Neuroinflammation and microglial priming in early Alzheimer's Disease¶
Notebook ID: nb-SDA-2026-04-04-gap-20260404-microglial-priming-early-ad · Analysis: SDA-2026-04-04-gap-20260404-microglial-priming-early-ad · Generated: 2026-04-20T08:58:50
Research question¶
Investigate mechanistic links between early microglial priming states, neuroinflammatory signaling, and downstream neurodegeneration in preclinical and prodromal AD.
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: 5
1. Target gene annotations (MyGene + Human Protein Atlas)¶
In [1]:
import pandas as pd
ann_rows = [{'gene': 'APOE', 'name': 'apolipoprotein E', 'protein_class': "['Cancer-related genes', 'Candidate cardiovascular disease g", 'disease_involvement': "['Alzheimer disease', 'Amyloidosis', 'Cancer-related genes', 'Disease variant', "}, {'gene': 'ARNTL', 'name': 'clock circadian regulator', 'protein_class': '—', 'disease_involvement': '—'}, {'gene': 'C1QA', 'name': 'complement C1q A chain', 'protein_class': "['Disease related genes', 'Human disease related genes', 'Pl", 'disease_involvement': '—'}, {'gene': 'C3', 'name': 'complement C3', 'protein_class': "['Candidate cardiovascular disease genes', 'Disease related ", 'disease_involvement': "['Age-related macular degeneration', 'Disease variant', 'FDA approved drug targe"}, {'gene': 'CLOCK', 'name': 'clock circadian regulator', 'protein_class': "['Enzymes', 'Metabolic proteins', 'Predicted intracellular p", 'disease_involvement': '—'}, {'gene': 'CX3CL1', 'name': 'C-X3-C motif chemokine ligand 1', 'protein_class': "['Plasma proteins', 'Predicted membrane proteins', 'Predicte", 'disease_involvement': '—'}, {'gene': 'CX3CR1', 'name': 'C-X3-C motif chemokine receptor 1', 'protein_class': "['Disease related genes', 'G-protein coupled receptors', 'Hu", 'disease_involvement': "['Age-related macular degeneration']"}, {'gene': 'DNMT3A', 'name': 'DNA methyltransferase 3 alpha', 'protein_class': "['Cancer-related genes', 'Disease related genes', 'Enzymes',", 'disease_involvement': "['Cancer-related genes', 'Disease variant', 'Dwarfism', 'Intellectual disability"}, {'gene': 'GPR109A', 'name': 'hydroxycarboxylic acid receptor 2', 'protein_class': '—', 'disease_involvement': '—'}, {'gene': 'GPR43', 'name': 'free fatty acid receptor 2', 'protein_class': '—', 'disease_involvement': '—'}, {'gene': 'HDAC1', 'name': 'histone deacetylase 1', 'protein_class': "['Enzymes', 'FDA approved drug targets', 'Plasma proteins', ", 'disease_involvement': "['FDA approved drug targets']"}, {'gene': 'HIF1A', 'name': 'hypoxia inducible factor 1 subunit alpha', 'protein_class': "['Cancer-related genes', 'Human disease related genes', 'Met", 'disease_involvement': "['Cancer-related genes']"}, {'gene': 'IGFBPL1', 'name': 'insulin like growth factor binding protein like 1', 'protein_class': "['Predicted secreted proteins']", 'disease_involvement': '—'}, {'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': 'IL6', 'name': 'interleukin 6', 'protein_class': "['Cancer-related genes', 'Candidate cardiovascular disease g", 'disease_involvement': "['Cancer-related genes', 'FDA approved drug targets']"}, {'gene': 'MULTIPLE', 'name': 'exostoses (multiple) 3', 'protein_class': '—', 'disease_involvement': '—'}, {'gene': 'NFKB1', 'name': 'nuclear factor kappa B subunit 1', 'protein_class': "['Cancer-related genes', 'Disease related genes', 'FDA appro", 'disease_involvement': "['Cancer-related genes', 'FDA approved drug targets']"}, {'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': 'TNF', 'name': 'tumor necrosis factor', 'protein_class': '—', 'disease_involvement': '—'}, {'gene': 'TNFA', 'name': 'tumor necrosis factor', 'protein_class': '—', 'disease_involvement': '—'}]
