Viral load measurement has become an indispensable tool for optimal clinical management of HIV-1-infected patients. Several different technologies have been utilized for measurement of HIV-1 RNA, including reverse transcriptase-polymerase chain reaction (RT-PCR), isothermal nucleic acid sequence-based amplification (NASBA) and branched DNA signal amplification (bDNA) [1–14]. HIV genetic diversity can compromise the reliability of detection and accuracy of quantity by conventional viral load tests, thus it is important to evaluate the performance of different assays on different HIV subtypes.
Three US Food and Drug Administration (FDA)-approved commercial HIV-1 viral load assays have been most widely used in monitoring the HIV-1 RNA level of HIV/AIDS patients: NucliSens HIV-1 QT assay by bioMerieux (based on NASBA); Quantiplex HIV-1 version 3.0 by Bayer Diagnostics (based on bDNA); and COBAS AmplicorTM HIV-1 Monitor by Roche Diagnostics (based on RT-PCR). Although these assays have been used reliably, they present different lower limits of detection and dynamic ranges. Moreover, the absolute HIV-1 RNA concentration determined by the different assays has been reported to differ between assays. Viral load quantification has also been reported to vary depending on HIV-1 genetic diversity, since RNA extracted from different HIV-1 Clades are not equally quantitated by different assays [15–21].
HIV-1 is characterized by a high level of genetic diversity, rendering it an especially challenging target for technologies that rely on hybridization of specific primers and/or probes. HIV-1 strains have been categorized into phylogenetically distinct groups (M, N, and O). Group M strains have been further subdivided into subtypes A–D, F–H, J, and K; group O strains also form multiple phylogenetically distinct clusters . Recombination, a major force in HIV-1 evolution, has substantially increased overall sequence complexity and given rise to at least 19 circulating recombinant forms (CRF) and numerous unique recombinant forms (URF). The distribution of groups, subtypes and recombinants is unequal and dynamic.
In China, where the non-B subtype is predominant, recent studies have revealed an increasing viral diversity of CRF. Molecular epidemiological data indicate that CRF_BC is the dominant subtype of HIV-1 virus, representing over 45% of the HIV-1 infected population in China [23–26]. At present the major forms of CRF_BC are CRF07_BC and CRF08_BC. The major difference of genetics between CRF07_BC and CRF08_BC is in env region (Los Alamos Genebank, http://hiv-web.lanl.gov).
Under the national policy of HIV/AIDS prevention and treatment, more and more AIDS patients have been enrolled in the antiretroviral treatment program. The number of treated patients has reached more than 30 000. Accordingly, HIV-1 viral load tests have been increasingly carried out in more treatment sites. In the present study, the HIV-1 viral load quantification results were compared among three assays commonly used in Chinese laboratories: NucliSens HIV-1 QT (together with its next generation product-NucliSENS easyQ HIV-1 v1.1); Amplicor HIV-1 monitor 1.5; and Quantiplex HIV-1 version 3.0. These data provide a practical reference for future monitoring of anti-retroviral therapy among patients with the CRF_BC subtype.
Patient samples and sample collection
The study subjects analyzed in this study were recruited from Guizhou province, one of the dominant HIV epidemic areas in south western China. HIV prevalence in this province comes mainly from the injection drug users (IDUs); the dominant subtype of HIV-1 virus prevalent in IDU is CRF_BC, which represents one of the current dominant subtypes prevalent in China. Whole blood was collected in EDTA-treated tubes from nineteen IDUs; among them, sixteen were HIV positive and three were HIV negative. HIV positive participants had been living with HIV for between two and five years and had not received any antiretroviral therapy at the time of sampling. Subject enrollment was approved by the local Institutional Review Board (IRB); all subjects signed informed consent and gave permission for the samples to be used for research purposes.
Plasma samples were prepared from the whole blood samples for viral load testing and genotyping. Plasma was separated by centrifugation at 1100 × g for 10 min, within 8 h of collection and stored in fresh RNase-free micro-tubes at −70°C until testing. The three HIV-1 negative samples served as negative controls. Samples were freeze-thawed no more than three times.
