Multiple Displacement Amplification and Whole Genome Sequencing for the Diagnosis of Infectious Diseases
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Next-generation sequencing technologies are revolutionising our ability to characterise and investigate infectious diseases. Utilising the power of high throughput sequencing, this study reports, the development of a sensitive, non-PCR based, unbiased amplification method. Which allows the rapid and accurate sequencing of multiple microbial pathogens directly from clinical samples. The method employs ɸ29 DNA polymerase, a highly efficient enzyme able to produce strand displacement during the polymerisation process with high fidelity. Problems with DNA secondary structure were overcome and the method optimised to produce sufficient DNA to sequence from a single bacterial cell in two hours. Evidence was also found that the enzyme requires at least six bases of single stranded DNA to initiate replication, and is not capable of amplification from nicks. ɸ29 multiple displacement amplification was shown to be suitable for a range of GC contents and bacterial cell wall types as well as for viral pathogens. The method was shown to be able to provide relative quantification of mixed cells, and a method for quantification of viruses using a known standard was developed. To complement the novel molecular biology workflow, a data analysis pipeline was developed to allow pathogen identification and characterisation without prior knowledge of input. The use of de novo assemblies for annotation was shown to be equivalent to the use of polished reference genomes. Single cell φ29 MDA samples had better assembly and annotation than non-amplification controls, a novel finding which, when combined with the very long DNA fragments produced, has interesting implications for a variety of analytical procedures. A sampling process was developed to allow isolation and amplification of pathogens directly from clinical samples, with good concordance shown between this method and traditional testing. The process was tested on a variety of modelled and real clinical samples showing good application to sterile site infections, particularly bacteraemia models. Within these samples multiple bacterial, viral and parasitic pathogens were identified, showing good application across multiple infection types. Emerging pathogens were identified including Onchocerca volvulus within a CSF sample, and Sneathia sanguinegens within an STI sample. Use of ɸ29 MDA allows rapid and accurate amplification of whole pathogen genomes. When this is coupled with the sample processing developed here it is possible to detect the presence of pathogens in sterile sites with a sensitivity of a single genome copy.
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