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Record W2939584742 · doi:10.1111/nph.15861

AtEDT1/HDG11 regulates stomatal density and water‐use efficiency via <i>ERECTA</i> and <i>E2Fa</i>

2019· article· en· W2939584742 on OpenAlexfundno aff
Xiaoyu Guo, Yao Wang, Ping‐Xia Zhao, Ping Xu, Guo‐Hua Yu, Li‐Yong Zhang, Yan Xiong, Cheng‐Bin Xiang

Bibliographic record

VenueNew Phytologist · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Molecular Biology Research
Canadian institutionsnot available
FundersArizona Biomedical Research CommissionInstitute of Genetics and Developmental Biology, Chinese Academy of SciencesMinistry of Science and Technology of the People's Republic of ChinaChinese Academy of SciencesInstitute of GeneticsNational Natural Science Foundation of China
KeywordsStomatal densityWater-use efficiencyBotanyPhotosynthesisChemistryBiology

Abstract

fetched live from OpenAlex

Improvement of crop drought resistance and water-use efficiency (WUE) has been a major endeavor in agriculture. Arabidopsis ENHANCED DROUGHT TOLERANCE1/HOMEODOMAIN GLABROUS11 (AtEDT1/HDG11), a homeodomain-START transcription factor we previously identified from the enhanced drought tolerance1 mutant (edt1), has been demonstrated to improve drought tolerance and WUE significantly in multiple plant species when constitutively overexpressed. Here, we report the genetic evidence suggesting a genetic pathway, which consists of EDT1/HDG11, ERECTA, and E2Fa loci, and regulates WUE by modulating stomatal density. AtEDT1/HDG11 transcriptionally activates ERECTA by binding to homeodomain-binding (HD) cis-elements in the ERECTA promoter. ERECTA, in turn, depends on E2Fa to modulate the expression of cell cycle-related genes. This modulation affects the transition from mitosis to endocycle, leading to increased ploidy levels in leaf cells, and therefore increased cell size and decreased stomatal density. Our results suggest a possible EDT1/HDG11-ERECTA-E2Fa genetic pathway that reduces stomatal density by increasing cell size and provide a new avenue to improve WUE of crops.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.215
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations83
Published2019
Admission routes1
Has abstractyes

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