Virologica Sinica

, Volume 28, Issue 4, pp 228–238 | Cite as

Inference of global HIV-1 sequence patterns and preliminary feature analysis

Research Article

Abstract

The epidemiology of HIV-1 varies in different areas of the world, and it is possible that this complexity may leave unique footprints in the viral genome. Thus, we attempted to find significant patterns in global HIV-1 genome sequences. By applying the rule inference algorithm RIPPER (Repeated Incremental Pruning to Produce Error Reduction) to multiple sequence alignments of Env sequences from four classes of compiled datasets, we generated four sets of signature patterns. We found that these patterns were able to distinguish southeastern Asian from nonsoutheastern Asian sequences with 97.5% accuracy, Chinese from non-Chinese sequences with 98.3% accuracy, African from non-African sequences with 88.4% accuracy, and southern African from non-southern African sequences with 91.2% accuracy. These patterns showed different associations with subtypes and with amino acid positions. In addition, some signature patterns were characteristic of the geographic area from which the sample was taken. Amino acid features corresponding to the phylogenetic clustering of HIV-1 sequences were consistent with some of the deduced patterns. Using a combination of patterns inferred from subtypes B, C, and all subtypes chimeric with CRF01_AE worldwide, we found that signature patterns of subtype C were extremely common in some sampled countries (for example, Zambia in southern Africa), which may hint at the origin of this HIV-1 subtype and the need to pay special attention to this area of Africa. Signature patterns of subtype B sequences were associated with different countries. Even more, there are distinct patterns at single position 21 with glycine, leucine and isoleucine corresponding to subtype C, B and all possible recombination forms chimeric with CRF01_AE, which also indicate distinct geographic features. Our method widens the scope of inference of signature from geographic, genetic, and genomic viewpoints. These findings may provide a valuable reference for epidemiological research or vaccine design.

Keywords

Pattern inference global HIV-1 sequence Repeated Incremental Pruning to Produce Error Reduction (RIPPER) 

