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      Maternal dysglycaemia, changes in the infant’s epigenome modified with a diet and physical activity intervention in pregnancy: Secondary analysis of a randomised control trial

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          Abstract

          Background

          Higher maternal plasma glucose (PG) concentrations, even below gestational diabetes mellitus (GDM) thresholds, are associated with adverse offspring outcomes, with DNA methylation proposed as a mediating mechanism. Here, we examined the relationships between maternal dysglycaemia at 24 to 28 weeks’ gestation and DNA methylation in neonates and whether a dietary and physical activity intervention in pregnant women with obesity modified the methylation signatures associated with maternal dysglycaemia.

          Methods and findings

          We investigated 557 women, recruited between 2009 and 2014 from the UK Pregnancies Better Eating and Activity Trial (UPBEAT), a randomised controlled trial (RCT), of a lifestyle intervention (low glycaemic index (GI) diet plus physical activity) in pregnant women with obesity (294 contol, 263 intervention). Between 27 and 28 weeks of pregnancy, participants had an oral glucose (75 g) tolerance test (OGTT), and GDM diagnosis was based on diagnostic criteria recommended by the International Association of Diabetes and Pregnancy Study Groups (IADPSG), with 159 women having a diagnosis of GDM. Cord blood DNA samples from the infants were interrogated for genome-wide DNA methylation levels using the Infinium Human MethylationEPIC BeadChip array. Robust regression was carried out, adjusting for maternal age, smoking, parity, ethnicity, neonate sex, and predicted cell-type composition. Maternal GDM, fasting glucose, 1-h, and 2-h glucose concentrations following an OGTT were associated with 242, 1, 592, and 17 differentially methylated cytosine-phosphate-guanine (dmCpG) sites (false discovery rate (FDR) ≤ 0.05), respectively, in the infant’s cord blood DNA. The most significantly GDM-associated CpG was cg03566881 located within the leucine-rich repeat-containing G-protein coupled receptor 6 (LGR6) (FDR = 0.0002). Moreover, we show that the GDM and 1-h glucose-associated methylation signatures in the cord blood of the infant appeared to be attenuated by the dietary and physical activity intervention during pregnancy; in the intervention arm, there were no GDM and two 1-h glucose-associated dmCpGs, whereas in the standard care arm, there were 41 GDM and 160 1-h glucose-associated dmCpGs. A total of 87% of the GDM and 77% of the 1-h glucose-associated dmCpGs had smaller effect sizes in the intervention compared to the standard care arm; the adjusted r 2 for the association of LGR6 cg03566881 with GDM was 0.317 (95% confidence interval (CI) 0.012, 0.022) in the standard care and 0.240 (95% CI 0.001, 0.015) in the intervention arm. Limitations included measurement of DNA methylation in cord blood, where the functional significance of such changes are unclear, and because of the strong collinearity between treatment modality and severity of hyperglycaemia, we cannot exclude that treatment-related differences are potential confounders.

          Conclusions

          Maternal dysglycaemia was associated with significant changes in the epigenome of the infants. Moreover, we found that the epigenetic impact of a dysglycaemic prenatal maternal environment appeared to be modified by a lifestyle intervention in pregnancy. Further research will be needed to investigate possible medical implications of the findings.

          Trial registration

          ISRCTN89971375.

          Abstract

          Karen Lillycrop and colleagues investigate whether a dietary and physical activity intervention in pregnant women with obesity modified the methylation signatures associated with maternal dysglycaemia.

          Author summary

          Why was this study done?
          • The incidence of gestational diabetes is increasing worldwide, concurrent with a rise in obesity with children born to mothers with gestational diabetes mellitus (GDM) having a heightened risk of obesity and metabolic disease, perpetuating an intergenerational cycle of metabolic disease.

          • High circulating levels of glucose in mothers with GDM have been suggested to trigger epigenetic changes (chemical modifications that affect gene activity and the amount of protein produced from them) during development of the fetus, resulting in an increased susceptibility to metabolic disease in later life.

          • As little is known of the epigenetic changes induced by maternal GDM within mothers with obesity, a high-risk population for GDM, we examined relationships between DNA methylation in infants born to mothers with obesity who developed GDM and those who did not and the mother’s blood glucose concentration. We then examined whether a dietary and physical activity intervention during pregnancy, designed to improve maternal glycaemia, modified the DNA methylation changes in the infant associated with maternal GDM exposure.

          What did the researchers do and find?
          • Using samples from the UK Pregnancies Better Eating and Activity Trial (UPBEAT), a randomised controlled trial (RCT) of lifestyle intervention (low glycaemic index (GI) diet plus physical activity) versus standard care in pregnant women with obesity, we investigated cord blood DNA methylation levels from 557 newborn infants.

          • Maternal GDM status and high circulating maternal glucose levels were associated with modest changes in DNA methylation in the infants.

          • The methylation changes observed in the infant associated with maternal GDM exposure appeared to be reduced by the pregnancy lifestyle intervention.

          What do these findings mean?
          • These findings suggest that the impact of high maternal circulating glucose levels on DNA methylation in the infant can be modified by a lifestyle intervention in pregnancy.

