Algorithm-Based Prediction of HIV-1 Subtype D Coreceptor Use

Abstract

We compared the coreceptor tropism-predicting performance of a specific genotypic algorithm for HIV-1 subtype D and that of the geno2pheno algorithm with different cutoffs. The D-specific algorithm and geno2pheno with a false-positivity rate cutoff of 2.5% had the same concordance with the phenotypic determination. The geno2pheno algorithm with a false-positivity rate cutoff of 2.5%, more sensitive but slightly less specific, seems to be an appropriate alternative. The robustness of the genotyping algorithms for predicting coreceptor tropism has been evaluated for HIV-1 subtype B (1, 2). While at least 40% of the patients monitored in Europe are infected with non-B subtypes, few data are available for these non-B genotypes. Recent studies on subtypes C, D, CRF01, and CRF02 (3-6) indicate that the rules could differ, depending on the subtype. The coreceptor tropism distributions of different HIV-1 subtypes also differ considerably (7-10). The disease progresses more rapidly in patients with a subtype D infection, perhaps because of the high prevalence of CXCR4 tropism and dual-mixed virus populations (11,12). It is thus essential to accurately predict tropism both for initiation of therapy targeting the CCR5 coreceptor and for pathophysiological studies. Raymond et al. built a specific rule for predicting CXCR4 usage for subtype D (3) based on one of the following criteria: (i) R (arginine) or K (lysine) at position 11 of hypervariable region V3 of gp120, (ii) R at position 25 of V3 and a net charge of Նϩ5, or (iii) a net charge of Նϩ6.This study compared the predictive capacities of the subtype D-specific rule and the geno2pheno (G2P) tool with different false-positivity rate (FPR) cutoffs with a new panel of subtype D samples.A total of 31 HIV-1 subtype D samples characterized in both the pol and env coding regions were collected from seven French centers. HIV-1 tropism was determined for the clinical management of the patients. The Toulouse tropism test (TTT), a recom- a The kappa coefficient was measured by using MedCalc to assess the agreement between the genotypic algorithms and the phenotypic assay. The correlation between two tests is usually considered good when the kappa coefficient is greater than 0.60.

Antiretroviral-Experienced HIV-1-Infected Patients Treated with Maraviroc: Factors Associated with Virological Response

“…[1][2][3] It is still unclear if there are any variations in intrinsic MVC activity against the B vs. non-B subtype or lack of sensitivity or specificity in the determination of tropism. [8][9][10][11] However, the HIV-1 tropism determination method does not seem to have an impact as the MVC effect on the non-B subtype was similar whatever method was used: phenotypes in MERIT and MOTIVATE or genotypes in our study.…”

Reliable Genotypic Tropism Tests for the Major HIV-1 Subtypes

“…On the other hand, genotypic algorithms enable most diagnostic laboratories to establish HIV-1 coreceptor usage by amplifying and sequencing the relatively short V3 region of env from patient blood samples, which compared to phenotypic tropism assays is a relatively inexpensive, rapid and straightforward process. Unfortunately, the majority of the currently available genotypic algorithms have been developed against HIV-1 subtype B V3 sequences and consequently they lack optimal predictive accuracy against non-B HIV-1 V3 sequences, as many of the V3 loop determinants of coreceptor specificity are subtype specific 3 13 22 23 29 38 39 40 41 42 43 44 45 46 47 . The lack of reliable genotypic algorithms that have been designed specifically for non-B HIV-1 subtypes is presently a major barrier to informing the appropriate use of maraviroc and future HIV-1 coreceptor blocking drugs in subjects infected with non-B HIV-1, which comprise approximately 90% of infections worldwide.…”

Subtype-Dependent Co-receptor Tropism in Cuban HIV-1–Infected Patients: Implications for Maraviroc Treatment

“…[10,11] These latter methods are rapid and easier to use; however, their development has been based mostly on sequences from HIV-1 subtype B isolates, without considering the viral diversity of the epidemic within countries. [1,[12][13][14] In Cuba, the HIV-1 epidemic is atypical when compared to the rest of the Latin American and Caribbean region. Cuba has a great diversity of HIV-1 subtypes including many recombinant forms (CRFs) such as BG recombinants (CRF20_BG, CRF23_BG and CRF24_BG) and complex CRFs, such as CRF18_cpx and CRF19_cpx.…”