An Integrated Metabolomic and Genomic Mining Workflow To Uncover the Biosynthetic Potential of Bacteria
ABSTRACT Microorganisms are a rich source of bioactives; however, chemical identification is a major bottleneck. Strategies that can prioritize the most prolific microbial strains and novel compounds are of great interest. Here, we present an integrated approach to evaluate the biosynthetic richness...
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American Society for Microbiology
2016
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oai:doaj.org-article:9906b1ac9e2b4d0ba8ac74b1086a3e872021-12-02T19:47:34ZAn Integrated Metabolomic and Genomic Mining Workflow To Uncover the Biosynthetic Potential of Bacteria10.1128/mSystems.00028-152379-5077https://doaj.org/article/9906b1ac9e2b4d0ba8ac74b1086a3e872016-06-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00028-15https://doaj.org/toc/2379-5077ABSTRACT Microorganisms are a rich source of bioactives; however, chemical identification is a major bottleneck. Strategies that can prioritize the most prolific microbial strains and novel compounds are of great interest. Here, we present an integrated approach to evaluate the biosynthetic richness in bacteria and mine the associated chemical diversity. Thirteen strains closely related to Pseudoalteromonas luteoviolacea isolated from all over the Earth were analyzed using an untargeted metabolomics strategy, and metabolomic profiles were correlated with whole-genome sequences of the strains. We found considerable diversity: only 2% of the chemical features and 7% of the biosynthetic genes were common to all strains, while 30% of all features and 24% of the genes were unique to single strains. The list of chemical features was reduced to 50 discriminating features using a genetic algorithm and support vector machines. Features were dereplicated by tandem mass spectrometry (MS/MS) networking to identify molecular families of the same biosynthetic origin, and the associated pathways were probed using comparative genomics. Most of the discriminating features were related to antibacterial compounds, including the thiomarinols that were reported from P. luteoviolacea here for the first time. By comparative genomics, we identified the biosynthetic cluster responsible for the production of the antibiotic indolmycin, which could not be predicted with standard methods. In conclusion, we present an efficient, integrative strategy for elucidating the chemical richness of a given set of bacteria and link the chemistry to biosynthetic genes. IMPORTANCE We here combine chemical analysis and genomics to probe for new bioactive secondary metabolites based on their pattern of distribution within bacterial species. We demonstrate the usefulness of this combined approach in a group of marine Gram-negative bacteria closely related to Pseudoalteromonas luteoviolacea, which is a species known to produce a broad spectrum of chemicals. The approach allowed us to identify new antibiotics and their associated biosynthetic pathways. Combining chemical analysis and genetics is an efficient “mining” workflow for identifying diverse pharmaceutical candidates in a broad range of microorganisms and therefore of great use in bioprospecting.Maria MaanssonNikolaj G. VynneAndreas KlitgaardJane L. NyboJette MelchiorsenDon D. NguyenLaura M. SanchezNadine ZiemertPieter C. DorresteinMikael R. AndersenLone GramAmerican Society for MicrobiologyarticlePseudoalteromonascomparative genomicsnatural productsuntargeted metabolomicsMicrobiologyQR1-502ENmSystems, Vol 1, Iss 3 (2016) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Pseudoalteromonas comparative genomics natural products untargeted metabolomics Microbiology QR1-502 |
| spellingShingle |
Pseudoalteromonas comparative genomics natural products untargeted metabolomics Microbiology QR1-502 Maria Maansson Nikolaj G. Vynne Andreas Klitgaard Jane L. Nybo Jette Melchiorsen Don D. Nguyen Laura M. Sanchez Nadine Ziemert Pieter C. Dorrestein Mikael R. Andersen Lone Gram An Integrated Metabolomic and Genomic Mining Workflow To Uncover the Biosynthetic Potential of Bacteria |
| description |
ABSTRACT Microorganisms are a rich source of bioactives; however, chemical identification is a major bottleneck. Strategies that can prioritize the most prolific microbial strains and novel compounds are of great interest. Here, we present an integrated approach to evaluate the biosynthetic richness in bacteria and mine the associated chemical diversity. Thirteen strains closely related to Pseudoalteromonas luteoviolacea isolated from all over the Earth were analyzed using an untargeted metabolomics strategy, and metabolomic profiles were correlated with whole-genome sequences of the strains. We found considerable diversity: only 2% of the chemical features and 7% of the biosynthetic genes were common to all strains, while 30% of all features and 24% of the genes were unique to single strains. The list of chemical features was reduced to 50 discriminating features using a genetic algorithm and support vector machines. Features were dereplicated by tandem mass spectrometry (MS/MS) networking to identify molecular families of the same biosynthetic origin, and the associated pathways were probed using comparative genomics. Most of the discriminating features were related to antibacterial compounds, including the thiomarinols that were reported from P. luteoviolacea here for the first time. By comparative genomics, we identified the biosynthetic cluster responsible for the production of the antibiotic indolmycin, which could not be predicted with standard methods. In conclusion, we present an efficient, integrative strategy for elucidating the chemical richness of a given set of bacteria and link the chemistry to biosynthetic genes. IMPORTANCE We here combine chemical analysis and genomics to probe for new bioactive secondary metabolites based on their pattern of distribution within bacterial species. We demonstrate the usefulness of this combined approach in a group of marine Gram-negative bacteria closely related to Pseudoalteromonas luteoviolacea, which is a species known to produce a broad spectrum of chemicals. The approach allowed us to identify new antibiotics and their associated biosynthetic pathways. Combining chemical analysis and genetics is an efficient “mining” workflow for identifying diverse pharmaceutical candidates in a broad range of microorganisms and therefore of great use in bioprospecting. |
| format |
article |
| author |
Maria Maansson Nikolaj G. Vynne Andreas Klitgaard Jane L. Nybo Jette Melchiorsen Don D. Nguyen Laura M. Sanchez Nadine Ziemert Pieter C. Dorrestein Mikael R. Andersen Lone Gram |
| author_facet |
Maria Maansson Nikolaj G. Vynne Andreas Klitgaard Jane L. Nybo Jette Melchiorsen Don D. Nguyen Laura M. Sanchez Nadine Ziemert Pieter C. Dorrestein Mikael R. Andersen Lone Gram |
| author_sort |
Maria Maansson |
| title |
An Integrated Metabolomic and Genomic Mining Workflow To Uncover the Biosynthetic Potential of Bacteria |
| title_short |
An Integrated Metabolomic and Genomic Mining Workflow To Uncover the Biosynthetic Potential of Bacteria |
| title_full |
An Integrated Metabolomic and Genomic Mining Workflow To Uncover the Biosynthetic Potential of Bacteria |
| title_fullStr |
An Integrated Metabolomic and Genomic Mining Workflow To Uncover the Biosynthetic Potential of Bacteria |
| title_full_unstemmed |
An Integrated Metabolomic and Genomic Mining Workflow To Uncover the Biosynthetic Potential of Bacteria |
| title_sort |
integrated metabolomic and genomic mining workflow to uncover the biosynthetic potential of bacteria |
| publisher |
American Society for Microbiology |
| publishDate |
2016 |
| url |
https://doaj.org/article/9906b1ac9e2b4d0ba8ac74b1086a3e87 |
| work_keys_str_mv |
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