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Comparative sequence analyses of rhodopsin and RPE65 reveal patterns of selective constraint across hereditary retinal disease mutations

Published online by Cambridge University Press:  11 January 2016

FRANCES E. HAUSER ,
RYAN K. SCHOTT ,
GIANNI M. CASTIGLIONE ,
ALEXANDER VAN NYNATTEN ,
ALEXANDER KOSYAKOV ,
PORTIA L. TANG ,
DANIEL A. GOW  and
BELINDA S.W. CHANG
FRANCES E. HAUSER
Affiliation:
Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
RYAN K. SCHOTT
Affiliation:
Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
GIANNI M. CASTIGLIONE
Affiliation:
Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
ALEXANDER VAN NYNATTEN
Affiliation:
Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
ALEXANDER KOSYAKOV
Affiliation:
Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
PORTIA L. TANG
Affiliation:
Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
DANIEL A. GOW
Affiliation:
Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
BELINDA S.W. CHANG*
Affiliation:
Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, Ontario, Canada
*
*Address correspondence to: Dr. Belinda Chang, Department of Cell & Systems Biology, Department of Ecology & Evolutionary Biology, University of Toronto, 25 Harbord St, Toronto, Ontario M5S 3G5, Canada. E-mail: belinda.chang@utoronto.ca
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Abstract

Retinitis pigmentosa (RP) comprises several heritable diseases that involve photoreceptor, and ultimately retinal, degeneration. Currently, mutations in over 50 genes have known links to RP. Despite advances in clinical characterization, molecular characterization of RP remains challenging due to the heterogeneous nature of causal genes, mutations, and clinical phenotypes. In this study, we compiled large datasets of two important visual genes associated with RP: rhodopsin, which initiates the phototransduction cascade, and the retinoid isomerase RPE65, which regenerates the visual cycle. We used a comparative evolutionary approach to investigate the relationship between interspecific sequence variation and pathogenic mutations that lead to degenerative retinal disease. Using codon-based likelihood methods, we estimated evolutionary rates (d N/d S) across both genes in a phylogenetic context to investigate differences between pathogenic and nonpathogenic amino acid sites. In both genes, disease-associated sites showed significantly lower evolutionary rates compared to nondisease sites, and were more likely to occur in functionally critical areas of the proteins. The nature of the dataset (e.g., vertebrate or mammalian sequences), as well as selection of pathogenic sites, affected the differences observed between pathogenic and nonpathogenic sites. Our results illustrate that these methods can serve as an intermediate step in understanding protein structure and function in a clinical context, particularly in predicting the relative pathogenicity (i.e., functional impact) of point mutations and their downstream phenotypic effects. Extensions of this approach may also contribute to current methods for predicting the deleterious effects of candidate mutations and to the identification of protein regions under strong constraint where we expect pathogenic mutations to occur.

Keywords

Molecular evolutionRetinitis pigmentosaMolecular phylogeneticsRhodopsinRPE65PhylomedicineLikelihood-based codon models of evolution
Type
Research Article
Information
Visual Neuroscience , Volume 33 , 2016 , E002
Copyright
Copyright © Cambridge University Press 2016 

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