Understanding and manipulating antibiotic production in actinomycetes

2013 ◽  
Vol 41 (6) ◽  
pp. 1355-1364 ◽  
Author(s):  
Mervyn J. Bibb
Keyword(s):  
Natural Product ◽  
Biological Activities ◽  
Antibiotic Production ◽  
Gene Clusters ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Natural Product Biosynthesis ◽  
Genomic Technologies ◽  
Wide Range ◽  
Recent Developments

Actinomycetes are prolific producers of natural products with a wide range of biological activities. Many of the compounds that they make (and derivatives thereof) are used extensively in medicine, most notably as clinically important antibiotics, and in agriculture. Moreover, these organisms remain a source of novel and potentially useful molecules, but maximizing their biosynthetic potential requires a better understanding of natural product biosynthesis. Recent developments in genome sequencing have greatly facilitated the identification of natural product biosynthetic gene clusters. In the present article, I summarize the recent contributions of our laboratory in applying genomic technologies to better understand and manipulate natural product biosynthesis in a range of different actinomycetes.

scholarly journals Mining of Cyanobacterial Genomes Indicates That Plasmids Are Involved in the Production of Natural Products

2020 ◽  
Author(s):  
Rafael Popin ◽  
Danillo Alvarenga ◽  
Raquel Castelo-Branco ◽  
David Fewer ◽  
Kaarina Sivonen
Keyword(s):  
Natural Products ◽  
Natural Product ◽  
Biological Activities ◽  
Gene Clusters ◽  
Biosynthetic Pathways ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Chemical Structures ◽  
Wide Range ◽  
Genes Encoding

Abstract Background Microbial natural products have unique chemical structures and diverse biological activities. Cyanobacteria commonly possess a wide range of biosynthetic gene clusters to produce natural products. Several studies have mapped the distribution of natural product biosynthetic gene clusters in cyanobacterial genomes. However, little attention has been paid to natural product biosynthesis in plasmids. Some genes encoding cyanobacterial natural product biosynthetic pathways are believed to be dispersed by plasmids through horizontal gene transfer. Thus, we examined complete cyanobacterial genomes to assess if plasmids are involved in the production and dissemination of natural products by cyanobacteria.Results The 185 analyzed genomes possessed 1 to 42 gene clusters and an average of 10. In total, 1816 biosynthetic gene clusters were found. Approximately 95% of these clusters were present in chromosomes. The remaining 5% were present in plasmids, from which homologs of the biosynthetic pathways for aeruginosin, anabaenopeptin, ambiguine, cryptophycin, hassallidin, geosmin, and microcystin were manually curated. The cryptophycin pathway was previously described as active while the other gene cluster include all genes for biosynthesis. Approximately 12% of the 424 analyzed cyanobacterial plasmids contained homologs of genes involved in conjugation. Large plasmids, previously named as “chromids”, were also observed to be widespread in cyanobacteria. Sixteen cryptic natural product biosynthetic gene clusters and geosmin biosynthetic gene clusters were located in those mobile plasmids.Conclusion Homologues of genes involved in the production of toxins, protease inhibitors, odorous compounds, antimicrobials, antitumorals, and other unidentified natural products are located in cyanobacterial plasmids. Some of these plasmids are predicted to be conjugative. The present study provides in silico evidence that plasmids are involved in the distribution of natural product biosynthetic pathways in cyanobacteria.

scholarly journals Global analysis of adenylate-forming enzymes reveals β-lactone biosynthesis pathway in pathogenic Nocardia

2020 ◽  
Vol 295 (44) ◽  
pp. 14826-14839
Author(s):  
Serina L. Robinson ◽  
Barbara R. Terlouw ◽  
Megan D. Smith ◽  
Sacha J. Pidot ◽  
Timothy P. Stinear ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Product ◽  
Global Analysis ◽  
Gene Clusters ◽  
Divergent Evolution ◽  
Acid Activation ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Predictive Tool ◽  
Wide Range

