Exposure to Entry Inhibitors Alters HIV Infectiousness and Sensitivity to Broadly Neutralizing Monoclonal Antibodies
Bibliographic record
Abstract
BACKGROUND: The development of envelope-specific neutralizing antibodies that can interfere with viral entry into target cells is important for the development of an HIV-1 vaccine. Another means of blocking viral entry is through the use of entry inhibitors such as the CCR5 inhibitor maraviroc (MVC), which can also repel cell-free virus particles from the cell surface. For this reason, we hypothesized that exposure to entry inhibitors might alter viral infectiousness and sensitivity to antibody-mediated neutralization. METHODS: The CCR5-tropic HIV-1 variants BaL, AD8, and CC 1/85 were used to infect PM-1 cells in the presence of 2 entry inhibitors, enfuvirtide and MVC. After 4 hours, culture fluids were ultrafiltered and the infectiousness and susceptibility to broadly neutralizing antibodies (2F5, 4E10, 2G12, b12, VRC01, PG9) of viruses exposed to these entry inhibitors were assessed using TZM-bl cells. RESULTS: Viruses exposed to the entry inhibitor MVC exhibited lower infectiousness than controls. Enfuvirtide exposure increased AD8 sensitivity to 2F5, 4E10, VRC01, and b12 and increased BaL sensitivity to 4E10 while lowering BaL sensitivity to b12 and VRC01. MVC-exposed BaL became less susceptible to the gp120-specific antibodies b12, 2G12, and VRC01. CONCLUSIONS: Exposure to entry inhibitors altered HIV-1 infectiousness and sensitivity to gp120-specific neutralizing antibodies. This alteration of entry inhibitor-exposed virus has implications for the development of future entry inhibitors and for vaccine development.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".