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REVIEW PAPER
Molecular evolution and phylogenetic implications in clinical research
1
Department of Virology, Medical University, Lublin, Poland
Ann Agric Environ Med. 2013;20(3):455-459
KEYWORDS
ABSTRACT
A phylogenetic tree shows graphically the evolutionary relationships among various organisms. The dynamic development of molecular biology and bioinformatics has led to a revolution in our knowledge of biological evolution and the kinships between living organisms and viruses. Nowadays, the available laboratory techniques and computer software allow reconstruction of the actual changes which occurred in the evolutionary process. The derivation of molecular evolution models and several methods for building phylogenetic trees have played a huge role in that enterprise. The emergence of new infectious agents is a problem afflicting mankind since prehistoric times. The study of phylogenetic implications among pathogenic microorganisms allows tracking the process of evolution, the indirect understanding of their biology, and thus facilitates the implementation of treatment.
The presented article demonstrates the basic methods for constructing phylogenetic trees, as well as the benefits of reconstructing the evolution process and kinship with the study of microorganisms; in particular, viruses are considered from the clinical aspect.
REFERENCES (41)
1.
Hall BG. Phylogenetic Trees Made Easy: A How-to Manual, Third Edition. Massachusetts: Sinauer Associates, 2008.
3.
Dayrat B. The Roots of Phylogeny: How Did Haeckel Build His Trees? Syst Biol. 2003; 52(4): 515–527.
4.
Baldauf SL. Phylogeny for the faint of heart: a tutorial. Trends Genet. 2003; 19: 345–351.
5.
Kimura M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. Mol Evol. 2003; 16: 111–120.
6.
Takahata N, Kimura M. A model of evolutionary base substitutions and its application with special reference to rapid change of pseudogenes. Genetics 1981; 98: 641–657.
7.
Higgs PG, Attwood TK. Bioinformatics and Molecular Evolution. New Jersey: Wiley-Blackwell, 2004.
8.
Bos DH, Posada D. Using models of nucleotide evolution to build phylogenetic trees. Dev Comp Immunol. 2005; 29: 211–227.
9.
Whelan S, Lio P, Goldman N. Molecular phylogenetics: state-of-theart methods for looking into the past. Trends Genet. 2001; 17: 262–272.
10.
Felsenstein J. Evolutionary Trees from DNA Sequences: A Maximum Likelihood Approach. J Mol Evol. 1981; 17: 368–376.
11.
Kimura M. Estimation of evolutionary distances between homologous nucleotide sequences. Proc NatL Acad Sci. USA 1981; 78(1): 454–458.
12.
Lio P, Goldman N. Models of Molecular Evolution and Phylogeny. Genome Res. 1998; 8: 1233–1244.
13.
Tamura K, Nei M. Estimation of the Number of Nucleotide Substitutions in the Control Region of Mitochondrial DNA in Humans and Chimpanzees. Mol Biol Evol. 1993; 10(3): 512–526.
14.
Criscuolo A, Gascuel O. Fast NJ-like algorithms to deal with incomplete distance matrices. BMC Bioinformatics 2008; 9: 166.
15.
Nei M, Roychoudhury AK. Evolutionary Relationships of Human Populations on a Global Scale. Mol Biol Evol. 1993; 10: 927–943.
16.
Saitou N, Nei M. The Neighbor-joining Method: A New Method for Reconstructing Phylogenetic Trees. Mol Biol Evol. 1987; 4: 406–425.
17.
Kolaczkowski B, Thornton JW.Performance of maximum parsimony and likelihood phylogenetics when evolution is heterogeneous. Nature 2004; 431: 980–984.
18.
Sober E. Parsimony in Systematics: Philosophical Issues Annual Review of Ecology and Systematics. Annu Rev Ecol Syst. 1983; 14: 335–357.
19.
Steel M, Penny D. Parsimony, Likelihood, and the Role of Models in Molecular Phylogenetics. Mol Biol Evol. 2000; 17: 839–850.
20.
Guindon S, Gascuel OA. Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood. Syst Biol. 2003; 52: 696–704.
21.
Holder M, Lewis PO. Phylogeny Estimation: Traditional and Bayesian Approaches. Nat Rev Genet. 2003; 4: 275–284.
22.
