Investigating potential drug targets for IgA nephropathy and membranous nephropathy through multi-queue plasma protein analysis: a Mendelian randomization study based on SMR and co-localization analysis.

IF 4 3区生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Biodata Mining Pub Date : 2024-11-08 DOI:10.1186/s13040-024-00405-w

Xinyi Xu, Changhong Miao, Shirui Yang, Lu Xiao, Ying Gao, Fangying Wu, Jianbo Xu

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引用次数: 0

Abstract

Background: Membranous nephropathy (MN) and IgA nephropathy (IgAN) pose challenges in clinical treatment with existing therapies primarily focusing on symptom relief and often yielding unsatisfactory outcomes. The search for novel drug targets remains crucial to address the shortcomings in managing both kidney diseases.

Methods: Utilizing GWAS data for MN (ncase = 2150, ncontrol = 5829) and IgAN (ncase = 15587, ncontrol = 462197), instrumental variables for plasma proteins were derived from recent GWAS. Sensitivity analysis involved bidirectional Mendelian randomization analysis, MR Steiger, Bayesian co-localization, and Phenotype scanning. The SMR analysis using eQTL data from the eQTLGen Consortium was conducted to assess the availability of selected protein targets. The PPI network was constructed to reveal potential associations with existing drug treatment targets.

Results: The study, subjected to the stringent Bonferroni correction, revealed significant associations: four proteins with MN and three proteins with IgAN. In plasma protein cis-pQTL data from two cohorts, an increase in one standard deviation in PLA2R1 (OR = 2.01, 95%CI = 1.83-2.21), AIF1 (OR = 9.04, 95%CI = 4.69-17.41), MLN (OR = 3.79, 95%CI = 2.12-6.78), and NFKB1 (OR = 29.43, 95%CI = 7.73-112.0) was associated with an increased risk of MN. Additionally, in plasma protein cis-pQTL data, a standard deviation increase in FCGR3B (OR = 1.15, 95%CI = 1.09-1.22) and BTN3A1 (OR = 4.05, 95%CI = 2.65-6.19) correlated with elevated IgAN risk, while AIF1 (OR = 0.58, 95%CI = 0.46-0.73) exhibited IgAN protection. Bayesian co-localization indicated that PLA2R1 (coloc.abf-PPH4 = 0.695), NFKB1 (coloc.abf-PPH4 = 0.949), FCGR3B (coloc.abf-PPH4 = 0.909), and BTN3A1 (coloc.abf-PPH4 = 0.685) share the same variants associated with MN and IgAN. The SMR analysis indicated a causal link between NFKB1 and BTN3A1 plasma protein eQTL in both conditions, and BTN3A1 was validated externally.

Conclusion: Genetically influenced plasma levels of PLA2R1 and NFKB1 impact MN risk, while FCGR3B and BTN3A1 levels are causally linked to IgAN risk, suggesting potential drug targets for further clinical exploration, notably BTN3A1 for IgAN.

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通过多队列血浆蛋白分析研究 IgA 肾病和膜性肾病的潜在药物靶点：基于 SMR 和共定位分析的孟德尔随机研究。

背景：膜性肾病（MN）和IgA肾病（IgAN）给临床治疗带来了挑战，现有疗法主要侧重于缓解症状，但结果往往不尽人意。寻找新的药物靶点对于解决这两种肾病的治疗缺陷仍然至关重要：利用MN（ncase = 2150，ncontrol = 5829）和IgAN（ncase = 15587，ncontrol = 462197）的GWAS数据，从最近的GWAS中得出血浆蛋白的工具变量。敏感性分析包括双向孟德尔随机分析、MR Steiger、贝叶斯共定位和表型扫描。利用来自 eQTLGen Consortium 的 eQTL 数据进行了 SMR 分析，以评估选定蛋白质靶标的可用性。构建了PPI网络，以揭示与现有药物治疗靶点的潜在关联：该研究经过严格的 Bonferroni 校正，发现了显著的关联：4 种蛋白质与 MN 关联，3 种蛋白质与 IgAN 关联。在来自两个队列的血浆蛋白顺式-pQTL数据中，PLA2R1（OR = 2.01，95%CI = 1.83-2.21）、AIF1（OR = 9.04，95%CI = 4.69-17.41）、MLN（OR = 3.79，95%CI = 2.12-6.78）和NFKB1（OR = 29.43，95%CI = 7.73-112.0）每增加一个标准差，就会增加罹患MN的风险。此外，在血浆蛋白顺式-pQTL 数据中，FCGR3B（OR = 1.15，95%CI = 1.09-1.22）和 BTN3A1（OR = 4.05，95%CI = 2.65-6.19）的标准差增加与 IgAN 风险升高相关，而 AIF1（OR = 0.58，95%CI = 0.46-0.73）则表现出 IgAN 保护作用。贝叶斯共定位表明，PLA2R1（coloc.abf-PPH4 = 0.695）、NFKB1（coloc.abf-PPH4 = 0.949）、FCGR3B（coloc.abf-PPH4 = 0.909）和 BTN3A1（coloc.abf-PPH4 = 0.685）共享与 MN 和 IgAN 相关的相同变异。SMR分析表明，在这两种疾病中，NFKB1和BTN3A1血浆蛋白eQTL之间存在因果联系，BTN3A1也得到了外部验证：结论：受基因影响的血浆 PLA2R1 和 NFKB1 水平会影响 MN 风险，而 FCGR3B 和 BTN3A1 水平则与 IgAN 风险有因果关系，这为进一步临床探索提供了潜在的药物靶点，尤其是用于 IgAN 的 BTN3A1。

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

Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-

CiteScore

7.90

自引率

0.00%

发文量

审稿时长

23 weeks

期刊介绍： BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.