Protecting HIV-1-infected cells from ADCC: role of Nef
Bibliographic record
Abstract
Despite the enormous efforts that are being made to develop new therapeutic strategies to fight HIV-1 infection, a better understanding of the elements contributing to HIV-1 virulence remains necessary to improve the effectiveness of these therapies.Emerging evidences suggest the importance role of Fc-mediated effector functions of anti-gp120 (glycoprotein-120) antibodies in the prevention and limitation of viral spread.This effector response was highlighted in the correlates of protection of the RV144 vaccine trial, the only trial that showed some levels of protection.However, the recognition of such antibodies relies on the necessity of CD4 and Envelope (Env) interaction that results in Env-conformational rearrangement and exposure of CD4induced (CD4i) epitopes.The implication of the HIV-1 accessory protein Nef was We also show that Nef's ability to reduce cell surface levels of NKG2D ligands also protects infected cells from ADCC.Cumulatively, our results suggest that in addition to the exposure of ADCC-mediating epitopes induced by the presence of CD4 at the cell surface, the accumulation of NKG2D activating ligands promotes NK cell cytotoxicity. Finally, during my PhD studies I also uncovered a new HIV-1 Env conformation (State2A) that is vulnerable to antibody attack, rendering cells susceptible to ADCC.Importantly, this conformation is counteracted by Nef and might explain why its ability to downregulate CD4 from the cell surface is highly conserved and important for HIV-1 pathogenesis.Altogether, the findings presented in this thesis emphasize the potential impact of ADCC in the development of new antiviral approaches while providing a better understanding of HIV-1 mechanisms of immune evasion.
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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) | 0.009 | 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".