CRISPR-Cas9 high-throughput screening to study drug resistance in <i>Leishmania infantum</i>
Bibliographic record
Abstract
ABSTRACT Parasites of the genus Leishmania pose a global health threat with limited treatment options. New drugs are urgently needed, and genomic screens have the potential to accelerate target discovery, mode of action, and resistance mechanisms against these new drugs. We describe here our effort in developing a genome-wide CRISPR-Cas9 screen in Leishmania , an organism lacking a functional nonhomologous end joining system that must rely on microhomology-mediated end joining, single-strand annealing, or homologous recombination for repairing Cas9-induced double-stranded DNA breaks. A new vector for cloning and expressing single guide RNAs (sgRNAs) was designed and proven to be effective in a small pilot project while enriching specific sgRNAs during drug selection. We then developed a whole-genome library of 49,754 sgRNAs, targeting all the genes of Leishmania infantum . This library was transfected in L. infantum expressing Cas9, and these cells were selected for resistance to two antileishmanials, miltefosine and amphotericin B. The sgRNAs the most enriched in the miltefosine screen targeted the miltefosine transporter gene, but sgRNAs targeting genes coding for a RING-variant protein and a transmembrane protein were also enriched. The sgRNAs the most enriched by amphotericin B targeted the sterol 24 C methyltransferase genes and a hypothetical gene. Through gene disruption experiments, we proved that loss of function of these genes was associated with resistance. This study describes the feasibility of carrying out whole-genome CRISPR-Cas9 screens in Leishmania provided that a strong selective pressure is applied. Such a screen can be used for accelerating the development of urgently needed antileishmanial drugs. IMPORTANCE Leishmaniasis, a global health threat, lacks adequate treatment options and drug resistance exacerbates the challenge. This study introduces a CRISPR-Cas9 screening approach in Leishmania infantum , unraveling mechanisms of drug resistance at a genome-wide scale. Our screen was applied against two main antileishmanial drugs, and guides were enriched upon drug selection. These guides targeted known and new targets, hence validating the use of this screen against Leishmania . This strategy provides a powerful tool to expedite drug discovery as well as potential therapeutic targets against this neglected tropical disease.
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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) | 0.001 | 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".