The associations of circulating inflammatory-related proteins with asthma: a Mendelian randomization study

The associations of circulating inflammatory-related proteins with asthma: a Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The associations of circulating inflammatory-related proteins with asthma: a Mendelian randomization study Yajun Xiong, Yanbing Sheng, Long Zhang, Yuntao Wei, Yuying Feng, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4271035/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Epidemiological evidence links inflammation to the etiology and pathophysiology of asthma. To assess the causal relationship between circulating inflammation-related proteins and asthma, we performed a two-sample Mendelian randomization (MR) analysis. Methods Protein quantitative trait locis (pQTLs) were derived from twelve genome-wide association studies (GWASs) cohorts on the circulating inflammation-related proteome. Genetic associations with asthma were obtained from a large-scale GWAS, categorized into childhood-onset asthma (COA) and adult-onset asthma (AOA). Bidirectional MR analysis, Bayesian co-localization, and phenotype scanning were employed to confirm the robustness of MR results. Furthermore, pathway enrichment analysis, protein-protein interaction (PPI) network analysis, and molecule docking were conducted to evaluate the druggability of identified proteins and prioritize potential therapeutic targets. These results were further validated in eQTLGen, GTEx Consortium, and two dependent cohorts. Results Collectively, elevated MMP-1 and decreased levels of three proteins (ADA, CD40L, CST5) were associated with an increased risk of both COA and AOA. CXCL6 had an adverse effect specifically on COA. These associations were validated in sensitivity analyses. Apart from CST5, the other proteins interacted with therapeutic targets of asthma medications. Furthermore, therapeutic targeting of three proteins (ADA, CD40L, MMP1) is currently under evaluation, while CST5 and CXCL6 are considered druggable. Molecular docking showed excellent binding between drugs and proteins (ADA and MMP-1) with available structural data. Conclusions This study identified five circulating inflammatory-related protein biomarkers associated with asthma and provided novel insights into its etiology. Drugs targeting these proteins are expected to facilitate future prioritization of drug targets for asthma. asthma Mendelian randomization circulating inflammatory-related proteome drug target Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Asthma is a common and heterogeneous chronic respiratory disease with significant morbidity, mortality, and financial burden worldwide[ 1 ]. The incidence of asthma has been increasing and the global prevalence rate reaches 300 million[ 2 ]. Childhood-onset asthma (COA) and adult-onset asthma (AOA) represent distinct phenotypes of asthma[ 3 ]. AOA is typically more severe and exhibits a lower remission rate compared to COA. Asthma is characterized by variable airflow limitation, bronchial hyperresponsiveness (BHR), airway inflammation[ 4 ]. Airways inflammation, particularly type 2 inflammation, is the core mechanism of asthma[ 5 ]. The majority of patients experience type 2 inflammation, a systemic allergic response that plays a key role in the pathophysiology of asthma, leading to progressive decline in lung function and exacerbations. Therapeutic strategies targeting inflammatory molecules, such as IL-13, IL-4, IL-33, are currently being evaluated for potential use in asthma treatment[ 6 ]. However, targeted therapies are limited by the underlying phenotypic and endotypic variation between patients. Moreover, the causal role of specific inflammatory-related proteins remains unclear, primarily due to the limitations of observational studies (e.g., residual confounding and reverse causality) and the lack of high-quality data from randomised trials. Nowadays, it is widely recognized that genetic component is a crucial contributor to asthma susceptibility[ 7 ]. Recently, a study comparing the genetic architecture of asthma found a greater role for genetic risk factors in COA than in AOA[ 8 ]. Identifying asthma-associated genetic loci, especially those related to inflammatory proteins, enhances our understanding of the molecular mechanisms and key biological pathways in asthma pathogenesis, facilitating the development of personalized therapeutic strategies. Genome-wide association studies (GWAS) of protein levels have identified genetic variants associated with proteins, referring to as “protein quantitative trait loci (pQTLs)”[ 9 ]. pQTLs provide valuable insights into the molecular basis of complex traits and diseases by mediating the relationship between genotype and phenotype[ 10 ]. Meanwhile, proteomic studies offer an opportunity to assess the causality of potential drug candidates on human diseases using Mendelian randomization (MR)[ 11 ], which has been widely used to infer causal effects between exposures and disease outcomes[ 12 ]. Since genetic variants are randomly assigned at conception before disease onset, MR analysis could overcome reverse causality bias and confounders inherent in observational studies[ 13 ]. Additionally, MR method should satisify three fundamental assumptions, as detailed in Fig. 1 . In this study, we performed a two-sample MR to estimate the causal relationships between circulating inflammatory-related proteins and asthma, as illustrated in Fig. 2 . We used genetic instrumental variables (IVs) for 91 inflammatory-related proteins from a cohort of 14824 participants[ 14 ] and derived genetic associations of asthma from Ferreira’s study[ 15 ]. To validate the robustness of our findings, we performed reverse causality detection, colocalization analyses, and phenotype scanning. Additionally, to assess the druggability of the identified proteins, we conducted pathway enrichment analysis, protein-protein interaction (PPI) network, and molecular docking. Finally, we verified our results using pQTL data from two published stuides[ 16 , 17 ] and eQTL data from the eQTLGen Consortium[ 18 ] and GTEx Consortium to strengthen our conclusions. Methods Data Sources The datasets used in our study were sourced from publicly available summarized GWAS data. For inflammatory-related proteins, we used data from a genome-wide pQTL study of 91 plasma proteins across 12 cohorts, totaling 14824 individuals of European descent[ 14 ]. Asthma data were derived from a study with 40544 cases and 300671 controls of European ancestry, categorized into COA (ages 0–19 years) and AOA (ages 20–60 years)[ 15 ]. Supplementary Table 1 presents the basic information of these datasets. Instrument variables selection For each protein, pQTLs from its GWAS data served as genetic instruments (IVs). First, we established P < 5×10 − 8 as the genome-wide significant threshold to select strongly associated pQTLs with inflammatory-related proteins. Second, to avoid linkage disequilibrium (LD), we clumped these pQTLs (kb = 10000, r 2 = 0.001). To mitigate bias resulting from weak instruments, we calculated F statistics for each pQTL to assess statistical strengthen, with an F statistics of at least 10 indicating no weak instrument bias[ 19 ]. Next, we extracted exposure pQTLs from the outcome data and excluded those associated with the outcome ( P < 0.05). Finally, we harmonized by aligning the alleles of exposure and outcome pQTLs, discarding palindromic and imcompatible pQTLs. Mendelian randomization analysis We employed two-sample MR to estimate the relationships between genetically predicted levels of inflammatory-related proteins and asthma. When only one pQTL was available for a protein, we applied Wald’s ratio method. If two or more pQTLs were available, we used the inverse-variance weighting (IVW) method. The odds ratios of the measured outcomes represent the likelihood of increased asthma risk for each additional unit of protein. To address multiple hypothesis testing, we calculated false discovery rate (FDR) adjusted P values in the MR analyses (q values). A q value less than 0.5 was considered significant[ 20 ]. All statistical analyses were conducted using R software (version 4.2.3) with TwoSampleMR package (version 0.5.5). Sensitivity analysis For the identified significant estimates ( P < 0.05), sensitivity analyses were then performed to assess the robustness and reliability of our primary findings. To determine if asthma has any causal effect on the identified inflammatory-related proteins, we selected genetic instruments for asthma from the GWAS data for bidirectional MR analysis. The effect was estimated using MR-IVW, MR-Egger, and weighted median. Results were considered statistically significant at P < 0.05. In addition, Steiger’s filtering was applied to test the directionality of causality between proteins and asthma[ 21 ]. We performed Bayesian co-localization analysis to test whether identified proteins and asthma share the same causal variant using the “coloc” package[ 22 ]. For each locus, the Bayesian method evaluated support for five exclusive hypotheses: 1) no association with either trait (H 1 ); 2) association with trait 1 only (H 2 ); 3) association with trait 2 only (H 3 ); 4) both traits have a causal SNP, but the SNPs are distinct (H 4 ); 5) both traits have a causal SNP, and share the same SNP (H 5 ). We focused on hypothesis H 5 , and posterior probability (PP) was used to calculate support for H 4 (PPH 4 ). We considered evidence of colocalization strong if PPH 4 was ≥ 0.75[ 23 ]. We also conducted phenotype scanning to assess whether the estimates were influenced by potential risk factors[ 24 ], including family history, age, gender, body mass index, obesity, smoking, occupational exposure, and respiratory tract infection. Pathway enrichment analysis We performed enrichment analysis to identify biological pathways associated with asthma risk loci. We merged all relevant genes to a gene set used for the enriched pathways, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The selected pathways were those significantly enriched with an FDR < 0.05. Protein and protein interaction network We conducted protein-protein interaction (PPI) analysis to investigate the association between identified inflammatory-related proteins and targets for current medictions. We obtained 17 asthma-modifying drugs from a recent review, along with their corresponding drug targets[ 25 ]. The PPI network was constructed using the STRING database ( https://string-db.org/ )[ 26 ], with a minimum required interaction score of 0.4. Druggability evaluation and molecular docking To evaluate the druggability of identified proteins, we searched for a list of druggable genes from a previous study[ 27 ], ChEMBL[ 28 ], and ClinicalTrials ( https://www.ClinicalTrials.gov ). We identified protein-related drug targets, along with information on drug name and the drug development process. To further explore the effect of drug candidates on drug target genes and the druggability of target genes, we performed molecular docking to determine the interaction