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Epigenetic Alterations of Maternal Tobacco Smoking during Pregnancy: A Narrative Review

Author

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  • Aurélie Nakamura

    (Université Grenoble Alpes, Inserm, CNRS, IAB, 38000 Grenoble, France)

  • Olivier François

    (Université Grenoble Alpes, Laboratoire TIMC, CNRS UMR 5525, 38000 Grenoble, France)

  • Johanna Lepeule

    (Université Grenoble Alpes, Inserm, CNRS, IAB, 38000 Grenoble, France)

Abstract

In utero exposure to maternal tobacco smoking is the leading cause of birth complications in addition to being associated with later impairment in child’s development. Epigenetic alterations, such as DNA methylation (DNAm), miRNAs expression, and histone modifications, belong to possible underlying mechanisms linking maternal tobacco smoking during pregnancy and adverse birth outcomes and later child’s development. The aims of this review were to provide an update on (1) the main results of epidemiological studies on the impact of in utero exposure to maternal tobacco smoking on epigenetic mechanisms, and (2) the technical issues and methods used in such studies. In contrast with miRNA and histone modifications, DNAm has been the most extensively studied epigenetic mechanism with regard to in utero exposure to maternal tobacco smoking. Most studies relied on cord blood and children’s blood, but placenta is increasingly recognized as a powerful tool, especially for markers of pregnancy exposures. Some recent studies suggest reversibility in DNAm in certain genomic regions as well as memory of smoking exposure in DNAm in other regions, upon smoking cessation before or during pregnancy. Furthermore, reversibility could be more pronounced in miRNA expression compared to DNAm. Increasing evidence based on longitudinal data shows that maternal smoking-associated DNAm changes persist during childhood. In this review, we also discuss some issues related to cell heterogeneity as well as downstream statistical analyses used to relate maternal tobacco smoking during pregnancy and epigenetics. The epigenetic effects of maternal smoking during pregnancy have been among the most widely investigated in the epigenetic epidemiology field. However, there are still huge gaps to fill in, including on the impact on miRNA expression and histone modifications to get a better view of the whole epigenetic machinery. The consistency of maternal tobacco smoking effects across epigenetic marks and across tissues will also provide crucial information for future studies. Advancement in bioinformatic and biostatistics approaches is key to develop a comprehensive analysis of these biological systems.

Suggested Citation

  • Aurélie Nakamura & Olivier François & Johanna Lepeule, 2021. "Epigenetic Alterations of Maternal Tobacco Smoking during Pregnancy: A Narrative Review," IJERPH, MDPI, vol. 18(10), pages 1-19, May.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:10:p:5083-:d:552418
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    References listed on IDEAS

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    1. World Health Organization, 2019. "WHO report on the global tobacco epidemic 2019: Offer help to quit tobacco use," University of California at San Francisco, Center for Tobacco Control Research and Education qt1g16k8b9, Center for Tobacco Control Research and Education, UC San Francisco.
    2. Sol Pía Juárez & Juan Merlo, 2013. "Revisiting the Effect of Maternal Smoking during Pregnancy on Offspring Birthweight: A Quasi-Experimental Sibling Analysis in Sweden," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-7, April.
    3. Parnian Kheirkhah Rahimabad & Thilani M. Anthony & A. Daniel Jones & Shakiba Eslamimehr & Nandini Mukherjee & Susan Ewart & John W. Holloway & Hasan Arshad & Sarah Commodore & Wilfried Karmaus, 2020. "Nicotine and Its Downstream Metabolites in Maternal and Cord Sera: Biomarkers of Prenatal Smoking Exposure Associated with Offspring DNA Methylation," IJERPH, MDPI, vol. 17(24), pages 1-15, December.
    4. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
    5. Allison A Appleton & David A Armstrong & Corina Lesseur & Joyce Lee & James F Padbury & Barry M Lester & Carmen J Marsit, 2013. "Patterning in Placental 11-B Hydroxysteroid Dehydrogenase Methylation According to Prenatal Socioeconomic Adversity," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-7, September.
    6. Kevin G. Murphy & Stephen R. Bloom, 2006. "Gut hormones and the regulation of energy homeostasis," Nature, Nature, vol. 444(7121), pages 854-859, December.
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    1. Mikael O. Ekblad & Julie Blanc & Ivan Berlin, 2021. "Special Issue on the Effects of Prenatal Smoking/Nicotine Exposure on the Child’s Health," IJERPH, MDPI, vol. 18(10), pages 1-4, May.

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