Expanded Studies of a Methylation-Based COVID-19 Classification Model To Predict Severity Of Disease and Its Ability To Differentiate From Other Respiratory Viruses

Authors Brett R. Peterson, Wenyu Zhou, Genelle F. Harrison, Meher Preethi Boorgula, Monica Campbell, Sameer Chavan, Bret Barnes, Rishi Porecha, Rasika A. Mathias, Ivana Yang, Christopher Gignoux, Alem Taye, Andrew Monte, Kathleen C. Barnes

SARS-CoV-2 infection triggers many molecular changes including epigenome patterns, in humans. Previously, we demonstrated that DNA methylation signatures differentiate patients with SARS-CoV-2 infection from uninfected individuals. Methylation Risk Scores (MRS) derived from the differential methylation signatures yielded highly predictive scores to determine the presence of SARS-CoV-2 infection (AUC=93.6%) and measure COVID-19 disease severity (AUC=79.1%-84.4%; Konigsberg et al 2021 Comm. Med.). In the original study, we observed a positive trend towards specificity of the COVID-19 disease signatures in comparison with other viral upper respiratory infections. However, the study was underpowered to determine the utility of the model to create disease classifiers specific to non-SARS-CoV-2 infections.

To expand on this work, we profiled peripheral blood samples from 304 additional patients on the customized Infinium MethylationEPIC array. These patients were either uninfected, infected with SARS-CoV-2, or SARS-CoV-2 negative but infected with other respiratory viruses. Measurements for disease severity, progression (hospitalization, ICU admittance, ventilator use), and vaccination and booster status were extracted from electronic health record data. An epigenome-wide association analysis in this expanded cohort validated genes and pathways that were previously found to be significantly associated with SARS-CoV-2 infection. The previously reported sparse regression based MRS, when tested in this expanded cohort, yielded higher AUCs for case-vs-control status, hospitalization, ICU admission, and progression to death. Additionally, we observed higher predictive scores for other respiratory viruses. In summary, the COVID-19-specific epigenetic signature (in peripheral blood) driven by expression/activation of key immune-related pathways was related to infection status, disease severity, and clinical deterioration. This study provides useful insights for diagnosis and prognosis in patients with COVID-19.