scholarly journals Identification, heterologous production and bioactivity of lentinulin A and dendrothelin A, two natural variants of backbone N-methylated peptide macrocycle omphalotin A

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Emmanuel Matabaro ◽  
Hannelore Kaspar ◽  
Paul Dahlin ◽  
Daniel L. V. Bader ◽  
Claudia E. Murar ◽  
...  
Keyword(s):  
Natural Products ◽  
Lentinula Edodes ◽  
Gene Clusters ◽  
Biosynthetic Gene Cluster ◽  
Precursor Protein ◽  
Fungal Genome ◽  
Homologous Gene ◽  
C Terminus ◽  
Natural Variants ◽  
Recombinant Peptides

AbstractBackbone N-methylation and macrocyclization improve the pharmacological properties of peptides by enhancing their proteolytic stability, membrane permeability and target selectivity. Borosins are backbone N-methylated peptide macrocycles derived from a precursor protein which contains a peptide α-N-methyltransferase domain autocatalytically modifying the core peptide located at its C-terminus. Founding members of borosins are the omphalotins from the mushroom Omphalotus olearius (omphalotins A-I) with nine out of 12 L-amino acids being backbone N-methylated. The omphalotin biosynthetic gene cluster codes for the precursor protein OphMA, the protease prolyloligopeptidase OphP and other proteins that are likely to be involved in other post-translational modifications of the peptide. Mining of available fungal genome sequences revealed the existence of highly homologous gene clusters in the basidiomycetes Lentinula edodes and Dendrothele bispora. The respective borosins, referred to as lentinulins and dendrothelins are naturally produced by L. edodes and D. bispora as shown by analysis of respective mycelial extracts. We produced all three homologous peptide natural products by coexpression of OphMA hybrid proteins and OphP in the yeast Pichia pastoris. The recombinant peptides differ in their nematotoxic activity against the plant pathogen Meloidogyne incognita. Our findings pave the way for the production of borosin peptide natural products and their potential application as novel biopharmaceuticals and biopesticides.

scholarly journals A Deep Learning Genome-Mining Strategy Improves Biosynthetic Gene Cluster Prediction

2018 ◽  
Author(s):  
Geoffrey D. Hannigan ◽  
David Prihoda ◽  
Andrej Palicka ◽  
Jindrich Soukup ◽  
Ondrej Klempir ◽  
...  
Keyword(s):  
Natural Products ◽  
Deep Learning ◽  
Learning Strategy ◽  
Genome Mining ◽  
Gene Clusters ◽  
Biosynthetic Gene Cluster ◽  
Biosynthetic Gene ◽  
Antimicrobial Drugs ◽  
Drug Candidates ◽  
Significant Step

AbstractNatural products represent a rich reservoir of small molecule drug candidates utilized as antimicrobial drugs, anticancer therapies, and immunomodulatory agents. These molecules are microbial secondary metabolites synthesized by co-localized genes termed Biosynthetic Gene Clusters (BGCs). The increase in full microbial genomes and similar resources has led to development of BGC prediction algorithms, although their precision and ability to identify novel BGC classes could be improved. Here we present a deep learning strategy (DeepBGC) that offers more accurate BGC identification and an improved ability to extrapolate and identify novel BGC classes compared to existing tools. We supplemented this with downstream random forest classifiers that accurately predicted BGC product classes and potential chemical activity. Application of DeepBGC to bacterial genomes uncovered previously undetectable BGCs that may code for natural products with novel biologic activities. The improved accuracy and classification ability of DeepBGC represents a significant step forward forin-silicoBGC identification.

scholarly journals Further biochemical profiling of Hypholoma fasciculare metabolome reveals its chemogenetic diversity

2020 ◽  
Author(s):  
Suhad A.A. Al-Salihi ◽  
Ian Bull ◽  
Raghad A. Al-Salhi ◽  
Paul J. Gates ◽  
Kifah Salih ◽  
...  
Keyword(s):  
Natural Products ◽  
Chemical Properties ◽  
Structural Diversity ◽  
Gene Clusters ◽  
Biosynthetic Gene Cluster ◽  
Biosynthetic Gene ◽  
Microbial Drug Resistance ◽  
Bioactive Natural Products ◽  
Highly Active ◽  
Comprehensive Picture

