Dataset Information

Computational identification of co-evolving multi-gene modules in microbial biosynthetic gene clusters.

ABSTRACT: The biosynthetic machinery responsible for the production of bacterial specialised metabolites is encoded by physically clustered group of genes called biosynthetic gene clusters (BGCs). The experimental characterisation of numerous BGCs has led to the elucidation of subclusters of genes within BGCs, jointly responsible for the same biosynthetic function in different genetic contexts. We developed an unsupervised statistical method able to successfully detect a large number of modules (putative functional subclusters) within an extensive set of predicted BGCs in a systematic and automated manner. Multiple already known subclusters were confirmed by our method, proving its efficiency and sensitivity. In addition, the resulting large collection of newly defined modules provides new insights into the prevalence and putative biosynthetic role of these modular genetic entities. The automated and unbiased identification of hundreds of co-evolving group of genes is an essential breakthrough for the discovery and biosynthetic engineering of high-value compounds.

SUBMITTER: Del Carratore F

PROVIDER: S-EPMC6395733 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Computational identification of co-evolving multi-gene modules in microbial biosynthetic gene clusters.

Del Carratore Francesco F Zych Konrad K Cummings Matthew M Takano Eriko E Medema Marnix H MH Breitling Rainer R

Communications biology 20190228

The biosynthetic machinery responsible for the production of bacterial specialised metabolites is encoded by physically clustered group of genes called biosynthetic gene clusters (BGCs). The experimental characterisation of numerous BGCs has led to the elucidation of subclusters of genes within BGCs, jointly responsible for the same biosynthetic function in different genetic contexts. We developed an unsupervised statistical method able to successfully detect a large number of modules (putative ...[more]

PMID: 30854475

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Project description:Microorganisms produce a wide variety of secondary/specialized metabolites (SMs), the majority of which are yet to be discovered. These natural products play multiple roles in microbiomes and are important for microbial competition, communication, and success in the environment. SMs have been our major source of antibiotics and are used in a range of biotechnological applications. In silico mining for biosynthetic gene clusters (BGCs) encoding the production of SMs is commonly used to assess the genetic potential of organisms. However, as BGCs span tens to over 200 kb, identifying complete BGCs requires genome data that has minimal assembly gaps within the BGCs, a prerequisite that was previously only met by individually sequenced genomes. Here, we assess the performance of the currently available genome mining platform antiSMASH on 1,080 high-quality metagenome-assembled bacterial genomes (HQ MAGs) previously produced from wastewater treatment plants (WWTPs) using a combination of long-read (Oxford Nanopore) and short-read (Illumina) sequencing technologies. More than 4,200 different BGCs were identified, with 88% of these being complete. Sequence similarity clustering of the BGCs implies that the majority of this biosynthetic potential likely encodes novel compounds, and few BGCs are shared between genera. We identify BGCs in abundant and functionally relevant genera in WWTPs, suggesting a role of secondary metabolism in this ecosystem. We find that the assembly of HQ MAGs using long-read sequencing is vital to explore the genetic potential for SM production among the uncultured members of microbial communities. IMPORTANCE Cataloguing secondary metabolite (SM) potential using genome mining of metagenomic data has become the method of choice in bioprospecting for novel compounds. However, accurate biosynthetic gene cluster (BGC) detection requires unfragmented genomic assemblies, which have been technically difficult to obtain from metagenomes until very recently with new long-read technologies. Here, we determined the biosynthetic potential of activated sludge (AS), the microbial community used in resource recovery and wastewater treatment, by mining high-quality metagenome-assembled genomes generated from long-read data. We found over 4,000 BGCs, including BGCs in abundant process-critical bacteria, with no similarity to the BGCs of characterized products. We show how long-read MAGs are required to confidently assemble complete BGCs, and we determined that the AS BGCs from different studies have very little overlap, suggesting that AS is a rich source of biosynthetic potential and new bioactive compounds.

Project description:BackgroundBiosynthetic gene clusters produce a wide range of metabolites with activities that are of interest to the pharmaceutical industry. Specific interest is shown towards those metabolites that exhibit antimicrobial activities against multidrug-resistant bacteria that have become a global health threat. Genera of the phylum Firmicutes are frequently identified as sources of such metabolites, but the biosynthetic potential of its Virgibacillus genus is not known. Here, we used comparative genomic analysis to determine whether Virgibacillus strains isolated from the Red Sea mangrove mud in Rabigh Harbor Lagoon, Saudi Arabia, may be an attractive source of such novel antimicrobial agents.ResultsA comparative genomics analysis based on Virgibacillus dokdonensis Bac330, Virgibacillus sp. Bac332 and Virgibacillus halodenitrificans Bac324 (isolated from the Red Sea) and six other previously reported Virgibacillus strains was performed. Orthology analysis was used to determine the core genomes as well as the accessory genome of the nine Virgibacillus strains. The analysis shows that the Red Sea strain Virgibacillus sp. Bac332 has the highest number of unique genes and genomic islands compared to other genomes included in this study. Focusing on biosynthetic gene clusters, we show how marine isolates, including those from the Red Sea, are more enriched with nonribosomal peptides compared to the other Virgibacillus species. We also found that most nonribosomal peptide synthases identified in the Virgibacillus strains are part of genomic regions that are potentially horizontally transferred.ConclusionsThe Red Sea Virgibacillus strains have a large number of biosynthetic genes in clusters that are not assigned to known products, indicating significant potential for the discovery of novel bioactive compounds. Also, having more modular synthetase units suggests that these strains are good candidates for experimental characterization of previously identified bioactive compounds as well. Future efforts will be directed towards establishing the properties of the potentially novel compounds encoded by the Red Sea specific trans-AT PKS/NRPS cluster and the type III PKS/NRPS cluster.

Dataset Information

Computational identification of co-evolving multi-gene modules in microbial biosynthetic gene clusters.

Publications

Computational identification of co-evolving multi-gene modules in microbial biosynthetic gene clusters.

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