scholarly journals Insights into Secondary Metabolism from a Global Analysis of Prokaryotic Biosynthetic Gene Clusters

Cell ◽  
2014 ◽  
Vol 158 (2) ◽  
pp. 412-421 ◽  
Author(s):  
Peter Cimermancic ◽  
Marnix H. Medema ◽  
Jan Claesen ◽  
Kenji Kurita ◽  
Laura C. Wieland Brown ◽  
...  
mSphere ◽  
2019 ◽  
Vol 4 (4) ◽  
Author(s):  
Laura A. Mike

ABSTRACT Laura A. Mike works in the field of bacterial pathogenesis. In this mSphere of Influence article, she reflects on how “Insights into Secondary Metabolism from a Global Analysis of Prokaryotic Biosynthetic Gene Clusters” by P. Cimermancic et al. (Cell 158:412–421, 2014, https://doi.org/10.1016/j.cell.2014.06.034) and “A Systematic Analysis of Biosynthetic Gene Clusters in the Human Microbiome Reveals a Common Family of Antibiotics” by M. S. Donia et al. (Cell 158:1402–1414, 2014, https://doi.org/10.1016/j.cell.2014.08.032) made an impact on her by systematically identifying microbiome-associated biosynthetic gene clusters predicted to synthesize secondary metabolites, which may facilitate interspecies interactions.


mBio ◽  
2015 ◽  
Vol 6 (4) ◽  
Author(s):  
Michalis Hadjithomas ◽  
I-Min Amy Chen ◽  
Ken Chu ◽  
Anna Ratner ◽  
Krishna Palaniappan ◽  
...  

ABSTRACTIn the discovery of secondary metabolites, analysis of sequence data is a promising exploration path that remains largely underutilized due to the lack of computational platforms that enable such a systematic approach on a large scale. In this work, we present IMG-ABC (https://img.jgi.doe.gov/abc), an atlas of biosynthetic gene clusters within the Integrated Microbial Genomes (IMG) system, which is aimed at harnessing the power of “big” genomic data for discovering small molecules. IMG-ABC relies on IMG's comprehensive integrated structural and functional genomic data for the analysis of biosynthetic gene clusters (BCs) and associated secondary metabolites (SMs). SMs and BCs serve as the two main classes of objects in IMG-ABC, each with a rich collection of attributes. A unique feature of IMG-ABC is the incorporation of both experimentally validated and computationally predicted BCs in genomes as well as metagenomes, thus identifying BCs in uncultured populations and rare taxa. We demonstrate the strength of IMG-ABC's focused integrated analysis tools in enabling the exploration of microbial secondary metabolism on a global scale, through the discovery of phenazine-producing clusters for the first time inAlphaproteobacteria. IMG-ABC strives to fill the long-existent void of resources for computational exploration of the secondary metabolism universe; its underlying scalable framework enables traversal of uncovered phylogenetic and chemical structure space, serving as a doorway to a new era in the discovery of novel molecules.IMPORTANCEIMG-ABC is the largest publicly available database of predicted and experimental biosynthetic gene clusters and the secondary metabolites they produce. The system also includes powerful search and analysis tools that are integrated with IMG's extensive genomic/metagenomic data and analysis tool kits. As new research on biosynthetic gene clusters and secondary metabolites is published and more genomes are sequenced, IMG-ABC will continue to expand, with the goal of becoming an essential component of any bioinformatic exploration of the secondary metabolism world.


Author(s):  
Takayuki Motoyama ◽  
Tomoaki Ishii ◽  
Takashi Kamakura ◽  
Hiroyuki Osada

Abstract The control of secondary metabolism in fungi is essential for the regulation of various cellular functions. In this study, we searched the RIKEN Natural Products Depository (NPDepo) chemical library for inducers of tenuazonic acid (TeA) production in the rice blast fungus Pyricularia oryzae and identified NPD938. NPD938 transcriptionally induced TeA production. We explored the mode of action of NPD938 and observed that this compound enhanced TeA production via LAE1, a global regulator of fungal secondary metabolism. NPD938 could also induce production of terpendoles and pyridoxatins in Tolypocladium album RK99-F33. Terpendole production was induced transcriptionally. We identified the pyridoxatin biosynthetic gene cluster among transcriptionally induced secondary metabolite biosynthetic gene clusters. Therefore, NPD938 is useful for the control of fungal secondary metabolism.


2020 ◽  
Vol 295 (44) ◽  
pp. 14826-14839
Author(s):  
Serina L. Robinson ◽  
Barbara R. Terlouw ◽  
Megan D. Smith ◽  
Sacha J. Pidot ◽  
Timothy P. Stinear ◽  
...  

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.


