Rhodococcus comparative genomics reveals a phylogenomic-dependent non-ribosomal peptide synthetase distribution: insights into biosynthetic gene cluster connection to an orphan metabolite.
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ABSTRACT:
SUBMITTER: Undabarrena A
PROVIDER: S-EPMC8477407 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Nonribosomal peptides represent a large class of metabolites with pharmaceutical relevance. Pteridines, such as pterins, folates, and flavins, are heterocyclic metabolites that often serve as redox-active cofactors. The biosynthetic machineries for construction of these distinct classes of small molecules operate independently in the cell. Here, we discovered an unprecedented nonribosomal peptide synthetase-like-pteridine synthase hybrid biosynthetic gene cluster in Photorhabdus luminescens using genome synteny analysis. P. luminescens is a Gammaproteobacterium that undergoes phenotypic variation and can have both pathogenic and mutualistic roles. Through extensive gene deletion, pathway-targeted molecular networking, quantitative proteomic analysis, and NMR, we show that the genetic locus affects the regulation of quorum sensing and secondary metabolic enzymes and encodes new pteridine metabolites functionalized with cis-amide acyl-side chains, termed pepteridine A (1) and B (2). The pepteridines are produced in the pathogenic phenotypic variant and represent the first reported metabolites to be synthesized by a hybrid NRPS-pteridine pathway. These studies expand our view of the combinatorial biosynthetic potential available in bacteria.
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Project description:Fungal secondary metabolites (SMs) contribute to the diversity of fungal ecological communities, niches, and lifestyles. Many fungal SMs have one or more medically and industrially important activities (e.g., antifungal, antibacterial, and antitumor). The genes necessary for fungal SM biosynthesis are typically located right next to each other in the genome and are known as biosynthetic gene clusters (BGCs). However, whether fungal SM bioactivity can be predicted from specific attributes of genes in BGCs remains an open question. We adapted machine learning models that predicted SM bioactivity from bacterial BGC data with accuracies as high as 80% to fungal BGC data. We trained our models to predict the antibacterial, antifungal, and cytotoxic/antitumor bioactivity of fungal SMs on two data sets: (i) fungal BGCs (data set comprised of 314 BGCs) and (ii) fungal (314 BGCs) and bacterial BGCs (1,003 BGCs). We found that models trained on fungal BGCs had balanced accuracies between 51% and 68%, whereas training on bacterial and fungal BGCs had balanced accuracies between 56% and 68%. The low prediction accuracy of fungal SM bioactivities likely stems from the small size of the data set; this lack of data, coupled with our finding that including bacterial BGC data in the training data did not substantially change accuracies currently limits the application of machine learning approaches to fungal SM studies. With >15,000 characterized fungal SMs, millions of putative BGCs in fungal genomes, and increased demand for novel drugs, efforts that systematically link fungal SM bioactivity to BGCs are urgently needed.IMPORTANCEFungi are key sources of natural products and iconic drugs, including penicillin and statins. DNA sequencing has revealed that there are likely millions of biosynthetic pathways in fungal genomes, but the chemical structures and bioactivities of >99% of natural products produced by these pathways remain unknown. We used artificial intelligence to predict the bioactivities of diverse fungal biosynthetic pathways. We found that the accuracies of our predictions were generally low, between 51% and 68%, likely because the natural products and bioactivities of only very few fungal pathways are known. With >15,000 characterized fungal natural products, millions of putative biosynthetic pathways present in fungal genomes, and increased demand for novel drugs, our study suggests that there is an urgent need for efforts that systematically identify fungal biosynthetic pathways, their natural products, and their bioactivities.
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Project description:Fungal secondary metabolites (SMs) play a significant role in the diversity of ecological communities, niches, and lifestyles in the fungal kingdom. Many fungal SMs have medically and industrially important properties including antifungal, antibacterial, and antitumor activity, and a single metabolite can display multiple types of bioactivities. The genes necessary for fungal SM biosynthesis are typically found in a single genomic region forming biosynthetic gene clusters (BGCs). However, whether fungal SM bioactivity can be predicted from specific attributes of genes in BGCs remains an open question. We adapted previously used machine learning models for predicting SM bioactivity from bacterial BGC data to fungal BGC data. We trained our models to predict antibacterial, antifungal, and cytotoxic/antitumor bioactivity on two datasets: 1) fungal BGCs (dataset comprised of 314 BGCs), and 2) fungal (314 BGCs) and bacterial BGCs (1,003 BGCs); the second dataset was our control since a previous study using just the bacterial BGC data yielded prediction accuracies as high as 80%. We found that the models trained only on fungal BGCs had balanced accuracies between 51-68%, whereas training on bacterial and fungal BGCs yielded balanced accuracies between 61-74%. The lower accuracy of the predictions from fungal data likely stems from the small number of BGCs and SMs with known bioactivity; this lack of data currently limits the application of machine learning approaches in studying fungal secondary metabolism. However, our data also suggest that machine learning approaches trained on bacterial and fungal data can predict SM bioactivity with good accuracy. With more than 15,000 characterized fungal SMs, millions of putative BGCs present in fungal genomes, and increased demand for novel drugs, efforts that systematically link fungal SM bioactivity to BGCs are urgently needed.
