Rienksma Rienk A, Suarez-Diez Maria, Spina Lucie, Schaap Peter J, Martins dos Santos Vitor A P
Laboratory of Systems and Synthetic Biology, Wageningen University and Research Centre, Dreijenplein 10, Wageningen 6703 HB, The Netherlands.
Centre National de la Rescherche Scientifique (CNRS), Institut de Pharmacologie et de Biologie Structurale (UMR 5089), Department of Tuberculosis and Infection Biology and Université de Toulouse (Université Paul Sabatier, Toulouse III), IPBS, 205 Route de Narbonne, BP 64182, F-31077 Toulouse, France.
Semin Immunol. 2014 Dec;26(6):610-22. doi: 10.1016/j.smim.2014.09.013. Epub 2014 Oct 23.
Systems-level metabolic network reconstructions and the derived constraint-based (CB) mathematical models are efficient tools to explore bacterial metabolism. Approximately one-fourth of the Mycobacterium tuberculosis (Mtb) genome contains genes that encode proteins directly involved in its metabolism. These represent potential drug targets that can be systematically probed with CB models through the prediction of genes essential (or the combination thereof) for the pathogen to grow. However, gene essentiality depends on the growth conditions and, so far, no in vitro model precisely mimics the host at the different stages of mycobacterial infection, limiting model predictions. These limitations can be circumvented by combining expression data from in vivo samples with a validated CB model, creating an accurate description of pathogen metabolism in the host. To this end, we present here a thoroughly curated and extended genome-scale CB metabolic model of Mtb quantitatively validated using 13C measurements. We describe some of the efforts made in integrating CB models and high-throughput data to generate condition specific models, and we will discuss challenges ahead. This knowledge and the framework herein presented will enable to identify potential new drug targets, and will foster the development of optimal therapeutic strategies.
系统水平的代谢网络重建以及由此衍生的基于约束的（CB）数学模型是探索细菌代谢的有效工具。结核分枝杆菌（Mtb）基因组中约四分之一的基因编码直接参与其代谢的蛋白质。这些基因代表了潜在的药物靶点，可以通过CB模型对病原体生长所必需的基因（或其组合）进行预测，从而对这些靶点进行系统探究。然而，基因的必需性取决于生长条件，并且到目前为止，尚无体外模型能够精确模拟分枝杆菌感染不同阶段的宿主情况，这限制了模型的预测能力。通过将体内样本的表达数据与经过验证的CB模型相结合，可以规避这些限制，从而准确描述宿主中病原体的代谢情况。为此，我们在此展示一个经过全面整理和扩展的Mtb全基因组规模的CB代谢模型，该模型已通过13C测量进行了定量验证。我们描述了在整合CB模型和高通量数据以生成特定条件模型方面所做的一些工作，并将讨论未来面临的挑战。本文所介绍的这些知识和框架将有助于识别潜在的新药物靶点，并推动最佳治疗策略的开发。
