Anyaegbunam Uchenna Alex, Vagiona Aimilia-Christina, Ten Cate Vincent, Bauer Katrin, Schmidlin Thierry, Distler Ute, Tenzer Stefan, Araldi Elisa, Bindila Laura, Wild Philipp, Andrade-Navarro Miguel A
Computational Biology and Data Mining Group (CBDM), Institute of Organismic and Molecular Evolution (iOME), Johannes Gutenberg University, 55122 Mainz, Germany.
Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstr. 1, 55131 Mainz, Germany.
Biomolecules. 2025 Mar 26;15(4):484. doi: 10.3390/biom15040484.
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.
多组学数据的整合为阐明生物过程和疾病背后的复杂分子机制提供了变革性潜力。在本研究中，我们构建了一个脂质-代谢物-蛋白质网络，该网络结合了蛋白质-蛋白质相互作用网络以及蛋白质与代谢物和脂质之间的酶促和遗传相互作用，为多组学整合提供了一个统一的框架。利用双曲嵌入，该网络可视化了各层组学之间的联系，可通过用户友好的Shiny R（版本1.10.0）软件包进行访问。该框架基于功能接近度对各层组学中的分子进行排序，便于直观探索。在心血管疾病（CVD）案例研究中的应用识别出了与CVD相关蛋白质有关的脂质和代谢物。分析证实了已知的关联，如胆固醇酯和鞘磷脂，并突出了潜在的新型生物标志物，如4-咪唑乙酸和吲哚乙醛。此外，我们利用该网络分析了恩格列净对脂质代谢的时间效应。对与脂质特征相关蛋白质的功能富集分析揭示了生物过程中的动态变化，早期效应影响磷脂代谢，长期效应影响鞘脂生物合成。我们的框架为假设生成、功能分析和生物标志物发现提供了一个多功能工具。通过连接分子层面，这种方法推进了我们对疾病机制和治疗效果的理解，在计算生物学和精准医学中具有广泛应用。
