Comprehensive proteome profile of the zebrafish (Danio rerio) gill: applications to physiological and biomarker discovery studies
Bibliographic record
Abstract
Zebrafish are an important model in vertebrate genetics, developmental biology, physiology, and toxicology. In this study, we established the first near comprehensive proteome profile of a teleost fish tissue using two‐dimensional liquid chromatography electrospray ionization tandem mass spectrometry. Coverage was significantly improved with the application of novel sequential solubilization and precursor ion exclusion methods. Six thousand one hundred seventy‐one proteins were identified and the proteome exhibited excellent coverage of important biochemical pathways relevant to the function of the gill in respiration, ion and acid‐base homeostasis, and energy metabolism. Numerous established and potential biomarkers of stress, disease, and environmental contamination were also expressed in the gill. Annotation information was completely lacking for greater than 30% of the expressed proteins, highlighting the need for advancements in bioinformatic analysis techniques to complement this research. Nevertheless, the results provide important insights into the physiological function of the gill as well as its role as an environmental interface. We discuss the significance of these findings in the context of exploratory physiological and toxicological studies. This research was supported by the Alberta Ingenuity Centre for Water Research.
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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 | 0.001 | 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".