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Record W3149158769 · doi:10.1128/mbio.03338-20

Cooperation between Different CRISPR-Cas Types Enables Adaptation in an RNA-Targeting System

2021· article· en· W3149158769 on OpenAlexafffund
Ville Hoikkala, Janne Ravantti, César Díez‐Villaseñor, Marja Tiirola, Rachel A. Conrad, Mark J. McBride, Sylvain Moineau, Lotta‐Riina Sundberg

Bibliographic record

VenuemBio · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversité Laval
FundersKoneen SäätiöGovernment of CanadaJane ja Aatos Erkon SäätiöAcademy of FinlandNatural Sciences and Engineering Research Council of CanadaEuropean CommissionU.S. Department of Agriculture
KeywordsCRISPRAdaptation (eye)RNAComputational biologyComputer scienceBiologyGeneticsGeneNeuroscience

Abstract

fetched live from OpenAlex

CRISPR-Cas systems are immune systems that protect bacteria and archaea against their viruses, bacteriophages. Immunity is achieved through the acquisition of short DNA fragments from the viral invader's genome. These fragments, called spacers, are integrated into a memory bank on the bacterial genome called the CRISPR array. The spacers allow for the recognition of the same invader upon subsequent infection. Most CRISPR-Cas systems target DNA, but recently, systems that exclusively target RNA have been discovered. RNA-targeting CRISPR-Cas systems often lack genes necessary for spacer acquisition, and it is thus unknown how new spacers are acquired and if they can be acquired from DNA phages. Here, we show that an RNA-targeting system "borrows" acquisition machinery from another CRISPR-Cas locus in the genome. Most new spacers in this locus are unable to target phage mRNA and are therefore likely redundant. Our results reveal collaboration between distinct CRISPR-Cas types and raise further questions on how other CRISPR-Cas loci may cooperate.

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 score0.038
Threshold uncertainty score0.346

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 head0.014
GPT teacher head0.287
Teacher spread0.273 · 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

Citations53
Published2021
Admission routes2
Has abstractyes

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