Comparative<i>N</i>‐glycoproteomic analysis of Tibetan and lowland chicken fertilized eggs: Implications on proteins biofunction and species evolution
Bibliographic record
Abstract
The characterization and functionality of protein glycosylation among different related species are of common interest. Herein, non-standard quantification and N-glycosylation enrichment technology combined with ultra-high liquid chromatography-tandem mass spectrometry were used to establish detailed N-glycoproteomics of fertilized eggs, and quantitatively compared between Tibetan and lowland chicken. A total of 396N-glycosites from 143 glycoproteins were found. Specifically, compared with lowland chicken egg white, 32N-glycosites of 22 glycoproteins were up-regulated and 57N-glycosites of 25 glycoproteins were down-regulated in Tibetan chicken egg white. Also, 137N-glycosites in 72 glycoproteins showed much higher-degree glycosylation and 36N-glycosites in 15 glycoproteins displayed lower-degree glycosylation in Tibetan chicken egg yolk than those in lowland chicken egg yolk. Through bioinformatic analysis, these varied glycoproteins were highly associated with antifreeze activity, hypoxia adaptation, coagulation cascade, and binding/immunity activities, which may be related to plateau hypoxia and cold stress. PRACTICAL APPLICATIONS: These findings provide a new insight on the role of biological egg N-glycoproteins related to environmental adaptation and evolution, which may be further applied in improving egg processing and human health, by developing biomolecules for food and medical industry.
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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 | 0.000 |
| 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".