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In this article, we aim to provide a thorough review of the Bayesian-inference-based methods applied to Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), and Human Immunodeficiency Virus (HIV) studies with a focus on the detection of the viral mutations and various problems which are correlated to these mutations. It is particularly difficult to detect and interpret these interacting mutation patterns, but by using Bayesian statistical modeling, it provides a groundbreaking opportunity to solve these problems. Here we summarize Bayesian-based statistical approaches, including the Bayesian Variable Partition (BVP) model, Bayesian Network (BN), and the Recursive Model Selection (RMS) procedure, which are designed to detect the mutations and to make further inferences to the comprehensive dependence structure among the interactions. BVP, BN, and RMS in which Markov Chain Monte Carlo (MCMC) methods are used have been widely applied in HBV, HCV, and HIV studies in the recent years. We also provide a summary of the Bayesian methods’ applications toward these viruses’ studies, where several important and useful results have been discovered. We envisage the applications of more modified Bayesian methods to other infectious diseases and cancer cells that will be following with critical medical results before long.


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Bayesian Analysis of Complex Mutations in HBV, HCV, and HIV Studies

Show Author's information Bing LiuShishi FengXuan GuoJing Zhang( )
Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303, USA.
Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, USA.

Abstract

In this article, we aim to provide a thorough review of the Bayesian-inference-based methods applied to Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), and Human Immunodeficiency Virus (HIV) studies with a focus on the detection of the viral mutations and various problems which are correlated to these mutations. It is particularly difficult to detect and interpret these interacting mutation patterns, but by using Bayesian statistical modeling, it provides a groundbreaking opportunity to solve these problems. Here we summarize Bayesian-based statistical approaches, including the Bayesian Variable Partition (BVP) model, Bayesian Network (BN), and the Recursive Model Selection (RMS) procedure, which are designed to detect the mutations and to make further inferences to the comprehensive dependence structure among the interactions. BVP, BN, and RMS in which Markov Chain Monte Carlo (MCMC) methods are used have been widely applied in HBV, HCV, and HIV studies in the recent years. We also provide a summary of the Bayesian methods’ applications toward these viruses’ studies, where several important and useful results have been discovered. We envisage the applications of more modified Bayesian methods to other infectious diseases and cancer cells that will be following with critical medical results before long.

Keywords:

Bayesian analysis, Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), Human Immunodeficiency Virus (HIV), complex mutations, Markov chain Monte Carlo
Received: 28 September 2018 Revised: 10 February 2019 Accepted: 16 February 2019 Published: 04 April 2019 Issue date: September 2019
References(67)
[1]
Hepatitis B foundation, Hepatitis B foundation statistics, , 2009.
[2]
World health organization, Hepatitis B: Fact sheet No. 204, , 2016.
[3]
M. Nettleman, Hepatitis B (HBV, Hep B), , 2019.
[4]
C. R. Bourne, S. P. Katen, M. R. Fulz, C. Packianathan, and A. Zlotnick, A mutant hepatitis B virus core protein mimics inhibitors of icosahedral capsid self-assembly, Biochemistry, vol. 48, no. 8, pp. 1736-1742, 2009.
[5]
World Health Organization, Hepatitis B: The Hepatitis B virus, , 2002.
[6]
J. P. Miguet and D. Dhumeaux, Progress in Hepatology 93. John Libbey Eurotext, 1993.
[7]
A. F. Voevodin and P. A. Marx, Simian Virology. Ames, IA, USA: Wiley-Blackwell, 2009.
[8]
C. Mirabelli, M. Surdo, F. Van Hemert, Z. C. Lian, R. Salpini, V. Cento, M. F. Cortese, M. Aragri, M. Pollicita, C. Alteri, et al., Specific mutations in the C-terminus domain of HBV surface antigen significantly correlate with low level of serum HBV-DNA in patients with chronic HBV infection, J. Infect., vol. 70, no. 3, pp. 288-298, 2015.
[9]
V. Svicher, V. Cento, M. Bernassola, M. Neumann-Fraune, F. Van Hemert, M. J. Chen, R. Salpini, C. Liu, R. Longo, M. Visca, et al., Novel HBsAg markers tightly correlate with occult HBV infection and strongly affect HBsAg detection, Antiviral Res., vol. 93, no. 1, pp. 86-93, 2012.
