Epigenetic reprogramming in aging neurons¶
Notebook ID: nb-SDA-2026-04-04-gap-epigenetic-reprog-b685190e · Analysis: SDA-2026-04-04-gap-epigenetic-reprog-b685190e · Generated: 2026-04-20T08:56:59
Research question¶
Investigate mechanisms of epigenetic reprogramming in aging neurons, including DNA methylation changes, histone modification dynamics, chromatin remodeling, and partial reprogramming approaches (e.g., Yamanaka factors) to reverse age-related epigenetic alterations in post-mitotic neurons.
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': 'BRD4', 'name': 'bromodomain containing 4', 'protein_class': "['Cancer-related genes', 'Disease related genes', 'Essential", 'disease_involvement': "['Cancer-related genes', 'Disease variant', 'Intellectual disability']"}, {'gene': 'HDAC', 'name': 'SIN3-HDAC complex associated factor', 'protein_class': '—', 'disease_involvement': '—'}, {'gene': 'HDAC3', 'name': 'histone deacetylase 3', 'protein_class': "['Enzymes', 'Essential proteins', 'FDA approved drug targets", 'disease_involvement': "['FDA approved drug targets']"}, {'gene': 'NAMPT', 'name': 'nicotinamide phosphoribosyltransferase', 'protein_class': "['Cancer-related genes', 'Enzymes', 'Metabolic proteins', 'P", 'disease_involvement': "['Cancer-related genes']"}, {'gene': 'OCT4', 'name': 'OCT4 hESC enhancer GRCh37_chr10:133148496-133148998', 'protein_class': '—', 'disease_involvement': '—'}, {'gene': 'SIRT1', 'name': 'sirtuin 1', 'protein_class': "['Enzymes', 'Metabolic proteins', 'Predicted intracellular p", 'disease_involvement': '—'}, {'gene': 'SIRT3', 'name': 'sirtuin 3', 'protein_class': "['Enzymes', 'Metabolic proteins', 'Predicted intracellular p", 'disease_involvement': '—'}, {'gene': 'SMARCA4', 'name': 'SWI/SNF related BAF chromatin remodeling complex subunit ATP', 'protein_class': "['Cancer-related genes', 'Disease related genes', 'Human dis", 'disease_involvement': "['Cancer-related genes', 'Deafness', 'Disease variant', 'Intellectual disability"}, {'gene': 'TET2', 'name': 'tet methylcytosine dioxygenase 2', 'protein_class': "['Cancer-related genes', 'Disease related genes', 'Enzymes',", 'disease_involvement': "['Cancer-related genes', 'Disease variant', 'Tumor suppressor']"}]
pd.DataFrame(ann_rows)
| gene | name | protein_class | disease_involvement | |
|---|---|---|---|---|
| 0 | BRD4 | bromodomain containing 4 | ['Cancer-related genes', 'Disease related gene... | ['Cancer-related genes', 'Disease variant', 'I... |
| 1 | HDAC | SIN3-HDAC complex associated factor | — | — |
| 2 | HDAC3 | histone deacetylase 3 | ['Enzymes', 'Essential proteins', 'FDA approve... | ['FDA approved drug targets'] |
| 3 | NAMPT | nicotinamide phosphoribosyltransferase | ['Cancer-related genes', 'Enzymes', 'Metabolic... | ['Cancer-related genes'] |
| 4 | OCT4 | OCT4 hESC enhancer GRCh37_chr10:133148496-1331... | — | — |
| 5 | SIRT1 | sirtuin 1 | ['Enzymes', 'Metabolic proteins', 'Predicted i... | — |
| 6 | SIRT3 | sirtuin 3 | ['Enzymes', 'Metabolic proteins', 'Predicted i... | — |
| 7 | SMARCA4 | SWI/SNF related BAF chromatin remodeling compl... | ['Cancer-related genes', 'Disease related gene... | ['Cancer-related genes', 'Deafness', 'Disease ... |
| 8 | TET2 | tet methylcytosine dioxygenase 2 | ['Cancer-related genes', 'Disease related gene... | ['Cancer-related genes', 'Disease variant', 'T... |
2. GO Biological Process enrichment (Enrichr)¶
In [2]:
go_bp = [{'rank': 1, 'term': 'Protein Deacylation (GO:0035601)', 'p_value': 7.154717101473363e-08, 'odds_ratio': 587.4705882352941, 'genes': ['HDAC3', 'SIRT1', 'SIRT3']}, {'rank': 2, 'term': 'Protein Deacetylation (GO:0006476)', 'p_value': 6.173082690121319e-07, 'odds_ratio': 269.64864864864865, 'genes': ['HDAC3', 'SIRT1', 'SIRT3']}, {'rank': 3, 'term': 'Positive Regulation Of Transcription By RNA Polymerase II (GO:0045944)', 'p_value': 7.790433066335926e-07, 'odds_ratio': 40.89914163090129, 'genes': ['HDAC3', 'NAMPT', 'TET2', 'SIRT1', 'BRD4', 'SMARCA4']}, {'rank': 4, 'term': 'Peptidyl-Lysine Deacetylation (GO:0034983)', 'p_value': 3.7754993322284675e-06, 'odds_ratio': 1142.057142857143, 'genes': ['SIRT1', 'SIRT3']}, {'rank': 5, 'term': 'Positive Regulation Of DNA-templated Transcription (GO:0045893)', 'p_value': 4.065006033168612e-06, 'odds_ratio': 30.321746160064674, 'genes': ['HDAC3', 'NAMPT', 'TET2', 'SIRT1', 'BRD4', 'SMARCA4']}, {'rank': 6, 'term': 'Negative Regulation Of Androgen Receptor Signaling Pathway (GO:0060766)', 'p_value': 1.633405632897404e-05, 'odds_ratio': 475.6904761904762, 'genes': ['SIRT1', 'SMARCA4']}, {'rank': 7, 'term': 'Regulation Of Androgen Receptor Signaling Pathway (GO:0060765)', 'p_value': 6.281272926601724e-05, 'odds_ratio': 228.18285714285713, 'genes': ['SIRT1', 'SMARCA4']}, {'rank': 8, 'term': 'Regulation Of Transcription By RNA Polymerase II (GO:0006357)', 'p_value': 6.91357556918871e-05, 'odds_ratio': 17.773491592482692, 'genes': ['HDAC3', 'NAMPT', 'TET2', 'SIRT1', 'BRD4', 'SMARCA4']}, {'rank': 9, 'term': 'Negative Regulation Of Intracellular Steroid Hormone Receptor Signaling Pathway (GO:0033144)', 'p_value': 8.313599120396748e-05, 'odds_ratio': 196.66995073891624, 'genes': ['SIRT1', 'SMARCA4']}, {'rank': 10, 'term': 'Histone Deacetylation (GO:0016575)', 'p_value': 0.00011250476197410829, 'odds_ratio': 167.7058823529412, 'genes': ['HDAC3', 'SIRT1']}]
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 | Protein Deacylation (GO:0035601) | 3 | 7.15e-08 | 587.5 | HDAC3, SIRT1, SIRT3 |
| 1 | Protein Deacetylation (GO:0006476) | 3 | 6.17e-07 | 269.6 | HDAC3, SIRT1, SIRT3 |
| 2 | Positive Regulation Of Transcription By RNA Po... | 6 | 7.79e-07 | 40.9 | HDAC3, NAMPT, TET2, SIRT1, BRD4, SMARCA4 |
| 3 | Peptidyl-Lysine Deacetylation (GO:0034983) | 2 | 3.78e-06 | 1142.1 | SIRT1, SIRT3 |
| 4 | Positive Regulation Of DNA-templated Transcrip... | 6 | 4.07e-06 | 30.3 | HDAC3, NAMPT, TET2, SIRT1, BRD4, SMARCA4 |
| 5 | Negative Regulation Of Androgen Receptor Signa... | 2 | 1.63e-05 | 475.7 | SIRT1, SMARCA4 |
| 6 | Regulation Of Androgen Receptor Signaling Path... | 2 | 6.28e-05 | 228.2 | SIRT1, SMARCA4 |
| 7 | Regulation Of Transcription By RNA Polymerase ... | 6 | 6.91e-05 | 17.8 | HDAC3, NAMPT, TET2, SIRT1, BRD4, SMARCA4 |
| 8 | Negative Regulation Of Intracellular Steroid H... | 2 | 8.31e-05 | 196.7 | SIRT1, SMARCA4 |
| 9 | Histone Deacetylation (GO:0016575) | 2 | 1.13e-04 | 167.7 | HDAC3, SIRT1 |
In [3]:
import matplotlib.pyplot as plt
import numpy as np
go_bp = [{'rank': 1, 'term': 'Protein Deacylation (GO:0035601)', 'p_value': 7.154717101473363e-08, 'odds_ratio': 587.4705882352941, 'genes': ['HDAC3', 'SIRT1', 'SIRT3']}, {'rank': 2, 'term': 'Protein Deacetylation (GO:0006476)', 'p_value': 6.173082690121319e-07, 'odds_ratio': 269.64864864864865, 'genes': ['HDAC3', 'SIRT1', 'SIRT3']}, {'rank': 3, 'term': 'Positive Regulation Of Transcription By RNA Polymerase II (GO:0045944)', 'p_value': 7.790433066335926e-07, 'odds_ratio': 40.89914163090129, 'genes': ['HDAC3', 'NAMPT', 'TET2', 'SIRT1', 'BRD4', 'SMARCA4']}, {'rank': 4, 'term': 'Peptidyl-Lysine Deacetylation (GO:0034983)', 'p_value': 3.7754993322284675e-06, 'odds_ratio': 1142.057142857143, 'genes': ['SIRT1', 'SIRT3']}, {'rank': 5, 'term': 'Positive Regulation Of DNA-templated Transcription (GO:0045893)', 'p_value': 4.065006033168612e-06, 'odds_ratio': 30.321746160064674, 'genes': ['HDAC3', 'NAMPT', 'TET2', 'SIRT1', 'BRD4', 'SMARCA4']}, {'rank': 6, 'term': 'Negative Regulation Of Androgen Receptor Signaling Pathway (GO:0060766)', 'p_value': 1.633405632897404e-05, 'odds_ratio': 475.6904761904762, 'genes': ['SIRT1', 'SMARCA4']}, {'rank': 7, 'term': 'Regulation Of Androgen Receptor Signaling Pathway (GO:0060765)', 'p_value': 6.281272926601724e-05, 'odds_ratio': 228.18285714285713, 'genes': ['SIRT1', 'SMARCA4']}, {'rank': 8, 'term': 'Regulation Of Transcription By RNA Polymerase II (GO:0006357)', 'p_value': 6.91357556918871e-05, 'odds_ratio': 17.773491592482692, 'genes': ['HDAC3', 'NAMPT', 'TET2', 'SIRT1', 'BRD4', 'SMARCA4']}]
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': 'SIRT1', 'protein2': 'SMARCA4', 'score': 0.422, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.423}, {'protein1': 'SIRT1', 'protein2': 'BRD4', 'score': 0.578, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.457, 'dscore': 0, 'tscore': 0.255}, {'protein1': 'POU5F1', 'protein2': 'SMARCA4', 'score': 0.57, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.57}, {'protein1': 'BRD4', 'protein2': 'SMARCA4', 'score': 0.709, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.457, 'dscore': 0, 'tscore': 0.487}, {'protein1': 'HDAC3', 'protein2': 'HDAC9', 'score': 0.511, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.457, 'dscore': 0, 'tscore': 0.137}, {'protein1': 'SMARCA4', 'protein2': 'HDAC9', 'score': 0.893, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.292, 'dscore': 0, 'tscore': 0.856}]
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)
6 STRING edges
| protein1 | protein2 | score | escore | tscore | |
|---|---|---|---|---|---|
| 5 | SMARCA4 | HDAC9 | 0.893 | 0.292 | 0.856 |
| 3 | BRD4 | SMARCA4 | 0.709 | 0.457 | 0.487 |
| 1 | SIRT1 | BRD4 | 0.578 | 0.457 | 0.255 |
| 2 | POU5F1 | SMARCA4 | 0.570 | 0.000 | 0.570 |
| 4 | HDAC3 | HDAC9 | 0.511 | 0.457 | 0.137 |
| 0 | SIRT1 | SMARCA4 | 0.422 | 0.000 | 0.423 |
In [5]:
import math
ppi = [{'protein1': 'SIRT1', 'protein2': 'SMARCA4', 'score': 0.422, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.423}, {'protein1': 'SIRT1', 'protein2': 'BRD4', 'score': 0.578, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.457, 'dscore': 0, 'tscore': 0.255}, {'protein1': 'POU5F1', 'protein2': 'SMARCA4', 'score': 0.57, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.57}, {'protein1': 'BRD4', 'protein2': 'SMARCA4', 'score': 0.709, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.457, 'dscore': 0, 'tscore': 0.487}, {'protein1': 'HDAC3', 'protein2': 'HDAC9', 'score': 0.511, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.457, 'dscore': 0, 'tscore': 0.137}, {'protein1': 'SMARCA4', 'protein2': 'HDAC9', 'score': 0.893, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.292, 'dscore': 0, 'tscore': 0.856}]
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': 'BRD4', 'n_pathways': 1, 'top_pathway': 'Potential therapeutics for SARS'}, {'gene': 'HDAC', 'n_pathways': 0, 'top_pathway': '—'}, {'gene': 'HDAC3', 'n_pathways': 8, 'top_pathway': 'p75NTR negatively regulates cell cycle via SC1'}, {'gene': 'NAMPT', 'n_pathways': 3, 'top_pathway': 'BMAL1:CLOCK,NPAS2 activates circadian expression'}, {'gene': 'OCT4', 'n_pathways': 7, 'top_pathway': 'POU5F1 (OCT4), SOX2, NANOG repress genes related to differentiation'}, {'gene': 'SIRT1', 'n_pathways': 8, 'top_pathway': 'Regulation of HSF1-mediated heat shock response'}, {'gene': 'SIRT3', 'n_pathways': 5, 'top_pathway': 'Transcriptional activation of mitochondrial biogenesis'}, {'gene': 'SMARCA4', 'n_pathways': 8, 'top_pathway': 'Interleukin-7 signaling'}, {'gene': 'TET2', 'n_pathways': 2, 'top_pathway': 'TET1,2,3 and TDG demethylate DNA'}]
pd.DataFrame(pw_rows).sort_values('n_pathways', ascending=False)
| gene | n_pathways | top_pathway | |
|---|---|---|---|
| 2 | HDAC3 | 8 | p75NTR negatively regulates cell cycle via SC1 |
| 7 | SMARCA4 | 8 | Interleukin-7 signaling |
| 5 | SIRT1 | 8 | Regulation of HSF1-mediated heat shock response |
| 4 | OCT4 | 7 | POU5F1 (OCT4), SOX2, NANOG repress genes relat... |
| 6 | SIRT3 | 5 | Transcriptional activation of mitochondrial bi... |
| 3 | NAMPT | 3 | BMAL1:CLOCK,NPAS2 activates circadian expression |
| 8 | TET2 | 2 | TET1,2,3 and TDG demethylate DNA |
| 0 | BRD4 | 1 | Potential therapeutics for SARS |
| 1 | HDAC | 0 | — |
5. Hypothesis ranking (9 hypotheses)¶
In [7]:
hyp_data = [('Chromatin Remodeling-Mediated Nutrient Sensing Restorat', 0.914), ('Nutrient-Sensing Epigenetic Circuit Reactivation', 0.907), ('Metabolic NAD+ Salvage Pathway Enhancement Through NAMP', 0.887), ('Selective HDAC3 Inhibition with Cognitive Enhancement', 0.779), ('Chromatin Accessibility Restoration via BRD4 Modulation', 0.768), ('Astrocyte-Mediated Neuronal Epigenetic Rescue', 0.725), ('Mitochondrial-Nuclear Epigenetic Cross-Talk Restoration', 0.701), ('Partial Neuronal Reprogramming via Modified Yamanaka Co', 0.672), ('Temporal TET2-Mediated Hydroxymethylation Cycling', 0.657)]
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('Epigenetic reprogramming in aging neurons')
ax.grid(axis='x', alpha=0.3)
plt.tight_layout(); plt.show()
6. Score dimension heatmap (top 10)¶
In [8]:
labels = ['Chromatin Remodeling-Mediated Nutrient S', 'Nutrient-Sensing Epigenetic Circuit Reac', 'Metabolic NAD+ Salvage Pathway Enhanceme', 'Selective HDAC3 Inhibition with Cognitiv', 'Chromatin Accessibility Restoration via ', 'Astrocyte-Mediated Neuronal Epigenetic R', 'Mitochondrial-Nuclear Epigenetic Cross-T', 'Partial Neuronal Reprogramming via Modif', 'Temporal TET2-Mediated Hydroxymethylatio']
matrix = np.array([[0.72, 0.92, 0.82, 0.9, 0.115, 0.9, 0.85, 0.9, 0.8], [0.7, 0.95, 0.85, 0.9, 0.115, 0.9, 0.85, 0.9, 0.8], [0.68, 0.84, 0.77, 0.9, 0.115, 0.9, 0.85, 0.9, 0.8], [0.85, 0.7, 0.8, 0.75, 0.062, 0.75, 0.7, 0.75, 0.55], [0.9, 0.6, 0.7, 0.65, 0.13, 0.7, 0.65, 0.95, 0.35], [0.95, 0.4, 0.75, 0.7, 0.135, 0.6, 0.5, 0.3, 0.4], [0.85, 0.5, 0.65, 0.6, 0.4, 0.65, 0.55, 0.5, 0.6], [0.95, 0.2, 0.8, 0.4, 0.42, 0.55, 0.35, 0.15, 0.25], [0.95, 0.25, 0.7, 0.55, 0.26, 0.6, 0.45, 0.2, 0.45]])
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: Chromatin Remodeling-Mediated Nutrient Sensing Restoration¶
Target genes: SMARCA4 · Composite score: 0.914
Molecular Mechanism and Rationale
The nutrient-sensing epigenetic circuit centered on AMPK-SIRT1-PGC1α becomes progressively silenced in aging neurons through chromatin compaction and histone modifications that restrict transcriptional access. This hypothesis proposes that targeted chromatin re
In [9]:
print('No PubMed results for hypothesis h-var-a09491064e')
No PubMed results for hypothesis h-var-a09491064e
Hypothesis 2: Nutrient-Sensing Epigenetic Circuit Reactivation¶
Target genes: SIRT1 · Composite score: 0.907
Molecular Mechanism and Rationale
The nutrient-sensing epigenetic circuit centered on AMPK-SIRT1-PGC1α represents a fundamental regulatory network that governs cellular energy homeostasis and metabolic adaptation. In aging neurons, this circuit becomes progressively silenced through multiple ep
In [10]:
print('No PubMed results for hypothesis h-4bb7fd8c')
No PubMed results for hypothesis h-4bb7fd8c
Hypothesis 3: Metabolic NAD+ Salvage Pathway Enhancement Through NAMPT Overexpressio¶
Target genes: NAMPT · Composite score: 0.887
The NAD+ salvage pathway represents the primary mechanism for maintaining cellular NAD+ homeostasis in neurons, with NAMPT (nicotinamide phosphoribosyltransferase) serving as the rate-limiting enzyme that converts nicotinamide to nicotinamide mononucleotide (NMN). During neurodegeneration, NAMPT exp
In [11]:
print('No PubMed results for hypothesis h-var-159030513d')
No PubMed results for hypothesis h-var-159030513d
Hypothesis 4: Selective HDAC3 Inhibition with Cognitive Enhancement¶
Target genes: HDAC3 · Composite score: 0.779
Molecular Mechanism and Rationale
Histone deacetylase 3 (HDAC3) represents a critical epigenetic regulator that orchestrates chromatin remodeling through targeted deacetylation of lysine residues on histone tails, particularly H3K27 and H4K16. In the aging brain, HDAC3 exhibits a paradoxical du
In [12]:
print('No PubMed results for hypothesis h-0e675a41')
No PubMed results for hypothesis h-0e675a41
Hypothesis 5: Chromatin Accessibility Restoration via BRD4 Modulation¶
Target genes: BRD4 · Composite score: 0.768
Molecular Mechanism and Rationale
BRD4 functions as a master epigenetic regulator through its unique ability to recognize and bind acetylated histone marks via two tandem bromodomains (BD1 and BD2). The BD1 domain preferentially binds H4K5ac and H4K8ac, while BD2 recognizes H3K14ac and H4K12ac
In [13]:
lit_data = [{'year': '2025', 'journal': 'Eur J Med Chem', 'title': 'Discovery of 4,5-dihydro-benzo[g]indazole-based hydroxamic acids as HDAC3/BRD4 d', 'pmid': '39764880'}, {'year': '2019', 'journal': 'Bioorg Chem', 'title': 'Design, synthesis and biological evaluation of novel indole derivatives as poten', 'pmid': '30554080'}, {'year': '2019', 'journal': 'J Neurosci', 'title': 'Enhancement of BDNF Expression and Memory by HDAC Inhibition Requires BET Bromod', 'pmid': '30504275'}]
if lit_data:
df = pd.DataFrame(lit_data)
print(f'{len(lit_data)} PubMed results')
display(df)
else:
print('No PubMed results')
3 PubMed results
| year | journal | title | pmid | |
|---|---|---|---|---|
| 0 | 2025 | Eur J Med Chem | Discovery of 4,5-dihydro-benzo[g]indazole-base... | 39764880 |
| 1 | 2019 | Bioorg Chem | Design, synthesis and biological evaluation of... | 30554080 |
| 2 | 2019 | J Neurosci | Enhancement of BDNF Expression and Memory by H... | 30504275 |
Hypothesis 6: Astrocyte-Mediated Neuronal Epigenetic Rescue¶
Target genes: HDAC · Composite score: 0.725
1. Molecular Mechanism and Rationale¶
The fundamental premise underlying astrocyte-mediated neuronal epigenetic rescue centers on the strategic manipulation of histone deacetylase (HDAC) activity through engineered paracrine signaling. HDACs comprise a family of 18 zinc-dependent enzymes divided
In [14]:
print('No PubMed results for hypothesis h-8fe389e8')
No PubMed results for hypothesis h-8fe389e8
Hypothesis 7: Mitochondrial-Nuclear Epigenetic Cross-Talk Restoration¶
Target genes: SIRT3 · Composite score: 0.701
Molecular Mechanism and Rationale¶
The mitochondrial-nuclear epigenetic cross-talk restoration hypothesis centers on the coordinated dysfunction of SIRT3, a critical NAD+-dependent deacetylase localized primarily to the mitochondrial matrix, and its intricate communication network with nuclear ch
In [15]:
print('No PubMed results for hypothesis h-0e614ae4')
No PubMed results for hypothesis h-0e614ae4
Hypothesis 8: Partial Neuronal Reprogramming via Modified Yamanaka Cocktail¶
Target genes: OCT4 · Composite score: 0.672
The hypothesis of partial neuronal reprogramming via a modified Yamanaka cocktail represents a paradigm shift in approaching neurodegeneration through epigenetic rejuvenation while preserving neuronal identity. This approach leverages the fundamental principle that cellular aging is largely driven b
In [16]:
print('No PubMed results for hypothesis h-baba5269')
No PubMed results for hypothesis h-baba5269
Hypothesis 9: Temporal TET2-Mediated Hydroxymethylation Cycling¶
Target genes: TET2 · Composite score: 0.657
Molecular Mechanism and Rationale¶
The temporal TET2-mediated hydroxymethylation cycling hypothesis centers on the dysregulation of Ten-Eleven Translocation 2 (TET2) enzyme activity in aged neurons and its profound impact on epigenetic landscape maintenance. TET2, a member of the α-ketoglutarate-
In [17]:
print('No PubMed results for hypothesis h-a90e2e89')
No PubMed results for hypothesis h-a90e2e89
8. Knowledge graph edges (132 total)¶
In [18]:
edge_data = [{'source': 'SIRT1', 'relation': 'associated_with', 'target': 'SIRT3', 'strength': 0.8}, {'source': 'OCT4', 'relation': 'activates', 'target': 'cellular_reprogramming', 'strength': 0.8}, {'source': 'SIRT1', 'relation': 'regulates', 'target': 'chromatin_remodeling', 'strength': 0.8}, {'source': 'SIRT1', 'relation': 'therapeutic_target', 'target': 'neurodegeneration', 'strength': 0.78}, {'source': 'TET2', 'relation': 'regulates', 'target': 'DNA_methylation', 'strength': 0.75}, {'source': 'diseases-huntingtons', 'relation': 'investigated_in', 'target': 'h-4bb7fd8c', 'strength': 0.75}, {'source': 'HDAC3', 'relation': 'therapeutic_target', 'target': 'neurodegeneration', 'strength': 0.73}, {'source': 'BRD4', 'relation': 'regulates', 'target': 'chromatin_remodeling', 'strength': 0.7}, {'source': 'SIRT3', 'relation': 'regulates', 'target': 'mitochondria', 'strength': 0.7}, {'source': 'BRD4', 'relation': 'therapeutic_target', 'target': 'neurodegeneration', 'strength': 0.66}, {'source': 'SIRT1', 'relation': 'promoted: Nutrient-Sensin', 'target': 'neurodegeneration', 'strength': 0.65}, {'source': 'SIRT3', 'relation': 'therapeutic_target', 'target': 'neurodegeneration', 'strength': 0.64}, {'source': 'NAMPT', 'relation': 'promoted: Metabolic NAD+ ', 'target': 'neurodegeneration', 'strength': 0.63}, {'source': 'SIRT1', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.62}, {'source': 'SIRT1', 'relation': 'participates_in', 'target': 'Sirtuin-1 / NAD+ metabolism / ', 'strength': 0.62}, {'source': 'HDAC3', 'relation': 'promoted: Selective HDAC3', 'target': 'neurodegeneration', 'strength': 0.57}, {'source': 'BRD4', 'relation': 'promoted: Chromatin Acces', 'target': 'neurodegeneration', 'strength': 0.56}, {'source': 'TET2', 'relation': 'therapeutic_target', 'target': 'neurodegeneration', 'strength': 0.56}, {'source': 'OCT4', 'relation': 'therapeutic_target', 'target': 'neurodegeneration', 'strength': 0.55}, {'source': 'BRD4', 'relation': 'participates_in', 'target': 'Epigenetic regulation', 'strength': 0.52}, {'source': 'BRD4', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.52}, {'source': 'HDAC', 'relation': 'participates_in', 'target': 'Astrocyte reactivity signaling', 'strength': 0.46}, {'source': 'HDAC', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.46}, {'source': 'SIRT3', 'relation': 'associated_with', 'target': 'neurodegeneration', 'strength': 0.45}, {'source': 'SIRT3', 'relation': 'participates_in', 'target': 'Sirtuin-3 / mitochondrial deac', 'strength': 0.45}]
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.