LPG2 Gene Duplication in Leishmania infantum: A Case for CRISPR-Cas9 Gene Editing
Bibliographic record
Abstract
On the surface of the Leishmania promastigote, phosphoglycans (PG) such as lipophosphoglycan (LPG), proteophosphoglycan (PPG), free phosphoglycan polymers (PGs) and acid phosphatases (sAP), are dominant and contribute to the invasion and survival of Leishmania within the host cell by modulating macrophage signaling and intracellular trafficking. Phosphoglycan synthesis depends on the Golgi GDP-mannose transporter encoded by the LPG2 gene. Aiming to investigate the role of PG-containing molecules in Leishmania infantum infection process, herein we describe the generation and characterization of L. infantum LPG2-deficient parasites. This gene was unexpectedly identified as duplicated in the L. infantum genome, which impaired gene targeting using the conventional homologous recombination approach. This limitation was circumvented by the use of CRISPR/Cas9 technology. Knockout parasites were selected by agglutination assays using CA7AE antibodies followed by a lectin (RCA 120). Five clones were isolated and molecularly characterized, all revealing the expected edited genome, as well as the complete absence of LPG and PG-containing molecule expression. Finally, the deletion of LPG2 was found to impair the outcome of infection in human neutrophils, as demonstrated by a pronounced reduction (~83%) in intracellular load compared to wild-type parasite infection. The results obtained herein reinforce the importance of LPG and other PGs as virulence factors in host-parasite interactions.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".