High-Throughput Characterization of Cascade type I-E CRISPR Guide Efficacy Reveals Unexpected PAM Diversity and Target Sequence Preferences
Bibliographic record
Abstract
Abstract Interactions between Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR) RNAs and CRISPR-associated (Cas) proteins form an RNA-guided adaptive immune system in prokaryotes. The adaptive immune system utilizes segments of the genetic material of invasive foreign elements in the CRISPR locus. The loci are transcribed and processed to produce small CRISPR RNAs (crRNAs), with degradation of invading genetic material directed by a combination of complementarity between RNA and DNA and in some cases recognition of adjacent motifs called PAMs (Protospacer Adjacent Motifs). Here we describe a general, high-throughput procedure to test the efficacy of thousands of targets, applying this to the Escherichia coli type I-E Cascade (CRISPR-associated complex for antiviral defense) system. These studies were followed with reciprocal experiments in which the consequence of CRISPR activity was survival in the presence of a lytic phage. From the combined analysis of the Cascade system, we found that (i) type I-E Cascade PAM recognition is more expansive than previously reported, with at least 22 distinct PAMs, with many of the noncanonical PAMs having CRISPR-interference abilities similar to the canonical PAMs; (ii) PAM positioning appears precise, with no evidence for tolerance to PAM slippage in interference; and (iii) while increased guanine-cytosine (GC) content in the spacer is associated with higher CRISPR-interference efficiency, high GC content (>62.5%) decreases CRISPR-interference efficiency. Our findings provide a comprehensive functional profile of Cascade type I-E interference requirements and a method to assay spacer efficacy that can be applied to other CRISPR-Cas systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".