Integrated transcriptome analysis across mitochondrial disease etiologies and tissues improves understanding of common cellular adaptations to respiratory chain dysfunction.

Mitochondrial diseases are heterogeneous, multi-systemic disorders for which mechanistic understanding is limited. To investigate common downstream effects of primary respiratory chain dysfunction on global gene expression and pathway regulation, we reanalyzed transcriptome datasets from all publicly available studies of respiratory chain dysfunction resulting from genetic disorders, acute pathophysiologic processes, or environmental toxins. A general overview is provided of the bioinformatic processing of transcriptome data to uncover biological insights into in vivo and in vitro adaptations to mitochondrial dysfunction, with specific examples discussed from a variety of independent cell, animal, and human tissue studies. To facilitate future community efforts to cohesively mine these data, all reanalyzed transcriptome datasets were deposited into a publicly accessible central web archive. Our own integrated meta-analysis of these data identified several commonly dysregulated genes across diverse mitochondrial disease etiologies, models, and tissue types. Overall, transcriptome analyses provide a useful means to survey cellular adaptation to mitochondrial diseases.