2017 ◽  
Vol 43 (5) ◽  
pp. 546-566 ◽  
Author(s):  
Raghavan Dinesh ◽  
Veeraraghavan Srinivasan ◽  
Sheeja T. E. ◽  
Muthuswamy Anandaraj ◽  
Hamza Srambikkal

Author(s):  
Krishnaveni Palaniappan ◽  
I-Min A Chen ◽  
Ken Chu ◽  
Anna Ratner ◽  
Rekha Seshadri ◽  
...  

Abstract Microbial secondary metabolism is a reservoir of bioactive compounds of immense biotechnological and biomedical potential. The biosynthetic machinery responsible for the production of these secondary metabolites (SMs) (also called natural products) is often encoded by collocated groups of genes called biosynthetic gene clusters (BGCs). High-throughput genome sequencing of both isolates and metagenomic samples combined with the development of specialized computational workflows is enabling systematic identification of BGCs and the discovery of novel SMs. In order to advance exploration of microbial secondary metabolism and its diversity, we developed the largest publicly available database of predicted BGCs combined with experimentally verified BGCs, the Integrated Microbial Genomes Atlas of Biosynthetic gene Clusters (IMG-ABC) (https://img.jgi.doe.gov/abc-public). Here we describe the first major content update of the IMG-ABC knowledgebase, since its initial release in 2015, refreshing the BGC prediction pipeline with the latest version of antiSMASH (v5) as well as presenting the data in the context of underlying environmental metadata sourced from GOLD (https://gold.jgi.doe.gov/). This update has greatly improved the quality and expanded the types of predicted BGCs compared to the previous version.


2020 ◽  
Author(s):  
Jacob L. Steenwyk ◽  
Matthew E. Mead ◽  
Sonja L. Knowles ◽  
Huzefa A. Raja ◽  
Christopher D. Roberts ◽  
...  

AbstractAspergillus fumigatus is a major human pathogen that causes hundreds of thousands of infections yearly with high mortality rates. In contrast, Aspergillus fischeri and the recently described Aspergillus oerlinghausenensis, the two species most closely related to A. fumigatus, are not known to be pathogenic. Some of the “cards of virulence” that A. fumigatus possesses are secondary metabolites that impair the host immune system, protect from host immune cell attacks, or acquire key nutrients. Secondary metabolites and the biosynthetic gene clusters (BGCs) that typically encode them often vary within and between fungal species. To gain insight into whether secondary metabolism-associated cards of virulence vary between A. fumigatus, A. oerlinghausenensis, and A. fischeri, we conducted extensive genomic and secondary metabolite profiling analyses. By analyzing multiple A. fumigatus, one A. oerlinghausenensis, and multiple A. fischeri strains, we identified both conserved and diverged secondary metabolism-associated cards of virulence. For example, we found that all species and strains examined biosynthesized the major virulence factor gliotoxin, consistent with the conservation of the gliotoxin BGC across genomes. However, species differed in their biosynthesis of fumagillin and pseurotin, both contributors to host tissue damage during invasive aspergillosis; these differences were reflected in sequence divergence of the intertwined fumagillin/pseurotin BGCs across genomes. These results delineate the similarities and differences in secondary metabolism-associated cards of virulence between a major fungal pathogen and its nonpathogenic closest relatives, shedding light into the genetic and phenotypic changes associated with the evolution of fungal pathogenicity.ImportanceThe major fungal pathogen Aspergillus fumigatus kills tens of thousands each year. In contrast, the two closest relatives of A. fumigatus, namely Aspergillus fischeri and Aspergillus oerlinghausenensis, are not considered pathogenic. A. fumigatus virulence stems, partly, from its ability to produce small molecules called secondary metabolites that have potent activities during infection. In this study, we examined whether A. fumigatus secondary metabolites and the metabolic pathways involved in their production are conserved in A. oerlinghausenensis and A. fischeri. We found that the nonpathogenic close relatives of A. fumigatus produce some, but not all, secondary metabolites thought to contribute to the success of A. fumigatus in causing human disease and that these similarities and differences were reflected in the underlying metabolic pathways involved in their biosynthesis. Compared to its nonpathogenic close relatives, A. fumigatus produces a distinct cocktail of secondary metabolites, which likely contributes to these organisms’ vastly different potentials to cause human disease. More broadly, the study of nonpathogenic organisms that have virulence-related traits, but are not currently considered agents of human disease, may facilitate the prediction of species capable of posing future threats to human health.


Marine Drugs ◽  
2018 ◽  
Vol 16 (2) ◽  
pp. 67 ◽  
Author(s):  
Stephen Jackson ◽  
Lisa Crossman ◽  
Eduardo Almeida ◽  
Lekha Margassery ◽  
Jonathan Kennedy ◽  
...  

2019 ◽  
Author(s):  
Serina L. Robinson ◽  
Barbara R. Terlouw ◽  
Megan D. Smith ◽  
Sacha J. Pidot ◽  
Tim P. Stinear ◽  
...  

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.


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