Abstract
Transcriptomic analysis of pharyngeal swab samples from carriers of Neisseria meningitidis (Nm) may help to predict whether protein antigen meningococcal vaccines might impact on herd protection. Although RNA extraction and detection of Nm gene transcripts from in vivo mucosal samples is challenging, a probe-based gene expression profiling platform, the NanoString nCounter system, can detect very low numbers of messenger RNA (mRNA) transcripts.RNA extraction methods were compared. The RNeasy mini kit method was easier to perform, gave more reproducible results, and delivered a higher quality and quantity of RNA than the alternative acid:phenol:chloroform method tested.
A panel of 47 Nm genes were selected for NanoString probe design that coded for proteins in current or potential vaccine candidates or that were important in colonisation of the pharyngeal mucosa.
The NanoString system was first assessed in Nm culture samples. The method showed high sensitivity of gene detection and high reproducibility. Exposure to heat, cold and iron depletion resulted in a range of gene expression across the panel. This study showed that mRNA can be successfully detected and quantified from Nm cultures without the need to amplify the genes and from very small quantities of total RNA.
In subsequent experiments, Nm genes were detected and counted from in vivo pharyngeal carriage samples for the first time. Isolates from the samples were whole genome sequenced to assess the gene expression results obtained by NanoString. Some limitations of the technique were demonstrated including cross reactivity between different species in samples containing multiple organisms and reduced specificity at low levels of gene expression. The methodologies established in this thesis should be important for future transcriptomic studies on Nm and other bacteria carried in the human pharynx, and for the assessment of actual and potential vaccine antigens.
| Date of Award | 28 Nov 2019 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Adam H R Finn (Supervisor) & Hannah Christensen (Supervisor) |