Smyth Laura J, Patterson Christopher C, Swan Elizabeth J, Maxwell Alexander P, McKnight Amy Jayne
Centre for Public Health, Queen's University Belfast, Belfast, United Kingdom.
Regional Nephrology Unit, Belfast City Hospital, Belfast, United Kingdom.
Front Cell Dev Biol. 2020 Oct 15;8:561907. doi: 10.3389/fcell.2020.561907. eCollection 2020.
A subset of individuals with type 1 diabetes will develop diabetic kidney disease (DKD). DKD is heritable and large-scale genome-wide association studies have begun to identify genetic factors that influence DKD. Complementary to genetic factors, we know that a person's epigenetic profile is also altered with DKD. This study reports analysis of DNA methylation, a major epigenetic feature, evaluating methylome-wide loci for association with DKD. Unique features ( = 485,577; 482,421 CpG probes) were evaluated in blood-derived DNA from carefully phenotyped White European individuals diagnosed with type 1 diabetes with (cases) or without (controls) DKD ( = 677 samples). Explicitly, 150 cases were compared to 100 controls using the 450K array, with subsequent analysis using data previously generated for a further 96 cases and 96 controls on the 27K array, and methylation data generated for replication in 139 cases and 96 controls. Following stringent quality control, raw data were quantile normalized and beta values calculated to reflect the methylation status at each site. The difference in methylation status was evaluated between cases and controls; resultant -values for array-based data were adjusted for multiple testing. Genes with significantly increased (hypermethylated) and/or decreased (hypomethylated) levels of DNA methylation were considered for biological relevance by functional enrichment analysis using KEGG pathways. Twenty-two loci demonstrated statistically significant fold changes associated with DKD and additional support for these associated loci was sought using independent samples derived from patients recruited with similar inclusion criteria. Markers associated with and genes are supported as differentially regulated loci ( < 10), with evidence also presented for , which has been identified from a meta-analysis and subsequent replication of genome-wide association studies. Further supporting evidence for differential gene expression in CCNL1 and ZNF187 is presented from kidney biopsy and blood-derived RNA in people with and without kidney disease from NephroSeq. Evidence confirming that methylation sites influence the development of DKD may aid risk prediction tools and stimulate research to identify epigenomic therapies which might be clinically useful for this disease.
1型糖尿病患者中的一部分会发展为糖尿病肾病（DKD）。DKD具有遗传性，大规模全基因组关联研究已开始确定影响DKD的遗传因素。与遗传因素相辅相成的是，我们知道一个人的表观遗传特征也会随着DKD而改变。本研究报告了对DNA甲基化（一种主要的表观遗传特征）的分析，评估全基因组范围内的位点与DKD的关联。在经过仔细表型分析的患有（病例）或未患有（对照）DKD的白种欧洲1型糖尿病患者（n = 677个样本）的血液来源DNA中评估了独特特征（n = 485,577；482,421个CpG探针）。具体而言，使用450K芯片将150例病例与100例对照进行比较，随后使用先前为另外96例病例和96例对照在27K芯片上生成的数据进行分析，并在139例病例和96例对照中生成甲基化数据用于重复验证。经过严格的质量控制后，对原始数据进行分位数归一化并计算β值以反映每个位点的甲基化状态。评估病例和对照之间甲基化状态的差异；对基于芯片的数据所得的P值进行多重检验校正。通过使用KEGG通路进行功能富集分析，考虑DNA甲基化水平显著升高（高甲基化）和/或降低（低甲基化）的基因的生物学相关性。22个位点显示出与DKD相关的统计学显著倍数变化，并使用来自具有相似纳入标准招募的患者的独立样本寻求对这些相关位点的额外支持。与ARHGAP22和ZNF187基因相关的标记被支持为差异调节位点（P < 0.01），同时也提供了来自全基因组关联研究的荟萃分析和后续重复验证中确定的ITGA8的证据。来自NephroSeq的有和没有肾脏疾病的人的肾脏活检和血液来源RNA提供了CCNL1和ZNF187中差异基因表达的进一步支持证据。证实甲基化位点影响DKD发展的证据可能有助于风险预测工具，并刺激研究以确定可能对该疾病具有临床实用性的表观基因组疗法。
