import pandas as pd
ann_rows = [{'gene': 'AQP4', 'name': 'aquaporin 4', 'protein_class': "['Human disease related genes', 'Metabolic proteins', 'Predi", 'disease_involvement': "['Disease variant']"}, {'gene': 'C1Q', 'name': 'complement C1q like 1', 'protein_class': '—', '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': 'CD38', 'name': 'CD38 molecule', 'protein_class': "['Cancer-related genes', 'CD markers', 'Enzymes', 'Metabolic", 'disease_involvement': "['Cancer-related genes', 'Diabetes mellitus']"}, {'gene': 'CGAS', 'name': 'cyclic GMP-AMP synthase', 'protein_class': "['Enzymes', 'Predicted intracellular proteins']", 'disease_involvement': '—'}, {'gene': 'DNASE2', 'name': 'deoxyribonuclease 2, lysosomal', 'protein_class': "['Disease related genes', 'Enzymes', 'Human disease related ", 'disease_involvement': "['Disease variant']"}, {'gene': 'GPX4', 'name': 'glutathione peroxidase 4', 'protein_class': "['Disease related genes', 'Enzymes', 'Essential proteins', '", 'disease_involvement': "['Dwarfism']"}, {'gene': 'HK2', 'name': 'hexokinase 2', 'protein_class': "['Cancer-related genes', 'Enzymes', 'Metabolic proteins', 'P", 'disease_involvement': "['Cancer-related genes']"}, {'gene': 'MMP2', 'name': 'matrix metallopeptidase 2', 'protein_class': "['Cancer-related genes', 'Candidate cardiovascular disease g", 'disease_involvement': "['Cancer-related genes', 'Disease variant']"}, {'gene': 'MMP9', 'name': 'matrix metallopeptidase 9', 'protein_class': "['Cancer-related genes', 'Candidate cardiovascular disease g", 'disease_involvement': "['Cancer-related genes']"}, {'gene': 'NAMPT', 'name': 'nicotinamide phosphoribosyltransferase', 'protein_class': "['Cancer-related genes', 'Enzymes', 'Metabolic proteins', 'P", 'disease_involvement': "['Cancer-related genes']"}, {'gene': 'PFKFB3', 'name': '6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3', 'protein_class': "['Enzymes', 'Metabolic proteins', 'Plasma proteins', 'Predic", 'disease_involvement': '—'}, {'gene': 'PLA2G4A', 'name': 'phospholipase A2 group IVA', 'protein_class': "['Disease related genes', 'Enzymes', 'Human disease related ", 'disease_involvement': "['Disease variant']"}, {'gene': 'PLA2G6', 'name': 'phospholipase A2 group VI', 'protein_class': "['Disease related genes', 'Enzymes', 'Human disease related ", 'disease_involvement': "['Disease variant', 'Dystonia', 'Neurodegeneration', 'Parkinson disease', 'Parki"}, {'gene': 'SLC7A11', 'name': 'solute carrier family 7 member 11', 'protein_class': "['FDA approved drug targets', 'Metabolic proteins', 'Plasma ", 'disease_involvement': "['FDA approved drug targets']"}, {'gene': 'STING1', 'name': 'stimulator of interferon response cGAMP interactor 1', 'protein_class': "['Disease related genes', 'Human disease related genes', 'Po", 'disease_involvement': "['Disease variant']"}]
pd.DataFrame(ann_rows)

