Heat shock responsive genes in Brassicaceae: genome-wide identification, phylogeny, and evolutionary associations within and between genera
Bibliographic record
Abstract
Heat stress affects the growth and development of Brassicaceae crops. Plant breeders aim to mitigate the effects of heat stress by selecting for heat stress tolerance, but the genes responsible for heat stress in Brassicaceae remain largely unknown. During heat stress, heat shock proteins (HSPs) function as molecular chaperones to aid in protein folding, and heat shock transcription factors (HSFs) serve as transcriptional regulators of HSP expression. We identified 5002 heat shock related genes, including HSPs and HSFs, across 32 genomes in Brassicaceae. Among these, 3347 genes were duplicated, with segmented duplication primarily contributing to their expansion. We identified 466 physical gene clusters, including 240 homogenous clusters and 226 heterogeneous clusters, shedding light on the organization of heat shock related genes. Notably, 37 genes were co-located with published thermotolerance quantitative trait loci, which supports their functional role in conferring heat stress tolerance. This study provides a comprehensive resource for the identification of functional Brassicaceae heat shock related genes, elucidates their clustering and duplication patterns and establishes the genomic foundation for future heat tolerance research. We hypothesise that genetic variants in HSP and HSF genes in certain species have potential for improving heat stress tolerance in Brassicaceae crops.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".