Laser capture microdissection of human pancreatic islets reveals novel eQTLs associated with type 2 diabetes
Laser capture microdissection of human pancreatic islets reveals novel eQTLs associated with type 2 diabetes
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DOI：
10.1016/j.molmet.2019.03.004
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发表时间：
2019-06-01
影响因子：
8.1
通讯作者：
Froguel, Philippe
中科院分区：
文献类型：
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作者：
Khamis, Amna;Canouil, Mickael;Froguel, Philippe
目的：针对2型糖尿病(T2D)的全基因组关联研究(GWAS)已发现常位于基因组非编码区的遗传位点，提示基因调控作用。我们结合遗传和转录分析从脑死亡器官捐赠者或手术患者的胰岛中检测表达数量性状基因座(EQTL)，并阐明这些基因的调控机制。方法：采用激光捕获显微解剖(LCM)和胶原酶消化法分离103例代谢性表型胰腺切除患者(PPP)和100例脑死亡器官捐赠者(OD)的胰岛。基因分型(>；870万个单核苷酸多态)和表达(>；47000个转录本和剪接变异体)分析相结合产生cis-eQTL。结果：在应用全基因组假发现率显著阈值后，我们在OD和PPP样本中分别检测到1173个和1021个eQTL。在OD和PPP之间共有的最强eQTL是CHURC1(OD p值=1.71 x 10(-24)；PPP p值=3.64 x 10(-24))和PSPH(OD p值=3.92 x 10(-26)；PPP p值=3.64 x 10(-24))。我们发现了与Gwas基因座T2D及其相关性状连锁不平衡的eQTL，包括TTLL6、MLX和KIF9基因座，它们与最近的基因没有关联。我们在PPP数据集中发现了11个在T2D中差异表达的eQTL基因，以及两个与HbA1c相关的基因(CYP4V2和TSEN2)，但在OD样本中没有表达。结论：通过对PPP中的LCM胰岛进行eQTL分析，我们发现了以前没有与胰岛生物学和T2D相关的新基因。通过eQTL方法获得的理解，特别是使用活体患者的手术样本，提供了比仅从遗传研究中获得的更准确的三维表示。(C)2019年提交人。由Elsevier GmbH出版。
Objective: Genome wide association studies (GWAS) for type 2 diabetes (T2D) have identified genetic loci that often localise in non-coding regions of the genome, suggesting gene regulation effects. We combined genetic and transcriptomic analysis from human islets obtained from brain-dead organ donors or surgical patients to detect expression quantitative trait loci (eQTLs) and shed light into the regulatory mechanisms of these genes.Methods: Pancreatic islets were isolated either by laser capture microdissection (LCM) from surgical specimens of 103 metabolically phenotyped pancreatectomized patients (PPP) or by collagenase digestion of pancreas from 100 brain-dead organ donors (OD). Genotyping (> 8.7 million single nucleotide polymorphisms) and expression (> 47,000 transcripts and splice variants) analyses were combined to generate cis-eQTLs.Results: After applying genome-wide false discovery rate significance thresholds, we identified 1,173 and 1,021 eQTLs in samples of OD and PPP, respectively. Among the strongest eQTLs shared between OD and PPP were CHURC1 (OD p-value=1.71 x 10(-24); PPP p-value = 3.64 x 10(-24)) and PSPH (OD p-value = 3.92 x 10(-26); PPP p-value = 3.64 x 10(-24)). We identified eQTLs in linkage-disequilibrium with GWAS loci T2D and associated traits, including TTLL6, MLXand KIF9 loci, which do not implicate the nearest gene. We found in the PPP datasets 11 eQTL genes, which were differentially expressed in T2D and two genes (CYP4V2 and TSEN2) associated with HbA1c but none in the OD samples.Conclusions: eQTL analysis of LCM islets from PPP led us to identify novel genes which had not been previously linked to islet biology and T2D. The understanding gained from eQTL approaches, especially using surgical samples of living patients, provides a more accurate 3-dimensional representation than those from genetic studies alone. (C) 2019 The Authors. Published by Elsevier GmbH.