Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-21T23:34:37.179Z Has data issue: false hasContentIssue false
  • English
  • Français

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: [email protected]
Get access Rights & Permissions [Opens in a new window]

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adzhubei, I.A., Schmidt, S., Peshkin, L., Ramensky, V.E., Gerasimova, A., Bork, P., Kondrashov, A.S. & Sunyaev, S.R. (2010). A method and server for predicting damaging missense mutations. Nature Methods 7, 248–249.Google Scholar
Anasagasti, A., Irigoyen, C., Barandika, O., López de Munain, A. & Ruiz-Ederra, J. (2012). Current mutation discovery approaches in retinitis pigmentosa. Vision Research 75, 117–129.Google Scholar
Anisimova, M. & Gascuel, O. (2006). Approximate likelihood-ratio test for branches: A fast, accurate, and powerful alternative. Systematic Biology 55, 539–552.Google Scholar
Bereta, G., Kiser, P.D., Golczak, M., Sun, W., Heon, E., Saperstein, D.A. & Palczewski, K. (2008). Impact of retinal disease-associated RPE65 mutations on retinoid isomerization. Biochemistry 47, 9856–9865.Google Scholar
Bowne, S.J., Sullivan, L.S., Koboldt, D.C., Ding, L., Fulton, R., Abbott, R.M., Sodergren, E.J., Birch, D.G., Wheaton, D.H., Heckenlively, J.R., Liu, Q., Pierce, E.A., Weinstock, G.M. & Daiger, S.P. (2011). Identification of disease-causing mutations in autosomal dominant retinitis pigmentosa (adRP) using next-generation DNA sequencing. Investigative Ophthalmology & Visual Science 52, 494–503.Google Scholar
Breikers, G., Portier-VandeLuytgaarden, M.J.M., Bovee-Geurts, P.H.M. & DeGrip, W.J. (2002). Retinitis pigmentosa-associated rhodopsin mutations in three membrane-located cysteine residues present three different biochemical phenotypes. Biochemical and Biophysical Research Communications 297, 847–853.Google Scholar
Briscoe, A.D., Gaur, C. & Kumar, S. (2004). The spectrum of human rhodopsin disease mutations through the lens of interspecific variation. Gene 332, 107–118.Google Scholar
Burk-Herrick, A., Scally, M., Amrine-Madsen, H., Stanhope, M.J. & Springer, M.S. (2006). Natural selection and mammalian BRCA1 sequences: Elucidating functionally important sites relevant to breast cancer susceptibility in humans. Mammalian Genome 17, 257–270.Google Scholar
Castellana, S., Rónai, J. & Mazza, T. (2015). MitImpact: An exhaustive collection of pre-computed pathogenicity predictions of human mitochondrial non-synonymous variants. Human Mutation 36, E2413–E2422.Google Scholar
Choe, H-W., Kim, Y.J., Park, J.H., Morizumi, T., Pai, E.F., Krauß, N., Hofmann, K.P., Scheerer, P. & Ernst, O.P. (2011). Crystal structure of metarhodopsin II. Nature 471, 651–655.Google Scholar
Choi, Y., Sims, G.E., Murphy, S., Miller, J.R. & Chan, A.P. (2012). Predicting the functional effect of amino acid substitutions and indels. PLoS One 7, e46688.Google Scholar
Cideciyan, A.V. (2010). Leber congenital amaurosis due to RPE65 mutations and its treatment with gene therapy. Progress in Retinal and Eye Research 29, 398–427.Google Scholar
Cooper, G.M., Brudno, M.; NISC Comparative Sequencing Program, Green, E.D., Batzoglou, S. & Sidow, A. (2003). Quantitative estimates of sequence divergence for comparative analyses of mammalian genomes. Genome Research 13, 813–820.Google Scholar
