• We are sorry, but NCBI web applications do not support your browser and may not function properly. More information
Logo of jcmPermissionsJournals.ASM.orgJournalJCM ArticleJournal InfoAuthorsReviewers
J Clin Microbiol. Jul 2009; 47(7): 2292–2294.
Published online May 13, 2009. doi:  10.1128/JCM.02439-08
PMCID: PMC2708518

Genotypic Prediction of Human Immunodeficiency Virus Type 1 CRF02-AG Tropism[down-pointing small open triangle]

Abstract

We assessed the performance of genotypic algorithms for predicting the tropism of human immunodeficiency virus type 1 coreceptor usage in 52 patients infected with the CRF02-AG subtype. The combined criteria of the 11/25 and net charge rules accurately detected CXCR4-using CRF02-AG viruses, whereas the Geno2pheno tool lacked sensitivity and the position-specific scoring matrix (PSSM) tool WebPSSM lacked specificity.

Human immunodeficiency virus type 1 (HIV-1) enters target cells through the sequential binding of the envelope glycoprotein (gp120) to CD4 and a chemokine receptor, CCR5 or CXCR4 (1). HIV-1 coreceptor usage must be identified before treatment with CCR5 antagonists, as they can only be used for patients harboring R5 viruses alone (7). The “gold standard” for characterization of HIV-1 tropism is a recombinant virus phenotypic entry assay, but genotypic methods based on the V3 sequence could be easier. We have previously shown that the V3 genotype accurately predicts the phenotype of HIV-1 coreceptor usage for subtype B viruses (5, 13). However, the V3-based genotypic algorithms could be unsuitable for predicting the tropism of non-B viruses because they were built using data sets of genotype-phenotype correlations from subtype B viruses (9). Indeed, the Geno2pheno and WebPSSM algorithms were not designed to be predictive for non-B viruses, except for a recent version of the position-specific scoring matrix (PSSM) designed for subtype C viruses (11, 12). It is thus necessary to study subtype-specific genotypic determinants of HIV-1 tropism. The CRF02-AG recombinant subtype predominates in West Africa (10) and accounts for an increasing proportion of cases in Western Europe, notably in France (6, 14). Various proportions of CXCR4-using viruses have been reported in subtype CRF02-AG-infected patients (2, 15, 16), but little is known about the genotypic determinants of HIV-1 tropism for subtype CRF02-AG viruses. Genotype-phenotype correlation studies are thus needed before genotypic algorithms can be used to predict the tropism of this particular HIV-1 subtype.

We characterized both genotypically and phenotypically the tropism of 52 HIV-1 CRF02-AG-infected individuals, recruited at the Department of Infectious Diseases of Toulouse University Hospital, France. These patients had a median plasma HIV-1 RNA load of 4.95 log copies/ml (interquartile range, 4.18 to 5.34), and a median CD4+ T-lymphocyte count of 210 cells/mm3 (interquartile range, 115 to 391). All viruses were identified as the HIV-1 CRF02-AG subtype by pol and env sequence analysis using the HIVseq program (http://hivdb.stanford.edu/) and the NCBI genotyping tool (http://www.ncbi.nlm.nih.gov/projects/genotyping/formpage.cgi). We confirmed that these viruses belonged to the CRF02-AG subtype by neighbor-joining phylogenetic analysis of the sequences studied here, together with HIV-1 subtype reference sequences from the Los Alamos National Laboratory (http://www.hiv.lanl.gov/content/sequence/NEWALIGN/align.html).

A region spanning gp120 and the ectodomain of the gp41 env gene of plasma HIV-1 RNA was amplified by reverse transcription-PCR. Two separate PCR amplifications were performed in parallel for each patient and pooled to prevent sampling bias of the assessed virus population. The V3 region from the env PCR product was bulk sequenced, blinded to the phenotype, as previously described (13). Bulk sequencing allows the detection of minor variants when present at a frequency of at least 20% in the viral population. The phenotype of HIV-1 coreceptor usage was determined using a recombinant virus entry assay (13). The sensitivity of the assay has been enhanced to detect minor amounts of CXCR4-using virus when they accounted for 0.5 to 1% of the virus population (data not shown).

