Molecular Systems Biology最新文献

Filamentous protein assemblies are essential for cellular functions but can also form aberrantly through mutations that induce self-interactions between folded protein subunits. These assemblies, which we refer to as agglomerates, differ from aggregates and amyloids that arise from protein misfolding. While cells have quality control mechanisms to identify, buffer, and eliminate aggregates, it is unknown whether similar mechanisms exist for agglomerates. Here, we define and characterize this distinct class of assemblies formed by the polymerization of folded proteins. To systematically assess their cellular impact, we developed a simple in-cell assay that distinguishes agglomerates from aggregates based on co-assembly with wild-type subunits. Unlike misfolded aggregates, we show that agglomerates retain their folded state, do not colocalize with the proteostasis machinery, and are not ubiquitinated. Moreover, agglomerates cause no detectable growth defects. Quantitative proteomics also revealed minor changes in protein abundance in cells expressing agglomerates. These results position agglomerates as a structurally and functionally distinct class of protein assemblies that are largely inert in cells, highlighting their potential as building blocks for intracellular engineering and synthetic biology.

Gene expression programs that establish and maintain specific cellular states are orchestrated through a regulatory network composed of transcription factors, cofactors, and chromatin regulators. Dysregulation of this network can lead to a broad range of diseases by altering gene programs. This article presents LaGrACE, a novel method designed to estimate dysregulation of gene programs combining omics data with clinical information. This approach facilitates the grouping of samples exhibiting similar patterns of gene program dysregulation, thereby enhancing the discovery of underlying molecular mechanisms in disease subpopulations. We rigorously evaluated LaGrACE's performance using synthetic data, bulk RNA-seq clinical datasets (breast cancer, chronic obstructive pulmonary disease (COPD)), and single-cell RNA-seq drug perturbation datasets. Our findings demonstrate that LaGrACE is exceptionally robust in identifying biologically meaningful and prognostic molecular subtypes. In addition, it effectively discerns drug response signals at a single-cell resolution. Moreover, the COPD analysis uncovered a new role of LEF1 regulator in COPD molecular mechanisms associated with mortality. Collectively, these results underscore the utility of LaGrACE as a valuable tool for elucidating the underlying mechanisms of diseases.

De novo protein design is of fundamental interest to synthetic biology, with a plethora of computational methods of various degrees of generality developed in recent years. Here, we introduce AlphaDesign, a hallucination-based computational framework for de novo protein design developed with maximum generality and usability in mind, which combines AlphaFold with autoregressive diffusion models to enable rapid generation and computational validation of proteins with controllable interactions, conformations and oligomeric state without the requirement for class-dependent model re-training or fine-tuning. We apply our framework to design and systematically validate in vivo active inhibitors of a family of bacterial phage defense systems with toxic effectors called retrons, paving the way towards efficient, rational design of novel proteins as biologics.

The in vivo continuous evolution system OrthoRep (orthogonal replication) is a powerful strategy for rapid enzyme evolution in Saccharomyces cerevisiae that diversifies genes at a rate exceeding the endogenous genome mutagenesis rate by several orders of magnitude. However, it is difficult to neofunctionalize genes using OrthoRep partly because of the way selection pressures are applied. Here we combine OrthoRep with optogenetics in a selection strategy we call OptoRep, which allows fine-tuning of selection pressure with light. With this capability, we evolved a truncated form of the endogenous monocarboxylate transporter JEN1 (JEN1t) into a de novo mevalonate importer. We demonstrate the functionality of the evolved JEN1t (JEN1t^Y180C/G) in the production of farnesene, a renewable aviation biofuel, from mevalonate fed to fermentation media or produced by microbial consortia. This study shows that the light-induced complementation of OptoRep may improve the ability to evolve functions not currently accessible for selection, while its fine tunability of selection pressure may allow the continuous evolution of genes whose desired function has a restrictive range between providing effective selection and cellular viability.

