The mechanism(s) underlying T1D initiation and progression remain elusive as individuals are asymptomatic and difficult to identify and study during the early stages of disease. Even if biomarkers could identify humans early in the disease process, pancreas specimens are not readily available to permit direct study of the events that occur in the target organ. To address this, we propose to use the virus-inducible BBDR rat model of T1D. The BBDR rat is the only natural animal model that develops T1D following viral infection. Its close similarity with human T1D pathology, availability of new approaches for studying beta cell stress, and novel technologies for RNAseq analyses of purified beta cells and for identifying microRNA (miRNA) signatures in plasma now permit systematic identification of events underlying early stages of T1D. Diabetes in BBDR rats occurs in four stages: 1) initiation of beta cell stress and 2) death during the pre-insulitis period. Inslitis (autoimmunity, stage 3), and T1D (stage 4) occurs after complete beta cell destruction. We hypothesize that beta cell stress and death occurs prior to the initiation of insulitis and that plasma biomarkers reflecting beta cell stress will be detectable during this pre-insulitis stage f disease.
Aim 1 will identify mRNA and microRNA signatures in beta cells at discrete stages of T1D development. We hypothesize that mRNA and microRNA beta cell expression profiles reflect the molecular mechanisms that are operational during each stage of progression of T1D. We will use RNAseq to generate mRNA and microRNA profiles to identify the molecular pathways that underlie beta cell responses at defined stages of T1D.
Aim 2 will interrogate islet cell heterogeneity using single cell sequencing technology and identify mRNA profiles of beta cells with disparate MHC class I or insulin expression. We hypothesize that beta cell responses to stress during the development of T1D are heterogeneous, and that this may lead to the spotty insulitis and beta cell loss observed in rat (and human) T1D. We will use single cell RNAseq analyses of isolated islet cells to interrogate gene expression changes that underlie beta cell heterogeneity at defined stages of diabetes progression and correlate mRNA signatures with MHC class I or insulin expression.
Aim 3 will identify stage-specific plasma microRNA signatures as T1D biomarkers. We hypothesize that the miRNA profile in the plasma will provide stage-specific signatures as biomarkers of T1D progression. We will use novel technology to recover small RNA from circulating plasma and perform RNAseq in the virus-induced BBDR rat and in our new model of Coxsackie B4-infected immunodeficient mice engrafted with human islets that will permit us to identify human miRNA signatures. These studies will permit detailed analyses of the mechanisms underlying beta cell decay prior to the development of insulitis and diabetes and associate them with ongoing events in the beta cells, leading to the identification of fundamental factors that underlie the earliest events of the disease. This will lay the foundation for future studies to look for miRNA biomarker signatures in humans that may be predisposed to T1D.
The ability to identify and intervene early in the development of type 1 diabetes is hindered in part by the inability to identify early pre-diabetic individuals. This project will use plasma from a predictable animal model of type 1 diabetes to identify biomarkers of early diabetes, and to use this information to identify the underlying causes of loss of insulin-producing cells.
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|Roberts, Frederick R; Hupple, Clinton; Norowski, Elaine et al. (2017) Possible type 1 diabetes risk prediction: Using ultrasound imaging to assess pancreas inflammation in the inducible autoimmune diabetes BBDR model. PLoS One 12:e0178641|
|Qaisar, Natasha; Lin, Suvana; Ryan, Glennice et al. (2017) A Critical Role for the Type I Interferon Receptor in Virus-Induced Autoimmune Diabetes in Rats. Diabetes 66:145-157|
|Derr, Alan; Yang, Chaoxing; Zilionis, Rapolas et al. (2016) End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data. Genome Res 26:1397-1410|