Non-obese @entity6 (NOD) @entity19 spontaneously develop @entity792 to the insulin producing beta cells leading to @entity1723 . 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 @entity19 . Cells were collected before @entity19 show signs of @entity792 (age, 2-4 weeks). We extracted both protein and RNA from NOD and C57BL/6 control @entity19 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 ( @entity18122 and @entity10674 ) and apoptosis-related genes ( @entity1379 and @entity185 ). The 273 genes with significant strain differences in RNA expression levels created six interconnected networks with a significant over-representation of genes related to @entity5 , cell cycle, and cell death. They contained many of the same genes found in the proteome networks (including @entity18122 and @entity10674 ). 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 @entity19 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.

New data analysis and mining approaches identify unique proteome and transcriptome markers of susceptibility to autoimmune XXXX .

@entity10674 :: ('18109(Tax:10090)', 'Gene') :: ['Mycn'] @entity18122 :: ('17869(Tax:10090)', 'Gene') :: ['Myc'] @entity6 :: ('MESH:D003920', 'Disease') :: ['diabetic'] @entity19 :: ('10090', 'Species') :: ['mice'] @entity5 :: ('MESH:D009369', 'Disease') :: ['cancer'] @entity185 :: ('12367(Tax:10090)', 'Gene') :: ['Casp3'] @entity1723 :: ('MESH:D003922', 'Disease') :: ['insulin-dependent diabetes'] @entity1379 :: ('12043(Tax:10090)', 'Gene') :: ['Bcl2'] @entity792 :: ('MESH:D001327', 'Disease') :: ['autoimmunity']
@entity6 :: (MESH:D003920,Disease) :: ['diabetes']
