We are analyzing https://link.springer.com/article/10.1007/s11306-005-4430-9.

Title:
Correlative GC-TOF-MS-based metabolite profiling and LC-MS-based protein profiling reveal time-related systemic regulation of metabolite–protein networks and improve pattern recognition for multiple biomarker selection | Metabolomics
Description:
A novel approach is presented combining quantitative metabolite and protein data and multivariate statistics for the analysis of time-related regulatory effects of plant metabolism at a systems level. For the analysis of metabolites, gas chromatography coupled to a time-of-flight mass analyzer (GC-TOF-MS) was used. Proteins were identified and quantified using a novel procedure based on shotgun sequencing as described recently (Weckwerth et al., 2004b, Proteomics 4, 78–83). For comparison, leaves of Arabidopsis thaliana wild type plants and starchless mutant plants deficient in phosphoglucomutase activity (PGM) were sampled at intervals throughout the day/night cycle. Using principal and independent components analysis, each dataset (metabolites and proteins) displayed discrete characteristics. Compared to the analysis of only metabolites or only proteins, independent components analysis (ICA) of the integrated metabolite/protein dataset resulted in an improved ability to distinguish between WT and PGM plants (first independent component) and, in parallel, to see diurnal variations in both plants (second independent component). Interestingly, levels of photorespiratory intermediates such as glycerate and glycine best characterized phases of diurnal rhythm, and were not influenced by high sugar accumulation in PGM plants. In contrast to WT plants, PGM plants showed an inversely regulated cluster of N-rich amino acid metabolites and carbohydrates, indicating a shift in C/N partitioning. This observation corresponds to altered utilization of urea cycle intermediates in PGM plants suggesting enhanced protein degradation and carbon utilization due to growth inhibition. Among the proteins chloroplastidic GAPDH (At3g26650) was the best discriminator between WT and PGM plants in contrast to the cytosolic isoform (At1g13440) according to the primary effect of mutation located in the chloroplast. The described method is applicable to all kinds of biological systems and enables the unbiased identification of biomarkers embedded in correlative metabolite–protein networks.
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Keywords {🔍}

article, google, scholar, cas, pubmed, analysis, acid, plant, plants, metabolic, data, weckwerth, metabolomics, profiling, protein, biol, fiehn, systems, mass, independent, metabolite, regulation, networks, wienkoop, proteins, diurnal, selbig, metabolites, pgm, component, identification, access, genomics, spectrometry, privacy, cookies, content, scholz, proteomics, arabidopsis, metabolomic, mol, willmitzer, publish, research, search, metaboliteprotein, multivariate, metabolism, components,

Topics {✒️}

lc/lc-ms/ms compound-specific delta-c-13 analyses high-speed gc/ms anal month download article/chapter correlative metabolite–protein networks molecular plant physiology cell-specific protein profiling shotgun proteomics systems biology ann diurnal rhythm time-related regulatory effects gc-tof-ms large-scale protein analysis diagnostic-technique acs symp metabolic profiles article metabolomics aims joachim selbig & wolfram weckwerth gc × gc-ms myo-inositol orn/arg multidimensional chromatography coupled multivariate statistical analysis improved methods metabolite–protein networks gas-chromatography time full article pdf trichome-located proteins involved post-genomic research stefanie wienkoop metabolic control analysis high-speed gc integrated extraction identification robot-based platform gas chromatography coupled author correspondence privacy choices/manage cookies multivariate analysis phenotypes plant mol comparing protein identifications tandem mass spectrometry diurnal starch turnover nonlinear pca biological processes plant shotgun sequencing multiple biomarker selection gc/tof metabolite profiling light period plant gc/ms complex metabolome data subcellular metabolite levels

Questions {❓}

Fiehn (2002) Can we discover novel pathways using metabolomic analysis?
Willmitzer (1999) Metabolic profiling: a Rosetta Stone for genomics?

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         headline:Correlative GC-TOF-MS-based metabolite profiling and LC-MS-based protein profiling reveal time-related systemic regulation of metabolite–protein networks and improve pattern recognition for multiple biomarker selection
         description:A novel approach is presented combining quantitative metabolite and protein data and multivariate statistics for the analysis of time-related regulatory effects of plant metabolism at a systems level. For the analysis of metabolites, gas chromatography coupled to a time-of-flight mass analyzer (GC-TOF-MS) was used. Proteins were identified and quantified using a novel procedure based on shotgun sequencing as described recently (Weckwerth et al., 2004b, Proteomics 4, 78–83). For comparison, leaves of Arabidopsis thaliana wild type plants and starchless mutant plants deficient in phosphoglucomutase activity (PGM) were sampled at intervals throughout the day/night cycle. Using principal and independent components analysis, each dataset (metabolites and proteins) displayed discrete characteristics. Compared to the analysis of only metabolites or only proteins, independent components analysis (ICA) of the integrated metabolite/protein dataset resulted in an improved ability to distinguish between WT and PGM plants (first independent component) and, in parallel, to see diurnal variations in both plants (second independent component). Interestingly, levels of photorespiratory intermediates such as glycerate and glycine best characterized phases of diurnal rhythm, and were not influenced by high sugar accumulation in PGM plants. In contrast to WT plants, PGM plants showed an inversely regulated cluster of N-rich amino acid metabolites and carbohydrates, indicating a shift in C/N partitioning. This observation corresponds to altered utilization of urea cycle intermediates in PGM plants suggesting enhanced protein degradation and carbon utilization due to growth inhibition. Among the proteins chloroplastidic GAPDH (At3g26650) was the best discriminator between WT and PGM plants in contrast to the cytosolic isoform (At1g13440) according to the primary effect of mutation located in the chloroplast. The described method is applicable to all kinds of biological systems and enables the unbiased identification of biomarkers embedded in correlative metabolite–protein networks.
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         ICA
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         diurnal rhythm
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         Cell Biology
         Developmental Biology
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