SARS-CoV-2 virus particles
Colorized scanning electron micrograph of a cell (pink) infected with a variant strain of SARS-CoV-2 virus particles (UK B.1.1.7; gold), isolated from a patient sample. Courtesy NIAID

UC San Diego researchers have found that a post-COVID lung disease shares origins with other scarring lung diseases, which may offer a path to effective therapies, according to a study released Wednesday.

Although most people recover relatively quickly from COVID-19, around one-third of survivors experience symptoms weeks and months after the initial infection. However, in the study published in Wednesday’s online issue of eBioMedicine, UCSD scientists studied interstitial lung disease, a form of “long COVID” that consists of a group of chronic pulmonary disorders characterized by inflammation and scarring of the lung.

The researchers said little is currently known about ILD — which can be fatal without a lung transplant in its most severe form. But they found insights into the causes and paths the disease may take.

“Using an artificial intelligence approach, we found that lung fibrosis caused by COVID-19 resembles idiopathic pulmonary fibrosis, the most common and the deadliest form of ILD,” said co-senior study author Dr. Pradipta Ghosh, professor in the departments of Medicine and Cellular and Molecular Medicine at UCSD School of Medicine. “At a fundamental level, both conditions display similar gene expression patterns in the lungs and blood, and dysfunctional processes within alveolar type II cells.”

Those AT2 cells play several roles in pulmonary function, including the production of lung surfactant that keeps lung cells from collapsing after exhalation and regeneration of lung cells after injury.

“The findings are insightful because AT2 cells are known to contain an elegant quality control network that responds to stress, internal or external,” Ghosh said. “Failure of quality control leads to broader organ dysfunction and, in this case, fibrotic remodeling of the lung.”

To conduct the study, Ghosh collaborated with co-author Debashis Sahoo, associate professor in the departments of Computer Science, Engineering and Pediatrics at UCSD for the AI assisted analysis among other aspects.

Ghosh and Sahoo said the approach would help them stay unbiased in navigating the unknowns of an emerging, post-pandemic disease. They analyzed more than 1,000 human lung datasets associated with various lung conditions, specifically looking for gene expression patterns, inflammation signaling and cellular changes. The disease with the closest match: IPF.

IPF affects around 100,000 people in the United States, with 30,000 to 40,000 new cases annually. The condition has a poor prognosis, with an estimated mean survival of 2 to 5 years from time of diagnosis.

City News Service contributed to this article.