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Mg<sup>2+</sup>-free buffer elevates transformation efficiency of<i>Vibrio parahaemolyticus</i>by electroporation

2009· article· en· W2107594265 on OpenAlexaff
H. Wang, Mansel W. Griffiths

Bibliographic record

VenueLetters in Applied Microbiology · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of GuelphCanadian Institute for Public Safety Research and Treatment
Fundersnot available
KeywordsElectroporationVibrio parahaemolyticusTransformation efficiencyNucleaseTransformation (genetics)ExtracellularBiologySucroseMicrobiologyVibrioPlasmidDNAChemistryBacteriaBiochemistryGeneAgrobacteriumGenetics

Abstract

fetched live from OpenAlex

AIMS: The ability to transform Vibrio spp. is limited by the extracellular nuclease that their cells secrete. The reported transformation efficiency of this organism is 10(2)-10(5) transformants per microgram DNA. We tried different buffers and conditions, aiming to elevate its transformation efficiency. METHODS AND RESULTS: MgCl(2) and sucrose are often included in the washing and/or electroporation buffers to stabilize the cell membrane. However, Mg(2+) is required for production and activity of the extracellular nuclease. A simple electroporation buffer lacking Mg(2+) was found to increase transformation efficiency dramatically, to levels 50-fold more than the buffers containing Mg(2+). To maintain the stability of the cell membranes, Mg(2+) was replaced with high concentrations of sucrose, from 272 to 408 mmol l(-1). With the new buffers, the transformation efficiency of Vibrio parahaemolyticus was increased to 2.2 x 10(6) transformants per microgram DNA. CONCLUSIONS: Mg(2+) in the buffer adversely affected transformation of V. parahaemolyticus by electroporation. The cell membranes of vibrio can be stabilized by high concentration of sucrose when Mg(2+) is absent. SIGNIFICANCE AND IMPACT OF THE STUDY: A greater transformation efficiency can facilitate the genetic analysis of an organism and its pathogenicity. Buffers lacking Mg(2+) can be used for other nuclease-producing organisms.

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 score0.071
Threshold uncertainty score0.772

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 head0.002
GPT teacher head0.217
Teacher spread0.215 · 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

Citations22
Published2009
Admission routes1
Has abstractyes

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