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J Infect Dis. Jul 15, 2011; 204(2): 291–298.
PMCID: PMC3114473
Chemokine (C-C Motif) Receptor 5 −2459 Genotype in Patients Receiving Highly Active Antiretroviral Therapy: Race-Specific Influence on Virologic Success
Rajeev K. Mehlotra,corresponding author1 Vinay K. Cheruvu,2a Melinda J. Blood Zikursh,1 Rebekah L. Benish,1 Michael M. Lederman,3 Robert A. Salata,3 Barbara Gripshover,3 Grace A. McComsey,3 Michelle V. Lisgaris,3 Scott Fulton,3 Carlos S. Subauste,3 Richard J. Jurevic,4 Chantal Guillemette,5 Peter A. Zimmerman,1 and Benigno Rodriguez3
1Center for Global Health and Diseases
2Department of Epidemiology and Biostatistics
3Division of Infectious Diseases and HIV Medicine, Case Western Reserve University School of Medicine
4Department of Biological Sciences, Case Western Reserve University School of Dental Medicine, Cleveland, Ohio
5Department of Pharmacy, Laval University, Quebec City, Quebec, Canada
corresponding authorCorresponding author.
Correspondence: Rajeev K. Mehlotra, PhD, Center for Global Health and Diseases, Wolstein Research Bldg, Room 4204, 2103 Cornell Rd, Cleveland, OH 44106-7286 (rkm/at/case.edu).
Potential conflict of interest: none reported.
Presented in part: XVIII International AIDS Conference, Vienna, Austria, July 2010. Abstract TUPE0077.
aCurrent affiliation: College of Public Health, Kent State University, Kent, OH.
Received November 10, 2010; Accepted March 16, 2011.
Background. In patients receiving highly active antiretroviral therapy (HAART), antiretroviral drug–metabolizing enzyme and transporter gene polymorphisms, as well as chemokine receptor gene polymorphisms, may influence response to treatment.
Methods. In a North American, treated, adherent human immunodeficiency virus (HIV)–positive cohort (self-identified whites, n = 175; blacks, n = 218), we investigated whether CYP2B6 (516G>T, 983T>C), UGT2B7 (IVS1+985A>G, 802C>T), MDR1 3435C>T, chemokine (C-C motif) receptor 2 (CCR2) 190G>A, and CCR5 (−2459G>A, Δ32) polymorphisms influenced the time to achieve virologic success (TVLS).
Results. No difference in TVLS was observed between races. In Kaplan-Meier analyses, only 516G>T (log-rank P = .045 for comparison of GG, GT, and TT and P = .02 GG + GT vs TT) and −2459G>A (log-rank P = .04 for GG, GA, and AA and P = .02 for GG + GA vs AA) genotypes were significantly associated with TVLS in black patients but not in white patients. However, in the Cox proportional hazards model that included age, sex, baseline CD4+ T cell count, and baseline viral load, no significant association was observed between 516G>T and TVLS, whereas the association between −2459G>A and TVLS remained significant even after including CCR2 190G>A as well as all the drug-metabolizing enzyme and transporter genotypes.
Conclusions. These findings suggest that CCR5 −2459G>A genotype had a strong, race-specific influence on TVLS in this cohort. Understanding the possible mechanisms underlying this influence requires further studies.
The introduction of highly active antiretroviral therapy (HAART) as a standard of care has markedly decreased the incidence of AIDS and improved prognoses in persons with AIDS or human immunodeficiency virus (HIV) infection [1]. However, a number of clinical factors, including regimen, adherence, and stage of infection or disease at the initiation of therapy, are known to influence response to HAART [24]. In addition, host-related genetic factors, including polymorphisms in antiretroviral drug-metabolizing enzyme and transporter genes, as well as those in the chemokine (C-C motif) receptor 5 (CCR5) and CCR2 genes, may influence the response to HAART [5].
