AI predicts how patients with viral infections, including COVID-19, will fare

This image shows specialized lung cells (resembling a pearl necklace) that can trigger a cytokine storm in response to certain viral infections.

Researchers at the University of California San Diego School of Medicine used an artificial intelligence (AI) algorithm to sift through terabytes of gene expression data – which genes are “on” or “off” during infection – to look for shared patterns in patients with past pandemic viral infections, including SARS, MERS and swine flu.

Two revealing signatures emerged from the study, published on June 11, 2021 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 predict the severity of a patient’s disease. For example, the need for hospitalization or the use of a mechanical ventilator. The utility of the algorithm was validated using lung tissue collected during autopsies of deceased patients with COVID-19 and animal models of the infection.

“These signatures associated with a viral pandemic tell us how a person’s immune system responds to a viral infection and how severe it can become, and it gives us a map for this pandemic and the future,” Pradipta Ghosh said. , MD, professor of cellular and molecular medicine at UC San Diego School of Medicine and the Moores Cancer Center.

Ghosh co-led the study with Debashis 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 guide immune cells to the site of infection to help get rid of the infection. Sometimes, however, the body releases too many cytokines, creating an uncontrollable immune system that attacks its own healthy tissue. This incident, known as the cytokine storm, is believed to be one of the reasons some patients infected with a virus, including some with the common flu, succumb to the infection while others do not. do not.

But the nature, extent and source of the deadly cytokine storms, who is most at risk and how it might be best treated has not been clear for a long time.

“When the COVID-19 pandemic started, I wanted to use my computer training to find something that all viral pandemics have in common – a universal truth that we could use as a guide as we try to make sense of one. new virus, ”Sahoo mentioned. “This coronavirus may be new to us, but there are only a limited number of ways our bodies can respond to infection. “

The data used to test and train the algorithm came from publicly available sources of patient gene expression data – all RNAs transcribed from patient genes and detected in tissue or blood samples. Whenever a new dataset of COVID-19 patients became available, the team tested it in their model. They saw the same signature gene expression patterns each time.

“In other words, it was what we call a prospective study, in which participants were enrolled in the study as they developed the disease and we used the genetic signatures that we have. found to navigate the uncharted territory of a completely new disease, ”Sahoo said.

By examining the source and function of these genes in the first set of signature genes, the study also revealed the source of cytokine storms: cells lining the pulmonary airways and white blood cells called macrophages and T lymphocytes. Plus, the findings shed light on the aftermath of the storm: damage to those same cells in the lung airways and natural killer cells, a specialized immune cell that kills cells infected with the virus.

“We could see and show the world that the alveolar cells in our lungs, which are normally designed to allow gas exchange and oxygenation in our blood, are one of the main sources of the cytokine storm and, therefore, serve as an eye for the cytokine. storm, ”Das said. “Next, our team at the HUMANOID Center models the human lungs in the context of COVID-19 infection to examine the acute and post-COVID-19 effects. “

The researchers believe the information could also help guide treatment approaches for patients facing a cytokine storm by providing cellular targets and benchmarks to measure improvement.

To test their theory, the team pretreated rodents with either a precursor version of Molnupiravir, a drug currently being tested in clinical trials for the treatment of COVID-19 patients, or antibodies that neutralize SARS-CoV-2. After exposure to SARS-CoV-2, lung cells from rodents treated with the control showed the expression signatures of 166 and 20 genes associated with the pandemic. The treated rodents did not, suggesting that the treatments were effective in alleviating the cytokine storm.

“It’s not about if, but when the next pandemic emerges,” said Ghosh, who is also director of the Institute for Network Medicine and executive director of the HUMANOID Center of Research Excellence at UC San Diego School. of Medicine. “We are building tools that are relevant not only for today’s pandemic, but for the next one around the corner.”

viral mapping

From a simple blood sample, gene expression models associated with pandemic viral infections could provide clinicians with a map to help define patients’ immune responses, measure disease severity, predict outcomes, and test for disease. therapies.

The co-authors of the study are: Gajanan D. Katkar, Soni Khandelwal, Mahdi Behroozikhah, 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.

Funding for this research came, in part, from the National Institutes for Health (grants R00-CA151673, R01-GM138385, R01-AI141630, CA100768, CA160911, R01DK107585, R01-AI155696, R00RG2628, R00RG2642 and U19-AI142742), UC San Diego Sanford Stem Cell Clinical Center, La Jolla Institute for Immunology Institutional Funds and VA San Diego Healthcare System.

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