Coupling the CRISPR/Cas9 System with Lambda Red Recombineering Enables Simplified Chromosomal Gene Replacement in Escherichia coli
Bibliographic record
Abstract
To date, most genetic engineering approaches coupling the type II Streptococcus pyogenes clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 system to lambda Red recombineering have involved minor single nucleotide mutations. Here we show that procedures for carrying out more complex chromosomal gene replacements in Escherichia coli can be substantially enhanced through implementation of CRISPR/Cas9 genome editing. We developed a three-plasmid approach that allows not only highly efficient recombination of short single-stranded oligonucleotides but also replacement of multigene chromosomal stretches of DNA with large PCR products. By systematically challenging the proposed system with respect to the magnitude of chromosomal deletion and size of DNA insertion, we demonstrated DNA deletions of up to 19.4 kb, encompassing 19 nonessential chromosomal genes, and insertion of up to 3 kb of heterologous DNA with recombination efficiencies permitting mutant detection by colony PCR screening. Since CRISPR/Cas9-coupled recombineering does not rely on the use of chromosome-encoded antibiotic resistance, or flippase recombination for antibiotic marker recycling, our approach is simpler, less labor-intensive, and allows efficient production of gene replacement mutants that are both markerless and "scar"-less.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".