strength between receptors and ligands. Drug (small molecule) structure data and corresponding IDs were derived from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ), while protein (ligand) structure data and corresponding PDB IDs were obtained from the Uniprot database ( https://www.uniprot.org/ ). We converted the SDF format of small molecules to mol2 format using OpenBabel-3.1.1[ 29 ]. Thereafter, we removed water molecules and added polar hydrogen atoms of the protein and small molecule in AutoDockTools 1.5.6[ 30 ]. Grid boxes were utilized to cover all structure domains of the proteins, and subsequently, the molecule docking process was initiated to determine the binding energy of each protein and small molecule. The results of molecular docking were visualized through PyMOL. Replication analysis We utilized summary data from GWAS on plasma proteins of European ancestry, obtained from studies by Zheng [ 16 ] (734 plasma proteins) and Ferkingstad[ 17 ] (4907 plasma proteins measured in 35559 participants), for replication in a proteome-wide MR analysis. Additionally, we used available expression quantitative trait locis (eQTLs) for drug target genes. The summary-level data for eQTLs were sourced from the eQTLGen Consortium ( https://www.eqtlgen.org )[ 18 ] and GTEx Consortium ( https://gtexportal.org/home ), with details provided in Supplementary Table 1 . We identified eQTLs significantly associated with the expression of genes corresponding to the identified proteins in blood and lung tissue. Results Screening circulating causal inflammatory-related proteins of asthma MR analysis identified seven circulating inflammatory-related proteins associated with asthma: adenosine deaminase (ADA), CD40L receptor (CD40L), cystatin D (CST5), interleukin-12 subunit beta (IL-12B), matrix metalloproteinase-1 (MMP-1), C-X-C motif chemokine 6 (CXCL6), and tumor necrosis factor ligand superfamily member 14 (TNFSF14). F -statistics for all instrument variants were above 30, indicating that weak instrument bias can be minimized in our study (Supplementary Table 2) . Results from IVW-MR revealed suggestive evidence for the association between increased expression of MMP-1 and higher risk of AOA (OR = 20.68; 95% CI, 15.47–27.65; P = 4.89 × 10 − 92 ) and COA (OR = 18.80; 95% CI, 11.45–30.88; P = 2.14 × 10 − 30 ). Strong evidence was observed between CXCL6 and the risk of COA (OR = 12.01; 95% CI, 6.00–24.03; P = 5.95 × 10 − 12 ). Conversely, ADA (OR = 0.33; 95% CI, 0.31–0.36; P = 1.32 × 10 − 132 ), CD40L (OR = 0.11; 95% CI, 0.08–0.15; P = 1.14 × 10 − 47 ), CST5 (OR = 0.08; 95% CI, 0.04–0.14; P = 3.04 × 10 − 16 ), and IL-12B (OR = 0.13; 95% CI, 0.10–0.17; P = 2.54 × 10 − 53 ) were associated with a decreased risk of AOA. Additionally, higher genetically predicted levels of ADA (OR = 0.33; 95% CI, 0.30–0.36; P = 1.38 × 10 − 87 ), CD40L (OR = 0.05; 95% CI, 0.04–0.07; P = 3.96 × 10 − 29 ), CST5 (OR = 0.07; 95% CI, 0.04–0.13; P = 7.51 × 10 − 20 ), IL-12B (OR = 0.13; 95% CI, 0.11–0.16; P = 1.29 × 10 − 96 ), and TNFSF14 (OR = 0.10; 95% CI, 0.02–0.46; P = 8.00 × 10 − 3 ) were associated with a lower risk of COA ( Fig. 3 and Supplementary Table 3) . Sensitivity analyses for asthma causal proteins To assess the robustness of our results, sensitivity analyses were conducted (Table 1 ). However, heterogeneity was observed in the Cochran Q statistics among the identified proteins and asthma ( Supplementary Table 3) . Despite the detected heterogeneity in our findings, it did not invalidate the MR estimates, as the random-effect IVW was applied in the current study, potentially balancing the pooled heterogeneity. Bidirectional MR analysis did not reveal any causal effect of asthma on the levels of the identified proteins. The Steiger directionality confirmed that the genetic associations were consistent with a causal effect of inflammatory-related proteins on asthma, rather than the opposite direction ( Supplementary Table 4 and Supplementary Fig. 1 ). Bayesian co-localization strongly suggested that the associations between four of seven proteins (CD40L, CST5, IL-12B, and MMP-1) and asthma were likely due to the same underlying causal variants (PP ≥ 0.75) (Table 1 and Supplementary Fig. 2 ). After phenotype scanning, IL-12B (rs76428106) was found to be associated with hypothyroidism or myxoedema and treatment with thyroxine product. IL-12B (rs10043720) was linked to Crohn’s disease (CD) and ulcerative colitis (UC). CD40L (rs1883832) was linked to chronic hepatitis B infection, rheumatoid arthritis (RA), and Kawasaki disease (KD) ( Supplementary Table 5 ). We cannot entirely exclude the possibility that hypothyroidism[ 32 ], inflammatory bowel disease (IBD)[ 33 ], exposure to hepatitis B[ 34 ], and RA[ 35 ] have a causal role in the association between inflammatory-related proteins and asthma because these conditions may be potential causes of asthma. We ruled out IL-12B and rs1883832 (CD40L), the causality between CD40L and AOA remained significant (OR = 0.06; 95% CI, 0.05–0.07; P = 1.60×10 − 252 , Supplementary Table 6 ). Table 1 Summary of reverse causality detection, Bayesian co-localization analysis, and phenotype scanning Outcome Protein UniProt ID Bidirectional MR (MR-IVW) Steiger filtering Colocalization PPH4 Previously reported associations AOA ADA P00813 1.002 (0.999–1.005) Passed (1.150×10 − 7 ) 0.043 AOA CD40L P25942 0.999 (0.998–1.001) Passed (2.566×10 − 8 ) 0.752 Chronic hepatitis B infection, RA, KD AOA CST5 P28325 1.001 (0.999–1.002) Passed (1.875×10 − 22 ) 0.834 AOA IL-12B P29460 0.996 (0.993–0.999) Passed (4.016×10 − 7 ) 0.919 IBD, CD, UC, hypothyroidism, Blood cells AOA MMP-1 P03956 0.999 (0.997–1.001) Passed (3.194×10 − 6 ) 0.844 COA ADA P00813 1.001 (1.000-1.003) Passed (1.561×10 − 4 ) 0.095 COA CD40L P25942 1.001 (1.000-1.002) Passed (1.542×10 − 5 ) 0.759 COA CST5 P28325 1.001 (1.000-1.002) Passed (7.849×10 − 4 ) 0.831 COA CXCL6 P80162 1.000 (0.999–1.002) Passed (1.483×10 − 6 ) 0.510 Blood cells COA IL-12B P29460 0.997 (0.995–0.999) Passed (1.057×10 − 4 ) 0.916 IBD, CD, UC, hypothyroidism, Blood cells COA MMP-1 P03956 1.000 (0.999–1.001) Passed (7.961×10 − 4 ) 0.945 COA TNFSF14 O43557 1.001 (1.000-1.003) Passed (1.896×10 − 4 ) 0.528 Monocyte count MR-IVW: Mendelian randomization with inverse variance weighted method; PP: posterior probability; RA: rheumatoid arthritis; KD: Kawasaki disease; IBD: inflammatory bowel disease; CD: Crohn’s disease; UC: ulcerative colitis; AOA: adult-onset asthma; COA: childhood-onset asthma. Enrichment analysis The GO analysis of identified potential targets was performed to reveal their biological functions. As depicted in Fig. 4 , the most significant pathways were predominantly enriched in the movement and chemotaxis of cells, and their response to chemotactic factors in the immune system, including leukocyte migration and chemotaxis, leukocyte homeostasis, neutrophil chemotaxis, and lymphocyte migration and chemotaxis. The KEGG results exhibited that the most involved pathways were the IL-17 signaling pathway, primary immunodeficiency, RA, NF-kappa B signaling pathway, and tumor necrosis factor (TNF) signaling pathway. Association of potential drug targets with current asthma medications We explored information on all asthma drugs and their targets ( Supplementary Table 7 ). A connection was identified between the potential therapeutic target and the target protein of current medications in the PPI network. Specifically, MMP-1 and CD40L showed associations with numerous cytokines, the targets of monoclonal antibodies (Fig. 5 ). In contrast, no significant association was observed for CST5 ( Supplementary Fig. 3 ). Druggability and clinical‑phase drug for candidate protein targets PPI analysis indicated potential avenues for asthma drug development. Consequently, we extensively searched for a list of druggable genes, the ChEMBL database, and clinical trial registry website to assess the druggability and drug development of the six candidate proteins. Specifically, ADA-targeted drug PENTOSTATIN is entering phase I/II trials for steroid refractory acute graft versus host disease, leukemia, lymphoma, and kidney cancer. CD40L-targeted drug DAPIROLIZUMAB PEGOL was in phase II trials for systemic lupus erythematosus (SLE). Additionally, CD40L-targeted drug AT-1501 was currently under evaluation in clinical trials for nephropathy, type 1 diabetes mellitus, amyotrophic lateral sclerosis, and kidney transplant, while DAZODALIBEP was in clinical trials for Sjogren's Syndrome. MMP1-targeted drug MARIMASTAT was currently undergoing evaluation in clinical trials for lung cancer, breast cancer and REBIMASTAT was in trials for HIV-related Kaposi's Sarcoma, non-small cell lung cancer, and prostate cancer. TNFSF14-targeted BAMINERCEPT was in clinical trials for primary Sjögren's Syndrome, RA, chronic HCV hepatitis C, and secondary progressive multiple sclerosis. Although no ongoing trials were found for CST5 and CXCL6, they are considered potential druggable targets ( Supplementary Table 8 ). Molecular docking analyses of potential targets and candidate drugs To assess the affinity of drug candidates for their targets and explore the druggability of the identified proteins, we conducted molecular docking. Since the structures of monoclonal antibodies on the PubChem database were unavailable, we only analyzed the binding energy between small molecule drugs and candidate proteins. We used AutoDock Tools v.1.5.6 to determine the binding sites and interactions of the five drug candidates with ADA and MMP-1, generating docking results for these two proteins with the drugs ( Supplementary Table 9 ). ADA and MMP-1, along with their corresponding small molecule drugs, exhibited binding energies below − 5 kcal/mol. The docking results were visualized through PyMOL ( Supplementary Fig. 4 ). External validation of potential drug targets for asthma Using the same variant and significant variant in different databases to validate our main findings ( Supplementary Table 9 ). Increasing CD40L also decreased the risk of COA and AOA. MMP-1 was shown to potentially increase the risk of COA and AOA. The causal relationship between CST5, CXCL6, and asthma was confirmed in Zheng’s study but not validated in Ferkingstad’s study. However, the causal relationship between TNFSF14 and asthma contradicts the result of the primary analysis ( Supplementary Table 10 ). To test whether the corresponding genes of the six target proteins were associated with asthma, we sought eQTL from the eQTLGen Consortium and GTEx Consortium. We identified that IV(s) for five of the six proteins (excluding CD40L) were significant eQTL in whole blood or lung tissue ( Supplementary Table 11 ). The expression of the ADA and CST5 genes in lung tissue, as well as the expression of the TNFSF14 gene in whole blood, has causal associations with asthma. Furthermore, the direction of their effects was consistent with the causality between their respective protein levels in circulation and asthma. However, the causal associations between MMP-1 gene expression in lung tissue and asthma, as well as between CXCL6 gene expression and COA in blood, exhibit opposite effects compared to our primary results (Fig. 6 and Supplementary Table 12 ). This discrepancy is likely due to the measurement of MMP-1 and CXCL6. The Olink assay only captures the circulating free MMP-1 and CXCL6, while all isoforms of MMP-1 and CXCL6 transcripts are captured by the gene expression measurements in blood and lung tissue. Discussion In this study, we conducted a thorough investigation into the causal associations between 91 circulating inflammatory-related proteins and asthma. We identified five protein markers (ADA, CD40L, CST5, MMP-1, and CXCL6), with CXCL6 being specific to COA, while four were shared among subsets. CD40L, CST5, and MMP-1 were supported by colocalization analyses. Bidirectional MR and Steiger filtering indicated that none of the identified proteins showed reverse causality. Pathway enrichment analysis revealed that the potential identified targets were mostly involved in inflammatory pathways like IL-17, NF-κB, and TNF signaling pathway, all associated with asthma development. A study suggested that IL-17 promotes the development of allergic asthma by enhancing airway dendritic cell (DC) activation, migration, and function[ 31 ]. Inhibition of the NF-κB signaling pathway could alleviate airway inflammation in asthma[ 32 ]. Natalie et al.