O‐Antigen Gene Cluster Reveals Genomic Variation in Chilean <i>Tenacibaculum dicentrarchi</i> Through Multiplex <scp>PCR</scp>‐Based Genotyping Scheme
Bibliographic record
Abstract
Tenacibaculum dicentrarchi emerged as a major pathogen in Chilean salmon farming in early 2023, causing 32.9% of Atlantic salmon (Salmo salar) mortalities. Although recent studies have provided valuable insights into T. dicentrarchi through virulence mechanisms and genome analysis, genetic diversity based on the O-antigen gene cluster remains largely unexplored. In this study, we conducted a comparative genomic analysis of O-antigen biosynthesis genes in 30 whole-genome sequenced strains, including the T. dicentrarchi type strain. The analysis identified a single O-antigen gene cluster (O-AGC) consisting of 20 genes involved in the biosynthesis of this component of the lipopolysaccharide layer. Variations in the O-AGC revealed antigenic diversity within the species, allowing classification into four distinct groups, designated Types 1 to 4. Based on these findings, we developed a multiplex PCR-based genotyping scheme, which was successfully applied to 25 bacterial isolates from Chilean fish farms. Most isolates were identified as Type 1 and 3, while Types 2 and 4 were less common, with the same number of isolates. We also investigated whether core genome phylogeny correlated with O-AGC Types by including publicly available genomes from Chile, Norway and Canada. Notably, T. dicentrarchi isolates clustered into two groups: one comprising isolates from Norway and Canada, all belonging to Type 1. Another group consisted of Chilean isolates with diverse O-AGC Types (i.e., 1, 2, 3 and 4), including the type strain. This multiplex PCR approach provided a valuable tool for rapid and reliable typing of T. dicentrarchi, facilitating epidemiological studies and aiding in the selection of appropriate isolates for the vaccine development against tenacibaculosis in fish farms.
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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) | 0.001 | 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 | 0.000 |
| 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".