Synthetic Biology Advanced 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.

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

Microbiology Resource Announcements ◽

10.1128/mra.00485-20 ◽

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.

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BiG-SLiCE: A Highly Scalable Tool Maps the Diversity of 1.2 Million Biosynthetic Gene Clusters

10.1101/2020.08.17.240838 ◽

2020 ◽

Cited By ~ 3

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.

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A supervised fingerprint-based strategy to connect natural product mass spectrometry fragmentation data to their biosynthetic gene clusters

10.1101/2021.10.05.463235 ◽

2021 ◽

Author(s):

Tiago F. Leao ◽

Mingxun Wang ◽

Ricardo da Silva ◽

Justin J.J. van der Hooft ◽

Anelize Bauermeister ◽

...

Keyword(s):

Mass Spectrometry ◽

Natural Products ◽

Natural Product ◽

Nearest Neighbor ◽

Gene Clusters ◽

Supervised Machine Learning ◽

Biosynthetic Gene ◽

Biosynthetic Gene Clusters ◽

Biosynthetic Genes ◽

Natural Product Discovery

AbstractMicrobial natural products, in particular secondary or specialized metabolites, are an important source and inspiration for many pharmaceutical and biotechnological products. However, bioactivity-guided methods widely employed in natural product discovery programs do not explore the full biosynthetic potential of microorganisms, and they usually miss metabolites that are produced at low titer. As a complementary method, the use of genome-based mining in natural products research has facilitated the charting of many novel natural products in the form of predicted biosynthetic gene clusters that encode for their production. Linking the biosynthetic potential inferred from genomics to the specialized metabolome measured by metabolomics would accelerate natural product discovery programs. Here, we applied a supervised machine learning approach, the K-Nearest Neighbor (KNN) classifier, for systematically connecting metabolite mass spectrometry data to their biosynthetic gene clusters. This pipeline offers a method for annotating the biosynthetic genes for known, analogous to known and cryptic metabolites that are detected via mass spectrometry. We demonstrate this approach by automated linking of six different natural product mass spectra, and their analogs, to their corresponding biosynthetic genes. Our approach can be applied to bacterial, fungal, algal and plant systems where genomes are paired with corresponding MS/MS spectra. Additionally, an approach that connects known metabolites to their biosynthetic genes potentially allows for bulk production via heterologous expression and it is especially useful for cases where the metabolites are produced at low amounts in the original producer.SignificanceThe pace of natural products discovery has remained relatively constant over the last two decades. At the same time, there is an urgent need to find new therapeutics to fight antibiotic resistant bacteria, cancer, tropical parasites, pathogenic viruses, and other severe diseases. To spark the enhanced discovery of structurally novel and bioactive natural products, we here introduce a supervised learning algorithm (K-Nearest Neighbor) that can connect known and analogous to known, as well as MS/MS spectra of yet unknowns to their corresponding biosynthetic gene clusters. Our Natural Products Mixed Omics tool provides access to genomic information for bioactivity prediction, class prediction, substrate predictions, and stereochemistry predictions to prioritize relevant metabolite products and facilitate their structural elucidation.

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BiG-SLiCE: A highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters

GigaScience ◽

10.1093/gigascience/giaa154 ◽

2021 ◽

Vol 10 (1) ◽

Cited By ~ 1

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

Abstract Background Genome 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 are hindered by a bottleneck caused by the expensive network-based approach used to group these BGCs into gene cluster families (GCFs). Results Here, 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 within 10 days on a typical 36-core 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. Conclusions BiG-SLiCE opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global and searchable interconnected network of BGCs. As more genomes are 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.

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

10.26434/chemrxiv.11316098.v1 ◽

2019 ◽

Cited By ~ 1

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.

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

mSystems ◽

10.1128/msystems.00160-17 ◽

2018 ◽

Vol 3 (2) ◽

Cited By ~ 14

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.

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Activation of Silent Natural Product Biosynthetic Gene Clusters Using Synthetic Biology Tools

Comprehensive Natural Products III ◽

10.1016/b978-0-12-409547-2.14725-0 ◽

2020 ◽

pp. 113-135

Author(s):

Bin Wang ◽

Hengqian Ren ◽

Qiqi Tian ◽

Huimin Zhao

Keyword(s):

Synthetic Biology ◽

Natural Product ◽

Gene Clusters ◽

Biosynthetic Gene ◽

Biosynthetic Gene Clusters

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Challenges and Advances in Genome Editing Technologies in Streptomyces

Biomolecules ◽

10.3390/biom10050734 ◽

2020 ◽

Vol 10 (5) ◽

pp. 734 ◽

Cited By ~ 2

Author(s):

Yawei Zhao ◽

Guoquan Li ◽

Yunliang Chen ◽

Yinhua Lu

Keyword(s):

Natural Product ◽

Genome Editing ◽

Genetic Manipulation ◽

Chemical Compounds ◽

Gene Clusters ◽

Future Research ◽

Biosynthetic Gene ◽

Biosynthetic Gene Clusters ◽

Research Focus ◽

Experimental Conditions

The genome of Streptomyces encodes a high number of natural product (NP) biosynthetic gene clusters (BGCs). Most of these BGCs are not expressed or are poorly expressed (commonly called silent BGCs) under traditional laboratory experimental conditions. These NP BGCs represent an unexplored rich reservoir of natural compounds, which can be used to discover novel chemical compounds. To activate silent BGCs for NP discovery, two main strategies, including the induction of BGCs expression in native hosts and heterologous expression of BGCs in surrogate Streptomyces hosts, have been adopted, which normally requires genetic manipulation. So far, various genome editing technologies have been developed, which has markedly facilitated the activation of BGCs and NP overproduction in their native hosts, as well as in heterologous Streptomyces hosts. In this review, we summarize the challenges and recent advances in genome editing tools for Streptomyces genetic manipulation with a focus on editing tools based on clustered regularly interspaced short palindrome repeat (CRISPR)/CRISPR-associated protein (Cas) systems. Additionally, we discuss the future research focus, especially the development of endogenous CRISPR/Cas-based genome editing technologies in Streptomyces.

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

Briefings in Bioinformatics ◽

10.1093/bib/bbx146 ◽

2017 ◽

Vol 20 (4) ◽

pp. 1103-1113 ◽

Cited By ~ 37

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.

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