2G12-Expressing B Cell Lines May Aid in HIV Carbohydrate Vaccine Design Strategies
Bibliographic record
Abstract
The highly conserved cluster of high-mannose glycans on the HIV-1 envelope glycoprotein, gp120, has been highlighted as a target for neutralizing antibodies. 2G12, the first HIV-1 antiglycan neutralizing antibody described, binds with an unusual domain-exchanged structure that creates a high-affinity multivalent binding surface. It is an interesting challenge for rational vaccine design to generate immunogens capable of eliciting domain-exchanged 2G12-like responses. We recently showed that di-mannose recognition by the variable domains of 2G12 is independent of domain exchange but that exchange is critical for virus neutralization. Carbohydrate-based immunogens aimed at inducing 2G12-like antibodies may need to drive both di-mannose recognition and domain exchange through interactions with B cell receptors. Here we assessed the ability of such immunogens to activate mouse B cell lines displaying domain-exchanged wild-type 2G12 (2G12 WT), a non-domain-exchanged Y-shaped variant (2G12 I19R), and germ line 2G12 (2G12 gl). We show that several immunogens, including heat-killed yeast and bacteria, can activate both 2G12 WT and 2G12 I19R B cells. However, only discrete clusters of high-mannose glycans, as on recombinant forms of the HIV-1 envelope trimer and oligodendrons, activate 2G12 WT B cells. Furthermore, no immunogen tested activated 2G12 gl cells. Our results support the hypothesis that in order to drive domain exchange of an antimannose antibody response, a boost with an immunogen displaying discrete clusters of high-mannose glycans not recognized by conventional Y-shaped antibodies will be required. Additionally, a molecule capable of activating 2G12 gl cells might also be required. The results highlight broadly neutralizing antibody-expressing mouse B cells as potentially useful tools for carbohydrate immunogen screening.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".