Dissecting virus-host interplay dynamics from a response regression perspective

Dynamic crosstalk between invading pathogens and host immunity determines the course and outcome of acute viral infections. Well‑coordinated immune responses mediate effective antiviral defence, while immune dysregulation fuels severe disease. However, standardized criteria for identifying antiviral immune dysregulation remain lacking, especially at early stages of immune disturbance. Previously, we developed a response‑regression model to separate viral-load-driven transcriptomic changes from other regulatory signals. In this study, we conceptualize dysregulated antiviral immunity as an independent regulatory input and apply this framework to longitudinal lung transcriptomic data from low‑ and high‑dose influenza A virus-infected mice, identifying functionally distinct gene sets. Some genes track disease progression, while others serve as candidate biomarkers or pathogenic mediators of severe disease. By calibrating host response direction and magnitude relative to viral fluctuations, our method advances the understanding of virus-host interplay dynamics and provides a new criterion to discriminate immune dysregulation from homeostasis.