Exploring biomarkers to predict pig disease resilience traits under a natural disease challenge
Creators: Yang, Ziqi
Publisher: name: University of Alberta Library
Year: 2024

<p>The intensification and consolidation of modern pig production is exposed to higher risks of endemic or pandemic infections. The complexity of the polymicrobial challenge and increasing concerns on antibiotics resistance make it pivotal to find an efficient way of controlling infections besides using vaccines. Breeding for disease resilience (maintaining productive performance during pathogen infections) could be a solution to circumvent this problem. This study was focused on three types of biological information in blood: serum acute phase proteins (APPs), whole blood transcriptome, and serum metabolome, which were reported with the potential for disease diagnosis and livestock production assessment. It is unknown whether they could be used to predict pig disease resilience before exposure to pathogens. The feasibility was tested using those molecules separately to identify biomarkers associated with disease resilience in a natural disease challenge model which simulates the polymicrobial environments in commercial farms. Identification of such biomarkers could help characterize disease resilience and provide a theoretical guide for commercial pig breeding.<br>
Plasma concentrations of alpha-1 acid glycoprotein (AGP), haptoglobin (HP), and C-reactive protein (CRP) were determined in 60 pigs before and after challenge using ELISA. The resilient pigs had a relatively low level of AGP in plasma before challenge. The concentrations of HP and CRP, but not AGP, were induced dramatically upon challenge in all the groups of pigs. Resilient pigs showed a slow response of both HP and CRP at the early stage of the challenge but had a sharp increase of CRP at the later stage. Correlation analysis between APPs and various performance traits suggested that they are more proper for assessing rather than predicting pig health and productivity under challenge even though AGP concentration before challenge showed some trends to correlate with productivity-related traits. <br>
Fifty eight pigs were chosen from their phenotypes after challenge and the whole blood transcriptome before challenge was determined by RNA-Seq. They were grouped into 4 groups (Resistant, Resilient, Susceptible, and Early<em>dead) in response to the natural disease challenge. Only two significant transcripts from the differential expression (DE) analysis were found higher in the Susceptible group compared to the others (q-value &lt; 0.1). They were mapped to the IgC gene and the SLAMF9 gene, respectively. A larger cohort of 209 pigs was utilized for validating the findings. Results from both of the cohorts supported a hypothetical hierarchical model for the baseline immunity: Resistant≧Resilient&gt;Early</em>dead&gt;Susceptible. The larger cohort with samples post disease challenge revealed that all the pigs activated innate immune response early after infection. Of note, the Resilient group exhibited a unique strategy of restricting the potency and energy expenditure of the immune response, implying that the resilient pigs maintain the productive performance under disease challenge via consuming less energy from their immune system. However, the results suggest that it may not be feasible using pre-challenge whole blood transcriptome to identify biomarkers for disease resilience in the context of this study. <br>
Plasma metabolomic profiles of 460 healthy pigs were determined by NMR spectroscopy before the natural disease challenge, and the pigs were then divided into four groups as in the RNA-Seq experiment, or groups defined by single or double trait records. Succinate and dimethylglycine in unchallenged pigs were found with significantly higher concentrations in the Early<em>dead group compared to the others. However, batch effect was found as the major causative factor for the metabolome variations, and pyruvic acid was found as the only hit with significantly lower concentration in the Early</em>dead group than the others. Machine learning was performed to test whether an integrated metabolite profile could be used to predict the pig phenotypes, but prediction accuracy was far from ideal. Together, a snapshot of the plasma metabolome only provided a limited prediction value for pig resilience, probably due to the prominent impact of the batch factor.<br>
In summary, this thesis created a framework for investigating proxy traits to assess or predict pig resilience, examined the viability of using various molecular information, including APPs, transcriptome, and metabolome derived from peripheral blood to predict pig disease resilience phenotype in a natural disease challenge model. Additionally, it improved knowledge of potential molecular processes influencing how pigs differentially respond to polymicrobial challenges, shedding light on how to use disease resilience as a breeding objective to meet the rapidly expanding demand for healthy pork products.</p>