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DNA methylation profiles in adults born at extremely low birth weight

Published online by Cambridge University Press:  19 October 2020

Karen J. Mathewson*
Affiliation:
Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, ON, Canada
Patrick O. McGowan
Affiliation:
Department of Biological Sciences, Cells and Systems Biology, Psychology and Physiology, University of Toronto, ON, Canada
Wilfred C. de Vega
Affiliation:
Department of Biological Sciences, Cells and Systems Biology, Psychology and Physiology, University of Toronto, ON, Canada
Ryan J. Van Lieshout
Affiliation:
Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
Katherine M. Morrison
Affiliation:
Department of Pediatrics, McMaster University, Hamilton, ON, Canada
Saroj Saigal
Affiliation:
Department of Pediatrics, McMaster University, Hamilton, ON, Canada
Louis A. Schmidt
Affiliation:
Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, ON, Canada
*
Author for Correspondence: Karen J. Mathewson, McMaster University, Hamilton, ONL8S 4K1, Canada. Email: mathewkj@mcmaster.ca

Abstract

Effects of stresses associated with extremely preterm birth may be biologically “recorded” in the genomes of individuals born preterm via changes in DNA methylation (DNAm) patterns. Genome-wide DNAm profiles were examined in buccal epithelial cells from 45 adults born at extremely low birth weight (ELBW; ≤1000 g) in the oldest known cohort of prospectively followed ELBW survivors (Mage = 32.35 years, 17 male), and 47 normal birth weight (NBW; ≥2500 g) control adults (Mage = 32.43 years, 20 male). Sex differences in DNAm profiles were found in both birth weight groups, but they were greatly enhanced in the ELBW group (77,895 loci) versus the NBW group (3,424 loci), suggesting synergistic effects of extreme prenatal adversity and sex on adult DNAm profiles. In men, DNAm profiles differed by birth weight group at 1,354 loci on 694 unique genes. Only two loci on two genes distinguished between ELBW and NBW women. Gene ontology (GO) and network analyses indicated that loci differentiating between ELBW and NBW men were abundant in genes within biological pathways related to neuronal development, synaptic transportation, metabolic regulation, and cellular regulation. Findings suggest increased sensitivity of males to long-term epigenetic effects of extremely preterm birth. Group differences are discussed in relation to particular gene functions.

Type
Regular Article
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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References

Anderson, P. J., De Luca, C. R., Hutchinson, E., Spencer-Smith, M. M., Roberts, G., Doyle, L. W., & Victorian Infant Collaborative Study Group. (2011). Attention problems in a representative sample of extremely preterm/extremely low birth weight children. Developmental Neuropsychology, 36, 5773. http://dx.doi.org/10.1080/87565641.2011.540538CrossRefGoogle Scholar
Aryee, M. J., Jaffe, A. E., Corrada-Bravo, H., Ladd-Acosta, C., Feinberg, A. P., Hansen, K. D., & Irizarry, R. A. (2014). Minfi: A flexible and comprehensive Bioconductor package for the analysis of Infinium DNAm microarrays. Bioinformatics, 30, 13631369. doi:10.1093/bioinformatics/btu049CrossRefGoogle ScholarPubMed
Back, S. A., Luo, N. L., Borenstein, N. S., Levine, J. M., Volpe, J. J., & Kinney, H. C. (2001). Late oligodendrocyte progenitors coincide with the developmental window of vulnerability for human perinatal white matter injury. Journal of Neuroscience, 21, 13021312. https://doi.org/10.1523/JNEUROSCI.21-04-01302.2001CrossRefGoogle ScholarPubMed
Bale, T. L. (2011). Sex differences in prenatal epigenetic programing of stress pathways. Stress, 14, 348356. doi:10.3109/10253890.2011.586447CrossRefGoogle Scholar
