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See detailIntegration of proteomic and metabolomic profiling as well as metabolic modeling for the functional analysis of metabolic networks.
May, Patrick UL; Christian, Nils UL; Ebenhoh, Oliver et al

in Methods in Molecular Biology (Clifton, N.J.) (2011), 694

The integrated analysis of different omics-level data sets is most naturally performed in the context of common process or pathway association. In this chapter, the two basic approaches for a metabolic ... [more ▼]

The integrated analysis of different omics-level data sets is most naturally performed in the context of common process or pathway association. In this chapter, the two basic approaches for a metabolic pathway-centric integration of proteomics and metabolomics data are described: the knowledge-based approach relying on existing metabolic pathway information, and a data-driven approach that aims to deduce functional (pathway) associations directly from the data. Relevant algorithmic approaches for the generation of metabolic networks of model organisms, their functional analysis, database resources, visualization and analysis tools will be described. The use of proteomics data in the process of metabolic network reconstruction will be discussed. [less ▲]

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See detailTargeted proteomics for Chlamydomonas reinhardtii combined with rapid subcellular protein fractionation, metabolomics and metabolic flux analyses.
Wienkoop, Stefanie; Weiss, Julia; May, Patrick UL et al

in Molecular Biosystems (2010), 6(6), 1018-31

In the era of fast genome sequencing a critical goal is to develop genome-wide quantitative molecular approaches. Here, we present a metaproteogenomic strategy to integrate proteomics and metabolomics ... [more ▼]

In the era of fast genome sequencing a critical goal is to develop genome-wide quantitative molecular approaches. Here, we present a metaproteogenomic strategy to integrate proteomics and metabolomics data for systems level analysis in the recently sequenced unicellular green algae Chlamydomonas reinhardtii. To achieve a representative proteome coverage we analysed different growth conditions with protein prefractionation and shotgun proteomics. For protein identification, different genome annotations as well as new gene model predictions with stringent peptide filter criteria were used. An overlapping proteome coverage of 25%, consistent for all databases, was determined. The data are stored in a public mass spectral reference database ProMEX (http://www.promexdb.org/home.shtml). A set of proteotypic peptides comprising Calvin cycle, photosynthetic apparatus, starch synthesis, glycolysis, TCA cycle, carbon concentrating mechanisms (CCM) and other pathways was selected from this database for targeted proteomics (Mass Western). Rapid subcellular fractionation in combination with targeted proteomics allowed for measuring subcellular protein concentrations in attomole per 1000 cells. From the same samples metabolite concentrations and metabolic fluxes by stable isotope incorporation were analyzed. Differences were found in the growth-dependent crosstalk of chloroplastidic and mitochondrial metabolism. A Mass Western survey of all detectable carbonic anhydrases partially involved in carbon-concentrating mechanism (CCM) revealed highest internal cell concentrations for a specific low-CO2-inducible mitochondrial CAH isoform. This indicates its role as one of the strongest CO2-responsive proteins in the crosstalk of air-adapted mixotrophic chloroplast and mitochondrial metabolism in Chlamydomonas reinhardtii. [less ▲]

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See detailMetabolomics- and proteomics-assisted genome annotation and analysis of the draft metabolic network of Chlamydomonas reinhardtii.
May, Patrick UL; Wienkoop, Stefanie; Kempa, Stefan et al

in Genetics (2008), 179(1), 157-66

We present an integrated analysis of the molecular repertoire of Chlamydomonas reinhardtii under reference conditions. Bioinformatics annotation methods combined with GCxGC/MS-based metabolomics and LC/MS ... [more ▼]

We present an integrated analysis of the molecular repertoire of Chlamydomonas reinhardtii under reference conditions. Bioinformatics annotation methods combined with GCxGC/MS-based metabolomics and LC/MS-based shotgun proteomics profiling technologies have been applied to characterize abundant proteins and metabolites, resulting in the detection of 1069 proteins and 159 metabolites. Of the measured proteins, 204 currently do not have EST sequence support; thus a significant portion of the proteomics-detected proteins provide evidence for the validity of in silico gene models. Furthermore, the generated peptide data lend support to the validity of a number of proteins currently in the proposed model stage. By integrating genomic annotation information with experimentally identified metabolites and proteins, we constructed a draft metabolic network for Chlamydomonas. Computational metabolic modeling allowed an identification of missing enzymatic links. Some experimentally detected metabolites are not producible by the currently known and annotated enzyme set, thus suggesting entry points for further targeted gene discovery or biochemical pathway research. All data sets are made available as supplementary material as well as web-accessible databases and within the functional context via the Chlamydomonas-adapted MapMan annotation platform. Information of identified peptides is also available directly via the JGI-Chlamydomonas genomic resource database (http://genome.jgi-psf.org/Chlre3/Chlre3.home.html). [less ▲]

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