Transcriptome profiles reveal response mechanisms and key role of PsNAC1 in Pinus sylvestris var. mongolica to drought stress
Bibliographic record
Abstract
BACKGROUND: Drought stress severely impedes plant growth, and only a limited number of species exhibit long-term resistance to such conditions. Pinus sylvestris var. mongolica, a dominant tree species in arid and semi-arid regions of China, exhibits strong drought resistance and plays a crucial role in the local ecosystem. However, the molecular mechanisms underlying this resistance remain poorly understood. RESULTS: Here, we conducted transcriptome sequence and physiological indicators analysis of needle samples during drought treatment and rehydration stages. De-novo assembly yielded approximately 114,152 unigenes with an N50 length of 1,363 bp. We identified 6,506 differentially expressed genes (DEGs), with the majority being concentrated in the heavy drought stage (4,529 DEGs). Functional annotation revealed enrichment of drought-related GO terms such as response to water (GO:0009415: enriched 108 genes) and response to water deprivation (GO:0009414: enriched 106 genes), as well as KEGG categories including MAPK signaling pathway (K04733: enriched 35 genes) and monoterpenoid biosynthesis (K21374: enriched 27 genes). Multiple transcription factor families and functional protein families were differentially expressed during drought treatment. Co-expression network analysis identified a potential drought regulatory network between cytochrome P450 genes (Unigene4122_c1_g1) and a core regulatory transcription factor Unigene9098_c3_g1 (PsNAC1) with highly significant expression differences. We validated PsNAC1 overexpression in Arabidopsis and demonstrated enhanced drought resistance. CONCLUSIONS: These findings provide insight into the molecular basis of drought resistance in P. sylvestris var. mongolica and lay the foundation for further exploration of its regulatory network.
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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".