Transcriptional analysis of islets of Langerhans from organ donors of different ages

Abstract

Insulin secretion is impaired with increasing age. In this study, we aimed to determine whether aging induces specific transcriptional changes in human islets. Laser capture microdissection was used to extract pancreatic islet tissue from 37 deceased organ donors aged 1–81 years. The transcriptomes of the extracted islets were analysed using Ion AmpliSeq sequencing. 346 genes that co-vary significantly with age were found. There was an increased transcription of genes linked to senescence, and several aspects of the cell cycle machinery were downregulated with increasing age. We detected numerous genes not linked to aging in previous studies likely because earlier studies analysed islet cells isolated by enzymatic digestion which might affect the islet transcriptome. Among the novel genes demonstrated to correlate with age, we found an upregulation of SPP1 encoding osteopontin. In beta cells, osteopontin has been seen to be protective against both cytotoxicity and hyperglycaemia. In summary, we present a transcriptional profile of aging in human islets and identify genes that could affect disease course in diabetes.

DNA damage models phenotypes of β cell senescence in Type 1 Diabetes

“…Furthermore, genes involved in endoplasmic reticulum (ER) stress and unfolded protein response (UPR) ( XBP1, DDIT3, EDEM1, ERN1, ATF4, ATF6, HSPA5, HSP90B1 ), which are involved in beta cell adaptive and terminal UPR, were not significantly changed between control and bleomycin treatment ( Supplementary Figure 6 ). Comparison of the list of differentially expressed genes in our dataset with recently generated human islet RNA-seq datasets during aging [44] and adult human islets treated with the cytokine IFNα [45] showed that there were only 74/385 (∼19%) age-related genes in common and only 256/1894 (∼13%) IFNα-regulated genes in common, respectively, ( Supplementary Figure 7A and 7B, Supplementary Table 1 ). This suggested that the transcriptional response during islet DDR activation and senescence is generally different from natural aging and cytokine exposure.…”

“…Using an unbiased analysis by RNA-seq, we found that DDR activation and senescence induction by bleomycin treatment of islets resulted in a coordinated p53-p21 transcriptional program, involving downregulation of genes involved in proliferation and cell cycle progression and upregulation of p53-targeted genes. Although there were a small subset of genes in common with other human islet RNA-seq datasets representing natural aging [48] or cytokine exposure [53] the changes during islet DDR activation and senescence were largely distinct. Importantly, the changes in this model occur 4 days after drug removal (6 days post-treatment) and thus reflect a stable phenotypic change rather than an acute response.…”

ZBED6 counteracts high-fat diet-induced glucose intolerance by maintaining beta cell area and reducing excess mitochondrial activation

“…It is therefore possible that ZBED6, by enhancing expression of this and other growth-controlling genes, maintains normal beta cell replication, ensuring an adequate beta cell mass for the long-term preservation of glucose tolerance. Interestingly, ZBED6 expression appears to be reduced in human beta cells with increasing age [ 30 ].…”

Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis

“…Expression profiling by high throughput sequencing dataset GSE162689 [25] was obtained from GEO database. The dataset comprised total 59 samples, of which 27 were from T1DM samples and 32 were from normal control samples and was based on the GPL24014 Ion Torrent S5 XL (Homo sapiens).…”

“…We downloaded expression profiling by high throughput sequencing dataset GSE162689 [25], from Gene Expression Omnibus database (GEO) (http://www.ncbi.nlm.nih.gov/geo/) [26], which contain gene expression data from T1DM samples and normal control samples. We then performed deep bioinformatics analysis, including identifying common differential expressed genes (DEGs), gene ontology (GO) enrichment analysis, REACTOME pathway enrichment analysis, and construction and analysis of protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network.…”