go_bp = [{'rank': 1, 'term': 'Platelet Activating Factor Metabolic Process (GO:0046469)', 'p_value': 1.2570489476168942e-05, 'odds_ratio': 570.8285714285714, 'genes': ['PLA2G4A', 'PLA2G6']}, {'rank': 2, 'term': 'Phosphatidylcholine Catabolic Process (GO:0034638)', 'p_value': 2.152942370892626e-05, 'odds_ratio': 407.6938775510204, 'genes': ['PLA2G4A', 'PLA2G6']}, {'rank': 3, 'term': 'Cellular Response To UV-A (GO:0071492)', 'p_value': 2.6899281719267998e-05, 'odds_ratio': 356.7142857142857, 'genes': ['MMP2', 'MMP9']}, {'rank': 4, 'term': 'Response To UV-A (GO:0070141)', 'p_value': 4.6560429993752554e-05, 'odds_ratio': 259.38961038961037, 'genes': ['MMP2', 'MMP9']}, {'rank': 5, 'term': 'Cellular Response To Exogenous dsRNA (GO:0071360)', 'p_value': 5.429523345740245e-05, 'odds_ratio': 237.76190476190476, 'genes': ['STING1', 'CGAS']}, {'rank': 6, 'term': 'Innate Immune Response-Activating Signaling Pathway (GO:0002758)', 'p_value': 7.15314919064766e-05, 'odds_ratio': 203.77551020408163, 'genes': ['STING1', 'CGAS']}, {'rank': 7, 'term': 'Cellular Response To dsRNA (GO:0071359)', 'p_value': 8.103128969688274e-05, 'odds_ratio': 190.1809523809524, 'genes': ['STING1', 'CGAS']}, {'rank': 8, 'term': 'Positive Regulation Of Vascular Associated Smooth Muscle Cell Proliferation (GO:1904707)', 'p_value': 0.00011304743026189121, 'odds_ratio': 158.46031746031747, 'genes': ['MMP2', 'MMP9']}, {'rank': 9, 'term': 'Cytosolic Pattern Recognition Receptor Signaling Pathway (GO:0002753)', 'p_value': 0.00015032135601996194, 'odds_ratio': 135.80272108843536, 'genes': ['STING1', 'CGAS']}, {'rank': 10, 'term': 'Negative Regulation Of Programmed Cell Death (GO:0043069)', 'p_value': 0.00019677324444621088, 'odds_ratio': 17.335985853227232, 'genes': ['GPX4', 'CD38', 'SLC7A11', 'MMP9']}]
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']]

import matplotlib.pyplot as plt
import numpy as np

go_bp = [{'rank': 1, 'term': 'Platelet Activating Factor Metabolic Process (GO:0046469)', 'p_value': 1.2570489476168942e-05, 'odds_ratio': 570.8285714285714, 'genes': ['PLA2G4A', 'PLA2G6']}, {'rank': 2, 'term': 'Phosphatidylcholine Catabolic Process (GO:0034638)', 'p_value': 2.152942370892626e-05, 'odds_ratio': 407.6938775510204, 'genes': ['PLA2G4A', 'PLA2G6']}, {'rank': 3, 'term': 'Cellular Response To UV-A (GO:0071492)', 'p_value': 2.6899281719267998e-05, 'odds_ratio': 356.7142857142857, 'genes': ['MMP2', 'MMP9']}, {'rank': 4, 'term': 'Response To UV-A (GO:0070141)', 'p_value': 4.6560429993752554e-05, 'odds_ratio': 259.38961038961037, 'genes': ['MMP2', 'MMP9']}, {'rank': 5, 'term': 'Cellular Response To Exogenous dsRNA (GO:0071360)', 'p_value': 5.429523345740245e-05, 'odds_ratio': 237.76190476190476, 'genes': ['STING1', 'CGAS']}, {'rank': 6, 'term': 'Innate Immune Response-Activating Signaling Pathway (GO:0002758)', 'p_value': 7.15314919064766e-05, 'odds_ratio': 203.77551020408163, 'genes': ['STING1', 'CGAS']}, {'rank': 7, 'term': 'Cellular Response To dsRNA (GO:0071359)', 'p_value': 8.103128969688274e-05, 'odds_ratio': 190.1809523809524, 'genes': ['STING1', 'CGAS']}, {'rank': 8, 'term': 'Positive Regulation Of Vascular Associated Smooth Muscle Cell Proliferation (GO:1904707)', 'p_value': 0.00011304743026189121, 'odds_ratio': 158.46031746031747, 'genes': ['MMP2', 'MMP9']}]
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()

ppi = [{'protein1': 'MMP2', 'protein2': 'MMP9', 'score': 0.9, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0.048}, {'protein1': 'CGAS', 'protein2': 'STING1', 'score': 0.477, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.292, 'dscore': 0, 'tscore': 0.292}]
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)

