Proteome distribution between nucleoplasm and nucleolus and its relation to ribosome biogenesis in<i>Arabidopsis thaliana</i>
Bibliographic record
Abstract
Ribosome biogenesis is an essential process initiated in the nucleolus. In eukaryotes, multiple ribosome biogenesis factors (RBFs) can be found in the nucleolus, the nucleus and in the cytoplasm. They act in processing, folding and modification of the pre-ribosomal (r)RNAs, incorporation of ribosomal proteins (RPs), export of pre-ribosomal particles to the cytoplasm, and quality control mechanisms. Ribosome biogenesis is best established for Saccharomyces cerevisiae. Plant ortholog assignment to yeast RBFs revealed the absence of about 30% of the yeast RBFs in plants. In turn, few plant specific proteins have been identified by biochemical experiments to act in plant ribosome biogenesis. Nevertheless, a complete inventory of plant RBFs has not been established yet. We analyzed the proteome of the nucleus and nucleolus of Arabidopsis thaliana and the post-translational modifications of these proteins. We identified 1602 proteins in the nucleolar and 2544 proteins in the nuclear fraction with an overlap of 1429 proteins. For a randomly selected set of proteins identified by the proteomic approach we confirmed the localization inferred from the proteomics data by the localization of GFP fusion proteins. We assigned the identified proteins to various complexes and functions and found about 519 plant proteins that have a potential to act as a RBFs, but which have not been experimentally characterized yet. Last, we compared the distribution of RBFs and RPs in the various fractions with the distribution established for yeast.
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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".