Genetic and Environmental Factors Influencing Infectious Bronchitis Virus and Vaccine Responses in Layer Chickens
Bibliographic record
Abstract
Infectious bronchitis virus (IBV) is a continuous challenge to poultry health and productivity worldwide despite widespread vaccination. This thesis investigated mechanisms of IBV variation, how IBV variation and environmental stressors particularly drinking water quality influence IBV infection and vaccine mediated immune response in layer chickens. First, genetic analysis of two newly detected Canadian IBV strains showed that both IBV strains were recombinants, likely arising from recombination between vaccine and field IBV strains. These viruses carried many changes in the spike (S) protein, including in important variable regions, and showed stronger binding to host cell receptors. We also discovered that infectious bronchitis (IB) vaccination can contribute to the evolution of new IBV variants. Second, we compared the pathogenicity of two IBV strains commonly present in Canadian poultry flocks, Massachusetts (Mass) and Delmarva (DMV/1639). The DMV/1639 IBV strain caused more severe disease, with higher drop in egg production, higher viral loads in tissues, more gross lesions, higher histopathological lesions, and increased cell death compared to the Mass IBV strain. Third, we assessed how water quality affects vaccine responses. A cross-sectional study across Alberta revealed that many flocks use water exceeding recommended limits for hardness, pH, sodium, and bicarbonates. Although no clear associations were seen at the farm level, controlled experiments confirmed that birds vaccinated while drinking poor-quality water developed lower antibody titers and reduced T-cell responses compared to those receiving tap water, which was used as the standard-quality water. Finally, we tested whether poor drinking water quality could worsen the outcome of IBV infection in chickens. The chickens given field water with higher pH, hardness, and bicarbonates showed higher viral loads and weaker immune responses compared to those given standard tap water, suggesting that suboptimal quality of drinking water can increase IB severity. Together, these studies show that both viral evolution and environmental factors such as water quality play important roles in IBV infection outcomes and IB vaccine effectiveness. Monitoring emerging IBV variants and improving water management are important steps for protecting poultry health and productivity in Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".