Respiratory Bacteria and Antimicrobial Resistance Genes Detected by Long-Read Metagenomic Sequencing Following Feedlot Arrival, Subsequent Treatment Risk and Phenotypic Resistance in Feedlot Calves
Bibliographic record
Abstract
Background/Objectives: Long-read metagenomic sequencing can assign antimicrobial resistance genes (ARGs) to speciated bacterial reads. This study evaluated whether metagenomic data from respiratory bacteria derived from feedlot calves sampled in the early feeding period were associated with subsequent bovine respiratory disease (BRD) treatment and phenotypic antimicrobial resistance (AMR) at treatment. Methods: Deep nasopharyngeal swabs (DNPSs) obtained at arrival processing (1 day on feed; DOF), 13 DOF, and the time of BRD treatment were cultured and subjected to antimicrobial susceptibility testing (AST) and long-read metagenomic sequencing. Analyses focused on macrolide (mphE-msrE, EstT) and tetracycline (tet(H)) ARGs within reads assigned to Mannheimia haemolytica, Pasteurella multocida, Histophilus somni, or Bibersteinia trehalosi. Generalized estimating equations assessed associations between metagenomic results from 1 and 13 DOF and subsequent BRD treatment risk and AST outcomes at treatment, at both the individual animal (calf) and pen levels. Results: Calf-level detection of H. somni at 13 DOF was associated with a greater BRD treatment risk between 14 and 45 DOF. An increased pen prevalence of either M. haemolytica or P. multocida at 13 DOF was associated with a greater BRD treatment risk from 14 to 45 DOF. At 13 DOF, detections of mphE-msrE, EstT, or tet(H) in target bacteria were associated with corresponding phenotypic AMR at BRD treatment. Similarly, a higher pen-level prevalence of mphE-msrE or EstT at 13 DOF was also associated with increased macrolide resistance at BRD treatment. Conclusions: The results from long-read metagenomic sequencing of DNPSs collected at 13 DOF were associated with both BRD risk and AMR at treatment. These findings align with prior culture-based results and support the potential utility of pen-level metagenomic testing for AMR surveillance and informing antimicrobial selection in feedlots.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".