Identification of Phosphoproteins in <i>Arabidopsis thaliana</i> Leaves Using Polyethylene Glycol Fractionation, Immobilized Metal-ion Affinity Chromatography, Two-Dimensional Gel Electrophoresis and Mass Spectrometry
Bibliographic record
Abstract
Reversible protein phosphorylation is a key regulatory mechanism in cells. Identification and characterization of phosphoproteins requires specialized enrichment methods, due to the relatively low abundance of these proteins, and is further complicated in plants by the high abundance of Rubisco in green tissues. We present a novel method for plant phosphoproteome analysis that depletes Rubisco using polyethylene glycol fractionation and utilizes immobilized metal-ion affinity chromatography to enrich phosphoproteins. Subsequent protein separation by one- and two-dimensional gel electrophoresis is further improved by extracting the PEG-fractionated protein samples with SDS/phenol and methanol/chloroform to remove interfering compounds. Using this approach, we identified 132 phosphorylated proteins in a partial Arabidopsis leaf extract. These proteins are involved in a range of biological processes, including CO(2) fixation, protein assembly and folding, stress response, redox regulation, and cellular metabolism. Both large and small subunits of Rubisco were phosphorylated at multiple sites, and depletion of Rubisco enhanced detection of less abundant phosphoproteins, including those associated with state transitions between photosystems I and II. The discovery of a phosphorylated form of AtGRP7, a self-regulating RNA-binding protein that affects floral transition, as well as several previously uncharacterized ribosomal proteins confirm the utility of this approach for phosphoproteome analysis and its potential to increase our understanding of growth and development in plants.
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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) | 0.001 | 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 | 0.001 |
| 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".