Posada D, Crandall KA. MODELTEST: testing the model of DNA substitution. Bioinformatics 1998; 14: 817–818.
23.
Posada D. jModelTest: Phylogenetic Model Averaging. Mol Biol Evol. 2008; 25: 1253–1256.
24.
Lunter G, Miklos I, Drummond A, Jensen JL, Hein J. Bayesian coestimation of phylogeny and sequence alignment. BMC Bioinformatics 2005; 6: 83.
25.
Bernard HU. The clinical importance of the nomenclature, evolution and taxonomy of human papillomaviruses. J Clin Virol. 2005; 32: S1-S6.
26.
de Villiers EM, Fauquet C, Broker TR, Bernard HU, zur Hausen H. Classification of papillomaviruses. Virology 2004; 324: 17–27.
27.
Bravo IG, de Sanjose S, Gottschling M. The clinical importance of understanding the evolution of papillomaviruses. Trends Microbiol. 2010; 18: 432–438.
28.
Chow LT, Broker TR, Steinberg BM. The natural history of human papillomavirus infections of the mucosal epithelia. Acta Path Micro Im C. 2010; 118: 422–449.
29.
Cantaloube JF, Gallian P, Attoui H, Biagini P, De Micco P, de Lamballerie X. Genotype Distribution and Molecular Epidemiology of Hepatitis C Virus in Blood Donors from Southeast France. J Clin Microbiol. 2005; 43: 3624–3629.
30.
Forbi JC, Vaughan G, Purdy MA, Campo DS, Xia G, Lilia M, GanovaRaeva LM, Ramachandran S, Thai H, Khudyakov YE. Epidemic History and Evolutionary Dynamics of Hepatitis B Virus Infection in Two Remote Communities in Rural Nigeria. PLoS One. 2010; 5: 1–14.
31.
Holmes EC. The phylogeography of human viruses. Mol Ecol. 2004; 13: 745–756.
32.
Mild M, Simon M, Albert J, Mirazimi A. Towards an understanding of the migration of Crimean–Congo hemorrhagic fever virus. J Gen Virol. 2010; 91: 199–207.
33.
Metzker ML, Mindell DP, Liu XM, Ptak RG, Gibbs RA, Hillis DM. Molecular evidence of HIV-1 transmission in a criminal case. P Natl Acad Sci. USA 2002; 99: 14292–14297.
34.
Smith TF, Waterman MS. The Continuing Case of the Florida Dentist. Science 1992; 256: 1155–1156.
35.
Wolfe ND, Dunavan CP, Diamond J. Origins of major human infectious diseases. Nature 2007; 447: 279–283.
36.
Dobson AP, Carper ER. Infectious Diseases and Human Population History. Bioscience 1996; 46: 115–126.
37.
Wolfe ND, Switzer WM, Carr JK, Bhullar VB, Shanmugam V, Tamoufe U, Prosser AT, Torimiro JN, Wright A, Mpoudi-Ngole E, McCutchan FE, Birx DL, Folks TM, Burke DS, Heneine W. Naturally acquired simian retrovirus infections in central African hunters. Lancet 2004; 363: 932–937.
38.
Wolfe ND, Heneine W, Carr JK, Garcia AD, Shanmugam V, Tamoufe U, Torimiro JN, Prosser AT, LeBreton M, Mpoudi-Ngole E, McCutchan FE, Birx DL, Folks TM, Burke DS, Switzer WM. Emergence of unique primate T-lymphotropic viruses among central African bushmeat hunters. P Natl Acad Sci. USA 2005; 102: 7994–7999.
39.
Zhu T, Korber BT, Nahmias AJ, Hooper E, Sharp PM, Ho DD. An African HIV-1 sequence from 1959 and implications for the origin of the epidemic. Nature 1998; 391: 594–597.
40.
Korber B, Gaschen B, Yusim K, Thakallapally R, Kesmir C, Detours V. Evolutionary and immunological implications of contemporary HIV-1 variation. Brit Med Bull. 2001; 58: 19–42.
41.
Pike BL, Saylors KE, Fair JN, LeBreton M, Tamoufe U, Djoko CF, Rimoin AW, Wolfe ND. The Origin and Prevention of Pandemics. Clin Infect Dis. 2010; 50: 1636–1640.
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