特发性肺纤维化的遗传学见解：一种用于识别潜在治疗靶点的多组学方法。

Genetic insights into idiopathic pulmonary fibrosis: a multi-omics approach to identify potential therapeutic targets.

作者信息

Wen Zhuofeng, Liang Weixuan, Yang Ziyang, Liu Junjie, Yang Jing, Xu Runge, Lin Keye, Pan Jia, Chen Zisheng

机构信息

1The Sixth School of Clinical Medicine, Department of Respiratory and Critical Care Medicine, the Affiliated Qingyuan Hospital (Qingyuan People's Hospital), Guangzhou Medical University, Qingyuan, China.

The First School of Clinical Medicine, Guangzhou Medical University, Guangzhou, China.

出版信息

J Transl Med. 2025 Mar 16;23(1):337. doi: 10.1186/s12967-025-06368-8.

DOI:10.1186/s12967-025-06368-8

PMID:40091050

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11912729/

Abstract

OBJECTIVE

To identify potential therapeutic targets and evaluate the safety profiles for Idiopathic Pulmonary Fibrosis (IPF) using a comprehensive multi-omics approach.

METHOD

We integrated genomic and transcriptomic data to identify therapeutic targets for IPF. First, we conducted a transcriptome-wide association study (TWAS) using the Omnibus Transcriptome Test using Expression Reference Summary data (OTTERS) framework, combining plasma expression quantitative trait loci (eQTL) data with IPF Genome-Wide Association Studies (GWAS) summary statistics from the Global Biobank (discovery) and Finngen (duplication). We then applied Mendelian randomization (MR) to explore causal relationships. RNA-seq co-expression analysis (bulk, single-cell and spatial transcriptomics) was used to identify critical genes, followed by molecular docking to evaluate their druggability. Finally, phenome-wide MR (PheW-MR) using GWAS data from 679 diseases in the UK Biobank assessed the potential adverse effects of the identified genes.

RESULT

We identified 696 genes associated with IPF in the discovery dataset and 986 genes in the duplication dataset, with 126 overlapping genes through TWAS. MR analysis revealed 29 causal genes in the discovery dataset, with 13 linked to increased and 16 to decreased IPF risk. Summary data-based MR (SMR) confirmed six essential genes: ANO9, BRCA1, CCDC200, EZH1, FAM13A, and SFR1. Bulk RNA-seq showed FAM13A upregulation and SFR1 and EZH1 downregulation in IPF. Single-cell RNA-seq revealed gene expression changes across cell types. Molecular docking identified binding solid affinities for essential genes with respiratory drugs, and PheW-MR highlighted potential side effects.

CONCLUSION

We identified six key genes-ANO9, BRCA1, CCDC200, EZH1, FAM13A, and SFR1-as potential drug targets for IPF. Molecular docking revealed strong drug affinities, while PheW-MR analysis highlighted therapeutic potential and associated risks. These findings offer new insights for IPF treatment and further investigation of potential side effects.

摘要

目的

采用综合多组学方法确定特发性肺纤维化（IPF）的潜在治疗靶点并评估其安全性。

方法

我们整合基因组和转录组数据以确定IPF的治疗靶点。首先，我们使用基于表达参考汇总数据的全转录组检验（OTTERS）框架进行全转录组关联研究（TWAS），将血浆表达定量性状位点（eQTL）数据与来自全球生物样本库（发现队列）和芬兰基因库（重复队列）的IPF全基因组关联研究（GWAS）汇总统计数据相结合。然后应用孟德尔随机化（MR）来探索因果关系。RNA测序共表达分析（批量、单细胞和空间转录组学）用于识别关键基因，随后进行分子对接以评估其成药潜力。最后，使用英国生物样本库中679种疾病的GWAS数据进行全表型孟德尔随机化（PheW-MR），评估已鉴定基因的潜在不良反应。

结果

我们在发现数据集中鉴定出696个与IPF相关的基因，在重复数据集中鉴定出986个基因，通过TWAS有126个重叠基因。MR分析在发现数据集中揭示了29个因果基因，其中13个与IPF风险增加相关，16个与IPF风险降低相关。基于汇总数据的MR（SMR）证实了六个关键基因：ANO9、BRCA1、CCDC200、EZH1、FAM13A和SFR1。批量RNA测序显示IPF中FAM13A上调，SFR1和EZH1下调。单细胞RNA测序揭示了不同细胞类型间的基因表达变化。分子对接确定了关键基因与呼吸药物的结合亲和力，PheW-MR突出了潜在的副作用。