IAPP-induced beta cell stress recapitulates the islet transcriptome in type 2 diabetes

Abstract

Aims/hypothesis Type 2 diabetes is characterised by islet amyloid and toxic oligomers of islet amyloid polypeptide (IAPP). We posed the questions, (1) does IAPP toxicity induce an islet response comparable to that in humans with type 2 diabetes, and if so, (2) what are the key transcriptional drivers of this response? Methods The islet transcriptome was evaluated in five groups of mice: beta cell specific transgenic for (1) human IAPP, (2) rodent IAPP, (3) human calpastatin, (4) human calpastatin and human IAPP, and (5) wild-type mice. RNA sequencing data was analysed by differential expression analysis and gene co-expression network analysis to establish the islet response to adaptation to an increased beta cell workload of soluble rodent IAPP, the islet response to increased expression of oligomeric human IAPP, and the extent to which the latter was rescued by suppression of calpain hyperactivation by calpastatin. Rank-rank hypergeometric overlap analysis was used to compare the transcriptome of islets from human or rodent IAPP transgenic mice vs humans with prediabetes or type 2 diabetes. Results The islet transcriptomes in humans with prediabetes and type 2 diabetes are remarkably similar. Beta cell overexpression of soluble rodent or oligomer-prone human IAPP induced changes in islet transcriptome present in prediabetes and type 2 diabetes, including decreased expression of genes that confer beta cell identity. Increased expression of human IAPP, but not rodent IAPP, induced islet inflammation present in prediabetes and type 2 diabetes in humans. Key mediators of the injury responses in islets transgenic for human IAPP or those from individuals with type 2 diabetes include STAT3, NF-κB, ESR1 and CTNNB1 by transcription factor analysis and COL3A1, NID1 and ZNF800 by gene regulatory network analysis. Conclusions/interpretation Beta cell injury mediated by IAPP is a plausible mechanism to contribute to islet inflammation and dedifferentiation in type 2 diabetes. Inhibition of IAPP toxicity is a potential therapeutic target in type 2 diabetes. Graphical abstract

Multi-omics regulatory networks reveal the key driver genes in the development of type 2 diabetes

“…In accordance with the present results, previous studies have demonstrated that HLA-DQB1 [35], COL1A1 [36], COL3A1 [37], COL4A1 [38], CD74 [39], and HLA-DQA1 [35] are highly related to T2D pathogenesis. While PSMB9, IRF7, and COL5A1 have not been previously reported to be associated with T2D in the literature.…”

It’s What and When You Eat: An Overview of Transcriptional and Epigenetic Responses to Dietary Perturbations in Pancreatic Islets

“…Consistent with this notion, transcriptional analysis of purified control and HF β-cells revealed that more than 50% of differential transcripts were sex dependant ( 82 ). HF-fed male β-cells were characterized by suppression of genes critical for oxidative phosphorylation and enrichment for islet amyloid polypeptide ( Iapp ), both features of failing β-cells in humans with T2DM ( 25 , 65 , 84 ). In contrast, female β-cells exhibited strong sex-specific differential expression of insulin synthesis and ER-stress related transcripts including activation of the endoplasmic reticulum oxidoreductase 1 beta ( Ero1b ) which is required for proinsulin processing and is directly correlated with the islet’s insulin secretory capacity ( 85 ).…”

“…fats have been implicated as causal factors in the development of obesity and T2DM; however, their relative contribution to pancreatic islet failure in T2DM is still under investigation (54)(55)(56). Despite differing perspectives on the dietary etiology of T2DM, long-term ingestion of a diet rich in saturated fatty acids and/or refined sugars has been clearly demonstrated to lead to increased visceral adiposity (57,58), reduced energy expenditure (59,60), peripheral insulin resistance (61,62), and pancreatic islet dysfunction (39,(63)(64)(65)(66). Enumerating the physiological, molecular, and genetic consequences of these dietary stressors on peripheral metabolic tissues may allow us to develop targeted therapies to reverse the effects of diet-induced metabolic dysfunction.…”

scCDC: a computational method for gene-specific contamination detection and correction in single-cell and single-nucleus RNA-seq data

“…The total RNA-seq data in mouse lactating and virgin mammary glands are from the GEO database with accession numbers: GSE115370 [39] and GSE52016 [40], respectively. The total RNA-seq data from mouse pancreatic islets were downloaded from GEO: GSE148809 [41]. The genes encoding secrecting proteins were predicted in SignalP 4.0 [42], and the list of protein-coding genes were obtained from the Refseq database [43].…”