NR4A Expression by Human Marginal Zone B-Cells
Bibliographic record
Abstract
We have previously characterized a human blood CD19+CD1c+IgM+CD27+CD21loCD10+ innate-like B-cell population, which presents features shared by both transitional immature and marginal zone (MZ) B-cells, named herein “precursor-like” MZ B-cells. B-cells with similar attributes have been associated with regulatory potential (Breg). In order to clarify this issue and better characterize this population, we have proceeded to RNA-Seq transcriptome profiling of mature MZ and precursor-like MZ B-cells taken from the blood of healthy donors. We report that ex vivo mature MZ and precursor-like MZ B-cells express transcripts for the immunoregulatory marker CD83 and nuclear receptors NR4A1, 2, and 3, known to be associated with T-cell regulatory (Treg) maintenance and function. Breg associated markers such as CD39 and CD73 were also expressed by both populations. We also show that human blood and tonsillar precursor-like MZ B-cells were the main B-cell population to express elevated levels of CD83 and NR4A1-3 proteins ex vivo and without stimulation. Sorted tonsillar precursor-like MZ B-cells exerted regulatory activity on autologous activated CD4+ T-cells, and this was affected by a CD83 blocking reagent. We believe these observations shed light on the Breg potential of MZ populations, and identify NR4A1-3 as potential Breg markers, which as for Tregs, may be involved in stabilization of a regulatory status. Since expression and activity of these molecules can be modulated therapeutically, our findings may be useful in strategies aiming at modulation of Breg responses.
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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) | 0.000 | 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".