<scp><i>M</i></scp><i>ycobacterium leprae</i>intracellular survival relies on cholesterol accumulation in infected macrophages: a potential target for new drugs for leprosy treatment
Bibliographic record
Abstract
We recently showed that Mycobacterium leprae (ML) is able to induce lipid droplet formation in infected macrophages. We herein confirm that cholesterol (Cho) is one of the host lipid molecules that accumulate in ML-infected macrophages and investigate the effects of ML on cellular Cho metabolism responsible for its accumulation. The expression levels of LDL receptors (LDL-R, CD36, SRA-1, SR-B1, and LRP-1) and enzymes involved in Cho biosynthesis were investigated by qRT-PCR and/or Western blot and shown to be higher in lepromatous leprosy (LL) tissues when compared to borderline tuberculoid (BT) lesions. Moreover, higher levels of the active form of the sterol regulatory element-binding protein (SREBP) transcriptional factors, key regulators of the biosynthesis and uptake of cellular Cho, were found in LL skin biopsies. Functional in vitro assays confirmed the higher capacity of ML-infected macrophages to synthesize Cho and sequester exogenous LDL-Cho. Notably, Cho colocalized to ML-containing phagosomes, and Cho metabolism impairment, through either de novo synthesis inhibition by statins or depletion of exogenous Cho, decreased intracellular bacterial survival. These findings highlight the importance of metabolic integration between the host and bacteria to leprosy pathophysiology, opening new avenues for novel therapeutic strategies to leprosy.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".