Abstract
Cryptosporidiosis is one of the most common human infectious diseases globally. The gp60 gene has been adopted as a key marker for molecular epidemiological investigations into this protozoan disease because of the capability to characterize genotypes and detect variants within Cryptosporidium species infecting humans. However, we know relatively little about the potential spatial and temporal variation in population demography that can be inferred from this gene beyond that it is recognized to be under selective pressure. Here, we analyzed the genetic variation in time and space within two putative populations of Cryptosporidium in New Zealand to infer the processes behind the patterns of sequence polymorphism. Analyses using Tajima’s D, Fu, and Li’s D* and F* tests show significant departures from neutrality in some populations and indicate the selective maintenance of alleles within some populations. Demographic analyses showed distortions in the pattern of the genetic variability caused by high recombination rates and population expansion, which was observed in case notification data. Our results showed that processes acting on populations that have similar effects can be distinguished from one another and multiple processes can be detected acting at the same time. These results are significant for prediction of the parasite dynamics and potential mechanisms of long-term changes in the risk of cryptosporidiosis in humans.
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References
Abal-Fabeiro JL, Maside X, Bello X, Llovo J, Bartolomé C (2013) Multilocus patterns of genetic variation across cryptosporidium species suggest balancing selection at the gp60 locus. Mol Ecol 22(18):4723–4732. doi:10.1111/mec.12425
Braverman JM, Hudson RR, Kaplan NL, Langley CH, Stephan W (1995) The hitchhiking effect on the site frequency spectrum of DNA polymorphisms. Genetics 140(2):783–796
Bruen TC, Philippe H, Bryant D (2006) A simple and robust statistical test for detecting the presence of recombination. Genetics 172(4):2665–2681. doi:10.1534/genetics.105.048975
Caldwell KS, Michelmore RW (2009) Arabidopsis thaliana Genes encoding defense signaling and recognition proteins exhibit contrasting evolutionary dynamics. Genetics 181(2):671–684. doi:10.1534/genetics.108.097279
Checkley W et al (2015) A review of the global burden, novel diagnostics, therapeutics, and vaccine targets for cryptosporidium. Lancet Infect Dis 15(1):85–94. doi:10.1016/S1473-3099(14)70772-8
Corbett-Detig RB, Hartl DL, Sackton TB (2015) Natural selection constrains neutral diversity across a wide range of species. PLoS Biol 13(4):e1002112. doi:10.1371/journal.pbio.1002112
Crawford AJ (2003) Huge populations and old species of costa Rican and Panamanian dirt frogs inferred from mitochondrial and nuclear gene sequences. Mol Ecol 12(10):2525–2540
Drummond AJ, et al. (2012) Geneious v.6.05. Available from http://www.geneious.com
Drumo R et al (2012) Evidence of host-associated populations of Cryptosporidium parvum in Italy. Appl Environ Microbiol 78(10):3523–3529. doi:10.1128/aem.07686-11
ESR (2017) New Zealand Public Health surveillance report, vol 15. Wellington, New Zealand
Fay JC, Wu C-I (1999) A human population bottleneck can account for the discordance between patterns of mitochondrial versus nuclear DNA variation. Mol Biol Evol 16(7):1003–1005
Fisher RA (1930) The genetical theory of natural selection. Oxford University Press, Oxford
Fu Y-X, Li W-H (1993) Statistical tests of neutrality of mutations. Genetics 133:693–709
Glaberman S, Moore JE, Lowery CJ, Chalmers RM, Sulaiman I, Elwin K (2002) Three drinking-water-associated cryptosporidiosis outbreaks, Northern Ireland. Emerg Infect Dis 8 doi:10.3201/eid0806.010368
