Journal of Molecular Evolution

, Volume 77, Issue 5–6, pp 231–245 | Cite as

Molecular Evolutionary Analysis of Vertebrate Transducins: A Role for Amino Acid Variation in Photoreceptor Deactivation

  • Yi G. Lin
  • Cameron J. Weadick
  • Francesco Santini
  • Belinda S. W. Chang
Original Article

Abstract

Transducin is a heterotrimeric G protein that plays a critical role in phototransduction in the rod and cone photoreceptor cells of the vertebrate retina. Rods, highly sensitive cells that recover from photoactivation slowly, underlie dim-light vision, whereas cones are less sensitive, recover more quickly, and underlie bright-light vision. Transducin deactivation is a critical step in photoreceptor recovery and may underlie the functional distinction between rods and cones. Rods and cones possess distinct transducin α subunits, yet they share a common deactivation mechanism, the GTPase activating protein (GAP) complex. Here, we used codon models to examine patterns of sequence evolution in rod (GNAT1) and cone (GNAT2) α subunits. Our results indicate that purifying selection is the dominant force shaping GNAT1 and GNAT2 evolution, but that GNAT2 has additionally been subject to positive selection operating at multiple phylogenetic scales; phylogeny-wide analysis identified several sites in the GNAT2 helical domain as having substantially elevated dN/dS estimates, and branch-site analysis identified several nearby sites as targets of strong positive selection during early vertebrate history. Examination of aligned GNAT and GAP complex crystal structures revealed steric clashes between several positively selected sites and the deactivating GAP complex. This suggests that GNAT2 sequence variation could play an important role in adaptive evolution of the vertebrate visual system via effects on photoreceptor deactivation kinetics and provides an alternative perspective to previous work that focused instead on the effect of GAP complex concentration. Our findings thus further the understanding of the molecular biology, physiology, and evolution of vertebrate visual systems.

Keyword

G proteins Vision Rhodopsin dN/dS Positive selection Maximum likelihood Codon models 

Supplementary material

239_2013_9589_MOESM1_ESM.pdf (322 kb)
Supplementary material 1 (PDF 400 kb)

