What are the mechanisms by which gut microbiome dysbiosis influences Parkinson's disease pathogenesis through the gut-brain axis?¶
Notebook ID: nb-SDA-2026-04-01-gap-20260401-225149 · Analysis: SDA-2026-04-01-gap-20260401-225149 · Generated: 2026-04-20T09:02:01
Research question¶
What are the mechanisms by which gut microbiome dysbiosis influences Parkinson's disease pathogenesis through the gut-brain axis?
Approach¶
This notebook is generated programmatically from real Forge tool calls and SciDEX debate data. Forge tools used: PubMed Search, MyGene, STRING PPI, Reactome pathways, Enrichr.
Debate Summary¶
Quality score: 0.95 · Rounds: 4
1. Target gene annotations (MyGene + Human Protein Atlas)¶
In [1]:
import pandas as pd
ann_rows = [{'gene': 'AADC', 'name': 'dopa decarboxylase', 'protein_class': '—', 'disease_involvement': '—'}, {'gene': 'AGER', 'name': 'advanced glycosylation end-product specific receptor', 'protein_class': '—', 'disease_involvement': '—'}, {'gene': 'AHR', 'name': 'aryl hydrocarbon receptor', 'protein_class': "['Cancer-related genes', 'Disease related genes', 'FDA appro", 'disease_involvement': "['Cancer-related genes', 'Disease variant', 'FDA approved drug targets', 'Retini"}, {'gene': 'AIM2', 'name': 'absent in melanoma 2', 'protein_class': "['Predicted intracellular proteins']", 'disease_involvement': "['Tumor suppressor']"}, {'gene': 'BDNF', 'name': 'brain derived neurotrophic factor', 'protein_class': "['Cancer-related genes', 'Human disease related genes', 'Pla", 'disease_involvement': "['Cancer-related genes']"}, {'gene': 'CASP1', 'name': 'caspase 1', 'protein_class': "['Cancer-related genes', 'Enzymes', 'Predicted intracellular", 'disease_involvement': "['Cancer-related genes']"}, {'gene': 'CHRNA7', 'name': 'cholinergic receptor nicotinic alpha 7 subunit', 'protein_class': "['FDA approved drug targets', 'Human disease related genes',", 'disease_involvement': "['FDA approved drug targets']"}, {'gene': 'CLDN1', 'name': 'claudin 1', 'protein_class': "['Disease related genes', 'Human disease related genes', 'Po", 'disease_involvement': "['Ichthyosis']"}, {'gene': 'CSGA', 'name': '—', 'protein_class': '—', 'disease_involvement': '—'}, {'gene': 'DDC', 'name': 'dopa decarboxylase', 'protein_class': "['Disease related genes', 'Enzymes', 'FDA approved drug targ", 'disease_involvement': "['Disease variant', 'FDA approved drug targets']"}, {'gene': 'DNMT1', 'name': 'DNA methyltransferase 1', 'protein_class': "['Disease related genes', 'Enzymes', 'Essential proteins', '", 'disease_involvement': "['Deafness', 'Disease variant', 'FDA approved drug targets', 'Neuropathy']"}, {'gene': 'GLP1R', 'name': 'glucagon like peptide 1 receptor', 'protein_class': "['FDA approved drug targets', 'G-protein coupled receptors',", 'disease_involvement': "['FDA approved drug targets']"}, {'gene': 'GPR109A', 'name': 'hydroxycarboxylic acid receptor 2', 'protein_class': '—', 'disease_involvement': '—'}, {'gene': 'HSPA1A', 'name': 'heat shock protein family A (Hsp70) member 1A', 'protein_class': '—', 'disease_involvement': '—'}, {'gene': 'IL10', 'name': 'interleukin 10', 'protein_class': "['Cancer-related genes', 'Candidate cardiovascular disease g", 'disease_involvement': "['Cancer-related genes']"}, {'gene': 'IL1B', 'name': 'interleukin 1 beta', 'protein_class': "['Cancer-related genes', 'Candidate cardiovascular disease g", 'disease_involvement': "['Cancer-related genes', 'FDA approved drug targets']"}, {'gene': 'MLCK', 'name': 'myosin light chain kinase 3', 'protein_class': '—', 'disease_involvement': '—'}, {'gene': 'NLRP3', 'name': 'NLR family pyrin domain containing 3', 'protein_class': "['Cancer-related genes', 'Human disease related genes', 'Pre", 'disease_involvement': "['Cancer-related genes']"}, {'gene': 'OCLN', 'name': 'occludin', 'protein_class': "['Disease related genes', 'Human disease related genes', 'Po", 'disease_involvement': "['Disease variant']"}, {'gene': 'PPIF', 'name': 'peptidylprolyl isomerase F', 'protein_class': "['Enzymes', 'FDA approved drug targets', 'Metabolic proteins", 'disease_involvement': "['FDA approved drug targets']"}]
pd.DataFrame(ann_rows)
| gene | name | protein_class | disease_involvement | |
|---|---|---|---|---|
| 0 | AADC | dopa decarboxylase | — | — |
| 1 | AGER | advanced glycosylation end-product specific re... | — | — |
| 2 | AHR | aryl hydrocarbon receptor | ['Cancer-related genes', 'Disease related gene... | ['Cancer-related genes', 'Disease variant', 'F... |
| 3 | AIM2 | absent in melanoma 2 | ['Predicted intracellular proteins'] | ['Tumor suppressor'] |
| 4 | BDNF | brain derived neurotrophic factor | ['Cancer-related genes', 'Human disease relate... | ['Cancer-related genes'] |
| 5 | CASP1 | caspase 1 | ['Cancer-related genes', 'Enzymes', 'Predicted... | ['Cancer-related genes'] |
| 6 | CHRNA7 | cholinergic receptor nicotinic alpha 7 subunit | ['FDA approved drug targets', 'Human disease r... | ['FDA approved drug targets'] |
| 7 | CLDN1 | claudin 1 | ['Disease related genes', 'Human disease relat... | ['Ichthyosis'] |
| 8 | CSGA | — | — | — |
| 9 | DDC | dopa decarboxylase | ['Disease related genes', 'Enzymes', 'FDA appr... | ['Disease variant', 'FDA approved drug targets'] |
| 10 | DNMT1 | DNA methyltransferase 1 | ['Disease related genes', 'Enzymes', 'Essentia... | ['Deafness', 'Disease variant', 'FDA approved ... |
| 11 | GLP1R | glucagon like peptide 1 receptor | ['FDA approved drug targets', 'G-protein coupl... | ['FDA approved drug targets'] |
| 12 | GPR109A | hydroxycarboxylic acid receptor 2 | — | — |
| 13 | HSPA1A | heat shock protein family A (Hsp70) member 1A | — | — |
| 14 | IL10 | interleukin 10 | ['Cancer-related genes', 'Candidate cardiovasc... | ['Cancer-related genes'] |
| 15 | IL1B | interleukin 1 beta | ['Cancer-related genes', 'Candidate cardiovasc... | ['Cancer-related genes', 'FDA approved drug ta... |
| 16 | MLCK | myosin light chain kinase 3 | — | — |
| 17 | NLRP3 | NLR family pyrin domain containing 3 | ['Cancer-related genes', 'Human disease relate... | ['Cancer-related genes'] |
| 18 | OCLN | occludin | ['Disease related genes', 'Human disease relat... | ['Disease variant'] |
| 19 | PPIF | peptidylprolyl isomerase F | ['Enzymes', 'FDA approved drug targets', 'Meta... | ['FDA approved drug targets'] |
2. GO Biological Process enrichment (Enrichr)¶
In [2]:
