DOI QR코드

DOI QR Code

Genetic signature of strong recent positive selection at interleukin-32 gene in goat

  • Asif, Akhtar Rasool (Key Lab of Animal Genetics, Breeding and Reproduction of Ministry Education, College of Animal Science and Technology, Huazhong Agricultural University) ;
  • Qadri, Sumayyah (Theriogenology Department, College of Veterinary and Animal Science, Jhang, Sub campus of University of Veterinary and Animal Sciences) ;
  • Ijaz, Nabeel (Key Lab of Animal Genetics, Breeding and Reproduction of Ministry Education, College of Animal Science and Technology, Huazhong Agricultural University) ;
  • Javed, Ruheena (Key Lab of Animal Genetics, Breeding and Reproduction of Ministry Education, College of Animal Science and Technology, Huazhong Agricultural University) ;
  • Ansari, Abdur Rahman (Theriogenology Department, College of Veterinary and Animal Science, Jhang, Sub campus of University of Veterinary and Animal Sciences) ;
  • Awais, Muhammd (Key Lab of Animal Genetics, Breeding and Reproduction of Ministry Education, College of Animal Science and Technology, Huazhong Agricultural University) ;
  • Younus, Muhammad (Theriogenology Department, College of Veterinary and Animal Science, Jhang, Sub campus of University of Veterinary and Animal Sciences) ;
  • Riaz, Hasan (Department of Biosciences, COMSATS Institute of Information Technology) ;
  • Du, Xiaoyong (Key Lab of Animal Genetics, Breeding and Reproduction of Ministry Education, College of Animal Science and Technology, Huazhong Agricultural University)
  • Received : 2015.11.19
  • Accepted : 2016.03.25
  • Published : 2017.07.01

Abstract

Objective: Identification of the candidate genes that play key roles in phenotypic variations can provide new information about evolution and positive selection. Interleukin (IL)-32 is involved in many biological processes, however, its role for the immune response against various diseases in mammals is poorly understood. Therefore, the current investigation was performed for the better understanding of the molecular evolution and the positive selection of single nucleotide polymorphisms in IL-32 gene. Methods: By using fixation index ($F_{ST}$) based method, IL-32 (9375) gene was found to be outlier and under significant positive selection with the provisional combined allocation of mean heterozygosity and $F_{ST}$. Using nucleotide sequences of 11 mammalian species from National Center for Biotechnology Information database, the evolutionary selection of IL-32 gene was determined using Maximum likelihood model method, through four models (M1a, M2a, M7, and M8) in Codeml program of phylogenetic analysis by maximum liklihood. Results: IL-32 is detected under positive selection using the $F_{ST}$ simulations method. The phylogenetic tree revealed that goat IL-32 was in close resemblance with sheep IL-32. The coding nucleotide sequences were compared among 11 species and it was found that the goat IL-32 gene shared identity with sheep (96.54%), bison (91.97%), camel (58.39%), cat (56.59%), buffalo (56.50%), human (56.13%), dog (50.97%), horse (54.04%), and rabbit (53.41%) respectively. Conclusion: This study provides evidence for IL-32 gene as under significant positive selection in goat.

Keywords

Positive Selection;Evolution;Interleukin-32;Goat

Acknowledgement

Supported by : National Nature Science Foundation of China, International Atomic Energy Agency(IAEA)

