Causality between cathepsins and idiopathic pulmonary fibrosis: a Mendelian randomization study

doi:10.21203/rs.3.rs-4190026/v1

Download PDF

Research Article

Causality between cathepsins and idiopathic pulmonary fibrosis: a Mendelian randomization study

https://doi.org/10.21203/rs.3.rs-4190026/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

The pathogenesis of idiopathic pulmonary fibrosis (IPF) is complex and difficult to diagnose and treat clinically. Cathepsins are involved in various physiological and pathological processes, and observational studies have shown an association between cathepsins and IPF. However, the causal relationship between them remains uncertain. Our aim was to assess the causal relationship between various cathepsins and IPF.

Methods

Protein quantitative trait loci (pQTL) data for cathepsins were obtained from INTERVAL studies, and summary statistics for IPF genome-wide association studies (GWAS) were obtained from the FinnGen R10 study. Univariable Mendelian randomization (UVMR), multivariable Mendelian randomization (MVMR), summary data-based Mendelian randomization (SMR) and Bayesian colocalization analysis were conducted in this study.

Results

UVMR analysis indicated that elevated cathepsin H levels reduced the overall risk of IPF (OR = 0.885,95%CI = 0.827 ~ 0.947, P = 3.86×10 − 4). MVMR showed that the effect of cathepsin H on IPF was still present after adjusting the interaction of cathepsins (OR = 0.895,95%CI = 0.834 ~ 0.961, P = 0.002). In addition, SMR analysis also suggested a causal association between cathepsin H and IPF (OR = 0.800,95%CI = 0.699 ~ 0.916, P = 0.001). Finally, we validated the results using the UK Biobank Pharma Proteomics Project (UKB-PPP) dataset (OR = 0.897,95%CI = 0.836 ~ 0.963, P = 0.003).

Conclusions

This study suggests that cathepsin H has a protective effect on IPF and may serve as a potential therapeutic target for IPF, providing inspiration and guidance for the diagnosis and treatment of IPF.

Cathepsins

Idiopathic pulmonary fibrosis

Protein quantitative trait loci

Mendelian randomization

Idiopathic pulmonary fibrosis (IPF) is the most common idiopathic interstitial pneumonia. According to statistics, the incidence of IPF is about 0.22 to 8.8 per 100,000 people, and the incidence is increasing year by year (1). The pathogenesis of IPF involves many risk factors, among which the synthesis and degradation of extracellular matrix proteins play an important role (2). In the pathogenesis of IPF, this dynamic balance of synthesis and degradation of extracellular matrix proteins is disrupted, especially the abnormal deposition of collagen, resulting in the destruction of lung structure and function (3). Therefore, protein homeostasis and proteolytic system activity are essential for the pathogenesis of IPF (4).

Cathepsins are commonly found in lysosomes and regulate various physiological and pathological processes (5). The papain superfamily of cysteine proteases, the best known cathepsins, are involved in the degradation of extracellular proteins and play critical roles in extracellular matrix remodeling (6).

Recent studies have revealed the role of several cathepsins in fibrosis of various substantial organs (7, 8). In addition, it has been reported that serum cathepsin B levels significantly differentiate IPF from healthy controls and have potential as a biomarker (9). However, there may be differences in the roles of different cathepsins and the causal relationship between different types of cathepsins and the risk of IPF has not yet been fully investigated. Therefore, there is a need for a comprehensive study of the causal relationship between different types of cathepsins and the risk of IPF.

With the development of genetic genomics, an increasing number of genome-wide association studies (GWAS) are revealing the role of genetic variation in disease. Mendelian randomization (MR) is an emerging and powerful analytical method that uses randomly assigned Single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to infer causal associations between exposures and outcomes (10). In this study, we performed univariable Mendelian randomization (UVMR) and multivariable Mendelian randomization (MVMR) to investigate the causal effects of different types of cathepsins on the risk of IPF. In addition, we performed summary data-based Mendelian randomization (SMR) and Bayesian colocalization analysis to ensure the robustness of the results. Finally, we used the UK Biobank Pharma Proteomics Project (UKB-PPP) dataset for further validation of the results.

