Specific gut bacterial strains produce short-chain fatty acids (SCFAs) that cross the blood-brain barrier and directly modulate α-synuclein aggregation through epigenetic modifications of chaperone proteins. Therapeutic supplementation with SCFA-producing bacteria could prevent or reverse pathological protein aggregation in PD.
Curated pathway from expert analysis
graph TD
A["Gut Microbiome<br/>B. longum, F. prausnitzii<br/>A. muciniphila"] --> B["SCFA Production<br/>Butyrate, Propionate<br/>Acetate"]
B --> C["Blood-Brain Barrier<br/>Crossing via MCT1/MCT2<br/>Transporters"]
C --> D["HDAC Inhibition<br/>HDAC1, HDAC3, HDAC6<br/>Suppression"]
D --> E["Histone Hyperacetylation<br/>H3/H4 Acetylation<br/>Chromatin Remodeling"]
E --> F["HSF1/NF-Y Transcription<br/>Factor Activation"]
F --> G["HSPA1A Upregulation<br/>Heat Shock Protein 70<br/>Expression"]
C --> H["DNMT1 Inhibition<br/>DNA Methylation<br/>Reduction"]
H --> I["Neuroprotective Gene<br/>Demethylation"]
G --> J["Enhanced Protein<br/>Chaperone Activity"]
I --> J
J --> K["alpha-Synuclein<br/>Disaggregation"]
L["alpha-Synuclein<br/>Pathological Aggregates"] --> K
K --> M["Reduced Neuronal<br/>Toxicity"]
M --> N["Neuroprotection<br/>Disease Modification"]
O["Therapeutic Intervention<br/>SCFA Supplementation<br/>Probiotic Administration"] --> BMedian TPM across 13 brain regions for SNCA, HSPA1A, DNMT1 from GTEx v10.
No curated ClinVar variants loaded for this hypothesis.
Run scripts/backfill_clinvar_variants.py to fetch P/LP/VUS variants.
No DepMap CRISPR Chronos data found for SNCA, HSPA1A, DNMT1.
Run python3 scripts/backfill_hypothesis_depmap.py to populate.
| Prediction | Predicted | Observed | Status | Conf |
|---|---|---|---|---|
| If hypothesis is true, intervention incorporate engineered bacterial strains with enhanced SCFA production capacity and targeted delivery mechanisms, including synthetic biology approaches to optimize | incorporate engineered bacterial strains with enhanced SCFA production capacity and targeted delivery mechanisms, including synthetic biology approaches to opti | — no observation — | pending | 0.40 |
| If hypothesis is true, intervention integrate pharmacogenomic profiling, microbiome analysis, and metabolomic signatures to optimize strain selection and dosing for individual patients | integrate pharmacogenomic profiling, microbiome analysis, and metabolomic signatures to optimize strain selection and dosing for individual patients | — no observation — | pending | 0.40 |