Multi-omics analysis reveals the physiological and molecular response to cold stress in different spring wheat cultivars at the booting stage
Bibliographic record
Abstract
Introduction: Cold stress at the booting stage can seriously affect wheat growth, development and yield. Methods: Therefore, this study employed integrated physiological, transcriptomic, proteomic and metabolomic approaches to examine the response of two wheat cultivars, Chuanmai 104 (CM104, cold-tolerant) and Chuanmai 42 (CM42, cold-sensitive), to cold stress at the booting stage. Results: The viability of pollen in CM104 was less affected by low-temperature stress compared to CM42, ensuring a higher seed-setting rate in CM104. The young spike of CM104 also synthesized more osmoregulatory substances, endogenous hormones and higher antioxidant enzyme activities under the cold treatment compared to CM42. Transcriptome analysis identified 7,362 and 5,328 differentially expressed genes (DEGs) between control and cold-treated CM104 and CM42 spike samples, respectively. More DEGs, such as transcription factors, late embryogenesis abundant protein and hormone signalling transduction involved in the key regulatory pathways associated with cold tolerance were expressed in CM104. Proteomic and metabolomic analyses identified 173 differentially expressed proteins and 180 differentially accumulated metabolites between control and cold-treated CM104 spike samples, with some thought to enhance the cold acclimation of the variety. Integrative multi-omics analysis highlighted the critical roles of starch and sucrose, and glycerophospholipid metabolism in response to cold stress in CM104. Discussion: This study uncovered the physiological changes, gene, protein and metabolite pathways involved in maintaining the osmotic balance and mitigating low-temperature stress in wheat spikes, and could serve as a crucial reference for selecting and breeding low-temperature tolerant wheat 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".