2 STRING edges

import math
ppi = [{'protein1': 'MMP2', 'protein2': 'MMP9', 'score': 0.9, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0.048}, {'protein1': 'CGAS', 'protein2': 'STING1', 'score': 0.477, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.292, 'dscore': 0, 'tscore': 0.292}]
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()

pw_rows = [{'gene': 'AQP4', 'n_pathways': 2, 'top_pathway': 'Vasopressin regulates renal water homeostasis via Aquaporins'}, {'gene': 'C1Q', 'n_pathways': 0, 'top_pathway': '—'}, {'gene': 'C3', 'n_pathways': 8, 'top_pathway': 'Alternative complement activation'}, {'gene': 'CD38', 'n_pathways': 1, 'top_pathway': 'Nicotinate metabolism'}, {'gene': 'CGAS', 'n_pathways': 1, 'top_pathway': 'STING mediated induction of host immune responses'}, {'gene': 'DNASE2', 'n_pathways': 1, 'top_pathway': 'Lysosome Vesicle Biogenesis'}, {'gene': 'GPX4', 'n_pathways': 7, 'top_pathway': 'Synthesis of 5-eicosatetraenoic acids'}, {'gene': 'HK2', 'n_pathways': 1, 'top_pathway': 'Glycolysis'}, {'gene': 'MMP2', 'n_pathways': 7, 'top_pathway': 'Collagen degradation'}, {'gene': 'MMP9', 'n_pathways': 8, 'top_pathway': 'Signaling by SCF-KIT'}, {'gene': 'NAMPT', 'n_pathways': 3, 'top_pathway': 'BMAL1:CLOCK,NPAS2 activates circadian expression'}, {'gene': 'PFKFB3', 'n_pathways': 1, 'top_pathway': 'Regulation of glycolysis by fructose 2,6-bisphosphate metabolism'}, {'gene': 'PLA2G4A', 'n_pathways': 8, 'top_pathway': 'phospho-PLA2 pathway'}, {'gene': 'PLA2G6', 'n_pathways': 5, 'top_pathway': 'Acyl chain remodelling of PC'}, {'gene': 'SLC7A11', 'n_pathways': 3, 'top_pathway': 'Basigin interactions'}, {'gene': 'STING1', 'n_pathways': 7, 'top_pathway': 'STING mediated induction of host immune responses'}]
pd.DataFrame(pw_rows).sort_values('n_pathways', ascending=False)

hyp_data = [('SASP-Driven Microglial Metabolic Reprogramming in Synap', 0.934), ('SASP-Mediated Complement Cascade Amplification', 0.91), ('SASP-Driven Aquaporin-4 Dysregulation', 0.782), ('SASP-Mediated Cholinergic Synapse Disruption', 0.763), ('Senescence-Activated NAD+ Depletion Rescue', 0.755), ('Senescent Cell Mitochondrial DNA Release', 0.742), ('Senescence-Associated Myelin Lipid Remodeling', 0.732), ('Senescence-Induced Lipid Peroxidation Spreading', 0.73)]
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('Senolytic therapy for age-related neurodegeneration')
ax.grid(axis='x', alpha=0.3)
plt.tight_layout(); plt.show()