Crottini, A., Madsen, O. & Poux, C. (2012). Vertebrate time-tree elucidates the biogeographic pattern of a major biotic change around the K–T boundary in Madagascar. Proceedings of the National Academy of Sciences of the United States of America 109, 5358–5363.CrossRefGoogle Scholar
Delport, W., Poon, A.F.Y., Frost, S.D.W. & Kosakovsky Pond, S.L. (2010). Datamonkey 2010: A suite of phylogenetic analysis tools for evolutionary biology. Bioinformatics 26, 2455–2457.Google Scholar
Dimitrieva, S. & Anisimova, M. (2014). Unraveling patterns of site-to-site synonymous rates variation and associated gene properties of protein domains and families. PLoS One 9, e95034.Google Scholar
Du, J., Dungan, S.Z., Sabouhanian, A. & Chang, B.S.W. (2014). Selection on synonymous codons in mammalian rhodopsins: A possible role in optimizing translational processes. BMC Evolutionary Biology 14, 96.Google Scholar
Ferrari, S., Di Iorio, E., Barbaro, V., Ponzin, D., Sorrentino, F.S. & Parmeggiani, F. (2011). Retinitis pigmentosa: Genes and disease mechanisms. Current Genomics 12, 238–249.Google Scholar
Flanagan, S.E., Patch, A-M. & Ellard, S. (2010). Using SIFT and PolyPhen to predict loss-of-function and gain-of-function mutations. Genetic Testing and Molecular Biomarkers 14, 533–537.Google Scholar
Fong, J.J., Brown, J.M., Fujita, M.K. & Boussau, B. (2012). A phylogenomic approach to vertebrate phylogeny supports a turtle-archosaur affinity and a possible paraphyletic lissamphibia. PLoS One 7, e48990.Google Scholar
Gaucher, E.A., De Kee, D.W. & Benner, S.A. (2006). Application of DETECTER, an evolutionary genomic tool to analyze genetic variation, to the cystic fibrosis gene family. BMC Genomics 7, 44.Google Scholar
Greenblatt, M.S., Beaudet, J.G., Gump, J.R., Godin, K.S., Trombley, L., Koh, J. & Bond, J.P. (2003). Detailed computational study of p53 and p16: Using evolutionary sequence analysis and disease-associated mutations to predict the functional consequences of allelic variants. Oncogene 22, 1150–1163.Google Scholar
Guindon, S., Dufayard, J.F., Lefort, V., Anisimova, M., Hordijk, W. & Gascuel, O. (2010). New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Systematic Biology 59, 307–321.Google Scholar
Hamosh, A. (2005). Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Research 33, D514–D517.Google Scholar
Hartong, D.T., Berson, E.L. & Dryja, T.P. (2006). Retinitis pigmentosa. Lancet 368, 1795–1809.Google Scholar
Hicks, S., Wheeler, D.A., Plon, S.E. & Kimmel, M. (2011). Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed. Human Mutation 32, 661–668.Google Scholar
Hollingsworth, T.J. & Gross, A.K. (2013). The severe autosomal dominant retinitis pigmentosa rhodopsin mutant Ter349Glu mislocalizes and induces rapid rod cell death. Journal of Biological Chemistry 288, 29047–29055.Google Scholar
Iannaccone, A., Man, D., Waseem, N., Jennings, B.J., Ganapathiraju, M., Gallaher, K., Reese, E., Bhattacharya, S.S. & Klein-Seetharaman, J. (2006). Retinitis pigmentosa associated with rhodopsin mutations: Correlation between phenotypic variability and molecular effects. Vision Research 46, 4556–4567.CrossRefGoogle ScholarPubMed
Janz, J.M., Fay, J.F. & Farrens, D.L. (2003). Stability of dark state rhodopsin is mediated by a conserved ion pair in intradiscal loop E-2. Journal of Biological Chemistry 278, 16982–16991.Google Scholar