Enzymes that cleave ATP to activate carboxylic acids play essential roles in primary and secondary metabolism in all domains of life. Class I adenylate-forming enzymes share a conserved structural fold but act on a wide range of substrates to catalyze reactions involved in bioluminescence, nonribosomal peptide biosynthesis, fatty acid activation, and β-lactone formation. Despite their metabolic importance, the substrates and functions of the vast majority of adenylate-forming enzymes are unknown without tools available to accurately predict them. Given the crucial roles of adenylate-forming enzymes in biosynthesis, this also severely limits our ability to predict natural product structures from biosynthetic gene clusters. Here we used machine learning to predict adenylate-forming enzyme function and substrate specificity from protein sequences. We built a web-based predictive tool and used it to comprehensively map the biochemical diversity of adenylate-forming enzymes across >50,000 candidate biosynthetic gene clusters in bacterial, fungal, and plant genomes. Ancestral phylogenetic reconstruction and sequence similarity networking of enzymes from these clusters suggested divergent evolution of the adenylate-forming superfamily from a core enzyme scaffold most related to contemporary CoA ligases toward more specialized functions including β-lactone synthetases. Our classifier predicted β-lactone synthetases in uncharacterized biosynthetic gene clusters conserved in >90 different strains of Nocardia. To test our prediction, we purified a candidate β-lactone synthetase from Nocardia brasiliensis and reconstituted the biosynthetic pathway in vitro to link the gene cluster to the β-lactone natural product, nocardiolactone. We anticipate that our machine learning approach will aid in functional classification of enzymes and advance natural product discovery.

scholarly journals Discovery and characterisation of an amidine-containing ribosomally-synthesised peptide that is widely distributed in nature

2021 ◽  
Author(s):  
Alicia H Russell ◽  
Natalia Miguel Vior ◽  
Edward Steven Hems ◽  
Rodney Lacret ◽  
Andrew William Truman
Keyword(s):  
Natural Product ◽  
Genome Mining ◽  
Gene Clusters ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Wide Range ◽  
Modified Peptides

Ribosomally synthesised and post-translationally modified peptides (RiPPs) are a structurally diverse class of natural product with a wide range of bioactivities. Genome mining for RiPP biosynthetic gene clusters (BGCs) is...

scholarly journals BiG-SLiCE: A Highly Scalable Tool Maps the Diversity of 1.2 Million Biosynthetic Gene Clusters

2020 ◽  
Author(s):  
Satria A. Kautsar ◽  
Justin J. J. van der Hooft ◽  
Dick de Ridder ◽  
Marnix H. Medema
Keyword(s):  
Natural Product ◽  
Biological Activities ◽  
Genome Mining ◽  
Gene Clusters ◽  
Genomic Diversity ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Microbial Genomes ◽  
Natural Product Discovery ◽  
User Friendly

AbstractBackgroundGenome mining for Biosynthetic Gene Clusters (BGCs) has become an integral part of natural product discovery. The >200,000 microbial genomes now publicly available hold information on abundant novel chemistry. One way to navigate this vast genomic diversity is through comparative analysis of homologous BGCs, which allows identification of cross-species patterns that can be matched to the presence of metabolites or biological activities. However, current tools suffer from a bottleneck caused by the expensive network-based approach used to group these BGCs into Gene Cluster Families (GCFs).ResultsHere, we introduce BiG-SLiCE, a tool designed to cluster massive numbers of BGCs. By representing them in Euclidean space, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion. We used BiG-SLiCE to analyze 1,225,071 BGCs collected from 209,206 publicly available microbial genomes and metagenome-assembled genomes (MAGs) within ten days on a typical 36-cores CPU server. We demonstrate the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify uncharted biosynthetic potential. BiG-SLiCE also provides a "query mode" that can efficiently place newly sequenced BGCs into previously computed GCFs, plus a powerful output visualization engine that facilitates user-friendly data exploration.ConclusionsBiG-SLiCE opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global, searchable interconnected network of BGCs. As more genomes get sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry. BiG-SLiCE is available via https://github.com/medema-group/bigslice.

scholarly journals Global analysis of adenylate-forming enzymes reveals β-lactone biosynthesis pathway in pathogenic Nocardia

2019 ◽  
Author(s):  
Serina L. Robinson ◽  
Barbara R. Terlouw ◽  
Megan D. Smith ◽  
Sacha J. Pidot ◽  
Tim P. Stinear ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Product ◽  
Sequence Similarity ◽  
Global Analysis ◽  
Gene Clusters ◽  
Acid Activation ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Predictive Tool ◽  
Wide Range

