Development of a military deployable metagenomic next-generation sequencing workflow for the diagnosis of infectious disease

Halford, Carl Matthew (2024) Development of a military deployable metagenomic next-generation sequencing workflow for the diagnosis of infectious disease. Doctoral thesis, University of East Anglia.

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Abstract

The conditions that deployed military personnel are exposed to can lead to high incidences of infectious diseases, with a significant impact on personnel wellbeing and the operational effectiveness of the deployment. Therefore, it is crucial that military clinicians can rapidly diagnose infectious diseases so that the optimal treatments are administered. The diagnostic tests currently available in deployed environments are limited by the logistical requirements, the time to provide an actionable result and the targeted nature of the tests.

Clinical metagenomics (CMg) has the potential to provide rapid, untargeted identification of the causative pathogen of infection and additional clinically relevant information. However, there are several limitations associated with CMg, including the high ratio of human to pathogen genetic material, the complexity of the methods, the logistical requirements and the difficulty of interpreting the results to inform treatments.

In this study, we aimed to address these limitations to develop, optimise and evaluate CMg methods suitable for use in deployed medical treatment facilities (MTF). CMg workflows were developed for the diagnosis of wound, gastrointestinal (GI) and respiratory infections. These methods were applied to samples collected from patients with suspected or confirmed infections and the results were compared against routine diagnostic tests. This included CMg workflows that were operated by a military biomedical scientist (BMS) within a deployed MTF, highlighting the technical and logistical challenges associated with CMg and providing the basis for the future development of CMg for deployed infectious disease diagnosis.

This work has demonstrated that CMg has potential as a future deployed diagnostic test, however, further optimisation, including reduced method complexity through automation and a better understanding of the interpretation of CMg results, is required to enable the successful implementation of CMg within the deployed diagnostic pathway.

Item Type:	Thesis (Doctoral)
Faculty \ School:	Faculty of Science > School of Biological Sciences
Depositing User:	Zoe White
Date Deposited:	18 Feb 2025 15:48
Last Modified:	18 Feb 2025 15:48
URI:	https://ueaeprints.uea.ac.uk/id/eprint/98542
DOI:

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