Quantification of HIV-1 RNA(4)
All of the nineteen samples were quantitated using the three methods, following each manufacturer's instructions. An internal control was used in each run.
For NucliSens HIV-1 QT (NASBA), 200 μl plasma for each sample, together with the internal calibrator, was processed using the standard NucliSens manual nucleic acid extraction method. HIV-1 RNA and calibrator were subjected to co-amplification, and amplicons were then detected with NucliSens ECL reader. The dynamic range of NucliSens HIV-1 QT (NASBA) was claimed to be 50–3 000 000 copies/ml.
Standard COBAS Amplicor™ Version 1.5 (RT-PCR) has a linear range of 400–750 000 copies/ml. It also uses 200 μl of plasma to undergo manual extraction and then amplification and detection automatically processed by Cobas Amplicor. For this method, one negative and two positive controls were processed per run.
VERSANT HIV–1 RNA 3.0 assay (bDNA) bears the linear range of 50–500 000 copies/ml and uses 1 ml of plasma. Internal and external controls were performed for each run to monitor the validity of detection results. Assays were performed using semi-automated Quantiplex bDNA system 340.
The gp41 region of the HIV-1 genome was amplified and sequenced. Viral RNA was extracted from plasma using the QIAquick Gel Extraction Kit (QIAGEN, Inc., Valencia, California, USA), Outer primers Gp40F1(5′–TCTTAGGAGCAGCAGGAAGCACTATGGG–3′), Gp41R1(5′–AACGACAAAGGTGAGTATCCCTGCCTAA–3′) and nested primers Gp46F2(5′–ACAATTATTGTCTGGTATAGTGCAACAGCA–3′), Gp47R2(5′–TTAAACCTATCAAGCCTCCTACTATCATTA–3′) were applied in the nested PCR amplification. Amplicons were sequenced using an ABI PRISM® 3100 Genetic Analyzer.
The gp41 sequences of unknown isolates were aligned with reference sequences of HIV-1 subtypes A through J using Clustal W, and compared using PHYLIP software. Phylogenetic trees were constructed using the neighbor-joining method in the distance methods in PHYLIP. The confidence of branching order was evaluated by bootstrapping.
To normalize the distributions, all viral load measurements from the three assays were transformed on the log10 scale prior to statistical analysis. Two major statistical analyses were performed with SPSS version 12.0 software (SPSS Inc., Chicago, Illinois, USA) as following: (1) correlation studies (Pearson and Spearman correlation coefficients), running linear correlation analysis with SPSS for each two assays respectively, we obtained three correlation equations and the correlation coefficients were apparently close to 1 (P < 0.0001), (2) two-tailed paired Student's t-test, statistics result from this analysis were capable of showing the mean difference between each two assays, this was intended to validate and illustrate the methodology difference.
DNA sequence analysis
Viral RNA were extracted from the 16 HIV-1 positive specimens and amplified by RT-PCR for the gp41 region. The gp41 amplicons were analyzed by DNA sequencer. Using PHYLIP software (neighbor-joining), a phylogenetic diagram was constructed with reference strains A,B,C,D,AE,F,G,H,J,K,AG,DF, CRF_BC. The analysis assigned all these sixteen samples to subtype CRF_BC (Fig. 1). The genetic variation among the sixteen samples and China subtype CRF_BC was less than 6.0%.
Comparison of viral load values among three HIV-1 viral load assays
The three HIV-1 negative specimens were correctly reported as below the limit of detection by all of the three HIV-1 viral load assays. The log values of the 16 HIV-1 positive samples tested using three assays were transformed by Log10 and the correlation between them was compared (Fig. 2 and Table 1).
