Skip to main content
Download PDF

Differential DNA methylation 7 months after SARS-CoV-2 infection

Clinical Epigenetics volume 17, Article number: 60 (2025) Cite this article

Abstract

Background

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), and SARS-CoV-2 has been linked to changes in DNA methylation (DNAm) patterns. Studies focused on post-SARS-CoV-2 infection and DNAm have been mainly carried out among severe COVID-19 cases or without distinguishing the severity of cases. However, investigations into mild and asymptomatic cases after SARS-CoV-2 infection are limited. In this study, we analyzed DNAm patterns of mild and asymptomatic cases seven months after SARS-CoV-2 infection in a household setting by conducting epigenome-wide association studies (EWAS).

Results

We identified DNAm changes at 42 CpG sites associated with anti-SARS-CoV-2 antibody levels. We additionally report EWAS between COVID-19 cases and controls, with the case status being confirmed by either an antibody test or a PCR test. The EWAS with an antibody test case definition identified 172 CpG sites to be differentially methylated, while the EWAS with a PCR test case definition identified 502 CpG sites. Two common sites, namely cg17126990 (annotated to AFAP1L2) and cg25483596 (annotated to PC), were identified to be hypermethylated across the three EWAS. Both CpG sites have been reported to be involved in molecular pathways after SARS-CoV-2 infection. While AFAP1L2 has been found to be upregulated after SARS-CoV-2 infection, the pyruvate carboxylase (PC) activity seems to be affected by SARS-CoV-2 infection resulting in changes to the host cell metabolism. Additionally, an EWAS to assess persistent health restrictions among PCR-confirmed cases showed 40 CpG sites to be differentially methylated.

Conclusions

We detected associations between DNAm in individuals who had asymptomatic and mild SARS-CoV-2 infections as compared to their household controls. These findings contribute to our understanding of the molecular consequences of SARS-CoV-2 infection observed months after infection.

Introduction

Coronavirus disease 2019 (COVID-19) has brought huge health challenges to the world; as of August 2023, more than 760 million cases and 6.9 million deaths have been documented worldwide [1]. Infected patients have a variety of clinical manifestations, from being asymptomatic or having mild symptoms, to severe illness including acute respiratory failure, septic shock, and multiple organ failure [2]. Most people who get sick with COVID-19 will recover without hospital treatment; however, the elderly, males, and those with pre-existing health conditions tend to have severer symptoms [3, 4]. Some people who have recovered from the initial COVID-19 experience long-lasting symptoms including fatigue, breathlessness, and cognitive dysfunction, which is known as post-COVID-19 condition [5].

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that causes COVID-19. Spike proteins on the surface of the SARS-CoV-2 mediate the host cells entry. The S1 subunit of the spike proteins binds to host entry receptor angiotensin-converting enzyme 2 (ACE2), and the S2 subunit mediates membrane fusion where spike proteins are cleaved by type 2 transmembrane serine protease (TMPRSS2) [6]. ACE2 and TMPRSS2 are widely expressed in lungs, hearts, kidneys, and multiple organs which intrigue the specific clinical manifestation of COVID-19 [7]. Both innate and adaptive immune responses are involved in the inflammatory response of the host defense, with increased viral loads leading to the activation and proliferation of immune cells, the production of proinflammatory and anti-inflammatory cytokines, ultimately resulting in various destructive events [8, 9].

DNAm is a molecular mechanism involved in the regulation of gene expression without changing the underlying DNA sequence. Methylation most often occurs in cytosine-phosphate-guanine (CpG) dinucleotides, where a methyl group is transferred to the C-5 position of the cytosine ring by DNA methyltransferases. DNAm plays a crucial role in normal development; its dysregulation is involved in the onset and progression of several human diseases. Therefore, DNAm changes are increasingly considered as diagnostic and prognostic biomarkers in clinical practice, for example, in human cancers [10].

Epigenetic regulation also plays an important part in the pathophysiology of COVID-19, as SARS-CoV-2-infected patients were reported to have DNAm alterations [11]. SARS-CoV-2 affects DNAm patterns that regulate ACE2 gene expression, which in turn is associated with the susceptibility to COVID-19 [12]. Studies also found that DNAm impacts COVID-19 severity by regulating the immune response [13, 14]. Differentially methylated regions were identified in blood samples of hospitalized individuals one year after recovering from acute illness [15]. An epigenome-wide association study (EWAS) conducted in the Norwegian Corona Cohort, which included both mild and severe COVID-19 patients, also found DNAm changes three months after infection [16].

A complete understanding of persistent DNAm variation in mild and asymptomatic cases after SARS-CoV-2 infection is missing. To better understand the molecular mechanisms underlying COVID-19, the aim of this study was to examine DNAm associations seven months post-SARS-CoV-2 infection in a household setting.

Methods

Study population

The prospective COVID-19 cohort Munich (KoCo19) was launched in 2020 in Munich, Germany. KoCo19-Shield was a substudy of KoCo19 to investigate SARS-CoV-2-specific immune responses in convalescent individuals more than 3 months after infection. In KoCo19-Shield, households with at least one person who had a polymerase chain reaction (PCR)-confirmed SARS-CoV-2 infection were recruited, as described in detail in a prior publication [17]. From September 2020 to January 2021, 177 PCR-positive individuals and 145 of their household members from 157 households were enrolled, either through house visits or at the study center at the Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich. In January 2021, 85 members of 36 households from KoCo19 were randomly selected as controls, all of them consistently tested negative for SARS-CoV-2-specific antibodies throughout the first year of the COVID-19 pandemic [18, 19]. All participants including PCR-confirmed individuals, their household members, and controls were asked to provide a venous blood sample with additional information about the course of the disease and their living situation. Personal and clinical data from participants were collected using the mobile data collection tool OpenDataKit via Android smartphones [20].

