Growth‐limiting drought increases sensitivity of Asian rice (<i>Oryza sativa</i>) leaves to heat shock through physiological and spatially distinct transcriptomic responses
Bibliographic record
Abstract
Growth-limiting droughts (GLD) impair tissue expansion and delay developmental transitions but are often not considered as stressors, as many physiological traits are only slightly altered relative to well-watered counterparts. Concurrently, cell size, biochemical makeup, and transcriptome profiles vary along the leaf blade in accordance with the partitioning of distinct functions to spatially defined regions of the leaf. This suggests that because different parts of the leaf have underlying differences in their transcriptome profiles, they might respond to GLD in distinctive ways. Moreover, how antagonistic stressors influence physiology and gene expression in different zones of leaves is an open question. In this study, we profiled growth, anatomy, and gas exchange in Asian rice (Oryza sativa) leaves developed in well-watered and GLD conditions, with or without a secondary heat shock. We dissected leaves into seven equal-length segments for transcriptome analysis in these conditions. We hypothesized that GLD would make the leaves more sensitive to heat shock and would disrupt the underlying heterogeneity of the leaf transcriptome. GLD plants were more strongly affected by heat shock with respect to gas exchange and the number and types of genes that were differentially expressed and that these differences varied along the leaf blade. We developed an eFP browser tool with these data to facilitate exploration and hypothesis testing. These findings show that even mild drought treatments are sufficient to impact responses to antagonistic stressors and that substantial within-organ variance exists with respect to stress responses.
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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".