RESUMEN
AIMS/HYPOTHESIS: Disruption of pancreatic islet function and glucose homeostasis can lead to the development of sustained hyperglycaemia, beta cell glucotoxicity and subsequently type 2 diabetes. In this study, we explored the effects of in vitro hyperglycaemic conditions on human pancreatic islet gene expression across 24 h in six pancreatic cell types: alpha; beta; gamma; delta; ductal; and acinar. We hypothesised that genes associated with hyperglycaemic conditions may be relevant to the onset and progression of diabetes. METHODS: We exposed human pancreatic islets from two donors to low (2.8 mmol/l) and high (15.0 mmol/l) glucose concentrations over 24 h in vitro. To assess the transcriptome, we performed single-cell RNA-seq (scRNA-seq) at seven time points. We modelled time as both a discrete and continuous variable to determine momentary and longitudinal changes in transcription associated with islet time in culture or glucose exposure. Additionally, we integrated genomic features and genetic summary statistics to nominate candidate effector genes. For three of these genes, we functionally characterised the effect on insulin production and secretion using CRISPR interference to knock down gene expression in EndoC-ßH1 cells, followed by a glucose-stimulated insulin secretion assay. RESULTS: In the discrete time models, we identified 1344 genes associated with time and 668 genes associated with glucose exposure across all cell types and time points. In the continuous time models, we identified 1311 genes associated with time, 345 genes associated with glucose exposure and 418 genes associated with interaction effects between time and glucose across all cell types. By integrating these expression profiles with summary statistics from genetic association studies, we identified 2449 candidate effector genes for type 2 diabetes, HbA1c, random blood glucose and fasting blood glucose. Of these candidate effector genes, we showed that three (ERO1B, HNRNPA2B1 and RHOBTB3) exhibited an effect on glucose-stimulated insulin production and secretion in EndoC-ßH1 cells. CONCLUSIONS/INTERPRETATION: The findings of our study provide an in-depth characterisation of the 24 h transcriptomic response of human pancreatic islets to glucose exposure at a single-cell resolution. By integrating differentially expressed genes with genetic signals for type 2 diabetes and glucose-related traits, we provide insights into the molecular mechanisms underlying glucose homeostasis. Finally, we provide functional evidence to support the role of three candidate effector genes in insulin secretion and production. DATA AVAILABILITY: The scRNA-seq data from the 24 h glucose exposure experiment performed in this study are available in the database of Genotypes and Phenotypes (dbGap; https://www.ncbi.nlm.nih.gov/gap/ ) with accession no. phs001188.v3.p1. Study metadata and summary statistics for the differential expression, gene set enrichment and candidate effector gene prediction analyses are available in the Zenodo data repository ( https://zenodo.org/ ) under accession number 11123248. The code used in this study is publicly available at https://github.com/CollinsLabBioComp/publication-islet_glucose_timecourse .
RESUMEN
We developed an efficient CRISPR prime editing protocol and generated isogenic-induced pluripotent stem cell (iPSC) lines carrying heterozygous or homozygous alleles for putatively causal single nucleotide variants at six type 2 diabetes loci (ABCC8, MTNR1B, TCF7L2, HNF4A, CAMK1D, and GCK). Our two-step sequence-based approach to first identify transfected cell pools with the highest fraction of edited cells significantly reduced the downstream efforts to isolate single clones of edited cells. We found that prime editing can make targeted genetic changes in iPSC and optimization of system components and guide RNA designs that were critical to achieve acceptable efficiency. Systems utilizing PEmax, epegRNA modifications, and MLH1dn provided significant benefit, producing editing efficiencies of 36-73%. Editing success and pegRNA design optimization required for each variant differed depending on the sequence at the target site. With attention to design, prime editing is a promising approach to generate isogenic iPSC lines, enabling the study of specific genetic changes in a common genetic background.