Zhang Tianlong, Cui Ying, Jiang Siyi, Jiang Lu, Song Lijun, Huang Lei, Li Yong, Yao Jiali, Li Min
Department of Critical Care Medicine, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.
Department of Critical Care Medicine, Jinhua Hospital Affiliated to Zhejiang University, Jinhua, Zhejiang, China.
Front Endocrinol (Lausanne). 2024 Jul 17;15:1396041. doi: 10.3389/fendo.2024.1396041. eCollection 2024.
Clinical studies have indicated a comorbidity between sepsis and kidney diseases. Individuals with specific mutations that predispose them to kidney conditions are also at an elevated risk for developing sepsis, and vice versa. This suggests a potential shared genetic etiology that has not been fully elucidated.
Summary statistics data on exposure and outcomes were obtained from genome-wide association meta-analysis studies. We utilized these data to assess genetic correlations, employing a pleiotropy analysis method under the composite null hypothesis to identify pleiotropic loci. After mapping the loci to their corresponding genes, we conducted pathway analysis using Generalized Gene-Set Analysis of GWAS Data (MAGMA). Additionally, we utilized MAGMA gene-test and eQTL information (whole blood tissue) for further determination of gene involvement. Further investigation involved stratified LD score regression, using diverse immune cell data, to study the enrichment of SNP heritability in kidney-related diseases and sepsis. Furthermore, we employed Mendelian Randomization (MR) analysis to investigate the causality between kidney diseases and sepsis.
In our genetic correlation analysis, we identified significant correlations among BUN, creatinine, UACR, serum urate, kidney stones, and sepsis. The PLACO analysis method identified 24 pleiotropic loci, pinpointing a total of 28 nearby genes. MAGMA gene-set enrichment analysis revealed a total of 50 pathways, and tissue-specific analysis indicated significant enrichment of five pairs of pleiotropic results in kidney tissue. MAGMA gene test and eQTL information (whole blood tissue) identified 33 and 76 pleiotropic genes, respectively. Notably, genes for BUN, for UACR, for creatinine, and for kidney stones were identified as shared risk genes by all three methods. In a series of immune cell-type-specific enrichment analyses of pleiotropy, we identified a total of 37 immune cells. However, MR analysis did not reveal any causal relationships among them.
This study lays the groundwork for shared etiological factors between kidney and sepsis. The confirmed pleiotropic loci, shared pathogenic genes, and enriched pathways and immune cells have enhanced our understanding of the multifaceted relationships among these diseases. This provides insights for early disease intervention and effective treatment, paving the way for further research in this field.
临床研究表明脓毒症与肾脏疾病之间存在共病现象。具有特定突变且易患肾脏疾病的个体发生脓毒症的风险也会升高,反之亦然。这表明存在一种尚未完全阐明的潜在共同遗传病因。
从全基因组关联荟萃分析研究中获取暴露和结局的汇总统计数据。我们利用这些数据评估遗传相关性,采用复合零假设下的多效性分析方法来识别多效性基因座。将这些基因座映射到相应基因后,我们使用全基因组关联研究数据的广义基因集分析(MAGMA)进行通路分析。此外,我们利用MAGMA基因检测和eQTL信息(全血组织)进一步确定基因的参与情况。进一步的研究包括使用不同的免疫细胞数据进行分层LD评分回归,以研究肾脏相关疾病和脓毒症中SNP遗传力的富集情况。此外,我们采用孟德尔随机化(MR)分析来研究肾脏疾病与脓毒症之间的因果关系。
在我们的遗传相关性分析中,我们确定了血尿素氮、肌酐、尿白蛋白肌酐比值、血清尿酸、肾结石和脓毒症之间存在显著相关性。PLACO分析方法确定了24个多效性基因座,共找出28个附近的基因。MAGMA基因集富集分析共揭示了50条通路,组织特异性分析表明在肾脏组织中有五对多效性结果显著富集。MAGMA基因检测和eQTL信息(全血组织)分别确定了33个和76个多效性基因。值得注意的是,血尿素氮、尿白蛋白肌酐比值、肌酐和肾结石的基因被这三种方法均确定为共同风险基因。在一系列针对多效性的免疫细胞类型特异性富集分析中,我们共确定了37种免疫细胞。然而,MR分析未揭示它们之间存在任何因果关系。
本研究为肾脏疾病和脓毒症之间的共同病因奠定了基础。已确认的多效性基因座、共同致病基因以及富集的通路和免疫细胞,增进了我们对这些疾病多方面关系的理解。这为疾病的早期干预和有效治疗提供了思路,为该领域的进一步研究铺平了道路。