Title: Expression profiling pre-diabetic mice to uncover drugs with clinical application to type 1 diabetes
Authors: Pang, D
Hamilton-Williams, EE
Harris, M
Thomas, HE
Mehdi, AM
Irvine, KM
Thomas, R
Issue Year: 2015
Publisher
Series Clin Transl Immunology:
Abstract In the NOD mouse model of type 1 diabetes (T1D), genetically identical mice in the same environment develop diabetes at different rates. Similar heterogeneity in the rate of progression to T1D exists in humans, but the underlying mechanisms are unclear. Here, we aimed to discover peripheral blood (PB) genes in NOD mice predicting insulitis severity and rate of progression to diabetes. We then wished to use these genes to mine existing databases to identify drugs effective in diabetes. In a longitudinal study, we analyzed gene expression in PB samples from NOD.CD45.2 mice at 10 weeks of age, then scored pancreatic insulitis at 14 weeks or determined age of diabetes onset. In a multilinear regression model, Tnf and Tgfb mRNA expression in PB predicted insulitis score (R (2)=0.56, P=0.01). Expression of these genes did not predict age of diabetes onset. However, by expression-profiling PB genes in 10-week-old NOD.CD45.2 mice, we found a signature of upregulated genes that predicted delayed or no diabetes. Major associated pathways included chromatin organization, cellular protein location and regulation of nitrogen compounds and RNA. In a clinical cohort, three of these genes were differentially expressed between first-degree relatives, T1D patients and controls. Bioinformatic analysis of differentially expressed genes in NOD.CD45.2 PB identified drugs that are predicted to delay or prevent diabetes. Of these drugs, 11 overlapped with drugs predicted to induce a human 'non-progressor' expression profile. These data demonstrate that disease heterogeneity in diabetes-prone mice can be exploited to mine novel clinical T1D biomarkers and drug targets.
URI: https://publications.svi.edu.au/publications/2433
ISSN
Other Identifiers 4(8):e41
Publication type Article
Grant ID GNT1037321; GNT1042735;
Find it online https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/26366287/