scholarly journals Bioactive Secondary Metabolites from Octocoral-Associated Microbes—New Chances for Blue Growth

Marine Drugs ◽  
2018 ◽  
Vol 16 (12) ◽  
pp. 485 ◽  
Author(s):  
Inês Raimundo ◽  
Sandra Silva ◽  
Rodrigo Costa ◽  
Tina Keller-Costa
Keyword(s):  
Natural Products ◽  
Genome Mining ◽  
Gene Clusters ◽  
Chemical Diversity ◽  
Chemical Structures ◽  
Novel Drugs ◽  
Blue Growth ◽  
Microbial Symbionts ◽  
Bacteria And Fungi ◽  
Near Future

Octocorals (Cnidaria, Anthozoa Octocorallia) are magnificent repositories of natural products with fascinating and unusual chemical structures and bioactivities of interest to medicine and biotechnology. However, mechanistic understanding of the contribution of microbial symbionts to the chemical diversity of octocorals is yet to be achieved. This review inventories the natural products so-far described for octocoral-derived bacteria and fungi, uncovering a true chemical arsenal of terpenes, steroids, alkaloids, and polyketides with antibacterial, antifungal, antiviral, antifouling, anticancer, anti-inflammatory, and antimalarial activities of enormous potential for blue growth. Genome mining of 15 bacterial associates (spanning 12 genera) cultivated from Eunicella spp. resulted in the identification of 440 putative and classifiable secondary metabolite biosynthetic gene clusters (BGCs), encompassing varied terpene-, polyketide-, bacteriocin-, and nonribosomal peptide-synthase BGCs. This points towards a widespread yet uncharted capacity of octocoral-associated bacteria to synthetize a broad range of natural products. However, to extend our knowledge and foster the near-future laboratory production of bioactive compounds from (cultivatable and currently uncultivatable) octocoral symbionts, optimal blending between targeted metagenomics, DNA recombinant technologies, improved symbiont cultivation, functional genomics, and analytical chemistry are required. Such a multidisciplinary undertaking is key to achieving a sustainable response to the urgent industrial demand for novel drugs and enzyme varieties.

scholarly journals Recent development of antiSMASH and other computational approaches to mine secondary metabolite biosynthetic gene clusters

2017 ◽  
Vol 20 (4) ◽  
pp. 1103-1113 ◽  
Author(s):  
Kai Blin ◽  
Hyun Uk Kim ◽  
Marnix H Medema ◽  
Tilmann Weber
Keyword(s):  
Natural Products ◽  
Small Molecules ◽  
Sequence Similarity ◽  
Genome Mining ◽  
Gene Clusters ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Rule Based ◽  
Chemical Structures ◽  
Annotation Quality

Abstract Many drugs are derived from small molecules produced by microorganisms and plants, so-called natural products. Natural products have diverse chemical structures, but the biosynthetic pathways producing those compounds are often organized as biosynthetic gene clusters (BGCs) and follow a highly conserved biosynthetic logic. This allows for the identification of core biosynthetic enzymes using genome mining strategies that are based on the sequence similarity of the involved enzymes/genes. However, mining for a variety of BGCs quickly approaches a complexity level where manual analyses are no longer possible and require the use of automated genome mining pipelines, such as the antiSMASH software. In this review, we discuss the principles underlying the predictions of antiSMASH and other tools and provide practical advice for their application. Furthermore, we discuss important caveats such as rule-based BGC detection, sequence and annotation quality and cluster boundary prediction, which all have to be considered while planning for, performing and analyzing the results of genome mining studies.

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.

