Molecular Systems Biology最新文献

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.

Human viruses rely on host translation resources, including the cellular tRNA pool, because they lack tRNA genes. Using tRNA sequencing, we profiled mature tRNAs during infections with human cytomegalovirus (HCMV) and SARS-CoV-2. HCMV-induced alterations in mature tRNA levels were predominantly virus-driven, with minimal influence from the cellular immune response. Certain post-transcriptional modifications, correlated with tRNA stability, were actively manipulated by HCMV. By contrast, SARS-CoV-2 caused minimal changes in mature tRNA levels or modifications. Comparing viral codon usage with proliferation- versus differentiation-associated codon-usage signatures in human genes revealed striking divergence. HCMV genes aligned with differentiation codon usage, whereas SARS-CoV-2 genes matched proliferation codon usage. Structural and gene-expression genes in both viruses showed strong adaptation to host tRNA pools. Finally, a systematic CRISPR screen of human tRNA genes and tRNA-modifying enzymes identified specific tRNAs and enzymes that either enhanced or restricted HCMV infectivity and influenced cellular growth. Together, these data define a dynamic interplay between the host tRNA landscape and viral infection, illuminating the mechanisms governing host-virus interactions.

Accurate prediction of protein complex structures by AlphaFold3 and similar programs has been used to predict the presence of protein-protein interactions (PPIs), but this technique has never been applied to an entire genome due to onerous computational requirements and questionable utility. Here we present pooled-PPI prediction, a technique that dramatically improves the accuracy of genome-scale screens compared to a paired approach while simultaneously reducing inference time (~twofold) and the number of jobs (~100-fold). We use this technique to predict the structure of all 113,050 pairwise PPIs in Mycoplasma genitalium using only 2027 AlphaFold3 jobs. This unbiased and comprehensive dataset was highly predictive of known interactions, revealed a previously unappreciated but widespread size bias in AlphaFold interface scores, correctly identified protein-protein interfaces in macromolecular complexes, and uncovered new biology in M. genitalium. This work establishes pooled-PPI prediction as a highly scalable method for uncovering protein-protein interactions and a powerful addition to the functional genomics toolkit.

Oncogenic mutations shape colorectal cancer (CRC) biology, yet their impact on transcriptional phenotypes remains incompletely understood, and their individual prognostic value is limited. Here, we perform a pooled single-cell transcriptomic screen of over 100,000 CRC cells with a comprehensive barcoded library of oncogenic variants across genetically diverse CRC lines. Using a variational autoencoder-based interpretable factor model, we identify ten conserved oncogene-driven transcriptional modules (TMOs) representing core cancer phenotypes such as cellular plasticity, inflammatory response, replicative stress, and epithelial-to-mesenchymal transition. Engagement of these modules can be context-dependent, reflecting interactions between oncogene-induced driver pathways and background genetics. TMO activity in patient tumors stratifies CRC cohorts into high- and low-risk groups, improving relapse-free survival prediction beyond existing classification systems. Our study systematically links oncogenic signaling to transcriptional states and clinical outcomes, establishing a functional framework for module-based patient stratification in precision oncology.

Following their success in natural language processing and protein biology, pretrained large language models have started appearing in genomics in large numbers. These genomic language models (gLMs), trained on diverse DNA and RNA sequences, promise improved performance on a variety of downstream prediction and understanding tasks. In this review, we trace the rapid evolution of gLMs, analyze current trends, and offer an overview of their application in genomic research. We investigate each gLM component in detail, from training data curation to the architecture, and highlight the present trends of increasing model complexity. We review major benchmarking efforts, suggesting that no single model dominates, and that task-specific design and pretraining data often outweigh general model scale or architecture. In addition, we discuss requirements for making gLMs practically useful for genomic research. While several applications, ranging from genome annotation to DNA sequence generation, showcase the potential of gLMs, their use highlights gaps and pitfalls that remain unresolved. This guide aims to equip researchers with a grounded understanding of gLM capabilities, limitations, and best practices for their effective use in genomics.

Why eukaryotic genomes are universally divided among multiple chromosomes remains an unresolved question. Although yeast and mouse cells can tolerate chromosomal fusions without impairing viability, we show here that chromosome length in mammalian cells is constrained by a biophysical limit governed by spindle geometry. Using engineered mouse cells carrying fused chromosomes of defined sizes, we identify ~308 Mb as the maximal length tolerated for faithful mitosis. Chromosomes exceeding this threshold disrupt segregation, leading to daughter cell re-coalescence and polyploidization. Aurora B kinase regulates this process by modulating spindle elongation; its inhibition induces mitotic failure even in chromosome configurations within the tolerated threshold of ~308 Mb. These findings explain the structural basis for genome fragmentation in animals and reveal a general mechanism linking chromosome size, spindle dynamics, and genome stability.