[10]
T. Poynard, V. Leroy, M. Cohard, T. Thevenot, P. Mathurin, P. Opolon, and J. P. Zarski, Meta-analysis of interferon randomized trials in the treatment of viral hepatitis C: Effects of dose and duration, Hepatology, vol. 24, no. 4, pp. 778-789, 1996.
[11]
G. L. Davis, R. Esteban-Mur, V. Rustgi, J. Hoefs, S. C. Gordon, C. Trepo, M. L. Shiffman, S. Zeuzem, A. Craxi, M. H. Ling, et al., Interferon alfa-2b alone or in combination with ribavirin for the treatment of relapse of chronic hepatitis C, N. Engl. J. Med., vol. 339, no. 21, pp. 1493-1499, 1998.
[12]
J. G. McHutchison, S. C. Gordon, E. R. Schiff, M. L. Shiffman, W. M. Lee, V. K. Rustgi, Z. D. Goodman, M. H. Ling, S. Cort, and J. K. Albrecht, Interferon alfa-2b alone or in combination with ribavirin as initial treatment for chronic hepatitis C, N. Engl. J. Med., vol. 339, no. 21, pp. 1485-1492, 1998.
[13]
A. Macdonald and M. Harris, Hepatitis C virus NS5A: Tales of a promiscuous protein, J. Gen. Virol., vol. 85, no. 9, pp. 2485-2502, 2004.
[14]
N. Enomoto, I. Sakuma, Y. Asahina, M. Kurosaki, T. Murakami, C. Yamamoto, Y. Ogura, N. Izumi, F. Marumo, and C. Sato, Mutations in the nonstructural protein 5A gene and response to interferon in patients with chronic hepatitis C virus 1b infection, N. Engl. J. Med., vol. 334, no. 2, pp. 77-82, 1996.
[15]
M. J. Jr. Gale, M. J. Korth, N. M. Tang, S. L. Tan, D. A. Hopkins, T. E. Dever, S. J. Polyak, D. R. Gretch, and M. G. Katze, Evidence that hepatitis C virus resistance to interferon is mediated through repression of the PKR protein kinase by the nonstructural 5A protein, Virology, vol. 230, no. 2, pp. 217-227, 1997.
[16]
M. Jr. Gale, C. M. Blakely, B. Kwieciszewski, S. L. Tan, M. Dossett, N. M. Tang, M. J. Korth, S. J. Polyak, D. R. Gretch, and M. G. Katze, Control of PKR protein kinase by hepatitis C virus nonstructural 5A protein: Molecular mechanisms of kinase regulation, Mol. Cell Biol., vol. 18, no. 9, pp. 5208-5218, 1998.
[17]
Y. Fu, G. Chen, X. Guo, J. Zhang, and Y. Pan, Analyzing the effects of pretreatment diversity on HCV drug treatment responsiveness using Bayesian partition methods, J. Bioinform. Proteom. Rev., vol. 1, no. 1, pp. 1-6, 2015.
[18]
N. Enomoto and C. Sato, Clinical relevance of hepatitis C virus quasispecies, J. Viral. Hepat., vol. 2, no. 6, pp. 267-272, 1995.
[19]
N. Enomoto, I. Sakuma, Y. Asahina, M. Kurosaki, T. Murakami, C. Yamamoto, N. Izumi, F. Marumo, and C. Sato, Comparison of full-length sequences of interferon- sensitive and resistant hepatitis C virus 1b. Sensitivity to interferon is conferred by amino acid substitutions in the NS5A region, J. Clin. Invest., vol. 96, no. 1, pp. 224-230, 1995.
[20]
M. Gerotto, F. Dal Pero, D. G. Sullivan, L. Chemello, L. Cavalletto, S. J. Polyak, P. Pontisso, D. R. Gretch, and A. Alberti, Evidence for sequence selection within the non-structural 5A gene of hepatitis C virus type 1b during unsuccessful treatment with interferon-α, J. Viral. Hepat., vol. 6, no. 5, pp. 367-372, 1999.
[21]
M. J. Clemens and A. Elia, The double-stranded RNA-dependent protein kinase PKR: Structure and function, J. Interf. Cytokine Res., vol. 17, no. 9, pp. 503-524, 1997.
[22]
M. Pirmohamed and D. J. Back, The pharmacogenomics of HIV therapy, Pharmacogenomics J., vol. 1, pp. 243-253, 2001.
[23]
T. Lengauer and T. Sing, Bioinformatics-assisted anti-HIV therapy, Nat. Rev. Microbiol., vol. 4, no. 10, pp. 790-797, 2006.
