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Pieter C Dorrestein - One of the best experts on this subject based on the ideXlab platform.
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Automated Genome Mining of Ribosomal Peptide Natural Products
2016Co-Authors: Pieter C DorresteinAbstract:ABSTRACT: Ribosomally synthesized and posttranslationally modified peptides (RiPPs), especially from microbial sources, are a large group of bioactive natural products that are a promising source of new (bio)chemistry and bioactivity.1 In light of exponentially increasing microbial Genome databases and improved mass spectrometry (MS)-based metabolomic platforms, there is a need for computa-tional tools that connect natural product genotypes predicted from microbial Genome sequences with their corresponding chemotypes from metabolomic data sets. Here, we introduce RiPPquest, a tandem mass spectrometry database search tool for identification of microbial RiPPs, and apply it to lanthipeptide discovery. RiPPquest uses genomics to limit search space to the vicinity of RiPP biosynthetic genes and proteomics to analyze extensive peptide modifications and compute p-values of peptide-spectrum matches (PSMs). We highlight RiPPquest by connecting multiple RiPPs from extracts of Streptomyces to their gene clusters and by the discovery of a new class III lanthipeptide, informatipeptin, from Streptomyces viridochromogenes DSM 40736 to reflect that it is a natural product that was discovered by mass spectrometry based Genome Mining using algorithmic tools rather than manual inspection of mas
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Molecular networking and pattern-based Genome Mining improves discovery of biosynthetic gene clusters and their products from salinispora species
Chemistry and Biology, 2015Co-Authors: Katherine R Duncan, Max Crüsemann, Anindita Sarkar, Nadine Ziemert, Nuno Bandeira, Bradley S. Moore, Anna Lechner, Jie Li, Pieter C DorresteinAbstract:Genome sequencing has revealed that bacteria contain many more biosynthetic gene clusters than predicted based on the number of secondary metabolites discovered to date. While this biosynthetic reservoir has fostered interest in new tools for natural product discovery, there remains a gap between gene cluster detection and compound discovery. Here we apply molecular networking and the new concept of pattern-based Genome Mining to 35 Salinispora strains, including 30 for which draft Genome sequences were either available or obtained for this study. The results provide a method to simultaneously compare large numbers of complex microbial extracts, which facilitated the identification of media components, known compounds and their derivatives, and new compounds that could be prioritized for structure elucidation. These efforts revealed considerable metabolite diversity and led to several molecular family-gene cluster pairings, of which the quinomycin-type depsipeptide retimycin A was characterized and linked to gene cluster NRPS40 using pattern-based bioinformatic approaches.
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Glycogenomics as a mass spectrometry-guided Genome-Mining method for microbial glycosylated molecules
Proceedings of the National Academy of Sciences of the United States of America, 2013Co-Authors: Roland D. Kersten, Nadine Ziemert, Pieter C Dorrestein, David Gonzalez, Brendan M. Duggan, Victor Nizet, Bradley S. MooreAbstract: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 experiment-guided 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.
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imaging mass spectrometry and Genome Mining via short sequence tagging identified the anti infective agent arylomycin in streptomyces roseosporus
Journal of the American Chemical Society, 2011Co-Authors: Wei Ting Liu, Roland D. Kersten, Bradley S. Moore, Yu Liang Yang, Pieter C DorresteinAbstract:Here, we described the discovery of anti-infective agent arylomycin and its biosynthetic gene cluster in an industrial daptomycin producing strain Streptomyces roseosporus. This was accomplished via the use of MALDI imaging mass spectrometry (IMS) along with peptidogenomic approach in which we have expanded to short sequence tagging (SST) described herein. Using IMS, we observed that prior to the production of daptomycin, a cluster of ions (1–3) was produced by S. roseosporus and correlated well with the decreased staphylococcal cell growth. With a further adopted SST peptidogenomics approach, which relies on the generation of sequence tags from tandem mass spectrometric data and query against Genomes to identify the biosynthetic genes, we were able to identify these three molecules (1–3) to arylomycins, a class of broad-spectrum antibiotics that target type I signal peptidase. The gene cluster was then identified. This highlights the strength of IMS and MS guided Genome Mining approaches in effectively b...
