Background: Neonatal sepsis is a systemic condition widely affecting preterm infants and characterized by pro-inflammatory and anti-inflammatory responses. However, its pathophysiology is not yet fully understood. Epigenetics regulates the immune system, and its alteration leads to the impaired immune response underlying sepsis. DNA methylation may contribute to sepsis-induced immunosuppression which, if persistent, will cause long-term adverse effects in neonates.Objective: To analyze the methylome of preterm infants in order to determine whether there are DNA methylation marks that may shed light on the pathophysiology of neonatal sepsis.Design: Prospective observational cohort study performed in the neonatal intensive care unit (NICU) of a tertiary care center.Patients: Eligible infants were premature ≤32 weeks admitted to the NICU with clinical suspicion of sepsis. The methylome analysis was performed in DNA from blood using Infinium Human Methylation EPIC microarrays to uncover methylation marks.Results: Methylation differential analysis revealed an alteration of methylation levels in genomic regions involved in inflammatory pathways which participate in both the innate and the adaptive immune response. Moreover, differences between early and late onset sepsis as compared to normal controls were assessed.Conclusions: DNA methylation marks can serve as a biomarker for neonatal sepsis and even contribute to differentiating between early and late onset sepsis.
Background: Neonatal sepsis is a heterogeneous condition affecting preterm infants whose underlying mechanisms remain unknown. The analysis of changes in the DNA methylation pattern can contribute to improving the understanding of molecular pathways underlying disease pathophysiology. Methylation EPIC 850K BeadChip technology is an excellent tool for genome-wide methylation analyses and the detection of differentially methylated regions (DMRs). Objective: The aim is to identify DNA methylation traits in complex diseases, such as neonatal sepsis, using data from Methylation EPIC 850K BeadChip arrays. Methods: Two different bioinformatic methods, DMRcate (a supervised approach) and mCSEA (an unsupervised approach), were used to identify DMRs using EPIC data from leukocytes of neonatal septic patients. Here, we describe with detail the implementation of both methods as well as their applicability, briefly discussing the results obtained for neonatal sepsis. Results: Differences in methylation levels were observed in neonatal sepsis patients. Moreover, differences were identified between the two subsets of the disease: Early-Onset neonatal Sepsis (EOS) and Late-Onset Neonatal Sepsis (LOS). Conclusion: This approach by using DMRcate and mCSA helped us to gain insight into the intricate mechanisms that may drive EOS and LOS development and progression in newborns.
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