Arabidopsis Seed Stored mRNAs are Degraded Constantly over Aging Time, as Revealed by New Quantification Methods
Bibliographic record
Abstract
How plant seeds age remains poorly understood and effective tools for monitoring seed aging are lacking. Dry seeds contain various stored mRNAs which are believed to be required for protein synthesis during early stages of seed germination. We reasoned that seed stored mRNAs would undergo degradation during seed aging, based on the propensity of mRNAs to degrade. We performed RT-PCR and qPCR analyses to study the changes in stored mRNA levels of Arabidopsis seeds during aging. All stored mRNAs analyzed were gradually degraded in both naturally and artificially aged seeds. The difference in Ct values between aged and control seeds (delta-Ct value) was highly correlated with the mRNA fragment size and seed aging time. We derived mathematical equations for estimating the relative amount of undamaged stored mRNAs and frequency of the breakdown at one nucleotide level for individual mRNAs. Stored mRNAs were found to break down randomly. The frequency of breaks per nucleotide per day, which we named (beta) value, remained fairly constant under the same aging conditions over aging time. This parameter should allow the effects of different conditions on stored mRNAs to be quantitatively compared. Also, we showed that the change in stored mRNA levels could serve as a more precise biomarker for seed aging assessment than three existing methods. These methods and findings will advance the studies of stored mRNAs and seed ageing in plants, and likely slow RNA degradation in non-plant systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".