[ 33 ] indicated that the elevation TNF levels are linked to clinical phenotypes of asthma, including neutrophilic and severe asthma. In addition, the potential targets mainly participated in immune system dysregulation, such as primary immunodeficiency and RA. This suggests that the identified proteins may play a role in the systemic immune response in asthma. CD40L, a membrane-bound protein of the TNF superfamily, is crucial for mediating the interaction between antigen-presenting cells (APCs) and lymphocytes[ 34 ]. Its engagement with CD40 plays a protective role in asthma by regulating the balance between Th1 and Th2 cells[ 35 ]. Notably, CD40L levels are significantly decreased in patients with allergic asthma[ 36 ], which consistent with our results. Our PPI analysis revealed that CD40L interacts with numerous cytokines targeted by monoclonal antibodies used in asthma treatment. Currently, CD40L is undergoing clinical trials for type 1 diabetes mellitus and kidney transplant. In addition, it was externally validated in two plasma proteins GWAS datasets. Therefore, it holds promise as a new druggable target for asthma. However, some studies have produced paradoxical results, suggesting that platelet depletion and CD40L depletion could attenuate asthma progression by inhibiting IL-4, IL-13, and IgE production, as well as leukocyte infiltration[ 37 ]. This controversy may stem from the diverse functions of CD40L across different cell types or phases of the immune response. Further investigation is needed to elucidate the detailed mechanism and biological significance of the CD40/CD40L interaction in asthma. The matrix metalloproteinases (MMPs) function in degrading extracellular matrix (ECM) components, including collagen, laminin, fibronectin, and others. MMP-1 is involved in degrading various types of collagen like I, II, III, VI, and X[ 38 ], and is recognized as contributing to airway inflammation, tissue remodeling, and asthma exacerbation[ 39 ]. Serum MMP-1 levels are elevated in chronic asthma and may distinguish between moderate and severe asthma[ 40 ]. MMP-1 overexpression has been observed in airway epithelial cells, inflammatory cells, and airway smooth muscle cells in asthma[ 41 ], particularly in fatal asthma cases[ 42 ]. Our results support these findings. MMP-1 is currently undergoing clinical trials for lung cancer and breast cancer, among others. Moreover, we have identified three potential drugs targeting MMP-1, providing a therapeutic approach for asthma. Molecular docking has reinforced the reliability of this association through molecular binding. Furthermore, external validation was conducted in independent cohorts. Adenosine deaminase (ADA) deficiency leads to the accumulation of toxic purine degradation by-products, most potently affecting lymphocytes, resulting in ADA-deficient severe combined immunodeficiency[ 43 ], which includes asthma[ 44 ]. Adenosine induces Ca 2+ oscillations and constriction in airway smooth muscle cells, ultimately leading to calcium-induced release. Epithelial injury and airway hyperresponsiveness are hallmarks of asthma. Removal of adenosine by ADA mitigates local epithelial injury and airway contraction, thereby alleviating asthma[ 45 ]. ADA is in clinical trials for leukemia, lymphoma, and kidney cancer. Additionlly, we conducted molecular docking to further validate the interaction strength between ADA and two small molecule drugs. Our results were also confirmed by eQTL analysis in lung tissue, providing further evidence for the causality between ADA and asthma. The findings suggest that ADA might be a therapeutic target. Additional druggable targets identified were CST5 and CXCL6. CST5 (Cystatin D) is a salivary cysteine protease inhibitor that blocks coronavirus replication at its physiologic concentration. Notably, CST5 levels were lower in COVID-19 patients compared to non-COVID-19 individuals[ 46 ]. We discovered that circulating ADA protein reduces the risk of asthma, which was validated at the genetic level in lung tissue. However, previous studies on CST5 in asthma are limited. Both CST3 and CST5 belong to the cystatin superfamily of protease inhibitors, with CST3 levels found to be higher in asthma compared to controls[ 47 ]. Further research is needed to explore the expression and function of CSTA in asthma. CXCL6, a member of the CXC chemokine family, serves as a chemotactic agent for neutrophil granulocytes. Verhoeckx[ 48 ] proposed that upregulation of CXCL6 could exacerbate the inflammatory progression of asthma. In addition, our PPI analysis revealed that CXCL6 interacts with IL-6 and TNF-α, therapeutic targets of Tocilizumab and Golimumab, respectively, both licensed medications for asthma. Thus, CXCL6 may represent a promising new target for asthma treatment. Several limitations should be considered when interpreting our findings. First, our analysis was restricted to individuals of European ancestry, potentially limiting the generalizability of our results to other ancestries. Further stuides involving non-European populations are necessary to broaden the discovery of genetic determinants of asthma. Second, all identified proteins had three or fewer SNPs, limiting alternative MR appproach, heterogeneity tests, and pleiotropy tests. However, the SNPs we selected were strong instruments with F statistics exceeding 30, indicating minimal weak instrumental variable bias. Third, colocalization analysis did not support ADA, CXCL6 and asthma sharing the same causal SNPs. Nonetheless, this does not invalidate the findings as colocalization methodologies typically exhibit a high false negative rate (around 60%). Finally, non-linear effects, time-dependent effects, or inflammation-environment interactions may exist between some proteins and asthma. Considering the possibility that a protein may influence asthma risk at extremely low or high levels is intriguing. However, detecting such effects in practical clinical settings could be challenging. Conclusions Our study suggests that genetically determined levels of circulating ADA, CD40L, CST5, MMP-1, and CXCL6 are causally associated with asthma. This provides new insights into the etiology of asthma and represents promising drug targets. Further experimental and clinical studies are required to explore the utility and efficacy of these candidates proteins in asthma treatment. Abbreviations MR Mendelian randomization pQTLs Protein quantitative trait locis GWASs Genome-wide association studies COA Childhood-onset asthma AOA Adult-onset asthma PPI Protein-protein interaction BHR Bronchial hyperresponsiveness IVs Instrumental variables LD Linkage disequilibrium IVW Inverse-variance weighting FDR False discovery rate PP Posterior probability GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes CD Crohn’s disease UC Ulcerative colitis RA Rheumatoid arthritis KD Kawasaki disease IBD Inflammatory bowel disease SLE Systemic lupus erythematosus DC Dendritic cell TNF Tumor necrosis factor APCs Antigen-presenting cells MMPs Matrix metalloproteinases ECM Extracellular matrix Declarations Ethics approval and consent to participate All studies were conducted in full accordance with the ethical principles out‑ lined in the Declaration of Helsinki, as revised in 2013, including adherence to protocols approved by their respective institutional ethics review committees and all participants provided written informed consent. Consent for publication Not applicable Availability of data and materials Summay data used for this study can be accessed through the following links: Circulating inflammatory-related proteins, https://www.phpc.cam.ac.uk/ceu/proteins/; Plasma proteins from MRC Integrative Epidemiology Unit, https://www.epigraphdb.org/pqtl/; Plasma proteins from deCODE genetics, https://www.decode.com/summarydata/; Whole blood eQTL data, https://www.eqtlgen.org/cis-eqtls.html; Lung tissue eQTL data, https://www.gtexportal.org/home/; AOA, https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST007799/; COA, https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST007800/ Competing interests The authors declare that they have no competing interests. Funding This project has received funding from Henan Provincial Science and Technology Research Project (SBGJ202001006). Authors' contributions Y.X. and Y.S. designed the work and wrote the draft. L.Z, Y.F., and Y.W. analyzed the data for Mendelian randomization analyses. X.Z., X.M., T.G., S.W., X.N., M.C., Y. C., and J.Z. reviewed, made revisions, and approved the article before submission. A.X. is the guarantor of this work, and took responsibility for the design of the work, the acquisition of funding, the administration of the project and the drafting and revising of the article. Acknowledgements We thank SCALLOP Consortium, MRC Integrative Epidemiology Unit, deCODE genetics, eQTLGen Consortium, GTEx Consortium, and IEU Open GWAS Project for providing GWAS summary statistics for our analysis. Graphical abstract was created with BioRender.com. References Reddel HK, Bacharier LB, Bateman ED, Brightling CE, Brusselle GG, Buhl R, Cruz AA, Duijts L, Drazen JM, FitzGerald JM et al. Global Initiative for Asthma Strategy 2021: executive summary and rationale for key changes. Eur Respir J. 2022; 59(1). Stern J, Pier J, Litonjua AA. Asthma epidemiology and risk factors. Semin Immunopathol. 2020;42(1):5–15. Lommatzsch M, Klein M, Stoll P, Virchow JC. 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AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009;30(16):2785–91. Jirmo AC, Busse M, Happle C, Skuljec J, Dalüge K, Habener A, Grychtol R, DeLuca DS, Breiholz OD, Prinz I, et al. IL-17 regulates DC migration to the peribronchial LNs and allergen presentation in experimental allergic asthma. Eur J Immunol. 