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Abstract
Mass spectrometry (MS) has become a widely used analytical technique to study the proteome of complex biological matrices. In this research, the gel based proteomic approach (GeLC-MS) was developed and applied to solve biological problems in different organisms such as Trypanosoma cruzi (T. cruzi) and embryonic stem cells. A membrane proteomic analysis of the protozoan parasite T. cruzi was performed. Using two individual membrane enrichment preparations, a total of 551 protein groups got identified from around 80 LC-MS/MS runs. Both two preparation strategies were effectively enriching some respective membrane proteins. The identified membrane proteins accounted for almost 40% of the protein identifications within the whole proteome, which shows great enrichment compared to regular global analyses which only have about 5%. The most attractive result for us is the identification of 87 trans-sialidases, 9 mucin associated surface protein (MASP), 3 mucins, and 2 GP63 proteins. These GPI anchored surface proteins are involved in parasite survival and cell invasions, thus could become potential vaccine targets. A comprehensive proteome analysis of T. cruzi intracellular amastigotes was introduced. Subcellular organelle and membrane enriched fractions as well as cytosol soluble fractions were individually obtained and analyzed using GeLC-MS/MS approach. In addition to matching the MS/MS spectra to the annotated proteome database, we performed a whole genome search in order to identify additional genes potentially missed in the annotation of the T. cruzi genome. We also utilized a hybrid identification tool (ByOnic) for the identification of unanticipated mutations caused by different T. cruzi strains. We also report here the application of GeLC-MS approach to resolve some protein isoforms identification including trans-sialidases, GP63, etc in T. cruzi. Additionally this technique was utilized to analyze the mouse embryonic stem cell proteome and focused on looking for some potential protein degradation products. Our identification data has shown that this approach is efficient and helpful for discovering the protein degradation process, which plays essential roles in biological cellular functions and activities.