Methylation patterns of the nasal epigenome of hospitalized SARS-CoV-2 positive patients reveal insights into molecular mechanisms of COVID-19

doi:10.21203/rs.3.rs-5026887/v1

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Methylation patterns of the nasal epigenome of hospitalized SARS-CoV-2 positive patients reveal insights into molecular mechanisms of COVID-19

https://doi.org/10.21203/rs.3.rs-5026887/v1

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Background: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has varied presentations from asymptomatic to death. Efforts to identify factors responsible for differential COVID-19 severity include but are not limited to genome wide association studies (GWAS) and transcriptomic analysis. More recently, variability in host epigenomic profiles have garnered attention, providing links to disease severity. However, whole epigenome analysis of the respiratory tract, the target tissue of SARS-CoV-2, remains ill-defined.

Results: We interrogated the nasal methylome to identify pathophysiologic drivers in COVID-19 severity through whole genome bisulfite sequencing (WGBS) of nasal samples from COVID-19 positive individuals with severe and mild presentation of disease. We noted differential DNA methylation in intergenic regions and low methylated regions (LMRs), demonstrating the importance of distal regulatory elements in COVID-19-induced gene regulation. Additionally, we demonstrated differential methylation of pathways implicated in immune cell recruitment and function, and the inflammatory response. We found significant hypermethylation (suppression) of the FUT4 promoter implicating impaired neutrophil adhesion in severe disease. We also identified hypermethylation of ELF5 binding sites suggesting downregulation of ELF5targets in the nasal cavity as a factor in COVID-19 phenotypic variability.

Conclusions: This study demonstrated DNA methylation as a marker of the immune response to SARS-CoV-2 infection, with enhancer-like elements playing significant roles. These differences in the nasal methylome may contribute to disease severity, or conversely the nasal immune system may respond to severe infection, through differential immune cell recruitment and immune function, and through differential regulation of the inflammatory response.

COVID-19

SARS-CoV-2

methylation

epigenome

Since its emergence in December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in nearly 775 million cases of coronavirus disease 2019 (COVID-19) and over 7 million deaths worldwide [1]. This placed COVID-19 as the third leading cause of death in the United States in 2021 [2]. The symptomatology of this disease is extremely variable, ranging from asymptomatic infection to mild respiratory complaints, to cardiopulmonary failure and death [3]. Mortality associated with COVID-19 has most commonly been attributed to septic shock and multiorgan failure, often secondary to suppurative pneumonia [4]. These wide-reaching and catastrophic outcomes associated with COVID-19 led to global efforts to define the pathophysiology of this disease in hopes of identifying therapeutic targets and pharmacologic interventions to reduce morbidity and mortality. However, many of the causative mechanisms that determine COVID-19 severity remain elusive.

Proposed mechanisms and associations of severe SARS-CoV-2 infections, evaluated mainly through transcriptomic and proteomic analyses, include increased levels of proinflammatory cytokines [5–7], modulations of immune cells including leukocyte exhaustion with depletion of T lymphocytes in particular [5, 8–10], and increased binding affinity of the SARS-CoV-2 spike protein to the host angiotensin 2 (ACE 2) receptor [11–13]. Large genome-wide association studies (GWAS) of common and rare variants have provided additional insight into the biological underpinnings of infection severity, identifying single nucleotide variants (SNV) in or near genes involved in the innate immune response to viral SARS-CoV-2 infections, type I interferon (IFN) immunity, blood group phenotype and viral entry [14–16].

Recently, interindividual differences in epigenetic footprints have been postulated as key drivers in some of the proposed pathways and determinants of differential clinical outcomes between patients [17–22]. DNA methylation, the most studied epigenetic mark, is cell-specific and occurs on cytosine residues in the context of cytosine-guanine dinucleotides (CpG). Generally, methylation (i.e., hypermethylation) of a gene promoter induces a closed chromatin configuration, such that methylation serves as a silencer of gene expression [23]. Conversely, lack of methylation (i.e., hypomethylation) is commonly associated with activation of gene transcription [24]. Though DNA methylation can be dynamic in response to environmental stimuli [25, 26], methylation patterns are typically propagated across cell divisions such that changes in methylation state can result in long-lasting effects on gene expression [23, 27, 28]. In fact, it was recently demonstrated that individuals previously hospitalized with COVID-19 exhibit changes in their methylomes that persist for at least one year after hospital discharge [29].

In the examination of DNA methylation during COVID-19 pathogenesis, the most highly considered contribution to disease pathogenesis has been related to methylation status of the ACE2 promoter region, with relative hypomethylation (i.e., increased gene expression) noted in individuals with severe disease as compared to uninfected controls [30]. Other associations suggesting methylation as a causative factor in COVID-19 severity include differentially methylated regions (DMRs) of interferon-related genes and interferon-effector genes in severe COVID-19 cases which correlate with observations of decreased transcriptional products of antiviral IFN genes [30, 31]. Further, hypomethylation of other inflammatory regulators leading to elevated cytokine/chemokine gene expression has been described [30]. Taken together, these findings indicate that differential methylation patterns impacting host-viral interactions may predispose certain individuals to more severe infection. Additionally, like other RNA viruses, SARS-CoV-2 may induce innate immune dysfunction thereby leading to impairment in host immune defenses.

However, these studies have limitations. Namely, many of the prior epigenome wide association studies (EWAS) examining COVID-19 were carried out in peripheral blood and thereby fail to evaluate the epigenetic modulations taking place in the respiratory tract which is the target of SARS-CoV-2 [30, 31]. Additionally, these investigations restricted analysis to a subset of predefined loci using array-based methods, thereby preventing a comprehensive whole genome approach to methylation analysis [30]. Whole-genome bisulfite sequencing (WGBS) is the gold-standard approach for untargeted DNA methylation interrogations at single base pair resolution.

