Methyl recycling activities are co-ordinately regulated during plant development
Bibliographic record
Abstract
A large number of compounds including lignin, phospholipids, pectin, DNA, mRNA, and proteins require methyl groups for their functionality. A detailed study of the expression and activities of two enzymes, adenosine kinase (ADK) and S-adenosylhomocysteine hydrolase (SAHH), which are both required for the maintenance and recycling of S-adenosylmethionine-dependent methylation in plants, was carried out. The abundance and tissue localization of ADK and SAHH transcripts and protein were monitored along with their enzyme activities in leaves, stems, buds, siliques, and roots of Arabidopsis. In all but roots and seed coats, the transcript abundance of ADK and SAHH fluctuated co-ordinately, matching changes in their protein and enzyme activities. To evaluate whether this expression pattern was associated with methyl recycling, the protein content and distribution of S-adenosylmethionine synthetase and phosphoethanolamine N-methyltransferase, a key methyltransferase involved in phospholipid synthesis, were investigated. These were found to accumulate in a pattern similar to ADK and SAHH. ADK and SAHH protein and transcript amounts were shown to fluctuate similarly in tissues accumulating lignin. Additionally, the amounts of ADK and SAHH mRNAs were also found at high levels in inflorescence meristems likely to support their higher rates of cell division. Thus, the results point to a co-ordinated and probably transcriptional regulation of these genes in most organs of Arabidopsis; SAHH abundance is distinctly higher in seeds and roots which suggests it may have a non-methyl-related role in these organs.
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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 | 0.001 | 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".