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Two dimensional gas chromatography coupled to time-of-flight mass spectrometry (GCxGC-TOF-MS) is a promising technique to overcome limits of complex metabolome analysis using one dimensional GC-TOF-MS. Especially at the stage of data export and data mining, however, convenient procedures to cope with the complexity of GCxGC-TOF-MS data are still in development. Here, we present a high sample throughput protocol exploiting first and second retention index for spectral library search and subsequent construction of a high dimensional data matrix useful for statistical analysis. The method was applied to the analysis of 13 C-labelling experiments in the unicellular green alga Chlamydomonas reinhardtii. We developed a rapid sampling and extraction procedure for Chlamydomonas reinhardtii laboratory strain (CC503), a cell wall deficient mutant. By testing all published quenching protocols we observed dramatic metabolite leakage rates for certain metabolites. To circumvent metabolite leakage, samples were directly quenched and analyzed without separation of the medium. The growth medium was adapted to this rapid sampling protocol to avoid interference with GCxGC-TOF-MS analysis. To analyse batches of samples a new software tool, MetMax, was implemented which extracts the isotopomer matrix from stable isotope labelling experiments together with the first and second retention index (RI1 and RI2). To exploit RI1 and RI2 for metabolite identification we used the Golm metabolome database (GMD [1] with RI1/ RI2-reference spectra and new search algorithms. Using those techniques we analysed the dynamics of (CO2)-C-13 and C-13- acetate uptake in Chlamydomonas reinhardtii cells in two different steady states namely photoautotrophic and mixotrophic growth conditions.
Biomarkers are used to predict phenotypical properties before these features become apparent and, therefore, are valuable tools for both fundamental and applied research. Diagnostic biomarkers have been discovered in medicine many decades ago and are now commonly applied. While this is routine in the field of medicine, it is of surprise that in agriculture this approach has never been investigated. Up to now, the prediction of phenotypes in plants was based on growing plants and assaying the organs of interest in a time intensive process. For the first time, we demonstrate in this study the application of metabolomics to predict agronomic important phenotypes of a crop plant that was grown in different environments. Our procedure consists of established techniques to screen untargeted for a large amount of metabolites in parallel, in combination with machine learning methods. By using this combination of metabolomics and biomathematical tools metabolites were identified that can be used as biomarkers to improve the prediction of traits. The predictive metabolites can be selected and used subsequently to develop fast, targeted and low-cost diagnostic biomarker assays that can be implemented in breeding programs or quality assessment analysis. The identified metabolic biomarkers allow for the prediction of crop product quality. Furthermore, marker-assisted selection can benefit from the discovery of metabolic biomarkers when other molecular markers come to its limitation. The described marker selection method was developed for potato tubers, but is generally applicable to any crop and trait as it functions independently of genomic information.
Mass Accuracy Precursor Alignment is a fast and flexible method for comparative proteome analysis that allows the comparison of unprecedented numbers of shotgun proteomics analyses on a personal computer in a matter of hours. We compared 183 LC-MS analyses and more than 2 million MS/MS spectra and could define and separate the proteomic phenotypes of field grown tubers of 12 tetraploid cultivars of the crop plant Solanum tuberosum. Protein isoforms of patatin as well as other major gene families such as lipoxygenase and cysteine protease inhibitor that regulate tuber development were found to be the primary source of variability between the cultivars. This suggests that differentially expressed protein isoforms modulate genotype specific tuber development and the plant phenotype. We properly assigned the measured abundance of tryptic peptides to different protein isoforms that share extensive stretches of primary structure and thus inferred their abundance. Peptides unique to different protein isoforms were used to classify the remaining peptides assigned to the entire subset of isoforms based on a common abundance profile using multivariate statistical procedures. We identified nearly 4000,proteins which we used for quantitative functional annotation making this the most extensive study of the tuber proteome to date.
Background: Protein sequence motifs are by definition short fragments of conserved amino acids, often associated with a specific function. Accordingly protein sequence profiles derived from multiple sequence alignments provide an alternative description of functional motifs characterizing families of related sequences. Such profiles conveniently reflect functional necessities by pointing out proximity at conserved sequence positions as well as depicting distances at variable positions. Discovering significant conservation characteristics within the variable positions of profiles mirrors group-specific and, in particular, evolutionary features of the underlying sequences. Results: We describe the tool PROfile analysis based on Mutual Information (PROMI) that enables comparative analysis of user-classified protein sequences. PROMI is implemented as a web service using Perl and R as well as other publicly available packages and tools on the server-side. On the client-side platform-independence is achieved by generally applied internet delivery standards. As one possible application analysis of the zinc finger C2H2-type protein domain is introduced to illustrate the functionality of the tool. Conclusion: The web service PROMI should assist researchers to detect evolutionary correlations in protein profiles of defined biological sequences. It is available at http:// promi.mpimpgolm. mpg.de where additional documentation can be found