Anti-CRISPRs and CRISPR-Cas: Characterization and Biotechnology
Bibliographic record
Abstract
Abstract Bacteria and phages (bacterial specific viruses) have been undergoing an evolutionary arms race for billions of years, whereby bacteria evolve mechanisms to inhibit phage infection and phages evolve mechanisms to evade bacterial defenses. Consequently, there are several mechanisms used by bacteria to inhibit phage infection. These include inhibiting phage adsorption, restriction modification systems, and the more recently discovered CRISPR-Cas immune systems. Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR) loci together with their accompanying CRISPR-associated (Cas) genes form the only known bacterial adaptive defense mechanism that effectively protects against the transfer of mobile genetic elements (MGEs) such as bacteriophages. CRISPR-Cas systems use an RNA-guided nuclease to bind and cleave foreign DNA, presenting a powerful barrier to phage infection. This strong evolutionary barrier led phages to evolve small protein inhibitors of CRISPR-Cas called anti-CRISPRs. In the first section of my work, I characterize the structure, function, and mechanism of action for an anti-CRISPR that inhibits the type I-E CRISPR-Cas system of Pseudomonas aeruginosa strain 4386. I show that beyond simply inhibiting the CRISPR-Cas system, anti-CRISPR AcrIE2 converts the CRISPR-Cas system from a DNA degradation complex to a transcriptional regulator. This modification suggests that anti-CRISPR proteins may function to do more than simply inhibit CRISPR-Cas targeting. Although CRISPR-Cas systems are a manifestation of the evolutionary arms race between bacteria and phages, CRISPR-Cas systems have also been used for genome editing in various organisms for research purposes. Considering this previous work, I developed a type I-E and type II-A CRISPR-Cas genome editing system to manipulate the genome of different P. aeruginosa strains for research. In the second section of my work, I discuss the methods I developed to efficiently edit the genome of P. aeruginosa. My work on genome editing in P. aeruginosa has and will allow for the development of new P. aeruginosa mutants for research purposes. Collectively, my work provides insight into the evolutionary interactions between phages and bacteria in the context of CRISPR-Cas and anti-CRISPRs. Moreover, it provides a genome editing tool for future P. aeruginosa studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".