Blood DNA methylation in post-acute sequelae of COVID-19 (PASC): a prospective cohort study

Abstract

Background: DNA methylation integrates environmental signals with transcriptional programs. COVID-19 infection induces changes in the host methylome. While post-acute sequelae of COVID-19 (PASC) is a long-term complication of acute illness, its association with DNA methylation is unknown. No universal blood marker of PASC, superseding single organ dysfunctions, has yet been identified.

Methods: In this single centre prospective cohort study, PASC, post-COVID without PASC, and healthy participants were enrolled to investigate their symptoms association with peripheral blood DNA methylation data generated with state-of-the-art whole genome sequencing. PASC-induced quality-of-life deterioration was scored with a validated instrument, SF-36. Analyses were conducted to identify potential functional roles of differentially methylated loci, and machine learning algorithms were used to resolve PASC severity.

Findings: 103 patients with PASC (22.3% male, 77.7% female), 15 patients with previous COVID-19 infection but no PASC (40.0% male, 60.0% female), and 27 healthy volunteers (48.1% male, 51.9% female) were enrolled. Whole genome methylation sequencing revealed 39 differentially methylated regions (DMRs) specific to PASC, each harbouring an average of 15 consecutive positions, that differentiate patients with PASC from the two control groups. Motif analyses of PASC-regulated DMRs identify binding domains for transcription factors regulating circadian rhythm and others. Some DMRs annotated to protein coding genes were associated with changes of RNA expression. Machine learning support vector algorithm and random forest hierarchical clustering reveal 28 unique differentially methylated positions (DMPs) in the genome discriminating patients with better and worse quality of life.

Interpretation: Blood DNA methylation levels identify PASC, stratify PASC severity, and suggest that DNA motifs are targeted by circadian rhythm-regulating pathways in PASC.

Funding: This project has been funded by the following agencies: NIH-AI173035 (A. Jaitovich and R. Alisch); and NIH-AG066179 (R. Alisch).

Keywords: COVID-19; DNA methylation; Machine learning; PASC; Quality-of-life.

Fig. 1

Prospective cohort design. Consort graph depicting the patients screened (N = 3479), considered (N = 829) and enrolled (N = 103) in this prospective cohort study. Out of the 30 healthy participants considered for enrolment, 27 were enrolled and analysed in the final dataset. 15 patients previously infected with COVID-19 but who had no symptoms of PASC were also enrolled one-year after their initial infection.

Fig. 2

Blood DNA methylation levels are altered in Patients with PASC. a, Global DNA methylation levels in healthy, Post-COVID, and PASC groups. b, Distribution of differentially methylated regions (DMRs) in genomic structures in PASC (N = 103) versus Healthy (N = 27) and PASC (N = 103) versus Post-COVID (N = 15) comparisons. c, Manhattan plot indicating the chromosomes that harbour the DMRs differentiating PASC versus healthy individuals. Significantly changing DMRs with q values less than 0.05 are indicated as solid dots above or below the dotted black lines. d, Manhattan plot indicating the chromosomes that harbour the DMRs differentiating PASC versus Post-COVID, non-PASC. Significantly changing DMRs with q values less than 0.05 are indicated as solid dots above or below the dotted black lines.

Fig. 3

PASC induces specific DNA methylation of transcription factors motifs. a, Comparative analysis was conducted to identify the DMRs that were present in both the PASC versus Healthy and PASC versus Post-COVID comparisons. b, HOMER motif analysis of the 39 overlapping PASC-specific DMRs identified several overrepresented motifs including BMAL1, NPAS, and others. c, Chromosomal distribution of the identified 39 DMRs across the genome shows concentration to certain chromosomes.

Fig. 4

PASC induces specific DNA methylation results in altered gene expression. Quantitative PCR analysis of the 7 protein-coding genes annotated to the 39 PASC-specific DMRs shows some consistent dysregulation of gene products in the leukocyte compartment associated with the altered methylation. Each dot represents an individual sample, and the bar marks the mean value. N = 27 healthy, 15 post-COVID no PASC, and 103 PASC. ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001 as determined by ANOVA.

Fig. 5

PASC-induced quality-of-life deterioration co-varies with blood DNA methylation levels. a, Circos plot shows the genomic distribution of differentially methylated positions (CpGs) across the human genome (outer ring) that are significantly associated with SF-36 as a continuous variable in the regression model. Each chromosome is shown as a different colour. Relative chromosome size is denoted by the arc bar length (inner rings). Hypermethylated CpGs are shown in red, and hypomethylated CpGs are shown in blue. Sex chromosomes were omitted from the analysis. These results indicate that 403 CpGs are differentially methylated in significant association with SF-36 quality-of-life score. The gradient scales below indicate that CpGs that have decreasing methylation levels with decreasing SF-36 are called hypomethylated and CpGs that have increasing methylation levels with decreasing SF-36 are called hypermethylated. See text for details. b, Principal component analysis indicates separation of patients with high (N = 52) and low SF-36 (N = 51), with some overlap between them. c, Heatmap indicating the result of a machine learning support vector algorithm and random forest hierarchical clustering analyses, which discriminated patients with high (N = 52) and low (N = 51) SF-36 scores using only 28 CpGs out of the 403 CpGs originally identified in the regression model.