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Using Bayesian Models to Locate Mutations for HBV Drug Resistance
Journal of Hematology & Thromboembolic Diseases

Journal of Hematology & Thromboembolic Diseases
Open Access

ISSN: 2329-8790

+44 1478 350008

Research Article - (2014) Volume 2, Issue 6

Using Bayesian Models to Locate Mutations for HBV Drug Resistance

Gadareth Higgs1,2*, Zhixiang Lin1,2, Valeria Cento3, Valentina Svicher3, Shilpa Hattangadi1,4,5 and Jing Zhang1,2,6
1Yale Combined Program in the Biological and Biomedical Sciences (BBS), USA
2Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA
33Department of Experimental Medicine and Biochemical Sciences, Virology Unit, Tor Vergata, University of Rome, Italy
4Pediatric Hematology & Oncology Program, Yale University, New Haven, CT, USA
5Pathology Program, Yale University, New Haven, CT, USA
6Department of Statistics, Yale University, New Haven, CT, USA
*Corresponding Author: Gadareth Higgs, PhD Student, Yale, Computational Biology & Bioinformatics, 65 Dwight Street, Apt H-73, New Haven, CT 06511, USA, Tel: 2035085687 Email:

Abstract

Introduction: The Hepatitis B virus (HBV) is very common, and has been difficult to treat, mainly because of the high mutation rate of the polymerase gene of its reverse transcriptase. The aim of our study was to use Bayesian statistics to determine the positions of mutations within the HBV genome.

Material and methods: The sequence data was derived from 73-treatment naïve and 215 treatment failures, of various drugs, from patient data provided by collaborators at the University of Tor Vergata. The Metropolis-Hastings algorithm was applied to the data to determine the mutation locations that correlate with drug resistance.

Results: For amino acid positions 80-250, nineteen positions were shown to have mutated in the treatment failure group. Fifteen of the nineteen positions were in the D genotype of HBV, while the other four were within the A genotype originating from the drug lamivudine (LMV). For amino acid positions 250-344, sixteen positions were mutated with seven of the sixteen originating from LMV in the D genotype. Four mutations originated from LMV in the A genotype.

Conclusion: This research identified previously unknown mutation positions and confirmed positions identified in previous research. Collaborators at the University of Rome, Tor Vergata, have validated the mutated positions experimentally with a 454-pyrosequencer. It is hoped that knowledge of these mutations would lead to improved treatment options. Also, with increased availability of genomic data, future research can be done on a larger HBV dataset and for other diseases.

Keywords: Hepatitis B virus (HBV); Drug resistance; Bayesian analysis; Metropolis-Hastings algorithm; Mutation identification; Markov chain; Posterior probability

Introduction

The Hepatitis B virus (HBV) is fairly common, infecting more than 2 billion people worldwide, including over 300 million carriers of the virus [1]. It is passed through the blood/blood-derived body fluids through percutaneous and permucosal means [2]. This virus can cause chronic infection, and, along with HCV, is the major causative factor for liver transplants.

The liver is vital to life, with over 500 diverse physiological functions including bile production, nutrient metabolism, drug processing, ammonia conversion, and coagulation factor creation [3]. Therefore, it is important that scientists identify and resolve causes of liver disease.

HBV is a circular DNA virus that is partially double stranded. It replicates via an RNA intermediate, and, like HIV, contains a reverse-transcriptase (RT) enzyme [4] (Figure 1).

haematology-thromboembolic-diseases-Genomic-Structure

Figure 1: Genomic Structure of HBV [5].

The Hepatitis B virus is divided into eight genotypes, A-H, based on overall sequence variation of the genome. In this analysis, only sequence data from genotypes A and D were used.

Like HIV, HBV has proven to be difficult to treat, mainly because of the high mutation rate of the polymerase gene of its reverse transcriptase [6]. Previous work by Zhang et al employed Bayesian statistics to determine the positions of mutations in the HIV genotype [7]. Whereas HIV mutates at a much faster rate than HBV, the same statistical method was successfully employed here.

Dataset

Prior studies undertaken by Zhixiang Lin, employed this method to analyze HCV data from a publicly available HCV database, and Hepatitis B virus sequences from an HBVrt database at Stanford University. These studies have proved helpful in identifying positions of mutations that confer general drug resistance. However, those studies did not look at mutations corresponding to particular drugs.

This study was conducted with sequence data on 73-treatment naïve and 215 treatment failures, of various drugs, from patient data collected by collaborators at the University of Tor Vergata. The sequence data were provided as fasta files for treatment naïve sets and treatment failures. They were then compiled and summarized in Excel sheets before processing in the R statistical package.

