Puerperal Psychosis is the most severe form of postnatal psychiatric illness, and is a psychiatric emergency. Human-based research to find a means of genetically predicting a woman's risk of puerperal psychosis has so far failed to reliably or reproducibly identify candidate genes or pathways, due to problems common within the field of psychiatric genetics, leading to the proposal of using an animal model in the form of Porcine Maternal Infanticide. In this project, the author has aimed to understand the pathophysiology of PMI using NGS technologies in order to 1) extend the validity of PMI as a model for PP; 2) determine future steps for development of the PMI model; and 3) generate insights into the management of PMI (and by extension PP) via prediction and detection of a puerperal trigger. These aims have been pursued via two experiments. In the first, the author has created RNA-Seq libraries from archival RNA, and then performed differential gene expression analysis. The results indicated that RNA-Seq technologies can be used with archival RNA samples, but using such samples introduces the risk of degradation-based bias. The substantial influence of outliers, confounding factors and sample size on the results prevented reliable identification of candidate genes" but provide concrete guidelines development of the Porcine Maternal Infanticide model. In the second, the author has created MBD-Seq libraries from archival tissue, and then performed differential methylation analysis. The results indicated that it is possible to use MBD-Seq technologies with Sus scrofa brain tissue. Once again, the effect of confounding factors and sample size on the results prevented reliable identification of candidate genes, but provide guidance for development of the Porcine Maternal Infanticide model.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:763758 |
| Date | January 2019 |
| Creators | Landers, Courtney Amaryllis |
| Contributors | Sargent, Carole Anne ; Affara, Nabeel |
| Publisher | University of Cambridge |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.repository.cam.ac.uk/handle/1810/284358 |
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