small RNA can move long distances through plant vasculature to influence gene expression in shoot apical meristems
Bibliographic record
Abstract
Abstract In plants, small RNA (sRNA) can regulate gene expression via post transcriptional gene silencing (PTGS) or through RNA-directed DNA methylation (RdDM) leading to transcriptional gene silencing (TGS). sRNA is mobile throughout the plant, with movement occurring short distances from cell-to-cell as well as long distances through the vasculature via phloem trafficking. The range of long-distance sRNA mediated signaling from the vasculature to the shoot apical meristem (SAM) is not clear. To investigate this, two independent transgenic approaches were used to examine trafficking of phloem-expressed sRNA to the SAM in Arabidopsis thaliana . First, the phloem companion-cell specific promoter SUC2 was used to drive expression of an inverted repeat complementary to FLOWERING LOCUS D ( FD) , a flowering time regulator expressed exclusively in the SAM. In a separate experiment, the SUC2 promoter was used to express an artificial microRNA (aMiR) designed to target a synthetic CLAVATA3 ( CLV3 ) target in the SAM stem cells. Both systems provide evidence of a phloem-to-SAM sRNA communication axis connecting distal regions of the plant to the stem cells of the SAM, which ultimately gives rise to all shoot tissues, including gametes. Thus, phloem-to-SAM sRNA movement defines an important link between sRNA expressed in distal regions of the plant and the growing shoot. Importantly, phloem-to-SAM sRNA trafficking may allow somatic sRNA to direct SAM RdDM, fixing transient sRNA expression events into stable epigenetic changes.
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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 | 0.000 |
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".