Study design

Figure 1 depicts the study design. We used UVMR to study the causal effects of nine cathepsins on IPF. We conducted the MR analysis following three main assumptions: [1] the selected IVs must be closely associated with cathepsins; [2] the selected IVs should not be associated with potential confounders; and [3] the selected IVs can only act on IPF through this pathway of cathepsins. In UVMR analyses, we used the traditional inverse variance weighted (IVW) method (11) to estimate the causal effect of cathepsins on IPF. In addition, in order to produce more stable and reliable results, several additional methods were added as supplements, including MR Egger (12), Weighted median (13), Weighted mode, Contamination mixture (14), Constrained maximum likelihood (15), Debiased inverse-variance weighted and Robust adjusted profile score (16). Considering the interaction between cathepsins, we performed UVMR along with MVMR analysis to determine the causal effect of different types of cathepsins alone on IPF. Secondly, to ensure the robustness of the results obtained and to further validate the results found in the previous step, we not only performed Bayesian colocalization analysis, but also integrated IPF GWAS summary data with expressed quantitative trait loci (eQTL) for SMR analyses. Finally, we performed replicate analyses using newly published plasma protein quantitative trait loci (pQTL) data from UK Biobank as external validation to strengthen conclusions. Our analysis process followed the STROBE-MR guidelines (17).

The data source and the selection of IVs

Table 1 describes the data used in this study. Genetic IVs for various cathepsins levels were obtained from the INTERVAL study, which identified 1,927 genetic associations with 1,478 proteins in 3,301 Europeans (18). The selection criteria of cathepsins-related IVs for MR analysis were as follows: [1] We selected SNPs that were closely related to cathepsins as candidate IVs(p < 5×10 − 6). [2] We used PLINK software to perform the clumping procedure, and the SNPs were pruned at a stringent linkage disequilibrium (LD) of R2 < 0.001 within a 10,000-kb window. LD was calculated using the 1000 Genomes Europe reference (19). [3] We excluded SNPs that were not available in IPF GWAS or had proxy SNPs. [4] We used the steiger test method to filter out SNPS that were highly correlated with outcomes to avoid reverse causality (20). The detailed information of SNPs serving as IVs can be seen in Additional file 1. The summary statistics for IPF was a GWAS in 409,798 individuals of European ancestry drawn from the FinnGen R10 study, consisting of 2,189 cases and 407,609 controls (21). The diagnosis of IPF was according to the ICD-10-J84.1 (International Classification of diseases) criteria. We extracted SNPs of cathepsins corresponding genes from the summary data of blood eQTL as IVs (p < 5×10 − 8). The summary data of eQTL of blood tissues were obtained from eQTLGen, which included genetic data on blood gene expression in 31,684 individuals (22). Our SMR analysis focuses only on cis- eQTL, which make up SNPs within 1Mb of the probe in any direction. For external validation of the results, we extracted cathepsins-associated SNPs from the large-scale GWAS of UKB-PPP as IVs (p < 5×10 − 8) and excluded SNPs with LD (r2 < 0.001,10,000Kb). UKB-PPP characterized the plasma proteomic profile of 54,219 participants using the Olink platform (23). The detailed information of SNPs associated with cathepsins extracted from UKB-PPP can be seen in Additional file 2. The data used in this study are publicly available and have been approved by the appropriate ethical review boards.