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Imaging mass spectrometry and Genome Mining via short sequence tagging identified the anti-infective agent arylomycin in streptomyces roseosporus
Journal of the American Chemical Society, 2011Co-Authors: Wei Ting Liu, Roland D. Kersten, Bradley S. Moore, Yu Liang Yang, Pieter C DorresteinAbstract:Here, we described the discovery of anti-infective agent arylomycin and its biosynthetic gene cluster in an industrial daptomycin producing strain Streptomyces roseosporus. This was accomplished via the use of MALDI imaging mass spectrometry (IMS) along with peptidogenomic approach in which we have expanded to short sequence tagging (SST) described herein. Using IMS, we observed that prior to the production of daptomycin, a cluster of ions (1-3) was produced by S. roseosporus and correlated well with the decreased staphylococcal cell growth. With a further adopted SST peptidogenomics approach, which relies on the generation of sequence tags from tandem mass spectrometric data and query against Genomes to identify the biosynthetic genes, we were able to identify these three molecules (1-3) to arylomycins, a class of broad-spectrum antibiotics that target type I signal peptidase. The gene cluster was then identified. This highlights the strength of IMS and MS guided Genome Mining approaches in effectively bridging the gap between phenotypes, chemotypes, and genotypes.
Bradley S. Moore - One of the best experts on this subject based on the ideXlab platform.
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Molecular networking and pattern-based Genome Mining improves discovery of biosynthetic gene clusters and their products from salinispora species
Chemistry and Biology, 2015Co-Authors: Katherine R Duncan, Max Crüsemann, Anindita Sarkar, Nadine Ziemert, Nuno Bandeira, Bradley S. Moore, Anna Lechner, Jie Li, Pieter C DorresteinAbstract:Genome sequencing has revealed that bacteria contain many more biosynthetic gene clusters than predicted based on the number of secondary metabolites discovered to date. While this biosynthetic reservoir has fostered interest in new tools for natural product discovery, there remains a gap between gene cluster detection and compound discovery. Here we apply molecular networking and the new concept of pattern-based Genome Mining to 35 Salinispora strains, including 30 for which draft Genome sequences were either available or obtained for this study. The results provide a method to simultaneously compare large numbers of complex microbial extracts, which facilitated the identification of media components, known compounds and their derivatives, and new compounds that could be prioritized for structure elucidation. These efforts revealed considerable metabolite diversity and led to several molecular family-gene cluster pairings, of which the quinomycin-type depsipeptide retimycin A was characterized and linked to gene cluster NRPS40 using pattern-based bioinformatic approaches.
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Identification of Thiotetronic Acid Antibiotic Biosynthetic Pathways by Target-directed Genome Mining
2015Co-Authors: Xiaoyu Tang, Natalie Millán-aguiñaga, Jia Jia Zhang, Ellis C. O’neill, Juan A. Ugalde, Paul R. Jensen, Simone M. Mantovani, Bradley S. MooreAbstract:Recent Genome sequencing efforts have led to the rapid accumulation of uncharacterized or “orphaned” secondary metabolic biosynthesis gene clusters (BGCs) in public databases. This increase in DNA-sequenced big data has given rise to significant challenges in the applied field of natural product Genome Mining, including (i) how to prioritize the characterization of orphan BGCs and (ii) how to rapidly connect genes to biosynthesized small molecules. Here, we show that by correlating putative antibiotic resistance genes that encode target-modified proteins with orphan BGCs, we predict the biological function of pathway specific small molecules before they have been revealed in a process we call target-directed Genome Mining. By querying the pan-Genome of 86 Salinispora bacterial Genomes for duplicated house-keeping genes colocalized with natural product BGCs, we prioritized an orphan polyketide synthase-nonribosomal peptide synthetase hybrid BGC (tlm) with a putative fatty acid synthase resistance gene. We employed a new synthetic double-stranded DNA-mediated cloning strategy based on transformation-associated recombination to efficiently capture tlm and the related ttm BGCs directly from genomic DNA and to heterologously express them in Streptomyces hosts. We show the production of a group of unusual thiotetronic acid natural products, including the well-known fatty acid synthase inhibitor thiolactomycin that was first described over 30 years ago, yet never at the genetic level in regards to biosynthesis and autoresistance. This finding not only validates the target-directed Genome Mining strategy for the discovery of antibiotic producing gene clusters without a priori knowledge of the molecule synthesized but also paves the way for the investigation of novel enzymology involved in thiotetronic acid natural product biosynthesis
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Glycogenomics as a mass spectrometry-guided Genome-Mining method for microbial glycosylated molecules
Proceedings of the National Academy of Sciences of the United States of America, 2013Co-Authors: Roland D. Kersten, Nadine Ziemert, Pieter C Dorrestein, David Gonzalez, Brendan M. Duggan, Victor Nizet, Bradley S. MooreAbstract: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 experiment-guided 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.
