About

Marc Chevrette is an Assistant Professor in the Department of Plant Pathology at the University of Wisconsin-Madison. He holds a PhD in Genetics and MSc degrees in Genetics and Biotechnology (Bioengineering), all from the University of Wisconsin-Madison, as well as a BSc in Molecular Biology & Bioinformatics from Rensselaer Polytechnic Institute. His research focuses broadly on secondary metabolism and its relation to interspecies interactions. He studies secondary metabolism in evolution, including genome mining and the development of computational tools to identify and characterize biosynthetic gene clusters (BGCs) in microbial genomes. His work explores the diversity and distribution of BGCs and biosynthetic enzymes across evolutionary space, as well as ancestral reconstruction of biosynthetic domains, genes, and pathways. Chevrette's research also investigates the evolutionary dynamics of enzyme specificity and promiscuity in natural systems, secondary metabolites from host-associated microbiomes such as amphibian skin, insect exoskeleton, and the rhizosphere, and soil bacteria, including those from Tiny Earth. Additionally, his lab employs metatranscriptomics and metametabolomics of synthetic microbial communities to understand genetic and regulatory mechanisms of secondary metabolite expression and interspecies interactions.

Research topics

• Biology
• Computer Science
• Bioinformatics
• Computational biology
• Biochemistry
• Genetics
• Microbiology
• Data science
• Sociology
• Ecology
• Stereochemistry
• Social Science
• Chemistry
• Political Science
• Botany
• Pharmacology
• Combinatorial chemistry
• Engineering ethics
• Engineering

Selected publications

• Community Curation of Microbial Metabolites Enables Biological Insights of Metabolomics Data

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-01-29

  articleOpen access

  Microbial metabolites play a critical role in regulating ecosystems, including the human body and its microbiota. However, understanding the physiologically relevant role of these molecules, especially through liquid chromatography tandem mass spectrometry (LC-MS/MS)-based untargeted metabolomics, poses significant challenges and often requires manual parsing of a large amount of literature, databases, and webpages. To address this gap, we established the Collaborative Microbial Metabolite Center knowledgebase (CMMC-KB), a platform that fosters collaborative efforts within the scientific community to curate knowledge about microbial metabolites. The CMMC-KB aims to collect comprehensive information about microbial molecules originating from microbial biosynthesis, drug metabolism, exposure-related molecules, food, host-derived molecules, and, whenever available, their known activities. Molecules from other sources, including host-produced, dietary, and pharmaceutical compounds, are also included. By enabling direct integration of this knowledgebase with downstream analytical tools, including molecular networking, we can deepen insights into microbiota and their metabolites, ultimately advancing our understanding of microbial ecosystems.

  PublisherOA PDFDOI

• Nematicidal indole oxazoles and chemoattractants from soil bacteria

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-01-20

  articleOpen access

  Abstract Ecological interactions between bacteria and nematodes in many environments provide a basis for the prediction that diverse bacteria produce anti-nematode compounds. The discovery of microbial secondary metabolites with broad-spectrum nematostatic or nematicidal properties can be hastened by drug screening approaches that include several nematode species and phenotypes. We cultured a collection of 22 soil-derived bacterial isolates that carry in their genomes putative pathways for production of unknown secondary metabolites. Isolates were cultured in various media to enhance natural product diversity and yield, and we evaluated culture filtrates for activity against two evolutionarily distinct nematode species: Clade V free-living nematode Caenorhabditis elegans and Clade III mammalian parasitic nematodes in the genus Brugia . Partitioned extracts from Pseudomonas sp. strain TE4607 stunted C. elegans development and caused motility defects in both blood-circulating larval and adult stages of Brugia . The primary active compound was identified as labradorin 1, an indole with known antibacterial and anticancer properties that had not been previously described as affecting nematodes. Notably, filtrates of Pseudomonas sp. TE4607 cultures attracted free-living nematodes in sensory assays, adding to evidence that certain Pseudomonas species modulate the behavior of free-living nematodes. These findings underscore the need to further explore the link between nematode sensory responses and whole-organism effects of microbial metabolites, with potential applications in anthelmintic discovery. Abstract Figure

