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Record W3089183881 · doi:10.1111/lam.13391

Disruption of <i>glpF</i> gene encoding the glycerol facilitator improves 1,3‐propanediol production from glucose via glycerol in <i>Escherichia coli</i>

2020· article· en· W3089183881 on OpenAlexfundno aff
Rintaro Sato, Tomonari Tanaka, Hitomi Ohara, Yuji Aso

Bibliographic record

VenueLetters in Applied Microbiology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsnot available
FundersAdvanced Low Carbon Technology Research and Development ProgramInstitute of GeneticsYale University
KeywordsGlycerolEscherichia coliBiochemistryBiologyChemistryFood scienceGene

Abstract

fetched live from OpenAlex

Engineered Escherichia coli has recently been applied to produce 1,3-propanediol (1,3-PDO) from glucose. A metabolic intermediate in the production pathway, glycerol, is partially secreted into the extracellular of E. coli through a glycerol facilitator encoded by glpF, and this secretion consequently decreases 1,3-PDO production. Therefore, we aimed to determine whether disrupting the glpF gene would improve 1,3-PDO production in E. coli. The intracellular glycerol concentration in a glpF-disruptant was 7·5 times higher than in a non-disruptant. The glpF-disrupted and non-disrupted E. coli strains produced 0·26 and 0·09 g l−1 of 1,3-PDO, respectively, from 1% glucose after 72 h of cultivation. The specific growth rate (μ) and the 1,3-PDO yield from glucose (YP/S) in the disruptant were higher than those in the non-disruptant (ΔglpF, μ = 0·08 ± 0·00 h−1, YP/S = 0·06 mol mol-glucose−1; BW25113, μ = 0·06 ± 0·00 h−1, YP/S = 0·02 mol mol-glucose−1). Disruption of the glpF gene decreased the production of the by-product, acetic acid. These results indicated that disruption of glpF increased the intracellular concentration of glycerol and consequently increased 1,3-PDO production in E. coli.

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 score0.006
Threshold uncertainty score0.844

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 head0.006
GPT teacher head0.188
Teacher spread0.182 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

Explore more

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