A mass spectrometry–guided genome mining approach for natural product peptidogenomics

Kersten, Roland D.; Yang, Yu Liang; Xu, Yuquan; Cimermančič, Peter; Nam, Sang Jip; Fenical, William; Fischbach, Michael A.; Moore, Bradley S.; Dorrestein, Pieter C.

doi:10.1038/nchembio.684

Cited by 339 publications

(358 citation statements)

References 53 publications

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“…Methods such as peptide and glycogenomics (Kersten et al ., 2011, 2013) connect computational predictions with state of the art mass spectrometric methods ( alternative methods for drug discovery ), whereas pattern‐based genome mining, for example, uses the huge amount of DNA sequence data available in a more comparative genomic approach combined with MS analysis (Duncan et al ., 2015). …”

Section: Novel Approaches For Drug Discoverymentioning

confidence: 99%

Antibiotic drug discovery

Wohlleben

Mast

Stegmann

et al. 2016

Microbial Biotechnology

143

113

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SummaryDue to the threat posed by the increase of highly resistant pathogenic bacteria, there is an urgent need for new antibiotics; all the more so since in the last 20 years, the approval for new antibacterial agents had decreased. The field of natural product discovery has undergone a tremendous development over the past few years. This has been the consequence of several new and revolutionizing drug discovery and development techniques, which is initiating a ‘New Age of Antibiotic Discovery’. In this review, we concentrate on the most significant discovery approaches during the last and present years and comment on the challenges facing the community in the coming years.

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Section: Novel Approaches For Drug Discoverymentioning

confidence: 99%

Antibiotic drug discovery

Wohlleben

Mast

Stegmann

et al. 2016

Microbial Biotechnology

143

113

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“…The same peak was absent not only from the conprimycin mutant but also from the ectoine mutant (Supplementary Figure S6a). To further connect the compound to its gene cluster, a peptidogenomic (Kersten et al, 2011) approach was used to reveal peptide fragments consistent with the postulated biosynthetic product (Figures 3c and d and Supplementary Figure S6b).…”

Section: Disease Suppressiveness and Rhizosphere Colonization By Strementioning

confidence: 99%

Microbial and biochemical basis of a Fusarium wilt-suppressive soil

et al. 2015

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Crops lack genetic resistance to most necrotrophic pathogens. To compensate for this disadvantage, plants recruit antagonistic members of the soil microbiome to defend their roots against pathogens and other pests. The best examples of this microbially based defense of roots are observed in disease-suppressive soils in which suppressiveness is induced by continuously growing crops that are susceptible to a pathogen, but the molecular basis of most is poorly understood. Here we report the microbial characterization of a Korean soil with specific suppressiveness to Fusarium wilt of strawberry. In this soil, an attack on strawberry roots by Fusarium oxysporum results in a response by microbial defenders, of which members of the Actinobacteria appear to have a key role. We also identify Streptomyces genes responsible for the ribosomal synthesis of a novel heat-stable antifungal thiopeptide antibiotic inhibitory to F. oxysporum and the antibiotic’s mode of action against fungal cell wall biosynthesis. Both classical- and community-oriented approaches were required to dissect this suppressive soil from the field to the molecular level, and the results highlight the role of natural antibiotics as weapons in the microbial warfare in the rhizosphere that is integral to plant health, vigor and development.

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“…This concept extends MS-guided genome mining beyond peptide natural products (20) to most biosynthetic classes of natural products that can be glycosylated. We show our approach by characterizing bioactive GNPs from actinobacterial metabolomes.…”

mentioning

confidence: 99%

“…This connection can be done either in the genotype-tochemotype direction by in silico-guided approaches (19) or in the chemotype-to-genotype direction by experiment-guided approaches (20). Many effective in silico-guided strategies have been developed using genetics (21), substrate labeling (22), and screening for predicted physicochemical properties (23) to characterize new natural products from cryptic and even silent gene clusters in genomes.…”

mentioning

confidence: 99%

“…However, these approaches target only one biosynthetic pathway per experiment, thereby resulting in a slow discovery rate. The experiment-guided approach, such as MS-guided genome mining of peptides, starts with an untargeted analytical step, e.g., MS n analysis of an extract (20), to identify biosynthetic building blocks of an unknown chemotype. This structural information is subsequently used to query the genome sequence of the target organism for corresponding genes associated with the enzymatic assembly of the chemotype based on biosynthetic principles.…”

mentioning

confidence: 99%

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Glycogenomics as a mass spectrometry-guided genome-mining method for microbial glycosylated molecules

Kersten

Ziemert

Gonzalez³

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

103

107

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Glycosyl groups are an essential mediator of molecular interactions in cells and on cellular surfaces. There are very few methods that directly relate sugar-containing molecules to their biosynthetic machineries. Here, we introduce glycogenomics as an experimentguided genome-mining approach for fast characterization of glycosylated natural products (GNPs) and their biosynthetic pathways from genome-sequenced microbes by targeting glycosyl groups in microbial metabolomes. Microbial GNPs consist of aglycone and glycosyl structure groups in which the sugar unit(s) are often critical for the GNP's bioactivity, e.g., by promoting binding to a target biomolecule. GNPs are a structurally diverse class of molecules with important pharmaceutical and agrochemical applications. Herein, O-and N-glycosyl groups are characterized in their sugar monomers by tandem mass spectrometry (MS) and matched to corresponding glycosylation genes in secondary metabolic pathways by a MS-glycogenetic code. The associated aglycone biosynthetic genes of the GNP genotype then classify the natural product to further guide structure elucidation. We highlight the glycogenomic strategy by the characterization of several bioactive glycosylated molecules and their gene clusters, including the anticancer agent cinerubin B from Streptomyces sp. SPB74 and an antibiotic, arenimycin B, from Salinispora arenicola CNB-527.secondary metabolite | drug discovery | microbial genomics | deoxysugar | polyketide

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A mass spectrometry–guided genome mining approach for natural product peptidogenomics

Cited by 339 publications

References 53 publications

Antibiotic drug discovery

Antibiotic drug discovery

Microbial and biochemical basis of a Fusarium wilt-suppressive soil

Glycogenomics as a mass spectrometry-guided genome-mining method for microbial glycosylated molecules

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