Molecular biology reports · 2026
Network toxicology and multi-omics identify potential interactions between between air pollutants and interferon-related signaling in tuberculosis.
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Cai Zihan, Ding Shoupeng, Han Jian, Gao Jinghua
Аннотация
BACKGROUND: Air pollution increases tuberculosis (TB) susceptibility, yet the underlying molecular mechanisms remain elusive.
METHODS: We integrated human genes associated with seven air pollutants with TB-associated genes from public databases. Utilizing network toxicology, we engineered a diagnostic pipeline evaluating 175 machine learning models across transcriptomic datasets to identify a core gene signature. This signature was validated via qPCR in an independent clinical cohort. Molecular docking and in silico single-cell knockout analyses were used to predict pollutant-protein interactions and potential downstream transcriptional perturbations.
RESULTS: We identified 271 intersecting genes enriched in inflammatory and immune-related pathways, including IL-17, TNF, and Toll-like receptor signaling. Machine learning identified a five-gene candidate signature consisting of STAT1, IFIH1, IFIT2, IFIT3, and CYBB. Clinical qRT-PCR further supported their upregulation in TB patients, with individual AUCs ranging from 0.76 to 0.89. Docking simulations predicted that toluene may form hydrophobic interactions with STAT1, IFIT2, and IFIT3. In silico STAT1 perturbation in monocytes predicted transcriptional alterations involving RETN and S100A9, with enrichment in IFN-γ-related pathways.
CONCLUSION: Air pollutants, particularly toluene and benzene, may contribute to TB susceptibility by interacting with interferon-related immune proteins. The identified five-gene signature may represent a potential biomarker panel for TB and warrants further validation in exposure-characterized cohorts.
METHODS: We integrated human genes associated with seven air pollutants with TB-associated genes from public databases. Utilizing network toxicology, we engineered a diagnostic pipeline evaluating 175 machine learning models across transcriptomic datasets to identify a core gene signature. This signature was validated via qPCR in an independent clinical cohort. Molecular docking and in silico single-cell knockout analyses were used to predict pollutant-protein interactions and potential downstream transcriptional perturbations.
RESULTS: We identified 271 intersecting genes enriched in inflammatory and immune-related pathways, including IL-17, TNF, and Toll-like receptor signaling. Machine learning identified a five-gene candidate signature consisting of STAT1, IFIH1, IFIT2, IFIT3, and CYBB. Clinical qRT-PCR further supported their upregulation in TB patients, with individual AUCs ranging from 0.76 to 0.89. Docking simulations predicted that toluene may form hydrophobic interactions with STAT1, IFIT2, and IFIT3. In silico STAT1 perturbation in monocytes predicted transcriptional alterations involving RETN and S100A9, with enrichment in IFN-γ-related pathways.
CONCLUSION: Air pollutants, particularly toluene and benzene, may contribute to TB susceptibility by interacting with interferon-related immune proteins. The identified five-gene signature may represent a potential biomarker panel for TB and warrants further validation in exposure-characterized cohorts.