Depression in pregnancy, infant birth weight and DNA methylation of imprint regulatory elements.

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2012-07

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Abstract

Depressed mood in pregnancy has been linked to low birth weight (LBW, 4,500 g) infants had 5.9% higher methylation at the PLAGL1 DMR compared with normal birth weight infants. Our findings confirm that severe maternal depressed mood in pregnancy is associated with LBW, and that MEG3 and IGF2 plasticity may play important roles.

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Humans, Pregnancy Complications, Birth Weight, Insulin-Like Growth Factor II, Cell Cycle Proteins, Tumor Suppressor Proteins, Transcription Factors, RNA, Untranslated, Sequence Analysis, DNA, Depression, DNA Methylation, Genomic Imprinting, Pregnancy, Adolescent, Adult, Infant, Newborn, Female, Male, Genetic Loci, High-Throughput Nucleotide Sequencing, RNA, Long Noncoding

Citation

Published Version (Please cite this version)

10.4161/epi.20734

Publication Info

Liu, Y, SK Murphy, AP Murtha, BF Fuemmeler, J Schildkraut, Z Huang, F Overcash, J Kurtzberg, et al. (2012). Depression in pregnancy, infant birth weight and DNA methylation of imprint regulatory elements. Epigenetics : official journal of the DNA Methylation Society, 7(7). pp. 735–746. 10.4161/epi.20734 Retrieved from https://hdl.handle.net/10161/24661.

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.

Scholars@Duke

Murphy

Susan Kay Murphy

Associate Professor in Obstetrics and Gynecology

Dr. Murphy is a tenured Associate Professor in the Department of Obstetrics and Gynecology and serves as Chief of the Division of Reproductive Sciences. As a molecular biologist with training in human epigenetics, her research interests are largely centered around the role of epigenetic modifications in health and disease. 

Dr. Murphy has ongoing projects on gynecologic malignancies, including approaches to eradicate ovarian cancer cells that survive chemotherapy and later give rise to recurrent disease. Dr. Murphy is actively involved in many collaborative projects relating to the Developmental Origins of Health and Disease (DOHaD).

Her lab is currently working on preconception environmental exposures in males, particularly on the impact of cannabis on the sperm epigenome and the potential heritability of these effects. They are also studying the epigenetic and health effects of in utero exposures, with primary focus on children from the Newborn Epigenetics STudy (NEST), a pregnancy cohort she co-founded who were recruited from central North Carolina between 2005 and 2011. Dr. Murphy and her colleagues continue to follow NEST children to determine relationships between prenatal exposures and later health outcomes.

Schildkraut

Joellen Martha Schildkraut

Professor Emeritus in Family Medicine and Community Health

Dr. Schildkraut is an epidemiologist whose research includes the molecular epidemiology of ovarian, breast and brain cancers. Dr. Schildkraut's research interests include the study of the interaction between genetic and environmental factors. She is currently involved in a large study of genome wide association and ovarian cancer risk and survival. Some of her work is also focused on particular genetic pathways including the DNA repair and apoptosis pathways. She currently leads a study of African American women diagnosed with ovarian cancer. She is also collaborating in a large a case-control study of meningioma risk factors and with which a genome wide association analysis is about to commence.

Huang

Zhiqing Huang

Assistant Professor in Obstetrics and Gynecology

Dr. Huang is an Assistant Professor in the Department of Obstetrics and Gynecology, Division of Reproductive Sciences, at Duke University Medical Center. She obtained her MD at North China Coal Medical University in China and her PhD at the University of Heidelberg in Germany under the mentorship of Dr. Ralph Witzgall. She did her postdoctoral training with Dr. Jiemin Wong at Baylor College of Medicine, studying how histone methylation and chromatin modifications regulate androgen receptor transcription. 

Dr. Huang’s research includes the following:

•The factors in the tumor microenvironment contribute to ovarian cancer progress;
•New drug development for recurrent ovarian cancer treatment;
•The early DNA methylation profiles contribute to cancer development in late life;
•The special changes in the tumor microenvironment;
•Epigenetics and epigenomics.
*The impact of lipid metabolism in the tumor microenvironment in cancer progression and treatment.
*Impact of ferroptosis in endometriosis development. 

Dr. Huang has received an R03 funding titled “Role of Age-Related Changes in the Tumor Microenvironment on Ovarian Cancer Progression” from NIA at NIH for 2021-2023.
Dr. Huang received Charles B. Hammond's Research Fund from the Department of Obstetrics and Gynecology at Duke University in November 2022, for a project titled "Single Cell Spatial Transcriptomics in Highly Aggressive and Less Aggressive Ovarian Cancer".
Dr. Huang has received Duke Cancer Institute 2023 spring pilot study award for07012023-06302024, the project title is "Age Effects on Chemotherapy Targeting Cells Causing Ovarian Cancer Recurrence”.
Dr. Huang has received the American Cancer Society -Duke Cancer Institute (ASC-DCI) 2024 spring pilot study award for 07012024-06302025. The project title is "Early Establishment of Epigenetic Profiles that Increase Cancer Risk in Late Life”.
Dr. Huang received Charles B. Hammond's Research Fund from the Department of Obstetrics and Gynecology at Duke University in November 2023 for 01012024-12312024. The project's title is "Age Effects on Chemotherapy Targeting Cells Causing Ovarian Cancer Recurrence".

Iversen

Edwin Severin Iversen

Research Professor of Statistical Science

Bayesian statistical modeling with application to problems in genetic
epidemiology and cancer research; models for epidemiological risk
assessment, including hierarchical methods for combining related
epidemiological studies; ascertainment corrections for high risk
family data; analysis of high-throughput genomic data sets.


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