DNAm analysis

Genomic DNA (750 ng) from 382 individuals was bisulfite converted using the EZ-96 DNA Methylation Kit (Zymo Research, Orange, CA, USA). Subsequent methylation analysis was performed on an Illumina (San Diego, CA, USA) iScan platform using the Infinium MethylationEPIC BeadChip v1 according to standard protocols provided by Illumina. GenomeStudio software version 2011.1 with Methylation Module version 1.9.0 was used for initial quality control of assay performance and for generation of methylation data export files. Further quality control and preprocessing of the data were performed in R v4.1.3 [21] with the package minfi v1.40.0 [22] and following primarily the CPACOR pipeline [23]. Raw intensities were read into R and background corrected. Probes with detection p-values > 10^{− 16} were set to missing and were retained as missing in all subsequent QC steps.

Before normalization, 4 samples were removed, as they had failed the sex prediction or the median intensity quality control steps or had > 20% missing values on the autosomes. A total of 68,017 probes were removed (some overlapping multiple categories): cross-reactive probes as given in published lists (N = 44,493); probes with SNPs with minor allele frequency > 5% at the CG position (N = 11,370) or the single base extension (N = 5,597) as given by minfi; and 15,667 with > 5% missing values (autosomes only). CpGs from the EPICv1 no longer appearing in the EPICv2 and CpGs from X and Y chromosomes were additionally removed, yielding a total of 690,738 available for analysis (79.77% of the probes).

Quantile normalization (QN, R package limma v3.50.3 [24]) was then performed separately on the signal intensities divided into the 6 probe types: type II red, type II green, type I green unmethylated, type I green methylated, type I red unmethylated, type I red methylated [23]. For the autosomes, QN was performed for all samples together. The transformed intensities were then used to generate methylation beta values, a measure from 0 to 1 indicating the percentage of methylated bead-type intensity to the total locus intensity.

Serology assessment

SARS-CoV-2 antibody reactivity was measured in plasma derived from ethylenediaminetetraacetic acid (EDTA)-coated blood tubes using Elecsys® Anti-SARS-CoV-2 (Roche, Mannheim, Germany). A threshold of 0.422 (instead of 1.0) for anti-SARS-CoV-2 antibody was used to determine seropositive in the participants [25].

In total, 346 participants with both clinical data and DNAm data were included in the analysis (Fig. 1). The study subjects were then categorized into five groups: 136 individuals who were PCR positive and seropositive (group1), 18 people who were PCR positive and seronegative (group2), 43 participants who were exposed household members and seropositive (group3), 70 individuals who were exposed members and seronegative (group4), and 79 individuals who were seronegative controls (group5). The case and non-case classification of antibody testing was not completely consistent with that of PCR testing, and the exposed household members with seropositive results were not confirmed by a PCR test. Therefore, in our subsequent analyses, anti-SARS-CoV-2 antibody levels, COVID-19 status based on antibody testing (group1 + group3 VS group2 + group4 + group5), and COVID-19 status based on PCR testing (group1 + group2 VS group4 + group5) were both analyzed as variables of interest.

Fig. 1
figure 1

Flow diagram of the study population

Full size image

Statistical analysis

Data analysis was conducted using R 4.3.1 [21]. An epigenome-wide association study approach using generalized estimating equation (GEE) models with an exchangeable correlation matrix and grouping factor households was applied to explore the relation between COVID-19 phenotypes and DNAm. DNAm outliers that were not within the range from 25th percentile−3*interquartile range (IQR) to 75th percentile + 3*IQR were removed, leading to the removal of 0.3% of the data points. The methylation beta-value was transformed to M-value as dependent variable, as it is more statistically valid and provides less biased results [26, 27]. To examine the impact of COVID-19, variables/phenotypes indicating anti-SARS-CoV-2 antibody levels, COVID-19 status based on antibody testing, and COVID-19 status based on PCR testing were included in the model, respectively. Additional variables included in the models to control for confounding were age, sex, CD8 + T-lymphocytes, CD4 + T-lymphocytes, natural killer cells, B cells, and monocytes. Granulocytes were excluded as a covariate to avoid multicollinearity.

To examine the long-term effect of COVID-19, PCR-confirmed individuals were divided into two groups and analyzed using the same model. The classification was based on participants’ responses to the question “consequence of illness from COVID-19.” Those who reported persistent health restrictions or long-term consequences were categorized into the persistent health restriction group, while those who reported recovery were categorized into the recovery group.

Sensitivity analysis included additional adjustment for smoking. Due to missingness and poor quality of self-reported smoking status, this variable was obtained by the classifier EpiSmokEr, which inferred smoking status based on 121 CpGs [28]. Although there were only 100 overlapping CpGs in our data, the performance of the classifier was validated using data from KORA F4, which resulted in a sensitivity of 71.1% and a specificity of 94.3% to discriminate current and non-current smokers. Another subset of participants was also considered in a secondary sensitivity analysis: This subset excluded 4 PCR-confirmed cases with no blood testing dates and their 5 household members, as well as 1 case for whom the interval between PCR testing and blood sample collection was only 19 days and one additional member of this household.

To identify the biological pathways, differentially methylated CpG sites of three models were mapped to Entrez Gene IDs and tested for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways by R package missMethyl [29].

