Evidence for gene essentiality in Leishmania using CRISPR
Bibliographic record
Abstract
The ability to determine the essentiality of a gene in the protozoan parasite Leishmania is important to identify potential targets for intervention and understanding the parasite biology. CRISPR gene editing technology has significantly improved gene targeting efficiency in Leishmania. There are two commonly used CRISPR gene targeting methods in Leishmania; the stable expression of the gRNA and Cas9 using a plasmid containing a Leishmania ribosomal RNA gene promoter (rRNA-P stable protocol) and the T7 RNA polymerase based transient gRNA expression system in promastigotes stably expressing Cas9 (T7 transient protocol). There are distinct advantages with both systems. The T7 transient protocol is excellent for high throughput gene deletions and has been used to successfully delete hundreds of Leishmania genes to study mutant phenotypes and several research labs are now using this protocol to target all the genes in L. mexicana genome. The rRNA-P stable protocol stably expresses the plasmid derived gRNA and has been used to delete or disrupt single and multicopy Leishmania genes, perform single nucleotide changes and provide evidence for gene essentiality by directly observing null mutant promastigotes dying in culture. In this study, the rRNA-P stable protocol was used to target 22 Leishmania genes in which null mutants were not generated using the T7 transient protocol. Notably, the rRNA-P stable protocol was able to generate alive null mutants for 8 of the 22 genes. These results demonstrate the rRNA-P stable protocol could be used alone or in combination with the T7 transient protocol to investigate gene essentiality in Leishmania.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".