Mining potential drug targets for idiopathic pulmonary fibrosis based on Mendelian randomization analysis_Chinese Journal of Respiratory and Critical Care Medicine

Authors：

TANG Linjuan ¹ , YANG Xiaojie ¹ , XU Kun ² ,  CHENG Li ³

1. NHC Key Laboratory of Pneumoconiosis, Department of Respiratory and Critical Care Medicine, First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030000, P. R. China;
2. Department of Ultrasound Imaging, First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030000, P. R. China;
3. Department of Clinical Laboratory and Pathology, Shanxi Armed Police Corps Hospital, Taiyuan, Shanxi 030000, P. R. China;

Corresponding?author：

CHENG Li, Email: chenglimed@163.com

Keywords：

Mendelian randomization analysis; idiopathic pulmonary fibrosis; drug targets; colocalization analysis

DOI：

10.7507/1671-6205.202601127

Video：

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Objective To identify new potential drug targets for idiopathic pulmonary fibrosis (IPF) in order to improve the current situation where there are very few effective treatments for IPF. Methods This study integrates protein quantitative trait loci (pQTL) data from the deCODE cohort and the Atherosclerosis Risk in Communities (ARIC) study, expression quantitative trait loci (eQTL) data of whole blood from the GTEx-V8 and eQTLGen databases, and genome-wide association study (GWAS) data of IPF, and employs a multi-dimensional genetic epidemiology approach for analysis. Specifically, it includes: assessing the causal relationship between protein levels and IPF risk using two-sample Mendelian randomization (MR) methods; examining the potential associations between gene expression and IPF using summary-data-based Mendelian randomization (SMR) analysis; and determining the sharing of genetic variants between pQTL/eQTL and GWAS signals using Bayesian colocalization analysis. On this basis, a protein-protein interaction (PPI) network was further constructed, and target druggability assessment and potential drug prediction were performed to evaluate the biological significance and therapeutic potential of candidate targets. Results This study identified two proteins significantly associated with IPF: BRSK2 (β=1.222 7, P=1.12×10^–10) and AP2A2 (β=2.854 3, P=1.22×10^–7). The analysis suggests that these two proteins may participate in the occurrence and progression of IPF by affecting the balance of lung tissue injury and repair or by modulating fibrosis-related signaling pathways, and increased levels of both BRSK2 and AP2A2 proteins were significantly associated with an increased risk of IPF. Further Bayesian colocalization analysis indicated that AP2A2 shares genetic variant loci with IPF, with posterior probabilities of PPH0=1.49×10^–11, PPH1=7.6×10^–5, PPH2=1.99×10^–10, PPH3=1.33×10^–5, and PPH4=0.999 9, suggesting a high degree of genetic signal concordance between them. For external validation, analyses based on the ARIC and UK Biobank databases further supported a potential causal association between BRSK2 and IPF, showing that genetic variants leading to increased BRSK2 protein levels also increased the risk of developing IPF (P=0.004). Conclusions At the protein and gene expression levels, this study provides genetic evidence supporting a potential causal association of AP2A2 and BRSK2 with IPF. These proteins may participate in the pathogenesis and progression of IPF by influencing the balance of lung tissue injury and repair or fibrosis-related signaling pathways. They may also serve as potential therapeutic targets for IPF. However, their specific mechanisms of action require further elucidation.

