LncRNA PMAT–PtoMYB46 module represses PtoMATE and PtoARF2 promoting Pb2+ uptake and plant growth in poplar
Bibliographic record
Abstract
Lead (Pb2+) is one of the most toxic heavy-metal contaminants. Fast-growing woody plants with substantial biomass are ideal for bioremediation. However, the transcriptional regulation of Pb2+ uptake in woody plants remains unclear. Here, we identified 226 Pb2+-induced, differentially expressed long non-coding RNAs (DELs) in Populus tomentosa. Functional annotation revealed that these DELs mainly regulate carbon metabolism, biosynthesis of secondary metabolites, energy metabolism, and signal transduction through their potential target genes. Association and epistasis analysis showed that the lncRNA PMAT (Pb2+-induced multidrug and toxic compound extrusion (MATE) antisense lncRNA) interacts epistatically with PtoMYB46 to regulate leaf dry weight, photosynthesis rate, and transketolase activity. Genetic transformation and molecular assays showed that PtoMYB46 reduces the expression of PtoMATE directly or indirectly through PMAT, thereby reducing the secretion of citric acid (CA) and ultimately promoting Pb2+ uptake. Meanwhile, PtoMYB46 targets auxin response factor 2 (ARF2) and reduces its expression, thus positively regulating plant growth. We concluded that the PMAT–PtoMYB46–PtoMATE–PtoARF2 regulatory module control Pb2+ tolerance, uptake, and plant growth. This study demonstrates the involvement of lncRNAs in response to Pb2+ in poplar, yielding new insight into the potential for developing genetically improved woody plant varieties for phytoremediating lead-contaminated soils.
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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) | 0.002 | 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".