A survey of the plant mitochondrial proteome in relation to development
Bibliographic record
Abstract
To expand the functional analysis of plant mitochondria, we have undertaken the building of the proteome of pea mitochondria purified from leaves (green and etiolated), roots and seeds. In the first stage, we focused our proteomic exploration on the soluble protein complement of the green leaf mitochondria. We used traditional two-dimensional polyacrylamide gel electrophoresis, in combination with size exclusion chromatography as a third dimension, to identify the major proteins and further resolve their macromolecular complexity. The two-dimensional map of soluble proteins of green leaf mitochondria revealed 433 spots (with Coomassie blue staining) and around 73% of the proteins (in mass) were identified using three different approaches: Edman degradation, matrix-assisted laser desorption/ionization mass spectrometry and electrospray ionization tandem mass spectrometry. Quite a lot of the polypeptides were present in multiforms which indicated the presence of isoforms or the occurrence of post-translational modifications. Among these proteins, we uncovered an abundant family that was identified as aldehyde dehydrogenases, representing approximately 7.5% of the soluble proteins. The comparative analysis of soluble mitochondrial proteomes led to the identification of a number of proteins which were specifically present in root or in seed mitochondria, thus revealing the impact of tissue differentiation at the mitochondrial level.
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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".