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<p>Psoriasis is a chronic skin disease influenced by genetic susceptibility and environmental factors. Ochratoxin A (OTA) is a ubiquitous foodborne mycotoxin known for its immunotoxicity, however, its specific role in psoriasis pathogenesis remains underexplored. This study implemented an integrated systems biology framework that combined network toxicology, machine learning, and molecular dynamics to elucidate the mechanisms of OTA-induced psoriasis. By intersecting OTA-associated targets with psoriasis-related genes, we identified 242 potential targets that were significantly enriched in the IL-17 and TNF signaling pathways. We utilized a weighted gene co-expression network analysis combined with nine machine learning algorithms to identify five hub genes based on their high feature importance and diagnostic robustness: <i>PNP</i>,<i> LCN2</i>,<i> HSPE1</i>,<i> TYMP</i>, and <i>CXCR2</i>, with an area under the curve of 0.988 in the training set and 1.00 in the external validation. These hub genes positively correlated with activated dendritic cells and eosinophils, suggesting that they mediate a pro-inflammatory microenvironment. Molecular docking and dynamics simulations demonstrated stable binding affinities (up to − 8.8&#xa0;kcal/mol) between OTA and the corresponding proteins. Our findings establish a mechanistic link whereby OTA directly interacts with key regulatory proteins to drive immune dysregulation, providing novel biomarkers and a theoretical basis for managing OTA-induced psoriasis.</p>

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Environmental toxicant ochratoxin A induces psoriasis based on network toxicology machine learning and molecular docking analyses

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月28日

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  • Jian Hu,
  • Ming Tang,
  • Quan-you Zheng,
  • Shen-ju Liang,
  • Gui-lian Xu,
  • Ke-qin Zhang

摘要

Psoriasis is a chronic skin disease influenced by genetic susceptibility and environmental factors. Ochratoxin A (OTA) is a ubiquitous foodborne mycotoxin known for its immunotoxicity, however, its specific role in psoriasis pathogenesis remains underexplored. This study implemented an integrated systems biology framework that combined network toxicology, machine learning, and molecular dynamics to elucidate the mechanisms of OTA-induced psoriasis. By intersecting OTA-associated targets with psoriasis-related genes, we identified 242 potential targets that were significantly enriched in the IL-17 and TNF signaling pathways. We utilized a weighted gene co-expression network analysis combined with nine machine learning algorithms to identify five hub genes based on their high feature importance and diagnostic robustness: PNP, LCN2, HSPE1, TYMP, and CXCR2, with an area under the curve of 0.988 in the training set and 1.00 in the external validation. These hub genes positively correlated with activated dendritic cells and eosinophils, suggesting that they mediate a pro-inflammatory microenvironment. Molecular docking and dynamics simulations demonstrated stable binding affinities (up to − 8.8 kcal/mol) between OTA and the corresponding proteins. Our findings establish a mechanistic link whereby OTA directly interacts with key regulatory proteins to drive immune dysregulation, providing novel biomarkers and a theoretical basis for managing OTA-induced psoriasis.