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      Genome-wide neonatal epigenetic changes associated with maternal exposure to the COVID-19 pandemic

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          Abstract

          Background

          During gestation, stressors to the fetus, including viral exposure or maternal psychological distress, can fundamentally alter the neonatal epigenome, and may be associated with long-term impaired developmental outcomes. The impact of in utero exposure to the COVID-19 pandemic on the newborn epigenome has yet to be described.

          Methods

          This study aimed to determine whether there are unique epigenetic signatures in newborns who experienced otherwise healthy pregnancies that occurred during the COVID-19 pandemic (Project RESCUE). The pre-pandemic control and pandemic cohorts (Project RESCUE) included in this study are part of a prospective observational and longitudinal cohort study that evaluates the impact of elevated prenatal maternal stress during the COVID-19 pandemic on early childhood neurodevelopment.

          Using buccal swabs collected at birth, differential DNA methylation analysis was performed using the Infinium MethylationEPIC arrays and linear regression analysis. Pathway analysis and gene ontology enrichment were performed on resultant gene lists.

          Results

          Widespread differential methylation was found between neonates exposed in utero to the pandemic and pre-pandemic neonates. In contrast, there were no apparent epigenetic differences associated with maternal COVID-19 infection during pregnancy. Differential methylation was observed among genomic sites that underpin important neurological pathways that have been previously reported in the literature to be differentially methylated because of prenatal stress, such as NR3C1.

          Conclusions

          The present study reveals potential associations between exposure to the COVID-19 pandemic during pregnancy and subsequent changes in the newborn epigenome. While this finding warrants further investigation, it is a point that should be considered in any study assessing newborn DNA methylation studies obtained during this period, even in otherwise healthy pregnancies.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12920-023-01707-4.

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          Most cited references67

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            A Global Measure of Perceived Stress

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              Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

              Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
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                Author and article information

                Contributors
                evilain@hs.uci.edu
                edelot@childrensnational.org
                Journal
                BMC Med Genomics
                BMC Med Genomics
                BMC Medical Genomics
                BioMed Central (London )
                1755-8794
                30 October 2023
                30 October 2023
                2023
                : 16
                : 268
                Affiliations
                [1 ]GRID grid.239560.b, ISNI 0000 0004 0482 1586, Center for Genetic Medicine Research, , Children’s National Research & Innovation Campus, ; Washington, DC USA
                [2 ]Department of Genomics & Precision Medicine, George Washington University, ( https://ror.org/00y4zzh67) Washington, DC USA
                [3 ]GRID grid.239560.b, ISNI 0000 0004 0482 1586, Developing Brain Institute, , Children’s National Hospital, ; Washington, DC USA
                [4 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Institute for Clinical and Translational Science, , University of California, ; Irvine, CA USA
                Article
                1707
                10.1186/s12920-023-01707-4
                10614377
                37899449
                4b3570de-f245-41bf-8ddc-64399e830966
                © The Author(s) 2023

                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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 20 March 2023
                : 17 October 2023
                Funding
                Funded by: A. James Clark Distinguished Chair of Molecular Genetics
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: NHLBI R01 HL116585-01
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100007857, Intellectual and Developmental Disabilities Research Center;
                Funded by: FundRef http://dx.doi.org/10.13039/100020056, A. James and Alice B. Clark Foundation;
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2023

                Genetics
                epigenetics,dna methylation,sars-cov-2,covid-19 pandemic,perinatal stress
                Genetics
                epigenetics, dna methylation, sars-cov-2, covid-19 pandemic, perinatal stress

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