Advertisement

Whole genome comparative analysis of CpG islands in camelid and other mammalian genomes

Abstract

Camels bear unique genotypes and phenotypes for adaptation in their environment, and as such could be very useful in the weather extremes accelerated by global climate change. Published sequences of Camelidae genomes provide an opportunity to elucidate the genomic architecture of these animals. CpG islands (CGIs) sequence patterns in complex genomes play important roles in gene regulation via epi-genetic change. Comparative large-scale genome analysis of CGIs in Camelidae was carried out using five different CGI detection algorithms, evaluating numbers of CGIs, CGI density and CGI length distribution in Camelidae. All algorithms identified the alpaca genome as having the largest number of CGIs, CGI density and average length of CGIs, though, the CGI length distribution and their number could be implicitly attributed to the characteristics of an algorithm for CGI identification rather than that of the species. In addition, high algorithm-to-algorithm variability of the observed CGI properties was shown. It was well expected, since there is no clear definition of a CGI and different algorithms could identify different genome regions as CGIs. Comparison with other mammalian genomes (human, mouse, dog, horse and cow) shows that CGIs features in cow were most similar to camelid genomes. Further analysis of camelid genomes may shed more light on molecular origins and mechanisms of adaptation in these extreme heat-adapted animals.

This is a preview of subscription content, log in to check access.

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

References

  1. Altaher, Y., Kandeel, M., 2015. Molecular analysis of some camel cytochrome P450 enzymes reveals lower evolution and drug-binding properties. J. Biomol. Struct. Dyn. 33, 1–10, https://doi.org/10.1080/07391102.2015.1014423.

  2. Barturen, G., Geisen, S., Dios, F., Hamberg, E.J.M., Hackenberg, M., Oliver, J.L., 2013. CpGislandEVO: a database and genome browser for comparative evolutionary genomics of CpG islands. Biomed Res. Int., 709042, https://doi.org/10.1155/2013/709042.

  3. Bird, A.P., 1987. CpG islands as gene markers in the vertebrate nucleus. Trends Genet. 3, 342–347, https://doi.org/10.1016/0168-9525(87)90294-0.

  4. Chuang, L.Y., Yang, C.H., Lin, M.C., 2012. CpGPAP: CpG island predictor analysis platform. BMC Genet. 13, 13, https://doi.org/10.1186/1471-2156-13-13.

  5. Cocozza, S., Scala, G., Miele, G., Castaldo, I., Monticelli, A., 2013. A distinct group of CpG islands shows differential DNA methylation between replicas of the same cell line in vitro. BMC Genomics 14, 692, https://doi.org/10.1186/1471-2164-14-692.

  6. Cui, P., Ji, R., Ding, F., Qi, D., Gao, H., Meng, H., Yu, J., Hu, S., Zhang, H., 2007. A complete mitochondrial genome sequence of the wild two-humped camel (Camelus bactrianus ferus): an evolutionary history of camelidae. BMC Genomics 8, 241, https://doi.org/10.1186/1471-2164-8-241.

  7. Daufresne, M., Lengfellner, K., Sommer, U., 2009. Global warming benefits the small in aquatic ecosystems. PNAS 106, 12788–12793, https://doi.org/10.1073/pnas.0902080106.

  8. Deaton, A.M., Bird, A., 2011. CpG islands and the regulation of transcription. Gene. Dev. 25, 1010–1022, https://doi.org/10.1101/gad.2037511.

  9. Dillon, M.E., Wang, G., Huey, R.B., 2010. Global metabolic impacts of recent climate warming. Nature 467, 704–706, https://doi.org/10.1038/nature09407.

  10. Edgar, R., Tan, P.P., Portales-Casamar, E., Pavlidis, P., 2014. Meta-analysis of human methylomes reveals stably methylated sequences surrounding CpG islands associated with high gene expression. Epigenet. Chromatin. 7, 28, https://doi.org/10.1186/1756-8935-7-28.

  11. FAO, 2014. FAOSTAT DATA (Accessed October 2014) https://doi.org/faostat3.fao.org/faostat-gateway/go/to/download/Q/QA/E.

  12. Faye, B., 2014. The camel today: assets and potentials. Anthropozoologica 49, 167–176, https://doi.org/10.5252/az2014n2a01.

  13. Gardiner-Garden, M., Frommer, M., 1987. CpG islands in vertebrate genomes. J. Mol. Biol. 196, 261–282, https://doi.org/10.1016/0022-2836(87)90689-9.

  14. Glass, J.L., Thompson, R.F., Khulan, B., Figueroa, M.E., Olivier, E.N., Oakley, E.J., Van Zant, G., Bouhassira, E.E., Melnick, A., Golden, A., Fazzari, M.J., Greally, J.M., 2007. CG dinucleotide clustering is a species-specific property of the genome. Nucleic Acids Res. 35, 6798–6807, https://doi.org/10.1093/nar/gkm489.