Generating longitudinal and multi-layered big biological data is crucial for effectively implementing artificial intelligence (AI) and systems biology approaches in characterising whole-body biological functions in health and complex disease states. Big biological data consists of multi-omics, clinical, wearable device, and imaging data, and information on diet, drugs, toxins, and other environmental factors. Given the significant advancements in omics technologies, human metabologenomics, and computational capabilities, several multi-omics studies are underway. Here, we first review the recent application of AI and systems biology in integrating and interpreting multi-omics data, highlighting their contributions to the creation of digital twins and the discovery of novel biomarkers and drug targets. Next, we review the multi-omics datasets generated worldwide to reveal interactions across multiple biological layers of information over time, which enhance precision health and medicine. Finally, we address the need to incorporate big biological data into clinical practice, supporting the development of a clinical decision support system essential for AI-driven hospitals and creating the foundation for an AI and systems biology-based healthcare model.

Characterizing the contribution of mutators to mutation accumulation is essential for understanding cellular adaptation and diseases like cancer. By measuring single and double mutation rates, including point mutations, segmental duplications, and reciprocal translocations, we found that wild-type yeast colonies exhibit double mutation rates up to 17 times higher than expected from experimentally determined single mutation rates. These double mutants retained wild-type mutation rates, indicating they originated from genetically normal cells that transiently expressed a mutator phenotype. Numerical simulations suggest that transient mutator subpopulations likely consist of less than a few thousand cells, and experience high-intensity mutational bursts for less than five generations. Most double mutations accumulated sequentially across cell cycles, with simultaneous acquisition being rare and likely linked to systemic genomic instability. Additionally, we explored the genetic control of transient hypermutation and found that the excess of double mutants can be modulated by replication stress and the DNA damage tolerance pathway. Our findings suggest that transient mutators play a significant role in genomic instability and contribute to the mutational load accumulating in growing isogenic populations.

Synthetic biology approaches such as whole-cell biosensing and 'sense-and-respond' therapeutics aim to enlist the vast sensing repertoire of gut microbes to drive cutting-edge clinical and research applications. However, well-characterised circuit components that sense health- and disease-relevant conditions within the gut remain limited. Here, we extend the flexibility and power of a biosensor screening platform using bacterial memory circuits. We construct libraries of sensory components sourced from diverse gut bacteria using a bespoke two-component system identification and cloning pipeline. Tagging unique strains using a hypervariable DNA barcode enables parallel tracking of thousands of unique clones, corresponding to ~150 putative biosensors, in a single experiment. Evaluating sensor activity and performance heterogeneity across various in vitro and in vivo conditions using mouse models, we identify several biosensors of interest. Validated hits include biosensors with relevance for autonomous control of synthetic functions within the mammalian gut and for non-invasive monitoring of inflammatory disease using faecal sampling. This approach will promote rapid biosensor engineering to advance the development of synthetic biology tools for deployment within complex environments.

Precision oncology led to the establishment and widespread application of molecular tumor boards (MTBs)-multidisciplinary units combining molecular and clinical assessment of individual cancer cases for swift selection of personalized treatments. Whole-exome or gene panel sequencing, combined with transcriptomic, immunohistochemical, and other molecular analyses, often permits dissection of molecular drivers of a tumor and identification of its potential targetable vulnerabilities, instructing clinical oncologists on sometimes unconventional treatment options. However, cancer drivers are often unleashed mutation-independently, especially in breast and gynecological cancers, and deleterious mutations are not always pathogenic. To complement the MTB arsenal, we chart here the molecular toolset we call Signalomics that permits fast and robust assessment of a panel of oncogenic signaling pathways in fresh tumor samples. Using transcriptional reporters introduced in primary tumor cells, this approach identifies the pathways overactivated in a given tumor and validates their sensitivity to targeted therapies, providing actionable insights for personalized treatment strategies. Integration of Signalomics into MTB workflows bridges the gap between molecular profiling and functional pathway analysis, refining clinical treatment decisions and advancing precision oncology.