Expanding the Natural Products Heterologous Expression Repertoire in the Model Cyanobacterium Anabaena sp. Strain PCC 7120: Production of Pendolmycin and Teleocidin B-4

2019 ◽  
Author(s):  
Patrick Videau ◽  
Kaitlyn Wells ◽  
Arun Singh ◽  
Jessie Eiting ◽  
Philip Proteau ◽  
...  
Keyword(s):  
Natural Products ◽  
Genome Mining ◽  
Gene Clusters ◽  
Combinatorial Biosynthesis ◽  
Test Case ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Cyanobacterium Anabaena ◽  
Anabaena Sp ◽  
Pcc 7120

Cyanobacteria are prolific producers of natural products and genome mining has shown that many orphan biosynthetic gene clusters can be found in sequenced cyanobacterial genomes. New tools and methodologies are required to investigate these biosynthetic gene clusters and here we present the use of <i>Anabaena </i>sp. strain PCC 7120 as a host for combinatorial biosynthesis of natural products using the indolactam natural products (lyngbyatoxin A, pendolmycin, and teleocidin B-4) as a test case. We were able to successfully produce all three compounds using codon optimized genes from Actinobacteria. We also introduce a new plasmid backbone based on the native <i>Anabaena</i>7120 plasmid pCC7120ζ and show that production of teleocidin B-4 can be accomplished using a two-plasmid system, which can be introduced by co-conjugation.

scholarly journals The Tripod for Bacterial Natural Product Discovery: Genome Mining, Silent Pathway Induction, and Mass Spectrometry-Based Molecular Networking

mSystems ◽  
2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Daniela B. B. Trivella ◽  
Rafael de Felicio
Keyword(s):  
Mass Spectrometry ◽  
Natural Products ◽  
Natural Product ◽  
Biosynthetic Pathway ◽  
Genome Mining ◽  
Chemical Compounds ◽  
Gene Clusters ◽  
Tandem Mass ◽  
Molecular Networking ◽  
Natural Product Discovery

ABSTRACT Natural products are the richest source of chemical compounds for drug discovery. Particularly, bacterial secondary metabolites are in the spotlight due to advances in genome sequencing and mining, as well as for the potential of biosynthetic pathway manipulation to awake silent (cryptic) gene clusters under laboratory cultivation. Further progress in compound detection, such as the development of the tandem mass spectrometry (MS/MS) molecular networking approach, has contributed to the discovery of novel bacterial natural products. The latter can be applied directly to bacterial crude extracts for identifying and dereplicating known compounds, therefore assisting the prioritization of extracts containing novel natural products, for example. In our opinion, these three approaches—genome mining, silent pathway induction, and MS-based molecular networking—compose the tripod for modern bacterial natural product discovery and will be discussed in this perspective.

scholarly journals Analysis of a large food chemical database: chemical space, diversity, and complexity

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 993 ◽  
Author(s):  
J. Jesús Naveja ◽  
Mariel P. Rico-Hidalgo ◽  
José L. Medina-Franco
Keyword(s):  
Natural Products ◽  
Chemical Space ◽  
Structural Complexity ◽  
Chemical Diversity ◽  
Chemical Structures ◽  
Novel Approach ◽  
Chemical Database ◽  
Protein Interactome ◽  
Limited Basis ◽  
Future Direction

Background: Food chemicals are a cornerstone in the food industry. However, its chemical diversity has been explored on a limited basis, for instance, previous analysis of food-related databases were done up to 2,200 molecules. The goal of this work was to quantify the chemical diversity of chemical compounds stored in FooDB, a database with nearly 24,000 food chemicals. Methods: The visual representation of the chemical space of FooDB was done with ChemMaps, a novel approach based on the concept of chemical satellites. The large food chemical database was profiled based on physicochemical properties, molecular complexity and scaffold content. The global diversity of FooDB was characterized using Consensus Diversity Plots. Results: It was found that compounds in FooDB are very diverse in terms of properties and structure, with a large structural complexity. It was also found that one third of the food chemicals are acyclic molecules and ring-containing molecules are mostly monocyclic, with several scaffolds common to natural products in other databases. Conclusions: To the best of our knowledge, this is the first analysis of the chemical diversity and complexity of FooDB. This study represents a step further to the emerging field of “Food Informatics”. Future study should compare directly the chemical structures of the molecules in FooDB with other compound databases, for instance, drug-like databases and natural products collections. An additional future direction of this work is to use the list of 3,228 polyphenolic compounds identified in this work to enhance the on-going polyphenol-protein interactome studies.