AbstractThere is a desperate need in continuing the search for natural products with novel mechanism to battle the constant increase of microbial drug resistance. Previously mushroom forming fungi were neglected as a source of novel antibiotics, due to the difficulties associated with their culture preparation and genetic tractability. However, modern fungal molecular and synthetic biology tools, renewed the interest in exploring mushroom fungi for novel therapeutics. The aim of this study was to have a comprehensive picture of nine basidiomycetes secondary metabolites (SM), screen their biological and chemical properties to describe the genetic pathways associated with their production. H. fasciculare revealed to be highly active antagonistic species, with antimicrobial activity against three different microorganisms - Bacillus subtilis, Escherichia coli and Saccharomyces cerevisiae-. Extensive genomic comparison and chemical analysis using analytical chromatography, led to the characterisation of more than 15 variant biosynthetic gene clusters and the first identification of a potent antibacterial metabolite-3, 5-dichloromethoxy benzoic acid (3, 5-D)-in this species, for which a biosynthetic gene cluster was predicted. This work demonstrates the great potential of mushroom forming fungi as a reservoir of bioactive natural products which are currently unexplored, and that access to their genomic data and structural diversity natural products via utilizing modern computational analysis and efficient chemical methods, could accelerate the development and applications of such distinct molecules in both pharmaceutical and agrochemical industry.

scholarly journals Biosynthetic Potential of a Novel Antarctic Actinobacterium Marisediminicola antarctica ZS314T Revealed by Genomic Data Mining and Pigment Characterization

Marine Drugs ◽  
2019 ◽  
Vol 17 (7) ◽  
pp. 388 ◽  
Author(s):  
Li Liao ◽  
Shiyuan Su ◽  
Bin Zhao ◽  
Chengqi Fan ◽  
Jin Zhang ◽  
...  
Keyword(s):  
Data Mining ◽  
Natural Products ◽  
Gene Cluster ◽  
Genomic Data ◽  
Gene Clusters ◽  
Biosynthetic Gene Cluster ◽  
The Novel ◽  
Biosynthetic Gene ◽  
Base Pairs ◽  
Peptide Synthetase

Rare actinobacterial species are considered as potential resources of new natural products. Marisediminicola antarctica ZS314T is the only type strain of the novel actinobacterial genus Marisediminicola isolated from intertidal sediments in East Antarctica. The strain ZS314T was able to produce reddish orange pigments at low temperatures, showing characteristics of carotenoids. To understand the biosynthetic potential of this strain, the genome was completely sequenced for data mining. The complete genome had 3,352,609 base pairs (bp), much smaller than most genomes of actinomycetes. Five biosynthetic gene clusters (BGCs) were predicted in the genome, including a gene cluster responsible for the biosynthesis of C50 carotenoid, and four additional BGCs of unknown oligosaccharide, salinixanthin, alkylresorcinol derivatives, and NRPS (non-ribosomal peptide synthetase) or amino acid-derived compounds. Further experimental characterization indicated that the strain may produce C.p.450-like carotenoids, supporting the genomic data analysis. A new xanthorhodopsin gene was discovered along with the analysis of the salinixanthin biosynthetic gene cluster. Since little is known about this genus, this work improves our understanding of its biosynthetic potential and provides opportunities for further investigation of natural products and strategies for adaptation to the extreme Antarctic environment.

scholarly journals Natural Product Gene Clusters in the Filamentous Nostocales Cyanobacterium HT-58-2

Life ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 356
Author(s):  
Xiaohe Jin ◽  
Eric S. Miller ◽  
Jonathan S. Lindsey
Keyword(s):  
Natural Products ◽  
Nitrogen Fixation ◽  
Natural Product ◽  
Microbial Consortium ◽  
Gene Clusters ◽  
Biosynthetic Gene Cluster ◽  
Nitrogen Stress ◽  
Biosynthetic Gene ◽  
Tetrapyrrole Biosynthesis ◽  
The Rich

Cyanobacteria are known as rich repositories of natural products. One cyanobacterial-microbial consortium (isolate HT-58-2) is known to produce two fundamentally new classes of natural products: the tetrapyrrole pigments tolyporphins A–R, and the diterpenoid compounds tolypodiol, 6-deoxytolypodiol, and 11-hydroxytolypodiol. The genome (7.85 Mbp) of the Nostocales cyanobacterium HT-58-2 was annotated previously for tetrapyrrole biosynthesis genes, which led to the identification of a putative biosynthetic gene cluster (BGC) for tolyporphins. Here, bioinformatics tools have been employed to annotate the genome more broadly in an effort to identify pathways for the biosynthesis of tolypodiols as well as other natural products. A putative BGC (15 genes) for tolypodiols has been identified. Four BGCs have been identified for the biosynthesis of other natural products. Two BGCs related to nitrogen fixation may be relevant, given the association of nitrogen stress with production of tolyporphins. The results point to the rich biosynthetic capacity of the HT-58-2 cyanobacterium beyond the production of tolyporphins and tolypodiols.

