Mitochondrial DNA Deletions With Low-Level Heteroplasmy in Adult-Onset Myopathy

A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism

BACKGROUND AND OBJECTIVE

Traditional targeted metabolomic investigations identify a pre-defined list of analytes in samples and have been widely used for decades in the diagnosis and monitoring of inborn errors of metabolism (IEMs). Recent technological advances have resulted in the development and maturation of untargeted metabolomics: a holistic, unbiased, analytical approach to detecting metabolic disturbances in human disease. We aim to provide a summary of untargeted metabolomics [focusing on tandem mass spectrometry (MS-MS)] and its application in the field of IEMs.

METHODS

Data for this review was identified through a literature search using PubMed, Google Scholar, and personal repositories of articles collected by the authors. Findings are presented within several sections describing the metabolome, the current use of targeted metabolomics in the diagnostic pathway of patients with IEMs, the more recent integration of untargeted metabolomics into clinical care, and the limitations of this newly employed analytical technique.

KEY CONTENT AND FINDINGS

Untargeted metabolomic investigations are increasingly utilized in screening for rare disorders, improving understanding of cellular and subcellular physiology, discovering novel biomarkers, monitoring therapy, and functionally validating genomic variants. Although the untargeted metabolomic approach has some limitations, this "next generation metabolic screening" platform is becoming increasingly affordable and accessible.

CONCLUSIONS

When used in conjunction with genomics and the other promising "-omic" technologies, untargeted metabolomics has the potential to revolutionize the diagnostics of IEMs (and other rare disorders), improving both clinical and health economic outcomes.

Blood biomarkers for assessment of mitochondrial dysfunction: An expert review

Although mitochondrial dysfunction is the known cause of primary mitochondrial disease, mitochondrial dysfunction is often difficult to measure and prove, especially when biopsies of affected tissue are not available. In order to identify blood biomarkers of mitochondrial dysfunction, we reviewed studies that measured blood biomarkers in genetically, clinically or biochemically confirmed primary mitochondrial disease patients. In this way, we were certain that there was an underlying mitochondrial dysfunction which could validate the biomarker. We found biomarkers of three classes: 1) functional markers measured in blood cells, 2) biochemical markers of serum/plasma and 3) DNA markers. While none of the reviewed single biomarkers may perfectly reveal all underlying mitochondrial dysfunction, combining biomarkers that cover different aspects of mitochondrial impairment probably is a good strategy. This biomarker panel may assist in the diagnosis of primary mitochondrial disease patients. As mitochondrial dysfunction may also play a significant role in the pathophysiology of multifactorial disorders such as Alzheimer's disease and glaucoma, the panel may serve to assess mitochondrial dysfunction in complex multifactorial diseases as well and enable selection of patients who could benefit from therapies targeting mitochondria.

Genetic basis of mitochondrial diseases

Mitochondrial disorders are monogenic disorders characterized by a defect in oxidative phosphorylation and caused by pathogenic variants in one of over 340 different genes. The implementation of whole-exome sequencing has led to a revolution in their diagnosis, duplicated the number of associated disease genes, and significantly increased the diagnosed fraction. However, the genetic etiology of a substantial fraction of patients exhibiting mitochondrial disorders remains unknown, highlighting limitations in variant detection and interpretation, which calls for improved computational and DNA sequencing methods, as well as the addition of OMICS tools. More intriguingly, this also suggests that some pathogenic variants lie outside of the protein-coding genes and that the mechanisms beyond the Mendelian inheritance and the mtDNA are of relevance. This review covers the current status of the genetic basis of mitochondrial diseases, discusses current challenges and perspectives, and explores the contribution of factors beyond the protein-coding regions and monogenic inheritance in the expansion of the genetic spectrum of disease.

Update Review about Metabolic Myopathies

The aim of this review is to summarize and discuss recent findings and new insights in the etiology and phenotype of metabolic myopathies. The review relies on a systematic literature review of recent publications. Metabolic myopathies are a heterogeneous group of disorders characterized by mostly inherited defects of enzymatic pathways involved in muscle cell metabolism. Metabolic myopathies present with either permanent (fixed) or episodic abnormalities, such as weakness, wasting, exercise-intolerance, myalgia, or an increase of muscle breakdown products (creatine-kinase, myoglobin) during exercise. Though limb and respiratory muscles are most frequently affected, facial, extra-ocular, and axial muscles may be occasionally also involved. Age at onset and prognosis vary considerably. There are multiple disease mechanisms and the pathophysiology is complex. Genes most recently related to metabolic myopathy include PGM1, GYG1, RBCK1, VMA21, MTO1, KARS, and ISCA2. The number of metabolic myopathies is steadily increasing. There is limited evidence from the literature that could guide diagnosis and treatment of metabolic myopathies. Treatment is limited to mainly non-invasive or invasive symptomatic measures. In conclusion, the field of metabolic myopathies is evolving with the more widespread availability and application of next generation sequencing technologies worldwide. This will broaden the knowledge about pathophysiology and putative therapeutic strategies for this group of neuromuscular disorders.

Bioinformatics Tools and Databases to Assess the Pathogenicity of Mitochondrial DNA Variants in the Field of Next Generation Sequencing

The development of next generation sequencing (NGS) has greatly enhanced the diagnosis of mitochondrial disorders, with a systematic analysis of the whole mitochondrial DNA (mtDNA) sequence and better detection sensitivity. However, the exponential growth of sequencing data renders complex the interpretation of the identified variants, thereby posing new challenges for the molecular diagnosis of mitochondrial diseases. Indeed, mtDNA sequencing by NGS requires specific bioinformatics tools and the adaptation of those developed for nuclear DNA, for the detection and quantification of mtDNA variants from sequence alignment to the calling steps, in order to manage the specific features of the mitochondrial genome including heteroplasmy, i.e., coexistence of mutant and wildtype mtDNA copies. The prioritization of mtDNA variants remains difficult, relying on a limited number of specific resources: population and clinical databases, and in silico tools providing a prediction of the variant pathogenicity. An evaluation of the most prominent bioinformatics tools showed that their ability to predict the pathogenicity was highly variable indicating that special efforts should be directed at developing new bioinformatics tools dedicated to the mitochondrial genome. In addition, massive parallel sequencing raised several issues related to the interpretation of very low mtDNA mutational loads, discovery of variants of unknown significance, and mutations unrelated to patient phenotype or the co-occurrence of mtDNA variants. This review provides an overview of the current strategies and bioinformatics tools for accurate annotation, prioritization and reporting of mtDNA variations from NGS data, in order to carry out accurate genetic counseling in individuals with primary mitochondrial diseases.