Insulin gene VNTR genotype associates with frequency and phenotype of the autoimmune response to proinsulin

Immune responses to autoantigens are in part controlled by deletion of autoreactive cells through genetically regulated selection mechanisms. We have directly analyzed peripheral CD4+ proinsulin (PI) 76–90 (SLQPLALEGSLQKRG)-specific T cells using soluble fluorescent major histocompatibility complex class II tetramers. Subjects with type I diabetes and healthy controls with high levels of peripheral proinsulin-specific T cells were characterized by the presence of a disease-susceptible polymorphism in the insulin variable number of tandem repeats (INS-VNTR) gene. Conversely, subjects with a ‘protective' polymorphism in the INS-VNTR gene had nearly undetectable levels of proinsulin tetramer-positive T cells. These results strongly imply a direct relationship between genetic control of autoantigen expression and peripheral autoreactivity, in which proinsulin genotype restricts the quantity and quality of the potential T-cell response. Using a modified tetramer to isolate low-avidity proinsulin-specific T cells from subjects with the susceptible genotype, transcript arrays identified several induced pro-apoptotic genes in the control, but not diabetic subjects, likely representing a second peripheral mechanism for maintenance of tolerance to self antigens

A Novel Repertoire of Blood Transcriptome Modules Based on Co-expression Patterns across sixteen Disease and Physiological States

Blood transcriptomics measures the abundance of circulating leukocyte RNA on a genome-wide scale. Dimension reduction is an important analytic step which condenses the number of variables and permits to enhance the robustness of data analyses and functional interpretation. An approach consisting in the construction of modular repertoires based on differential co-expression observed across multiple biological states of a given system has been described before. In this report, a new blood transcriptome modular repertoire is presented based on an expended range of disease and physiological states (16 in total, encompassing 985 unique transcriptome profiles). The input datasets have been deposited in NCBI public repository, GEO. The composition of the set of 382 modules constituting the repertoire is shared, along with extensive functional annotations and a custom fingerprint visualization scheme. Finally, the similarities and differences between the blood transcriptome profiles of this wide range of biological states are presented and discussed