go_bp = [{'rank': 1, 'term': 'Regulation Of Inflammatory Response (GO:0050727)', 'p_value': 1.8166472903495823e-13, 'odds_ratio': 170.31034482758622, 'genes': ['AIM2', 'IL1B', 'CASP1', 'NLRP3', 'AHR', 'AGER', 'TLR4', 'SNCA']}, {'rank': 2, 'term': 'Positive Regulation Of Inflammatory Response (GO:0050729)', 'p_value': 1.5386754484756792e-11, 'odds_ratio': 202.9591836734694, 'genes': ['AIM2', 'IL1B', 'CASP1', 'NLRP3', 'TLR4', 'SNCA']}, {'rank': 3, 'term': 'Positive Regulation Of Defense Response (GO:0031349)', 'p_value': 4.504919112958996e-11, 'odds_ratio': 168.38983050847457, 'genes': ['AIM2', 'IL1B', 'CASP1', 'NLRP3', 'TLR4', 'SNCA']}, {'rank': 4, 'term': 'Positive Regulation Of Response To External Stimulus (GO:0032103)', 'p_value': 1.7477293684651717e-10, 'odds_ratio': 133.1476510067114, 'genes': ['AIM2', 'IL1B', 'CASP1', 'NLRP3', 'TLR4', 'SNCA']}, {'rank': 5, 'term': 'Cellular Response To Molecule Of Bacterial Origin (GO:0071219)', 'p_value': 4.817494616694516e-09, 'odds_ratio': 126.76020408163265, 'genes': ['IL1B', 'CASP1', 'NLRP3', 'AHR', 'TLR4']}, {'rank': 6, 'term': 'Positive Regulation Of NF-kappaB Transcription Factor Activity (GO:0051092)', 'p_value': 1.8005744353667828e-08, 'odds_ratio': 96.40913508260446, 'genes': ['AIM2', 'IL1B', 'NLRP3', 'AGER', 'TLR4']}, {'rank': 7, 'term': 'Positive Regulation Of NIK/NF-kappaB Signaling (GO:1901224)', 'p_value': 2.1406875727816016e-08, 'odds_ratio': 203.4591836734694, 'genes': ['IL1B', 'NLRP3', 'AGER', 'TLR4']}, {'rank': 8, 'term': 'Response To Lipopolysaccharide (GO:0032496)', 'p_value': 2.257207399455398e-08, 'odds_ratio': 91.99443413729128, 'genes': ['IL1B', 'CASP1', 'NLRP3', 'TLR4', 'SNCA']}, {'rank': 9, 'term': 'Positive Regulation Of Interleukin-1 Beta Production (GO:0032731)', 'p_value': 2.8840214365837964e-08, 'odds_ratio': 188.06603773584905, 'genes': ['AIM2', 'CASP1', 'NLRP3', 'TLR4']}, {'rank': 10, 'term': 'Positive Regulation Of Interleukin-1 Production (GO:0032732)', 'p_value': 4.628601835611114e-08, 'odds_ratio': 166.06666666666666, 'genes': ['AIM2', 'CASP1', 'NLRP3', 'TLR4']}]
go_df = pd.DataFrame(go_bp)[['term','p_value','odds_ratio','genes']]
go_df['p_value'] = go_df['p_value'].apply(lambda p: f'{p:.2e}')
go_df['odds_ratio'] = go_df['odds_ratio'].round(1)
go_df['term'] = go_df['term'].str[:60]
go_df['n_hits'] = go_df['genes'].apply(len)
go_df['genes'] = go_df['genes'].apply(lambda g: ', '.join(g))
go_df[['term','n_hits','p_value','odds_ratio','genes']]
| term | n_hits | p_value | odds_ratio | genes | |
|---|---|---|---|---|---|
| 0 | Regulation Of Inflammatory Response (GO:0050727) | 8 | 1.82e-13 | 170.3 | AIM2, IL1B, CASP1, NLRP3, AHR, AGER, TLR4, SNCA |
| 1 | Positive Regulation Of Inflammatory Response (... | 6 | 1.54e-11 | 203.0 | AIM2, IL1B, CASP1, NLRP3, TLR4, SNCA |
| 2 | Positive Regulation Of Defense Response (GO:00... | 6 | 4.50e-11 | 168.4 | AIM2, IL1B, CASP1, NLRP3, TLR4, SNCA |
| 3 | Positive Regulation Of Response To External St... | 6 | 1.75e-10 | 133.1 | AIM2, IL1B, CASP1, NLRP3, TLR4, SNCA |
| 4 | Cellular Response To Molecule Of Bacterial Ori... | 5 | 4.82e-09 | 126.8 | IL1B, CASP1, NLRP3, AHR, TLR4 |
| 5 | Positive Regulation Of NF-kappaB Transcription... | 5 | 1.80e-08 | 96.4 | AIM2, IL1B, NLRP3, AGER, TLR4 |
| 6 | Positive Regulation Of NIK/NF-kappaB Signaling... | 4 | 2.14e-08 | 203.5 | IL1B, NLRP3, AGER, TLR4 |
| 7 | Response To Lipopolysaccharide (GO:0032496) | 5 | 2.26e-08 | 92.0 | IL1B, CASP1, NLRP3, TLR4, SNCA |
| 8 | Positive Regulation Of Interleukin-1 Beta Prod... | 4 | 2.88e-08 | 188.1 | AIM2, CASP1, NLRP3, TLR4 |
| 9 | Positive Regulation Of Interleukin-1 Productio... | 4 | 4.63e-08 | 166.1 | AIM2, CASP1, NLRP3, TLR4 |
In [3]:
import matplotlib.pyplot as plt
import numpy as np
go_bp = [{'rank': 1, 'term': 'Regulation Of Inflammatory Response (GO:0050727)', 'p_value': 1.8166472903495823e-13, 'odds_ratio': 170.31034482758622, 'genes': ['AIM2', 'IL1B', 'CASP1', 'NLRP3', 'AHR', 'AGER', 'TLR4', 'SNCA']}, {'rank': 2, 'term': 'Positive Regulation Of Inflammatory Response (GO:0050729)', 'p_value': 1.5386754484756792e-11, 'odds_ratio': 202.9591836734694, 'genes': ['AIM2', 'IL1B', 'CASP1', 'NLRP3', 'TLR4', 'SNCA']}, {'rank': 3, 'term': 'Positive Regulation Of Defense Response (GO:0031349)', 'p_value': 4.504919112958996e-11, 'odds_ratio': 168.38983050847457, 'genes': ['AIM2', 'IL1B', 'CASP1', 'NLRP3', 'TLR4', 'SNCA']}, {'rank': 4, 'term': 'Positive Regulation Of Response To External Stimulus (GO:0032103)', 'p_value': 1.7477293684651717e-10, 'odds_ratio': 133.1476510067114, 'genes': ['AIM2', 'IL1B', 'CASP1', 'NLRP3', 'TLR4', 'SNCA']}, {'rank': 5, 'term': 'Cellular Response To Molecule Of Bacterial Origin (GO:0071219)', 'p_value': 4.817494616694516e-09, 'odds_ratio': 126.76020408163265, 'genes': ['IL1B', 'CASP1', 'NLRP3', 'AHR', 'TLR4']}, {'rank': 6, 'term': 'Positive Regulation Of NF-kappaB Transcription Factor Activity (GO:0051092)', 'p_value': 1.8005744353667828e-08, 'odds_ratio': 96.40913508260446, 'genes': ['AIM2', 'IL1B', 'NLRP3', 'AGER', 'TLR4']}, {'rank': 7, 'term': 'Positive Regulation Of NIK/NF-kappaB Signaling (GO:1901224)', 'p_value': 2.1406875727816016e-08, 'odds_ratio': 203.4591836734694, 'genes': ['IL1B', 'NLRP3', 'AGER', 'TLR4']}, {'rank': 8, 'term': 'Response To Lipopolysaccharide (GO:0032496)', 'p_value': 2.257207399455398e-08, 'odds_ratio': 91.99443413729128, 'genes': ['IL1B', 'CASP1', 'NLRP3', 'TLR4', 'SNCA']}]
terms = [t['term'][:45] for t in go_bp][::-1]
neglogp = [-np.log10(max(t['p_value'], 1e-300)) for t in go_bp][::-1]
fig, ax = plt.subplots(figsize=(9, 4.5))
ax.barh(terms, neglogp, color='#4fc3f7')
ax.set_xlabel('-log10(p-value)')
ax.set_title('Top GO:BP enrichment (Enrichr)')
ax.grid(axis='x', alpha=0.3)
plt.tight_layout(); plt.show()
3. STRING protein interaction network¶
In [4]:
ppi = [{'protein1': 'IL1B', 'protein2': 'TLR4', 'score': 0.633, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.633}, {'protein1': 'IL1B', 'protein2': 'CASP1', 'score': 0.79, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.457, 'dscore': 0, 'tscore': 0.629}, {'protein1': 'NLRP3', 'protein2': 'CASP1', 'score': 0.998, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.292, 'dscore': 0.9, 'tscore': 0.983}, {'protein1': 'NLRP3', 'protein2': 'AIM2', 'score': 0.998, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0.982}, {'protein1': 'AIM2', 'protein2': 'CASP1', 'score': 0.999, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.626, 'dscore': 0.9, 'tscore': 0.982}, {'protein1': 'TLR4', 'protein2': 'SNCA', 'score': 0.518, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.518}, {'protein1': 'TLR4', 'protein2': 'AGER', 'score': 0.75, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.75}, {'protein1': 'TH', 'protein2': 'SNCA', 'score': 0.812, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.812}]
ppi_df = pd.DataFrame(ppi).sort_values('score', ascending=False)
display_cols = [c for c in ['protein1','protein2','score','escore','tscore'] if c in ppi_df.columns]
print(f'{len(ppi_df)} STRING edges')
ppi_df[display_cols].head(20)
8 STRING edges
| protein1 | protein2 | score | escore | tscore | |
|---|---|---|---|---|---|
| 4 | AIM2 | CASP1 | 0.999 | 0.626 | 0.982 |
| 3 | NLRP3 | AIM2 | 0.998 | 0.000 | 0.982 |
