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Record W2991193264 · doi:10.1038/s41467-019-13403-y

CRISPR-Switch regulates sgRNA activity by Cre recombination for sequential editing of two loci

2019· article· en· W2991193264 on OpenAlexaff
Krzysztof Chylinski, Maria Hubmann, Ruth E. Hanna, Connor Yanchus, Georg Michlits, Esther C. H. Uijttewaal, John G. Doench, Daniel Schramek, Ulrich Elling

Bibliographic record

VenueNature Communications · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsCRISPRGenome editingCas9Subgenomic mRNAGuide RNABiologyComputational biologyHomologous recombinationGeneticsGene

Abstract

fetched live from OpenAlex

CRISPR-Cas9 is an efficient and versatile tool for genome engineering in many species. However, inducible CRISPR-Cas9 editing systems that regulate Cas9 activity or sgRNA expression often suffer from significant limitations, including reduced editing capacity, off-target effects, or leaky expression. Here, we develop a precisely controlled sgRNA expression cassette that can be combined with widely-used Cre systems, termed CRISPR-Switch (SgRNA With Induction/Termination by Cre Homologous recombination). Switch-ON facilitates controlled, rapid induction of sgRNA activity. In turn, Switch-OFF-mediated termination of editing improves generation of heterozygous genotypes and can limit off-target effects. Furthermore, we design sequential CRISPR-Switch-based editing of two loci in a strictly programmable manner and determined the order of mutagenic events that leads to development of glioblastoma in mice. Thus, CRISPR-Switch substantially increases the versatility of gene editing through precise and rapid switching ON or OFF sgRNA activity, as well as switching OVER to secondary sgRNAs.

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 score0.050
Threshold uncertainty score0.397

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 head0.010
GPT teacher head0.345
Teacher spread0.335 · 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

Citations45
Published2019
Admission routes1
Has abstractyes

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