In this study, we sought to overcome these limitations by applying WGBS to define the global epigenomic landscape of the nasal mucosa as it relates to the host response in severe versus mild cases of SARS-CoV-2 infection utilizing biospecimens collected early in the COVID-19 pandemic, representing primary infections prior to the advent of the vaccination initiative. We demonstrate supporting data of differential antiviral responses and immune cell populations between disease severities. We highlight the importance and interplay between multiple inflammatory mechanisms, including the phosphoinositide 3-kinase/serine‐threonine kinase (PI3K/Akt) pathway, Notch, and nuclear factor kappa B (NF-κB) signaling. We identify differential methylation of FUT4 – an immature neutrophil marker and adhesion molecule— as putative factors in severe COVID-19 pathogenesis. Finally, we expand the understanding of the roles of ELF4 and ELF5 in controlling transcriptional regulation as it relates to COVID-19 severity.

Characterization of the regulatory genomic landscape in nasal mucosa by WGBS

Nasal samples from 61 subjects positive for the alpha or beta variants of SARS-CoV-2 presenting to a single center at the time of symptomatic presentation concerning for COVID-19 (4 severe and 57 mild) from April 8, 2020, through June 8, 2020 were included in the study. Demographic features of hospitalized (defined as severe; n = 4) versus non-hospitalized (defined as mild; n = 57) patients are summarized in Table 1. Of note, all hospitalized subjects required intensive care unit admission. Three of the four hospitalized subjects required supplemental oxygen support with two of the four hospitalized subjects requiring intubation and mechanical ventilation.

Table 1

Demographic features of hospitalized *versus* non-hospitalized COVID-19 positive subjects.
Item	Hospitalized (n = 4)	Non-hospitalized (n = 57)
^aAge (years)	60 (50, 66.75)	37 (27, 51)
^bGender Female Male	1 (25) 3 (75)	25 (44) 32 (56)
^bRace Black White Other	1 (25) 0 (0) 3 (75)	27 (47) 13 (23) 17 (30)
^a Presented as median (IQR) ^b Presented as n (%)

WGBS data was generated at high depth, identifying on average 13.2 million CpGs per sample each at > 10X coverage. Hierarchical clustering was performed on the top 25th percentile most variably methylated regions showing no clustering structure for confounders such as self-reported race and gender (Supplemental Fig. 1). Using the combined WGBS datasets of severe and mild COVID-19 cases, we characterized active regulatory regions in nasal mucosa. Specifically, we performed methylation segmentations to extract unmethylated regions (UMR) and low methylated regions (LMR) which are known to correlate with promoter- and enhancer-like elements, respectively [25]. We identified 19,187 UMRs (average 2,366 bp) with methylation across the regions being < 5% and containing on average 117 CpGs per region. LMRs were, as expected, more abundant identifying 43,924 regions with an intermediate methylation status (5–50%) and more CpG-sparse (8 CpGs per region, average 642 bp). We annotated these regions based on publicly available reference maps of regulatory DNA based on DNase I hypersensitive sites (DHSs) across 16 different cell types [32] and found 99% and 98% of UMRs and LMRs, respectively, overlapped a DHS. Of these annotated regions, the vast majority (98%) of the UMRs overlapped with a DHS detected in multiple cell types. In contrast, LMRs were shown to represent to a larger extent cell-specific regulatory DNA with 27% of LMRs (N = 11,976) overlapping a DHS unique to a specific cell type. Of these 11,976 cell-specific LMRs from our aggregated COVID-19 positive samples (severe and mild disease), we noted 11% and 20% being lymphoid and myeloid regulatory elements, respectively (Supplemental Table 1).

To better characterize the cell types in which differential methylation may be associated with COVID-19 severity, we examined the UMRs and LMRs of severe and mild WGBS datasets separately, as well as WGBS data from nasal samples derived from non-infected individuals. While there were not substantial differences in the cell-type proportions of identified DHSs between severe and mild cases of COVID-19 (Supplemental Table 2), there were notable differences between COVID-19 positive (combined severe and mild cases) and COVID-19 negative individuals, particularly in the case of LMRs (Fig. 1, Supplemental Table 1). Most notably, when comparing COVID-19 positive versus negative individuals, we found that 46% compared to 25% of LMRs overlapped with immune cell regulatory elements (X² = 5189.6, df = 1, p < 2.2 x 10^–16). More specifically, in COVID-19 positive compared to COVID-19 negative individuals, 24% versus 15% of LMRs overlapped with lymphoid cell regulatory elements (X² = 1264.0, df = 1, p < 2.2 x 10^–16), and 28% versus 12% of LMRs overlapped with myeloid cell regulatory elements (X² = 4557.1, df = 1, p < 2.2 x 10^–16). While the same trend was seen for UMRs, the magnitude of effect was strikingly stronger when contrasting LMRs in COVID-19 positive versus COVID-19 negative individuals (Fig. 1).

When narrowing our focus to only those UMRs and LMRs appreciated for a single cell type, we noted similar findings. Our comparisons of cell-type specific UMRs and LMRs were relatively similar between those with severe and mild disease (Supplemental Table 2). However, when comparing the cell-specific UMRs and LMRs in SARS-CoV-2 positive as compared to negative individuals, substantial differences were appreciated in LMR distribution. Specifically, when examining cell-specific UMRs in COVID-19 positive versus negative individuals, 8% and 4% of cell-specific UMRs overlapped with immune regulatory elements (p = 0.064) and 0.6% and 3% overlapped with epithelial regulatory elements (p = 0.032) (Supplemental Table 1). Regarding cell-specific LMRs in COVID-19 positive as compared to negative individuals, 31% and 8% of cell-specific LMRs overlapped immune regulatory elements (p = 0.0037), 11% and 4% overlapped with lymphoid regulatory elements (p = 5.6 x 10^–16), 20% and 3% overlapped with myeloid regulatory elements (p < 2.2 x 10^–16), 2% and 3% overlapped with pulmonary elements (p < 2.2 x 10^–16), and 3% and 11% overlapped with epithelial regulatory elements (p < 2.2 x 10^–16) (Supplemental Table 1).

