Host methylation predicts SARS-CoV-2 infection and clinical outcome

Abstract

Since the onset of the SARS-CoV-2 pandemic, most clinical testing has focused on RT-PCR. Host epigenome manipulation post coronavirus infection 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. We customized Illumina’s Infinium Methylation EPIC 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. 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. 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.

A machine learning approach utilizing DNA methylation as an accurate classifier of COVID-19 disease severity

“…Genome-wide DNA methylation of SARS-CoV-2-infected and -uninfected patients using Illumina Infinium MethylationEPIC profiling array platform from whole blood was publicly available through NCBI Gene Expression Omnibus (GEO). GSE167202 25 (training cohort) consisting of 525 individuals (164 COVID-19 infected, 296 COVID-19 uninfected, and 65 with other Non-COVID-19 infections) were obtained. Participants were classified by COVID-19 severity score (SS) as follows: 0.…”