pd.DataFrame(ann_rows)
| gene | name | protein_class | disease_involvement | |
|---|---|---|---|---|
| 0 | APOE | apolipoprotein E | ['Cancer-related genes', 'Candidate cardiovasc... | ['Alzheimer disease', 'Amyloidosis', 'Cancer-r... |
| 1 | ARNTL | clock circadian regulator | — | — |
| 2 | C1QA | complement C1q A chain | ['Disease related genes', 'Human disease relat... | — |
| 3 | C3 | complement C3 | ['Candidate cardiovascular disease genes', 'Di... | ['Age-related macular degeneration', 'Disease ... |
| 4 | CLOCK | clock circadian regulator | ['Enzymes', 'Metabolic proteins', 'Predicted i... | — |
| 5 | CX3CL1 | C-X3-C motif chemokine ligand 1 | ['Plasma proteins', 'Predicted membrane protei... | — |
| 6 | CX3CR1 | C-X3-C motif chemokine receptor 1 | ['Disease related genes', 'G-protein coupled r... | ['Age-related macular degeneration'] |
| 7 | DNMT3A | DNA methyltransferase 3 alpha | ['Cancer-related genes', 'Disease related gene... | ['Cancer-related genes', 'Disease variant', 'D... |
| 8 | GPR109A | hydroxycarboxylic acid receptor 2 | — | — |
| 9 | GPR43 | free fatty acid receptor 2 | — | — |
| 10 | HDAC1 | histone deacetylase 1 | ['Enzymes', 'FDA approved drug targets', 'Plas... | ['FDA approved drug targets'] |
| 11 | HIF1A | hypoxia inducible factor 1 subunit alpha | ['Cancer-related genes', 'Human disease relate... | ['Cancer-related genes'] |
| 12 | IGFBPL1 | insulin like growth factor binding protein like 1 | ['Predicted secreted proteins'] | — |
| 13 | IL1B | interleukin 1 beta | ['Cancer-related genes', 'Candidate cardiovasc... | ['Cancer-related genes', 'FDA approved drug ta... |
| 14 | IL6 | interleukin 6 | ['Cancer-related genes', 'Candidate cardiovasc... | ['Cancer-related genes', 'FDA approved drug ta... |
| 15 | MULTIPLE | exostoses (multiple) 3 | — | — |
| 16 | NFKB1 | nuclear factor kappa B subunit 1 | ['Cancer-related genes', 'Disease related gene... | ['Cancer-related genes', 'FDA approved drug ta... |
| 17 | NLRP3 | NLR family pyrin domain containing 3 | ['Cancer-related genes', 'Human disease relate... | ['Cancer-related genes'] |
| 18 | TNF | tumor necrosis factor | — | — |
| 19 | TNFA | tumor necrosis factor | — | — |
2. GO Biological Process enrichment (Enrichr)¶
In [2]:
go_bp = [{'rank': 1, 'term': 'Positive Regulation Of NF-kappaB Transcription Factor Activity (GO:0051092)', 'p_value': 9.091770008863585e-11, 'odds_ratio': 73.657824933687, 'genes': ['CX3CR1', 'IL6', 'IL1B', 'NLRP3', 'TNF', 'CLOCK', 'CX3CL1']}, {'rank': 2, 'term': 'Regulation Of Smooth Muscle Cell Proliferation (GO:0048660)', 'p_value': 1.7837089378996073e-09, 'odds_ratio': 135.58503401360545, 'genes': ['IL6', 'HDAC1', 'APOE', 'TNF', 'CX3CL1']}, {'rank': 3, 'term': 'Cellular Response To Lipopolysaccharide (GO:0071222)', 'p_value': 1.8147739036301002e-09, 'odds_ratio': 72.13801452784503, 'genes': ['CX3CR1', 'IL6', 'IL1B', 'NLRP3', 'TNF', 'NFKB1']}, {'rank': 4, 'term': 'Positive Regulation Of DNA-binding Transcription Factor Activity (GO:0051091)', 'p_value': 2.6448307696605873e-09, 'odds_ratio': 44.47602188606373, 'genes': ['CX3CR1', 'IL6', 'IL1B', 'NLRP3', 'TNF', 'CLOCK', 'CX3CL1']}, {'rank': 5, 'term': 'Microglial Cell Activation (GO:0001774)', 'p_value': 3.4856231082538815e-09, 'odds_ratio': 311.9375, 'genes': ['CX3CR1', 'C1QA', 'TNF', 'CX3CL1']}, {'rank': 6, 'term': 'Regulation Of Lipid Storage (GO:0010883)', 'p_value': 4.303029496793e-09, 'odds_ratio': 293.5735294117647, 'genes': ['C3', 'IL6', 'TNF', 'NFKB1']}, {'rank': 7, 'term': 'Regulation Of Neurogenesis (GO:0050767)', 'p_value': 5.40302432763974e-09, 'odds_ratio': 107.08602150537635, 'genes': ['CX3CR1', 'IL6', 'IL1B', 'TNF', 'CX3CL1']}, {'rank': 8, 'term': 'Positive Regulation Of Vascular Endothelial Growth Factor Production (GO:0010575)', 'p_value': 7.625177432915028e-09, 'odds_ratio': 249.5, 'genes': ['C3', 'IL6', 'IL1B', 'HIF1A']}, {'rank': 9, 'term': 'Positive Regulation Of I-kappaB Phosphorylation (GO:1903721)', 'p_value': 8.538975432762311e-09, 'odds_ratio': 1762.764705882353, 'genes': ['CX3CR1', 'TNF', 'CX3CL1']}, {'rank': 10, 'term': 'Regulation Of Calcidiol 1-Monooxygenase Activity (GO:0060558)', 'p_value': 8.538975432762311e-09, 'odds_ratio': 1762.764705882353, 'genes': ['IL1B', 'TNF', 'NFKB1']}]
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 | Positive Regulation Of NF-kappaB Transcription... | 7 | 9.09e-11 | 73.7 | CX3CR1, IL6, IL1B, NLRP3, TNF, CLOCK, CX3CL1 |
| 1 | Regulation Of Smooth Muscle Cell Proliferation... | 5 | 1.78e-09 | 135.6 | IL6, HDAC1, APOE, TNF, CX3CL1 |
| 2 | Cellular Response To Lipopolysaccharide (GO:00... | 6 | 1.81e-09 | 72.1 | CX3CR1, IL6, IL1B, NLRP3, TNF, NFKB1 |
| 3 | Positive Regulation Of DNA-binding Transcripti... | 7 | 2.64e-09 | 44.5 | CX3CR1, IL6, IL1B, NLRP3, TNF, CLOCK, CX3CL1 |
| 4 | Microglial Cell Activation (GO:0001774) | 4 | 3.49e-09 | 311.9 | CX3CR1, C1QA, TNF, CX3CL1 |
| 5 | Regulation Of Lipid Storage (GO:0010883) | 4 | 4.30e-09 | 293.6 | C3, IL6, TNF, NFKB1 |
| 6 | Regulation Of Neurogenesis (GO:0050767) | 5 | 5.40e-09 | 107.1 | CX3CR1, IL6, IL1B, TNF, CX3CL1 |
| 7 | Positive Regulation Of Vascular Endothelial Gr... | 4 | 7.63e-09 | 249.5 | C3, IL6, IL1B, HIF1A |
| 8 | Positive Regulation Of I-kappaB Phosphorylatio... | 3 | 8.54e-09 | 1762.8 | CX3CR1, TNF, CX3CL1 |
| 9 | Regulation Of Calcidiol 1-Monooxygenase Activi... | 3 | 8.54e-09 | 1762.8 | IL1B, TNF, NFKB1 |
In [3]:
import matplotlib.pyplot as plt
import numpy as np
go_bp = [{'rank': 1, 'term': 'Positive Regulation Of NF-kappaB Transcription Factor Activity (GO:0051092)', 'p_value': 9.091770008863585e-11, 'odds_ratio': 73.657824933687, 'genes': ['CX3CR1', 'IL6', 'IL1B', 'NLRP3', 'TNF', 'CLOCK', 'CX3CL1']}, {'rank': 2, 'term': 'Regulation Of Smooth Muscle Cell Proliferation (GO:0048660)', 'p_value': 1.7837089378996073e-09, 'odds_ratio': 135.58503401360545, 'genes': ['IL6', 'HDAC1', 'APOE', 'TNF', 'CX3CL1']}, {'rank': 3, 'term': 'Cellular Response To Lipopolysaccharide (GO:0071222)', 'p_value': 1.8147739036301002e-09, 'odds_ratio': 72.13801452784503, 'genes': ['CX3CR1', 'IL6', 'IL1B', 'NLRP3', 'TNF', 'NFKB1']}, {'rank': 4, 'term': 'Positive Regulation Of DNA-binding Transcription Factor Activity (GO:0051091)', 'p_value': 2.6448307696605873e-09, 'odds_ratio': 44.47602188606373, 'genes': ['CX3CR1', 'IL6', 'IL1B', 'NLRP3', 'TNF', 'CLOCK', 'CX3CL1']}, {'rank': 5, 'term': 'Microglial Cell Activation (GO:0001774)', 'p_value': 3.4856231082538815e-09, 'odds_ratio': 311.9375, 'genes': ['CX3CR1', 'C1QA', 'TNF', 'CX3CL1']}, {'rank': 6, 'term': 'Regulation Of Lipid Storage (GO:0010883)', 'p_value': 4.303029496793e-09, 'odds_ratio': 293.5735294117647, 'genes': ['C3', 'IL6', 'TNF', 'NFKB1']}, {'rank': 7, 'term': 'Regulation Of Neurogenesis (GO:0050767)', 'p_value': 5.40302432763974e-09, 'odds_ratio': 107.08602150537635, 'genes': ['CX3CR1', 'IL6', 'IL1B', 'TNF', 'CX3CL1']}, {'rank': 8, 'term': 'Positive Regulation Of Vascular Endothelial Growth Factor Production (GO:0010575)', 'p_value': 7.625177432915028e-09, 'odds_ratio': 249.5, 'genes': ['C3', 'IL6', 'IL1B', 'HIF1A']}]
