Do β-amyloid plaques and neurofibrillary tangles cause or result from cholinergic dysfunction?¶
Notebook ID: nb-SDA-2026-04-12-20260411-082446-2c1c9e2d · Analysis: SDA-2026-04-12-20260411-082446-2c1c9e2d · Generated: 2026-04-17
Research question¶
The abstract explicitly questions whether AD's hallmark pathologies induce cholinergic dysfunction or vice versa. This fundamental causality question is critical for determining therapeutic targets but remains unresolved despite evidence that β-amyloid affects cholinergic receptors.
Gap type: open_question Source paper: The cholinergic system in aging and neuronal degeneration. (2011, Behavioural brain research, PMID:21145918)
Approach¶
This notebook is generated programmatically from real Forge tool calls and SciDEX debate data. Code cells load cached evidence bundles from data/forge_cache/seaad/*.json and query live data from scidex.db. Re-run python3 scripts/regenerate_notebooks.py --analysis SDA-2026-04-12-20260411-082446-2c1c9e2d --force to refresh.
3 hypotheses were generated and debated. The knowledge graph has 2 edges.
Debate Summary¶
Quality score: 0.79 · Rounds: 4 · Personas: Theorist, Skeptic, Domain_Expert, Synthesizer
1. Forge tool provenance¶
In [1]:
import json, sys, sqlite3
from pathlib import Path
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
matplotlib.rcParams['figure.dpi'] = 110
matplotlib.rcParams['figure.facecolor'] = 'white'
REPO = Path('.').resolve()
sys.path.insert(0, str(REPO))
CACHE_SUB = 'seaad'
CACHE = REPO / 'data' / 'forge_cache' / CACHE_SUB
def load(name):
p = CACHE / f'{name}.json'
if p.exists():
return json.loads(p.read_text())
return {}
db_path = Path('/home/ubuntu/scidex/scidex.db')
try:
db = sqlite3.connect(str(db_path))
prov = pd.read_sql_query('''
SELECT skill_id, status, COUNT(*) AS n_calls,
ROUND(AVG(duration_ms),0) AS mean_ms
FROM tool_calls
WHERE created_at >= date('now','-30 days')
GROUP BY skill_id, status
ORDER BY n_calls DESC
''', db)
db.close()
prov['tool'] = prov['skill_id'].str.replace('tool_', '', regex=False)
print(f'{len(prov)} tool-call aggregates (last 30 days):')
prov[['tool','status','n_calls','mean_ms']].head(20)
except Exception as e:
print(f'Provenance unavailable: {e}')
181 tool-call aggregates (last 30 days):
2. Target gene annotations¶
In [2]:
ann_rows = []
for g in ['CHAT', 'NICOTINIC']:
mg = load(f'mygene_{g}')
hpa = load(f'hpa_{g}')
if not mg and not hpa:
ann_rows.append({'gene': g, 'name': '—', 'protein_class': '—',
'disease_involvement': '—'})
continue
ann_rows.append({
'gene': g,
'name': (mg.get('name') or '')[:55],
'protein_class': ', '.join((hpa.get('protein_class') or [])[:2])[:55]
if isinstance(hpa.get('protein_class'), list)
else str(hpa.get('protein_class') or '—')[:55],
'disease_involvement': ', '.join((hpa.get('disease_involvement') or [])[:2])[:55]
if isinstance(hpa.get('disease_involvement'), list)
else str(hpa.get('disease_involvement') or '')[:55],
})
pd.DataFrame(ann_rows)
| gene | name | protein_class | disease_involvement | |
|---|---|---|---|---|
| 0 | CHAT | — | — | — |
| 1 | NICOTINIC | — | — | — |
3. GO Biological Process enrichment (Enrichr)¶
In [3]:
go_bp = load('enrichr_GO_Biological_Process')
if isinstance(go_bp, list) and go_bp:
go_df = pd.DataFrame(go_bp[:10])[['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']]
else:
print('No GO:BP enrichment data')
In [4]:
# Visualize top GO BP enrichment
go_bp = load('enrichr_GO_Biological_Process')
if isinstance(go_bp, list) and go_bp:
top = go_bp[:8]
terms = [t['term'][:45] for t in top][::-1]
neglogp = [-np.log10(max(t['p_value'], 1e-300)) for t in top][::-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()