We used a genotypic rule based on amino acid residues at positions 11 and 25 and the overall net charge of V3 to predict HIV-1 tropism from the V3 genotype (3, 4, 8). One of the following criteria is required for predicting CXCR4 coreceptor usage: (i) R or K at position 11 of V3 and/or K at position 25, (ii) R at position 25 of V3 and a net charge of ≥+5, or (iii) a net charge of ≥+6 (13). The V3 net charge was calculated by subtracting the number of negatively charged amino acids (D and E) from the number of positively charged ones (K and R). We have previously shown that these combined criteria are better for predicting HIV-1 coreceptor usage of subtype B viruses than the 11/25 and net charge rules used separately (5, 13). We have now assessed the performance of these combined criteria for predicting the tropism of subtype CRF02-AG viruses and those of the bioinformatic tools Geno2pheno (false-positive rate of 10%) and WebPSSM with the SI/NSI and X4/R5 matrices (WebPSSMSI/NSI and WebPSSMX4/R5, respectively). Geno2pheno is available at http://coreceptor.bioinf.mpi-sb.mpg.de/cgi-bin/coreceptor.pl (September 2008). WebPSSM is available at http://ubik.microbiol.washington.edu/computing/pssm/ (September 2008).

The phenotypic assay revealed 42 virus populations with an R5 phenotype and 10 virus populations with a dual/mixed R5X4 phenotype but no virus population with a pure X4 phenotype. The genotypic classifications based on the combined criteria from the 11/25 and net charge rules and the Geno2pheno and WebPSSM tools were compared to the phenotype of the subtype CRF02-AG viruses (Table (Table1).1). The combined criteria from the 11/25 and net charge rules misclassified only four of the samples from the 52 patients (global concordance, 92%), while Geno2pheno misclassified 10 samples (global concordance, 81%), PSSMX4/R5 misclassified 12 samples, and PSSMSI/NSI misclassified 7 samples (global concordance, 77 to 87%). The combined 11/25 and net charge rule criteria successfully detected CRF02-AG subtype CXCR4-using viruses with a sensitivity of 70% and a specificity of 98%. Geno2pheno lacked sensitivity (40%), while PSSMX4/R5 was sensitive (80%) but less specific (76%).

TABLE 1.
Comparison of genotypic prediction of HIV tropism and the observed phenotype and performances of the V3 genotype for predicting CXCR4 usage of the HIV-1 CRF02-AG subtype

A recent study reported that the genotypic algorithms currently used lack sensitivity for detecting CXCR4-using viruses among non-B subtypes, but no details were given of their performance for particular subtypes (9). Subtype-specific genotype-phenotype correlations should be assessed because the genotypic determinants of coreceptor usage for some particular subtypes may be different. We found that the Geno2pheno tool lacked sensitivity for predicting the CXCR4 usage of subtype CRF02-AG viruses, although it performs well for subtype B viruses (13). In contrast, the combined 11/25 and net charge rule criteria were equally good at predicting the CXCR4 usage of both subtype CRF02-AG and subtype B viruses (13). Bulk sequencing is less sensitive than the phenotypic assay at detecting minor CXCR4-using variants in the virus population, but the impact of such minor variants on the clinical response to CCR5 antagonists remains to be determined. Multicenter studies analyzing the correlations between the genotypic determination of HIV-1 tropism and clinical response to CCR5 antagonists are needed to validate this approach in clinical practice.

In conclusion, the combined criteria from the 11/25 and net charge rules performed well for predicting the tropism of HIV-1 subtype CRF02-AG, while the Geno2pheno bioinformatic tool did not. Simple genotypic methods could make the clinical use of CCR5 antagonists easier and cheaper than using phenotypic assays. Additional studies are needed to assess the performances of the various genotypic algorithms for predicting the tropism of other HIV-1 non-B subtypes.

Acknowledgments

Financial support for this work was provided by INSERM U563.

Footnotes

[down-pointing small open triangle]Published ahead of print on 13 May 2009.