Behrman, R. E., & Butler, A. S. (2007). Preterm birth: Causes, Consequences, and Prevention. Committee on Understanding Premature Birth and Assuring Healthy Outcomes Board on Health Sciences Policy. Washington: Institute of Medicine of the National Academies, pp. 1490. http://www.nap.edu/catalog/11622.htmlGoogle Scholar
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57, 289300. https://www.jstor.org/stable/2346101CrossRefGoogle Scholar
Bibikova, M., Barnes, B., Tsan, C., Ho, V., Klotzle, B., Le, J. M., … Fan, J. B. (2011). High density DNAm array with single CpG site resolution. Genomics, 98, 288295. https://doi.org/10.1016/j.ygeno.2011.07.007CrossRefGoogle Scholar
Black, M. J., Sutherland, M. R., Gubhaju, L., Kent, A. L., Dahlstrom, J. E., & Moore, L. (2013). When birth comes early: Effects on nephrogenesis. Nephrology, 18, 180182. doi:10.1111/nep.12028CrossRefGoogle ScholarPubMed
Boyle, M. H., Miskovic, V., Van Lieshout, R., Duncan, L., Schmidt, L. A., Hoult, L., … Saigal, S. (2011). Psychopathology in young adults born at extremely low birth weight. Psychological Medicine, 41, 17631774. http://dx.doi.org/10.1017/S0033291710002357CrossRefGoogle ScholarPubMed
Brettell, R., Yeh, P. S., & Impey, L. W. (2008). Examination of the association between male gender and preterm delivery. European Journal of Obstetrics & Gynecology and Reproductive Biology, 141, 123126. doi:10.1016/j.ejogrb.2008.07.030CrossRefGoogle ScholarPubMed
Challis, J., Newnham, J., Petraglia, F., Yeganegi, M., & Bocking, A. (2013). Fetal sex and preterm birth. Placenta, 34, 9599. http://dx.doi.org/10.1016/j.placenta.2012.11.007CrossRefGoogle ScholarPubMed
Chau, C. M. Y., Ranger, M., Sulistyoningrum, D., Devlin, A. M., Oberlander, T. F., & Grunau, R. E. (2014). Neonatal pain and COMT Val158Met genotype in relation to serotonin transporter (SLC6A4) promoter methylation in very preterm children at school age. Frontiers in Behavioral Neuroscience, 8, 112. https://doi.org/10.3389/fnbeh.2014.00409CrossRefGoogle ScholarPubMed
Chen, Y. A., Lemire, M., Choufani, S., Butcher, D. T., Grafodatskaya, D., Zanke, B. W., … Weksberg, R. (2013). Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics, 8, 203209. https://doi.org/10.4161/epi.23470CrossRefGoogle ScholarPubMed
Clifton, V. L. (2010). Sex and the human placenta: mediating differential strategies of fetal growth and survival. Placenta, 31, S33S39. doi:10.1016/j.placenta.2009.11.010CrossRefGoogle ScholarPubMed
Conradt, E., Adkins, D. E., Crowell, S. E., Raby, K. L., Diamond, L. M., & Ellis, B. (2018). Incorporating epigenetic mechanisms to advance fetal programming theories. Development and Psychopathology, 30, 807824. doi:10.1017/S0954579418000469CrossRefGoogle ScholarPubMed
Constable, R. T., Ment, L. R., Vohr, B. R., Kesler, S. R., Fulbright, R. K., Lacadie, C., … Makuch, R. W. (2008). Prematurely born children demonstrate white matter microstructural differences at 12 years of age, relative to term control subjects: an investigation of group and gender effects. Pediatrics, 121, 306316. doi:10.1542/peds.2007-0414CrossRefGoogle ScholarPubMed
Cruickshank, M. N., Oshlack, A., Theda, C., Davis, P. G., Martino, D., Sheehan, P., … Craig, J. M. (2013). Analysis of epigenetic changes in survivors of preterm birth reveals the effect of gestational age and evidence for a long term legacy. Genome Medicine, 5, 96. http://genomemedicine.com/content/5/10/96CrossRefGoogle ScholarPubMed
Davies, M. N., Volta, M., Pidsley, R., Lunnon, K., Dixit, A., Lovestone, S., … Al-Sarraj, S. (2012). Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood. Genome Biology, 13, R43. http://genomebiology.com/2012/13/6/R43CrossRefGoogle ScholarPubMed
de Goede, O. M., Lavoie, P. M., & Robinson, W. P. (2017). Cord blood hematopoietic cells from preterm infants display altered DNAm patterns. Clinical Epigenetics, 9, 39. doi:10.1186/s13148-017-0339-1CrossRefGoogle ScholarPubMed
