{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T07:16:17Z","timestamp":1788419777251,"version":"build-2803163510"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Air pollution has been associated with the development and exacerbation of atopic dermatitis (AD), but the molecular signatures connecting pollutant-related targets with AD-associated immune dysregulation remain incompletely characterized.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We applied an integrated systems toxicology and transcriptomic framework to prioritize candidate pollutant-related immune signatures in AD. Pollutant-associated targets were intersected with high-confidence AD-related genes, followed by protein\u2013protein interaction analysis, GO\/KEGG enrichment, machine learning, immune infiltration analysis, single-cell transcriptomics,\n                      <jats:italic>in silico<\/jats:italic>\n                      CCL22 perturbation, 1-fluoro-2, 4-dinitrobenzene (DNFB)-induced AD-like mouse validation, and exploratory molecular docking.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Shared pollutant\u2013AD targets were mainly enriched in cytokine activity, chemokine signaling, pattern-recognition receptor activity, IL-17 signaling, cytokine\u2013cytokine receptor interaction, and Toll-like receptor-related inflammatory pathways. A machine learning framework based on 15 algorithms and 175 predictive combinations identified plsRglm + AdaBoost as the optimal model, with an AUC of 0.963 in the training cohort and AUCs of 1.000, 0.909, and 0.966 in three validation cohorts. The model identified a pollutant-prioritized AD signature including CCL22, CCL5, CSF2, F2RL1, HRH4, ICAM1, IFNG, IL10, IL17A, and IL18. CCL22 was upregulated in AD samples and mainly localized to dendritic cells and macrophages.\n                      <jats:italic>In silico<\/jats:italic>\n                      CCL22 perturbation was associated with extracellular matrix and stromal remodeling programs, while DNFB-induced AD-like dermatitis confirmed increased CCL22 expression in lesional skin.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings identify CCL22-associated immune and stromal remodeling signatures as candidate molecular features of air pollution-related AD and generate testable hypotheses for future controlled exposure studies.<\/jats:p>\n                  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