In all, these results point towards activation of immune cells in nasal mucosa after SARS-CoV-2 infection and indicate that WGBS can capture methylation signatures specific to these cell types. This suggests that our WGBS analysis of the nasal methylome can be used to infer differential regulation of immune-mediated pathways in the setting of severe as compared to mild SARS-CoV-2 infection.

Preponderance of hypomethylated regions in severe COVID-19 patients

To identify DMRs between individuals suffering from severe COVID-19 (i.e. requiring inpatient admission) versus individuals experiencing mild COVID-19 (i.e. remaining outpatient) we performed tiling window analysis using the WGBS data sets and logistic regression models with the self-reported measures of age, race, and gender included as covariates. To evaluate meaningful methylation differences, the top 10,000 DMRs as ranked by q-value (q-value < 1.20 x 10^–15) were selected for further analysis. These DMRs demonstrated a preponderance of hypomethylated regions (n = 7,256) as compared to hypermethylated regions (n = 2,744) in hospitalized versus non-hospitalized subjects (Fig. 2A). Among these regions, 3,599 (49.6%) hypomethylated DMRs fell in intergenic regions as compared to hypermethylated DMRs where only 378 (13.8%) mapped to similar regions (Χ² = 1,064.4, df = 1, p < 2.2 x 10^–16). We then queried genes associated with hypo- and hypermethylated DMRs using Genomic Regions Enrichment of Annotations Tool (GREAT) algorithm [33, 34]. This yielded associations with 487 genes hypomethylated regions and 503 genes in hypermethylated regions (Supplemental Tables 3 and 4). This similarity in gene counts associated with hypo- versus hypermethylated regions despite the substantial difference in DMR suggests interdependency of regulatory elments involved in gene activation upon SARS-CoV-2 infection. Of note, this state of global hypomethylation has been previously appreciated in response to other viral infections [35].

To discern the relevant biologic pathways associated with these differentially methylated genes (DMGs) we performed pathway enrichment analysis. Enrichment of hypomethylated and hypermethylated DMGs in severe versus mild COVID-19 patients were carried out separately. Pathway enrichment analysis using Coronascape [36] was performed on 487 relatively hypomethylated genes with a p-value threshold of ≤ 0.01 (Log₁₀P ≤-2) resulting in 353 Gene Ontology (GO) processes [37, 38], 28 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways [39], 21 Reactome Gene Sets [40], 10 cannonical pathways [41], and 2 CORUM complexes [42] (total 414 enriched pathways) previously associated with SARS-CoV-2 infection (Supplemental Table 5). Of these, associations with p ≤ 10^− 3 were manually evaluated (n = 124) and results associated with immune response were extracted (n = 17) (Fig. 2B, C). Themes emerging from these relatively hypomethylated immune-related pathways in severe compared to mild COVID-19 cases included processes related to Th1, Th2 and Th17 cell differentiation, regulation of T lymphocytes and hematopoietic cell lines, regulation of the Notch signaling pathway, cytokine production and chemotaxis, and regulation of macrophages and phagocytosis (Fig. 2B). In the similar analysis of relatively hypermethylated DMGs in severe versus mild COVID-19 cases, 423 GO Biological processes [37, 38], 40 KEGG pathways [39], 57 Reactome Gene Sets [40], and 12 cannonical pathways [41] (total 532 enriched pathways) met the p-value threshold of ≤ 0.01 (LogP ≤-2), while 181 had an associated p-value of ≤ 10^− 3 (Supplemental Table 6). Of these 181 enriched pathways, 36 were identified as being associated with the immune response (Fig. 2C). Emerging themes of these enriched immune-related pathways associated with hypermethylated genes in severe versus mild COVID-19 patients included processes associated with cell activation, proliferation, and differentiation including hematopoietic cells from myeloid (e.g., neutrophils) and lymphoid (e.g., T cells) populations, leukocyte migration and adhesion, neutrophil degranulation, adaptive and leukocyte mediated immunity, cytokine signaling, and host stress responses (Fig. 2C). These cumulative findings support previous reports of aberrancies in immune response in the face of mild/moderate as opposed to severe COVID-19 [43–45].

Differentially methylated genes in severe COVID-19 overlap genetic loci

To add validity to our identified differentially methylated gene list in individuals with severe as compared to mild SARS-CoV-2 infection, we used data from a recent GWAS meta-analysis published by the COVID-19 Host Genetics Initiative [46] reporting 23 genetic loci associated with SARS-CoV-2 infection susceptibility and/or COVID-19 disease severity. Within our dataset, we identified differential methylation of eight genes that overlapped with those implicated in this previously published meta-analysis (Table 2). Within our dataset, we found that relative hypermethylation of ABO was present in severely as compared to mildly affected individuals. In severe as compared to mild cases, we noted relative hypomethylation of FDPS, CEP97, HLA-DPA1, OBP2B, MUC5B, PPP1R15A, and NAPSA.

