Large-Scale Modeling Approach Reveals Functional Metabolic Shifts during Hepatic Differentiation.,Journal of Proteome Research

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Large-Scale Modeling Approach Reveals Functional Metabolic Shifts during Hepatic Differentiation.
Journal of Proteome Research ( IF 3.8 ) Pub Date : 2018-11-19 , DOI: 10.1021/acs.jproteome.8b00524
Nathalie Poupin ₁ , Anne Corlu ₂ , Nicolas J Cabaton ₁ , Hélène Dubois-Pot-Schneider ₂ , Cécile Canlet ₁ , Elodie Person ₁ , Sandrine Bruel ₁ , Clément Frainay ₁ , Florence Vinson ₁ , Florence Maurier ₁ , Fabrice Morel ₂ , Marie-Anne Robin ₂ , Bernard Fromenty ₂ , Daniel Zalko ₁ , Fabien Jourdan ₁

Affiliation

Being able to explore the metabolism of broad metabolizing cells is of critical importance in many research fields. This article presents an original modeling solution combining metabolic network and omics data to identify modulated metabolic pathways and changes in metabolic functions occurring during differentiation of a human hepatic cell line (HepaRG). Our results confirm the activation of hepato-specific functionalities and newly evidence modulation of other metabolic pathways, which could not be evidenced from transcriptomic data alone. Our method takes advantage of the network structure to detect changes in metabolic pathways that do not have gene annotations and exploits flux analyses techniques to identify activated metabolic functions. Compared to the usual cell-specific metabolic network reconstruction approaches, it limits false predictions by considering several possible network configurations to represent one phenotype rather than one arbitrarily selected network. Our approach significantly enhances the comprehensive and functional assessment of cell metabolism, opening further perspectives to investigate metabolic shifts occurring within various biological contexts.

中文翻译：

大规模建模方法揭示了肝分化过程中的功能性代谢变化。

在许多研究领域中，能够探索广泛代谢的细胞的代谢至关重要。本文提出了一种结合代谢网络和组学数据的原始建模解决方案，以识别调节的代谢途径和人类肝细胞系（HepaRG）分化过程中发生的代谢功能变化。我们的结果证实了肝特异性功能的激活和新的其他代谢途径的调节，这不能仅从转录组数据中得到证实。我们的方法利用网络结构来检测没有基因注释的代谢途径的变化，并利用流量分析技术来识别激活的代谢功能。与通常的细胞特异性代谢网络重建方法相比，它通过考虑几种可能的网络配置来代表一种表型而不是一种任意选择的网络，从而限制了错误的预测。我们的方法大大增强了细胞代谢的综合和功能评估，为研究在各种生物学环境下发生的代谢变化提供了进一步的视角。

更新日期：2018-11-20

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