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Record W3202952473 · doi:10.3390/v13101925

Comparison of CRISPR–Cas9 Tools for Transcriptional Repression and Gene Disruption in the BEVS

2021· article· en· W3202952473 on OpenAlexafffund
Mark R. Bruder, Sadru‐Dean Walji, Marc G. Aucoin

Bibliographic record

VenueViruses · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicViral Infectious Diseases and Gene Expression in Insects
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCRISPRRecombineeringCas9BiologyGenome editingGenePsychological repressionCRISPR interferenceRegulation of gene expressionGeneticsReporter geneRNA interferenceComputational biologyGenomeGene expressionRNA

Abstract

fetched live from OpenAlex

The generation of knock-out viruses using recombineering of bacmids has greatly accelerated scrutiny of baculovirus genes for a variety of applications. However, the CRISPR–Cas9 system is a powerful tool that simplifies sequence-specific genome editing and effective transcriptional regulation of genes compared to traditional recombineering and RNAi approaches. Here, the effectiveness of the CRISPR–Cas9 system for gene disruption and transcriptional repression in the BEVS was compared. Cell lines constitutively expressing the cas9 or dcas9 gene were developed, and recombinant baculoviruses delivering the sgRNA were evaluated for disruption or repression of a reporter green fluorescent protein gene. Finally, endogenous AcMNPV genes were targeted for disruption or downregulation to affect gene expression and baculovirus replication. This study provides a proof-of-concept that CRISPR–Cas9 technology may be an effective tool for efficient scrutiny of baculovirus genes through targeted gene disruption and transcriptional repression.

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 score0.032
Threshold uncertainty score0.203

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 head0.075
GPT teacher head0.390
Teacher spread0.316 · 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

Citations12
Published2021
Admission routes2
Has abstractyes

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