Nature Reviews Genetics ( IF 39.1 ) Pub Date : 2024-09-19 , DOI: 10.1038/s41576-024-00768-0
Adil Mardinoglu, Bernhard Ø. Palsson
Metabologenomics integrates metabolomics with other omics data types to comprehensively study the genetic and environmental factors that influence metabolism. These multi-omics data can be incorporated into genome-scale metabolic models (GEMs), which are highly curated knowledge bases that explicitly account for genes, transcripts, proteins and metabolites. By including all known biochemical reactions catalysed by enzymes and transporters encoded in the human genome, GEMs analyse and predict the behaviour of complex metabolic networks. Continued advancements to the scale and scope of GEMs — from cells and tissues to microbiomes and the whole body — have helped to design effective treatments and develop better diagnostic tools for metabolic diseases. Furthermore, increasing amounts of multi-omics data are incorporated into GEMs to better identify the underlying mechanisms, biomarkers and potential drug targets of metabolic diseases.
中文翻译:
人类代谢基因组学的基因组规模模型
代谢基因组学将代谢组学与其他组学数据类型相结合,全面研究影响代谢的遗传和环境因素。这些多组学数据可以纳入基因组规模的代谢模型(GEM)中,这些模型是精心策划的知识库,可以明确解释基因、转录本、蛋白质和代谢物。通过包含人类基因组中编码的酶和转运蛋白催化的所有已知生化反应,GEM 可以分析和预测复杂代谢网络的行为。 GEM 的规模和范围(从细胞和组织到微生物组和整个身体)的持续进步有助于设计有效的治疗方法并开发更好的代谢疾病诊断工具。此外,越来越多的多组学数据被纳入 GEM,以更好地识别代谢疾病的潜在机制、生物标志物和潜在药物靶点。