Table 2

DMGs overlapping with known loci associated with COVID-19 risk.
Gene	Chromosome	Start	Stop	Methylation Difference (%)	q-value
FDPS	chr1	155306501	155307000	-12.73	3.32 x 10^–19
CEP97	chr3	101727251	101727750	-19.01	4.03 x 10^–16
HLA-DPA1	chr6	33080751	33081250	-20.08	1.07 x 10^–16
OBP2B	chr9	133211251	133211750	-15.64	8.28 x 10^–16
ABO	chr9	133275751	133276250	13.43	2.14 x 10^–29
MUC5B	chr11	1225751	1226250	-23.41	1.11 x 10–¹⁶
PPP1R15A	chr19	48871251	48871750	-12.33	7.39 x 10^–23
NAPSA	chr19	50369501	50370000	-35.15	3.38 x 10–⁴³

Differential methylation of genes within the PI3K/Akt pathway and COVID-19 severity

Upon closer manual evaluation of genes implicated in various enriched GO terms, KEGG pathways, and Reactome gene sets, we noted differential methylation between severe and mild COVID-19 individuals in many genes involved in the PI3K/Akt pathway (Fig. 3). Specifically, in the severe cohort compared to those with mild COVID-19, we noted relative hypomethylation of promoters whose genes have interplay with the PI3K/Akt pathway and SARS-CoV-2 infection, including genes within the NF-κB (e.g., NFKBIA) and Notch (e.g., NOTCH1) signaling pathways (Fig. 3A). Genes mapping to the Notch signaling pathway were similarly appreciated as a significantly hypomethylated GO term in our enrichment analysis of severe as compared to mild COVID-19 patients (Fig. 2B).

Comparing severe versus mild SARS-CoV-2 infection, DMGs included relative hypomethylation of the AKT1 promoter (chr14: 104796001–104796500, methylation difference = -18.38%, q = 2.22 x 10^–22) and of its downstream target, the type I interferon signaling molecule, ISG15 (chr1: 1013751–1014250, methylation difference = -32.27%, q = 2.49 x 10^–35) (Fig. 3B, C). Further support implicating the PI3K/Akt pathway in the severe COVID-19 phenotype are the relative hypomethylation of ZEB2 (chr2: 144524251–144524750, methylation difference = -24.39%, q = 2.52 x 10^–32) and SNAI1(chr20: 49983251–49983750, methylation difference = -28.98%, q = 6.11 x 10^–16) promoters in severe versus mild disease. Coordinates and methylation difference of DMRs in genes associated with the PI3k/Akt pathway from our dataset are summarized in Supplemental Table 7.

In order to validate our findings indicating relative upregulation of the PI3K/Akt pathway in severe as compared to mild cases, we utilized the publicly available single cell RNA sequencing (scRNA-seq) data from Chua et al. accessed through the UCSC Cell Browser (https://covid-airways.cells.ucsc.edu) [45, 47]. In the dataset of Chua et al. we were able to examine scRNA-seq data derived from nasopharyngeal samples of individuals with critical cases of COVID-19, moderate cases, and control subjects (Fig. 4A). These data similarly showed greater expression of AKT1, TLR5, and MARK4 in secretory and ciliated cells of individuals with moderate and severe COVID-19 as compared to healthy controls. ZEB2, NOTCH1, and ITGB2, showed increased expression in neutrophils and non-resident macrophage populations of individuals with severe COVID-19. IRF7, NFKBIA, and ISG15 showed upregulation in secretory, ciliated, and squamous cells, as well as in cytotoxic T lymphocytes, T regulatory cells, non-resident macrophages, and neutrophils of severe/moderate cases compared to healthy controls (Fig. 4, Supplemental Fig. 2). These comparative data further strengthen our findings suggesting differential immune cell expression of PI3K/Akt-related genes in the nasopharynx of individuals with severe COVID-19 disease.

Hypermethylation of immune cell surface markers in severe cases of COVID-19

Differential expression of various immune cell surface makers have been noted in the setting of severe SARS-CoV-2 infections, including CD15 (the gene product of FUT4) and CD8 [8, 43, 48–55]. In examination of the most significant differentially methylated bins in severe versus mild COVID-19 cases, we found the FUT4 promoter to be relatively hypermethylated over a long region in hospitalized as compared to non-hospitalized subjects (chr11: 94545001–94552500, q = 2.90 x 10^–112) (Fig. 5A). To further evaluate this finding, we visualized this region using the UCSC Genome Browser [56] comparing severe to mildly infected individuals, in addition to pooled samples from healthy controls (n = 7) and found that this relatively hypermethylated state of the FUT4 promoter among severe COVID-19 patients persisted across comparison groups (Fig. 5A). CD15, the gene product of FUT4, is predominately expressed in myeloid cells, as is shown in Fig. 5B, generated using the dataset of Monaco et al. [57] and the Human Protein Atlas (proteinatlas.org) [58]. We additionally noted significant hypermethylation of the CD8A promoter (chr2: 86809001–86809750, q = 1.50 x 10^–21). Cumulatively, these data provide evidence that myeloid cell dysfunction is involved in COVID-19 severity, and that differential regulation of cell surface genes (e.g., FUT4, CD8A) potentially play a contributory roles in disease pathogenesis.

Enrichment of ELF4 and ELF5 transcriptional motifs in hypermethylated regions

Table 3: Most significantly enriched transcription factor binding motifs of hypomethylated regions in hospitalized versus non-hospitalized COVID-19 patients.

^aFold change represented as % Targeted Sequences/% Background sequences

To better understand the regulatory pathways involved in COVID-19 pathogenesis and severity, we examined transcription factor binding sites among DMRs in severe versus mild COVID-19 samples. The top 25,000 hypo- and hypermethylated bins as determined by q-value between severe and mildly infected individuals were evaluated separately using HOMER motif analysis (Table 3, 4) [59]. Evaluation of hypermethylated regions revealed targets of ELF4 (p = 1 x 10^–59, target sequences with motif = 11.34%, background sequences with motif = 8.33%) and ELF5 (p = 1 x 10^–54, target sequences with motif = 8.84%, background sequences with motif = 6.31%) as among the most significantly enriched motifs (Table 4).

Table 4: Most significantly enriched transcription factor binding motifs of hypermethylated regions in hospitalized versus non-hospitalized COVID-19 patients.

