Towards an in vitro model of Congenital Hyperinsulinism of Infancy

Abstract

Congenital hyperinsulinism of infancy (CHI), one of the common forms of profound and persistent hypoglycemia in early childhood or infancy, is characterised by uncontrolled insulin secretion and beta cell hyperproliferation. Mutations in a number of genes have been identified to be associated with CHI; the most common being inactivating mutations in the ABCC8 and KCNJ11 genes encoding the SUR1 and Kir6.2 subunits of KATP channel in beta cells. However, for most of the cases, the aetiology of the disease is yet to be understood. Because of the limited availability of CHI tissue samples to be analysed, this study aimed to generate a model cell line that could mimic CHI-like behaviour in terms of insulin secretion and cell proliferation. Reports suggest that insulin could act as cell proliferation inducer, so we hypothesised that increased insulin secretion might promote pancreatic beta cells to acquire CHI-like characteristics. Insulin secretagogues (ISGs)- KCl (40 mM), tolbutamide (200 or 500 µM), leucine (10 mM), arginine (10 mM) and glibenclamide (10 µM) were tested for their abilities to promote cell proliferation or insulin secretion. No significant changes in insulin secretion or cell proliferation were observed in cells (MIN6 or EndoCβH1) acutely (48 hours) or chronically (up to 16 weeks) treated with ISGs. Also, there were no changes in expression of some of the key ion (Na+, K+ and Ca2+) channels associated with the insulin secretion pathway. Thus, ISGs were not an appropriate option to transform beta cells into a CHI-like model system. As an alternative approach, KATP channel genes were edited (introducing single nucleotide polymorphism or deletion of a fragment) with CRISPR-Cas9. EndoCβH1, a human model pancreatic beta cell line, and CHI-iPS, an iPS cell line generated by our research group, were explored in this study. As part of this work, it was shown that insulin-producing beta cells could be generated from this CHI-iPS cell line through an already published controlled differentiation process. Our results showed successful cell transfection with gene editing tools followed by target gene editing in both CHI-iPS and EndoCβH1 cells. However, none of the clonal cell lines generated from edited single CHI-iPS cells could retain their stem cell properties after a couple of passages. For EndoCβH1 cell line, it was not possible to grow and expand a single cell. Thus, attempts were made to generate a population of cells with a greater proportion of gene-edited cells. However, only ~20% of cells were observed to have gene edited and this low proportion of edited cells made the cell population unfit for further analysis. As part of this study, genome-wide differential gene expression was studied on CHI pancreas tissue samples with a view to identify novel candidate genes related to this disease. Differential gene expression analysis identified 39 potential candidate genes related to insulin secretion pathways, pancreas developmental pathway and glucose homeostasis etc. A network biology approach was explored to identify candidate genes based on already published protein-protein interaction data. On this basis, 12 more potential CHI candidate genes were predicted which are associated with cell signalling, exocytosis, cell proliferation, immune response etc. To validate these new findings in future, approach was taken to generate transient CHI model cells by knocking down the expression of KATP channels using gene-specific short interfering RNA. Our results suggested ~55% of channel protein knockdown in the cell population. This knocked down cell population showed increased basal insulin secretion and other experiments with KATP channel opener and inhibitor also suggested this increased secretion was the result of loss-of-function of KATP channels. Due to time limitations, this cell population could not be used to validate the association of CHI candidate genes predicted by microarray analysis. However, our preliminary

Date of Award	1 Aug 2020
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Mark Dunne (Supervisor) & Jean-Marc Schwartz (Supervisor)