Pandemic-Associated Genetic Signatures Help Predict Immune Responses to Infections

Researchers at University of California San Diego School of Medicine used an artificial intelligence algorithm to sift through terabytes of gene expression data and to look for shared patterns in patients with past pandemic viral infections, including SARS, MERS and swine flu.

Two telltale signatures emerged from the study, published in eBiomedicine. One, a set of 166 genes, reveals how the human immune system responds to viral infections. A second set of 20 signature genes predicts the severity of a patient's disease. For example, the need to hospitalize or use a mechanical ventilator. The algorithm's utility was validated using lung tissues collected at autopsies from deceased patients with COVID-19 and animal models of the infection. "These viral pandemic-associated signatures tell us how a person's immune system responds to a viral infection and how severe it might get, and that gives us a map for this and future pandemics," said lead researcher Pradipta Ghosh. 

The data used to test and train the algorithm came from publicly available sources of patient gene expression data—all the RNA transcribed from patients' genes and detected in tissue or blood samples. Each time a new set of data from patients with COVID-19 became available, the team tested it in their model. They saw the same signature gene expression patterns every time. By examining the source and function of those genes in the first signature gene set, the study also revealed the source of cytokine storms: the cells lining lung airways and white blood cells known as macrophages and T cells. In addition, the results illuminated the consequences of the storm: damage to those same lung airway cells and natural killer cells, a specialized immune cell that kills virus-infected cells.

"We could see and show the world that the alveolar cells in our lungs that are normally designed to allow gas exchange and oxygenation of our blood, are one of the major sources of the cytokine storm, and hence, serve as the eye of the cytokine storm," co-lead researcher Soumita Das said. "Next, our HUMANOID Center team is modeling human lungs in the context of COVID-19 infection in order to examine both acute and post-COVID-19 effects." The researchers think the information might also help guide treatment approaches for patients experiencing a cytokine storm by providing cellular targets and benchmarks to measure improvement.