Whole blood DNA methylation analysis reveals respiratory environmental traits involved in COVID-19 severity following SARS-CoV-2 infection

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

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.

The COVID-19 legacy: consequences for the human DNA methylome and therapeutic perspectives

The COVID-19 pandemic has left a lasting legacy on human health, extending beyond the acute phase of infection. This article explores the evidence suggesting that SARS-CoV-2 infection can induce persistent epigenetic modifications, particularly in DNA methylation patterns, with potential long-term consequences for individuals' health and aging trajectories. The review discusses the potential of DNA methylation-based biomarkers, such as epigenetic clocks, to identify individuals at risk for accelerated aging and tailor personalized interventions. Integrating epigenetic clock analysis into clinical management could mark a new era of personalized treatment for COVID-19, possibly helping clinicians to understand patient susceptibility to severe outcomes and establish preventive strategies. Several valuable reviews address the role of epigenetics in infectious diseases, including the Sars-CoV-2 infection. However, this article provides an original overview of the current understanding of the epigenetic dimensions of COVID-19, offering insights into the long-term health implications of the pandemic. While acknowledging the limitations of current data, we emphasize the need for future research to unravel the precise mechanisms underlying COVID-19-induced epigenetic changes and to explore potential approaches to target these modifications.

Harnessing Epigenetics: Innovative Approaches in Diagnosing and Combating Viral Acute Respiratory Infections

Acute respiratory infections (ARIs) caused by viruses such as SARS-CoV-2, influenza viruses, and respiratory syncytial virus (RSV), pose significant global health challenges, particularly for the elderly and immunocompromised individuals. Substantial evidence indicates that acute viral infections can manipulate the host's epigenome through mechanisms like DNA methylation and histone modifications as part of the immune response. These epigenetic alterations can persist beyond the acute phase, influencing long-term immunity and susceptibility to subsequent infections. Post-infection modulation of the host epigenome may help distinguish infected from uninfected individuals and predict disease severity. Understanding these interactions is crucial for developing effective treatments and preventive strategies for viral ARIs. This review highlights the critical role of epigenetic modifications following viral ARIs in regulating the host's innate immune defense mechanisms. We discuss the implications of these modifications for diagnosing, preventing, and treating viral infections, contributing to the advancement of precision medicine. Recent studies have identified specific epigenetic changes, such as hypermethylation of interferon-stimulated genes in severe COVID-19 cases, which could serve as biomarkers for early detection and disease progression. Additionally, epigenetic therapies, including inhibitors of DNA methyltransferases and histone deacetylases, show promise in modulating the immune response and improving patient outcomes. Overall, this review provides valuable insights into the epigenetic landscape of viral ARIs, extending beyond traditional genetic perspectives. These insights are essential for advancing diagnostic techniques and developing innovative treatments to address the growing threat of emerging viruses causing ARIs globally.

Applicability of epigenetic age models to next-generation methylation arrays

BACKGROUND

Epigenetic clocks are mathematical models used to estimate epigenetic age based on DNA methylation at specific CpG sites. As new methylation microarrays are developed and older models discontinued, existing epigenetic clocks might become obsolete. Here, we explored the effects of the changes introduced in the new EPICv2 DNA methylation array on existing epigenetic clocks.

METHODS

We tested the performance of four epigenetic clocks on the probeset of the EPICv2 array using a dataset of 10,835 samples. We developed a new epigenetic age prediction model compatible across the 450 k, EPICv1, and EPICv2 microarrays and validated it on 2095 samples. We estimated technical noise and intra-subject variation using two datasets with repeated sampling. We used data from (i) cancer survivors who had undergone different therapies, (ii) breast cancer patients and controls, and (iii) an exercise-based interventional study, to test the ability of our model to detect alterations in epigenetic age acceleration in response to theoretically antiaging interventions.

RESULTS

The results of the four epiclocks tested are significantly distorted by the EPICv2 probeset, causing an average difference of up to 25 years. Our new model produced highly accurate chronological age predictions, comparable to a state-of-the-art epiclock. The model reported the lowest epigenetic age acceleration in normal populations, as well as the lowest variation across technical replicates and repeated samples from the same subjects. Finally, our model reproduced previous results of increased epigenetic age acceleration in cancer patients and in survivors treated with radiation therapy, and no changes from exercise-based interventions.