[24]
I. Kozyryev and J. Zhang, Bayesian analysis of complex interacting mutations in HIV drug resistance and cross-resistance, in Advance in Structural Bioinformatics, D. Q. Wei, Q. Xu, T. Z. Zhao, and H. Dai, eds. Springer, 2015, pp. 367-383.
[25]
A. Engelman and P. Cherepanov, The structural biology of HIV-1: Mechanistic and therapeutic insights, Nat. Rev. Microbiol., vol. 10, no. 4, pp. 279-290, 2012.
[26]
C. Flexner, HIV drug development: The next 25 years, Nat. Rev. Drug Discov., vol. 6, no. 12, pp. 959-966, 2007.
[27]
E. A. Berger, R. W. Doms, E. M. Fenyö, B. T. M. Korber, D. R. Littman, J. P. Moore, Q. J. Sattentau, H. Schuitemaker, J. Sodroski, and R. A. Weiss, A new classification for HIV-1, Nature, vol. 391, no. 6664, p. 240, 1998.
[28]
R. R. Regoes and S. Bonhoeffer, The HIV coreceptor switch: A population dynamical perspective, Trends Microbiol., vol. 13, no. 6, pp. 269-277, 2005.
[29]
T. L. Hoffman and R. W. Doms, HIV-1 envelope determinants for cell tropism and chemokine receptor use, Mol. Membr. Biol., vol. 16, no. 1, pp. 57-65, 1999.
[30]
M. J. Chen, V. Svicher, A. Artese, G. Costa, C. Alteri, F. Ortuso, L. Parrotta, Y. Liu, C. Liu, C. F. Perno, et al., Detecting and understanding genetic and structural features in HIV-1 B subtype V3 underlying HIV-1 co-receptor usage, Bioinformatics, vol. 29, no. 4, pp. 451-460, 2013.
[31]
N. Yu, Z. H. Li, and Z. Yu, Survey on encoding schemes for genomic data representation and feature learning — From signal processing to machine learning, Big Data Min. Anal., vol. 1, no. 3, pp. 191-210, 2018.
[32]
O. Zagordi, L. Geyrhofer, V. Roth, and N. Beerenwinkel, Deep sequencing of a genetically heterogeneous sample: Local haplotype reconstruction and read error correction, J. Comput. Biol., vol. 17, no. 3, pp. 417-428, 2010.
[33]
O. Zagordi, R. Klein, M. Däumer, and N. Beerenwinkel, Error correction of next-generation sequencing data and reliable estimation of HIV quasispecies, Nucl. Acids Res., vol. 38, no. 21, pp. 7400-7409, 2010.
[34]
J. Zhang, T. J. Hou, W. Wang, and J. S. Liu, Detecting and understanding combinatorial mutation patterns responsible for HIV drug resistance, Proc. Natl. Acad. Sci. USA., vol. 107, no. 4, pp. 1321-1326, 2010.
[35]
H. Thai, D. S. Campo, J. Lara, Z. Dimitrova, S. Ramachandran, G. L. Xia, L. Ganova-Raeva, C. G. Teo, A. Lok, and Y. Khudyakov, Convergence and coevolution of hepatitis B virus drug resistance, Nat. Commun., vol. 3, p. 789, 2012.
[36]
N. Beerenwinkel, H. Montazeri, H. Schuhmacher, P. Knupfer, V. Von Wyl, H. Furrer, M. Battegay, B. Hirschel, M. Cavassini, P. Vernazza, et al., The individualized genetic barrier predicts treatment response in a large cohort of HIV-1 infected patients, PLoS Comput. Biol., vol. 9, no. 8, p. e1003203, 2013.
[37]
A. Chaillon, M. Nakazawa, J. O. Wertheim, S. J. Little, D. M. Smith, S. R. Mehta, and S. Gianella, No substantial evidence for sexual transmission of minority HIV drug resistance mutations in men who have sex with men, J. Virol., vol. 91, no. 21, p. e00769-17, 2017.
[38]
V. Svicher, C. Alteri, A. Artese, J. M. Zhang, G. Costa, F. Mercurio, R. D’Arrigo, S. Alcaro, G. Palù, M. Clementi, et al., Identification and structural characterization of novel genetic elements in the HIV-1 V3 loop regulating coreceptor usage, Antivir. Ther., vol. 16, no. 7, pp. 1035-1045, 2011.
