HIV-1 Promotes Intake of Leishmania Parasites by Enhancing Phosphatidylserine-Mediated, CD91/LRP-1-Dependent Phagocytosis in Human Macrophages
Bibliographic record
Abstract
Over the past decade, the number of reported human immunodeficiency virus type-1 (HIV-1)/Leishmania co-infections has risen dramatically, particularly in regions where both diseases are endemic. Although it is known that HIV-1 infection leads to an increase in susceptibility to Leishmania infection and leishmaniasis relapse, little remains known on how HIV-1 contributes to Leishmania parasitaemia. Both pathogens infect human macrophages, and the intracellular growth of Leishmania is increased by HIV-1 in co-infected cultures. We now report that uninfected bystander cells, not macrophages productively infected with HIV-1, account for enhanced phagocytosis and higher multiplication of Leishmania parasites. This effect can be driven by HIV-1 Tat protein and transforming growth factor-beta (TGF-β). Furthermore, we show for the first time that HIV-1 infection increases surface expression of phosphatidylserine receptor CD91/LRP-1 on human macrophages, thereby leading to a Leishmania uptake by uninfected bystander cells in HIV-1-infected macrophage populations. The more important internalization of parasites is due to interactions between the scavenger receptor CD91/LRP-1 and phosphatidylserine residues exposed at the surface of Leishmania. We determined also that enhanced CD91/LRP-1 surface expression occurs rapidly following HIV-1 infection, and is triggered by the activation of extracellular TGF-β. Thus, these results establish an intricate link between HIV-1 infection, Tat, surface CD91/LRP-1, TGF-β, and enhanced Leishmania phosphatidylserine-mediated phagocytosis.
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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".