Application of bioinformatics and scalable computing to perform proteomic analysis of stomach tissue from diabetic mice

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Edward O. List
Darlene E. Berryman
Amanda J. Palmer
Elahu Gosney
Shigeru Okada
Bruce Kelder
Jens Lichtenberg
Lonnie R. Welch
John J. Kopchick


Modern molecular biology experiments generate large amounts of data, which the biologists then have to analyze these manually through slow and error-prone processes. Bioinformatics and scalable computing provide essential tools for a speedup of the proteomics analysis. An example for such a proteomic analysis and the influences of bioinformatics is presented in this paper.

The stomach is a versatile organ with a prominent role in digestion and with endocrine function. While stomach has been the target of several proteomic analyses interested in cancer or ulcer biology, no studies have analyzed the proteome of stomach with the onset of diabetes, despite the fact that diabetes has a significant impact on gastric function. In this study, proteomic analyses were performed on stomach samples collected from C57BL/6J mice with obesity and diabetes and compared with samples from non-diabetic, lean controls. Obesity and diabetes were induced in mice by placing 3 week old mice on a high fat diet for 16 weeks, while control mice remained on a low fat standard chow diet. Once diabetes was established in the high fat fed mice, 4 diabetic and 4 control mice were sacrificed and stomachs removed for proteomic analysis using 2 dimension gel electrophoresis (2-DE). The protein spots that made up the stomach proteomic profiles were quantified using PDQuest 7.0.0 software. Protein spots found to be increased or decreased in diabetic stomach as compared to control stomach were removed from the gel for identification by database searches from peak lists generated by both MALDI-TOF and MS/MS analyses. In conclusion, a total of 23 proteins are reported herein with 14 being increased and 9 being decreased in diabetic stomach as compared to control.

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