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imaging mass spectrometry and Genome Mining via short sequence tagging identified the anti infective agent arylomycin in streptomyces roseosporus
Journal of the American Chemical Society, 2011Co-Authors: Wei Ting Liu, Roland D. Kersten, Bradley S. Moore, Yu Liang Yang, Pieter C DorresteinAbstract:Here, we described the discovery of anti-infective agent arylomycin and its biosynthetic gene cluster in an industrial daptomycin producing strain Streptomyces roseosporus. This was accomplished via the use of MALDI imaging mass spectrometry (IMS) along with peptidogenomic approach in which we have expanded to short sequence tagging (SST) described herein. Using IMS, we observed that prior to the production of daptomycin, a cluster of ions (1–3) was produced by S. roseosporus and correlated well with the decreased staphylococcal cell growth. With a further adopted SST peptidogenomics approach, which relies on the generation of sequence tags from tandem mass spectrometric data and query against Genomes to identify the biosynthetic genes, we were able to identify these three molecules (1–3) to arylomycins, a class of broad-spectrum antibiotics that target type I signal peptidase. The gene cluster was then identified. This highlights the strength of IMS and MS guided Genome Mining approaches in effectively b...
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Imaging mass spectrometry and Genome Mining via short sequence tagging identified the anti-infective agent arylomycin in streptomyces roseosporus
Journal of the American Chemical Society, 2011Co-Authors: Wei Ting Liu, Roland D. Kersten, Bradley S. Moore, Yu Liang Yang, Pieter C DorresteinAbstract:Here, we described the discovery of anti-infective agent arylomycin and its biosynthetic gene cluster in an industrial daptomycin producing strain Streptomyces roseosporus. This was accomplished via the use of MALDI imaging mass spectrometry (IMS) along with peptidogenomic approach in which we have expanded to short sequence tagging (SST) described herein. Using IMS, we observed that prior to the production of daptomycin, a cluster of ions (1-3) was produced by S. roseosporus and correlated well with the decreased staphylococcal cell growth. With a further adopted SST peptidogenomics approach, which relies on the generation of sequence tags from tandem mass spectrometric data and query against Genomes to identify the biosynthetic genes, we were able to identify these three molecules (1-3) to arylomycins, a class of broad-spectrum antibiotics that target type I signal peptidase. The gene cluster was then identified. This highlights the strength of IMS and MS guided Genome Mining approaches in effectively bridging the gap between phenotypes, chemotypes, and genotypes.
Wei Ting Liu - One of the best experts on this subject based on the ideXlab platform.