labels = ['SASP-Driven Microglial Metabolic Reprogr', 'SASP-Mediated Complement Cascade Amplifi', 'SASP-Driven Aquaporin-4 Dysregulation', 'SASP-Mediated Cholinergic Synapse Disrup', 'Senescence-Activated NAD+ Depletion Resc', 'Senescent Cell Mitochondrial DNA Release', 'Senescence-Associated Myelin Lipid Remod', 'Senescence-Induced Lipid Peroxidation Sp']
matrix = np.array([[0.8, 0.7, 0.75, 0.75, 0.05, 0.8, 0.7, 0.85, 0.6], [0.85, 0.75, 0.8, 0.75, 0.4, 0.75, 0.7, 0.85, 0.6], [0.65, 0.6, 0.72, 0.75, 0.712, 0.62, 0.58, 0.65, 0.45], [0.75, 0.65, 0.65, 0.6, 0.666, 0.6, 0.55, 0.6, 0.45], [0.75, 0.7, 0.75, 0.65, 0.436, 0.8, 0.75, 0.9, 0.65], [0.85, 0.45, 0.6, 0.55, 0.436, 0.45, 0.45, 0.4, 0.5], [0.8, 0.45, 0.5, 0.4, 0.436, 0.35, 0.3, 0.55, 0.4], [0.7, 0.55, 0.55, 0.45, 0.452, 0.5, 0.4, 0.65, 0.5]])
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')

print('No PubMed results for hypothesis h-var-ac50d1e3d1')

No PubMed results for hypothesis h-var-ac50d1e3d1

print('No PubMed results for hypothesis h-58e4635a')

No PubMed results for hypothesis h-58e4635a

print('No PubMed results for hypothesis h-807d7a82')

No PubMed results for hypothesis h-807d7a82

print('No PubMed results for hypothesis h-1acdd55e')

No PubMed results for hypothesis h-1acdd55e

print('No PubMed results for hypothesis h-cb833ed8')

No PubMed results for hypothesis h-cb833ed8

print('No PubMed results for hypothesis h-1a34778f')

No PubMed results for hypothesis h-1a34778f

print('No PubMed results for hypothesis h-bb518928')

No PubMed results for hypothesis h-bb518928

print('No PubMed results for hypothesis h-7957bb2a')

No PubMed results for hypothesis h-7957bb2a

edge_data = [{'source': 'SDA-2026-04-01-gap-013', 'relation': 'generated', 'target': 'h-807d7a82', 'strength': 0.95}, {'source': 'SDA-2026-04-01-gap-013', 'relation': 'generated', 'target': 'h-7957bb2a', 'strength': 0.95}, {'source': 'SDA-2026-04-01-gap-013', 'relation': 'generated', 'target': 'h-58e4635a', 'strength': 0.95}, {'source': 'SDA-2026-04-01-gap-013', 'relation': 'generated', 'target': 'h-cb833ed8', 'strength': 0.95}, {'source': 'SDA-2026-04-01-gap-013', 'relation': 'generated', 'target': 'h-1acdd55e', 'strength': 0.95}, {'source': 'MMP9', 'relation': 'remodels', 'target': 'extracellular matrix', 'strength': 0.8}, {'source': 'SASP', 'relation': 'induces', 'target': 'neuroinflammation', 'strength': 0.8}, {'source': 'CD38', 'relation': 'regulates', 'target': 'NAD+ metabolism', 'strength': 0.8}, {'source': 'PLA2G6', 'relation': 'modifies', 'target': 'myelin lipids', 'strength': 0.8}, {'source': 'IL1B', 'relation': 'downregulates', 'target': 'AQP4', 'strength': 0.8}, {'source': 'STING1', 'relation': 'triggers', 'target': 'neuroinflammation', 'strength': 0.8}, {'source': 'SLC7A11', 'relation': 'mediates', 'target': 'cystine import', 'strength': 0.8}, {'source': 'C1Q', 'relation': 'initiates', 'target': 'complement cascade', 'strength': 0.8}, {'source': 'AQP4', 'relation': 'enables', 'target': 'glymphatic system', 'strength': 0.8}, {'source': 'TNF', 'relation': 'downregulates', 'target': 'AQP4', 'strength': 0.8}, {'source': 'senescent cells', 'relation': 'contributes_to', 'target': 'neurodegeneration', 'strength': 0.8}, {'source': 'NAMPT', 'relation': 'catalyzes', 'target': 'NAD+ biosynthesis', 'strength': 0.8}, {'source': 'MMP2', 'relation': 'degrades', 'target': 'perineuronal nets', 'strength': 0.8}, {'source': 'C3', 'relation': 'mediates', 'target': 'synapse elimination', 'strength': 0.8}, {'source': 'CGAS', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.75}, {'source': 'PLA2G6', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.75}, {'source': 'diseases-ftd', 'relation': 'investigated_in', 'target': 'h-58e4635a', 'strength': 0.75}, {'source': 'GPX4', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.75}, {'source': 'diseases-corticobasal-syndrome', 'relation': 'investigated_in', 'target': 'h-58e4635a', 'strength': 0.75}, {'source': 'C1Q', 'relation': 'participates_in', 'target': 'C1q / complement-mediated syna', 'strength': 0.71}]
if edge_data:
    pd.DataFrame(edge_data).head(25)
else:
    print('No KG edge data available')

