Molecular Systems Biology最新文献

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.

Essentiality studies have traditionally focused on coding regions, often overlooking other small genetic regulatory elements. To address this, we combined transposon libraries containing promoter or terminator sequences to obtain a high-resolution essentiality map of a genome-reduced bacterium, at near-single-nucleotide precision when considering non-essential genes. By integrating temporal transposon-sequencing data by k-means unsupervised clustering, we present a novel essentiality assessment approach, providing dynamic and quantitative information on the fitness contribution of different genomic regions. We compared the insertion tolerance and persistence of the two engineered libraries, assessing the local impact of transcription and termination on cell fitness. Essentiality assessment at the local base-level revealed essential protein domains and small genomic regions that are either essential or inaccessible to transposon insertion. We also identified structural regions within essential genes that tolerate transposon disruptions, resulting in functionally split proteins. Overall, this study presents a nuanced view of gene essentiality, shifting from static and binary models to a more accurate perspective. Additionally, it provides valuable insights for genome engineering and enhances our understanding of the biology of genome-reduced cells.

Xenobiotics like drugs are recognised as key influencers of gut bacterial growth. Yet, their impact on the production of metabolites involved in microbiota-host interactions is largely unknown. Here, we report the impact of commonly ingested xenobiotics-therapeutic drugs, pesticides, industrial chemicals, and sweeteners-on gut bacterial amine metabolism. We tested >13,000 interactions between >1700 compounds and 4 amine-producing bacteria, uncovering 747 xenobiotic-species-metabolite interactions involving 275 compounds. These compounds span all tested classes, with the majority being antimicrobial drugs. In 66% of the cases, amine production was correlated with growth, while the rest showed xenobiotic-induced decoupling between growth and metabolite production. The latter includes transient bursts in polyamine production by Escherichia coli in response to β-lactam antibiotics, and overproduction of aromatic amines by Ruminococcus gnavus treated with 15 diverse chemicals. Xenobiotics thus can disrupt metabolic homeostasis in both growth-dependent and -independent manner. We also find that metabolic responses have non-monotonic dose-dependency, resulting in lower doses sometimes having stronger effects. Our results bring forward the potential of common xenobiotics to disrupt the amine metabolism of gut bacteria.

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.