References

  1. Zeder MA, Hesse B. The initial domestication of goats (Capra hircus) in the Zagros Mountains 10,000 years ago. Science 2000;287:2254-7. https://doi.org/10.1126/science.287.5461.2254
  2. Zhao W, Zhong T, Wang LJ, Li L, Zhang HP. Extensive female-mediated gene flow and low phylogeography among seventeen goat breeds in southwest China. Biochem Genet 2014;52:355-64. https://doi.org/10.1007/s10528-014-9652-y
  3. Du L. Animal genetic resources in china: sheep and goats. 62. Beijing, China: Agriculture Press; 2011.
  4. Graham AL, Cattadori IM, Lloyd-Smith JO, Ferrari MJ, Bjornstad ON. Transmission consequences of coinfection: cytokines writ large? Trends Parasitol 2007;23:284-91. https://doi.org/10.1016/j.pt.2007.04.005
  5. Xu N, Li X, Zhong Y. Inflammatory cytokines: potential biomarkers of immunologic dysfunction in autism spectrum disorders. Mediators Inflamm 2015;Article ID 531518.
  6. Turner AK, Begon M, Jackson JA, Bradley JE, Paterson S. Genetic diversity in cytokines associated with immune variation and resistance to multiple pathogens in a natural rodent population. PLoS Genet 2011;7:e1002343-e. https://doi.org/10.1371/journal.pgen.1002343
  7. Brocker C, Thompson D, Matsumoto A, Nebert DW, Vasiliou V. Evolutionary divergence and functions of the human interleukin (IL) gene family. Hum Genomics 2010;5:30-55. https://doi.org/10.1186/1479-7364-5-1-30
  8. Commins SP, Borish L, Steinke JW. Immunologic messenger molecules: cytokines, interferons, and chemokines. J Allergy Clin Immunol 2010;125:S53-S72. https://doi.org/10.1016/j.jaci.2009.07.008
  9. Li W, Liu Y, Mukhtar MM, et al. Activation of interleukin-32 proinflammatory pathway in response to influenza A virus infection. PLoS One 2008;3:e1985-e. https://doi.org/10.1371/journal.pone.0001985
  10. Zhou Y, Zhu Y. Important role of the IL-32 inflammatory network in the host response against viral infection. Viruses 2015;7:3116-29. https://doi.org/10.3390/v7062762
  11. Kim S-H, Han S-Y, Azam T, Yoon D-Y, Dinarello CA. Interleukin-32: a cytokine and inducer of $TNF{\alpha}$. Immunity 2005;22:131-42.
  12. Kim S. Interleukin-32 in inflammatory autoimmune diseases. Immune Netw 2014;14:123-7. https://doi.org/10.4110/in.2014.14.3.123
  13. Blows MW, Hoffmann AA. A reassessment of genetic limits to evolutionary change. Ecology 2005;86:1371-84. https://doi.org/10.1890/04-1209
  14. Agrawal AF, Stinchcombe JR. How much do genetic covariances alter the rate of adaptation? ProcR Soc London B Biol Sci 2009;276:1183-91. https://doi.org/10.1098/rspb.2008.1671
  15. Mukesh M, Sodhi M, Bhatia S, Mishra B. Genetic diversity of Indian native cattle breeds as analysed with 20 microsatellite loci. J Anim Breed Genet 2004;121:416-24. https://doi.org/10.1111/j.1439-0388.2004.00468.x
  16. Ellegren H, Moore S, Robinson N, et al. Microsatellite evolution--a reciprocal study of repeat lengths at homologous loci in cattle and sheep. Mol Biol Evol 1997;14:854-60. https://doi.org/10.1093/oxfordjournals.molbev.a025826
  17. Ijaz N, Liu G, Jiang X, et al. Genetic signature of strong recent positive selection at $FSH{\beta}$ gene in goats. Pak J Agri Sci 2015;52:1113-8.
  18. Barendse W, Harrison BE, Bunch RJ, Thomas MB, Turner LB. Genome wide signatures of positive selection: the comparison of independent samples and the identification of regions associated to traits. BMC Genomics 2009;10:1. https://doi.org/10.1186/1471-2164-10-1
  19. Stella A, Ajmone-Marsan P, Lazzari B, Boettcher P. Identification of selection signatures in cattle breeds selected for dairy production. Genetics 2010;185:1451-61. https://doi.org/10.1534/genetics.110.116111
  20. Beaumont MA, Nichols RA. Evaluating loci for use in the genetic analysis of population structure. Proc R Soc London B Biol Sci 1996; 263:1619-26. https://doi.org/10.1098/rspb.1996.0237