All analyses were performed on complete data for all variables included in the model. Bacon method was used to control inflation by applying the empirical null distribution [30]. False discovery rate (FDR) correction method by Benjamini–Hochberg was applied to address multiple testing problems, with a significant set of p < 0.05 [31]. Findings were compared with previous published EWAS results and databases like the EWAS catalog [32]. The publicly accessible database “Genetics of DNA Methylation Consortium” (GoDMC) was used to extract cis- and trans-methylation quantitative trait loci (meQTL) and expression quantitative trait methylation (eQTM) [33,34,35].

Results

Characteristics of the study population

A total of 346 individuals from 171 households were considered in the study (Table 1). None of the PCR-confirmed COVID-19 cases was hospitalized, and they presented a mild course of illness or reported asymptomatic. The average age of all study participants was 42 years old, and the median age ranged from 35 to 42 years across groups. Forty-nine percent of study participants were females, and the proportion of females ranged from 45% to 67% across groups. The median days from PCR testing to blood sample collection were 242 days.

Table 1 Characteristics of participants according to COVID-19 status groups
Full size table

Based on SARS-CoV-2 antibody testing, the median age of the case group (group 1 + group 3) was 41 years, the median body mass index (BMI) was 23.77 kg/m2, 47% were females, and 12.2% were current smokers; the median age of the control group (group 2 + group 4 + group 5) was 42 years, the median BMI was 23.51 kg/m2, 50% were females, and 15.6% were current smokers. Based on PCR testing, the cases group (group1 + group2) had a median age of 41.5 years and a median BMI of 24.24 kg/m2, with females making up 47.4% and 15.6% being current smokers, while the control group (group4 + group5) had a median age of 43 years, a median BMI of 23.36 kg/m2, with a proportion of female 48.3% and 15.4% current smoker. The Wilcoxon rank sum test for age and BMI and the Chi-square test for sex and smoking status all showed that the study population was well balanced across case–control groups.

Of the PCR-confirmed cases, 121 (83%) reported recovery from the illness, with a balanced ratio of median age, BMI, and sex in both groups (Table 2).

Table 2 Characteristics of PCR-positive individuals with different post-infection health statuses
Full size table

DNAm changes associated with anti-SARS-CoV-2 antibody levels

The Miami plot illustrates the EWAS results for DNAm in relation to anti-SARS-CoV-2 antibody levels (Fig. 2). After inflation correction, the inflation factor lambda was 1.357 (Supplementary file 1, Figs. 1A and 2A) [30]. Forty-two differentially methylated CpG sites were identified to be associated with antibody levels at FDR < 0.05 (Supplementary file 2, Table S1). Among the significant CpG sites, 18 (43%) were hypermethylated. In addition, the results were compared with independent top cis- and trans- meQTLs and eQTMs. Three out of 42 CpGs (cg02942825 annotated to GIPR, cg10118093 annotated to PIK3C2B, cg26589785 annotated to ITPKA) were found to be cis-meQTLs and cis-eQTMs, suggesting that genetic variants might contribute to the regulation of local gene expression.

Fig. 2
figure 2

X-axis represents the position of the CpGs along the chromosomes, and y-axis represents -log10 (p-value). The dash black line represents the Bonferroni threshold (7.24e-08). The gray line represents the FDR threshold (3.35e-06). The top panel shows positive effect sizes, while the bottom panel shows negative effect sizes. The top 5 significant CpG sites in each panel and their annotated genes are shown in boxes

Miami plot displaying EWAS results for anti-SARS-CoV-2 antibody level (n = 346).

Full size image

Differential DNAm according to COVID-19 status

DNAm patterns between COVID-19 cases and controls were also examined. The Miami plot displays the EWAS results between COVID-19 cases and controls based on different case definitions (antibodies test VS PCR test) (Fig. 3). The Bacon correction to control for inflation and bias resulted in an inflation factor of 1.118 (Supplementary file 1, Figs. 1B and 2B) and 1.265 (Supplementary file 1, Figs. 1C and 2C) for the two approaches, respectively. The EWAS on anti-SARS-CoV-2 antibody levels showed 172 differentially methylated CpG sites (Supplementary file 2, Table S2), of which 125 (73%) were hypermethylated; 22 (13%) CpGs had a cis-meQTL, but none had cis-eQTM, thus indicating variation in genetic sequence to be associated with variation in DNAm level in 13% of the identified CpGs. In the analysis of cases and controls based on the PCR test results, 502 CpG sites were found to be differentially methylated (Supplementary file 2, Table S3). Of these, 428 (53%) were hypermethylated and 2 CpGs (cg20307496 in gene CDC42EP1, cg22950153 in gene IGDCC3) were found in both cis-meQTL and eQTM, suggesting that genetic variants might regulate local gene expression by DNAm variation.

Fig. 3
figure 3

X-axis represents the position of the CpGs along the chromosomes, and y-axis represents -log10 (p-value). The top panel shows positive effect of EWAS, while the bottom panel shows negative effect of EWAS. The top 5 significant CpG sites in each panel and their annotated genes are shown in boxes. The dash black line represents the Bonferroni threshold (7.24e-08). (A) EWAS between antibody testing cases and controls (n = 346), the gray line represents the FDR threshold (1.34e-05). (B) EWAS between PCR testing cases and controls (n = 305), the gray line represents the FDR threshold (3.90e-05)

Miami plot of EWAS results between cases and controls.

Full size image

Persistent health restriction in PCR-positive patients

By investigating the long-term effect of SARS-CoV-2 infection, we found 40 differentially methylated CpG sites between persistent health restriction cases and recovery cases (Supplementary file 2, Table S4), 21 (53%) of which were hypermethylated (Fig. 4).