References

  1. Abascal F, Zardoya R, Posada D (2005) Prottest: selection of best-fit models of protein evolution. Bioinformatics 21:2104–2105PubMedCrossRefGoogle Scholar
  2. Alfaro ME, Santini F, Brock C, Alamillo H, Dornburg A, Rabosky DL, Carnevale G, Harmon LJ (2009) Nine exceptional radiations plus high turnover explain species diversity in jawed vertebrates. Proc Natl Acad Sci 106:13410–13414PubMedCentralPubMedCrossRefGoogle Scholar
  3. Anisimova M, Liberles DA (2012) Detecting and understanding natural selection. In: Cannarozzi GM, Schneider A (eds) Codon evolution: mechanisms and models. Oxford University Press, Oxford, pp 73–96CrossRefGoogle Scholar
  4. Anisimova M, Bielawski JP, Yang Z (2001) Accuracy and power of the likelihood ratio test in detecting adaptive molecular evolution. Mol Biol Evol 18:1585–1592PubMedCrossRefGoogle Scholar
  5. Bielawski JP, Yang ZH (2004) A maximum likelihood method for detecting functional divergence at individual codon sites, with application to gene family evolution. J Mol Evol 59:121–132PubMedCrossRefGoogle Scholar
  6. Burns ME, Pugh EN (2009) Rgs9 concentration matters in rod phototransduction. Biophys J 97:1538–1547PubMedCentralPubMedCrossRefGoogle Scholar
  7. Burns ME, Pugh EN (2010) Lessons from photoreceptors: turning off g-protein signaling in living cells. Physiology 25:72–84PubMedCentralPubMedCrossRefGoogle Scholar
  8. Carretero-Paulet L, Albert VA, Fares MA (2013) Molecular evolutionary mechanisms driving functional diversification of the hsp90a family of heat shock proteins in eukaryotes. Mol Biol Evol 30:2035–2043PubMedCrossRefGoogle Scholar
  9. Cheever ML, Snyder JT, Gershburg S, Siderovski DP, Harden TK, Sondek J (2008) Crystal structure of the multifunctional g beta 5-rgs9 complex. Nat Struct Mol Biol 15:155–162PubMedCentralPubMedCrossRefGoogle Scholar
  10. Chen CK, Woodruff ML, Chen FS, Shim H, Cilluffo MC, Fain GL (2010) Replacing the rod with the cone transducin alpha subunit decreases sensitivity and accelerates response decay. J Physiol (London) 588:3231–3241CrossRefGoogle Scholar
  11. Cowan CW, Fariss RN, Sokal I, Palczewski K, Wensel TG (1998) High expression levels in cones of rgs9, the predominant gtpase accelerating protein of rods. Proc Natl Acad Sci USA 95:5351–5356PubMedCentralPubMedCrossRefGoogle Scholar
  12. Delport W, Poon AFY, Frost SDW, Pond SLK (2010) Datamonkey 2010: a suite of phylogenetic analysis tools for evolutionary biology. Bioinformatics 26:2455–2457PubMedCentralPubMedCrossRefGoogle Scholar
  13. Ebrey T, Koutalos Y (2001) Vertebrate photoreceptors. Prog Retin Eye Res 20:49–94PubMedCrossRefGoogle Scholar
  14. Fain GL, Hardie R, Laughlin SB (2010) Phototransduction and the evolution of photoreceptors. Curr Biol 20:R114–R124PubMedCentralPubMedCrossRefGoogle Scholar
  15. Fletcher W, Yang Z (2010) The effect of insertions, deletions, and alignment errors on the branch-site test of positive selection. Mol Biol Evol 27:2257–2267PubMedCrossRefGoogle Scholar
  16. Fritsches KA, Brill RW, Warrant EJ (2005) Warm eyes provide superior vision in swordfishes. Curr Biol 15:55–58PubMedCrossRefGoogle Scholar
  17. Gharib WH, Robinson-Rechavi M (2013) The branch-site test of positive selection is surprisingly robust but lacks power under synonymous substitution saturation and variation in gc. Mol Biol Evol 30:1675–1686PubMedCentralPubMedCrossRefGoogle Scholar
  18. Gojobori T (1983) Codon substitution in evolution and the “saturation” of synonymous changes. Genetics 105:1011–1027PubMedCentralPubMedGoogle Scholar
