Vpu Exerts a Positive Effect on HIV-1 Infectivity by Down-modulating CD4 Receptor Molecules at the Surface of HIV-1-producing Cells
Bibliographic record
Abstract
Human immunodeficiencey virus, type 1 (HIV-1) encodes three proteins, Nef, Vpu, and gp160, that down-modulate surface expression of the CD4 receptor during viral infection. In the present study, we have investigated the role of CD4 down-modulation in the HIV-1 infection cycle, primarily from the perspective of Vpu function. We report here that, like Nef, Vpu-mediated CD4 degradation modulates positively HIV-1 infectivity. Our data reveal that accumulation of CD4 at the cell surface of Vpu-deficient HIV-1-producing cells leads to an efficient recruitment of CD4 into virions and to an impairment of viral infectivity. This CD4-mediated inhibition of viral infectivity was not observed when a CD4 mutant unable to bind Env gp120 was used or when VSV-G glycoprotein was utilized to pseudotype viruses, suggesting that an interaction between CD4 and gp120 is required for interference. Indeed, protein analysis of Vpu-defective viral particles reveals that CD4 recruitment is associated with an increased formation of gp120-CD4 complexes at the virion surface. Interestingly, we did not detect any difference at the level of total virion-associated Env glycoproteins between wild-type and Vpu-defective virus, indicating that accumulation of CD4 at the cell surface and recruitment of CD4 into Vpu-defective HIV-1 particles exert a negative effect on viral infectivity, most likely by promoting the formation of nonfunctional gp120-CD4 complexes at the virion surface. Finally, we show that both Vpu- and Nef-induced CD4 down-modulation activities are required for production of fully infectious particles in CD4+ T cell lines and primary cells, an observation that has clear implications for viral spread in vivo.
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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".