Guo Y, et al. (2015) Comparative genomic analysis reveals occurrence of genetic recombination in virulent Cryptosporidium hominis subtypes and telomeric gene duplications in Cryptosporidium parvum. BMC Genomics 16(320) doi:10.1186/s12864-015-1517-1
Haldane JBS (1932) The causes of evolution. Longmans, Green & Co., London
Hammer MF et al (2004) Heterogeneous patterns of variation among multiple human X-linked loci: the possible role of diversity-reducing selection in non-Africans. Genetics 167(4):1841–1853. doi:10.1534/genetics.103.025361
Hedrick PW (2011) Population genetics of malaria resistance in humans. Heredity 107(4):283–304. doi:10.1038/hdy.2011.16
Horn B, Lopez L, Cressey P, Pirie R (2014) Annual report concerning foodborne disease in New Zealand 2013. ESR, Christchurch
Hudson RR (1990) Gene genealogies and the coalescent process. In: Futuyma D, Antonovics J (eds) Oxford Surveys in Evolutionary Biology, volume 7. Oxford Surveys in Evolutionary Biology, vol 7, p 1–44
Hughes AL (2007) Looking for Darwin in all the wrong places: the misguided quest for positive selection at the nucleotide sequence level. Heredity 99(4):364–373
Hughes AL, Nei M (1992) Models of host-parasite interaction and MHC polymorphism. Genetics 132(3):863–864
Huson DH, Bryant D (2006) Application of phylogenetic networks in evolutionary studies. Mol Biol Evol 23(2):254–267. doi:10.1093/molbev/msj030
Jensen JD, Wong A, Aquadro CF (2007) Approaches for identifying targets of positive selection. Trends Genet 23(11):568–577. doi:10.1016/j.tig.2007.08.009
Lewontin RC, Krakauer J (1973) Distribution of gene frequency as a test of the theory of the selective neutrality of polymorphisms. Genetics 74(1):175–195
Librado P, Rozas J (2009) DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25(11):1451–1452. doi:10.1093/bioinformatics/btp187
Liu K et al (2012) SATé-II: very fast and accurate simultaneous estimation of multiple sequence alignments and phylogenetic trees. Syst Biol 61(1):90–106. doi:10.1093/sysbio/syr095
Martin DP, Lemey P, Posada D (2011) Analysing recombination in nucleotide sequences. Mol Ecol Resour 11(6):943–955. doi:10.1111/j.1755-0998.2011.03026.x
Maynard Smith J, Haigh J (1974) The hitch-hiking effect of a favourable gene. Cambridge University Press 23:23–35. doi:10.1017/S0016672300014634
Na L et al (2013) Genetic recombination and Cryptosporidium hominis virulent subtype IbA10G2. Emerg Infect Dis 19(10):1573. doi:10.3201/eid1910.121361
O’Connor RM, Wanyiri JW, Cevallos AM, Priest JW, Ward HD (2007) Cryptosporidium parvum Glycoprotein gp40 localizes to the sporozoite surface by association with gp15. Mol Biochem Parasitol 156(1):80–83. doi:10.1016/j.molbiopara.2007.07.010
Polley SD, Conway DJ (2001) Strong diversifying selection on domains of the Plasmodium falciparum apical membrane antigen 1 gene. Genetics 158(4):1505–1512
Rogers AR, Harpending H (1992) Population growth makes waves in the distribution of pairwise genetic differences. Mol Biol Evol 9(3):552–569
Snel SJ, Baker MG, Kamalesh V, French N, Learmonth J (2009) A tale of two parasites: the comparative epidemiology of cryptosporidiosis and giardiasis. Epidemiol Infect 137(11):1641–1650. doi:10.1017/S0950268809002465
Strong WB, Gut J, Nelson RG (2000) Cloning and sequence analysis of a highly polymorphic Cryptosporidium parvum gene encoding a 60-kilodalton glycoprotein and characterization of its 15- and 45-kilodalton zoite surface antigen products. Infect Immun 68(7):4117–4134
Tajima F (1989a) The effect of change in population size on DNA polymorphism. Genetics 123(3):597–601
Tajima F (1989b) Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123:585–595
