Whole-Blood Transcriptome Analysis of Feedlot Cattle With and Without Bovine Respiratory Disease
Bibliographic record
Abstract
Bovine respiratory disease (BRD) is one of the main factors leading to morbidity and mortality in feedlot operations in North America. A complex of viral and bacterial pathogens can individually or collectively establish BRD in cattle, and to date, most disease characterization studies using transcriptomic techniques examine bronchoalveolar and transtracheal fluids, lymph node, and lung tissue as well as nasopharyngeal swabs, with limited studies investigating the whole-blood transcriptome. Here, we aimed to identify differentially expressed (DE) genes involved in the host immune response to BRD using whole blood and RNA sequencing. Samples were collected from heifers (average arrival weight = 215.0 ± 5.3 kg) with ( n = 25) and without ( n = 18) BRD at a commercial feedlot in Western Canada. RNAseq analysis showed a distinct whole-blood transcriptome profile between BRD and non-BRD heifers. Further examination of the DE genes revealed that those involved in the host inflammatory response and infectious disease pathways were enriched in the BRD animals, while gene networks associated with metabolism and cell growth and maintenance were downregulated. Overall, the transcriptome profile derived from whole blood provided evidence that a distinct antimicrobial peptide-driven host immune response was occurring in the animals with BRD. The blood transcriptome of the BRD animals shows similarities to the transcriptome profiles obtained from lung and bronchial lymph nodes in other studies. This suggests that the blood transcriptome is a potential diagnostic tool for the identification of biomarkers of BRD infection and can be measured in live animals and used to further understand infection and disease in cattle. It may also provide a useful tool to increase the understanding of the genes involved in establishing BRD in beef cattle and be used to investigate potential therapeutic applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".