Genotypic Prediction of HIV-1 CRF01-AE Tropism

Raymond, Stéphanie; Delobel, Pierre; Ranger-Rogez, Sylvie; Encinas, Stéphanie; Bruel, Patrick; Pasquier, Christophe; Sandres-Sauné, Karine; Marchou, B.; Massip, Patrice; Izopet, Jacques

doi:10.1128/jcm.02328-12

Cited by 34 publications

(43 citation statements)

References 29 publications

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“…[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.…”

Section: Discussionmentioning

confidence: 77%

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

SoulieCathia

PeytavinGilles²,

CharpentierCharlotte³

et al. 2015

AIDS Research and Human Retroviruses

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There are few data on the clinical and virological factors associated with the virological response (VR) of maraviroc (MVC) in clinical practice. This study aimed to identify factors associated with the VR to MVCcontaining regimens in 104 treatment-experienced but CCR5 inhibitor-naive HIV-1 patients. VR was defined at month 3 (M3) as HIV-1 RNA viral load (VL) < 50 copies/ml. The impact on VR of age, sex, baseline tropism, HIV subtype (B vs. non-B), nadir CD4 cell count and CD4 cell count, baseline VL, genotypic susceptibility score of treatment, once or twice daily treatment, presence of raltegravir in optimized background therapy, and MVC concentrations was investigated. Median baseline VL was 3.3 log 10 copies/ml (range 1.7-6.0 log 10 copies/ml) and CD4 cell count was 299 cells/mm 3 (range 7-841 cells/mm 3 ). At M3, 53.8% of patients were responders. In univariate analysis, a better efficacy of the MVC-containing regimen was associated with a high CD4 cell count ( p = 0.0069) and there was a trend for low baseline VL, high nadir CD4 cell count, and HIV subtype (B versus non-B). Only low baseline VL remained significantly associated with better VR in the multivariate analysis. This study demonstrated a VR of an optimized antiretroviral treatment including MVC in clinical practice similar to that observed in clinical trials. The factors associated with VR were higher baseline CD4 cell count in univariate analysis and lower baseline VL in multivariate analysis.

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Section: Discussionmentioning

confidence: 77%

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

SoulieCathia

PeytavinGilles²,

CharpentierCharlotte³

et al. 2015

AIDS Research and Human Retroviruses

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“…The V3 net charge has not been used alone quite as widely as the other genotyping algorithms, and its use is also complicated because of the ambiguity of the H (histidine) residue in charge calculation methodology [74, 250, 287]. The V3 net charge has often been used in combination with the 11/25 rule, and this approach has increased the specificity of the assay up to 98% in both subtype B and non-subtype B, but it is rather insensitive [247, 289]. …”

Section: Pitfalls Of Co-receptor Usage Predictionmentioning

confidence: 99%

“…Although a majority of predictive tools have been constructed based on HIV-1 subtype B, with the exception of PSSM-C, which offers only an SI/NSI matrix, viral tropism prediction in non-subtype B viruses has remained incomplete as compared with subtype B [281, 287, 289]. Considering only HIV-1 subtype B analysis, PSSM exhibited better concordance with respect to genotyping predictions, and applying the threshold score of −8 enhanced the sensitivity of X4 detection [281, 285].…”

Section: Pitfalls Of Co-receptor Usage Predictionmentioning

confidence: 99%

“…Viral tropism analyses within non-subtype B viruses have shown that PSSM has greater sensitivity and specificity in subtypes A, B, and G, but not in subtype F (Table 2) [287]. The combination of classic 11/25 and V3 net charge rules has also been shown to be a good candidate with respect to predicting the co-receptor usage of non-B viruses with a high degree of specificity (over 90%); nevertheless, sensitivity in detecting the X4 or dual-tropic virus has been more uncertain, depending on the subtype [287, 289]. The combined 11/25 and V3 net charge algorithm was utilized in co-receptor prediction analysis of HIV-1 CRF01-AE and the level of specificity was greater than that of geno2pheno but the sensitivity was much lower [289].…”

Section: Pitfalls Of Co-receptor Usage Predictionmentioning

confidence: 99%

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Bioinformatic Analysis of HIV-1 Entry and Pathogenesis

Aiamkitsumrit¹,

Dampier²,

Antell

et al. 2014

CHR

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The evolution of human immunodeficiency virus type 1 (HIV-1) with respect to co-receptor utilization has been shown to be relevant to HIV-1 pathogenesis and disease. The CCR5-utilizing (R5) virus has been shown to be important in the very early stages of transmission and highly prevalent during asymptomatic infection and chronic disease. In addition, the R5 virus has been proposed to be involved in neuroinvasion and central nervous system (CNS) disease. In contrast, the CXCR4-utilizing (X4) virus is more prevalent during the course of disease progression and concurrent with the loss of CD4+ T cells. The dual-tropic virus is able to utilize both co-receptors (CXCR4 and CCR5) and has been thought to represent an intermediate transitional virus that possesses properties of both X4 and R5 viruses that can be encountered at many stages of disease. The use of computational tools and bioinformatic approaches in the prediction of HIV-1 co-receptor usage has been growing in importance with respect to understanding HIV-1 pathogenesis and disease, developing diagnostic tools, and improving the efficacy of therapeutic strategies focused on blocking viral entry. Current strategies have enhanced the sensitivity, specificity, and reproducibility relative to the prediction of co-receptor use; however, these technologies need to be improved with respect to their efficient and accurate use across the HIV-1 subtypes. The most effective approach may center on the combined use of different algorithms involving sequences within and outside of the env-V3 loop. This review focuses on the HIV-1 entry process and on co-receptor utilization, including bioinformatic tools utilized in the prediction of co-receptor usage. It also provides novel preliminary analyses for enabling identification of linkages between amino acids in V3 with other components of the HIV-1 genome and demonstrates that these linkages are different between X4 and R5 viruses.

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“…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)(4)(5)(6) indicate that the rules could differ, depending on the subtype. The coreceptor tropism distributions of different HIV-1 subtypes also differ considerably (7)(8)(9)(10).…”

mentioning

confidence: 99%

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

Dina¹,

Raymond

Maillard

et al. 2013

J Clin Microbiol

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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.

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Genotypic Prediction of HIV-1 CRF01-AE Tropism

Cited by 34 publications

References 29 publications

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

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

Bioinformatic Analysis of HIV-1 Entry and Pathogenesis

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

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