The DOI matches its Crossref record; 121 of the 121 references it cites were found in a registry or an index.
Influence of environmental exposure on human epigenetic regulation
Open accessAbstract
Environmental toxicants can alter epigenetic regulatory features such as DNA methylation and microRNA expression. As the sensitivity of epigenomic regulatory features may be greatest during the in utero period, when critical windows are narrow, and when epigenomic profiles are being set, this review will highlight research focused on that period. I will focus on work in human populations, where the impact of environmental toxicants in utero, including cigarette smoke and toxic trace metals such as arsenic, mercury and manganese, on genome-wide, gene-specific DNA methylation has been assessed. In particular, arsenic is highlighted, as this metalloid has been the focus of a number of studies and its detoxification mechanisms are well understood. Importantly, the tissues and cells being examined must be considered in context in order to interpret the findings of these studies. For example, by studying the placenta, it is possible to identify potential epigenetic adaptations of key genes and pathways that may alter the developmental course in line with the developmental origins of health and disease paradigm. Alternatively, studies of newborn cord blood can be used to examine how environmental exposure in utero can impact the composition of cells within the peripheral blood, leading to immunological effects of exposure. The results suggest that in humans, like other vertebrates, there is a susceptibility for epigenomic alteration by the environment during intrauterine development, and this may represent a mechanism of plasticity of the organism in response to its environment as well as a mechanism through which long-term health consequences can be shaped.
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Cites
Epigenetic programming by maternal behavior
Weaver, I. C. G., Cervoni, N., Champagne, F. A., D'Alessio, A. C., Sharma, S., Seckl, J. R., Dymov, S., Szyf, M., & Meaney, M. J. (2004) · Nature Neuroscience · doi:10.1038/nn1276 · Cited by 6,177
Methylation-specific PCR: a novel PCR assay for methylation status of CpG islands.
Herman, J. G., Graff, J. R., Myöhänen, S., Nelkin, B. D., & Baylin, S. B. (1996) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.93.18.9821 · Cited by 5,646
Genome-wide methylation profiles reveal quantitative views of human aging rates
Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S., Klotzle, B., Bibikova, M., Fan, J.-B., Gao, Y., et al. (2013) · Molecular Cell · doi:10.1016/j.molcel.2012.10.016 · Cited by 4,270
WEIGHT IN INFANCY AND DEATH FROM ISCHAEMIC HEART DISEASE
Barker, D. J. P., Osmond, C., Winter, P. D., Margetts, B., & Simmonds, S. J. (1989) · The Lancet · doi:10.1016/s0140-6736(89)90710-1 · Cited by 3,574
Frommer, M., McDonald, L. E., Millar, D. S., Collis, C. M., Watt, F., Grigg, G. W., Molloy, P. L., & Paul, C. L. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.5.1827 · Cited by 3,238
DNA methylation arrays as surrogate measures of cell mixture distribution
Houseman, E. A., Accomando, W. P., Koestler, D. C., Christensen, B. C., Marsit, C. J., Nelson, H. H., Wiencke, J. K., & Kelsey, K. T. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-86 · Cited by 3,175
Self-citationPersistent epigenetic differences associated with prenatal exposure to famine in humans
Heijmans, B. T., Tobi, E. W., Stein, A. D., Putter, H., Blauw, G. J., Susser, E. S., Slagboom, P. E., & Lumey, L. H. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0806560105 · Cited by 3,082
Environmental epigenomics and disease susceptibility
Jirtle, R. L., & Skinner, M. K. (2007) · Nature Reviews Genetics · doi:10.1038/nrg2045 · Cited by 2,492
Transgenerational epigenetic inheritance: myths and mechanisms
Heard, E., & Martienssen, R. A. (2014) · Cell · doi:10.1016/j.cell.2014.02.045 · Cited by 1,864
Principles and challenges of genome-wide DNA methylation analysis
Laird, P. W. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2732 · Cited by 1,572
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Arsenic and human health effects: A review
Mohammed Abdul, K. S., Jayasinghe, S. S., Chandana, E. P. S., Jayasumana, C., & De Silva, P. M. C. S. (2015) · Environmental Toxicology and Pharmacology · doi:10.1016/j.etap.2015.09.016 · Cited by 907
Ralli, M., Angeletti, D., Fiore, M., D'Aguanno, V., Lambiase, A., Artico, M., de Vincentiis, M., & Greco, A. (2020) · Autoimmunity Reviews · doi:10.1016/j.autrev.2020.102649 · Cited by 600
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Subramanian, M., Wojtusciszyn, A., Favre, L., Boughorbel, S., Shan, J., Letaief, K. B., Pitteloud, N., & Chouchane, L. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-020-02658-5 · Cited by 362
The Placenta as a Mediator of Stress Effects on Neurodevelopmental Reprogramming
Bronson, S. L., & Bale, T. L. (2016) · Neuropsychopharmacology · doi:10.1038/npp.2015.231 · Cited by 287
Associating cellular epigenetic models with human phenotypes
Lappalainen, T., & Greally, J. M. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.32 · Cited by 282
Tarrade, A., Panchenko, P., Junien, C., & Gabory, A. (2015) · Journal of Experimental Biology · doi:10.1242/jeb.110320 · Cited by 251
Kompier, N. F., Keysers, C., Gazzola, V., Lucassen, P. J., & Krugers, H. J. (2019) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2019.00143 · Cited by 53
The immunobiology of female predominance in primary biliary cholangitis
Gerussi, A., Cristoferi, L., Carbone, M., Asselta, R., & Invernizzi, P. (2018) · Journal of Autoimmunity · doi:10.1016/j.jaut.2018.10.015 · Cited by 41
Understanding asthma and allergies by the lens of biodiversity and epigenetic changes
Fiuza, B. S. D., Fonseca, H. F., Meirelles, P. M., Marques, C. R., da Silva, T. M., & Figueiredo, C. A. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.623737 · Cited by 38
Maternal smoking during pregnancy is associated with mitochondrial DNA methylation
Armstrong, D. A., Green, B. B., Blair, B. A., Guerin, D. J., Litzky, J. F., Chavan, N. R., Pearson, K. J., & Marsit, C. J. (2016) · Environmental Epigenetics · doi:10.1093/eep/dvw020 · Cited by 36
Self-citationRibeiro, A. C. R., Jahr, F. M., Hawkins, E., Kronfol, M. M., Younis, R. M., McClay, J. L., & Deshpande, L. S. (2021) · Life Sciences · doi:10.1016/j.lfs.2021.119765 · Cited by 30
Metadata
- Authors
- Marsit, C. J.
- Venue
- Journal of Experimental Biology
- Publisher
- The Company of Biologists
- Published
- 2015-01-01
- Volume
- 218
- Issue
- 1
- Pages
- 71-79
- ISSN
- 0022-0949 (ISSN-L)
- 1477-9145
- OpenAlex
- W1992068823
- Cited by
- 253
- References
- 121
- FWCI
- 9.91
- Type
- journal article
- Language
- en
- Open access
- green
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