scholarly journals Further Biochemical Profiling of Hypholoma fasciculare Metabolome Reveals Its Chemogenetic Diversity

2021 ◽  
Vol 9 ◽  
Author(s):  
Suhad A. A. Al-Salihi ◽  
Ian D. Bull ◽  
Raghad Al-Salhi ◽  
Paul J. Gates ◽  
Kifah S. M. Salih ◽  
...  
Keyword(s):  
Natural Products ◽  
Chemical Properties ◽  
Gene Clusters ◽  
Biosynthetic Gene Cluster ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Microbial Drug Resistance ◽  
Bioactive Natural Products ◽  
Highly Active ◽  
Hypholoma Fasciculare

Natural products with novel chemistry are urgently needed to battle the continued increase in microbial drug resistance. Mushroom-forming fungi are underutilized as a source of novel antibiotics in the literature due to their challenging culture preparation and genetic intractability. However, modern fungal molecular and synthetic biology tools have renewed interest in exploring mushroom fungi for novel therapeutic agents. The aims of this study were to investigate the secondary metabolites of nine basidiomycetes, screen their biological and chemical properties, and then investigate the genetic pathways associated with their production. Of the nine fungi selected, Hypholoma fasciculare was revealed to be a highly active antagonistic species, with antimicrobial activity against three different microorganisms: Bacillus subtilis, Escherichia coli, and Saccharomyces cerevisiae. Genomic comparisons and chromatographic studies were employed to characterize more than 15 biosynthetic gene clusters and resulted in the identification of 3,5-dichloromethoxy benzoic acid as a potential antibacterial compound. The biosynthetic gene cluster for this product is also predicted. This study reinforces the potential of mushroom-forming fungi as an underexplored reservoir of bioactive natural products. Access to genomic data, and chemical-based frameworks, will assist the development and application of novel molecules with applications in both the pharmaceutical and agrochemical industries.

scholarly journals Deep-BGCpred: A unified deep learning genome-mining framework for biosynthetic gene cluster prediction

2021 ◽  
Author(s):  
Ziyi Yang ◽  
Benben Liao ◽  
Changyu Hsieh ◽  
Chao Han ◽  
Liang Fang ◽  
...  
Keyword(s):  
Natural Products ◽  
Deep Learning ◽  
High Throughput Sequencing ◽  
Short Term Memory ◽  
Genome Mining ◽  
Gene Clusters ◽  
Biosynthetic Gene Cluster ◽  
Bioactive Molecules ◽  
Dual Model ◽  
Biosynthetic Gene

Natural products produced by microorganisms constitute an important source of essential pharmaceuticals, including antimicrobial and anti-tumor drugs. These bioactive molecules are microbial secondary metabolites synthesized by co-localized genes termed Biosynthetic Gene Clusters (BGCs). The rapid increase of microbial genomics resources, due to the availability of high-throughput sequencing technologies, has spurred the development of computational methods for microbial genome mining for BGC discovery. Current machine learning methods, however, have limited successes in uncovering novel BGCs due to an excessive number of false positives in their predictions. To this end, we propose Deep-BGCpred, a framework that effectively addresses the aforementioned issue by improving a deep learning model termed DeepBGC. The new model embeds multi-source protein family domains and employs a stacked Bidirectional Long Short-Term Memory model to boost accuracy for BGC identifications. In particular, it integrates two customized strategies, sliding window strategy and dual-model serial screening, to improve the model's performance stability and reduce the number of false positive in BGC predictions. We compare the proposed model against other well-established methods on common benchmarks and achieve new state-of-the-art results with convincing evidences. We expect that researchers working on genome mining for natural products may be greatly benefited from our newly proposed method, Deep-BGCpred.

scholarly journals Structural evolution of a DNA repair self-resistance mechanism targeting genotoxic secondary metabolites

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Elwood A. Mullins ◽  
Jonathan Dorival ◽  
Gong-Li Tang ◽  
Dale L. Boger ◽  
Brandt F. Eichman
Keyword(s):  
Natural Products ◽  
Selective Pressure ◽  
Alkylating Agents ◽  
Resistance Mechanism ◽  
Structural Evolution ◽  
Gene Clusters ◽  
Biosynthetic Gene Cluster ◽  
Structural Basis