          • Follow-up studies are needed to establish whether the reduction in DNA methylation changes observed in infants from mothers with GDM undertaking the lifestyle intervention is accompanied by improved health outcomes of the children in later life.

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          Most cited references73

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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 reference for human genetic variation

            The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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              Adjusting batch effects in microarray expression data using empirical Bayes methods.

              Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
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                Author and article information

                Contributors
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – original draft
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – original draft
                Role: Formal analysisRole: MethodologyRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: ValidationRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: Formal analysisRole: Writing – review & editing
                Role: Formal analysisRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draft
                Role: ConceptualizationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draft
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draft
                Role: Academic Editor
                Journal
                PLoS Med
                PLoS Med
                plos
                plosmed
                PLoS Medicine
                Public Library of Science (San Francisco, CA USA )
                1549-1277
                1549-1676
                5 November 2020
                November 2020
                : 17
                : 11
                : e1003229
                Affiliations
                [1 ] Biological Sciences, Institute of Developmental Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, United Kingdom
                [2 ] Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
                [3 ] MRC Lifecourse Epidemiology Unit, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
                [4 ] Department of Women and Children’s Health, School of Life Course Sciences, King’s College London, London, United Kingdom
                [5 ] NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Trust, Southampton, United Kingdom
                [6 ] BC Childrens Hospital Research Institute, Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, Canada
                Chinese University of Hong Kong, CHINA
                Author notes

                Competing Interests:I have read the journal's policy and the authors of this manuscript have the following competing interests: KMG and GCB have received reimbursement for speaking at conferences sponsored by companies selling nutritional products. KAL and KMG are part of academic research programs that have received research funding from Abbott Nutrition, Nestec, Danone and BenevolentAI Bio Ltd. GCB has received research funding from Abbott Nutrition, Nestec and Danone and has been a scientific advisor to BASF. LP have received funding from Abbott Nutrition and Danone.The remaining authors declare no competing interests.

                ‡ EA and NTK are joint first authors. LP, KMG, and KAL are joint senior authors.

                Author information
                https://orcid.org/0000-0002-9477-1564
                https://orcid.org/0000-0001-7518-9096
                https://orcid.org/0000-0002-7797-8571
                https://orcid.org/0000-0003-0958-6725
                https://orcid.org/0000-0001-5268-4203
                https://orcid.org/0000-0003-4963-4242
                https://orcid.org/0000-0002-2383-1742
                https://orcid.org/0000-0001-7904-7933
                https://orcid.org/0000-0001-5695-9446
                https://orcid.org/0000-0002-7665-2967
                https://orcid.org/0000-0001-7979-0508
                https://orcid.org/0000-0002-4643-0618
                https://orcid.org/0000-0001-7350-5489
                Article
                PMEDICINE-D-20-00268
                10.1371/journal.pmed.1003229
                7643947
                33151971
                323db3b9-20d6-4788-b738-9ac343da7c4a
                © 2020 Antoun et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 30 January 2020
                : 6 October 2020
                Page count
                Figures: 5, Tables: 6, Pages: 29
                Funding
                Funded by: Diabetes UK
                Award ID: 16/0005454
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000272, National Institute for Health Research;
                Award ID: RP-0407-10452
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100014589, Chief Scientist Office, Scottish Government Health and Social Care Directorate;
                Award ID: CZB/A/680
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MC_UU_12011/4
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000272, National Institute for Health Research;
                Award ID: NF-SI-0515-10042
                Award Recipient :
                Funded by: EU
                Award ID: 573651EPP-1-2016-1-DE-EPPKA2-CBHE-JP
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000274, British Heart Foundation;
                Award ID: FS/17/71/32953
                Award Recipient :
                Funding: EA, NTK, PT, JH, GCB, SLW, PS,KG, LP and KAL were funded by the Diabetes UK (16/0005454) ( www.diabetes.og.uk). The UPBEAT trial was supported by the National Institute for Health Research (NIHR) ( www.nihr.ac.uk) under the Programme Grants for Applied Research Programme (RP-0407-10452) and the Chief Scientist Office, Scottish Government Health Directorates (Edinburgh) (CZB/A/680) ( www.cso.scot.nhs.uk). LP and SW are supported by the NIHR Biomedical Research Centre at Guys & St Thomas NHS Foundation Trust & King’s College London. KMG is supported by the UK Medical Research Council (MC_UU_12011/4)( www.mrc.ukri.org), the National Institute for Health Research (NIHR Senior Investigator (NF-SI-0515-10042) and the NIHR Southampton Biomedical Research Centre) and the European Union (Erasmus+ Programme Early Nutrition eAcademy Southeast Asia-573651-EPP-1-2016-1-DE-EPPKA2-CBHE-JP) ( www.ec.europa.eu). KVD is supported by the British Heart Foundation FS/17/71/32953( www.bhf.org.uk). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
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                Custom metadata
                The data underlying the results presented in the study are available from Gene Expression Omnibus under accession no. GSE141065.

                Medicine
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