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糖尿病风险基因座相关通路在代谢组织中是共享的。

Diabetes risk loci-associated pathways are shared across metabolic tissues.

机构信息

Department of Cell and Chemical Biology, Leiden University Medical Center, Einthovenweg 20, 2333ZC, Leiden, the Netherlands.

Department of Epidemiology and Data Science, Amsterdam UMC, Location VUMC, Amsterdam Public Health Institute, Amsterdam, the Netherlands.

出版信息

BMC Genomics. 2022 May 14;23(1):368. doi: 10.1186/s12864-022-08587-5.

DOI:10.1186/s12864-022-08587-5
PMID:35568807
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9107144/
Abstract

AIMS/HYPOTHESIS: Numerous genome-wide association studies have been performed to understand the influence of genetic variation on type 2 diabetes etiology. Many identified risk variants are located in non-coding and intergenic regions, which complicates understanding of how genes and their downstream pathways are influenced. An integrative data approach will help to understand the mechanism and consequences of identified risk variants.

METHODS

In the current study we use our previously developed method CONQUER to overlap 403 type 2 diabetes risk variants with regulatory, expression and protein data to identify tissue-shared disease-relevant mechanisms.

RESULTS

One SNP rs474513 was found to be an expression-, protein- and metabolite QTL. Rs474513 influenced LPA mRNA and protein levels in the pancreas and plasma, respectively. On the pathway level, in investigated tissues most SNPs linked to metabolism. However, in eleven of the twelve tissues investigated nine SNPs were linked to differential expression of the ribosome pathway. Furthermore, seven SNPs were linked to altered expression of genes linked to the immune system. Among them, rs601945 was found to influence multiple HLA genes, including HLA-DQA2, in all twelve tissues investigated.

CONCLUSION

Our results show that in addition to the classical metabolism pathways, other pathways may be important to type 2 diabetes that show a potential overlap with type 1 diabetes.

摘要

目的/假设:为了了解遗传变异对 2 型糖尿病病因的影响,已经进行了许多全基因组关联研究。许多已确定的风险变异位于非编码和基因间区域,这增加了理解基因及其下游途径如何受到影响的难度。综合数据方法将有助于理解已确定风险变异的机制和后果。

方法

在目前的研究中,我们使用我们之前开发的方法 CONQUER 将 403 个 2 型糖尿病风险变异与调节、表达和蛋白质数据重叠,以识别组织共享的疾病相关机制。

结果

发现一个 SNP rs474513 是一个表达、蛋白质和代谢物 QTL。rs474513 分别影响胰腺和血浆中的 LPA mRNA 和蛋白质水平。在途径水平上,在所研究的组织中,大多数与代谢相关的 SNP 都与代谢有关。然而,在所研究的 12 个组织中的 11 个中,9 个 SNP 与核糖体途径的差异表达有关。此外,有七个 SNP 与免疫系统相关基因的表达改变有关。其中,rs601945 被发现影响包括 HLA-DQA2 在内的多个 HLA 基因,在所有 12 个组织中都有影响。

结论

我们的结果表明,除了经典的代谢途径外,其他途径可能对 2 型糖尿病也很重要,这与 1 型糖尿病有潜在的重叠。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0fc/9107144/3c964a614687/12864_2022_8587_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0fc/9107144/963ac52edc1c/12864_2022_8587_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0fc/9107144/62551fa02d9e/12864_2022_8587_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0fc/9107144/f86e4d422020/12864_2022_8587_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0fc/9107144/3c964a614687/12864_2022_8587_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0fc/9107144/963ac52edc1c/12864_2022_8587_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0fc/9107144/62551fa02d9e/12864_2022_8587_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0fc/9107144/f86e4d422020/12864_2022_8587_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0fc/9107144/3c964a614687/12864_2022_8587_Fig4_HTML.jpg

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本文引用的文献

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Nat Commun. 2019 Oct 21;10(1):4788. doi: 10.1038/s41467-019-12703-7.
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Laser capture microdissection of human pancreatic islets reveals novel eQTLs associated with type 2 diabetes.
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Mol Metab. 2019 Jun;24:98-107. doi: 10.1016/j.molmet.2019.03.004. Epub 2019 Mar 18.
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Developing a network view of type 2 diabetes risk pathways through integration of genetic, genomic and functional data.通过整合遗传、基因组和功能数据来构建 2 型糖尿病风险途径的网络视图。
Genome Med. 2019 Mar 26;11(1):19. doi: 10.1186/s13073-019-0628-8.
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Identification of 28 new susceptibility loci for type 2 diabetes in the Japanese population.在日本人群中鉴定出 28 个 2 型糖尿病的新易感位点。
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