Journal: Molecular Systems Biology

Article Title: A methylation clock model of mild SARS‐CoV ‐2 infection provides insight into immune dysregulation

doi: 10.15252/msb.202211361

Figure Lengend Snippet: A Schematic of the SARS‐CoV‐2 study design and alignment of human subjects by infection timing. Examples of 3 subject trajectories are shown arranged by study time (top) and infection pseudotime, aligned by diagnosis (bottom). B Number of DMS or DEG in each pseudotime period vs. preinfection controls (nominal P < 10 −4 ). Numbers were either corrected for cell‐type proportions or uncorrected. See Fig . C Scatter plots of differential methylation at the sites in (B) for asymptomatic ( n = 68) vs. mild ( n = 65) infections. For each differential contrast, we first selected DMS in Fig (all subjects) that were also differentially methylated (FDR < 0.05) within symptomatic or asymptomatic groups. See Fig . D Principal component analysis of the Mid vs. Control DEG or DMS (with FDR < 0.05 and fold change > 1.5 for DEG) at all time periods. Other DMS, are DMS that do not map to a DEG. We note that for gene expression, Post time points are very close to Control, while this is not the case for methylation. Moreover, the pattern is similar for DEG‐associated and other differential probes. E Scatter plots of differential expression (log2 fold change) or methylation (normalized delta beta) at the indicated periods for the DEG and DMS in (D). Performed assays were RNA‐seq and methylation microarray, and the limma method was applied for differential analysis of either dataset.

Article Snippet: We adopted the ChAMP pipeline (Tian et al , ) to process the raw (IDAT) files from Illumina Methylation microarray platform.

Techniques: Infection, Methylation, Expressing, RNA Sequencing Assay, Microarray