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Record W4401503221 · doi:10.1101/pdb.prot108434

Compositional Analysis of Cutin in Maize Leaves

2024· article· en· W4401503221 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCold Spring Harbor Protocols · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Surface Properties and Treatments
Canadian institutionsEssar Steel Algoma (Canada)Algoma University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsNational Science Foundation
KeywordsCutinDepolymerizationChemistryWaxGas chromatographyChromatographyMonomerSuberinCuticle (hair)Flame ionization detectorGas chromatography–mass spectrometryOrganic chemistryMass spectrometryBiochemistryPolymerLignin

Abstract

fetched live from OpenAlex

The cuticle is a lipid barrier that covers the air-exposed surfaces of plants. It consists of waxes and cutin, a cell wall–attached lipid polyester of oxygenated fatty acids and glycerol. Unlike waxes, cutin is insoluble in organic solvents, and its composition is typically studied by chemical depolymerization followed by monomer analysis by gas chromatography (GC). Here, we describe a method for the chemical depolymerization of cutin in maize leaves and subsequent compositional analysis of the constituent lipid monomers. The method has been adapted from protocols for cutin analysis developed for Arabidopsis , by both optimizing the amount of leaf tissue used and including a data analysis process specific to the monomers present in maize cutin. The approach uses base-catalyzed transmethylation, which produces fatty acid methyl esters, and silylation, which gives trimethylsilyl ether derivatives of hydroxyl groups for gas chromatographic analysis. For monomer identification, a few representative samples are first analyzed by GC–mass spectrometry (GC-MS). This is then followed by analysis of all replicates by gas chromatography coupled to a flame ionization detector (GC-FID) for monomer quantification, because the flame ionization detector provides a linear response over a wide mass range, is relatively simple to operate, and is more cost-effective to maintain compared to mass spectrometry detectors. Although the protocol bypasses time-consuming cuticle isolation steps by using whole-leaf samples, this means that a fraction of the compounds in the chromatographic profiles do not derive from cutin. Accordingly, we discuss some considerations for the interpretation of the resulting depolymerization products. Our protocol offers specific guidance on preparing maize leaf samples, ensuring reproducible results, and enabling the detection of subtle variations in cutin monomer composition among plant genotypes or developmental stages.

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)0.000
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 score0.773
Threshold uncertainty score0.219

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 head0.028
GPT teacher head0.258
Teacher spread0.231 · 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