A New High Throughput Sequencing Assay for Characterizing the Diversity of Natural Vibrio Communities and Its Application to a Pacific Oyster Mortality Event
Bibliographic record
Abstract
The Vibrio genus is notable for including several pathogens of marine animals and humans, yet characterisation of Vibrio diversity using routine 16S rRNA sequencing methods is often constrained by poor resolution beyond the genus level. Here, a new high throughput sequencing approach targeting the heat shock protein (hsp60) as a phylogenetic marker was developed to more precisely discriminate members of the Vibrio genus in environmental samples. The utility of this new assay was tested using mock communities constructed from known dilutions of Vibrio isolates. Relative to standard and Vibrio-specific 16S rRNA sequencing assays, the hsp60 assay delivered high levels of fidelity with the mock community composition at the species level, including discrimination of species within the Vibrio harveyi clade. This assay was subsequently applied to characterise Vibrio community composition in seawater and delivered substantially improved taxonomic resolution of Vibrio species compared to 16S rRNA analysis. Finally, this assay was applied to examine patterns in the Vibrio community within oysters during a Pacific oyster mortality event. In these oysters, the hsp60 assay identified species-level Vibrio community shifts prior to disease onset, pinpointing V. harveyi as a putative pathogen. Given that shifts in the Vibrio community can precede, cause and follow disease onset in numerous marine organisms, there is a need for an accurate high throughput assay for defining Vibrio community composition in natural samples. This Vibrio-centric hsp60 sequencing assay offers the potential for precise high throughput characterisation of Vibrio diversity, providing an enhanced platform for dissecting Vibrio dynamics in the environment.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".