Delineating the key virulence factors and intraspecies divergence of <i>Vibrio harveyi</i> via whole-genome sequencing
Bibliographic record
Abstract
Vibrio harveyi is one of the major pathogens in aquaculture. To identify the key virulence factors affecting pathogenesis of V. harveyi towards fish, we conducted a field investigation for three representative fish farms infected with V. harveyi. Multilocus sequence typing (MLST) and whole-genome sequencing were conducted to delineate the phylogenetic relationship and genetic divergence of V. harveyi. A total of 25 V. harveyi strains were isolated from the diseased fish and groundwater and were subtyped into 12 sequence types by MLST. Five virulence genes, mshB, pilA, hutR, ureB, and ureG, were variably presented in the sequenced strains. The virulence gene profiles strongly correlated with the distinct pathogenicity of V. harveyi strains, with a strain harboring all five genes exhibiting the highest virulence towards fish. Phenotype assay confirmed that reduced virulence correlated with decreased motility and biofilm formation ability. Additionally, three types of type VI secretion system, namely T6SS1, T6SS2, and T6SS3, were identified in V. harveyi strains, which can be classified into six, four, and 12 subtypes, respectively. In conclusion, the results indicated that the virulence level of V. harveyi is mainly determined by the above virulence genes, which may play vital roles in environmental adaptation for V. harveyi.
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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 | 0.001 |
| 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".