scholarly journals Uncovering the unexplored diversity of thioamidated ribosomal peptides in Actinobacteria using the RiPPER genome mining tool

2018 ◽  
Author(s):  
Javier Santos-Aberturas ◽  
Govind Chandra ◽  
Luca Frattaruolo ◽  
Rodney Lacret ◽  
Thu H. Pham ◽  
...  
Keyword(s):  
Genome Mining ◽  
Gene Clusters ◽  
Chemical Diversity ◽  
Bioactive Molecules ◽  
Post Translational Modification ◽  
Biosynthetic Gene Clusters ◽  
New Family ◽  
The Family ◽  
Modified Peptides ◽  
Mining Tool

ABSTRACTThe rational discovery of new specialized metabolites by genome mining represents a very promising strategy in the quest for new bioactive molecules. Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a major class of natural product that derive from genetically encoded precursor peptides. However, RiPP gene clusters are particularly refractory to reliable bioinformatic predictions due to the absence of a common biosynthetic feature across all pathways. Here, we describe RiPPER, a new tool for the family-independent identification of RiPP precursor peptides and apply this methodology to search for novel thioamidated RiPPs in Actinobacteria. Until now, thioamidation was believed to be a rare post-translational modification, which is catalyzed by a pair of proteins (YcaO and TfuA) in Archaea. In Actinobacteria, the thioviridamide-like molecules are a family of cytotoxic RiPPs that feature multiple thioamides, and it has been proposed that a YcaO-TfuA pair of proteins also catalyzes their formation. Potential biosynthetic gene clusters encoding YcaO and TfuA protein pairs are common in Actinobacteria but the chemical diversity generated by these pathways is almost completely unexplored. A RiPPER analysis reveals a highly diverse landscape of precursor peptides encoded in previously undescribed gene clusters that are predicted to make thioamidated RiPPs. To illustrate this strategy, we describe the first rational discovery of a new family of thioamidated natural products, the thiovarsolins from Streptomyces varsoviensis.

scholarly journals Natural Products, Traditional Uses and Pharmacological Activities of the Genus Biebersteinia (Biebersteiniaceae)

Plants ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 595 ◽  
Author(s):  
Benyin Zhang ◽  
Xiaona Jin ◽  
Hengxia Yin ◽  
Dejun Zhang ◽  
Huakun Zhou ◽  
...  
Keyword(s):  
Natural Products ◽  
Molecular Mechanisms ◽  
Chemical Properties ◽  
Pharmacological Properties ◽  
Pharmacological Activities ◽  
Drug Discovery And Development ◽  
Chemical Structures ◽  
Traditional Uses ◽  
Novel Drugs ◽  
Major Chemical

Medicinal plants have been known as a rich source of natural products (NPs). Due to their diverse chemical structures and remarkable pharmacological activities, NPs are regarded as important repertoires for drug discovery and development. Biebersteinia plant species belong to the Biebersteiniaceae family, and have been used in folk medicines in China and Iran for ages. However, the chemical properties, bioactivities and modes of action of the NPs produced by medicinal Biebersteinia species are poorly understood despite the fact that there are only four known Biebersteinia species worldwide. Here, we reviewed the chemical classifications and diversity of the various NPs found in the four known Biebersteinia species. We found that the major chemical categories in these plants include flavonoids, alkaloids, phenylpropanoids, terpenoids, essential oils and fatty acids. We also discussed the anti-inflammatory, analgesic, antibacterial, antioxidant, antihypertensive and hypoglycemic effects of the four Biebersteinia species. We believe that the present review will facilitate the exploration of traditional uses and pharmacological properties of Biebersteinia species, extraction of the NPs and elucidation of their molecular mechanisms, as well as the development of novel drugs based on the reported properties and mode-of-action.

