tetano
Editor, Senior Moderator
Virol J
. 2026 Aug 8;23(1):254.
doi: 10.1186/s12985-026-03272-5.
Zexing Chen # 1 , Yanling Xiang # 2 , Jinyi Zhu 2 , Shixuan Li 2 , Xinhua Wang 2 , Changsheng Deng 3 , Wanyi Huang 4 5
Affiliations Expand
Background: Depression has been associated with increased susceptibility to influenza; however, the underlying mechanisms remain unclear.
Methods: Mendelian randomization (MR) analysis was performed to assess the causal relationship between depression and influenza. Differential expression genes (DEGs) analyses were performed using Gene Expression Omnibus (GEO) datasets related to influenza A virus (IAV) infection and depression. Protein-protein interaction (PPI) analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were employed to explore underlying mechanisms and identify hub genes involved in depression combined with IAV infection. Receiver operating characteristic (ROC) curve validation and external datasets were conducted to validate the key gene. Immune infiltration and correlation analyses, together with single-cell RNA (scRNA) sequencing and virtual knockout (KO) simulation, were used to investigate altered immune cells and their associations with the key gene. Molecular docking and molecular dynamics simulations were performed to screen potential compounds targeting the key gene. Finally, in vitro experiments using IAV-infected Jurkat T cells further validated the roles of the key gene and candidate compound.
Results: MR analysis indicated that depression exerts a detrimental causal effect on influenza susceptibility. 24 overlapping DEGs and 13 hub genes were identified through DEG analysis. PPI, GO and KEGG enrichment analyses revealed these genes were mainly enriched in immune and inflammatory pathways. IL7R was identified as a key gene and was positively correlated with multiple T-cell subsets while negatively correlated with macrophages, as determined by ROC curve analysis, external validation datasets, and immune infiltration analysis. scRNA-seq analysis demonstrated that IAV infection reduced CD4 + and CD8 + T cells infiltration while increasing macrophage-like monocytes abundance and significantly downregulating IL7R expression, particularly in CD4 + T cells. Virtual KO of IL7R led to the upregulation of MT-CYB and MT-CO1 in CD4 + T cells. Molecular docking and molecular dynamics simulations revealed that sanguinarine exhibited the strongest binding affinity for IL7R and maintained stable interaction. In vitro experiments further confirmed that sanguinarine reversed IAV-induced downregulation of IL7R and reduced viral replication.
Conclusion: IL7R may serve as a potential therapeutic target, and sanguinarine represents a promising candidate for the treatment of depression combined with IAV infection through immune modulation.
Keywords: Bioinformatics; Depression; IL7R; Influenza A virus; Sanguinarine.
. 2026 Aug 8;23(1):254.
doi: 10.1186/s12985-026-03272-5.
An integrated bioinformatics analysis reveals that targeted activation of IL7R may reduce influenza A virus infection risk in patients with depression
Zexing Chen # 1 , Yanling Xiang # 2 , Jinyi Zhu 2 , Shixuan Li 2 , Xinhua Wang 2 , Changsheng Deng 3 , Wanyi Huang 4 5
Affiliations Expand
- PMID: 43004330
- PMCID: PMC13749781
- DOI: 10.1186/s12985-026-03272-5
Abstract
Background: Depression has been associated with increased susceptibility to influenza; however, the underlying mechanisms remain unclear.
Methods: Mendelian randomization (MR) analysis was performed to assess the causal relationship between depression and influenza. Differential expression genes (DEGs) analyses were performed using Gene Expression Omnibus (GEO) datasets related to influenza A virus (IAV) infection and depression. Protein-protein interaction (PPI) analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were employed to explore underlying mechanisms and identify hub genes involved in depression combined with IAV infection. Receiver operating characteristic (ROC) curve validation and external datasets were conducted to validate the key gene. Immune infiltration and correlation analyses, together with single-cell RNA (scRNA) sequencing and virtual knockout (KO) simulation, were used to investigate altered immune cells and their associations with the key gene. Molecular docking and molecular dynamics simulations were performed to screen potential compounds targeting the key gene. Finally, in vitro experiments using IAV-infected Jurkat T cells further validated the roles of the key gene and candidate compound.
Results: MR analysis indicated that depression exerts a detrimental causal effect on influenza susceptibility. 24 overlapping DEGs and 13 hub genes were identified through DEG analysis. PPI, GO and KEGG enrichment analyses revealed these genes were mainly enriched in immune and inflammatory pathways. IL7R was identified as a key gene and was positively correlated with multiple T-cell subsets while negatively correlated with macrophages, as determined by ROC curve analysis, external validation datasets, and immune infiltration analysis. scRNA-seq analysis demonstrated that IAV infection reduced CD4 + and CD8 + T cells infiltration while increasing macrophage-like monocytes abundance and significantly downregulating IL7R expression, particularly in CD4 + T cells. Virtual KO of IL7R led to the upregulation of MT-CYB and MT-CO1 in CD4 + T cells. Molecular docking and molecular dynamics simulations revealed that sanguinarine exhibited the strongest binding affinity for IL7R and maintained stable interaction. In vitro experiments further confirmed that sanguinarine reversed IAV-induced downregulation of IL7R and reduced viral replication.
Conclusion: IL7R may serve as a potential therapeutic target, and sanguinarine represents a promising candidate for the treatment of depression combined with IAV infection through immune modulation.
Keywords: Bioinformatics; Depression; IL7R; Influenza A virus; Sanguinarine.