Fig. 4
figure 4

Miami plot of EWAS results between persistent health restriction and recovery cases (n = 139)

Full size image

X-axis represents the position of the CpGs along the chromosomes, and y-axis represents -log10 (p-value). The dash black line represents the Bonferroni threshold (7.24e-08). The gray line represents the FDR threshold (3.15e-06). The top panel shows positive effect of EWAS, while the bottom panel shows negative effect of EWAS. The top 5 significant CpG sites in each panel and their annotated genes are shown in boxes.

Common CpG sites across the findings

Comparing the results of the different models, two CpG sites, cg17126990 and cg25483596, both hypermethylated, appeared in all three models (Table 3). Cg17126990 was annotated to gene AFAP1L2 and is located on chromosome 10. Another CpG site, cg25483596, annotated to the gene Pyruvate carboxylase (PC), is located within intron 10 of the gene on chromosome 11. Additionally, cg03498173 was differentially methylated in both the antibody-level association EWAS and the EWAS between antibody testing cases and controls, while cg13725803 and cg16238815 were differentially methylated CpG sites identified in both the EWAS on antibody levels and the EWAS on the PCR-based case status (Table 3).

Table 3 Common significant CpGs across the three EWASs
Full size table

In addition to the two overlapping CpG sites, 124 common differentially methylated CpG sites were identified from the two EWASs of the different COVID-19 status test methods (Supplementary file 2, Table S5). The effect sizes between the two EWASs (PCR-based and antibody-based methods) were strongly consistent (R = 0.88). Among the results, cg05655377 on chromosome 4 was one of the hypermethylated top effect sites in both EWASs. Cg11586124 and cg19722371 on chromosome 12 were the hypomethylated top effect sites common to both EWASs. Cg19722371 was annotated to gene DARM1.

Comparing the findings of the different exploration approaches with the EWAS catalog, none of the CpG sites had been previously reported in relation to COVID-19. Enrichment analysis showed that both GO and KEGG terms returned no statistically significant results.

Sensitivity analysis

Given that smoking status has a great impact on DNAm patterns, regression models with the same covariates plus smoking status were used in the analysis. Using three models, with phenotypes indicating anti-SARS-CoV-2 antibody levels, COVID-19 status based on antibody testing, and COVID-19 status based on PCR testing, we identified 15, 130, and 394 differentially methylated CpG sites after bacon correction at FDR < 0.05, respectively (Supplementary file 2, Table S6-S8). Among these CpGs, 15 (100%), 129 (99.2%), and 389 (98.7%) out of them appeared in the main analysis and all of them had a consistent direction, showing that the primary findings was robust to smoking status.

By removing participants without clear blood sample collection dates and with short intervals between PCR test and blood collection, we eliminated 11 persons and analyzed the remaining subset using regression models with the same covariates. The EWAS results can be found in Supplementary file 2, Table S9-S11. Overall, 37 (88%) differentially methylated CpGs in the first model (association with anti-SARS-CoV-2 antibody levels), 158 (91.8%) CpGs in the second model (COVID-19 status based on antibody testing), and 443 (88.2%) CpGs in the third model (COVID-19 status based on PCR testing), were also discovered in the main analysis and the effect directions were consistent. Additionally, 3 common differentially methylated CpGs (cg04900672, cg17126990, cg25483596) were identified, and 2 of them (cg17126990, cg25483596) were the main findings in the primary analysis, indicating that the association of the two CpGs with antibody levels and case status described in main results were robust to variations in the estimated intervals from infection to blood collection.

Discussion

We investigated DNAm changes with anti-SARS-CoV-2 antibody levels and compared DNAm patterns between mild and asymptomatic patients and healthy individuals after 7 months of infection in a household setting. Overall, numerous differentially methylated CpG sites were identified, demonstrating that DNAm changes of post-infection are associated with antibody levels and that patients with mild and asymptomatic symptoms have DNAm patterns that differ from never infected controls. Two common CpG sites, namely cg17126990 (AFAP1L2) and cg25483596 (PC), showed positive associations with both antibody levels and case status.

AFAP1L2 enables SH3 domain binding activity and protein tyrosine kinase activator activity and is involved in the positive regulation of the epidermal growth factor receptor signaling pathway, associated with vesicoureteral reflux and cartilage cancer [36]. Studies showed that AFAP1L2 was upregulated in A549-ACE2 cells [37], human nasal epithelial cells [38], and mouse kidneys after SARS-CoV-2 infection [39]. According to data from blood cell lines, cg17126990 overlapped both a DNasel hypersensitivity and an open chromatin regions, which suggests this CpG is involved in the transcription process [40]. Additionally, a genome browser search showed that both transcription factors ELAVL1 and PABPC1 bind the region containing cg17126990 [40]. ELAVL1 is highly expressed in cancer cells and is also involved in inflammation by regulating mRNA stability, splicing, and translation [41]. Lu et al. [42] found that ELAVL1 was upregulated in COVID-19 patients and can effectively predict SARS-CoV-2 infection with other six m6A-related genes. The second identified CpG site, cg25483596, is annotated to gene PC. PC is a protein-coding gene involved in gluconeogenesis, lipogenesis, insulin secretion, and synthesis of the neurotransmitter glutamate [36]. SARS-CoV2 infection was found to alter host cell metabolism by upregulating PC activity to increase carbon entry into the TCA cycle [43]. Analyses in cell lines show that cg25483596 overlapped regions with histone modifications, weak enhancers, and weakly transcribed regions in blood cells [40].

We also found different DNAm patterns between persistent health restriction cases and recovered cases. Cg27139398 (annotated to ADAM22) and cg23815787 (annotated to UBE2N) were two of the top 5 negatively correlated CpG sites. EI-Agnaf et al. [44] discovered that ADAM22 may contribute to neurological complications in post-severe COVID-19 patients, while UBE2N was downregulated in SARS-CoV-2 late-stage infection in human blood samples [45].

