Characterization of the activation history and antigen specificity of B cells in meningeal clusters in Central Nervous System autoimmunity
Bibliographic record
Abstract
Abstract An important role for B cells in the pathology of Multiple Sclerosis (MS) is demonstrated by the therapeutic benefit of broad B cell depletion by anti-CD20 monoclonal antibodies. However, little is known about the subset(s) of B cells involved, the pathogenic mechanism(s), or the anatomical location from which they drive disease. Clusters of B cells have been observed in the meninges of some MS brains post mortem and it has been hypothesized that B cells drive pathology from these structures within the inflamed central nervous system (CNS). We used spontaneous and induced models of CNS autoimmune disease that incorporate B cell recognition of myelin autoantigen to investigate the identity and activation history of meningeal B cells in disease. B cells accumulated in the meninges in both models, often in clusters directly associated with demyelinating lesions. We found little evidence of organization into true lymphoid follicles nor evidence of typical B cell activation, despite clear ongoing inflammation and demyelination within the white matter. B cells had a largely naïve-like phenotype with little class-switch. Nevertheless, when compared to naïve lymph node B cells, these cells expressed more CD80 and less CD62L indicating some level of activation. To determine if meningeal B cells are specific for myelin antigen, we transferred fluorescent myelin-reactive T and B cells into wild-type recipient mice and induced EAE. While myelin reactive T cells dominated the T cell infiltrate of the spinal cord, as expected, almost no myelin reactive B cells were evident. This suggests that, unlike T cells, autoimmune B cells are excluded from the CNS in this model.
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 | 0.001 | 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".