Metabolomics: Applications and Promise in Mycobacterial Disease
Metabolomics: Applications and Promise in Mycobacterial Disease
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DOI：
10.1513/annalsats.201505-279ps
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发表时间：
2015-09-01
影响因子：
8.3
通讯作者：
Schraufnagel, Dean E.
中科院分区：
文献类型：
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作者：
Mirsaeidi, Mehdi;Banoei, Mohammad Mehdi;Schraufnagel, Dean E.
直到最近，分枝杆菌疾病的研究还停留在基于培养的技术上，这种技术已经有世纪的历史了。核酸扩增的使用正在改变这一点，强大的新技术即将出现。代谢组学是对细菌和宿主的代谢产物集的研究，正在用于阐明疾病的机制，并可以识别导致更好地诊断，治疗和预防分枝杆菌疾病的变化。代谢谱是基因在其环境中的生化产物的阵列。这些复杂的模式是生物标志物，可以比基因组学或蛋白质组学更全面地了解细胞功能，功能障碍和干扰。代谢组学可能预示着个性化医疗和临床试验设计的全面进步，但代谢组学的挑战也很大。测量的代谢物浓度随条件内的时间、内在生物学、仪器和样品制备而变化。代谢随着年龄、性别、肠道微生物植物群的变化和生活方式而发生深刻的变化。生物标志物的验证因测量准确性、选择性、线性、再现性、稳健性和检测限而变得复杂。统计方面的挑战包括分析、解释和描述所产生的大量数据。尽管有这些缺点，代谢组学提供了很大的机会和潜力，了解和管理分枝杆菌疾病。
Until recently, the study of mycobacterial diseases was trapped in culture-based technology that is more than a century old. The use of nucleic acid amplification is changing this, and powerful new technologies are on the horizon. Metabolomics, which is the study of sets of metabolites of both the bacteria and host, is being used to clarify mechanisms of disease, and can identify changes leading to better diagnosis, treatment, and prognostication of mycobacterial diseases. Metabolomic profiles are arrays of biochemical products of genes in their environment. These complex patterns are biomarkers that can allow a more complete understanding of cell function, dysfunction, and perturbation than genomics or proteomics. Metabolomics could herald sweeping advances in personalized medicine and clinical trial design, but the challenges in metabolomics are also great. Measured metabolite concentrations vary with the timing within a condition, the intrinsic biology, the instruments, and the sample preparation. Metabolism profoundly changes with age, sex, variations in gut microbial flora, and lifestyle. Validation of biomarkers is complicated by measurement accuracy, selectivity, linearity, reproducibility, robustness, and limits of detection. The statistical challenges include analysis, interpretation, and description of the vast amount of data generated. Despite these drawbacks, metabolomics provides great opportunity and the potential to understand and manage mycobacterial diseases.