Decoding Long‐Distance Communication Under Mineral Stress: Advances in Vascular Signalling and Molecular Tools for Plant Resilience
Bibliographic record
Abstract
Mineral nutrients are essential for plant growth, development and crop yield. Under mineral deficient conditions, plants rely on a sophisticated network of signalling pathways to coordinate their molecular, physiological, and morphological responses. Recent research has shown that long-distance signalling pathways play a pivotal role in maintaining mineral homeostasis and optimising growth. This review explores the intricate mechanisms of long-distance signalling under mineral deficiencies, emphasising its importance as a communication network between roots and shoots. Through the vascular tissues, plants transport an array of signalling molecules, including phytohormones, small RNAs, proteins, small peptides, and mobile mRNAs, to mediate systemic responses. Vascular tissues, particularly companion cells, are critical hubs for sensing and relaying mineral deficiency signals, leading to rapid changes in mineral uptake and optimised root morphology. We highlight the roles of key signalling molecules in regulating mineral acquisition and stress adaptation. Advances in molecular tools, including TRAP-Seq, heterografting, and single-cell RNA sequencing, have recently unveiled novel aspects of long-distance signalling and its regulatory components. These insights underscore the essential role of vascular-mediated communication in enabling plants to navigate heterogeneous mineral distribution environments and suggest new avenues for improving crop resilience and mineral use efficiency.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".