Table 1

Characteristics of data in this study
Types	Source	Cases/controls	Population	PMID/Year
Various cathepsins pQTL	INTERVAL	3301	European	29875488/2018
IPF GWAS	FinnGen	2189/407609	European	36653562/2023
Cathepsin H eQTL	eQTLGen	31684	European	34475573/2021
Cathepsin H pQTL	UKB-PPP	54219	European	37794186/2023

pQTL(Protein quantitative trait loci),IPF(Idiopathic pulmonary fibrosis),GWAS(Genome-wide association studies),eQTL(Expressed quantitative trait loci),UKB-PPP(UK Biobank Pharma Proteomics Project)

Statistical analysis

We use odds ratios (OR) and 95% confidence interval (CI) of IVW to report effect estimates for MR. In addition, we used several additional methods to verify the robustness of the MR results (MR Egger, Weighted median, Weighted mode, Contamination mixture, Robust adjusted profile score, Debiased inverse- variance weighted and Constrained maximum likelihood). In the sensitivity analysis, we used Cochran's Q test to examine heterogeneity. When there was significant heterogeneity among SNPs, the random-effect IVW model was adopted; otherwise, the fixed-effect IVW model was adopted (24). We used MR-Egger regression and Mendelian Randomized Pleiotropic Residuals and Outliers (MR-PRESSO) methods to test and correct the potential horizontal pleiotropy of the selected IVs (25, 26). In addition, scatter plots, Leave-one-out plots and funnel plots were used.

We performed reverse MR analysis to assess reverse causality. The effects of various cathepsins on each other were then adjusted by using MVMR analysis. In addition, we performed Bayesian colocalization analysis to test whether cathepsins and IPF share genetic effects in a given region (27). We performed the above tests using the Two Sample MR, MR-Presso, Mendelian Randomization and coloc packages in R software (version 4.2.2).

We used SMR software for Linux version 1.3.1 to perform SMR and the heterogeneity in dependent instruments (HEIDI) test at the command line using the default options (SNP threshold p was set to 5 × 10 − 8 and for HDIEI test threshold p was set to 1.57 × 10 − 3, trimming out SNPs with r2 greater than 0.9 and less than 0.05) (28).

Exploration of the causal effect of cathepsins on IPF

To investigate the causal effect of various cathepsins on IPF, we performed two-sample MR analysis of nine cathepsins (cathepsin B, E, F, G, H, O, S, L2 and Z) and IPF. UVMR analysis showed that high levels of cathepsin H reduced the risk of developing IPF. The OR of cathepsin H for IPF risk was estimated by IVW method to be 0.885(95%CI = 0.827 ~ 0.947, P = 3.86×10 − 4). Several other methods yielded similar results: MR Egger (OR = 0.871,95%CI = 0.794 ~ 0.956, P = 0.018); Weighted median(OR = 0.878,95%CI = 0.817 ~ 0.945, P = 4.74×10 − 4); Weighted mode (OR = 0.880,95%CI = 0.820 ~ 0.944, P = 0.005); MR-PRESSO (OR = 0.885,95%CI = 0.828 ~ 0.943, P = 0.004); Contamination mixture (OR = 0.884,95%CI = 0.831 ~ 0.943, P = 0.017); Constrained maximum likelihood (OR = 0.892,95%CI = 0.819 ~ 0.971, P = 0.008); Debiased inverse-variance weighted(OR = 0.884,95%CI = 0.826 ~ 0.947, P = 4.12×10 − 4); Robust adjusted profile score (OR = 0.883,95%CI = 0.823 ~ 0.947, P = 4.98×10 − 4) (Fig. 2)。Additionally, both the intercept of MR-Egger and the global test of MR-PRESSO provided no evidence of horizontal pleiotropy in Additional file 3. However, we did not find a causal relationship between other types of cathepsins and IPF.

To explore the possibility that IPF affects various cathepsins, we performed reverse MR analysis. We found that the evidence provided by reverse MR analysis did not support a causal relationship between IPF and various cathepsins (Additional file 4).

In addition, we performed MVMR to assess the association of genetic susceptibility involving multiple cathepsins with IPF risk. Our MVMR analysis showed that the protective association of cathepsin H against IPF persisted even after adjusting for other types of cathepsins (IVW: OR = 0.895, 95% CI = 0.834 to 0.961, P = 0.002) (Fig. 3). In addition, the MR-Egger intercept analysis did not show horizontal pleiotropy (Additional file 5). It is worth mentioning that neither our UVMR nor MVMR analyses showed evidence of a causal relationship between other types of cathepsins and IPF.

