Biology subjects

Chevrette, M.

Publications and source records attributed to Chevrette, M..

5 recordsLinked to original sources

bioRxiv2025

Using cross-species co-expression to predict metabolic interactions in microbiomes.

In microbial ecosystems, metabolic interactions are key determinants of species relative abundance and activity. Given the immense number of possible interactions in microbial communities, their experimental characterization is best guided by testable hypotheses generated through computational predictions. However, widely adopted software tools - such as those utilizing microbial co-occurrence - typically fail to highlight the pathways underlying these interactions. Bridging this gap will require methods that utilize microbial activity data to infer putative target pathways for experimental validation. In this study, we explored a novel approach by applying cross-species co-expression to predict interactions from microbial co-culture RNA-sequencing data. Specifically, we investigated the extent to which co-expression between genes and pathways of different bacterial species can predict competition, cross-feeding, and specialized metabolic interactions. Our analysis of the Mucin and Diet-based Minimal Microbiome (MDb-MM) data yielded results consistent with previous findings and demonstrated the methods potential to identify pathways that are subject to resource competition. Our analysis of the Hitchhikers of the Rhizosphere (THOR) data showed links between related specialized functions, for instance, between antibiotic and multidrug efflux system expression. Additionally, siderophore co-expression and further evidence suggested that increased siderophore production of the Pseudomonas koreensis koreenceine BGC deletion-mutant drives siderophore production in the other community members. In summary, our findings confirm the feasibility of using cross-species co-expression to predict pathways potentially involved in microbe-microbe interactions. We anticipate that the approach will also facilitate the discovery of novel gene functions through their association with other species metabolic pathways, for example, those involved in antibiotic response. ImportanceAn improved mechanistic understanding of microbial interactions can guide targeted interventions or inform the rational design of microbial communities to optimize them for applications such as pathogen control, food fermentation, and various biochemical processes. Existing methodologies for inferring the mechanisms behind microbial interactions often rely on complex model-building and are therefore sensitive to the introduction of biases from the incorporated existing knowledge and model-building assumptions. We highlight the microbial interaction prediction potential of cross-species co-expression analysis, which contrasts with these methods by its data-driven nature. We describe the utility of cross-species co-expression for various types of interactions and thereby inform future studies on use-cases of the approach and the opportunities and pitfalls that can be expected in its application.

bioinformatics↗

bioRxiv2025

Learning gene interactions and functional landscapes from entire bacterial proteomes

Unraveling complex gene interactions and understanding their functions in the genomes of bacteria will provide critical advancements in fields including bacterial genome evolution, microbiome studies, as well as drug and natural product discovery. This is a challenging problem due to the structural and functional complexity of bacterial genomes, and issues including poor gene annotation in non-model species. Language models (LMs) provide a feasible framework for learning the complex interactions among genes from a large number of publicly available, unannotated bacterial genomes. However, applications of language models have mostly been limited to developing models trained on short genomic sequences. Here, we introduce the first whole bacteria proteome foundation model to our knowledge. Our model was trained on ESM embeddings of tens of thousands of full-size proteomes and can generate contextual embeddings for individual proteins as well as embeddings representing the entire genome. We show that our model captures gene-gene interactions and genomic integrity. We further demonstrate that the learned embeddings can be used to achieve state-of-the-art performances for downstream tasks such as identifying operons, and predicting genotype-phenotype maps.