  PublisherOA PDFDOI

• The antimicrobial potential from insect microbiomes of Streptomyces

  The Catalogue of Life · 2026-02-16

  datasetOpen access1st authorCorresponding
  PublisherDOI

• Antinociceptive Benzothiazine and Benzothiazole Natural Products from Marine <i>Croceibacter atlanticus</i>

  Journal of Natural Products · 2026-04-09

  article

  With the goal of identifying new therapeutic leads to treat pain, we screened a library of microbial chemical extracts using the Embryonic Zebrafish Irritant-evoked Hyperlocomotion (EZIH) assay, an in vivo phenotypic assay for analgesic/antinociceptive activity. We found that extracts from the bacterium Croceibacter atlanticus substantially altered behavioral responses. Using behavior-guided fractionation to identify the active partitions, we determined that the known 1,4-benzothiazin-3-one BPSS2111–2113:C and the new benzothiazole croceithiazole A suppress behavioral responses to painful and/or nociceptive stimuli without apparent acute toxicity. Further investigation with radioligand binding assays revealed that BPSS2111–2113:C shows selective binding to both the serotonin receptor 5-HT2B and the benzodiazepine site of GABAA receptors, while croceithiazole A appears to bind the peripheral benzodiazepine receptor. These results suggest that BPSS2111–2113:C and croceithiazole A are interesting leads as analgesics, sedatives, anesthetics, or molecular probes.

  PublisherDOI

• Integrating targeted genome mining and structure-guided modeling reveals unexplored 7-deazapurine-containing pathways

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-19

  articleOpen access

  Abstract 7-deazapurines are nucleoside analogs that play key roles in nucleic acid modification and can serve as building blocks for diverse, bioactive secondary metabolites. Despite their biological significance, their biosynthetic diversity, distribution, and enzymatic determinants of structural diversification remain poorly understood. Here, we leverage large-scale targeted genome mining, phylogenetic, and network analysis to explore 7-deazapurine-containing pathways across ∼2 million bacterial genomes. We identified over 900 candidate biosynthetic gene clusters (BGCs), grouped into more than 100 families, most of which remain uncharacterized. These GATOR-GC-predicted BGCs were predominantly found in Streptomyces . We then examined enzyme-substrate interactions in three representative pathways: (i) peptidyl-deazapurines, (ii) huimycin, and (iii) dapiramicin A. Molecular docking and molecular dynamics (MD) simulations recapitulated known enzyme-substrate interactions and highlighted candidate catalytic residues governing amide bond formation, methylation, and glycosylation. Using this genome- and structure-guided framework, we identified a candidate BGC for dapiramicin A and proposed tailoring steps, including scaffold methylation and deoxy-sugar formation. These findings expand the known diversity of 7-deazapurine-containing BGCs and demonstrate how integrating genome mining with structural modeling can link BGCs to chemical function, providing a foundation for discovering and characterizing 7-deazapurine-containing secondary metabolites. Graphical abstract

  PublisherDOI

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

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-16

  preprintOpen access

  Abstract 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 method’s 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. Importance An 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.

  PublisherOA PDFDOI

• Fueling Future Antibiotic Discovery: An Analysis of Taxonomic and Biosynthetic Diversity in Antimicrobial-Producing Soil Bacterial Isolates

  UF Journal of Undergraduate Research · 2025-11-05

  articleOpen accessSenior author

  More than 80% of clinical antibiotics are compounds produced by soil bacteria, and there are many more unknown pathogen-inhibiting compounds encoded by clusters of co-localized genes (i.e., biosynthetic gene clusters) in soil bacterial genomes. To find novel antimicrobial drug candidates using a genome-level approach, biosynthetic gene clusters (BGCs) can be identified and compared against a database of known BGCs to infer what secondary metabolite(s) may be encoded. This study aims to uncover the diversity of these BGCs and characterize evolutionary patterns of BGC distribution across a phylogeny of 305 soil-derived bacterial isolates. BGCs were predicted through antiSMASH and taxonomic classification was conducted via GTDB-tk. OrthoFinder was used to perform a multi-locus sequence analysis and phylogenetic tree construction. The results of this investigation, an annotated phylogeny with mapped BGC data, will provide future antibiotic discovery researchers with deeper genetic insight into the biosynthetic potential and evolutionary patterns of BGCs for the investigated bacterial strains.