	term	n_hits	p_value	odds_ratio	genes
0	Platelet Activating Factor Metabolic Process (...	2	1.26e-05	570.8	PLA2G4A, PLA2G6
1	Phosphatidylcholine Catabolic Process (GO:0034...	2	2.15e-05	407.7	PLA2G4A, PLA2G6
2	Cellular Response To UV-A (GO:0071492)	2	2.69e-05	356.7	MMP2, MMP9
3	Response To UV-A (GO:0070141)	2	4.66e-05	259.4	MMP2, MMP9
4	Cellular Response To Exogenous dsRNA (GO:0071360)	2	5.43e-05	237.8	STING1, CGAS
5	Innate Immune Response-Activating Signaling Pa...	2	7.15e-05	203.8	STING1, CGAS
6	Cellular Response To dsRNA (GO:0071359)	2	8.10e-05	190.2	STING1, CGAS
7	Positive Regulation Of Vascular Associated Smo...	2	1.13e-04	158.5	MMP2, MMP9
8	Cytosolic Pattern Recognition Receptor Signali...	2	1.50e-04	135.8	STING1, CGAS
9	Negative Regulation Of Programmed Cell Death (...	4	1.97e-04	17.3	GPX4, CD38, SLC7A11, MMP9

Senolytic therapy for age-related neurodegeneration

Research question¶

Approach¶

Debate Summary¶

1. Target gene annotations (MyGene + Human Protein Atlas)¶

2. GO Biological Process enrichment (Enrichr)¶

3. STRING protein interaction network¶

4. Reactome pathway footprint¶

5. Hypothesis ranking (8 hypotheses)¶

6. Score dimension heatmap (top 10)¶

7. PubMed literature per hypothesis¶

Hypothesis 1: SASP-Driven Microglial Metabolic Reprogramming in Synaptic Phagocytosi¶

Molecular Mechanism¶

Hypothesis 2: SASP-Mediated Complement Cascade Amplification¶

Hypothesis 3: SASP-Driven Aquaporin-4 Dysregulation¶

Hypothesis 4: SASP-Mediated Cholinergic Synapse Disruption¶

Hypothesis 5: Senescence-Activated NAD+ Depletion Rescue¶

Hypothesis 6: Senescent Cell Mitochondrial DNA Release¶

Hypothesis 7: Senescence-Associated Myelin Lipid Remodeling¶

Molecular Mechanism and Rationale¶

Hypothesis 8: Senescence-Induced Lipid Peroxidation Spreading¶

8. Knowledge graph edges (340 total)¶

Caveats¶

	gene	name	protein_class	disease_involvement
0	AQP4	aquaporin 4	['Human disease related genes', 'Metabolic pro...	['Disease variant']
1	C1Q	complement C1q like 1	—	—
2	C3	complement C3	['Candidate cardiovascular disease genes', 'Di...	['Age-related macular degeneration', 'Disease ...
3	CD38	CD38 molecule	['Cancer-related genes', 'CD markers', 'Enzyme...	['Cancer-related genes', 'Diabetes mellitus']
4	CGAS	cyclic GMP-AMP synthase	['Enzymes', 'Predicted intracellular proteins']	—
5	DNASE2	deoxyribonuclease 2, lysosomal	['Disease related genes', 'Enzymes', 'Human di...	['Disease variant']
6	GPX4	glutathione peroxidase 4	['Disease related genes', 'Enzymes', 'Essentia...	['Dwarfism']
7	HK2	hexokinase 2	['Cancer-related genes', 'Enzymes', 'Metabolic...	['Cancer-related genes']
8	MMP2	matrix metallopeptidase 2	['Cancer-related genes', 'Candidate cardiovasc...	['Cancer-related genes', 'Disease variant']
9	MMP9	matrix metallopeptidase 9	['Cancer-related genes', 'Candidate cardiovasc...	['Cancer-related genes']
10	NAMPT	nicotinamide phosphoribosyltransferase	['Cancer-related genes', 'Enzymes', 'Metabolic...	['Cancer-related genes']
11	PFKFB3	6-phosphofructo-2-kinase/fructose-2,6-biphosph...	['Enzymes', 'Metabolic proteins', 'Plasma prot...	—
12	PLA2G4A	phospholipase A2 group IVA	['Disease related genes', 'Enzymes', 'Human di...	['Disease variant']
13	PLA2G6	phospholipase A2 group VI	['Disease related genes', 'Enzymes', 'Human di...	['Disease variant', 'Dystonia', 'Neurodegenera...
14	SLC7A11	solute carrier family 7 member 11	['FDA approved drug targets', 'Metabolic prote...	['FDA approved drug targets']
15	STING1	stimulator of interferon response cGAMP intera...	['Disease related genes', 'Human disease relat...	['Disease variant']

	gene	n_pathways	top_pathway
2	C3	8	Alternative complement activation
9	MMP9	8	Signaling by SCF-KIT
12	PLA2G4A	8	phospho-PLA2 pathway
6	GPX4	7	Synthesis of 5-eicosatetraenoic acids
8	MMP2	7	Collagen degradation
15	STING1	7	STING mediated induction of host immune responses
13	PLA2G6	5	Acyl chain remodelling of PC
10	NAMPT	3	BMAL1:CLOCK,NPAS2 activates circadian expression
14	SLC7A11	3	Basigin interactions
0	AQP4	2	Vasopressin regulates renal water homeostasis ...
3	CD38	1	Nicotinate metabolism
5	DNASE2	1	Lysosome Vesicle Biogenesis
4	CGAS	1	STING mediated induction of host immune responses
7	HK2	1	Glycolysis
11	PFKFB3	1	Regulation of glycolysis by fructose 2,6-bisph...
1	C1Q	0	—

	protein1	protein2	score	escore	tscore
0	MMP2	MMP9	0.900	0.000	0.048
1	CGAS	STING1	0.477	0.292	0.292

Senolytic therapy for age-related neurodegeneration

Senolytic therapy for age-related neurodegeneration¶

Research question¶

Approach¶

Debate Summary¶

1. Target gene annotations (MyGene + Human Protein Atlas)¶

2. GO Biological Process enrichment (Enrichr)¶

3. STRING protein interaction network¶

4. Reactome pathway footprint¶

5. Hypothesis ranking (8 hypotheses)¶

6. Score dimension heatmap (top 10)¶

7. PubMed literature per hypothesis¶

Hypothesis 1: SASP-Driven Microglial Metabolic Reprogramming in Synaptic Phagocytosi¶

Molecular Mechanism¶

Hypothesis 2: SASP-Mediated Complement Cascade Amplification¶

Hypothesis 3: SASP-Driven Aquaporin-4 Dysregulation¶

Hypothesis 4: SASP-Mediated Cholinergic Synapse Disruption¶

Hypothesis 5: Senescence-Activated NAD+ Depletion Rescue¶

Hypothesis 6: Senescent Cell Mitochondrial DNA Release¶

Hypothesis 7: Senescence-Associated Myelin Lipid Remodeling¶

Molecular Mechanism and Rationale¶

Hypothesis 8: Senescence-Induced Lipid Peroxidation Spreading¶

8. Knowledge graph edges (340 total)¶

Caveats¶