Labory Justine, Fierville Morgane, Ait-El-Mkadem Samira, Bannwarth Sylvie, Paquis-Flucklinger Véronique, Bottini Silvia
Université Côte d'Azur, Center of Modeling, Simulation and Interactions, Nice, France.
Université Côte d'Azur, Inserm U1081, CNRS UMR 7284, Institute for Research on Cancer and Aging, Nice (IRCAN), Centre hospitalier universitaire (CHU) de Nice, Nice, France.
Front Mol Biosci. 2020 Nov 2;7:590842. doi: 10.3389/fmolb.2020.590842. eCollection 2020.
Mitochondrial diseases (MD) are rare disorders caused by deficiency of the mitochondrial respiratory chain, which provides energy in each cell. They are characterized by a high clinical and genetic heterogeneity and in most patients, the responsible gene is unknown. Diagnosis is based on the identification of the causative gene that allows genetic counseling, prenatal diagnosis, understanding of pathological mechanisms, and personalized therapeutic approaches. Despite the emergence of Next Generation Sequencing (NGS), to date, more than one out of two patients has no diagnosis in the absence of identification of the responsible gene. Technologies currently used for detecting causal variants (genetic alterations) is far from complete, leading many variants of unknown significance (VUS) and mainly based on the use of whole exome sequencing thus neglecting the identification of non-coding variants. The complexity of human genome and its regulation at multiple levels has led biologists to develop several assays to interrogate the different aspects of biological processes. While one-dimension single omics investigation offers a peek of this complex system, the combination of different omics data allows the discovery of coherent signatures. The community of computational biologists and bioinformaticians, in order to integrate data from different omics, has developed several approaches and tools. However, it is difficult to understand which suits the best to predict diverse phenotypic outcome. First attempts to use multi-omics approaches showed an improvement of the diagnostic power. However, we are far from a complete understanding of MD and their diagnosis. After reviewing multi-omics algorithms developed in the latest years, we are proposing here a novel data-driven classification and we will discuss how multi-omics will change and improve the diagnosis of MD. Due to the growing use of multi-omics approaches in MD, we foresee that this work will contribute to set up good practices to perform multi-omics data integration to improve the prediction of phenotypic outcomes and the diagnostic power of MD.
线粒体疾病（MD）是由线粒体呼吸链功能缺陷引起的罕见疾病，线粒体呼吸链为每个细胞提供能量。其特点是临床和遗传异质性高，在大多数患者中，致病基因尚不清楚。诊断基于对致病基因的鉴定，这有助于进行遗传咨询、产前诊断、了解病理机制以及采取个性化治疗方法。尽管新一代测序（NGS）技术已经出现，但迄今为止，在未鉴定出致病基因的情况下，超过半数的患者仍无法确诊。目前用于检测致病变异（基因改变）的技术还远不完善，导致许多意义未明的变异（VUS），并且主要基于全外显子组测序的应用，从而忽略了非编码变异的鉴定。人类基因组的复杂性及其在多个层面的调控促使生物学家开发了多种检测方法来探究生物过程的不同方面。虽然一维单组学研究能让我们初步了解这个复杂系统，但不同组学数据的结合能发现连贯的特征。为了整合来自不同组学的数据，计算生物学家和生物信息学家群体已经开发了多种方法和工具。然而，很难确定哪种方法最适合预测不同的表型结果。首次尝试使用多组学方法显示诊断能力有所提高。然而，我们对线粒体疾病及其诊断仍远未完全了解。在回顾了近年来开发的多组学算法后，我们在此提出一种新的数据驱动分类方法，并将讨论多组学将如何改变和改善线粒体疾病的诊断。由于多组学方法在线粒体疾病中的应用越来越广泛，我们预计这项工作将有助于建立良好的实践方法，以进行多组学数据整合，从而改善表型结果的预测和线粒体疾病的诊断能力。
