Reliability of Rapid Subtyping Tools Compared to That of Phylogenetic Analysis for Characterization of Human Immunodeficiency Virus Type 1 Non-B Subtypes and Recombinant Forms

Holguín, África; López, Marisa; Soriano, Vincent

doi:10.1128/jcm.00515-08

Cited by 23 publications

(25 citation statements)

References 24 publications

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“…The 177 failure to identify subtype B strains in three cases using the REGA tool was probably due to shorter 178 sequence lengths. As described previously [Holguín et al, 2008], also in this study misclassification of 179 some non-B subtypes was observed when using the rapid on-line tools, which reinforces the need to 180 use phylogenetic analyses to confirm subtyping results. 181…”

supporting

confidence: 78%

Characteristics of HIV‐1 non‐B subtype infections in Northwest Poland

Parczewski¹,

Leszczyszyn-Pynka²,

Bander³

et al. 2010

Journal of Medical Virology

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The number of non‐B subtype HIV‐1 infections in Europe has been increasing even though major regional differences have been observed. This trend was investigated in northwestern Poland using sequence and epidemiological data from a cohort of 102 HIV‐1‐infected patients from Szczecin, Poland. HIV‐1 subtypes were defined by phylogenetic analysis of viral reverse transcriptase‐ and protease‐partial coding regions, and results were compared with online subtyping by Standford and REGA tools. Subtype analysis using on‐line subtyping methods produced varying results if compared to phylogenesis, with concordant variant assignment obtained for 98% (100/102) of sequences by Stanford and 85% (87/102) by REGA. In the population studied, non‐B subtype infections comprised 21% of the infections and consisted of subtype D (57%, n = 12), CRF01_AE (19%, n = 4), A and C clades (9.5%, n = 2), and the CRF13_cpx recombinant isolate (4.8%, n = 1). Patients carrying non‐B subtypes were predominantly heterosexuals with high percentage (57%) of women observed in the group. All HIV‐1 non‐B women were Caucasian with majority (83%) of infections acquired in Poland; however, among 12 travelers included in the study a higher proportion of non‐B infections was noted (50%, P = 0.01). Moreover, lower baseline lymphocyte CD4 counts (P = 0.01), higher baseline HIV‐1 viremia (P = 0.08), and a more advanced stage of the disease (P = 0.03) were observed among individuals infected with non‐B subtypes. The data indicated that the proportion of HIV‐1 non‐B subtype infections was higher than previously reported in Poland consisting of a high subtype D prevalence. Furthermore, subtype D transmission occurred primarily between heterosexual Caucasian individuals from this region. J. Med. Virol. 82:1306–1313, 2010. © 2010 Wiley‐Liss, Inc.

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supporting

confidence: 78%

Characteristics of HIV‐1 non‐B subtype infections in Northwest Poland

Parczewski¹,

Leszczyszyn-Pynka²,

Bander³

et al. 2010

Journal of Medical Virology

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“…In this study, we aimed at assessing the necessity of updating MPhy, as well as the current performance of commonly used automated subtyping tools, in an era of changing molecular epidemiology in Western countries, including Italy (7,12,13,15,16,28). Of note, the Italian clinical context is experiencing a rapid change in circulating HIV-1 strains, mainly due to a diversified migration system, ongoing infection in the high-risk populations (mostly men having sex with men [MSM]), and specific transmission clusters (12,13).…”

Section: Discussionmentioning

confidence: 99%

“…However, they have considerable limitations compared to Mphy especially in assigning non-B variants: outputs of different tools are usually in disagreement (26)(27)(28), their algorithms are not regularly updated, and they have only a limited number of CRFs in the reference data set. In routine clinical practice, the most commonly used automated tools are statistically based use of partial matching compression algorithms (context-based modeling for expeditious typing [COMET] (29), a similarity-based tool (Stanford HIV drug resistance database [Stanford]), and phylogenetics-based tools (REGA and subtype classification using evolutionary algo-…”

mentioning

confidence: 99%

Comparative Evaluation of Subtyping Tools for Surveillance of Newly Emerging HIV-1 Strains