References

  1. Avenue M, Hill M, Cohen W W, Of C, and Pruning R. 1994. Fast E ective Rule Induction 2 Previous work 1 in introduction.Google Scholar
  2. Bello G, Eyer-Silva W a, Couto-Fernandez J C, Guimarães M L, Chequer-Fernandez S L, Teixeira S L M, and Morgado M G. 2007. Demographic history of HIV-1 subtypes B and F in Brazil. Infection, genetics and evolution: journal of molecular epidemiology and evolutionary genetics in infectious diseases, 7: 263–270.PubMedCrossRefGoogle Scholar
  3. Blair C, and Murphy R W. 2011. Recent trends in molecular phylogenetic analysis: where to next? The Journal of heredity, 102: 130–138.PubMedCrossRefGoogle Scholar
  4. Buonaguro L, Tagliamonte M, Tornesello M L, and Buonaguro F M. 2007. Genetic and phylogenetic evolution of HIV-1 in a low subtype heterogeneity epidemic: the Italian example. Retrovirology, 4: 34–34.PubMedCrossRefGoogle Scholar
  5. Butler I F, Pandrea I, Marx P a, and Apetrei C. 2007. HIV genetic diversity: biological and public health consequences. Current HIV research, 5: 23–45.PubMedCrossRefGoogle Scholar
  6. Cai Y-D, Lu L, Chen L, and He J-F. 2010. Predicting subcellular location of proteins using integrated-algorithm method. Molecular diversity, 14: 551–558.PubMedCrossRefGoogle Scholar
  7. Crooks G E, Hon G, Chandonia J-m, and Brenner S E. 2004. WebLogo: A Sequence Logo Generator. 1188–1190.Google Scholar
  8. Delano W L, and Ph D. 2004. PyMOL User’ s Guide written by.Google Scholar
  9. Delatorre E O, and Bello G. 2012. Phylodynamics of HIV-1 subtype C epidemic in east Africa. PloS one, 7: e41904–e41904.PubMedCrossRefGoogle Scholar
  10. Dybowski J N, Riemenschneider M, Hauke S, Pyka M, Verheyen J, Hoffmann D, and Heider D. 2011. Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers. BioData mining, 4: 26–26.PubMedCrossRefGoogle Scholar
  11. Edgar R C. 2004. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic acids research, 32: 1792–1797.PubMedCrossRefGoogle Scholar
  12. Fauci A S, Johnston M I, Dieffenbach C W, Burton D R, Hammer S M, Hoxie J a, Martin M, Overbaugh J, Watkins D I, Mahmoud A, and Greene W C. 2008. HIV vaccine research: the way forward. Science (New York, N.Y.), 321: 530–532.CrossRefGoogle Scholar
  13. Fryer H R, and McLean A R. 2011. Modelling the spread of HIV immune escape mutants in a vaccinated population. PLoS computational biology, 7: e1002289–e1002289.PubMedCrossRefGoogle Scholar
  14. Gentleman R C, Carey V J, Bates D M, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, and Gentry J. 2004. Bioconductor: open software development for computational biology and bioinformatics. Genome biology, 5: R80PubMedCrossRefGoogle Scholar
  15. Gilbert M T P, Rambaut A, Wlasiuk G, Spira T J, Pitchenik A E, and Worobey M. 2007. The emergence of HIV/AIDS in the Americas and beyond. Proceedings of the National Academy of Sciences of the United States of America, 104: 18566–18570.PubMedCrossRefGoogle Scholar
  16. Grant B J, Rodrigues A P C, ElSawy K M, McCammon J A, and Caves L S D. 2006. Bio3d: an R package for the comparative analysis of protein structures. Bioinformatics, 22: 2695–2696.PubMedCrossRefGoogle Scholar
  17. Hemelaar J. 2012. The origin and diversity of the HIV-1 pandemic. Trends in Molecular Medicine, 18: 182–192.PubMedCrossRefGoogle Scholar
  18. Hornik K, Buchta C, and Zeileis A. 2009. Open-source machine learning: R meets Weka. Computational Statistics, 24: 225–232.CrossRefGoogle Scholar
  19. Junqueira D M, de Medeiros R M, Matte M C C, Araújo L A L, Chies J A B, Ashton-Prolla P, and Almeida S E D M. 2011. Reviewing the history of HIV-1: spread of subtype B in the Americas. PloS one, 6: e27489–e27489.PubMedCrossRefGoogle Scholar
  20. Kallings L O. 2008. The first postmodern pandemic: 25 years of HIV/AIDS. Journal of internal medicine, 263: 218–243.PubMedCrossRefGoogle Scholar
  21. Karlsson Hedestam G B, Fouchier R a M, Phogat S, Burton D R, Sodroski J, and Wyatt R T. 2008. The challenges of eliciting neutralizing antibodies to HIV-1 and to influenza virus. Nature reviews. Microbiology, 6: 143–155.PubMedCrossRefGoogle Scholar
  22. Li Y, Uenishi R, Hase S, Liao H, Li X-J, Tsuchiura T, Tee K K, Pybus O G, and Takebe Y. 2010. Explosive HIV-1 subtype B’ epidemics in Asia driven by geographic and risk group founder events. Virology, 402: 223–227.PubMedCrossRefGoogle Scholar
