Quantitative Phosphoproteomic and Physiological Analyses Provide Insights into the Formation of the Variegated Leaf in Catalpa fargesii
Bibliographic record
Abstract
Variegated plants are valuable materials for investigating leaf color regulated mechanisms. To unveil the role of posttranslational modification in the variegated phenotype, we conducted global quantitative phosphoproteomic analysis on different leaf color sectors of Maiyuanjinqiu and the corresponding of Catalpa fargesii using Ti4+-IMAC phosphopeptide enrichment. A total of 3778 phosphorylated sites assigned to 1646 phosphoproteins were identified, and 3221 in 1434 proteins were quantified. Differential phosphoproteins (above 1.5 or below 1/1.5) in various leaf color sectors were selected for functional enrichment analyses. Gene ontology (GO) enrichment revealed that processes of photosynthesis, regulation of the generation of precursor metabolites, response to stress, homeostasis, amino acid metabolism, transport–related processes, and most of the energy metabolisms might contribute to leaf color. KEGG pathway enrichment analysis was performed based on differential phosphoproteins (DPs) in different organelles. The result showed that most enriched pathways were located in the chloroplasts and cytosol. The phosphorylation levels of glycometabolism enzymes might greatly affect leaf variegation. Measurements of fluorescence parameters and enzyme activities confirmed that protein phosphorylation could affect plant physiology by regulating enzyme activity. These results provide new clues for further study the formation mechanisms of naturally variegated phenotype.
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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".