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Record W2912169624 · doi:10.1186/s13068-019-1358-2

Identification of genes associated with ricinoleic acid accumulation in Hiptage benghalensis via transcriptome analysis

2019· article· en· W2912169624 on OpenAlexafffund
Bo Tian, Tianquan Lu, Yang Xu, Ruling Wang, Guanqun Chen

Bibliographic record

VenueBiotechnology for Biofuels · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid metabolism and biosynthesis
Canadian institutionsUniversity of Alberta
FundersKunming Institute of Botany, Chinese Academy of SciencesNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaCanada Research Chairs
KeywordsTranscriptomeRicinoleic acidIdentification (biology)GeneBiologyBiotechnologyComputational biologyBotanyGeneticsGene expressionBiochemistry

Abstract

fetched live from OpenAlex

Ricinoleic acid is a high-value hydroxy fatty acid with broad industrial applications. Hiptage benghalensis seed oil contains a high amount of ricinoleic acid (~ 80%) and represents an emerging source of this unusual fatty acid. However, the mechanism of ricinoleic acid accumulation in H. benghalensis is yet to be explored at the molecular level, which hampers the exploration of its potential in ricinoleic acid production. To explore the molecular mechanism of ricinoleic acid biosynthesis and regulation, H. benghalensis seeds were harvested at five developing stages (13, 16, 19, 22, and 25 days after pollination) for lipid analysis. The results revealed that the rapid accumulation of ricinoleic acid occurred at the early–mid-seed development stages (16–22 days after pollination). Subsequently, the gene transcription profiles of the developing seeds were characterized via a comprehensive transcriptome analysis with second-generation sequencing and single-molecule real-time sequencing. Differential expression patterns were identified in 12,555 transcripts, including 71 enzymes in lipid metabolic pathways, 246 putative transcription factors (TFs) and 124 long noncoding RNAs (lncRNAs). Twelve genes involved in diverse lipid metabolism pathways, including fatty acid biosynthesis and modification (hydroxylation), lipid traffic, triacylglycerol assembly, acyl editing and oil-body formation, displayed high expression levels and consistent expression patterns with ricinoleic acid accumulation in the developing seeds, suggesting their primary roles in ricinoleic acid production. Subsequent co-expression network analysis identified 57 TFs and 35 lncRNAs, which are putatively involved in the regulation of ricinoleic acid biosynthesis. The transcriptome data were further validated by analyzing the expression profiles of key enzyme-encoding genes, TFs and lncRNAs with quantitative real-time PCR. Finally, a network of genes associated with ricinoleic acid accumulation in H. benghalensis was established. This study was the first step toward the understating of the molecular mechanisms of ricinoleic acid biosynthesis and oil accumulation in H. benghalensis seeds and identified a pool of novel genes regulating ricinoleic acid accumulation. The results set a foundation for developing H. benghalensis into a novel ricinoleic acid feedstock at the transcriptomic level and provided valuable candidate genes for improving ricinoleic acid production in other plants.

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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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

Citations60
Published2019
Admission routes2
Has abstractyes

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