TREM2 agonism vs antagonism in DAM microglia¶
Notebook ID: nb-SDA-2026-04-01-gap-001 · Analysis: SDA-2026-04-01-gap-001 · Generated: 2026-04-17
Research question¶
The disease-associated microglia (DAM) phenotype involves TREM2 upregulation, but whether therapeutic agonism or antagonism of TREM2 is beneficial remains contested across disease stages.
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-2026-04-01-gap-001 --force to refresh.
7 hypotheses were generated and debated. The knowledge graph has 31 edges.
Debate Summary¶
Quality score: 0.57 · 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 ['AXIS', 'PATHWAY', 'TYROBP']:
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 | AXIS | — | — | — |
| 1 | PATHWAY | — | — | — |
| 2 | TYROBP | transmembrane immune signaling adaptor TYROBP | Disease related genes, Human disease related g... |
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 ['AXIS', 'PATHWAY', 'TYROBP']:
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 | |
|---|---|---|---|
| 2 | TYROBP | 6 | Immunoregulatory interactions between a Lympho... |
| 0 | AXIS | 0 | — |
| 1 | PATHWAY | 0 | — |
7. Allen Brain Atlas ISH regional expression¶
In [9]:
ish_rows = []
for g in ['AXIS', 'PATHWAY', 'TYROBP']:
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 | AXIS | 0 | — | — |
| 1 | PATHWAY | 0 | — | — |
| 2 | TYROBP | 0 | — | — |
8. Hypothesis ranking (7 hypotheses)¶
In [10]:
hyp_data = [('PLCG2 Allosteric Modulation as a Precision Therapeutic ', 0.936), ('TREM2-APOE Axis Dissociation for Selective DAM Activati', 0.89), ('TYROBP (DAP12) Conditional Antagonism for Early-Stage N', 0.844), ('CSF1R-TREM2 Co-Agonism for Sustained Microglial Expansi', 0.828), ('TREM2-mTOR Co-Agonism for Metabolic Reprogramming', 0.811), ('CX3CR1-TREM2 Integration for Synapse Pruning Normalizat', 0.776), ('INPP5D (SHIP1) Inhibition to Shift Microglial Polarizat', 0.758)]
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('TREM2 agonism vs antagonism in DAM microglia')
ax.grid(axis='x', alpha=0.3)
plt.tight_layout(); plt.show()
9. Score dimension heatmap (top 10)¶
In [11]:
labels = ['PLCG2 Allosteric Modulation as a Precisi', 'TREM2-APOE Axis Dissociation for Selecti', 'TYROBP (DAP12) Conditional Antagonism fo', 'CSF1R-TREM2 Co-Agonism for Sustained Mic', 'TREM2-mTOR Co-Agonism for Metabolic Repr', 'CX3CR1-TREM2 Integration for Synapse Pru', 'INPP5D (SHIP1) Inhibition to Shift Micro']
matrix = np.array([[0.85, 0.42, 0.72, 0.75, 0, 0.58, 0.62, 0.35, 0.48], [0.78, 0.35, 0.68, 0.7, 0, 0.6, 0.58, 0.28, 0.42], [0.82, 0.28, 0.58, 0.55, 0, 0.55, 0.52, 0.25, 0.38], [0.72, 0.25, 0.62, 0.58, 0, 0.48, 0.52, 0.22, 0.45], [0.7, 0.22, 0.65, 0.52, 0, 0.52, 0.48, 0.18, 0.35], [0.65, 0.25, 0.55, 0.48, 0, 0.45, 0.42, 0.22, 0.4], [0.72, 0.22, 0.48, 0.42, 0, 0.35, 0.38, 0.32, 0.32]])
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: PLCG2 Allosteric Modulation as a Precision Therapeutic for TREM2-Depen¶
Target genes: PLCG2 · Composite score: 0.936
Direct allosteric modulation of PLCG2 to bypass upstream TREM2 deficits while preserving TREM2-independent inflammatory signaling
In [12]:
hid = 'h-0f025d94'
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: TREM2-APOE Axis Dissociation for Selective DAM Activation¶
Target genes: TREM2-APOE axis · Composite score: 0.89
Pharmacological dissociation of the TREM2-APOE axis—agonizing TREM2 while blocking APOE effects—to enable beneficial phagocytosis without APOE-driven lipid accumulation and inflammatory skewing
In [13]:
hid = 'h-5b378bd3'
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: TYROBP (DAP12) Conditional Antagonism for Early-Stage Neuroprotection¶
Target genes: TYROBP · Composite score: 0.844
Temporal antagonism during acute phase (first 72 hours post-injury) combined with subsequent TREM2 agonism to prevent inflammatory damage while preserving eventual phagocytic clearance