| 2 | NLRP3 | CASP1 | 0.998 | 0.292 | 0.983 |
| 7 | TH | SNCA | 0.812 | 0.000 | 0.812 |
| 1 | IL1B | CASP1 | 0.790 | 0.457 | 0.629 |
| 6 | TLR4 | AGER | 0.750 | 0.000 | 0.750 |
| 0 | IL1B | TLR4 | 0.633 | 0.000 | 0.633 |
| 5 | TLR4 | SNCA | 0.518 | 0.000 | 0.518 |
In [5]:
import math
ppi = [{'protein1': 'IL1B', 'protein2': 'TLR4', 'score': 0.633, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.633}, {'protein1': 'IL1B', 'protein2': 'CASP1', 'score': 0.79, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.457, 'dscore': 0, 'tscore': 0.629}, {'protein1': 'NLRP3', 'protein2': 'CASP1', 'score': 0.998, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.292, 'dscore': 0.9, 'tscore': 0.983}, {'protein1': 'NLRP3', 'protein2': 'AIM2', 'score': 0.998, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0.982}, {'protein1': 'AIM2', 'protein2': 'CASP1', 'score': 0.999, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.626, 'dscore': 0.9, 'tscore': 0.982}, {'protein1': 'TLR4', 'protein2': 'SNCA', 'score': 0.518, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.518}, {'protein1': 'TLR4', 'protein2': 'AGER', 'score': 0.75, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.75}, {'protein1': 'TH', 'protein2': 'SNCA', 'score': 0.812, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.812}]
if ppi:
nodes = sorted({p for e in ppi for p in (e['protein1'], e['protein2'])})
n = len(nodes)
pos = {n_: (math.cos(2*math.pi*i/n), math.sin(2*math.pi*i/n)) for i, n_ in enumerate(nodes)}
fig, ax = plt.subplots(figsize=(7, 7))
for e in ppi:
x1,y1 = pos[e['protein1']]; x2,y2 = pos[e['protein2']]
ax.plot([x1,x2],[y1,y2], color='#888', alpha=0.3+0.5*e.get('score',0))
for name,(x,y) in pos.items():
ax.scatter([x],[y], s=450, color='#ffd54f', edgecolors='#333', zorder=3)
ax.annotate(name, (x,y), ha='center', va='center', fontsize=8, fontweight='bold', zorder=4)
ax.set_aspect('equal'); ax.axis('off')
ax.set_title(f'STRING PPI network ({len(ppi)} edges)')
plt.tight_layout(); plt.show()
4. Reactome pathway footprint¶
In [6]:
pw_rows = [{'gene': 'AADC', 'n_pathways': 2, 'top_pathway': 'Catecholamine biosynthesis'}, {'gene': 'AGER', 'n_pathways': 3, 'top_pathway': 'TAK1-dependent IKK and NF-kappa-B activation'}, {'gene': 'AHR', 'n_pathways': 5, 'top_pathway': 'PPARA activates gene expression'}, {'gene': 'AIM2', 'n_pathways': 2, 'top_pathway': 'Cytosolic sensors of pathogen-associated DNA'}, {'gene': 'BDNF', 'n_pathways': 8, 'top_pathway': 'PIP3 activates AKT signaling'}, {'gene': 'CASP1', 'n_pathways': 8, 'top_pathway': 'NOD1/2 Signaling Pathway'}, {'gene': 'CHRNA7', 'n_pathways': 1, 'top_pathway': 'Highly calcium permeable postsynaptic nicotinic acetylcholine receptor'}, {'gene': 'CLDN1', 'n_pathways': 1, 'top_pathway': 'Tight junction interactions'}, {'gene': 'CSGA', 'n_pathways': 0, 'top_pathway': '—'}, {'gene': 'DDC', 'n_pathways': 2, 'top_pathway': 'Catecholamine biosynthesis'}, {'gene': 'DNMT1', 'n_pathways': 7, 'top_pathway': 'PRC2 methylates histones and DNA'}, {'gene': 'GLP1R', 'n_pathways': 3, 'top_pathway': 'Glucagon-like Peptide-1 (GLP1) regulates insulin secretion'}, {'gene': 'GPR109A', 'n_pathways': 3, 'top_pathway': 'Hydroxycarboxylic acid-binding receptors'}, {'gene': 'HSPA1A', 'n_pathways': 8, 'top_pathway': 'Viral RNP Complexes in the Host Cell Nucleus'}, {'gene': 'IL10', 'n_pathways': 6, 'top_pathway': 'Interleukin-10 signaling'}, {'gene': 'IL1B', 'n_pathways': 7, 'top_pathway': 'Interleukin-1 processing'}, {'gene': 'MLCK', 'n_pathways': 0, 'top_pathway': '—'}, {'gene': 'NLRP3', 'n_pathways': 6, 'top_pathway': 'Metalloprotease DUBs'}, {'gene': 'OCLN', 'n_pathways': 2, 'top_pathway': 'Apoptotic cleavage of cell adhesion proteins'}, {'gene': 'PPIF', 'n_pathways': 0, 'top_pathway': '—'}]
pd.DataFrame(pw_rows).sort_values('n_pathways', ascending=False)
| gene | n_pathways | top_pathway | |
|---|---|---|---|
| 5 | CASP1 | 8 | NOD1/2 Signaling Pathway |
| 13 | HSPA1A | 8 | Viral RNP Complexes in the Host Cell Nucleus |
| 4 | BDNF | 8 | PIP3 activates AKT signaling |
| 10 | DNMT1 | 7 | PRC2 methylates histones and DNA |
| 15 | IL1B | 7 | Interleukin-1 processing |
| 14 | IL10 | 6 | Interleukin-10 signaling |
| 17 | NLRP3 | 6 | Metalloprotease DUBs |
| 2 | AHR | 5 | PPARA activates gene expression |
| 12 | GPR109A | 3 | Hydroxycarboxylic acid-binding receptors |
| 11 | GLP1R | 3 | Glucagon-like Peptide-1 (GLP1) regulates insul... |
| 1 | AGER | 3 | TAK1-dependent IKK and NF-kappa-B activation |
| 0 | AADC | 2 | Catecholamine biosynthesis |
| 3 | AIM2 | 2 | Cytosolic sensors of pathogen-associated DNA |
| 18 | OCLN | 2 | Apoptotic cleavage of cell adhesion proteins |
| 9 | DDC | 2 | Catecholamine biosynthesis |
| 6 | CHRNA7 | 1 | Highly calcium permeable postsynaptic nicotini... |
| 7 | CLDN1 | 1 | Tight junction interactions |
| 8 | CSGA | 0 | — |
| 16 | MLCK | 0 | — |
| 19 | PPIF | 0 | — |
5. Hypothesis ranking (20 hypotheses)¶
In [7]:
hyp_data = [('Gut Microbiome Remodeling to Prevent Systemic NLRP3 Pri', 0.888), ('Microglial AIM2 Inflammasome as the Primary Driver of T', 0.824), ('Astrocyte-Intrinsic NLRP3 Inflammasome Activation by Al', 0.822), ('Mitochondrial DAMPs-Driven AIM2 Inflammasome Activation', 0.805), ('Calcium-Dysregulated mPTP Opening as an Alternative mtD', 0.804), ('Mitochondrial DNA-Driven AIM2 Inflammasome Activation i', 0.803), ('Selective TLR4 Modulation to Prevent Gut-Derived Neuroi', 0.789), ('Microbial Inflammasome Priming Prevention', 0.723), ('Targeted Butyrate Supplementation for Microglial Phenot', 0.7), ('Enhancing Vagal Cholinergic Signaling to Restore Gut-Br', 0.669), ('Gut Barrier Permeability-α-Synuclein Axis Modulation', 0.663), ('Vagal Afferent Microbial Signal Modulation', 0.66), ('Targeting Bacterial Curli Fibrils to Prevent α-Synuclei', 0.642), ('Microbial Metabolite-Mediated α-Synuclein Disaggregatio', 0.626), ('Enteric Nervous System Prion-Like Propagation Blockade', 0.625), ('Blocking AGE-RAGE Signaling in Enteric Glia to Prevent ', 0.613), ('Restoring Neuroprotective Tryptophan Metabolism via Tar', 0.612), ('Correcting Gut Microbial Dopamine Imbalance to Support ', 0.606), ('Microbiome-Derived Tryptophan Metabolite Neuroprotectio', 0.605), ('Bacterial Enzyme-Mediated Dopamine Precursor Synthesis', 0.59)]
titles = [h[0] for h in hyp_data][::-1]
scores = [h[1] for h in hyp_data][::-1]
fig, ax = plt.subplots(figsize=(10, max(8, len(titles)*0.4)))
colors = ['#ef5350' if s >= 0.6 else '#ffa726' if s >= 0.5 else '#66bb6a' for s in scores]
ax.barh(range(len(titles)), scores, color=colors)
ax.set_yticks(range(len(titles))); ax.set_yticklabels(titles, fontsize=7)
ax.set_xlabel('Composite Score'); ax.set_title("What are the mechanisms by which gut microbiome dysbiosis influences Parkinson's disease pathogenesis through the gut-brain axis?")
