Saiyed, N. | bioRxiv Science

bioRxiv2025

Whole-genome DNA methylation profiling in COVID-19 positive patients reveals alterations in pathways linked to neurological dysfunction

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmissible RNA betacoronavirus, causing coronavirus disease-19 (COVID-19). Infection with SARS-CoV-2 can result in a broad spectrum of clinical outcomes, ranging from asymptomatic or mild to a severe, deadly illness. Emerging evidence suggests SARS-CoV-2 affects host gene regulation through epigenetic mechanisms, such as DNA methylation, potentially contributing to immune dysregulation and post-acute sequelae, including neurological and psychiatric disorders. However, the extent and functional relevance of these epigenetic changes remain uncertain. Methods and resultsWe employed whole-genome bisulfite sequencing to profile DNA methylation in peripheral blood from SARS-CoV-2-positive patients across a spectrum of symptom severity, ranging from asymptomatic to severe (n=101), in comparison to SARS-CoV-2-negative individuals (n=105). We observed a widespread hypomethylation in the genomes of infected individuals, which was more pronounced in severe cases. Notably, we identified differentially methylated genes in patients with mild (19 genes), moderate (19 genes), and severe (35 genes) symptoms. These genes included those involved in canonical immune responses as well as known to be linked to neurodegenerative diseases. Subsequent pathway enrichment analysis further supported the significant association between the differentially methylated genes and those implicated in Alzheimers and Parkinsons disease, as well as neuropsychiatric conditions, suggesting potential epigenetic links between acute SARS-CoV-2 infection and long-term neurological outcomes. This is one of the first studies to comprehensively map severity-stratified genome-wide DNA methylation changes in COVID-19 patients. ConclusionOur findings underscore the potential importance of epigenetic regulation in the acute responses to SARS-CoV-2 infection and highlight an overlap with epigenetic mechanisms relevant for neuropsychiatric disease processes.

neuroscience↗

bioRxiv2018

Placental epigenetics for evaluation of fetal congenital heart defects: Ventricular septal defect (VSD)

Ventricular Septal Defect (VSD), the most common congenital heart defect, is characterized by a hole in the septum between the right and left ventricles. The pathogenesis of VSD is unknown in most clinical cases. There is a paucity of data relevant to epigenetic changes in VSD. The placenta is a fetal tissue and is a potentially useful surrogate for the evaluation of fetal organ development. To understand epigenetic mechanisms that may play a role in the development of VSD, a genome-wide DNA methylation assay of the placentas of 8 term subjects with isolated VSD and no known or suspected genetic syndromes and 10 normal controls was performed using the Illumina HumanMethylation450 BeadChip assay. The study identified a total of 80 highly accurate potential epigenomic markers in 80 genes for the detection of VSD; area under the receiver operating characteristic curve (AUC ROC) = 1.0 with significant 95% CI (FDR) p-values < 0.05. The biological processes and functions for these differentially methylated genes are known to be associated with heart development or heart disease, including cardiac ventricle development (HEY2, ISL1), heart looping (SRF), cardiac muscle cell differentiation (ACTC1, HEY2), cardiac septum development (ISL1), heart morphogenesis (SRF, HEY2, ISL1, HEYL), Notch signaling pathway (HEY2, HEYL), cardiac chamber development (ISL1), and cardiac muscle tissue development (ACTC1, ISL1). The study also identified eight microRNA genes that have the potential to be biomarkers for the early detection of VSD including miR-191, miR-548F1, miR-148A, miR-423, miR-92B, miR-611, miR-2110, and miR-548H4. To our knowledge this is the first report in which placental analysis has been used for determining the pathogenesis of and predicting CHD.

genomics↗