  19. Goldman N, Whelan S (2000) Statistical tests of gamma-distributed rate heterogeneity in models of sequence evolution in phylogenetics. Mol Biol Evol 17:975–978PubMedCrossRefGoogle Scholar
  20. Goldman N, Yang ZH (1994) Codon-based model of nucleotide substitution for protein-coding DNA-sequences. Mol Biol Evol 11:725–736PubMedGoogle Scholar
  21. Gopalakrishna KN, Boyd KK, Artemyev NO (2012) Comparative analysis of cone and rod transducins using chimeric galpha subunits. Biochemistry 51:1617–1624PubMedCentralPubMedCrossRefGoogle Scholar
  22. He W, Cowan CW, Wensel TG (1998) Rgs9, a gtpase accelerator for phototransduction. Neuron 20:95–102PubMedCrossRefGoogle Scholar
  23. Hu G, Wensel TG (2002) R9ap, a membrane anchor for the photoreceptor gtpase accelerating protein, rgs9-1. Proc Natl Acad Sci USA 99:9755–9760PubMedCentralPubMedCrossRefGoogle Scholar
  24. Huelsenbeck JP, Rannala B (1997) Phylogenetic methods come of age: testing hypotheses in an evolutionary context. Science 276:227–232PubMedCrossRefGoogle Scholar
  25. Hughes AL (2007) Looking for Darwin in all the wrong places: the misguided quest for positive selection at the nucleotide sequence level. Heredity (Edinb) 99:364–373CrossRefGoogle Scholar
  26. Jones DT, Taylor WR, Thornton JM (1992) The rapid generation of mutation data matrices from protein sequences. Comput Appl Biosci 8:275–282PubMedGoogle Scholar
  27. Jordan G, Goldman N (2012) The effects of alignment error and alignment filtering on the sitewise detection of positive selection. Mol Biol Evol 29:1125–1139PubMedCrossRefGoogle Scholar
  28. Kosakovsky Pond SL, Murrell B, Fourment M, Frost SDW, Delport W, Scheffler K (2011) A random effects branch-site model for detecting episodic diversifying selection. Mol Biol Evol 28:3033–3043PubMedCentralPubMedCrossRefGoogle Scholar
  29. Lagman D, Sundstrom G, Ocampo Daza D, Abalo XM, Larhammar D (2012) Expansion of transducin subunit gene families in early vertebrate tetraploidizations. Genomics 100:203–211PubMedCrossRefGoogle Scholar
  30. Lamb TD (1984) Effects of temperature changes on toad rod photocurrents. J Physiol 346:557–578PubMedCentralPubMedCrossRefGoogle Scholar
  31. Lambright DG, Noel JP, Hamm HE, Sigler PB (1994) Structural determinants for activation of the alpha-subunit of a heterotrimeric g-protein. Nature 369:621–628PubMedCrossRefGoogle Scholar
  32. Laskowski RA, Macarthur MW, Moss DS, Thornton JM (1993) Procheck—a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26:283–291CrossRefGoogle Scholar
  33. Leipe DD, Wolf YI, Koonin EV, Aravind L (2002) Classification and evolution of p-loop gtpases and related atpases. J Mol Biol 317:41–72PubMedCrossRefGoogle Scholar
  34. Lerea CL, Somers DE, Hurley JB, Klock IB, Buntmilam AH (1986) Identification of specific transducin alpha-subunits in retinal rod and cone photoreceptors. Science 234:77–80PubMedCrossRefGoogle Scholar
  35. 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. Mol Biol Evol 2:150–174PubMedGoogle Scholar
  36. Lochrie MA, Hurley JB, Simon MI (1985a) Sequence of the alpha subunit of photoreceptor g protein: homologies between transducin, ras, and elongation factors. Science 228:96–99PubMedCrossRefGoogle Scholar
  37. Lochrie MA, Hurley JB, Simon MI (1985b) Sequence of the alpha-subunit of photoreceptor-g protein—homologies between transducin, ras, and elongation-factors. Science 228:96–99PubMedCrossRefGoogle Scholar
  38. Long JA, Gordon MS (2004) The greatest step in vertebrate history: a paleobiological review of the fish-tetrapod transition. Physiol Biochem Zool 77:700–719PubMedCrossRefGoogle Scholar
