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Record W1975982655 · doi:10.1007/s10295-010-0915-2

Construction and functional screening of a metagenomic library using a T7 RNA polymerase-based expression cosmid vector

2010· article· en· W1975982655 on OpenAlexaff
François-Xavier Lussier, Olivier Chambenoit, Amélie Côté, Jean-François Hupé, François Denis, Pierre Juteau, Réjean Beaudet, François Shareck

Bibliographic record

VenueJournal of Industrial Microbiology & Biotechnology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsCosmidBiologyShuttle vectorHeterologous expressionMetagenomicsEscherichia coliGeneGenomic libraryRNA polymeraseInsert (composites)T7 RNA polymeraseComputational biologyMolecular biologyGeneticsVector (molecular biology)Recombinant DNABacteriophage

Abstract

fetched live from OpenAlex

The metagenomic approach has greatly accelerated the discovery of new enzymes by giving access to the genetic potential of microorganisms from various environments. Function-based screening depends on adequate expression of the foreign genes in the heterologous host, which can be challenging in large-insert libraries. In this study, the shuttle cosmid vector pFX583 was used for the construction and screening of a metagenomic library. This vector allows T7 RNA polymerase-directed transcription of the cloned DNA and can be used in Escherichia coli and Streptomyces lividans. The DNA used for the library construction was obtained from an enriched biomass. The library was screened for lipolytic and proteolytic activities using E. coli and S. lividans as hosts. Numerous E. coli clones with lipolytic activity were detected. Unfortunately, proteases could not be detected in both hosts. From the lipolytic activity screen, a gene coding for a new lipase was isolated, and partial characterization was conducted.

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 score0.017
Threshold uncertainty score0.826

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 integrity0.0010.001
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 head0.014
GPT teacher head0.204
Teacher spread0.190 · 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

Citations24
Published2010
Admission routes1
Has abstractyes

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