Integrative multi-omics, machine learning, computational toxicology, and experimental validation reveal PFAS-associated immune–inflammatory signatures in chronic obstructive pulmonary disease

Background Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants increasingly implicated in respiratory disorders. However, the systems-level connections among PFAS-associated molecular perturbations, immune dysregulation, and chronic obstructive pulmonary disease (COPD) remain poorly understood. This study aimed to prioritize candidate links between PFAS-associated target information and COPD-related transcriptional and inflammatory signatures. Methods PFAS-associated target information was integrated with bulk transcriptomic differential expression and weighted gene co-expression network analysis to identify overlapping candidate genes. Protein–protein interaction analysis and 113 machine-learning model combinations were used for candidate prioritization and cross-cohort classification evaluation. CIBERSORT and xCell provided computational immune-cell signatures, with Benjamini–Hochberg correction applied to group comparisons and gene–signature correlations; single-cell transcriptomics provided cell-type expression context. Candidate expression was examined in cigarette-smoke-extract-treated cells, clinical samples, and public human and murine datasets. Molecular docking and molecular dynamics simulations explored candidate PFAS–protein interactions. Results HMOX1, JUNB, CXCL12, and PTGS2 were prioritized as candidate genes overlapping PFAS-associated target information and COPD-related expression patterns. Enrichment and immune-deconvolution analyses suggested associations with inflammatory and immune-related signatures, while single-cell analysis described their distribution across annotated cell types. Exploratory plasma ELISA measurements supported higher levels of the four candidate proteins in participants with COPD, with additional expression support varying by gene and experimental model. Docking and molecular dynamics generated structural hypotheses for PFOA/PFOS–protein interactions, including HMOX1. Conclusions This integrative systems-immunology framework delineates immune–inflammatory signatures at the PFAS–COPD interface and identifies HMOX1, JUNB, CXCL12, and PTGS2 as convergent molecular nodes. The findings do not establish exposure causality but provide a multi-scale basis for future epidemiological and mechanistic studies of how persistent environmental pollutants may contribute to COPD-associated immune and inflammatory alterations.

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Journal
Frontiers in Immunology
Published
2026-11-06
DOI
https://doi.org/10.3389/fimmu.2026.1965629
Primary Topic
Per- and polyfluoroalkyl substances research
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article
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article

Integrative multi-omics, machine learning, computational toxicology, and experimental validation reveal PFAS-associated immune–inflammatory signatures in chronic obstructive pulmonary disease

桂珍珍, Pixin Ran, Guilan Yang, Zhanbing Ma et al.
6 links
Frontiers in Immunology
Per- and polyfluoroalkyl substances research
article

Integrative multi-omics, machine learning, computational toxicology, and experimental validation reveal PFAS-associated immune–inflammatory signatures in chronic obstructive pulmonary disease

桂珍珍, Pixin Ran, Guilan Yang, Zhanbing Ma, Miaomiao Nian, Zhengyu Hu, Hongxia Xin, Jialin Yu, Xia Cao, Yuanlan Yang, Xiuyan Wang, Juan Chen, Jieda Cui
article en

Abstract

Background Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants increasingly implicated in respiratory disorders. However, the systems-level connections among PFAS-associated molecular perturbations, immune dysregulation, and chronic obstructive pulmonary disease (COPD) remain poorly understood. This study aimed to prioritize candidate links between PFAS-associated target information and COPD-related transcriptional and inflammatory signatures. Methods PFAS-associated target information was integrated with bulk transcriptomic differential expression and weighted gene co-expression network analysis to identify overlapping candidate genes. Protein–protein interaction analysis and 113 machine-learning model combinations were used for candidate prioritization and cross-cohort classification evaluation. CIBERSORT and xCell provided computational immune-cell signatures, with Benjamini–Hochberg correction applied to group comparisons and gene–signature correlations; single-cell transcriptomics provided cell-type expression context. Candidate expression was examined in cigarette-smoke-extract-treated cells, clinical samples, and public human and murine datasets. Molecular docking and molecular dynamics simulations explored candidate PFAS–protein interactions. Results HMOX1, JUNB, CXCL12, and PTGS2 were prioritized as candidate genes overlapping PFAS-associated target information and COPD-related expression patterns. Enrichment and immune-deconvolution analyses suggested associations with inflammatory and immune-related signatures, while single-cell analysis described their distribution across annotated cell types. Exploratory plasma ELISA measurements supported higher levels of the four candidate proteins in participants with COPD, with additional expression support varying by gene and experimental model. Docking and molecular dynamics generated structural hypotheses for PFOA/PFOS–protein interactions, including HMOX1. Conclusions This integrative systems-immunology framework delineates immune–inflammatory signatures at the PFAS–COPD interface and identifies HMOX1, JUNB, CXCL12, and PTGS2 as convergent molecular nodes. The findings do not establish exposure causality but provide a multi-scale basis for future epidemiological and mechanistic studies of how persistent environmental pollutants may contribute to COPD-associated immune and inflammatory alterations.

Frontiers in ImmunologyVol. 17
Guangzhou Institutes of Biomedicine and Health (CN), First Affiliated Hospital of Guangzhou Medical University (CN), Ningxia Medical University (CN), Ningxia Medical University General Hospital (CN), State Key Laboratory of Respiratory Disease (CN), Guangzhou Laboratory (CN), Guangzhou Institute of Respiratory Health (CN), Guangzhou Medical University (CN)
Good health and well-being
Openalex Percentile: Top 28%
Per- and polyfluoroalkyl substances research
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