REFERENCES

1. Berger, E. A., P. M. Murphy, and J. M. Farber. 1999. Chemokine receptors as HIV-1 coreceptors: roles in viral entry, tropism, and disease. Annu. Rev. Immunol. 17657-700. [PubMed]
2. Brandful, J. A., M. E. Coetzer, T. Cilliers, M. Phoswa, M. A. Papathanasopoulos, L. Morris, and P. L. Moore. 2007. Phenotypic characterization of HIV type 1 isolates from Ghana. AIDS Res. Hum. Retrovir. 23144-152. [PubMed]
3. Briggs, D. R., D. L. Tuttle, J. W. Sleasman, and M. M. Goodenow. 2000. Envelope V3 amino acid sequence predicts HIV-1 phenotype (co-receptor usage and tropism for macrophages). AIDS 142937-2939. [PubMed]
4. De Jong, J. J., A. De Ronde, W. Keulen, M. Tersmette, and J. Goudsmit. 1992. Minimal requirements for the human immunodeficiency virus type 1 V3 domain to support the syncytium-inducing phenotype: analysis by single amino acid substitution. J. Virol. 666777-6780. [PMC free article] [PubMed]
5. Delobel, P., M. T. Nugeyre, M. Cazabat, C. Pasquier, B. Marchou, P. Massip, F. Barre-Sinoussi, N. Israel, and J. Izopet. 2007. Population-based sequencing of the V3 region of env for predicting the coreceptor usage of human immunodeficiency virus type 1 quasispecies. J. Clin. Microbiol. 451572-1580. [PMC free article] [PubMed]
6. Descamps, D., M. L. Chaix, P. Andre, V. Brodard, J. Cottalorda, C. Deveau, M. Harzic, D. Ingrand, J. Izopet, E. Kohli, B. Masquelier, S. Mouajjah, P. Palmer, I. Pellegrin, J. C. Plantier, C. Poggi, S. Rogez, A. Ruffault, V. Schneider, A. Signori-Schmuck, C. Tamalet, M. Wirden, C. Rouzioux, F. Brun-Vezinet, L. Meyer, and D. Costagliola. 2005. French national sentinel survey of antiretroviral drug resistance in patients with HIV-1 primary infection and in antiretroviral-naive chronically infected patients in 2001-2002. J. Acquir. Immune Defic. Syndr. 38545-552. [PubMed]
7. Dorr, P., M. Westby, S. Dobbs, P. Griffin, B. Irvine, M. Macartney, J. Mori, G. Rickett, C. Smith-Burchnell, C. Napier, R. Webster, D. Armour, D. Price, B. Stammen, A. Wood, and M. Perros. 2005. Maraviroc (UK-427,857), a potent, orally bioavailable, and selective small-molecule inhibitor of chemokine receptor CCR5 with broad-spectrum anti-human immunodeficiency virus type 1 activity. Antimicrob. Agents Chemother. 494721-4732. [PMC free article] [PubMed]
8. Fouchier, R. A., M. Groenink, N. A. Kootstra, M. Tersmette, H. G. Huisman, F. Miedema, and H. Schuitemaker. 1992. Phenotype-associated sequence variation in the third variable domain of the human immunodeficiency virus type 1 gp120 molecule. J. Virol. 663183-3187. [PMC free article] [PubMed]
9. Garrido, C., V. Roulet, N. Chueca, E. Poveda, A. Aguilera, K. Skrabal, N. Zahonero, S. Carlos, F. Garcia, J. L. Faudon, V. Soriano, and C. de Mendoza. 2008. Evaluation of eight different bioinformatics tools to predict viral tropism in different human immunodeficiency virus type 1 subtypes. J. Clin. Microbiol. 46887-891. [PMC free article] [PubMed]
10. Hemelaar, J., E. Gouws, P. D. Ghys, and S. Osmanov. 2006. Global and regional distribution of HIV-1 genetic subtypes and recombinants in 2004. AIDS 20W13-W23. [PubMed]
11. Jensen, M. A., M. Coetzer, A. B. van 't Wout, L. Morris, and J. I. Mullins. 2006. A reliable phenotype predictor for human immunodeficiency virus type 1 subtype C based on envelope V3 sequences. J. Virol. 804698-4704. [PMC free article] [PubMed]
12. Jensen, M. A., F. S. Li, A. B. van 't Wout, D. C. Nickle, D. Shriner, H.-X. He, S. McLaughlin, R. Shankarappa, J. B. Margolick, and J. I. Mullins. 2003. Improved coreceptor usage prediction and genotypic monitoring of R5-to-X4 transition by motif analysis of human immunodeficiency virus type 1 env V3 loop sequences. J. Virol. 7713376-13388. [PMC free article] [PubMed]
13. Raymond, S., P. Delobel, M. Mavigner, M. Cazabat, C. Souyris, K. Sandres-Saune, L. Cuzin, B. Marchou, P. Massip, and J. Izopet. 2008. Correlation between genotypic predictions based on V3 sequences and phenotypic determination of HIV-1 tropism. AIDS 22F11-F16. [PubMed]
14. Taylor, B. S., and S. M. Hammer. 2008. The challenge of HIV-1 subtype diversity. N. Engl. J. Med. 3591965-1966. [PubMed]
15. Tebit, D. M., L. Zekeng, L. Kaptue, M. Salminen, H. G. Krausslich, and O. Herchenroder. 2002. Genotypic and phenotypic analysis of HIV type 1 primary isolates from western Cameroon. AIDS Res. Hum. Retrovir. 1839-48. [PubMed]
16. Vergne, L., A. Bourgeois, E. Mpoudi-Ngole, R. Mougnutou, J. Mbuagbaw, F. Liegeois, C. Laurent, C. Butel, L. Zekeng, E. Delaporte, and M. Peeters. 2003. Biological and genetic characteristics of HIV infections in Cameroon reveals dual group M and O infections and a correlation between SI-inducing phenotype of the predominant CRF02_AG variant and disease stage. Virology 310254-266. [PubMed]

Articles from Journal of Clinical Microbiology are provided here courtesy of American Society for Microbiology (ASM)
PubReader format: click here to try

Formats:

Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...

Links

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...