Tamura K, Stecher G, Peterson D, Filipski A, Kumar S (2013) MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol 30(12):2725–2729. doi:10.1093/molbev/mst197
Waldron LS, Ferrari BC, Power ML (2009) Glycoprotein 60 diversity in C. hominis and C. parvum causing human cryptosporidiosis in NSW, Australia. Exp Parasitol 122(2):124–127. doi:10.1016/j.exppara.2009.02.006
Weedall GD, Conway DJ (2010) Detecting signatures of balancing selection to identify targets of anti-parasite immunity. Trends Parasitol 26(7):363–369. doi:10.1016/j.pt.2010.04.002
Widmer G (2009) Meta-analysis of a polymorphic surface glycoprotein of the parasitic protozoa Cryptosporidium parvum and Cryptosporidium hominis. Epidemiol Infect 137(12):1800–1808. doi:10.1017/S0950268809990215
Widmer G, Lee Y (2010) Comparison of single- and multilocus genetic diversity in the protozoan parasites Cryptosporidium parvum and C. hominis. Appl Environ Microbiol 76(19):6639–6644. doi:10.1128/AEM.01268-10
Widmer G, Lee Y, Hunt P, Martinelli A, Tolkoff M, Bodi K (2012) Comparative genome analysis of two Cryptosporidium parvum isolates with different host range. Infect Genet Evol 12(6):1213–1221. doi:10.1016/j.meegid.2012.03.027
Widmer G, Ras R, Chalmers RM, Elwin K, Desoky E, Badawy A (2015) Population structure of natural and propagated isolates of Cryptosporidium parvum, C. hominis and C. meleagridis. Environ Microbiol 17(4):984–993. doi:10.1111/1462-2920.12447
Wright S (1920) The relative importance of heredity and environment in determining the piebald pattern of Guinea-pigs. Proc Natl Acad Sci 6(6):320–332
Yang Z, Bielawski JP (2000) Statistical methods for detecting molecular adaptation. Trends Ecol Evol 15(12):496–503. doi:10.1016/S0169-5347(00)01994-7
Acknowledgments
The first author (JCGR) thanks the New Zealand Ministry of Health for support. mEpiLab members provided useful discussions on different stages of the study.
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Table S1
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Figure S1
Mismatch distribution analyses showing the frequencies of pairwise differences of C. hominis in Auckland 2012 (A), Auckland 2013 (B), Auckland 2014 (C), Auckland 2015 (D), and Canterbury 2014 (E). The observed distributions (red dotted line) are compared to the expected distribution (black solid line) under a model of sudden expansion. X-axis: number of pairwise differences, Y-axis: frequency. (JPEG 3507 kb)
Figure S2
Mismatch distribution analyses showing the frequencies of pairwise differences of C. parvum in Auckland 2010 (A), Auckland 2011 (B), Auckland 2012 (C), Auckland 2013 (D), Auckland 2014 (E), Auckland 2015 (F), Canterbury 2010 (G), Canterbury 2013 (H), Canterbury 2014 (I) and Canterbury 2015 (J). The observed distributions (red dotted line) are compared to the expected distribution (black solid line) under a model of sudden expansion. X-axis: number of pairwise differences, Y-axis: frequency. (JPEG 5661 kb)
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Appendix 1
Appendix 1
Alignments of gp60 sequences from the populations used in this study. Files are in nexus format with blocks of information containing the number of individuals per year of C. hominis in Auckland (Dataset 1), C. hominis in Canterbury (Dataset 2), C. parvum in Auckland (Dataset 3) and C. parvum in Canterbury (Dataset 4).
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Garcia-R, J.C., Hayman, D.T.S. Evolutionary processes in populations of Cryptosporidium inferred from gp60 sequence data. Parasitol Res 116, 1855–1861 (2017). https://doi.org/10.1007/s00436-017-5459-1
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DOI: https://doi.org/10.1007/s00436-017-5459-1