Metabolomic and transcriptomic analyses reveal positive roles of root border cells in salinity resistance in cotton (Gossypium hirsutum L.)
Bibliographic record
Abstract
Root border cells (RBCs) play important roles in plant stress tolerance. However, their roles and underlying mechanisms in salinity stress responses remain largely unknown. To elucidate the salinity-induced metabolic adaptations and transcriptional responses of RBCs in cotton ( Gossypium hirsutum L.), we performed a comparative analysis of the metabolomes and transcriptomes of RBCs and adjacent naked root tips (NRTs, RBCs removed) under salinity stress. A total of 150 and 195 differentially accumulated metabolites, along with 10,593 and 7270 differentially expressed genes (DEGs) were identified in RBCs and NRTs, respectively. RBCs exhibited elevated accumulation of glycerophospholipids, sterols, unsaturated fatty acids and betaine relative to NRTs, which are crucial for maintaining membrane stability and osmoregulation. Enrichment analysis revealed that the α-linolenic acid metabolism pathway, participating in both lipid metabolism and jasmonic acid (JA) biosynthesis, was specially enriched in RBCs. DEGs associated with JA and salicylic acid signaling pathways showed markedly higher upregulation in RBCs than in NRTs, indicating stronger stress-responsive signaling in RBCs under salinity stress. Notably, azelaic acid (AZA), a lipid signaling molecule, was accumulated at higher levels in RBCs. Exogenous AZA application increased the production of RBCs and improved cotton seedling salinity tolerance. Taken together, higher accumulation of membrane-stabilizing and signaling lipids, as well as stronger JA/SA signal transduction promote salinity tolerance in RBCs. These findings expand our understanding of plant metabolic alterations in response to salinity stress and offer potential targets for improving cotton salinity tolerance.
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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 | 0.001 | 0.001 |
| 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".