Kirwan, J.D., Bekaert, M., Commins, J.M., Davies, K.T.J., Rossiter, S.J. & Teeling, E.C. (2013). A phylomedicine approach to understanding the evolution of auditory sensory perception and disease in mammals. Evolutionary Applications 6, 412–422.Google Scholar
Kiser, P.D. & Palczewski, K. (2010). Membrane-binding and enzymatic properties of RPE65. Progress in Retinal and Eye Research 29, 428–442.Google Scholar
Kiser, P.D., Golczak, M., Lodowski, D.T., Chance, M.R. & Palczewski, K. (2009). Crystal structure of native RPE65, the retinoid isomerase of the visual cycle. Proceedings of the National Academy of Sciences of the United States of America 106, 17325–17330.Google Scholar
Kosakovsky Pond, S.L. (2005). Not so different after all: A comparison of methods for detecting amino acid sites under selection. Molecular Biology and Evolution 22, 1208–1222.Google Scholar
Kosakovsky Pond, S.L., Frost, S.D.W. & Muse, S.V. (2005). HyPhy: Hypothesis testing using phylogenies. Bioinformatics 21, 676–679.Google Scholar
Kumar, S., Dudley, J.T., Filipski, A. & Liu, L. (2011). Phylomedicine: An evolutionary telescope to explore and diagnose the universe of disease mutations. Trends in Genetics 27, 377–386.Google Scholar
Li, S., Hu, J., Jin, R.J., Aiyar, A., Jacobson, S.G., Bok, D. & Jin, M. (2015). Temperature-sensitive retinoid isomerase activity of RPE65 mutants associated with Leber Congenital Amaurosis. Journal of Biochemistry 158, 115–125.Google Scholar
Li, S., Izumi, T., Hu, J., Jin, H.H., Siddiqui, A-A.A., Jacobson, S.G., Bok, D. & Jin, M. (2014). Rescue of enzymatic function for disease-associated RPE65 proteins containing various missense mutations in non-active sites. Journal of Biological Chemistry 289, 18943–18956.Google Scholar
Li, W.H., Wu, C.I. & Luo, C.C. (1985). A new method for estimating synonymous and nonsynonymous rates of nucleotide substitution considering the relative likelihood of nucleotide and codon changes. Molecular Biology and Evolution 2, 150–174.Google Scholar
Liu, M.Y., Liu, J., Mehrotra, D., Liu, Y., Guo, Y., Baldera-Aguayo, P.A., Mooney, V.L., Nour, A.M. & Yan, E.C.Y. (2013). Thermal stability of rhodopsin and progression of retinitis pigmentosa: Comparison of S186W and D190N rhodopsin mutants. Journal of Biological Chemistry 288, 17698–17712.Google Scholar
Löytynoja, A. & Goldman, N. (2005). An algorithm for progressive multiple alignment of sequences with insertions. Proceedings of the National Academy of Sciences of the United States of America 102, 10557–10562.Google Scholar
Löytynoja, A. & Goldman, N. (2008). Phylogeny-aware gap placement prevents errors in sequence alignment and evolutionary analysis. Science 320, 1632–1635.Google Scholar
Magrane, M. & Uniprot Consortium (2011). UniProt Knowledgebase: A hub of integrated protein data. Database 2011, bar009.Google Scholar
McKeone, R., Wikstrom, M., Kiel, C. & Rakoczy, P.E. (2014). Assessing the correlation between mutant rhodopsin stability and the severity of retinitis pigmentosa. Molecular Vision 20, 183–199.Google Scholar
Mendes, H.F., van der Spuy, J., Chapple, J.P. & Cheetham, M.E. (2005). Mechanisms of cell death in rhodopsin retinitis pigmentosa: Implications for therapy. Trends in Molecular Medicine 11, 177–185.Google Scholar
