Lipidomics is of growing importance for clinical and biomedical research due to many associations between lipid metabolism and diseases. The discovery of these associations is facilitated by improved lipid identification and quantification. Sophisticated computational methods are advantageous for interpreting such large-scale data for understanding metabolic processes and their underlying (patho)mechanisms. To generate hypothesis about these mechanisms, the combination of metabolic networks and graph algorithms is a powerful option to pinpoint molecular disease drivers and their interactions. Here we present lipid network explorer (LINEX$^2$), a lipid network analysis framework that fuels biological interpretation of alterations in lipid compositions. By integrating lipid-metabolic reactions from public databases, we generate dataset-specific lipid interaction networks. To aid interpretation of these networks, we present an enrichment graph algorithm that infers changes in enzymatic activity in the context of their multispecificity from lipidomics data. Our inference method successfully recovered the MBOAT7 enzyme from knock-out data. Furthermore, we mechanistically interpret lipidomic alterations of adipocytes in obesity by leveraging network enrichment and lipid moieties. We address the general lack of lipidomics data mining options to elucidate potential disease mechanisms and make lipidomics more clinically relevant.

Lipids play an important role in biological systems and have the potential to serve as biomarkers in medical applications. Advances in lipidomics allow identification of hundreds of lipid species from biological samples. However, a systems biological analysis of the lipidome, by incorporating pathway information remains challenging, leaving lipidomics behind compared to other omics disciplines. An especially uncharted territory is the integration of statistical and network-based approaches for studying global lipidome changes. Here we developed the Lipid Network Explorer (LINEX), a web-tool addressing this gap by providing a way to visualize and analyze functional lipid metabolic networks. It utilizes metabolic rules to match biochemically connected lipids on a species level and combine it with a statistical correlation and testing analysis. Researchers can customize the biochemical rules considered, to their tissue or organism specific analysis and easily share them. We demonstrate the benefits of combining network-based analyses with statistics using publicly available lipidomics data sets. LINEX facilitates a biochemical knowledge-based data analysis for lipidomics. It is availableas a web-application and as a publicly available docker container.

Lipidomics is of growing importance for clinical and biomedical research due to an increasing number of discovered associations between lipid metabolism and diseases. However, sophisticated computational methods are required for biological interpretation including an understanding of metabolic processes, and their underlying (patho)mechanisms from lipidomics data. This can be achieved by using metabolic networks in combination with graph algorithms. Here, we present a lipid network analysis framework (Lipid Network Explorer, short LINEX) that allows biological interpretation of changes in lipidome composition. We developed an algorithm to generate data-specific lipid species networks from reaction database information. Using these networks, we developed a network enrichment algorithm that infers changes in enzymatic activity from lipidomics data, by leveraging multispecificity of lipid enzymes. Our inference method successfully recovered the MBOAT7 enzyme from knock-out lipidomics data. Additionally, we predict a PLA2 member to be at the center of dysregulation in adipose tissue in obesity. In our work, we showed the potential of lipidomics data to unravel mechanisms of metabolic regulation. Thereby our presented method can make lipidomics more clinically relevant by elucidating potential disease mechanisms.