DNA methylation mediates the effect of maternal smoking during pregnancy on birthweight of the offspring

Background: We examined whether the effect of maternal smoking during pregnancy on birthweight of the offspring was mediated by smoking-induced changes to DNA methylation in cord blood. Methods: First, we used cord blood of 129 Dutch children exposed to maternal smoking vs 126 unexposed to maternal and paternal smoking (53% male) participating in the GECKO Drenthe birth cohort. DNA methylation was measured using the Illumina HumanMethylation450 Beadchip. We performed an epigenome-wide association study for the association between maternal smoking and methylation followed by a mediation analysis of the top signals [false-discovery rate (FDR)<0.05]. We adjusted both analyses for maternal age, education, pre-pregnancy BMI, offspring's sex, gestational age and white blood cell composition. Secondly, in 175 exposed and 1248 unexposed newborns from two independent birth cohorts, we replicated and meta-analysed results of eight cytosine-phosphate-guanine (CpG) sites in the GFI1 gene, which showed the most robust mediation. Finally, we performed functional network and enrichment analysis. Results: We found 35 differentially methylated CpGs (FDR<0.05) in newborns exposed vs unexposed to smoking, of which 23 survived Bonferroni correction (P<1×10-7). These 23 CpGs mapped to eight genes: AHRR, GFI1, MYO1G, CYP1A1, NEUROG1, CNTNAP2, FRMD4A and LRP5. We observed partial confirmation as three of the eight CpGs in GFI1 replicated. These CpGs partly mediated the effect of maternal smoking on birthweight (Sobel P<0.05) in meta-analysis of GECKO and the two replication cohorts. Differential methylation of these three GFI1 CpGs explained 12-19% of the 202 g lower birthweight in smoking mothers. Functional enrichment analysis pointed towards activation of cell-mediated immunity. Conclusions: Maternal smoking during pregnancy was associated with cord blood methylation differences. We observed a potentially mediating role of methylation in the association between maternal smoking during pregnancy and birthweight of the offspring. Functional network analysis suggested a role in activating the immune system

Lifelines COVID-19 cohort:investigating COVID-19 infection and its health and societal impacts in a Dutch population-based cohort

Purpose The Lifelines COVID-19 cohort was set up to assess the psychological and societal impacts of the COVID-19 pandemic and investigate potential risk factors for COVID-19 within the Lifelines prospective population cohort.Participants Participants were recruited from the 140 000 eligible participants of Lifelines and the Lifelines NEXT birth cohort, who are all residents of the three northern provinces of the Netherlands. Participants filled out detailed questionnaires about their physical and mental health and experiences on a weekly basis starting in late March 2020, and the cohort consists of everyone who filled in at least one questionnaire in the first 8 weeks of the project.Findings to date &gt;71 000 unique participants responded to the questionnaires at least once during the first 8 weeks, with &gt;22 000 participants responding to seven questionnaires. Compiled questionnaire results are continuously updated and shared with the public through the Corona Barometer website. Early results included a clear signal that younger people living alone were experiencing greater levels of loneliness due to lockdown, and subsequent results showed the easing of anxiety as lockdown was eased in June 2020.Future plans Questionnaires were sent on a (bi)weekly basis starting in March 2020 and on a monthly basis starting July 2020, with plans for new questionnaire rounds to continue through 2020 and early 2021. Questionnaire frequency can be increased again for subsequent waves of infections. Cohort data will be used to address how the COVID-19 pandemic developed in the northern provinces of the Netherlands, which environmental and genetic risk factors predict disease susceptibility and severity and the psychological and societal impacts of the crisis. Cohort data are linked to the extensive health, lifestyle and sociodemographic data held for these participants by Lifelines, a 30-year project that started in 2006, and to data about participants held in national databases