By using publicly available data, cg02942825 (annotated to GIPR), cg10118093 (annotated to PIK3C2B), and cg26589785 (annotated to ITPKA) were found to be reported cis-meQTLs and cis-eQTMs, indicating that the CpG sites are influenced by genetic variants nearby, and might potentially mediate the association between genetic variants and downstream phenotype. GIPR, PIK3C2B, and ITPKA are all protein-coding genes related to multiple traits [46]. Previous studies showed an association between GIPR and C-reactive protein levels [47] and ITPKA was found to be related to respiratory system diseases [48], while PIK3C2B was reported to be associated with lung function [49, 50].

Lee et al. [16] investigated the DNAm profile between COVID-19 cases and controls after 3 months post-infection; they report DNAm patterns in long COVID patients, highlighting the immune response associated gene IFI44L as their main finding. We found no overlapping CpG sites when comparing our results with those of Lee et al. One reason might be that we have only mild and asymptomatic cases, while Lee et al. had a study population with severe symptoms. Another reason could be the follow-up time post-infection for the DNAm measurement, as the epigenetic profile likely further changes after an additional four month post-infection. There are also no common CpG sites between our findings and COVID-19-related studies reported in the EWAS catalog, indicating that mild cases could have a different DNAm pattern at 7 months of post-infection.

There are some strengths of our study. Different COVID-19 case definitions were used in this study, providing a more comprehensive estimation. To our knowledge, this is the first study to focus on asymptomatic and mild COVID-19 cases with a relatively long follow-up after infection 7 months in a household setting. In addition, the GEE model takes the correlation of DNAm patterns within household members into account, leading to a reliable and robust estimate.

There are also a few limitations. The study population was 14 years and older, living in private households in the Munich area, which may limit its generalizability. We also found differentially methylated CpG sites between cases with self-reported health restriction cases and those fully recovered, yet these results are limited by the instrument used to determine health restrictions and potential misclassification of the groups. We used publicly available data to identify meQTLs and eQTMs, which might not necessarily reflect associations in our study population.

Conclusions

We investigated DNAm changes in asymptomatic, mild cases, and healthy individuals 7 months post-SARS-CoV-2 infection. Cg17126990 (annotated to AFAP1L2) and cg25483596 (annotated to PC) were the common CpG sites identified to be differentially methylated across the different study approaches, thus suggesting their relevance in the aftermath of COVID-19. Both CpGs have been reported to be involved in molecular pathways associated with SARS-CoV-2 infection. These findings may be useful for further understanding the molecular mechanism after SARS-CoV-2 infection in mild and asymptomatic individuals.

Data availability

Data at individual level are not available due to protection of data privacy of our study subjects. However, data are accessible subject to data protection regulations upon reasonable request to the KoCo19 investigators. Requests will be scientifically reviewed following the cohort’s review process.

Abbreviations

COVID-19:

Coronavirus disease 2019

SARS-CoV-2:

Severe acute respiratory syndrome coronavirus 2

DNAm:

DNA methylation

ACE2:

Angiotensin-converting enzyme 2

TMPRSS2:

Type 2 transmembrane serine protease

CpG:

Cytosine-phosphate-guanine

EWAS:

Epigenome-wide association study

KoCo19:

Prospective COVID-19 cohort Munich

PCR:

Polymerase chain reaction

QN:

Quantile normalization

EDTA:

Ethylenediaminetetraacetic acid

GEE:

Generalized estimating equation

IQR:

Interquartile range

GO:

Gene ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

FDR:

False discovery rate

meQTL:

Methylation quantitative trait loci

eQTM:

Expression quantitative trait methylation

PC:

Pyruvate carboxylase

BMI:

Body mass index

References

  1. World Health Organisation. Coronavirus disease (COVID-19) [Internet]. Available from: https://www.who.int/news-room/fact-sheets/detail/coronavirus-disease-(covid-19)

  2. M C, A A. Features, Evaluation, and Treatment of Coronavirus (COVID-19) [Updated 2023 Aug 18]. [Internet]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK554776/

  3. Li X, Xu S, Yu M, Wang K, Tao Y, Zhou Y, et al. Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan. J Allergy Clin Immunol. 2020;146(1):110–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Flaherty GT, Hession P, Liew CH, Lim BCW, Leong TK, Lim V, et al. COVID-19 in adult patients with pre-existing chronic cardiac, respiratory and metabolic disease: a critical literature review with clinical recommendations. Trop Dis Travel Med Vac. 2020;6(1):16.

    Article  Google Scholar 

  5. World Health Organisation. Coronavirus disease (COVID-19): Post COVID-19 condition [Internet]. Available from: https://www.who.int/news-room/questions-and-answers/item/coronavirus-disease-(covid-19)-post-covid-19-condition

  6. Lamers MM, Haagmans BL. SARS-CoV-2 pathogenesis. Nat Rev Microbiol. 2022;20(5):270–84.

    Article  CAS  PubMed  Google Scholar 

  7. Beyerstedt S, Casaro EB, Rangel ÉB. COVID-19: angiotensin-converting enzyme 2 (ACE2) expression and tissue susceptibility to SARS-CoV-2 infection. Eur J Clin Microbiol Infect Dis. 2021;40(5):905–19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Hanna R, Dalvi S, Sălăgean T, Pop ID, Bordea IR, Benedicenti S. Understanding COVID-19 pandemic: molecular mechanisms and potential therapeutic strategies. An evidence-based review. J Inflamm Res. 2021;14:13–56.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Zhu Y, Sharma L, Chang D. Pathophysiology and clinical management of coronavirus disease (COVID-19): a mini-review. Front Immunol. 2023;14(14):1116131.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Lopomo A, Coppedè F. Epigenetic Signatures in the Diagnosis and Prognosis of Cancer. In: Epigenetic Mechanisms in Cancer [Internet]. Elsevier; 2018 [cited 2024 Jul 8]. p. 313–43. Available from: https://linkinghub.elsevier.com/retrieve/pii/B9780128095522000127