^aFold change represented as % Targeted Sequences/% Background sequences

Among these relatively hypermethylated DMRs in hospitalized as compared to non-hospitalized individuals, 2,829 were found to be targets of ELF4. More specifically, when annotating these regions based on DHSs [32], we found that 11.2% (n = 317) of these ELF4 targets overlapped signatures of myeloid cells as compared to non-ELF4 targets, of which 8.6% (n = 1,906) overlapped with myeloid signatures (X² = 20.75, df = 1, p = 5.23 x 10^− 6). Regarding lymphoid signatures, we found that the percentage of ELF4 and non-ELF4 targets overlapping with lymphoid cell DHSs were roughly equivalent, 9.9% (n = 280) and 9.1% (n = 2,024), respectively (X² = 1.68, df = 1, p = 0.19). These findings are in keeping with the known preferential upregulation of ELF4 within myeloid cell lines (Supplemental Fig. 3) as well as its known role host antiviral response [60]. The distribution of ELF4 transcription factor binding motifs across cell types is summarized in Supplemental Fig. 4).

We identified 2,199 relatively hypermethylated DMRs as targets of ELF5 in severe as compared to mild COVID-19 individuals. Additionally, we noted relative hypermethylation (i.e., downregulation) of the ELF5 promoter in COVID-19 positive individuals (severe or mild) as compared to COVID-19 negative (n = 7) patients (Fig. 6). Recently, Pietzner et al. [61]suggested several genes to be potentially regulated or co-expressed with ELF5, of which 23 were also identified among the within our dataset as being relatively hypermethylated targets of ELF5 in individuals with severe as compared to mild COVID-19 (Supplemental Table 8). Notably, we found that C1orf116 (chr1: 207031251–207031750, q = 7.85 x 10^− 7), PLAC8 (chr4: 83128251–83128750, q = 4.43 x 10^− 10), and IFRD1 (chr7: 112421501–112422000, q = 8.15 x 10^− 9) were among these relatively hypermethylated ELF5 targets in severe as compared to mild COVID-19, all of which have been implicated in the host response to SARS-CoV-2 infection [62–64].

In our exploration of the nasal epigenome, we highlight differential methylation status as a key correlate of COVID-19 severity. Importantly, our demonstration of increased proportions of the LMR overlapping with immune cell regulatory elements in COVID-19 positive individuals, along with the proponderence hypomethylated DMRs in intergenic regions among individuals with severe disease shed new light to the importance of enhancer-like regions and distal regulatory elements in the immune response to SARS-CoV-2 and the development of severe COVID-19.

As we explore our findings individually, our pathway enrichment analysis suggests the presence of abberancies in immune response in the face of severe as opposed to mild COVID-19, which is in keeping with prior studies [43–45]. In the comparison of severe versus mild COVID-19, we appreciate differential activation of Th17, Th1, and Th2 as well as T cell selection (hypomethylated), but also note downregulation (hypermethylation) in leukocyte activation, neutrophil degranulation and negative regulation of macrophage differentiation. These findings suggest differential activation of T cell subsets is occurring in severe as compared to mild cases of COVID-19 and indicate impairment or dysregulation in host innate immunity in those individuals who go on to develop more severe phenotypes.

Looking at the specific immune cell lineages and functions that may be dysregulated regulated in severe versus mild disease, our findings show differential methylation of genes impacting innate and adaptive immune cell functions between disease severities. We note the substantial hypermethylation of the FUT4 promoter in individuals with severe as compared to mild SARS-CoV-2 infection. FUT4 has previously been highlighted as a marker of immature neutrophil or “proneutrophil” populations in the examination of peripheral blood of patients with severe COVID-19 [43, 44, 52]. In contrast to our finding of striking hypermethylation of the FUT4 promoter, suggesting decreased gene expression in those with severe COVID-19, prior studies of SARS-CoV-2 infection demonstrated circulating FUT4⁺ neutrophils to be present in greater abundance in individuals with severe COVID-19 when compared to healthy controls and patients with mild COVID-19 cases [43, 44, 54, 55]. However, more recently and in keeping with our data, Karawajczyk et al. showed decreased neutrophilic expression of the cell surface marker and gene product of FUT4, CD15, in severe COVID-19 patients [50]. They focused on the functional role of CD15 as an adhesion molecule, noting the simultaneous lack of upregulation of other adhesion molecules in the presence of severe infection. The relative hypermethylation of the FUT4 promoter in severe disease in our dataset, taken together with the significantly enriched GO terms involved in leukocyte migration, adhesion, and tethering among hypermethylated genes implicate impaired localization of leukocytes as a potential pathophysiologic player in severe COVID-19. Building on this further, our data indicate that in a broader sense, neutrophil dysregulation may be playing a role in COVID-19 severity. This is supported by our finding of relative hypermethylation of genes implicated in neutrophil degranulation (e.g., ELANE, AZU1, CD59, GSDMD, SERPINB1) in severe COVID-19.

We also identified relative hypermethylation of CD8A in individuals with severe as compared to mild COVID-19. Throughout the COVID-19 pandemic, data has accumulated demonstrating the importance of CD8⁺ T cells in the antiviral response to SARS-CoV-2 [8, 49, 53, 65–67]. Robust populations of SARS-CoV-2-specific CD8⁺ T cells are associated with mild COVID-19 presentations [53], whereas CD8⁺ T cell depletion and exhaustion have been shown to correlate with worsened disease severity and increased mortality [8, 49, 67]. It is possible that hypermethylation of genes responsible for the CD8 antigen (e.g., CD8A) contribute to the decreased expression of CD8⁺ T cells and is among the reasons behind the CD8⁺ cytopenia observed in severe COVID-19 cases.

In addition to differential immune cell line regulation between COVID-19 severities, we also appreciated differential methylation of genes related to the inflammatory and cytokine responses. For example, relative hypomethylation of PPP1R15A in individuals with severe COVID-19. PPP1R15A is a stress-response gene. Prior studies indicate increased levels of PPP1R15A expression is present within immune cells of severely affected COVID-19 patients [18, 68]. More specifically, PPP1R15A has been reported to have highest expression in immune cells containing the highest levels of viral RNA [68]. It is thought that this increased expression of PPP1R15A may contribute to COVID-19 severity through the induction of proinflammatory cytokines and by enhancing the survival and multiplication of infected cells [18].

