MétaCan
Menu
Back to cohort
Record W4408175679 · doi:10.1186/s12870-025-06313-3

The CGA1-SNAT regulatory module potentially contributes to cytokinin-mediated melatonin biosynthesis and drought tolerance in wheat

2025· article· en· W4408175679 on OpenAlexaff
Roohollah Shamloo‐Dashtpagerdi, Angelica Lindlöf, Massume Aliakbari

Bibliographic record

VenueBMC Plant Biology · 2025
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsEtobicoke General Hospital
FundersHögskolan i Skövde
KeywordsBiologyMelatoninCytokininBiosynthesisDrought toleranceBotanyCell biologyComputational biologyGeneBiochemistryNeuroscienceAuxin

Abstract

fetched live from OpenAlex

BACKGROUND: Melatonin plays a pivotal role in alleviating abiotic stresses, yet its biosynthesis regulation in crops, particularly wheat, remains unclear. This study explores regulatory components of melatonin biosynthesis under drought stress using bioinformatic, physiochemical, and molecular approaches in contrasting wheat genotypes. RESULTS: Bioinformatic analysis identified SNAT, a key melatonin biosynthesis gene, and 88 transcription factors (TFs) from 26 families as potential regulators. The regulatory network for SNAT highlighted CYTOKININ-RESPONSIVE GATA FACTOR 1 (CGA1) as a significant TF. Under drought stress, contrasting wheat genotypes exhibited distinct CGA1-SNAT module expression, melatonin and cytokinin levels, photosynthetic activity, and oxidative damage. Cytokinin treatments regulated the CGA1-SNAT module, altering melatonin content, SPAD values, and chloroplast numbers, particularly in drought-susceptible genotypes. CONCLUSIONS: This study uncovers the pivotal role of the CGA1-SNAT module and its interaction with the cytokinin pathway in regulating melatonin biosynthesis during drought stress. These findings enhance our understanding of the molecular mechanisms underpinning drought tolerance and offer promising targets for genetic and biochemical interventions to improve crop resilience.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.236
Teacher spread0.223 · 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

Citations7
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueBMC Plant BiologySame topicCircadian rhythm and melatoninFrench-language works237,207