[39]
V. Svicher, M. Chen, C. Alteri, G. Costa, S. Dimonte, L. Chang, L. Parrotta, C. Dimaio, M. Surdo, P. Saccomandi, et al., Key genetic elements in HIV-1 gp120 V1, V2 and C4 domains tightly and differentially modulate gp120 interaction with the CCR5 and CXCR4 N-terminus and HIV-1 antigenic potential, in Proc. 2nd Int. Workshop on HIV & Hepatitis Virus Drug Resistance and Curatove Strategies, Los Cabos, Mexico, 2011.
[40]
V. Svicher, V. Cento, M. Bernassola, M. Neumann-Fraune, M. Chen, R. Salpini, L. Chang, R. Longo, M. Visca, S. Romano, et al., Specific HBsAg genetic determinants are associated with occult HBV infection in vivo and HBsAg detection, in Proc. 2nd Int. Workshop on HIV & Hepatitis Virus Drug Resistance and Curatove Strategies, Los Cabos, Mexico, 2011.
[41]
J. Zhang, T. J. Hou, Y. Liu, G. Chen, X. Yang, J. S. Liu, and W. Wang, Systematic investigation on interactions for HIV drug resistance and cross-resistance among protease inhibitors, J. Proteome Sci. Comput. Biol., vol. 1, no. 1, p. 2, 2012.
[42]
Y. Fu, G. Chen, L. Z. Fu, and J. Zhang, Investigating genotype 1a HCV drug resistance in NS5A region via Bayesian inference, Tsinghua Sci. Technol., vol. 20, no. 5, pp. 484-490, 2015.
[43]
J. Fellay, D. L. Ge, K. V. Shianna, S. Colombo, B. Ledergerber, E. T. Cirulli, T. J. Urban, K. L. Zhang, C. E. Gumbs, J. P. Smith, et al., Common genetic variation and the control of HIV-1 in humans, PLoS Genet., vol. 5, no. 12, p. e1000791, 2009.
[44]
R. M. Neal, Markov chain sampling methods for dirichlet process mixture models, J. Comput. Graph. Stat., vol. 9, no. 2, pp. 249-265, 2000.
[45]
D. Dash and M. J. Druzdzel, Robust independence testing for constraint-based learning of causal structure, in Proc. 19th Conf. Uncertainty in Artificial Intelligence, Acapulco, Mexico, 2002, pp. 167-174.
[46]
J. Cheng, R. Greiner, J. Kelly, D. Bell, and W. R. Liu, Learning Bayesian networks from data: An information-theory based approach, Artif. Intell., vol. 137, nos. 1&2, pp. 43-90, 2002.
[47]
J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Elsevier, 2014.
[48]
Z. C. Lian, Q. N. Tian, Y. Liu, V. Cento, R. Salpini, C. F. Perno, V. Svicher, G. Chen, C. Li, and J. Zhang, Detecting hepatitis B viral amino acid sequence mutations in occult hepatitis B infections via bayesian partition model, J. Proteomics Bioinform., .
[49]
O. Lada, Y. Benhamou, A. Cahour, C. Katlama, T. Poynard, and V. Thibault, In vitro susceptibility of lamivudine-resistant hepatitis B virus to adefovir and tenofovir, Antivir. Ther., vol. 9, no. 3, pp. 353-363, 2004.
[50]
J. Sheldon, B. Ramos, J. Garcia-Samaniego, P. Rios, A. Bartholomeusz, M. Romero, S. Locarnini, F. Zoulim, and V. Soriano, Selection of Hepatitis B Virus (HBV) vaccine escape mutants in HBV-infected and HBV/HIV-coinfected patients failing antiretroviral drugs with anti-HBV activity, J. Acquir. Immune Defic. Syndr., vol. 46, no. 3, pp. 279-282, 2007.
[51]
R. Gish, J. D. Jia, S. Locarnini, and F. Zoulim, Selection of chronic hepatitis B therapy with high barrier to resistance, Lancet Infect. Dis., vol. 12, no. 4, pp. 341-353, 2012.
