PT  - JOURNAL ARTICLE
AU  - Pedro Milanez-Almeida
AU  - Andrew J. Martins
AU  - Parizad Torabi-Parizi
AU  - Luis M. Franco
AU  - John S. Tsang
AU  - Ronald N. Germain
TI  - Blood gene expression-based prediction of lethality after respiratory infection by influenza A virus in mice
AID  - 10.1101/2020.10.27.357053
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.10.27.357053
4099  - http://biorxiv.org/content/early/2020/10/27/2020.10.27.357053.short
4100  - http://biorxiv.org/content/early/2020/10/27/2020.10.27.357053.full
AB  - Lethality 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.Competing Interest StatementThe authors have declared no competing interest.