<scp>CBF4</scp>/<scp>DREB1D</scp> represses <scp><i>XERICO</i></scp> to attenuate <scp>ABA</scp>, osmotic and drought stress responses in Arabidopsis
Bibliographic record
Abstract
Water stress can severely impact plant growth, productivity and yield. Consequently, plants have evolved various strategies through which they can respond and adapt to their environment. XERICO (XER) is a stress-responsive RING E3 ubiquitin ligase that modulates abscisic acid (ABA) levels and promotes drought tolerance when overexpressed. To better understand the biological role of XER in stress responses, we characterized a xer-1 hypomorphic mutant and a CRISPR/Cas9-induced xer-2 null mutant in Arabidopsis. Both xer mutant alleles exhibited increased drought sensitivity, supporting the results from overexpression studies. Furthermore, we discovered that both xer mutants have greater stomatal indices and that XER is expressed in epidermal cells, indicating that XER functions in the epidermis to repress stomatal development. To explore XER spatiotemporal and stress-dependent regulation, we conducted a yeast one-hybrid screen and found that CBF4/DREB1D associates with the XER 5' untranslated region (5'-UTR). We generated three cbf4 null mutants with CRISPR/Cas9 and showed that CBF4 negatively regulates ABA responses, promotes stomatal development and reduces drought tolerance, in contrast to the roles shown for XER. CBF4 is induced by ABA and osmotic stress, and localizes to the nucleus where it downregulates XER expression via the DRE element in its 5'-UTR. Lastly, genetic interaction studies confirmed that xer is epistatic to cbf4 in stomatal development and in ABA, osmotic and drought stress responses. We propose that the repression of XER by CBF4 functions to attenuate ABA signaling and stress responses to maintain a balance between plant growth and survival under adverse environmental conditions.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".