The HIV-1 accessory protein Nef increases surface expression of the checkpoint receptor Tim-3 in infected CD4+ T cells
Bibliographic record
Abstract
Prolonged immune activation drives the upregulation of multiple checkpoint receptors on the surface of virus-specific T cells, inducing their exhaustion. Reversing HIV-1-induced T cell exhaustion is imperative for efficient virus clearance; however, viral mediators of checkpoint receptor upregulation remain largely unknown. The enrichment of checkpoint receptors on T cells upon HIV-1 infection severely constrains the generation of an efficient immune response. Herein, we examined the role of HIV-1 Nef in mediating the upregulation of checkpoint receptors on peripheral blood mononuclear cells. We demonstrate that the HIV-1 accessory protein Nef upregulates cell surface levels of the checkpoint receptor T-cell immunoglobulin mucin domain-3 (Tim-3) and that this is dependent on Nef's dileucine motif LL 164/165 . Furthermore, we used a bimolecular fluorescence complementation assay to demonstrate that Nef and Tim-3 form a complex within cells that is abrogated upon mutation of the Nef dileucine motif. We also provide evidence that Nef moderately promotes Tim-3 shedding from the cell surface in a dileucine motif–dependent manner. Treating HIV-1-infected CD4 + T cells with a matrix metalloprotease inhibitor enhanced cell surface Tim-3 levels and reduced Tim-3 shedding. Finally, Tim-3-expressing CD4 + T cells displayed a higher propensity to release the proinflammatory cytokine interferon-gamma. Collectively, our findings uncover a novel mechanism by which HIV-1 directly increases the levels of a checkpoint receptor on the surface of infected CD4 + T cells.
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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 | 0.000 | 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".