Faculty Opinions recommendation of Bacteriophage genes that inactivate the CRISPR/Cas bacterial immune system.
Bibliographic record
Abstract
A widespread system used by bacteria for protection against potentially dangerous foreign DNA molecules are the clustered regularly interspaced short palindromic repeats (CRISPR) loci coupled with cas (CRISPR-associated) genes 1 .Similar to RNA interference (RNAi) in eukaryotes 2 , these CRISPR/Cas systems utilize small RNAs for sequence-specific detection and neutralization of invading genomes 3 .Here we describe the first examples of genes that mediate the inhibition of a CRISPR/Cas system.Five distinct "anti-CRISPR" genes were found in the genomes of phages infecting Pseudomonas aeruginosa.Mutation of the anti-CRISPR gene of a phage rendered it unable to infect bacteria with a functional CRISPR/Cas system, and the addition of the same gene to the genome of a CRISPR/Cas-targeted phage allowed it to evade the CRISPR/Cas system.Phage-encoded anti-CRISPR genes may represent a widespread mechanism for phages to overcome the highly prevalent CRISPR/Cas systems.The existence of anti-CRISPR genes presents new avenues for the elucidation of CRISPR/Cas functional mechanisms and provides new insight into the coevolution of phages and bacteria.Predation by phages presents a major challenge to bacterial survival 4 , and bacteria have evolved numerous mechanisms to resist phage infection 5 .One such system is the CRISPR/Cas immune system, which is found in 48% of eubacteria and 95% of archaea 6 .CRISPR loci contain multiple repeated sequences of approximately 30 base pairs, separated by variable "spacer" sequences of similar length, which are often identical to segments of phage genomes or other mobile genetic elements 6 .The large single transcript from a CRISPR locus is processed within the repeat regions into small CRISPR RNAs (crRNAs) 7,8 that are complexed with Cas proteins 3,9 .Using the crRNAs as guides, crRNA/Cas complexes cleave foreign DNA molecules at sites bearing complementarity to the crRNAs,
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.111 | 0.066 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".