A scalable CRISPR-Cas9 gene editing system facilitates CRISPR screens in the malaria parasite <i>Plasmodium berghei</i>
Bibliographic record
Abstract
Abstract Many Plasmodium genes remain uncharacterised due to low genetic tractability. Previous large scale knockout screens have only been able to target about half of the genome in the more genetically tractable rodent malaria parasite Plasmodium berghei . To overcome this limitation, we have developed a scalable CRISPR system called PbHiT, which uses a single cloning step to generate targeting vectors with 100 bp homology arms physically linked to a guide RNA (gRNA) that effectively integrate into the target locus. We show that PbHiT coupled with gRNA sequencing robustly recapitulates known knockout mutant phenotypes in pooled transfections. Furthermore, we provide vector designs and sequences to target the entire P. berghei genome and scale-up vector production using a pooled ligation approach. This work presents for the first time a tool for high-throughput CRISPR screens in Plasmodium for studying the parasite’s biology at scale.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".