Quasispecies dynamics and treatment outcome during early hepatitis C infection in a cohort of HIV-infected men

Hepatitis C virus (HCV) is emerging as one of the leading causes of morbidity and mortality in individuals infected with HIV and has overtaken AIDS-defining illnesses as a cause of death in HIV patient populations who have access to highly active antiretroviral therapy. For many years, the clonal analysis was the reference method for investigating viral diversity. In this thesis, a next generation sequencing (NGS) approach was developed using 454 pyrosequencing and Illumina-based technology. A sequencing pipeline was developed using two different NGS approaches, nested PCR, and metagenomics. The pipeline was used to study the viral populations in the sera of HCV-infected patients from a unique cohort of 160 HIV-positive patients with early HCV infection. These pipelines resulted in an improved understanding of HCV quasispecies dynamics, especially regarding studying response to treatment. Low viral diversity at baseline correlated with sustained virological response (SVR) while high viral diversity at baseline was associated with treatment failure. The emergence of new viral strains following treatment failure was most commonly associated with emerging dominance of pre-existing minority variants rather than re-infection. In the new era of direct-acting antivirals, next generation sequencing technologies are the most promising tool for identifying minority variants present in the HCV quasispecies populations at baseline. In this cohort, several mutations conferring resistance were detected in genotype 1a treatment-naïve patients. Further research into the impact of baseline HCV variants on SVR rates should be carried out in this population. A clearer understanding of the properties of viral quasispecies would enable clinicians to make improved treatment choices for their patients.

The impact of denture related disease on the oral microbiome of denture wearers

Advances in healthcare over the last 100 years has resulted in an ever increasing elderly population. This presents greater challenges for adequate systemic and oral healthcare delivery. With increasing age there is a natural decline in oral health, leading to the loss of teeth and ultimately for some having to wear denture prosthesis. It is currently estimated that approximately one fifth of the UK and US populations have some form of removable prosthesis. The microbiology of denture induced mucosal inflammation is a pivotal factor to consider in denture care management, similar to many other oral diseases of microbial influence, such as caries, gingivitis and periodontitis. Dentures support the growth of microbial biofilms, structures commonly known as denture plaque. Microbiologically, denture stomatitis (DS) is a disease primarily considered to be of yeast aetiology, with the literature disproportionately focussed on Candida spp. However, the denture surface is capable of carrying up to 1011 microbes per milligram, the majority of which are bacteria. Thus it is apparent that denture plaque is more diverse than we assume. There is a fundamental gap in our understanding of the bacterial composition of denture plaque and the role that they may play in denture related disease such as DS. This is categorised as inflammation of the oral mucosa, a disease affecting around half of all denture wearers. It has been proposed that bacteria and fungi interact on the denture surface and that these polymicrobial interactions lead to synergism and increased DS pathogenesis. Therefore, understanding the denture microbiome composition is the key step to beginning to understand disease pathogenesis, and ultimately help improve treatments and identify novel targets for therapeutic and preventative strategies. A group of 131 patients were included within this study in which they provided samples from their dentures, palatal mucosa, saliva and dental plaque. Microbes residing on the denture surface were quantified using standard Miles and Misra culture technique which investigated the presence of Candida, aerobes and anaerobes. These clinical samples also underwent next generation sequencing using the Miseq Illumina platform to give a more global representation of the microbes present at each of these sites in the oral cavity of these denture wearers. This data was then used to compare the composition and diversity of denture, mucosal and dental plaque between one another, as well as between healthy and diseased individuals. Additional comparisons included denture type and the presence or absence of natural teeth. Furthermore, microbiome data was used to assess differences between patients with varying levels of oral hygiene. The host response to the denture microbiome was investigated by screening the patients saliva for the presence and quantification of a range of antimicrobial peptides that are associated with the oral cavity. Based on the microbiome data an in vitro biofilm model was developed that reflected the composition of denture plaque. These biofilms were then used to assess quantitative and compositional changes over time and in response to denture cleansing treatments. Finally, the systemic implications of denture plaque were assessed by screening denture plaque samples for the presence of nine well known respiratory pathogens using quantitative PCR. The results from this study have shown that the bacterial microbiome composition of denture wearers is not consistent throughout the mouth and varies depending on sample site. Moreover, the presence of natural dentition has a significant impact on the microbiome composition. As for healthy and diseased patients the data suggests that compositional changes responsible for disease progression are occurring at the mucosa, and that dentures may in fact be a reservoir for these microbes. In terms of denture hygiene practices, sleeping with a denture in situ was found to be a common occurrence. Furthermore, significant shifts in denture microbiome composition were found in these individuals when compared to the denture microbiome of those that removed their denture at night. As for the host response, some antimicrobial peptides were found to be significantly reduced in the absence of natural dentition, indicating that the oral immune response is gradually impaired with the loss of teeth. This study also identified potentially serious systemic implications in terms of respiratory infection, as 64.6% of patients carried respiratory pathogens on their denture. In conclusion, this is the first study to provide a detailed understanding of the oral microbiome of denture wearers, and has provided evidence that DS development is more complex than simply a candidal infection. Both fungal and bacterial kingdoms clearly play a role in defining the progression of DS. The biofilm model created in this study demonstrated its potential as a platform to test novel actives. Future use of this model will aid in greater understanding of host: biofilm interactions. Such findings are applicable to oral health and beyond, and may help to identify novel therapeutic targets for the treatment of DS and other biofilm associated diseases.

Mixed genotype Hepatitis C virus infections: incidence in Scotland and methods for detection

The diagnosis of mixed genotype hepatitis C virus (HCV) infection is rare and information on incidence in the UK, where genotypes 1a and 3 are the most prevalent, is sparse. Considerable variations in the efficacies of direct-acting antivirals (DAAs) for the HCV genotypes have been documented and the ability of DAAs to treat mixed genotype HCV infections remains unclear, with the possibility that genotype switching may occur. In order to estimate the prevalence of mixed genotype 1a/3 infections in Scotland, a cohort of 512 samples was compiled and then screened using a genotype-specific nested PCR assay. Mixed genotype 1a/3 infections were found in 3.8% of samples tested, with a significantly higher prevalence rate of 6.7% (p<0.05) observed in individuals diagnosed with genotype 3 infections than genotype 1a (0.8%). An analysis of the samples using genotypic-specific qPCR assays found that in two-thirds of samples tested, the minor strain contributed <1% of the total viral load. The potential of deep sequencing methods for the diagnosis of mixed genotype infections was assessed using two pan-genotypic PCR assays compatible with the Illumina MiSeq platform that were developed targeting the E1-E2 and NS5B regions of the virus. The E1-E2 assay detected 75% of the mixed genotype infections, proving to be more sensitive than the NS5B assay which identified only 25% of the mixed infections. Studies of sequence data and linked patient records also identified significantly more neurological disorders in genotype 3 patients. Evidence of distinctive dinucleotide expression within the genotypes was also uncovered. Taken together these findings raise interesting questions about the evolutionary history of the virus and indicate that there is still more to understand about the different genotypes. In an era where clinical medicine is frequently more personalised, the development of diagnostic methods for HCV providing increased patient stratification is increasingly important. This project has shown that sequence-based genotyping methods can be highly discriminatory and informative, and their use should be encouraged in diagnostic laboratories. Mixed genotype infections were challenging to identify and current deep sequencing methods were not as sensitive or cost-effective as Sanger-based approaches in this study. More research is needed to evaluate the clinical prognosis of patients with mixed genotype infection and to develop clinical guidelines on their treatment.