Method

The Metropolis-Hastings algorithm was applied to sequence data to determine the mutation locations that correlate with drug resistance. This algorithm employs Bayesian statistics and Markov modeling to test each sequence position for four hypotheses:

H1: the corresponding treated and untreated positions are independent from each other, and have the same probability distribution

H2: the corresponding treated and untreated positions are independent from each other, but have different probability distributions

H3: the corresponding treated and untreated positions are dependent on each other, and have the same probability distribution

H4: the corresponding treated and untreated positions are dependent on each other, but have different probability distributions

The positions corresponding to hypothesis 4 with a 95% threshold cut-off were later collected.

The patients were segregated into groups based on the viral genotype (A or D) and the combinations of drugs used for treatment, namely ADF, ETV, LMV, Peg-IFN, TBV, and TDF [8].

ADF: adefovir dipivoxil (Hepsera)

ETV: entecavir (Baraclude)

LMV: lamivudine (Epivir)

Peg-IFN: pegylated interferon

TBV: telbivudine (Tyzeka)

TDF: tenofivir (Viread)

Of all the combinations used on patients, only those samples with more than 20 elements were included in this analysis. These groups were:

ADF treatment failures of genotype D

ADF&LMV treatment failures of genotype D

ETV treatment failures of genotype D

LMV treatment failures of genotype D

LMV treatment failures of genotype A

Most of the data came from patients with the HBV D genotype, which is the prevailing genotype of Western regions [9].

Results

The DNA sequences were then transcribed and translated, with the resulting peptide sequences split into sections from 80-250 and from 250-344. Using a program developed in R, these sections were analyzed for hypotheses 1-4 against the corresponding lengths of the treatment naïve data.

Ten Markov chains were simulated with 20,000 iterations. The burn-in was set to 5,000 and the program chose the Markov chain with the highest probability to create the following charts.

Figure 2 shows the optimal Markov chain posterior probabilities for positions 80-250 of ADF-treatment failures of the D genotype. Positions making the 95% threshold cut-off for H4 were 34, 71, 76, and 117.

haematology-thromboembolic-diseases-Posterior-Probabilities

Figure 2: ADF-D 80-250 Posterior Probabilities.

Figure 3 shows the optimal Markov chain posterior probabilities for positions 80-250 of ADF/LMV-treatment failures of the D genotype. Positions making the 95% threshold cut-off for H4 were 23, 37, 68, and 76.

haematology-thromboembolic-diseases-Posterior-Probabilities

Figure 3: ADF/LMV-D 80-250 Posterior Probabilities

Figure 4 shows the optimal Markov chain posterior probabilities for positions 80-250 of ETV-treatment failures of the D genotype. Positions making the 95% threshold cut-off for H4 were 1, 3, 21, 134, and 168.

haematology-thromboembolic-diseases-Posterior-Probabilities

Figure 4: ETV-D 80-250 Posterior Probabilities.

Figure 5 shows the optimal Markov chain posterior probabilities for positions 80-250 of LMV-treatment failures of the A genotype. Positions making the 95% threshold cut-off for H4 were 43, 45, 50, and 84.

haematology-thromboembolic-diseases-Posterior-Probabilities

Figure 5: LMV-A 80-250 Posterior Probabilities

Figure 6 shows the optimal Markov chain posterior probabilities for positions 80-250 of LMV-treatment failures of the D genotype. Positions making the 95% threshold cut-off for H4 were 101 and 125.

haematology-thromboembolic-diseases-Posterior-Probabilities

Figure 6: LMV-D 80-250 Posterior Probabilities.

Figure 7 shows the optimal Markov chain posterior probabilities for positions 250-344 of ADF-treatment failures of the D genotype. Positions making the 95% threshold cut-off for H4 were 49 and 62.

haematology-thromboembolic-diseases-Posterior-Probabilities

Figure 7: ADF-D 250-344 Posterior Probabilities.

Figure 8 shows the optimal Markov chain posterior probabilities for positions 250-344 of ADF/LMV-treatment failures of the D genotype. The only position making the 95% threshold cut-off for H4 was 40.

haematology-thromboembolic-diseases-Posterior-Probabilities

Figure 8: ADF/LMV-D 250-344 Posterior Probabilities.

Figure 9 shows the optimal Markov chain posterior probabilities for positions 250-344 of ETV-treatment failures of the D genotype. Positions making the 95% threshold cut-off for H4 were 16, and 38.

haematology-thromboembolic-diseases-Posterior-Probabilities

Figure 9: ETV-D 250-344 Posterior Probabilities

Figure 10 shows the optimal Markov chain posterior probabilities for positions 250-344 of LMV-treatment failures of the A genotype. Positions making the 95% threshold cut-off for H4 were 7, 8, 22, and 68.

haematology-thromboembolic-diseases-Posterior-Probabilities

Figure 10: LMV-A 250-344 Posterior Probabilities.

