1. Publications

Global Genomic And Transcriptomic Analysis Of Human Pancreatic Islets Reveals Novel Genes Influencing Glucose Metabolism

João Fadista, Petter Vikman, Emilia Ottosson Laakso, Inês Guerra Mollet, Jonathan Lou Esguerra, Jalal Taneera, Petter Storm, Peter Osmark, Claes Ladenvall, Rashmi B. Prasad, Karin B. Hansson, Francesca Finotello, Kristina Uvebrant, Jones K. Ofori, Barbara Di Camillo, Ulrika Krus, Corrado M. Cilio, Ola Hansson, Lena Eliasson, Anders H. Rosengren, Erik Renström, Claes B. Wollheim, and Leif Groop.
PNAS. 2014-09-23;111(38):13924-13929.
Abstract
Genetic variation can modulate gene expression, and thereby phenotypic variation and susceptibility to complex diseases such as type 2 diabetes (T2D). Here we harnessed the potential of DNA and RNA sequencing in human pancreatic islets from 89 deceased donors to identify genes of potential importance in the pathogenesis of T2D. We present a catalog of genetic variants regulating gene expression (eQTL) and exon use (sQTL), including many long noncoding RNAs, which are enriched in known T2D-associated loci. Of 35 eQTL genes, whose expression differed between normoglycemic and hyperglycemic individuals, siRNA of tetraspanin 33 (TSPAN33), 5′-nucleotidase, ecto (NT5E), transmembrane emp24 protein transport domain containing 6 (TMED6), and p21 protein activated kinase 7 (PAK7) in INS1 cells resulted in reduced glucose-stimulated insulin secretion. In addition, we provide a genome-wide catalog of allelic expression imbalance, which is also enriched in known T2D-associated loci. Notably, allelic imbalance in paternally expressed gene 3 (PEG3) was associated with its promoter methylation and T2D status. Finally, RNA editing events were less common in islets than previously suggested in other tissues. Taken together, this study provides new insights into the complexity of gene regulation in human pancreatic islets and better understanding of how genetic variation can influence glucose metabolism.
Consortium data used in this publication
GSE50398 , GSE50244, GSE50397. Database deposition: The data reported in this paper have been deposited in the Gene Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (accession no. GSE50398).
Datasets
TSTSR098766, TSTSR638245, TSTSR670978, TSTSR679367, TSTSR721246, TSTSR542173, TSTSR630808, TSTSR323939, TSTSR784943, TSTSR316095, TSTSR184180, TSTSR642613, TSTSR676179, TSTSR899587, TSTSR155444, TSTSR134102, TSTSR135624, TSTSR164023, TSTSR656226, TSTSR906512, TSTSR616161, TSTSR951732, TSTSR508689, TSTSR805462, TSTSR033006, TSTSR352653, TSTSR485182, TSTSR919428, TSTSR928462, TSTSR703811, TSTSR176374, TSTSR094527, TSTSR699269, TSTSR367552, TSTSR848696, TSTSR470708, TSTSR680791, TSTSR239401, TSTSR811572, TSTSR791965, TSTSR727416, TSTSR311763, TSTSR813985, TSTSR686983, TSTSR410515, TSTSR491807, TSTSR380173, TSTSR981178, TSTSR179417, TSTSR147234, TSTSR273106, TSTSR991456, TSTSR987459, TSTSR924491, TSTSR549154, TSTSR181798, TSTSR276475, TSTSR013596, TSTSR198980, TSTSR581882, TSTSR273808, TSTSR321616, TSTSR706548, TSTSR135505, TSTSR719753, TSTSR827474, TSTSR507725, TSTSR153940, TSTSR141857, TSTSR665908, TSTSR152756, TSTSR948151, TSTSR913698, TSTSR791956, TSTSR854416, TSTSR050096, TSTSR720157, TSTSR872790, TSTSR060542, TSTSR588435, TSTSR083075, TSTSR087172, TSTSR303949, TSTSR519379, TSTSR527369, TSTSR868815, TSTSR117115, TSTSR538488, TSTSR247284, TSTSR383636, DSR394SAU, DSR276ICN