Meredith, R.W., Janečka, J.E., Gatesy, J., Ryder, O.A., Fisher, C.A., Teeling, E.C., Goodbla, A., Eizirik, E., Simão, T.L., Stadler, T., Rabosky, D.L., Honeycutt, R.L., Flynn, J.J., Ingram, C.M., Steiner, C., Williams, T.L., Robinson, T.J., Burk-Herrick, A., Westerman, M., Ayoub, N.A., Springer, M.S. & Murphy, W.J. (2011). Impacts of the cretaceous terrestrial revolution and KPg extinction on mammal diversification. Science 334, 521–524.Google Scholar
Miller, M.P. & Kumar, S. (2001). Understanding human disease mutations through the use of interspecific genetic variation. Human Molecular Genetics 10, 2319–2328.Google Scholar
Moiseyev, G., Chen, Y., Takahashi, Y., Wu, B.X. & Ma, J-X. (2005). RPE65 is the isomerohydrolase in the retinoid visual cycle. Proceedings of the National Academy of Sciences of the United States of America 102, 12413–12418.Google Scholar
Mooney, S.D. & Klein, T.E. (2002). The functional importance of disease-associated mutation. BMC Bioinformatics 3, 24.Google Scholar
Morgan, C.C., Mc Cartney, A.M., Donoghue, M.T.A., Loughran, N.B., Spillane, C., Teeling, E.C. & O’Connell, M.J. (2013). Molecular adaptation of telomere associated genes in mammals. BMC Evolutionary Biology 13, 251.Google Scholar
Morrow, J.M. & Chang, B.S.W. (2015). Comparative mutagenesis studies of retinal release in light-activated zebrafish rhodopsin using fluorescence spectroscopy. Biochemistry 54, 4507–4518.Google Scholar
Murrell, B., Moola, S., Mabona, A., Weighill, T., Sheward, D., Kosakovsky Pond, S.L. & Scheffler, K. (2013). FUBAR: A fast, unconstrained bayesian approximation for inferring selection. Molecular Biology and Evolution 30, 1196–1205.Google Scholar
Neidhardt, J., Barthelmes, D., Farahmand, F., Fleischhauer, J.C. & Berger, W. (2006). Different amino acid substitutions at the same position in rhodopsin lead to distinct phenotypes. Investigative Ophthalmology & Visual Science 47, 1630–1635.Google Scholar
Neveling, K., Collin, R.W., Gilissen, C., van Huet, R.A., Visser, L., Kwint, M.P., Gijsen, S.J., Zonneveld, M.N., Wieskamp, N., de Ligt, J., Siemiatkowska, A.M., Hoefsloot, L.H., Buckley, M.F., Kellner, U., Branham, K.E., den Hollander, A.I., Hoischen, A., Hoyng, C., Klevering, B.J., van den Born, L.I., Veltman, J.A., Cremers, F.P. & Scheffer, H. (2012). Next-generation genetic testing for retinitis pigmentosa. Human Mutation 33, 963–972.Google Scholar
Ng, P.C. & Henikoff, S. (2003). SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Research 31, 3812–3814.Google Scholar
Nishiguchi, K.M., Tearle, R.G., Liu, Y.P., Oh, E.C., Miyake, N., Benaglio, P., Harper, S., Koskiniemi-Kuendig, H., Venturini, G., Sharon, D., Koenekoop, R.K., Nakamura, M., Kondo, M., Ueno, S., Yasuma, T.R., Beckmann, J.S., Ikegawa, S., Matsumoto, N., Terasaki, H., Berson, E.L., Katsanis, N. & Rivolta, C. (2013). Whole genome sequencing in patients with retinitis pigmentosa reveals pathogenic DNA structural changes and NEK2 as a new disease gene. Proceedings of the National Academy of Sciences of the United States of America 110, 16139–16144.CrossRefGoogle ScholarPubMed
Okada, T., Sugihara, M., Bondar, A-N., Elstner, M., Entel, P. & Buss, V. (2004). The retinal conformation and its environment in rhodopsin in light of a new 2.2Å crystal structure. Journal of Molecular Biology 342, 571–583.Google Scholar
Opefi, C.A., South, K., Reynolds, C.A., Smith, S.O. & Reeves, P.J. (2013). Retinitis pigmentosa mutants provide insight into the role of the N-terminal cap in rhodopsin folding, structure, and function. Journal of Biological Chemistry 288, 33912–33926.Google Scholar