2020;50(7):1019–33. Huang W, Li ML, Xia MY, Shao JY. Fisetin-treatment alleviates airway inflammation through inhbition of MyD88/NF-κB signaling pathway. Int J Mol Med. 2018;42(1):208–18. Niessen NM, Gibson PG, Baines KJ, Barker D, Yang IA, Upham JW, Reynolds PN, Hodge S, James AL, Jenkins C, et al. Sputum TNF markers are increased in neutrophilic and severe asthma and are reduced by azithromycin treatment. Allergy. 2021;76(7):2090–101. Tang T, Cheng X, Truong B, Sun L, Yang X, Wang H. Molecular basis and therapeutic implications of CD40/CD40L immune checkpoint. Pharmacol Ther. 2021;219:107709. Takahashi H, Ebihara S, Kanda A, Kamanaka M, Sato T, Habu S, Kikutani H, Sasaki H. Increased susceptibility to airway responses in CD40-deficient mice. Clin Exp Immunol. 2003;133(1):22–9. Sobkowiak P, Narożna B, Wojsyk-Banaszak I, Bręborowicz A, Szczepankiewicz A. Expression of proteins associated with airway fibrosis differs between children with allergic asthma and allergic rhinitis. Int J Immunopathol Pharmacol. 2021;35:2058738421990493. Tian J, Zhu T, Liu J, Guo Z, Cao X. Platelets promote allergic asthma through the expression of CD154. Cell Mol Immunol. 2015;12(6):700–7. de Souza AP, Trevilatto PC, Scarel-Caminaga RM, Brito RB, Line SR. MMP-1 promoter polymorphism: association with chronic periodontitis severity in a Brazilian population. J Clin Periodontol. 2003;30(2):154–8. Chen LH, Li CH, Wang SC, Chiu KL, Wu MF, Yang JS, Tsai CW, Chang WS, Hsia TC, Bau DT. Association of Matrix Metalloproteinase-1 Promoter Polymorphisms With Asthma Risk. Vivo. 2024;38(1):365–71. Prabha A, Lokesh KS, Chaya SK, Jayaraj BS, Malamardi S, Subbarao M, Beck SC, Krishna MT, Mahesh PA. Pilot study investigating diagnostic utility of serum MMP-1 and TGF-β1 in asthma in 'real world' clinical practice in India. J Clin Pathol. 2022;75(4):222–5. Rogers NK, Clements D, Dongre A, Harrison TW, Shaw D, Johnson SR. Extra-cellular matrix proteins induce matrix metalloproteinase-1 (MMP-1) activity and increase airway smooth muscle contraction in asthma. PLoS ONE. 2014;9(2):e90565. Dolhnikoff M, da Silva LF, de Araujo BB, Gomes HA, Fernezlian S, Mulder A, Lindeman JH, Mauad T. The outer wall of small airways is a major site of remodeling in fatal asthma. J Allergy Clin Immunol. 2009;123(5):1090–7. 1097.e1091. Flinn AM, Gennery AR. Adenosine deaminase deficiency: a review. Orphanet J Rare Dis. 2018;13(1):65. Scott O, Kim VH, Reid B, Pham-Huy A, Atkinson AR, Aiuti A, Grunebaum E. Long-Term Outcome of Adenosine Deaminase-Deficient Patients-a Single-Center Experience. J Clin Immunol. 2017;37(6):582–91. Zhou J, Alvarez-Elizondo MB, Botvinick E, George SC. Adenosine A(1) and prostaglandin E receptor 3 receptors mediate global airway contraction after local epithelial injury. Am J Respir Cell Mol Biol. 2013;48(3):299–305. Molinero M, Gómez S, Benítez ID, Vengoechea JJ, González J, Polanco D, Gort-Paniello C, Moncusí-Moix A, García-Hidalgo MC, Perez-Pons M, et al. Multiplex protein profiling of bronchial aspirates reveals disease-, mortality- and respiratory sequelae-associated signatures in critically ill patients with ARDS secondary to SARS-CoV-2 infection. Front Immunol. 2022;13:942443. Cimerman N, Brguljan PM, Krasovec M, Suskovic S, Kos J. Serum cystatin C, a potent inhibitor of cysteine proteinases, is elevated in asthmatic patients. Clin Chim Acta. 2000;300(1–2):83–95. Verhoeckx KC, Doornbos RP, Witkamp RF, van der Greef J, Rodenburg RJ. Beta-adrenergic receptor agonists induce the release of granulocyte chemotactic protein-2, oncostatin M, and vascular endothelial growth factor from macrophages. Int Immunopharmacol. 2006;6(1):1–7. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure.docx SupplementaryTable.xlsx floatimage1.png Graphical Abstract Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4271035","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291773137,"identity":"fdb733d0-a2ed-4995-8ea9-4ca44d415bf9","order_by":0,"name":"Yajun Xiong","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yajun","middleName":"","lastName":"Xiong","suffix":""},{"id":291773138,"identity":"6d59ab92-f06d-4969-aba2-17bed9fbc02f","order_by":1,"name":"Yanbing Sheng","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yanbing","middleName":"","lastName":"Sheng","suffix":""},{"id":291773139,"identity":"55d348f6-e808-4d7b-949f-84a48b602459","order_by":2,"name":"Long Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Zhang","suffix":""},{"id":291773140,"identity":"461d05de-8b37-479a-8938-1233e2384f71","order_by":3,"name":"Yuntao Wei","email":"","orcid":"","institution":"University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Yuntao","middleName":"","lastName":"Wei","suffix":""},{"id":291773141,"identity":"c416d721-a93d-4404-9ccb-961c92384c3e","order_by":4,"name":"Yuying Feng","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou 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16:54:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4271035/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4271035/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54912616,"identity":"5086f9c1-1b8c-4c0e-85f5-7168ef89c290","added_by":"auto","created_at":"2024-04-18 13:20:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":94573,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResearch overview and design of Mendelian randomization analysis.\u003c/strong\u003e 1) Genetic variants and exposure are strongly correlated (“relevance”); 2) Genetic variants are not associated with any potential confounding variables (“independence”); 3) Genetic variants are not directly related to the outcome (“exclusion restriction”). AOA: adult-onset asthma; COA: childhood-onset asthma.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4271035/v1/58ff6d8c2b8a4fe77e4463d0.png"},{"id":54912615,"identity":"6e8e4c3a-4b17-4a98-bac8-4c9b0f64b3cd","added_by":"auto","created_at":"2024-04-18 13:20:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":336651,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the study\u003c/strong\u003e. AOA: adult-onset asthma; COA: childhood-onset asthma; IVW: inverse-variance weighting; MR: Mendelian Randomization; cis-eQTL: cis-acting expression quantitative trait locus; GTEx: Genotype-Tissue Expression.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4271035/v1/8e32e9e1a007e006cdc1a192.png"},{"id":54911556,"identity":"5f9cad57-fcd2-4605-aef9-227c844cdbac","added_by":"auto","created_at":"2024-04-18 13:04:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":117814,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMR analysis for potential causal inflammatory-related proteins on (a) AOA and\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b) COA\u003c/strong\u003e. The squares were the causal estimates on the OR scale, and the whiskers represented the 95% CI for these ORs. nSNPs: number of SNPs used for the estimation of the causal effects in this plot. OR for increased risk of asthma were expressed as per SD increase in protein levels. \u003cem\u003eP\u003c/em\u003e values were determined from the IVW or Wald ratio. AOA: adult-onset asthma; COA: childhood-onset asthma; MR: Mendelian randomization; OR: odds ratio; CI: confidence interval; SD: standard deviation; IVW: inverse-variance-weighted.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4271035/v1/cc8e07d6edeb7ee7614f2dd5.png"},{"id":54912126,"identity":"31e1489f-5374-4a5f-a3e4-21f938a8e9f4","added_by":"auto","created_at":"2024-04-18 13:12:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":161802,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe top lists of (a) GO enrichment and (b) KEGG pathways of targets.\u003c/strong\u003e GO: Gene Ontology; KEGG：Kyoto Encyclopedia of Genes and Genomes.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4271035/v1/b5b863863fac758d4aaa3685.png"},{"id":54911562,"identity":"683e4030-6066-4054-afec-326c434fc1e3","added_by":"auto","created_at":"2024-04-18 13:04:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":365417,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInteraction between asthma medication targets and (a) ADA, MMP-1, TNFSF14; (b) CXCL6, CD40L.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4271035/v1/e9606e00f96a20e81f1bbab9.png"},{"id":54911560,"identity":"906642b0-5048-4c7e-a6f0-413d3491b2b9","added_by":"auto","created_at":"2024-04-18 13:04:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":125992,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummary of MR results between identified proteins and corresponding genes and asthma (AOA and COA).\u003c/strong\u003eColour and circle size are scaled based on the MR beta estimates. AOA: adult-onset asthma; COA: childhood-onset asthma；eQTL: expression quantitative trait loci. * means \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4271035/v1/e7626f4f49f40a0f923965aa.png"},{"id":54913463,"identity":"4cca415f-2bab-44c1-859e-7fff75a271fa","added_by":"auto","created_at":"2024-04-18 13:36:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1707939,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4271035/v1/0c7b8901-c266-4204-8a7d-8bb9f91274a4.pdf"},{"id":54911564,"identity":"4fc48fc2-f55f-46cd-8f86-98ec6bfd3b2b","added_by":"auto","created_at":"2024-04-18 13:04:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9422546,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-4271035/v1/a0b6fc7c2f35597a6276f01f.docx"},{"id":54911558,"identity":"afaa5db1-5bed-4146-8d3d-5d56100b8e9c","added_by":"auto","created_at":"2024-04-18 13:04:53","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":68865,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4271035/v1/5540052fe4004fe0762a2db6.xlsx"},{"id":54912123,"identity":"554c419a-0cb4-43e1-8e06-8ab1f00aec4b","added_by":"auto","created_at":"2024-04-18 13:12:53","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":434437,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4271035/v1/cc02894c11f90615a38f98cd.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"The associations of circulating inflammatory-related proteins with asthma: a Mendelian randomization study","fulltext":[{"header":"Background","content":"\u003cp\u003eAsthma is a common and heterogeneous chronic respiratory disease with significant morbidity, mortality, and financial burden worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The incidence of asthma has been increasing and the global prevalence rate reaches 300 million[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Childhood-onset asthma (COA) and adult-onset asthma (AOA) represent distinct phenotypes of asthma[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. AOA is typically more severe and exhibits a lower remission rate compared to COA.