Among the most important differentially methylated pathways between severe and mild disease in our dataset was the differential methylation within the PI3K/Akt signaling pathway. The PI3K/Akt pathway is critical to regulation of IFN signaling and IFN effector genes as part of the host’s antiviral response. Increasingly, studies have demonstrated delayed or decreased type I IFN (IFN-I) and type III IFN (IFN-III) responses in severe COVID-19 cases [14, 69–72]. We appreciated hypomethylation of the interferon-stimulated gene, ISG15, in individuals with severe as compared to mild COVID-19, which is consistent with findings of the single cell profiling of airway cells in the COVID-19 Cell Atlas demonstrating ISG15 as among the top three most differentially expressed genes between severe COVID-19 disease and non-severe cases (https://www.covid19cellatlas.org/meyer21_airway/) [73]. ISG15 was suggested by Munnur et al. to be involved in COVID-19 pathogenesis at multiple levels: SARS-CoV-2 stimulates the release of intracellular interferon-stimulated gene 15 (ISG15) from infected macrophages, and extracellular ISG15 acts to exaggerate the cytokine/chemokine inflammatory response [74]. Interestingly, we found within our data that some IFN-I genes were downregulated in severe as compared to mild COVID-19 cases (i.e., IRF1), however other positive regulators of the IFN-I responses (e.g., IRF3, IRF7, IRF8) were hypomethylated indicative of increased expression. We also noted relative hypermethylation of ELF4 targets among individuals with severe disease. ELF4 has a crucial role in the host antiviral response including contributing to NK cell development and function, regulating cell cycle arrest in naïve CD8⁺ cells in the face of viral infection, and driving IFN-I responses [60, 75, 76]. Cumulatively, these findings emphasize that disordered regulation of the IFN response may be related to a more severe COVID-19 phenotype.

Even prior to the SARS-CoV-2 pandemic, the PI3K/Akt/mTOR pathway was recognized as crucial to the pathogenesis of other coronaviruses, namely the related Middle East respiratory syndrome coronavirus (MERS-CoV) in which in vitro studies demonstrated inhibition of this pathway could block viral proliferation [77, 78]. Building on this historical role of PI3K/Akt in RNA coronaviruses, PI3K/Akt signaling has been implicated in SARS-CoV-2 pathogenesis in multiple organ systems and studies have demonstrated inhibition of SARS-CoV-2 replication in response to PI3K/Akt/mTOR blockade [77, 79, 80].

A possible explanation for the differential immune cell recruitment and inflammatory pathway activation seen in our dataset could be related to the concept of immune tolerance. Immune tolerance typically refers to an immune cell’s inability to activate gene transcription and perform its function in response to restimulation by a previously encountered antigen [81]. However, it has also been demonstrated that exposure to one pathogen can induce tolerance of the immune response to an unrelated pathogen (i.e., “heterologous immune tolerance”) [82]. Regardless of the primary exposure, immune tolerance leads to a less effective response to secondary stimuli. The subjects of our current study were evaluated prior to the advent of the SARS-CoV-2 vaccination initiative; however it is possible that remote exposures to viruses with homologous antigens to SARS-CoV-2, including the seasonal human cornoaviruses (HCoVs) that are most typically associated with mild respiratory disease, may have induced this tolerance to the SARS-CoV-2 virus. In this way, it is possible that methylation status of the immune regulatory genes could have been altered in response to remote exposures and modulated the host’s COVID-19 severity risk.

We found a relative degree of hypermethylation of ELF5 binding sites among individuals with severe as compared to mild COVID-19. Pietzner et al. recently demonstrated increased ELF5 expression within respiratory epithelial cells as a risk factor for severe COVID-19 [61]. However, they also noted substantially diminished ELF5 expression in the injured olfactory mucosa in indivduals who experienced rapid death secondary to severe COVID-19 as compared to healthy controls. This finding of decreased ELF5 expression in the olfactory mucosa is in keeping with our noted hypermethylation of ELF5 targets in these nasal mucosal swabs of individuals with severe COVID-19.

Within our dataset, relatively hypermethylated targets of ELF5 in individuals with severe as compared to mild COVID-19 included C1orf116 and PLAC8 (mediators of viral entry), and IFRD1 (an interferon-stimulated gene). Interestingly and in contrast to our data, COVID-19 studies using bronchoalveolar lavage samples, cell cultures, and in silico models, C1orf116, PLAC8, and IFRD1 have been overexpressed in severe cases [62–64]. It is possible that these differences arise due to differential cell populations of study (i.e., nasal mucosa as compared to the more distal respiratory tract). The nasopharynx represents the initial interface between host and the SARS-CoV-2 virus, and as such, the nasal mucosa plays a critical role in inducing early innate and acquired immune responses [83]. It is possible that hypermethylation of these ELF5 targets in the nasal mucosa contribute to impairments in early encounters between host and virus. This could result in delayed local and systemic responses, thereby providing the virus opportunity to infect distal airways before meeting host defenses.

Our study does have some limitations. We have a relatively small sample size, particularly with regard to our severe COVID-19 cohort. In this way, we may have been limited in our ability to identify significant associations of methylation and disease severity, as these may have been masked by interindividual variability within the severely affected group. As these samples were collected at the time of diagnosis, it is difficult to discern whether the methylation differences we appreciated were the cause of severe versus mild disease outcomes, or if these differences were the result of having severe as compared to mild disease. Finally, though we can extrapolate patterns of gene expression based on our knowledge of methylation and based on the transcription analyses of others, we do not have direct measures of gene expression for the individuals within our dataset.

