Combination of multidisciplinary approaches reveals potential causal associations between influenza and immune cells: Single-cell RNA sequencing and Mendelian randomization

Background The relationship between immune cells and influenza is a battle between the host immune system and viral invaders, however, the causality and underlying mechanisms have not been fully elucidated. Methods This study first analysed disability-adjusted life years (DALYs) and mortality of influenza using descriptive epidemiology based on the Global Burden of Disease (GBD) data from 1990 to 2019. Potential causal associations between 731 immune cells and influenza were then explored using univariate Mendelian randomization (UVMR), followed by validation of the cellular subpopulations to which the immune cells identified by UVMR belonged at the single-cell level, and then enrichment analysis has been performed. Finally, we also performed MR of key genes in cellular subpopulations, reverse MR analysis, colocalization analysis, potential drug prediction and molecular docking for genes satisfying causal associations. Results Joinpoint regression trend analysis showed a general downward trend in the change of influenza DALYs rate and mortality rate, and then UVMR results showed a strong association between the immune cell HLA-DR on CD14+ CD16- monocyte and influenza ( P _IVW = 5.47E-05, P _FDR = 0.03). The single-cell sequencing (scRNA-Seq) results verified that the immune cell HLA-DR on CD14+ CD16- monocyte identified by UVMR belonged to the Classical monocytes (CMs) subpopulation. MR analysis of key genes in the cellular subpopulation identified a total of 7 genes as causally associated with influenza, and no reverse causal association was found. The 3 genes were identified as druggable by drug prediction, namely VIM, CTSA and CSF3R. Finally, molecular docking results demonstrated the strong potential of the CSF3R gene as a drug target. Conclusions Our study provides new insights into future prevention and treatment strategies for influenza from epidemiology to genetics to bioinformatic analyses and genomic.