Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Nguyen DD

PROVIDER: S-EPMC3710860 | biostudies-other | 2013 Jul

REPOSITORIES: biostudies-other

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

Nguyen Don Duy DD Wu Cheng-Hsuan CH Moree Wilna J WJ Lamsa Anne A Medema Marnix H MH Zhao Xiling X Gavilan Ronnie G RG Aparicio Marystella M Atencio Librada L Jackson Chanaye C Ballesteros Javier J Sanchez Joel J Watrous Jeramie D JD Phelan Vanessa V VV van de Wiel Corine C Kersten Roland D RD Mehnaz Samina S De Mot René R Shank Elizabeth A EA Charusanti Pep P Nagarajan Harish H Duggan Brendan M BM Moore Bradley S BS Bandeira Nuno N Palsson Bernhard Ø BØ Pogliano Kit K Gutiérrez Marcelino M Dorrestein Pieter C PC

Proceedings of the National Academy of Sciences of the United States of America 20130624 28

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 f ...[more]

PMID: 23798442

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Project description:Identifying genomic regions that descended from a common ancestor is important for understanding the function and evolution of genomes. In distantly related genomes, clusters of homologous gene pairs are evidence of candidate homologous regions. Demonstrating the statistical significance of such "gene clusters" is an essential component of comparative genomic analyses. However, currently there are no practical statistical tests for gene clusters that model the influence of the number of homologs in each gene family on cluster significance. In this work, we demonstrate empirically that failure to incorporate gene family size in gene cluster statistics results in overestimation of significance, leading to incorrect conclusions. We further present novel analytical methods for estimating gene cluster significance that take gene family size into account. Our methods do not require complete genome data and are suitable for testing individual clusters found in local regions, such as contigs in an unfinished assembly. We consider pairs of regions drawn from the same genome (paralogous clusters), as well as regions drawn from two different genomes (orthologous clusters). Determining cluster significance under general models of gene family size is computationally intractable. By assuming that all gene families are of equal size, we obtain analytical expressions that allow fast approximation of cluster probabilities. We evaluate the accuracy of this approximation by comparing the resulting gene cluster probabilities with cluster probabilities obtained by simulating a realistic, power-law distributed model of gene family size, with parameters inferred from genomic data. Surprisingly, despite the simplicity of the underlying assumption, our method accurately approximates the true cluster probabilities. It slightly overestimates these probabilities, yielding a conservative test. We present additional simulation results indicating the best choice of parameter values for data analysis in genomes of various sizes and illustrate the utility of our methods by applying them to gene clusters recently reported in the literature. Mathematical code to compute cluster probabilities using our methods is available as supplementary material.

Project description:BACKGROUND:Herbicidin F has an undecose tricyclic furano-pyrano-pyran structure with post-decorations. It was detected from Streptomyces mobaraensis US-43 fermentation broth as a trace component by HPLC-MS analysis. As herbicidins exhibit herbicidal, antibacterial, antifungal and antiparasitic activities, we are attracted to explore more analogues for further development. RESULTS:The genome of S. mobaraensis US-43 was sequenced and a herbicidin biosynthetic gene cluster (hcd) was localized. The cluster contains structural genes, one transporter and three potential transcription regulatory genes. Overexpression of the three regulators respectively showed that only hcdR2 overexpression significantly improved the production of herbicidin F, and obviously increased the transcripts of 7 structural genes as well as the transporter gene. After performing homology searches using BLASTP in the GenBank database, 14 hcd-like clusters were found with a cluster-situated hcdR2 homologue. These HcdR2 orthologues showed overall structural similarity, especially in the C-terminal DNA binding domain. Based on bioinformatics analysis, a 21-bp consensus binding motif of HcdR2 was detected within 30 promoter regions in these genome-mined clusters. EMSA results verified that HcdR2 bound to the predicted consensus sequence. Additionally, we employed molecular networking to explore novel herbicidin analogues in hcdR2 overexpression strain. As a result, ten herbicidin analogues including six new compounds were identified based on MS/MS fragments. Herbicidin O was further purified and confirmed by 1H NMR spectrum. CONCLUSIONS:A herbicidin biosynthetic gene cluster (hcd) was identified in S. mobaraensis US-43. HcdR2, a member of LuxR family, was identified as the pathway-specific positive regulator, and the production of herbicidin F was dramatically increased by overexpression of hcdR2. Combined with molecular networking, ten herbicidin congeners including six novel herbicidin analogues were picked out from the secondary metabolites of hcdR2 overexpression strain. The orthologues of herbicidin F pathway-specific regulator HcdR2 were present in most of the genome-mined homologous biosynthetic gene clusters, which possessed at least one consensus binding motif with LuxR family characteristic. These results indicated that the combination of overexpression of hcdR2 orthologous regulator and molecular networking might be an effective way to exploit the "cryptic" herbicidin-related biosynthetic gene clusters for discovery of novel herbicidin analogues.

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

Publications

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

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