MétaCan
Menu
Back to cohort
Record W4415954479 · doi:10.1016/j.isci.2025.113963

Ectopic expression of pectate lyase PtxtPL1-27 in aspen affects leaf cuticle development

2025· article· en· W4415954479 on OpenAlexfundno aff
Ajaya K. Biswal, Alicja Banasiak, Josefina-Patricia Fernández-Moreno, Madhusree Mitra, Jesper Harholt, Marta Derba‐Maceluch, Mateusz Majda, Sunita Kushwah, Vikash Kumar, Ilka N. Abreu, Pramod Sivan, Sivakumar Pattathil, Peter Immerzeel, András Gorzsás, Thomas Möritz, Henrik Vibe Scheller, Asaph Aharoni, Ewa J. Mellerowicz

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Surface Properties and Treatments
Canadian institutionsnot available
FundersCenter for Bioenergy InnovationBiological and Environmental ResearchStiftelsen för Strategisk ForskningLawrence Berkeley National LaboratoryVetenskapsrådetKnut och Alice Wallenbergs StiftelseSvenska Forskningsrådet FormasOffice of ScienceVINNOVAStiftelsen för Strategisk ForskningCanada Research Coordinating CommitteeEuropean Molecular Biology OrganizationSkogs- och Jordbrukets ForskningsrådU.S. Department of Energy
KeywordsCutinCuticle (hair)Cell wallPectate lyaseSuberinEpidermis (zoology)UltrastructurePlant cuticle

Abstract

fetched live from OpenAlex

PL1-27 had pleiotropic effects on shoot development, including the reduction of cuticle thickness and changes in cutin and wax composition, but the expression of cutin biosynthetic genes was little affected. Despite a reduction in homogalacturonan content in the leaves, labeling with the homogalacturonan-specific antibody JIM5 in the outer epidermal cell wall layer increased and displayed an altered pattern. Moreover, the ultrastructure of cell walls was changed concomitant with lipid accumulation. We propose that the disruption of homogalacturonan integrity affected the cutinsome-dependent transport and polymerization of cutin monomers in the cell wall.

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 score0.368
Threshold uncertainty score0.101

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 head0.019
GPT teacher head0.226
Teacher spread0.206 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueiScienceSame topicPlant Surface Properties and TreatmentsFrench-language works237,207