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Phytochromes pp 265-276 | Cite as

Phylogenetic Methods to Study Light Signaling

  • Fay-Wei Li
  • Sarah MathewsEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 2026)

Abstract

Phylogenetic comparative methods (PCM) represent a rigorous approach for inferring functional evolution. To infer the origin and evolution of a function, PCM use a phylogenetic tree of the species in which the function has evolved and functional data from those species. These data enable reconstruction of ancestral states and inference of how the function evolved along the branches of the species tree. PCM can be applied to understand any aspect of light signaling, from early events in photoactivation, to interactions with signaling partners, to physiological responses. Integrating evolutionary histories of individual aspects of light signaling obtained through PCM with network modeling of protein–protein interactions for light signaling would enable a deep understanding of the evolution in light signaling pathways and their roles in helping plants adapt to changing environments. Here we describe the steps for using PCM to infer functional evolution using a species tree and trait data.

Keywords

Functional evolution Light signaling Phytochromes Phylogenetic comparative methods 

References

  1. 1.
    Wang H, Wang H (2015) Phytochrome signaling: time to tighten up the loose ends. Mol Plant 8:540–551CrossRefGoogle Scholar
  2. 2.
    Pagel M (1999) The Maximum Likelihood approach to reconstructing ancestral character states of discrete characters on phylogenies. Syst Biol 48:612–622Google Scholar
  3. 3.
    Li F-W, Melkonian M, Rothfels CJ, Villarreal JC, Stevenson DW, Graham SW, Wong GK-S, Pryer KM, Mathews S (2015) Phytochrome diversity in green plants and the origin of canonical plant phytochromes. Nat Commun 6:7852CrossRefGoogle Scholar
  4. 4.
    Ranwez V, Harispe S, Delsuc F, Douzery EJP (2011) MACSE: Multiple Alignment of Coding SEquences accounting for frameshifts and stop codons. PLoS One 6:e22594CrossRefGoogle Scholar
  5. 5.
    Castresana J (2000) Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol Biol Evol 17:540–552CrossRefGoogle Scholar
  6. 6.
    Kück P, Meusemann K (2010) FASconCAT: convenient handling of data matrices. Mol Phylogenet Evol 56:1115–1118CrossRefGoogle Scholar
  7. 7.
    Kearse M, Moir R, Wilson A et al (2012) Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28:1647–1649CrossRefGoogle Scholar
  8. 8.
    Cheng S, Melkonian M, Smith SA, Brockington S, Archibald JM, Delaux PM et al (2018) 10KP: A phylodiverse genome sequencing plan. Gigascience 7:1–9CrossRefGoogle Scholar
  9. 9.
    Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL (2009) BLAST+: architecture and applications. BMC Bioinformatics 10:421CrossRefGoogle Scholar
  10. 10.
    Finn RD, Clements J, Eddy SR (2011) HMMER web server: interactive sequence similarity searching. Nucleic Acids Res 39:W29–W37CrossRefGoogle Scholar
  11. 11.
    Mathews S, Tremonte D (2012) Tests of the link between functional innovation and positive selection at phytochrome A: the phylogenetic distribution of far-red high-irradiance responses in seedling development. Int J Plant Sci 173:662–672CrossRefGoogle Scholar
  12. 12.
    Maddison WP, Maddison DR (2017) Mesquite: a modular system for evolutionary analysis. http://mesquiteproject.orgGoogle Scholar
  13. 13.
    Wickett NJ, Mirarab S, Nguyen N et al (2014) Phylotranscriptomic analysis of the origin and early diversification of land plants. Proc Natl Acad Sci U S A 111:E4859–E4868CrossRefGoogle Scholar
  14. 14.
    Ruhfel BR, Gitzendanner MA, Soltis PS, Soltis DE, Burleigh JG (2014) From algae to angiosperms–inferring the phylogeny of green plants (Viridiplantae) from 360 plastid genomes. BMC Evol Biol 14:23CrossRefGoogle Scholar
  15. 15.
    Hinchliff CE, Smith SA, Allman JF et al (2015) Synthesis of phylogeny and taxonomy into a comprehensive tree of life. Proc Natl Acad Sci U S A 112:12764–12769CrossRefGoogle Scholar
  16. 16.
    Huson DH, Richter DC, Rausch C, Dezulian T, Franz M, Rupp R (2007) Dendroscope: An interactive viewer for large phylogenetic trees. BMC Bioinformatics 8:460CrossRefGoogle Scholar
  17. 17.
    Levin RA, Whelan A, Miller JS (2009) The utility of nuclear conserved ortholog set II (COSII) genomic regions for species-level phylogenetic inference in Lycium (Solanaceae). Mol Phylogenet Evol 53:881–890CrossRefGoogle Scholar
  18. 18.
    Jeong Y-M, Chung W-H, Chung H, Kim N, Park B-S, Lim K-B, Yu H-J, Mun J-H (2014) Comparative analysis of the radish genome based on a conserved ortholog set (COS) of Brassica. Theor Appl Genet 127:1975–1989CrossRefGoogle Scholar
  19. 19.
    Smith SA, Beaulieu JM, Donoghue MJ (2009) Mega-phylogeny approach for comparative biology: an alternative to supertree and supermatrix approaches. BMC Evol Biol 9:37CrossRefGoogle Scholar
  20. 20.
    Freyman WA (2015) SUMAC: constructing phylogenetic supermatrices and assessing partially decisive taxon coverage. Evol Bioinformatics Online 11:263–266Google Scholar
  21. 21.
    Katoh K, Kuma K, Toh H, Miyata T (2005) MAFFT version 5: improvement in accuracy of multiple sequence alignment. Nucleic Acids Res 33:511–518CrossRefGoogle Scholar
  22. 22.
    Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32:1792–1797CrossRefGoogle Scholar
  23. 23.
    Page RDM, Holmes EC (1998) Molecular evolution: a phylogenetic approach. Blackwell Science, Oxford/Malden, MAGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Boyce Thompson InstituteCornell UniversityIthacaUSA
  2. 2.Australian National HerbariumCSIRO National Research Collections AustraliaCanberraAustralia
  3. 3.Department of Biological SciencesLousiana State UniversityBaton RougeUSA

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