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Automated Genome Mining of Ribosomal Peptide Natural Products
2015Co-Authors: Hosein Mohimani, Wei Ting Liu, Nuno Bandeira, Roland D. Kersten, Mingxun Wang, Samuel O. Purvine, Heather M. Brewer, Ljiljana Pasa-tolic, Bradley S. MooreAbstract:Ribosomally synthesized and posttranslationally modified peptides (RiPPs), especially from microbial sources, are a large group of bioactive natural products that are a promising source of new (bio)chemistry and bioactivity. In light of exponentially increasing microbial Genome databases and improved mass spectrometry (MS)-based metabolomic platforms, there is a need for computational tools that connect natural product genotypes predicted from microbial Genome sequences with their corresponding chemotypes from metabolomic data sets. Here, we introduce RiPPquest, a tandem mass spectrometry database search tool for identification of microbial RiPPs, and apply it to lanthipeptide discovery. RiPPquest uses genomics to limit search space to the vicinity of RiPP biosynthetic genes and proteomics to analyze extensive peptide modifications and compute p-values of peptide-spectrum matches (PSMs). We highlight RiPPquest by connecting multiple RiPPs from extracts of Streptomyces to their gene clusters and by the discovery of a new class III lanthipeptide, informatipeptin, from Streptomyces viridochromogenes DSM 40736 to reflect that it is a natural product that was discovered by mass spectrometry based Genome Mining using algorithmic tools rather than manual inspection of mass spectrometry data and genetic information. The presented tool is available at cyclo.ucsd.edu
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imaging mass spectrometry and Genome Mining via short sequence tagging identified the anti infective agent arylomycin in streptomyces roseosporus
Journal of the American Chemical Society, 2011Co-Authors: Wei Ting Liu, Roland D. Kersten, Bradley S. Moore, Yu Liang Yang, Pieter C DorresteinAbstract:Here, we described the discovery of anti-infective agent arylomycin and its biosynthetic gene cluster in an industrial daptomycin producing strain Streptomyces roseosporus. This was accomplished via the use of MALDI imaging mass spectrometry (IMS) along with peptidogenomic approach in which we have expanded to short sequence tagging (SST) described herein. Using IMS, we observed that prior to the production of daptomycin, a cluster of ions (1–3) was produced by S. roseosporus and correlated well with the decreased staphylococcal cell growth. With a further adopted SST peptidogenomics approach, which relies on the generation of sequence tags from tandem mass spectrometric data and query against Genomes to identify the biosynthetic genes, we were able to identify these three molecules (1–3) to arylomycins, a class of broad-spectrum antibiotics that target type I signal peptidase. The gene cluster was then identified. This highlights the strength of IMS and MS guided Genome Mining approaches in effectively b...
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Imaging mass spectrometry and Genome Mining via short sequence tagging identified the anti-infective agent arylomycin in streptomyces roseosporus
Journal of the American Chemical Society, 2011Co-Authors: Wei Ting Liu, Roland D. Kersten, Bradley S. Moore, Yu Liang Yang, Pieter C DorresteinAbstract:Here, we described the discovery of anti-infective agent arylomycin and its biosynthetic gene cluster in an industrial daptomycin producing strain Streptomyces roseosporus. This was accomplished via the use of MALDI imaging mass spectrometry (IMS) along with peptidogenomic approach in which we have expanded to short sequence tagging (SST) described herein. Using IMS, we observed that prior to the production of daptomycin, a cluster of ions (1-3) was produced by S. roseosporus and correlated well with the decreased staphylococcal cell growth. With a further adopted SST peptidogenomics approach, which relies on the generation of sequence tags from tandem mass spectrometric data and query against Genomes to identify the biosynthetic genes, we were able to identify these three molecules (1-3) to arylomycins, a class of broad-spectrum antibiotics that target type I signal peptidase. The gene cluster was then identified. This highlights the strength of IMS and MS guided Genome Mining approaches in effectively bridging the gap between phenotypes, chemotypes, and genotypes.
Roland D. Kersten - One of the best experts on this subject based on the ideXlab platform.
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Glycogenomics as a mass spectrometry-guided Genome-Mining method for microbial glycosylated molecules
Proceedings of the National Academy of Sciences of the United States of America, 2013Co-Authors: Roland D. Kersten, Nadine Ziemert, Pieter C Dorrestein, David Gonzalez, Brendan M. Duggan, Victor Nizet, Bradley S. MooreAbstract: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 experiment-guided 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.
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imaging mass spectrometry and Genome Mining via short sequence tagging identified the anti infective agent arylomycin in streptomyces roseosporus
Journal of the American Chemical Society, 2011Co-Authors: Wei Ting Liu, Roland D. Kersten, Bradley S. Moore, Yu Liang Yang, Pieter C DorresteinAbstract:Here, we described the discovery of anti-infective agent arylomycin and its biosynthetic gene cluster in an industrial daptomycin producing strain Streptomyces roseosporus. This was accomplished via the use of MALDI imaging mass spectrometry (IMS) along with peptidogenomic approach in which we have expanded to short sequence tagging (SST) described herein. Using IMS, we observed that prior to the production of daptomycin, a cluster of ions (1–3) was produced by S. roseosporus and correlated well with the decreased staphylococcal cell growth. With a further adopted SST peptidogenomics approach, which relies on the generation of sequence tags from tandem mass spectrometric data and query against Genomes to identify the biosynthetic genes, we were able to identify these three molecules (1–3) to arylomycins, a class of broad-spectrum antibiotics that target type I signal peptidase. The gene cluster was then identified. This highlights the strength of IMS and MS guided Genome Mining approaches in effectively b...
