NTB-A and 2B4 Natural Killer Cell Receptors Modulate the Capacity of a Cocktail of Non-Neutralizing Antibodies and a Small CD4-Mimetic to Eliminate HIV-1-Infected Cells by Antibody-Dependent Cellular Cytotoxicity
Bibliographic record
Abstract
Natural Killer (NK) cells have the potential to eliminate HIV-1-infected cells by antibody-dependent cellular cytotoxicity (ADCC). NK cell activation is tightly regulated by the engagement of its inhibitory and activating receptors. The activating receptor CD16 drives ADCC upon binding to the Fc portion of antibodies; NK cell activation is further sustained by the co-engagement of activating receptors NTB-A and 2B4. During HIV-1 infection, Nef and Vpu accessory proteins contribute to ADCC escape by downregulating the ligands of NTB-A and 2B4. HIV-1 also evades ADCC by keeping its envelope glycoproteins (Env) in a "closed" conformation which effectively masks epitopes recognized by non-neutralizing antibodies (nnAbs) which are abundant in the plasma of people living with HIV. To achieve this, the virus uses its accessory proteins Nef and Vpu to downregulate the CD4 receptor, which otherwise interacts with Env and exposes the epitopes recognized by nnAbs. Small CD4-mimetic compounds (CD4mc) have the capacity to expose these epitopes, thus sensitizing infected cells to ADCC. Given the central role of NK cell co-activating receptors NTB-A and 2B4 in Fc-effector functions, we studied their contribution to CD4mc-mediated ADCC. Despite the fact that their ligands are partially downregulated by HIV-1, we found that both co-activating receptors significantly contribute to CD4mc sensitization of HIV-1-infected cells to ADCC.
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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) | 0.002 | 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".