  11. Konigsberg IR, Barnes B, Campbell M, Davidson E, Zhen Y, Pallisard O, et al. Host methylation predicts SARS-CoV-2 infection and clinical outcome. Commun Med. 2021;1(1):42.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Daniel G, Paola AR, Nancy G, Fernando SO, Beatriz A, Zulema R, et al. Epigenetic mechanisms and host factors impact ACE2 gene expression: Implications in COVID-19 susceptibility. Infect Genet Evol. 2022;104: 105357.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Barturen G, Carnero-Montoro E, Martínez-Bueno M, Rojo-Rello S, Sobrino B, Porras-Perales Ó, et al. Whole blood DNA methylation analysis reveals respiratory environmental traits involved in COVID-19 severity following SARS-CoV-2 infection. Nat Commun. 2022;13(1):4597.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Castro de Moura M, Davalos V, Planas-Serra L, Alvarez-Errico D, Arribas C, Ruiz M, et al. Epigenome-wide association study of COVID-19 severity with respiratory failure. EBioMedicine. 2021;66:103339.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Balnis J, Madrid A, Hogan KJ, Drake LA, Adhikari A, Vancavage R, et al. Persistent blood DNA methylation changes one year after SARS-CoV-2 infection. Clin Epigen. 2022;14(1):94.

    Article  CAS  Google Scholar 

  16. Lee Y, Riskedal E, Kalleberg KT, Istre M, Lind A, Lund-Johansen F, et al. EWAS of post-COVID-19 patients shows methylation differences in the immune-response associated gene, IFI44L, three months after COVID-19 infection. Sci Rep. 2022;12(1):11478.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Brand I, Gilberg L, Bruger J, Garí M, Wieser A, Eser TM, et al. Broad T cell targeting of structural proteins after SARS-CoV-2 infection: high throughput assessment of T cell reactivity using an automated interferon gamma release assay. Front Immunol. 2021;20(12): 688436.

    Article  Google Scholar 

  18. Radon K, Bakuli A, Pütz P, Le Gleut R, Guggenbuehl Noller JM, Olbrich L, et al. From first to second wave: follow-up of the prospective COVID-19 cohort (KoCo19) in Munich (Germany). BMC Infect Dis. 2021;21(1):925.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Pritsch M, Radon K, Bakuli A, Le Gleut R, Olbrich L, Guggenbüehl Noller J, et al. Prevalence and risk factors of infection in the representative COVID-19 cohort Munich. Int J Environ Res Public Health. 2021;18(7):3572.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hartung C, Lerer A, Anokwa Y, Tseng C, Brunette W, Borriello G. Open data kit: tools to build information services for developing regions. In: Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development [Internet]. London United Kingdom: ACM; 2010 [cited 2024 Jun 22]. p. 1–12. https://doiorg.publicaciones.saludcastillayleon.es/10.1145/2369220.2369236

  21. R Core Team. R: a language and environment for statistical computing [Internet]. R Foundation for Statistical Computing, Vienna, Austria; Available from: https://www.R-project.org/

  22. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinforma Oxf Engl. 2014;30(10):1363–9.

    Article  CAS  Google Scholar 

  23. Lehne B, Drong AW, Loh M, Zhang W, Scott WR, Tan ST, et al. A coherent approach for analysis of the Illumina HumanMethylation450 BeadChip improves data quality and performance in epigenome-wide association studies. Genome Biol. 2015;16(1):37.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7): e47.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Olbrich L, Castelletti N, Schälte Y, Garí M, Pütz P, Bakuli A, et al. Head-to-head evaluation of seven different seroassays including direct viral neutralisation in a representative cohort for SARS-CoV-2. J Gen Virol [Internet]. 2021 Nov 1 [cited 2025 Feb 27];102(10). https://doiorg.publicaciones.saludcastillayleon.es/10.1099/jgv.0.001653

  26. Du P, Zhang X, Huang CC, Jafari N, Kibbe WA, Hou L, et al. Comparison of beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinform. 2010;11(1):587.

    Article  CAS  Google Scholar 

  27. Xie C, Leung YK, Chen A, Long DX, Hoyo C, Ho SM. Differential methylation values in differential methylation analysis. Bioinformatics. 2019;35(7):1094–7.

    Article  CAS  PubMed  Google Scholar 

  28. Bollepalli S, Korhonen T, Kaprio J, Anders S, Ollikainen M. EpiSmokEr: a robust classifier to determine smoking status from DNA methylation data. Epigenomics. 2019;11(13):1469–86.

    Article  CAS  PubMed  Google Scholar 

  29. Phipson B, Maksimovic J, Oshlack A. MissMethyl: an R package for analyzing data from Illumina’s HumanMethylation450 platform. Bioinformatics. 2016;32(2):286–8.

    Article  CAS  PubMed  Google Scholar 

  30. Van Iterson M, Van Zwet EW, Heijmans BT. Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution. Genome Biol. 2017;18(1):19.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Stat Methodol. 1995;57(1):289–300.

    Article  Google Scholar 

  32. Battram T, Yousefi P, Crawford G, Prince C, Sheikhali Babaei M, Sharp G, et al. The EWAS catalog: a database of epigenome-wide association studies. Wellcome Open Res. 2022;31(7):41.