The emerging landscape of dynamic DNA methylation in early childhood

Background: DNA methylation has been found to associate with disease, aging and environmental exposure, but it is unknown how genome, environment and disease influence DNA methylation dynamics in childhood. Results: By analysing 538 paired DNA blood samples from children at birth and at 4-5 years old and 726 paired samples from children at 4 and 8 years old from four European birth cohorts using the Illumina Infinium Human Methylation 450 k chip, we have identified 14,150 consistent age-differential methylation sites (a-DMSs) at epigenome-wide significance of rho <1.14x10(-7). Genes with an increase in age-differential methylation were enriched in pathways related to 'development', and were more often located in bivalent transcription start site (TSS) regions, which can silence or activate expression of developmental genes. Genes with a decrease in age-differential methylation were involved in cell signalling, and enriched on H3K27ac, which can predict developmental state. Maternal smoking tended to decrease methylation levels at the identified da-DMSs. We also found 101 a-DMSs (0.71%) that were regulated by genetic variants using cis-differential Methylation Quantitative Trait Locus (cis-dMeQTL) mapping. Moreover, a-DMS-associated genes during early development were significantly more likely to be linked with disease. Conclusion: Our study provides new insights into the dynamic epigenetic landscape of the first 8 years of life.Peer reviewe

Proton pump inhibitors affect the gut microbiome

BACKGROUND AND AIMS: Proton pump inhibitors (PPIs) are among the top 10 most widely used drugs in the world. PPI use has been associated with an increased risk of enteric infections, most notably Clostridium difficile. The gut microbiome plays an important role in enteric infections, by resisting or promoting colonisation by pathogens. In this study, we investigated the influence of PPI use on the gut microbiome. METHODS: The gut microbiome composition of 1815 individuals, spanning three cohorts, was assessed by tag sequencing of the 16S rRNA gene. The difference in microbiota composition in PPI users versus non-users was analysed separately in each cohort, followed by a meta-analysis. RESULTS: 211 of the participants were using PPIs at the moment of stool sampling. PPI use is associated with a significant decrease in Shannon's diversity and with changes in 20% of the bacterial taxa (false discovery rate <0.05). Multiple oral bacteria were over-represented in the faecal microbiome of PPI-users, including the genus Rothia (p=9.8×10(-38)). In PPI users we observed a significant increase in bacteria: genera Enterococcus, Streptococcus, Staphylococcus and the potentially pathogenic species Escherichia coli. CONCLUSIONS: The differences between PPI users and non-users observed in this study are consistently associated with changes towards a less healthy gut microbiome. These differences are in line with known changes that predispose to C. difficile infections and can potentially explain the increased risk of enteric infections in PPI users. On a population level, the effects of PPI are more prominent than the effects of antibiotics or other commonly used drugs

Environmental factors shaping the gut microbiome in a Dutch population

The gut microbiome is associated with diverse diseases(1-3), but a universal signature of a healthy or unhealthy microbiome has not been identified, and there is a need to understand how genetics, exposome, lifestyle and diet shape the microbiome in health and disease. Here we profiled bacterial composition, function, antibiotic resistance and virulence factors in the gut microbiomes of 8,208 Dutch individuals from a three-generational cohort comprising 2,756 families. We correlated these to 241 host and environmental factors, including physical and mental health, use of medication, diet, socioeconomic factors and childhood and current exposome. We identify that the microbiome is shaped primarily by the environment and cohabitation. Only around 6.6% of taxa are heritable, whereas the variance of around 48.6% of taxa is significantly explained by cohabitation. By identifying 2,856 associations between the microbiome and health, we find that seemingly unrelated diseases share a common microbiome signature that is independent of comorbidities. Furthermore, we identify 7,519 associations between microbiome features and diet, socioeconomics and early life and current exposome, with numerous early-life and current factors being significantly associated with microbiome function and composition. Overall, this study provides a comprehensive overview of gut microbiome and the underlying impact of heritability and exposures that will facilitate future development of microbiome-targeted therapies