T-bet+ B cells are induced by human viral infections and dominate the HIV gp140 response
Bibliographic record
Abstract
Humoral immunity is critical for viral control, but the identity and mechanisms regulating human antiviral B cells are unclear. Here, we characterized human B cells expressing T-bet and analyzed their dynamics during viral infections. T-bet+ B cells demonstrated an activated phenotype, a distinct transcriptional profile, and were enriched for expression of the antiviral immunoglobulin isotypes IgG1 and IgG3. T-bet+ B cells expanded following yellow fever virus and vaccinia virus vaccinations and also during early acute HIV infection. Viremic HIV-infected individuals maintained a large T-bet+ B cell population during chronic infection that was associated with increased serum and cell-associated IgG1 and IgG3 expression. The HIV gp140-specific B cell response was dominated by T-bet-expressing memory B cells, and we observed a concomitant biasing of gp140-specific serum immunoglobulin to the IgG1 isotype. These findings suggest that T-bet induction promotes antiviral immunoglobulin isotype switching and development of a distinct T-bet+ B cell subset that is maintained by viremia and coordinates the HIV Env-specific humoral response.
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) | 0.000 | 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".