metabolomics

name	metabolomics
description	Metabolomics-specific analysis strategies and domain knowledge
category	domain

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)

Resources:

• KEGG pathways: https://www.genome.jp/kegg/pathway.html
• BioCyc: https://biocyc.org/

2. Flux Index Calculation

When: You want to infer enzymatic activity

How:

# Simple flux proxy: Product / Substrate
flux_index = data["Product"] / data["Substrate"]

# Compare across groups
t_test(flux_index[group1], flux_index[group2])

Common indices:

• Glycolysis flux: FBP / G6P
• TCA flux: Citrate / Acetyl-CoA (if available)
• Salvage flux: Product / Precursor

3. Energy Charge Calculation

When: Assessing cellular energy state

Formula:

# Adenylate energy charge
AEC = (ATP + 0.5*ADP) / (ATP + ADP + AMP)
# Range: 0 (depleted) to 1 (high energy)

# Similar for GTP, CTP, UTP

Interpretation:

• AEC > 0.8: High energy state
• AEC < 0.5: Energy crisis

4. Redox State Assessment

When: Investigating oxidative stress or metabolic state

Ratios:

NAD_ratio = NAD+ / NADH  # High = oxidized state
NADP_ratio = NADP+ / NADPH  # High = oxidative stress
GSH_ratio = GSH / GSSG  # Low = oxidative stress

Metabolomics-Specific Hypotheses

Template Hypotheses

H1: Pathway Shift Hypothesis

"Condition X shifts metabolism from [pathway A] to [pathway B]
due to [mechanism], evidenced by [metabolite pattern]"

H2: Enzymatic Bottleneck Hypothesis

"Enzyme [E] activity is reduced in condition X, causing accumulation
of substrate [S] and depletion of product [P]"

H3: Cofactor Limitation Hypothesis

"Limited availability of cofactor [C] constrains pathway [P],
causing metabolite pattern [M]"

H4: Energy State Hypothesis

"Condition X induces low-energy state, triggering metabolic
reprogramming to salvage pathways"

Literature Search Strategies

Effective Search Queries

For pathway context:

"[metabolite] metabolism pathway"
"[metabolite] biosynthesis regulation"

For mechanistic insights:

"[condition] [metabolite] mechanism"
"[enzyme] regulation [condition]"

For flux studies:

"[pathway] flux analysis"
"[metabolite] turnover rate"

Key Databases

1. KEGG: Pathway maps and enzyme info
2. HMDB: Human Metabolome Database
3. PubChem: Chemical structures and properties
4. MetaboAnalyst: Analysis tools and pathway info

Common Pitfalls

❌ Assuming directionality

• Many reactions are reversible
• Check enzyme and equilibrium constants

❌ Ignoring compartmentalization

• Metabolites exist in different cellular compartments
• Mitochondrial vs cytoplasmic pools may differ

❌ Overinterpreting single metabolites

• Always consider pathway context
• One metabolite change can have multiple explanations

❌ Confusing correlation with regulation

• Co-regulation doesn't mean direct interaction
• Use pathway knowledge to infer relationships

❌ Forgetting isomers

• Many metabolites have isomers (e.g., leucine/isoleucine)
• Mass spec may not distinguish them

Quality Checks

Before interpreting results, verify:

• Metabolite identifications are confident (not just m/z matches)
• Normalization was appropriate (sample weight, protein, etc.)
• Missing values handled correctly
• Batch effects addressed
• Biological replicates have reasonable variance

Example Analysis Flow

Observation: ATP levels decreased 30% (p=0.01)

Step 1: Check related metabolites

# Check adenylate pool
print(data[["ATP", "ADP", "AMP"]])

Step 2: Calculate energy charge

AEC = (ATP + 0.5*ADP) / (ATP + ADP + AMP)

Step 3: Search literature

search_pubmed("[condition] ATP depletion mechanism")

Step 4: Generate hypotheses

• H1: Increased energy demand (check ATP consumers)
• H2: Reduced ATP synthesis (check TCA metabolites)
• H3: ATP degradation (check breakdown products)

Step 5: Test hypotheses

# H2: Check TCA cycle metabolites
tca_metabolites = ["Citrate", "Isocitrate", "α-KG", "Succinate", "Fumarate", "Malate"]
test_pathway(tca_metabolites, group_var)

Key Principle

Metabolism is a network, not a list.

Single metabolite changes are clues, not answers. Build mechanistic models by connecting metabolites through known biochemical pathways.