Molecular Systems Biology最新文献

Modeling and simulating gene regulatory networks (GRNs) is crucial for understanding biological processes, predicting system behavior, interpreting experimental data and guiding the design of synthetic systems. In synthetic biology, GRNs are fundamental to enable the design and control of complex functions. However, GRN simulations can be time-consuming and often require specialized expertise. To address this challenge, we developed GRN_modeler - a user-friendly tool with a graphical user interface that enables users without programming experience to create phenomenological models, while also offering command-line support for advanced users. GRN_modeler supports the analysis of both dynamical behaviors and spatial pattern formation. We demonstrate its versatility through several examples in synthetic biology, including the design of novel oscillator families capable of robust oscillation with an even number of nodes, complementing the classical repressilator family, which requires odd-numbered nodes. Furthermore, we showcase how GRN_modeler allowed us to develop a light-detecting biosensor in Escherichia coli that tracks light intensity over several days and leaves a record in the form of ring patterns in bacterial colonies.

Most organismal traits result from the complex interplay of many genetic and environmental factors, making their prediction difficult. Here, we used machine learning (ML) models to explore phenotype predictions for 223 traits measured across 1011 genome-sequenced Saccharomyces cerevisiae strains isolated worldwide. We benchmarked a ML pipeline with multiple linear and non-linear models to predict phenotypes from genotypes and gene expression, and determined gradient boosting machines as the best-performing model. Gene function disruption scores and gene presence/absence emerged as best predictors, suggesting a considerable contribution of the accessory genome in controlling phenotypes. The prediction accuracy broadly varied among phenotypes, with stress resistance being easier to predict compared to growth across nutrients. ML identified relevant genomic features linked to phenotypes, including high-impact variants with established relationships to phenotypes, despite these being rare in the population. Near-perfect accuracies were achieved when other phenomics data mostly in similar conditions were used, suggesting that useful information can be conveyed across phenotypes. Overall, our study underscores the power of ML to interpret the functional outcome of genetic variants.

Overflow metabolism refers to the widespread phenomenon of cells excreting metabolic by-products into their environment. Although overflow is observed in virtually all living organisms, it has been studied independently and given different names in different species. This review highlights emerging evidence that overflow metabolism is governed by common principles in prokaryotic and eukaryotic organisms. We examine the similarities and specificities in the structure, function, and regulation of overflow pathways in bacterial, yeast, and mammalian cells, with a focus on model species and common by-products. Our reinterpretation of previous findings points to the existence of universal principles governing overflow fluxes. We also emphasize the need to reconsider the roles of overflow metabolites, not as cellular stress-inducing toxic waste, but as nutrients and regulators, influencing metabolism at both cellular and community levels, often to the benefit of the producing cells. Finally, we review prevailing theories of overflow metabolism and explore avenues toward a potential unified theory of overflow. This review offers fundamental insights into this widespread metabolic process and proposes a conceptual foundation for future research.

Osimertinib is the first-line therapy for EGFR-mutated non-small cell lung cancer, but acquired resistance emerges in most patients and remains a major barrier for complete cure. This phenomenon is most likely associated with the drug-tolerant persister (DTP) cell phenotype, a reversible state that enables survival under treatment and leads to irreversible drug resistance. To uncover the molecular mechanism driving this distinct phenotype, we applied data-independent acquisition mass spectrometry (DIA-MS) to establish the dynamic proteomic and phosphoproteomic landscape in the osimertinib DTPs. While osimertinib initially blocks EGFR signaling, ribosome synthesis and protein translation related pathways arise in DTP phase, and resistance developed through the reactivation of EGFR downstream pathways and anti-apoptotic mechanisms such as YAP1 and mTOR-BAD hyperphosphorylation, as validated by growth combination assays. Kinase enrichment revealed elevated phosphorylation of multiple CDK1 substrates in DTP phase and pharmacological or genetic inhibition of CDK1-mediated SAMHD1 activation significantly impair DTP growth and survival. This study illuminates the dynamic landscape underlying the DTPs biology and identifies biomarker and new targets to potentially prevent or delay the onset of resistance.

