Investigating disease persistence in host vector systems: dengue as a case study

The investigation of pathogen persistence in vector-borne diseases is important in different ecological and epidemiological contexts. In this thesis, I have developed deterministic and stochastic models to help investigating the pathogen persistence in host-vector systems by using efficient modelling paradigms. A general introduction with aims and objectives of the studies conducted in the thesis are provided in Chapter 1. The mathematical treatment of models used in the thesis is provided in Chapter 2 where the models are found locally asymptotically stable. The models used in the rest of the thesis are based on either the same or similar mathematical structure studied in this chapter. After that, there are three different experiments that are conducted in this thesis to study the pathogen persistence. In Chapter 3, I characterize pathogen persistence in terms of the Critical Community Size (CCS) and find its relationship with the model parameters. In this study, the stochastic versions of two epidemiologically different host-vector models are used for estimating CCS. I note that the model parameters and their algebraic combination, in addition to the seroprevalence level of the host population, can be used to quantify CCS. The study undertaken in Chapter 4 is used to estimate pathogen persistence using both deterministic and stochastic versions of a model with seasonal birth rate of the vectors. Through stochastic simulations we investigate the pattern of epidemics after the introduction of an infectious individual at different times of the year. The results show that the disease dynamics are altered by the seasonal variation. The higher levels of pre-existing seroprevalence reduces the probability of invasion of dengue. In Chapter 5, I considered two alternate ways to represent the dynamics of a host-vector model. Both of the approximate models are investigated for the parameter regions where the approximation fails to hold. Moreover, three metrics are used to compare them with the Full model. In addition to the computational benefits, these approximations are used to investigate to what degree the inclusion of the vector population in the dynamics of the system is important. Finally, in Chapter 6, I present the summary of studies undertaken and possible extensions for the future work.

Phylodynamic modelling of foot-and-mouth disease virus sequence data

The under-reporting of cases of infectious diseases is a substantial impediment to the control and management of infectious diseases in both epidemic and endemic contexts. Information about infectious disease dynamics can be recovered from sequence data using time-varying coalescent approaches, and phylodynamic models have been developed in order to reconstruct demographic changes of the numbers of infected hosts through time. In this study I have demonstrated the general concordance between empirically observed epidemiological incidence data and viral demography inferred through analysis of foot-and-mouth disease virus VP1 coding sequences belonging to the CATHAY topotype over large temporal and spatial scales. However a more precise and robust relationship between the effective population size (

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.

An investigation of the relationship between the components of tumour microenvironment, signal transduction pathways and outcome in breast cancer

