Gut dysbiosis modulates the immune response to factor VIII in murine hemophilia A
Bibliographic record
Abstract
The development of neutralizing FVIII antibodies is the most serious complication of hemophilia A treatment. The currently known patient- and treatment-related risk factors for inhibitor development do not accurately predict this adverse event in all patients. The composition of the gut microbiota has been shown to influence immune-mediated diseases at distant anatomical sites (eg, lungs, brain, and joints). We demonstrate that a disrupted gut microbiota can be created in a mouse model of hemophilia A using a broad-spectrum antibiotic. Under controlled conditions, this sustained dysbiosis was associated with an increase in splenic B cells and the development of higher titer, FVIII-specific immunoglobulin G antibodies after FVIII challenge. Splenic and mesenteric lymph node cytokines, T cells, and dendritic cells were unaffected before administration of FVIII. However, the immune transcriptome of both aforementioned secondary lymphoid organs was significantly modified. Short-chain fatty acids (SCFAs), which are immunomodulatory microbial metabolites, were depleted in cecal contents of the dysbiotic mice. Furthermore, supplementation of the drinking water with butyrate, the most immunologically active SCFA, successfully achieved attenuation of the FVIII immune response. Collectively, data from this exploratory study suggest that the composition of the gut microbiota alters the FVIII immune response via the action of specific microbial metabolites on the immune cell transcriptome and that oral supplementation with butyrate effectively reduces the FVIII immune response.
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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".