phuEGO:一种基于网络的方法,可从磷酸蛋白组学数据集中重建活跃的信号通路。

IF 6.1 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Molecular & Cellular Proteomics Pub Date : 2024-06-01 Epub Date: 2024-04-19 DOI:10.1016/j.mcpro.2024.100771
Girolamo Giudice, Haoqi Chen, Thodoris Koutsandreas, Evangelia Petsalaki
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引用次数: 0

摘要

信号网络对几乎所有细胞功能都至关重要。我们目前对细胞信号的了解主要集中在信号通路数据库中,这些数据库虽然有用,但偏重于研究得比较透彻的过程,无法捕捉特定环境下的网络线路或通路交叉。基于质谱的磷酸化蛋白质组学数据可提供特定背景下活跃细胞信号过程的更无偏见的视图,但它存在信噪比低和不同实验间可重复性差的问题。虽然从此类数据中提取活跃信号特征的方法取得了进展,但在平衡偏差和可解释性方面仍有局限。在这里,我们提出了 phuEGO,它将多达三层的网络传播与自我网络分解相结合,提供了由活跃功能信号模块组成的小型网络。phuEGO 提高了全局磷酸化蛋白质组学数据集的信噪比,丰富了功能性磷酸位点网络,并改进了数据集之间的比较和整合。我们将 phuEGO 应用于五个磷酸化蛋白质组学数据集,这些数据集来自感染 SARS CoV2 后收集的细胞系。phuEGO 能够更好地识别不同数据集的共同活性功能,并指出一个富含已知 COVID-19 靶点的子网络。总之,phuEGO 为改进全球磷酸化蛋白质组学数据集的功能解释提供了一个灵活的工具。
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phuEGO: A Network-Based Method to Reconstruct Active Signaling Pathways From Phosphoproteomics Datasets.

Signaling networks are critical for virtually all cell functions. Our current knowledge of cell signaling has been summarized in signaling pathway databases, which, while useful, are highly biased toward well-studied processes, and do not capture context specific network wiring or pathway cross-talk. Mass spectrometry-based phosphoproteomics data can provide a more unbiased view of active cell signaling processes in a given context, however, it suffers from low signal-to-noise ratio and poor reproducibility across experiments. While progress in methods to extract active signaling signatures from such data has been made, there are still limitations with respect to balancing bias and interpretability. Here we present phuEGO, which combines up-to-three-layer network propagation with ego network decomposition to provide small networks comprising active functional signaling modules. PhuEGO boosts the signal-to-noise ratio from global phosphoproteomics datasets, enriches the resulting networks for functional phosphosites and allows the improved comparison and integration across datasets. We applied phuEGO to five phosphoproteomics data sets from cell lines collected upon infection with SARS CoV2. PhuEGO was better able to identify common active functions across datasets and to point to a subnetwork enriched for known COVID-19 targets. Overall, phuEGO provides a flexible tool to the community for the improved functional interpretation of global phosphoproteomics datasets.

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来源期刊
Molecular & Cellular Proteomics
Molecular & Cellular Proteomics 生物-生化研究方法
CiteScore
11.50
自引率
4.30%
发文量
131
审稿时长
84 days
期刊介绍: The mission of MCP is to foster the development and applications of proteomics in both basic and translational research. MCP will publish manuscripts that report significant new biological or clinical discoveries underpinned by proteomic observations across all kingdoms of life. Manuscripts must define the biological roles played by the proteins investigated or their mechanisms of action. The journal also emphasizes articles that describe innovative new computational methods and technological advancements that will enable future discoveries. Manuscripts describing such approaches do not have to include a solution to a biological problem, but must demonstrate that the technology works as described, is reproducible and is appropriate to uncover yet unknown protein/proteome function or properties using relevant model systems or publicly available data. Scope: -Fundamental studies in biology, including integrative "omics" studies, that provide mechanistic insights -Novel experimental and computational technologies -Proteogenomic data integration and analysis that enable greater understanding of physiology and disease processes -Pathway and network analyses of signaling that focus on the roles of post-translational modifications -Studies of proteome dynamics and quality controls, and their roles in disease -Studies of evolutionary processes effecting proteome dynamics, quality and regulation -Chemical proteomics, including mechanisms of drug action -Proteomics of the immune system and antigen presentation/recognition -Microbiome proteomics, host-microbe and host-pathogen interactions, and their roles in health and disease -Clinical and translational studies of human diseases -Metabolomics to understand functional connections between genes, proteins and phenotypes
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