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MSEA: metabolite set enrichment analysis in the MeltDB metabolomics software platform: metabolic profiling of Corynebacterium glutamicum as an example

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Abstract

Gene set enrichment analysis (GSEA) has been successfully employed in transcriptomics and proteomics research for over 5 years. We have applied a modified GSEA approach to metabolomics, called metabolite set enrichment analysis (MSEA), and have integrated this method into the MeltDB platform. With the novel integrated functionality, we evaluate the applicability of the approach for metabolomics research by analyzing the metabolic profiles of industrial amino acid producer strains of Corynebacterium glutamicum. Metabolite sets are obtained from metabolic pathways defined in the KEGG and the newly created CglCyc databases. In the first experiment using MSEA, the metabolic profiling analyses compared glucose- and acetate-grown strains, and, in the second, a production strain series in which the carbon flow is shifted step-wise from the lysine pathway to the branching threonine, isoleucine, and methionine pathways. By identifying changes of the metabolic profile MSEA was able to identify the metabolic pathways activated while utilizing different carbon sources or those that have been genetically modified. The presented experimental results are publicly available at http://meltdb.cebitec.uni-bielefeld.de.

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Acknowledgments

HN and LJS acknowledge the financial support of the SysLogics Project (BMBF 0315275A) and MP that of the SysMAP Project (BMBF 0313704). NK acknowledges the support by a fellowship from the CLIB-Graduate Cluster Industrial Biotechnology. The authors wish to thank the BRF team for expert technical support. Furthermore, the authors thank Daniel Stephen Brooks (Department of Philosophy, University of Bielefeld, Germany) and John Quimby (Massachusetts Institute of Technology, Sinskey Laboratory, Cambridge, USA) for carefully reading the manuscript and helping with wording and phrasing.

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  1. Jens Plassmeier

    Present address: Department of Biology, Massachusetts Institute of Technology, 31 Ames Street, Bldg. 68-346, Cambridge, MA, 02142, USA

Authors and Affiliations

  1. Institut für Genomforschung und Systembiologie, Centrum für Biotechnologie (CeBiTec), University of Bielefeld, Universitätsstrasse 27, 33615, Bielefeld, Germany

    Marcus Persicke, Christian Rückert, Jens Plassmeier & Jörn Kalinowski

  2. Computational Genomics, Centrum für Biotechnologie (CeBiTec), University of Bielefeld, Universitätsstrasse 27, 33615, Bielefeld, Germany

    Leonhardt Jonathan Stutz, Nikolas Kessler, Alexander Goesmann & Heiko Neuweger

  3. Biodata Mining & Applied Neuroinformatics, Centrum für Biotechnologie (CeBiTec), University of Bielefeld, Universitätsstrasse 27, 33615, Bielefeld, Germany

    Nikolas Kessler

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11306_2011_311_MOESM1_ESM.tif

Suppl. Figure 1: The visualization of the Enrichment Analysis results generated out of SVG graphics from the web interface. The best five results for the CglCyc metabolic pathways (A) exhibiting the highest enrichment scores were presented. The running ES score is plotted using blue color if the metabolites belonging to MS are predominantly found in the top part of the ordered List L. The red color indicates that the metabolites are found at the lower part of L. All metabolites present in the experiment are represented as gray boxes and the ones belonging to MS are labeled and highlighted in green. On the right, the computed m-values (B), the t-test probabilities, and the signal to noise ratios (SNR) are listed for all compounds identified in the analyzed metabolomics experiment of C. glutamicum DM1730 in comparison to the wild type. For the background of the table cells, a color mapping function of the m-values from red to green was chosen. (TIFF 35,835 kb)

11306_2011_311_MOESM2_ESM.tif

Suppl. Figure 2: The visualization of the Enrichment Analysis results generated out of SVG graphics from the web interface. The best five results for the CglCyc metabolic pathways (A) exhibiting the highest enrichment scores were presented. The running ES score is plotted using blue color if the metabolites belonging to MS are predominantly found in the top part of the ordered List L. The red color indicates that the metabolites are found at the lower part of L. All metabolites present in the experiment are represented as gray boxes and the ones belonging to MS are labeled and highlighted in green. On the right, the computed m-values (B), the t-test probabilities, and the signal to noise ratios (SNR) are listed for all compounds identified in the analyzed metabolomics experiment of C. glutamicum MP001 in comparison to the wild type. For the background of the table cells, a color mapping function of the m-values from red to green was chosen. (TIFF 35,119 kb)

11306_2011_311_MOESM3_ESM.tif

Suppl. Figure 3: The visualization of the Enrichment Analysis results generated out of SVG graphics from the web interface. The best five results for the CglCyc metabolic pathways (A) exhibiting the highest enrichment scores were presented. The running ES score is plotted using blue color if the metabolites belonging to MS are predominantly found in the top part of the ordered List L. The red color indicates that the metabolites are found at the lower part of L. All metabolites present in the experiment are represented as gray boxes and the ones belonging to MS are labeled and highlighted in green. On the right, the computed m-values (B), the t-test probabilities, and the signal to noise ratios (SNR) are listed for all compounds identified in the analyzed metabolomics experiment of C. glutamicum DM1795 in comparison to the wild type. For the background of the table cells, a color mapping function of the m-values from red to green was chosen. (TIFF 35,261 kb)

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Persicke, M., Rückert, C., Plassmeier, J. et al. MSEA: metabolite set enrichment analysis in the MeltDB metabolomics software platform: metabolic profiling of Corynebacterium glutamicum as an example. Metabolomics 8, 310–322 (2012). https://doi.org/10.1007/s11306-011-0311-6

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