Survey of human mitochondrial diseases using new genomic/proteomic tools
1 BioMolecular Engineering Research Center and Department of Pharmacology, Boston University School of Medicine, 80 E Concord Street, Boston, MA 02118, USA
2 BioMolecular Engineering Research Center, Boston University College of Engineering, 36 Cummington Street, Boston, MA 02215, USA
3 BioMolecular Engineering Research Center and Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, MA 02215, USA
Genome Biology 2001, 2:research0021-research0021.16 doi:10.1186/gb-2001-2-6-research0021Published: 1 June 2001
We have constructed Bayesian prior-based, amino-acid sequence profiles for the complete yeast mitochondrial proteome and used them to develop methods for identifying and characterizing the context of protein mutations that give rise to human mitochondrial diseases. (Bayesian priors are conditional probabilities that allow the estimation of the likelihood of an event - such as an amino-acid substitution - on the basis of prior occurrences of similar events.) Because these profiles can assemble sets of taxonomically very diverse homologs, they enable identification of the structurally and/or functionally most critical sites in the proteins on the basis of the degree of sequence conservation. These profiles can also find distant homologs with determined three-dimensional structures that aid in the interpretation of effects of missense mutations.
This survey reports such an analysis for 15 missense mutations, one insertion and three deletions involved in Leber's hereditary optic neuropathy, Leigh syndrome, mitochondrial neurogastrointestinal encephalomyopathy, Mohr-Tranebjaerg syndrome, iron-storage disorders related to Friedreich's ataxia, and hereditary spastic paraplegia. We present structural correlations for seven of the mutations.
Of the 19 mutations analyzed, 14 involved changes in very highly conserved parts of the affected proteins. Five out of seven structural correlations provided reasonable explanations for the malfunctions. As additional genetic and structural data become available, this methodology can be extended. It has the potential for assisting in identifying new disease-related genes. Furthermore, profiles with structural homologs can generate mechanistic hypotheses concerning the underlying biochemical processes - and why they break down as a result of the mutations.