Metagenomic Detection of Viruses of the Respiratory Tract in Arriving Feedlot Calves to Inform Vaccine Gaps and Risk Assessment for Bovine Respiratory Disease
Bibliographic record
Abstract
Bovine respiratory disease (BRD) is a leading cause of morbidity and economic loss in feedlot cattle, driven by complex interactions among viral and bacterial pathogens, host immunity, and environmental stressors. Traditional diagnostic methods often target a limited range of pathogens, restricting the scope of surveillance and early intervention. The objective of this thesis was to use nanopore metagenomic sequencing and Bayesian modeling to enhance pathogen detection, evaluate diagnostic performance, and assess scalable sampling strategies under field conditions in the early feeding period in feedlot cattle. This work considers the potential of a “one-test-for-all” approach—where a single, scalable assay can simultaneously detect viruses, bacteria, and antimicrobial resistance genes (ARGs)—to support BRD diagnostics and surveillance. Nanopore metagenomic sequencing, in Chapter 2, was applied to short nasal swabs (SNS) from fall-placed calves (FPC) and yearlings (YRL) at arrival and 14 days on feed (DOF) across western Canadian feedlots. Twenty-one distinct viruses were identified with the most prevalent being bovine coronavirus (BCoV). BRD-associated viruses, such as bovine respiratory syncytial virus (BRSV) and bovine parainfluenza virus 3 (BPIV-3), were more likely to be detected at 14 DOF in both FPC and YRL, as was influenza D virus (IDV) in FPC. BRSV and BPIV-3 were more likely to be detected in arrival samples from YRL than FPC (P = 0.01). In 14 DOF samples, BPIV-3 (P = 0.02) and BVDV-2 (P = 0.01) were identified more frequently in YRL than FPC. Respiratory bacteria and ARGs were also characterized in the data resulting from the viral sequencing protocol. When comparing samples from FPC collected at 14 DOF and arrival, M. haemolytica increased (P = 0.02), while P. multocida decreased (P = 0.03). In YRL, no significant temporal changes were identified for M. haemolytica, P. multocida or H. somni. Thirty-three different ARGs were identified in these samples, with detection more frequent at 14 DOF than arrival in both FPC (P = 0.03) and YRL (P = 0.01), with identified ARGs most associated with resistance to lincosamides, aminoglycosides, and tetracyclines. The diagnostic performance of qPCR and metagenomic sequencing was assessed using Bayesian latent class modeling (BLCM) in Chapter 3 using the sequencing data described for the 760 SNS. While qPCR demonstrated higher sensitivity for BCoV and bovine herpesvirus 1 (BoHV-1), sequencing had slightly higher sensitivity than qPCR for BRSV and showed comparable results for BPIV-3 and IDV. Specificity was generally similar across methods, with sequencing outperforming qPCR for BCoV. The specificity and sensitivity for detection of BRD-associated bacteria from the same metagenomic data were also similar to those estimated for culture and qPCR results for the same samples. These findings support metagenomic sequencing as a viable laboratory tool capable of detecting multiple BRD pathogens in a single test. Chapter 4 investigated whether early viral detection could predict subsequent risk of BRD in beef calves arriving at a research feedlot. Deep nasopharyngeal swabs (DNPS) were sequenced from steer calves at arrival (n=729), at 13 DOF (n=389), and from sick calves (n=93) at initial BRD treatment. Although multiple viruses were detected in a pattern similar to that described in the earlier chapters, neither viral detection at arrival nor 13 DOF were associated with increased risk of BRD treatment. However, BCoV was more prevalent in sick calves than in pen and DOF matched controls (OR = 20.3, 95% CI 8.4 – 48.9; P< 0.001). Pathogen detection was compared for paired SNS and DNPS collected from 150 calves at 13 DOF in Chapter 5. Read counts and the frequency of detection were higher for SNS than DNPS for key viruses (BCoV, IDV) and bacteria (M. haemolytica, P. multocida), while Mesomycoplasma dispar was more prevalent in DNPS. Detection of Histophilus somni, Bibersteinia trehalosi, and Mycoplasmopsis bovis did not significantly differ between swab types. Despite variable agreement between swab types, SNS proved to be a sensitive, scalable, and practical alternative to DNPS for field-based surveillance. Collectively, this thesis advances understanding of BRD pathogen dynamics, diagnostic test performance, and sampling strategies in feedlot cattle. The findings support the integration of metagenomic sequencing into routine surveillance using easily collected SNS. This study also demonstrates the potential for a single sequencing protocol to provide comprehensive detection of both respiratory viruses and bacteria at scale on samples collected under field conditions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".