The results of Bayesian colocalization analysis

Our MR study suggested a potential causal relationship between cathepsin H and IPF. We then performed Bayesian colocalization analysis to test whether cathepsin H and IPF share genetic effects. Colocalization analysis has four hypotheses: H0: All SNPs loci in the region are not significantly correlated between the two traits. H1/H2: One of the two traits was significantly correlated at SNPs loci within the region. H3: SNPs loci in the region are significantly correlated between the two traits but are driven by different causal loci. H4: The two traits are significantly correlated at SNPs loci within the region and driven by the same causal variant locus. Cathepsin H is encoded by the CTSH gene, and we performed colocalization analysis of SNPs within a 50Kb window at the CTSH gene locus. The results showed that the posterior probability of H4 was 0.80 (Additional file 6). Therefore, we can assume that there is a common genetic effect between cathepsin H and IPF. In summary, combining the results of our analyses, we conclude that high levels of cathepsin H are protective factors for the development of IPF and are driven by the same genetic variant loci (Fig. 4).

The results of SMR analysis

We integrated eQTL data of CTSH and GWAS data of IPF for SMR analysis. High expression of CTSH gene in blood tissues was found to reduce the risk of IPF (OR = 0.800, 95% CI = 0.699–0.916, p = 0.001). In addition, we included 16 SNPs for HEIDI test, and the results showed that the P-value of HEIDI test was > 0.05(p_HEIDI = 0.446), indicating that the association between CTSH and IPF was not driven by LD. The detailed information of SMR results can be seen in Additional file 7. In conclusion, our SMR results verified a robust causal relationship between CTSH and IPF.

External verification of causality between cathepsin H and IPF

We extracted closely related IVs to cathepsin H from the UKB-PPP data for two-sample MR analysis. The results of the analysis showed that elevated cathepsin H levels significantly reduced the risk of IPF (IVW: OR = 0.897, 95% CI = 0.836–0.963, P = 0.003). In sensitivity analyses, no evidence of horizontal pleiotropy was found by both MR-PRESSO global test and MR-Egger intercept (Additional file 8). Scatter plot, Leave-one-out plot and funnel plot also indicated the stability of the results (Fig. 5). In addition, we performed a reverse MR analysis, which showed no reverse causality between cathepsin H and IPF (Additional file 9).

The initiation and development of fibrosis is a complex process in which the proteolytic system plays a crucial role (4). Among the important members associated with these processes, cathepsins have attracted considerable interest. Using relevant genetic data, we systematically investigated the causal relationship between nine different cathepsins and IPF risk. To our knowledge, this is the first MR analysis to investigate the causal relationship between multiple cathepsins and IPF. Based on the results of comprehensive UVMR analysis, MVMR analysis, Bayesian colocalization analysis and SMR analysis, we can conclude that cathepsin H is an important protective factor for IPF. Furthermore, we did not find a reverse causal relationship between cathepsin H and IPF.

Cathepsin H is a lysosomal cysteine proteinase with unique aminopeptidase and endopeptidase activities that plays important roles in physiological and pathological processes (29). However, current research on cathepsin H is mainly focused on tumor. For example, Deletion of cathepsin H significantly impaired angiogenesis and resulted in a reduction in the subsequent number of tumors that subsequently formed (30). In addition, a study showed that cathepsin H promotes the invasion of the tumor cells by degrading the extracellular matrix (31). In the lung, cathepsin H is found in lamellar bodies, dense multivesicular bodies and compound vesicles of type II pneumocytes and is involved in the processing of lung surfactant (32). A research team observed lower levels of pulmonary surfactant protein B in bronchoalveolar lavage fluid from mice lacking cathepsin H, resulting in impaired physical properties of pulmonary surfactant (33). The relationship between cathepsin H and lung disease is therefore complex. For example, pulmonary alveolar proteinosis is associated with a reduced ability of alveolar macrophages to clear surfactant, leading to the accumulation of different types of pulmonary surfactant (34). Studies have reported increased levels and activity of cathepsin H in bronchoalveolar lavage fluid from patients with pulmonary alveolar proteinosis (35). However, there are currently no studies characterizing the relationship between cathepsin H levels and IPF. Notably, this study is the first to demonstrate a causal relationship between cathepsin H and IPF.

