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Record W4389935020 · doi:10.1021/acssynbio.3c00399

An Expanded Genetic Toolbox to Accelerate the Creation of <i>Acholeplasma laidlawii</i> Driven by Synthetic Genomes

2023· article· en· W4389935020 on OpenAlexafffund
Daniel P. Nucifora, Nidhi D. Mehta, Daniel J. Giguere, Bogumil J. Karas

Bibliographic record

VenueACS Synthetic Biology · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaUniversiteit van AmsterdamGovernment of Canada
KeywordsMollicutesGenomeBiologyElectroporationTransposable elementSynthetic biologyGeneticsMobile genetic elementsBacterial genome sizeGeneComputational biologyBacteria

Abstract

fetched live from OpenAlex

We have developed genetic tools for the atypical bacterium Acholeplasma laidlawii . A. laidlawii is a member of the class Mollicutes, which lacks cell walls, has small genomes, and has limited metabolic capabilities, requiring many metabolites from their hosts. Several of these traits have facilitated the development of genome transplantation for some Mollicutes, consequently enabling the generation of synthetic cells. Here, we propose the development of genome transplantation for A. laidlawii . We first investigated a donor–recipient relationship between two strains, PG-8A and PG-8195, through whole-genome sequencing. We then created multihost shuttle plasmids and used them to optimize an electroporation protocol. We also evolved a superior strain for DNA uptake via electroporation. We created a PG-8A donor strain with a Tn5 transposon carrying a tetracycline resistance gene. These tools will enhance Acholeplasma research and accelerate the effort toward creating A. laidlawii strains with synthetic genomes.

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 categoriesInsufficient payload (model declined to judge)
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 score0.099
Threshold uncertainty score0.999

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)0.0010.001

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 head0.019
GPT teacher head0.296
Teacher spread0.277 · 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.

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

Citations2
Published2023
Admission routes2
Has abstractyes

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