  23. Liao H, Tee K K, Hase S, Uenishi R, Li X-J, Kusagawa S, Thang P H, Hien N T, Pybus O G, and Takebe Y. 2009. Phylodynamic analysis of the dissemination of HIV-1 CRF01_AE in Vietnam. Virology, 391: 51–56.PubMedCrossRefGoogle Scholar
  24. Lihana R W. 2012. Update on HIV-1 Diversity in Africa: A Decade in Review. 83–100.Google Scholar
  25. Liu J, and Zhang C. 2011. Phylogeographic analyses reveal a crucial role of Xinjiang in HIV-1 CRF07_BC and HCV 3a transmissions in Asia. PloS one, 6: e23347–e23347.PubMedCrossRefGoogle Scholar
  26. Lundegaard C, Lamberth K, Harndahl M, Buus S, Lund O, and Nielsen M. 2008. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11. Nucleic Acids Research, 36: W509–W512.PubMedCrossRefGoogle Scholar
  27. Lynch R M, Shen T, Gnanakaran S, and Derdeyn C a. 2009. Appreciating HIV type 1 diversity: subtype differences in Env. AIDS research and human retroviruses, 25: 237–248.PubMedCrossRefGoogle Scholar
  28. Masciotra S, Livellara B, Belloso W, Clara L, Tanuri a, Ramos a C, Baggs J, Lal R, and Pieniazek D. 2000. Evidence of a high frequency of HIV-1 subtype F infections in a heterosexual population in Buenos Aires, Argentina. AIDS research and human retroviruses, 16: 1007–1014.PubMedCrossRefGoogle Scholar
  29. Meng Z, Xin R, Zhong P, Zhang C, Abubakar Y F, Li J, Liu W, Zhang X, and Xu J. 2012. A new migration map of HIV-1 CRF07_BC in China: analysis of sequences from 12 provinces over a decade. PloS one, 7: e52373–e52373.PubMedCrossRefGoogle Scholar
  30. Moran D, and Jordaan J a. 2007. HIV/AIDS in Russia: determinants of regional prevalence. International journal of health geographics, 6: 22–22.PubMedCrossRefGoogle Scholar
  31. Morcos F, Pagnani A, Lunt B, Bertolino A, Marks D S, Sander C, Zecchina R, Onuchic J N, Hwa T, and Weigt M. Direct-coupling analysis of residue coevolution captures native contacts across many protein families. Proceedings of the National Academy of Sciences of the United States of America, 108: E1293–E1301.Google Scholar
  32. Morris C N, and Ferguson a G. 2006. Estimation of the sexual transmission of HIV in Kenya and Uganda on the trans-Africa highway: the continuing role for prevention in high risk groups. Sexually transmitted infections, 82: 368–371.PubMedCrossRefGoogle Scholar
  33. Njai H F, Gali Y, Vanham G, Clybergh C, Jennes W, Vidal N, Butel C, Mpoudi-Ngolle E, Peeters M, and Ariën K K. 2006. The predominance of Human Immunodeficiency Virus type 1 (HIV-1) circulating recombinant form 02 (CRF02_AG) in West Central Africa may be related to its replicative fitness. Retrovirology, 3: 40–40.PubMedCrossRefGoogle Scholar
  34. Paradis E, Claude J, and Strimmer K. 2004. APE: Analyses of Phylogenetics and Evolution in R language. Bioinformatics, 20: 289–290.PubMedCrossRefGoogle Scholar
  35. Paraschiv S, Otelea D, Batan I, Baicus C, Magiorkinis G, and Paraskevis D. 2012. Molecular typing of the recently expanding subtype B HIV-1 epidemic in Romania: evidence for local spread among MSMs in Bucharest area. Infection, genetics and evolution: journal of molecular epidemiology and evolutionary genetics in infectious diseases, 12: 1052–1057.PubMedCrossRefGoogle Scholar
  36. Paraskevis D, Pybus O, Magiorkinis G, Hatzakis A, Wensing A M, van de Vijver D a, Albert J, Angarano G, Asjö B, Balotta C, Boeri E, Camacho R, Chaix M-L, Coughlan S, Costagliola D, De Luca A, de Mendoza C, Derdelinckx I, Grossman Z, Hamouda O, Hoepelman I, Horban A, Korn K, Kücherer C, Leitner T, Loveday C, Macrae E, Maljkovic-Berry I, Meyer L, Nielsen C, Op de Coul E L, Ormaasen V, Perrin L, Puchhammer-Stöckl E, Ruiz L, Salminen M O, Schmit J-C, Schuurman R, Soriano V, Stanczak J, Stanojevic M, Struck D, Van Laethem K, Violin M, Yerly S, Zazzi M, Boucher C a, and Vandamme A-M. 2009. Tracing the HIV-1 subtype B mobility in Europe: a phylogeographic approach. Retrovirology, 6: 49–49.PubMedCrossRefGoogle Scholar
  37. Pérez L, Thomson M M, Bleda M J, Aragonés C, González Z, Pérez J, Sierra M, Casado G, Delgado E, and Nájera R. 2006. HIV Type 1 molecular epidemiology in cuba: high genetic diversity, frequent mosaicism, and recent expansion of BG intersubtype recombinant forms. AIDS research and human retroviruses, 22: 724–733.PubMedCrossRefGoogle Scholar
  38. Pollakis G, Abebe A, Kliphuis A, De Wit T F R, Fisseha B, Tegbaru B, Tesfaye G, Negassa H, Mengistu Y, Fontanet A L, Cornelissen M, and Goudsmit J. 2003. Recombination of HIV type 1C (C′;/C″) in Ethiopia: possible link of EthHIV-1C′ to subtype C sequences from the high-prevalence epidemics in India and Southern Africa. AIDS research and human retroviruses, 19: 999–1008.PubMedCrossRefGoogle Scholar
