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Record W4384523402 · doi:10.3389/fpls.2023.1204016

Identification of new potential molecular actors related to fiber quality in flax through Omics

2023· article· en· W4384523402 on OpenAlexfundno aff
Malika Chabi, Estelle Goulas, Dmitry Galinousky, Anne‐Sophie Blervacq, Anca Lucau‐Danila, Godfrey Neutelings, Sébastien Grec, Arnaud Day, Brigitte Chabbert, Katharina Haag, Jörg Müssig, Sandrine Arribat, Sébastien Planchon, Jenny Renaut, Simon Hawkins

Bibliographic record

VenueFrontiers in Plant Science · 2023
Typearticle
Languageen
FieldMedicine
TopicPhytoestrogen effects and research
Canadian institutionsnot available
FundersFP7 Food, Agriculture and Fisheries, BiotechnologyNatural Sciences and Engineering Research Council of CanadaAgence Nationale de la RechercheMinisterio de Economía y CompetitividadBundesministerium für Bildung und ForschungUniversité de LilleGenome Canada
KeywordsIdentification (biology)Quality (philosophy)BiologyComputational biologyFiberOmicsBiotechnologyData scienceGeneticsBotanyComputer scienceChemistry

Abstract

fetched live from OpenAlex

One of the biggest challenges for a more widespread utilization of plant fibers is to better understand the different molecular factors underlying the variability in fineness and mechanical properties of both elementary and scutched fibers. Accordingly, we analyzed genome-wide transcription profiling from bast fiber bearing tissues of seven different flax varieties (4 spring, 2 winter fiber varieties and 1 winter linseed) and identified 1041 differentially expressed genes between varieties, of which 97 were related to cell wall metabolism. KEGG analysis highlighted a number of different enriched pathways. Subsequent statistical analysis using Partial Least-Squares Discriminant Analysis showed that 73% of the total variance was explained by the first 3 X-variates corresponding to 56 differentially expressed genes. Calculation of Pearson correlations identified 5 genes showing a strong correlation between expression and morphometric data. Two-dimensional gel proteomic analysis on the two varieties showing the most discriminant and significant differences in morphometrics revealed 1490 protein spots of which 108 showed significant differential abundance. Mass spectrometry analysis successfully identified 46 proteins representing 32 non-redundant proteins. Statistical clusterization based on the expression level of genes corresponding to the 32 proteins showed clear discrimination into three separate clusters, reflecting the variety type (spring-/winter-fiber/oil). Four of the 32 proteins were also highly correlated with morphometric features. Examination of predicted functions for the 9 (5 + 4) identified genes highlighted lipid metabolism and senescence process. Calculation of Pearson correlation coefficients between expression data and retted fiber mechanical measurements (strength and maximum force) identified 3 significantly correlated genes. The genes were predicted to be connected to cell wall dynamics, either directly (Expansin-like protein), or indirectly (NAD(P)-binding Rossmann-fold superfamily protein). Taken together, our results have allowed the identification of molecular actors potentially associated with the determination of both in-planta fiber morphometrics, as well as ex-planta fiber mechanical properties, both of which are key parameters for elementary fiber and scutched fiber quality in flax.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.345
Teacher spread0.322 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2023
Admission routes1
Has abstractyes

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