genetics↗

bioRxiv2024

Convergent Evolution of the Antimycobacterial Lasso Peptide Triculamin

Triculamin is a ribosomally synthesized and post-translationally modified peptide (RiPP) lasso peptide with potent antimycobacterial activity, produced by an unusual, non-canonical biosynthetic gene cluster (BGC). In this study, we elucidate the biosynthetic pathway of triculamin through heterologous expression and show that the biosynthesis proceeds in the presence of a precursor (triA), macrocyclase (triC), and acetyltransferase (triT). Through in vitro triT acetylation and bioactivity assays, we show that acetylation functions as a resistance mechanism. Genomic searches of triculamin BGC genes across bacteria show that triculamin is more widely distributed than previously anticipated, as triculamin-like core peptides are found in at least three phyla in contrast to previously described lasso peptides that are typically restricted to one phylum. Triculamin BGCs with both canonical and non-canonical RiPP biosynthetic genes were identified. Two strains containing canonical triculamin-like BGCs were chemically characterized and shown to produce the novel triculamin-like lasso peptides palmamin and gelatinamin, the latter of which appears to have an unprecedented additional ring formation. Detailed phylogenetic investigation of the macrocyclases from triculamin-like BGCs suggests that these molecules are products of convergent evolution. These findings broaden the evolutionary and functional landscape of lasso peptides, revealing their unexpected diversification and cross-phylum distribution.

biochemistry↗

bioRxiv2024

Surface colonization by Flavobacterium johnsoniae promotes its survival in a model microbial community

Flavobacterium johnsoniae is a ubiquitous soil and rhizosphere bacterium, but despite its abundance, the factors contributing to its success in communities are poorly understood. Using a model microbial community, The Hitchhikers Of the Rhizosphere (THOR), we determined the effects of colonization on fitness of F. johnsoniae in the community. Insertion sequencing (INSeq), a massively parallel transposon mutant screen, on sterile sand identified 25 genes likely to be important for surface colonization. We constructed in-frame deletions of nine candidate genes predicted to be involved in cell membrane biogenesis, motility, signal transduction, and transport of amino acids and lipids. All mutants poorly colonized sand, glass, and polystyrene and produced less biofilm than the wild type, indicating the importance of the targeted genes in surface colonization. Eight of the nine colonization-defective mutants were also unable to form motile biofilms, or zorbs, thereby suggesting that the affected genes play a role in group movement and linking stationary and motile biofilm formation genetically. Furthermore, we showed that deletion of colonization genes in F. johnsoniae affected its behavior and survival in THOR on surfaces, suggesting that the same traits are required for success in a multispecies microbial community. Our results provide insight into the mechanisms of surface colonization by F. johnsoniae and form the basis for further understanding its ecology in the rhizosphere. IMPORTANCEMicrobial communities direct key environmental processes through multispecies interactions. Understanding these interactions is vital for manipulating microbiomes to promote health in human, environmental, and agricultural systems. However, microbiome complexity can hinder our understanding of the underlying mechanisms in microbial community interactions. As a first step towards unraveling these interactions, we explored the role of surface colonization in microbial community interactions using THOR, a genetically tractable model community of three bacterial species, Flavobacterium johnsoniae, Pseudomonas koreensis, and Bacillus cereus. We identified F. johnsoniae genes important for surface colonization in solitary conditions and in the THOR community. Understanding the mechanisms that promote success of bacteria in microbial communities brings us closer to targeted manipulations to achieve outcomes that benefit agriculture, the environment, and human health.

microbiology↗

bioRxiv2021

Nutrient availability shifts the biosynthetic potential of soil-derived microbial communities

Secondary metabolites produced by microorganisms are the main source of antimicrobials other pharmaceutical drugs. Soil microbes have been the primary discovery source for these secondary metabolites, often producing complex organic compounds with specific biological activities. Research suggests that secondary metabolism broadly shapes microbial ecological interactions, but little is known about the factors that shape the abundance, distribution, and diversity of biosynthetic gene clusters in the context of microbial communities. In this study, we investigate the role of nutrient availability on the abundance of biosynthetic gene clusters in soil-derived microbial consortia. We found that soil microbial consortia enriched in medium with 150 mg/L of glucose and 200 mg/L of trehalose (here defined as high sugar) had more biosynthetic gene cluster and higher inhibitory activity than soil microbial consortia enriched in medium with 15 mg/L of glucose + 20 mg/L of trehalose (here defined as low sugar). Our results demonstrate that laboratory microbial communities are a promising tool to study ecology of specialized metabolites.

microbiology↗