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Metabolomics: Applications and Promise in Mycobacterial Disease

机译:代谢组学:在分枝杆菌疾病中的应用和前景

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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.
机译:直到最近,对分枝杆菌疾病的研究还停留在一个多世纪以前的基于文化的技术中。核酸扩增的使用正在改变这种状况,并且强大的新技术即将出现。代谢组学是对细菌和宿主代谢产物的研究,正在用于阐明疾病的机制,并可以识别导致更好的诊断,治疗和预后的分枝杆菌疾病的变化。代谢组学谱是其环境中基因生化产物的阵列。这些复杂的模式是生物标记,可以比基因组学或蛋白质组学更全面地了解细胞功能,功能障碍和微扰。代谢组学可能预示着个性化医学和临床试验设计的飞速发展,但是代谢组学的挑战也很大。测量的代谢物浓度随条件,内在生物学,仪器和样品制备的时间而变化。代谢随着年龄,性别,肠道微生物菌群的变化和生活方式的不同而发生深刻变化。测量结果的准确性,选择性,线性,可重复性,稳健性和检测极限使生物标志物的验证变得复杂。统计挑战包括对生成的大量数据的分析,解释和描述。尽管有这些缺点,代谢组学还是提供了巨大的机会,也为了解和管理分枝杆菌疾病提供了潜力。

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