else:
print('No GO:BP data to plot')
4. KEGG pathway enrichment¶
In [5]:
kegg = load('enrichr_KEGG_Pathways')
if isinstance(kegg, list) and kegg:
kegg_df = pd.DataFrame(kegg[:10])[['term','p_value','odds_ratio','genes']]
kegg_df['genes'] = kegg_df['genes'].apply(lambda g: ', '.join(g))
kegg_df['p_value'] = kegg_df['p_value'].apply(lambda p: f'{p:.2e}')
kegg_df['odds_ratio'] = kegg_df['odds_ratio'].round(1)
kegg_df
else:
print('No KEGG enrichment data')
No KEGG enrichment data
5. STRING protein interaction network¶
In [6]:
ppi = load('string_network')
if isinstance(ppi, list) and ppi:
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)
else:
print('No STRING edges returned')
11 STRING edges
In [7]:
# Network figure
ppi = load('string_network')
if isinstance(ppi, list) and ppi:
import math
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['score'],
linewidth=0.5+2*e['score'])
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()
else:
print('No STRING data to visualize')
6. Reactome pathway footprint¶
In [8]:
pw_rows = []
for g in ['CHAT', 'NICOTINIC']:
pws = load(f'reactome_{g}')
if isinstance(pws, list):
pw_rows.append({'gene': g, 'n_pathways': len(pws),
'top_pathway': (pws[0]['name'] if pws else '—')[:70]})
else:
pw_rows.append({'gene': g, 'n_pathways': 0, 'top_pathway': '—'})
pd.DataFrame(pw_rows).sort_values('n_pathways', ascending=False)
| gene | n_pathways | top_pathway | |
|---|---|---|---|
| 0 | CHAT | 0 | — |
| 1 | NICOTINIC | 0 | — |
7. Allen Brain Atlas ISH regional expression¶
In [9]:
ish_rows = []
for g in ['CHAT', 'NICOTINIC']:
ish = load(f'allen_ish_{g}')
regions = ish.get('regions') or [] if isinstance(ish, dict) else []
ish_rows.append({
'gene': g,
'n_ish_regions': len(regions),
'top_region': (regions[0].get('structure','') if regions else '—')[:45],
'top_energy': round(regions[0].get('expression_energy',0), 2) if regions else None,
})
pd.DataFrame(ish_rows)
| gene | n_ish_regions | top_region | top_energy | |
|---|---|---|---|---|
| 0 | CHAT | 0 | — | — |
| 1 | NICOTINIC | 0 | — | — |
8. Hypothesis ranking (3 hypotheses)¶
In [10]:
hyp_data = [('Multi-Target Hypothesis: Aβ-Induced Cholinergic Damage ', 0.887), ('Vicious Cycle Hypothesis: Cholinergic Dysfunction Exace', 0.785), ('Direct Toxicity Hypothesis: β-Amyloid Directly Impairs ', 0.769)]
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('Do β-amyloid plaques and neurofibrillary tangles cause or result from cholinergic dysfunction?')
ax.grid(axis='x', alpha=0.3)
plt.tight_layout(); plt.show()
9. Score dimension heatmap (top 10)¶
In [11]:
labels = ['Multi-Target Hypothesis: Aβ-Induced Chol', 'Vicious Cycle Hypothesis: Cholinergic Dy', 'Direct Toxicity Hypothesis: β-Amyloid Di']
matrix = np.array([[0.55, 0.6, 0.85, 0, 0, 0, 0, 0, 0], [0.75, 0.45, 0.7, 0, 0, 0, 0, 0, 0], [0.5, 0.55, 0.6, 0, 0, 0, 0, 0, 0]])
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')
10. PubMed evidence per hypothesis¶
Hypothesis 1: Multi-Target Hypothesis: Aβ-Induced Cholinergic Damage is Partially Ir¶
Target genes: APP/PSEN1 (Aβ production), CHAT (cholinergic synthesis) · Composite score: 0.887
Multi-Target Hypothesis: Aβ-Induced Cholinergic Damage is Partially Irreversible¶
Mechanistic Description¶
1. Mechanism of Action¶
The cholinergic hypothesis of Alzheimer's disease (AD) posits that early dysfunction and progressive loss of cholinergic neurons in the basal forebrain constitutes a primary driver of cognitive decline, independent of—and synergistic with—amyloid-beta (Aβ) pathology. Under this expanded multi-target framework, Aβ accumulation initiates a cascade of even