de la Rica, L., Urquiza, J. M., Gómez-Cabrero, D., Islam, A. B., López-Bigas, N., Tegnér, J., … Ballestar, E. (2013). Identification of novel markers in rheumatoid arthritis through integrated analysis of DNAm and microRNA expression. Journal of Autoimmunity, 41, 616. https://doi.org/10.1016/j.jaut.2012.12.005CrossRefGoogle Scholar
de Vega, W. C., Herrera, S., Vernon, S. D., & McGowan, P. O. (2017). Epigenetic modifications and glucocorticoid sensitivity in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). BMC Medical Genomics, 10, 11. doi:10.1186/s12920-017-0248-3CrossRefGoogle Scholar
Di Renzo, G. C., Rosati, A., Sarti, R. D., Cruciani, L., & Cutuli, A. M. (2007). Does fetal sex affect pregnancy outcome? Gender Medicine, 4, 1930. https://doi.org/10.1016/S1550-8579(07)80004-0CrossRefGoogle ScholarPubMed
Essex, M. J., Boyce, W. T., Hertzman, C., Lam, L. L., Armstrong, J. M., Neumann, S. M., & Kobor, M. S. (2013). Epigenetic vestiges of early developmental adversity: Childhood stress exposure and DNAm in adolescence. Child Development, 84, 5875. doi:10.1111/j.1467-8624.2011.01641.xCrossRefGoogle Scholar
Fernando, F., Keijser, R., Henneman, P., van der Kevie, A. M. F., Mannens, M. M., van der Post, J. A., … Ris-Stalpers, C. (2015). The idiopathic preterm delivery methylation profile in umbilical cord blood DNA. BMC Genomics, 16, 736. doi:10.1186/s12864-015-1915-4CrossRefGoogle ScholarPubMed
Frey, H. A., & Klebanoff, M. A. (2016). The epidemiology, etiology, and costs of preterm birth. In Seminars in Fetal and Neonatal Medicine. 21, 6873. http://dx.doi.org/10.1016/j.siny.2015.12.011Google Scholar
Galichon, P., Mesnard, L., Hertig, A., Stengel, B., & Rondeau, E. (2012). Unrecognized sequence homologies may confound genome-wide association studies. Nucleic Acids Research, 40, 47744782. https://doi.org/10.1093/nar/gks169CrossRefGoogle ScholarPubMed
Gluckman, P. D., & Hanson, M. A. (2004a). Developmental origins of disease paradigm: A mechanistic and evolutionary perspective. Pediatric Research, 56, 311317. doi:10.1203/01.PDR.0000135998.08025.FB.CrossRefGoogle Scholar
Gluckman, P. D., & Hanson, M. A. (2004b). Living with the past: Evolution, development, and patterns of disease. Science, 305, 17331736. doi:10.1126/science.1095292CrossRefGoogle Scholar
Gluckman, P. D., Hanson, M. A., & Buklijas, T. (2010). A conceptual framework for the developmental origins of health and disease. Journal of Developmental Origins of Health and Disease, 1, 618. doi:10.1017/S2040174409990171CrossRefGoogle ScholarPubMed
Gluckman, P. D., Hanson, M. A., Cooper, C., & Thornburg, K. L. (2008). Effect of in utero and early-life conditions on adult health and disease. New England Journal of Medicine, 359, 6173. doi:10.1056/NEJMra0708473CrossRefGoogle ScholarPubMed
Hack, M., Flannery, D. J., Schluchter, M., Cartar, L., Borawski, E., & Klein, N. (2002). Outcomes in young adulthood for very-low-birth-weight infants. New England Journal of Medicine, 346, 149157. doi:10.1056/NEJMoa010856CrossRefGoogle ScholarPubMed
Hack, M., Taylor, H. G., Schluchter, M., Andreias, L., Drotar, D., & Klein, N. (2009). Behavioral outcomes of extremely low birth weight children at age 8 years. Journal of Developmental and Behavioral Pediatrics: JDBP, 30, 122130. doi:10.1097/DBP.0b013e31819e6a16HCrossRefGoogle ScholarPubMed
Halmøy, A., Klungsøyr, K., Skjærven, R., & Haavik, J. (2012). Pre-and perinatal risk factors in adults with attention-deficit/hyperactivity disorder. Biological Psychiatry, 71, 474481. doi:10.1016/j.biopsych.2011.11.013CrossRefGoogle ScholarPubMed
