Browse by author
Lookup NU author(s): Dr Joanna Elson, Dr Paul Smith
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
Despite the identification of a large number of potentially pathogenic variants in the mitochondrially encoded rRNA (mt-rRNA) genes, we lack direct methods to firmly establish their pathogenicity. In the absence of such methods, we have devised an indirect approach named heterologous inferential analysis or HIA that can be used to make predictions on the disruptive potential of a large subset of mt-rRNA variants. First, due to the high evolutionary conservation of the rRNA fold, comparison of phylogenetically derived secondary structures of the human mt-rRNAs and those from model organisms allows the location of structurally equivalent residues. Second, visualization of the heterologous equivalent residue in high-resolution structures of the ribosome allows a preliminary structural characterization of the residue and its neighboring region. Third, an exhaustive search for biochemical and genetic information on the residue and its surrounding region is performed to understand their degree of involvement in ribosomal function. Additional rounds of visualization in biochemically relevant high-resolution structures will lead to the structural and functional characterization of the residue’s role in ribosomal function and to an assessment of the disruptive potential of mutations at this position. Notably, in the case of certain mitochondrial variants for which sufficient information regarding their genetic and pathological manifestation is available; HIA data alone can be used to predict their pathogenicity. In other cases, HIA will serve to prioritize variants for additional investigation. In the context of a scoring system specifically designed for these variants, HIA could lead to a powerful diagnostic tool.
Author(s): Elson JL, Smith PM, Vila-Sanjurjo A
Publication type: Article
Publication status: Published
Journal: Methods in Molecular Biology
Year: 2015
Volume: 1264
Pages: 369-383
Print publication date: 06/01/2015
ISSN (print): 1064-3745
Publisher: Springer New York
URL: http://dx.doi.org/10.1007/978-1-4939-2257-4_32
DOI: 10.1007/978-1-4939-2257-4_32
Altmetrics provided by Altmetric