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

2020 ◽  
Author(s):  
Alicia H. Russell ◽  
Natalia M. Vior ◽  
Edward S. Hems ◽  
Rodney Lacret ◽  
Andrew W. Truman
Keyword(s):  
Natural Product ◽  
Genome Mining ◽  
Gene Clusters ◽  
Model Organisms ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Post Translational Modifications ◽  
Streptomyces Albus ◽  
Modified Peptides ◽  
Mining Tool

ABSTRACTRibosomally synthesised and post-translationally modified peptides (RiPPs) are a structurally diverse class of natural product with a range of bioactivities. Genome mining for RiPP biosynthetic gene clusters (BGCs) is often hampered by poor detection of the short precursor peptides that are ultimately modified into the final molecule. Here, we utilise a previously described genome mining tool, RiPPER, to identify novel RiPP precursor peptides near YcaO-domain proteins, enzymes that catalyse various RiPP post-translational modifications including heterocyclisation and thioamidation. Using this dataset, we identified a novel, diverse and highly conserved family of RiPP BGCs spanning over 230 species of Actinobacteria and Firmicutes. A representative BGC from Streptomyces albus J1074 was characterised, leading to the discovery of streptamidine, a novel-amidine containing RiPP. This highlights the breadth of unexplored natural products with structurally rare features, even in model organisms.

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 Genomic Assemblies of Members of Burkholderia and Related Genera as a Resource for Natural Product Discovery

2020 ◽  
Vol 9 (42) ◽  
Author(s):  
Alex J. Mullins ◽  
Cerith Jones ◽  
Matthew J. Bull ◽  
Gordon Webster ◽  
Julian Parkhill ◽  
...  
Keyword(s):  
Natural Product ◽  
Genome Mining ◽  
Genomic Analysis ◽  
Gene Clusters ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Natural Product Discovery

ABSTRACT The genomes of 450 members of Burkholderiaceae, isolated from clinical and environmental sources, were sequenced and assembled as a resource for genome mining. Genomic analysis of the collection has enabled the identification of multiple metabolites and their biosynthetic gene clusters, including the antibiotics gladiolin, icosalide A, enacyloxin, and cepacin A.

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.

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 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 A Single Biosynthetic Gene Cluster Is Responsible for the Production of Bagremycin Antibiotics and Ferroverdin Iron Chelators

mBio ◽  
2019 ◽  
Vol 10 (4) ◽  
Author(s):  
Loïc Martinet ◽  
Aymeric Naômé ◽  
Benoit Deflandre ◽  
Marta Maciejewska ◽  
Déborah Tellatin ◽  
...  
Keyword(s):  
Natural Product ◽  
Gene Cluster ◽  
Biosynthetic Pathway ◽  
Genome Mining ◽  
Gene Clusters ◽  
Bioactive Molecules ◽  
Bioactive Metabolites ◽  
Biosynthetic Gene ◽  
Single Family ◽  
Biosynthetic Gene Clusters

ABSTRACT Biosynthetic gene clusters (BGCs) are organized groups of genes involved in the production of specialized metabolites. Typically, one BGC is responsible for the production of one or several similar compounds with bioactivities that usually only vary in terms of strength and/or specificity. Here we show that the previously described ferroverdins and bagremycins, which are families of metabolites with different bioactivities, are produced from the same BGC, whereby the fate of the biosynthetic pathway depends on iron availability. Under conditions of iron depletion, the monomeric bagremycins are formed, representing amino-aromatic antibiotics resulting from the condensation of 3-amino-4-hydroxybenzoic acid with p-vinylphenol. Conversely, when iron is abundantly available, the biosynthetic pathway additionally produces a molecule based on p-vinylphenyl-3-nitroso-4-hydroxybenzoate, which complexes iron to form the trimeric ferroverdins that have anticholesterol activity. Thus, our work shows a unique exception to the concept that BGCs should only produce a single family of molecules with one type of bioactivity and that in fact different bioactive molecules may be produced depending on the environmental conditions. IMPORTANCE Access to whole-genome sequences has exposed the general incidence of the so-called cryptic biosynthetic gene clusters (BGCs), thereby renewing their interest for natural product discovery. As a consequence, genome mining is the often first approach implemented to assess the potential of a microorganism for producing novel bioactive metabolites. By revealing a new level of complexity of natural product biosynthesis, we further illustrate the difficulty of estimation of the panel of molecules associated with a BGC based on genomic information alone. Indeed, we found that the same gene cluster is responsible for the production of compounds which differ in terms of structure and bioactivity. The production of these different compounds responds to different environmental triggers, which suggests that multiplication of culture conditions is essential for revealing the entire panel of molecules made by a single BGC.

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 Synthetic Biology Advanced Natural Product Discovery

Metabolites ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 785
Author(s):  
Junyang Wang ◽  
Jens Nielsen ◽  
Zihe Liu
Keyword(s):  
Natural Products ◽  
Synthetic Biology ◽  
Natural Product ◽  
Rapid Development ◽  
Genome Mining ◽  
Gene Clusters ◽  
Future Research ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Natural Product Discovery

A wide variety of bacteria, fungi and plants can produce bioactive secondary metabolites, which are often referred to as natural products. With the rapid development of DNA sequencing technology and bioinformatics, a large number of putative biosynthetic gene clusters have been reported. However, only a limited number of natural products have been discovered, as most biosynthetic gene clusters are not expressed or are expressed at extremely low levels under conventional laboratory conditions. With the rapid development of synthetic biology, advanced genome mining and engineering strategies have been reported and they provide new opportunities for discovery of natural products. This review discusses advances in recent years that can accelerate the design, build, test, and learn (DBTL) cycle of natural product discovery, and prospects trends and key challenges for future research directions.

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.

Sign in / Sign up

Export Citation Format

Share Document