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Record W7122351148

Metagenomic Detection of Viruses of the Respiratory Tract in Arriving Feedlot Calves to Inform Vaccine Gaps and Risk Assessment for Bovine Respiratory Disease

2025· article· en· W7122351148 on OpenAlexfundaboutno aff
Emmanuel Donbraye

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsnot available
FundersGenome PrairieGenome AlbertaMinistry of Agriculture - SaskatchewanBeef Cattle Research CouncilGenome Canada
KeywordsBovine respiratory diseaseMetagenomicsVirusFeedlotTransmission (telecommunications)PandemicRespiratory tract infectionsCoronavirus
DOInot available

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.230
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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