Heijmans, B. T., Tobi, E. W., Stein, A. D., Putter, H., Blauw, G. J., Susser, E. S., … Lumey, L. H. (2008). Persistent epigenetic differences associated with prenatal exposure to famine in humans. Proceedings of the National Academy of Sciences of the United States of America, 105, 1704617049. doi:10.1073/pnas.0806560105CrossRefGoogle ScholarPubMed
Heiss, J. A., Brennan, K. J., Baccarelli, A. A., Téllez-Rojo, M. M., Estrada-Gutiérrez, G., Wright, R. O., & Just, A. C. (2019). Battle of epigenetic proportions: comparing Illumina's EPIC methylation microarrays and TruSeq targeted bisulfite sequencing. Epigenetics, 15(1–2), 174182. https://doi.org/10.1080/15592294.2019.1656159.CrossRefGoogle ScholarPubMed
Herrera, S., de Vega, W. C., Ashbrook, D., Vernon, S. D., & McGowan, P. O. (2018). Genome-epigenome interactions associated with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Epigenetics, 13, 11741190. https://doi.org/10.1080/15592294.2018.154976CrossRefGoogle ScholarPubMed
Hertzman, C. (2012). Putting the concept of biological embedding in historical perspective. Proceedings of the National Academy of Sciences, 109, 1716017167. https://doi.org/10.1073/pnas.1202203109CrossRefGoogle ScholarPubMed
Hintz, S. R., Kendrick, D. E., Vohr, B. R., Poole, W. K., Higgins, R. D., & NICHD Neonatal Research Network. (2006). Gender differences in neurodevelopmental outcomes among extremely preterm, extremely-low-birth weight infants. Acta Paediatrica, 95, 12391248. doi:10.1080/08035250600599727CrossRefGoogle Scholar
Hollingshead, A. B. (1969). Two-Factor Index of Social Position. New Haven, CT: Yale University Press.Google Scholar
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009a). Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research, 37, 113. doi:10.1093/nar/gkn923CrossRefGoogle Scholar
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009b). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols, 4, 4457. doi:10.1038/nprot.2008.211CrossRefGoogle Scholar
Ingemarsson, I. (2003). Gender aspects of preterm birth. BJOG: An International Journal of Obstetrics & Gynaecology, 110, 3438. doi:10.1016/S1470-0328(03)00022-3CrossRefGoogle ScholarPubMed
Inoshita, M., Numata, S., Tajima, A., Kinoshita, M., Umehara, H., Yamamori, H., … Ohmori, T. (2015). Sex differences of leukocytes DNAm adjusted for estimated cellular proportions. Biology of Sex Differences, 6, 11. doi:10.1186/s13293-015-0029-7CrossRefGoogle Scholar
Jansson, T., & Powell, T. L. (2007). Role of the placenta in fetal programming: underlying mechanisms and potential interventional approaches. Clinical Science, 113, 113. doi:10.1042/CS20060339CrossRefGoogle ScholarPubMed
Johnson, S., Hollis, C., Kochhar, P., Hennessy, E., Wolke, D., & Marlow, N. (2010). Autism spectrum disorders in extremely preterm children. Journal of Pediatrics, 156, 525531. http://dx.doi.org/10.1016/j.jpeds.2009.10.041CrossRefGoogle ScholarPubMed
Johnson, W. E., Li, C., & Rabinovic, A. (2007). Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics, 8, 118–27. https://doi.org/10.1093/biostatistics/kxj037CrossRefGoogle ScholarPubMed
Johnson, S., & Marlow, N. (2014). Growing up after extremely preterm birth: lifespan mental health outcomes. Seminars in Fetal and Neonatal Medicine, 19, 97104. http://dx.doi.org/10.1016/j.siny.2013.11.004CrossRefGoogle ScholarPubMed
Kent, A. L., Wright, I. M., & Abdel-Latif, M. E. (2012). Mortality and adverse neurologic outcomes are greater in preterm male infants. Pediatrics, 129, 124131. doi:10.1542/peds.2011-1578CrossRefGoogle ScholarPubMed
Kramer, M. S., Platt, R. W., Wen, S. W., Joseph, K. S., Allen, A., Abrahamowicz, M.Fetal/Infant Health Study Group of the Canadian Perinatal Surveillance System. (2001). A new and improved population-based Canadian reference for birth weight for gestational age. Pediatrics, 108, E35. doi:10.1542/peds.108.2.e35CrossRefGoogle ScholarPubMed
