What does this strategy enable?
Metabologenomics treats a microbial strain collection as a panel of naturally varying experiments. Genome mining defines BGCs or GCFs, metabolomics defines spectra or molecular families, and co-occurrence across strains is scored to prioritize plausible BGC–metabolite links. The strategy can reveal both known and previously uncharacterized natural-product families without requiring a prior genetic hypothesis.
Transferable workflow
Workflow
- 1
Profile a genetically diverse strain collection
Generate comparable LC-MS/MS data across many sequenced isolates or conditions so metabolite occurrence can be compared across the collection.
- 2
Mine genomes for biosynthetic gene clusters
Identify BGCs and cluster related BGCs into gene-cluster families using genome-mining tools.
- 3
Organize metabolite features or molecular families
Use molecular networking or related spectral grouping to define recurring chemical features across strains.
- 4
Score BGC–metabolite co-occurrence
Correlate the presence/abundance of metabolite features or molecular families with BGC/GCF occurrence across strains.
- 5
Prioritize and validate links
Use genetics, targeted cultivation, isolation, MS/NMR or heterologous expression to validate high-priority BGC–metabolite associations.
Connected resources
Tools used in this strategy
Strategy profile
Related objectives
Molecular-family discovery, Unknown identification
Components
Data acquisition, Preprocessing and feature extraction, Molecular networking, Metadata analysis, Statistical analysis, Biological validation
Sample types
Microbial culture or biomass
Biological context
Microbial
Scope and considerations
Designed for
- Linking specialized-metabolite chemistry to candidate biosynthetic gene clusters across strain collections.
- Prioritizing biosynthetic hypotheses before direct genetic validation.
Important requirements
- A sufficiently diverse, sequenced strain collection with comparable metabolomics data.
- Consistent identifiers linking each genome/isolate to its metabolomics samples.
Considerations
- Co-occurrence supports association, not causation; population structure and phylogeny can confound links.
- Silent BGCs and condition-dependent metabolite production create false negatives.
- The linked contemporary resources illustrate ways to implement the strategy; they are not all software used in the original publication.
Reusable resources
Publication
Goering AW, McClure RA, Doroghazi JR, et al. Metabologenomics: Correlation of Microbial Gene Clusters with Metabolites Drives Discovery of a Nonribosomal Peptide with an Unusual Amino Acid Monomer. ACS Central Science (2016).