IPF is a progressive chronic lung disease whose histological feature is collagen accumulation produced by highly proliferative fibroblasts (36). Uncontrolled immune responses can lead to the onset and development of IPF (37). The role of the immune system in IPF cannot be ignored. Several factors have been implicated in the pathogenesis of IPF, including dysregulation of the immune response (38). The potential mechanism by which cathepsin H reduces the risk of IPF is not clear. A recent study has highlighted the role of cathepsin H in antimicrobial immunity (39). In addition, a research team reported that knocking out cathepsin H in a mouse model of experimental autoimmune encephalomyelitis resulted in increased Th1 cell differentiation and early onset of autoimmune encephalomyelitis (40). Our study suggests that elevated cathepsin H levels reduce the overall risk of IPF (OR = 0.885, 95%CI = 0.827 ~ 0.947, P = 3.86×10 − 4). Combined with our findings, we speculate that the possible mechanism is that cathepsin H plays a role in regulating immune responses and maintaining pulmonary surfactant, thereby altering susceptibility to developing IPF through its effects on fibrosis processes and alveolar function.

It is worth noting that a recent observational study showed that serum cathepsin B levels were significantly higher in the progressive IPF group than in the non-progressive group and healthy controls, and could significantly predict IPF progression (9). Interestingly, both our UVMR and MVMR analyses found no evidence of a causal relationship between cathepsin B levels and IPF (OR = 0.960, 95%CI = 0.841 ~ 1.095, P = 0.540) (OR = 0.922, 95%CI = 0.832 ~ 1.022, P = 0.124). More importantly, more precise models (such as knockout mouse models) are needed to further determine whether cathepsin B has an impact on the onset and development of IPF.

This study conducted MR analysis based on the results of large-scale GWAS cohorts, with a large sample size and high statistical efficiency. Secondly, this study used genetic IVs and multiple MR analysis methods to explore the causal relationship between different types of cathepsins and IPF. MVMR analysis minimized confounding, and reverse MR analysis also avoided reverse causation bias. Sensitivity analyses also indicated that the results were not affected by horizontal pleiotropy. In addition, colocalization analysis and SMR analysis further demonstrated the robustness of the results. Finally, we validated the findings using UKB-PPP data, which strengthened the final causal inference. However, it is worth noting that this study also has limitations. Firstly, this study was limited to European populations, which restricts the generalizability of our conclusions to other populations. Additionally, due to lack of individual information, the current study cannot conduct further stratified analyses on specific features of interest.

In conclusion, our MR research suggests that cathepsin H reduces the risk of developing IPF and may serve as a potential therapeutic target for IPF. This finding provides significant insights for the study of the biological mechanism of IPF and provides valuable clues for the diagnosis and treatment of IPF.

IPF

Idiopathic pulmonary fibrosis

GWAS

Genome-wide association studies

Mendelian randomization

SNPs

Single nucleotide polymorphisms

IVs

Instrumental variables

UVMR

Univariable Mendelian randomization

MVMR

Multivariable Mendelian randomization

SMR

Summary data-based Mendelian randomization

UKB-PPP

UK Biobank Pharma Proteomics Project

IVW

Inverse variance weighted

eQTL

Expressed quantitative trait loci

pQTL

Protein quantitative trait loci

Linkage disequilibrium

Odds ratios

Confidence interval

MR-PRESSO

Mendelian Randomized Pleiotropic Residuals and Outliers

HEIDI

Heterogeneity in dependent instruments

Ethics approval and consent to participate: Not applicable.

Consent for publication: Not applicable.

