Department of Computer Science, Memorial University, St. John's, Newfoundland and Labrador, Canada.
Faculty of Medicine, Memorial University, St. John's, Newfoundland and Labrador, Canada.
PLoS Comput Biol. 2018 Mar 1;14(3):e1005986. doi: 10.1371/journal.pcbi.1005986. eCollection 2018 Mar.
Metabolomics studies use quantitative analyses of metabolites from body fluids or tissues in order to investigate a sequence of cellular processes and biological systems in response to genetic and environmental influences. This promises an immense potential for a better understanding of the pathogenesis of complex diseases. Most conventional metabolomics analysis methods exam one metabolite at a time and may overlook the synergistic effect of combining multiple metabolites. In this article, we proposed a new bioinformatics framework that infers the non-linear synergy among multiple metabolites using a symbolic model and subsequently, identify key metabolites using network analysis. Such a symbolic model is able to represent a complex non-linear relationship among a set of metabolites associated with osteoarthritis (OA) and is automatically learned using an evolutionary algorithm. Applied to the Newfoundland Osteoarthritis Study (NFOAS) dataset, our methodology was able to identify nine key metabolites including some known osteoarthritis-associated metabolites and some novel metabolic markers that have never been reported before. The results demonstrate the effectiveness of our methodology and more importantly, with further investigations, propose new hypotheses that can help better understand the OA disease.
代谢组学研究使用定量分析来自体液或组织中的代谢物，以研究一系列细胞过程和生物系统对遗传和环境影响的反应。这有望为更好地理解复杂疾病的发病机制提供巨大的潜力。大多数传统的代谢组学分析方法一次检查一种代谢物，可能会忽略组合多种代谢物的协同作用。在本文中，我们提出了一种新的生物信息学框架，该框架使用符号模型推断多种代谢物之间的非线性协同作用，然后使用网络分析识别关键代谢物。这种符号模型能够表示与骨关节炎（OA）相关的一组代谢物之间的复杂非线性关系，并使用进化算法自动学习。将我们的方法应用于纽芬兰骨关节炎研究（NFOAS）数据集，能够鉴定出包括一些已知的与骨关节炎相关的代谢物和一些以前从未报道过的新代谢标志物在内的 9 种关键代谢物。结果表明了我们方法的有效性，更重要的是，通过进一步研究，提出了有助于更好地理解 OA 疾病的新假说。