  15. Hackenberg, M., Barturen, G., Carpena, P., Luque-Escamilla, P.L., Previti, C., Oliver, J.L., 2010. Prediction of CpG-island function: CpG clustering vs. Sliding-window methods. BMC Genomics 11, 327, https://doi.org/10.1186/1471-2164-11-327.

  16. Hackenberg, M., Previti, C., Luque-Escamilla, P.L., Carpena, P., Martinez-Aroza, J., Oliver, J.L., 2006. CpGcluster: a distance-based algorithm for CpG-island detection. BMC Bioinf. 7, 446, https://doi.org/10.1186/1471-2105-7-446.

  17. Haerter, J.O., Lövkvist, C., Dodd, I.B., Sneppen, K., 2014. Collaboration between CpG sites is needed for stable somatic inheritance of DNA methylation states. Nucleic Acids Res. 42, 2235–2244, https://doi.org/10.1093/nar/gkt1235.

  18. Han, L., Su, B., Li, W.H., Zhao, Z., 2008. CpG island density and its correlations with genomic features in mammalian genomes. Genome Biol. 9, R79, https://doi.org/10.1186/gb-2008-9-5-r79.

  19. Han, L., Zhao, Z., 2008. Comparative analysis of CpG islands in four fish genomes. Comp. Funct. Genom, 565631, https://doi.org/10.1155/2008/565631.

  20. Han, L., Zhao, Z., 2009a. Contrast features of CpG islands in the promoter and other regions in the dog genome. Genomics 94, 117–124, https://doi.org/10.1016/j.ygeno.2009.04.007.

  21. Han, L., Zhao, Z., 2009b. CpG islands or CpG clusters: how to identify functional GC-rich regions in a genome. BMC Bioinf. 10, 1–6, https://doi.org/10.1186/1471-2105-10-65.

  22. Illingworth, R.S., Bird, A.P., 2009. CpG islands-‘a rough guide’. FEBS Lett. 583, 1713–1720, https://doi.org/10.1016/j.febslet.2009.04.012.

  23. Irizarry, R.A., Wu, H., Feinberg, A.P., 2009. A species-generalized probabilistic model-based definition of CpG islands. Mamm. Genome 20, 674–680, https://doi.org/10.1007/s00335-009-9222-5.

  24. Jair, K.W, Bachman, K.E., Suzuki, H., Ting, A.H., Rhee, I., Yen, R.W, Baylin, S.B., Schuebel, K.E., 2006. De novo CpG island methylation in human cancer cells. Cancer Res. 66, 682–692, https://doi.org/10.1158/0008-5472.CAN-05-1980.

  25. Jia, M., Gao, X., Zhang, Y., Hoffmeister, M., Brenner, H., 2016. Different definitions of CpG island methylator phenotype and outcomes of colorectal cancer: a systematic review. Clin. Epigenet. 8, 25, https://doi.org/10.1186/s13148-016-0191-8.

  26. Jiang, C., Han, L., Su, B., Li, W.H., Zhao, Z., 2007. Features and trend of loss of promoter-associated CpG islands in the human and mouse genomes. Mol. Biol. Evol. 24, 1991–2000, https://doi.org/10.1093/molbev/msm128.

  27. Jirimutu, Wang, Z., Ding, G., Chen, G., Sun, Y., Sun, Y., Zhang, H., Wang, L., Hasi, S., Zhang, Y., Li, J., et al., 2012. Genome sequences of wild and domestic bactrian camels. Nat. Commun. 3, 1202, https://doi.org/10.1038/ncomms2192.

  28. Kakumani, R., Ahmad, O., Devabhaktuni, V., 2012. Identification of CpG islands in DNA sequences using statistically optimal null filters. EURASIP J. Bioinf. Sys. Biol. 2012, 12, https://doi.org/10.1186/1687-4153-2012-12.

  29. Kashiwabara, A.Y., Bonadio, I., Onuchic, V., Amado, F., Mathias, R., Durham, A.M., 2013. ToPS: a framework to manipulate probabilistic models of sequence data. PLoS Comput. Biol. 9, e1003234, https://doi.org/10.1371/journal.pcbi.1003234.

  30. Kastelic, D., Frkovic-Grazio, S., Baty, D., Truan, G., Komel, R., Pompon, D., 2009. A single-step procedure of recombinant library construction for the selection of efficiently produced llama VH binders directed against cancer markers. J. Immunol. Methods 350, 54–62, https://doi.org/10.1016/jjim.2009.08.016.

  31. Koh, Y.W, Chun, S.M., Park, Y.S., Song, J.S., Lee, G.K., Khang, S.K, Jang, S.J., 2016. Association between the CpG island methylator phenotype and its prognostic significance in primary pulmonary adenocarcinoma. J. Immunother. Emphasis Tumor Immunol. 37, 1–10, https://doi.org/10.1007/s13277-016-4932-2.

