Application of Isotope Coded Affinity Tag (ICAT) Analysis for the Identification of Differentially Expressed Proteins Following Infection of Atlantic Salmon (<i>Salmo </i><i>s</i><i>alar)</i> with Infectious Hematopoietic Necrosis Virus (IHNV) or <i>Renibacterium </i><i>s</i><i>almoninarum</i> (BKD)
Bibliographic record
Abstract
Aquaculture and commercial fisheries worldwide suffer from significant economic loss due to diseases of net-pen reared fish. In British Columbia, infectious hematopoietic necrosis (IHN) and bacterial kidney disease (BKD) epidemics occur because there are currently no commercially available drugs or fully licensed vaccines to treat these diseases. With a better understanding of the molecular mechanisms underlying these diseases, this circumstance might be significantly improved. In the present study, we have used a proteomics approach in an effort to identify and quantitate differentially expressed proteins in the liver and kidneys of diseased and healthy Atlantic salmon (Salmo salar). Isotope coded affinity tagging (ICAT), 2D gel electrophoresis, and multidimensional liquid chromatography coupled to tandem mass spectrometry (2D-LC MS/MS) were used to identify hundreds of differentially expressed proteins. While the direct significance of changes in expression levels of many proteins remains to be elucidated, others appear to be more clearly related to the infectious process. Examples of the latter are discussed here and include, a natural killer cell enhancement factor (NKEF), procathepsin L, superoxide-producing NADPH oxidase and interferon-induced viral resistance protein Mx (IFI-Mx).
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".