Bovine Gamma Delta T Cells Contribute to Exacerbated IL-17 Production in Response to Co-Infection with Bovine RSV and Mannheimia haemolytica
Bibliographic record
Abstract
Human respiratory syncytial virus (HRSV) is a leading cause of severe lower respiratory tract infection in children under five years of age. IL-17 and Th17 responses are increased in children infected with HRSV and have been implicated in both protective and pathogenic roles during infection. Bovine RSV (BRSV) is genetically closely related to HRSV and is a leading cause of severe respiratory infections in young cattle. While BRSV infection in the calf parallels many aspects of human infection with HRSV, IL-17 and Th17 responses have not been studied in the bovine. Here we demonstrate that calves infected with BRSV express significant levels of IL-17, IL-21 and IL-22; and both CD4 T cells and γδ T cells contribute to this response. In addition to causing significant morbidity from uncomplicated infections, BRSV infection also contributes to the development of bovine respiratory disease complex (BRDC), a leading cause of morbidity in both beef and dairy cattle. BRDC is caused by a primary viral infection, followed by secondary bacterial pneumonia by pathogens such as Mannheimia haemolytica. Here, we demonstrate that in vivo infection with M. haemolytica results in increased expression of IL-17, IL-21 and IL-22. We have also developed an in vitro model of BRDC and show that co-infection of PBMC with BRSV followed by M. haemolytica leads to significantly exacerbated IL-17 production, which is primarily mediated by IL-17-producing γδ T cells. Together, our results demonstrate that calves, like humans, mount a robust IL-17 response during RSV infection; and suggest a previously unrecognized role for IL-17 and γδ T cells in the pathogenesis of BRDC.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".