Among a variety of antiretroviral drugs that are included in HAART, metabolism is relatively well defined for the nonnucleoside reverse-transcriptase inhibitors (NNRTIs) efavirenz (EFV) and nevirapine (NVP), as well as for the nucleoside reverse-transcriptase inhibitor (NRTI) zidovudine (AZT). The hepatic cytochrome P450 2B6 enzyme (CYP2B6) plays a major role in the hydroxylation of both EFV and NVP [6, 7]. CYP2B6 is highly polymorphic, and of all the functional single-nucleotide polymorphisms (SNPs) in this gene, 516G>T (rs3745274, exon 4, Gln172His) and 983T>C (rs28399499, exon 7, Ile328Thr) have been consistently found to affect the pharmacokinetics of EFV and NVP in HIV-infected patients [813]. UDP-glucuronosyltransferase 2B7 (UGT2B7) catalyzes direct glucuronidation of AZT as well as EFV [14]. Of all the functional SNPs in UGT2B7, the most prevalent 802C>T (rs7439366, exon 2, His268Tyr) affects the glucuronidation of AZT but not of EFV [14]. Recently, 802C>T was found to be significantly associated with lower EFV plasma concentrations in HIV-infected patients in univariate analysis, but not in multivariate analysis [15]. However, the influence of 802C>T on treatment responses to AZT and EFV is unknown. Furthermore, the phenotypic consequences are unknown for another highly prevalent SNP, IVS1+985A>G (rs62298861; intron 1; frequency, .09–.37 in North American populations [Mehlotra et al, unpublished observation]) [16], which may have effects on the glucuronidation of and treatment responses to these drugs. Finally, P-glycoprotein (P-gp), an efflux transporter encoded by multidrug resistance 1 gene (MDR1 [ABCB1]), is involved in the absorption, distribution, and elimination of a wide variety of drugs, including protease inhibitors [17]. Fewer data are available in the literature regarding affinity of NNRTIs and NRTIs for P-gp. EFV and NVP are not known to be P-gp substrates, but AZT may be one [17]. MDR1 is also highly polymorphic, and of all the exonic SNPs in this gene, the most functionally significant 3435C>T (rs1045642, exon 26, Ile1145Ile) [18], alone or together with the CYP2B6 SNPs, has been the focus of attempts to understand consequences of variation in MDR1 on EFV pharmacokinetics and treatment response; these attempts have yielded discordant results [8, 9, 11, 12, 19].
Among the chemokine receptor polymorphisms, a 32–base pair (bp) deletion in the CCR5 open reading frame (ORF) (Δ32, rs333), a SNP in the CCR5 promoter region (−2459G>A, rs1799987), and a SNP in the CCR2 ORF (190G>A, rs1799864, Val64Ile) have been the focus of a variety of studies examining their effects on HIV disease progression and response to HAART [2032]. The CCR5 −2459G/A allelic variants are also known as 59029G/A (based on GenBank accession no. U95626) and 303G/A [33], and the A allele–containing most prevalent haplotype is called HHE [33]. The −2459G allele has been associated with significantly lower expression of CCR5 on leukocytes compared with the −2459A allele [34]. In some studies, the CCR5 Δ32 and −2459G alleles have been shown to be associated with improved clinical outcomes among HAART-treated patients, consistent with observations in treatment-naive patients, reported elsewhere [20, 2427, 30, 32]. On the other hand, some studies reported an insignificant trend or no effect of the CCR5 polymorphisms on response to HAART [2123, 28, 29, 31]. Regarding the CCR2 190A (64Ile) allele, most studies found an insignificant trend or no effect on response to HAART [20, 28, 31, 32], with a few exceptions reporting a positive [29] or negative effect [25].
Most previous studies designed to examine genetic associations with response to HAART have focused on polymorphisms in either drug-metabolizing enzyme and transporter [813, 19] or chemokine receptor sets of genes [2024, 2632], and thus may have reached less comprehensive conclusions. In the present study, we explored associations between polymorphisms in both of these sets of genes and virologic response to HAART. This study was conducted in a North American HIV-positive cohort that had received HAART regimens containing EFV, NVP, and/or AZT and had a high rate of adherence to treatment. Furthermore, the cohort had near equal representation of 2 major races, white (European American) and black (African American). The major aims of the study were to determine (1) whether race or certain CYP2B6, UGT2B7, and MDR1 genotypes influence the time to achieve virologic success (TVLS) and (2), given that this was a cohort of treated patients with high adherence, whether certain CCR2 and CCR5 genotypes, known to influence HIV infection and progression to AIDS, also influence TVLS. For these aims, we first asked whether TVLS differs by race. We then asked whether TVLS differs by genotype within each race. Our results provide new insights regarding the influence of CYP2B6 516G>T and CCR5 −2459G>A genotypes on TVLS.
Study Cohort and Genomic DNA Extraction
All subjects (n = 393) were adults with confirmed HIV infection, receiving care at the Special Immunology Unit of Case Western Reserve University/University Hospitals Case Medical Center, Cleveland, OH. Patients were included if their first HAART regimen (defined as ≥3 drugs, including ≥1 NNRTI or protease inhibitor) contained EFV (41.5%), NVP (11.2%), and/or AZT (72.3%). No patient received the CCR5 antagonist maraviroc. These patients were followed up for ≥6 months after initiation of HAART and had available viral load results during that period. They were also required to have a recorded adherence to their HAART regimen of ≥90% during the 6 months after HAART initiation, with 72-hour recall of missed doses used to assess adherence. The predictive value of this tool in this population has been previously assessed in 833 patients and found to be closely associated with the probability of virologic failure at subsequent visits [35]. Information on adherence was available for 84.3% of visits for patients receiving EFV-containing, 90% of visits for those receiving NVP-containing, and 83.7% of visits for those receiving AZT-containing HAART regimens.
Deidentified packed blood pellets, collected from eligible patients during 1996–2007, were obtained from the Case Western Reserve University Center for AIDS Research specimen repository. Genomic DNAs were extracted from 200 μL of packed blood pellets with use of a QIAamp 96 DNA blood kit (Qiagen) [36]. All patients provided written informed consent for deidentified clinical data and specimen collection, storage, and usage in genetic and nongenetic studies. The data and specimen collection protocol was approved by the Institutional Review Board of University Hospitals Case Medical Center.
