Badkas Apurva, Nguyen Thanh-Phuong, Caberlotto Laura, Schneider Jochen G, De Landtsheer Sébastien, Sauter Thomas
Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, L-4365 Esch-sur-Alzette, Luxembourg.
Megeno S.A., L-4362 Esch-sur-Alzette, Luxembourg.
Biology (Basel). 2021 Feb 3;10(2):107. doi: 10.3390/biology10020107.
A large percentage of the global population is currently afflicted by metabolic diseases (MD), and the incidence is likely to double in the next decades. MD associated co-morbidities such as non-alcoholic fatty liver disease (NAFLD) and cardiomyopathy contribute significantly to impaired health. MD are complex, polygenic, with many genes involved in its aetiology. A popular approach to investigate genetic contributions to disease aetiology is biological network analysis. However, data dependence introduces a bias (noise, false positives, over-publication) in the outcome. While several approaches have been proposed to overcome these biases, many of them have constraints, including data integration issues, dependence on arbitrary parameters, database dependent outcomes, and computational complexity. Network topology is also a critical factor affecting the outcomes. Here, we propose a simple, parameter-free method, that takes into account database dependence and network topology, to identify central genes in the MD network. Among them, we infer novel candidates that have not yet been annotated as MD genes and show their relevance by highlighting their differential expression in public datasets and carefully examining the literature. The method contributes to uncovering connections in the MD mechanisms and highlights several candidates for in-depth study of their contribution to MD and its co-morbidities.
目前，全球很大一部分人口患有代谢性疾病（MD），且在未来几十年发病率可能会翻倍。与MD相关的合并症，如非酒精性脂肪性肝病（NAFLD）和心肌病，会显著损害健康。MD是复杂的多基因疾病，其病因涉及许多基因。研究基因对疾病病因贡献的一种常用方法是生物网络分析。然而，数据依赖性会给结果带来偏差（噪声、假阳性、过度发表）。虽然已经提出了几种方法来克服这些偏差，但其中许多方法都有局限性，包括数据整合问题、对任意参数的依赖、依赖数据库的结果以及计算复杂性。网络拓扑结构也是影响结果的关键因素。在此，我们提出一种简单的、无参数的方法，该方法考虑了数据库依赖性和网络拓扑结构，以识别MD网络中的核心基因。其中，我们推断出尚未被注释为MD基因的新候选基因，并通过突出它们在公共数据集中的差异表达以及仔细查阅文献来展示它们的相关性。该方法有助于揭示MD机制中的联系，并突出了几个值得深入研究其对MD及其合并症贡献的候选基因。
