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Record W2807553904 · doi:10.1038/s41598-017-18074-7

Development of a high-throughput assay for rapid screening of butanologenic strains

2018· article· en· W2807553904 on OpenAlexfundno aff
Chidozie Victor Agu, Stella M. Lai, Victor Ujor, Pradip Kumar Biswas, A. G. Jones, Venkat Gopalan, Thaddeus Chukwuemeka Ezeji

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsnot available
FundersOhio Agricultural Research and Development Center, Ohio State UniversityWageningen University and ResearchCenter for Applied Plant Sciences, Ohio State UniversityUniversity of WaterlooOregon State University
KeywordsButanolAlcohol dehydrogenaseFermentationHigh-throughput screeningEthanolChemistryAcetoneChromatographyBiochemistryAcetic acidAlcohol

Abstract

fetched live from OpenAlex

Abstract We report a Thermotoga hypogea ( Th ) alcohol dehydrogenase (ADH)-dependent spectrophotometric assay for quantifying the amount of butanol in growth media, an advance that will facilitate rapid high-throughput screening of hypo- and hyper-butanol-producing strains of solventogenic Clostridium species. While a colorimetric nitroblue tetrazolium chloride-based assay for quantitating butanol in acetone-butanol-ethanol (ABE) fermentation broth has been described previously, we determined that Saccharomyces cerevisiae ( Sc ) ADH used in this earlier study exhibits approximately 13-fold lower catalytic efficiency towards butanol than ethanol. Any Sc ADH-dependent assay for primary quantitation of butanol in an ethanol-butanol mixture is therefore subject to “ethanol interference”. To circumvent this limitation and better facilitate identification of hyper-butanol-producing Clostridia , we searched the literature for native ADHs that preferentially utilize butanol over ethanol and identified Th ADH as a candidate. Indeed, recombinant Th ADH exhibited a 6-fold higher catalytic efficiency with butanol than ethanol, as measured using the reduction of NADP + to NADPH that accompanies alcohol oxidation. Moreover, the assay sensitivity was not affected by the presence of acetone, acetic acid or butyric acid (typical ABE fermentation products). We broadened the utility of our assay by adapting it to a high-throughput microtiter plate-based format, and piloted it successfully in an ongoing metabolic engineering initiative.

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 score0.091
Threshold uncertainty score0.298

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 head0.015
GPT teacher head0.245
Teacher spread0.230 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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