the power of spatially resolved quantitative proteomics in understanding early carcinogenesis and provides a rich resource for biomarker and drug target research.

Bacterial colonization of tumors is widespread, yet the dynamics during colonization remain underexplored. Here we discover strong variability in the sizes of intratumor bacterial clones and use this variability to infer the mechanisms of colonization. We monitored bacterial population dynamics in murine tumors after introducing millions of genetically barcoded Escherichia coli cells. Results from intravenous injection revealed that roughly a hundred bacteria seeded a tumor and that colonizers underwent rapid, yet highly nonuniform growth. Within a day, bacteria reached a steady-state and then sustained load and clone diversity. Intratumor injections, circumventing colonization bottlenecks, revealed that the nonuniformity persists and that the sizes of bacterial progenies followed a scale-free distribution. Theory suggested that our observations are compatible with a growth model constrained by a local niche load, global resource competition, and noise. Our work provides the first dynamical model of tumor colonization and may allow distinguishing genuine tumor microbiomes from contamination.

Single-cell RNA sequencing allows defining cellular identities based on transcriptional similarity using unsupervised clustering. However, a single clustering resolution may not yield groups of cells that represent both broad, well-defined populations and smaller subpopulations simultaneously. Therefore, when cell identities are not known prior to sequencing, robust comparison and annotation of inferred de novo clusters remains a challenge. Here, we introduce CANTAO, in which we propose the average overlap metric to define the distance between single-cell clusters by comparing ranked lists of differentially expressed genes in a top-weighted manner. We benchmark CANTAO in truth-known datasets comprised of similar yet distinct cell populations and show that evaluating clusters with average overlap results in a consistent, precise, and biologically meaningful recapitulation of true cell identities. We then analyze unsorted mouse thymocytes and characterize stages of T-cell development in the thymus, including minor populations of double-negative (CD4-CD8-) T cells that are difficult to confidently detect among unsorted single cells. We demonstrate that CANTAO enables robust, reproducible characterization of single-cell data and clarifies biological interpretation of underlying identities in homogeneous populations.

The gut microbiome plays fundamental roles in physiological and pathological processes, yet its interaction with host gene expression and contribution to disease remain underexplored. Here, we integrate the genetic regulatory maps of 116 microbial genera with gene expression quantitative trait loci (eQTLs) and DNA methylation QTLs (mQTLs) in three intestinal tissues to dissect host-microbiome interaction. We identify 6088, 5810, and 2398 gene-to-microbiome regulatory loci in the transverse colon, sigmoid colon, and ileum, respectively. Among these, 13.2% of genes show broad regulatory effects on multiple genera, with functional enrichments in developmental, metabolic, and immune-related pathways. Integrative analysis with genome-wide association studies (GWASs) reveals 283 microbiome-dependent disease loci. We observe pleiotropic effects mediated by the gene-to-microbiome regulation at both microbiome and disease layers. Notably, we predict and experimentally validate the suppressive effect of Allisonella on depression through regulating bile acid abundance, and the regulation of Parasutterella on short-chain fatty acid and its contribution to allergic rhinitis. The gene-microbiome-disease regulatory maps are available at our interactive database ( https://xiongxslab.github.io/microbiomeMR/ ).

Single-cell atlases provide valuable insights into gene expression states but lack information on cellular dynamics. Understanding cell turnover rates-the time between a cell's birth and death-can shed light on stemness potential and susceptibility to damage. However, measuring turnover rates in human organs has been a significant challenge. In this study, we integrate transcriptomic data from both tissue and shed cells to assign turnover scores to individual cells, leveraging their expression profiles in spatially resolved expression atlases. By performing RNA sequencing on shed cells from the upper gastrointestinal tract, collected via nasogastric tubes, we infer turnover rates in the human esophagus, stomach, and small intestine. In addition, we analyze colonic fecal washes to map turnover patterns in the human large intestine. Our findings reveal a subset of short-lived, interferon-stimulated colonocytes within a distinct pro-inflammatory microenvironment. Our approach introduces a dynamic dimension to single-cell atlases, offering broad applicability across different organs and diseases.

Precision medicine for paediatric and adult cancers that incorporates drug sensitivity profiling can identify effective therapies for individual patients. However, obtaining adequate biopsy samples for high-throughput (HTP) screening remains challenging, with tumours needing to be expanded in culture or patient-derived xenografts, this is time-consuming and often unsuccessful. Herein, we have developed paediatric patient-derived tumour models using an engineered extracellular matrix (ECM) tissue mimic hydrogel system and HTP 3D bioprinting. Gene expression analysis from a neuroblastoma and sarcoma paediatric patient cohort identified key components of the ECM in these tumour types. Engineered hydrogels with ECM-mimic peptides were used to bioprint and create patient-specific tumouroids using patient-derived cells from xenograft models, and the approach was further confirmed on direct patient tumour samples. Bioprinted tumouroids from the PDX models recapitulated the genetic and phenotypic characteristics of the original tumours and retained tumourigenicity. HTP drug screening of these models identified individualised drug sensitivities. Our approach offers a timely and clinically relevant technology platform for precision medicine in paediatric cancers, potentially transforming preclinical testing across multiple cancer types.

Deciphering patient-specific mechanisms of cancer cell reprogramming remains a crucial challenge in systems oncology, as it is key to improving patient diagnosis and treatment. For this reason, comprehensive and patient-specific multi-omic characterization of tumor specimens has become increasingly common in clinical practice. Here, we developed PatientProfiler, a computational workflow that integrates proteogenomic data with curated causal interaction networks to generate mechanistic models of signal transduction for individual patients. PatientProfiler allows multi-omic data analysis and standardization, generation of patient-specific mechanistic models of signal transduction, and extraction of network-based prognostic biomarkers. We successfully benchmarked the tool on proteogenomic and clinical data derived from 122 biopsies of treatment-naïve breast cancer, available through the CPTAC portal. We identified patient-specific mechanistic models that recapitulate oncogenic signaling pathways. In-depth topological exploration of these networks revealed seven subgroups of patients, associated with unique transcriptomic signatures and distinct prognostic values. We identified well-known Basal-like 1 and 2 subtypes, while also highlighting distinct mechanistic drivers such as the MYC-CDK4/6 axis or NF-kappaB-mediated inflammatory programs. Beyond breast cancer, PatientProfiler offers a generalizable framework to transform cohort-level multi-omic data into interpretable mechanistic models, making it applicable across diverse cancer types and other complex diseases.

Genetic oscillators drive precisely timed gene expression, crucial for development and physiology. Using the C. elegans molting clock as a model, we investigate how oscillators can schedule the orderly expression of thousands of genes. Single-cell RNA sequencing reveals a broad peak phase dispersion in individual tissues, mirrored by rhythmic changes in chromatin accessibility at thousands of regulatory elements identified by time-resolved ATAC-seq. We develop a linear model to predict chromatin dynamics based on the binding of >200 transcription factors. This identifies nine key regulators acting additively to determine the peak phase and amplitude of each regulatory element. Strikingly, these factors can also generate constitutive, non-rhythmic activity through destructive interference. Validating its power, the model accurately predicts the impact of GRH-1/Grainyhead perturbation on both chromatin and transcript dynamics. This work provides a conceptual framework for understanding how combinatorial, non-cooperative transcription factor binding schedules complex gene expression patterns in development and other dynamic biological processes.