ax.grid(axis='x', alpha=0.3)
plt.tight_layout(); plt.show()
6. Score dimension heatmap (top 10)¶
In [8]:
labels = ['Gut Microbiome Remodeling to Prevent Sys', 'Microglial AIM2 Inflammasome as the Prim', 'Astrocyte-Intrinsic NLRP3 Inflammasome A', 'Mitochondrial DAMPs-Driven AIM2 Inflamma', 'Calcium-Dysregulated mPTP Opening as an ', 'Mitochondrial DNA-Driven AIM2 Inflammaso', 'Selective TLR4 Modulation to Prevent Gut', 'Microbial Inflammasome Priming Preventio', 'Targeted Butyrate Supplementation for Mi', 'Enhancing Vagal Cholinergic Signaling to']
matrix = np.array([[0, 0, 0, 0.8, 0.037, 0.8, 0.7, 0.9, 0.6], [0, 0, 0, 0.8, 0.037, 0.8, 0.7, 0.9, 0.6], [0, 0, 0, 0.8, 0.037, 0.8, 0.7, 0.9, 0.6], [0, 0, 0, 0.8, 0.037, 0.8, 0.7, 0.9, 0.6], [0, 0, 0, 0.8, 0.037, 0.8, 0.7, 0.9, 0.6], [0, 0, 0, 0.8, 0.037, 0.8, 0.7, 0.9, 0.6], [0.7, 0.8, 0.7, 0.7, 0.13, 0.7, 0.7, 0.8, 0.6], [0.7, 0.8, 0.8, 0.8, 0.037, 0.8, 0.7, 0.9, 0.6], [0.6, 0.9, 0.8, 0.8, 0.13, 0.8, 0.8, 0.9, 0.9], [0.8, 0.7, 0.7, 0.6, 0.328, 0.6, 0.6, 0.6, 0.8]])
dims = ['novelty_score', 'feasibility_score', 'impact_score', 'mechanistic_plausibility_score', 'clinical_relevance_score', 'data_availability_score', 'reproducibility_score', 'druggability_score', 'safety_profile_score']
if matrix.size:
fig, ax = plt.subplots(figsize=(10, 5))
im = ax.imshow(matrix, cmap='RdYlGn', aspect='auto', vmin=0, vmax=1)
ax.set_xticks(range(len(dims)))
ax.set_xticklabels([d.replace('_score','').replace('_',' ').title() for d in dims],
rotation=45, ha='right', fontsize=8)
ax.set_yticks(range(len(labels))); ax.set_yticklabels(labels, fontsize=7)
ax.set_title('Score dimensions — top hypotheses')
plt.colorbar(im, ax=ax, shrink=0.8)
plt.tight_layout(); plt.show()
else:
print('No score data available')
7. PubMed literature per hypothesis¶
Hypothesis 1: Gut Microbiome Remodeling to Prevent Systemic NLRP3 Priming in Neurode¶
Target genes: NLRP3, CASP1, IL1B, PYCARD · Composite score: 0.888
Molecular Mechanism and Rationale¶
The core molecular mechanism involves a two-step process where intestinal dysbiosis creates systemic NLRP3 inflammasome priming through bacterial lipopolysaccharide (LPS) translocation, followed by secondary activation triggers in the central nervous system. Cir
In [9]:
lit_data = [{'year': '2020', 'journal': 'Prog Neurobiol', 'title': 'Microbiota-gut-brain axis in health and disease: Is NLRP3 inflammasome at the cr', 'pmid': '32473843'}, {'year': '2024', 'journal': 'Heliyon', 'title': 'Electroacupuncture blocked motor dysfunction and gut barrier damage by modulatin', 'pmid': '38774094'}, {'year': '2020', 'journal': 'Int J Mol Sci', 'title': 'Focus on the Role of NLRP3 Inflammasome in Diseases.', 'pmid': '32545788'}, {'year': '2025', 'journal': 'Exp Neurobiol', 'title': 'G protein-coupled Estrogen Receptor Activation Exerts Protective Effects via Mod', 'pmid': '41152022'}, {'year': '2025', 'journal': 'J Neuroimmune Pharmacol', 'title': 'Modulation of Intestinal Inflammation and Protection of Dopaminergic Neurons in ', 'pmid': '39826038'}]
if lit_data:
df = pd.DataFrame(lit_data)
print(f'{len(lit_data)} PubMed results')
display(df)
else:
print('No PubMed results')
5 PubMed results
| year | journal | title | pmid | |
|---|---|---|---|---|
| 0 | 2020 | Prog Neurobiol | Microbiota-gut-brain axis in health and diseas... | 32473843 |
| 1 | 2024 | Heliyon | Electroacupuncture blocked motor dysfunction a... | 38774094 |
| 2 | 2020 | Int J Mol Sci | Focus on the Role of NLRP3 Inflammasome in Dis... | 32545788 |
| 3 | 2025 | Exp Neurobiol | G protein-coupled Estrogen Receptor Activation... | 41152022 |
| 4 | 2025 | J Neuroimmune Pharmacol | Modulation of Intestinal Inflammation and Prot... | 39826038 |
Hypothesis 2: Microglial AIM2 Inflammasome as the Primary Driver of TDP-43 Proteinop¶
Target genes: AIM2, CASP1, IL1B, PYCARD, TARDBP · Composite score: 0.824
Molecular Mechanism and Rationale¶
The AIM2 inflammasome in microglia represents a critical cytosolic DNA sensing pathway that bridges TDP-43 proteinopathy-induced mitochondrial dysfunction with sustained neuroinflammation in ALS and FTD. When TDP-43 mislocalizes from the nucleus to the cytoplasm
In [10]:
lit_data = [{'year': '2025', 'journal': 'Front Immunol', 'title': "Loss of DJ-1 alleviates microglia-mediated neuroinflammation in Parkinson's dise", 'pmid': '40990010'}, {'year': '2024', 'journal': 'Heliyon', 'title': 'Crocin attenuates the lipopolysaccharide-induced neuroinflammation via expressio', 'pmid': '38356604'}, {'year': '2022', 'journal': 'Glia', 'title': 'Microglial AIM2 alleviates antiviral-related neuro-inflammation in mouse models ', 'pmid': '35959803'}, {'year': '2021', 'journal': 'J Neuroimmunol', 'title': 'A selective NLRP3 inflammasome inhibitor attenuates behavioral deficits and neur', 'pmid': '33714750'}, {'year': '2024', 'journal': 'Front Immunol', 'title': 'The roles of AIM2 in neurodegenerative diseases: insights and therapeutic implic', 'pmid': '39076969'}]
if lit_data:
df = pd.DataFrame(lit_data)
print(f'{len(lit_data)} PubMed results')
display(df)
else:
print('No PubMed results')
5 PubMed results
| year | journal | title | pmid | |
|---|---|---|---|---|
| 0 | 2025 | Front Immunol | Loss of DJ-1 alleviates microglia-mediated neu... | 40990010 |
| 1 | 2024 | Heliyon | Crocin attenuates the lipopolysaccharide-induc... | 38356604 |
| 2 | 2022 | Glia | Microglial AIM2 alleviates antiviral-related n... | 35959803 |
| 3 | 2021 | J Neuroimmunol | A selective NLRP3 inflammasome inhibitor atten... | 33714750 |
| 4 | 2024 | Front Immunol | The roles of AIM2 in neurodegenerative disease... | 39076969 |
Hypothesis 3: Astrocyte-Intrinsic NLRP3 Inflammasome Activation by Alpha-Synuclein A¶
Target genes: NLRP3, CASP1, IL1B, PYCARD · Composite score: 0.822
Molecular Mechanism and Rationale¶
The NLRP3 inflammasome pathway in astrocytes represents a critical neuroinflammatory cascade initiated by alpha-synuclein (α-Syn) aggregate recognition and subsequent intracellular danger signal processing. Extracellular α-Syn fibrils bind to astrocytic Toll-lik
In [11]:
lit_data = [{'year': '2020', 'journal': 'Prog Neurobiol', 'title': 'Microbiota-gut-brain axis in health and disease: Is NLRP3 inflammasome at the cr', 'pmid': '32473843'}, {'year': '2024', 'journal': 'Heliyon', 'title': 'Electroacupuncture blocked motor dysfunction and gut barrier damage by modulatin', 'pmid': '38774094'}, {'year': '2020', 'journal': 'Int J Mol Sci', 'title': 'Focus on the Role of NLRP3 Inflammasome in Diseases.', 'pmid': '32545788'}, {'year': '2025', 'journal': 'Exp Neurobiol', 'title': 'G protein-coupled Estrogen Receptor Activation Exerts Protective Effects via Mod', 'pmid': '41152022'}, {'year': '2025', 'journal': 'J Neuroimmune Pharmacol', 'title': 'Modulation of Intestinal Inflammation and Protection of Dopaminergic Neurons in ', 'pmid': '39826038'}]
if lit_data:
