Future-proofing our food: increasing tolerance to abiotic stress in hexaploid bread wheat using transcriptomics
Bibliographic record
Abstract
Over four billion people around the world rely on hexaploid bread wheat (Triticum aestivum L.) \nas a major constituent of their diet. However, a warming climate, with increasingly common \nfluctuations in temperature and rainfall, threatens wheat yields, and, subsequently, the lives \nand livelihoods of billions of people who depend on the crop for food. To future-proof wheat \nagainst a hostile and variable climate, where periods of heat and drought stress occur more \nintensely and unpredictably in some regions, a better understanding of how the response to \nthese stresses, and inherent stress tolerance are regulated is required. This thesis introduces \nthe YoGI wheat landrace panel, a diverse selection of 342 accessions taken from several \nlandrace collections, and utilizes them to better understand the regulation of the transcriptional \nand physiological responses to early heat and drought stress exposure, as well as the \ntranscriptional regulation of inherent thermotolerance. This thesis primarily employs a network \napproach, weighted gene co-expression network analysis (WGCNA), to identify candidate \nmaster-regulators of these processes, whilst comparative transcriptomic analyses provide \ninsights in to how the wheat transcriptome is affected by these stresses. This thesis also \nexamines whether exposure to, and then removal of, early heat stress leads to any \nphysiological changes and yield effects later in development, identifying a novel delayed \nflowering phenotype after this stress treatment, and potential transcriptional determinants of \nthis delay. In all, this thesis represents an exploratory examination of the hexaploid wheat \ntranscriptome; identifying genes which may determine inherent stress tolerance, or which may \nact to coordinate the transcriptional and physiological responses to heat and drought stresses \n– genes which could, one day, aid the production of climate-resilient wheat varieties, better \nable to grow in an increasingly hostile climate
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".