The increasing prevalence of antifungal resistance represents a major clinical challenge. To explore potential new therapeutic avenues, we investigated fitness trade-offs associated with azole and echinocandin resistance in Nakaseomyces glabratus (syn. Candida glabrata), a priority yeast pathogen showing growing incidence of drug and multidrug resistance. For this, we comprehensively phenotyped a large collection (n = 77) of azole- and echinocandin-resistant strains to uncover resistance-associated stress sensitivity trade-offs. Our results show that increased stress sensitivity is a common trade-off of drug resistance in this species, with 98% of resistant strains exhibiting reduced fitness under at least one of six assayed stresses. Despite the diversity of genetic backgrounds and resistance mechanisms represented by our collection, we identified consistent trends in some resistance-associated vulnerabilities. Using multivariate modeling we uncovered complex genetic interactions underlying these trade-offs. As a proof of concept for therapeutic potential, we experimentally validated the inhibitory effects of targeting some fitness trade-offs. Cyclosporin A selectively inhibited anidulafungin-resistant strains, while NaCl effectively suppressed the emergence of fluconazole resistance. This study highlights the widespread occurrence of fitness costs associated with antifungal resistance and emphasizes their potential as a novel therapeutic strategy against this growing threat.

The function of a protein is determined by its structure, which may change dynamically in response to post-translational modifications, interaction with other molecules, or environmental factors like temperature. Limited proteolysis-coupled mass spectrometry (LiP-MS) captures such structural alterations on a proteome-wide scale via the detection of altered protease susceptibility patterns of proteins. However, this technique has so far required cell lysis, which exposes proteins to non-native conditions and can disrupt labile interactions such as those occurring within biomolecular condensates. To study protein structures directly within cells, we developed in-cell LiP-MS. We optimized conditions for introduction of proteinase K into human cells using electroporation and validated that intracellular cleavage occurs. In-cell LiP-MS captured the known binding of rapamycin to FKBP1A within the cell. Moreover, it detected global protein structural alterations upon sodium arsenite treatment and captured the structural dynamics of hundreds of proteins from biomolecular condensates with peptide level resolution and within live human cells. The data allowed monitoring of structural alterations of individual sites on the involved proteins, such as known RNA-binding and intrinsically-disordered regions, and dissected the timing of the different events. We detected known (G3BP1) and novel structural alterations of proteins from stress granules as well as from nuclear speckles and validated alteration of nuclear speckles by fluorescence microscopy and of the protein SERBP1 by polysome profiling. Our dataset further provides a resource describing the structural changes of human proteins in response to a cellular stress leading to biomolecular condensation and pinpoints structurally altered regions. Comparison of LiP-based structural fingerprints before and after cell lysis revealed which human proteins are susceptible to structural change upon cell lysis, therefore guiding the design of future experiments requiring native protein structures.

Dictyostelids are a species-rich clade of cellular slime molds that are widely found in soils and have been studied for over a century. Due to a lack of genome editing methods, most molecular research in Dictyostelids has focused on only a single species, Dictyostelium discoideum, which has severely limited broad-scale comparative analyses. Here, we introduce the first CRISPR-Cas9 editing approach that is cloning-free, selection-free, highly efficient, and effective across Dictyostelid species that diverged millions of years ago. Depending on the CRISPR-Cas9 target site, our editing approach generates knock-out efficiencies of up to 90% and knock-in efficiencies of up to 50% without a selective marker. We show that mutants can be isolated as soon as one day post-transfection, vastly outpacing existing methods for generating knock-outs, fusion proteins, and expression reporters. Leveraging single-cell sorting and fluorescent microscopy, we could readily apply our CRISPR-Cas9 editing approach to phylogenetically distant Dictyostelid species, including those that have never been genome edited before. Our methods therefore open the door to performing broad-scale genetic interrogations across the Dictyostelids.

Persisters represent a transient, antibiotic-tolerant subpopulation within isogenic bacterial populations, contributing to infection relapses. However, the mechanisms driving persister formation and resuscitation remain elusive. Here, we developed nano-flow cytometry (nFCM)-based methods for single-cell quantification of toxin (T) RelE and antitoxin (A) RelB levels, as well as for monitoring persister states through cell wall growth. We demonstrate that bacteria elevate the T/A ratio through two distinct TA expression modalities to withstand bacteriostatic antibiotic challenge, with T/A = 1.0 as a critical threshold. Intriguingly, single-cell resuscitation dynamics revealed that subinhibitory antibiotic exposure promotes entry into a deeper dormant state characterized by elevated T/A ratios, underscoring the importance of maximizing therapeutic antibiotic concentrations. Crucially, we uncovered a triphasic detoxification process during resuscitation where progressive toxin depletion drives T/A ratio reduction to a critical proliferation-permissive threshold. Proteomic profiling unveiled that persisters with high RelE production have increased transmembrane transporter levels linked to stress response and drug efflux. Our findings offer pivotal molecular insights underlying persister transitions and underscore the need for high-throughput, single-cell analysis of these heterogeneity phenotypes.