Boccard Julien, Schvartz Domitille, Codesido Santiago, Hanafi Mohamed, Gagnebin Yoric, Ponte Belén, Jourdan Fabien, Rudaz Serge
School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland.
Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland.
Front Mol Biosci. 2021 May 14;8:682559. doi: 10.3389/fmolb.2021.682559. eCollection 2021.
Because of its ability to generate biological hypotheses, metabolomics offers an innovative and promising approach in many fields, including clinical research. However, collecting specimens in this setting can be difficult to standardize, especially when groups of patients with different degrees of disease severity are considered. In addition, despite major technological advances, it remains challenging to measure all the compounds defining the metabolic network of a biological system. In this context, the characterization of samples based on several analytical setups is now recognized as an efficient strategy to improve the coverage of metabolic complexity. For this purpose, chemometrics proposes efficient methods to reduce the dimensionality of these complex datasets spread over several matrices, allowing the integration of different sources or structures of metabolic information. Bioinformatics databases and query tools designed to describe and explore metabolic network models offer extremely useful solutions for the contextualization of potential biomarker subsets, enabling mechanistic hypotheses to be considered rather than simple associations. In this study, network principal component analysis was used to investigate samples collected from three cohorts of patients including multiple stages of chronic kidney disease. Metabolic profiles were measured using a combination of four analytical setups involving different separation modes in liquid chromatography coupled to high resolution mass spectrometry. Based on the chemometric model, specific patterns of metabolites, such as N-acetyl amino acids, could be associated with the different subgroups of patients. Further investigation of the metabolic signatures carried out using genome-scale network modeling confirmed both tryptophan metabolism and nucleotide interconversion as relevant pathways potentially associated with disease severity. Metabolic modules composed of chemically adjacent or close compounds of biological relevance were further investigated using carbon transfer reaction paths. Overall, the proposed integrative data analysis strategy allowed deeper insights into the metabolic routes associated with different groups of patients to be gained. Because of their complementary role in the knowledge discovery process, the association of chemometrics and bioinformatics in a common workflow is therefore shown as an efficient methodology to gain meaningful insights in a clinical context.
由于代谢组学能够生成生物学假设，它在包括临床研究在内的许多领域提供了一种创新且有前景的方法。然而，在这种情况下收集样本可能难以标准化，尤其是当考虑不同疾病严重程度的患者群体时。此外，尽管有重大的技术进步，但测量定义生物系统代谢网络的所有化合物仍然具有挑战性。在这种背景下，基于多种分析设置对样本进行表征现在被认为是提高代谢复杂性覆盖范围的有效策略。为此，化学计量学提出了有效的方法来降低分布在多个矩阵上的这些复杂数据集的维度，从而能够整合不同来源或结构的代谢信息。旨在描述和探索代谢网络模型的生物信息学数据库和查询工具为潜在生物标志物子集的背景化提供了极其有用的解决方案，使人们能够考虑机制假设而非简单关联。在本研究中，网络主成分分析被用于研究从包括慢性肾病多个阶段的三个患者队列中收集的样本。使用四种涉及液相色谱不同分离模式并与高分辨率质谱联用的分析设置组合来测量代谢谱。基于化学计量学模型，特定的代谢物模式，如N - 乙酰氨基酸，可能与不同的患者亚组相关。使用基因组规模网络建模对代谢特征进行的进一步研究证实色氨酸代谢和核苷酸相互转化都是可能与疾病严重程度相关的相关途径。使用碳转移反应路径进一步研究了由具有生物学相关性的化学相邻或相近化合物组成的代谢模块。总体而言，所提出的综合数据分析策略使人们能够更深入地了解与不同患者群体相关的代谢途径。由于化学计量学和生物信息学在知识发现过程中的互补作用，因此在一个共同的工作流程中将它们结合起来被证明是在临床背景下获得有意义见解的有效方法。
