New insights into autoimmune mediated neonatal diabetes
Thesis or dissertation
University of Exeter
Monogenic autoimmune diseases are highly variable syndromes that usually have onset in the first year of life and are often fatal in early childhood. Identifying monogenic autoimmune diabetes is important as it can have implications for medical management of patients, informs families and clinicians of prognosis and recurrence risk, and gives insights into beta-cell autoimmunity and immune tolerance. The first section of this thesis introduces monogenic autoimmune disease, with focus on the conditions that have autoimmune endocrine disorders as part of their clinical phenotype. The following section details the methodologies used throughout this thesis. In chapter 1, we used a type 1 diabetes genetic risk score (T1D-GRS) based on the top 10 risk alleles for T1D to identify patients with monogenic autoimmunity from patients with early-onset polygenic diabetes and additional autoimmunity. We showed that the T1D-GRS was highly discriminatory of monogenic autoimmunity, especially when combined with age of onset (ROC-AUC 0.88). We also identified 16 families for gene discovery studies. Furthermore, this work shows that polygenic risk for the development of T1D does not affect the development of diabetes in monogenic autoimmunity. Chapter 2 describes the genetic and phenotypic information for the largest cohort of patients with IPEX syndrome, caused by hemizygous mutations in FOXP3, reported to date (n=48). We analysed this data to determine if there were any genotypic or clinical characteristics of IPEX syndrome that could predict prognosis. We did not find evidence of phenotype-genotype relationships and showed that presenting feature did not predict prognosis. Medical management of IPEX syndrome cannot, therefore, be based on genotype or presentation. In chapter 3 we employed whole exome sequencing to look for causal variant(s) in a patient with diabetes (diagnosed aged 7 weeks) and autoimmune lymphoproliferative disease. This identified recessively inherited causative variants in LRBA. We then used targeted next generation sequencing (NGS) to screen a large cohort of patients (n=169) and identified an additional 8 probands and an affected family member. This confirms the role of LRBA as a neonatal diabetes gene, bringing the total number of genes to 25. In chapter 4, we assessed if immunoglobulin E (IgE) could be useful to identify patients with early-onset multisystem autoimmune disease caused by gain of function (GOF) STAT3 mutations. We showed that serum IgE was below the lower limit of the normal reference range (2KU/L) in all patients with STAT3 GOF (n=6), giving this threshold a sensitivity of 100% (95% CI: 54.1 – 100) and specificity 97.2% (95% CI: 96.2-97.9). We also found that IgE in patients with IPEX (n=16) was significantly higher than those with STAT3 GOF (p=0.002) suggesting it could be useful to identify IPEX from STAT3 GOF in non-consanguineous males with early-onset autoimmunity. The final concluding section summarises the key findings of each chapter, the impact of these findings and suggests future avenues for research. Identifying monogenic autoimmunity has enabled prenatal diagnoses, given families and clinicians knowledge on recurrence risk, and could enable targeted therapies to be employed. This body of work will enable better discrimination of monogenic autoimmunity from polygenic clustering of early-onset autoimmunity, and gives insights into the factors that determine disease phenotype and clinical course in monogenic autoimmunity. Gene discovery on the remaining patients will give new insights into the mechanisms of beta-cell autoimmunity and the regulation of the adaptive immune system and maintenance of immune tolerance.
Johnson MB et al. (2017) Recessively Inherited LRBA Mutations Cause Autoimmunity Presenting as Neonatal Diabetes. Diabetes 66(8):2316-2322
Johnson MB et al. (2016) Monogenic autoimmune diseases of the endocrine system. Lancet Diabetes and Endocrinology 4(10):862-872
Johnson MB et al. (2016) Low IgE is a useful tool to identify STAT3 gain-of-function mutations. Clinical Chemistry 62(11):1536-1538
PhD in Medical Studies