  39. Loytynoja A, Goldman N (2005) An algorithm for progressive multiple alignment of sequences with insertions. Proc Natl Acad Sci USA 102:10557–10562PubMedCentralPubMedCrossRefGoogle Scholar
  40. Loytynoja A, Vilella AJ, Goldman N (2012) Accurate extension of multiple sequence alignments using a phylogeny-aware graph algorithm. Bioinformatics 28:1684–1691PubMedCentralPubMedCrossRefGoogle Scholar
  41. Lythgoe JN (1979) The ecology of vision. Clarendon Press, OxfordGoogle Scholar
  42. Lyubarsky A, Chen CK, Naarendorp F, Zhang X, Wensel T, Simon M, Pugh E (2001) Rgs9-1 is required for normal inactivation of mouse cone phototransduction. Mol Vis 7:71–78PubMedGoogle Scholar
  43. Maddison DR, Schulz K-S (eds) (2007) The tree of life web project. http://tolweb.org
  44. Makino ER, Handy JW, Li TS, Arshavsky VY (1999) The gtpase activating factor for transducin in rod photoreceptors is the complex between rgs9 and type 5 g protein beta subunit. Proc Natl Acad Sci USA 96:1947–1952PubMedCentralPubMedCrossRefGoogle Scholar
  45. Mao W, Miyagishima KJ, Yao Y, Soreghan B, Sampath AP, Chen J (2013) Functional comparison of rod and cone galphat on the regulation of light sensitivity. J Biol Chem 288:5257–5267PubMedCentralPubMedCrossRefGoogle Scholar
  46. Maynard Smith J (1994) Estimating selection by comparing synonymous and substitutional changes. J Mol Evol 39:123–128Google Scholar
  47. Miyata T, Yasunaga T (1980) Molecular evolution of mRNA: a method for estimating evolutionary rates of synonymous and amino acid substitutions from homologous nucleotide sequences and its application. J Mol Evol 16:23–36PubMedCrossRefGoogle Scholar
  48. Muradov H, Kerov V, Boyd KK, Artemyev NO (2008) Unique transducins expressed in long and short photoreceptors of lamprey petromyzon marinus. Vis Res 48:2302–2308PubMedCentralPubMedCrossRefGoogle Scholar
  49. Muse SV, Gaut BS (1994) A likelihood approach for comparing synonymous and nonsynonymous nucleotide substitution rates, with application to the chloroplast genome. Mol Biol Evol 11:715–724PubMedGoogle Scholar
  50. Nagai H, Terai Y, Sugawara T, Imai H, Nishihara H, Hori M, Okada N (2011) Reverse evolution in rh1 for adaptation of cichlids to water depth in Lake Tanganyika. Mol Biol Evol 28:1769–1776PubMedCrossRefGoogle Scholar
  51. Nei M, Gojobori T (1986) Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions. Mol Biol Evol 3:418–426PubMedGoogle Scholar
  52. Noel JP, Hamm HE, Sigler PB (1993) The 2.2-angstrom crystal-structure of transducin-alpha complexed with gtp-gamma-s. Nature 366:654–663PubMedCrossRefGoogle Scholar
  53. Nordstrom K, Larsson TA, Larhammar D (2004) Extensive duplications of phototransduction genes in early vertebrate evolution correlate with block (chromosome) duplications. Genomics 83:852–872PubMedCrossRefGoogle Scholar
  54. Nozawa M, Suzuki Y, Nei M (2009) Reliabilities of identifying positive selection by the branch-site and the site-prediction methods. Proc Natl Acad Sci USA 106:6700–6705PubMedCentralPubMedCrossRefGoogle Scholar
  55. Oldham WM, Hamm HE (2008) Heterotrimeric g protein activation by g-protein-coupled receptors. Nat Rev Mol Cell Bio 9:60–71CrossRefGoogle Scholar
  56. Penn O, Privman E, Ashkenazy H, Landan G, Graur D, Pupko T (2010a) Guidance: a web server for assessing alignment confidence scores. Nucleic Acids Res 38:W23–W28PubMedCentralPubMedCrossRefGoogle Scholar