ABSTRACTEnzymes that cleave ATP to activate carboxylic acids play essential roles in primary and secondary metabolism in all domains of life. Class I adenylate-forming enzymes share a conserved structural fold but act on a wide range of substrates to catalyze reactions involved in bioluminescence, nonribosomal peptide biosynthesis, fatty acid activation, and β-lactone formation. Despite their metabolic importance, the substrates and catalytic functions of the vast majority of adenylate-forming enzymes are unknown without tools available to accurately predict them. Given the crucial roles of adenylate-forming enzymes in biosynthesis, this also severely limits our ability to predict natural product structures from biosynthetic gene clusters. Here we used machine learning to predict adenylate-forming enzyme function and substrate specificity from protein sequence. We built a web-based predictive tool and used it to comprehensively map the biochemical diversity of adenylate-forming enzymes across >50,000 candidate biosynthetic gene clusters in bacterial, fungal, and plant genomes. Ancestral enzyme reconstruction and sequence similarity networking revealed a ‘hub’ topology suggesting radial divergence of the adenylate-forming superfamily from a core enzyme scaffold most related to contemporary aryl-CoA ligases. Our classifier also predicted β-lactone synthetases in novel biosynthetic gene clusters conserved across >90 different strains of Nocardia. To test our computational predictions, we purified a candidate β-lactone synthetase from Nocardia brasiliensis and reconstituted the biosynthetic pathway in vitro to link the gene cluster to the β-lactone natural product, nocardiolactone. We anticipate our machine learning approach will aid in functional classification of enzymes and advance natural product discovery.

scholarly journals Mining metagenomes for natural product biosynthetic gene clusters: unlocking new potential with ultrafast techniques

2021 ◽  
Author(s):  
Emiliano Pereira-Flores ◽  
Marnix Medema ◽  
Pier Luigi Buttigieg ◽  
Peter Meinicke ◽  
Frank Oliver Glöckner ◽  
...  
Keyword(s):  
Natural Products ◽  
Natural Product ◽  
Human Microbiome ◽  
Gene Clusters ◽  
Human Microbiome Project ◽  
Biosynthetic Gene Cluster ◽  
Metagenomic Data ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Wide Range

Microorganisms produce an immense variety of natural products through the expression of Biosynthetic Gene Clusters (BGCs): physically clustered genes that encode the enzymes of a specialized metabolic pathway. These natural products cover a wide range of chemical classes (e.g., aminoglycosides, lantibiotics, nonribosomal peptides, oligosaccharides, polyketides, terpenes) that are highly valuable for industrial and medical applications1. Metagenomics, as a culture-independent approach, has greatly enhanced our ability to survey the functional potential of microorganisms and is growing in popularity for the mining of BGCs. However, to effectively exploit metagenomic data to this end, it will be crucial to more efficiently identify these genomic elements in highly complex and ever-increasing volumes of data2. Here, we address this challenge by developing the ultrafast Biosynthetic Gene cluster MEtagenomic eXploration toolbox (BiG-MEx). BiG-MEx rapidly identifies a broad range of BGC protein domains, assess their diversity and novelty, and predicts the abundance profile of natural product BGC classes in metagenomic data. We show the advantages of BiG-MEx compared to standard BGC-mining approaches, and use it to explore the BGC domain and class composition of samples in the TARA Oceans3 and Human Microbiome Project datasets4. In these analyses, we demonstrate BiG-MEx’s applicability to study the distribution, diversity, and ecological roles of BGCs in metagenomic data, and guide the exploration of natural products with clinical applications.

scholarly journals BiG-SLiCE: A highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters

GigaScience ◽  
2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Satria A Kautsar ◽  
Justin J J van der Hooft ◽  
Dick de Ridder ◽  
Marnix H Medema
Keyword(s):  
Natural Product ◽  
Biological Activities ◽  
Genome Mining ◽  
Gene Clusters ◽  
Genomic Diversity ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Microbial Genomes ◽  
Natural Product Discovery ◽  
User Friendly

Abstract Background Genome mining for biosynthetic gene clusters (BGCs) has become an integral part of natural product discovery. The >200,000 microbial genomes now publicly available hold information on abundant novel chemistry. One way to navigate this vast genomic diversity is through comparative analysis of homologous BGCs, which allows identification of cross-species patterns that can be matched to the presence of metabolites or biological activities. However, current tools are hindered by a bottleneck caused by the expensive network-based approach used to group these BGCs into gene cluster families (GCFs). Results Here, we introduce BiG-SLiCE, a tool designed to cluster massive numbers of BGCs. By representing them in Euclidean space, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion. We used BiG-SLiCE to analyze 1,225,071 BGCs collected from 209,206 publicly available microbial genomes and metagenome-assembled genomes within 10 days on a typical 36-core CPU server. We demonstrate the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify uncharted biosynthetic potential. BiG-SLiCE also provides a “query mode” that can efficiently place newly sequenced BGCs into previously computed GCFs, plus a powerful output visualization engine that facilitates user-friendly data exploration. Conclusions BiG-SLiCE opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global and searchable interconnected network of BGCs. As more genomes are sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry. BiG-SLiCE is available via https://github.com/medema-group/bigslice.

scholarly journals Identification of a New Antimicrobial, Desertomycin H, Utilizing a Modified Crowded Plate Technique

