Identification of the immunological parameters that associate with severe influenza disease or recovery in mice

Abstract

Abstract The severity of influenza virus infection is largely determined by the interplay between the virus and the host. Understanding the pathological drivers of influenza A viruses serves to rapidly characterize the host response to newly emerging influenza viruses and derive prognostic signatures of disease severity. We implemented a differential network approach to uncover novel distinctions between the global transcriptional host response to highly pathogenic and low pathogenic H1N1, H7N9, H7N7, and H5N1 influenza A infections in BALB/c mice. We identified crucial mediators of inflammatory, coagulation, and tissue repair responses that discriminate between disease outcomes. It has been demonstrated that these responses are largely impacted by the immune cell influx into the site of infection. We examined the contribution of resident and lung-infiltrating immune cell quantity and activation state to disease phenotypes by implementing digital cell quantification (DCQ), which allowed us to model the in vivo dynamics of immune cells across viral strains based on time and strain-dependent infected lung transcriptional profiles. Through linear regression modeling, we revealed that distinct monocyte, granulocyte, DC, and T cell populations predict disease morbidity. Additionally, we demonstrated that both the kinetics of CD8+ T cell infiltration and the extent of their activation state, serve as a key predictor of immunopathological consequences of influenza infection. Overall, we provide a multidimensional analysis that delineates how innate immune responses relate to aberrant tissue repair responses and drive subsequent host adaptive immune response that control the severity of influenza virus disease in the murine model.