Genetics of mitochondrial diseases: Identifying mutations to help diagnosis

Abstract

Mitochondrial diseases are amongst the most genetically and phenotypically diverse groups of inherited diseases. The vast phenotypic overlap with other disease entities together with the absence of reliable biomarkers act as driving forces for the integration of unbiased methodologies early in the diagnostic algorithm, such as whole exome sequencing (WES) and whole genome sequencing (WGS). Such approaches are used in variant discovery and in combination with high-throughput functional assays such as transcriptomics in simultaneous variant discovery and validation. By capturing all genes, they not only increase the diagnostic rate in heterogenous mitochondrial disease patients, but accelerate novel disease gene discovery, and are valuable in side-stepping the risk of overlooking unexpected or even treatable genetic disease diagnoses.

Keywords: Diagnostic; Genetic; Mitochondrial disease; Multi-omic; Mutation.

Fig. 1

Mitochondrial disease genes. Mitochondrial disease genes (338) divided into six subsets according to their functional roles: (1) OXPHOS subunits, assembly factors, and electron carriers (102/338 genes), (2) mitochondrial DNA maintenance, expression, and translation (102/338 genes), (3) mitochondrial dynamics, homoeostasis, and quality control (43/338 genes), (4) metabolism of substrates (40/338 genes), (5) metabolism of cofactors (41/338 genes), and (6) metabolism of toxic compounds (10/338 genes). Numerous genes have dual roles across these categories (indicated by an asterisk). The mode of inheritance is autosomal recessive in 262 genes, maternal in 36, autosomal dominant in 8, X-linked dominant in 6, X-linked recessive in 4, and 22 genes inherited by a combination of AR and AD inheritance.

Fig. 2

Methods of disease gene discovery. Demonstration of the shift from mt-DNA and candidate gene sequencing to NGS, marked by the dashed lined in 2010, and reflected by an acceleration in disease gene discovery. Studies marked with an asterisk include a combination of linkage analysis, homozygosity mapping, and candidate gene sequencing.

Fig. 3

Diagnostic rate of WES in suspected mitochondrial disease. WES studies of greater cohort size typically encompass more heterogenous cohorts and show a trend towards reduced diagnostic rate. They are, however more reflective of every day clinical practice.

Fig. 4

Evolution of the diagnostic approach. Schematic comparing a conventional phenotype driven approach to molecular diagnostics, where deep phenotyping of the patient in combination with invasive muscle biopsy determines choice of genetic investigation, to the unbiased approaches of WES and WGS. Variants of uncertain significance (VUS) resulting from the analyses require functional validation and often necessitate patient materials such as a biopsy (B) or cell line (C), optimally undertaken in the affected tissue. Beyond the global “omic” methodologies, depicted here are a selection of assays capable of capturing the consequence of substantial subgroups of mitochondrial disease genes.

Fig. 5

Integration of transcriptomic data with genetic data. Tissue selection in transcriptomics influences disease gene detection. Over 80% of the mitochondrial disease genes and 50%–75% of OMIM disease genes are captured across a range of different tissues. In the captured transcripts, aberrant splicing, aberrant expression, and allelic imbalance may be analysed. Of 46 identified pathogenic variants in diagnostic studies to date (Table 2), 40 induce aberrant splice events. The majority of these events were pseudoexon creation or intron retention resulting from deep intronic variants, and exon creation or exon skipping as a result of splice region variants.