Availability of data and materials

The datasets analyzed during the current study are available on the following public website.

(https://gwas.mrcieu.ac.uk/)(https://r10.finngen.fi/)(http://www.eqtlgen.org/)(https://www.synapse.org/)

Competing interests: All authors declare no competing interests.

Funding: There is no funding to report.

Authors' contributions

YH and ZE-Z conducted the design of this study. ZE-Z, RC-X and CH-X performed data analysis and wrote the manuscript. YL-Z and QX contributed suggestions for manuscript revision and revised the manuscript. All authors read and approved the final manuscript.

Acknowledgements

We want to acknowledge the participants and investigators of the FinnGen study, and we are grateful to associations that make summary statistics data publicly available.

Kaunisto J, Salomaa ER, Hodgson U, Kaarteenaho R, Myllärniemi M. Idiopathic pulmonary fibrosis–a systematic review on methodology for the collection of epidemiological data. BMC Pulm Med. 2013;13:53.
Sgalla G, Iovene B, Calvello M, Ori M, Varone F, Richeldi L. Idiopathic pulmonary fibrosis: pathogenesis and management. Respir Res. 2018;19(1):32.
Knudsen L, Ruppert C, Ochs M. Tissue remodelling in pulmonary fibrosis. Cell Tissue Res. 2017;367(3):607–26.
Roque W, Boni A, Martinez-Manzano J, Romero F. A Tale of Two Proteolytic Machines: Matrix Metalloproteinases and the Ubiquitin-Proteasome System in Pulmonary Fibrosis. Int J Mol Sci. 2020;21(11).
Reiser J, Adair B, Reinheckel T. Specialized roles for cysteine cathepsins in health and disease. J Clin Invest. 2010;120(10):3421–31.
Fonović M, Turk B. Cysteine cathepsins and extracellular matrix degradation. Biochim Biophys Acta. 2014;1840(8):2560–70.
Zuo T, Xie Q, Liu J, Yang J, Shi J, Kong D, et al. Macrophage-Derived Cathepsin S Remodels the Extracellular Matrix to Promote Liver Fibrogenesis. Gastroenterology. 2023;165(3):746–e6116.
Yang G, Yang W, Jiang H, Yi Q, Ma W. Hederagenin inhibits high glucose-induced fibrosis in human renal cells by suppression of NLRP3 inflammasome activation through reducing cathepsin B expression. Chem Biol Drug Des. 2023;102(6):1409–20.
Yeo HJ, Ha M, Shin DH, Lee HR, Kim YH, Cho WH. Development of a Novel Biomarker for the Progression of Idiopathic Pulmonary Fibrosis. Int J Mol Sci. 2024;25(1).
Kim MS, Song M, Shin JI, Won HH. How to interpret studies using Mendelian randomisation. BMJ Evid Based Med. 2023;28(4):251–4.
Burgess S, Small DS, Thompson SG. A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res. 2017;26(5):2333–55.
Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017;46(6):1985–98.
Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40(4):304–14.
Burgess S, Foley CN, Allara E, Staley JR, Howson JMM. A robust and efficient method for Mendelian randomization with hundreds of genetic variants. Nat Commun. 2020;11(1):376.
Yin Q, Zhu L. Does co-localization analysis reinforce the results of Mendelian randomization? Brain. 2023.
Yu K, Chen XF, Guo J, Wang S, Huang XT, Guo Y, et al. Assessment of bidirectional relationships between brain imaging-derived phenotypes and stroke: a Mendelian randomization study. BMC Med. 2023;21(1):271.
Skrivankova VW, Richmond RC, Woolf BAR, Davies NM, Swanson SA, VanderWeele TJ, et al. Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration. BMJ. 2021;375:n2233.
Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR, Blackshaw J, et al. Genomic atlas of the human plasma proteome. Nature. 2018;558(7708):73–9.
Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68–74.
Hemani G, Tilling K, Davey Smith G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 2017;13(11):e1007081.