scholarly journals Multi-Omic Profiling of Melophlus Sponges Reveals Diverse Metabolomic and Microbiome Architectures that Are Non-overlapping with Ecological Neighbors

Marine Drugs ◽  
2020 ◽  
Vol 18 (2) ◽  
pp. 124 ◽  
Author(s):  
Ipsita Mohanty ◽  
Sheila Podell ◽  
Jason S. Biggs ◽  
Neha Garg ◽  
Eric E. Allen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Natural Products ◽  
Candidate Gene ◽  
Marine Sponge ◽  
Gene Clusters ◽  
Marine Sponges ◽  
Quantitative Comparison ◽  
Filter Feeding ◽  
Chemical Structures ◽  
Ecological Relevance

Marine sponge holobionts, defined as filter-feeding sponge hosts together with their associated microbiomes, are prolific sources of natural products. The inventory of natural products that have been isolated from marine sponges is extensive. Here, using untargeted mass spectrometry, we demonstrate that sponges harbor a far greater diversity of low-abundance natural products that have evaded discovery. While these low-abundance natural products may not be feasible to isolate, insights into their chemical structures can be gleaned by careful curation of mass fragmentation spectra. Sponges are also some of the most complex, multi-organismal holobiont communities in the oceans. We overlay sponge metabolomes with their microbiome structures and detailed metagenomic characterization to discover candidate gene clusters that encode production of sponge-derived natural products. The multi-omic profiling strategy for sponges that we describe here enables quantitative comparison of sponge metabolomes and microbiomes to address, among other questions, the ecological relevance of sponge natural products and for the phylochemical assignment of previously undescribed sponge identities.

scholarly journals In vivo characterization of the activities of novel cyclodipeptide oxidases: new tools for increasing chemical diversity of bioproduced 2,5-diketopiperazines in Escherichia coli

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Fabien Le Chevalier ◽  
Isabelle Correia ◽  
Lucrèce Matheron ◽  
Morgan Babin ◽  
Mireille Moutiez ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Biological Activities ◽  
Gene Clusters ◽  
Chemical Diversity ◽  
E Coli ◽  
Chemical Structures ◽  
In Vivo Characterization ◽  
Culture Supernatants

Abstract Background Cyclodipeptide oxidases (CDOs) are enzymes involved in the biosynthesis of 2,5-diketopiperazines, a class of naturally occurring compounds with a large range of pharmaceutical activities. CDOs belong to cyclodipeptide synthase (CDPS)-dependent pathways, in which they play an early role in the chemical diversification of cyclodipeptides by introducing Cα-Cβ dehydrogenations. Although the activities of more than 100 CDPSs have been determined, the activities of only a few CDOs have been characterized. Furthermore, the assessment of the CDO activities on chemically-synthesized cyclodipeptides has shown these enzymes to be relatively promiscuous, making them interesting tools for cyclodipeptide chemical diversification. The purpose of this study is to provide the first completely microbial toolkit for the efficient bioproduction of a variety of dehydrogenated 2,5-diketopiperazines. Results We mined genomes for CDOs encoded in biosynthetic gene clusters of CDPS-dependent pathways and selected several for characterization. We co-expressed each with their associated CDPS in the pathway using Escherichia coli as a chassis and showed that the cyclodipeptides and the dehydrogenated derivatives were produced in the culture supernatants. We determined the biological activities of the six novel CDOs by solving the chemical structures of the biologically produced dehydrogenated cyclodipeptides. Then, we assessed the six novel CDOs plus two previously characterized CDOs in combinatorial engineering experiments in E. coli. We co-expressed each of the eight CDOs with each of 18 CDPSs selected for the diversity of cyclodipeptides they synthesize. We detected more than 50 dehydrogenated cyclodipeptides and determined the best CDPS/CDO combinations to optimize the production of 23. Conclusions Our study establishes the usefulness of CDPS and CDO for the bioproduction of dehydrogenated cyclodipeptides. It constitutes the first step toward the bioproduction of more complex and diverse 2,5-diketopiperazines.

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.

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