Polymerase Chain Reaction and Agarose Gel Electrophoresis
The primers and conditions to selectively amplify CYP2B6 exons 4 and 7 [37, 38], UGT2B7 exon 2 [36], and MDR1 exon 26 [39] regions have been described elsewhere. The primers and conditions to selectively amplify UGT2B7 intron 1 (411 bp), CCR2 ORF (327 bp), CCR5 promoter (1118 bp), and CCR5 ORF (312/280 bp) regions are described in Table 1; online only.
SNP and CCR5 Δ32 Genotyping
After polymerase chain reaction amplification, genotyping of CYP2B6 (516G>T and 983T>C) [37, 38], UGT2B7 802C>T [36], and MDR1 3435C>T [39] SNPs was performed by using an oligonucleotide ligation detection reaction–fluorescent microsphere assay on the Bio-Plex suspension array system (Bio-Rad Laboratories), as described elsewhere. Using the same assay, we performed genotyping of UGT2B7 IVS1+985A>G, CCR2 190G>A, and CCR5 −2459G>A SNPs. The ligation detection reaction primers and conditions to genotype these SNPs, and the mean log-transformed fluorescent values corresponding to each of the genotypes (with 95% confidence intervals [CIs]), are presented in Table 1; online only. For the CCR5 Δ32 mutation, difference in the polymerase chain reaction amplicon size determined the presence (280 bp, deletion allele) or absence (312 bp, normal allele) of the mutation.
Statistical Analysis
From the genotype data, CYP2B6, UGT2B7, MDR1, CCR2, and CCR5 allele frequencies were calculated and the Hardy-Weinberg exact test was performed for each group of patients using GenePop software (http://genepop.curtin.edu.au/). Differences in the allele frequencies between groups were measured using Fisher’s exact test (http://www.langsrud.com/fisher.htm). To analyze linkage disequilibrium (LD) between alleles, we calculated Lewontin’s D′ and correlation coefficient (r2) parameters using SHEsis software (http://analysis.bio-x.cn/myAnalysis.php).
Since the observation period began in 1996, viral loads of a significant proportion of patients were measured using the Amplicor HIV-1 Monitor test (Roche), with a lower limit of detection of 400 copies/mL. Therefore, virologic success was defined as a viral load <400 copies/mL within 6 months of HAART initiation, and the only patients included were those with viral loads ≥400 copies/mL at HAART initiation. TVLS was expressed as the median number of days. Analysis started at the beginning of the first HAART regimen, even if patients had received antiretrovirals before then.
We analyzed associations between CYP2B6 UGT2B7, MDR1, CCR2, and CCR5 genotypes and TVLS using Kaplan-Meier analysis and the Cox proportional hazards model (Cox model). To analyze genotype-phenotype associations, polymorphisms in CYP2B6 (n = 2), UGT2B7 (n = 2), and CCR2-CCR5 (n = 3) were considered singly, not as haplotypes. The association analyses were performed using SAS software (version 9.2). For all statistical analyses, differences were considered significant at P < .05.
The characteristics of the cohort are presented in Table 1. Self-identified whites (n = 175) and blacks (n = 218) were approximately equally represented, and the sex distribution reflected the demographics of our clinic, with a predominance of male subjects. CD4+ T-cell counts and viral loads at the initiation of HAART were similar between races, and the distribution of HAART regimens did not differ significantly between races (Table 2). The majority of these patients had a history of exposure to antiretrovirals before their first HAART regimen; prior antiretroviral exposure was relatively evenly distributed across the 3 medication groups (EFV, 74%; NVP, 88.6%; AZT, 73.8%) and did not differ significantly between races (P = .224).
Table 1.
Table 1.
Characteristics of the Human Immunodeficiency Virus–Positive Cohort
Table 2.
Table 2.
Distribution of HAART Regimens by Race
Allele Frequencies and Linkage Disequilibrium
The observed frequencies of the CYP2B6, UGT2B7, MDR1, CCR2, and CCR5 alleles in white and black patients are presented in Table 3. We observed significant differences between races in CYP2B6 516T and 983C, UGT2B7 IVS1+985G and 802T, MDR1 3435T, and CCR5 Δ32 allele frequencies, and marginally significant differences in CCR2 190A and CCR5 −2459A allele frequencies. There was no significant departure from Hardy-Weinberg equilibrium among white or black patients (data not shown). The pairwise LD in white and black patients, represented by both D′ and r2 parameters, is presented in Table 4. The patterns of LD were similar between races. The allele frequency and LD results are in agreement with those reported elsewhere for comparable populations [3640].
Table 3.
Table 3.
Frequencies of CYP2B6, UGT2B7, MDR1, CCR2, and CCR5 Alleles in Patients With Human Immunodeficiency Virus Infection
Table 4.
Table 4.