The viral load in all sixteen samples ranged from 630 to 12 000 copies/ml by NASBA test, from 2930 to 102 000 copies/ml by RT-PCR test, and from 1816 to 23 561 by bDNA test, respectively. Viral load values were higher by RT-PCR than that by bDNA or NASBA (RT-PCR > bDNA > NASBA). The difference between RT-PCR and bDNA was 0.43 (CI 95%, 0.083–0.776); 0.40 (CI 95%, 0.214–0.578) for bDNA and NASBA; and 0.83 (CI 95%, 0.520–1.131) for RT-PCR and NASBA.
The Pearson's correlation between RT-PCR and bDNA was 0.969, (P < 0.001); for RT-PCR and NASBA the correlation was 0.968 (p<0.001); and for RT-PCR and bDNA it was 0.980 (P < 0.0001).
The mean bias in viral load between RT-PCR and bDNA assays is 0.429 log. The mean difference between bDNA and NASBA assays is 0.401 log. The mean difference between RT-PCR and NASBA assays is 0.830 log.
Viral load measurement has become a routine procedure for therapeutic management of HIV-1infected patients. Unfortunately, recombination has increased quickly and become more popular. Given the importance of accurate viral load measurements for optimal management of patients, the viral load assay must also reliably quantify genetically divergent strains of virus. In fact, several recent studies have raised concerns regarding the reliability of the results that can be obtained from the different commercially-available viral load assays – each of which is based on a different principle – when measuring highly polymorphic targets [1,7,11]. Thus, it is important that the performance of different viral load assays is critically evaluated for different HIV subtypes.
The ability to accurately quantitate all HIV-1 subtypes by different assays may vary significantly. RT-PCR has been reported to give higher values than bDNA for HIV-1 subtypes B, C, and D, while giving lower values for subtypes A, E, and F. The values across the subtypes for bDNA and RT-PCR differed by as much as 3– 450-fold. RT-PCR does not detect subtypes A, E, and F with the same efficiency as it detects subtypes B, C, and D (the fitted regression line was y = 0.733x + 1.627)  and this may be related to the design of the primers and probes in the RT-PCR assay. Sequence variation among different HIV-1 subtypes may cause mis-matches to the primer or probes, resulting in reduced hybridization and therefore under-quantitation of some subtypes .
The comparison study of NASBA and RT-PCR assays by Dyer and colleague  indicated a correlation between the two assays (y = 0.931x + 0.167; where y is log10 NASBA and x is log10 RT-PCR HIV RNA concentration). In contrast, Ginocchio and colleagues  found that, while the intercept of 0.30 was not significantly different from 0 (95% confidence interval, –0.01 to 0.61), the slope of 0.87 was significantly different from 1 (95% confidence interval, 0.80 to 0.95), indicating that although the two values correlate significantly, the NucliSens values are consistently higher than Quantiplex values.
The genetic variation between the sixteen samples and China subtype CRF_BC is less than 6.0%. Also this result indicated that the ratio of CRF_BC subtype in injected drug users (IDU) is very high in China mainland, especially in the west of China.
The three assays (RT-PCR, bDNA, NASBA) showed good correlation. Thus we surmised that the three assays have good correlation while quantifying RNA viral load of CRF_BC subtype of China with RT-PCR, bDNA, NASBA.
The viral load value is important for validating the drug resistance . In China, more than 45% of HIV-1 infected patients are subtype CRF_BC. This study suggests that all of the three viral load methods could provide RNA quantitation for subtype CRF_BC for monitoring anti-retroviral therapy. According to the present investigation, the CRF_BC subtype will extend and increase quickly in China, especially among IDU. Therefore, a comprehensive understanding of viral load testing assays for this subtype provides valuable information for monitoring the epidemic in China.
The authors would like to thank Dr. Sheena Sullivan for critical review of manuscript.
Sponsorship: This work was supported by the Clinton HIV/AIDS Initiative, China.
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Keywords:© 2007 Lippincott Williams & Wilkins, Inc.
HIV; sequence variability/subtypes; Viral load; CRF_BC; bDNA; RT-PCR; NASBA