Whole genome sequencing of non-aureus Staphylococcus species from poultry barn bioaerosols across Alberta reveals circulation of isolates with multiple antibiotic resistant genes and disinfectant resistant genes
Bibliographic record
Abstract
OBJECTIVES: Poultry environment has vast variety of bacterial species and non-aureus Staphylococcus species (NASS) are predominantly found. Various NASS are normal inhabitant of gastrointestinal tract and skin of chickens. The study objective was to isolate and identify NASS from bioaerosols of layer chicken barns across Alberta, leading to the phenotypic and genotypic antimicrobial resistance (AMR) profiling of the isolated bacterial colonies. METHODS: A total of 15 barns were sampled in this study with an XMX-CV microbial air sampler. Phenotypic AMR profiles were determined using the Sensititre® broth microdilution method on the standard CMV3AGPF plates and whole genome sequencing (WGS) was conducted to confirm the species and AMR genes. RESULTS: Seven Staphylococcus equorum, 5 Staphylococcus shinii, 1 Staphylococcus pseudoxylosus, 1 Staphylococcus cohnii, and 1 Staphylococcus gallinarum isolates were confirmed and identified by WGS and comparative genomic analysis. One of the S. equorum isolate was genetically highly divergent from the remaining isolates of the study. The phenotypic susceptibility profile showed 73.3% (11/15) of the isolates were resistant to lincomycin, 66.7% (10/15) were resistant to tetracycline, 33.3% (5/15) were resistant to streptomycin, 20% (3/15) were resistant to erythromycin, and 6.3% were resistant to gentamicin. A total of 10 antimicrobial resistance genes (ARGs) for antibiotics and 1 gene encoding resistance to disinfectants were detected among the study isolates by WGS. CONCLUSIONS: Normal bioaerosol microflora having ARGs may lead to reduced therapeutic effectiveness of antibiotics in poultry and these ARGs can be a source of serious public health concern.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".