CONCLUSION

Existing epigenetic clocks require updates for full EPICv2 compatibility. Our new model translates the capabilities of state-of-the-art epigenetic clocks to the EPICv2 platform and is cross-compatible with older microarrays. The characterization of epigenetic age prediction variation provides useful metrics to contextualize the relevance of epigenetic age alterations. The analysis of data from subjects influenced by radiation, cancer, and exercise-based interventions shows that despite being good predictors of chronological age, neither a pathological state like breast cancer, a hazardous environmental factor (radiation), nor exercise (a beneficial intervention) caused significant changes in the values of the "epigenetic age" determined by these first-generation models.

The emerging role of SARS-CoV-2 nonstructural protein 1 (nsp1) in epigenetic regulation of host gene expression

Infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes widespread changes in epigenetic modifications and chromatin architecture in the host cell. Recent evidence suggests that SARS-CoV-2 nonstructural protein 1 (nsp1) plays an important role in driving these changes. Previously thought to be primarily involved in host translation shutoff and cellular mRNA degradation, nsp1 has now been shown to be a truly multifunctional protein that affects host gene expression at multiple levels. The functions of nsp1 are surprisingly diverse and include not only the downregulation of cellular mRNA translation and stability, but also the inhibition of mRNA export from the nucleus, the suppression of host immune signaling, and, most recently, the epigenetic regulation of host gene expression. In this review, we first summarize the current knowledge on SARS-CoV-2-induced changes in epigenetic modifications and chromatin structure. We then focus on the role of nsp1 in epigenetic reprogramming, with a particular emphasis on the silencing of immune-related genes. Finally, we discuss potential molecular mechanisms underlying the epigenetic functions of nsp1 based on evidence from SARS-CoV-2 interactome studies.

Map of epigenetic age acceleration: A worldwide analysis

We present a systematic analysis of epigenetic age acceleration based on by far the largest collection of publicly available DNA methylation data for healthy samples (93 datasets, 23 K samples), focusing on the geographic (25 countries) and ethnic (31 ethnicities) aspects around the world. We employed the most popular epigenetic tools for assessing age acceleration and examined their quality metrics and ability to extrapolate to epigenetic data from different tissue types and age ranges different from the training data of these models. In most cases, the models proved to be inconsistent with each other and showed different signs of age acceleration, with the PhenoAge model tending to systematically underestimate and different versions of the GrimAge model tending to systematically overestimate the age prediction of healthy subjects. Referring to data availability and consistency, most countries and populations are still not represented in GEO, moreover, different datasets use different criteria for determining healthy controls. Because of this, it is difficult to fully isolate the contribution of "geography/environment", "ethnicity" and "healthiness" to epigenetic age acceleration. Among the explored metrics, only the DunedinPACE, which measures aging rate, appears to adequately reflect the standard of living and socioeconomic indicators in countries, although it has a limited application to blood methylation data only. Invariably, by epigenetic age acceleration, males age faster than females in most of the studied countries and populations.

Quantifying the stochastic component of epigenetic aging

DNA methylation clocks can accurately estimate chronological age and, to some extent, also biological age, yet the process by which age-associated DNA methylation (DNAm) changes are acquired appears to be quasi-stochastic, raising a fundamental question: how much of an epigenetic clock's predictive accuracy could be explained by a stochastic process of DNAm change? Here, using DNAm data from sorted immune cells, we build realistic simulation models, subsequently demonstrating in over 22,770 sorted and whole-blood samples from 25 independent cohorts that approximately 66-75% of the accuracy underpinning Horvath's clock could be driven by a stochastic process. This fraction increases to 90% for the more accurate Zhang's clock, but is lower (63%) for the PhenoAge clock, suggesting that biological aging is reflected by nonstochastic processes. Confirming this, we demonstrate that Horvath's age acceleration in males and PhenoAge's age acceleration in severe coronavirus disease 2019 cases and smokers are not driven by an increased rate of stochastic change but by nonstochastic processes. These results significantly deepen our understanding and interpretation of epigenetic clocks.