    Article  Google Scholar 

  33. Min JL, Hemani G, Hannon E, Dekkers KF, Castillo-Fernandez J, Luijk R, et al. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation. Nat Genet. 2021;53(9):1311–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Zhernakova DV, Deelen P, Vermaat M, Van Iterson M, Van Galen M, Arindrarto W, et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat Genet. 2017;49(1):139–45.

    Article  CAS  PubMed  Google Scholar 

  35. Bonder MJ, Luijk R, Zhernakova DV, Moed M, Deelen P, et al. Disease variants alter transcription factor levels and methylation of their binding sites. Nat Genet. 2017;1:131–8.

    Article  Google Scholar 

  36. Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, et al. (2016) The genecards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinforma.54:1.30.1–1.30.33.

  37. Hoagland DA, Clarke DJB, Møller R, Han Y, Yang L, Wojciechowicz ML, et al. (2020) Modulating the transcriptional landscape of SARS-CoV-2 as an effective method for developing antiviral compounds [Internet]. [cited 2024 Jul 16]. https://doiorg.publicaciones.saludcastillayleon.es/10.1101/2020.07.12.199687

  38. Gamage AM, Tan KS, Chan WOY, Liu J, Tan CW, Ong YK, et al. Infection of human nasal epithelial cells with SARS-CoV-2 and a 382-nt deletion isolate lacking ORF8 reveals similar viral kinetics and host transcriptional profiles. PLoS Pathog. 2020;16(12): e1009130.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Li S, Ma F, Yokota T, Garcia GJ, Palermo A, Wang Y, et al. Metabolic reprogramming and epigenetic changes of vital organs in SARS-CoV-2-induced systemic toxicity. JCI Insight. 2021;6(2): 145027.

    Article  PubMed  Google Scholar 

  40. Nassar LR, Barber GP, Benet-Pagès A, Casper J, Clawson H, Diekhans M, et al. The UCSC genome browser database: 2023 update. Nucleic Acids Res. 2023;51(D1):D1188–95.

    Article  CAS  PubMed  Google Scholar 

  41. Yang Y, Sun BF, Xiao W, Yang X, Sun HY, Zhao YL, et al. Dynamic m 6 a modification and its emerging regulatory role in mRNA splicing. Sci Bull. 2015;60(1):21–32.

    Article  CAS  Google Scholar 

  42. Lu L, Li Y, Ao X, Huang J, Liu B, Wu L, et al. The risk of COVID-19 can be predicted by a nomogram based on m6A-related genes. Infect Genet Evol. 2022;106: 105389.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Mullen PJ, Garcia G, Purkayastha A, Matulionis N, Schmid EW, Momcilovic M, et al. SARS-CoV-2 infection rewires host cell metabolism and is potentially susceptible to mTORC1 inhibition. Nat Commun. 2021;12(1):1876.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. El-Agnaf O, Bensmail I, Al-Nesf MAY, Flynn J, Taylor M, Majbour NK, et al. Uncovering a neurological protein signature for severe COVID-19. Neurobiol Dis. 2023;182: 106147.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. McClain MT, Constantine FJ, Henao R, Liu Y, Tsalik EL, Burke TW, et al. Dysregulated transcriptional responses to SARS-CoV-2 in the periphery. Nat Commun. 2021;12(1):1079.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Cerezo M, Sollis E, Ji Y, Lewis E, Abid A, Bircan KO, et al. The NHGRI-EBI GWAS Catalog: standards for reusability, sustainability and diversity. Nucleic Acids Res. 2025;53(D1):D998-1005.

    Article  PubMed  Google Scholar 

  47. Han X, Ong JS, An J, Hewitt AW, Gharahkhani P, MacGregor S. Using Mendelian randomization to evaluate the causal relationship between serum C-reactive protein levels and age-related macular degeneration. Eur J Epidemiol. 2020;35(2):139–46.

    Article  CAS  PubMed  Google Scholar 

  48. Kichaev G, Bhatia G, Loh PR, Gazal S, Burch K, Freund MK, et al. Leveraging polygenic functional enrichment to improve GWAS power. Am J Hum Genet. 2019;104(1):65–75.

    Article  CAS  PubMed  Google Scholar 

  49. Shrine N, Guyatt AL, Erzurumluoglu AM, Jackson VE, Hobbs BD, Melbourne CA, et al. New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries. Nat Genet. 2019;51(3):481–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Shrine N, Izquierdo AG, Chen J, Packer R, Hall RJ, Guyatt AL, et al. Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk. Nat Genet. 2023;55(3):410–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the laboratory staff at the Helmholtz Zentrum München for the technical support, Dr. Thomas Delerue for the normalization and quality control of the DNA methylation data, Dr. Elisabeth Thiering for the statistical support, and the KoCo19 study group members: Emad Alamoudi, Jared Anderson, Abhishek Bakuli, Marc Becker, Franziska Bednarzki, Olimbek Bemirayev, Jessica Beyerl, Patrick Bitzer, Rebecca Boehnlein, Friedrich Caroli, Lorenzo Contento, Alina Czwienzek, Flora Deák, Maximilian N. Diefenbach, Gerhard Dobler, Jürgen Durner, Judith Eckstein, Philine Falk, Volker Fingerle, Felix Forster, Turid Frahnow, Guenter Froeschl, Otto Geisenberger, Kristina Gillig, Philipp Girl, Pablo Gutierrez, Anselm Haderer, Marlene Hannes, Jan Hasenauer, Tim Haselwarter, Alejandra Hernandes, Matthias Herrmann, Leah Hillari, Christian Hinske, Tim Hofberger, Sacha Horn, Kristina Huber, Christian Janke, Ursula Kappl, Antonia Kessler, Zohaib N. Khan, Johanna Kresin, Arne Kroidl, Magdalena Lang, Silvan Lange, Michael Laxy, Ronan Le Gleut, Reiner Leidl, Leopold Liedl, Xhovana Lucaj, Petra Mang, Alisa Markgraf, Rebecca Mayrhofer, Dafni Metaxa, Hannah Mueller, Katharina Mueller, Laura Olbrich, Ivana Paunovic, Claire Pleimelding, Michel Pletschette, Stephan Prueckner, Kerstin Puchinger, Peter Puetz, Katja Radon, Elba Raimundéz, Jakob Reich, Friedrich Riess, Camilla Rothe, Viktoria Ruci, Nicole Schaefer, Yannik Schaelte, Benedikt Schluse, Elmar Saathoff, Lara Schneider, Mirjam Schunk, Lars Schwettmann, Peter Sothmann, Kathrin Strobl, Jeni Tang, Fabian Theis, Verena Thiel, Jonathan von Lovenberg, Julia Waibel, Claudia Wallrauch, Roman Woelfel, Julia Wolff, Tobias Wuerfel, Sabine Zange, Eleftheria Zeggini, Anna Zielke.