Palczewski, K., Kumasaka, T., Hori, T., Behnke, C.A., Motoshima, H., Fox, B.A., Le Trong, I., Teller, D.C., Okada, T., Stenkamp, R.E., Yamamoto, M. & Miyano, M. (2000). Crystal structure of rhodopsin: A G protein-coupled receptor. Science 289, 739–745.Google Scholar
Pettersen, E.F., Goddard, T.D., Huang, C.C., Couch, G.S., Greenblatt, D.M., Meng, E.C. & Ferrin, T.E. (2004). UCSF Chimera—A visualization system for exploratory research and analysis. Journal of Computational Chemistry 25, 1605–1612.CrossRefGoogle ScholarPubMed
Philpa, A.R., Jin, M., Li, S., Schindler, E.I., Iannaccone, A., Lam, B.L., Weleber, R.G., Fishman, G.A., Jacobson, S.G., Mullins, R.F., Travis, G.H. & Stone, E.M. (2009). Predicting the pathogenicity of RPE65 mutations. Human Mutation 30, 1183–1188.CrossRefGoogle Scholar
Piechnick, R., Ritter, E., Hildebrand, P.W., Ernst, O.P., Scheerer, P., Hofmann, K.P. & Heck, M. (2012). Effect of channel mutations on the uptake and release of the retinal ligand in opsin. Proceedings of the National Academy of Sciences of the United States of America 109, 5247–5252.Google Scholar
Pierrottet, C.O., Zuntini, M., Digiuni, M., Bazzanella, I., Ferri, P., Paderni, R., Rossetti, L.M., Cecchin, S., Orzalesi, N. & Bertelli, M. (2014). Syndromic and non-syndromic forms of retinitis pigmentosa: A comprehensive Italian clinical and molecular study reveals new mutations. Genetics and Molecular Research 13, 8815–8833.CrossRefGoogle ScholarPubMed
Plotkin, J.B. & Kudla, G. (2011). Synonymous but not the same: The causes and consequences of codon bias. Nature Reviews Genetics 12, 32–42.Google Scholar
Pope, A., Eilers, M., Reeves, P.J. & Smith, S.O. (2014). Amino acid conservation and interactions in rhodopsin: Probing receptor activation by NMR spectroscopy. Biochimica et Biophysica Acta 1837, 683–693.Google Scholar
Porter, M.L., Blasic, J.R., Bok, M.J., Cameron, E.G., Pringle, T., Cronin, T.W. & Robinson, P.R. (2011). Shedding new light on opsin evolution. Proceedings of the Royal Society B: Biological Sciences 279, 3–14.Google Scholar
Redmond, T.M., Poliakov, E., Yu, S., Tsai, J-Y., Lu, Z. & Gentleman, S. (2005). Mutation of key residues of RPE65 abolishes its enzymatic role as isomerohydrolase in the visual cycle. Proceedings of the National Academy of Sciences of the United States of America 102, 13658–13663.Google Scholar
Rishishwar, L., Varghese, N., Tyagi, E., Harvey, S.C., Jordan, I.K. & McCarty, N.A. (2012). Relating the disease mutation spectrum to the evolution of the Cystic Fibrosis transmembrane conductance regulator (CFTR). PLoS One 7, e42336.Google Scholar
Rivolta, C., Sharon, D., DeAngelis, M.M. & Dryja, T.P. (2002). Retinitis pigmentosa and allied diseases: Numerous diseases, genes, and inheritance patterns. Human Molecular Genetics 11, 1219–1227.Google Scholar
Ronquist, F. & Huelsenbeck, J.P. (2003). MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574.Google Scholar
Sauna, Z.E. & Kimchi-Sarfaty, C. (2011). Understanding the contribution of synonymous mutations to human disease. Nature Reviews Genetics 12, 683–691.Google Scholar
Sauna, Z.E., Kimchi-Sarfaty, C., Ambudkar, S.V. & Gottesman, M.M. (2007). Silent polymorphisms speak: How they affect pharmacogenomics and the treatment of cancer. Cancer Research 67, 9609–9612.Google Scholar
