AbstractLethality after respiratory infection with influenza A virus (IAV) is associated with potent immune activation and lung tissue damage. In a well-controlled animal model of infection, we sought to determine if one could predict lethality using transcriptional information obtained from whole blood early after influenza virus exposure. We started with publicly available transcriptomic data from the lung, which is the primary site of the infection and pathology, to derive a multigene transcriptional signature of death reflective of innate inflammation associated with tissue damage. We refined this affected tissue signature with data from infected mouse and human blood to develop and validate a machine learning model that can robustly predict survival in mice after IAV challenge using data obtained from as little as 10 μl of blood from early time points post infection. Furthermore, in genetically identical, cohoused mice infected with the same viral bolus, the same model can predict the lethality of individual animals but, intriguingly, only within a specific time window that overlapped with the early effector phase of adaptive immunity. These findings raise the possibility of predicting disease outcome in respiratory virus infections with blood transcriptional data and pave the way for translating such approaches to humans.
Text-Based Illness Monitoring for Detection of Novel Influenza A Virus Infections During an Influenza A (H3N2)v Virus Outbreak in Michigan, 2016: Surveillance and Survey
