Next-generation metabolic screening: targeted and untargeted metabolomics for the diagnosis of inborn errors of metabolism in individual patients
Next-generation metabolic screening: targeted and untargeted metabolomics for the diagnosis of inborn errors of metabolism in individual patients
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DOI：
10.1007/s10545-017-0131-6
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发表时间：
2018-05-01
影响因子：
4.2
通讯作者：
Wevers, Ron A.
中科院分区：
文献类型：
--
作者：
Coene, Karlien L. M.;Kluijtmans, Leo A. J.;Wevers, Ron A.
全外显子组测序在临床诊断中的实施产生了对遗传变体的功能评估的需要。在先天性代谢缺陷（IEM）领域，采用多种靶向生化测定来分析有限量的代谢物。我们现在提出了一个单一的平台，高分辨率液相色谱四极杆飞行时间（LC-QTOF）的方法，可应用于整体代谢谱在个别IEM怀疑患者的血浆。这种方法，我们称之为“下一代代谢筛选”（NGMS），可以在每个样本中检测超过10，000个特征。在NGMS工作流程中，使用“各种形式的色谱质谱（XCMS）”软件包对患者和对照样品中鉴定的特征进行比对。随后，使用人类代谢组数据库注释所有特征，并进行统计学检验以鉴定与对照相比患者样品中显著扰动的代谢物浓度。我们提出了三种主要的模式来分析复杂的。非靶向代谢组学数据。首先，可以基于在代谢途径中不确定意义的鉴定的遗传变体进行有针对性的评估。其次，我们开发了一组LEM相关代谢物来过滤非靶向代谢组学数据。基于这种IEM面板方法，我们为46个IEM中的42个提供了正确的诊断。作为最后一种模式，代谢组学数据可以在非目标环境中进行分析，我们称之为“开放代谢组学”分析。这种方法鉴定了已知IEM中潜在的新生物标志物，并导致鉴定了未知IEM的生物标志物。我们相信NGMS是IEM实验室诊断的前进方向。
The implementation of whole-exome sequencing in clinical diagnostics has generated a need for functional evaluation of genetic variants. In the field of inborn errors of metabolism (IEM), a diverse spectrum of targeted biochemical assays is employed to analyze a limited amount of metabolites. We now present a single-platform, high-resolution liquid chromatography quadrupole time of flight (LC-QTOF) method that can be applied for holistic metabolic profiling in plasma of individual IEM-suspected patients. This method, which we termed "next-generation metabolic screening" (NGMS), can detect >10,000 features in each sample. In the NGMS workflow, features identified in patient and control samples are aligned using the "various forms of chromatography mass spectrometry (XCMS)" software package. Subsequently, all features are annotated using the Human Metabolome Database, and statistical testing is performed to identify significantly perturbed metabolite concentrations in a patient sample compared with controls. We propose three main modalities to analyze complex. untargeted metabolomics data. First, a targeted evaluation can be done based on identified genetic variants of uncertain significance in metabolic pathways. Second, we developed a panel of LEM-related metabolites to filter untargeted metabolomics data. Based on this IEM-panel approach, we provided the correct diagnosis for 42 of 46 IEMs. As a last modality, metabolomics data can be analyzed in an untargeted setting, which we term "open the metabolome" analysis. This approach identifies potential novel biomarkers in known IEMs and leads to identification of biomarkers for as yet unknown IEMs. We are convinced that NGMS is the way forward in laboratory diagnostics of IEMs.