Identification of Novel Protein Drug Targets for Respiratory Diseases by Integrating Human Plasma Proteome with Genome

MA Xinqian; NI Wentao

doi:10.11999/JEIT250796

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MA Xinqian, NI Wentao. Identification of Novel Protein Drug Targets for Respiratory Diseases by Integrating Human Plasma Proteome with Genome[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250796

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MA Xinqian, NI Wentao. Identification of Novel Protein Drug Targets for Respiratory Diseases by Integrating Human Plasma Proteome with Genome[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250796

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Identification of Novel Protein Drug Targets for Respiratory Diseases by Integrating Human Plasma Proteome with Genome

doi: 10.11999/JEIT250796 cstr: 32379.14.JEIT250796

MA Xinqian,
NI Wentao^,

Department of Respiratory and Critical Care Medicine, Peking University People’s Hospital, Beijing 100044, China

Funds: The National Natural Science Foundation of China (81903672)

Received Date: 2025-08-26
Accepted Date: 2026-01-12
Rev Recd Date: 2026-01-07

Available Online: 2026-01-30

Abstract

Abstract

Objective Respiratory diseases are a major cause of global morbidity and mortality and place a heavy socioeconomic burden on healthcare systems. Epidemiological data indicate that Chronic Obstructive Pulmonary Disease (COPD), pneumonia, asthma, lung cancer, and tuberculosis are the five most significant pulmonary diseases worldwide. The COronaVIrus Disease 2019 (COVID-19) pandemic has introduced additional challenges for respiratory health and emphasizes the need for new diagnostic and therapeutic strategies. Integrating proteomics with Genome-Wide Association Studies (GWAS) provides a framework for connecting genetic variation to clinical phenotypes. Genetic variants associated with plasma protein levels, known as protein Quantitative Trait Loci (pQTLs), link the genome to complex respiratory phenotypes. This study evaluates the causal effects of druggable proteins on major respiratory diseases through proteome-wide Mendelian Randomization (MR) and colocalization analyses. The aim is to identify causal associations that can guide biomarker development and drug discovery, and to prioritize candidates for therapeutic repurposing. Methods Summary-level data for circulating protein levels are obtained from two large pQTL studies: the deCODE study and the UK Biobank Pharma Proteomics Project (UKB-PPP). Strictly defined cis-pQTLs are selected to ensure robust genetic instruments, yielding 2,918 proteins for downstream analyses. For disease outcomes, large GWAS summary statistics for 27 respiratory phenotypes are collected from previously published studies and international consortia. A two-sample MR design is applied to estimate the effects of plasma proteins on these phenotypes. To reduce confounding driven by Linkage Disequilibrium (LD), Bayesian colocalization analysis is used to assess whether genetic signals for protein levels and respiratory outcomes share a causal variant. The Posterior Probability of hypothesis 4 (PP4) serves as the primary metric, and PP4 > 0.8 is considered strong evidence of shared causality. Summary-data-based Mendelian Randomization (SMR) and the HEterogeneity In Dependent Instruments (HEIDI) test are used to validate the causal associations. Bidirectional MR and the Steiger test are applied to evaluate potential reverse causality. Protein-Protein Interaction (PPI) networks are generated through the STRING database to visualize functional connectivity and biological pathways associated with the causal proteins. Results and Discussions The causal effects of 2 918 plasma proteins on 27 respiratory phenotypes are evaluated (Fig. 1). A total of 694 protein–trait associations meet the Bonferroni-corrected threshold (P<1.7×10^–5) when cis-instrumental variables are used (Fig. 2). The MR-Egger intercept test identifies 94 protein–disease associations with evidence of directional pleiotropy, which are excluded. Colocalization analysis indicates that 29 protein–phenotype associations show high-confidence evidence of a shared causal variant (PP4>0.8), and 39 show medium-level evidence (0.5<PP4<0.8). SMR validation confirms 26 associations (P<1.72×10^–3), and 21 pass the HEIDI test (P>0.05). The findings provide insights into several respiratory diseases. For COPD, five proteins—NRX3A, NRX3B, ERK-1, COMMD1, and PRSS27—are identified as causal. The association between NRXN3 and COPD suggests a genetic connection between nicotine-addiction pathways and chronic lung decline. For asthma, TEF, CASP8, and IL7R show causal evidence, and the robust association between IL7R and asthma suggests that modulation of T-cell homeostasis may provide a therapeutic opportunity. The FUT3_FUT5 complex is uniquely associated with Idiopathic Pulmonary Fibrosis (IPF). CSF3 and LTBP2 are significantly associated with severe COVID-19. For lung cancer, subtype-specific causal proteins are identified, including BTN2A1 for squamous cell lung cancer, BTN1A1 for small cell lung carcinoma, and EHBP1 for lung adenocarcinoma. These findings provide a basis for the development of subtype-specific precision therapies. Conclusions This study identifies 29 plasma proteins with high-confidence causal associations across major respiratory diseases. Using MR and colocalization, a comprehensive map of molecular drivers of respiratory conditions is generated. These findings may support precision medicine strategies. However, the findings are limited by the focus on European populations and potential heterogeneity arising from different proteomic platforms. The associations are based on computational analysis, and further validation in independent cohorts and animal models is needed. Additional experimental studies and clinical trials are required to clarify the pathogenic roles and biological mechanisms of the identified proteins to support therapeutic innovation in respiratory medicine.
- Respiratory diseases,
- Plasma protein,
- Mendelian randomization,
- Biomarker,
- Drug target

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