df = pd.DataFrame(lit_data)
print(f'{len(lit_data)} PubMed results')
display(df)
else:
print('No PubMed results')
5 PubMed results
| year | journal | title | pmid | |
|---|---|---|---|---|
| 0 | 2020 | Prog Neurobiol | Microbiota-gut-brain axis in health and diseas... | 32473843 |
| 1 | 2024 | Heliyon | Electroacupuncture blocked motor dysfunction a... | 38774094 |
| 2 | 2020 | Int J Mol Sci | Focus on the Role of NLRP3 Inflammasome in Dis... | 32545788 |
| 3 | 2025 | Exp Neurobiol | G protein-coupled Estrogen Receptor Activation... | 41152022 |
| 4 | 2025 | J Neuroimmune Pharmacol | Modulation of Intestinal Inflammation and Prot... | 39826038 |
Hypothesis 4: Mitochondrial DAMPs-Driven AIM2 Inflammasome Activation in Neurodegene¶
Target genes: AIM2, CASP1, IL1B, PYCARD · Composite score: 0.805
Molecular Mechanism and Rationale¶
The AIM2 inflammasome pathway represents a critical cytosolic DNA-sensing mechanism that becomes aberrantly activated during neurodegeneration through mitochondrial dysfunction. Upon mitochondrial membrane permeabilization, fragmented mitochondrial DNA (mtDNA) t
In [12]:
lit_data = [{'year': '2025', 'journal': 'Front Immunol', 'title': "Loss of DJ-1 alleviates microglia-mediated neuroinflammation in Parkinson's dise", 'pmid': '40990010'}, {'year': '2024', 'journal': 'Heliyon', 'title': 'Crocin attenuates the lipopolysaccharide-induced neuroinflammation via expressio', 'pmid': '38356604'}, {'year': '2022', 'journal': 'Glia', 'title': 'Microglial AIM2 alleviates antiviral-related neuro-inflammation in mouse models ', 'pmid': '35959803'}, {'year': '2021', 'journal': 'J Neuroimmunol', 'title': 'A selective NLRP3 inflammasome inhibitor attenuates behavioral deficits and neur', 'pmid': '33714750'}, {'year': '2024', 'journal': 'Front Immunol', 'title': 'The roles of AIM2 in neurodegenerative diseases: insights and therapeutic implic', 'pmid': '39076969'}]
if lit_data:
df = pd.DataFrame(lit_data)
print(f'{len(lit_data)} PubMed results')
display(df)
else:
print('No PubMed results')
5 PubMed results
| year | journal | title | pmid | |
|---|---|---|---|---|
| 0 | 2025 | Front Immunol | Loss of DJ-1 alleviates microglia-mediated neu... | 40990010 |
| 1 | 2024 | Heliyon | Crocin attenuates the lipopolysaccharide-induc... | 38356604 |
| 2 | 2022 | Glia | Microglial AIM2 alleviates antiviral-related n... | 35959803 |
| 3 | 2021 | J Neuroimmunol | A selective NLRP3 inflammasome inhibitor atten... | 33714750 |
| 4 | 2024 | Front Immunol | The roles of AIM2 in neurodegenerative disease... | 39076969 |
Hypothesis 5: Calcium-Dysregulated mPTP Opening as an Alternative mtDNA Release Mech¶
Target genes: AIM2, CASP1, IL1B, PYCARD, PPIF · Composite score: 0.804
Molecular Mechanism and Rationale¶
The mPTP-mediated mtDNA release pathway operates through calcium-dependent conformational changes in cyclophilin D (PPIF), which regulates pore formation at the inner mitochondrial membrane in association with the adenine nucleotide translocator and voltage-depe
In [13]:
lit_data = [{'year': '2025', 'journal': 'Front Immunol', 'title': "Loss of DJ-1 alleviates microglia-mediated neuroinflammation in Parkinson's dise", 'pmid': '40990010'}, {'year': '2024', 'journal': 'Heliyon', 'title': 'Crocin attenuates the lipopolysaccharide-induced neuroinflammation via expressio', 'pmid': '38356604'}, {'year': '2022', 'journal': 'Glia', 'title': 'Microglial AIM2 alleviates antiviral-related neuro-inflammation in mouse models ', 'pmid': '35959803'}, {'year': '2021', 'journal': 'J Neuroimmunol', 'title': 'A selective NLRP3 inflammasome inhibitor attenuates behavioral deficits and neur', 'pmid': '33714750'}, {'year': '2024', 'journal': 'Front Immunol', 'title': 'The roles of AIM2 in neurodegenerative diseases: insights and therapeutic implic', 'pmid': '39076969'}]
if lit_data:
df = pd.DataFrame(lit_data)
print(f'{len(lit_data)} PubMed results')
display(df)
else:
print('No PubMed results')
5 PubMed results
| year | journal | title | pmid | |
|---|---|---|---|---|
| 0 | 2025 | Front Immunol | Loss of DJ-1 alleviates microglia-mediated neu... | 40990010 |
| 1 | 2024 | Heliyon | Crocin attenuates the lipopolysaccharide-induc... | 38356604 |
| 2 | 2022 | Glia | Microglial AIM2 alleviates antiviral-related n... | 35959803 |
| 3 | 2021 | J Neuroimmunol | A selective NLRP3 inflammasome inhibitor atten... | 33714750 |
| 4 | 2024 | Front Immunol | The roles of AIM2 in neurodegenerative disease... | 39076969 |
Hypothesis 6: Mitochondrial DNA-Driven AIM2 Inflammasome Activation in Neurodegenera¶
Target genes: AIM2, CASP1, IL1B, PYCARD · Composite score: 0.803
Molecular Mechanism and Rationale¶
The AIM2 inflammasome represents a critical cytosolic DNA-sensing pathway that becomes aberrantly activated in neurodegeneration through mitochondrial DNA (mtDNA) release. Under conditions of cellular stress, mitochondrial outer membrane permeabilization (MOMP)
In [14]:
lit_data = [{'year': '2025', 'journal': 'Front Immunol', 'title': "Loss of DJ-1 alleviates microglia-mediated neuroinflammation in Parkinson's dise", 'pmid': '40990010'}, {'year': '2024', 'journal': 'Heliyon', 'title': 'Crocin attenuates the lipopolysaccharide-induced neuroinflammation via expressio', 'pmid': '38356604'}, {'year': '2022', 'journal': 'Glia', 'title': 'Microglial AIM2 alleviates antiviral-related neuro-inflammation in mouse models ', 'pmid': '35959803'}, {'year': '2021', 'journal': 'J Neuroimmunol', 'title': 'A selective NLRP3 inflammasome inhibitor attenuates behavioral deficits and neur', 'pmid': '33714750'}, {'year': '2024', 'journal': 'Front Immunol', 'title': 'The roles of AIM2 in neurodegenerative diseases: insights and therapeutic implic', 'pmid': '39076969'}]
if lit_data:
df = pd.DataFrame(lit_data)
print(f'{len(lit_data)} PubMed results')
display(df)
else:
print('No PubMed results')
5 PubMed results
| year | journal | title | pmid | |
|---|---|---|---|---|
| 0 | 2025 | Front Immunol | Loss of DJ-1 alleviates microglia-mediated neu... | 40990010 |
| 1 | 2024 | Heliyon | Crocin attenuates the lipopolysaccharide-induc... | 38356604 |
| 2 | 2022 | Glia | Microglial AIM2 alleviates antiviral-related n... | 35959803 |
| 3 | 2021 | J Neuroimmunol | A selective NLRP3 inflammasome inhibitor atten... | 33714750 |
| 4 | 2024 | Front Immunol | The roles of AIM2 in neurodegenerative disease... | 39076969 |
Hypothesis 7: Selective TLR4 Modulation to Prevent Gut-Derived Neuroinflammatory Pri¶
Target genes: TLR4 · Composite score: 0.789
Selective TLR4 Modulation to Prevent Gut-Derived Neuroinflammatory Priming proposes targeting the Toll-like receptor 4 (TLR4) signaling axis as the critical bridge between intestinal barrier dysfunction and CNS neuroinflammation. Chronic low-grade endotoxemia — elevated circulating bacterial lipopol
In [15]:
