Study On Type2Diabetes Mellitus Relevant Metabolomics And Bioinformatics Research

Abstract:In this thesis,I summarized my researches on type2diabetes mellitus (T2DM) relevant metabolomics and bioinformatics.As we all know, T2DM is a complex metabolic disease which affects by many effects,such as environment, obesity, lock of practices.Using multi-methods to study this disease will provide new opportunities for discovering pathological processes,assessing the efficacy of drugs,and disease early diagnosing.Clinical manifestations of T2DM are the metabolic disturbances of saccharides,lipid, and amino acid.These make using the metabolomics methods to study T2DM is very suitable.In this thesis,my research group and I developed some methods for analysis the T2DM relevant samples which collected from animal models,T2DM patients,and corresponding control groups.The detailed metabolomics studies are listed as follows:We combined the GC-MS analytical methods with random forest (RF) algorithm to analyze the metabolites variation in AMPK gene knock-out mice and healthy C57mice.Not only the differences between the normal C57mice and C57-AMPK gene knocked-out mice were observed, but also the gender-related metabolites differences of the C57-AMPK gene knocked-out mice were obviously visualized; The models constructed by RF could visually discriminate type2diabetic mice from healthy control group and represent the variance of metabolic profiles of diabetic mice in the therapeutic process with repaglinide and rosiglitazone.Simultaneous,some informative metabolites have been successfully discovered by means of variable importance ranking in RF program;sophisticated feature selection approaches are required to extract the information hidden in such complex’omics’data.In this study, we proposed a new and robust selective method by combining random forests (RF)with model population analysis (MPA),for selecting informative metabolites from three metabolomic datasets.Then, we did some T2DM relevant bioinformatics work which focused on G protein coupled receptor (GPCR) post-translational modification sites and lymphocyte epitopes identification.The detailed work can be stated as follows:Insulin (3cell can coordinate the effects caused by nutriment, neurotransmitter, and hormone, to make the insulin in a stable level.The dysfunctions of islet are one of the most important pathogenic factors of T2DM.Most of the neurotransmitter and hormone can control the insulin secretion by GPCR mediated signal transduction system.And the GPCR mediated signal transduction system is controlled and affected by many factors,and protein post-translational modifications are one of these factors.In this thesis,we used the support vector machine,ensemble support vector machine, and random forest algorithm to identify Phosphorylation post-translational modification,Glycosylation post-translational modification, and Palmitoylation post-translational modification, respectively. The prediction results for these three methods were satisfactory. These work had great help for further discovering the GPCR signal transmission mechanism,explaining the relationships with insulin secretion.And our further works are focused on expliciting the interactions of these different post-translational modifications,and constructing their interaction network.Previous researchers found that the T2DM was connected with islet chronic inflammation and autoimmunization.Based on these studies,we have done some work for discovering the relationships between T-cell and B-cell mediated specific immunity response;The processes of specific immunity response contains three steps:first, the antigen is specific recognized by epitopes, then conform antigen-epitopes complex and sent to lymphocyte cell receptor, final the immunity response and immunity regulation is active.The epitopes specific recognize the antigen is the first step of immunity response.Identify the exact epitope sites is vitally important for T-cell and B-cell mediated specific immunity response.In this thesis,we developed a new peptide sequence feature description method, which combined the peptide amino acid physicochemical properties and chemical molecular properties.Our research showed that this combined feature had a good performance in T cell and B cell epitopes identification.In the last part of this thesis we introduced the online serves for these proposed methods, which could provide a free test for the researchers all over the world. The aims of online serves are to store the date collected from T2DM metabolomics experiments and bioinformatics researches, update the models by time, and make a solid foundation for explaining the T2DM from a systems biology view.