Diagnosis and Microbial Characterization
Identification of the causative agent of disease typically relies on demonstrating that a particular pathogen is present in a given clinical sample. Conventional testing relies on the use of culture based methods perfromed in conjunction in some cases with pathogen-specific molecular techniques such as gene specific PCR. In order to isolate specific pathogens, specialized media and culture conditions are often required. These techniques can be time consuming, labour intensive and are of limited use when the causative agent is difficult to cultivate as in the case of fastidious bacteria such as Treponema palidum and Mycobacterium leprae, and with several major viruses including Hepatitis A, B, C and E viruses. In many instances, culture based methods are being now routinely complemented with or even replaced by nucleic acid-based tests such as PCR. These tests are generally fast, cheap, can be automated and offer a high degree of sensitivity, and specificity. These tests are however usually gene specific, which restricts their use to identifying predefined targets, and therefore relies a hypothesis driven diagnosis. Furthermore many molecular tests are low resolution and are unable to discriminate between genotypes.
Whole genome sequencing can potentially revolutionize the way in which pathogens are detected and disease diagnosed, and may be particularly useful for slow growing organisms such as Mycobacterium tuberculosis and other fastidious and difficult to culture organisms. This technology offers a rapid and high resolution alternative to other molecular techniques and can potentially be used to identify pathogens directly from clinical samples without the need for a hypothesis driven workflow.
The objectives of this research priority are therefore to demonstrate the utility of using whole genome sequencing for the detection and characterization of pathogenic organisms. This technology should allow for the more rapid and accurate identification of disease causing pathogens, which may potentially help clinicians to make more informed treatment choices.