Host methylation predicts SARS-CoV-2 infection and clinical outcome
Iain R. Konigsberg, Bret Barnes, Monica Campbell, Elizabeth Davidson, Yingfei Zhen, Olivia Pallisard, Meher P. Boorgula, Corey Cox, Debmalya Nandy, Souvik Seal, Kristy Crooks, Evan Sticca, Genelle F. Harrison, Andrew Hopkinson, Alexis Vest, Cosby G. Arnold, Michael G. Kahn, David Kao, Brett Peterson, Stephen J. Wicks, Debashis Ghosh, Steve Horvath, Wanding Zhou, Rasika A. Mathias, Paul J. Norman, Rishi Porecha, Ivana V. Yang, Christopher R. Gignoux, Andrew A. Monte, Alem Taye, Kathleen C. Barnes
Abstract
BACKGROUND: suggests that DNA methylation signatures may differentiate patients with SARS-CoV-2 infection from uninfected individuals, and help predict COVID-19 disease severity, even at initial presentation. METHODS: We customized Illumina's Infinium MethylationEPIC array to enhance immune response detection and profiled peripheral blood samples from 164 COVID-19 patients with longitudinal measurements of disease severity and 296 patient controls. RESULTS: Epigenome-wide association analysis revealed 13,033 genome-wide significant methylation sites for case-vs-control status. Genes and pathways involved in interferon signaling and viral response were significantly enriched among differentially methylated sites. We observe highly significant associations at genes previously reported in genetic association studies (e.g. IRF7, OAS1). Using machine learning techniques, models built using sparse regression yielded highly predictive findings: cross-validated best fit AUC was 93.6% for case-vs-control status, and 79.1%, 80.8%, and 84.4% for hospitalization, ICU admission, and progression to death, respectively. CONCLUSIONS: In summary, the strong COVID-19-specific epigenetic signature in peripheral blood driven by key immune-related pathways related to infection status, disease severity, and clinical deterioration provides insights useful for diagnosis and prognosis of patients with viral infections.