Automated genome mining for natural products

Li, Michael H. T.; Ung, Peter M.U.; Zajkowski, James; Garneau‐Tsodikova, Sylvie; Sherman, David H.

doi:10.1186/1471-2105-10-185

Cited by 236 publications

(170 citation statements)

References 37 publications

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“…In the future, specific algorithms can be developed that will overcome this limitation, especially in conjunction with high-resolution MS/MS data. Instead of carrying out peptidogenomics analysis on a single organism as previously described, the peptide backbones of all of the NRPS gene clusters from all available genome sequences of bacilli and pseudomonads in the public databases were predicted using a batch form of antibiotics and Secondary Metabolite Analysis SHell (antiSMASH) as well as curation using other Adomain prediction tools (4,16,43,44,57) (Tables S2 and S3). By combining amino acid MS/MS signatures with the predicted amino acid specificity of NRPS A domains, we obtained candidate matches of MS/MS signatures to particular GCFs.…”

Section: Significancementioning

confidence: 99%

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MS/MS networking guided analysis of molecule and gene cluster families

Nguyen

Moree

et al. 2013

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

235

238

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The ability to correlate the production of specialized metabolites to the genetic capacity of the organism that produces such molecules has become an invaluable tool in aiding the discovery of biotechnologically applicable molecules. Here, we accomplish this task by matching molecular families with gene cluster families, making these correlations to 60 microbes at one time instead of connecting one molecule to one organism at a time, such as how it is traditionally done. We can correlate these families through the use of nanospray desorption electrospray ionization MS/MS, an ambient pressure MS technique, in conjunction with MS/MS networking and peptidogenomics. We matched the molecular families of peptide natural products produced by 42 bacilli and 18 pseudomonads through the generation of amino acid sequence tags from MS/MS data of specific clusters found in the MS/MS network. These sequence tags were then linked to biosynthetic gene clusters in publicly accessible genomes, providing us with the ability to link particular molecules with the genes that produced them. As an example of its use, this approach was applied to two unsequenced Pseudoalteromonas species, leading to the discovery of the gene cluster for a molecular family, the bromoalterochromides, in the previously sequenced strain P. piscicida JCM 20779 T . The approach itself is not limited to 60 related strains, because spectral networking can be readily adopted to look at molecular family-gene cluster families of hundreds or more diverse organisms in one single MS/MS network.MS/MS molecular networking | mass spectrometry | microbial ecology T ens of thousands of sequenced microbial genomes or rough drafts of genomes are available at this time, and this number is predicted to grow into the millions over the next decades. This wealth of sequence data has the potential to be used for the discovery of small bioactive molecules through genome mining (1-6). Genome mining is a process in which small molecules are discovered by predicting what compound will be genetically encoded based on the sequences of biosynthetic gene clusters. However, the process of mining genetically encoded small molecules is not keeping pace with the rate by which genome sequences are being obtained. In general, genome mining is still done one gene cluster at a time and requires many person-years of effort to annotate a single molecule. The time and significant expertise that current genome mining requires also make genome mining very expensive. In light of this extensive effort and cost, alternative approaches to genome mining and annotating specialized metabolites must be developed that not only take advantage of the sequenced resources available and make it efficient to perform genome mining on a more global scale but also enable the molecular analysis of unsequenced organisms. Such methods will then significantly reduce the cost of genome mining by increasing the speed with which molecules are connected to candidate genes and using resources already available. Here, we put fo...

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Section: Significancementioning

confidence: 99%

“…This wealth of sequence data has the potential to be used for the discovery of small bioactive molecules through genome mining (1)(2)(3)(4)(5)(6). Genome mining is a process in which small molecules are discovered by predicting what compound will be genetically encoded based on the sequences of biosynthetic gene clusters.…”

mentioning

confidence: 99%

MS/MS networking guided analysis of molecule and gene cluster families

Nguyen

Moree

et al. 2013

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

235

238

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“…Oftentimes, the distribution of the ion of interest is interfacial and the producer is ambiguous. By employing analytical and biological approaches, such as microbial IMS, IMS of multiple time points (67), traditional solvent extraction, purification, and structural determination methods, such as tandem MS and nuclear magnetic resonance (NMR) (18,30,33,67,68), genetics and microbiology, 16S rRNA sequencing (54), established MALDI-TOF protocols (12,49,53), genome mining approaches (7,13,15,45,63) and predictive programs (2,3,16,33, 40,48,55,62,65,68), peptidogenomics (31), and literature and database searches (23, 52) (AntiMarin database, Dictionary of Natural Products, the National Institute of Standards and Technology [NIST] databases, and the SciFinder database), one can typically annotate microbial IMS data. Two main strategies to confirm the producing microbe and the resultant phenotype are genetic knockout and complementation studies or assays with purified compound, in combination with more IMS.…”