  32. Lander, E., Linton, L., Birren, B., Nusbaum, C., International Human Genome Sequencing Consortium (IHGSC), 2001. Initial sequencing and analysis of the human genome. Nature 409, 860–921, https://doi.org/10.1038/35057062.

  33. Medvedeva, Y.A., 2011. Algorithms for CpG Islands search:New advantages and Old problems. In: Mahdavi, M.A. (Ed.), Bioinformatics - Trends and Methodologies. InTechOpen, pp. 449–470.

  34. Nguyen, V.K., Hamers, R., Wyns, L., Muyldermans, S., 2000. Camel heavy-chain antibodies: diverse germline V(H)H and specific mechanisms enlarge the antigen-binding repertoire. EMBO J. 19, 921–930, https://doi.org/10.1093/emboj/19.5.921.

  35. O’Connor, M.I., Selig, E.R., Pinsky, M.L., Altermatt, F., 2012. Toward a conceptual synthesis for climate change responses. Global Eco. Biogeog. 21, 693–703, https://doi.org/10.1111/j.1466-8238.2011.00713.x.

  36. Ponger, L., Mouchiroud, D., 2002. CpGProD: identifying CpG islands associated with transcription start sites in large genomic mammalian sequences. Bioinformatics 18, 631–633, https://doi.org/10.1093/bioinformatics/18.4.631.

  37. Scholtz, M.M., van Zyl, J.P., Theunissen, A., 2014. The effect of epigenetic changes on animal production. Appl. Anim. Husb. Rural Develop. 7, 7–10.

  38. Sheridan, J.A., Bickford, D., 2011. Shrinking body size as an ecological response to climate change. Nat. Clim. Change. 1, 401–406, https://doi.org/10.1038/nclimate1259.

  39. Stanley, H.F., Kadwell, M., Wheeler, J.C., 1994. Molecular evolution of the family Camelidae: a mitochondrial DNA study. P. R. Soc. B-Biol. Sci. 256, 1–6, https://doi.org/10.1098/rspb.1994.0041.

  40. Su, J., Zhang, Y., Lv, J., Liu, H., Tang, X., Wang, F., Qi, Y., Feng, Y., Li, X., 2010. CpG_MI: a novel approach for identifying functional CpG islands in mammalian genomes. Nucleic Acids Res. 38, e6, https://doi.org/10.1093/nar/gkp882.

  41. Takai, D., Jones, P., 2003. The CpG island searcher: a new WWW resource. In Silico Biol. (Gedrukt) 3, 235–240.

  42. Takai, D., Jones, P.A., 2002. Comprehensive analysis of CpG islands in human chromosomes 21 and 22. PNAS. 99, 3740–3745, https://doi.org/10.1073/pnas.052410099.

  43. Tian, X.C., 2012. Bovine Epigenetics and Epigenomics. in. In: Womack, J.E. (Ed.), Bovine Genomics. Wiley-Blackwell, pp. 144–168.

  44. Walther, G.R., 2010. Community and ecosystem responses to recent climate change. Philos. Trans. Biol. Sci. 365, 2019–2024, https://doi.org/10.1098/rstb.2010.0021.

  45. Wang, Y., Leung, F.C., 2004. An evaluation of new criteria for CpG islands in the human genome as gene markers. Bioinformatics 20, 1170–1177, https://doi.org/10.1093/bioinformatics/bth059.

  46. Westerlund, F., Bjørnholm, T., 2009. Directed assembly of gold nanoparticles. Curr. Opin. Colloid In. Sci. 14, 126–134, https://doi.org/10.1016/j.cocis.2008.07.002.

  47. Wu, H., Caffo, B., Jaffee, H.A., Irizarry, R.A., Feinberg, A.P., 2010. Redefining CpG islands using hidden Markov models. Biostatistics 11, 499–514, https://doi.org/10.1093/biostatistics/kxq005.

  48. Wu, H., Guang, X., Al-Fageeh, M.B., Cao, J., Pan, S., Zhou, H., Zhang, L., Abutarboush, M.H., Xing, Y., Xie, Z., Zhang, Y., Yao, Q., 2014. Camelid genomes reveal evolution and adaptation to desert environments. Nat. Commun. 5, 5188, https://doi.org/10.1038/ncomms6188.

Download references

Author information

Correspondence to Mohammadreza Mohammadabadi or Mostafa Ghaderi-Zefrehei.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Barazandeh, A., Mohammadabadi, M., Ghaderi-Zefrehei, M. et al. Whole genome comparative analysis of CpG islands in camelid and other mammalian genomes. Mamm Biol 98, 73–79 (2019). https://doi.org/10.1016/j.mambio.2019.07.007

Download citation

Keywords

  • Adaptation
  • Camelid genomes
  • CGIs
  • Detection algorithms