Bruton’s tyrosine kinase inhibitors rescue neuronal impairment caused by B cell–mediated lymphotoxin-α release
Bibliographic record
Abstract
Although B cell depletion slows disability accrual in people with multiple sclerosis (pwMS), the role of B cells in MS-associated neuronal injury remains elusive. B cells release cytokines such as lymphotoxin-α (LTα), and this proinflammatory protein is also present in the cerebrospinal fluid (CSF) and meninges of pwMS. B cell-specific ablation of LTα alleviates disease severity in a preclinical model of MS. To further study the impact of B cell-derived cytokines on neuronal function, we performed patch-clamp recordings on human iPSC-derived neurons. Coculture with proinflammatory human B cells led to depolarization and aberrant firing. Pretreatment of proinflammatory B cells with a Bruton's tyrosine kinase inhibitor (BTKi), reported in patients to beneficially affect disability progression even in the absence of inflammatory relapse activity, prevented neuronal impairment and inhibited LTα release from B cells. Blocking LTα, tumor necrosis factor receptor 1 (TNFR1), or receptor-interacting protein kinase 1 or 3 also prevented neuronal impairment, but blocking TNFα or LTβ had no such effect. Neuronal impairment was reversible by BTKi or blockage of LTα, and this reversibility was dependent on the activity of acid sphingomyelinase. In pwMS, LTα correlated with elevated neurofilament light chain (NfL) abundance in CSF, and anti-CD20 B cell-depletion therapy led to a reduction in circulating LTα, supporting the role of B cells as a regulator for LTα. These findings highlight the negative impact of B cell-derived LTα on neurons and suggest potential treatment avenues for MS-associated neuronal injury.
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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) | 0.001 | 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".