Integrating network toxicology, machine learning, and single-cell data to explore the mechanism of sepsis-related
Meng Zhang1, Peiyu Zhang2, Linxiao Wang3
1Department of Emergency, Xijing Hospital, Air Force Military Medical University, Xi'an, Shaanxi 710032, People's Republic of China; School of Medicine, Northwest University, Xian, Shaanxi 710069, People's Republic of China; Department of Interventional Vascular, The Affiliated Hospital of Northwest University, Xi'an, Shaanxi, People's Republic of China; Xi'an Key Laboratory of Metabolic Disease Imaging, The Affiliated Hospital of Northwest University, Xi'an, Shaanxi, People's Republic of China.
Abstract:
Sepsis, a systemic inflammatory response syndrome induced by infection, has seen a rising incidence in recent years, partly attributed to environmental factors. Several studies have suggested that TCDD, a toxic environmental pollutant, may contribute to an elevated risk of sepsis. As a Class I human carcinogen, TCDD disrupts normal vascular and immune system functions-critical risk factors for sepsis onset. However, the key targets and underlying mechanisms by which TCDD influences sepsis remain unclear. Through network toxicology, transcriptomics, machine learning, and Mendelian randomization, the biomarkers PPARG and CASPASE-3 were identified, both of which are significantly associated with oxidative phosphorylation and other signaling pathways. These biomarkers also correlate with γδT cell infiltration in the immune response. Molecular docking studies confirm the stable interaction between TCDD, PPARG, and CASPASE-3, supporting their involvement in sepsis progression. A single-cell dataset was used to analyze the dynamic changes of key immune cells in sepsis pathogenesis, highlighting their connection to PPARG and CASPASE-3. The study conducted rigorous validation and in-depth analysis of the identified biomarkers by incorporating an external validation set and single-cell data, significantly strengthening the reliability of the results. This comprehensive approach offers valuable clues for identifying potential therapeutic targets for sepsis.
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