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': 'CX3CL1', 'protein2': 'CX3CR1', 'score': 0.999, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.618, 'dscore': 0.9, 'tscore': 0.983}, {'protein1': 'NFKB1', 'protein2': 'TNF', 'score': 0.846, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.846}, {'protein1': 'NFKB1', 'protein2': 'HDAC1', 'score': 0.975, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.564, 'dscore': 0, 'tscore': 0.947}, {'protein1': 'DNMT3A', 'protein2': 'HDAC1', 'score': 0.989, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.457, 'dscore': 0, 'tscore': 0.982}, {'protein1': 'HDAC1', 'protein2': 'HIF1A', 'score': 0.912, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.457, 'dscore': 0, 'tscore': 0.845}, {'protein1': 'HCAR2', 'protein2': 'FFAR2', 'score': 0.569, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.569}, {'protein1': 'ARNTL', 'protein2': 'HIF1A', 'score': 0.922, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.552, 'dscore': 0, 'tscore': 0.834}, {'protein1': 'ARNTL', 'protein2': 'CLOCK', 'score': 0.999, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.965, 'dscore': 0.8, 'tscore': 0.986}]
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 | |
|---|---|---|---|---|---|
| 0 | CX3CL1 | CX3CR1 | 0.999 | 0.618 | 0.983 |
| 7 | ARNTL | CLOCK | 0.999 | 0.965 | 0.986 |
| 3 | DNMT3A | HDAC1 | 0.989 | 0.457 | 0.982 |
| 2 | NFKB1 | HDAC1 | 0.975 | 0.564 | 0.947 |
| 6 | ARNTL | HIF1A | 0.922 | 0.552 | 0.834 |
| 4 | HDAC1 | HIF1A | 0.912 | 0.457 | 0.845 |
| 1 | NFKB1 | TNF | 0.846 | 0.000 | 0.846 |
| 5 | HCAR2 | FFAR2 | 0.569 | 0.000 | 0.569 |
In [5]:
import math
ppi = [{'protein1': 'CX3CL1', 'protein2': 'CX3CR1', 'score': 0.999, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.618, 'dscore': 0.9, 'tscore': 0.983}, {'protein1': 'NFKB1', 'protein2': 'TNF', 'score': 0.846, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.846}, {'protein1': 'NFKB1', 'protein2': 'HDAC1', 'score': 0.975, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.564, 'dscore': 0, 'tscore': 0.947}, {'protein1': 'DNMT3A', 'protein2': 'HDAC1', 'score': 0.989, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.457, 'dscore': 0, 'tscore': 0.982}, {'protein1': 'HDAC1', 'protein2': 'HIF1A', 'score': 0.912, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.457, 'dscore': 0, 'tscore': 0.845}, {'protein1': 'HCAR2', 'protein2': 'FFAR2', 'score': 0.569, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.569}, {'protein1': 'ARNTL', 'protein2': 'HIF1A', 'score': 0.922, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.552, 'dscore': 0, 'tscore': 0.834}, {'protein1': 'ARNTL', 'protein2': 'CLOCK', 'score': 0.999, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.965, 'dscore': 0.8, 'tscore': 0.986}]
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': 'APOE', 'n_pathways': 8, 'top_pathway': 'Nuclear signaling by ERBB4'}, {'gene': 'ARNTL', 'n_pathways': 8, 'top_pathway': 'BMAL1:CLOCK,NPAS2 activates circadian expression'}, {'gene': 'C1QA', 'n_pathways': 3, 'top_pathway': 'Initial triggering of complement'}, {'gene': 'C3', 'n_pathways': 8, 'top_pathway': 'Alternative complement activation'}, {'gene': 'CLOCK', 'n_pathways': 8, 'top_pathway': 'BMAL1:CLOCK,NPAS2 activates circadian expression'}, {'gene': 'CX3CL1', 'n_pathways': 2, 'top_pathway': 'Chemokine receptors bind chemokines'}, {'gene': 