lit_data = [{'year': '2021', 'journal': 'Microbiome', 'title': "Fecal microbiota transplantation protects rotenone-induced Parkinson's disease m", 'pmid': '34784980'}, {'year': '2021', 'journal': 'Acta Pharm Sin B', 'title': 'Novel compound FLZ alleviates rotenone-induced PD mouse model by suppressing TLR', 'pmid': '34589401'}, {'year': '2018', 'journal': 'Brain Behav Immun', 'title': 'Neuroprotective effects of fecal microbiota transplantation on MPTP-induced Park', 'pmid': '29471030'}, {'year': '2023', 'journal': 'Front Immunol', 'title': "How Toll-like receptors influence Parkinson's disease in the microbiome-gut-brai", 'pmid': '37207228'}, {'year': '2020', 'journal': 'Expert Opin Ther Targets', 'title': "The gut-brain axis and gut inflammation in Parkinson's disease: stopping neurode", 'pmid': '32349553'}]
if lit_data:
df = pd.DataFrame(lit_data)
print(f'{len(lit_data)} PubMed results')
display(df)
else:
print('No PubMed results')
5 PubMed results
| year | journal | title | pmid | |
|---|---|---|---|---|
| 0 | 2021 | Microbiome | Fecal microbiota transplantation protects rote... | 34784980 |
| 1 | 2021 | Acta Pharm Sin B | Novel compound FLZ alleviates rotenone-induced... | 34589401 |
| 2 | 2018 | Brain Behav Immun | Neuroprotective effects of fecal microbiota tr... | 29471030 |
| 3 | 2023 | Front Immunol | How Toll-like receptors influence Parkinson's ... | 37207228 |
| 4 | 2020 | Expert Opin Ther Targets | The gut-brain axis and gut inflammation in Par... | 32349553 |
Hypothesis 8: Microbial Inflammasome Priming Prevention¶
Target genes: NLRP3, CASP1, IL1B, PYCARD · Composite score: 0.723
Molecular Mechanism and Rationale
The pathogenesis of neuroinflammatory processes through microbial inflammasome activation represents a sophisticated molecular cascade involving intricate interactions between host immune systems and microbial environmental triggers. At the core of this mechani
In [16]:
lit_data = [{'year': '2020', 'journal': 'Prog Neurobiol', 'title': 'Microbiota-gut-brain axis in health and disease: Is NLRP3 inflammasome at the cr', 'pmid': '32473843'}, {'year': '2024', 'journal': 'Heliyon', 'title': 'Electroacupuncture blocked motor dysfunction and gut barrier damage by modulatin', 'pmid': '38774094'}, {'year': '2020', 'journal': 'Int J Mol Sci', 'title': 'Focus on the Role of NLRP3 Inflammasome in Diseases.', 'pmid': '32545788'}, {'year': '2025', 'journal': 'Exp Neurobiol', 'title': 'G protein-coupled Estrogen Receptor Activation Exerts Protective Effects via Mod', 'pmid': '41152022'}, {'year': '2025', 'journal': 'J Neuroimmune Pharmacol', 'title': 'Modulation of Intestinal Inflammation and Protection of Dopaminergic Neurons in ', 'pmid': '39826038'}]
if lit_data:
df = pd.DataFrame(lit_data)
print(f'{len(lit_data)} PubMed results')
display(df)
else:
print('No PubMed results')
5 PubMed results
| year | journal | title | pmid | |
|---|---|---|---|---|
| 0 | 2020 | Prog Neurobiol | Microbiota-gut-brain axis in health and diseas... | 32473843 |
| 1 | 2024 | Heliyon | Electroacupuncture blocked motor dysfunction a... | 38774094 |
| 2 | 2020 | Int J Mol Sci | Focus on the Role of NLRP3 Inflammasome in Dis... | 32545788 |
| 3 | 2025 | Exp Neurobiol | G protein-coupled Estrogen Receptor Activation... | 41152022 |
| 4 | 2025 | J Neuroimmune Pharmacol | Modulation of Intestinal Inflammation and Prot... | 39826038 |
Hypothesis 9: Targeted Butyrate Supplementation for Microglial Phenotype Modulation¶
Target genes: GPR109A · Composite score: 0.7
Targeted Butyrate Supplementation for Microglial Phenotype Modulation proposes leveraging the gut-brain axis to restore microglial homeostasis in neurodegenerative diseases through precision delivery of butyrate — a short-chain fatty acid (SCFA) produced by commensal gut bacteria. Parkinson's diseas
In [17]:
lit_data = [{'year': '2025', 'journal': 'Nutrients', 'title': "Beyond the Gut: Unveiling Butyrate's Global Health Impact Through Gut Health and", 'pmid': '40284169'}]
if lit_data:
df = pd.DataFrame(lit_data)
print(f'{len(lit_data)} PubMed results')
display(df)
else:
print('No PubMed results')
1 PubMed results
| year | journal | title | pmid | |
|---|---|---|---|---|
| 0 | 2025 | Nutrients | Beyond the Gut: Unveiling Butyrate's Global He... | 40284169 |
Hypothesis 10: Enhancing Vagal Cholinergic Signaling to Restore Gut-Brain Anti-Inflam¶
Target genes: CHRNA7 · Composite score: 0.669
Gut dysbiosis disrupts vagal cholinergic anti-inflammatory pathways by reducing acetylcholine-producing bacteria and damaging enteric neurons. Vagus nerve stimulation combined with choline supplementation could restore this protective pathway and reduce systemic inflammation driving Parkinson's dise
In [18]:
print('No PubMed results for hypothesis h-a4e259e0')
No PubMed results for hypothesis h-a4e259e0
Hypothesis 11: Gut Barrier Permeability-α-Synuclein Axis Modulation¶
Target genes: CLDN1, OCLN, ZO1, MLCK · Composite score: 0.663
Molecular Mechanism and Rationale
The gut-brain axis represents a critical bidirectional communication pathway that has emerged as a central player in neurodegenerative disease pathogenesis, particularly in α-synucleinopathies such as Parkinson's disease. The molecular foundation of this hypoth
In [19]:
print('No PubMed results for hypothesis h-6c83282d')
No PubMed results for hypothesis h-6c83282d
Hypothesis 12: Vagal Afferent Microbial Signal Modulation¶
Target genes: GLP1R, BDNF · Composite score: 0.66
Molecular Mechanism and Rationale¶
The vagus nerve represents a critical bidirectional communication highway between the gut microbiome and the central nervous system, with vagal afferent neurons serving as primary transducers of microbial metabolites and inflammatory signals. This hypothesis
In [20]:
print('No PubMed results for hypothesis h-ee1df336')
No PubMed results for hypothesis h-ee1df336
Hypothesis 13: Targeting Bacterial Curli Fibrils to Prevent α-Synuclein Cross-Seeding¶
Target genes: CSGA · Composite score: 0.642
Background and Rationale
Parkinson's disease (PD) is characterized by the accumulation of misfolded α-synuclein aggregates, primarily in the form of Lewy bodies and Lewy neurites. While the precise mechanisms underlying α-synuclein aggregation remain incompletely understood, emerging evidence s
In [21]:
lit_data = [{'year': '2021', 'journal': 'Proc Natl Acad Sci U S A', 'title': 'Genome-wide screen identifies curli amyloid fibril as a bacterial component prom', 'pmid': '34413194'}, {'year': '2022', 'journal': 'Front Pharmacol', 'title': 'Using Caenorhabditis elegans to Model Therapeutic Interventions of Neurodegenera', 'pmid': '35571084'}]
if lit_data:
df = pd.DataFrame(lit_data)
print(f'{len(lit_data)} PubMed results')
display(df)
else:
print('No PubMed results')
2 PubMed results
| year | journal | title | pmid | |
|---|---|---|---|---|
| 0 | 2021 | Proc Natl Acad Sci U S A | Genome-wide screen identifies curli amyloid fi... | 34413194 |
