Multi-Omics Approaches to Improve Mitochondrial Disease Diagnosis: Challenges, Advances, and Perspectives

Abstract

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.

Integration strategies of multi-omics data for machine learning analysis

“…It is intended for computational biologists looking for general approaches and ideas in handling their omics datasets. For more specific reviews on multi-omics integration, we suggest several other reviews either methodologically oriented on subject such as variable selection [23] , dimensionality reduction [24] , autoencoders [25] , clustering [26] , [27] or network-based methods [10] , [11] , [28] , [29] , or biologically oriented on subjects like metabolomics [30] , [31] , phosphoproteomics [32] , toxicology [33] , host interactions [34] , [35] and others [36] , [37] , [38] , [39] .…”

Mitochondrial Syndromes Revisited

“…The development of next generation sequencing has led to a significant improvement in the genetic diagnostic capacity in the last ten years, dramatically expanding the scenario of the genetic basis of mitochondrial diseases [ 69 ]. The diagnostic power may improve with “multi-omics” integrative approaches too [ 70 ].…”

The History of Gene Hunting in Hereditary Spinocerebellar Degeneration: Lessons From the Past and Future Perspectives

“…A holistic approach that integrates data obtained from multiple omics layers (multi-omics approach) has a higher biological reliability and can substantially enhance new Mendelian genes’ discovery than the single-omics approaches ( Labory et al, 2020 ). The multi-omics have been successfully used in many diseases, including neurological diseases, and is thus a promising approach to enhance the discovery of new SCD genes ( Crowther et al, 2018 ; Kerr et al, 2020 ; Labory et al, 2020 ).…”