Figure 11 shows the optimal Markov chain posterior probabilities for positions 250-344 of LMV-treatment failures of the D genotype. Positions making the 95% threshold cut-off for H4 were 10, 34, 36, 39, 54, 59, and 70.

haematology-thromboembolic-diseases-Posterior-Probabilities

Figure 11: LMV-D 250-344 Posterior Probabilities

The following Table 1 summarizes the results corresponding to the 95% threshold cut-off for positions 80-250.

Group Positions
ADF-D 34, 71, 76, 117
ADF/LMV-D 23, 37, 68, 76
ETV-D 1, 3, 21, 134, 168
LMV-A 43, 45, 50, 84
LMV-D 101, 125

Table 1: H4 Positions for Sequence Segments 80-250

The following Table 2 summarizes the results corresponding to the 95% threshold cut-off for positions 250-344.

Group Positions
ADF-D 49, 62
ADF/LMV-D 40
ETV-D 16, 38
LMV-A 7, 8, 22, 68
LMV-D 10,34, 36, 39,54,59,70

Table 2: H4 Positions for Sequence Segments 250-344

Discussion

As proteins serve as the workhorse for cells, single amino acid changes can be devastating. Differences in hydrophobicity and binding properties can lead to dysfunctional aggregation-prone structures. However, in viruses, such changes may confer drug resistance and therefore be selected for. The rtM204V/I mutations are well-established markers for LAM resistance, while rtN236T and rtA181V are thought to be responsible for ADV resistance [6].

Previous research using Bayesian modeling looked at all mutations without segregating them by drug treatments. This study segregated a sample into treatment failures with different drug combinations in order to find specific mutation points that correlate with resistance for each drug.

For Figures 2-11 and the Tables, amino acid changes were studied instead of individual nucleotide changes. Working with peptide sequences, instead of genomic sequences, was advantageous for three main reasons. Firstly, it allowed each graph to convey more information. Secondly, it circumvented the issue of analyzing the un-translated region of the HBV genome, and thirdly, it ensured that it filtered out less important synonymous mutations. But while this put the focus on non-synonymous and non-silent mutations, it is possible that this process may have overlooked seemingly unimportant silent and synonymous mutations that may influence the development of drug resistance.

Another limitation of this study is that some amino acid positions were not identifiable and were denoted with an X. Sequences with missing values were still included in the overall analysis. However, it is hoped that additional experiments with a newly acquired 454 pyrosequencer will analyze full-length HBV sequences. Future research would also employ this Bayesian selection tool on a larger pool of HBV data.

It is hoped that this information can be used in targeting the identified locations to improve patient treatment and drug design. With increased availability of genomic data, the Bayesian method used here may even be employed with other viruses or diseases to identify other regions of interest.

References

  1. Sharma SK, Saini N, Chwla Y (2005) Hepatitis B virus: inactive carriers.Virol J 2: 82.
  2. Guidotti LG, Chisari FV (2006) Immunobiology and pathogenesis of viral hepatitis.Annu Rev Pathol 1: 23-61.
  3. Robbins SL, Kumar V, Cotran RS (2010) Robbins and Cotran pathologic basis of disease. In., 8th edn. Philadelphia, PA: Saunders/Elsevier, 1 online resource, 14-1450.
  4. Shaw T, Bartholomeusz A, Locarnini S (2006) HBV drug resistance: mechanisms, detection and interpretation.J Hepatol 44: 593-606.
  5. Seeger C, Mason WS (2000) Hepatitis B virus biology.MicrobiolMolBiol Rev 64: 51-68.
  6. Pastor R, Habersetzer F, Fafi-Kremer S, Doffoel M, Baumert TF, et al. (2009) Hepatitis B virus mutations potentially conferring adefovir/tenofovir resistance in treatment-naive patients.World J Gastroenterol 15: 753-755.
  7. Zhang J, Hou T, Wang W, Liu JS (2010) Detecting and understanding combinatorial mutation patterns responsible for HIV drug resistance.ProcNatlAcadSci U S A 107: 1321-1326.
  8. Dienstag JL (2008) Hepatitis B virus infection.N Engl J Med 359: 1486-1500.
  9. Svicher V, Cento V, Bernassola M, Neumann-Fraune M, Van Hemert F, et al. (2012) Novel HBsAg markers tightly correlate with occult HBV infection and strongly affect HBsAg detection.Antiviral Res 93: 86-93.
Citation: Higgs G, Lin Z, Cento V, Svicher V, Hattangadi S et al. (2014) Using Bayesian Models to Locate Mutations for HBV Drug Resistance. J Hematol Thrombo Dis 2:166.

Copyright: © 2014 Higgs G, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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