Application of CRISPR/Cas9-Mediated Genome Editing in Leishmania
Bibliographic record
Abstract
CRISPR-Cas9 is an RNA guided endonuclease derived from the bacterium Streptococcus pyogenes. Due to its simplicity, versatility, and high efficiency, it has been widely used for genome editing in a variety of organisms including the protozoan parasite Leishmania, the causative agent of human leishmaniasis. Compared to the traditional homologous recombination gene targeting method, CRISPR-Cas9 has been shown to be a more efficient method to delete or disrupt Leishmania genes, generate point mutations, and add tags to endogenous genes. Notably, the stable CRISPR expression systems were shown to delete multicopy family Leishmania genes and genes present in multiploid chromosomes, identify essential Leishmania genes, and create specific chromosome translocations. In this chapter, we describe detailed procedures on using the stable CRISPR expression system for genome editing in Leishmania. These procedures include CRISPR targeting site selection, gRNA design, cloning single and double gRNA coding sequences into the Leishmania CRISPR vector pLdCN, oligonucleotide donor and drug resistance selection donor design, Leishmania cell transfection, screening, and isolation of CRISPR-edited mutants. As the principles of gene editing are generally similar, many of these procedures could also apply to the transient Leishmania CRISPR systems described by other labs.
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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 | 0.001 |
| 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".