Plant gene silencing signals move from the phloem to influence gene expression in shoot apical meristems
Bibliographic record
Abstract
BACKGROUND: Small RNAs (sRNA) are potent regulators of gene expression that can diffuse short distances between cells and move long distances through plant vasculature. However, the degree to which sRNA silencing signals can move from the phloem to the shoot apical meristem (SAM) remains unclear. RESULTS: Two independent transgenic approaches were used to examine whether phloem sRNA silencing can reach different domains of the SAM and silence SAM-expressed genes. First, the phloem companion-cell specific SUCROSE-PROTON SYMPORTER2 (SUC2) promoter was used to drive expression of an inverted repeat to target the FD gene, an exclusively SAM-localized floral regulator. Second, the SUC2 promoter was used to express an artificial microRNA (aMiR) designed to target a synthetic CLAVATA3 (CLV3) transgene in SAM stem cells. Both phloem silencing signals phenocopied the loss of function of their targets and altered target gene expression suggesting that a phloem-to-SAM silencing communication axis exists, connecting distal regions of the plant to SAM stem cells. CONCLUSIONS: Demonstration of phloem-to-SAM silencing reveals a regulatory link between somatic sRNA expressed in distal regions of the plant and the growing shoot. Since the SAM stem cells ultimately produce the gametes, we discuss the intriguing possibility that phloem-to-SAM sRNA trafficking could allow transient somatic sRNA expression to manifest stable, transgenerational 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".