Investigation of the age-at-onset heterogeneity in type 1 diabetes through mathematical modeling

Investigation of coordination and order in transcription regulation of innate and adaptive immunity genes in type 1 diabetes

Background

Type 1 diabetes (T1D) is an autoimmune disease and extensive evidence has indicated a critical role of both the innate and the adaptive arms of immune system in disease development. To date most clinical trials of immunomodulation therapies failed to show efficacy. A number of gene expression studies of T1D have been carried out. However, a systems analysis of the expression variations of the innate and adaptive immunity gene sets, or their co-expression network structures in cohorts at different disease states or of different disease risks, is not available till now.

Methods

We utilized data from a large gene expression study that included transcription profiles of control peripheral blood mononuclear cells (PBMC) exposed to plasma of 148 human subjects from four cohorts that included unrelated healthy controls (uHC), recent onset T1D patients (RO-T1D), and healthy siblings of probands that possess high (HRS, High Risk Sibling) or low (LRS, Low Risk Sibling) risk HLA haplotypes. Both weighted and non-weighted co-expression networks were constructed in each cohort separately, and edge weight distribution and the activation of known protein complexes were examined. The co-expression networks of the innate and adaptive immunity genes were further examined in more detail through a number of network measures that included network density, Shannon entropy, h-index, and the scaling exponent γ of degree distribution. Pathway analysis was carried out using CoGA, a tool for detecting significant network structural changes of a gene set.

Results

Weighted network edge distribution revealed a globally weakened co-expression network induced by the RO-T1D cohort as compared to that by the uHC, suggesting a broad spectrum loss of transcriptional coordination. The two healthy T1D family cohorts (HRS and LRS) induced more active but heterogeneous transcription coordination globally, and among both the innate and the adaptive immunity genes, than the uHC. This finding is consistent with our previous report of these cohorts sharing a heightened innate inflammatory state. The spike-in of IL-1RA to RO-T1D sera improved co-expression network strength of both the innate and the adaptive immunity genes, and enabled a global order recovery in transcription regulation that resulted in significantly increased number of activated protein complexes. Many of the top pathways that showed significant difference in co-expression network structures and order between RO-T1D and uHC have strong links to T1D.

Conclusions

Network level analysis of the innate and adaptive immunity genes, and the whole genome, revealed striking cohort-dependent differences in co-expression network structural measures, suggesting their potential in cohort classification and disease-relevant pathway identification. The results demonstrated the advantages of systems analysis in defining molecular signatures as well as in predicting targets in future research.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-017-0243-8) contains supplementary material, which is available to authorized users.

Autoimmune responses in T1DM: quantitative methods to understand onset, progression, and prevention of disease

Understanding the physiological processes that underlie autoimmune disorders and identifying biomarkers to predict their onset are two pressing issues that need to be thoroughly sorted out by careful thought when analyzing these diseases. Type 1 diabetes (T1D) is a typical example of such diseases. It is mediated by autoreactive cytotoxic CD4⁺ and CD8⁺ T-cells that infiltrate the pancreatic islets of Langerhans and destroy insulin-secreting β-cells, leading to abnormal levels of glucose in affected individuals. The disease is also associated with a series of islet-specific autoantibodies that appear in high-risk subjects (HRS) several years prior to the onset of diabetes-related symptoms. It has been suggested that T1D is relapsing-remitting in nature and that islet-specific autoantibodies released by lymphocytic B-cells are detectable at different stages of the disease, depending on their binding affinity (the higher, the earlier they appear). The multifaceted nature of this disease and its intrinsic complexity make this disease very difficult to analyze experimentally as a whole. The use of quantitative methods, in the form of mathematical models and computational tools, to examine the disease has been a very powerful tool in providing predictions and insights about the underlying mechanism(s) regulating its onset and development. Furthermore, the models developed may have prognostic implications by aiding in the enrollment of HRS into trials for T1D prevention. In this review, we summarize recent advances made in determining T- and B-cell involvement in T1D using these quantitative approaches and delineate areas where mathematical modeling can make further contributions in unraveling certain aspect of this disease.

The Virtual NOD Mouse: Applying Predictive Biosimulation to Research in Type 1 Diabetes

Type 1 diabetes is a complex, multifactorial disease characterized by T cell-mediated autoimmune destruction of insulin-secreting pancreatic beta cells. To facilitate research in type 1 diabetes, a large-scale dynamic mathematical model of the female non-obese diabetic (NOD) mouse was developed. In this model, termed the Entelos Type 1 Diabetes PhysioLab platform, virtual NOD mice are constructed by mathematically representing components of the immune system and islet beta cell physiology important for the pathogenesis of type 1 diabetes. This report describes the scope of the platform and illustrates some of its capabilities. Specifically, using two virtual NOD mice with either average or early diabetes-onset times, we demonstrate the reproducibility of experimentally observed dynamics involved in diabetes progression, therapeutic responses to exogenous IL-10, and heterogeneity in disease onset. Additionally, we use the Type 1 Diabetes PhysioLab platform to investigate the impact of disease heterogeneity on the effectiveness of exogenous IL-10 therapy to prevent diabetes onset. Results indicate that the inability of a previously published IL-10 therapy protocol to protect NOD mice who exhibit early diabetes onset is due to high levels of pancreatic lymph node (PLN) inflammation, islet infiltration, and beta cell destruction at the time of treatment initiation. Further, simulation indicates that earlier administration of the treatment protocol can prevent NOD mice from developing diabetes by initiating treatment during the period when the disease is still sensitive to IL-10's protective function.