Enhanced metagenomic surveillance for bovine respiratory disease pathogens and antimicrobial resistance by hybridization capture sequencing
Bibliographic record
Abstract
ABSTRACT Bovine respiratory disease (BRD) is the primary disease of cattle and is responsible for most of the antibiotic use in the beef industry, both for metaphylaxis and treatment. Infection prevention and targeted treatments would benefit from detecting and identifying bacterial pathogens and, ideally, assessing antibiotic sensitivity. Here, we report success refining targeted metagenomics by hybridization capture sequencing (CapSeq) to detect and genotype bacterial pathogens and genes for antibiotic resistance in BRD. We developed a novel multi-locus sequence typing (MLST) strategy to both enhance assay sensitivity and provide epidemiological data compatible with traditional typing assays. Phylogenetically informed panel design by ProbeTools coupled with hybridization capture enabled the sequencing of novel genotypes and elucidated coinfection by distinct strains of Mycoplasmopsis bovis . CapSeq was orders of magnitude more sensitive than classical metagenomics at a scale compatible with low-throughput DNA sequencing capacity in diagnostic laboratories. Iterative design and testing of longer typing loci confirmed improvements in locus detection through a strategy applicable to future target selection. CapSeq detected bacterial pathogens that evaded detection by traditional metagenomics, and it demonstrated greater range and sensitivity for antibiotic resistance gene detection. IMPORTANCE Shotgun metagenomic sequencing for infectious disease diagnostics and surveillance benefits from simultaneous detection of multiple pathogens in a sample. Adding a hybridization probe capture step to the metagenomics workflow enriches targeted loci to improve the sensitivity of pathogen detection without compromising the ability to detect pathogen variants. Our custom probe panel enables multi-locus sequence typing of bovine respiratory disease agents as well as capture of antibiotic resistance genes, which improves the sensitivity of metagenomic testing and provides genotyping data compatible with traditional assays. This study demonstrates the utility of new design principles for probe panels; it also demonstrates how targeted metagenomics provides important new insights into co-infections and is equally useful for surveillance of environmental reservoirs of disease agents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".