Linkage Disequilibrium Between Pairs of Polymorphisms in Patients With Human Immunodeficiency Virus Infection
Race and Virologic Success
A total of 262 patients (67%) achieved virologic success (white patients, n = 126 [72%]; black patients, n = 136 [62%]). Using Kaplan-Meier analysis, we found no significant difference in TVLS between races (93 days for white patients and 94 days for black patients; log-rank P = .67).
Drug-Metabolizing Enzyme/Transporter Genotypes and Virologic Success
In white patients, Kaplan-Meier analysis showed no significant difference in TVLS among CYP2B6 516G>T (log-rank P = .45), UGT2B7 IVS1+985A>G (log-rank P = .82), UGT2B7 802C>T (log-rank P = .45), and MDR1 3435C>T (log-rank P = .52) genotypes.
In black patients, Kaplan-Meier analysis showed no significant difference in TVLS among CYP2B6 983T>C (log-rank P = .16), UGT2B7 IVS1+985A>G (log-rank P = .47), UGT2B7 802C>T (log-rank P = .17), and MDR1 3435C>T (log-rank P = .52) genotypes. On the other hand, we observed a significant association between CYP2B6 516G>T genotype and TVLS; patients carrying the 516G allele achieved virologic success significantly earlier (GG, GT, and TT, 155, 161, and 177 days, respectively [log-rank P = .045]; GG + GT and TT, 161 and 177 days, respectively; [log-rank P = .02]). However, in the Cox model that included age, sex, baseline CD4+ T-cell count, and baseline viral load, no significant association was observed between CYP2B6 516G>T genotype and TVLS (P = .12 for GG or GG + GT vs TT; P = .78 for GG vs GT).
Chemokine Receptor Genotypes and Virologic Success
In white patients, Kaplan-Meier analysis showed no significant difference in TVLS among CCR2 190G>A (log-rank P = .44), CCR5 −2459G>A with (log-rank P = .4 [n = 175]) or without (log-rank P = .49 [n = 155]) CCR5 Δ32, and CCR5 Δ32 (log-rank P = .31) genotypes.
In black patients, Kaplan-Meier analysis, showed no significant difference in TVLS among CCR2 190G>A genotypes (log-rank P = .93). On the other hand, we observed a strong association between CCR5 −2459G>A genotype and TVLS; patients carrying the −2459G allele achieved virologic success significantly earlier (GG, GA, and AA, 76, 98 and 131 days, respectively [log-rank P = .04]; GG + GA and AA, 88 and131 days, respectively [log-rank P = .02]) (Figure 1a, 1b). Furthermore, in the Cox model that included age, sex, baseline CD4+ T-cell count, baseline viral load, and CCR2 190G>A genotype, association between CCR5 −2459G>A genotype and TVLS remained significant (Table 5). In this model, when we further included all the drug-metabolizing enzyme and transporter genotypes, the association still remained significant (relative hazard [RH] for GG vs AA, 2.60 [95% CI, 1.36–5.00; P = .004]; RH for GG vs GA, 1.53 [95% CI, .94–2.47; P = .08]; RH for GG + GA vs AA, 1.88 [95% CI, 1.10–3.22; P = .02]). Finally, no significant association was observed between CCR5 −2459G>A genotype and TVLS when white and black patients were combined (log-rank P = .51), indicating that the lack of association in white patients was not due to inadequate sample size.
Figure 1.
Figure 1.
Influence of CCR5 −2459G>A genotypes on time to achieve virologic success (TimeVLSucc; measured in days) in black patients, as determined by Kaplan-Meier analysis. A, In this analysis, all 3 genotypes were considered separately. B, In (more ...)
Table 5.
Table 5.
Virologic Success in Black Patients by CCR5 −2459G>A Genotypes
In the present study, conducted in a North American HIV-positive cohort including 2 major races, white and black, 3 main observations were made regarding TVLS as the response to treatment with HAART: First, the treatment response did not differ significantly by race. Disparate virologic responses to HAART between races or ethnicities may [41] or may not occur [42, 43]. Interethnic differences in certain allele frequencies (eg, higher frequencies of the CYP2B6 516T and 983C alleles in black populations and of the CCR5 Δ32 allele in white populations) may be correlated with differences in response to HAART. In our study, despite significant differences in the allele frequencies of the drug-metabolizing enzyme, transporter, and chemokine receptor genes between white and black patients (Table 3), we did not observe a significant difference in TVLS between the 2 groups.
