Identification and Characterization of Putative Virulence Genes and Gene Clusters in <i>Aeromonas hydrophila</i> PPD134/91
Bibliographic record
Abstract
Aeromonas hydrophila is a gram-negative opportunistic pathogen of animals and humans. The pathogenesis of A. hydrophila is multifactorial. Genomic subtraction and markers of genomic islands (GIs) were used to identify putative virulence genes in A. hydrophila PPD134/91. Two rounds of genomic subtraction led to the identification of 22 unique DNA fragments encoding 19 putative virulence factors and seven new open reading frames, which are commonly present in the eight virulence strains examined. In addition, four GIs were found, including O-antigen, capsule, phage-associated, and type III secretion system (TTSS) gene clusters. These putative virulence genes and gene clusters were positioned on a physical map of A. hydrophila PPD134/91 to determine their genetic organization in this bacterium. Further in vivo study of insertion and deletion mutants showed that the TTSS may be one of the important virulence factors in A. hydrophila pathogenesis. Furthermore, deletions of multiple virulence factors such as S-layer, serine protease, and metalloprotease also increased the 50% lethal dose to the same level as the TTSS mutation (about 1 log) in a blue gourami infection model. This observation sheds light on the multifactorial and concerted nature of pathogenicity in A. hydrophila. The large number of putative virulence genes identified in this study will form the basis for further investigation of this emerging pathogen and help to develop effective vaccines, diagnostics, and novel therapeutics.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".