| 1 | 2022 | Front Pharmacol | Using Caenorhabditis elegans to Model Therapeu... | 35571084 |
Hypothesis 14: Microbial Metabolite-Mediated α-Synuclein Disaggregation¶
Target genes: SNCA, HSPA1A, DNMT1 · Composite score: 0.626
Molecular Mechanism and Rationale
The pathogenesis of Parkinson's disease (PD) centers on the misfolding and aggregation of α-synuclein protein, encoded by the SNCA gene, into toxic oligomers and fibrillar structures known as Lewy bodies. This hypothesis proposes that specific gut bacterial str
In [22]:
lit_data = [{'year': '2024', 'journal': 'Redox Biol', 'title': 'Gut microbiome, short-chain fatty acids, alpha-synuclein, neuroinflammation, and', 'pmid': '38377788'}, {'year': '2022', 'journal': 'Metab Brain Dis', 'title': 'Age-dependent aggregation of α-synuclein in the nervous system of gut-brain axis', 'pmid': '35089485'}, {'year': '2024', 'journal': 'Front Cell Infect Microbiol', 'title': 'Dysbiosis of the gut microbiota and its effect on α-synuclein and prion protein ', 'pmid': '38435303'}, {'year': '2023', 'journal': 'EBioMedicine', 'title': 'P.\xa0mirabilis-derived pore-forming haemolysin, HpmA drives intestinal alpha-synuc', 'pmid': '37995468'}, {'year': '2023', 'journal': 'J Neurochem', 'title': 'IL-1β/IL-1R1 signaling is involved in the propagation of α-synuclein pathology o', 'pmid': '37434423'}]
if lit_data:
df = pd.DataFrame(lit_data)
print(f'{len(lit_data)} PubMed results')
display(df)
else:
print('No PubMed results')
5 PubMed results
| year | journal | title | pmid | |
|---|---|---|---|---|
| 0 | 2024 | Redox Biol | Gut microbiome, short-chain fatty acids, alpha... | 38377788 |
| 1 | 2022 | Metab Brain Dis | Age-dependent aggregation of α-synuclein in th... | 35089485 |
| 2 | 2024 | Front Cell Infect Microbiol | Dysbiosis of the gut microbiota and its effect... | 38435303 |
| 3 | 2023 | EBioMedicine | P. mirabilis-derived pore-forming haemolysin, ... | 37995468 |
| 4 | 2023 | J Neurochem | IL-1β/IL-1R1 signaling is involved in the prop... | 37434423 |
Hypothesis 15: Enteric Nervous System Prion-Like Propagation Blockade¶
Target genes: TLR4, SNCA · Composite score: 0.625
Molecular Mechanism and Rationale¶
The enteric nervous system (ENS) represents a critical junction where gut microbiome dysfunction intersects with neurodegenerative disease pathogenesis, particularly through the gut-brain axis mediated by α-synuclein prion-like propagation. This hypothesis c
In [23]:
lit_data = [{'year': '2021', 'journal': 'Microbiome', 'title': "Fecal microbiota transplantation protects rotenone-induced Parkinson's disease m", 'pmid': '34784980'}, {'year': '2021', 'journal': 'Acta Pharm Sin B', 'title': 'Novel compound FLZ alleviates rotenone-induced PD mouse model by suppressing TLR', 'pmid': '34589401'}, {'year': '2018', 'journal': 'Brain Behav Immun', 'title': 'Neuroprotective effects of fecal microbiota transplantation on MPTP-induced Park', 'pmid': '29471030'}, {'year': '2023', 'journal': 'Front Immunol', 'title': "How Toll-like receptors influence Parkinson's disease in the microbiome-gut-brai", 'pmid': '37207228'}, {'year': '2020', 'journal': 'Expert Opin Ther Targets', 'title': "The gut-brain axis and gut inflammation in Parkinson's disease: stopping neurode", 'pmid': '32349553'}]
if lit_data:
df = pd.DataFrame(lit_data)
print(f'{len(lit_data)} PubMed results')
display(df)
else:
print('No PubMed results')
5 PubMed results
| year | journal | title | pmid | |
|---|---|---|---|---|
| 0 | 2021 | Microbiome | Fecal microbiota transplantation protects rote... | 34784980 |
| 1 | 2021 | Acta Pharm Sin B | Novel compound FLZ alleviates rotenone-induced... | 34589401 |
| 2 | 2018 | Brain Behav Immun | Neuroprotective effects of fecal microbiota tr... | 29471030 |
| 3 | 2023 | Front Immunol | How Toll-like receptors influence Parkinson's ... | 37207228 |
| 4 | 2020 | Expert Opin Ther Targets | The gut-brain axis and gut inflammation in Par... | 32349553 |
Hypothesis 16: Blocking AGE-RAGE Signaling in Enteric Glia to Prevent Neuroinflammato¶
Target genes: AGER · Composite score: 0.613
Background and Rationale
The gut-brain axis has emerged as a critical bidirectional communication pathway in neurodegeneration, with mounting evidence suggesting that intestinal dysfunction precedes and contributes to central nervous system pathology. Advanced glycation end-products (AGEs) repr
In [24]:
lit_data = [{'year': '2025', 'journal': 'Neuropharmacology', 'title': 'Vortioxetine attenuates rotenone-induced enteric neuroinflammation via modulatio', 'pmid': '40010563'}]
if lit_data:
df = pd.DataFrame(lit_data)
print(f'{len(lit_data)} PubMed results')
display(df)
else:
print('No PubMed results')
1 PubMed results
| year | journal | title | pmid | |
|---|---|---|---|---|
| 0 | 2025 | Neuropharmacology | Vortioxetine attenuates rotenone-induced enter... | 40010563 |
Hypothesis 17: Restoring Neuroprotective Tryptophan Metabolism via Targeted Probiotic¶
Target genes: TDC · Composite score: 0.612
Background and Rationale
The gut-brain axis has emerged as a critical bidirectional communication pathway in neurodegeneration, with mounting evidence demonstrating that intestinal microbiota composition significantly influences central nervous system health. Tryptophan, an essential amino acid
In [25]:
lit_data = [{'year': '2020', 'journal': 'Clin Physiol Funct Imaging', 'title': 'Heat-related changes in skin tissue dielectric constant (TDC).', 'pmid': '31677329'}, {'year': '2025', 'journal': 'Sensors (Basel)', 'title': 'Arrayable TDC with Voltage-Controlled Ring Oscillator for dToF Image Sensors.', 'pmid': '40807755'}, {'year': '2020', 'journal': 'Dalton Trans', 'title': 'Fluorometric detection of iodine by MIL-53(Al)-TDC.', 'pmid': '32338666'}, {'year': '2021', 'journal': 'J Inflamm Res', 'title': 'GARP and GARP-Treated tDC Prevented the Formation of Atherosclerotic Plaques in ', 'pmid': '34326655'}, {'year': '2025', 'journal': 'Front Immunol', 'title': 'Unraveling porcine dendritic-cell diversity: welcome tDC and DC3.', 'pmid': '41235223'}]
if lit_data:
df = pd.DataFrame(lit_data)
print(f'{len(lit_data)} PubMed results')
display(df)
else:
print('No PubMed results')
5 PubMed results
| year | journal | title | pmid | |
|---|---|---|---|---|
| 0 | 2020 | Clin Physiol Funct Imaging | Heat-related changes in skin tissue dielectric... | 31677329 |
| 1 | 2025 | Sensors (Basel) | Arrayable TDC with Voltage-Controlled Ring Osc... | 40807755 |
| 2 | 2020 | Dalton Trans | Fluorometric detection of iodine by MIL-53(Al)... | 32338666 |
| 3 | 2021 | J Inflamm Res | GARP and GARP-Treated tDC Prevented the Format... | 34326655 |
| 4 | 2025 | Front Immunol | Unraveling porcine dendritic-cell diversity: w... | 41235223 |
Hypothesis 18: Correcting Gut Microbial Dopamine Imbalance to Support Systemic Dopami¶
Target genes: DDC · Composite score: 0.606
Background and Rationale
