Increasing the resilience of plant immunity to a warming climate
Bibliographic record
Abstract
Abstract Extreme weather conditions associated with climate change affect many aspects of plant and animal life, including the response to infectious diseases. Production of salicylic acid (SA), a central plant defence hormone 1–3 , is particularly vulnerable to suppression by short periods of hot weather above the normal plant growth temperature range via an unknown mechanism 4–7 . Here we show that suppression of SA production in Arabidopsis thaliana at 28 °C is independent of PHYTOCHROME B 8,9 (phyB) and EARLY FLOWERING 3 10 (ELF3), which regulate thermo-responsive plant growth and development. Instead, we found that formation of GUANYLATE BINDING PROTEIN-LIKE 3 (GBPL3) defence-activated biomolecular condensates 11 (GDACs) was reduced at the higher growth temperature. The altered GDAC formation in vivo is linked to impaired recruitment of GBPL3 and SA-associated Mediator subunits to the promoters of CBP60g and SARD1 , which encode master immune transcription factors. Unlike many other SA signalling components, including the SA receptor and biosynthetic genes, optimized CBP60g expression was sufficient to broadly restore SA production, basal immunity and effector-triggered immunity at the elevated growth temperature without significant growth trade-offs. CBP60g family transcription factors are widely conserved in plants 12 . These results have implications for safeguarding the plant immune system as well as understanding the concept of the plant–pathogen–environment disease triangle and the emergence of new disease epidemics in a warming climate.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".