\u003c/p\u003e \u003cp\u003eAsthma is characterized by variable airflow limitation, bronchial hyperresponsiveness (BHR), airway inflammation[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Airways inflammation, particularly type 2 inflammation, is the core mechanism of asthma[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The majority of patients experience type 2 inflammation, a systemic allergic response that plays a key role in the pathophysiology of asthma, leading to progressive decline in lung function and exacerbations. Therapeutic strategies targeting inflammatory molecules, such as IL-13, IL-4, IL-33, are currently being evaluated for potential use in asthma treatment[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, targeted therapies are limited by the underlying phenotypic and endotypic variation between patients. Moreover, the causal role of specific inflammatory-related proteins remains unclear, primarily due to the limitations of observational studies (e.g., residual confounding and reverse causality) and the lack of high-quality data from randomised trials.\u003c/p\u003e \u003cp\u003eNowadays, it is widely recognized that genetic component is a crucial contributor to asthma susceptibility[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Recently, a study comparing the genetic architecture of asthma found a greater role for genetic risk factors in COA than in AOA[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Identifying asthma-associated genetic loci, especially those related to inflammatory proteins, enhances our understanding of the molecular mechanisms and key biological pathways in asthma pathogenesis, facilitating the development of personalized therapeutic strategies.\u003c/p\u003e \u003cp\u003eGenome-wide association studies (GWAS) of protein levels have identified genetic variants associated with proteins, referring to as \u0026ldquo;protein quantitative trait loci (pQTLs)\u0026rdquo;[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. pQTLs provide valuable insights into the molecular basis of complex traits and diseases by mediating the relationship between genotype and phenotype[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Meanwhile, proteomic studies offer an opportunity to assess the causality of potential drug candidates on human diseases using Mendelian randomization (MR)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], which has been widely used to infer causal effects between exposures and disease outcomes[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Since genetic variants are randomly assigned at conception before disease onset, MR analysis could overcome reverse causality bias and confounders inherent in observational studies[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Additionally, MR method should satisify three fundamental assumptions, as detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we performed a two-sample MR to estimate the causal relationships between circulating inflammatory-related proteins and asthma, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. We used genetic instrumental variables (IVs) for 91 inflammatory-related proteins from a cohort of 14824 participants[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and derived genetic associations of asthma from Ferreira\u0026rsquo;s study[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To validate the robustness of our findings, we performed reverse causality detection, colocalization analyses, and phenotype scanning. Additionally, to assess the druggability of the identified proteins, we conducted pathway enrichment analysis, protein-protein interaction (PPI) network, and molecular docking. Finally, we verified our results using pQTL data from two published stuides[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and eQTL data from the eQTLGen Consortium[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and GTEx Consortium to strengthen our conclusions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Sources\u003c/h2\u003e \u003cp\u003eThe datasets used in our study were sourced from publicly available summarized GWAS data. For inflammatory-related proteins, we used data from a genome-wide pQTL study of 91 plasma proteins across 12 cohorts, totaling 14824 individuals of European descent[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Asthma data were derived from a study with 40544 cases and 300671 controls of European ancestry, categorized into COA (ages 0\u0026ndash;19 years) and AOA (ages 20\u0026ndash;60 years)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e presents the basic information of these datasets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eInstrument variables selection\u003c/h2\u003e \u003cp\u003eFor each protein, pQTLs from its GWAS data served as genetic instruments (IVs). First, we established \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e as the genome-wide significant threshold to select strongly associated pQTLs with inflammatory-related proteins. Second, to avoid linkage disequilibrium (LD), we clumped these pQTLs (kb\u0026thinsp;=\u0026thinsp;10000, \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.001). To mitigate bias resulting from weak instruments, we calculated \u003cem\u003eF\u003c/em\u003e statistics for each pQTL to assess statistical strengthen, with an \u003cem\u003eF\u003c/em\u003e statistics of at least 10 indicating no weak instrument bias[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Next, we extracted exposure pQTLs from the outcome data and excluded those associated with the outcome (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Finally, we harmonized by aligning the alleles of exposure and outcome pQTLs, discarding palindromic and imcompatible pQTLs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMendelian randomization analysis\u003c/h2\u003e \u003cp\u003eWe employed two-sample MR to estimate the relationships between genetically predicted levels of inflammatory-related proteins and asthma. When only one pQTL was available for a protein, we applied Wald\u0026rsquo;s ratio method. If two or more pQTLs were available, we used the inverse-variance weighting (IVW) method. The odds ratios of the measured outcomes represent the likelihood of increased asthma risk for each additional unit of protein. To address multiple hypothesis testing, we calculated false discovery rate (FDR) adjusted \u003cem\u003eP\u003c/em\u003e values in the MR analyses (q values). A q value less than 0.5 was considered significant[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. All statistical analyses were conducted using R software (version 4.2.3) with TwoSampleMR package (version 0.5.5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eFor the identified significant estimates (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), sensitivity analyses were then performed to assess the robustness and reliability of our primary findings.\u003c/p\u003e \u003cp\u003eTo determine if asthma has any causal effect on the identified inflammatory-related proteins, we selected genetic instruments for asthma from the GWAS data for bidirectional MR analysis. The effect was estimated using MR-IVW, MR-Egger, and weighted median. Results were considered statistically significant at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. In addition, Steiger\u0026rsquo;s filtering was applied to test the directionality of causality between proteins and asthma[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe performed Bayesian co-localization analysis to test whether identified proteins and asthma share the same causal variant using the \u0026ldquo;coloc\u0026rdquo; package[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For each locus, the Bayesian method evaluated support for five exclusive hypotheses: 1) no association with either trait (H\u003csub\u003e1\u003c/sub\u003e); 2) association with trait 1 only (H\u003csub\u003e2\u003c/sub\u003e); 3) association with trait 2 only (H\u003csub\u003e3\u003c/sub\u003e); 4) both traits have a causal SNP, but the SNPs are distinct (H\u003csub\u003e4\u003c/sub\u003e); 5) both traits have a causal SNP, and share the same SNP (H\u003csub\u003e5\u003c/sub\u003e). We focused on hypothesis H\u003csub\u003e5\u003c/sub\u003e, and posterior probability (PP) was used to calculate support for H\u003csub\u003e4\u003c/sub\u003e (PPH\u003csub\u003e4\u003c/sub\u003e). We considered evidence of colocalization strong if PPH\u003csub\u003e4\u003c/sub\u003e was \u0026ge;\u0026thinsp;0.75[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe also conducted phenotype scanning to assess whether the estimates were influenced by potential risk factors[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], including family history, age, gender, body mass index, obesity, smoking, occupational exposure, and respiratory tract infection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePathway enrichment analysis\u003c/h2\u003e \u003cp\u003eWe performed enrichment analysis to identify biological pathways associated with asthma risk loci. We merged all relevant genes to a gene set used for the enriched pathways, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The selected pathways were those significantly enriched with an \u003cem\u003eFDR\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eProtein and protein interaction network\u003c/h2\u003e \u003cp\u003eWe conducted protein-protein interaction (PPI) analysis to investigate the association between identified inflammatory-related proteins and targets for current medictions. We obtained 17 asthma-modifying drugs from a recent review, along with their corresponding drug targets[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The PPI network was constructed using the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], with a minimum required interaction score of 0.4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDruggability evaluation and molecular docking\u003c/h2\u003e \u003cp\u003eTo evaluate the druggability of identified proteins, we searched for a list of druggable genes from a previous study[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], ChEMBL[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and ClinicalTrials (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ClinicalTrials.gov\u003c/span\u003e\u003cspan address=\"https://www.ClinicalTrials.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We identified protein-related drug targets, along with information on drug name and the drug development process.\u003c/p\u003e \u003cp\u003eTo further explore the effect of drug candidates on drug target genes and the druggability of target genes, we performed molecular docking to determine the interaction strength between receptors and ligands. Drug (small molecule) structure data and corresponding IDs were derived from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), while protein (ligand) structure data and corresponding PDB IDs were obtained from the Uniprot database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We converted the SDF format of small molecules to mol2 format using OpenBabel-3.1.1[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Thereafter, we removed water molecules and added polar hydrogen atoms of the protein and small molecule in AutoDockTools 1.5.6[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Grid boxes were utilized to cover all structure domains of the proteins, and subsequently, the molecule docking process was initiated to determine the binding energy of each protein and small molecule. The results of molecular docking were visualized through PyMOL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eReplication analysis\u003c/h2\u003e \u003cp\u003eWe utilized summary data from GWAS on plasma proteins of European ancestry, obtained from studies by Zheng [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] (734 plasma proteins) and Ferkingstad[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] (4907 plasma proteins measured in 35559 participants), for replication in a proteome-wide MR analysis.