Master of Science / Department of Clinical Sciences / Michael D. Apley / One hundred ninety four steers, bulls, and heifers weighing 182-318 kg were purchased at an Arkansas sale barn and shipped 12 hours to a northern Kansas feedlot. There was no previous history of treatment and the cattle had been delivered to the sale barn within the 24 hour period prior to the sale. The objectives of the study were to evaluate (1) bacterial pathogen isolates in different locations in the respiratory tract, (2) pathogen load in clinically ill and clinically normal calves, and (3) compare histological damage that may be a result of clinical disease. Fifteen calves were identified with signs of acute bovine respiratory disease (BRD) based on clinical score and a minimum rectal temperature of 40° C. An additional 5 calves with no clinical signs and rectal temperatures < 40° C were selected as controls. Cattle were humanely euthanized following recording of antemortem clinical observations. At postmortem, samples for microbiologic and histologic (hematoxylin and eosin stain) analysis were collected from grossly normal and/or consolidated tissue in each lung lobe. Samples were also collected from the tonsils and trachea. Quantification of the BRD pathogens per gram were determined for each positive site and then converted to total counts for each animal. Total colony forming units (CFU) of pathogens in the entire lung for cattle with identified pathogens ranged from 2x10[superscript]7 – 2x10[superscript]8 CFU for Pasturella multocida and 9x10[superscript]6 – 9x10[superscript]8 CFU for Mannheimmia haemolytica. Total visual estimated percent consolidation ranged from 0.0% to 45.0% of the total lung. Isolated pathogens from the upper and lower respiratory tract were compared and showed to have no significant agreement. Histology scores of 0-4 were assigned to the tissue samples and compared to the quantified BRD pathogens to test a possible association between the pathologic process and the total agents in that tissue sample. A significant difference in bacterial counts between histology scores of zero or 1 and a histology score of 4 was observed.

A host can adopt two strategies when facing infection: resistance, where host immune responses prevent or reduce pathogen replication; or tolerance, which refers to all mechanisms that reduce the impact of the infection on host health or performance. Both strategies may be under host genetic control, and could thus be targeted for genetic improvement. Although there is ample evidence of genetic variation in resistance to infection, there is limited evidence to suggest that individuals also differ genetically in tolerance. Furthermore, although resistance and tolerance are typically considered as alternative host defense mechanisms, relatively little is known about the genetic relationship between them and how they change together over time and jointly determine infection outcome. In this thesis, two datasets from experimental challenge infection experiments were considered for investigating tolerance genetics: Porcine Reproductive & Respiratory Syndrome (PRRS), an endemic viral disease which causes loss of growth and mortality in growing pigs; and Listeria monoctyogenes (Lm), a bacterium which causes food-borne infections in mammals. The two datasets differed substantially in size and genetic structure; the PRRS dataset consists of thousands of records from outbred commercial pig populations, whereas the Listeria dataset comprises much fewer records from genetically diverse highly inbred strains of a mice as a model species. The aims of this thesis were to: 1) Identify if genetic variation in host tolerance to infection exists, with case studies in PRRS and listeria, using conventional reaction-norm methodology; 2) Identify if host tolerance, along with resistance, changes longitudinally as infection progresses; 3) Identify whether the WUR genotype is associated with tolerance slope; 4) Analyse the dynamic relationship between host performance and pathogen load over the time-course of infection by examining the relationship at different stages of infection using GWAS; 5) Develop novel trajectory methodology to offer insight into health-infection dynamics, and identify whether there is genetic variation in trajectories; 6) Develop novel trajectory-derived phenotypes that analyse changes in host performance with respect to changes in pathogen load, as an alternative to tolerance, and identify whether genetic variation exists. This study found that conventional reaction-norm methodology is limited to capture genetic variation in tolerance in outbred populations without measures of performance in the absence of infection. However, by utilising repeated longitudinal data on the same dataset, stages of infection (early, mid and late) were defined for each individual, based on host pathogen load. Using these stages of infection, genetic variation in tolerance was identified over all stages of infection and at mid to late stage of infection. Genetic correlation between resistance and tolerance was strong and positive over all stages of infection, and evidence suggested that resistance and tolerance may be under pleiotropic control. Furthermore, this research found that genetic correlations between resistance and growth changed considerably over time, and that individuals who expressed high genetic resistance early in infection tended to grow slower during that time-period, but were more likely to clear the virus by late stage, and thus recover in growth. However, at mid-late stage of infection, those with high virus load also had high growth, indicating potential epidemiological problems with genetic selection of host resilience to infection. Furthermore, genome wide association studies for pathogen load and growth associated with different stages of infection did not identify novel genetic loci associated with these traits than those previously reported for the whole infection period. By adopting conventional methodology, this study found genetic variation in tolerance of genetically diverse mouse strains to Lm and pigs to PRRS, despite statistical problems. The relationship between resistance and tolerance indicated that both traits should be considered in genetic selection programs. By adopting novel trajectory analysis, this study demonstrated that level of expression of resistance and tolerance changed throughout the experimental infection period and, furthermore, that expression of resistance, followed by tolerance, determined survival of infection. Survivors and non-survivors followed different infection trajectories, which were partially determined by genetics. By deriving novel phenotypes from trajectories that explained changes in growth in relation to change in pathogen load at specific time points, and applying these to the PRRS data, this study did not identify genetic variation in these phenotypes. The genetic signal in these phenotypes may have been masked by the fact that individuals were likely at different stages of infection. In summary, this study has shown that genetic improvement of tolerance, in addition to resistance may be desirable, but could be difficult to achieve in practice due to shortcomings in obtaining accurate and unbiased tolerance estimates based on conventional reaction-norms. Infection trajectories have proven to be a promising tool for achieving an optimally timed balance between resistance and tolerance, but further work is needed to incorporate them in genetic improvement programs.