The gut-brain axis has emerged as a critical bidirectional communication pathway that significantly influences neurological health and disease progression. In Parkinson's disease (PD), mounting evidence suggests that the enteric nervous system and gut microbiome play fu
In [26]:
lit_data = [{'year': '2023', 'journal': 'Int J Mol Sci', 'title': 'DDC-Promoter-Driven Chemogenetic Activation of SNpc Dopaminergic Neurons Allevia', 'pmid': '36768816'}, {'year': '2024', 'journal': 'Neural Netw', 'title': 'M-DDC: MRI based demyelinative diseases classification with U-Net segmentation a', 'pmid': '37890361'}, {'year': '2025', 'journal': 'Vet Microbiol', 'title': 'Multi-epitope vaccines Xlc and Ddc against Glaesserella parasuis infection in mi', 'pmid': '40154005'}, {'year': '2020', 'journal': 'Gene', 'title': 'DDC expression is not regulated by NFAT5 (TonEBP) in dopaminergic neural cell li', 'pmid': '32165301'}, {'year': '1993', 'journal': 'Ann Pharmacother', 'title': 'Zalcitabine.', 'pmid': '8097417'}]
if lit_data:
df = pd.DataFrame(lit_data)
print(f'{len(lit_data)} PubMed results')
display(df)
else:
print('No PubMed results')
5 PubMed results
| year | journal | title | pmid | |
|---|---|---|---|---|
| 0 | 2023 | Int J Mol Sci | DDC-Promoter-Driven Chemogenetic Activation of... | 36768816 |
| 1 | 2024 | Neural Netw | M-DDC: MRI based demyelinative diseases classi... | 37890361 |
| 2 | 2025 | Vet Microbiol | Multi-epitope vaccines Xlc and Ddc against Gla... | 40154005 |
| 3 | 2020 | Gene | DDC expression is not regulated by NFAT5 (TonE... | 32165301 |
| 4 | 1993 | Ann Pharmacother | Zalcitabine. | 8097417 |
Hypothesis 19: Microbiome-Derived Tryptophan Metabolite Neuroprotection¶
Target genes: AHR, IL10, TGFB1 · Composite score: 0.605
Molecular Mechanism and Rationale
The gut-brain axis represents a critical bidirectional communication pathway that fundamentally influences neuroinflammation and neurodegeneration through microbial metabolite signaling. Central to this mechanism is the tryptophan-aryl hydrocarbon receptor (AHR
In [27]:
print('No PubMed results for hypothesis h-f9c6fa3f')
No PubMed results for hypothesis h-f9c6fa3f
Hypothesis 20: Bacterial Enzyme-Mediated Dopamine Precursor Synthesis¶
Target genes: TH, AADC · Composite score: 0.59
Molecular Mechanism and Rationale
The engineered probiotic approach leverages the direct biosynthesis of L-3,4-dihydroxyphenylalanine (L-DOPA) through bacterial expression of two critical enzymes in the dopamine synthesis pathway: tyrosine hydroxylase (TH) and aromatic L-amino acid decarboxylas
In [28]:
lit_data = [{'year': '2024', 'journal': 'Int J Mol Sci', 'title': 'Tyrosine Hydroxylase Inhibitors and Dopamine Receptor Agonists Combination Thera', 'pmid': '38731862'}, {'year': '2022', 'journal': 'Cell Mol Life Sci', 'title': "The role of tyrosine hydroxylase-dopamine pathway in Parkinson's disease pathoge", 'pmid': '36409355'}, {'year': '2024', 'journal': 'J Neural Transm (Vienna)', 'title': "Catecholamines and Parkinson's disease: tyrosine hydroxylase (TH) over tetrahydr", 'pmid': '37638996'}, {'year': '2024', 'journal': 'Exp Neurol', 'title': 'Moderate intensity aerobic exercise alleviates motor deficits in 6-OHDA lesioned', 'pmid': '38944332'}, {'year': '2021', 'journal': 'J Neurochem', 'title': 'Too much for your own good: Excessive dopamine damages neurons and contributes t', 'pmid': '34184261'}]
if lit_data:
df = pd.DataFrame(lit_data)
print(f'{len(lit_data)} PubMed results')
display(df)
else:
print('No PubMed results')
5 PubMed results
| year | journal | title | pmid | |
|---|---|---|---|---|
| 0 | 2024 | Int J Mol Sci | Tyrosine Hydroxylase Inhibitors and Dopamine R... | 38731862 |
| 1 | 2022 | Cell Mol Life Sci | The role of tyrosine hydroxylase-dopamine path... | 36409355 |
| 2 | 2024 | J Neural Transm (Vienna) | Catecholamines and Parkinson's disease: tyrosi... | 37638996 |
| 3 | 2024 | Exp Neurol | Moderate intensity aerobic exercise alleviates... | 38944332 |
| 4 | 2021 | J Neurochem | Too much for your own good: Excessive dopamine... | 34184261 |
8. Knowledge graph edges (505 total)¶
In [29]:
edge_data = [{'source': 'SNCA', 'relation': 'encodes', 'target': 'alpha_synuclein', 'strength': 1.0}, {'source': 'SDA-2026-04-01-gap-20260401-22', 'relation': 'generated', 'target': 'h-74777459', 'strength': 0.95}, {'source': 'SDA-2026-04-01-gap-20260401-22', 'relation': 'generated', 'target': 'h-6c83282d', 'strength': 0.95}, {'source': 'SDA-2026-04-01-gap-20260401-22', 'relation': 'generated', 'target': 'h-e7e1f943', 'strength': 0.95}, {'source': 'SDA-2026-04-01-gap-20260401-22', 'relation': 'generated', 'target': 'h-f9c6fa3f', 'strength': 0.95}, {'source': 'SDA-2026-04-01-gap-20260401-22', 'relation': 'generated', 'target': 'h-7bb47d7a', 'strength': 0.95}, {'source': 'GPR109A', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.79}, {'source': 'diseases-atypical-parkinsonism', 'relation': 'investigated_in', 'target': 'h-74777459', 'strength': 0.75}, {'source': 'diseases-atypical-parkinsonism', 'relation': 'investigated_in', 'target': 'h-2e7eb2ea', 'strength': 0.75}, {'source': 'CHRNA7', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.66}, {'source': 'NLRP3', 'relation': 'interacts_with', 'target': 'CASP1', 'strength': 0.65}, {'source': 'NLRP3', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.65}, {'source': 'PYCARD', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.65}, {'source': 'NLRP3', 'relation': 'interacts_with', 'target': 'IL1B', 'strength': 0.65}, {'source': 'IL1B', 'relation': 'interacts_with', 'target': 'PYCARD', 'strength': 0.65}, {'source': 'IL1B', 'relation': 'interacts_with', 'target': 'NLRP3', 'strength': 0.65}, {'source': 'IL1B', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.65}, {'source': 'CASP1', 'relation': 'interacts_with', 'target': 'PYCARD', 'strength': 0.65}, {'source': 'CASP1', 'relation': 'interacts_with', 'target': 'IL1B', 'strength': 0.65}, {'source': 'CASP1', 'relation': 'interacts_with', 'target': 'NLRP3', 'strength': 0.65}, {'source': 'PYCARD', 'relation': 'interacts_with', 'target': 'CASP1', 'strength': 0.65}, {'source': 'IL1B', 'relation': 'interacts_with', 'target': 'CASP1', 'strength': 0.65}, {'source': 'CASP1', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.65}, {'source': 'NLRP3', 'relation': 'interacts_with', 'target': 'PYCARD', 'strength': 0.65}, {'source': 'PYCARD', 'relation': 'interacts_with', 'target': 'NLRP3', 'strength': 0.65}]
if edge_data:
pd.DataFrame(edge_data).head(25)
else:
print('No KG edge data available')
Caveats¶
This notebook uses real Forge tool calls from live APIs:
- Enrichment is against curated gene-set libraries (Enrichr)
- STRING/Reactome/HPA/MyGene reflect curated knowledge
- PubMed literature is search-relevance ranked, not systematic review
The cached evidence bundle is the minimum viable real-data analysis for this topic.