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

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.

scholarly journals Synthetic Biology Advanced Natural Product Discovery

Metabolites ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 785
Author(s):  
Junyang Wang ◽  
Jens Nielsen ◽  
Zihe Liu
Keyword(s):  
Natural Products ◽  
Synthetic Biology ◽  
Natural Product ◽  
Rapid Development ◽  
Genome Mining ◽  
Gene Clusters ◽  
Future Research ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
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.

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

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.

Native and engineered promoters in natural product discovery

2016 ◽  
Vol 33 (8) ◽  
pp. 1006-1019 ◽  
Author(s):  
Maksym Myronovskyi ◽  
Andriy Luzhetskyy
Keyword(s):  
Natural Product ◽  
Transcriptional Activation ◽  
Gene Clusters ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Natural Product Discovery

Transcriptional activation of biosynthetic gene clusters.

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

2020 ◽  
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.

A Natural Product Chemist's Guide to Unlocking Silent Biosynthetic Gene Clusters

2021 ◽  
Vol 90 (1) ◽  
Author(s):  
Brett C. Covington ◽  
Fei Xu ◽  
Mohammad R. Seyedsayamdost
Keyword(s):  
Natural Products ◽  
Natural Product ◽  
Gene Clusters ◽  
Annual Review ◽  
Publication Date ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Research And Innovation ◽  
Therapeutic Leads ◽  
Microbial Natural Products

Microbial natural products have provided an important source of therapeutic leads and motivated research and innovation in diverse scientific disciplines. In recent years, it has become evident that bacteria harbor a large, hidden reservoir of potential natural products in the form of silent or cryptic biosynthetic gene clusters (BGCs). These can be readily identified in microbial genome sequences but do not give rise to detectable levels of a natural product. Herein, we provide a useful organizational framework for the various methods that have been implemented for interrogating silent BGCs. We divide all available approaches into four categories. The first three are endogenous strategies that utilize the native host in conjunction with classical genetics, chemical genetics, or different culture modalities. The last category comprises expression of the entire BGC in a heterologous host. For each category, we describe the rationale, recent applications, and associated advantages and limitations. Expected final online publication date for the Annual Review of Biochemistry, Volume 90 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals A chromatogram-simplified Streptomyces albus host for heterologous production of natural products

2019 ◽  
Author(s):  
Asif Fazal ◽  
Divya Thankachan ◽  
Ellie Harris ◽  
Ryan F. Seipke
Keyword(s):  
Natural Products ◽  
Natural Product ◽  
Gene Clusters ◽  
Heterologous Production ◽  
Biosynthetic Gene ◽  
Critical Aspect ◽  
Biosynthetic Gene Clusters ◽  
Streptomyces Albus ◽  
Microbial Natural Products ◽  
Suitable Expression

AbstractCloning natural product biosynthetic gene clusters from cultured or uncultured sources and their subsequent expression by genetically tractable heterologous hosts is an essential strategy for the elucidation and characterisation of novel microbial natural products. The availability of suitable expression hosts is a critical aspect of this workflow. In this work, we mutagenised five endogenous biosynthetic gene clusters from Streptomyces albus S4, which reduced the complexity of chemical extracts generated from the strain and eliminated antifungal and antibacterial bioactivity. We showed that the resulting quintuple mutant can express foreign BGCs by heterologously producing actinorhodin, cinnamycin and prunustatin. We envisage that our strain will be a useful addition to the growing suite of heterologous expression hosts available for exploring microbial secondary metabolism.

scholarly journals The Application of Ribosome Engineering to Natural Product Discovery and Yield Improvement in Streptomyces

Antibiotics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 133 ◽  
Author(s):  
Saibin Zhu ◽  
Yanwen Duan ◽  
Yong Huang
Keyword(s):  
Natural Product ◽  
Ribosomal Proteins ◽  
Cost Effective ◽  
Gene Clusters ◽  
Product Formation ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Yield Improvement ◽  
Ribosome Engineering ◽  
Natural Product Discovery

Microbial natural product drug discovery and development has entered a new era, driven by microbial genomics and synthetic biology. Genome sequencing has revealed the vast potential to produce valuable secondary metabolites in bacteria and fungi. However, many of the biosynthetic gene clusters are silent under standard fermentation conditions. By rational screening for mutations in bacterial ribosomal proteins or RNA polymerases, ribosome engineering is a versatile approach to obtain mutants with improved titers for microbial product formation or new natural products through activating silent biosynthetic gene clusters. In this review, we discuss the mechanism of ribosome engineering and its application to natural product discovery and yield improvement in Streptomyces. Our analysis suggests that ribosome engineering is a rapid and cost-effective approach and could be adapted to speed up the discovery and development of natural product drug leads in the post-genomic era.

scholarly journals Survey of Biosynthetic Gene Clusters from Sequenced Myxobacteria Reveals Unexplored Biosynthetic Potential

2019 ◽  
Vol 7 (6) ◽  
pp. 181 ◽  
Author(s):  
Katherine Gregory ◽  
Laura A. Salvador ◽  
Shukria Akbar ◽  
Barbara I. Adaikpoh ◽  
D. Cole Stevens
Keyword(s):  
Natural Product ◽  
Chemical Space ◽  
Sequence Similarity ◽  
Gene Clusters ◽  
Biologically Active ◽  
Critical Component ◽  
Biosynthetic Gene ◽  
Bacterial Genomes ◽  
Biosynthetic Gene Clusters ◽  
Natural Product Discovery

Coinciding with the increase in sequenced bacteria, mining of bacterial genomes for biosynthetic gene clusters (BGCs) has become a critical component of natural product discovery. The order Myxococcales, a reputable source of biologically active secondary metabolites, spans three suborders which all include natural product producing representatives. Utilizing the BiG-SCAPE-CORASON platform to generate a sequence similarity network that contains 994 BGCs from 36 sequenced myxobacteria deposited in the antiSMASH database, a total of 843 BGCs with lower than 75% similarity scores to characterized clusters within the MIBiG database are presented. This survey provides the biosynthetic diversity of these BGCs and an assessment of the predicted chemical space yet to be discovered. Considering the mere snapshot of myxobacteria included in this analysis, these untapped BGCs exemplify the potential for natural product discovery from myxobacteria.

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