Second, in the drug-metabolizing enzyme and transporter set of genetic polymorphisms, the treatment response differed significantly only by CYP2B6 516G>T genotype in black patients, but not in white patients. Among black patients, those who carried the 516G allele achieved virologic success significantly earlier. Interestingly, this association was nonsignificant in the Cox model that included age, sex, baseline CD4+ T-cell count, and baseline viral load. Many studies involving ethnically diverse patients have analyzed the association between CYP2B6 genotypes and the pharmacokinetics of EFV and NVP [813] and also between CYP2B6 and/or MDR1 genotypes and EFV-related treatment responses [8, 9, 11, 12, 19]. Although the results pertaining to the association between CYP2B6 genotypes and the pharmacokinetics of these drugs are mostly consistent across these studies, those pertaining to the association between CYP2B6 and/or MDR1 genotypes and EFV-related treatment responses remain unclear. Furthermore, the results of these studies [813] suggest that the interrelationship among ethnicity, CYP2B6/MDR1 genotypes, and the phenotypes considered therein is far from clear; ethnicity may [8, 1012] or may not [9, 13] influence genotype-phenotype associations. Our study was not designed to analyze the pharmacokinetics of any of these drugs or any treatment response other than TVLS. However, when we consider our results together with those of other studies [8, 9, 11, 12, 19], it seems either that the drug-metabolizing enzyme/transporter genotypes considered so far do not substantially influence response to HAART or that the extent of their influence may be cohort dependent.
To our knowledge, whether UGT2B7 SNPs affect antiretroviral treatment response has not been reported elsewhere. Several promoter variants were reported to alter expression of UGT2B7 [44], and it is still unknown whether coding SNPs other than 802C>T result in impaired catalytic activity toward EFV. Recently, a synonymous coding SNP, 735A>G, was found to be associated with faster AZT clearance in patients coinfected with HIV and tuberculosis, and with higher AZT glucuronidation in vitro [45]. Therefore, additional polymorphisms in UGT2B7 as well as relationships between UGT2B7 polymorphisms and treatment responses to AZT and EFV in other cohorts should be investigated.
Third, in the chemokine receptor set of genetic polymorphisms, the treatment response differed significantly only by CCR5 −2459G>A genotype in black patients, but not in white patients. Among black patients, those who carried the −2459G allele achieved virologic success significantly earlier. No significant differences in baseline viral load (P = .61) or baseline CD4+ T-cell count (P = .48) were observed among the 3 CCR5 −2459 genotypes in black patients. The association between the −2459G allele and TVLS remained significant even when CCR2 190G>A as well as all the drug-metabolizing enzyme and transporter genotypes, including CYP2B6 516G>T, were included in the Cox model, suggesting that CCR5 −2459G>A genotype had a stronger influence on TVLS in black patients. As described above, studies that have analyzed the effects of chemokine receptor gene polymorphisms on response to HAART have yielded inconsistent results [2032]. Because these studies were conducted in various populations under a variety of designs, it is difficult to compare our findings directly with the findings of these studies. Nevertheless, the race-specific influence of the −2459G allele, observed in black patients in our study, was not reported in any of the other studies.
Some of the studies reported elsewhere found a significant association of CCR5 Δ32 with improved responses to HAART [24, 26, 27, 30]. In our white patients, we did not observe significant association between this deletion and TVLS. In addition to probable differences in cohort and design between those studies and ours, it may be that the frequency of the Δ32 allele in our white patients (.06) was too low for us to detect a difference in the outcome between wild-type (wtwt) homozygotes (n = 155 [88.57%]) and wtΔ32 heterozygotes (n = 20 [11.43%]).
The observation that CCR5 −2459G exerted a race-specific influence on response to HAART raises the question of whether the genetic characteristics of this cohort are in any way unique. In an attempt to answer this question, we further analyzed patterns of LD at the CCR2-CCR5 locus in both groups of patients, by including other known SNPs in the CCR5 promoter region (−2733A>G, −2554G>T, −2135T>C, −2132C>T, −2086A>G, and −1835C>T). When we performed pairwise LD analysis of all the 9 CCR2-CCR5 polymorphisms, the overall patterns of LD were similar between whites and blacks (data not shown) and were similar to the patterns seen in comparable populations [46]. We also quantified admixture in both groups of patients by using the Duffy blood group antigen (FY) as a population-specific marker. Among the 3 most common FY alleles, FY*A, FY*B, and FY*BES, FY*BES is a key marker for African ancestry [47, 48]. Among white patients, frequency of the FY*BES allele was .01, reflecting the extent (1%) of the African ancestry contribution to European American populations in the continental United States [47]. Among black patients, frequency of the FY*BES allele was .81 (FY*A, .08; FY*B, .11), reflecting the extent (19%) of the European ancestry contribution to African American populations in the continental United States [47]. Thus, our analysis of the CCR2-CCR5 locus and admixture proportions, based on FY alleles, did not reveal any unique genetic characteristics of this cohort. However, analyses should take into consideration genetic characteristics based on other genes potentially important in HIV/AIDS pathogenesis, located on chromosome 3 along with CCR2-CCR5, or on other chromosomes [40, 46, 49]. Finally, to determine whether the race-specific influence of CCR5 −2459G in black patients is unique to this HIV-positive cohort requires future study of various North American and African cohorts.