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Imaging mass spectrometry and Genome Mining via short sequence tagging identified the anti-infective agent arylomycin in streptomyces roseosporus
Journal of the American Chemical Society, 2011Co-Authors: Wei Ting Liu, Roland D. Kersten, Bradley S. Moore, Yu Liang Yang, Pieter C DorresteinAbstract:Here, we described the discovery of anti-infective agent arylomycin and its biosynthetic gene cluster in an industrial daptomycin producing strain Streptomyces roseosporus. This was accomplished via the use of MALDI imaging mass spectrometry (IMS) along with peptidogenomic approach in which we have expanded to short sequence tagging (SST) described herein. Using IMS, we observed that prior to the production of daptomycin, a cluster of ions (1-3) was produced by S. roseosporus and correlated well with the decreased staphylococcal cell growth. With a further adopted SST peptidogenomics approach, which relies on the generation of sequence tags from tandem mass spectrometric data and query against Genomes to identify the biosynthetic genes, we were able to identify these three molecules (1-3) to arylomycins, a class of broad-spectrum antibiotics that target type I signal peptidase. The gene cluster was then identified. This highlights the strength of IMS and MS guided Genome Mining approaches in effectively bridging the gap between phenotypes, chemotypes, and genotypes.
Yu Liang Yang - One of the best experts on this subject based on the ideXlab platform.
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imaging mass spectrometry and Genome Mining via short sequence tagging identified the anti infective agent arylomycin in streptomyces roseosporus
Journal of the American Chemical Society, 2011Co-Authors: Wei Ting Liu, Roland D. Kersten, Bradley S. Moore, Yu Liang Yang, Pieter C DorresteinAbstract:Here, we described the discovery of anti-infective agent arylomycin and its biosynthetic gene cluster in an industrial daptomycin producing strain Streptomyces roseosporus. This was accomplished via the use of MALDI imaging mass spectrometry (IMS) along with peptidogenomic approach in which we have expanded to short sequence tagging (SST) described herein. Using IMS, we observed that prior to the production of daptomycin, a cluster of ions (1–3) was produced by S. roseosporus and correlated well with the decreased staphylococcal cell growth. With a further adopted SST peptidogenomics approach, which relies on the generation of sequence tags from tandem mass spectrometric data and query against Genomes to identify the biosynthetic genes, we were able to identify these three molecules (1–3) to arylomycins, a class of broad-spectrum antibiotics that target type I signal peptidase. The gene cluster was then identified. This highlights the strength of IMS and MS guided Genome Mining approaches in effectively b...
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Imaging mass spectrometry and Genome Mining via short sequence tagging identified the anti-infective agent arylomycin in streptomyces roseosporus
Journal of the American Chemical Society, 2011Co-Authors: Wei Ting Liu, Roland D. Kersten, Bradley S. Moore, Yu Liang Yang, Pieter C DorresteinAbstract:Here, we described the discovery of anti-infective agent arylomycin and its biosynthetic gene cluster in an industrial daptomycin producing strain Streptomyces roseosporus. This was accomplished via the use of MALDI imaging mass spectrometry (IMS) along with peptidogenomic approach in which we have expanded to short sequence tagging (SST) described herein. Using IMS, we observed that prior to the production of daptomycin, a cluster of ions (1-3) was produced by S. roseosporus and correlated well with the decreased staphylococcal cell growth. With a further adopted SST peptidogenomics approach, which relies on the generation of sequence tags from tandem mass spectrometric data and query against Genomes to identify the biosynthetic genes, we were able to identify these three molecules (1-3) to arylomycins, a class of broad-spectrum antibiotics that target type I signal peptidase. The gene cluster was then identified. This highlights the strength of IMS and MS guided Genome Mining approaches in effectively bridging the gap between phenotypes, chemotypes, and genotypes.