  57. Penn O, Privman E, Landan G, Graur D, Pupko T (2010b) An alignment confidence score capturing robustness to guide tree uncertainty. Mol Biol Evol 27:1759–1767PubMedCentralPubMedCrossRefGoogle Scholar
  58. Perler F, Efstratiadis A, Lomedico P, Gilbert W, Kolodner R, Dodgson J (1980) The evolution of genes: the chicken preproinsulin gene. Cell 20:555–566PubMedCrossRefGoogle Scholar
  59. Sali A, Blundell TL (1993) Comparative protein modeling by satisfaction of spatial restraints. J Mol Biol 234:779–815PubMedCrossRefGoogle Scholar
  60. Shen MY, Sali A (2006) Statistical potential for assessment and prediction of protein structures. Protein Sci 15:2507–2524PubMedCentralPubMedCrossRefGoogle Scholar
  61. Skiba NP, Yang CS, Huang T, Bae H, Hamm HE (1999) The alpha-helical domain of galphat determines specific interaction with regulator of g protein signaling 9. J Biol Chem 274:8770–8778PubMedCrossRefGoogle Scholar
  62. Skiba NP, Martemyanov KA, Elfenbein A, Hopp JA, Bohm A, Simonds WF, Arshavsky VY (2001) Rgs9-g beta 5 substrate selectivity in photoreceptors. Opposing effects of constituent domains yield high affinity of rgs interaction with the g protein-effector complex. J Biol Chem 276:37365–37372PubMedCrossRefGoogle Scholar
  63. Slep KC, Kercher MA, He W, Cowan CW, Wensel TG, Sigler PB (2001) Structural determinants for regulation of phosphodiesterase by a g protein at 2.0 angstrom. Nature 409:1071–1077PubMedCrossRefGoogle Scholar
  64. Sondek J, Lambright DG, Noel JP, Hamm HE, Sigler PB (1994) Gtpase mechanism of g proteins from the 1.7-angstrom crystal-structure of transducin alpha-center-dot-gdp-center-dot-alf4(−). Nature 372:276–279PubMedCrossRefGoogle Scholar
  65. Soundararajan M, Willard FS, Kimple AJ, Turnbull AP, Ball LJ, Schoch GA, Gileadi C, Fedorov OY, Dowler EF, Higman VA, Hutsell SQ, Sundstrom M, Doyle DA, Siderovski DP (2008) Structural diversity in the rgs domain and its interaction with heterotrimeric g protein alpha-subunits. Proc Natl Acad Sci USA 105:6457–6462PubMedCentralPubMedCrossRefGoogle Scholar
  66. Sprang SR (1997) G protein mechanisms: insights from structural analysis. Annu Rev Biochem 66:639–678PubMedCrossRefGoogle Scholar
  67. Studer RA, Penel S, Duret L, Robinson-Rechavi M (2008) Pervasive positive selection on duplicated and nonduplicated vertebrate protein coding genes. Genome Res 18:1393–1402PubMedCentralPubMedCrossRefGoogle Scholar
  68. Swanson WJ, Nielsen R, Yang QF (2003) Pervasive adaptive evolution in mammalian fertilization proteins. Mol Biol Evol 20:18–20PubMedCrossRefGoogle Scholar
  69. Tamura K, Dudley J, Nei M, Kumar S (2007) Mega4: molecular evolutionary genetics analysis (mega) software version 4.0. Mol Biol Evol 24:1596–1599PubMedCrossRefGoogle Scholar
  70. Thompson JD, Higgins DG, Gibson TJ (1994) Clustal-w—improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22:4673–4680PubMedCentralPubMedCrossRefGoogle Scholar
  71. Trezise AE, Collin SP (2005) Opsins: evolution in waiting. Curr Biol 15:R794–R796PubMedCrossRefGoogle Scholar
  72. Walls GL (1942) The vertebrate eye and its adaptive radiation. Oxford, England: Cranbrook Institute of Science, p 785Google Scholar
  73. Weadick CJ, Chang BS (2009) Molecular evolution of the œ ≤ œ ≥ lens crystallin superfamily: evidence for a retained ancestral function in œ ≥ n crystallins? Mol Biol Evol 26:1127–1142PubMedCrossRefGoogle Scholar
  74. Weadick CJ, Chang BSW (2012) An improved likelihood ratio test for detecting site-specific functional divergence among clades of protein-coding genes. Mol Biol Evol 29:1297–1300PubMedCrossRefGoogle Scholar
