脂质 - 代谢物 - 蛋白质网络的地图,以帮助多基因组合集成
Uchenna Alex Anyaegbunam1, Aimilia-Christina Vagiona1, Vincent Ten Cate2,3,4
1Computational Biology and Data Mining Group (CBDM), Institute of Organismic and Molecular Evolution (iOME), Johannes Gutenberg University, 55122 Mainz, Germany.
Biomolecules
|April 30, 2025
概括
这项研究引入了一种新的多omics网络,集成脂质,代谢物和蛋白质数据. 该工具有助于发现生物标志物和了解疾病机制,特别是心血管疾病 (CVD).
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 生物化学 生物化学
背景情况:
- 了解复杂疾病需要整合多学科数据.
- 现有的方法往往难以统一多样化的分子信息,如脂质,代谢物和蛋白质.
- 需要一个统一的框架来探索跨学科的相互作用和生物机制.
研究的目的:
- 通过脂质代谢物-蛋白质网络开发一个统一的数据集成框架.
- 为了可视化和分析跨不同奥米克层的分子连接.
- 识别新的生物标志物,了解疾病机制和治疗效果.
主要方法:
- 开发了一个集成蛋白质-蛋白质相互作用与代谢物和脂质数据的网络.
- 利用超标嵌入用于网络可视化和分析.
- 应用了一个用户友好的Shiny R软件包,用于直观的探索.
- 进行功能丰富分析以确定生物过程.
主要成果:
- 确定了心血管疾病 (CVD) 的已知和潜在的新生物标志物.
- 已证实胆固醇和斯芬戈米林与心血管疾病相关蛋白质的相关性.
- 突出显示了4-imidazole acetate和indoleacetaldehyde作为潜在的新生物标志物.
- 分析了empagliflozin对脂质代谢的时间效应,揭示了脂和脂通路的动态变化.
结论:
- 开发的多omics网络为产生假设,生物标志物发现和功能分析提供了多功能工具.
- 这一框架通过桥接分子层来增强对疾病机制和药物效应的理解.
- 这种方法在计算生物学,精准医学和药物发现方面具有广泛的应用.
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