Dynamic changes in immune gene co-expression networks predict development of type 1 diabetes.

Significant progress has been made in elucidating genetic risk factors influencing Type 1 diabetes (T1D); however, features other than genetic variants that initiate and/or accelerate islet autoimmunity that lead to the development of clinical T1D remain largely unknown. We hypothesized that genetic and environmental risk factors can both contribute to T1D through dynamic alterations of molecular interactions in physiologic networks. To test this hypothesis, we utilized longitudinal blood transcriptomic profiles in The Environmental Determinants of Diabetes in the Young (TEDDY) study to generate gene co-expression networks. In network modules that contain immune response genes associated with T1D, we observed highly dynamic differences in module connectivity in the 600 days (~ 2 years) preceding clinical diagnosis of T1D. Our results suggest that gene co-expression is highly plastic and that connectivity differences in T1D-associated immune system genes influence the timing and development of clinical disease.

在阐明影响1型糖尿病（T1D）的遗传风险因素方面已经取得了重大进展；然而，除了引发和/或加速导致临床T1D发生的胰岛自身免疫的遗传变异之外的其他特征在很大程度上仍然未知。我们假设遗传和环境风险因素都可以通过生理网络中分子相互作用的动态改变而促成T1D。为了验证这一假设，我们在青少年糖尿病环境决定因素（TEDDY）研究中利用纵向血液转录组图谱来构建基因共表达网络。在包含与T1D相关的免疫反应基因的网络模块中，我们观察到在T1D临床诊断前的600天（约2年）内模块连接性存在高度动态的差异。我们的研究结果表明基因共表达具有高度可塑性，并且与T1D相关的免疫系统基因的连接性差异影响临床疾病的发病时间和发展。