Anti-V3 Monoclonal Antibodies Display Broad Neutralizing Activities against Multiple HIV-1 Subtypes
Bibliographic record
Abstract
BACKGROUND: The V3 loop of the HIV-1 envelope (Env) glycoprotein gp120 was identified as the "principal neutralizing domain" of HIV-1, but has been considered too variable to serve as a neutralizing antibody (Ab) target. Structural and immunochemical data suggest, however, that V3 contains conserved elements which explain its role in binding to virus co-receptors despite its sequence variability. Despite this evidence of V3 conservation, the ability of anti-V3 Abs to neutralize a significant proportion of HIV-1 isolates from different subtypes (clades) has remained controversial. METHODS: HIV-1 neutralization experiments were conducted in two independent laboratories to test human anti-V3 monoclonal Abs (mAbs) against pseudoviruses (psVs) expressing Envs of diverse HIV-1 subtypes from subjects with acute and chronic infections. Neutralization was defined by 50% inhibitory concentrations (IC(50)), and was statistically assessed based on the area under the neutralization titration curves (AUC). RESULTS: Using AUC analyses, statistically significant neutralization was observed by >or=1 anti-V3 mAbs against 56/98 (57%) psVs expressing Envs of diverse subtypes, including subtypes A, AG, B, C and D. Even when the 10 Tier 1 psVs tested were excluded from the analysis, significant neutralization was detected by >or=1 anti-V3 mAbs against 46/88 (52%) psVs from diverse HIV-1 subtypes. Furthermore, 9/24 (37.5%) Tier 2 viruses from the clade B and C standard reference panels were neutralized by >or=1 anti-V3 mAbs. Each anti-V3 mAb tested was able to neutralize 28-42% of the psVs tested. By IC(50) criteria, 40/98 (41%) psVs were neutralized by >or=1 anti-V3 mAbs. CONCLUSIONS: Using standard and new statistical methods of data analysis, 6/7 anti-V3 human mAbs displayed cross-clade neutralizing activity and revealed that a significant proportion of viruses can be neutralized by anti-V3 Abs. The new statistical method for analysis of neutralization data provides many advantages to previously used analyses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".