Dataset Information

Structuring Microbial Metabolic Responses to Multiplexed Stimuli via Self-Organizing Metabolomics Maps.

ABSTRACT: Secondary metabolite biosynthesis in microorganisms responds to discrete chemical and biological stimuli; however, untargeted identification of these responses presents a significant challenge. Herein we apply multiplexed stimuli to Streptomyces coelicolor and collect the resulting response metabolomes via ion mobility-mass spectrometric analysis. Self-organizing map (SOM) analytics adapted for metabolomic data demonstrate efficient characterization of the subsets of primary and secondary metabolites that respond similarly across stimuli. Over 60% of all metabolic features inventoried from responses are either not observed under control conditions or produced at greater than 2-fold increase in abundance in response to at least one of the multiplexing conditions, reflecting how metabolites encode phenotypic changes in an organism responding to multiplexed challenges. Using abundance as an additional filter, each of 16 known S. coelicolor secondary metabolites is prioritized via SOM and observed at increased levels (1.2- to 22-fold compared with unperturbed) in response to one or more challenge conditions.

SUBMITTER: Goodwin CR

PROVIDER: S-EPMC4537791 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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Structuring Microbial Metabolic Responses to Multiplexed Stimuli via Self-Organizing Metabolomics Maps.

Goodwin Cody R CR Covington Brett C BC Derewacz Dagmara K DK McNees C Ruth CR Wikswo John P JP McLean John A JA Bachmann Brian O BO

Chemistry & biology 20150430 5

Secondary metabolite biosynthesis in microorganisms responds to discrete chemical and biological stimuli; however, untargeted identification of these responses presents a significant challenge. Herein we apply multiplexed stimuli to Streptomyces coelicolor and collect the resulting response metabolomes via ion mobility-mass spectrometric analysis. Self-organizing map (SOM) analytics adapted for metabolomic data demonstrate efficient characterization of the subsets of primary and secondary metabo ...[more]

PMID: 25937311

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Project description:BackgroundParallel high-throughput microarray and sequencing experiments produce vast quantities of multidimensional data which must be arranged and analyzed in a concerted way. One approach to addressing this challenge is the machine learning technique known as self organizing maps (SOMs). SOMs enable a parallel sample- and gene-centered view of genomic data combined with strong visualization and second-level analysis capabilities. The paper aims at bridging the gap between the potency of SOM-machine learning to reduce dimension of high-dimensional data on one hand and practical applications with special emphasis on gene expression analysis on the other hand.ResultsThe method was applied to generate a SOM characterizing the whole genome expression profiles of 67 healthy human tissues selected from ten tissue categories (adipose, endocrine, homeostasis, digestion, exocrine, epithelium, sexual reproduction, muscle, immune system and nervous tissues). SOM mapping reduces the dimension of expression data from ten of thousands of genes to a few thousand metagenes, each representing a minicluster of co-regulated single genes. Tissue-specific and common properties shared between groups of tissues emerge as a handful of localized spots in the tissue maps collecting groups of co-regulated and co-expressed metagenes. The functional context of the spots was discovered using overrepresentation analysis with respect to pre-defined gene sets of known functional impact. We found that tissue related spots typically contain enriched populations of genes related to specific molecular processes in the respective tissue. Analysis techniques normally used at the gene-level such as two-way hierarchical clustering are better represented and provide better signal-to-noise ratios if applied to the metagenes. Metagene-based clustering analyses aggregate the tissues broadly into three clusters containing nervous, immune system and the remaining tissues.ConclusionsThe SOM technique provides a more intuitive and informative global view of the behavior of a few well-defined modules of correlated and differentially expressed genes than the separate discovery of the expression levels of hundreds or thousands of individual genes. The program is available as R-package 'oposSOM'.

Dataset Information

Structuring Microbial Metabolic Responses to Multiplexed Stimuli via Self-Organizing Metabolomics Maps.

Publications

Structuring Microbial Metabolic Responses to Multiplexed Stimuli via Self-Organizing Metabolomics Maps.

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