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Title: Statistical modelling and analysis of the infection dynamics of PRRSV in vivo infections
Author: Islam, Zeenath Ul
ISNI:       0000 0004 7225 1482
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2017
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Porcine reproductive and respiratory syndrome (PRRS) is one of the most economically significant viral diseases facing the global swine industry. Viraemia profiles of PRRS virus challenged pigs reflect the severity and progression of infection within the host and provide crucial information for subsequent control measures. In this thesis we analyse the largest longitudinal PRRS viraemia dataset from an in-vivo experiment, and corresponding immune measures in the form of cytokine data and neutralising antibodies. In the PRRS Host Genetic Consortium (PHGC) trials, pigs were challenged with one of two PRRSV isolates (NVSL and KS06, respectively). In Chapter 2 we derive a statistical description of the temporal changes in viraemia and determine the influence of diverse factors (e.g. PRRSV strain, pig genetic background, resistance genotype, etc.) on viraemia profiles. The well-established methodology of linear mixed modelling with a repeated measures model and fitting a linearized Wood’s function, a gamma-type function, is applied to the viraemia dataset. The virus isolate had a significant impact on the viraemia profiles which was captured by statistically significant differences in model parameters via both statistical methods. The more virulent NVSL isolate had higher early viraemia predictions and a faster rate of decline than KS06. In line with previous studies the WUR “resistance” genotype, associated with lower AUC viraemia found in previous studies, also resulted in lower viraemia predictions in the statistical models. The typical time trends of the viraemia profiles were a rise to a peak followed by a period of decline with dynamics and magnitude influenced by the virus isolate. Both uni and bimodal viraemia profiles were observed. The Wood’s model appeared a suitable candidate model for the data associated with uni-modal profiles and captured the time trends concisely in only three model parameters which also had a biological relevance. Overall the best fitting Wood’s model (y=atbe-ct) was when there was a random effect in Wood’s parameters b and c. Bimodal profiles significantly reduced the model fit, particularly in the later phase of infection resulting in large model residuals. However bimodal profiles did not impact upon the significance of the differences between the LSM repeated measures estimates nor the LSM linearized Wood’s model parameter estimates. The longitudinal viraemia measures from the PRRSV challenge experiment revealed substantial differences in the viraemia profiles between hosts infected with the same PRRSV challenge dose, pointing to considerable variation in the host response to PRRSV infections. In Chapter 3 we provide a suitable mathematical description of all viraemia profiles with biologically meaningful parameters for quantitative analysis of profile characteristics. The Wood’s function and a biphasic extended Wood’s function were fit to the individual profiles using Bayesian inference with a likelihood framework in Chapter 3. Using maximum likelihood inference and numerous fit criteria, we established that the broad spectrum of viraemia trends could be adequately represented by either uni-or biphasic Wood’s functions. Three viraemic categories emerged: cleared (uni-modal and below detection within 42 days post infection(dpi)), persistent (transient experimental persistence over 42 dpi) and rebound (biphasic within 42 dpi). The convenient biological interpretation of the model parameters estimates, allowed us not only to quantify inter-host variation, but also to establish common viraemia curve characteristics and their predictability. The convenient biological interpretation of the model parameters estimates, allowed us not only to quantify inter-host variation, but also to establish common viraemia curve characteristics and their predictability, which were utilized in subsequent quantitative genetic analyses to identify genomic regions associated with these new resistance traits. The Bayesian approach for curve fitting in Chapter 3 led to better model fits than the classical linear mixed models approach of Chapter 2. Furthermore in Chapter 4 we explored the association between the observed PRRS viraemia profile characteristics and the corresponding measures of the immune response in the form of: neutralising antibody (nAb) cross protection data and longitudinal cytokine profiles. Statistical analysis of the profile characteristics revealed that persistent profiles were distinguishable already within the first 21 dpi, whereas it is not possible to predict the onset of viraemia rebound. Analysis of the neutralizing antibody (nAb) data indicated that there was a ubiquitous strong response to the homologous PRRSV challenge, but high variability in the range of cross-protection of the nAbs. Persistent pigs were found to have a significantly higher nAb cross-protectivity than pigs that either cleared viraemia or experienced rebound within 42 dpi. We determined the typical features and time trends of each cytokine profile, examined the associations between cytokines, and characterised the cytokine response. A stronger association was found in the direction of cytokines driving the ensuing viraemia characteristics as opposed to vice versa. It was found that viraemia class differences were best captured in the anti-viral cytokine IFNA and also the chemokine CCL2, furthermore these key cytokines were the most strongly associated with viraemia measures. The breadth of the cytokine responsiveness was associated with viral profile class and genetic background but not the WUR genotype. The statistical categorization of pigs from each PHGC trial through model fitting provides a critical basis for the generation of new desirable host phenotypes, and of potential use in the genetic selection of pigs with favourable infection traits. Our study provides novel insights into the nature and degree of variation of hosts’ responses to infection as well as new informative traits for subsequent genetic and modelling studies.
Supervisor: Wilson, Andrea ; Bishop, Stephen Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: PRRSV ; longitudinal viraemia profiles ; PRRS viraemia ; infection responses