metabolomics

Metabolomics Analysis

When to Use This Skill

• When data contains metabolite measurements
• When analyzing metabolic pathways or fluxes
• When interpreting biochemical mechanisms

Core Concepts

Metabolite Naming

Metabolites have multiple naming conventions:

• IUPAC names: Chemical nomenclature (e.g., "2-aminoethanesulfonic acid")
• Common names: Biology names (e.g., "Taurine")
• Abbreviations: Shorthand (e.g., "Tau")

Always verify metabolite identity before interpreting results.

Pathway Context

Metabolites exist in biochemical pathways:

• Substrates → Enzymes → Products
• Changes in one metabolite affect connected metabolites
• Pathway analysis is more informative than individual metabolites

Example pathway:

Glucose → (HK) → G6P → (G6PDH) → 6PG

If G6P ↑ and 6PG unchanged → suggests bottleneck at G6PDH enzyme

Flux vs Concentration

Concentration: Amount of metabolite present Flux: Rate of metabolite conversion

Key insight:

• High concentration + low downstream product = bottleneck (slow flux)
• Low concentration + high downstream product = high flux
• Calculate flux proxies using ratios: Product/Substrate

Common Metabolomics Patterns

Pattern 1: Substrate Depletion

Precursor ↓↓, Product ↑↑
→ Interpretation: Active consumption, increased flux

Pattern 2: Bottleneck

Substrate ↑↑, Product ↓↓ or unchanged
→ Interpretation: Enzymatic bottleneck, blocked conversion

Pattern 3: Pathway Shutdown

All pathway metabolites ↓↓
→ Interpretation: Reduced pathway activity

Pattern 4: Salvage vs De Novo

De novo intermediates ↓, Salvage products ↑
→ Interpretation: Metabolic shift to energy-efficient salvage

Analysis Strategies

1. Pathway Enrichment

When: You have many differentially abundant metabolites

How:

# Group metabolites by pathway
pathway_metabolites = {
    "Glycolysis": ["Glucose", "G6P", "F6P", "FBP", ...],
    "TCA Cycle": ["Citrate", "Isocitrate", "α-KG", ...],
    "Purine Metabolism": ["AMP", "ADP", "ATP", "IMP", ...]
}

# Count hits per pathway
for pathway, metabolites in pathway_metabolites.items():
    hits = [m for m in significant_metabolites if m in metabolites]
    enrichment_score = len(hits) / len(metabolites)