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Record W4417015476 · doi:10.5376/mgg.2025.16.0030

Dynamic DNA Methylation Landscapes in Maize Roots Under Salt Stress

2025· article· W4417015476 on OpenAlexvenueno aff
Wang Wei, Minghua Li

Bibliographic record

VenueMaize Genomics and Genetics · 2025
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicPlant Molecular Biology Research
Canadian institutionsnot available
Fundersnot available
KeywordsDNA methylationEpigeneticsMethylationLimitingMechanism (biology)Abiotic stressSalt (chemistry)

Abstract

fetched live from OpenAlex

Salt stress is one of the main abiotic factors limiting the yield of maize ( Zea mays  L.), especially having a significant impact on root growth, water absorption and ion homeostasis. Recent studies have shown that epigenetic regulatory mechanisms, especially DNA methylation, play a significant role in plants’ response to adverse stress. However, there is still a lack of systematic research on the dynamic changes of the whole-genome DNA methylation map of maize roots under salt stress conditions. This study reviews the physiological and molecular response characteristics of maize root systems under salt stress, as well as the biological functions of DNA methylation in plant stress responses. It introduces the types of methylation and their detection techniques, analyzes in detail the dynamic change characteristics and functional enrichment pathways of methylation profiles under salt stress, and compares the methylation differences between typical salt-tolerant and sensitive maize varieties. It reveals the possible mechanism of epigenetic regulation in the formation of salt tolerance. This study explored the dynamic regulatory mechanism of DNA methylation in maize roots under salt stress, providing a new perspective for a deeper understanding of plant epigenetic responses and also offering a theoretical basis and data support for the development of salt-tolerant maize varieties.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.696

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 head0.011
GPT teacher head0.252
Teacher spread0.241 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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