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1. George PM, Spagnolo P, Kreuter M, et al. Progressive fibrosing interstitial lung disease: clinical uncertainties, consensus recommendations, and research priorities[J]. Lancet Respir Med, 2020, 8(9): 925-934.
2. Meltzer EB, Noble PW. Idiopathic pulmonary fibrosis[J]. Orphanet J Rare Dis, 2008, 3: 8.
3. Ni S, Song M, Guo W, et al. Biomarkers and their potential functions in idiopathic pulmonary fibrosis[J]. Expert Rev Respir Med, 2020, 14(6): 593-602.
4. Martin MD, Chung JH, Kanne JP. Idiopathic pulmonary fibrosis[J]. J Thorac Imaging, 2016, 31(3): 127-139.
5. Raghu G, Remy-Jardin M, Richeldi L, et al. Idiopathic pulmonary fibrosis (an update) and progressive pulmonary fibrosis in adults: an official ATS/ERS/JRS/ALAT clinical practice guideline[J]. Am J Respir Crit Care Med, 2022, 205(9): e18-e47.
6. Manolio TA, Collins FS, Cox NJ, et al. Finding the missing heritability of complex diseases[J]. Nature, 2009, 461(7265): 747-753.
7. Hanash S. Disease proteomics[J]. Nature, 2003, 422(6928): 226-232.
8. Ferkingstad E, Sulem P, Atlason BA, et al. Large-scale integration of the plasma proteome with genetics and disease[J]. Nat Genet, 2021, 53(12): 1712-1721.
9. Zhang J, Dutta D, K?ttgen A, et al. Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies[J]. Nat Genet, 2022, 54(5): 593-602.
10. Kurki MI, Karjalainen J, Palta P, et al. FinnGen provides genetic insights from a well-phenotyped isolated population[J]. Nature, 2023, 613(7944): 508-518.
11. Duckworth A, Gibbons MA, Allen RJ, et al. Telomere length and risk of idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease: a Mendelian randomisation study[J]. Lancet Respir Med, 2021, 9(3): 285-294.
12. Burgess S, Small DS, Thompson SG. A review of instrumental variable estimators for Mendelian randomization[J]. Stat Methods Med Res, 2017, 26(5): 2333-2355.
13. Sedgwick P. Multiple hypothesis testing and Bonferroni's correction[J]. BMJ, 2014, 349: g6284.
14. Wang K. Support interval for two-sample summary data-based Mendelian randomization[J]. Genes, 2023, 14(1): 211.
15. Sheng X, Guan Y, Ma Z, et al. Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments[J]. Nat Genet, 2021, 53(9): 1322-1333.
16. Mei Q, Liu Z, Zuo H, et al. Idiopathic pulmonary fibrosis: an update on pathogenesis[J]. Front Pharmacol, 2022, 12: 797292.
17. Confalonieri P, Volpe MC, Jacob J, et al. Regeneration or repair? The role of alveolar epithelial cells in the pathogenesis of idiopathic pulmonary fibrosis (IPF)[J]. Cells, 2022, 11(13): 2095.
18. Wolters PJ, Collard HR, Jones KD. Pathogenesis of idiopathic pulmonary fibrosis[J]. Annu Rev Pathol, 2014, 9: 157-179.
19. Ng B, Dong J, D'Agostino G, et al. Interleukin-11 is a therapeutic target in idiopathic pulmonary fibrosis[J]. Sci Transl Med, 2019, 11(511): eaaw1237.
20. Hao M, Fu R, Tai J, et al. S1PR1 serves as a viable drug target against pulmonary fibrosis by increasing the integrity of the endothelial barrier of the lung[J]. Acta Pharm Sin B, 2023, 13(3): 1110-1127.
21. Schuliga M, Kanwal A, Read J, et al. A cGAS-dependent response links DNA damage and senescence in alveolar epithelial cells: a potential drug target in IPF[J]. Am J Physiol Lung Cell Mol Physiol, 2021, 321(5): L859-L871.
22. Ballester B, Milara J, Montero P, et al. MUC16 is overexpressed in idiopathic pulmonary fibrosis and induces fibrotic responses mediated by transforming growth factor-β1 canonical pathway[J]. Int J Mol Sci, 2021, 22(12): 6502.