'CX3CR1', 'n_pathways': 4, 'top_pathway': 'Chemokine receptors bind chemokines'}, {'gene': 'DNMT3A', 'n_pathways': 6, 'top_pathway': 'PRC2 methylates histones and DNA'}, {'gene': 'GPR109A', 'n_pathways': 3, 'top_pathway': 'Hydroxycarboxylic acid-binding receptors'}, {'gene': 'GPR43', 'n_pathways': 2, 'top_pathway': 'G alpha (q) signalling events'}, {'gene': 'HDAC1', 'n_pathways': 8, 'top_pathway': 'Transcription of E2F targets under negative control by DREAM complex'}, {'gene': 'HIF1A', 'n_pathways': 8, 'top_pathway': 'Regulation of gene expression by Hypoxia-inducible Factor'}, {'gene': 'IGFBPL1', 'n_pathways': 0, 'top_pathway': '—'}, {'gene': 'IL1B', 'n_pathways': 7, 'top_pathway': 'Interleukin-1 processing'}, {'gene': 'IL6', 'n_pathways': 8, 'top_pathway': 'Interleukin-6 signaling'}, {'gene': 'MULTIPLE', 'n_pathways': 0, 'top_pathway': '—'}, {'gene': 'NFKB1', 'n_pathways': 8, 'top_pathway': 'Activation of NF-kappaB in B cells'}, {'gene': 'NLRP3', 'n_pathways': 6, 'top_pathway': 'Metalloprotease DUBs'}, {'gene': 'TNF', 'n_pathways': 8, 'top_pathway': 'Transcriptional regulation of white adipocyte differentiation'}, {'gene': 'TNFA', 'n_pathways': 8, 'top_pathway': 'Transcriptional regulation of white adipocyte differentiation'}]
pd.DataFrame(pw_rows).sort_values('n_pathways', ascending=False)
| gene | n_pathways | top_pathway | |
|---|---|---|---|
| 0 | APOE | 8 | Nuclear signaling by ERBB4 |
| 1 | ARNTL | 8 | BMAL1:CLOCK,NPAS2 activates circadian expression |
| 3 | C3 | 8 | Alternative complement activation |
| 4 | CLOCK | 8 | BMAL1:CLOCK,NPAS2 activates circadian expression |
| 16 | NFKB1 | 8 | Activation of NF-kappaB in B cells |
| 11 | HIF1A | 8 | Regulation of gene expression by Hypoxia-induc... |
| 10 | HDAC1 | 8 | Transcription of E2F targets under negative co... |
| 14 | IL6 | 8 | Interleukin-6 signaling |
| 19 | TNFA | 8 | Transcriptional regulation of white adipocyte ... |
| 18 | TNF | 8 | Transcriptional regulation of white adipocyte ... |
| 13 | IL1B | 7 | Interleukin-1 processing |
| 7 | DNMT3A | 6 | PRC2 methylates histones and DNA |
| 17 | NLRP3 | 6 | Metalloprotease DUBs |
| 6 | CX3CR1 | 4 | Chemokine receptors bind chemokines |
| 2 | C1QA | 3 | Initial triggering of complement |
| 8 | GPR109A | 3 | Hydroxycarboxylic acid-binding receptors |
| 9 | GPR43 | 2 | G alpha (q) signalling events |
| 5 | CX3CL1 | 2 | Chemokine receptors bind chemokines |
| 15 | MULTIPLE | 0 | — |
| 12 | IGFBPL1 | 0 | — |
5. Hypothesis ranking (14 hypotheses)¶
In [7]:
hyp_data = [('Microbiota-Microglia Axis Modulation', 0.651), ('Epigenetic Reprogramming of Microglial Memory', 0.647), ('Cardiovascular-Neuroinflammatory Dual Targeting', 0.627), ('Perinatal Immune Challenge Prevention', 0.616), ('Synaptic Pruning Precision Therapy', 0.612), ('APOE4-Lipid Metabolism Correction', 0.61), ('Cardiovascular-Neuroinflammation Crosstalk Interruption', 0.587), ('Gut-Brain Axis Microbiome Modulation', 0.585), ('IGFBPL1-Mediated Homeostatic Restoration', 0.584), ('Complement-Mediated Synaptic Protection', 0.58), ('IGFBPL1-Mediated Microglial Reprogramming', 0.579), ('TREM2-P2RY12 Balance Restoration Therapy', 0.571), ('Temporal Gating of Microglial Responses', 0.565), ('Perinatal Hypoxia-Primed Microglia Targeting', 0.548)]
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("Neuroinflammation and microglial priming in early Alzheimer's Disease")
ax.grid(axis='x', alpha=0.3)
plt.tight_layout(); plt.show()
6. Score dimension heatmap (top 10)¶
In [8]:
labels = ['Microbiota-Microglia Axis Modulation', 'Epigenetic Reprogramming of Microglial M', 'Cardiovascular-Neuroinflammatory Dual Ta', 'Perinatal Immune Challenge Prevention', 'Synaptic Pruning Precision Therapy', 'APOE4-Lipid Metabolism Correction', 'Cardiovascular-Neuroinflammation Crossta', 'Gut-Brain Axis Microbiome Modulation', 'IGFBPL1-Mediated Homeostatic Restoration', 'Complement-Mediated Synaptic Protection']
matrix = np.array([[0.6, 0.6, 0.5, 0.4, 0, 0.4, 0.3, 0.7, 0.8], [0.8, 0.8, 0.7, 0.7, 0, 0.7, 0.8, 0.9, 0.6], [0.4, 0.8, 0.6, 0.6, 0, 0.8, 0.7, 0.9, 0.6], [0.9, 0.1, 0.4, 0.3, 0, 0.2, 0.1, 0.3, 0.2], [0.7, 0.6, 0.8, 0.8, 0, 0.8, 0.7, 0.6, 0.5], [0.7, 0.4, 0.6, 0.5, 0, 0.6, 0.4, 0.4, 0.7], [0.5, 0.8, 0.7, 0.6, 0, 0.7, 0.8, 0.9, 0.4], [0.8, 0.4, 0.6, 0.5, 0, 0.5, 0.4, 0.3, 0.8], [0.9, 0.3, 0.8, 0.7, 0, 0.6, 0.6, 0.2, 0.5], [0.6, 0.5, 0.7, 0.6, 0, 0.5, 0.5, 0.6, 0.3]])
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: Microbiota-Microglia Axis Modulation¶
Target genes: Multiple · Composite score: 0.651
Molecular Mechanism and Rationale¶
The microbiota-microglia axis represents a sophisticated bidirectional communication network that fundamentally influences neuroinflammatory processes and microglial phenotypic states. This therapeutic approach targets the transition from homeostatic microglia t
In [9]:
print('No PubMed results for hypothesis h-6f21f62a')
No PubMed results for hypothesis h-6f21f62a
Hypothesis 2: Epigenetic Reprogramming of Microglial Memory¶
Target genes: DNMT3A, HDAC1/2 · Composite score: 0.647
Epigenetic Reprogramming of Microglial Memory: A Novel Approach to Preventing Neurodegeneration¶
Scientific Background¶
Neuroinflammation represents a critical pathological hallmark of neurodegenerative diseases, with microglia—the resident immune cells of the central nervous system—emerging as
In [10]:
print('No PubMed results for hypothesis h-e5f1182b')
No PubMed results for hypothesis h-e5f1182b
Hypothesis 3: Cardiovascular-Neuroinflammatory Dual Targeting¶
Target genes: TNF/IL6 · Composite score: 0.627
Cardiovascular-Neuroinflammatory Dual Targeting¶
Mechanistic Hypothesis Overview¶
The "Cardiovascular-Neuroinflammatory Dual Targeting" hypothesis proposes that the strong epidemiological link between cardiovascular risk factors (hypertension, hypercholesterolemia, atherosclerosis, type 2 dia
In [11]:
print('No PubMed results for hypothesis h-6f1e8d32')
No PubMed results for hypothesis h-6f1e8d32
Hypothesis 4: Perinatal Immune Challenge Prevention¶
Target genes: Multiple · Composite score: 0.616
Perinatal Immune Challenge Prevention¶
Mechanistic Hypothesis Overview¶
This hypothesis proposes a disease-modifying strategy centered on Perinatal Immune Challenge Prevention as a mechanistic intervention point in neurodegeneration. The core claim is that the biological process represent
In [12]:
print('No PubMed results for hypothesis h-8f9633d9')
No PubMed results for hypothesis h-8f9633d9
Hypothesis 5: Synaptic Pruning Precision Therapy¶
Target genes: C1QA, C3, CX3CR1, CX3CL1 · Composite score: 0.612
Synaptic Pruning Precision Therapy: Targeting Complement and Chemokine Signaling to Preserve Neuronal Connectivity¶
Scientific Background¶
Synaptic pruning represents a developmentally regulated process whereby immature or redundant synaptic connections are selectively eliminated to refine neur
In [13]:
print('No PubMed results for hypothesis h-494861d2')
No PubMed results for hypothesis h-494861d2
Hypothesis 6: APOE4-Lipid Metabolism Correction¶
Target genes: APOE · Composite score: 0.61