Correlation between Genomic Variants and Worldwide COVID-19 Epidemiology

COVID-19 is a systemic disease caused by the etiologic agent SARS-CoV-2, first reported in Hubei Province in Wuhan, China, in late 2019. The SARS-CoV-2 virus has evolved over time with distinct transmissibility subvariants from ancestral lineages. The clinical manifestations of the disease vary according to their severity and can range from asymptomatic to severe. Due to the rapid evolution to a pandemic, epidemiological studies have become essential to understand and effectively combat COVID-19, as the incidence and mortality of this disease vary between territories and populations. This study correlated epidemiological data on the incidence and mortality of COVID-19 with frequencies of important SNPs in GWAS studies associated with the susceptibility and mortality of this disease in different populations. Our results indicated significant correlations for 11 genetic variants (rs117169628, rs2547438, rs2271616, rs12610495, rs12046291, rs35705950, rs2176724, rs10774671, rs1073165, rs4804803 and rs7528026). Of these 11 variants, 7 (rs12046291, rs117169628, rs1073165, rs2547438, rs2271616, rs12610495 and rs35705950) were positively correlated with the incidence rate, these variants were more frequent in EUR populations, suggesting that this population is more susceptible to COVID-19. The rs2176724 variant was inversely related to incidence rates; therefore, the higher the frequency of the allele is, the lower the incidence rate. This variant was more frequent in the AFR population, which suggests a protective factor against SARS-CoV-2 infection in this population. The variants rs10774671, rs4804803, and rs7528026 showed a significant relationship with mortality rates. SNPs rs10774671 and rs4804803 were inversely related to mortality rates and are more frequently present in the AFR population. The rs7528026 variant, which is more frequent in the AMR population, was positively related to mortality rates. The study has the potential to identify and correlate the genetic profile with epidemiological data, identify populations that are more susceptible to severe forms of COVID-19, and relate them to incidence and mortality.

An improved epigenetic counter to track mitotic age in normal and precancerous tissues

The cumulative number of stem cell divisions in a tissue, known as mitotic age, is thought to be a major determinant of cancer-risk. Somatic mutational and DNA methylation (DNAm) clocks are promising tools to molecularly track mitotic age, yet their relationship is underexplored and their potential for cancer risk prediction in normal tissues remains to be demonstrated. Here we build and validate an improved pan-tissue DNAm counter of total mitotic age called stemTOC. We demonstrate that stemTOC's mitotic age proxy increases with the tumor cell-of-origin fraction in each of 15 cancer-types, in precancerous lesions, and in normal tissues exposed to major cancer risk factors. Extensive benchmarking against 6 other mitotic counters shows that stemTOC compares favorably, specially in the preinvasive and normal-tissue contexts. By cross-correlating stemTOC to two clock-like somatic mutational signatures, we confirm the mitotic-like nature of only one of these. Our data points towards DNAm as a promising molecular substrate for detecting mitotic-age increases in normal tissues and precancerous lesions, and hence for developing cancer-risk prediction strategies.

The Initial COVID-19 Reliable Interactive DNA Methylation Markers and Biological Implications

Earlier research has established the existence of reliable interactive genomic biomarkers. However, reliable DNA methylation biomarkers, not to mention interactivity, have yet to be identified at the epigenetic level. This study, drawing from 865,859 methylation sites, discovered two miniature sets of Infinium MethylationEPIC sites, each having eight CpG sites (genes) to interact with each other and disease subtypes. They led to the nearly perfect (96.87-100% accuracy) prediction of COVID-19 patients from patients with other diseases or healthy controls. These CpG sites can jointly explain some post-COVID-19-related conditions. These CpG sites and the optimally performing genomic biomarkers reported in the literature become potential druggable targets. Among these CpG sites, cg16785077 (gene MX1), cg25932713 (gene PARP9), and cg22930808 (gene PARP9) at DNA methylation levels indicate that the initial SARS-CoV-2 virus may be better treated as a transcribed viral DNA into RNA virus, i.e., not as an RNA virus that has concerned scientists in the field. Such a discovery can significantly change the scientific thinking and knowledge of viruses.

Cell-free DNA methylation reveals cell-specific tissue injury and correlates with disease severity and patient outcomes in COVID-19

BACKGROUND

The recently identified methylation patterns specific to cell type allows the tracing of cell death dynamics at the cellular level in health and diseases. This study used COVID-19 as a disease model to investigate the efficacy of cell-specific cell-free DNA (cfDNA) methylation markers in reflecting or predicting disease severity or outcome.

METHODS

Whole genome methylation sequencing of cfDNA was performed for 20 healthy individuals, 20 cases with non-hospitalized COVID-19 and 12 cases with severe COVID-19 admitted to intensive care unit (ICU). Differentially methylated regions (DMRs) and gene ontology pathway enrichment analyses were performed to explore the locus-specific methylation difference between cohorts. The proportion of cfDNA derived from lung and immune cells to a given sample (i.e. tissue fraction) at cell-type resolution was estimated using a novel algorithm, which reflects lung injuries and immune response in COVID-19 patients and was further used to evaluate clinical severity and patient outcome.

