Large-scale Genetic Exploration of Genome-wide Models: an Application to Human Cancer Metabolism

Computational modelling of metabolic processes has proven to be a useful approach to formulate our knowledge and improve our understanding of core biochemical systems that are crucial to maintain cellular functions. Recently, it has become evident that metabolism is not only responsible for generating the required energy and controlling the abundance of metabolites within a cell, but also has an important role in and influence on cellular fate specification. Toward understanding the broader role of metabolism on cellular decision-making in healthy and disease conditions, it is important to integrate the study of metabolism with other core regulatory systems within the cell, including gene regulatory networks that control gene expression patterns. However, the sheer size of human metabolism models makes large-scale integration and discovery difficult to perform extensively. After quantitatively integrating gene expression profiles with a genome-scale reconstruction of human metabolism, we propose combinatorial methods to reverse engineer gene expression profiles and combinations of genetic modifications that simultaneously optimize multi-objective cellular goals. This enables us to suggest classes of transcriptomic profiles that are most suitable to achieve given metabolic phenotypes. As a result, we can express objectives on the phenotypes and characterize genotypes able to reach the set objectives. We demonstrate how our techniques are able to compute beneficial, neutral or ``toxic'' combinations of gene expression levels. We test our method on a manually curated improved version of the human metabolic reconstruction (Recon 2), and on nine cell-specific cancer models, comparing our outcomes with the corresponding normal cells. When applied to cancer models, toxic genes become simultaneous targets for potential therapies. Our methods enable large scale studies of metabolic reconstructions coupled to gene expression models, and open the way to a broad class of applications that require understanding of the interplay among genotype, metabolism, and cellular behavior. The code is freely available as a MATLAB toolbox.

代谢过程的计算建模已被证实是一种有效梳理既有认知、深化对维持细胞功能至关重要的核心生化系统理解的研究手段。近期研究表明，代谢不仅负责细胞内能量生成与代谢物丰度调控，还在细胞命运决定过程中发挥关键作用并产生重要影响。为阐明代谢在健康与疾病状态下对细胞决策的更广范围作用，需将代谢研究与细胞内其他核心调控系统（包括控制基因表达模式的基因调控网络（gene regulatory networks））相结合。然而，人类代谢模型的庞大规模使得大规模整合与分析难以全面开展。在将基因表达谱与人类基因组规模代谢重建模型（genome-scale reconstruction of human metabolism）进行定量整合后，我们提出了组合式方法，用于逆向工程构建基因表达谱，以及同时优化多目标细胞功能需求的遗传修饰组合方案。该方法可帮助我们筛选出最适配特定代谢表型的转录组谱（transcriptomic profiles）类别。借此，我们能够针对表型设定研究目标，并表征出可达成既定目标的基因型特征。我们验证了所提技术可用于计算基因表达水平的有益、中性乃至“毒性”组合。我们在经人工手动整理优化的人类代谢重建模型（Recon 2）以及9种细胞特异性癌症模型上对所提方法进行了测试，并将测试结果与对应正常细胞进行了对比。将该方法应用于癌症模型时，毒性基因可成为潜在治疗的协同靶点。我们的方法可为代谢重建模型与基因表达模型的耦合大规模研究提供支撑，并为一系列需要解析基因型、代谢与细胞行为间相互作用的应用场景开辟了道路。相关代码以MATLAB工具箱（MATLAB toolbox）的形式免费公开。