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

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.