N<sup>6</sup>‐methyladenosine mRNA methylation positively regulated the response of poplar to salt stress
Bibliographic record
Abstract
Abstract As the most abundant form of methylation modification in messenger RNA (mRNA), the distribution of N 6 ‐methyladenosine (m 6 A) has been preliminarily revealed in herbaceous plants under salt stress, but its function and mechanism in woody plants were still unknown. Here, we showed that global m 6 A levels increased during poplar response to salt stress. Methylated RNA immunoprecipitation sequencing (MeRIP‐seq) revealed that m 6 A significantly enriched in the coding sequence region and 3′‐untranslated regions in poplar, by recognising the conserved motifs, AGACU, GGACA and UGUAG. A large number of differential m 6 A transcripts have been identified, and some have been proved involving in salt response and plant growth and development. Further combined analysis of MeRIP‐seq and RNA‐seq revealed that the m 6 A hypermethylated and enrich in the CDS region preferred to positively regulate expression abundance. Writer inhibitor, 3‐deazaneplanocin A treatment increased the sensitivity of poplar to salt stress by reducing mRNA stability to regulate the expression of salt‐responsive transcripts PagMYB48 , PagGT2 , PagNAC2 , PagGPX8 and PagARF2 . Furthermore, we verified that the methyltransferase PagFIP37 plays a positively role in the response of poplar to salt stress, overexpressed lines have stronger salt tolerance, while RNAi lines were more sensitive to salt, which relied on regulating mRNA stability in an m 6 A manner of salt‐responsive transcripts PagMYB48 , PagGT2 , PagNAC2 , PagGPX8 and PagARF2 . Collectively, these results revealed the regulatory role of m 6 A methylation in poplar response to salt stress, and revealed the importance and mechanism of m 6 A methylation in the response of woody plants to salt stress for the first time.
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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 | 0.000 |
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".