Multi-Omics Analysis of Heat Stress Tolerance in Chickpea
Bibliographic record
Abstract
Heat stress is an important factor affecting the yield and adaptability of chickpeas, especially in the context of global climate change. To breed varieties that are both stress-resistant and high-yielding, it is necessary to conduct in-depth research on its heat-resistant molecular basis. This study reviewed the physiological and molecular responses of chickpeas at high temperatures, such as changes in photosynthetic efficiency, membrane stability, heat shock proteins (HSPs), and hormone signaling. Transcriptome studies have discovered many differentially expressed genes and regulatory networks; Proteomic and metabolomic analyses revealed that some stress-resistant related proteins, antioxidant substances, as well as metabolites such as proline and soluble sugars would accumulate at high temperatures. Genomic methods (such as QTL mapping and SNP analysis) have helped identify candidate gene loci, while the role of epigenetic modifications in heat tolerance responses has gradually been discovered. This study also presents a comparative case, conducting multi-omics analyses on heat-resistant and sensitive strains, demonstrating how to integrate the results to identify key candidate genes and metabolic pathways and apply them to the development of molecular markers. This study also systematically summarized the progress of multi-omics in the heat tolerance research of chickpeas, pointed out the difficulties faced in data integration, and proposed future research directions, which are expected to improve stress resistance in molecular design breeding and accelerate the breeding of heat-tolerant chickpea varieties.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".