23. Staab-Weijnitz CA, Fernandez IE, Knüppel L, et al. FK506-binding protein 10, a potential novel drug target for idiopathic pulmonary fibrosis[J]. Am J Respir Crit Care Med, 2015, 192(4): 455-467.
24. Newman DR, Sills WS, Hanrahan K, et al. Expression of WNT5A in idiopathic pulmonary fibrosis and its control by TGF-β and WNT7B in human lung fibroblasts[J]. J Histochem Cytochem, 2016, 64(2): 99-111.
25. Clarke LA, Botelho HM, Sousa L, et al. Transcriptome meta-analysis reveals common differential and global gene expression profiles in cystic fibrosis and other respiratory disorders and identifies CFTR regulators[J]. Genomics, 2015, 106(5): 268-277.
26. Wagener BM, Marjon NA, Revankar CM, et al. Adaptor protein-2 interaction with arrestin regulates GPCR recycling and apoptosis[J]. Traffic, 2009, 10(9): 1286-1300.
27. Ren J, Zhang J, Rudemiller NP, et al. Twist1 in infiltrating macrophages attenuates kidney fibrosis via matrix metallopeptidase 13-mediated matrix degradation[J]. J Am Soc Nephrol, 2019, 30(9): 1674-1685.
28. Sundqvist M, Holdfeldt A, Wright SC, et al. Barbadin selectively modulates FPR2-mediated neutrophil functions independent of receptor endocytosis[J]. Biochim Biophys Acta Mol Cell Res, 2020, 1867(12): 118849.
29. Takano M, Kawami M, Aoki A, et al. Receptor-mediated endocytosis of macromolecules and strategy to enhance their transport in alveolar epithelial cells[J]. Expert Opin Drug Deliv, 2015, 12(5): 813-825.
30. Markham A. Fostamatinib: first global approval[J]. Drugs, 2018, 78(9): 959-963.
31. Wang M, Zhang Z, Liu J, et al. Gefitinib and fostamatinib target EGFR and SYK to attenuate silicosis: a multi-omics study with drug exploration[J]. Signal Transduct Target Ther, 2022, 7(1): 157.
32. Pamuk ON, Can G, Ayvaz S, et al. Spleen tyrosine kinase (Syk) inhibitor fostamatinib limits tissue damage and fibrosis in a bleomycin-induced scleroderma mouse model[J]. Clin Exp Rheumatol, 2015, 33(4 Suppl 91): S15-S22.
33. Tamir TY, Bowman BM, Agajanian MJ, et al. Gain-of-function genetic screen of the kinome reveals BRSK2 as an inhibitor of the NRF2 transcription factor[J]. J Cell Sci, 2020, 133(14): jcs241356.
34. Bellezza I, Giambanco I, Minelli A, et al. Nrf2-Keap1 signaling in oxidative and reductive stress[J]. Biochim Biophys Acta Mol Cell Res, 2018, 1865(5): 721-733.
35. Larson-Casey JL, He C, Carter AB. Mitochondrial quality control in pulmonary fibrosis[J]. Redox Biol, 2020, 33: 101426.
36. Zhang Z, Qu J, Zheng C, et al. Nrf2 antioxidant pathway suppresses Numb-mediated epithelial-mesenchymal transition during pulmonary fibrosis[J]. Cell Death Dis, 2018, 9(2): 83.
37. Lee J, Jang J, Park SM, et al. An update on the role of Nrf2 in respiratory disease: molecular mechanisms and therapeutic approaches[J]. Int J Mol Sci, 2021, 22(16): 8406.
38. Wang Y, Wei J, Deng H, et al. The role of Nrf2 in pulmonary fibrosis: molecular mechanisms and treatment approaches[J]. Antioxidants (Basel), 2022, 11(9): 1685.
39. Cannon M, Stevenson J, Stahl K, et al. DGIdb 5.0: rebuilding the drug-gene interaction database for precision medicine and drug discovery platforms[J]. Nucleic Acids Res, 2024, 52(D1): D1227-D1235.

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Latest ArticlesMining potential drug targets for idiopathic pulmonary fibrosis based on Mendelian randomization analysis

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