Harnessing T Follicular Helper Cell Responses for HIV Vaccine Development
Bibliographic record
Abstract
Passive administration of broadly neutralizing antibodies (bNAbs) capable of recognizing a broad range of viral strains to non-human primates has led to protection from infection with chimeric SIV/HIV virus (SHIV). This data suggests that generating protective antibody responses could be an effective strategy for an HIV vaccine. However, classic vaccine approaches have failed so far to induce such protective antibodies in HIV vaccine trials. HIV-specific bNAbs identified in natural infection show high levels of somatic hypermutations, demonstrating that they underwent extensive affinity maturation. It is likely that to gain ability to recognize diverse viral strains, vaccine-induced humoral responses will also require complex, iterative maturation. T follicular helper cells (Tfh) are a specialized CD4+ T cell subset that provides help to B cells in the germinal center for the generation of high-affinity and long-lasting humoral responses. It is therefore probable that the quality and quantity of Tfh responses upon vaccination will impact development of bNAbs. Here, we review studies that advanced our understanding of Tfh differentiation, function and regulation. We discuss correlates of Tfh responses and bNAb development in natural HIV infection. Finally, we highlight recent strategies to optimize Tfh responses upon vaccination and their impact on prophylactic HIV vaccine research.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".