\u003c/p\u003e \u003cp\u003eAdditionally, we used available expression quantitative trait locis (eQTLs) for drug target genes. The summary-level data for eQTLs were sourced from the eQTLGen Consortium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eqtlgen.org\u003c/span\u003e\u003cspan address=\"https://www.eqtlgen.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and GTEx Consortium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gtexportal.org/home\u003c/span\u003e\u003cspan address=\"https://gtexportal.org/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with details provided in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e. We identified eQTLs significantly associated with the expression of genes corresponding to the identified proteins in blood and lung tissue.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eScreening circulating causal inflammatory-related proteins of asthma\u003c/h2\u003e\n \u003cp\u003eMR analysis identified seven circulating inflammatory-related proteins associated with asthma: adenosine deaminase (ADA), CD40L receptor (CD40L), cystatin D (CST5), interleukin-12 subunit beta (IL-12B), matrix metalloproteinase-1 (MMP-1), C-X-C motif chemokine 6 (CXCL6), and tumor necrosis factor ligand superfamily member 14 (TNFSF14). \u003cem\u003eF\u003c/em\u003e-statistics for all instrument variants were above 30, indicating that weak instrument bias can be minimized in our study \u003cstrong\u003e(Supplementary Table\u0026nbsp;2)\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eResults from IVW-MR revealed suggestive evidence for the association between increased expression of MMP-1 and higher risk of AOA (OR\u0026thinsp;=\u0026thinsp;20.68; 95% CI, 15.47\u0026ndash;27.65; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.89 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;92\u003c/sup\u003e) and COA (OR\u0026thinsp;=\u0026thinsp;18.80; 95% CI, 11.45\u0026ndash;30.88; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.14 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;30\u003c/sup\u003e). Strong evidence was observed between CXCL6 and the risk of COA (OR\u0026thinsp;=\u0026thinsp;12.01; 95% CI, 6.00\u0026ndash;24.03; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.95 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;12\u003c/sup\u003e). Conversely, ADA (OR\u0026thinsp;=\u0026thinsp;0.33; 95% CI, 0.31\u0026ndash;0.36; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.32 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;132\u003c/sup\u003e), CD40L (OR\u0026thinsp;=\u0026thinsp;0.11; 95% CI, 0.08\u0026ndash;0.15; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.14 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;47\u003c/sup\u003e), CST5 (OR\u0026thinsp;=\u0026thinsp;0.08; 95% CI, 0.04\u0026ndash;0.14; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.04 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e), and IL-12B (OR\u0026thinsp;=\u0026thinsp;0.13; 95% CI, 0.10\u0026ndash;0.17; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.54 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;53\u003c/sup\u003e) were associated with a decreased risk of AOA. Additionally, higher genetically predicted levels of ADA (OR\u0026thinsp;=\u0026thinsp;0.33; 95% CI, 0.30\u0026ndash;0.36; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.38 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;87\u003c/sup\u003e), CD40L (OR\u0026thinsp;=\u0026thinsp;0.05; 95% CI, 0.04\u0026ndash;0.07; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.96 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;29\u003c/sup\u003e), CST5 (OR\u0026thinsp;=\u0026thinsp;0.07; 95% CI, 0.04\u0026ndash;0.13; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.51 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;20\u003c/sup\u003e), IL-12B (OR\u0026thinsp;=\u0026thinsp;0.13; 95% CI, 0.11\u0026ndash;0.16; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.29 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;96\u003c/sup\u003e), and TNFSF14 (OR\u0026thinsp;=\u0026thinsp;0.10; 95% CI, 0.02\u0026ndash;0.46; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.00 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) were associated with a lower risk of COA \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cstrong\u003eand Supplementary Table\u0026nbsp;3)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eSensitivity analyses for asthma causal proteins\u003c/h2\u003e\n \u003cp\u003eTo assess the robustness of our results, sensitivity analyses were conducted (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). However, heterogeneity was observed in the Cochran Q statistics among the identified proteins and asthma (\u003cstrong\u003eSupplementary Table\u0026nbsp;3)\u003c/strong\u003e. Despite the detected heterogeneity in our findings, it did not invalidate the MR estimates, as the random-effect IVW was applied in the current study, potentially balancing the pooled heterogeneity.\u003c/p\u003e\n \u003cp\u003eBidirectional MR analysis did not reveal any causal effect of asthma on the levels of the identified proteins. The Steiger directionality confirmed that the genetic associations were consistent with a causal effect of inflammatory-related proteins on asthma, rather than the opposite direction (\u003cstrong\u003eSupplementary Table\u0026nbsp;4 and Supplementary Fig.\u0026nbsp;1\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003eBayesian co-localization strongly suggested that the associations between four of seven proteins (CD40L, CST5, IL-12B, and MMP-1) and asthma were likely due to the same underlying causal variants (PP\u0026thinsp;\u0026ge;\u0026thinsp;0.75) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cstrong\u003eand Supplementary Fig.\u0026nbsp;2\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003eAfter phenotype scanning, IL-12B (rs76428106) was found to be associated with hypothyroidism or myxoedema and treatment with thyroxine product. IL-12B (rs10043720) was linked to Crohn\u0026rsquo;s disease (CD) and ulcerative colitis (UC). CD40L (rs1883832) was linked to chronic hepatitis B infection, rheumatoid arthritis (RA), and Kawasaki disease (KD) (\u003cstrong\u003eSupplementary Table\u0026nbsp;5\u003c/strong\u003e). We cannot entirely exclude the possibility that hypothyroidism[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e], inflammatory bowel disease (IBD)[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e], exposure to hepatitis B[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e], and RA[\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e] have a causal role in the association between inflammatory-related proteins and asthma because these conditions may be potential causes of asthma. We ruled out IL-12B and rs1883832 (CD40L), the causality between CD40L and AOA remained significant (OR\u0026thinsp;=\u0026thinsp;0.06; 95% CI, 0.05\u0026ndash;0.07; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.60\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;252\u003c/sup\u003e, \u003cstrong\u003eSupplementary Table\u0026nbsp;6\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of reverse causality detection, Bayesian co-localization analysis, and phenotype scanning\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUniProt ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBidirectional MR (MR-IVW)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSteiger filtering\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eColocalization PPH4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePreviously reported associations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAOA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP00813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.002 (0.999\u0026ndash;1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassed (1.150\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAOA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD40L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP25942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.999 (0.998\u0026ndash;1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassed (2.566\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic hepatitis B infection, RA, KD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAOA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCST5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP28325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.001 (0.999\u0026ndash;1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassed (1.875\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;22\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAOA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-12B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP29460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.996 (0.993\u0026ndash;0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassed (4.016\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIBD, CD, UC, hypothyroidism, Blood cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAOA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMP-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP03956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.999 (0.997\u0026ndash;1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassed (3.194\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP00813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.001 (1.000-1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassed (1.561\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD40L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP25942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.001 (1.000-1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassed (1.542\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCST5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP28325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.001 (1.000-1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassed (7.849\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCXCL6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP80162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000 (0.999\u0026ndash;1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassed (1.483\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-12B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP29460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.997 (0.995\u0026ndash;0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassed (1.057\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIBD, CD, UC, hypothyroidism, Blood cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMP-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP03956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000 (0.999\u0026ndash;1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassed (7.961\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNFSF14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO43557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.001 (1.000-1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassed (1.896\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonocyte count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eMR-IVW: Mendelian randomization with inverse variance weighted method; PP: posterior probability; RA: rheumatoid arthritis; KD: Kawasaki disease; IBD: inflammatory bowel disease; CD: Crohn\u0026rsquo;s disease; UC: ulcerative colitis; AOA: adult-onset asthma; COA: childhood-onset asthma.