Detection and editing of the updated plastid- and mitochondrial-encoded proteomes for <i>Arabidopsis</i> with PeptideAtlas
Bibliographic record
Abstract
Abstract Arabidopsis thaliana Col-0 has plastid and mitochondrial genomes encoding for over one hundred proteins and several ORFs. Public databases ( e.g. Araport11) have redundancy and discrepancies in gene identifiers for these organelle-encoded proteins. RNA editing results in changes to specific amino acid residues or creation of start and stop codons for many of these proteins, but the impact of such RNA editing at the protein level is largely unexplored due to the complexities of detection. This study first assembled the non-redundant set of identifiers, their correct protein sequences, and 452 predicted non-synonymous editing sites of which 56 are edited at lower frequency. Accumulation of edited and/or unedited proteoforms was then determined by searching ∼259 million raw MSMS spectra from ProteomeXchange as part of Arabidopsis PeptideAtlas ( www.peptideatlas.org/builds/arabidopsis/ ). All mitochondrial proteins and all except three plastid-encoded proteins (NDHG/NDH6, PSBM, RPS16), but none of the ORFs, were identified; we suggest that all ORFs and RPS16 are pseudogenes. Detection frequencies for each edit site and type of edit ( e.g. S to L/F) were determined at the protein level, cross-referenced against the metadata ( e.g. tissue), and evaluated for technical challenges of detection.167 predicted edit sites were detected at the proteome level. Minor frequency sites were indeed also edited at low frequency at the protein level. However, except for sites RPL5-22 and CCB382-124, proteins only accumulate in edited form (>98 –100% edited) even if RNA editing levels are well below 100%. This study establishes that RNA editing for major editing sites is required for stable protein accumulation.
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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 | 0.001 |
| 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 | 0.001 |
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".