  39. Poonpiriya V, Sungkanuparph S, Leechanachai P, Pasomsub E, Watitpun C, Chunhakan S, and Chantratita W. 2008. A study of seven rule-based algorithms for the interpretation of HIV-1 genotypic resistance data in Thailand. Journal of virological methods, 151: 79–86.PubMedCrossRefGoogle Scholar
  40. Restif O. 2009. Evolutionary epidemiology 20 years on: challenges and prospects. Infection, genetics and evolution: journal of molecular epidemiology and evolutionary genetics in infectious diseases, 9: 108–123.PubMedCrossRefGoogle Scholar
  41. Sharp P M, and Hahn B H. 2011. Origins of HIV and the AIDS Pandemic. 1–22.Google Scholar
  42. Sharp P M, and Hahn B H. 2011. Origins of HIV and the AIDS pandemic. Cold Spring Harbor perspectives in medicine, 1: a006841–a006841.PubMedCrossRefGoogle Scholar
  43. Shen C, Craigo J, Ding M, Chen Y, and Gupta P. 2011. Origin and dynamics of HIV-1 subtype C infection in India. PloS one, 6: e25956–e25956.PubMedCrossRefGoogle Scholar
  44. Sierra M, Thomson M M, Posada D, Pérez L, Aragonés C, González Z, Pérez J, Casado G, and Nájera R. 2007. Identification of 3 phylogenetically related HIV-1 BG intersubtype circulating recombinant forms in Cuba. Journal of acquired immune deficiency syndromes (1999), 45: 151–160.CrossRefGoogle Scholar
  45. Silveira J, Santos A F, Martínez A M B, Góes L R, Mendoza-Sassi R, Muniz C P, Tupinambás U, Soares M a, and Greco D B. 2012. Heterosexual transmission of human immunodeficiency virus type 1 subtype C in southern Brazil. Journal of clinical virology: the official publication of the Pan American Society for Clinical Virology, 54: 36–41.CrossRefGoogle Scholar
  46. Spira S. 2003. Impact of clade diversity on HIV-1 virulence, antiretroviral drug sensitivity and drug resistance. Journal of Antimicrobial Chemotherapy, 51: 229–240.PubMedCrossRefGoogle Scholar
  47. Taylor B S, and Hammer S M. 2008. The challenge of HIV-1 subtype diversity. The New England journal of medicine, 359: 1965–1966.PubMedCrossRefGoogle Scholar
  48. Tebit D M, and Arts E J. 2011. Tracking a century of global expansion and evolution of HIV to drive understanding and to combat disease. The Lancet Infectious Diseases, 11: 45–56.PubMedCrossRefGoogle Scholar
  49. Villanova F E. 2010. Diversity of HIV-1 Subtype B: Implications to the Origin of BF Recombinants. 5: 1–9.Google Scholar
  50. Walker B D, and Burton D R. 2008. Toward an AIDS vaccine. Science (New York, N.Y.), 320: 760–764.CrossRefGoogle Scholar
  51. Walker P R, Pybus O G, Rambaut A, and Holmes E C. 2005. Comparative population dynamics of HIV-1 subtypes B and C: subtype-specific differences in patterns of epidemic growth. Infection, genetics and evolution: journal of molecular epidemiology and evolutionary genetics in infectious diseases, 5: 199–208.PubMedCrossRefGoogle Scholar
  52. Wang Y, Rawi R, Wilms C, Heider D, Yang R, and Hoffmann D. 2013. A small set of succinct signature patterns distinguishes Chinese and non-Chinese HIV-1 genomes. PloS one, 8: e58804–e58804.PubMedCrossRefGoogle Scholar
  53. Witten I H, Frank E, and Hall M A. 2011. Data Mining: Practical Machine Learning Tools and Techniques: Practical Machine Learning Tools and Techniques. ElsevierGoogle Scholar
  54. Worobey M, Gemmel M, Teuwen D E, Haselkorn T, Kunstman K, Bunce M, Muyembe J-j, Kabongo J-m M, Kalengayi R M, Van Marck E, Gilbert M T P, Wolinsky S M, Kalengayi M, and Marck E V. 2008. Direct evidence of extensive diversity of HIV-1 in Kinshasa by 1960. Nature, 455: 661–664.PubMedCrossRefGoogle Scholar
  55. Yang O O. 2009. Candidate vaccine sequences to represent intraand inter-clade HIV-1 variation. PloS one, 4: e7388–e7388.PubMedCrossRefGoogle Scholar
  56. Zhao Y. 2011. R and Data Mining: Examples and Case Studies 1.Google Scholar
  57. Zhu T, Korber B T, Nahmias a J, Hooper E, Sharp P M, and Ho D D. 1998. An African HIV-1 sequence from 1959 and implications for the origin of the epidemic. Nature, 391: 594–597.PubMedCrossRefGoogle Scholar

Copyright information

© Wuhan Institute of Virology, CAS and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.AIDS and HIV Research Group, State Key Laboratory of Virology, Wuhan Institute of VirologyChinese Academy of SciencesWuhanChina
  2. 2.Research Group for Bioinformatics, Center for Medical BiologyUniversity of Duisburg-EssenEssenGermany

Personalised recommendations