Repurposing CRISPR/Cas to Discover SARS‐CoV‐2 Detecting and Neutralizing Aptamers
Bibliographic record
Abstract
RNA aptamers provide useful biological probes and therapeutic agents. New methodologies to screen RNA aptamers will be valuable by complementing the traditional Systematic Evolution of Ligands by Exponential Enrichment (SELEX). Meanwhile, repurposing clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated systems (Cas) has expanded their utility far beyond their native nuclease function. Here, CRISmers, a CRISPR/Cas-based novel screening system for RNA aptamers based on binding to a chosen protein of interest in a cellular context, is presented. Using CRISmers, aptamers are identified specifically targeting the receptor binding domain (RBD) of the spike glycoprotein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Two aptamer leads enable sensitive detection and potent neutralization of SARS-CoV-2 Delta and Omicron variants in vitro. Intranasal administration of one aptamer, further modified with 2'-fluoro pyrimidines (2'-F), 2'-O-methyl purines (2'-O), and conjugation with both cholesterol and polyethylene glycol of 40 kDa (PEG40K), achieves effective prophylactic and therapeutic antiviral activity against live Omicron BA.2 variants in vivo. The study concludes by demonstrating the robustness, consistency, and potential broad utility of CRISmers using two newly identified aptamers but switching CRISPR, selection marker, and host species.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".