Anti-myelin autoreactive B cells have a reduced response to BCR stimulation 2922
Bibliographic record
Abstract
Abstract Description Peripheral B-cell tolerance typically requires regular exposure to autoantigens to induce B-cell anergy. However, B cells in the periphery do not regularly encounter tissue-restricted autoantigens, like those sequestered within the CNS. We demonstrated that B cells reactive to myelin oligodendrocyte glycoprotein (MOG) have a weaker response to BCR stimulation, yet do not exhibit a phenotype consistent with tolerance from regular autoantigens exposure. How B cell tolerance to tissue-restricted autoantigens is induced or maintained remains unclear. To understand the mechanism(s) limiting the anti-MOG B cell response, we analyzed in vitro B cell responses in IgHMOG mice, where 30% of B cells are MOG-specific (MOG-sp). Compared to MOG-nonsp B cells, MOG-sp B cells showed defective proliferation and failed to downregulate IgD upon activation. Further, BCR-proximal signalling (p-Syk, p-Tyr, Ca2+ flux) in MOG-sp B cells was not reduced, as is typical with B cell anergy. We also found that downstream BCR-signalling pathways (p-ERK, p-AKT, p-p38, NF-κB) were intact in MOG-sp B cells. Finally, we observed that MOG-sp B cells preferentially underwent apoptosis after BCR stimulation. We will further characterize their metabolic phenotype, transcriptome, and epigenetic regulation to understand the underlying mechanisms. This work may identify a novel mechanism that limits B cells specific for tissue-restricted autoantigens, independent of regular autoantigens exposure. Topic Categories Basic Autoimmunity (BA)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.005 | 0.002 |
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".