Modelling monogenic diabetes mutations using authentic human beta cell models
Keywords: HNF1A
monogenic diabetes
MODY
beta cell
diabetes
insulin
iPSCs

Genome-wide association studies have identified over 400 loci associated with risk for type 2 diabetes (T2D), a large proportion are associated with pancreatic islet dysfunction. The parallel emergence of genome editing technology such as CRISPR/Cas9, alongside the ability to derive endocrine pancreatic cells from human stem cells, provides unprecedented opportunity to investigate and examine the mechanisms that underpin beta cell dysfunction. Given that several monogenic genes are also associated with T2D risk, investigating the mechanisms by which beta cell dysfunction occurs in monogenic diabetes also presents the opportunity to gain additional insight into T2D pathogenesis. The aim of my thesis was to model monogenic diabetes variants in INS and HNF1A in human beta cell models using both genome-edited human induced pluripotent stem cells (hiPSCs) and EndoC-βH1 cells.
To investigate the role of INS mutations on beta cell function, I generated the first p.C43G INS hiPSC line, including a reporter line, using CRISPR/Cas9 editing and differentiated these cells along the pancreatic endocrine lineage. Gene expression analysis revealed no influence of the p.C43G mutation on either differentiation efficiency or ER stress, reflecting that differentiated hiPSCs may require further functional maturation in order to model INS-associated monogenic diabetes.
Expression of the p.C43G INS mutant in the recently generated INS-KO EndoC-βH1 line was appropriately packaged into secretory vesicles. Subsequent gene expression and RNA-Seq analysis showed no significant differences between cells transfected with either the wildtype or p.C43G construct, echoing the findings from hiPSC differentiations. This would suggest that the negative effect of the mutation might translate through different cellular mechanisms. However, this study revealed various technical challenges, such as with transfection efficiency that considerably reduced the ability to fully characterise p.C43G expression in beta cells.
Finally, I sought to investigate another gene involved in monogenic diabetes, HNF1A. As mouse models of HNF1A-diabetes do not fully recapitulate the human phenotype, I utilised a hiPSC line to characterise HNF1A knockout in parallel with a knockdown (KD) model of HNF1A within the EndoC-βH1 cell line. Loss of HNF1A led to decreased expression of insulin and glucagon at the final stage of differentiation. The HNF1A KO line, in comparison to the wildtype line, had an enrichment of differentially expressed genes involved in regulation of beta cell function, beta cell development and insulin secretion, recapitulating several features of the patient phenotype. Further comparisons with differentiated cells from a patient with an HNF1A-MODY causing variant and with my HNF1A KD EndoC-βH1 data, showed a significant overlap between the datasets demonstrating that many of the same gene signatures can be captured between different cellular models of HNF1A.
My thesis illustrates the complementarity of utilising both iPSCs and EndoC-βH1 for disease modelling with each addressing different aspects of diabetes pathogenesis. This approach, although still being optimised, provides a better understanding of the cellular physiology that underlies genetic predisposition for diabetes.