Global gene expression responses of Atlantic salmon skin to Moritella viscosa
Bibliographic record
Abstract
Moritella viscosa is a Gram-negative pathogen that causes large, chronic ulcers, known as winter-ulcer disease, in the skin of several fish species including Atlantic salmon. We used a bath challenge approach to profile the transcriptome responses of M. viscosa-infected Atlantic salmon skin at the lesion (Mv-At) and away from the lesion (Mv-Aw) sites. M. viscosa infection was confirmed through RNA-based qPCR assays. RNA-Seq identified 5212 and 2911 transcripts differentially expressed in the Mv-At compared to no-infection control and Mv-Aw groups, respectively. Also, there were 563 differentially expressed transcripts when comparing the Mv-Aw to control samples. Our results suggest that M. viscosa caused massive and strong, but largely infection site-focused, transcriptome dysregulations in Atlantic salmon skin, and its effects beyond the skin lesion site were comparably subtle. The M. viscosa-induced transcripts of Atlantic salmon were mainly involved in innate and adaptive immune response-related pathways, whereas the suppressed transcripts by this pathogen were largely connected to developmental and cellular processes. As validated by qPCR, M. viscosa dysregulated transcripts encoding receptors, signal transducers, transcription factors and immune effectors playing roles in TLR- and IFN-dependent pathways as well as immunoregulation, antigen presentation and T-cell development. This study broadened the current understanding of molecular pathways underlying M. viscosa-triggered responses of Atlantic salmon, and identified biomarkers that may assist to diagnose and combat this pathogen.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".