Antimicrobial susceptibility and genomic determinants of resistance and virulence in Mycoplasma cynos and Mycoplasma felis
Bibliographic record
Abstract
Mycoplasma cynos and Mycoplasma felis are important respiratory pathogens in dogs and cats. Due to the challenges of culturing these fastidious bacteria, little is known about their antimicrobial susceptibility or mechanisms of pathogenicity. Treatment is typically empirical, as in vitro antimicrobial activity has not been evaluated, and therapeutic efficacy remains unclear. This study aimed to assess in vitro susceptibility and identify genetic markers of antimicrobial resistance (AMR) and virulence in M. cynos and M. felis clinical isolates. Minimum inhibitory concentrations (MICs) for doxycycline, tetracycline, minocycline, enrofloxacin, marbofloxacin, and azithromycin were determined using a broth microdilution assay developed for this study. Hybrid genomes were generated using Oxford Nanopore and Illumina sequencing. AMR-associated single-nucleotide polymorphisms (SNPs) in the gyrA gene correlated with high MICs to enrofloxacin and marbofloxacin in both species. Mutations in 23S rRNA were associated with reduced susceptibility to azithromycin. In M. felis, novel variants in gyrA and the 50S ribosomal protein L4 were linked to decreased susceptibility to fluoroquinolones and azithromycin, respectively. The data also suggest potential intrinsic resistance to azithromycin in M. felis. Low MICs were observed for tetracyclines, and resistance mutations were not identified in the 16S rRNA gene, supporting tetracyclines as effective first-line treatment options. Virulence genes, particularly those associated with adhesion and immune evasion, were detected in both M. cynos and M. felis. This study presents the first comprehensive genomic and phenotypic analysis of AMR and virulence in M. cynos and M. felis, providing new insights into their pathogenicity and informing evidence-based therapeutic strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".