MetaboGraph: A Framework for Metabolomics and Lipidomics Annotation and Pathway Network Analysis

Abstract

Untargeted metabolomics and lipidomics generate high-dimensional data sets whose biological interpretation remains challenging, particularly at the pathway and network levels. Here, we present MetaboGraph, a standalone Python-based workflow for end-to-end metabolomics and lipidomics analysis, enabling pathway-level interpretation from small-molecule data. MetaboGraph integrates automated data cleaning, comprehensive multidatabase metabolite and lipid annotation, pathway mapping, and direction-aware pathway inference. A central feature of the platform is its ability to predict pathway direction by integrating metabolite/lipid-level fold changes with pathway membership structure, supporting biologically interpretable pathway and network analyses beyond conventional enrichment approaches. MetaboGraph supports multiomics integration and comparative analysis, enabling consistent pathway-level interpretation across metabolomics, lipidomics, and multiple studies. We demonstrate the platform using untargeted LC-MS/MS metabolomics and lipidomics data comparing two breast cancer cell lines with distinct metastatic potential, MCF7 (HTB22; less metastatic) and MDA-MB-453 (HTB131; more metastatic). Relative to HTB22, the HTB131 cells exhibited coordinated metabolic remodeling, including altered amino acid and nitrogen metabolism, increased nucleotide biosynthetic demand, lipid remodeling, and changes in energy-associated pathways. These pathway-level alterations are consistent with established metabolic adaptations associated with increased cancer aggression. MetaboGraph expands the analytical toolbox for small-molecule biology and facilitates reproducible, biologically grounded insights from metabolomics and lipidomics data sets.