Jalal Taneera1, Sarah Dhaiban2, Abdul Khader Mohammed2, Debasmita Mukhopadhyay2, Hayat Aljaibeji2, Nabil Sulaiman2, Joao Fadista3, Albert Salehi4. 1. Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates. Electronic address: jtaneera@sharjah.ac.ae. 2. Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates. 3. Department of Epidemiology Research, Statens Serum Institute, Copenhagen, Denmark; Lund University Diabetes Centre (LUDC), Department of Clinical Sciences, Lund University, Malmö, Sweden. 4. Lund University Diabetes Centre (LUDC), Department of Clinical Sciences, Lund University, Malmö, Sweden.
Abstract
BACKGROUND: Type 2 diabetes (T2D) is a complex polygenic disease with unclear mechanism. In an attempt to identify novel genes involved in β-cell function, we harness a bioinformatics method called Loss-of-function tool (LoFtool) gene score. METHODS: RNA-sequencing data from human islets were used to cross-reference genes within the 1st quartile of most intolerant LoFtool score with the 100th most expressed genes in human islets. Out of these genes, GNAS and EEF1A1 genes were selected for further investigation in diabetic islets, metabolic tissues along with their correlation with diabetic phenotypes. The influence of GNAS and EEF1A1 on insulin secretion and β-cell function were validated in INS-1 cells. RESULTS: A comparatively higher expression level of GNAS and EEF1A1 was observed in human islets than fat, liver and muscle tissues. Furthermore, diabetic islets displayed a reduced expression of GNAS, but not of EEF1A, compared to non-diabetic islets. The expression of GNAS was positively correlated with insulin secretory index, GLP1R, GIPR and inversely correlated with HbA1c. Diabetic human islets displayed a reduced cAMP generation and insulin secretory capacity in response to glucose. Moreover, siRNA silencing of GNAS in INS-1 cells reduced insulin secretion, insulin content, and cAMP production. In addition, the expression of Insulin, PDX1, and MAFA was significantly down-regulated in GNAS-silenced cells. However, cell viability and apoptosis rate were unaffected. CONCLUSION: LoFtool is a powerful tool to identify genes associated with pancreatic islets dysfunction. GNAS is a crucial gene for the β-cell insulin secretory capacity.
BACKGROUND:Type 2 diabetes (T2D) is a complex polygenic disease with unclear mechanism. In an attempt to identify novel genes involved in β-cell function, we harness a bioinformatics method called Loss-of-function tool (LoFtool) gene score. METHODS: RNA-sequencing data from human islets were used to cross-reference genes within the 1st quartile of most intolerant LoFtool score with the 100th most expressed genes in human islets. Out of these genes, GNAS and EEF1A1 genes were selected for further investigation in diabetic islets, metabolic tissues along with their correlation with diabetic phenotypes. The influence of GNAS and EEF1A1 on insulin secretion and β-cell function were validated in INS-1 cells. RESULTS: A comparatively higher expression level of GNAS and EEF1A1 was observed in human islets than fat, liver and muscle tissues. Furthermore, diabetic islets displayed a reduced expression of GNAS, but not of EEF1A, compared to non-diabetic islets. The expression of GNAS was positively correlated with insulin secretory index, GLP1R, GIPR and inversely correlated with HbA1c. Diabetichuman islets displayed a reduced cAMP generation and insulin secretory capacity in response to glucose. Moreover, siRNA silencing of GNAS in INS-1 cells reduced insulin secretion, insulin content, and cAMP production. In addition, the expression of Insulin, PDX1, and MAFA was significantly down-regulated in GNAS-silenced cells. However, cell viability and apoptosis rate were unaffected. CONCLUSION: LoFtool is a powerful tool to identify genes associated with pancreatic islets dysfunction. GNAS is a crucial gene for the β-cell insulin secretory capacity.
Authors: Lina Sui; Yurong Xin; Qian Du; Daniela Georgieva; Giacomo Diedenhofen; Leena Haataja; Qi Su; Michael V Zuccaro; Jinrang Kim; Jiayu Fu; Yuan Xing; Yi He; Danielle Baum; Robin S Goland; Yong Wang; Jose Oberholzer; Fabrizio Barbetti; Peter Arvan; Sandra Kleiner; Dieter Egli Journal: JCI Insight Date: 2021-03-08