AbstractMicrobes produce a broad spectrum of antibiotic natural products, including many DNA-damaging genotoxins. Among the most potent of these are DNA alkylating agents in the spirocyclopropylcyclohexadienone (SCPCHD) family, which includes the duocarmycins, CC-1065, gilvusmycin, and yatakemycin. The yatakemycin biosynthesis cluster in Streptomyces sp. TP-A0356 contains an AlkD-related DNA glycosylase, YtkR2, that serves as a self-resistance mechanism against yatakemycin toxicity. We previously reported that AlkD, which is not present in an SCPCHD producer, provides only limited resistance against yatakemycin. We now show that YtkR2 and C10R5, a previously uncharacterized homolog found in the CC-1065 biosynthetic gene cluster of Streptomyces zelensis, confer far greater resistance against their respective SCPCHD natural products. We identify a structural basis for substrate specificity across gene clusters and show a correlation between in vivo resistance and in vitro enzymatic activity indicating that reduced product affinity—not enhanced substrate recognition—is the evolutionary outcome of selective pressure to provide self-resistance against yatakemycin and CC-1065.

scholarly journals Recapitulation of the evolution of biosynthetic gene clusters reveals hidden chemical diversity on bacterial genomes

2015 ◽  
Author(s):  
Pablo Cruz-Morales ◽  
Christian E. Martínez-Guerrero ◽  
Marco A. Morales-Escalante ◽  
Luis Yáñez-Guerra ◽  
Johannes Florian Kopp ◽  
...  
Keyword(s):  
Natural Products ◽  
Chemical Space ◽  
Streptomyces Coelicolor ◽  
Genome Mining ◽  
Gene Clusters ◽  
Biosynthetic Gene Cluster ◽  
Chemical Diversity ◽  
Biosynthetic Gene ◽  
Bacterial Genomes ◽  
Biosynthetic Gene Clusters

AbstractNatural products have provided humans with antibiotics for millennia. However, a decline in the pace of chemical discovery exerts pressure on human health as antibiotic resistance spreads. The empirical nature of current genome mining approaches used for natural products research limits the chemical space that is explored. By integration of evolutionary concepts related to emergence of metabolism, we have gained fundamental insights that are translated into an alternative genome mining approach, termed EvoMining. As the founding assumption of EvoMining is the evolution of enzymes, we solved two milestone problems revealing unprecedented conversions. First, we report the biosynthetic gene cluster of the ‘orphan’ metabolite leupeptin in Streptomyces roseus. Second, we discover an enzyme involved in formation of an arsenic-carbon bond in Streptomyces coelicolor and Streptomyces lividans. This work provides evidence that bacterial chemical repertoire is underexploited, as well as an approach to accelerate the discovery of novel antibiotics from bacterial genomes.

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 Targeted Genome Mining—From Compound Discovery to Biosynthetic Pathway Elucidation

2020 ◽  
Vol 8 (12) ◽  
pp. 2034
Author(s):  
Nils Gummerlich ◽  
Yuriy Rebets ◽  
Constanze Paulus ◽  
Josef Zapp ◽  
Andriy Luzhetskyy
Keyword(s):  
Natural Products ◽  
Polyketide Synthase ◽  
Genomic Library ◽  
Mutational Analysis ◽  
Genome Mining ◽  
Gene Clusters ◽  
Biosynthetic Gene Cluster ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Feeding Experiments

Natural products are an important source of novel investigational compounds in drug discovery. Especially in the field of antibiotics, Actinobacteria have been proven to be a reliable source for lead structures. The discovery of these natural products with activity- and structure-guided screenings has been impeded by the constant rediscovery of previously identified compounds. Additionally, a large discrepancy between produced natural products and biosynthetic potential in Actinobacteria, including representatives of the order Pseudonocardiales, has been revealed using genome sequencing. To turn this genomic potential into novel natural products, we used an approach including the in-silico pre-selection of unique biosynthetic gene clusters followed by their systematic heterologous expression. As a proof of concept, fifteen Saccharothrixespanaensis genomic library clones covering predicted biosynthetic gene clusters were chosen for expression in two heterologous hosts, Streptomyceslividans and Streptomycesalbus. As a result, two novel natural products, an unusual angucyclinone pentangumycin and a new type II polyketide synthase shunt product SEK90, were identified. After purification and structure elucidation, the biosynthetic pathways leading to the formation of pentangumycin and SEK90 were deduced using mutational analysis of the biosynthetic gene cluster and feeding experiments with 13C-labelled precursors.

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