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Record W4411669863 · doi:10.3389/fpls.2025.1571825

Phenylpropanoids metabolism: recent insight into stress tolerance and plant development cues

2025· review· en· W4411669863 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Plant Science · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Gene Expression Analysis
Canadian institutionsBiotechnology Research Institute
FundersChinese Academy of Agricultural Sciences
KeywordsPhenylpropanoidAbiotic componentAbiotic stressBiotic stressBiologyLigninSecondary metabolismPlant metabolismDrought toleranceComputational biologyBiotechnologyBotanyBiosynthesisBiochemistryEcologyGene

Abstract

fetched live from OpenAlex

The phenylpropanoid pathway remains a key target for most climate-resilient crop development, owing to it being a precursor to over 8000 metabolites, including flavonoids and lignin compounds, including their derivatives. These metabolites are involved in biotic and abiotic stress tolerance, inviting several studies into their roles in plant defense, drought, temperature, UV, and nutrient stress tolerance. Literature is currently inundated with cutting-edge reports on the phenylpropanoid pathways and their functions. Here, we provide a comprehensive update on the biosynthesis of phenylpropanoids, mainly lignin and flavonoids, their roles in biotic and abiotic interaction, and transcending topics, including pest and diseases, drought, temperature, and UV stress tolerance. We further reviewed the post-transcriptional, post-translational, and epigenetic modifications regulating phenylpropanoid metabolism and highlighted their applications and optimization strategies for large-scale production. This review provides an all-inclusive update on recent reports on the metabolism of phenylpropanoids in plants.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.941
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.012
GPT teacher head0.269
Teacher spread0.256 · 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