IL-10-Producing B Cells Are Induced Early in HIV-1 Infection and Suppress HIV-1-Specific T Cell Responses
Bibliographic record
Abstract
A rare subset of IL-10-producing B cells, named regulatory B cells (Bregs), suppresses adaptive immune responses and inflammation in mice. In this study, we examined the role of IL-10-producing B cells in HIV-1 infection. Compared to uninfected controls, IL-10-producing B cell frequencies were elevated in both blood and sigmoid colon during the early and chronic phase of untreated HIV-1 infection. Ex vivo IL-10-producing B cell frequency in early HIV-1 infection directly correlated with viral load. IL-10-producing B cells from HIV-1 infected individuals were enriched in CD19(+)TIM-1(+) B cells and were enriched for specificity to trimeric HIV-1 envelope protein. Anti-retroviral therapy was associated with reduced IL-10-producing B cell frequencies. Treatment of B cells from healthy donors with microbial metabolites and Toll-like receptor (TLR) agonists could induce an IL-10 producing phenotype, suggesting that the elevated bacterial translocation characteristic of HIV-1 infection may promote IL-10-producing B cell development. Similar to regulatory B cells found in mice, IL-10-producing B cells from HIV-1-infected individuals suppressed HIV-1-specific T cell responses in vitro, and this suppression is IL-10-dependent. Also, ex vivo IL-10-producing B cell frequency inversely correlated with contemporaneous ex vivo HIV-1-specific T cell responses. Our findings show that IL-10-producing B cells are induced early in HIV-1 infection, can be HIV-1 specific, and are able to inhibit effective anti-HIV-1 T cell responses. HIV-1 may dysregulate B cells toward Bregs as an immune evasion strategy.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".