Inferring secretory and metabolic pathway activity from omic data with secCellFie.
Inferring secretory and metabolic pathway activity from omic data with secCellFie.
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使用 secCellFie 从组学数据推断分泌和代谢途径活性。
DOI：
10.1101/2023.05.04.539316
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Lewis,NathanE
中科院分区：
文献类型：
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作者：
Masson,HelenO;Samoudi,Mojtaba;Robinson,CaressaM;Kuo,Chih-Chung;Weiss,Linus;Doha,KmShamsUd;Campos,Alex;Tejwani,Vijay;Dahodwala,Hussain;Menard,Patrice;Voldborg,BjornG;Sharfstein,SusanT;Lewis,NathanE
了解蛋白质分泌在生物技术中具有相当重要的意义，并且在广泛的正常和病理条件下具有重要意义，包括发育，免疫学和组织功能。虽然在研究分泌途径中的单个蛋白质方面已经取得了很大进展，但由于所涉及的生物分子系统的复杂性，测量和量化途径活性的机械变化仍然具有挑战性。系统生物学已经开始通过开发用于分析生物途径的算法工具来解决这个问题;然而，这些工具中的大多数仍然只有具有丰富计算经验的系统生物学专家才能使用。在这里，我们扩展了用户友好的CellFie工具，该工具从组学数据中量化代谢活动，包括分泌途径功能，允许任何科学家从组学数据中推断蛋白质分泌的特性。我们展示了CellFie（secCellFie）的分泌扩增如何帮助预测不同免疫细胞的代谢和分泌功能，NAFLD细胞模型中的肝细胞因子分泌，以及中国人卵巢细胞中的抗体产生。
Understanding protein secretion has considerable importance in biotechnology and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer properties of protein secretion from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can help predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.