In [12]:
hid = 'hyp-SDA-2026-04-12-20260411-082446-2c1c9e2d-1'
papers = load(f'pubmed_{hid}')
if isinstance(papers, list) and papers:
lit = pd.DataFrame(papers)
cols = [c for c in ['year','journal','title','pmid'] if c in lit.columns]
if cols:
lit = lit[cols]
lit['title'] = lit['title'].str[:80]
if 'journal' in lit.columns:
lit['journal'] = lit['journal'].str[:30]
lit.sort_values('year', ascending=False, inplace=True)
display_df = lit
else:
display_df = pd.DataFrame(papers[:5])
else:
display_df = pd.DataFrame([{'note':'no PubMed results'}])
display_df
| note | |
|---|---|
| 0 | no PubMed results |
Hypothesis 2: Vicious Cycle Hypothesis: Cholinergic Dysfunction Exacerbates Amyloid¶
Target genes: CHRNA7 (α7 nicotinic receptor), BACE1 · Composite score: 0.785
Vicious Cycle Hypothesis: Cholinergic Dysfunction Exacerbates Amyloid Pathology¶
Mechanistic Description¶
The basal forebrain cholinergic system, comprising the medial septum, vertical and horizontal diagonal bands, and nucleus basalis of Meynert (corresponding to Ch1–Ch4 cell groups), provides the principal cholinergic innervation to the hippocampus, amygdala, and widespread cortical regions. These neurons are among the earliest and most severely affected in Alzheimer's disease pathology,
In [13]:
hid = 'hyp-SDA-2026-04-12-20260411-082446-2c1c9e2d-2'
papers = load(f'pubmed_{hid}')
if isinstance(papers, list) and papers:
lit = pd.DataFrame(papers)
cols = [c for c in ['year','journal','title','pmid'] if c in lit.columns]
if cols:
lit = lit[cols]
lit['title'] = lit['title'].str[:80]
if 'journal' in lit.columns:
lit['journal'] = lit['journal'].str[:30]
lit.sort_values('year', ascending=False, inplace=True)
display_df = lit
else:
display_df = pd.DataFrame(papers[:5])
else:
display_df = pd.DataFrame([{'note':'no PubMed results'}])
display_df
| note | |
|---|---|
| 0 | no PubMed results |
Hypothesis 3: Direct Toxicity Hypothesis: β-Amyloid Directly Impairs Cholinergic Sig¶
Target genes: CHRNA7, CHRM1 · Composite score: 0.769
Direct Toxicity Hypothesis: β-Amyloid Directly Impairs Cholinergic Signaling¶
Mechanistic Overview¶
The Direct Toxicity Hypothesis proposes that soluble β-amyloid (Aβ) oligomers exert their pathogenic effects on cholinergic signaling through direct, high-affinity interactions with key cholinergic receptors—namely the α7 nicotinic acetylcholine receptor (α7-nAChR) and the M1 muscarinic acetylcholine receptor (M1 mAChR). This hypothesis challenges the traditional view that cholinergic dysfunc
In [14]:
hid = 'hyp-SDA-2026-04-12-20260411-082446-2c1c9e2d-3'
papers = load(f'pubmed_{hid}')
if isinstance(papers, list) and papers:
lit = pd.DataFrame(papers)
cols = [c for c in ['year','journal','title','pmid'] if c in lit.columns]
if cols:
lit = lit[cols]
lit['title'] = lit['title'].str[:80]
if 'journal' in lit.columns:
lit['journal'] = lit['journal'].str[:30]
lit.sort_values('year', ascending=False, inplace=True)
display_df = lit
else:
display_df = pd.DataFrame(papers[:5])
else:
display_df = pd.DataFrame([{'note':'no PubMed results'}])
display_df
| note | |
|---|---|
| 0 | no PubMed results |
11. Knowledge graph edges (2 total)¶
In [15]:
edge_data = [{'source': 'BACE1', 'relation': 'co_discussed', 'target': 'ERK', 'strength': 0.4}, {'source': 'BACE1', 'relation': 'co_discussed', 'target': 'PI3K', 'strength': 0.4}]
if edge_data:
pd.DataFrame(edge_data).head(25)
else:
print('No KG edge data available')
12. Caveats¶
This notebook uses real Forge tool calls cached from live APIs, but:
- Enrichment is against curated gene-set libraries, not genome-wide screens
- 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.