The influence of secondary structure, selection and recombination on rubella virus nucleotide substitution rate estimates

Virology Journal

Gordon W Harkins²  Darren P Martin¹  Brejnev M Muhire¹  Emil P Tanov²  Leendert J Cloete² 

[1] Institute of Infectious Diseases and Molecular Medicine, Computational Biology Group, University of Cape Town, Cape Town, South Africa;South African National Bioinformatics Institute, SA Medical Research Council Unit for Bioinformatics Capacity Development, University of the Western Cape, Cape Town, South Africa

关键词: Bayesian phylogenetic analyses;    Nucleic acid secondary structure;    Recombination;    Synonymous substitution rates;    Nucleotide substitution rates;    Congenital rubella syndrome;    Rubella virus;   

Others  :  1148416
DOI  :  10.1186/1743-422X-11-166

 received in 2014-02-28, accepted in 2014-09-11,  发布年份 2014

【 摘 要 】

Background

Annually, rubella virus (RV) still causes severe congenital defects in around 100 000 children globally. An attempt to eradicate RV is currently underway and analytical tools to monitor the global decline of the last remaining RV lineages will be useful for assessing the effectiveness of this endeavour. RV evolves rapidly enough that much of this information might be inferable from RV genomic sequence data.

Methods

Using BEASTv1.8.0, we analysed publically available RV sequence data to estimate genome-wide and gene-specific nucleotide substitution rates to test whether current estimates of RV substitution rates are representative of the entire RV genome. We specifically accounted for possible confounders of nucleotide substitution rate estimates, such as temporally biased sampling, sporadic recombination, and natural selection favouring either increased or decreased genetic diversity (estimated by the PARRIS and FUBAR methods), at nucleotide sites within the genomic secondary structures (predicted by the NASP method).

Results

We determine that RV nucleotide substitution rates range from 1.19 × 10^-3 substitutions/site/year in the E1 region to 7.52 × 10^-4 substitutions/site/year in the P150 region. We find that differences between substitution rate estimates in different RV genome regions are largely attributable to temporal sampling biases such that datasets containing higher proportions of recently sampled sequences, will tend to have inflated estimates of mean substitution rates. Although there exists little evidence of positive selection or natural genetic recombination in RV, we show that RV genomes possess pervasive biologically functional nucleic acid secondary structure and that purifying selection acting to maintain this structure contributes substantially to variations in estimated nucleotide substitution rates across RV genomes.

Conclusion

Both temporal sampling biases and purifying selection favouring the conservation of RV nucleic acid secondary structures have an appreciable impact on substitution rate estimates but do not preclude the use of RV sequence data to date ancestral sequences. The combination of uniformly high substitution rates across the RV genome and strong temporal structure within the available sequence data, suggests that such data should be suitable for tracking the demographic, epidemiological and movement dynamics of this virus during eradication attempts.