We acknowledge that our study has some limitations. Most of the patients in our study had prior exposure to antiretrovirals, and this exposure did not differ significantly between races. Thus, it is important to note that for these patients, the first HAART regimen was not necessarily their initial therapy. However, we did not consider data regarding prior antiretroviral exposure into our genotype-phenotype association analyses. Adherence is a significant factor in response to HAART [50], and the tool we used to assess adherence, self-reported 72-hour recall of missed doses, is admittedly less precise than other measures, such as pill counts or electronic monitoring. Nevertheless, this tool has been clinically validated in our patient population [35], and we therefore feel confident that it provides a meaningful measure of adherence in this cohort. We intentionally selected a stringently high level of adherence as an inclusion criterion (≥90%), in an attempt to minimize the confounding effect of adherence on the results.
In conclusion, a strong, race-specific influence of CCR5 −2459G>A genotype on TVLS was observed in a North American, treated, adherent HIV-positive cohort. Understanding the possible mechanisms underlying this influence is important, because it would probably enhance our understanding of genetic factors influencing response to antiretroviral drugs in diverse worldwide populations, complementing our current efforts to further lessen the morbidity and mortality due to this global killer.
Funding
This study was supported by a development award from the Center for AIDS Research University Hospitals Case Medical Center (National Institutes of Health [NIH] grant AI36219) (R. K. M. and C. G.); an Infectious Diseases research support from STERIS Corporation (R. K. M. and B. R.); a large pilot grant from Case Western Reserve University/Cleveland Clinic CTSA grant number UL1RR024989 (National Center for Research Resources) (R. K. M.); and the National Institute of Dental and Craniofacial Research (NIH grant 1P01DE019759-01 to R. J. J.). C. G. is the Canada Research Chair in Pharmacogenetics.
Acknowledgments
We thank Dave McNamara, Dr Carolyn Myers, Dr Walter Blank, Dr Gopal Yadavalli, Cara Henry-Halldin, and Zachary Kloos for their critical comments on the manuscript. We are deeply grateful to all study subjects for contributing blood samples and to Dominic Dorazio for organizing the patient blood samples for this study.
1. Palella FJ, Jr, Delaney KM, Moorman AC, et al. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV Outpatient Study Investigators. N Engl J Med. 1998;338:853–60. [PubMed]
2. Conway B. The role of adherence to antiretroviral therapy in the management of HIV infection. J Acquir Immune Defic Syndr. 2007;45(Suppl 1):S14–8. [PubMed]
3. Easterbrook PJ, Phillips AN, Hill T, et al. Patterns and predictors of the use of different antiretroviral drug regimens at treatment initiation in the UK. HIV Med. 2008;9:47–56. [PubMed]
4. Kitahata MM, Gange SJ, Abraham AG, et al. Effect of early versus deferred antiretroviral therapy for HIV on survival. N Engl J Med. 2009;360:1815–26. [PMC free article] [PubMed]
5. Telenti A, Zanger UM. Pharmacogenetics of anti-HIV drugs. Annu Rev Pharmacol Toxicol. 2008;48:227–56. [PubMed]
6. Erickson DA, Mather G, Trager WF, Levy RH, Keirns JJ. Characterization of the in vitro biotransformation of the HIV-1 reverse transcriptase inhibitor nevirapine by human hepatic cytochromes P-450. Drug Metab Dispos. 1999;27:1488–95. [PubMed]
7. Ward BA, Gorski JC, Jones DR, Hall SD, Flockhart DA, Desta Z. The cytochrome P450 2B6 (CYP2B6) is the main catalyst of efavirenz primary and secondary metabolism: implication for HIV/AIDS therapy and utility of efavirenz as a substrate marker of CYP2B6 catalytic activity. J Pharmacol Exp Ther. 2003;306:287–300. [PubMed]
8. Haas DW, Ribaudo HJ, Kim RB, et al. Pharmacogenetics of efavirenz and central nervous system side effects: an Adult AIDS Clinical Trials Group study. AIDS. 2004;18:2391–400. [PubMed]
9. Haas DW, Smeaton LM, Shafer RW, et al. Pharmacogenetics of long-term responses to antiretroviral regimens containing efavirenz and/or nelfinavir: an Adult Aids Clinical Trials Group Study. J Infect Dis. 2005;192:1931–42. [PubMed]
10. Mahungu T, Smith C, Turner F, et al. Cytochrome P450 2B6 516G>T is associated with plasma concentrations of nevirapine at both 200 mg twice daily and 400 mg once daily in an ethnically diverse population. HIV Med. 2009;10:310–7. [PubMed]
11. Motsinger AA, Ritchie MD, Shafer RW, et al. Multilocus genetic interactions and response to efavirenz-containing regimens: an adult AIDS clinical trials group study. Pharmacogenet Genomics. 2006;16:837–45. [PubMed]
12. Ribaudo HJ, Liu H, Schwab M, et al. Effect of CYP2B6, ABCB1, and CYP3A5 polymorphisms on efavirenz pharmacokinetics and treatment response: an AIDS Clinical Trials Group study. J Infect Dis. 2010;202:717–22. [PMC free article] [PubMed]
