Endoplasmic reticulum‐associated N‐glycan degradation of cold‐upregulated glycoproteins in response to chilling stress in <i>Arabidopsis</i>
Bibliographic record
Abstract
N-glycosylation has a great impact on glycoprotein structure, conformation, stability, solubility, immunogenicity and enzyme activity. Structural characterization of N-glycoproteome has been challenging but can provide insights into the extent of protein folding and surface topology. We describe a highly sensitive proteomics method for large-scale identification and quantification of glycoproteins in Arabidopsis through (15) N-metabolic labeling, selective enrichment of glycopeptides, data-dependent MS/MS analysis and automated database searching. In-house databases of Arabidopsis glycoproteins and glycopeptides containing Asn-X-Ser/Thr/Cys motifs were constructed by reducing 20% and 90% of the public database size, respectively, to enable a rapid analysis of large datasets for comprehensive identification and quantification of glycoproteins and heterogeneous N-glycans in a complex mixture. Proteome-wide analysis identified c. 100 stress-related N-glycoproteins, of which the endoplasmic reticulum (ER) resident proteins were examined to be up-regulated. Quantitative measurements provided a molecular signature specific to glycoproteins for determining the degree of plant stress at low temperature. Structural N-glycoproteomics following time-course cold treatments revealed the stress-responsive degradation of high-mannose type N-glycans in ER in response to chilling stress, which may aid in elucidating the cellular mechanisms of protein relocation, transport, trafficking, misfolding and degradation under stress conditions.
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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 | 0.001 |
| 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".