Funding

Open Access funding enabled and organized by Projekt DEAL. This study was supported by the Program for Advancement of Corona Research Projects by the Bavarian Ministry for Science and Arts. The Koco19-study is funded by Bavarian State Ministry of Science and the Arts, University Hospital, LMU Munich, Helmholtz Centre Munich, University of Bonn, University of Bielefeld, German Ministry for Education and Research (prom. nr.: 01KI20271.). This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 101016167, ORCHESTRA (Connecting European Cohorts to Increase Common and Effective Response to SARS-CoV-2 Pandemic).

Author information

Authors and Affiliations

  1. Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

    Peizhen Hong, Melanie Waldenberger, Annette Peters & Pamela R. Matías-García

  2. Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

    Peizhen Hong, Melanie Waldenberger, Annette Peters & Pamela R. Matías-García

  3. Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Faculty of Medicine, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany

    Peizhen Hong & Annette Peters

  4. Pettenkofer School of Public Health, Munich, Germany

    Peizhen Hong & Annette Peters

  5. German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany

    Melanie Waldenberger, Annette Peters & Pamela R. Matías-García

  6. Institute of Infectious Diseases and Tropical Medicine, LMU University Hospital, LMU Munich, Munich, Germany

    Michael Pritsch, Leonard Gilberg, Isabel Brand, Jan Bruger, Jonathan Frese, Noemi Castelletti, Christof Geldmacher & Michael Hoelscher

  7. German Center for Infection Research (DZIF), Partner Site Munich, Munich, Germany

    Michael Pritsch, Christof Geldmacher & Michael Hoelscher

  8. Department of Infectious Diseases, LMU University Hospital, LMU Munich, Munich, Germany

    Leonard Gilberg

  9. Division of Clinical Pharmacology, Department of Medicine IV, LMU University Hospital, LMU, Munich, Germany

    Isabel Brand

  10. Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

    Mercè Garí

  11. Center for International Health (CIH), University Hospital, LMU Munich, Munich, Germany

    Michael Hoelscher

  12. Immunology, Infection and Pandemic Research, Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, 80799, Munich, Germany

    Christof Geldmacher & Michael Hoelscher

Authors
  1. Peizhen Hong
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Melanie Waldenberger
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Michael Pritsch
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Leonard Gilberg
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Isabel Brand
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Jan Bruger
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Jonathan Frese
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Noemi Castelletti
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Mercè Garí
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Christof Geldmacher
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Michael Hoelscher
    View author publications

    You can also search for this author inPubMed Google Scholar

  12. Annette Peters
    View author publications

    You can also search for this author inPubMed Google Scholar

  13. Pamela R. Matías-García
    View author publications

    You can also search for this author inPubMed Google Scholar

Consortia

ORCHESTRA-study group

Contributions

P.H. performed the statistical analysis, drafted the main manuscript, and prepared all included tables and figures. P.MG. contributed to the study design, provided statistical and epidemiological guidance, and reviewed the manuscript. M.P. designed KoCo19-Shield, led sample collection, and contributed to study design. L.G. contributed to sample collection, participant recruitment, and cohort description. I.B., J.B., and J.F. contributed to sample collection and participant recruitment. N.C. contributed to data cleaning. M.G. contributed to data cleaning and statistical analysis. C.G. contributed to study design. M.H. and A.P. contributed to study design and acquisition of funding. M.W. initiated the study and contributed to the study design, data evaluation and reviewed the manuscript, as well as provided epidemiological guidance. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Peizhen Hong or Pamela R. Matías-García.

Ethics declarations

Conflict of interests

The authors declare no competing interests.

Ethics approval and consent to participate

The study protocol was reviewed and approved by the Institutional Review Board of the Medical Faculty at Ludwig-Maximilians-University Munich, Germany, under the project number 20–692 (vote of approval dated Sept. 21st, 2020) and 20–371 (vote of approval dated May 15th, 2020). Oral and written informed consent was obtained from all study subjects. For youths (ages 14 to 17) age-appropriate versions of the information and consent forms were used.

Consent for publication

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hong, P., Waldenberger, M., Pritsch, M. et al. Differential DNA methylation 7 months after SARS-CoV-2 infection. Clin Epigenet 17, 60 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13148-025-01866-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13148-025-01866-4

Keywords

Clinical Epigenetics

ISSN: 1868-7083

Contact us