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Record W2806930066 · doi:10.18331/brj2018.5.2.3

Lipid accumulation from glucose and xylose in an engineered, naturally oleaginous strain of Saccharomyces cerevisiae

2018· article· en· W2806930066 on OpenAlexvenueno aff
Eric P. Knoshaug, Stefanie Van Wychen, Arjun Singh, Min Zhang

Bibliographic record

VenueBiofuel Research Journal · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsnot available
FundersOffice of Energy EfficiencyOffice of Energy Efficiency and Renewable EnergyNational Renewable Energy LaboratoryU.S. Department of Energy
KeywordsXyloseSaccharomyces cerevisiaeBiochemistryChemistryFermentationOleic acidPalmitic acidStrain (injury)YeastDiacylglycerol kinaseStearic acidPalmitoleic acidFatty acidFood scienceBiologyKinaseProtein kinase COrganic chemistry

Abstract

fetched live from OpenAlex

Saccharomyces cerevisiae, a well-known industrial yeast for alcoholic fermentation, is not historically known to accumulate lipids. Four S. cerevisiae strains used in industrial applications were screened for their ability to accumulate neutral lipids. Only one, D5A, was found to accumulate up to 20% dry cell weight (dcw) lipids. This strain was further engineered by knocking out ADP-activated serine/threonine kinase (SNF1) which increased lipid accumulation to 35% dcw lipids. In addition, we engineered D5A to utilize xylose and found that D5A accumulates up to 37% dcw lipids from xylose as the sole carbon source. Further we over-expressed different diacylglycerol acyltransferase (DGA1) genes and boosted lipid accumulation to 50%. Fatty acid speciation showed that 94% of the extracted lipids consisted of 5 fatty acid species, C16:0 (palmitic), C16:1n7 (palmitoleic), C18:0 (stearic), C18:1n7 (vaccenic), and C18:1n9 (oleic), while the relative distributions changed depending on growth conditions. In addition, this strain accumulated lipids concurrently with ethanol production.

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)0.001
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 score0.077
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.048
GPT teacher head0.361
Teacher spread0.313 · 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

Citations25
Published2018
Admission routes1
Has abstractyes

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