The present study compared genetically modified (GM) crops with crops from different farming practices using high-resolution tandem mass spectrometry (HR-MS) and proteomics bioinformatics tools. In... Show moreThe present study compared genetically modified (GM) crops with crops from different farming practices using high-resolution tandem mass spectrometry (HR-MS) and proteomics bioinformatics tools. In a previously pub-lished study, a number of significant differences regarding nutritional and elemental composition between a selection of GM, non-GM conventionally farmed, and organic soybeans have been found. In the present study, the proteome-level equivalence of the same samples was assessed using HR-MS. Direct comparison of tandem mass spectra and bottom-up proteomics bioinformatics indicated that proteomes of all samples investigated were very similar overall, with only a few distinct protein expression clusters obtained for GM and organic samples. Standard bottom-up proteome analyses identified 1025 soy proteins; of these 39 were found to be differentially expressed (p < 0.01) between GM, non-GM conventionally farmed, and organically farmed soybeans. Subsequent bioinformatics analyses of these proteins highlighted several potentially affected biochemical pathways that could contribute to the compositional differences reported earlier. In addition, protein markers separating conventionally, and organically farmed soybean seeds were found and peptide markers for the detection of GM soy in food and feed samples are described. Taken together, the data presented here shows that HR-MS based proteomics approaches can be used for the detection of transgenic events in food and feed grade soy, the dif-ferentiation of organically and conventionally farmed plants, and provide mechanistic explanations of effects observed on the phenotypic level of GM plants. HR-MS and proteomic bioinformatics thus should be considered key tools when developing molecular panel approaches for detection and safety assessments of novel crop va-rieties destined for use in feed and food. Show less
This dissertation describes the development of glyco-bioinformatics tools that facilitate the high-throughput data processing of glycomics and glycoproteomics experiments, specifically for both... Show moreThis dissertation describes the development of glyco-bioinformatics tools that facilitate the high-throughput data processing of glycomics and glycoproteomics experiments, specifically for both MALDI-TOF-MS (Chapter 2) and LC-ESI-MS (Chapter 3). The developed methods also provide various quality control parameters that assist the researcher in curating both the measured spectra and quantified analytes, thereby providing high-quality data in a high-throughput manner.The tools that were developed within this thesis have been used to identify the influence of glycosylation on trypsin efficacy of Immunoglobulin G (Chapter 3) and two biological cohorts. Specifically, to investigate the serum N-glycosylation during and after pregnancy (Chapter 5) and to identify the differences in the N-glycosylation between maternal and fetal serum and IgG (Chapter 6). Show less
