Protease Polymorphisms in HIV-1 Subtype CRF01_AE Represent Selection by Antiretroviral Therapy and Host Immune Pressure

Weerawat Manosuthi1,2, David M. Butler2, Josué Perez2, Art F. Y. Poon2, Sergei Kosakovsky Pond2, Satish K. Pillai3, Sanjay R. Mehta2, Mary E. Pacold2, Douglas D. Richman2,4, Davey M. Smith2,4 (Contact: drweerawat@hotmail.com)

1Bamrasnaradura Infectious Diseases Institute, Ministry of Public Health, Thailand 2University of California San Diego, CA 3University of California San Francisco, CA 4Veterans Affairs San Diego Healthcare System, San Diego, CA, USA

▪ Resistance to protease inhibitors (PIs) develops with the acquisition of mutations in the protease gene (pro). These resistance-associated mutations can be classified as either major or accessory (Hammer 2008).

▪ Continued viral replication in the setting of PIs allows for the development and

accumulation of accessory mutations in HIV pro, which can compensate for impaired

fitness conferred by other mutations or can increase the level of phenotypic resistance

(Hirsch 2008; Johnson 2008).

▪ Intersubtype genetic differences and the genetic background of the infected host

population can also influence PI susceptibility and viral replication capacity (Hirsch 2008).

▪ To investigate how CRF01_AE protease polymorphisms and protease resistance-

associated mutations (PRAMs) have been affected by the introduction of antiretroviral

therapy and the frequency of HLA haplotypes in studied populations (Thai and Chinese).

▪ All HIV-1 CRF01_AE pro sequences were downloaded from the Los Alamos National Laboratory database on April 28, 2008.

▪ The recombinant identification program, RIP 3.0 (available at http://lanl.gov/), was used to

identify and confirm subtype and presence of recombination in retrieved sequences.

▪ The dataset was checked for uniqueness using the UNIX command “uniq” and sequences

were aligned using Multiple Sequence Comparison (Edgar 2004).

▪ Geneious Basic version 3.7.1 software (Biomatters Ltd., Auckland, NZ) was used to

construct a neighbor-joining tree using the Jukes-Cantor 69 method for phylogenetic

analysis.

▪ HYPERMUT 2.0 and Epitope Location Finder (available at http://lanl.gov/) were used to

detect APOBEC-induced hypermutation and searched CTL recognized epitopes from viral

amino acid sequences by using sequence code 01_AE.AU.X.CT1 as a reference sequence

(Rose and Korber 2000).

▪ PRAMs and polymorphisms were identified based on data derived from clade B virus

using the Stanford Resistance Database (http://hivdb.stanford.edu/).

▪ The prevalence of specific HLA alleles in the populations studied (Thai and Chinese) were

obtained from the dbMHC database of the National Center for Biotechnology Information (

http://www.ncbi.nlm.nih.gov/projects/gv/mhc).

▪ HLA binding affinities were determined using the Immune Epitope Database and Analysis

resource (IEDB, www.immuneepitope.org) by stabilized matrix method.

▪ Spearman correlations and logistic regression models were used for further analyses.

Acknowledgement: The authors would like to thank The University of California, San Diego Center for AIDS Research (UCSD CFAR) for their support. This work was supported by grants AI69432 (ACTG), AI043638 (AIEDRP), MH62512 (HNRC), MH083552 (Clade), AI077304 (Dual Infection), AI36214 (the Viral Pathogenesis Core of the UCSD Center for AIDS Research),

AI047745 (Dynamics), AI74621 (Transmission) from the National Institutes of Health and the California HIV/AIDS Research Program RN07-SD-702.

▪ 4 polymorphisms had an association with a PRAM with P values <0.2 in Fisher’s exact test univariate analysis. In multivariate analysis, three of four polymorphisms were associated with the presence of a major PRAM (P <0.05) (Figure 2).

▪ At five residue sites, 30 HLA-polymorphism associations were found, distributed among 11 (37%) HLA-A, 17 (56%) HLA-B and 2 (7%) HLA-C haplotypes (Figure 3). Of these associations, 24 (80%) were positive correlations (i.e. the subtype B consensus amino acid was favored) and 6 (20%) were negative correlations.

▪ The nine HLA-polymorphism associations with the largest changes in predicted binding affinity are presented in Figure 4.

▪ There were no substantial differences in hydrophobicity or chemical classifications of these residues between subtype B and CRF01_AE consensus sequences (Table 2).

