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Record W3212233672 · doi:10.1038/s41467-021-26989-z

Gene editing with CRISPR-Cas12a guides possessing ribose-modified pseudoknot handles

2021· article· en· W3212233672 on OpenAlexaff
Eman A. Ageely, Ramadevi Chilamkurthy, Sunit Kumar Jana, Leonora Abdullahu, Daniel O’Reilly, Philip J. Jensik, Masad J. Damha, Keith T. Gagnon

Bibliographic record

VenueNature Communications · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsMcGill University
FundersNational Institute of General Medical SciencesNational Institutes of HealthFoundation for the National Institutes of Health
KeywordsPseudoknotCRISPRRNAComputational biologyGenome editingTrans-activating crRNANucleotideRiboseGeneCas9ChemistryCRISPR interferenceBiologyCombinatorial chemistryBiochemistryEnzyme

Abstract

fetched live from OpenAlex

CRISPR-Cas12a is a leading technology for development of model organisms, therapeutics, and diagnostics. These applications could benefit from chemical modifications that stabilize or tune enzyme properties. Here we chemically modify ribonucleotides of the AsCas12a CRISPR RNA 5' handle, a pseudoknot structure that mediates binding to Cas12a. Gene editing in human cells required retention of several native RNA residues corresponding to predicted 2'-hydroxyl contacts. Replacing these RNA residues with a variety of ribose-modified nucleotides revealed 2'-hydroxyl sensitivity. Modified 5' pseudoknots with as little as six out of nineteen RNA residues, with phosphorothioate linkages at remaining RNA positions, yielded heavily modified pseudoknots with robust cell-based editing. High trans activity was usually preserved with cis activity. We show that the 5' pseudoknot can tolerate near complete modification when design is guided by structural and chemical compatibility. Rules for modification of the 5' pseudoknot should accelerate therapeutic development and be valuable for CRISPR-Cas12a diagnostics.

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 score0.250
Threshold uncertainty score0.582

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 head0.017
GPT teacher head0.344
Teacher spread0.327 · 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

Citations35
Published2021
Admission routes1
Has abstractyes

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