This whole genome interrogation of the nasal methylome suggests that methylation is linked to the host immune response to SARS-CoV-2 infection and appears contributory to the severity of COVID-19. Differences in the nasal methylome between individuals with severe as compared to mild COVID-19 appear to modulate innate immunity through disruptions in neutrophil adhesion, localization, and degranulation. In the adaptive immune response, differential methylation between individuals with severe and mild disease may lead to alterations in T cell populations. In part these differences in immune response and differential regulation of inflammatory pathways (e.g., PI3K/Akt pathway) could be associated with immune tolerance. Further, impairments in the early immune defenses of the nasal mucosa may be contributory to COVID-19 severity. These findings highlight the continued need for exploration into potential causative pathways as we seek further understanding of the SARS-CoV-2 viral pathophysiology and gives evidence supporting investigation of these paths as putative therapeutic targets.

WGBS Sample characteristics

Salvage nasal mucosa derived from patients presenting to the emergency department at University Health Truman Medical Center were accessed and collected from mid-turbinate nasal flocked swabs as part of routine testing for SARS-CoV-2 infection. Samples were obtained from 4 individuals with severe COVID-19, 57 with mild disease, and two pools of COVID-19 negative individuals (n = 8 and n = 7, respectively). Samples were stored in 3 mL of Universal Transport Medium where 200ul of each specimen was tested for SARS-CoV-2 and remaining aliquot was saved in -80C freezer.

DNA Isolation

Nasal specimens were stored at -80⁰C and were brought to room temperature. DNA was isolated with a DNeasy Blood and Tissue Kit (Qiagen, Cat No. 69504) with the following modifications to kit protocol: 8uL of RNase A was used instead of 4ul during the optional RNase A step and the lysis incubation time at 56⁰C was increased to at least 3 hours to ensure complete lysis of the specimens. After isolation, the DNA concentration of each sample was determined using a Qubit dsDNA HS Assay Kit (Fisher, Cat No. Q32851).

WGBS Library Preparation and Sequencing

At least minimum of 100ng of DNA was aliquoted from each sample. Unmethylated λDNA was added to each sample at 0.5% w/v and the samples were sheared mechanically using a Covaris LE220-plus system to a length of 350 bp, using the settings recommended by the manufacturer. The sizing was determined by a High Sensitivity D1000 ScreenTape and Reagents (Agilent, Cat. No. 5067–5584 and 5067–5585) on the TapeStation platform. Once the input DNA was at the proper fragment size, the samples were concentrated with a SpeedVac to a volume of 20µL. The samples then underwent bisulfite conversion with an EZ DNA Methylation- Gold kit (Zymo, Cat. No. D5006). The samples were eluted off the spin columns with 15µl of low EDTA TE buffer (Swift, Cat. No. 30024) before library preparation.

The low-input libraries were prepared using an ACCEL-NGS Methyl-Seq Library kit (Swift, Cat. No. 30024) with a Methyl-Seq Set A Indexing Kit (Swift, Cat. No. 36024), following the protocol associated with the library kit. During the protocol, bead cleanup steps were performed with SPRIselect beads (Beckman Coulter, Cat. No. B23318). Following the recommendation of the kit, 6 PCR cycles were performed to amplify the samples. The final libraries were quantified with a Qubit dsDNA HS Assay Kit and the size was determined by using a BioAnalyzer High Sensitivity DNA Kit (Agilent, Cat. No. 5067 − 4626). The libraries were then sequenced on the Illumina NovaSeq6000 System using 150bp paired-end sequencing.

WGBS data processing

WGBS data was processed using the Epigenome Pipeline available from the DRAGEN Bio-IT platform (Edico Genomics/Illumina). Sequence reads were demultiplexed into FASTQ files using Illumina's bcl2Fastq2-2.19.1 software and trimmed for quality (phred33 > = 20) and Illumina adapters using trimgalore v.0.4.2 (https://github.com/FelixKrueger/TrimGalore). Reads were then aligned to the bisulfite-converted GRCh38 reference genome using DRAGEN EP v2.6.3 in paired-end mode using the directional/Lister methylation protocol presets. Alignments were calculated for both Watson and Crick strands and the highest quality unique alignment was retained. Duplicated reads were removed using picard v 2.17.8 [84]. A genome-wide cytosine methylation report was generated by DRAGEN to record counts of methylated and unmethylated cytosines at each cytosine position in the genome. Methylation counts were provided for the CpG, CHG and CHH cytosine contexts but only CpG was considered in the study. To avoid potential biases in downstream analyses, CpGs were further filtered by removing CpGs: covered by five or less reads, and located within genomic regions that are known to have anomalous, unstructured, high signal/read counts as reported in DAC blacklisted regions (DBRs) or Duke excluded regions (DERs) generated by the ENCODE project [85].

Differential methylation analysis

Filtered methylation data from all nasal samples were merged according to disease severity. Only CpGs covered by at least 10 reads and present in at least 2 samples per group (50% of the severe sample size) were kept. DMRs of destranded autosomes were evaluated in an overlapping tiling window analysis with window size 500 bp and step size 250 bp through a logistic regression analysis with age, gender, and race included as covariates using the R-package, methylKit [86]. P-values were adjusted to q-values using SLIM method.

Comparative analysis of differential proportions of hypo- versus hypermethylated regions was carried out using Chi-square test of independence.

Gene Annotation

Following differential methylation analysis, gene annotation was limited to those bins with a q-value < 0.01 and an absolute average methylation difference of > 10% between comparison groups. In the case of evaluation of DMRs between hospitalized versus non-hospitalized subjects, gene annotation was limited to those 10,000 most statistically significant differences by q-value. Initial gene annotation was performed using Genomic Regions Enrichment of Annotations Tool (GREAT) algorithm [33, 34] (Association rule: Single nearest gene: 5000 bp max extension, curated regulatory domains included). with identification of the single nearest gene regulatory domain within 5 kb of the region of interest. Subsequent gene ontology was further explored using Coronascape with hypo- and hypermethylated DMRs evaluated separately. Additional pathway analysis was performed using QIAGEN Ingenuity Pathway Analysis (IPA) version 01-21-03 (QIAGEN, Venlo, Netherlands) examining all gene-associated loci with a q-value of < 0.05.

