Animal inference on human mitochondrial diseases. (June 2016)

• Record Type: Journal Article
• Authors: Nardi, Francesco; Frati, Francesco; Liò, Pietro
• Is Part Of: Computational biology and chemistry. Volume 62(2016)
• Page Start: 17
• Page End: 28
• Publication Date: 2016-06
• Subjects: Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2016.02.002
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: Several pathological mutations in the human mitochondrial genome have been characterized based on medical, genetic and biochemical evidence. The observation that the structure and core functions of the mitochondrial genome are conserved from animals to man suggests that the analysis of animal variation may be informative to further characterize, and possibly predict, human pathological variants. We studied the distribution of sequence site-wise diversity and structural heterogeneity (based on several scales of hydrophobicity and supercomplex classification of mitochondrial genes) at different taxonomic levels in ∼15, 000 human and animal genomes. We found that human pathological mutations tend to lay in regions of low diversity and that states that are pathological in humans appear to be extremely rare in animals, with two noticeable exceptions (T10663C and C14568T). Focusing on hydrophobicity, as possibly the most general site-wise functional parameter of a protein, we deploy the observed range of hydrophobicity in mammals as a proxy for the range of permissible states compatible with an efficient functioning of the mitochondrial machinery. We show that, while non pathological human variants tend to fall within the hypothesized range, pathological mutations generally fall outside this range. We further analyzed this distribution quantitatively to show that the estimated probability of observed states can indeed be used to predict the pathogenicity of a mutation in humans. This study provides a proof of principle that animal data can indeed be informative to predict the pathogenicity of a human mutation alongside, or in the absence of, additional evidence.