利用差异基因表达预测多细胞生物代谢改变的计算方法

IF 3.743 Q2 Biochemistry, Genetics and Molecular Biology
Lvxing Zhu, Haoran Zheng, Xinying Hu and Yang Xu
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引用次数: 5

摘要

代谢的改变通常被认为是生理和发病机制的原因或结果。但由于缺乏明确的代谢目标函数,多细胞生物的代谢通量分布难以预测。在此,我们提出了一种计算方法,可以成功地描述两种不同条件下的大规模代谢差异。通过将基因表达数据与现有的全球人类代谢网络的综合重建相结合,我们在没有事先知识或代谢物摄取和分泌率的情况下,定性地预测了显著差异的通量。因此,该方法既适用于微生物,也适用于多细胞生物。与传统的富集分析方法和基于约束的模型不同,我们同时考虑了代谢网络中的条件和相互作用。为了应用所提出的方法,我们预测了大肠杆菌菌株和透明细胞肾细胞癌的通量变化,同时大肠杆菌菌株在不同稀释率的趋化器中有氧生长,并将透明细胞肾细胞癌与正常肾细胞进行比较。然后,我们将显著差异的反应映射到原始代谢网络中定义的代谢子系统,以观察ccRCC的代谢变化。与已有的研究结果相比,我们的结果表明大肠杆菌实验的准确性较高,对ccRCC实验的预测更为合理。本文提出的方法为微生物和多细胞生物在双条件下改变代谢的全基因组研究提供了一种计算方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A computational method using differential gene expression to predict altered metabolism of multicellular organisms†

A computational method using differential gene expression to predict altered metabolism of multicellular organisms†

Altered metabolism is often identified as a cause or an effect of physiology and pathogenesis. But it is difficult to predict the metabolic flux distributions of multicellular organisms due to the lack of an explicit metabolic objective function. Here we present a computational method which can successfully describe the differences in metabolism between two different conditions on a large scale. By integrating gene expression data with an existing comprehensive reconstruction of the global human metabolic network, we qualitatively predicted significantly differential fluxes without prior knowledge or the rate of metabolite uptake and secretion. Therefore, this method can be applied for both microorganisms and multicellular organisms. Different from traditional enrichment analysis methods and constraint-based models, we consider conditions and interactions within the metabolic network simultaneously. To apply the proposed method, we predicted altered fluxes for E. coli strains and clear cell renal cell carcinoma, while the E. coli strains are growing aerobically in a chemostat with different dilution rates and clear cell renal cell carcinoma is compared with normal kidney cells. Then we map the significantly differential reactions to metabolic subsystems defined in the original metabolic network for ccRCC to observe the altered metabolism. In contrast with existing studies, our results show a high accuracy of the E. coli experiment and a more reasonable prediction of the ccRCC experiment. The method presented here provides a computational approach for the genome-wide study of altered metabolism under pairs of conditions for both microorganisms and multicellular organisms.

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来源期刊
Molecular BioSystems
Molecular BioSystems 生物-生化与分子生物学
CiteScore
2.94
自引率
0.00%
发文量
0
审稿时长
2.6 months
期刊介绍: Molecular Omics publishes molecular level experimental and bioinformatics research in the -omics sciences, including genomics, proteomics, transcriptomics and metabolomics. We will also welcome multidisciplinary papers presenting studies combining different types of omics, or the interface of omics and other fields such as systems biology or chemical biology.
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