Pneumonia is often associated with highly pathogenic respiratory RNA viruses and bacterial infection, posing a tremendous threat to global public health. Identifying unknown host factors driving the pneumonia is crucial for the development of immune intervention strategies. Weighted gene co-expression network analysis (WGCNA) of public transcriptomic data from pneumonia patients and machine learning methods were used to identify genes linked to the disease phenotype. Gene set enrichment analysis (GSEA) was performed to verify the potential molecular pathways associated with ITM2C. Two epithelial cell lines were used for exploring the role of ITM2C. The mRNA and protein levels of ITM2C or NF-κB components were measured by real-time quantitative PCR and western blot. Cell viability and levels of IL-6 and IL-8 were determined using CCK-8 and ELISA kits. WGCNA and machine learning methods identified ITM2C as the gene linked to pneumonia. GSEA showed that ITM2C was enriched in ten pathways, including influenza infection and neutrophil degranulation. Airway epithelial cells upon polyinosinic-polycytidylic acid (poly I:C) exposure exhibited elevated IL-6 and IL-8 levels and ITM2C expression. The overexpression of ITM2C resulted in increased levels of IL-6 and IL-8, whereas the silencing of ITM2C led to decreased levels of IL-6 and IL-8. ITM2C knockdown impaired the activation of the NF-κB pathway. ITM2C was upregulated and promoted inflammatory response in poly I:C-treated airway epithelial cells via activating NF-κB pathway, providing a potentially new regulatory mechanism for inflammatory response.
Zhang et al. (Sat,) studied this question.