title: New data analysis and mining approaches identify unique proteome and transcriptome markers of susceptibility to autoimmune diabetes.
abstract: Non-obese diabetic (NOD) mice spontaneously develop autoimmunity to the insulin producing beta cells leading to insulin-dependent diabetes. In this study we developed and used new data analysis and mining approaches on combined proteome and transcriptome (molecular phenotype) data to define pathways affected by abnormalities in peripheral leukocytes of young NOD female mice. Cells were collected before mice show signs of autoimmunity (age, 2-4 weeks). We extracted both protein and RNA from NOD and C57BL/6 control mice to conduct both proteome analysis by two-dimensional gel electrophoresis and transcriptome analysis on Affymetrix expression arrays. We developed a new approach to analyze the two-dimensional gel proteome data that included two-way analysis of variance, cluster analysis, and principal component analysis. Lists of differentially expressed proteins and transcripts were subjected to pathway analysis using a commercial service. From the list of 24 proteins differentially expressed between strains we identified two highly significant and interconnected networks centered around oncogenes (Myc and Mycn) and apoptosis-related genes (Bcl2 and Casp3). The 273 genes with significant strain differences in RNA expression levels created six interconnected networks with a significant over-representation of genes related to cancer, cell cycle, and cell death. They contained many of the same genes found in the proteome networks (including Myc and Mycn). The combination of the eight, highly significant networks created one large network of 272 genes of which 82 had differential expression between strains either at the protein or the RNA level. We conclude that new proteome data analysis strategies and combined information from proteome and transcriptome can enhance the insights gained from either type of data alone. The overall systems biology of prediabetic NOD mice points toward abnormalities in regulation of the opposing processes of cell renewal and cell death even before there are any clear signatures of immune system activation.
authors.affiliation: Department of Medicine, College of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee 28104, USA. igerling@utmem.edu
authors.forename: Ivan C
authors.initials: IC
authors.lastname: Gerling
authors.forename: Sudhir
authors.initials: S
authors.lastname: Singh
authors.forename: Nataliya I
authors.initials: NI
authors.lastname: Lenchik
authors.forename: Dana R
authors.initials: DR
authors.lastname: Marshall
authors.forename: Jian
authors.initials: J
authors.lastname: Wu
mesh_terms: D000704:Analysis of Variance; D000818:Animals; D016000:Cluster Analysis; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics* / Q000378:metabolism*; D004198:Disease Susceptibility; D020869:Gene Expression Profiling; D005819:Genetic Markers; D020022:Genetic Predisposition to Disease / Q000235:genetics; D023281:Genomics / Q000379:methods*; D051379:Mice; D008810:Mice, Inbred C57BL; D016688:Mice, Inbred NOD; D025341:Principal Component Analysis; D020543:Proteome; D040901:Proteomics / Q000379:methods*; D014158:Transcription, Genetic / Q000235:genetics*
chemical_list: D005819:Genetic Markers; D020543:Proteome