Maternal haemoglobin levels in pregnancy and child DNA methylation : a study in the pregnancy and childhood epigenetics consortium

Altered maternal haemoglobin levels during pregnancy are associated with pre-clinical and clinical conditions affecting the fetus. Evidence from animal models suggests that these associations may be partially explained by differential DNA methylation in the newborn with possible long-term consequences. To test this in humans, we meta-analyzed the epigenome-wide associations of maternal haemoglobin levels during pregnancy with offspring DNA methylation in 3,967 newborn cord blood and 1,534 children and 1,962 adolescent whole-blood samples derived from 10 cohorts. DNA methylation was measured using Illumina Infinium Methylation 450K or MethylationEPIC arrays covering 450,000 and 850,000 methylation sites, respectively. There was no statistical support for the association of maternal haemoglobin levels with offspring DNA methylation either at individual methylation sites or clustered in regions. For most participants, maternal haemoglobin levels were within the normal range in the current study, whereas adverse perinatal outcomes often arise at the extremes. Thus, this study does not rule out the possibility that associations with offspring DNA methylation might be seen in studies with more extreme maternal haemoglobin levels.Peer reviewe

Longitudinal associations of DNA methylation and sleep in children : a meta-analysis

Publisher Copyright: © 2022, The Author(s).Background: Sleep is important for healthy functioning in children. Numerous genetic and environmental factors, from conception onwards, may influence this phenotype. Epigenetic mechanisms such as DNA methylation have been proposed to underlie variation in sleep or may be an early-life marker of sleep disturbances. We examined if DNA methylation at birth or in school age is associated with parent-reported and actigraphy-estimated sleep outcomes in children. Methods: We meta-analysed epigenome-wide association study results. DNA methylation was measured from cord blood at birth in 11 cohorts and from peripheral blood in children (4–13 years) in 8 cohorts. Outcomes included parent-reported sleep duration, sleep initiation and fragmentation problems, and actigraphy-estimated sleep duration, sleep onset latency and wake-after-sleep-onset duration. Results: We found no associations between DNA methylation at birth and parent-reported sleep duration (n = 3658), initiation problems (n = 2504), or fragmentation (n = 1681) (p values above cut-off 4.0 × 10–8). Lower methylation at cg24815001 and cg02753354 at birth was associated with longer actigraphy-estimated sleep duration (p = 3.31 × 10–8, n = 577) and sleep onset latency (p = 8.8 × 10–9, n = 580), respectively. DNA methylation in childhood was not cross-sectionally associated with any sleep outcomes (n = 716–2539). Conclusion: DNA methylation, at birth or in childhood, was not associated with parent-reported sleep. Associations observed with objectively measured sleep outcomes could be studied further if additional data sets become available.Peer reviewe

Maternal haemoglobin levels in pregnancy and child DNA methylation: a study in the pregnancy and childhood epigenetics consortium

Altered maternal haemoglobin levels during pregnancy are associated with pre-clinical and clinical conditions affecting the fetus. Evidence from animal models suggests that these associations may be partially explained by differential DNA methylation in the newborn with possible long-term consequences. To test this in humans, we meta-analyzed the epigenome-wide associations of maternal haemoglobin levels during pregnancy with offspring DNA methylation in 3,967 newborn cord blood and 1,534 children and 1,962 adolescent whole-blood samples derived from 10 cohorts. DNA methylation was measured using Illumina Infinium Methylation 450K or MethylationEPIC arrays covering 450,000 and 850,000 methylation sites, respectively. There was no statistical support for the association of maternal haemoglobin levels with offspring DNA methylation either at individual methylation sites or clustered in regions. For most participants, maternal haemoglobin levels were within the normal range in the current study, whereas adverse perinatal outcomes often arise at the extremes. Thus, this study does not rule out the possibility that associations with offspring DNA methylation might be seen in studies with more extreme maternal haemoglobin levels

Maternal–fetal stress and DNA methylation signatures in neonatal saliva: an epigenome-wide association study

