Dumas Marc-Emmanuel
Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, Exhibition Road, South Kensington, London SW7 2AZ, UK.
Mol Biosyst. 2012 Oct;8(10):2494-502. doi: 10.1039/c2mb25167a.
The characterization of the metabolome has rapidly evolved over two decades, from early developments in analytical chemistry to systems biology. Metabolites and small molecules are not independent; they are organized in biochemical pathways and in a wider metabolic network, which is itself dependent on various genetic and signaling networks for its regulation. Recent advances in genomics, transcriptomics, proteomics and metabolomics have been matched by the development of publicly available repositories, which have helped shaping a new generation of integrative studies using metabolite measurements in molecular epidemiology and genetic studies. Although the environment influences metabolism, the identification of the genetic determinants of metabolic phenotypes (metabotypes) was made possible by the development of metabotype quantitative trait locus (mQTL) mapping and metabolomic genome-wide association studies (mGWAS) in a rigorous statistical genetics framework, deriving associations between metabolite concentrations and genetic polymorphisms. However, given the complexity of the biomolecular events involved in the regulation of metabolic patterns, alternative network biology approaches have also been recently introduced, such as integrated metabolome and interactome mapping (iMIM). This unprecedented convergence of metabolic biochemistry, quantitative genetics and network biology already has had a strong impact on the role of the metabolome in biomedical sciences, and this review gives a foretaste of its anticipated successes in eventually delivering personalized medicine.
在过去的二十年里,代谢组学的特征描述已从分析化学的早期发展迅速演进至系统生物学。代谢物和小分子并非独立存在;它们在生化途径以及更广泛的代谢网络中组织排列,而该网络本身的调节依赖于各种基因和信号网络。基因组学、转录组学、蛋白质组学和代谢组学的最新进展与公开可用数据库的发展相匹配,这些数据库有助于开展新一代整合研究,即在分子流行病学和基因研究中利用代谢物测量数据。尽管环境会影响新陈代谢,但在严格的统计遗传学框架下,代谢型定量性状位点(mQTL)定位和代谢组全基因组关联研究(mGWAS)的发展使得识别代谢表型(代谢型)的遗传决定因素成为可能,从而得出代谢物浓度与基因多态性之间的关联。然而,鉴于代谢模式调节中涉及的生物分子事件的复杂性,最近也引入了其他网络生物学方法,如整合代谢组和相互作用组图谱绘制(iMIM)。代谢生物化学、定量遗传学和网络生物学的这一前所未有的融合,已经对代谢组在生物医学科学中的作用产生了重大影响,本综述预先展示了其在最终实现个性化医疗方面预期取得的成功。