Leishmania major Dihydrolipoyl dehydrogenase (DLD) is a key metabolic enzyme that drives parasite proliferation, pathology and host immune response
Bibliographic record
Abstract
Identifying antigens that elicit protective immunity is pivotal for developing effective vaccines and therapeutics against cutaneous leishmaniasis. Dihydrolipoyl dehydrogenase (DLD), a mitochondrial enzyme involved in oxidizing lipoamides to facilitate electron transfer for energy production and metabolism, plays a critical role in virulence of fungi and bacteria. However, its function in Leishmania virulence and pathogenesis remains unexplored. Using a CRISPR-Cas9-based approach, we generated DLD-deficient Leishmania (L.) major parasites and a complementary add-back strain by episomally reintroducing DLD gene into the knockout parasites. Loss of DLD significantly impaired parasite proliferation in axenic cultures and infected macrophages compared to wild-type (WT) and add-back control parasites. These defects were linked to reduced ROS production, impaired mitochondrial permeability, an enhanced oxygen consumption rate, and alterations in mitochondrial ultrastructure. In murine models, DLD-deficient parasites failed to cause observable lesions and exhibited significantly reduced parasite burdens compared to WT and add-back control strains. Notably, mice infected with DLD-deficient parasites displayed blunted immune responses compared to their WT controls. Importantly, vaccination with DLD-deficient parasites conferred robust protection against virulent L. major challenge, characterized by a strong IFN-γ-mediated immune response. These findings establish DLD as an essential metabolic enzyme for L. major intracellular survival and pathogenesis. Targeting DLD not only impairs parasite viability but also holds promise as a novel strategy for vaccine development to combat cutaneous leishmaniasis.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".