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To treat or not to treat: investigating the impact of depression and antidepressant use in pregnancy.

  • Full or part time
  • Application Deadline
    Monday, November 25, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Depression in pregnancy, often occurring in combination with anxiety and a stressful environment, increases the risk of adverse outcomes for offspring with males showing more attention deficits, cognitive problems and externalising behaviour and females tending to present with anxiety, depression and internalising symptoms. Selective serotonin reuptake inhibitors (SSRIs) are the mostly commonly prescribed drugs designed to reduce symptoms of depression and anxiety in pregnancy but may themselves confer a risk to offspring, specifically for autism spectrum disorders. Our study will uniquely test the hypothesis that maternal depression damages the placenta contributing to adverse behavioural outcomes for mothers and offspring. We will also assess the benefits and risks of SSRI treatment at a molecular and behavioural level using an experimental model system and the Grown in Wales Study. Inclusion of human placenta in the analysis is an important component for three reasons: Firstly, the human placenta represents one of the few easily available tissues that can be collected at birth for gene expression and epigenetic analysis; secondly, the placenta is a fetally-derived tissue with the same genes as the fetus providing a “record” of exposures in pregnancy; thirdly and most importantly, we have shown in experimental models that placental dysfunction per se can cause fetal growth restriction, deficits in maternal behaviour and contribute to detrimental behavioural outcomes. We have recently identified a distinct transcriptional (RNAseq data) and epigenetic signature (EPIC array data) in human placenta exposed to prenatal depression and a different signature when depressed mothers report taking SSRIs. Together, these findings suggest that the human placenta responds to the stressors contributing to adverse outcomes for both mothers and offspring, potentially ameliorated or exacerbated by SSRIs.
To test the hypothesis that maternal depression damages the placenta and assess the impact of antidepressants, a pregnancy stress model will be developed and behaviourally characterised. The response of epigenetically sensitive loci to different stressors and treatments will be assessed by in vivo imaging of fetuses and placenta generated under different conditions of stressor and antidepressant using an existing luciferase model. Placenta from different models will be characterised using classic histology, state of the art imaging (RNAscope) and RNAseq. Human and experimental placenta RNAseq datasets will be interrogated to identify and compare disrupted pathways. At the end of this study, we will have a better idea on how untreated depression impacts placental development and the potential benefits and risk of treating pregnant women with antidepressants to address the unmet need for consistent data around SSRI use in pregnancy. This work will support clinicians deciding whether to prescribe antidepressants in pregnancy, and women making the decision to take them.

How to apply

Applicants must apply directly to the DTP as well as through the standard Cardiff University process: https://www.gw4biomed.ac.uk/doctoral-students/

Funding Notes

This studentship is funded through GW4 BioMed MRC Doctoral Training Partnership. It consists of full UK/EU tuition fees, as well as a Doctoral Stipend matching UK Research Council National Minimum (£15,009 for 2019/20, updated each year) for 3.5 years, or the part-time equivalent.
Additional funding, known as the Research Training and Support Grant, of between £2,000 to £5,000 per annum (dependent on the research requirements of the project), is available over the course of the programme. This is to cover costs such as training, conferences and travel, and research consumables.


References

Reviews
Janssen AB, Kertes DA, McNamara GI, Braithwaite EC, Creeth HD, Glover VI, John RM. A Role for the Placenta in Programming Maternal Mood and Childhood Behavioural Disorders. J Neuroendocrinol. 2016 Aug;28(8).
Creeth HDJ, McNamara GI, Isles AR, John RM. Imprinted genes influencing the quality of maternal care. Front Neuroendocrinol. 2019 Apr;53:100732.
John RM, Rougeulle C. Developmental Epigenetics: Phenotype and the Flexible Epigenome. Front Cell Dev Biol. 2018 Oct 11;6:130.
John RM. Imprinted genes and the regulation of placental endocrine function: Pregnancy and beyond. Placenta. 2017 Aug;56:86-90.
Research papers
Janssen AB, Savory KA, Garay SM, Sumption L, Watkins W, Garcia-Martin I, Savory NA, Ridgway A, Isles AR, Penketh R, Jones IR, John RM. Persistence of anxiety symptoms after elective caesarean delivery. BJPsych Open. 2018 Aug 17;4(5):354-360.
Creeth HDJ, McNamara GI, Tunster SJ, Boque-Sastre R, Allen B, Sumption L, Eddy JB, Isles AR, John RM. Maternal care boosted by paternal imprinting in mammals. PLoS Biol. 2018 Jul 31;16(7):e2006599.
McNamara GI, Creeth HDJ, Harrison DJ, Tansey KE, Andrews RM, Isles AR, John RM. Loss of offspring Peg3 reduces neonatal ultrasonic vocalizations and increases maternal anxiety in wild-type mothers. Hum Mol Genet. 2018 Feb 1;27(3):440-450.
Van de Pette M, Abbas A, Feytout A, McNamara G, Bruno L, To WK, Dimond A, Sardini A, Webster Z, McGinty J, Paul EJ, Ungless MA, French PMW, Withers DJ, Uren A, Ferguson-Smith AC, Merkenschlager M, John RM, Fisher AG. Visualizing Changes in Cdkn1c Expression Links Early-Life Adversity to Imprint Mis-regulation in Adults. Cell Rep. 2017 Jan 31;18(5):1090-1099.
Janssen AB, Capron LE, O'Donnell K, Tunster SJ, Ramchandani PG, Heazell AE, Glover V, John RM. Maternal prenatal depression is associated with decreased placental expression of the imprinted gene PEG3. Psychol Med. 2016 Oct;46(14):2999-3011.

How good is research at Cardiff University in Biological Sciences?

FTE Category A staff submitted: 54.70

Research output data provided by the Research Excellence Framework (REF)

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