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

doi:10.21203/rs.3.rs-4271035/v1

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

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.

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, r² = 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 (H₁); 2) association with trait 1 only (H₂); 3) association with trait 2 only (H₃); 4) both traits have a causal SNP, but the SNPs are distinct (H₄); 5) both traits have a causal SNP, and share the same SNP (H₅). We focused on hypothesis H₅, and posterior probability (PP) was used to calculate support for H₄ (PPH₄). We considered evidence of colocalization strong if PPH₄ 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.

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.

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 Ca²⁺ 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.

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.

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

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.

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No competing interests reported.

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

Status:

Version 1

Abstract

Figures

Background

Methods

Data Sources

Instrument variables selection

Mendelian randomization analysis

Sensitivity analysis

Pathway enrichment analysis

Protein and protein interaction network

Druggability evaluation and molecular docking

Replication analysis

Results

Screening circulating causal inflammatory-related proteins of asthma

Sensitivity analyses for asthma causal proteins

Enrichment analysis

Association of potential drug targets with current asthma medications

Druggability and clinical‑phase drug for candidate protein targets

Molecular docking analyses of potential targets and candidate drugs

External validation of potential drug targets for asthma

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1