Kurki MI, Karjalainen J, Palta P, Sipilä TP, Kristiansson K, Donner KM, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613(7944):508–18.
Võsa U, Claringbould A, Westra HJ, Bonder MJ, Deelen P, Zeng B, et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet. 2021;53(9):1300–10.
Sun BB, Chiou J, Traylor M, Benner C, Hsu YH, Richardson TG, et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature. 2023;622(7982):329–38.
Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017;46(6):1734–9.
Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512–25.
Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693–8.
Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10(5):e1004383.
Wu Y, Zeng J, Zhang F, Zhu Z, Qi T, Zheng Z, et al. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat Commun. 2018;9(1):918.
Wang Y, Zhao J, Gu Y, Wang H, Jiang M, Zhao S, et al. Cathepsin H: Molecular characteristics and clues to function and mechanism. Biochem Pharmacol. 2023;212:115585.
Gocheva V, Chen X, Peters C, Reinheckel T, Joyce JA. Deletion of cathepsin H perturbs angiogenic switching, vascularization and growth of tumors in a mouse model of pancreatic islet cell cancer. Biol Chem. 2010;391(8):937–45.
Fröhlich E, Möhrle M, Klessen C. Cathepsins in basal cell carcinomas: activity, immunoreactivity and mRNA staining of cathepsins B, D, H and L. Arch Dermatol Res. 2004;295(10):411–21.
Brasch F, Ten Brinke A, Johnen G, Ochs M, Kapp N, Müller KM, et al. Involvement of cathepsin H in the processing of the hydrophobic surfactant-associated protein C in type II pneumocytes. Am J Respir Cell Mol Biol. 2002;26(6):659–70.
Bühling F, Kouadio M, Chwieralski CE, Kern U, Hohlfeld JM, Klemm N, et al. Gene targeting of the cysteine peptidase cathepsin H impairs lung surfactant in mice. PLoS ONE. 2011;6(10):e26247.
Khan A, Agarwal R. Pulmonary alveolar proteinosis. Respir Care. 2011;56(7):1016–28.
Woischnik M, Bauer A, Aboutaam R, Pamir A, Stanzel F, de Blic J, et al. Cathepsin H and napsin A are active in the alveoli and increased in alveolar proteinosis. Eur Respir J. 2008;31(6):1197–204.
Yan P, Liu J, Li Z, Wang J, Zhu Z, Wang L et al. Glycolysis Reprogramming in Idiopathic Pulmonary Fibrosis: Unveiling the Mystery of Lactate in the Lung. Int J Mol Sci. 2023;25(1).
Xu Y, Lan P, Wang T. The Role of Immune Cells in the Pathogenesis of Idiopathic Pulmonary Fibrosis. Med (Kaunas). 2023;59(11).
Mutsaers SE, Miles T, Prêle CM, Hoyne GF. Emerging role of immune cells as drivers of pulmonary fibrosis. Pharmacol Ther. 2023;252:108562.
Wang Y, Xu H, Sun B. Cathepsin H and cathepsin B of Cynoglossus semilaevis are involved in anti-bacterial immunity against Edwardsiella tarda. Fish Shellfish Immunol. 2023;134:108594.
Okada R, Zhang X, Harada Y, Wu Z, Nakanishi H. Cathepsin H deficiency in mice induces excess Th1 cell activation and early-onset of EAE though impairment of toll-like receptor 3 cascade. Inflamm Res. 2018;67(5):371–4.

No competing interests reported.

Download PDF

Editor assigned by journal
20 May, 2024
Editor invited by journal
02 Apr, 2024
Submission checks completed at journal
02 Apr, 2024
First submitted to journal
29 Mar, 2024

You are reading this latest preprint version

Causality between cathepsins and idiopathic pulmonary fibrosis: a Mendelian randomization study

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials and methods

Study design

The data source and the selection of IVs

Statistical analysis

Results

Exploration of the causal effect of cathepsins on IPF

The results of Bayesian colocalization analysis

The results of SMR analysis

External verification of causality between cathepsin H and IPF

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1