Exogenous auxins for proline regulation in heat-stressed plants
Bibliographic record
Abstract
Abstract Microbial indole-3-acetic acid (IAA) has long been recognized as a driver of plant growth and developmental plasticity. Recent studies show that microbial IAA production is sustained or even enhanced at elevated temperatures, suggesting that microbial auxins may contribute to plant thermotolerance by stabilizing auxin signalling and supporting metabolic adaptation. Here, we synthesize emerging molecular and physiological evidence linking microbial IAA to proline turnover during thermomorphogenesis. We propose that microbial IAA establishes a regulatory window in which proline metabolism transitions between early osmoprotective synthesis and later catabolism that fuels elongation and redox balance. This integration involves crosstalk between the HSP90-TIR1 auxin perception module, heat-responsive MPK-IAA8 signalling, mitochondrial redox regulators (SSR1, HSCA2), and hormonal interactions with abscisic acid and ethylene. We outlined four mechanistic hypotheses and associated experiments to test how microbial IAA modulates proline homeostasis. This framework highlights microbial auxins as metabolic integrators during heat stress and provides a conceptual basis for leveraging auxin-producing microbes to enhance plant resilience under global warming. Highlight Microbial auxins modulate the proline cycle during heat stress by integrating hormonal, redox, and mitochondrial signals, offering a mechanistic framework for how microbial IAA enhances thermomorphogenesis and plant heat resilience.
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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) | 0.002 | 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".