PosterM-105

INTRODUCTION

OBJECTIVE

METHODS

0 10 20 30 40

L90M

V82AFMS

M46IL

I54VA

I84V

N88S

D30N

L76V1990-2000 (n=188)

2001-2007 (n=537)

No data (n=263)

P = 0.067

P = 0.031

4.8% 2.0%

2.6% 0.5%

Frequencies of PRAMs

Number

0 100 200 300 400 500 600 700 800 900

M36I

I13V

K20RMI

L10V

I93L

V82I

D60E

I62V

V77I

L33V 1990-2000

2001-2007

No data

P = 0.002

P = 0.007

95.2% 87.1%

85.6% 76.3%

Frequencies of polymorphisms

Number

Figure 1. Frequencies of major PRAMs and polymorphisms by time period

-10 0 10 20 30 40 50 60

L10V*L10V

I13VK20RMI

*K20RMIL33VM36IM36I

D60EI62V

*I62VV77IV82II93L

Adjusted odds ratios

Unadjusted odds ratios

* P <0.001

10.955 3.236-37.092

6.460 3.972-10.505

4.263 2.585-7.032

OR 95%CI

Odds ratio and 95% confidence interval (CI)

Polymorphisms

Figure 3. Associations between HLA haplotypes and polymorphisms

A positive HLA-polymorphism association was defined as expression of a subtype B consensus residue that would be expected during CTL escape with the HLA allele expression in the study population. A negative HLA-polymorphism association was defined when expression of the CRF01_AE consensus residue was associated with HLA expression and CTL escape.

Figure 2. Logistic regression analysis of major PRAMs and each polymorphism

Figure 4. Predicted fold changes of HLA class I binding affinities from the eptiope of subtype B consensus to CRF01_AE

B*4001 B*4430 B*4402 B*1801 A*6802 A*6801 A*0301 A*0301 A*0101

-40

-30

-20

-10

0

Epitope position: 34-42 34-42 34-42 34-42 30-38 30-38 11-20 12-20 7-15

HLA Allele:

39.7

36.5

26.7

18.9

4.9 4.1 3.0 2.41.1

Protease

B: EEMNLPGRWAE: EDINLPGKW

B: DTVLEEMNLAE: DTVLEEINL

Fol

d ch

ange

s

RESULTS

Positive correlation Negative correlation

Polymorphic mutation

Correlation coefficient

P value Polymorphic mutation

Correlation coefficient

P value

10 and 62 0.154 <0.001 20 and 36 -1.333 <0.001

20 and 62 0.140 <0.001 13 and 93 -0.400 <0.001

20 and 93 0.125 <0.001 10 and 93 -0.089 0.005

60 and 93 0.104 0.001

10 and 20 0.095 0.003

77 and 93 0.094 0.003

10 and 13 0.004 0.093

13 and 82 0.069 0.030

Residue Subtype BHydrophobicit

y*Chemical

classificationsSubtype

AEHydrophobicit

y*Chemical

classifications

13 I 1 Aliphatic group V 1 Aliphatic group

35 E 4 Negative charge D 4 Negative charge

36 M 2Sulphur containing

groupI 1 Aliphatic group

41 R 5 Positive charge K 4 Positive charge

69 H 4 Positive charge K 4 Positive charge

89 L 1 Aliphatic group M 2Sulphur containing

group* Hydrophobicity 1 to 5 = from the highest hydrophobicity to the least hydrophobicity

Table 2. Hydrophobicity and chemical classifications of six different residues

Table 1. Correlations among each pair of polymorphisms

CONCLUSION

Polymorphisms in CRF01_AE protease gene are common with M36I being the most frequent.

Polymorphisms at residue 10, 20 and 62 were associated with major PRAMs and therefore PI

use, while R41K and H69K variants are more likely attributable to recognition of epitopes by

the HLA haplotypes in the population. Additionally, protease function most likely requires

maintaining the same biochemical properties at these residues.

▪ 1,322 sequences between 1990 - 2007 were retrieved. 328 duplicated sequences and 6 non-

CRF01_AE sequences were excluded. Analysis demonstrated that all remaining 988 sequences

were not hypermutated.

▪ 846 (86%) sequences were from Asian countries and 142 (14%) sequences came from non-Asian

countries. Asian countries included Thailand (22%), Vietnam (18%), China (17%), Cambodia (13%),

Singapore (10%), Japan (5%) and others (1%).

▪ Phylogenetic analysis demonstrated that the branch length between CRF01_AE pro sequences

revealed relatively low genetic diversity and overall percentage of pairwise similarity was 96%.

▪ The frequencies of major PRAMs and polymorphisms for the time periods of before and after the

introduction of PIs in the Asia region were also investigated (Figure 1).

▪ The correlation between each pair of polymorphisms was investigated (Table 1). There were

eight positive correlations and three negative correlations (all with P <0.05).