metabolome
Definition
The metabolome represents the complete set of small-molecule metabolites (typically <1,500 Da) present in a biological system at a specific time point. This includes primary metabolites like amino acids, sugars, and lipids, as well as secondary metabolites such as hormones and signaling molecules. The metabolome serves as the functional readout of cellular activity, reflecting the downstream consequences of genomic, transcriptomic, and proteomic changes. Unlike the relatively static genome, the metabolome is highly dynamic, responding rapidly to genetic modifications, environmental perturbations, disease states, and therapeutic interventions. Metabolome analysis provides direct insight into biochemical phenotypes and metabolic flux, making it essential for understanding cellular physiology, disease mechanisms, and drug responses.
Visualize metabolome in Nodes Bio
Researchers can visualize metabolome data as networks where nodes represent individual metabolites and edges indicate biochemical transformations, co-regulation patterns, or statistical correlations. Nodes Bio enables integration of metabolomic profiles with genomic and proteomic data to identify multi-omics signatures of disease states, map perturbed metabolic pathways, and discover biomarker panels. Network clustering reveals coordinated metabolic modules responding to experimental conditions.
Visualization Ideas:
- Metabolite-metabolite correlation networks showing co-regulated compounds
- Integrated metabolome-proteome networks linking enzymes to their substrate and product metabolites
- Pathway enrichment networks displaying perturbed metabolic routes in disease versus control conditions
Example Use Case
A pharmaceutical team investigating non-alcoholic fatty liver disease (NAFLD) performs untargeted metabolomics on patient liver biopsies across disease stages. They identify 347 significantly altered metabolites and map them to KEGG pathways. Using network analysis, they discover a tightly connected cluster of bile acid metabolites and lipid species that distinguishes early-stage from advanced fibrosis. Integration with transcriptomic data reveals that these metabolic changes correlate with dysregulation of specific nuclear receptors, suggesting novel therapeutic targets for preventing disease progression.