Long non-coding RNAs regulate the expression of cell surface receptors in plants
Bibliographic record
Abstract
Abstract Plants are exposed to a variety of growth, developmental, and environmental cues during their lifespan. To survive and thrive, plants have developed sophisticated ways of responding to these signals that involve regulation at the transcriptional, post-transcriptional, translational, and post-translational levels. Leucine-rich repeat receptor-like kinases are the largest family of receptor-like kinases in plants and respond to a range of external and internal stimuli. They act as crucial regulators of plant growth, development, and immunity. To fully understand LRR-RLK function, it is essential to understand how their expression is regulated under different conditions. While there have been numerous studies on post-translational regulation of LRR-RLKs through phosphorylation and ubiquitination, there is little known about the mechanisms of transcriptional and post-transcriptional regulation of LRR-RLKs. In this study, we show that natural antisense transcript long non-coding RNAs are central regulators of LRR-RLK expression at the transcriptional and post-transcriptional levels. LRR-RLK genes are almost universally associated with cis-NATs and we confirm cis-NAT expression in planta using strand-specific RT-PCR. We leverage several well-studied LRR-RLKs to demonstrate that cis-NATs regulate LRR-RLK expression and function. For cis-NATs to fine-tune LRR-RLK expression, their expression and regulatory activity must be tightly controlled and cell autonomous. Using a combination of GUS reporter assays and tissue-specific promoters, we provide evidence that cis-NATs have these characteristics, positioning them as key regulators of LRR-RLK function. We also demonstrate that the association of LRR-RLK genes with cis-NATs is conserved across much of plant evolution, suggesting that this previously unexplored regulatory mechanism serves an important and ancient purpose.
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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 | 0.001 |
| 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".