  21. Akey JM, Zhang G, Zhang K, Jin L, Shriver MD. Interrogating a highdensity SNP map for signatures of natural selection. Genome Res 2002;12:1805-14. https://doi.org/10.1101/gr.631202
  22. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol 2013;30:2725-9. https://doi.org/10.1093/molbev/mst197
  23. Li K-B. ClustalW-MPI: ClustalW analysis using distributed and parallel computing. Bioinformatics 2003;19:1585-6. https://doi.org/10.1093/bioinformatics/btg192
  24. Pond SLK, Muse SV. HyPhy: hypothesis testing using phylogenies. Statistical methods in molecular evolution: Springer; 2005. p. 125-81.
  25. Brown E, Pilkington J, Nussey D, et al. Detecting genes for variation in parasite burden and immunological traits in a wild population: testing the candidate gene approach. Mol Ecol 2013;22:757-73. https://doi.org/10.1111/j.1365-294X.2012.05757.x
  26. 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;164:1229-36.
  27. Barreiro LB, Quintana-Murci L. From evolutionary genetics to human immunology: how selection shapes host defence genes. Nat Rev Genet 2010;11:17-30. https://doi.org/10.1038/nrg2698
  28. Neves F, Abrantes J, Steinke JW, Esteves PJ. Maximum-likelihood approaches reveal signatures of positive selection in IL genes in mammals. Innate immun 2014;20:184-91. https://doi.org/10.1177/1753425913486687
  29. Hayes BJ, Lien S, Nilsen H, et al. The origin of selection signatures on bovine chromosome 6. Anim Genet 2008;39:105-11. https://doi.org/10.1111/j.1365-2052.2007.01683.x
  30. Consortium BH. Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds. Science 2009;324:528-32. https://doi.org/10.1126/science.1167936
  31. Ryu J, Lee C. Identification of contemporary selection signatures using composite log likelihood and their associations with marbling score in Korean cattle. Anim Genet 2014;45:765-70. https://doi.org/10.1111/age.12209
  32. Qanbari S, Pimentel E, Tetens J, et al. A genome-wide scan for signatures of recent selection in Holstein cattle. Anim Genet 2010;41:377-89.
  33. Bustamante CD, Fledel-Alon A, Williamson S, et al. Natural selection on protein-coding genes in the human genome. Nature 2005;437:1153-7. https://doi.org/10.1038/nature04240
  34. Nielsen R, Bustamante C, Clark AG, et al. A scan for positively selected genes in the genomes of humans and chimpanzees. PLoS Biol 2005;3:e170. https://doi.org/10.1371/journal.pbio.0030170
  35. Thompson EE, Kuttab-Boulos H, Witonsky D, et al. CYP3A variation and the evolution of salt-sensitivity variants. Am J Hum Genet 2004; 75:1059-69. https://doi.org/10.1086/426406
  36. Fullerton SM, Bartoszewicz A, Ybazeta G, et al. Geographic and haplotype structure of candidate type 2 diabetes-susceptibility variants at the calpain-10 locus. Am J Hum Genet 2002;70:1096-106. https://doi.org/10.1086/339930
  37. Rockman MV, Hahn MW, Soranzo N, Goldstein DB, Wray GA. Positive selection on a human-specific transcription factor binding site regulating IL4 expression. Curr Biol 2003;13:2118-23. https://doi.org/10.1016/j.cub.2003.11.025
  38. Sakagami T, Witherspoon D, Nakajima T, et al. Local adaptation and population differentiation at the interleukin 13 and interleukin 4 loci. Genes Immun 2004;5:389-97. https://doi.org/10.1038/sj.gene.6364109
  39. Akey JM, Eberle MA, Rieder MJ, et al. Population history and natural selection shape patterns of genetic variation in 132 genes. PLoS Biol 2004;2:1591-9.
  40. Rockman MV, Hahn MW, Soranzo N, et al. Positive selection on MMP3 regulation has shaped heart disease risk. Curr Biol 2004;14:1531-9. https://doi.org/10.1016/j.cub.2004.08.051
  41. Nakajima T, Wooding S, Sakagami T, et al. Natural selection and population history in the human angiotensinogen gene (AGT): 736 complete AGT sequences in chromosomes from around the world. Am J Hum Genet 2004;74:898-916. https://doi.org/10.1086/420793

Cited by

  1. Positive selection of IL-33 in adaptive immunity of domestic Chinese goats vol.7, pp.6, 2017, https://doi.org/10.1002/ece3.2813