In [14]:
hid = 'h-f503b337'
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 4: CSF1R-TREM2 Co-Agonism for Sustained Microglial Expansion¶
Target genes: CSF1R-TREM2 · Composite score: 0.828
Combined low-dose CSF1R agonism to expand the microglial pool with TREM2 agonism to direct differentiation toward protective DAM
In [15]:
hid = 'h-0cbe9bac'
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 5: TREM2-mTOR Co-Agonism for Metabolic Reprogramming¶
Target genes: TREM2-mTOR pathway · Composite score: 0.811
Co-targeting TREM2 agonism with mTOR activation to restore metabolic competence, enabling proper DAM transition in TREM2 risk variant carriers
In [16]:
hid = 'h-2e03f316'
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 6: CX3CR1-TREM2 Integration for Synapse Pruning Normalization¶
Target genes: CX3CR1-TREM2 · Composite score: 0.776
Balanced activation of CX3CR1 and TREM2 to maintain homeostatic pruning while preventing neurodegeneration-associated excessive pruning
In [17]:
hid = 'h-7597968b'
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 7: INPP5D (SHIP1) Inhibition to Shift Microglial Polarization¶
Target genes: INPP5D · Composite score: 0.758
Selective INPP5D inhibition to amplify pro-survival PI3K signaling in the presence of TREM2 agonism, enabling stronger DAM commitment under stress conditions
In [18]:
hid = 'h-39148342'
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 (31 total)¶
In [19]:
edge_data = [{'source': 'h-0f025d94', 'relation': 'targets', 'target': 'PLCG2', 'strength': 0.5}, {'source': 'h-5b378bd3', 'relation': 'targets', 'target': 'TREM2-APOE axis', 'strength': 0.5}, {'source': 'h-0cbe9bac', 'relation': 'targets', 'target': 'CSF1R-TREM2', 'strength': 0.5}, {'source': 'h-f503b337', 'relation': 'targets', 'target': 'TYROBP', 'strength': 0.5}, {'source': 'h-2e03f316', 'relation': 'targets', 'target': 'TREM2-mTOR pathway', 'strength': 0.5}, {'source': 'h-7597968b', 'relation': 'targets', 'target': 'CX3CR1-TREM2', 'strength': 0.5}, {'source': 'h-39148342', 'relation': 'targets', 'target': 'INPP5D', 'strength': 0.5}, {'source': 'PLCG2', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.4}, {'source': 'PLCG2', 'relation': 'implicated_in', 'target': 'neurodegeneration', 'strength': 0.4}, {'source': 'TREM2-APOE axis', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.4}, {'source': 'TREM2-APOE axis', 'relation': 'implicated_in', 'target': 'neurodegeneration', 'strength': 0.4}, {'source': 'CSF1R-TREM2', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.4}, {'source': 'CSF1R-TREM2', 'relation': 'implicated_in', 'target': 'neurodegeneration', 'strength': 0.4}, {'source': 'TYROBP', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.4}, {'source': 'TREM2-mTOR pathway', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.4}, {'source': 'TREM2-mTOR pathway', 'relation': 'implicated_in', 'target': 'neurodegeneration', 'strength': 0.4}, {'source': 'CX3CR1-TREM2', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.4}, {'source': 'CX3CR1-TREM2', 'relation': 'implicated_in', 'target': 'neurodegeneration', 'strength': 0.4}, {'source': 'INPP5D', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.4}, {'source': 'INPP5D', 'relation': 'implicated_in', 'target': 'neurodegeneration', 'strength': 0.4}, {'source': 'PLCG2', 'relation': 'co_associated_with', 'target': 'TREM2', 'strength': 0.3}, {'source': 'TREM2-APOE axis', 'relation': 'co_associated_with', 'target': 'TREM2', 'strength': 0.3}, {'source': 'TREM2-APOE axis', 'relation': 'co_associated_with', 'target': 'APOE', 'strength': 0.3}, {'source': 'TREM2-APOE axis', 'relation': 'co_associated_with', 'target': 'DAM', 'strength': 0.3}, {'source': 'CSF1R-TREM2', 'relation': 'co_associated_with', 'target': 'CSF1R', 'strength': 0.3}]
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.