Filling gaps in bacterial catabolic pathways with computation and high-throughput genetics

Abstract

To discover novel catabolic enzymes and transporters, we combined high-throughput genetic data from 29 bacteria with an automated tool to find gaps in their catabolic pathways. GapMind for carbon sources automatically annotates the uptake and catabolism of 62 compounds in bacterial and archaeal genomes. For the compounds that are utilized by the 29 bacteria, we systematically examined the gaps in GapMind’s predicted pathways, and we used the mutant fitness data to find additional genes that were involved in their utilization. We identified novel pathways or enzymes for the utilization of glucosamine, citrulline, myo-inositol, lactose, and phenylacetate, and we annotated 299 diverged enzymes and transporters. We also curated 125 proteins from published reports. For the 29 bacteria with genetic data, GapMind finds high-confidence paths for 85% of utilized carbon sources. In diverse bacteria and archaea, 38% of utilized carbon sources have high-confidence paths, which was improved from 27% by incorporating the fitness-based annotations and our curation. GapMind for carbon sources is available as a web server (http://papers.genomics.lbl.gov/carbon) and takes just 30 seconds for the typical genome.

Systems biology illuminates alternative metabolic niches in the human gut microbiome

“…Initial testing indicated that the model was incapable of biomass production in EDM1 media, so we performed additional curation of model reactions and transporters ( Table S3 ). We curated the reconstruction based on genome annotations from multiple sources (Henry et al, 2010; Pascal Andreu et al, 2021; Price et al, 2022) and added transporters for strongly depleted and produced compounds that were identified with high confidence in our metabolomics data. Throughout this process, we compared model results with experimentally observed growth in chemically defined media conditions, using these results to inform the curation process and add missing reactions where supported by experimental data.…”

“…In order to facilitate interpretation of FBA results and avoid excess gap-filled reactions, we used the simpler E. lenta ATCC 25559 type strain reconstruction as the basis for subsequent curation and analysis. We transferred reactions present in the DSM 2243 reconstruction into this version if they were supported by genome annotations from other sources (Prokka (Seemann, 2014), GapMind (Price et al, 2022)) and/or by experimental growth or metabolomics data. We also performed several additional custom curations.…”

“…Transporters were added for metabolites identified with high confidence (Metabolomics Standards Initiative level 1) and detected as secreted or depleted with a log 2 fold change greater than 2 in the stationary phase strain collection metabolomics dataset (Figure 4 ) . Several pathways were also modified based on growth assay results and/or pathway annotation software (Price et al, 2022) and (Pascal Andreu et al, 2021)). Curations were checked for viable growth in EDM1 using flux balance analysis.…”

“…The copyright holder for this preprint this version posted September 19, 2022. ; https://doi.org/10.1101/2022.09.19.508335 doi: bioRxiv preprint were only curated to ensure growth in EDM1 and to allow import/export based on metabolomics data, but not based on genome analysis with GapMind (Price et al, 2022) or the results of leave-one-out growth experiments, since those were only performed using the type strain. A complete summary of all curation steps is found in Table S3.…”

“…GapMind (Price et al, 2022)) and/or by experimental growth or metabolomics data. We also performed several additional custom curations.…”