Large amounts of publicly available data and heterogeneity between datasets present a unique opportunity to turn the traditional research paradigm in biomedical and translational research. We have developed a computational framework to identify generalizable disease signature for novel diagnostics and prognostics, drug target discovery and drug repurposing. We have repeatedly demonstrated its successful application across a broad range of inflammatory conditions including organ transplant, infectious diseases, autoimmune diseases, cancers, and vaccines. The four pandemic viral outbreaks in the last decade have underscored the need for a generalizable diagnostic and prognostic tests in our pandemic preparedness. Tests that are readily usable in clinical practice, irrespective of novel or re-emerging virus, for distinguishing patients at higher risk of severe outcome from those with mild infection could help to avoid overwhelming healthcare systems worldwide. Using our computational framework, we integrated 4,780 blood transcriptome profiles from patients (<12 months to 73 years) with one of 16 viral infections across 34 independent cohorts from 18 countries, and scRNA-seq profiles of 264,000 immune cells from 71 samples across 3 independent cohorts to identify host response modules associated with severity of viral infection irrespective of virus. Despite the biological, clinical, and technical heterogeneity across these cohorts, we found that a myeloid cell-dominated conserved host immune response to viral infection is associated with severity, and identifies distinct trajectories for mild or severe outcomes in patients with viral infection, irrespective of infecting virus. Analyses of these trajectories showed increased hematopoiesis, myelopoiesis, and myeloid-derived suppressor cells, and reduced NK and T cells are associated with increased severity of viral infections across all cohorts, irrespective of the infecting virus. We found interferon response is decoupled from protective host response module in patients with severe viral infection, but not in those with mild infection. Finally, we defined SoM score using these modules that accurately distinguish patients with mild or moderate viral infections from those with severe outcomes. Together, our findings offer crucial insights into the underlying immune dynamics of severity of viral infection, and identify factors that may influence infection outcomes.
To understand heterogeneity between datasets is a blessing in disguise to accelerate clinical translationa
Learn a computational framework for in silico systems biology analysis or large amounts of data
How publicly-available data can increase reproducibility and find generalizable disease signatures
Identification of conserved host immune response to viral infections including SARS-CoV-2, Ebola, chikungunya, influenza, RSV and HRV
Developing diagnostics and prognostics for predicting severity of viral infection and improve our pandemic preparedness