  75. Wiederstein M, Sippl MJ (2007) Prosa-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 35:W407–W410PubMedCentralPubMedCrossRefGoogle Scholar
  76. Williams PD, Pollock DD, Blackburne BP, Goldstein RA (2006) Assessing the accuracy of ancestral protein reconstruction methods. PLoS Comput Biol 2:e69PubMedCentralPubMedCrossRefGoogle Scholar
  77. Wittinghofer A (1994) The structure of transducin-g(alpha-t)—more to view than just ras. Cell 76:201–204PubMedCrossRefGoogle Scholar
  78. Yang ZH (1994) Maximum-likelihood phylogenetic estimation from DNA-sequences with variable rates over sites—approximate methods. J Mol Evol 39:306–314PubMedCrossRefGoogle Scholar
  79. Yang ZH (2007) Paml 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol 24:1586–1591PubMedCrossRefGoogle Scholar
  80. Yang Z, dos Reis M (2011) Statistical properties of the branch-site test of positive selection. Mol Biol Evol 28:1217–1228PubMedCrossRefGoogle Scholar
  81. Yang Z, Nielsen R (2000) Estimating synonymous and nonsynonymous substitution rates under realistic evolutionary models. Mol Biol Evol 17:32–43PubMedCrossRefGoogle Scholar
  82. Yang ZH, Nielsen R (2002) Codon-substitution models for detecting molecular adaptation at individual sites along specific lineages. Mol Biol Evol 19:908–917PubMedCrossRefGoogle Scholar
  83. Yang ZH, Kumar S, Nei M (1995) A new method of inference of ancestral nucleotide and amino-acid-sequences. Genetics 141:1641–1650PubMedCentralPubMedGoogle Scholar
  84. Yang ZH, Nielsen R, Goldman N, Pedersen AMK (2000) Codon-substitution models for heterogeneous selection pressure at amino acid sites. Genetics 155:431–449PubMedCentralPubMedGoogle Scholar
  85. Yang ZH, Wong WSW, Nielsen R (2005) Bayes empirical bayes inference of amino acid sites under positive selection. Mol Biol Evol 22:1107–1118PubMedCrossRefGoogle Scholar
  86. Yang Z, Nielsen R, Goldman N (2009) In defense of statistical methods for detecting positive selection. Proc Natl Acad Sci USA 106:E95; author reply E96Google Scholar
  87. Yau KW, Hardie RC (2009) Phototransduction motifs and variations. Cell 139:246–264PubMedCentralPubMedCrossRefGoogle Scholar
  88. Yokoyama S, Starmer WT (1992) Phylogeny and evolutionary rates of g protein alpha subunit genes. J Mol Evol 35:230–238PubMedCrossRefGoogle Scholar
  89. Yoshida I, Sugiura W, Shibata J, Ren FR, Yang ZH, Tanaka H (2011) Change of positive selection pressure on hiv-1 envelope gene inferred by early and recent samples. PLoS ONE 6(4):e18630Google Scholar
  90. Zhai W, Nielsen R, Goldman N, Yang Z (2012) Looking for Darwin in genomic sequences—validity and success of statistical methods. Mol Biol Evol 29:2889–2893PubMedCrossRefGoogle Scholar
  91. Zhang X, Wensel TG, Kraft TW (2003) Gtpase regulators and photoresponses in cones of the eastern chipmunk. J Neurosci 23:1287–1297PubMedGoogle Scholar
  92. Zhang JZ, Nielsen R, Yang ZH (2005) Evaluation of an improved branch-site likelihood method for detecting positive selection at the molecular level. Mol Biol Evol 22:2472–2479PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Yi G. Lin
    • 1
    • 2
  • Cameron J. Weadick
    • 1
  • Francesco Santini
    • 1
  • Belinda S. W. Chang
    • 1
    • 2
    • 3
  1. 1.Department of Ecology and Evolutionary BiologyUniversity of TorontoTorontoCanada
  2. 2.Department of Cell and Systems BiologyUniversity of TorontoTorontoCanada
  3. 3.Centre for the Analysis of Genome Evolution and FunctionUniversity of TorontoTorontoCanada

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