【 授权许可】

   
2014 Cloete et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

【 参考文献 】

• [1]Peltola H, Davidkin I, Paunio M, Valle M, Leinikki P, Heinonen O: Mumps and rubella eliminated from Finland. JAMA 2000, 284:2643-2647.
• [2]Icenogle J, Frey T, Abernathy E, Reef S, Schnurr D, Stewart J: Genetic analysis of rubella viruses found in the United States between 1966 and 2004: evidence that indigenous rubella viruses have been eliminated. Clin Infect Dis 2006, 43(Suppl 3):S133-S140.
• [3]Song N, Gao Z, Wood J, Hueston L, Gilbert G, MacIntyre C, Quinn H, Menzies R, McIntyre P: Current epidemiology of rubella and congenital rubella syndrome in Australia: progress towards elimination. Vaccine 2012, 30:4073-4078.
• [4]World Health Organization (WHO): Global measles and rubella strategic plan: 2012- 2020. Geneva: World Health Organization Press; 2012:10-13.
• [5]Centers for Disease Control and Prevention (CDC): Elimination of rubella and congenital rubella syndrome - United States, 1969 - 2004. MMWR Morb Mortal Wkly Rep 2005, 54:279-282.
• [6]Frey T: Molecular biology of rubella virus. Adv Virus Res 1994, 44:69-160.
• [7]World Health Organization (WHO): Standardization of the nomenclature for genetic characteristics of wild-type rubella viruses. Wkly Epidemiol Rec 2005, 80:126-132.
• [8]World Health Organization (WHO): Update of standard nomenclature for wild-type rubella viruses. Wkly Epidemiol Rec 2007, 82:209-224.
• [9]World Health Organization (WHO): Rubella virus nomenclature update: 2013. Wkly Epidemiol Rec 2013, 88:337-348.
• [10]Katow S: Molecular epidemiology of rubella virus in Asia: utility for reduction in the burden of diseases due to congenital rubella syndrome. Pediatr Int 2004, 46:207-213.
• [11]Abernathy E, Hübschen J, Muller C, Jin L, Brown D, Komase K, Mori Y, Xu W, Zhu Z, Siqueira M, Shulga S, Tikhonova N, Pattamadilok S, Incomserb P, Smit S, Akoua-Koffi C, Bwogi J, Lim W, Woo G, Triki H, Jee Y, Mulders M, de Filippis A, Ahmed H, Ramamurty N, Featherstone D, Icenogle J: Status of global virologic surveillance for rubella viruses. J Infect Dis 2011, 204(Suppl 1):S524-S532.
• [12]Jenkins G, Rambaut A, Pybus O, Holmes E: Rates of molecular evolution in RNA viruses: a quantitative phylogenetic analysis. J Mol Evol 2002, 54:156-165.
• [13]Zhu Z, Cui A, Wang H, Zhang Y, Liu C, Wang C, Zhou S, Chen X, Zhang Z, Feng D, Wang Y, Chen H, Pan Z, Zeng X, Zhou J, Wang S, Chang X, Lei Y, Tian H, Liu Y, Zhou S, Zhan J, Chen H, Gu S, Tian X, Liu J, Chen Y, Fu H, Yang X, Zheng H, Liu L, Zheng L, Gao H, He J, Sun L, Xu W: Emergence and continuous evolution of genotype 1E rubella viruses in China. J Clin Microbiol 2011, 50:353-363.
• [14]Cherian S, Walimbe A, Jadhav S, Gandhe S, Hundekar S, Mishra A, Arankalle V: Evolutionary rates and timescale comparison of Chikungunya viruses inferred from the whole genome/E1 gene with special reference to the 2005–07 outbreak in the Indian subcontinent. Infect Genet Evol 2009, 9:16-23.
• [15]Volk S, Chen R, Tsetsarkin K, Adams A, Garcia T, Sall A, Nasar F, Schuh A, Holmes E, Higgs S, Maharaj P, Brault A, Weaver S: Genome-scale phylogenetic analyses of chikungunya virus reveal independent emergences of recent epidemics and various evolutionary rates. J Virol 2010, 84:6497-6504.
• [16]Suwannakarn K, Theamboonlers A, Poovorawan Y: Molecular genome tracking of East, Central and South African genotype of Chikungunya virus in South–east Asia between 2006 and 2009. Asian Pac J Trop Med 2011, 4:535-540.
• [17]Simmonds P, Tuplin A, Evans D: Detection of genome-scale ordered RNA structure (GORS) in genomes of positive-stranded RNA viruses: implications for virus evolution and host persistence. RNA 2004, 10:1337-1351.
• [18]Markham N, Zuker M: UNAFold: software for nucleic acid folding and hybridization. Methods Mol Biol 2008, 453:3-31.
• [19]Semegni JY, Wamalwa M, Gaujoux R, Harkins GW, Gray A, Martin DP: NASP: a parallel program for identifying evolutionarily conserved nucleic acid secondary structures from nucleotide sequence alignments. Bioinforma Oxf Engl 2011, 27:2443-2445.
• [20]Chen M, Frey T: Mutagenic analysis of the 3’ cis-acting elements of the rubella virus genome. J Virol 1999, 73:3386-3403.
• [21]Tzeng W, Frey T: Mapping the rubella virus subgenomic promoter. J Virol 2002, 76:3189-3201.
