Remodeling of the Host Cell Plasma Membrane by HIV-1 Nef and Vpu: A Strategy to Ensure Viral Fitness and Persistence
Bibliographic record
Abstract
The plasma membrane protects the cell from its surroundings and regulates cellular communication, homing, and metabolism. Not surprisingly, the composition of this membrane is highly controlled through the vesicular trafficking of proteins to and from the cell surface. As intracellular pathogens, most viruses exploit the host plasma membrane to promote viral replication while avoiding immune detection. This is particularly true for the enveloped human immunodeficiency virus (HIV), which assembles and obtains its lipid shell directly at the plasma membrane. HIV-1 encodes two proteins, negative factor (Nef) and viral protein U (Vpu), which function primarily by altering the quantity and localization of cell surface molecules to increase virus fitness despite host antiviral immune responses. These proteins are expressed at different stages in the HIV-1 life cycle and employ a variety of mechanisms to target both unique and redundant surface proteins, including the viral receptor CD4, host restriction factors, immunoreceptors, homing molecules, tetraspanins and membrane transporters. In this review, we discuss recent progress in the study of the Nef and Vpu targeting of host membrane proteins with an emphasis on how remodeling of the cell membrane allows HIV-1 to avoid host antiviral immune responses leading to the establishment of systemic and persistent infection.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".