Microbiota‐Driven Activation of Intrahepatic B Cells Aggravates NASH Through Innate and Adaptive Signaling
Bibliographic record
Abstract
BACKGROUND AND AIMS: Nonalcoholic steatohepatitis is rapidly becoming the leading cause of liver failure and indication for liver transplantation. Hepatic inflammation is a key feature of NASH but the immune pathways involved in this process are poorly understood. B lymphocytes are cells of the adaptive immune system that are critical regulators of immune responses. However, the role of B cells in the pathogenesis of NASH and the potential mechanisms leading to their activation in the liver are unclear. APPROACH AND RESULTS: In this study, we report that NASH livers accumulate B cells with elevated pro-inflammatory cytokine secretion and antigen-presentation ability. Single-cell and bulk RNA sequencing of intrahepatic B cells from mice with NASH unveiled a transcriptional landscape that reflects their pro-inflammatory function. Accordingly, B-cell deficiency ameliorated NASH progression, and adoptively transferring B cells from NASH livers recapitulates the disease. Mechanistically, B-cell activation during NASH involves signaling through the innate adaptor myeloid differentiation primary response protein 88 (MyD88) as B cell-specific deletion of MyD88 reduced hepatic T cell-mediated inflammation and fibrosis, but not steatosis. In addition, activation of intrahepatic B cells implicates B cell-receptor signaling, delineating a synergy between innate and adaptive mechanisms of antigen recognition. Furthermore, fecal microbiota transplantation of human NAFLD gut microbiotas into recipient mice promoted the progression of NASH by increasing the accumulation and activation of intrahepatic B cells, suggesting that gut microbial factors drive the pathogenic function of B cells during NASH. CONCLUSION: Our findings reveal that a gut microbiota-driven activation of intrahepatic B cells leads to hepatic inflammation and fibrosis during the progression of NASH through innate and adaptive immune mechanisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".