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Imaging and Quantifying Mitochondrial Morphology in C. elegans During Aging
Published on: January 17, 2025
Mitochondrial Genomics: A complex field now coming of age
Elizabeth M McCormick1, Colleen C Muraresku1, Marni J Falk1,2
1Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
Purpose Of Review:
The groundwork for mitochondrial medicine was laid 30 years ago with identification of the first disease-causing mitochondrial DNA (mtDNA) mutations in 1988. Three decades later, mutations in nearly 300 genes involving every possible mode of inheritance within both nuclear and mitochondrial genomes are now recognized to collectively comprise the largest class of inherited metabolic disease affecting at least 1 in 4,300 individuals across all ages. Significant progress has been made in recent years to improve understanding of mitochondrial biology and disease pathophysiology.
Recent Findings:
Markedly improved understanding of the highly diverse molecular etiologies of multi-systemic phenotypes in primary mitochondrial disease has resulted from massively parallel genomic sequencing technologies and improved bioinformatic resources that enable identification in individual patients of their disease's precise genetic etiology. Key informatics resources of particular utility to the mitochondrial disease genomics community have been developed, including: (1) Mitocarta 2.0 repository of 1200+ verified mitochondria-localized proteins, (2) MITOMAP Web resource of curated mtDNA genome variants, and (3) Mitochondrial Disease Sequence Data Resource (MSeqDR) that centralizes Web curation and annotation of mitochondrial disease genes and variants in both genomes, ontology-defined phenotypes, and access to many analytic tools to support genomic data mining and interpretation. Gene and mutation-based disease categorization has proven particularly useful to identify the full clinical spectrum of disease that may affect a given individual.
Summary:
Extensive genomic advances, both in technologic platforms and bioinformatics resources, have facilitated dramatic improvement in the accurate recognition and understanding of primary mitochondrial disease.
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