A new classification for HIV-1

Berger, Edward A.; Doms, Robert W.; Fenyõ, Éva Mária; Korber, Bette T.; Littman, Dan R.; Moore, John P.; Sattentau, Quentin J.; Schuitemaker, Hanneke; Sodroski, Joseph; Weiss, Robin A.

doi:10.1038/34571

Cited by 724 publications

(473 citation statements)

References 6 publications

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“…Indeed, HIV-1, HIV-2, and both pathogenic and nonpathogenic SIVs use CCR5 in association with the CD4 molecule. [175][176][177] Moreover, CD4 þ T cells expressing CCR5 decreases at the peak of viral replication in both pathogenic and nonpathogenic SIV-infected monkeys. 166,[178][179][180] Altogether, these observations may indicate that depletion of CCR5 þ CD4 þ T cells is an important event but likely is not the only factor involved in AIDS pathogenesis.…”

Section: Cell Cycle Dysregulationmentioning

confidence: 99%

Apoptosis in SIV infection

et al. 2005

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Section: Cell Cycle Dysregulationmentioning

confidence: 99%

Apoptosis in SIV infection

et al. 2005

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“…Recent very unexpected findings have indicated that chemokines and their receptors play pivotal roles in HIV infection. In addition to CCR5 and CXCR4, at least nine other chemokine or orphan receptors, including CCR2b, CCR3, CCR8, GPR1, GPR15, STRL33, US28, V28, and ChemR23, can function as coreceptors to support the cellular entry of one or more HIV strains into various types of cells [12][13][14]. Chemokines, such as macrophage inflammatory protein 1␣ (MIP-1␣), MIP-1␤, RANTES, monocyte chemotactic protein 2 (MCP-2), eotaxin, and SDF-1, have been shown to block the entry of certain HIV strains into its target cells by the corresponding chemokine receptors [15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

IL-4 and a glucocorticoid up-regulate CXCR4 expression on human CD4+ T lymphocytes and enhance HIV-1 replication

Wang

Harada

Matsushita

et al. 1998

Journal of Leukocyte Biology

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“…Based on the ability to use CCR5 or CXCR4, HIV-1 variants are classified as CCR5 tropic (R5), CXCR4 tropic (X4), or dual-mixed tropic (X4R5) (2). R5 strains are generally responsible for the establishment of early infection, while the use of the CXCR4 coreceptor is generally seen in more advanced disease stages.…”

mentioning

confidence: 99%

Evaluation of Eight Different Bioinformatics Tools To Predict Viral Tropism in Different Human Immunodeficiency Virus Type 1 Subtypes

et al. 2008

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Human immunodeficiency virus type 1 (HIV-1) tropism can be assessed using phenotypic assays, but this is quite laborious, expensive, and time-consuming and can be made only in sophisticated laboratories. More accessible albeit reliable tools for testing of HIV-1 tropism are needed in view of the prompt introduction of CCR5 antagonists in clinical practice. Bioinformatics tools based on V3 sequences might help to predict HIV-1 tropism; however, most of these methods have been designed by taking only genetic information derived from HIV-1 subtype B into consideration. The aim of this study was to evaluate the performances of several genotypic tools to predict HIV-1 tropism in non-B subtypes, as data on this issue are scarce. Plasma samples were tested using a new phenotypic tropism assay (Phenoscript-tropism; Eurofins), and results were compared with estimates of coreceptor usage using eight different genotypic predictor softwares (Support Vector Machine [SVM], C4.5, C4.5 with positions 8 to 12 only, PART, Charge Rule, geno2pheno coreceptor, Position-Specific Scoring Matrix X4R5 [PSSM X4R5 ], and PSSM sinsi ). A total of 150 samples were tested, with 115 belonging to patients infected with non-B subtypes and 35 drawn from subtype B-infected patients, which were taken as controls. When non-B subtypes were tested, the concordances between the results obtained using the phenotypic assay and distinct genotypic tools were as follows: 78.8% for SVM, 77.5% for C4.5, 82.5% for C4.5 with positions 8 to 12 only, 82.5% for PART, 82.5% for Charge Rule, 82.5% for PSSM X4R5 , 83.8% for PSSM sinsi , and 71.3% for geno2pheno. When clade B viruses were tested, the best concordances were seen for PSSM X4R5 (91.4%), PSSM sinsi (88.6%), and geno2pheno (88.6%). The sensitivity for detecting X4 variants was lower for non-B than for B viruses, especially in the case of PSSM sinsi (38.4% versus 100%, respectively), SVM wetcat (46% versus 100%, respectively), and PART (30% versus 90%, respectively). In summary, while inferences of HIV-1 coreceptor usage using genotypic tools seem to be reliable for clade B viruses, their performances are poor for non-B subtypes, in which they particularly fail to detect X4 variants.

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A new classification for HIV-1

Cited by 724 publications

References 6 publications

Apoptosis in SIV infection

Apoptosis in SIV infection

IL-4 and a glucocorticoid up-regulate CXCR4 expression on human CD4+ T lymphocytes and enhance HIV-1 replication

Evaluation of Eight Different Bioinformatics Tools To Predict Viral Tropism in Different Human Immunodeficiency Virus Type 1 Subtypes

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