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eEnrichment analysis\u003c/h2\u003e\n \u003cp\u003eThe GO analysis of identified potential targets was performed to reveal their biological functions. As depicted in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, the most significant pathways were predominantly enriched in the movement and chemotaxis of cells, and their response to chemotactic factors in the immune system, including leukocyte migration and chemotaxis, leukocyte homeostasis, neutrophil chemotaxis, and lymphocyte migration and chemotaxis. The KEGG results exhibited that the most involved pathways were the IL-17 signaling pathway, primary immunodeficiency, RA, NF-kappa B signaling pathway, and tumor necrosis factor (TNF) signaling pathway.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociation of potential drug targets with current asthma medications\u003c/h2\u003e\n \u003cp\u003eWe explored information on all asthma drugs and their targets (\u003cstrong\u003eSupplementary Table\u0026nbsp;7\u003c/strong\u003e). A connection was identified between the potential therapeutic target and the target protein of current medications in the PPI network. Specifically, MMP-1 and CD40L showed associations with numerous cytokines, the targets of monoclonal antibodies (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). In contrast, no significant association was observed for CST5 (\u003cstrong\u003eSupplementary Fig.\u0026nbsp;3\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eDruggability and clinical‑phase drug for candidate protein targets\u003c/h2\u003e\n \u003cp\u003ePPI analysis indicated potential avenues for asthma drug development. Consequently, we extensively searched for a list of druggable genes, the ChEMBL database, and clinical trial registry website to assess the druggability and drug development of the six candidate proteins. Specifically, ADA-targeted drug PENTOSTATIN is entering phase I/II trials for steroid refractory acute graft versus host disease, leukemia, lymphoma, and kidney cancer. CD40L-targeted drug DAPIROLIZUMAB PEGOL was in phase II trials for systemic lupus erythematosus (SLE). Additionally, CD40L-targeted drug AT-1501 was currently under evaluation in clinical trials for nephropathy, type 1 diabetes mellitus, amyotrophic lateral sclerosis, and kidney transplant, while DAZODALIBEP was in clinical trials for Sjogren\u0026apos;s Syndrome. MMP1-targeted drug MARIMASTAT was currently undergoing evaluation in clinical trials for lung cancer, breast cancer and REBIMASTAT was in trials for HIV-related Kaposi\u0026apos;s Sarcoma, non-small cell lung cancer, and prostate cancer. TNFSF14-targeted BAMINERCEPT was in clinical trials for primary Sj\u0026ouml;gren\u0026apos;s Syndrome, RA, chronic HCV hepatitis C, and secondary progressive multiple sclerosis. Although no ongoing trials were found for CST5 and CXCL6, they are considered potential druggable targets (\u003cstrong\u003eSupplementary Table\u0026nbsp;8\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eMolecular docking analyses of potential targets and candidate drugs\u003c/h2\u003e\n \u003cp\u003eTo assess the affinity of drug candidates for their targets and explore the druggability of the identified proteins, we conducted molecular docking. Since the structures of monoclonal antibodies on the PubChem database were unavailable, we only analyzed the binding energy between small molecule drugs and candidate proteins. We used AutoDock Tools v.1.5.6 to determine the binding sites and interactions of the five drug candidates with ADA and MMP-1, generating docking results for these two proteins with the drugs (\u003cstrong\u003eSupplementary Table\u0026nbsp;9\u003c/strong\u003e). ADA and MMP-1, along with their corresponding small molecule drugs, exhibited binding energies below \u0026minus;\u0026thinsp;5 kcal/mol. The docking results were visualized through PyMOL (\u003cstrong\u003eSupplementary Fig.\u0026nbsp;4\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eExternal validation of potential drug targets for asthma\u003c/h2\u003e\n \u003cp\u003eUsing the same variant and significant variant in different databases to validate our main findings (\u003cstrong\u003eSupplementary Table\u0026nbsp;9\u003c/strong\u003e). Increasing CD40L also decreased the risk of COA and AOA. MMP-1 was shown to potentially increase the risk of COA and AOA. The causal relationship between CST5, CXCL6, and asthma was confirmed in Zheng\u0026rsquo;s study but not validated in Ferkingstad\u0026rsquo;s study. However, the causal relationship between TNFSF14 and asthma contradicts the result of the primary analysis (\u003cstrong\u003eSupplementary Table\u0026nbsp;10\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003eTo test whether the corresponding genes of the six target proteins were associated with asthma, we sought eQTL from the eQTLGen Consortium and GTEx Consortium. We identified that IV(s) for five of the six proteins (excluding CD40L) were significant eQTL in whole blood or lung tissue (\u003cstrong\u003eSupplementary Table\u0026nbsp;11\u003c/strong\u003e). The expression of the ADA and CST5 genes in lung tissue, as well as the expression of the TNFSF14 gene in whole blood, has causal associations with asthma. Furthermore, the direction of their effects was consistent with the causality between their respective protein levels in circulation and asthma. However, the causal associations between MMP-1 gene expression in lung tissue and asthma, as well as between CXCL6 gene expression and COA in blood, exhibit opposite effects compared to our primary results (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cstrong\u003eSupplementary Table\u0026nbsp;12\u003c/strong\u003e). This discrepancy is likely due to the measurement of MMP-1 and CXCL6. The Olink assay only captures the circulating free MMP-1 and CXCL6, while all isoforms of MMP-1 and CXCL6 transcripts are captured by the gene expression measurements in blood and lung tissue.\u003c/p\u003e\n\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we conducted a thorough investigation into the causal associations between 91 circulating inflammatory-related proteins and asthma. We identified five protein markers (ADA, CD40L, CST5, MMP-1, and CXCL6), with CXCL6 being specific to COA, while four were shared among subsets. CD40L, CST5, and MMP-1 were supported by colocalization analyses. Bidirectional MR and Steiger filtering indicated that none of the identified proteins showed reverse causality.\u003c/p\u003e \u003cp\u003ePathway enrichment analysis revealed that the potential identified targets were mostly involved in inflammatory pathways like IL-17, NF-κB, and TNF signaling pathway, all associated with asthma development. A study suggested that IL-17 promotes the development of allergic asthma by enhancing airway dendritic cell (DC) activation, migration, and function[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Inhibition of the NF-κB signaling pathway could alleviate airway inflammation in asthma[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Natalie et al.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] indicated that the elevation TNF levels are linked to clinical phenotypes of asthma, including neutrophilic and severe asthma. In addition, the potential targets mainly participated in immune system dysregulation, such as primary immunodeficiency and RA. This suggests that the identified proteins may play a role in the systemic immune response in asthma.\u003c/p\u003e \u003cp\u003eCD40L, a membrane-bound protein of the TNF superfamily, is crucial for mediating the interaction between antigen-presenting cells (APCs) and lymphocytes[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Its engagement with CD40 plays a protective role in asthma by regulating the balance between Th1 and Th2 cells[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Notably, CD40L levels are significantly decreased in patients with allergic asthma[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which consistent with our results. Our PPI analysis revealed that CD40L interacts with numerous cytokines targeted by monoclonal antibodies used in asthma treatment. Currently, CD40L is undergoing clinical trials for type 1 diabetes mellitus and kidney transplant. In addition, it was externally validated in two plasma proteins GWAS datasets. Therefore, it holds promise as a new druggable target for asthma. However, some studies have produced paradoxical results, suggesting that platelet depletion and CD40L depletion could attenuate asthma progression by inhibiting IL-4, IL-13, and IgE production, as well as leukocyte infiltration[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This controversy may stem from the diverse functions of CD40L across different cell types or phases of the immune response. Further investigation is needed to elucidate the detailed mechanism and biological significance of the CD40/CD40L interaction in asthma.\u003c/p\u003e \u003cp\u003eThe matrix metalloproteinases (MMPs) function in degrading extracellular matrix (ECM) components, including collagen, laminin, fibronectin, and others. MMP-1 is involved in degrading various types of collagen like I, II, III, VI, and X[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and is recognized as contributing to airway inflammation, tissue remodeling, and asthma exacerbation[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Serum MMP-1 levels are elevated in chronic asthma and may distinguish between moderate and severe asthma[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. MMP-1 overexpression has been observed in airway epithelial cells, inflammatory cells, and airway smooth muscle cells in asthma[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], particularly in fatal asthma cases[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Our results support these findings. MMP-1 is currently undergoing clinical trials for lung cancer and breast cancer, among others. Moreover, we have identified three potential drugs targeting MMP-1, providing a therapeutic approach for asthma. Molecular docking has reinforced the reliability of this association through molecular binding. Furthermore, external validation was conducted in independent cohorts.