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Record W2539676704 · doi:10.1371/journal.pone.0164624

Genetics and Biochemistry of Zero-Tannin Lentils

2016· article· en· W2539676704 on OpenAlexafffund
Mahla Mirali, Randy W. Purves, Rob Stonehouse, Rui Song, Kirstin E. Bett, Albert Vandenberg

Bibliographic record

VenuePLoS ONE · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Gene Expression Analysis
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaSaskatchewan Pulse Growers
KeywordsProanthocyanidinBiologyMyricetinLocus (genetics)GeneTanninGeneticsPhenotypeBiochemistryStructural geneCondensed tanninFlavonoidCandidate geneBotanyPolyphenolAntioxidant

Abstract

fetched live from OpenAlex

The zero-tannin trait in lentil is controlled by a single recessive gene (tan) that results in a phenotype characterized by green stems, white flowers, and thin, transparent, or translucent seed coats. Genes that result in zero-tannin characteristics are useful for studies of seed coat pigmentation and biochemical characters because they have altered pigmentation. In this study, one of the major groups of plant pigments, phenolic compounds, was compared among zero-tannin and normal phenotypes and genotypes of lentil. Biochemical data were obtained by liquid chromatography-mass spectrometry (LC-MS). Genomic sequencing was used to identify a candidate gene for the tan locus. Phenolic compound profiling revealed that myricetin, dihydromyricetin, flavan-3-ols, and proanthocyanidins are only detected in normal lentil phenotypes and not in zero-tannin types. The molecular analysis showed that the tan gene encodes a bHLH transcription factor, homologous to the A gene in pea. The results of this study suggest that tan as a bHLH transcription factor interacts with the regulatory genes in the biochemical pathway of phenolic compounds starting from flavonoid-3',5'-hydroxylase (F3'5'H) and dihydroflavonol reductase (DFR).

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 score0.010
Threshold uncertainty score0.269

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 head0.218
Teacher spread0.199 · 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

Citations42
Published2016
Admission routes2
Has abstractyes

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