The Integration of Genome Mining, Comparative Genomics, and Functional Genetics for Biosynthetic Gene Cluster Identification
Antimicrobial resistance is a worldwide health crisis for which new antibiotics are needed. One strategy for antibiotic discovery is identifying unique antibiotic biosynthetic gene clusters that may produce novel compounds. The aim of this study was to demonstrate how an integrated approach that com...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2020-12-01
|
| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2020.600116/full |
| _version_ | 1818880316369010688 |
|---|---|
| author | Ashley N. Williams Naveen Sorout Alexander J. Cameron John Stavrinides |
| author_facet | Ashley N. Williams Naveen Sorout Alexander J. Cameron John Stavrinides |
| author_sort | Ashley N. Williams |
| collection | DOAJ |
| description | Antimicrobial resistance is a worldwide health crisis for which new antibiotics are needed. One strategy for antibiotic discovery is identifying unique antibiotic biosynthetic gene clusters that may produce novel compounds. The aim of this study was to demonstrate how an integrated approach that combines genome mining, comparative genomics, and functional genetics can be used to successfully identify novel biosynthetic gene clusters that produce antimicrobial natural products. Secondary metabolite clusters of an antibiotic producer are first predicted using genome mining tools, generating a list of candidates. Comparative genomic approaches are then used to identify gene suites present in the antibiotic producer that are absent in closely related non-producers. Gene sets that are common to the two lists represent leading candidates, which can then be confirmed using functional genetics approaches. To validate this strategy, we identified the genes responsible for antibiotic production in Pantoea agglomerans B025670, a strain identified in a large-scale bioactivity survey. The genome of B025670 was first mined with antiSMASH, which identified 24 candidate regions. We then used the comparative genomics platform, EDGAR, to identify genes unique to B025670 that were not present in closely related strains with contrasting antibiotic production profiles. The candidate lists generated by antiSMASH and EDGAR were compared with standalone BLAST. Among the common regions was a 14 kb cluster consisting of 14 genes with predicted enzymatic, transport, and unknown functions. Site-directed mutagenesis of the gene cluster resulted in a reduction in antimicrobial activity, suggesting involvement in antibiotic production. An integrated approach that combines genome mining, comparative genomics, and functional genetics yields a powerful, yet simple strategy for identifying potentially novel antibiotics. |
| first_indexed | 2024-12-19T14:44:02Z |
| format | Article |
| id | doaj.art-99722be9eb9f45cc956619845cf31763 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-19T14:44:02Z |
| publishDate | 2020-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-99722be9eb9f45cc956619845cf317632022-12-21T20:16:59ZengFrontiers Media S.A.Frontiers in Genetics1664-80212020-12-011110.3389/fgene.2020.600116600116The Integration of Genome Mining, Comparative Genomics, and Functional Genetics for Biosynthetic Gene Cluster IdentificationAshley N. WilliamsNaveen SoroutAlexander J. CameronJohn StavrinidesAntimicrobial resistance is a worldwide health crisis for which new antibiotics are needed. One strategy for antibiotic discovery is identifying unique antibiotic biosynthetic gene clusters that may produce novel compounds. The aim of this study was to demonstrate how an integrated approach that combines genome mining, comparative genomics, and functional genetics can be used to successfully identify novel biosynthetic gene clusters that produce antimicrobial natural products. Secondary metabolite clusters of an antibiotic producer are first predicted using genome mining tools, generating a list of candidates. Comparative genomic approaches are then used to identify gene suites present in the antibiotic producer that are absent in closely related non-producers. Gene sets that are common to the two lists represent leading candidates, which can then be confirmed using functional genetics approaches. To validate this strategy, we identified the genes responsible for antibiotic production in Pantoea agglomerans B025670, a strain identified in a large-scale bioactivity survey. The genome of B025670 was first mined with antiSMASH, which identified 24 candidate regions. We then used the comparative genomics platform, EDGAR, to identify genes unique to B025670 that were not present in closely related strains with contrasting antibiotic production profiles. The candidate lists generated by antiSMASH and EDGAR were compared with standalone BLAST. Among the common regions was a 14 kb cluster consisting of 14 genes with predicted enzymatic, transport, and unknown functions. Site-directed mutagenesis of the gene cluster resulted in a reduction in antimicrobial activity, suggesting involvement in antibiotic production. An integrated approach that combines genome mining, comparative genomics, and functional genetics yields a powerful, yet simple strategy for identifying potentially novel antibiotics.https://www.frontiersin.org/articles/10.3389/fgene.2020.600116/fullPantoeasecondary metabolitesbiosynthetic gene clusteragar overlay assaygenome miningantiSMASH |
| spellingShingle | Ashley N. Williams Naveen Sorout Alexander J. Cameron John Stavrinides The Integration of Genome Mining, Comparative Genomics, and Functional Genetics for Biosynthetic Gene Cluster Identification Frontiers in Genetics Pantoea secondary metabolites biosynthetic gene cluster agar overlay assay genome mining antiSMASH |
| title | The Integration of Genome Mining, Comparative Genomics, and Functional Genetics for Biosynthetic Gene Cluster Identification |
| title_full | The Integration of Genome Mining, Comparative Genomics, and Functional Genetics for Biosynthetic Gene Cluster Identification |
| title_fullStr | The Integration of Genome Mining, Comparative Genomics, and Functional Genetics for Biosynthetic Gene Cluster Identification |
| title_full_unstemmed | The Integration of Genome Mining, Comparative Genomics, and Functional Genetics for Biosynthetic Gene Cluster Identification |
| title_short | The Integration of Genome Mining, Comparative Genomics, and Functional Genetics for Biosynthetic Gene Cluster Identification |
| title_sort | integration of genome mining comparative genomics and functional genetics for biosynthetic gene cluster identification |
| topic | Pantoea secondary metabolites biosynthetic gene cluster agar overlay assay genome mining antiSMASH |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2020.600116/full |
| work_keys_str_mv | AT ashleynwilliams theintegrationofgenomeminingcomparativegenomicsandfunctionalgeneticsforbiosyntheticgeneclusteridentification AT naveensorout theintegrationofgenomeminingcomparativegenomicsandfunctionalgeneticsforbiosyntheticgeneclusteridentification AT alexanderjcameron theintegrationofgenomeminingcomparativegenomicsandfunctionalgeneticsforbiosyntheticgeneclusteridentification AT johnstavrinides theintegrationofgenomeminingcomparativegenomicsandfunctionalgeneticsforbiosyntheticgeneclusteridentification AT ashleynwilliams integrationofgenomeminingcomparativegenomicsandfunctionalgeneticsforbiosyntheticgeneclusteridentification AT naveensorout integrationofgenomeminingcomparativegenomicsandfunctionalgeneticsforbiosyntheticgeneclusteridentification AT alexanderjcameron integrationofgenomeminingcomparativegenomicsandfunctionalgeneticsforbiosyntheticgeneclusteridentification AT johnstavrinides integrationofgenomeminingcomparativegenomicsandfunctionalgeneticsforbiosyntheticgeneclusteridentification |