Marine Drugs ◽  
2021 ◽  
Vol 19 (8) ◽  
pp. 424
Author(s):  
Osama G. Mohamed ◽  
Sadaf Dorandish ◽  
Rebecca Lindow ◽  
Megan Steltz ◽  
Ifrah Shoukat ◽  
...  
Keyword(s):  
Antibiotic Production ◽  
Gene Clusters ◽  
Multidrug Resistant ◽  
Microbial Interactions ◽  
Mass Spectrometry Data ◽  
Metagenomic Data ◽  
Resistant Bacteria ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Plate Technique

The antibiotic-resistant bacteria-associated infections are a major global healthcare threat. New classes of antimicrobial compounds are urgently needed as the frequency of infections caused by multidrug-resistant microbes continues to rise. Recent metagenomic data have demonstrated that there is still biosynthetic potential encoded in but transcriptionally silent in cultivatable bacterial genomes. However, the culture conditions required to identify and express silent biosynthetic gene clusters that yield natural products with antimicrobial activity are largely unknown. Here, we describe a new antibiotic discovery scheme, dubbed the modified crowded plate technique (mCPT), that utilizes complex microbial interactions to elicit antimicrobial production from otherwise silent biosynthetic gene clusters. Using the mCPT as part of the antibiotic crowdsourcing educational program Tiny Earth®, we isolated over 1400 antibiotic-producing microbes, including 62, showing activity against multidrug-resistant pathogens. The natural product extracts generated from six microbial isolates showed potent activity against vancomycin-intermediate resistant Staphylococcus aureus. We utilized a targeted approach that coupled mass spectrometry data with bioactivity, yielding a new macrolactone class of metabolite, desertomycin H. In this study, we successfully demonstrate a concept that significantly increased our ability to quickly and efficiently identify microbes capable of the silent antibiotic production.

scholarly journals Phytoplankton trigger the production of cryptic metabolites in the marine actinobacteria Salinispora tropica

2020 ◽  
Author(s):  
Audam Chhun ◽  
Despoina Sousoni ◽  
Maria del Mar Aguiló-Ferretjans ◽  
Lijiang Song ◽  
Christophe Corre ◽  
...  
Keyword(s):  
Natural Products ◽  
Secondary Metabolites ◽  
Antibiotic Production ◽  
Antimicrobial Effect ◽  
Gene Clusters ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Marine Actinobacteria ◽  
Salinispora Tropica ◽  
Eukaryotic Phytoplankton

AbstractBacteria from the Actinomycete family are a remarkable source of natural products with pharmaceutical potential. The discovery of novel molecules from these organisms is, however, hindered because most of the biosynthetic gene clusters (BGCs) encoding these secondary metabolites are cryptic or silent and are referred to as orphan BGCs. While co-culture has proven to be a promising approach to unlock the biosynthetic potential of many microorganisms by activating the expression of these orphan BGCs, it still remains an underexplored technique. The marine actinobacteria Salinispora tropica, for instance, produces valuable compounds such as the anti-cancer molecule salinosporamide A but half of its putative BGCs are still orphan. Although previous studies have looked into using marine heterotrophs to induce orphan BGCs in Salinispora, the potential impact of co-culturing marine phototrophs with Salinispora has yet to be investigated. Following the observation of clear antimicrobial phenotype of the actinobacterium on a range of phytoplanktonic organisms, we here report the discovery of novel cryptic secondary metabolites produced by S. tropica in response to its co-culture with photosynthetic primary producers. An approach combining metabolomics and proteomics revealed that the photosynthate released by phytoplankton influences the biosynthetic capacities of S. tropica with both production of new molecules and the activation of orphan BGCs. Our work pioneers the use of phototrophs as a promising strategy to accelerate the discovery of novel natural products from actinobacteria.ImportanceThe alarming increase of antimicrobial resistance has generated an enormous interest in the discovery of novel active compounds. The isolation of new microbes to untap novel natural products is currently hampered because most biosynthetic gene clusters (BGC) encoded by these microorganisms are not expressed under standard laboratory conditions, i.e. mono-cultures. Here we show that co-culturing can be an easy way for triggering silent BGC. By combining state-of-the-art metabolomics and high-throughput proteomics, we characterized the activation of cryptic metabolites and silent biosynthetic gene clusters in the marine actinobacteria Salinispora tropica by the presence of phytoplankton photosynthate. We further suggest a mechanistic understanding of the antimicrobial effect this actinobacterium has on a broad range of prokaryotic and eukaryotic phytoplankton species and reveal a promising candidate for antibiotic production.

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