Background: Maternal stress before, during and after pregnancy has profound effects on the development and lifelong function of the infant’s neurocognitive development. We hypothesized that the programming of the central nervous system (CNS), hypothalamic–pituitary–adrenal (HPA) axis and autonomic nervous system (ANS) induced by prenatal stress (PS) is reflected in electrophysiological and epigenetic biomarkers. In this study, we aimed to find noninvasive epigenetic biomarkers of PS in the newborn salivary DNA. Results: A total of 728 pregnant women were screened for stress exposure using Cohen Perceived Stress Scale (PSS), 164 women were enrolled, and 114 dyads were analyzed. Prenatal Distress Questionnaire (PDQ) was also administered to assess specific pregnancy worries. Transabdominal fetal electrocardiograms (taECG) were recorded to derive coupling between maternal and fetal heart rates resulting in a ‘Fetal Stress Index’ (FSI). Upon delivery, we collected maternal hair strands for cortisol measurements and newborn’s saliva for epigenetic analyses. DNA was extracted from saliva samples, and DNA methylation was measured using EPIC BeadChip array (850 k CpG sites). Linear regression was used to identify associations between PSS/PDQ/FSI/Cortisol and DNA methylation. We found epigenome-wide significant associations for 5 CpG with PDQ and cortisol at FDR < 5%. Three CpGs were annotated to genes (Illumina Gene annotation file): YAP1, TOMM20 and CSMD1, and two CpGs were located approximately lay at 50 kb from SSBP4 and SCAMP1. In addition, two differentiated methylation regions (DMR) related to maternal stress measures PDQ and cortisol were found: DAXX and ARL4D. Conclusions: Genes annotated to these CpGs were found to be involved in secretion and transportation, nuclear signaling, Hippo signaling pathways, apoptosis, intracellular trafficking and neuronal signaling. Moreover, some CpGs are annotated to genes related to autism, post-traumatic stress disorder (PTSD) and schizophrenia. However, our results should be viewed as hypothesis generating until replicated in a larger sample. Early assessment of such noninvasive PS biomarkers will allow timelier detection of babies at risk and a more effective allocation of resources for early intervention programs to improve child development. A biomarker-guided early intervention strategy is the first step in the prevention of future health problems, reducing their personal and societal impact.Fil: Sharma, Ritika. Technische Universitat München; AlemaniaFil: Frasch, Martin Gerbert. University of Washington; Estados UnidosFil: Zelgert, Camila. Technische Universitat München; AlemaniaFil: Zimmermann, Peter. Technische Universitat München; AlemaniaFil: Fabre, Bibiana. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Fisiopatología y Bioquímica Clínica; ArgentinaFil: Wilson, Rory. Helmholtz Zentrum Munich; AlemaniaFil: Waldenberger, Melanie. Helmholtz Zentrum Munich; AlemaniaFil: MacDonald, James W.. University of Washington; Estados UnidosFil: Bammler, Theo K.. University of Washington; Estados UnidosFil: Lobmaier, Silvia M.. Technische Universitat München; AlemaniaFil: Antonelli, Marta Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; Argentin

Epigenome-Wide Analysis of DNA Methylation and Optimism in Women and Men

Objective: Higher optimism is associated with reduced mortality and a lower risk of age-related chronic diseases. DNA methylation (DNAm) may provide insight into mechanisms underlying these relationships. We hypothesized that DNAm would differ among older individuals who are more versus less optimistic. Methods: Using cross-sectional data from two population-based cohorts of women with diverse races/ethnicities (n = 3816) and men (only White, n = 667), we investigated the associations of optimism with epigenome-wide leukocyte DNAm. Random-effects meta-analyses were subsequently used to pool the individual results. Significantly differentially methylated cytosine-phosphate-guanines (CpGs) were identified by the “number of independent degrees of freedom” approach: effective degrees of freedom correction using the number of principal components (PCs), explaining >95% of the variation of the DNAm data (PC-correction). We performed regional analyses using comb-p and pathway analyses using the Ingenuity Pathway Analysis software. Results: We found that essentially all CpGs (total probe N = 359,862) were homogeneous across sex and race/ethnicity in the DNAm-optimism association. In the single CpG site analyses based on homogeneous CpGs, we identified 13 significantly differentially methylated probes using PC-correction. We found four significantly differentially methylated regions and two significantly differentially methylated pathways. The annotated genes from the single CpG site and regional analyses are involved in psychiatric disorders, cardiovascular disease, cognitive impairment, and cancer. Identified pathways were related to cancer, and neurodevelopmental and neurodegenerative disorders. Conclusion: Our findings provide new insights into possible mechanisms underlying optimism and health