Lahat, A., Van Lieshout, R. J., Mathewson, K. J., Mackillop, J., Saigal, S., Morrison, K. M., … Schmidt, L. A. (2016). Extremely low birth weight babies grown up: Gene–environment interaction predicts internalizing problems in the third and fourth decades of life. Development and Psychopathology, 29, 837843. doi:10.1017/S0954579416000511CrossRefGoogle ScholarPubMed
Lam, L. L., Emberly, E., Fraser, H. B., Neumann, S. M., Chen, E., Miller, G. E., & Kobor, M. S. (2012). Factors underlying variable DNAm in a human community cohort. Proceedings of the National Academy of Sciences, 109(Supplement 2), 1725317260. www.pnas.org/cgi/doi/10.1073/pnas.1121249109.CrossRefGoogle Scholar
Lee, H., Jaffe, A. E., Feinberg, J. I., Tryggvadottir, R., Brown, S., Montano, C., … Goldman, L. R. (2012). DNAm shows genome-wide association of NFIX, RAPGEF2 and MSRB3 with gestational age at birth. International Journal of Epidemiology, 41, 188199. doi:10.1093/ije/dyr237CrossRefGoogle Scholar
Lefebvre, F., Mazurier, É, & Tessier, R. (2005). Cognitive and educational outcomes in early adulthood for infants weighing 1000 grams or less at birth. Acta Paediatrica, 94, 733740. doi:10.1080/08035250510025987CrossRefGoogle ScholarPubMed
Lester, B. M., Marsit, C. J., Conradt, E., Bromer, C., & Padbury, J. F. (2012). Behavioral epigenetics and the developmental origins of child mental health disorders. Journal of Developmental Origins of Health and Disease, 3, 395408. doi:10.1017/S2040174412000426CrossRefGoogle ScholarPubMed
Liu, Y., Aryee, M. J., Padyukov, L., Fallin, M. D., Hesselberg, E., Runarsson, A., … Shchetynsky, K. (2013). Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nature Biotechnology, 31, 142147. doi:10.1038/nbt.2487CrossRefGoogle ScholarPubMed
Liu, J., Morgan, M., Hutchison, K., & Calhoun, V. D. (2010). A study of the influence of sex on genome wide methylation. PloS One, 5, e10028. doi:10.1371/journal.pone.0010028CrossRefGoogle ScholarPubMed
Liyanage, V., Jarmasz, J., Murugeshan, N., Del Bigio, M., Rastegar, M., & Davie, J. (2014). DNA modifications: Function and applications in normal and disease states. Biology, 3, 670723. doi:10.3390/biology3040670CrossRefGoogle Scholar
Loucks, E. B., Huang, Y. T., Agha, G., Chu, S., Eaton, C. B., Gilman, S. E., … Kelsey, K. T. (2016). Epigenetic mediators between childhood socioeconomic disadvantage and mid-life body mass index: The New England Family Study. Psychosomatic Medicine, 78, 10531065. doi:10.1097/PSY.0000000000000411CrossRefGoogle ScholarPubMed
Lowe, R., Gemma, C., Beyan, H., Hawa, M. I., Bazeos, A., Leslie, R. D., … Ramagopalan, S. V. (2013). Buccals are likely to be a more informative surrogate tissue than blood for epigenome-wide association studies. Epigenetics, 8, 445454. doi:10.4161/epi.24362CrossRefGoogle ScholarPubMed
Luu, T. M., Katz, S. L., Leeson, P., Thébaud, B., & Nuyt, A. M. (2016). Preterm birth: Risk factor for early-onset chronic diseases. CMAJ, 188, 736746. doi:10.1503 /cmaj.150450CrossRefGoogle ScholarPubMed
Maksimovic, J., Gordon, L., & Oshlack, A. (2012). SWAN: Subset-quantile within array normalization for illumina infinium HumanMethylation450 BeadChips. Genome Biology, 13, R44. http://genomebiology.com/2012/13/6/R44CrossRefGoogle ScholarPubMed
Massart, R., Nemoda, Z., Suderman, M. J., Sutti, S., Ruggiero, A. M., Dettmer, A. M., … Szyf, M. (2016). Early life adversity alters normal sex-dependent developmental dynamics of DNAm. Development and Psychopathology, 28, 12591272. doi:10.1017/S0954579416000833CrossRefGoogle Scholar
Mathewson, K. J., Chow, C. H., Dobson, K. G., Pope, E. I., Schmidt, L. A., & Van Lieshout, R. J. (2017). Mental health of extremely low birth weight survivors: A systematic review and meta-analysis. Psychological Bulletin, 143, 347383. http://dx.doi.org/10.1037/bul0000091CrossRefGoogle ScholarPubMed