\u003c/p\u003e \u003cp\u003eAdenosine deaminase (ADA) deficiency leads to the accumulation of toxic purine degradation by-products, most potently affecting lymphocytes, resulting in ADA-deficient severe combined immunodeficiency[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], which includes asthma[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Adenosine induces Ca\u003csup\u003e2+\u003c/sup\u003e oscillations and constriction in airway smooth muscle cells, ultimately leading to calcium-induced release. Epithelial injury and airway hyperresponsiveness are hallmarks of asthma. Removal of adenosine by ADA mitigates local epithelial injury and airway contraction, thereby alleviating asthma[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. ADA is in clinical trials for leukemia, lymphoma, and kidney cancer. Additionlly, we conducted molecular docking to further validate the interaction strength between ADA and two small molecule drugs. Our results were also confirmed by eQTL analysis in lung tissue, providing further evidence for the causality between ADA and asthma. The findings suggest that ADA might be a therapeutic target.\u003c/p\u003e \u003cp\u003eAdditional druggable targets identified were CST5 and CXCL6. CST5 (Cystatin D) is a salivary cysteine protease inhibitor that blocks coronavirus replication at its physiologic concentration. Notably, CST5 levels were lower in COVID-19 patients compared to non-COVID-19 individuals[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. We discovered that circulating ADA protein reduces the risk of asthma, which was validated at the genetic level in lung tissue. However, previous studies on CST5 in asthma are limited. Both CST3 and CST5 belong to the cystatin superfamily of protease inhibitors, with CST3 levels found to be higher in asthma compared to controls[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Further research is needed to explore the expression and function of CSTA in asthma.\u003c/p\u003e \u003cp\u003eCXCL6, a member of the CXC chemokine family, serves as a chemotactic agent for neutrophil granulocytes. Verhoeckx[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] proposed that upregulation of CXCL6 could exacerbate the inflammatory progression of asthma. In addition, our PPI analysis revealed that CXCL6 interacts with IL-6 and TNF-α, therapeutic targets of Tocilizumab and Golimumab, respectively, both licensed medications for asthma. Thus, CXCL6 may represent a promising new target for asthma treatment.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered when interpreting our findings. First, our analysis was restricted to individuals of European ancestry, potentially limiting the generalizability of our results to other ancestries. Further stuides involving non-European populations are necessary to broaden the discovery of genetic determinants of asthma. Second, all identified proteins had three or fewer SNPs, limiting alternative MR appproach, heterogeneity tests, and pleiotropy tests. However, the SNPs we selected were strong instruments with \u003cem\u003eF\u003c/em\u003e statistics exceeding 30, indicating minimal weak instrumental variable bias. Third, colocalization analysis did not support ADA, CXCL6 and asthma sharing the same causal SNPs. Nonetheless, this does not invalidate the findings as colocalization methodologies typically exhibit a high false negative rate (around 60%). Finally, non-linear effects, time-dependent effects, or inflammation-environment interactions may exist between some proteins and asthma. Considering the possibility that a protein may influence asthma risk at extremely low or high levels is intriguing. However, detecting such effects in practical clinical settings could be challenging.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study suggests that genetically determined levels of circulating ADA, CD40L, CST5, MMP-1, and CXCL6 are causally associated with asthma. This provides new insights into the etiology of asthma and represents promising drug targets. Further experimental and clinical studies are required to explore the utility and efficacy of these candidates proteins in asthma treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMR Mendelian randomization\u003c/p\u003e\n\u003cp\u003epQTLs Protein quantitative trait locis\u003c/p\u003e\n\u003cp\u003eGWASs Genome-wide association studies\u003c/p\u003e\n\u003cp\u003eCOA Childhood-onset asthma\u003c/p\u003e\n\u003cp\u003eAOA Adult-onset asthma\u003c/p\u003e\n\u003cp\u003ePPI Protein-protein interaction\u003c/p\u003e\n\u003cp\u003eBHR Bronchial hyperresponsiveness\u003c/p\u003e\n\u003cp\u003eIVs Instrumental variables\u003c/p\u003e\n\u003cp\u003eLD Linkage disequilibrium\u003c/p\u003e\n\u003cp\u003eIVW Inverse-variance weighting\u003c/p\u003e\n\u003cp\u003eFDR False discovery rate\u003c/p\u003e\n\u003cp\u003ePP Posterior probability\u003c/p\u003e\n\u003cp\u003eGO Gene Ontology\u003c/p\u003e\n\u003cp\u003eKEGG Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eCD Crohn\u0026rsquo;s disease\u003c/p\u003e\n\u003cp\u003eUC Ulcerative colitis\u003c/p\u003e\n\u003cp\u003eRA Rheumatoid arthritis\u003c/p\u003e\n\u003cp\u003eKD Kawasaki disease\u003c/p\u003e\n\u003cp\u003eIBD Inflammatory bowel disease\u003c/p\u003e\n\u003cp\u003eSLE Systemic lupus erythematosus\u003c/p\u003e\n\u003cp\u003eDC Dendritic cell\u003c/p\u003e\n\u003cp\u003eTNF Tumor necrosis factor\u003c/p\u003e\n\u003cp\u003eAPCs Antigen-presenting cells\u003c/p\u003e\n\u003cp\u003eMMPs Matrix metalloproteinases\u003c/p\u003e\n\u003cp\u003eECM Extracellular matrix\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll studies were conducted in full accordance with the ethical principles out‑\u003c/p\u003e\n\u003cp\u003elined in the Declaration of Helsinki, as revised in 2013, including adherence to \u003c/p\u003e\n\u003cp\u003eprotocols approved by their respective institutional ethics review committees \u003c/p\u003e\n\u003cp\u003eand all participants provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSummay data used for this study can be accessed through the following links: Circulating inflammatory-related proteins, https://www.phpc.cam.ac.uk/ceu/proteins/; Plasma proteins from MRC Integrative Epidemiology Unit, https://www.epigraphdb.org/pqtl/; Plasma proteins from deCODE genetics, https://www.decode.com/summarydata/; Whole blood eQTL data, https://www.eqtlgen.org/cis-eqtls.html; Lung tissue eQTL data, https://www.gtexportal.org/home/; AOA, https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST007799/; COA, https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST007800/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project has received funding from Henan Provincial Science and Technology Research Project (SBGJ202001006).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.X. and Y.S. designed the work and wrote the draft. L.Z, Y.F., and Y.W. analyzed the data for Mendelian randomization analyses. X.Z., X.M., T.G., S.W., X.N., M.C., Y. C., and J.Z. reviewed, made revisions, and approved the article before submission. A.X. is the guarantor of this work, and took responsibility for the design of the work, the acquisition of funding, the administration of the project and the drafting and revising of the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank SCALLOP Consortium, MRC Integrative Epidemiology Unit, deCODE genetics, eQTLGen Consortium, GTEx Consortium, and IEU Open GWAS Project for providing GWAS summary statistics for our analysis. Graphical abstract was created with BioRender.com.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eReddel HK, Bacharier LB, Bateman ED, Brightling CE, Brusselle GG, Buhl R, Cruz AA, Duijts L, Drazen JM, FitzGerald JM et al. 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Int Immunopharmacol. 2006;6(1):1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"asthma, Mendelian randomization, circulating inflammatory-related proteome, drug target","lastPublishedDoi":"10.21203/rs.3.rs-4271035/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4271035/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEpidemiological evidence links inflammation to the etiology and pathophysiology of asthma. To assess the causal relationship between circulating inflammation-related proteins and asthma, we performed a two-sample Mendelian randomization (MR) analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtein quantitative trait locis (pQTLs) were derived from twelve genome-wide association studies (GWASs) cohorts on the circulating inflammation-related proteome. Genetic associations with asthma were obtained from a large-scale GWAS, categorized into childhood-onset asthma (COA) and adult-onset asthma (AOA). Bidirectional MR analysis, Bayesian co-localization, and phenotype scanning were employed to confirm the robustness of MR results. Furthermore, pathway enrichment analysis, protein-protein interaction (PPI) network analysis, and molecule docking were conducted to evaluate the druggability of identified proteins and prioritize potential therapeutic targets. These results were further validated in eQTLGen, GTEx Consortium, and two dependent cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCollectively, elevated MMP-1 and decreased levels of three proteins (ADA, CD40L, CST5) were associated with an increased risk of both COA and AOA. CXCL6 had an adverse effect specifically on COA. These associations were validated in sensitivity analyses. Apart from CST5, the other proteins interacted with therapeutic targets of asthma medications. Furthermore, therapeutic targeting of three proteins (ADA, CD40L, MMP1) is currently under evaluation, while CST5 and CXCL6 are considered druggable. Molecular docking showed excellent binding between drugs and proteins (ADA and MMP-1) with available structural data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study identified five circulating inflammatory-related protein biomarkers associated with asthma and provided novel insights into its etiology. Drugs targeting these proteins are expected to facilitate future prioritization of drug targets for asthma.\u003c/p\u003e","manuscriptTitle":"The associations of circulating inflammatory-related proteins with asthma: a Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-18 13:04:48","doi":"10.21203/rs.3.rs-4271035/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5447cb48-06c7-4890-a7ac-b297ba868bd9","owner":[],"postedDate":"April 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-18T13:04:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-18 13:04:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4271035","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4271035","identity":"rs-4271035","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}