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Prenatal maternal psychological distress increases risk for adverse infant outcomes. However, the biological mechanisms underlying this association remain unclear. Prenatal stress can impact fetal epigenetic regulation that could underlie changes in infant stress responses. It has been suggested that maternal glucocorticoids may mediate this epigenetic effect. We examined this hypothesis by determining the impact of maternal cortisol and depressive symptoms during pregnancy on infant NR3C1 and BDNF DNA methylation. Fifty-seven pregnant women were recruited during the second or third trimester. Participants self-reported depressive symptoms and salivary cortisol samples were collected diurnally and in response to a stressor. Buccal swabs for DNA extraction and DNA methylation analysis were collected from each infant at 2 months of age, and mothers were assessed for postnatal depressive symptoms. Prenatal depressive symptoms significantly predicted increased NR3C1 1F DNA methylation in male infants (β = 2.147, P = 0.044). Prenatal depressive symptoms also significantly predicted decreased BDNF IV DNA methylation in both male and female infants (β = −3.244, P = 0.013). No measure of maternal cortisol during pregnancy predicted infant NR3C1 1F or BDNF promoter IV DNA methylation. Our findings highlight the susceptibility of males to changes in NR3C1 DNA methylation and present novel evidence for altered BDNF IV DNA methylation in response to maternal depression during pregnancy. The lack of association between maternal cortisol and infant DNA methylation suggests that effects of maternal depression may not be mediated directly by glucocorticoids. Future studies should consider other potential mediating mechanisms in the link between maternal mood and infant outcomes. 相似文献
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The genus Macaca: a review of taxonomy and evolution 总被引:1,自引:0,他引:1
JOHN E. FA 《Mammal Review》1989,19(2):45-81
74.
Wouter Meuleman Judith YMN Engwegen Marie-Christine W Gast Jos H Beijnen Marcel JT Reinders Lodewyk FA Wessels 《BMC bioinformatics》2008,9(1):88
Background
Mass spectrometry for biological data analysis is an active field of research, providing an efficient way of high-throughput proteome screening. A popular variant of mass spectrometry is SELDI, which is often used to measure sample populations with the goal of developing (clinical) classifiers. Unfortunately, not only is the data resulting from such measurements quite noisy, variance between replicate measurements of the same sample can be high as well. Normalisation of spectra can greatly reduce the effect of this technical variance and further improve the quality and interpretability of the data. However, it is unclear which normalisation method yields the most informative result. 相似文献75.
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