[52]
N. Beerenwinkel, M. Däumer, T. Sing, J. Rahnenführer, T. Lengauer, J. Selbig, D. Hoffmann, and R. Kaiser, Estimating HIV evolutionary pathways and the genetic barrier to drug resistance, J. Infect. Dis., vol. 191, no. 11, pp. 1953-1960, 2005.
[53]
J. H. Albert and S. Chib, Bayesian analysis of binary and polychotomous response data, J. Am. Stat. Assoc., vol. 88, no. 422, pp. 669-679, 1993.
[54]
J. O. Berger and L. R. Pericchi, The intrinsic Bayes factor for model selection and prediction, J. Am. Stat. Assoc., vol. 91, no. 433, pp. 109-122, 1996.
[55]
P. M. Lee, Bayesian Statistics: An Introduction, 4th Edition. John Wiley & Sons, 2012.
[56]
M. Surdo, M. F. Cortese, C. Mirabelli, R. Salpini, J. Zhang, F. Van Hemert, V. Cento, M. Pollicita, G. Gubertini, G. M. De Sanctis, et al., Key patterns of HBsAg mutations correlate with mechanisms underlying levels of serum HBVDNA, J. Hepatol., vol. 60, no. S1, p. S299, 2014.
[57]
V. Svicher, F. Mercurio, and A. Artese, Signature mutations in V3 and bridging sheet domain of HIV-1 gp120 HIV-1 are specifically associated with dual tropism and modulate the interaction with CCR5 N-terminus, Infection, vol. 39, no. 1, pp. S11-S91, 2011.
[58]
X. Guo, J. Zhang, Z. P. Cai, D. Z. Du, and Y. Pan, Searching genome-wide multi-locus associations for multiple diseases based on Bayesian inference, IEEE/ACM Trans. Comput. Biol. Bioinform., vol. 14, no. 3, pp. 600-610, 2017.
[59]
J. X. Wang, T. Joshi, B. Valliyodan, H. Y. Shi, Y. C. Liang, H. T. Nguyen, J. Zhang, and D. Xu, A Bayesian model for detection of high-order interactions among genetic variants in genome-wide association studies, BMC Genomics, vol. 16, p. 1011, 2015.
[60]
H. Wang, Y. Liu, and W. Huang, Random forest and Bayesian prediction for Hepatitis B virus reactivation, in 2017 13th Int. Conf. Natural Computation, Fuzzy Systems and Knowledge Discovery, Guilin, China, 2017.
[61]
L. Zapata, H. Susak, O. Drechsel, M. Friedlander, X. Estivill, and O. Stephan, Bayesian inference of cancer driver genes using signatures of positive selection, bioRxiv: 059360, 2017.
[62]
L. Xu, Y. B. Zheng, J. Liu, D. Rakheja, S. Singleterry, T. W. Laetsch, J. F. Shern, J. Khan, T. J. Triche, D. S. Hawkins, et al., Integrative Bayesian analysis identifies rhabdomyosarcoma disease genes, Cell Rep., vol. 24, no. 1, pp. 238-251, 2018.
[63]
X. Guo, B. Liu, L. Chen, G. T. Chen, Y. Pan, and J. Zhang, Bayesian inference for functional dynamics exploring in fMRI data, Comput. Math. Methods Med., vol. 2016, p. 3279050, 2016.
[64]
X. C. Xiao, B. Liu, J. Zhang, X. L. Xiao, and Y. Pan, Detecting change points in fMRI data via bayesian inference and genetic algorithm model, in Bioinformatics Research and Applications, Z. P. Cai, O. Daescu, and M. Li, eds. Springer, 2017, pp. 314-324.
[65]
X. Xiao, B. Liu, J. Zhang, X. Xiao, and Y. Pan, An optimized method for Bayesian connectivity change point model, J. Comput. Biol., vol. 25, no. 3, pp. 337-347, 2018.
[66]
B. Liu, X. Guo, and J. Zhang, Bayesian Bi-cluster change-point model for exploring functional brain dynamics, in Proc. 2018 Int. Conf. Bioinformatics and Computational Biology, Las Vegas, NV, USA, 2018.
[67]
J. Zhang, T. Liu, and G. Deshpande, Probablistic methods in computational neuroscience, IEEE/ACM Trans. Comput. Biol. Bioinform., vol. 15, no. 2, pp. 535-536, 2018.
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Received: 28 September 2018
Revised: 10 February 2019
Accepted: 16 February 2019
Published: 04 April 2019
Issue date: September 2019

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