BAFF Blockade Attenuates B Cell MALT Formation in Conditional <i>Nlrc5</i> ‐Deficient Mice With <i>Helicobacter felis</i> Infection
Bibliographic record
Abstract
ABSTRACT Helicobacter infection is a key cause of gastric B cell mucosa–associated lymphoid tissue (MALT) lymphoma. This study examined the role of B cell–activating factor (BAFF), a major driver of B cell proliferation and many B cell disorders, in this malignancy using a model in which conditional knockout mice for NOD‐like receptor family CARD domain‐containing 5 ( Nlrc5 ) are infected with Helicobacter felis . Gastric BAFF production was significantly increased in H. felis –infected Nlrc5 mø‐KO mice compared to wild‐type. Blocking BAFF signalling, before or after the onset of Helicobacter ‐induced gastritis, significantly reduced MALT development, with fewer gastric B cell follicles and reduced gland hyperplasia. BAFF blockade also reshaped the immune cell landscape in the stomach, resulting in fewer CD4 + T cells, Tregs, macrophages and dendritic cells. Using a cell culture model, we identified the protein‐coding BAFF transcripts that are upregulated in NLRC5‐deficient macrophages stimulated with either H. felis or the NLRC5 agonist, lipopolysaccharide. Among the upregulated variants, TNFSF13B (BAFF)‐206 acts as a transcription factor and is reported to enhance BAFF production in autoimmune diseases and cancer. Altogether, these findings implicate the NLRC5–BAFF signalling axis in Helicobacter ‐induced B cell MALT lymphoma, highlighting BAFF inhibition as a potential therapeutic approach.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".