Histological chorioamnionitis (HCA) is inflammation of the fetal membranes. Understanding the fetal membrane microbiota linked to inflammation and bacterial infection is incomplete. This research aimed to increase current knowledge of the fetal membrane microbiota in HCA, including bacterial profile, bacterial load and correlation to inflammatory response. In addition, to investigate the application of Formalin-Fixed Paraffin-Embedded (FFPE) tissues to microbiota research. Finally, to optimise Raman spectroscopy on low biomass cultures for future application to HCA detection.
A retrospective cohort study was employed on fetal membranes from patients with preterm spontaneous labour and HCA (n=12), or preterm (n=6) and term labour without HCA (n=6), plus low risk term patients (n=58). To investigate bacterial profiles, 16S rRNA Illumina sequencing was performed. Bacterial loads were assessed by 16S rRNA BactQuant qPCR, with bacterial loads also correlated to inflammatory marker data. The spectral fingerprint and biochemical composition of three bacteria were analysed using Raman spectroscopy.
Bacterial loads were significantly greater from HCA patients (5013.066 copies/µl) compared to preterm (288.873 copies/µl) and term without HCA (254.819 copies/µl). Increased bacterial load was positively correlated with maternal inflammatory staging, and the expression of five inflammatory markers. Non-HCA patients, low risk term patients and negative controls did not display distinct bacterial loads (200-300 copies/µl). A trend for increased Prevotella with increased inflammation was detected. Over half of sequences from FFPE fetal membranes were identified as contaminants. Bacteria were identified by one Raman spectral peak (1003 cm-1).
Inflammatory HCA involves bacterial infection and increased bacterial load in a dose response relationship, with an association to increased Prevotella. Bacteria is not acquired in utero on fetal membranes without an inflammatory condition. Frozen tissues remain the gold standard for microbiota research. Optimisation of Raman microspectroscopy for bacterial detection on clinical tissues is required to improve diagnosis.
|Date of Award||19 Jun 2020|
|Supervisor||Caroline Orr (Supervisor) & Gillian Taylor (Supervisor)|