Matthews, T. J., MacDorman, M. F., & Thoma, M. E. (2015). Infant mortality statistics from the 2013 period linked birth/infant death data set. National Vital Statistics Reports, 64, 130. https://www.cdc.gov/nchs/products/nvsr.htmGoogle ScholarPubMed
Matthews, S., & McGowan, P. (2019). Developmental programming of the HPA axis and related behaviours: Epigenetic mechanisms. Journal of Endocrinology, 242, T69T79. doi: 10.1530/JOE-19-0057CrossRefGoogle ScholarPubMed
McGowan, P. O., Sasaki, A., D'alessio, A. C., Dymov, S., Labonté, B., Szyf, M., … Meaney, M. J. (2009). Epigenetic regulation of the glucocorticoid receptor in human brain associates with childhood abuse. Nature Neuroscience, 12, 342348. doi:10.1038/nn.2270CrossRefGoogle ScholarPubMed
Meaney, M. J., & Szyf, M. (2005). Environmental programming of stress responses through DNAm: life at the interface between a dynamic environment and a fixed genome. Dialogues in Clinical Neuroscience, 7, 103123. doi:10.1038/npp.2015.180Google Scholar
Merico, D., Isserlin, R., Stueker, O., Emili, A., & Bader, G. D. (2010). Enrichment map: A network-based method for gene-set enrichment visualization and interpretation. PLoS One, 5, e13984. doi:10.1371/journal.pone.0013984CrossRefGoogle ScholarPubMed
Moisiadis, V. G., & Matthews, S. G. (2014). Glucocorticoids and fetal programming part 2: mechanisms. Nature Reviews Endocrinology, 10, 403–11. http://dx.doi.org/10.1038/nrendo.2014.74CrossRefGoogle ScholarPubMed
Montirosso, R., Provenzi, L., Giorda, R., Fumagalli, M., Morandi, F., Sirgiovanni, I., … Borgatti, R. (2016). SLC6A4 promoter region methylation and socio-emotional stress response in very preterm and full-term infants. Epigenomics, 8, 895907. https://doi.org/10.2217/epi-2016-0010CrossRefGoogle ScholarPubMed
Murgatroyd, C., & Spengler, D. (2011). Epigenetic programming of the HPA axis: Early life decides. Stress, 14, 581589. doi:10.3389/fpsyt.2011.00016CrossRefGoogle ScholarPubMed
Murphy, V. E., Gibson, P. G., Giles, W. B., Zakar, T., Smith, R., Bisits, A. M., … Clifton, V. L. (2003). Maternal asthma is associated with reduced female fetal growth. American Journal of Respiratory and Critical Care Medicine, 168, 13171323. https://doi.org/10.1164/rccm.200303-374OCCrossRefGoogle ScholarPubMed
Nosarti, C., Nam, K. W., Walshe, M., Murray, R. M., Cuddy, M., Rifkin, L., & Allin, M. P. (2014). Preterm birth and structural brain alterations in early adulthood. NeuroImage: Clinical, 6, 180191. https://doi.org/10.1016/j.nicl.2014.08.005CrossRefGoogle ScholarPubMed
Parets, S. E., Conneely, K. N., Kilaru, V., Fortunato, S. J., Syed, T. A., Saade, G., … Menon, R. (2013). Fetal DNAm associates with early spontaneous preterm birth and gestational age. PloS One, 8, e67489. doi:10.1371/journal.pone.0067489CrossRefGoogle Scholar
Peacock, J. L., Marston, L., Marlow, N., Calvert, S. A., & Greenough, A. (2012). Neonatal and infant outcome in boys and girls born very prematurely. Pediatric Research, 71, 305310. doi:10.1038/pr.2011.50CrossRefGoogle ScholarPubMed
Petronis, A. (2010). Epigenetics as a unifying principle in the aetiology of complex traits and diseases. Nature, 465, 721727. doi:10.1038/nature09230CrossRefGoogle ScholarPubMed
Pidsley, R., Zotenko, E., Peters, T. J., Lawrence, M. G., Risbridger, G. P., Molloy, P., … Clark, S. J. (2016). Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biology, 17, 208. doi:10.1186/s13059-016-1066-1CrossRefGoogle ScholarPubMed
Provenzi, L., Guida, E., & Montirosso, R. (2018). Preterm behavioral epigenetics: A systematic review. Neuroscience & Biobehavioral Reviews, 84, 262271. https://doi.org/10.1016/j.neubiorev.2017.08.020CrossRefGoogle ScholarPubMed
Purisch, S. E., & Gyamfi-Bannerman, C. (2017). Epidemiology of preterm birth. In Seminars in Perinatology, 41, 387391. http://dx.doi.org/10.1053/j.semperi.2017.07.009Google Scholar
