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Record W2164998058 · doi:10.1534/genetics.113.160846

Simultaneous Expression of Multiple Proteins Under a Single Promoter in <i>Caenorhabditis elegans</i> via a Versatile 2A-Based Toolkit

2013· article· en· W2164998058 on OpenAlexfundno aff
Arnaud Ahier, Sophie Jarriault

Bibliographic record

VenueGenetics · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsnot available
FundersFondation Générale de SantéUniversity of British ColumbiaCentre National de la Recherche ScientifiqueFondation ARC pour la Recherche sur le CancerInstitute of GeneticsFondation pour la Recherche Médicale
KeywordsCaenorhabditis elegansBiologyComputational biologyTranslation (biology)GeneticsPlasmidCaenorhabditisGeneCell biologyMessenger RNA

Abstract

fetched live from OpenAlex

Caenorhabditis elegans is a powerful in vivo model in which transgenesis is highly developed. However, while the analysis of biological phenomena often require the expression of more than one protein of interest, no reliable tool exists to ensure efficient concomitant and equivalent expression of more than two polypeptides from a single promoter. We report the use of viral 2A peptides, which trigger a "ribosomal-skip" or "STOP&GO" mechanism during translation, to express multiple proteins from a single vector in C. elegans. Although none of the viruses known to infect C. elegans contain 2A-like sequences, our results show that 2A peptides allow the production of separate functional proteins in all cell types and at all developmental stages tested in the worm. In addition, we constructed a toolkit including a 2A-based polycistronic plasmid and reagents to generate 2A-tagged fosmids. 2A peptides constitute an important tool to ensure the delivery of multiple polypeptides in specific cells, enabling several novel applications such as the reconstitution of multi-subunit complexes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.006
GPT teacher head0.231
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations129
Published2013
Admission routes1
Has abstractyes

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