Epigenetic rewiring of pathways related to odour perception in immune cells exposed to SARS-CoV-2 in vivo and in vitro

Abstract

A majority of SARS-CoV-2 recoverees develop only mild-to-moderate symptoms, while some remain completely asymptomatic. Although viruses, including SARS-CoV-2, may evade host immune responses by epigenetic mechanisms including DNA methylation, little is known about whether these modifications are important in defence against and healthy recovery from COVID-19 in the host. To this end, epigenome-wide DNA methylation patterns from COVID-19 convalescents were compared to uninfected controls from before and after the pandemic. Peripheral blood mononuclear cell (PBMC) DNA was extracted from uninfected controls, COVID-19 convalescents, and symptom-free individuals with SARS-CoV-2-specific T cell-responses, as well as from PBMCs stimulated in vitro with SARS-CoV-2. Subsequently, the Illumina MethylationEPIC 850K array was performed, and statistical/bioinformatic analyses comprised differential DNA methylation, pathway over-representation, and module identification analyses. Differential DNA methylation patterns distinguished COVID-19 convalescents from uninfected controls, with similar results in an experimental SARS-CoV-2 infection model. A SARS-CoV-2-induced module was identified in vivo, comprising 66 genes of which six (TP53, INS, HSPA4, SP1, ESR1, and FAS) were present in corresponding in vitro analyses. Over-representation analyses revealed involvement in Wnt, muscarinic acetylcholine receptor signalling, and gonadotropin-releasing hormone receptor pathways. Furthermore, numerous differentially methylated and network genes from both settings interacted with the SARS-CoV-2 interactome. Altered DNA methylation patterns of COVID-19 convalescents suggest recovery from mild-to-moderate SARS-CoV-2 infection leaves longstanding epigenetic traces. Both in vitro and in vivo exposure caused epigenetic modulation of pathways thataffect odour perception. Future studies should determine whether this reflects host-induced protective antiviral defense or targeted viral hijacking to evade host defence.

Keywords: DNA methylation; PBMC; SARS-CoV-2; in vitro stimulation; interactome; mild-to-moderate; module identification; network analysis.

Figure 1.

Outline of included participants, experimental procedures as well as statistical and bioinformatic approaches utilized in the present study. CC19 – convalescent COVID-19, Con – non-infected control, DMG – differentially methylated gene, Pre20 – Pre-2020 non-infected control, SFT – symptom-free individuals with SARS-CoV-2-specific T cell response, SMIA – suspension multiplex immunoassay.

Figure 2.

Principal component and differential DNAm analysis of PBMC DNA methylomes in COVID-19 convalescents and uninfected controls. Upon filtering and normalisation, the DNAm data were subjected to PCA. Panel A shows a 3D-PCA plot of principal components (PC)1, PC3 and PC5, where the group means are illustrated as centroids. DMCs were identified comparing CC19s to Cons and Pre20s by computing a linear model on the DNAm data. Panel B illustrates a volcano plot of the CC19 vs. Con + Pre20 DNAm data. The dash-dotted horizontal line represents a nominal p-value cut-off of 0.01, and the vertical lines represent a cut-off in mean methylation difference (MMD) in CC19 vs. Con + Pre20 of > ± 0.2. Panel C shows a heatmap representing an unsupervised hierarchical clustering analysis of individual β values of the 87 identified DMCs in B. The individuals’ antibody status is indicated as a grey-scale (unknown = anonymous Pre20 blood donors, orange).

Figure 3.

Network illustration of SARS-CoV-2-induced module genes from the in vivo comparison. A network module constructed by means of the graph clustering algorithm MCODE with the 54 DMGs from the in vivo setting as input. Nodes (n = 66) represent genes and connecting lines represent high-confidence protein–protein interactions within the network (STRING combined score > 0.7). Combined ranked scores of centrality quantification of degree, betweenness and closeness is visualized as a colour (light orange to dark red) continuum, with dark red nodes constituting the most central parts of the network. Nodes that were also found both when utilising two other module identifying methods (DIAMOnD and WGCNA) and when performing the same analyses on the in vitro data set using MCODE are enclosed with a black line.

Figure 4.

Differential DNAm analyses of PBMCs stimulated in vitro with SARS-CoV-2. Venn diagrams depicting the overlap of DMCs from the SARS-CoV-2 in vitro stimulated PBMCs in pre-2020 non-infected individuals. PBMCs from non-infected pre-2020 individuals (n = 4, collected in 2014–2019 before the start of the pandemic) were stimulated with SARS-CoV-2 in vitro for 48 h (MOI = 0.01) or left unstimulated (non-infected mock). Results from the subsequent 850 K DNA methylation analyses were thereafter performed, by making. intra-individual comparisons of differential DNAm in treated vs. untreated PBMCs. DMCs were defined as a fold change in M-value >|2|. These DMCs were further mapped to their corresponding annotated genes (DMGs, n = 542).