Gut Microbiota and Short-Chain Fatty Acid Profiles in Facioscapulohumeral Dystrophy: Associations with Epigenetic Alterations
Bibliographic record
Abstract
BACKGROUND: Gut microbiota (GM) affects muscle homeostasis, and growing evidence indicates dysbiosis of GM may be a contributing factor in the pathogenesis of dystrophies. Furthermore, GM metabolites can interact with DNA methylation. Facioscapulohumeral muscular dystrophy (FSHD) is the second common dystrophy with hypomethylation of DR1 and 5P regions of D4Z4 repeat on 4qter. OBJECTIVE: Considering alteration of GM may be a contributing factor, we investigated (i) GM alterations and (ii) the correlation of microbial-derived free fatty acids (FFAs) with methylation of DR1 and 5P regions in FSHD. METHODS: Twenty-eight FSHD patients and 28 gender-age-matched controls were included. GM characterisation was performed through 16S-rRNA sequencing. Methylation levels of DR1 and 5P regions were assessed by bisulphite sequencing. Faecal and circulating FFAs including short-chain fatty acids (SCFAs), medium-chain fatty acids (MCFAs) and long-chain fatty acids (LCFAs) were analysed with gas chromatography-mass spectrometry. RESULTS: FFAs in patients, while no correlation was identified in healthy controls. The severely affected patients exhibited a notable increase in the prevalence of Pasteurellaceae, while the FFA profile was similar among mild and severely affected patients. This is the first study revealing that FSHD patients showed compositional and functional GM dysbiosis. A strong association between proximal D4Z4 hypomethylation with microbial-derived SCFAs was identified. CONCLUSION: These findings suggest that GM modulation with its metabolites could be a promising strategy for interventions in FSHD management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".