Methylation Segmentation

UMRs and LMRs for each samples set were called based on a pooled aggregate methylation profile across the samples using MethylSeekR package (v 1.38) [87] from Bioconductor (v 3.16) [88, 89].

MethylSeekR: Burger L, Gaidatzis D, Schubeler D, Stadler MB (2013). “Identification of active regulatory regions from DNA methylation data.” Nucleic Acids Research. doi:10.1093/nar/gkt599, http://nar.oxfordjournals.org/content/early/2013/07/04/nar.gkt599.long.

Bioconductor: http://www.nature.com/nmeth/journal/v12/n2/abs/nmeth.3252.html, https://genomebiology.biomedcentral.com/articles/10.1186/gb-2004-5-10-r80

Annotation of regulatory elements

Genomic regions were further annotated for overlaps with the entire DNase I Hypersensitive Site (DHS) vocabulary using the intersect function in the Bedtools suite (v 2.30.0) [90] with minimum overlap of 1 nucleotide. The DHS coordinates were accessed from

https://zenodo.org/record/3838751/files/DHS_Index_and_Vocabulary_hg38_WM20190703.txt.gz using 16 different vocabulary representatives as outlined in Meuleman et al., 2020 [32].

BedTools: Quinlan AR and Hall IM, 2010. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 26, 6, pp. 841–842.

Transcription Factor Binding Analysis

Transcription factor binding site (TFBS) motif analysis was performed using the Homer software (HOMER findMotifsGenome.pl v4.11.1) [87] using the central 200bp of regions. Motif analysis was performed using HOMER software examining hypo- and hypermethylated regions separately, however, DMRs were expanded to include all regions with an absolute methylation difference of > 10% between groups and a q-value 1x10^− 5.

SARS-CoV-2: Severe acute respiratory syndrome coronavirus 2

COVID-19: Coronavirus disease 2019

ACE 2: Angiotensin 2

GWAS: Genome-wide association studies

SNV: Single nucleotide variants

IFN: Interferon

DMR: Differentially methylated region

EWAS: Epigenome wide association studies

WGBS: Whole-genome bisulfite sequencing

PI3K/Akt: Phosphoinositide 3‐kinase/serine‐threonine kinase

NF-κB: Nuclear factor kappa B

UMR: Unmethylated regions

LMR: Low methylated regions

DHS: DNase I hypersensitive site

GREAT: Genomic Regions Enrichment of Annotations Tool

DMG: Differentially methylated gene

GO: Gene Ontology

KEGG: Kyoto Encyclopedia of Genes and Genomes

scRNA-seq: Single cell RNA sequencing

IFN-I: Type I interferon

IFN-III: Type III interferon

ISG15: Interferon-stimulated gene 15

MERS-CoV: Middle East respiratory syndrome coronavirus

HCoVs: Human coronaviruses

DBR: DAC blacklisted region

DER: Duke excluded region

IPA: Ingenuity Pathway Analysis

TFBS: Transcription factor binding site

IRB: Institutional Review Board

Ethics approval and consent to participate

All experimental protocols were determined as non-human subjects research by The Office of Research Integrity at Children’s Mercy Research Institute (#Study00001258) as only collection of existing deidentified specimens were included with no identifying information that would permit access to protected health information. Accordingly, an Institutional Review Board (IRB) review was waived as this is only needed for studies involving human subjects research.

Consent for publication

Not applicable.

Availability of data and materials

The datasets supporting the conclusions of this article will be available in the Gene Expression Omnibus repository.

Competing interest

The authors declare no competing interests.

Funding

This work was supported by a CTSA grant from NCATS awarded to the University of Kansas for Frontiers: University of Kansas Clinical and Translational Science Institute (# UL1TR002366) The contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH or NCATS.

Authors’ contributions

The project was conceptualized by EG and RS. Clinical coordination was performed by DB and RS. Data and bioinformatics analyses were done by BS and BK. BS and RM prepared all figures. BS wrote the original manuscript draft with contributions from all authors. BS, BK, RM, DB, KL, TB, RS, and EG read and approved the final manuscript. EG supervised the project and was responsible for funding acquisition.

Acknowledgements

The authors would like to thank Daniel Louiselle and Rebecca Biswell for their work in sample processing. EG holds the Roberta D. Harding & William F. Bradley, Jr. Endowed Chair in Genomic Research. BLS was supported in part by The Sam and Helen Kaplan Research Fund in Pediatric Nephrology, and The McLaughlin Family Endowed Chair in Nephrology.

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No competing interests reported.

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Methylation patterns of the nasal epigenome of hospitalized SARS-CoV-2 positive patients reveal insights into molecular mechanisms of COVID-19

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Version 1

Abstract

Figures

Background

Results

Characterization of the regulatory genomic landscape in nasal mucosa by WGBS

Preponderance of hypomethylated regions in severe COVID-19 patients

Differentially methylated genes in severe COVID-19 overlap genetic loci

Differential methylation of genes within the PI3K/Akt pathway and COVID-19 severity

Hypermethylation of immune cell surface markers in severe cases of COVID-19

Enrichment of ELF4 and ELF5 transcriptional motifs in hypermethylated regions

Discussion

Conclusions

Methods

WGBS Sample characteristics

DNA Isolation

WGBS Library Preparation and Sequencing

WGBS data processing

Differential methylation analysis

Gene Annotation

Methylation Segmentation

Annotation of regulatory elements

Transcription Factor Binding Analysis

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1