13. Wyen C, Hendra H, Vogel M, et al. Impact of CYP2B6 983T>C polymorphism on non-nucleoside reverse transcriptase inhibitor plasma concentrations in HIV-infected patients. J Antimicrob Chemother. 2008;61:914–8. [PubMed]
14. Belanger AS, Caron P, Harvey M, Zimmerman PA, Mehlotra RK, Guillemette C. Glucuronidation of the antiretroviral drug efavirenz by UGT2B7 and an in vitro investigation of drug-drug interaction with zidovudine. Drug Metab Dispos. 2009;37:1793–6. [PubMed]
15. Kwara A, Lartey M, Sagoe KW, Kenu E, Court MH. CYP2B6, CYP2A6 and UGT2B7 genetic polymorphisms are predictors of efavirenz mid-dose concentration in HIV-infected patients. AIDS. 2009;23:2101–6. [PMC free article] [PubMed]
16. Innocenti F, Liu W, Fackenthal D, et al. Single nucleotide polymorphism discovery and functional assessment of variation in the UDP-glucuronosyltransferase 2B7 gene. Pharmacogenet Genomics. 2008;18:683–97. [PMC free article] [PubMed]
17. Owen A, Chandler B, Back DJ. The implications of P-glycoprotein in HIV: friend or foe? Fundam Clin Pharmacol. 2005;19:283–96. [PubMed]
18. Fung KL, Gottesman MM. A synonymous polymorphism in a common MDR1 (ABCB1) haplotype shapes protein function. Biochim Biophys Acta. 2009;1794:860–71. [PMC free article] [PubMed]
19. Fellay J, Marzolini C, Meaden ER, et al. Response to antiretroviral treatment in HIV-1-infected individuals with allelic variants of the multidrug resistance transporter 1: a pharmacogenetics study. Lancet. 2002;359:30–6. [PubMed]
20. Bogner JR, Lutz B, Klein HG, Pollerer C, Troendle U, Goebel FD. Association of highly active antiretroviral therapy failure with chemokine receptor 5 wild type. HIV Med. 2004;5:264–72. [PubMed]
21. Bratt G, Karlsson A, Leandersson AC, Albert J, Wahren B, Sandstrom E. Treatment history and baseline viral load, but not viral tropism or CCR-5 genotype, influence prolonged antiviral efficacy of highly active antiretroviral treatment. AIDS. 1998;12:2193–202. [PubMed]
22. Brumme ZL, Chan KJ, Dong W, et al. CCR5Delta32 and promoter polymorphisms are not correlated with initial virological or immunological treatment response. AIDS. 2001;15:2259–66. [PubMed]
23. Brumme ZL, Henrick BM, Brumme CJ, Hogg RS, Montaner JS, Harrigan PR. Association of the CCR5delta32 mutation with clinical response and >5-year survival following initiation of first triple antiretroviral regimen. Antivir Ther. 2005;10:849–53. [PubMed]
24. Guerin S, Meyer L, Theodorou I, et al. CCR5 delta32 deletion and response to highly active antiretroviral therapy in HIV-1-infected patients. AIDS. 2000;14:2788–90. [PubMed]
25. Hendrickson SL, Jacobson LP, Nelson GW, et al. Host genetic influences on highly active antiretroviral therapy efficacy and AIDS-free survival. J Acquir Immune Defic Syndr. 2008;48:263–71. [PubMed]
26. Kasten S, Goldwich A, Schmitt M, et al. Positive influence of the Delta32CCR5 allele on response to highly active antiretroviral therapy (HAART) in HIV-1 infected patients. Eur J Med Res. 2000;5:323–8. [PubMed]
27. Laurichesse JJ, Persoz A, Theodorou I, Rouzioux C, Delfraissy JF, Meyer L. Improved virological response to highly active antiretroviral therapy in HIV-1-infected patients carrying the CCR5 Delta32 deletion. HIV Med. 2007;8:213–9. [PubMed]
28. O'Brien TR, McDermott DH, Ioannidis JP, et al. Effect of chemokine receptor gene polymorphisms on the response to potent antiretroviral therapy. AIDS. 2000;14:821–6. [PubMed]
29. Passam AM, Zafiropoulos A, Miyakis S, et al. CCR2-64I and CXCL12 3'A alleles confer a favorable prognosis to AIDS patients undergoing HAART therapy. J Clin Virol. 2005;34:302–9. [PubMed]
30. Valdez H, Purvis SF, Lederman MM, Fillingame M, Zimmerman PA. Association of the CCR5delta32 mutation with improved response to antiretroviral therapy. JAMA. 1999;282:734. [PubMed]
31. Wit FW, van Rij RP, Weverling GJ, Lange JM, Schuitemaker H. CC chemokine receptor 5 delta32 and CC chemokine receptor 2 64I polymorphisms do not influence the virologic and immunologic response to antiretroviral combination therapy in human immunodeficiency virus type 1-infected patients. J Infect Dis. 2002;186:1726–32. [PubMed]
32. Yamashita TE, Phair JP, Munoz A, et al. Immunologic and virologic response to highly active antiretroviral therapy in the Multicenter AIDS Cohort Study. AIDS. 2001;15:735–46. [PubMed]
33. Gonzalez E, Bamshad M, Sato N, et al. Race-specific HIV-1 disease-modifying effects associated with CCR5 haplotypes. Proc Natl Acad Sci USA. 1999;96:12004–9. [PubMed]
34. Hladik F, Liu H, Speelmon E, et al. Combined effect of CCR5-Delta32 heterozygosity and the CCR5 promoter polymorphism -2459 A/G on CCR5 expression and resistance to human immunodeficiency virus type 1 transmission. J Virol. 2005;79:11677–84. [PMC free article] [PubMed]
35. Chang S, Gripshover B, Kuchia M, Sethi AK. In: AIDS 2006-XVI International AIDS Conference. Toronto, Canada: 2006. An operationalized simple adherence assessment predicts future viral failure among patients attending an urban U.S. HIV care clinic [abstract TUPE0124] http://www.iasociety.org/Abstracts/A2198088.aspx. Accessed 9 May 2011.
