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tooluniverse-aging-senescence

Investigates aging genes, senescence pathways, and senolytic drugs using ToolUniverse biomedical databases.

1.7k|254|Updated Mar 3, 2025
Search Tags:#drug discovery#pathway analysis#longevity#gwas#aging#senolytics#cellular senescence
One-click install
npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-aging-senescence
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tooluniverse-aging-senescence
Source: https://github.com/mims-harvard/ToolUniverse/tree/main/plugins/tooluniverse/skills/tooluniverse-aging-senescence
Command: npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-aging-senescence

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Aging and senescence research requires synthesizing evidence across GWAS catalogs, pathway databases, drug interaction resources, and clinical trial registries, while carefully distinguishing correlative findings from causal evidence. This Skill provides a structured, evidence-graded workflow for investigating aging genes, senescence markers, and senolytic drug candidates without conflating cell-culture results with human genetic evidence.

Core Features & Use Cases

  • Hallmarks-Based Classification: Maps genes and queries to the 12 hallmarks of aging (Lopez-Otin et al., Cell 2023) to guide targeted pathway and disease investigation.
  • Evidence-Graded Genetic Analysis: Queries Open Genes, GWAS Catalog, OpenTargets, and PubMed to triage longevity genes (APOE, FOXO3, TERT, CDKN2A) with T1-T4 evidence grading.
  • Senolytic Drug Discovery: Finds senolytic and geroprotector candidates (dasatinib+quercetin, navitoclax, fisetin, rapamycin) via DGIdb, ChEMBL, and clinical trial searches with clinical status checks.
  • Use Case: Ask how CDKN2A relates to aging, and the Skill classifies it under cellular senescence, pulls curated aging evidence from Open Genes, analyzes KEGG pathway hsa04218, checks senolytic drug interactions, and returns an evidence-graded report.

Quick Start

Ask the agent to investigate how the gene FOXO3 relates to human longevity and what senolytic drugs target its pathway.

Frequently Asked Questions about tooluniverse-aging-senescence

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I find genes associated with human longevity?▼

Start with OpenGenes_get_gene for curated aging evidence per gene, then query gwas_get_snps_for_gene for GWAS loci. Supplement with PubMed searches for centenarian studies, since targeted-genotyping studies like FOXO3 often miss the GWAS Catalog.

What are the best markers of cellular senescence?▼

No single marker is definitive; use a panel combining p16 (CDKN2A), SA-beta-gal, SASP factors like IL-6 and IL-8, and persistent gamma-H2AX foci. Each marker alone has false-positive sources such as cancer, quiescence, or infection.

How do I find senolytic drug candidates for a disease?▼

Query DGIdb for drug-gene interactions against BCL2, BCL2L1, TP53, and CDKN2A, then search clinical trials for senolytic conditions. Established candidates include dasatinib plus quercetin, navitoclax, and fisetin, mostly in Phase I/II trials.

Does this skill cover epigenetic clock analysis?▼

No. Epigenetic clocks like Horvath and Hannum require methylation array data processing that is not directly queryable through ToolUniverse. The skill covers epigenetic alteration genes such as DNMT and TET families instead.

When should I use a different skill instead of this one?▼

Use tooluniverse-rare-disease-diagnosis for rare disease genetics and tooluniverse-disease-research for general disease questions. This skill is specific to aging hallmarks, senescence biology, and longevity genetics.

Why does mouse lifespan data not translate to human aging?▼

Mice differ substantially from humans in telomere biology and metabolic rate, so lifespan extension in mice often fails to replicate clinically. The skill grades evidence T1-T4 and prioritizes T1 human genetic evidence over model organism data.

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