Prenatal metal exposure, cord blood DNA methylation and persistence in childhood: an epigenome-wide association study of 12 metals

Background Prenatal exposure to essential and non-essential metals impacts birth and child health, including fetal growth and neurodevelopment. DNA methylation (DNAm) may be involved in pathways linking prenatal metal exposure and health. In the Project Viva cohort, we analyzed the extent to which metals (As, Ba, Cd, Cr, Cs, Cu, Hg, Mg, Mn, Pb, Se, and Zn) measured in maternal erythrocytes were associated with differentially methylated positions (DMPs) and regions (DMRs) in cord blood and tested if associations persisted in blood collected in mid-childhood. We measured metal concentrations in first-trimester maternal erythrocytes, and DNAm in cord blood (N = 361) and mid-childhood blood (N = 333, 6–10 years) with the Illumina HumanMethylation450 BeadChip. For each metal individually, we tested for DMPs using linear models (considered significant at FDR < 0.05), and for DMRs using comb-p (Sidak p < 0.05). Covariates included biologically relevant variables and estimated cell-type composition. We also performed sex-stratified analyses. Results Pb was associated with decreased methylation of cg20608990 (CASP8) (FDR = 0.04), and Mn was associated with increased methylation of cg02042823 (A2BP1) in cord blood (FDR = 9.73 × 10–6). Both associations remained significant but attenuated in blood DNAm collected at mid-childhood (p < 0.01). Two and nine Mn-associated DMPs were identified in male and female infants, respectively (FDR < 0.05), with two and six persisting in mid-childhood (p < 0.05). All metals except Ba and Pb were associated with ≥ 1 DMR among all infants (Sidak p < 0.05). Overlapping DMRs annotated to genes in the human leukocyte antigen (HLA) region were identified for Cr, Cs, Cu, Hg, Mg, and Mn. Conclusions Prenatal metal exposure is associated with DNAm, including DMRs annotated to genes involved in neurodevelopment. Future research is needed to determine if DNAm partially explains the relationship between prenatal metal exposures and health outcomes

An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays

Epigenome-wide association studies (EWASs) have become increasingly popular for studying DNA methylation (DNAm) variations in complex diseases. The Illumina methylation arrays provide an economical, high-throughput and comprehensive platform for measuring methylation status in EWASs. A number of software tools have been developed for identifying disease-associated differentially methylated regions (DMRs) in the epigenome. However, in practice, we found these tools typically had multiple parameter settings that needed to be specified and the performance of the software tools under different parameters was often unclear. To help users better understand and choose optimal parameter settings when using DNAm analysis tools, we conducted a comprehensive evaluation of 4 popular DMR analysis tools under 60 different parameter settings. In addition to evaluating power, precision, area under precision-recall curve, Matthews correlation coefficient, F1 score and type I error rate, we also compared several additional characteristics of the analysis results, including the size of the DMRs, overlap between the methods and execution time. The results showed that none of the software tools performed best under their default parameter settings, and power varied widely when parameters were changed. Overall, the precision of these software tools were good. In contrast, all methods lacked power when effect size was consistent but small. Across all simulation scenarios, comb-p consistently had the best sensitivity as well as good control of false-positive rate

Multi-Omics Approaches in Immunological Research

The immune system plays a vital role in health and disease, and is regulated through a complex interactive network of many different immune cells and mediators. To understand the complexity of the immune system, we propose to apply a multi-omics approach in immunological research. This review provides a complete overview of available methodological approaches for the different omics data layers relevant for immunological research, including genetics, epigenetics, transcriptomics, proteomics, metabolomics, and cellomics. Thereafter, we describe the various methods for data analysis as well as how to integrate different layers of omics data. Finally, we discuss the possible applications of multi-omics studies and opportunities they provide for understanding the complex regulatory networks as well as immune variation in various immune-related diseases

