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Record W2805400981 · doi:10.1038/s41467-018-05092-w

Widespread anti-CRISPR proteins in virulent bacteriophages inhibit a range of Cas9 proteins

2018· article· en· W2805400981 on OpenAlexafffund
Alexander P. Hynes, Geneviève M. Rousseau, Daniel Agudelo, Adeline Goulet, Béatrice Amigues, Jérémy Loehr, Dennis Romero, Christophe Fremaux, Philippe Horvath, Yannick Doyon, Christian Cambillau, Sylvain Moineau

Bibliographic record

VenueNature Communications · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsCentre hospitalier universitaire de QuébecUniversité Laval
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of Canada
KeywordsCRISPRStreptococcus thermophilusVirulenceCas9GenomeBiologyGenome editingComputational biologyGeneticsGeneBacteria

Abstract

fetched live from OpenAlex

CRISPR-Cas systems are bacterial anti-viral systems, and bacterial viruses (bacteriophages, phages) can carry anti-CRISPR (Acr) proteins to evade that immunity. Acrs can also fine-tune the activity of CRISPR-based genome-editing tools. While Acrs are prevalent in phages capable of lying dormant in a CRISPR-carrying host, their orthologs have been observed only infrequently in virulent phages. Here we identify AcrIIA6, an Acr encoded in 33% of virulent Streptococcus thermophilus phage genomes. The X-ray structure of AcrIIA6 displays some features unique to this Acr family. We compare the activity of AcrIIA6 to those of other Acrs, including AcrIIA5 (also from S. thermophilus phages), and characterize their effectiveness against a range of CRISPR-Cas systems. Finally, we demonstrate that both Acr families from S. thermophilus phages inhibit Cas9-mediated genome editing of human cells.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.322
Teacher spread0.311 · 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
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

Citations206
Published2018
Admission routes2
Has abstractyes

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Same venueNature CommunicationsSame topicCRISPR and Genetic EngineeringFrench-language works237,207