APOE4-Lipid Metabolism Correction¶
Mechanistic Hypothesis Overview¶
This hypothesis proposes a disease-modifying strategy centered on APOE4-Lipid Metabolism Correction as a mechanistic intervention point in neurodegeneration. The core claim is that the biological process represented by ap
In [14]:
print('No PubMed results for hypothesis h-69bde12f')
No PubMed results for hypothesis h-69bde12f
Hypothesis 7: Cardiovascular-Neuroinflammation Crosstalk Interruption¶
Target genes: IL1B, TNFA, NLRP3 · Composite score: 0.587
Cardiovascular-Neuroinflammation Crosstalk Interruption: Targeting Shared Inflammatory Mediators in Neurodegeneration¶
Scientific Background¶
Cardiovascular disease and neurodegenerative pathology share more than epidemiological correlation—they are mechanistically linked through chronic system
In [15]:
print('No PubMed results for hypothesis h-cc1076c1')
No PubMed results for hypothesis h-cc1076c1
Hypothesis 8: Gut-Brain Axis Microbiome Modulation¶
Target genes: GPR43, GPR109A · Composite score: 0.585
Gut-Brain Axis Microbiome Modulation: Preventing Neurodegeneration Through GPR43/GPR109A Signaling¶
Scientific Background¶
The gut microbiota exerts profound influence over central nervous system (CNS) homeostasis through the gut-brain axis, a bidirectional communication network involving neura
In [16]:
print('No PubMed results for hypothesis h-48775971')
No PubMed results for hypothesis h-48775971
Hypothesis 9: IGFBPL1-Mediated Homeostatic Restoration¶
Target genes: IGFBPL1 · Composite score: 0.584
IGFBPL1-Mediated Homeostatic Restoration: Targeting Microglial Priming in Neurodegeneration¶
Scientific Background¶
Neuroinflammation, characterized by sustained microglial activation, represents a critical pathological feature across multiple neurodegenerative conditions including Alzheimer's
In [17]:
print('No PubMed results for hypothesis h-d4ff5555')
No PubMed results for hypothesis h-d4ff5555
Hypothesis 10: Complement-Mediated Synaptic Protection¶
Target genes: C1QA · Composite score: 0.58
Complement-Mediated Synaptic Protection¶
Mechanistic Hypothesis Overview¶
The "Complement-Mediated Synaptic Protection" hypothesis proposes that excessive activation of the classical complement cascade — specifically the C1q-C3-C3aR and C4b pathways — drives synaptic loss in Alzheimer's disea
In [18]:
print('No PubMed results for hypothesis h-f19b8ac8')
No PubMed results for hypothesis h-f19b8ac8
Hypothesis 11: IGFBPL1-Mediated Microglial Reprogramming¶
Target genes: IGFBPL1 · Composite score: 0.579
IGFBPL1-Mediated Microglial Reprogramming¶
Mechanistic Hypothesis Overview¶
This hypothesis proposes a disease-modifying strategy centered on IGFBPL1-Mediated Microglial Reprogramming as a mechanistic intervention point in neurodegeneration. The core claim is that the biological process r
In [19]:
print('No PubMed results for hypothesis h-6880f29b')
No PubMed results for hypothesis h-6880f29b
Hypothesis 12: TREM2-P2RY12 Balance Restoration Therapy¶
Target genes: TREM2 · Composite score: 0.571
TREM2-P2RY12 Balance Restoration Therapy¶
Mechanistic Hypothesis Overview¶
This hypothesis proposes a disease-modifying strategy centered on TREM2-P2RY12 Balance Restoration Therapy as a mechanistic intervention point in neurodegeneration. The core claim is that the biological process rep
In [20]:
print('No PubMed results for hypothesis h-ea3274ff')
No PubMed results for hypothesis h-ea3274ff
Hypothesis 13: Temporal Gating of Microglial Responses¶
Target genes: CLOCK, ARNTL · Composite score: 0.565