Epigenetic Profiling of Human Placenta Throughout Early Gestation

The differentiation of the placenta, especially during early gestation is important for pregnancy success. Whilst emerging evidence has shown that DNA methylation (DNAm) in placenta varies over gestation, to date, most studies have compared DNAm in a relatively short gestational age range. Little is known about the dynamics of DNAm patterns across early pregnancy from as early as 6 weeks’, and as late as 23 weeks’ gestation. This comprehensive analysis of the placental methylome will help to elucidate the previously poorly understood relationship between DNA methylation, placental development, and complications of pregnancy. The overall aim of this thesis is to characterise and interpret this relationship through the analysis of DNA methylation profiles from strictly phenotyped samples of human placenta, and matched maternal leukocytes, across early to mid-gestation, through bioinformatics analyses. All data herein were obtained using the Illumina Infinium® MethylationEPIC BeadChips (EPIC arrays). In this study, we first compared three different bioinformatics methods implemented with different algorithms for detecting differentially methylated regions (DMRs) between sample groups. Subsequent to these analyses we aimed to establish an inhouse pipeline for the quality control and analysis of EPIC array methylation data obtained from both GEO database, and from this study. The three methods used for the discovery of DMRs were bumphunter, Probe Lasso and DMRcate. After comparison of these three methods we were able to demonstrate unique advantages and disadvantages of each. Overall, DMRcate was considered the most appropriate method for the identification of DMRs in EPIC array methylation data from our placenta samples, with a better sensitivity than Probe Lasso and bumphunter methods and less false positive regions than the Probe Lasso method. Next, the established in-house pipeline was used for array data analyses. Initial unsupervised clustering using a PCA analysis of methylation data revealed several outliers within our data. These 6 samples did not cluster as expected with other placenta samples of the same gestational age. To investigate whether these outliers were caused by complicated pregnancy or technical issues, we compared the data from samples in our study with publicly available data from samples of placenta and placenta-associated tissues. Given the otherwise strong gestational age clustering observed in the PCA analysis, and the unknown pregnancy outcomes of the tissue in question, we hypothesized that the samples which failed to cluster within their gestational age group would cluster with other like samples. After preprocessing we included the public data in a new PCA analysis with results indicating that the outliers we identified were not pure placenta villous tissue, but rather these samples were a mix of both placental and maternal tissue. After assessing the quality of all placenta samples, and removing samples identified as containing maternal tissue, an epigenome wide DNAm study of placenta (n = 125) across 6-23 weeks’ gestation was performed. Placental DNA methylation changed throughout gestation, with methylation differences also found between groups up to and after 10 weeks’ gestation. Since maternal blood starts to flow into placenta at approximately 10 weeks’ gestation, these DNA methylation changes could be associated with a change in oxygen tension in the placenta. Further to the DNA methylation changes identified across early gestation, DNA methylation levels at partially methylated domains and imprinting control regions were stable in placenta across early gestation, suggesting an association with these regions and the basic function and development of the human placenta. Finally, DNA methylation changes of maternal leukocytes from matched maternal blood were investigated. We identified DNA methylation changes in maternal leukocytes associated with maternal smoking and with maternal age, and to a lesser degree we were able to identify changes in DNA methylation of maternal leukocytes that were associated with gestational age. Changes of cell proportion for maternal leukocytes were identified and a potential accelerated aging was found in pregnant women compared with non-pregnant women. These findings provide more information for real time assessment of pregnancy health using DNA methylation in maternal circulating leukocytes. In summary, the research reported here provides an insight into performing bioinformatics analyses and quality control of placental DNA methylation data obtained from EPIC array analyses. Further, this thesis adds to our understanding of placental development, health and disease through the characterisation of the DNA methylome of placenta and matched maternal leukocytes across early gestation.Thesis (Ph.D.) -- University of Adelaide, Adelaide Medical School, 202

