Decoding the air pollutant-psoriasis axis: A multi-layered systems toxicology investigation

Area of Science:

• Environmental Health
• Immunotoxicology
• Computational Biology

Background:

• Epidemiological data links air pollution to immune-mediated diseases like psoriasis.
• Molecular mechanisms connecting air pollution and psoriasis remain largely unknown.
• Traditional toxicology's focus on single agents limits understanding of complex environmental exposures.

Purpose of the Study:

• To investigate the immunotoxic mechanisms by which air pollutants may affect psoriasis.
• To identify potential immune targets and biomarkers associated with air pollution exposure in psoriasis.
• To develop a computational framework for analyzing environmental exposures and immune-mediated diseases.

Main Methods:

• Compiled psoriasis-related and pollutant-associated genes from public databases.
• Performed network toxicology analyses (protein-protein interactions, pathway enrichment).
• Developed machine learning classifiers to identify a diagnostic gene signature and conducted molecular docking simulations.

Main Results:

• Identified 51 overlapping genes enriched in IL-17, JAK-STAT, and cytokine signaling pathways.
• A 12-gene signature showed high diagnostic performance (AUCs 0.911-0.966) across validation datasets.
• Molecular docking revealed toluene's high binding affinity to immune targets AHR, JAK1, and NOS2.

Conclusions:

• An integrative framework combining network toxicology and machine learning elucidates air pollution's immunotoxic effects in psoriasis.
• Identified candidate biomarkers for air pollution-related immune disruption.
• Findings support targeted environmental health interventions for psoriasis prevention and management.