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Project description:Previously, DNA microarrays analysis showed that, in co-culture with Bacillus subtilis, a biosynthetic gene cluster anchored with a nonribosomal peptides synthetase of Aspergillus niger is downregulated. Based on phylogenetic and synteny analyses, we show here that this gene cluster, NRRL3_00036-NRRL3_00042, comprises genes predicted to encode a nonribosomal peptides synthetase, a FAD-binding domain-containing protein, an uncharacterized protein, a transporter, a cytochrome P450 protein, a NAD(P)-binding domain-containing protein and a transcription factor. We overexpressed the in-cluster transcription factor gene NRRL3_00042. The overexpression strain, NRRL3_00042OE, displays reduced growth rate and production of a yellow pigment, which by mass spectrometric analysis corresponds to two compounds with masses of 409.1384 and 425.1331. We deleted the gene encoding the NRRL3_00036 nonribosomal peptides synthetase in the NRRL3_00042OE strain. The resulting strain reverted to the wild-type phenotype. These results suggest that the biosynthetic gene cluster anchored by the NRRL3_00036 nonribosomal peptides synthetase gene is regulated by the in-cluster transcriptional regulator gene NRRL3_00042, and that it is involved in the production of two previously uncharacterized compounds.
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Project description:BackgroundThe antifungal therapy caspofungin is a semi-synthetic derivative of pneumocandin B0, a lipohexapeptide produced by the fungus Glarea lozoyensis, and was the first member of the echinocandin class approved for human therapy. The nonribosomal peptide synthetase (NRPS)-polyketide synthases (PKS) gene cluster responsible for pneumocandin biosynthesis from G. lozoyensis has not been elucidated to date. In this study, we report the elucidation of the pneumocandin biosynthetic gene cluster by whole genome sequencing of the G. lozoyensis wild-type strain ATCC 20868.ResultsThe pneumocandin biosynthetic gene cluster contains a NRPS (GLNRPS4) and a PKS (GLPKS4) arranged in tandem, two cytochrome P450 monooxygenases, seven other modifying enzymes, and genes for L-homotyrosine biosynthesis, a component of the peptide core. Thus, the pneumocandin biosynthetic gene cluster is significantly more autonomous and organized than that of the recently characterized echinocandin B gene cluster. Disruption mutants of GLNRPS4 and GLPKS4 no longer produced the pneumocandins (A0 and B0), and the Δglnrps4 and Δglpks4 mutants lost antifungal activity against the human pathogenic fungus Candida albicans. In addition to pneumocandins, the G. lozoyensis genome encodes a rich repertoire of natural product-encoding genes including 24 PKSs, six NRPSs, five PKS-NRPS hybrids, two dimethylallyl tryptophan synthases, and 14 terpene synthases.ConclusionsCharacterization of the gene cluster provides a blueprint for engineering new pneumocandin derivatives with improved pharmacological properties. Whole genome estimation of the secondary metabolite-encoding genes from G. lozoyensis provides yet another example of the huge potential for drug discovery from natural products from the fungal kingdom.
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Project description:We report a comparative study of 29 representative genomes of the animal pathogen Rhodococcus equi The analyses showed that R. equi is genetically homogeneous and clonal, with a large core genome accounting for ?80% of an isolates' gene content. An open pangenome, even distribution of accessory genes among the isolates, and absence of significant core-genome recombination, indicated that gene gain/loss is a main driver of R. equi genome evolution. Traits previously predicted to be important in R. equi physiology, virulence and niche adaptation were part of the core genome. This included the lack of a phosphoenolpyruvate:carbohydrate transport system (PTS), unique among the rhodococci except for the closely related Rhodococcus defluvii, reflecting selective PTS gene loss in the R. equi-R. defluvii sublineage. Thought to be asaccharolytic, rbsCB and glcP non-PTS sugar permease homologues were identified in the core genome and, albeit inefficiently, R. equi utilized their putative substrates, ribose and (irregularly) glucose. There was no correlation between R. equi whole-genome phylogeny and host or geographical source, with evidence of global spread of genomovars. The distribution of host-associated virulence plasmid types was consistent with the exchange of the plasmids (and corresponding host shifts) across the R. equi population, and human infection being zoonotically acquired. Phylogenomic analyses demonstrated that R. equi occupies a central position in the Rhodococcus phylogeny, not supporting the recently proposed transfer of the species to a new genus.
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