Time Anti-Inflammatory Interventions to Circadian Windows of Maximal Microglial Priming for Enhanced Efficacy
Overview¶
The brain's immune system does not operate uniformly across the day. Microglia, the primary immune cells of the central nervous system, exhibit profound circadian rhythmicity i
In [21]:
print('No PubMed results for hypothesis h-828b3729')
No PubMed results for hypothesis h-828b3729
Hypothesis 14: Perinatal Hypoxia-Primed Microglia Targeting¶
Target genes: HIF1A, NFKB1 · Composite score: 0.548
Perinatal Hypoxia-Primed Microglia Targeting¶
Mechanistic Hypothesis Overview¶
This hypothesis proposes a disease-modifying strategy centered on Perinatal Hypoxia-Primed Microglia Targeting as a mechanistic intervention point in neurodegeneration. The core claim is that the biological pro
In [22]:
print('No PubMed results for hypothesis h-646ae8f1')
No PubMed results for hypothesis h-646ae8f1
8. Knowledge graph edges (108 total)¶
In [23]:
edge_data = [{'source': 'h-d4ff5555', 'relation': 'targets', 'target': 'IGFBPL1', 'strength': 0.8}, {'source': 'h-d4ff5555', 'relation': 'implicated_in', 'target': "Alzheimer's disease", 'strength': 0.8}, {'source': 'h-494861d2', 'relation': 'targets', 'target': 'C3', 'strength': 0.7}, {'source': 'h-494861d2', 'relation': 'implicated_in', 'target': "Alzheimer's disease", 'strength': 0.7}, {'source': 'h-494861d2', 'relation': 'targets', 'target': 'C1QA', 'strength': 0.7}, {'source': 'h-494861d2', 'relation': 'targets', 'target': 'CX3CR1', 'strength': 0.7}, {'source': 'h-494861d2', 'relation': 'targets', 'target': 'CX3CL1', 'strength': 0.7}, {'source': 'h-e5f1182b', 'relation': 'implicated_in', 'target': "Alzheimer's disease", 'strength': 0.6}, {'source': 'h-e5f1182b', 'relation': 'targets', 'target': 'HDAC1', 'strength': 0.6}, {'source': 'h-e5f1182b', 'relation': 'targets', 'target': 'DNMT3A', 'strength': 0.6}, {'source': 'h-e5f1182b', 'relation': 'targets', 'target': '2', 'strength': 0.6}, {'source': 'CX3CR1', 'relation': 'associated_with_microglia', 'target': "Alzheimer's disease", 'strength': 0.5}, {'source': 'microbiota', 'relation': 'modulates', 'target': 'microglia_activation', 'strength': 0.5}, {'source': 'IGFBPL1', 'relation': 'associated_with_microglia', 'target': "Alzheimer's disease", 'strength': 0.5}, {'source': 'C3', 'relation': 'associated_with_microglia', 'target': "Alzheimer's disease", 'strength': 0.5}, {'source': 'C1QA', 'relation': 'associated_with_microglia', 'target': "Alzheimer's disease", 'strength': 0.5}, {'source': 'P2RY12', 'relation': 'maintains', 'target': 'homeostatic_microglia', 'strength': 0.5}, {'source': 'TNF', 'relation': 'drives', 'target': 'neuroinflammation', 'strength': 0.5}, {'source': 'NFKB1', 'relation': 'associated_with_microglia', 'target': "Alzheimer's disease", 'strength': 0.5}, {'source': 'HDAC2', 'relation': 'associated_with_microglia', 'target': "Alzheimer's disease", 'strength': 0.5}, {'source': 'HIF1A', 'relation': 'associated_with_microglia', 'target': "Alzheimer's disease", 'strength': 0.5}, {'source': 'CLOCK', 'relation': 'associated_with_microglia', 'target': "Alzheimer's disease", 'strength': 0.5}, {'source': 'ARNTL', 'relation': 'associated_with_microglia', 'target': "Alzheimer's disease", 'strength': 0.5}, {'source': 'C1QA, C3, CX3CR1, CX3CL1', 'relation': 'associated_with', 'target': "Alzheimer's disease", 'strength': 0.5}, {'source': 'GPR109A', 'relation': 'associated_with_microglia', 'target': "Alzheimer's disease", 'strength': 0.5}]
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.