36. Mehlotra RK, Bockarie MJ, Zimmerman PA. Prevalence of UGT1A9 and UGT2B7 nonsynonymous single nucleotide polymorphisms in West African, Papua New Guinean, and North American populations. Eur J Clin Pharmacol. 2007;63:1–8. [PMC free article] [PubMed]
37. Mehlotra RK, Bockarie MJ, Zimmerman PA. CYP2B6 983T>C polymorphism is prevalent in West Africa but absent in Papua New Guinea: implications for HIV/AIDS treatment. Br J Clin Pharmacol. 2007;64:391–5. [PubMed]
38. Mehlotra RK, Ziats MN, Bockarie MJ, Zimmerman PA. Prevalence of CYP2B6 alleles in malaria-endemic populations of West Africa and Papua New Guinea. Eur J Clin Pharmacol. 2006;62:267–75. [PubMed]
39. Benish RL, Rodriguez B, Zimmerman PA, Mehlotra RK. Comparative description of haplotype structure and genetic diversity of MDR1 (ABCB1) in HIV-positive and HIV-negative populations. Infect Genet Evol. 2010;10:60–7. [PubMed]
40. Clark VJ, Dean M. Haplotype structure and linkage disequilibrium in chemokine and chemokine receptor genes. Hum Genomics. 2004;1:255–73. [PubMed]
41. Weintrob AC, Grandits GA, Agan BK, et al. Virologic response differences between African Americans and European Americans initiating highly active antiretroviral therapy with equal access to care. J Acquir Immune Defic Syndr. 2009;52:574–80. [PubMed]
42. de Arellano ER, Benito JM, Soriano V, Lopez M, Holguin A. Impact of ethnicity and HIV type 1 subtype on response to first-line antiretroviral therapy. AIDS Res Hum Retroviruses. 2007;23:891–4. [PubMed]
43. Jensen-Fangel S, Pedersen L, Pedersen C, et al. The effect of race/ethnicity on the outcome of highly active antiretroviral therapy for human immunodeficiency virus type 1-infected patients. Clin Infect Dis. 2002;35:1541–8. [PubMed]
44. Duguay Y, Baar C, Skorpen F, Guillemette C. A novel functional polymorphism in the uridine diphosphate-glucuronosyltransferase 2B7 promoter with significant impact on promoter activity. Clin Pharmacol Ther. 2004;75:223–33. [PubMed]
45. Kwara A, Lartey M, Boamah I, et al. Interindividual variability in pharmacokinetics of generic nucleoside reverse transcriptase inhibitors in TB/HIV-coinfected Ghanaian patients: UGT2B7*1c is associated with faster zidovudine clearance and glucuronidation. J Clin Pharmacol. 2009;49:1079–90. [PMC free article] [PubMed]
46. Gonzalez E, Dhanda R, Bamshad M, et al. Global survey of genetic variation in CCR5, RANTES, and MIP-1alpha: impact on the epidemiology of the HIV-1 pandemic. Proc Natl Acad Sci USA. 2001;98:5199–204. [PubMed]
47. Parra EJ, Marcini A, Akey J, et al. Estimating African American admixture proportions by use of population-specific alleles. Am J Hum Genet. 1998;63:1839–51. [PubMed]
48. Zimmerman PA, Woolley I, Masinde GL, et al. Emergence of FY*A(null) in a Plasmodium vivax-endemic region of Papua New Guinea. Proc Natl Acad Sci USA. 1999;96:13973–7. [PubMed]
49. Gonzalez E, Kulkarni H, Bolivar H, et al. The influence of CCL3L1 gene-containing segmental duplications on HIV-1/AIDS susceptibility. Science. 2005;307:1434–40. [PubMed]
50. McMahon JH, Jordan MR, Kelley K, et al. Pharmacy adherence measures to assess adherence to antiretroviral therapy: review of the literature and implications for treatment monitoring. Clin Infect Dis. 2011;52:493–506. [PMC free article] [PubMed]
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