2 resultados para Genotyping by sequencing

em Glasgow Theses Service


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Trypanosomiasis has been identified as a neglected tropical disease in both humans and animals in many regions of sub-Saharan Africa. Whilst assessments of the biology of trypanosomes, vectors, vertebrate hosts and the environment have provided useful information about life cycles, transmission, and pathogenesis of the parasites that could be used for treatment and control, less information is available about the effects of interactions among multiple intrinsic factors on trypanosome presence in tsetse flies from different sites. It is known that multiple species of tsetse flies can transmit trypanosomes but differences in their vector competence has normally been studied in relation to individual factors in isolation, such as: intrinsic factors of the flies (e.g. age, sex); habitat characteristics; presence of endosymbionts (e.g. Wigglesworthia glossinidia, Sodalis glossinidius); feeding pattern; host communities that the flies feed on; and which species of trypanosomes are transmitted. The purpose of this study was to take a more integrated approach to investigate trypanosome prevalence in tsetse flies. In chapter 2, techniques were optimised for using the Polymerase Chain Reaction (PCR) to identify species of trypanosomes (Trypanosoma vivax, T. congolense, T. brucei, T. simiae, and T. godfreyi) present in four species of tsetse flies (Glossina austeni, G. brevipalpis, G. longipennis and G. pallidipes) from two regions of eastern Kenya (the Shimba Hills and Nguruman). Based on universal primers targeting the internal transcribed spacer 1 region (ITS-1), T. vivax was the predominant pathogenic species detected in flies, both singly and in combination with other species of trypanosomes. Using Generalised Linear Models (GLMs) and likelihood ratio tests to choose the best-fitting models, presence of T. vivax was significantly associated with an interaction between subpopulation (a combination between collection sites and species of Glossina) and sex of the flies (X2 = 7.52, df = 21, P-value = 0.0061); prevalence in females overall was higher than in males but this was not consistent across subpopulations. Similarly, T. congolense was significantly associated only with subpopulation (X2 = 18.77, df = 1, P-value = 0.0046); prevalence was higher overall in the Shimba Hills than in Nguruman but this pattern varied by species of tsetse fly. When associations were analysed in individual species of tsetse flies, there were no consistent associations between trypanosome prevalence and any single factor (site, sex, age) and different combinations of interactions were found to be significant for each. The results thus demonstrated complex interactions between vectors and trypanosome prevalence related to both the distribution and intrinsic factors of tsetse flies. The potential influence of the presence of S. glossinidius on trypanosome presence in tsetse flies was studied in chapter 3. A high number of Sodalis positive flies was found in the Shimba Hills, while there were only two positive flies from Nguruman. Presence or absence of Sodalis was significantly associated with subpopulation while trypanosome presence showed a significant association with age (X2 = 4.65, df = 14, P-value = 0.0310) and an interaction between subpopulation and sex (X2 = 18.94, df = 10, P-value = 0.0043). However, the specific associations that were significant varied across species of trypanosomes, with T. congolense and T. brucei but not T. vivax showing significant interactions involving Sodalis. Although it has previously been concluded that presence of Sodalis increases susceptibility to trypanosomes, the results presented here suggest a more complicated relationship, which may be biased by differences in the distribution and intrinsic factors of tsetse flies, as well as which trypanosome species are considered. In chapter 4 trypanosome status was studied in relation to blood meal sources, feeding status and feeding patterns of G. pallidipes (which was the predominant fly species collected for this study) as determined by sequencing the mitochondrial cytochrome B gene using DNA extracted from abdomen samples. African buffalo and African elephants were the main sources of blood meals but antelopes, warthogs, humans, giraffes and hyenas were also identified. Feeding on multiple hosts was common in flies sampled from the Shimba Hills but most flies from Nguruman had fed on single host species. Based on Multiple Correspondence Analysis (MCA), host-feeding patterns showed a correlation with site of sample collection and Sodalis status, while trypanosome status was correlated with sex and age of the flies, suggesting that recent host-feeding patterns from blood meal analysis cannot predict trypanosome status. In conclusion, the complexity of interactions found suggests that strategies of tsetse fly control should be specific to particular epidemic areas. Future studies should include laboratory experiments that use local colonies of tsetse flies, local strains of trypanosomes and local S. glossinidius under controlled environmental conditions to tease out the factors that affect vector competence and the relative influence of external environmental factors on the dynamics of these interactions.

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Background: Rheumatoid arthritis (RA) is a chronic inflammatory arthritis that causes significant morbidity and mortality and has no cure. Although early treatment strategies and biologic therapies such as TNFα blocking antibodies have revolutionised treatment, there still remains considerable unmet need. JAK kinase inhibitors, which target multiple inflammatory cytokines, have shown efficacy in treating RA although their exact mechanism of action remains to be determined. Stratified medicine promises to deliver the right drug to the right patient at the right time by using predictive ‘omic biomarkers discovered using bioinformatic and “Big Data” techniques. Therefore, knowledge across the realms of clinical rheumatology, applied immunology, bioinformatics and data science is required to realise this goal. Aim: To use bioinformatic tools to analyse the transcriptome of CD14 macrophages derived from patients with inflammatory arthritis and define a JAK/STAT signature. Thereafter to investigate the role of JAK inhibition on inflammatory cytokine production in a macrophage cell contact activation assay. Finally, to investigate JAK inhibition, following RA synovial fluid stimulation of monocytes. Methods and Results: Using bioinformatic software such as limma from the Bioconductor repository, I determined that there was a JAK/STAT signature in synovial CD14 macrophages from patients with RA and this differed from psoriatic arthritis samples. JAK inhibition using a JAK1/3 inhibitor tofacitinib reduced TNFα production when macrophages were cell contact activated by cytokine stimulated CD4 T-cells. Other pro-inflammatory cytokines such as IL-6 and chemokines such as IP-10 were also reduced. RA synovial fluid failed to stimulate monocytes to phosphorylate STAT1, 3 or 6 but CD4 T-cells activated STAT3 with this stimulus. RNA sequencing of synovial fluid stimulated CD4 T-cells showed an upregulation of SOCS3, BCL6 and SBNO2, a gene associated with RA but with unknown function and tofacitinib reversed this. Conclusion: These studies demonstrate that tofacitinib is effective at reducing inflammatory mediator production in a macrophage cell contact assay and also affects soluble factor mediated stimulation of CD4 T-cells. This suggests that the effectiveness of JAK inhibition is due to inhibition of multiple cytokine pathways such as IL-6, IL-15 and interferon. RNA sequencing is a useful tool to identify non-coding RNA transcripts that are associated with synovial fluid stimulation and JAK inhibition but these require further validation. SBNO2, a gene that is associated with RA, may be biomarker of tofacitinib treatment but requires further investigation and validation in wider disease cohorts.