Breast cancer, the most commonly diagnosed type of cancer in women, is a major cause of morbidity and mortality in the western world. Well-established risk factors of breast cancer are mostly related to women’s reproductive history, such as early menarche, late first pregnancy and late menopause. Survival rates have improved due to a combination of factors, including better health education, early detection with large-scale use of screening mammogram, improved surgical techniques, as well as widespread use of adjuvant therapy. At initial presentation, clinicopathological features of breast cancer such as age, nodal status, tumour size, tumour grade, and hormonal receptor status are considered to be the standard prognostic and predictive markers of patient survival, and are used to guide appropriate treatment strategies. Lymphovascular invasion (LBVI), including lymphatic (LVI) and blood (BVI) vessel invasion, has been reported to be prognostic and merit accurate evaluation, particularly in patients with node negative tumours who might benefit from adjuvant chemotherapy. There is a lack of standard assessment and agreement on distinguishing LVI from BVI despite the major challenges in the field. A systematic review of the literatures, examining methods of detection and the prognostic significance of LBVI, LVI and BVI, was carried out. The majority of studies used haematoxylin and eosin (H&E) and classical histochemistry to identify LVI and BVI. Only few recent studies used immunohistochemistry (IHC) staining of the endothelium lining lymphatic and blood vessels, and were able to show clear differences between LVI and BVI. The prognostic significance of LBVI and LVI was well-documented and strongly associated with aggressive features of breast tumours, while the prognostic value and the optimal detection method of BVI were unclear. Assessment and prognostic value of LBVI on H&E sections (LBVIH&E) was examined and compared to that of LVI and BVI detected using IHC with D2-40 for LVI (LVID2–40) and Factor VIII for BVI (BVIFVIII) in patients with breast cancer including node negative and triple negative patients (n=360). LBVIH&E, LVID2–40 and BVIFVIII were present in 102 (28%), 127 (35%) and 59 (16%) patients respectively. In node negative patients (206), LBVIH&E, LVID2–40 and BVIFVIII were present in 41 (20%), 53 (26%) and 21 (10%) respectively. In triple negative patients (102), LBVIH&E, LVID2–40 and BVIFVIII were present in 35 (29%), 36 (35%) and 14 (14%) respectively. LBVIH&E, LVID2–40 and BVIFVIII were all significantly associated with tumour recurrence in all cohorts. On multivariate survival analysis, only LVID2–40 and BVIFVIII were independent predictors of cancer specific survival (CSS) in the whole cohort (P=0.022 and P<0.001 respectively), node negative (P=0.008 and P=0.001 respectively) and triple negative patients (P=0.014 and P<0.001 respectively). Assessment of LVI and BVI by IHC, using D2-40 and Factor VIII, improves prediction of outcome in patients with node negative and triple negative breast cancer and was superior to the conventional detection method. Breast cancer is recognised as a complex molecular disease and histologically identical tumours may have highly variable outcomes, including different responses to therapy. Therefore, there is a compelling need for new prognostic and predictive markers helpful of selecting patients at risk and patients with aggressive diseases who might benefit from adjuvant and targeted therapy. It is increasingly recognised that the development and progression of human breast cancer is not only determined by genetically abnormal cells, but also dependent on complex interactions between malignant cells and the surrounding microenvironment. This has led to reconsider the features of tumour microenvironment as potential predictive and prognostic markers. Among these markers, tumour stroma percentage (TSP) and tumour budding, as well as local tumour inflammatory infiltrate have received recent attention. In particular, the local environment of cytokines, proteases, angiogenic and growth factors secreted by inflammatory cells and stromal fibroblasts has identified crucial roles in facilitating tumour growth, and metastasis of cancer cells through lymphatic and/or blood vessel invasion. This might help understand the underlying process promoting tumour invasion into these vessels. An increase in the proportion of tumour stroma and an increase in the dissociation of tumour cells have been associated with poorer survival in a number of solid tumours, including breast cancer. However, the interrelationship between these variables and other features of the tumour microenvironment in different subgroups of breast cancer are not clear. Also, whether their prognostic values are independent of other components of the tumour microenvironment have yet to be identified. Therefore, the relationship between TSP, clinicopathological characteristics and outcome in patients with invasive ductal breast cancer, in particular node negative and triple negative disease was examined in patients with invasive ductal breast cancer (n=361). The TSP was assessed on the haematoxylin and eosin-stained tissue sections. With a cut-off value of 50% TSP, patients with ≤50% stroma were classified as the low-TSP group and those with >50% stroma were classified as the high-TSP group. A total of 109 (30%) patients had high TSP. Patients with high TSP were old age (P=0.035), had involved lymph node (P=0.049), Her-2 positive tumours (P=0.029), low-grade peri-tumoural inflammatory infiltrate (P=0.034), low CD68+ macrophage infiltrate (P<0.001), low CD4+ (P=0.023) and low CD8+ T-lymphocytes infiltrate (P=0.017), tumour recurrence (P=0.015) and shorter CSS (P<0.001). In node negative patients (n=207), high TSP was associated with low CD68+ macrophage infiltrate (P=0.001), low CD4+ (P=0.040) and low CD8+ T-lymphocytes infiltrate (P=0.016) and shorter CSS (P=0.005). In triple negative patients (n=103), high TSP was associated with increased tumour size (P=0.017) high tumour grade (P=0.014), low CD8+ T-lymphocytes infiltrate (P=0.048) and shorter CSS (P=0.041). The 15-year cancer specific survival rate was 79% vs 21% in the low-TSP group vs high-TSP group. On multivariate survival analysis, a high TSP was associated with reduced CSS in the whole cohort (P=0.007), node negative patients (P=0.005) and those who received systemic adjuvant therapy (P=0.016), independent of other pathological characteristics including local host inflammatory responses. Therefore, a high TSP in invasive ductal breast cancer was associated with recurrence and poorer long-term survival. The inverse relation with the tumour inflammatory infiltrate highlights the importance of the amount of tumour stroma on immunological response in patients with invasive ductal breast cancer. Implementing this simple and reproducible parameter in routine pathological examination may help optimise risk stratification in patients with breast cancer. Similarly, the relationship between tumour budding, clinicopathological characteristics and outcome was examined in patients with invasive ductal breast cancer (n=474), using routine pathological sections. Tumour budding was associated with several adverse pathological characteristics, including positive lymph node (P=0.009), presence of LVI (P<0.001), and high TSP (P=0.001) and low-grade general peri-tumural inflammatory infiltrative (P=0.002). In node negative patients, a high tumour budding was associated with presence of LVI (P<0.001) and low-grade general peri-tumural inflammatory infiltrative (P=0.038). On multivariate survival analysis, tumour budding was associated with reduced CSS (P=0.001), independent of nodal status, tumour necrosis, CD8+ and CD138+ inflammatory cells infiltrate, LVI, BVI and TSP. Furthermore, tumour budding was independently associated with reduced CSS in node negative patients (P=0.004) and in those who have low TSP (P=0.003) and high-grade peri-tumoural inflammatory infiltrative (P=0.012). A high tumour budding was significantly associated with shorter CSS in luminal B and triple negative breast cancer subtypes (all P<0.001). Therefore, tumour budding was a significant predictor of poor survival in patients with invasive ductal breast cancer, independent of adverse pathological characteristics and components of tumour microenvironment. These results suggest that tumour budding may promote disease progression through a direct effect on local and distant invasion into lymph nodes and lymphatic vessels. Therefore, detection of tumour buds at the stroma invasive front might therefore represent a morphologic link between tumour progression, lymphatic invasion, spread of tumour cells to regional lymph nodes, and the establishment of metastatic dissemination. Given the potential importance of the tumour microenvironment, the characterisation of intracellular signalling pathways is important in the tumour microenvironment and is of considerable interest. One plausible signalling molecule that links tumour stroma, inflammatory cell infiltrate and tumour budding is the signal transducer and activator of transcription (STAT). The relationship between total and phosphorylated STAT1 (ph-STAT1), and total and ph-STAT3 tumour cell expression, components of tumour microenvironment and survival in patients with invasive ductal breast cancer was examined. IHC of total and ph-STAT1/STAT3 was performed on tissue microarray of 384 breast cancer specimens. Cellular STAT1 and cellular STAT3 expression at both cytoplasmic and nuclear locations were combined and identified as STAT1/STAT3 tumour cell expression. These results were then related to CSS and phenotypic features of the tumour and host. A high ph-STAT1 and a high ph-STAT3 tumour cell expression was associated with increased ER (P=0.001 and P<0.001 respectively) and PR (all P<0.05), reduced tumour grade (P=0.015 and P<0.001 respectively) and necrosis (all P=0.001). Ph-STAT1 was associated with increased general peri-tumoural inflammatory infiltrate (P=0.007) and ph-STAT3 was associated with lower CD4+ T-lymphocyte infiltrate (P=0.024). On multivariate survival analysis, including both ph-STAT1 and ph-STAT3 tumour cell expression, only high ph-STAT3 tumour cell expression was significantly associated with improved CSS (P=0.010) independent of other tumour and host-based factors. In patients with high necrosis grade, high ph-STAT3 tumour cell expression was independent predictor of improved CSS (P=0.021). Ph-STAT1 and ph-STAT3 were also significantly associated with improved cancer specific survival in luminal A and B subtypes. STAT1 and STAT3 tumour cell expression appeared to be an important determinant of favourable outcome in patients with invasive ductal breast cancer. The present results suggest that STATs may affect disease outcome through direct impact on tumour cells, and the surrounding microenvironment. The above observations of the present thesis point to the importance of the tumour microenvironment in promoting tumour budding, LVI and BVI. The observations from STATs work may suggest that an important driving mechanism for the above associations is the presence of tumour necrosis, probably secondary to hypoxia. Further work is needed to examine the interaction of other molecular pathways involved in the tumour microenvironment, such as HIF and NFkB in patients with invasive ductal breast cancer.