Reiss, A. L., Kesler, S. R., Vohr, B., Duncan, C. C., Katz, K. H., Pajot, S., … Ment, L. R. (2004). Sex differences in cerebral volumes of 8-year-olds born preterm. Journal of Pediatrics, 145, 242249. doi:10.1016/j.jpeds.2004.04.031CrossRefGoogle ScholarPubMed
Saigal, S., Day, K. L., Van Lieshout, R. J., Schmidt, L. A., Morrison, K. M., & Boyle, M. H. (2016). Health, wealth, social integration, and sexuality of extremely low birth weight prematurely born adults in the fourth decade of life. JAMA Pediatrics, 170, 678686. doi:10.1001/jamapediatrics.2016.0289CrossRefGoogle ScholarPubMed
Saigal, S., Rosenbaum, P., Hattersley, B., & Milner, R. (1989). Decreased disability rate among 3-year-old survivors weighing 501 to 1000 grams at birth and born to residents of a geographically defined region from 1981 to 1984 compared with 1977 to 1980. Journal of Pediatrics, 114, 839846. https://doi.org/10.1016/S0022-3476(89)80150-7CrossRefGoogle ScholarPubMed
Saigal, S., Stoskopf, B., Boyle, M., Paneth, N., Pinelli, J., Streiner, D., & Goddeeris, J. (2007). Comparison of current health, functional limitations, and health care use of young adults who were born with extremely low birth weight and normal birth weight. Pediatrics, 119, e562e573. doi:10.1542/peds.2006-2328CrossRefGoogle ScholarPubMed
Saigal, S., Szatmari, P., Rosenbaum, P., Campbell, D., & King, S. (1991). Cognitive abilities and school performance of extremely low birth weight children and matched term control children at age 8 years: a regional study. Journal of Pediatrics, 118, 751760. https://doi.org/10.1016/S0022-3476(05)80043-5CrossRefGoogle ScholarPubMed
Sandman, C. A., Glynn, L. M., & Davis, E. P. (2013). Is there a viability–vulnerability tradeoff? Sex differences in fetal programming. Journal of Psychosomatic Research, 75, 327335. http://dx.doi.org/10.1016/j.jpsychores.2013.07.009CrossRefGoogle Scholar
Sandoval, J., Heyn, H., Moran, S., Serra-Musach, J., Pujana, M. A., Bibikova, M., & Esteller, M. (2011). Validation of a DNAm microarray for 450,000 CpG sites in the human genome. Epigenetics, 6, 692702. https://doi.org/10.4161/epi.6.6.16196CrossRefGoogle Scholar
Shonkoff, J. P., Boyce, W. T., & McEwen, B. S. (2009). Neuroscience, molecular biology, and the childhood roots of health disparities: Building a new framework for health promotion and disease prevention. JAMA, 301, 22522259. doi:10.1001/jama.2009.754CrossRefGoogle ScholarPubMed
Skiöld, B., Alexandrou, G., Padilla, N., Blennow, M., Vollmer, B., & Ådén, U. (2014). Sex differences in outcome and associations with neonatal brain morphology in extremely preterm children. Journal of Pediatrics, 164, 10121018. http://dx.doi.org/10.1016/j.jpeds.2013.12.051CrossRefGoogle ScholarPubMed
Smith, A. K., Kilaru, V., Klengel, T., Mercer, K. B., Bradley, B., Conneely, K. N., … Binder, E. B. (2015). DNA extracted from saliva for methylation studies of psychiatric traits: Evidence for tissue specificity and relatedness to brain. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 168, 3644. doi: 10.1002/ajmg.b.32278CrossRefGoogle Scholar
Sparrow, S., Manning, J. R., Cartier, J., Anblagan, D., Bastin, M. E., Piyasena, C., … Evans, M. (2016). Epigenomic profiling of preterm infants reveals DNAm differences at sites associated with neural function. Translational Psychiatry, 6, e716. doi:10.1038/tp.2015.210CrossRefGoogle ScholarPubMed
Spiers, H., Hannon, E., Schalkwyk, L. C., Smith, R., Wong, C. C., O'Donovan, M. C., … Mill, J. (2015). Methylomic trajectories across human fetal brain development. Genome Research, 25, 338352. doi:10.1101/gr.180273.114.CrossRefGoogle ScholarPubMed
Suderman, M., Borghol, N., Pappas, J. J., Pereira, S. M. P., Pembrey, M., Hertzman, C., … Szyf, M. (2014). Childhood abuse is associated with methylation of multiple loci in adult DNA. BMC Medical Genomics, 7, 13. http://www.biomedcentral.com/1755-8794/7/1CrossRefGoogle ScholarPubMed
