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Title: Pregnancy risk stratification using DESI-MS profiling of vaginal mucosa
Author: Lewis, Holly Victoria
ISNI:       0000 0004 9349 9882
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2019
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Preterm birth is the leading cause of childhood mortality. Despite decades of research, the pathophysiology of spontaneous preterm birth (SPTB) remains poorly understood. Prevention strategies are limited by our inability to reliably predict women at risk and stratify depending on underlying aetiology. There is an established association between ascending vaginal infection and SPTB. More recently, highly diverse vaginal bacterial communities deplete of Lactobacillus species have been associated with SPTB. However, not all pregnant women with such community structures deliver preterm, highlighting the importance of individual host response. Medical swabs are routinely used for microbiological screening with culture-based techniques. However, these are time-consuming, have a narrow focus for specific microbes and provide no information regarding host response. We hypothesised that metabolic profiling of cervico-vaginal mucosa (CVM) may offer the ability to assess interactions between the vaginal microbiota and the pregnant host that are useful for prediction and stratification of SPTB risk. To address this hypothesis, we developed a technique using DESI-MS that enabled rapid acquisition of metabolic information directly from vaginal swabs. In Chapter 3, method optimisation is described and its capacity to detect variations in the CVM associated with physiological changes in the host (e.g. pregnancy) and disruptions in bacterial community compositions during pregnancy (e.g. bacterial vaginosis) are presented. The DESI-MS swab profiling approach was then used to characterise and compare CVM metabolic profiles associated with SPTB risk (Chapter 4). These results showed that the CVM metabolome associated with subsequent SPTB was highly variable, reflecting the heterogeneity of SPTB aetiology. In support of this, DESI-MS more effectively discriminated samples with differing severity of SPTB (early vs late) and phenotypes (SPTL and PPROM). In Chapter 5, DESI-MS profiling of CVM was shown to facilitate prediction of PPROM as well as enable its robust diagnosis. DESI-MS also had capacity to characterise microbial compositions following PPROM suggesting its potential to assist in directed treatment strategies based on underlying aetiology. This thesis highlights the predictive and therapeutic potential of DESI-MS in pregnancy.
Supervisor: MacIntyre, David ; Bennett, Phillip ; Takats, Zoltan Sponsor: Imperial College Healthcare NHS Trust ; Genesis Research Trust
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral