tooluniverse-aging-senescence

# Aging & Cellular Senescence Research

## Aging Research Reasoning

Before querying any tool, ask the central question: is this a cause or consequence of aging?

Senescence markers (SA-β-gal, p16/CDKN2A, SASP factors like IL-6 and IL-8) indicate that senescent cells are present. But their presence does not prove that senescence is driving the phenotype. Correlation is easy to establish. Causation requires an intervention. If senolytic drugs (dasatinib+quercetin, fisetin, navitoclax) clear senescent cells and the age-related phenotype improves, that is causal evidence. If clearing senescent cells has no effect, something else is driving the pathology.

Apply this reasoning when interpreting any gene or pathway query: classify it first by hallmark, then ask whether the evidence for its role is correlative (expression data, GWAS association) or causal (functional assay, genetic knockout, senolytic intervention).

Evidence grade the findings: T1 is human genetic evidence (GWAS, centenarian studies). T2 is model organism lifespan data. T3 is cell culture senescence data. T4 is computational prediction. Do not conflate T3 cell culture data with T1 human evidence — they are very different levels of confidence.

A final principle: cellular senescence is one hallmark of aging, not aging itself. Distinguish senescence from organismal aging, from age-related disease, and from progeria (accelerated aging syndromes). These require different tools and different interpretations.

## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.

## When to Use

- "What genes are associated with longevity?"
- "Find senolytic drug candidates for [disease]"
- "What are the markers of cellular senescence?"
- "How does [gene] relate to aging?"
- "GWAS hits for age-related diseases"
- "Pathways involved in cellular senescence"
- "What drugs target senescent cells?"

**Not this skill**: For rare disease genetics, use `tooluniverse-rare-disease-diagnosis`. For general disease research, use `tooluniverse-disease-research`.

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## Workflow

```
Phase 0: Query Parsing — aging gene, senescence marker, age-related disease, or drug query
    |
Phase 1: Hallmarks Classification — map to the 12 hallmarks of aging framework
    |
Phase 2: Genetic Evidence — GWAS, longevity loci, model organism data
    |
Phase 3: Pathway Analysis — senescence, autophagy, telomere, epigenetic pathways
    |
Phase 4: Senolytic/Geroprotector Drug Discovery — existing drugs, clinical trials
    |
Phase 5: Literature & Clinical Context — published evidence, ongoing trials
    |
Phase 6: Interpretation & Report — evidence-graded findings with translational potential
```

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## Phase 1: Hallmarks Classification

Organize findings around the 12 hallmarks of aging (Lopez-Otin et al., Cell 2023). When a user asks about an aging gene, first classify which hallmark(s) it belongs to, then investigate that hallmark's pathway and disease connections. This prevents scattershot querying — each hallmark has specific pathways and tool strategies.

The hallmarks most amenable to ToolUniverse investigation are: genomic instability (DNA repair genes: ATM, ATR, BRCA1/2, TP53), telomere attrition (TERT, TERC, POT1), epigenetic alterations (DNMT1/3, TET1-3, SIRT1-7), loss of proteostasis (autophagy pathway hsa04140), deregulated nutrient sensing (mTOR pathway hsa04150, FOXO pathway hsa04068, AMPK, IGF1), mitochondrial dysfunction (PINK1, PARKIN, PGC1α), and cellular senescence (CDKN2A/p16, CDKN1A/p21, TP53, RB — KEGG pathway hsa04218).

For altered intercellular communication, focus on SASP factors: IL6, IL8, MCP1 (CCL2), MMP3, MMP9, PAI1, IGFBP7, VEGF. These are the secreted signals that make senescent cells pathological for surrounding tissue.

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## Phase 2: Genetic Evidence

The best human evidence for aging genes comes from longevity GWAS and centenarian studies. Well-established loci include: APOE (19q13.32, strongest longevity signal), FOXO3 (5q33.3, replicated across multiple centenarian cohorts), TERT (10q24, telomere length GWAS), and CDKN2A/B (9p21.3, GWAS for CVD, cancer, and T2D — all age-related diseases sharing this locus).

Important caveat: many FOXO3 longevity studies (Willcox 2008, Flachsbart 2009) used targeted genotyping rather than GWAS arrays, so they do not appear in the GWAS Catalog. Always supplement GWAS Catalog queries with PubMed literature searches for centenarian studies.

**Start gene-centric questions with Open Genes** — a manually-curated aging-gene database that already aggregates the experimental evidence per gene (lifespan-change studies, longevity associations, age-related expression changes, progeria associations) plus the aging mechanism(s) and functional cluster(s). `OpenGenes_get_gene(symbol="FOXO3")` returns the aging mechanisms, curator confidence level, and an `evidence_counts` summary (e.g. FOXO3 → 58 longevity-association studies) — use it to triage whether a gene is an established aging gene and by what mechanism before drilling into GWAS/OpenTargets/PubMed. `OpenGenes_search_genes` browses the full catalog (~2400 genes). It catches the targeted-genotyping FOXO3 evidence that the GWAS Catalog misses.

```python
# Aging-evidence triage FIRST — mechanisms + curated study counts per gene
OpenGenes_get_gene(symbol="FOXO3")

# Best for gene-centric GWAS analysis
gwas_get_snps_for_gene(gene_symbol="FOXO3")

# For trait queries — note "longevity" is not a standard EFO term; try "lifespan" or specific diseases
gwas_search_associations(query="telomere length")

# OpenTargets aggregated evidence
OpenTargets_get_associated_targets_by_disease_efoId(efoId="EFO_0004847", limit=20)

# Essential for centenarian studies not in GWAS Catalog
PubMed_search_articles(query="FOXO3 GWAS longevity centenarian meta-analysis")
```

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## Phase 3: Pathway Analysis

The