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Record W3025746691 · doi:10.1073/pnas.2004945117

Transcriptomic network analyses shed light on the regulation of cuticle development in maize leaves

2020· article· en· W3025746691 on OpenAlexaff
Pengfei Qiao, Richard Bourgault, Marc Mohammadi, Susanne Matschi, Glenn Philippe, Laurie G. Smith, Michael A. Gore, Isabel Molina, Michael J. Scanlon

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Surface Properties and Treatments
Canadian institutionsAlgoma University
FundersNational Science Foundation
KeywordsCutinCuticle (hair)BiologyPhyscomitrella patensLaser capture microdissectionEpidermis (zoology)Cell biologyMutantPlant cuticleBotanyArabidopsisMossWaxGeneBiochemistryAnatomyGene expression

Abstract

fetched live from OpenAlex

Plant cuticles are composed of wax and cutin and evolved in the land plants as a hydrophobic boundary that reduces water loss from the plant epidermis. The expanding maize adult leaf displays a dynamic, proximodistal gradient of cuticle development, from the leaf base to the tip. Laser microdissection RNA Sequencing (LM-RNAseq) was performed along this proximodistal gradient, and complementary network analyses identified potential regulators of cuticle biosynthesis and deposition. A weighted gene coexpression network (WGCN) analysis suggested a previously undescribed function for PHYTOCHROME-mediated light signaling during the regulation of cuticular wax deposition. Genetic analyses reveal that phyB1 phyB2 double mutants of maize exhibit abnormal cuticle composition, supporting the predictions of our coexpression analysis. Reverse genetic analyses also show that phy mutants of the moss Physcomitrella patens exhibit abnormal cuticle composition, suggesting an ancestral role for PHYTOCHROME-mediated, light-stimulated regulation of cuticle development during plant evolution.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.106

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.114
GPT teacher head0.276
Teacher spread0.162 · 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

Citations45
Published2020
Admission routes1
Has abstractyes

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