Secondary B Cell Receptor Diversification Is Necessary for T Cell Mediated Neuro-Inflammation during Experimental Autoimmune Encephalomyelitis
Bibliographic record
Abstract
BACKGROUND: Clinical studies of B cell depletion in Multiple Sclerosis (MS) have revealed that B Lymphocytes are involved in the neuro-inflammatory process, yet it remains unclear how B cells can exert pro- and anti-inflammatory functions during MS. Experimental Autoimmune Encephalomyelitis (EAE) is an animal model of MS whereby myelin-specific T cells become activated and subsequently migrate to the Central Nervous System (CNS) where they perform pro-inflammatory functions such as cytokine secretion. Typically EAE is induced by immunization of mice of a susceptible genetic background with peptide antigen emulsified in Complete Freund's Adjuvant. However, novel roles for B-lymphocytes in EAE may also be explored by immunization with full-length myelin oligodendrocyte glycoprotein (MOG) that contains the B cell conformational epitope. Here we show that full length MOG immunization promotes a chronic disease in mice that depends on antigen-driven secondary diversification of the B cell receptor. METHODS: Activation-Induced Deaminase (AID) is an enzyme that is essential for antigen-driven secondary diversification of the B cell receptor. We immunized AID(-/-) mice with the extracellular domain (amino acids 1-120) of recombinant human MOG protein (rhMOG) and examined the incidence and severity of disease in AID(-/-) versus wild type mice. Corresponding with these clinical measurements, we also evaluated parameters of T cell activation in the periphery and the CNS as well as the generation of anti-MOG antibodies (Ab). CONCLUSIONS: AID(-/-) mice exhibit reduced severity and incidence of EAE. This suggests that the secondary diversification of the B cell receptor is required for B cells to exert their full encephalogenic potential during rhMOG-induced EAE, and possibly also during MS.
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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) | 0.002 | 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; 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".