The Impact of HIV on B Cell Compartment and Its Implications for COVID-19 Vaccinations in People with HIV
Bibliographic record
Abstract
HIV causes intense polyclonal activation of B cells, resulting in increased numbers of spontaneously antibody-secreting cells in the circulation and hypergammaglobulinemia. It is accompanied by significant perturbations in various B cell subsets, such as increased frequencies of immature/transitional B cells, activated memory B cells, atypical memory B cells, short-lived plasmablasts and regulatory B cells, as well as by decreased frequencies of resting memory and resting naïve B cells. Furthermore, both memory and antigen-inexperienced naïve B cells show exhausted and immune-senescent phenotypes. HIV also drives the expansion and functional impairment of CD4+ T follicular helper cells, which provide help to B cells, crucial for the generation of germinal center reactions and production of long-lived plasma and memory B cells. By suppressing viral replication, anti-retroviral therapy reverses the virus-induced perturbations and functional defects, albeit inadequately. Due to HIV’s lingering impact on B cells, immune senescence and residual chronic inflammation, people with HIV (PWH), especially immune non-responders, are immunocompromised and mount suboptimal antibody responses to vaccination for SARS-CoV-2. Here, we review how functionally and phenotypically distinct B cell subsets are induced in response to a vaccine and an infection and how HIV infection and anti-retroviral therapy (ART) impact them. We also review the role played by HIV-induced defects and perturbations in B cells in the induction of humoral immune responses to currently used anti-SARS-CoV-2 vaccines in PWH on ART. We also outline different strategies that could potentially enhance the vaccine-induced antibody responses in PWH. The review will provide guidance and impetus for further research to improve the immunogenicity of these vaccines in this human population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".