  PublisherDOI

• Learning gene interactions and functional landscapes from entire bacterial proteomes

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-19 · 1 citations

  preprintOpen access

  Abstract 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.

  PublisherOA PDFDOI

• Targeted genome mining with GATOR-GC maps the evolutionary landscape of biosynthetic diversity

  Nucleic Acids Research · 2025-06-14 · 6 citations

  articleOpen accessSenior author

  Gene clusters, groups of physically adjacent genes that work collectively, are pivotal to bacterial fitness and valuable in biotechnology and medicine. While various genome mining tools can identify and characterize gene clusters based on homology, they often overlook their evolutionary diversity, a crucial factor in revealing novel cluster functions and applications. To address this gap, we developed GATOR-GC, a targeted, homology-based genome mining tool that enables comprehensive and flexible exploration of gene clusters in a single execution. We show that GATOR-GC identified a diversity of over 4 million gene clusters similar to experimentally validated biosynthetic gene clusters (BGCs) that antiSMASH version 7 fails to detect. To highlight the utility of GATOR-GC, we identified previously uncharacterized co-occurring conserved genes potentially involved in mycosporine-like amino acid biosynthesis and mapped the taxonomic and evolutionary patterns of genomic islands that modify DNA with 7-deazapurines. Additionally, with its proximity-weighted similarity scoring, GATOR-GC successfully differentiated BGCs of the FK family of metabolites (e.g. rapamycin, FK506/520) according to their chemistries. When benchmarked on the FK-family of BGCs, GATOR-GC outperformed cblaster, zol, and fai. We anticipate GATOR-GC will be a valuable tool to assess gene cluster diversity for targeted, exploratory, and flexible genome mining.

  PublisherOA PDFDOI

• Targeted genome mining with GATOR-GC maps the evolutionary landscape of biosynthetic diversity

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-02-28 · 1 citations

  preprintOpen accessSenior authorCorresponding

  Gene clusters, groups of physically adjacent genes that work collectively, are pivotal to bacterial fitness and valuable in biotechnology and medicine. While various genome mining tools can identify and characterize gene clusters, they often overlook their evolutionary diversity, a crucial factor in revealing novel cluster functions and applications. To address this gap, we developed GATOR-GC, a targeted genome mining tool that enables comprehensive and flexible exploration of gene clusters in a single execution. We show that GATOR-GC identified a diversity of over 4 million gene clusters similar to experimentally validated biosynthetic gene clusters (BGCs) that other tools fail to detect. To highlight the utility of GATOR-GC, we identified previously uncharacterized co-occurring conserved genes potentially involved in mycosporine-like amino acid biosynthesis and mapped the taxonomic and evolutionary patterns of genomic islands that modify DNA with 7-deazapurines. Additionally, with its proximity-weighted similarity scoring, GATOR-GC successfully differentiated BGCs of the FK-family of metabolites (e.g., rapamycin, FK506/520) according to their chemistries. We anticipate GATOR-GC will be a valuable tool to assess gene cluster diversity for targeted, exploratory, and flexible genome mining. GATOR-GC is available at https://github.com/chevrettelab/gator-gc.

  PublisherOA PDFDOI

Frequent coauthors

• Cameron R. Currie

  University of Wisconsin–Madison

  44 shared

• Jo Handelsman

  University of Wisconsin–Madison

  35 shared

• Tim S. Bugni

  University of Wisconsin–Madison

  23 shared

• Timothy J. Triche

  Van Andel Institute

  20 shared

• Amanda Hurley

  Wisconsin Institutes for Discovery

  20 shared

• Casey S. Greene

  18 shared

• Francisco Barona‐Gómez

  Center for Research and Advanced Studies of the National Polytechnic Institute

  18 shared

• Gabriel L. Lozano

  Wisconsin Institutes for Discovery

  16 shared

Labs

• Chevrette LabPI

Education

• Ph.D., Plant Pathology

  University of Wisconsin-Madison

  1990

• M.S., Plant Pathology

  University of Wisconsin-Madison

  1986

• B.S., Botany

  University of Wisconsin-Madison

  1982