Implicating type 2 diabetes effector genes in relevant metabolic cellular models using promoter-focused Capture-C.

IF 8.4 1区 医学 Q1 ENDOCRINOLOGY & METABOLISM
Nicholas A Wachowski, James A Pippin, Keith Boehm, Sumei Lu, Michelle E Leonard, Elisabetta Manduchi, Ursula W Parlin, Martin Wabitsch, Alessandra Chesi, Andrew D Wells, Struan F A Grant, Matthew C Pahl
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引用次数: 0

Abstract

Aims/hypothesis: Genome-wide association studies (GWAS) have identified hundreds of type 2 diabetes loci, with the vast majority of signals located in non-coding regions; as a consequence, it remains largely unclear which 'effector' genes these variants influence. Determining these effector genes has been hampered by the relatively challenging cellular settings in which they are hypothesised to confer their effects.

Methods: To implicate such effector genes, we elected to generate and integrate high-resolution promoter-focused Capture-C, assay for transposase-accessible chromatin with sequencing (ATAC-seq) and RNA-seq datasets to characterise chromatin and expression profiles in multiple cell lines relevant to type 2 diabetes for subsequent functional follow-up analyses: EndoC-BH1 (pancreatic beta cell), HepG2 (hepatocyte) and Simpson-Golabi-Behmel syndrome (SGBS; adipocyte).

Results: The subsequent variant-to-gene analysis implicated 810 candidate effector genes at 370 type 2 diabetes risk loci. Using partitioned linkage disequilibrium score regression, we observed enrichment for type 2 diabetes and fasting glucose GWAS loci in promoter-connected putative cis-regulatory elements in EndoC-BH1 cells as well as fasting insulin GWAS loci in SGBS cells. Moreover, as a proof of principle, when we knocked down expression of the SMCO4 gene in EndoC-BH1 cells, we observed a statistically significant increase in insulin secretion.

Conclusions/interpretation: These results provide a resource for comparing tissue-specific data in tractable cellular models as opposed to relatively challenging primary cell settings.

Data availability: Raw and processed next-generation sequencing data for EndoC-BH1, HepG2, SGBS_undiff and SGBS_diff cells are deposited in GEO under the Superseries accession GSE262484. Promoter-focused Capture-C data are deposited under accession GSE262496. Hi-C data are deposited under accession GSE262481. Bulk ATAC-seq data are deposited under accession GSE262479. Bulk RNA-seq data are deposited under accession GSE262480.

Abstract Image

利用启动子聚焦捕获-C 在相关代谢细胞模型中揭示 2 型糖尿病效应基因。
目的/假设:全基因组关联研究(GWAS)发现了数百个 2 型糖尿病基因位点,其中绝大多数信号位于非编码区;因此,这些变异对哪些 "效应 "基因产生影响在很大程度上仍不清楚。确定这些效应基因的工作受到了相对困难的细胞环境的阻碍:为了确定这些效应基因,我们选择生成并整合高分辨率启动子聚焦捕获-C、转座酶可接触染色质测序(ATAC-seq)和 RNA-seq 数据集,以描述与 2 型糖尿病相关的多个细胞系的染色质和表达谱,以便进行后续功能跟踪分析:这些细胞系包括:EndoC-BH1(胰腺β细胞)、HepG2(肝细胞)和辛普森-戈拉比-贝赫默综合征(SGBS;脂肪细胞):结果:随后进行的变异到基因分析在 370 个 2 型糖尿病风险基因位点上发现了 810 个候选效应基因。通过分区连锁不平衡得分回归,我们观察到 EndoC-BH1 细胞中与启动子连接的推定顺式调节元件富集了 2 型糖尿病和空腹血糖 GWAS 基因位点,SGBS 细胞中也富集了空腹胰岛素 GWAS 基因位点。此外,作为原理证明,当我们敲除 EndoC-BH1 细胞中 SMCO4 基因的表达时,我们观察到胰岛素分泌有显著的统计学增长:与相对具有挑战性的原代细胞设置相比,这些结果为在可控的细胞模型中比较组织特异性数据提供了资源:EndoC-BH1、HepG2、SGBS_undiff和SGBS_diff细胞的原始和处理过的新一代测序数据存放在GEO的Superseries accession GSE262484中。Promoter-focused Capture-C 数据以 GSE262496 编号保存。Hi-C 数据以登录号 GSE262481 保存。批量 ATAC-seq 数据以登录号 GSE262479 保存。批量 RNA-seq 数据以登录号 GSE262480 保存。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Diabetologia
Diabetologia 医学-内分泌学与代谢
CiteScore
18.10
自引率
2.40%
发文量
193
审稿时长
1 months
期刊介绍: Diabetologia, the authoritative journal dedicated to diabetes research, holds high visibility through society membership, libraries, and social media. As the official journal of the European Association for the Study of Diabetes, it is ranked in the top quartile of the 2019 JCR Impact Factors in the Endocrinology & Metabolism category. The journal boasts dedicated and expert editorial teams committed to supporting authors throughout the peer review process.
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