Transcriptional Profiling Implicates Novel Interactions between Abiotic Stress and Hormonal Responses in Thellungiella, a Close Relative of Arabidopsis
Bibliographic record
Abstract
Thellungiella, an Arabidopsis (Arabidopsis thaliana)-related halophyte, is an emerging model species for studies designed to elucidate molecular mechanisms of abiotic stress tolerance. Using a cDNA microarray containing 3,628 unique sequences derived from previously described libraries of stress-induced cDNAs of the Yukon ecotype of Thellungiella salsuginea, we obtained transcript profiles of its response to cold, salinity, simulated drought, and rewatering after simulated drought. A total of 154 transcripts were differentially regulated under the conditions studied. Only six of these genes responded to all three stresses of drought, cold, and salinity, indicating a divergence among the end responses triggered by each of these stresses. Unlike in Arabidopsis, there were relatively few transcript changes in response to high salinity in this halophyte. Furthermore, the gene products represented among drought-responsive transcripts in Thellungiella associate a down-regulation of defense-related transcripts with exposure to water deficits. This antagonistic interaction between drought and biotic stress response may demonstrate Thellungiella's ability to respond precisely to environmental stresses, thereby conserving energy and resources and maximizing its survival potential. Intriguingly, changes of transcript abundance in response to cold implicate the involvement of jasmonic acid. While transcripts associated with photosynthetic processes were repressed by cold, physiological responses in plants developed at low temperature suggest a novel mechanism for photosynthetic acclimation. Taken together, our results provide useful starting points for more in-depth analyses of Thellungiella's extreme stress tolerance.
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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 | 0.000 | 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 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".