Szyf, M., & Bick, J. (2013). DNAm: a mechanism for embedding early life experiences in the genome. Child Development, 84, 4957. doi:10.1111/j.1467-8624.2012.01793.xCrossRefGoogle Scholar
Tan, Q., Li, S., Frost, M., Nygaard, M., Soerensen, M., Larsen, M., … Christiansen, L. (2018). Epigenetic signature of preterm birth in adult twins. Clinical Epigenetics, 10, 87. https://doi.org/10.1186/s13148-018-0518-8CrossRefGoogle ScholarPubMed
Theda, C., Hwang, S. H., Czajko, A., Loke, Y. J., Leong, P., & Craig, J. M. (2018). Quantitation of the cellular content of saliva and buccal swab samples. Scientific Reports, 8, 6944. doi:10.1038/s41598-018-25311-0CrossRefGoogle ScholarPubMed
Thompson, R. F., & Einstein, F. H. (2010). Epigenetic basis for fetal origins of age-related disease. Journal of Women's Health, 19, 581587. doi:10.1089=jwh.2009.1408CrossRefGoogle ScholarPubMed
Tobi, E. W., Slieker, R. C., Luijk, R., Dekkers, K. F., Stein, A. D., Xu, K. M., … Biobank-based Integrative Omics Studies Consortium. (2018). DNAm as a mediator of the association between prenatal adversity and risk factors for metabolic disease in adulthood. Science Advances, 4, eaao4364. doi:10.1126/sciadv.aao4364CrossRefGoogle ScholarPubMed
Twilhaar, E. S., de Kieviet, J. F., Aarnoudse-Moens, C. S. H., van Elburg, R. M., & Oosterlaan, J. (2018). Academic performance of children born preterm: a meta-analysis and meta-regression. Archives of Disease in Childhood-Fetal Neonatal Edition, 103, F322–330. http://dx.doi.org/10.1136/archdischild-2017-312916CrossRefGoogle ScholarPubMed
Van Lieshout, R. J., Boyle, M. H., Saigal, S., Morrison, K., & Schmidt, L. A. (2015). Mental health of extremely low birth weight survivors in their 30s. Pediatrics, 135, 452459. doi:10.1542/peds.2014-3143CrossRefGoogle ScholarPubMed
van Tilborg, E., Heijnen, C. J., Benders, M. J., van Bel, F., Fleiss, B., Gressens, P., & Nijboer, C. H. (2016). Impaired oligodendrocyte maturation in preterm infants: potential therapeutic targets. Progress in Neurobiology, 136, 2849. https://doi.org/10.1016/j.pneurobio.2015.11.002CrossRefGoogle ScholarPubMed
Vatten, L. J., & Skjærven, R. (2004). Offspring sex and pregnancy outcome by length of gestation. Early Human Development, 76, 4754. doi:10.1016/j.earlhumdev.2003.10.006CrossRefGoogle ScholarPubMed
Wehkalampi, K., Muurinen, M., Wirta, S. B., Hannula-Jouppi, K., Hovi, P., Järvenpää, A. L., … Kajantie, E. (2013). Altered methylation of IGF2 locus 20 years after preterm birth at very low birth weight. PLoS One, 8, e67379. doi:10.1371/journal.pone.0067379CrossRefGoogle ScholarPubMed
Wilson-Costello, D., Friedman, H., Minich, N., Fanaroff, A. A., & Hack, M. (2005). Improved survival rates with increased neurodevelopmental disability for extremely low birth weight infants in the 1990s. Pediatrics, 115, 9971003. doi:10.1542/peds.2004-0221CrossRefGoogle ScholarPubMed
Xu, H., Wang, F., Liu, Y., Yu, Y., Gelernter, J., & Zhang, H. (2014). Sex-biased methylome and transcriptome in human prefrontal cortex. Human Molecular Genetics, 23, 12601270. doi:10.1093/hmg/ddt516CrossRefGoogle ScholarPubMed
Zeskind, P. S., & Gingras, J. L. (2006). Maternal cigarette-smoking during pregnancy disrupts rhythms in fetal heart rate. Journal of Pediatric Psychology, 31, 514. doi:10.1093/jpepsy/jsj031CrossRefGoogle ScholarPubMed
Zheng, S. C., Webster, A. P., Dong, D., Feber, A., Graham, D. G., Sullivan, R., … Teschendorff, A. E. (2018). A novel cell-type deconvolution algorithm reveals substantial contamination by immune cells in saliva, buccal and cervix. Epigenomics, 10, 925940. doi:10.2217/epi-2018-0037CrossRefGoogle ScholarPubMed
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