Gene expression patterns associated with pandemic viral infections provide a map for defining patients’ immune responses, measuring disease severity, predicting outcomes, and testing therapies – for current and future pandemics
Researchers at the University of California San Diego School of Medicine used an artificial intelligence (AI) algorithm to sift terabytes of gene expression data – which genes are “on” or “off” during infection – to find common patterns in patients with past pandemics to look for viral infections, including SARS, MERS, and swine flu.
The study, which was published in study on June 11, 2021, resulted in two telltale signatures eBiomedicine. One, a set of 166 genes, shows how the human immune system reacts to viral infections. A second set of 20 signature genes predicts the severity of a patient’s disease. For example, the need to be hospitalized or to use a mechanical ventilator. The algorithm’s usefulness has been validated using lung tissue obtained from autopsies of deceased patients with COVID-19 and animal models of the infection.
“These signatures associated with a virus pandemic tell us how a person’s immune system reacts to a viral infection and how severe it could get, and that gives us a map for this and future pandemics,” said Pradipta Ghosh, MD, Professor of Cellular and Moleculars Medicine at the UC San Diego School of Medicine and the Moores Cancer Center.
Ghosh co-led the study with Debashi’s Sahoo, PhD, Assistant Professor of Pediatrics at UC San Diego School of Medicine and Computer Science and Engineering at Jacobs School of Engineering, and Soumita Das, PhD, Associate Professor of Pathology at UC San Diego School of medicine.
During a viral infection, the immune system releases small proteins called cytokines into the blood. These proteins direct immune cells to the site of infection to get rid of the infection. However, sometimes the body releases too many cytokines, creating a runaway immune system that attacks its own healthy tissues. This mishap, known as a cytokine storm, is believed to be one of the reasons why some virus-infected patients, including some with the common flu, succumb to the infection while others do not.
But the nature, extent, and source of deadly cytokine storms, who is most at risk, and how best to treat them have long been unclear.
“When the COVID-19 pandemic started, I wanted to use my computer science background to find something that all viral pandemics have in common – a universal truth that we could use as a guide when trying to understand a new virus,” said Sahoo, ”said. “This coronavirus may be new to us, but the ways our bodies can respond to an infection are limited.”
The data used to test and train the algorithm comes from publicly available sources of patient gene expression data – all of the RNA transcribed from the patient’s genes and detected in tissue or blood samples. Every time a new data set of patients with COVID-19 became available, the team tested them in their model. They saw the same typical gene expression patterns every time.
“In other words, this was a prospective study where participants were enrolled in the study as they developed the disease, and we used the gene signatures we found to break new ground on an entirely new disease,” said Sahoo.
By examining the source and function of these genes in the first distinctive set of genes, the study also revealed the source of cytokine storms: the cells that line the airways of the lungs and white blood cells known as macrophages and T cells. The results also highlighted the consequences of the storm: damage to the same lung airway cells and natural killer cells, a specialized immune cell that kills virus-infected cells.
“We were able to see and show the world that the alveolar cells in our lungs, which are normally designed to allow gas exchange and the oxygenation of our blood, are one of the main sources of the cytokine storm and therefore serve as the eye of the cytokine.” Storm “, said that. “Next, our HUMANOID Center team will model the human lungs in the context of a COVID-19 infection in order to investigate both acute and post-COVID-19 effects.”
The researchers believe the information could also help guide treatment approaches for patients experiencing a cytokine storm by providing cellular targets and benchmarks to measure improvement.
To test their theory, the team treated rodents with either a precursor version of molnupiravir, a drug currently being tested in clinical trials to treat COVID-19 patients, or with SARS-CoV-2 neutralizing antibodies. After exposure to SARS-CoV-2, the lung cells of control-treated rodents showed the pandemic-associated 166 and 20 gene expression signatures. The treated rodents did not, suggesting the treatments were effective in mitigating the cytokine storm.
“It’s not about if, but when the next pandemic will occur,” said Ghosh, who is also director of the Institute for Network Medicine and executive director of the HUMANOID Center of Research Excellence at the UC San Diego School of Medicine. “We are building tools that are not only relevant for today’s pandemic, but also for the next one.”
Co-authors of the study are: Gajanan D. Katkar, Soni Khandelwal, Mahdi Behhoozikhah, Amanraj Claire, Vanessa Castillo, Courtney Tindle, MacKenzie Fuller, Sahar Taheri, Stephen A. Rawlings, Victor Pretorius, David M. Smith, Jason Duran, UC San Diego; Thomas F. Rogers, Scripps Research and UC San Diego; Nathan Beutler, Dennis R. Burton, Scripps Research; Sydney I. Ramirez, La Jolla Institute of Immunology; Laura E. Crotty Alexander, VA San Diego Healthcare System and UC San Diego; Shane Crotty, Jennifer M. Dan, La Jolla Institute for Immunology, and UC San Diego.