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Record W3110743609 · doi:10.1186/s12870-021-02948-0

Parallel analysis of global garlic gene expression and alliin content following leaf wounding

2021· article· en· W3110743609 on OpenAlexfundno aff
Xuqin Yang, Yiren Su, Jiaying Wu, Wen Wan, Huijian Chen, Xiaoying Cao, Junjuan Wang, Zhong Zhang, Youzhi Wang, Deliang Ma, Gary J. Loake, Jihong Jiang

Bibliographic record

VenueBMC Plant Biology · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGarlic and Onion Studies
Canadian institutionsnot available
FundersInstitute of Genetics and Developmental Biology, Chinese Academy of SciencesBiotechnology and Biological Sciences Research CouncilShanghai Jiao Tong UniversityJiangsu Normal UniversityGovernment of Jiangsu ProvinceChinese Academy of SciencesInstitute of GeneticsNational Natural Science Foundation of China
KeywordsAlliinAllicinAllium sativumBiosynthesisSerineBiologyBiochemistryCysteineTranscriptomeGene expressionGeneBotanyEnzyme

Abstract

fetched live from OpenAlex

BACKGROUND: Allium sativum (garlic) is an economically important food source and medicinal plant rich in sulfides and other protective substances such as alliin, the precursor of allicin biosynthesis. Cysteine, serine and sulfur is the precursor of alliin biosynthesis. However, little is known about the alliin content under abiotic stress or the mechanism by which it is synthesized. RESULTS: The findings revealed that the content of alliin was lowest in the garlic roots, and highest in the buds. Furthermore, alliin levels decreased in mature leaves following wounding. Transcriptome data generated over time after wounding further revealed significant up-regulation of genes integral to the biosynthetic pathways of cysteine and serine in mature garlic leaves. CONCLUSIONS: The findings suggest that differential expression of cysteine, serine and sulfide-related genes underlies the accumulation of alliin and its precursors in garlic, providing a basis for further analyses of alliin biosynthesis.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.138

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 head0.081
GPT teacher head0.267
Teacher spread0.187 · 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 designObservational
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

Citations14
Published2021
Admission routes1
Has abstractyes

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