Section: Challenges In Microbial Imsmentioning

confidence: 99%

Primer on Agar-Based Microbial Imaging Mass Spectrometry

et al. 2012

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Matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) imaging mass spectrometry (IMS) applied directly to microbes on agar-based medium captures global information about microbial molecules, allowing for direct correlation of chemotypes to phenotypes. This tool was developed to investigate metabolic exchange factors of intraspecies, interspecies, and polymicrobial interactions. Based on our experience of the thousands of images we have generated in the laboratory, we present five steps of microbial IMS: culturing, matrix application, dehydration of the sample, data acquisition, and data analysis/interpretation. We also address the common challenges encountered during sample preparation, matrix selection and application, and sample adherence to the MALDI target plate. With the practical guidelines described herein, microbial IMS use can be extended to bio-based agricultural, biofuel, diagnostic, and therapeutic discovery applications.

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“…Streptomyces is a representative of a filamentous bacteria responsible for producing variety of valuable secondary metabolites, such as antibiotics, parasiticides, herbicides, and pharmacologically active substances, including antitumor agents and immunosuppressants (Waksman and Woodruff, 1940;Ditsler et al, 1992;Euverink, 1995;Newman et al, 2000;El Hassan et al, 2001;Bentley et al, 2002;Bibb, 2005;Quintana et al, 2008;Li et al, 2009;Doroghazi et al, 2014). Genome sequencing analyses of Streptomyces avermitilis (Omura et al, 2001), Streptomyces coelicolor A3(2) (Bentley et al, 2002), Streptomyces griseus IFO 13350 (Ohnishi et al, 2008), and Streptomyces bingchenggensis (Wang et al, 2010) revealed that Streptomyces possess an unexpected abundance of natural product biosynthetic gene clusters and thus that they have the potential to make many more compounds than previously thought.…”

Section: Introductionmentioning

confidence: 99%

SECONDARY BIOACTIVE METABOLITE GENE CLUSTERS IDENTIFICATION OF ANTICANDIDA-PRODUCING Streptomyces Sp. GMR22 ISOLATED FROM WANAGAMA FOREST AS REVEALED BY GENOME MINING APPROACH

Herdini

Miceli

Hariwiyanto

et al. 2017

Indonesian J. Pharm.

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Streptomyces are a group of Gram-positive bacteria belonging to the Actinobacteria class, which are among the most important bacteria for producing secondary bioactive metabolites such as antibiotics, chemotherapeutics, insecticides and other high-value chemicals. Genome mining of gene clusters that encode the biosynthetic pathways for these metabolites has become a key methodology for novel compound discovery. Recently, we have isolated the Streptomyces sp. GMR22 from Cajuput rhizospheric soil at Wanagama Forest, Indonesia. GMR22 produced a secondary metabolite that inhibited Candida albicans with IC 50 of 62.5μg/mL. The objective of this work was to reveal the novel secondary metabolites from GMR22 by genome mining approach. The antiSMASH 3.0 was used to predict gene clusters that encode the biosynthetic pathways of secondary metabolites in the genome of GMR22, and their core chemical structures. The phylogenomic analysis showed that GMR22 was closely related to Streptomyces bingchenggensis BCW1, as well as to the large genome size (9.5-12.7Mbp) groups of Streptomyces. AntiSMASH 3.0 analysis revealed that the genome of Streptomyces sp. GMR22 harbored at least 63 gene clusters that encode the biosynthetic pathways of secondary metabolites. It was the highest number of gene clusters had been observed among the members of Streptomyces groups, with polyketide synthetase (PKS) was predicted as the major groups of the identified gene cluster products. The results suggested that GMR22 could be a strong potential candidate for secondary bioactive metabolites source.

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Automated genome mining for natural products

Cited by 236 publications

References 37 publications

MS/MS networking guided analysis of molecule and gene cluster families

MS/MS networking guided analysis of molecule and gene cluster families

Primer on Agar-Based Microbial Imaging Mass Spectrometry

SECONDARY BIOACTIVE METABOLITE GENE CLUSTERS IDENTIFICATION OF ANTICANDIDA-PRODUCING Streptomyces Sp. GMR22 ISOLATED FROM WANAGAMA FOREST AS REVEALED BY GENOME MINING APPROACH

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