# A Map of the Lipid–Metabolite–Protein Network to Aid Multi-Omics Integration

## TL;DR

This paper introduces a new network framework that connects lipids, metabolites, and proteins to better understand complex biological processes and diseases.

## Contribution

The novel contribution is a unified lipid–metabolite–protein network for multi-omics integration, enabling functional analysis and biomarker discovery.

## Key findings

- The network identified CVD-related lipids and metabolites, including novel biomarkers like 4-imidazoleacetate and indoleacetaldehyde.
- Analysis of empagliflozin's effects revealed early impacts on phospholipid metabolism and long-term effects on sphingolipid biosynthesis.
- The framework confirmed known associations like cholesterol esters and sphingomyelin with cardiovascular disease.

## Abstract

The integration of multi-omics data offers transformative potential for elucidating complex molecular mechanisms underlying biological processes and diseases. In this study, we developed a lipid–metabolite–protein network that combines a protein–protein interaction network and enzymatic and genetic interactions of proteins with metabolites and lipids to provide a unified framework for multi-omics integration. Using hyperbolic embedding, the network visualizes connections across omics layers, accessible through a user-friendly Shiny R (version 1.10.0) software package. This framework ranks molecules across omics layers based on functional proximity, enabling intuitive exploration. Application in a cardiovascular disease (CVD) case study identified lipids and metabolites associated with CVD-related proteins. The analysis confirmed known associations, like cholesterol esters and sphingomyelin, and highlighted potential novel biomarkers, such as 4-imidazoleacetate and indoleacetaldehyde. Furthermore, we used the network to analyze empagliflozin’s temporal effects on lipid metabolism. Functional enrichment analysis of proteins associated with lipid signatures revealed dynamic shifts in biological processes, with early effects impacting phospholipid metabolism and long-term effects affecting sphingolipid biosynthesis. Our framework offers a versatile tool for hypothesis generation, functional analysis, and biomarker discovery. By bridging molecular layers, this approach advances our understanding of disease mechanisms and therapeutic effects, with broad applications in computational biology and precision medicine.