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Phenotyping placental disease in women with diabetes and chronic hypertension using transcriptomic analysis


Project Description

Placental disease triggers medically-indicated preterm birth in 1 in 6 pregnancies complicated by chronic hypertension and/or diabetes (cardiometabolic disease). In these pregnancies, making a diagnosis of placental disease using standard clinical signs is challenging and imprecise. We have observed at least two distinct longitudinal profiles of placental growth factor in pregnancies complicated by pre-eclampsia; it is plausible that these represent different placental disease aetiologies1. Recent work by our collaborator (Cox) has identified molecular subtypes of placental disease using gene expression profiles2,3. It is not known to what extent these molecular subtypes will overlap the placental disease observed in women with cardiometabolic disease. In order to improve the antenatal diagnosis, and ultimately the treatment of placental disease, it is crucial that we understand the relationship between maternal disease biomarkers (angiogenic markers, metabolic, inflammatory and haemodynamic status), fetal growth trajectory and the underlying placental disease.

Through our established existing cohorts, we have already collected maternal characteristics, disease biomarkers and blood samples, and fetal growth data from more than 1000 women with cardiometabolic disease in pregnancy. We have an established placental collection infrastructure within the Manchester Maternal & Fetal Health Research Centre and have placentas stored in RNAlater and snap frozen from >300 of these.
Research questions: (1) Do gene expression changes in placentas from women with cardiometabolic disease overlap previously reported pre-eclampsia/FGR placental transcriptome subtypes, (2) What patterns of maternal disease biomarkers and fetal growth associate with molecular placental disease subtypes.
mRNA analysis of placental tissues will be carried out in collaboration with Professor Cox (University of Toronto) who has expertise in the bioinformatic processing of transcriptomic data sets and will contribute to the supervision of the project.
Impact: (1) novel molecular characterisation of placental disease in women with cardiometabolic disease (2) improved diagnosis and risk stratification of placental disease through the identification of maternal disease biomarkers which delineate placental disease subtypes

Training/techniques to be provided:
Transcriptomic analysis of placental tissue. Processing and biostatistics of longitudinal clinical data. Bioinformatic analysis using supervised and unsupervised clustering techniques, multivariate analysis using techniques developed by Professor Cox.

Entry Requirements:
Candidates are expected to hold (or be about to obtain) a minimum upper second class honours degree (or equivalent) in a related area / subject. Candidates with experience in molecular biology and/or biostatistics or with an interest in bioinformatics are encouraged to apply.

Funding Notes

Applications are invited from self-funded students. This project has a Band 3 fee. Details of our different fee bands can be found on our website (View Website). For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (View Website).

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

References

1. A Dempsey, E Johnstone, C Chmiel, G Marshall, J Horn, and J Myers. Longitudinal placental growth factor (PlGF) in pregnancies complicated by pre-existing diabetes in the context of maternal hypertensive disease. Brit J Obstet Gynae, 2017.
2. K Leavey, SJ Benton, D Grynspan, JC Kingdom, SA Bainbridge, and BJ Cox. Unsupervised Placental Gene Expression Profiling Identifies Clinically Relevant Subclasses of Human Preeclampsia. Hypertension, 2016. doi:10.1161/HYPERTENSIONAHA.116.07293
3. Leavey, SA Bainbridge, and BJ Cox. Large scale aggregate microarray analysis reveals three distinct molecular subclasses of human preeclampsia. PloS one, 2015. doi:10.1371/journal.pone.0116508

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