Scheffler, K., Martin, D.P. & Seoighe, C. (2006). Robust inference of positive selection from recombining coding sequences. Bioinformatics 22, 2493–2499.Google Scholar
Schott, R.K., Refvik, S.P., Hauser, F.E., López-Fernández, H. & Chang, B.S.W. (2014). Divergent positive selection in rhodopsin from lake and riverine cichlid fishes. Molecular Biology and Evolution 31, 1149–1165.Google Scholar
Sherry, S.T., Ward, M.H., Kholodov, M., Baker, J., Phan, L., Smigielski, E.M. & Sirotkin, K. (2001). dbSNP: The NCBI database of genetic variation. Nucleic Acids Research 29, 308–311.Google Scholar
Stenson, P.D., Mort, M., Ball, E.V., Shaw, K., Phillips, A.D. & Cooper, D.N. (2013). The human gene mutation database: Building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Human Genetics 133, 1–9.Google Scholar
Stover, D.A. & Verrelli, B.C. (2010). Comparative vertebrate evolutionary analyses of type I collagen: Potential of COL1a1 gene structure and intron variation for common bone-related diseases. Molecular Biology and Evolution 28, 533–542.Google Scholar
Sugawara, T., Imai, H., Nikaido, M., Imamoto, Y. & Okada, N. (2010). Vertebrate rhodopsin adaptation to dim light via rapid meta-II intermediate formation. Molecular Biology and Evolution 27, 506–519.Google Scholar
Takahashi, Y., Moiseyev, G. & Ma, J.X. (2014). Identification of key residues determining isomerohydrolase activity of human RPE65. Journal of Biological Chemistry 289, 26743–26751.Google Scholar
Ueyama, H., Miraki-Oda, S., Yamade, S., Tanabe, S., Yamashita, T., Shichida, Y. & Ogita, H. (2012). Unique haplotype in exon 3 of cone opsin mRNA affects splicing of its precursor, leading to congenital color vision defect. Biochemical and Biophysical Research Communications 424, 152–157.Google Scholar
Vincent, A.L., Carroll, J., Fishman, G.A., Sauer, A., Sharp, D., Summerfelt, P., Williams, V., Dubis, A.M., Kohl, S. & Wong, F. (2013). Rhodopsin F45L allele does not cause autosomal dominant retinitis pigmentosa in a large caucasian family. Translational Vision Science and Technology 2, 4.Google Scholar
Webb, A.E., Gerek, Z.N., Morgan, C.C., Walsh, T.A., Loscher, C.E., Edwards, S.V. & O’Connell, M.J. (2015). Adaptive evolution as a predictor of species-specific innate immune response. Molecular Biology and Evolution 32, 1717–1729.Google Scholar
Weitz, C.J. & Nathans, J. (1992). Histidine residues regulate the transition of photoexcited rhodopsin to its active conformation, metarhodopsin II. Neuron 8, 465–472.Google Scholar
Yang, G., Xie, S., Feng, N., Yuan, Z., Zhang, M. & Zhao, J. (2014). Spectrum of rhodopsin gene mutations in Chinese patients with retinitis pigmentosa. Molecular Vision 20, 1132–1136.Google Scholar
Yang, Z. (2005). Bayes empirical bayes inference of amino acid sites under positive selection. Molecular Biology and Evolution 22, 1107–1118.Google Scholar
Yang, Z. (2007). PAML 4: Phylogenetic analysis by maximum likelihood. Molecular Biology and Evolution 24, 1586–1591.Google Scholar
Zhao, H., Ru, B., Teeling, E.C., Faulkes, C.G., Zhang, S. & Rossiter, S.J. (2009). Rhodopsin molecular evolution in mammals inhabiting low light environments. PLoS One 4, e8326.Google Scholar
Supplementary material: PDF

Hauser supplementary material

Figures S1-S8 and Tables S1-S9

Download Hauser supplementary material(PDF)
PDF 2.3 MB