Excess BAFF Alters NR4As Expression Levels and Breg Function of Human Precursor-like Marginal Zone B-Cells in the Context of HIV-1 Infection
Bibliographic record
Abstract
We have reported excess B-cell activating factor (BAFF) in the blood of HIV-infected progressors, which was concomitant with increased frequencies of precursor-like marginal zone (MZp) B-cells, early on and despite antiretroviral therapy (ART). In controls, MZp possess a strong B-cell regulatory (Breg) potential. They highly express IL-10, the orphan nuclear receptors (NR)4A1, NR4A2 and NR4A3, as well as the ectonucleotidases CD39 and CD73, all of which are associated with the regulation of inflammation. Furthermore, we have shown MZp regulatory function to involve CD83 signaling. To address the impact of HIV infection and excessive BAFF on MZp Breg capacities, we have performed transcriptomic analyses by RNA-seq of sorted MZp B-cells from the blood of HIV-infected progressors. The Breg profile and function of blood MZp B-cells from HIV-infected progressors were assessed by flow-cytometry and light microscopy high-content screening (HCS) analyses, respectively. We report significant downregulation of NR4A1, NR4A2, NR4A3 and CD83 gene transcripts in blood MZp B-cells from HIV-infected progressors when compared to controls. NR4A1, NR4A3 and CD83 protein expression levels and Breg function were also downregulated in blood MZp B-cells from HIV-infected progressors and not restored by ART. Moreover, we observe decreased expression levels of NR4A1, NR4A3, CD83 and IL-10 by blood and tonsillar MZp B-cells from controls following culture with excess BAFF, which significantly diminished their regulatory function. These findings, made on a limited number of individuals, suggest that excess BAFF contributes to the alteration of the Breg potential of MZp B-cells during HIV infection and possibly in other situations where BAFF is found in excess.
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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 | 0.000 |
| 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".