Epigenetic hotspots in cancer

DNA methylation is one of the most studied epigenetic events. In normal cells, it assures the regulation of gene expression without changing the genetic code. However, alterations in DNA methylation are now widely recognized as a contributing factor in tumorigenesis. The bulk of research done in cancer epigenetics focuses on one of two events: promoter hypermethylation and global hypomethylation. Advances in the understanding of how DNA methylation shapes the chromatin’s organization and how the later affects gene expression have been made. Less is known about how DNA methylation affects genes not only locally but also at a distance. We hypothesized that during tumorigenesis specific genomic regions are more susceptible to DNA methylation (epi-hotspots) and other are resistance to DNA methylation changes (epi-blackholes). We also hypothesized that these regions might persist in tumor cells by exerting some selective pressure in the primary tumor clones. By performing a pan-cancer analysis comparing normal to stage-I primary stage-I primary tumor samples gathered from TCGA consortium, we observed that both epi-hotspots and epi-blackholes occurred in all of the analyzed cancer cohorts. Furthermore, generally, epi-hotspots were able to predict gene expression alterations during tumorigenesis, and epi-blackholes were predictors of maintenance of gene expression during tumor initiation, which was in accordance with our hypothesis. We also found that several epi-hotspots and epi-blackholes are predictors of survival in stage-III tumor patients, which may provide potential study targets for candidate prognostic biomarkers. In summary, this study provides new evidence that regional methylation patterns potentially might exert selective pressure in tumor initiation by influencing genome-wide gene expression, and that these traits might be used to develop novel diagnostic and prognostic candidate biomarkers.O cancro é um conjunto heterogéneo de várias doenças que são caracterizadas por uma taxa de crescimento e divisão celular anormais. Durante o processo tumorigénico, as células tumorais vão sucessivamente adquirindo alterações genéticas e epigenéticas, o que leva a uma continua seleção de subclones tumorais. Durante esta evolução tumoral, as células sofrem alterações a nível da metilação de DNA que, tal como as mutações, podem ser propagadas para as células-filha. Estes tipos de alterações contribuem não só para o início do processo tumorigénico, como também para o seu continuo desenvolvimento, sem alterarem a sequência de DNA. Alterações a nível da metilação de DNA participam no processo tumorigénico influenciando diretamente a expressão génica, e afetando a conformação da cromatina que, por sua vez, está relacionada com a toda a expressão génica na célula. Para serem ativamente expressos, os genes têm de estar acessíveis a fatores regulatórios. Por outro lado, genes que têm a sua expressão silenciada tendem a estar compactados na cromatina, de forma a estarem inacessíveis às proteínas responsáveis pela sua transcrição. Alterações a nível da conformação da cromatina podem promover a tumorigénese pelo facto de mudarem a acessibilidade de certas regiões de DNA, assim alterando o padrão global de expressão génica da célula. A maioria dos tumores apresenta um padrão de metilação de DNA global anormal. Uma vez que estes mesmos padrões têm um papel importante na modulação da acessibilidade da cromatina, que por sua vez tem impacto no fenótipo da célula, surgiu a pergunta biológica: “ durante o processo de iniciação tumoral, serão certas regiões genómicas mais suscetíveis a alterações a nível de metilação de DNA?”. E no caso da resposta a esta pergunta ser afirmativa, surge ainda a questão: “Será que estas regiões estão associadas à alteração de padrões de expressão génica nas células tumorais?”