Size Distribution of Small Interfering RNAs in Various Organs at Different Developmental Stages is Primarily Determined by the Dicing Activity of Dicer-Like Proteins in Plants
Bibliographic record
Abstract
RNA silencing is a fundamental mechanism to maintain plant growth and development, and regulation of the size distribution of small interfering RNAs (siRNAs) is critical in the control of normal gene expression throughout a plant's life cycle. However, the cause of organ- and developmental stage-specific accumulation of siRNAs has never been reported. Whereas 24 nt siRNAs accumulated about 5.3-fold more than 21 nt siRNAs in Arabidopsis rosette leaves, 21 and 24 nt siRNAs accumulated to similar levels in Arabidopsis pollen grains, rice spikelets and maize anthers. We successfully detected two distinct double-stranded RNA (dsRNA)-cleaving activities that produced 21 and 24 nt RNAs in cell-free extracts prepared from various organs at different developmental stages of A. thaliana, Brassica rapa, rice and maize. Although DCL4 transcript was expressed more than DCL3 transcript in most organs, the 21 nt RNA-producing activity of DCL4 or its orthologs was very low and was 5- to 10-fold lower than the 24 nt RNA-producing activity of DCL3 or its orthologs particularly in leaves, indicating that DCL4 activity is negatively regulated translationally or post-translationally in leaves. High dicing activity of DCL3 and DCL4 was detected in immature inflorescences, developing seeds, germinating embryos and callus, all of which contain actively dividing cells. In various organs at different developmental stages, the size distribution of siRNAs was positively correlated with the dicing activity of two Dicers, DCL3 and DCL4, or their orthologs. Taken together, the size distribution of siRNAs in most organs is primarily determined by the dicing activity of DCL3 and DCL4.
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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 | 0.000 | 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 | 0.001 |
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".