Propionic acid regulates immune tolerant properties in B Cells
Bibliographic record
Abstract
Interleukin 10 (IL-10)-producing B cells (B10 cells) are a canonical cell fraction for regulating other activities of immune cells. Posttranscriptional modification of IL-10 in B10 cells is not yet fully understood. Short-chain fatty acids play an important role to regulate the functions of immune cells. This study aims to clarify the role of propionic acid (PA), a short-chain fatty acid, in regulating the expression of IL-10 in B10 cells. Blood samples were collected from patients with food allergy (FA) and healthy subjects. Serum and cellular components were prepared with the samples, and analysed by enzyme-linked immunosorbent assay and flow cytometry, respectively. The results showed that serum PA levels were lower in FA patients. PA concentrations were negatively correlated with serum cytokine Th2 concentrations, specific IgE concentrations in serum and skin prick test results. The peripheral frequency of B10 cells and the production of IL-10 in B cells were also associated with serum PA concentrations. Activation of B cells by CpG induced the production of IL-10 and tristetretrprolin (TTP), in which TTP caused the spontaneous decay of IL-10 mRNA. PA was necessary to stabilize the IL-10 mRNA in B cells by inducing the production of granzyme B, which resulted in the degradation of the IL-10 mRNA. Administration of PA attenuated FA response in mice by maintaining homeostasis of B10 cells. In conclusion, PA is needed to stabilize the expression of IL-10 in B10 cells. PA administration can mitigate experimental FA by maintaining B10 cell functions.
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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".