. No caso de existirem zonas genómicas de maior suscetibilidade a alterações de metilação de DNA, e estas estarem associadas a alterações a nível de expressão génica, surge ainda a hipótese que estas regiões poderão ter valor de prognóstico em pacientes com doença avançada. Numa tentativa de respondermos a estas questões, realizámos uma análise a doze tipos de cancro (adenocarcinoma do colon, adenocarcinoma do pâncreas, carcinoma da mama, colangiocarcinoma, carcinoma do esófago, cancro da cabeça e pescoço, carcinoma de células renais de células claras, carcinoma de células renais papilar, carcinoma hepatocelular, adenocarcinoma do pulmão, carcinoma do pulmão de células escamosas, e carcinoma da tireoide), onde comparámos dados de metilação entre amostras de tecido normal com amostras de tecido tumoral em estádio I. Por forma a se encontrarem regiões de maior suscetibilidade a alterações de metilação de DNA, aplicámos dois algoritmos de identificação de regiões diferencialmente metiladas e intercetámos os resultados. As regiões genómicas identificadas por ambos os métodos foram designadas epi-hotspots. De modo a aferir se os epi-hotspots estavam associados a alterações de expressão génica no processo de iniciação tumoral, efetuou-se ainda uma análise de regressão linear múltipla entre cada gene diferencialmente expresso em estádio I e cada epi-hotspot. Os genes diferencialmente expressos, cuja variação entre tecido normal e tumor estádio I podia ser explicada por epi-hotspots, foram sujeitos a um estudo de ontologia genética, por forma a se compreender se estes genes potencialmente epigeneticamente regulados enriqueciam algum processo celular. Este processo foi também repetido por forma a se identificarem regiões de baixa suscetibilidade a alterações de metilação de DNA, que designámos epi-blackholes. De modo a testar se estas regiões estavam associadas a genes não-diferencialmente expressos, realizou-se ainda uma análise de regressão linear múltipla entre cada gene não-diferencialmente expresso em estádio I e cada epi-blackhole. Estudou-se ainda o grau de semelhança entre os doze tipos de cancro aqui analisádos relativamente à presença de epi-hotspots e epi-blackholes por meio de uma análise de agrupamento hierárquico. Finalmente, examinou-se o potencial de prognóstico de cada epi-hotspot e cada epi-blackhole em pacientes tumorais de estádio III, fazendo uso de uma análise baseada em regressão multivariada de Cox. Os nossos resultados indicam que, apesar de existirem pequenas semelhanças, o número e localização de epi-hospots e epi-blackholes é característico de cada tipo de cancro, o que sugere que tanto a alteração como a manutenção dos padrões de metilação nestas regiões dependem da célula de origem. Verificou-se ainda que os padrões de metilação em epi-hotspots estavam associados a padrões alterados de expressão génica, em amostras de tecido tumoral em estádio I. Este resultado suporta a hipótese de que alterações regionais de metilação de DNA podem conferir vantagem seletiva na evolução clonal do tumor por influenciarem a expressão génica. De uma forma geral, os genes cuja variação em iniciação tumoral era explicada pela variação da metilação de epi-hotspots enriquecem processos celulares de forma distinta nos diferentes tipos de cancro analisados. Por outro lado, padrões de metilação em epi-blackholes estavam associados à manutenção dos padrões de expressão génica, em amostras de tecido tumoral em estádio I, o que sugere que a conservação de padrões de metilação em certas regiões do DNA pode também ser relevante para a tumorigénese. Observou-se também que em dois terços dos tipos de cancro analisados, a metilação das CpGs de pelo menos um epi-hotspot ou epi-blackhole foi capaz de dividir os pacientes oncológicos de estádio III em dois grupos com padrões de sobrevida distintos, independentemente da idade dos pacientes. Apesar de nem todas as regiões aqui identificadas terem demonstrado potencial de prognóstico, este estudo sugere que os padrões de metilação de DNA em epi-hotspots e epi-blackholes podem ser potênciais candidatos para biomarcadores de prognóstico em pacientes oncológicos de estádio III. Em suma, este trabalho demonstra que, durante o processo de iniciação tumoral, há uma alteração do padrão de metilação de DNA de certas regiões genómicas (epi-hotspots). Por outro lado, parece também haver uma conservação do padrão de metilação de DNA de outras regiões (epi-blackholes). Além disso, parece existir uma associação entre a variação da expressão génica na iniciação tumoral e a metilação dos epi-hotspots. A manutenção do padrão de metilação dos epi-blackholes identificados parece estar associada com a ausência de variação de expressão de determinados genes. Este estudo revela ainda que epi-hotspots e epi-blackholes podem ter também exercer uma pressão seletiva no tumor, já que para além de estarem associados à expressão génica são ainda capazes de prever o prognóstico de pacientes oncológicos em estádio III