RESULTS

COVID‑19 patients had globally reduced cfDNA methylation level compared with healthy controls. Compared with non-hospitalized COVID-19 patients, the cfDNA methylation pattern was significantly altered in severe patients with the identification of 11,156 DMRs, which were mainly enriched in pathways related to immune response. Markedly elevated levels of cfDNA derived from lung and more specifically alveolar epithelial cells, bronchial epithelial cells, and lung endothelial cells were observed in COVID-19 patients compared with healthy controls. Compared with non-hospitalized patients or healthy controls, severe COVID-19 had significantly higher cfDNA derived from B cells, T cells and granulocytes and lower cfDNA from natural killer cells. Moreover, cfDNA derived from alveolar epithelial cells had the optimal performance to differentiate COVID-19 with different severities, lung injury levels, SOFA scores and in-hospital deaths, with the area under the receiver operating characteristic curve of 0.958, 0.941, 0.919 and 0.955, respectively.

CONCLUSION

Severe COVID-19 has a distinct cfDNA methylation signature compared with non-hospitalized COVID-19 and healthy controls. Cell type-specific cfDNA methylation signature enables the tracing of COVID-19 related cell deaths in lung and immune cells at cell-type resolution, which is correlated with clinical severities and outcomes, and has extensive application prospects to evaluate tissue injuries in diseases with multi-organ dysfunction.

Epigenetic modulation of myeloid cell functions in HIV and SARS-CoV-2 infection

Myeloid cells play a vital role in innate immune responses as they recognize and phagocytose pathogens like viruses, present antigens, produce cytokines, recruit other immune cells to combat infections, and contribute to the attenuation of immune responses to restore homeostasis. Signal integration by pathogen recognition receptors enables myeloid cells to adapt their functions by a network of transcription factors and chromatin remodelers. This review provides a brief overview of the subtypes of myeloid cells and the main epigenetic regulation mechanisms. Special focus is placed on the epigenomic alterations in viral nucleic acids of HIV and SARS-CoV-2 along with the epigenetic changes in the host's myeloid cell compartment. These changes are important as they lead to immune suppression and promote the progression of the disease. Finally, we highlight some promising examples of 'epidrugs' that modulate the epigenome of immune cells and could be used as therapeutics for viral infections.

Epigenetic perspectives associated with COVID-19 infection and related cytokine storm: an updated review

PURPOSE

The COVID-19 pandemic caused by the novel Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) has put the world in a medical crisis for the past three years; nearly 6.3 million lives have been diminished due to the virus outbreak. This review aims to update the recent findings on COVID-19 infections from an epigenetic scenario and develop future perspectives of epi-drugs to treat the disease.

METHODS

Original research articles and review studies related to COVID-19 were searched and analyzed from the Google Scholar/PubMed/Medline databases mainly between 2019 and 2022 to brief the recent work.

RESULTS

Numerous in-depth studies of the mechanisms used by SARS-CoV-2 have been going on to minimize the consequences of the viral outburst. Angiotensin-Converting Enzyme 2 receptors and Transmembrane serine protease 2 facilitate viral entry to the host cells. Upon internalization, it uses the host machinery to replicate viral copies and alter the downstream regulation of the normal cells, causing infection-related morbidities and mortalities. In addition, several epigenetic regulations such as DNA methylation, acetylation, histone modifications, microRNA, and other factors (age, sex, etc.) are responsible for the regulations of viral entry, its immune evasion, and cytokine responses also play a major modulatory role in COVID-19 severity, which has been discussed in detail in this review.

CONCLUSION

Findings of epigenetic regulation of viral pathogenicity open a new window for epi-drugs as a possible therapeutical approach against COVID-19.

The role of DNA methylation in personalized medicine for immune-related diseases

Epigenetics functions as a bridge between host genetic & environmental factors, aiding in human health and diseases. Many immune-related diseases, including infectious and allergic diseases, have been linked to epigenetic mechanisms, particularly DNA methylation. In this review, we summarized an updated overview of DNA methylation and its importance in personalized medicine, and demonstrated that DNA methylation has excellent potential for disease prevention, diagnosis, and treatment in a personalized manner. The future implications and limitations of the DNA methylation study have also been well-discussed.

