Genotyping and biofilm formation of Mycoplasma hyopneumoniae and their association with virulence
Bibliographic record
Abstract
Mycoplasma hyopneumoniae, the causative agent of swine respiratory disease, demonstrates differences in virulence. However, factors associated with this variation remain unknown. We herein evaluated the association between differences in virulence and genotypes as well as phenotype (i.e., biofilm formation ability). Strains 168 L, RM48, XLW-2, and J show low virulence and strains 232, 7448, 7422, 168, NJ, and LH show high virulence, as determined through animal challenge experiments, complemented with in vitro tracheal mucosa infection tests. These 10 strains with known virulence were then subjected to classification via multilocus sequence typing (MLST) with three housekeeping genes, P146-based genotyping, and multilocus variable-number tandem-repeat analysis (MLVA) of 13 loci. MLST and P146-based genotyping identified 168, 168 L, NJ, and RM48 as the same type and clustered them in a single branch. MLVA assigned a different sequence type to each strain. Simpson's index of diversity indicates a higher discriminatory ability for MLVA. However, no statistically significant correlation was found between genotypes and virulence. Furthermore, we investigated the correlation between virulence and biofilm formation ability. The strains showing high virulence demonstrate strong biofilm formation ability, while attenuated strains show low biofilm formation ability. Pearson correlation analysis revealed a significant positive correlation between biofilm formation ability and virulence. To conclude, there was no association between virulence and our genotyping data, but virulence was found to be significantly associated with the biofilm formation ability of M. hyopneumoniae.
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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 | 0.002 |
| 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) | 0.000 | 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 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".