Filling gaps in bacterial catabolic pathways with computation and high-throughput genetics.
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 geno...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-04-01
|
| Series: | PLoS Genetics |
| Online Access: | https://doi.org/10.1371/journal.pgen.1010156 |
| _version_ | 1811257801763192832 |
|---|---|
| author | Morgan N Price Adam M Deutschbauer Adam P Arkin |
| author_facet | Morgan N Price Adam M Deutschbauer Adam P Arkin |
| author_sort | Morgan N Price |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T18:03:17Z |
| format | Article |
| id | doaj.art-55e631ee343046f4a1b51e3e24b557a1 |
| institution | Directory Open Access Journal |
| issn | 1553-7390 1553-7404 |
| language | English |
| last_indexed | 2024-04-12T18:03:17Z |
| publishDate | 2022-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Genetics |
| spelling | doaj.art-55e631ee343046f4a1b51e3e24b557a12022-12-22T03:22:04ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042022-04-01184e101015610.1371/journal.pgen.1010156Filling gaps in bacterial catabolic pathways with computation and high-throughput genetics.Morgan N PriceAdam M DeutschbauerAdam P ArkinTo 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.https://doi.org/10.1371/journal.pgen.1010156 |
| spellingShingle | Morgan N Price Adam M Deutschbauer Adam P Arkin Filling gaps in bacterial catabolic pathways with computation and high-throughput genetics. PLoS Genetics |
| title | Filling gaps in bacterial catabolic pathways with computation and high-throughput genetics. |
| title_full | Filling gaps in bacterial catabolic pathways with computation and high-throughput genetics. |
| title_fullStr | Filling gaps in bacterial catabolic pathways with computation and high-throughput genetics. |
| title_full_unstemmed | Filling gaps in bacterial catabolic pathways with computation and high-throughput genetics. |
| title_short | Filling gaps in bacterial catabolic pathways with computation and high-throughput genetics. |
| title_sort | filling gaps in bacterial catabolic pathways with computation and high throughput genetics |
| url | https://doi.org/10.1371/journal.pgen.1010156 |
| work_keys_str_mv | AT morgannprice fillinggapsinbacterialcatabolicpathwayswithcomputationandhighthroughputgenetics AT adammdeutschbauer fillinggapsinbacterialcatabolicpathwayswithcomputationandhighthroughputgenetics AT adamparkin fillinggapsinbacterialcatabolicpathwayswithcomputationandhighthroughputgenetics |