• [22]Pugachev K, Frey T: Effects of defined mutations in the 5’ nontranslated region of rubella virus genomic RNA on virus viability and macromolecule synthesis. J Virol 1998, 72:641-650.
• [23]Murrell B, Moola S, Mabona A, Weighill T, Sheward D, Kosakovsky Pond S, Scheffler K: FUBAR: a fast, unconstrained bayesian approximation for inferring selection. Mol Biol Evol 2013, 30:1196-1205.
• [24]Scheffler K, Martin D, Seoighe C: Robust inference of positive selection from recombining coding sequences. Bioinformatics 2006, 22:2493-2499.
• [25]Poon A, Lewis F, Frost S, Kosakovsky Pond S: Spidermonkey: rapid detection of co-evolving sites using Bayesian graphical models. Bioinformatics 2008, 24:1949-1950.
• [26]Muhire B, Golden M, Murrell B, Lefeuvre P, Lett J, Gray A, Poon A, Ngandu N, Semegni Y, Tanov EP, Monjane A, Harkins G, Varsani A, Shepherd D, Martin D: Evidence of pervasive biologically functional secondary structures within the genomes of eukaryotic single-stranded DNA viruses. J Virol 2014, 88:1972-1989.
• [27]Schierup M, Hein J: Recombination and the molecular clock. Mol Biol Evol 2000, 17:1578-1579.
• [28]Posada D, Crandall K: The effect of recombination on the accuracy of phylogeny estimation. J Mol Evol 2002, 54:396-402.
• [29]Zheng D, Frey T, Icenogle J, Katow S, Abernathy E, Song K, Xu W, Yarulin V, Desjatskova R, Aboudy Y: Global distribution of rubella virus genotypes. Emerg Infect Dis 2003, 9:1523.
• [30]Zhou Y, Ushijima H, Frey T: Genomic analysis of diverse rubella virus genotypes. J Gen Virol 2007, 88:932-941.
• [31]Abernathy E, Chen M, Bera J, Shrivastava S, Kirkness E, Zheng Q, Bellini W, Icenogle J: Analysis of whole genome sequences of 16 strains of rubella virus from the United States, 1961–2009. Virol J 2013, 10:1-9. BioMed Central Full Text
• [32]Hofmann J, Renz M, Meyer S, von Haeseler A, Liebert U: Phylogenetic analysis of rubella virus including new genotype I isolates. Virus Res 2003, 96:123-128.
• [33]Martin D, Lemey P, Lott M, Moulton V, Posada D, Lefeuvre P: RDP3: a flexible and fast computer program for analyzing recombination. Bioinformatics 2010, 26:2462-2463.
• [34]Han G, Worobey M: Homologous recombination in negative sense RNA viruses. Viruses 2011, 3:1358-1373.
• [35]Drummond A, Pybus O, Rambaut A: Inference of viral evolutionary rates from molecular sequences. Adv Parasitol 2003, 54:331-358.
• [36]Rambaut A: Path-O-Gen. 2013. http://tree.bio.ed.ac.uk/software/pathogen webcite
• [37]Duffy S, Shackelton L, Holmes E: Rates of evolutionary change in viruses: patterns and determinants. Nat Rev Genet 2008, 9:267-276.
• [38]Edgar R: MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucl Acids Res 2004, 32:1792-1797.
• [39]Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S: MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol 2011, 28:2731-2739.
• [40]Muhire B, Martin D, Brown J, Navas-Castillo J, Moriones E, Zerbini F, Rivera-Bustamante R, Malathi V, Briddon R, Varsani A: A genome-wide pairwise-identity-based proposal for the classification of viruses in the genus Mastrevirus (family Geminiviridae). Arch Virol 2013, 158:1411-1424.
• [41]Rambaut A, Suchard M, Drummond A: Tracer. 2009. http://tree.bio.ed.ac.uk/software/tracer/ webcite
• [42]Golden M, Martin D: DOOSS: a tool for visual analysis of data overlaid on secondary structures. Bioinformatics 2013, 29:271-272.
• [43]Martin D, Lemey P, Posada D: Analysing recombination in nucleotide sequences. Mol Ecol Res 2011, 11:943-955.
• [44]Anisimova M, Nielsen R, Yang Z: Effect of recombination on the accuracy of the likelihood method for detecting positive selection at amino acid sites. Genetics 2003, 164:1229-1236.
• [45]Kosakovsky Pond S, Posada D, Gravenor M, Woelk C, Frost S: GARD: a genetic algorithm for recombination detection. Bioinformatics 2006, 22:3096-3098.
• [46]Delport W, Poon A, Frost S, Kosakovsky Pond S: Datamonkey 2010: a suite of phylogenetic analysis tools for evolutionary biology. Bioinformatics 2010, 26:2455-2457.
• [47]Delport W, Poon A, Frost S, Kosakovsky Pond S: Datamonkey Webserver. 2010. http://www.datamonkey.org webcite
• [48]Drummond A, Rambaut A: BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol 2007, 7:214. BioMed Central Full Text