Phages Fight Back: Inactivation of the CRISPR-Cas Bacterial Immune System by Anti-CRISPR Proteins
Bibliographic record
Abstract
Phage infection poses a major threat to bacterial survival, and bacteria have evolved many mechanisms to protect against it. One such system is the CRISPR-Cas system, which utilizes sequence memory to protect bacteria from phage infection. CRISPR-Cas is a highly specific adaptive defense mechanism that protects against invasion by all mobile genetic elements, including phages, plasmids, and conjugative elements [1,2]. CRISPRs were first recognized in Escherichia coli in 1987 [3] and have since been identified in the genomes of approximately 48% of eubacteria and 95% of archaea [4]. Their widespread occurrence highlights the important role they play in the evolution of microbial and phage genomes. CRISPR-Cas systems are composed of clustered regularly interspaced short palindromic repeats (CRISPR) separated by short “spacer” sequences, and CRISPR-associated (cas) genes. CRISPR systems are classified according to their gene complement and mechanism of action [2]. There are several different types (I, II, III) and subtypes (e.g., I-E, I-F) of CRISPR-Cas systems, most of which target DNA. The CRISPR system functions by incorporating one or more CRISPR spacers at the leader end of a CRISPR locus when a novel phage or other mobile genetic element infects the bacterial cell. These spacers are derived from the DNA of the invading phage, and their introduction into the CRISPR locus provides immunity to further infection by that phage. This adaptive immunity serves to protect the bacterial progeny; if the same phage is encountered in the future, the CRISPR-Cas system will be activated and the foreign genome destroyed. In turn, the phages mutate to evade CRISPR targeting, thereby creating a constant evolutionary battle between them and the bacteria they infect.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".