Project description:Methanococcus maripaludis is a methanogenic archaeon. Within its genome, there are two operons for membrane associated hydrogenases, eha and ehb. To investigate the regulation of ehb on the cell, an S40 mutant was constructed in such a way that a portion of the ehb operon was replaced by pac cassette in the wild type parental strain S2 (done by Whitman's group at the University of Georgia). The S40 and S2 strains were grown in 14N and 15N media with acetate separately. A biological replicate was made by switching the media. Mass spectrometry based quantitative proteomics were done on the mixtures to investigate the differences in expression patterns between S40 and S2. Keywords: isotope labeling mass spectrometry, quantitative proteomics
Project description:Data analysis and mass spectrometry tools have advanced significantly in the last decade. This ongoing revolution has elevated the status of analytical chemistry within the big-data omics era. High resolution mass spectrometers (HRMS) can now distinguish different metabolites with mass to charge ratios (i.e. m/z) that differ by 0.01 Da or less. This unprecedented level of resolution not only enables identification of previously unknown compounds but also presents an opportunity to establish active metabolic pathways through quantification of isotope enrichment. Studies with stable isotope tracers continue to contribute to our knowledge of biological pathways in human, plant and bacterial species, however most current studies have been based on targeted analyses. The capacity of HRMS to resolve near-overlapping isotopologues and identify compounds with high mass precision presents a strategy to assess ‘active’ pathways de novo from data generated in an untargeted way, that is blind to the metabolic network and therefore unbiased. Currently, identifying metabolic features, enriched with stable isotopes, at an ‘omics’ level remains an experimental bottleneck, limiting our capacity to understand biological network operation at the metabolic level. We developed data analysis tools that: i) use labeling information and exact mass to determine the elemental composition of each isotopically enriched ion, ii) apply correlation-based approaches to cluster metabolite peaks with similar patterns of isotopic labels and, iii) leverage this information to build directed metabolic networks de novo. Using Camelina sativa, an emerging oilseed model, we demonstrate the power of stable isotope labeling in combination with imaging and HRMS to reconstruct lipid metabolic networks in developing seeds and are currently addressing questions about lipid and central metabolism. Tools developed in this study will have a broader application to assess context specific operation of metabolic pathways.
Project description:Stable isotope labeling of peptides is the basis for numerous mass spectrometry-based quantification strategies. Isobaric tagging and metabolic labeling, namely TMT and SILAC, are among the most widely used techniques for relative protein quantification. Here we report an alternative, precursor-based quantification method using non-isobaric TMT variants: TMT0 (TMTzero) and shTMT (super-heavy TMT). We term this strategy mTMT (mass difference tandem mass tagging), as these TMT variants differ by 11 mass units; yet, peptides labeled with these reagents co-elute, analogous to SILAC-labeled sample analysis. As proof-of-concept, we profiled the proteomes of two cell lines that are frequently used in neuroscience studies, SH-SY5Y and SVGp12, using mTMT and standard SILAC-labeling approaches. We show similar numbers of quantified proteins and peptides using each method with highly correlated fold changes between workflows. We conclude that mTMT is a suitable alternative for precursor-based protein quantification.