Specifically modified Env immunogens activate B-cell precursors of broadly neutralizing HIV-1 antibodies in transgenic mice
Bibliographic record
Abstract
VRC01-class broadly neutralizing HIV-1 antibodies protect animals from experimental infection and could contribute to an effective vaccine response. Their predicted germline forms (gl) bind Env inefficiently, which may explain why they are not elicited by HIV-1 Env-immunization. Here we show that an optimized Env immunogen can engage multiple glVRC01-class antibodies. Furthermore, this immunogen activates naive B cells expressing the human germline heavy chain of 3BNC60, paired with endogenous mouse light chains in vivo. To address whether it activates B cells expressing the fully humanized gl3BNC60 B-cell receptor (BCR), we immunized mice carrying both the heavy and light chains of gl3BNC60. B cells expressing this BCR display an autoreactive phenotype and fail to respond efficiently to soluble forms of the optimized immunogen, unless it is highly multimerized. Thus, specifically designed Env immunogens can activate naive B cells expressing human BCRs corresponding to precursors of broadly neutralizing HIV-1 antibodies even when the B cells display an autoreactive phenotype.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".