Viruses are genetic parasites of cellular life. Tolerance to genetic change, high mutation rates, adaptations to hosts, and immune escape have driven extensive sequence divergence of viral genes, hampering phylogenetic inference and functional annotation. Protein structure, however, is more conserved, allowing searches for distant homologs and revealing otherwise obscured evolutionary histories. Viruses are underrepresented in current protein structure databases, but this can be addressed by recent advances in machine learning. Using AlphaFold2-ColabFold and ESMFold, we predicted structures for >85,000 proteins from >4400 viruses, expanding viral coverage 30 times compared to experimental structures. Using this data, we map form and function across the human and animal virosphere and examine the evolutionary history of viral class-I fusion glycoproteins, revealing the potential origins of coronavirus spike glycoprotein. Our database, Viro3D ( https://viro3d.cvr.gla.ac.uk/ ), will allow the virology community to fully benefit from the structure prediction revolution, facilitating fundamental molecular virology and structure-informed design of therapies and vaccines.

Enzymes in a pathway often form metabolons through weak protein-protein interactions (PPI) that localize and protect labile metabolites. Due to their transient nature, the structural architecture of these enzyme assemblies has largely remained elusive, limiting our abilities to re-engineer novel metabolic pathways. Here, we delineate a complete PPI map of 1225 interactions in the E. coli 1-carbon metabolism pathway using bimolecular fluorescence complementation that can capture transient interactions in vivo and show strong intra- and inter-pathway clusters within the folate and purine biosynthesis pathways. Scanning mutagenesis experiments along with AlphaFold predictions and metadynamics simulations reveal that most proteins use conserved "dedicated" interfaces distant from their active sites to interact with multiple partners. Diffusion-reaction simulations with shared interaction surfaces and realistic PPI networks reveal a dramatic speedup in metabolic pathway fluxes. Overall, this study sheds light on the fundamental features of metabolon biophysics and structural aspects of transient binary complexes.

Evolutionary repair refers to the compensatory evolution that follows perturbations in cellular processes. While evolutionary trajectories are often reproducible, other studies suggest they are shaped by genotype-by-environment (GxE) interactions. Here, we test the predictability of evolutionary repair in response to DNA replication stress-a severe perturbation impairing the conserved mechanisms of DNA synthesis, resulting in genetic instability. We conducted high-throughput experimental evolution on Saccharomyces cerevisiae experiencing constitutive replication stress, grown under different glucose availability. We found that glucose levels impact the physiology and adaptation rate of replication stress mutants. However, the genetics of adaptation show remarkable robustness across environments. Recurrent mutations collectively recapitulated the fitness of evolved lines and are advantageous across macronutrient availability. We also identified a novel role of the mediator complex of RNA polymerase II in adaptation to replicative stress. Our results highlight the robustness and predictability of evolutionary repair mechanisms to DNA replication stress and provide new insights into the evolutionary aspects of genome stability, with potential implications for understanding cancer development.

Deep mutational scanning (DMS) can systematically assess the effects of thousands of genetic variants in a single assay, providing insights into protein function, evolution, host-pathogen interactions, and clinical impacts. Accurate scoring of variant effects is crucial, yet the diversity of tools and experimental designs contributes considerable heterogeneity that complicates data analysis. Here, we review and compare 12 computational tools for processing DMS sequencing data and scoring variant effects. We systematically outline each tool's statistical approaches, supported experimental designs, input/output requirements, software implementation, visualisation capabilities, and key assumptions. By highlighting the strengths and limitations of these tools, we hope to guide researchers in selecting methods appropriate for their specific experiments. Furthermore, we discuss current challenges, including the need for standardised analysis protocols and sustainable software maintenance, as well as opportunities for future methods development. Ultimately, this review seeks to advance the application and adoption of DMS, facilitating deeper biological understanding and improved clinical translation.

The activity of bacterial transcription factors (TFs) is typically modulated through direct interactions with small molecules. However, these input signals remain unknown for most TFs, even in well-studied model bacteria. Identifying these signals typically requires tedious experiments for each TF. Here, we develop a systematic workflow for the identification of TF input signals in bacteria based on metabolomics and transcriptomics data. We inferred the activity of 173 TFs from published transcriptomics data and determined the abundance of 279 metabolites across 40 matched experimental conditions in Escherichia coli. By correlating TF activities with metabolite abundances, we successfully identified previously known TF-metabolite interactions and predicted novel TF effector metabolites for 41 TFs. To validate our predictions, we conducted in vitro assays and confirmed a predicted effector metabolite for LeuO. As a result, we established a network of 80 regulatory interactions between 71 metabolites and 41 E. coli TFs. This network includes 76 novel interactions that encompass a diverse range of chemical classes and regulatory patterns, bringing us closer to a comprehensive TF regulatory network in E. coli.