Epigenome-wide association analysis of infant bronchiolitis severity: a multicenter prospective cohort study

Bronchiolitis is the most common lower respiratory infection in infants, yet its pathobiology remains unclear. Here we present blood DNA methylation data from 625 infants hospitalized with bronchiolitis in a 17-center prospective study, and associate them with disease severity. We investigate differentially methylated CpGs (DMCs) for disease severity. We characterize the DMCs based on their association with cell and tissues types, biological pathways, and gene expression. Lastly, we also examine the relationships of severity-related DMCs with respiratory and immune traits in independent cohorts. We identify 33 DMCs associated with severity. These DMCs are differentially methylated in blood immune cells. These DMCs are also significantly enriched in multiple tissues (e.g., lung) and cells (e.g., small airway epithelial cells), and biological pathways (e.g., interleukin-1-mediated signaling). Additionally, these DMCs are associated with respiratory and immune traits (e.g., asthma, lung function, IgE levels). Our study suggests the role of DNA methylation in bronchiolitis severity.

A meta-analysis of immune-cell fractions at high resolution reveals novel associations with common phenotypes and health outcomes

BACKGROUND

Changes in cell-type composition of tissues are associated with a wide range of diseases and environmental risk factors and may be causally implicated in disease development and progression. However, these shifts in cell-type fractions are often of a low magnitude, or involve similar cell subtypes, making their reliable identification challenging. DNA methylation profiling in a tissue like blood is a promising approach to discover shifts in cell-type abundance, yet studies have only been performed at a relatively low cellular resolution and in isolation, limiting their power to detect shifts in tissue composition.

METHODS

Here we derive a DNA methylation reference matrix for 12 immune-cell types in human blood and extensively validate it with flow-cytometric count data and in whole-genome bisulfite sequencing data of sorted cells. Using this reference matrix, we perform a directional Stouffer and fixed effects meta-analysis comprising 23,053 blood samples from 22 different cohorts, to comprehensively map associations between the 12 immune-cell fractions and common phenotypes. In a separate cohort of 4386 blood samples, we assess associations between immune-cell fractions and health outcomes.

RESULTS

Our meta-analysis reveals many associations of cell-type fractions with age, sex, smoking and obesity, many of which we validate with single-cell RNA sequencing. We discover that naïve and regulatory T-cell subsets are higher in women compared to men, while the reverse is true for monocyte, natural killer, basophil, and eosinophil fractions. Decreased natural killer counts associated with smoking, obesity, and stress levels, while an increased count correlates with exercise and sleep. Analysis of health outcomes revealed that increased naïve CD4 + T-cell and N-cell fractions associated with a reduced risk of all-cause mortality independently of all major epidemiological risk factors and baseline co-morbidity. A machine learning predictor built only with immune-cell fractions achieved a C-index value for all-cause mortality of 0.69 (95%CI 0.67-0.72), which increased to 0.83 (0.80-0.86) upon inclusion of epidemiological risk factors and baseline co-morbidity.

CONCLUSIONS

This work contributes an extensively validated high-resolution DNAm reference matrix for blood, which is made freely available, and uses it to generate a comprehensive map of associations between immune-cell fractions and common phenotypes, including health outcomes.

Cellular and molecular features of COVID-19 associated ARDS: therapeutic relevance

The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection can be asymptomatic or cause a disease (COVID-19) characterized by different levels of severity. The main cause of severe COVID-19 and death is represented by acute (or acute on chronic) respiratory failure and acute respiratory distress syndrome (ARDS), often requiring hospital admission and ventilator support.The molecular pathogenesis of COVID-19-related ARDS (by now termed c-ARDS) is still poorly understood. In this review we will discuss the genetic susceptibility to COVID-19, the pathogenesis and the local and systemic biomarkers correlated with c-ARDS and the therapeutic options that target the cell signalling pathways of c-ARDS.

Sex-biased expression of the TLR7 gene in severe COVID-19 patients: Insights from transcriptomics and epigenomics

There is abundant epidemiological data indicating that the incidence of severe cases of coronavirus disease (COVID-19) is significantly higher in males than females worldwide. Moreover, genetic variation at the X-chromosome linked TLR7 gene has been associated with COVID-19 severity. It has been suggested that the sex-biased incidence of COVID-19 might be related to the fact that TLR7 escapes X-chromosome inactivation during early embryogenesis in females, thus encoding a doble dose of its gene product compared to males. We analyzed TLR7 expression in two acute phase cohorts of COVID-19 patients that used two different technological platforms, one of them in a multi-tissue context including saliva, nasal, and blood samples, and a third cohort that included different post-infection timepoints of long-COVID-19 patients. We additionally explored methylation patterns of TLR7 using epigenomic data from an independent cohort of COVID-19 patients stratified by severity and sex. In line with genome-wide association studies, we provide supportive evidence indicating that TLR7 has altered CpG methylation patterns and it is consistently downregulated in males compared to females in the most severe cases of COVID-19.