Analysis of mRNA Levels of Ten Genes Under Water Stress in Triticum turgidum subsp. durum
Bibliographic record
Abstract
Drought is one of the major causes of dramatic yield loss in crop plants. Plants perceive and respond to stress. Upon perception of stress, a signal is communicated to downstream components resulting in change of gene expression and thereby of proteins required for the initial damage-repair and physiological re-programming for better adaptation. In this work, a set of 10 genes from Triticum turgidum subsp. durum, were tested for their expression under drought conditions. These drought responsive genes selected for expressional analyses can be classified into two groups. The first group includes functional proteins already known to be involved in the response to water stress such as late embryogenesis protein. The second group comprises protein factors involved in the regulation of signal transduction and gene expression, such as transcription factors. We have used the real-time quantitative PCR to monitor the expression patterns of these 10 genes in Triticum durum leaves under drought stress. The results showed a high quantitative up-regulation of some genes belonging to dehydrin, transcription factors (DREBS), cell wall polysaccharides and regulation of secretion categories. Moreover, the actin binding protein and an ethylene-responsive element binding factor genes were slightly down-regulated and not significantly affected by the drought stress. The result of this study extended our knowledge of drought induced genes and may provide better understanding of the molecular mechanisms of drought response inTriticum durum. Identifying genes turned on or off in response to water stress will help in enhancing drought tolerance and providing genes that can be tested by many biotechnology-based approaches. The long term objective of this study is to choose the optimum condition for subsequent whole transcriptomic analysis by cDNA-AFLP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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".