High-fidelity CRISPR genome editing of single-nucleotide mutation with near-complementary guide RNA via enhanced target binding kinetics
Bibliographic record
Abstract
Abstract The CRISPR-Cas9 system is a powerful genome editing tool capable of precisely recognizing and cleaving specific DNA sequences, and has been extensively investigated as a strategy for correcting mutations associated with genetic diseases and cancer. However, conventional CRISPR genome engineering often fail to discriminate single-nucleotide mutations from wild-type alleles when the mutation is located outside the protospacer adjacent motif (PAM) sequence. To address this limitation, we developed a RNA engineering approach for designing near-complementary single guide RNA (sgRNA) that contain intentional mismatches within the seed region of the sgRNA. Single molecule kinetic analyses showed that the near-complementary sgRNA selectively reduces the binding affinity of CRISPR ribonucleoprotein complex by via differentiated increment in the dissociation rates to the wild-type target DNA compared to the mutant allele. The engineered kinetic characteristics of near-complementary sgRNAs enable highly specific genome editing of single-base mutations without reliance on PAM proximity. We demonstrate the application of the strategy to the a cancer-specific single-nucleotide G228A (-124C > T) mutation in the TERT promoter, frequently found in glioblastomas and other tumors, that does not generate a canonical PAM sequence. Our near-complementary sgRNA successfully induced selective editing of the mutant allele while sparing the wild-type sequence. Furthermore, single-molecule fluorescence resonance energy transfer (smFRET) analyss revealed distinct differences in binding kinetics between mutant and wild-type DNA, providing kinetic insight into the discrimination process. We conclude that the near-complementary sgRNA CRISPR editing strategy facilitates precise PAM-independent targeting of single-nucleotide mutations without protein engineering and offers a molecular framework for expanding the specificity and applicability of CRISPR-based genome and epigenome editing technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".