et al. 2017

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HIV-1 non-B subtypes/circulating recombinant forms (CRFs) are increasing worldwide. Since subtype identification can be clinically relevant, we assessed the added value in HIV-1 subtyping using updated molecular phylogeny (Mphy) and the performance of routinely used automated tools. Updated Mphy (2015 updated reference sequences), used as a gold standard, was performed to subtype 13,116 HIV-1 protease/reverse transcriptase sequences and then compared with previous Mphy (reference sequences until 2014) and with COMET, REGA, SCUEAL, and Stanford subtyping tools. Updated Mphy classified subtype B as the most prevalent (73.4%), followed by CRF02_AG (7.9%), C (4.6%), F1 (3.4%), A1 (2.2%), G (1.6%), CRF12_BF (1.2%), and other subtypes (5.7%). A 2.3% proportion of sequences were reassigned as different subtypes or CRFs because of misclassification by previous Mphy. Overall, the tool most concordant with updated Mphy was Stanford-v8.1 (95.4%), followed by COMET (93.8%), REGA-v3 (92.5%), Stanford-old (91.1%), and SCUEAL (85.9%). All the tools had a high sensitivity (Ն98.0%) and specificity (Ն95.7%) for subtype B. Regarding non-B subtypes, Stanford-v8.1 was the best tool for C, D, and F subtypes and for CRFs 01, 02, 06, 11, and 36 (sensitivity, Ն92.6%; specificity, Ն99.1%). A1 and G subtypes were better classified by COMET (92.3%) and REGA-v3 (98.6%), respectively. Our findings confirm Mphy as the gold standard for accurate HIV-1 subtyping, although Stanford-v8.1, occasionally combined with COMET or REGA-v3, represents an effective subtyping approach in clinical settings. Periodic updating of HIV-1 reference sequences is fundamental to improving subtype characterization in the context of an effective epidemiological surveillance of non-B strains.

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“…HIV-1 subtypes were previously defined by phylogenetic analysis in the pol gene including protease and reverse transcriptase coding regions (17,18,19,22), using as reference HIV-1 sequences belonging to HIV-1 group M available at the GenBank. The tree topology was obtained by using the neighbor-joining method.…”

Section: Methodsmentioning

confidence: 99%

Performance of Three Commercial Viral Load Assays, Versant Human Immunodeficiency Virus Type 1 (HIV-1) RNA bDNA v3.0, Cobas AmpliPrep/Cobas TaqMan HIV-1, and NucliSens HIV-1 EasyQ v1.2, Testing HIV-1 Non-B Subtypes and Recombinant Variants

Holguín

López²,

Molinero³

et al. 2008

J Clin Microbiol

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Monitoring antiretroviral therapy requires that human immunodeficiency virus type 1 (HIV-1) viremia assays are applicable to all distinct variants. This study evaluates the performance of three commercial viral load assays-Versant HIV-1 RNA bDNA v3.0, Cobas AmpliPrep/Cobas TaqMan HIV-1, and NucliSens HIV-1 EasyQ v1.2-in testing 83 plasma specimens from patients carrying HIV-1 non-B subtypes and recombinants previously defined by phylogenetic analysis of the pol gene. All 28 specimens from patients under treatment presented viremia values below the detection limit with the three methods. In the remaining 55 specimens from naive individuals viremia could not be detected in 32.7, 20, and 14.6% using the NucliSens, Versant, or TaqMan tests, respectively, suggesting potential viral load underestimation of some samples by all techniques. Only 32 (58.2%) samples from naive subjects were quantified by the three methods; the NucliSens test provided the highest HIV RNA values (mean, 4.87 log copies/ml), and the Versant test provided the lowest (mean, 4.16 log copies/ml). Viremia differences of greater than 1 log were seen in 8 (14.5%) of 55 specimens, occurring in 10.9, 7.3, and 5.4%, respectively, of the specimens in comparisons of Versant versus NucliSens, Versant versus TaqMan, and TaqMan versus NucliSens. Differences greater than 0.5 log, considered significant for clinicians, occurred in 45.5, 27.3, and 29% when the same assays were compared. Some HIV-1 strains, such as subtype G and CRF02_AG, showed more discrepancies in distinct quantification methods than others. In summary, an adequate design of primers and probes is needed for optimal quantitation of plasma HIV-RNA in non-B subtypes. Our data emphasize the need to use the same method for monitoring patients on therapy and also the convenience of HIV-1 subtyping.

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Reliability of Rapid Subtyping Tools Compared to That of Phylogenetic Analysis for Characterization of Human Immunodeficiency Virus Type 1 Non-B Subtypes and Recombinant Forms

Cited by 23 publications

References 24 publications

Characteristics of HIV‐1 non‐B subtype infections in Northwest Poland

Characteristics of HIV‐1 non‐B subtype infections in Northwest Poland

Comparative Evaluation of Subtyping Tools for Surveillance of Newly Emerging HIV-1 Strains

Performance of Three Commercial Viral Load Assays, Versant Human Immunodeficiency Virus Type 1 (HIV-1) RNA bDNA v3.0, Cobas AmpliPrep/Cobas TaqMan HIV-1, and NucliSens HIV-1 EasyQ v1.2, Testing HIV-1 Non-B Subtypes and Recombinant Variants

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