Literature DB >> 17933727

Genotypic coreceptor analysis.

S Sierra1, R Kaiser, A Thielen, T Lengauer.   

Abstract

HIV infects target cells by binding of its envelope gp120 protein to CD4 and a coreceptor on the cell surface. In vivo, the different HIV-strains use either CCR5 or CXCR4 as coreceptor. CCR5-using strains are named R5 viruses, while CXCR4-using strains are named X4. X4 viruses usually occur in the later stages. Coreceptor usage is a marker for disease progression. Additionally interest on coreceptors continually raises as a consequence of the development of a new class of antiretroviral drugs, namely the coreceptor antagonists or blockers. These specific drugs block the CCR5 or the CXCR4 coreceptors. So far, the CXCR4 blockers are not allowed to be used in the clinical practice due to their severe side effects. On the other hand, CCR5 blockers are currently in clinical practice, although they can only be administered after a baseline determination of the coreceptor usage of the predominant viral strain. Most of the coreceptor analyses in clinical cohorts have been performed with commercially available phenotypic assays. As for resistance testing of NRTIs, NNRTIs and PIs, efforts have also been made to predict the coreceptor usage from the genotype of the viruses. Different rules have been published based on the amino acid sequence of the Env-V3 region of HIV-gp120, which is known to be the major determinant of coreceptor usage. Among these, the most widely used is the 11/25 rule. Recently, bioinformatics driven prediction systems have been developed. Three of the interpretation systems are freely available via internet: WetCat, WebPSSM, geno2pheno[coreceptor]. All three systems focus on the Env-V3 region and take the amino acid sequence only into account. They learn from phenotypic and corresponding genotypic data. So far, two cohorts have been analyzed with such a genotypic approach and provided frequencies of R5 virus strains that are within the range of those reported with phenotypic assays. For one of the systems, geno2pheno[coreceptor], additional clinical data (e.g. CD4+T-cell counts) or structural information can be used to improve the prediction. Such genotypic systems provide the possibility for rapid screening of patients who may be administered with CCR5 blockers like the recently licensed Maraviroc.

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Year:  2007        PMID: 17933727

Source DB:  PubMed          Journal:  Eur J Med Res        ISSN: 0949-2321            Impact factor:   2.175


  17 in total

1.  Deep sequencing to infer HIV-1 co-receptor usage: application to three clinical trials of maraviroc in treatment-experienced patients.

Authors:  Luke C Swenson; Theresa Mo; Winnie W Y Dong; Xiaoyin Zhong; Conan K Woods; Mark A Jensen; Alexander Thielen; Douglass Chapman; Marilyn Lewis; Ian James; Jayvant Heera; Hernan Valdez; P Richard Harrigan
Journal:  J Infect Dis       Date:  2011-01-15       Impact factor: 5.226

Review 2.  Bioinformatic analysis of HIV-1 entry and pathogenesis.

Authors:  Benjamas Aiamkitsumrit; Will Dampier; Gregory Antell; Nina Rivera; Julio Martin-Garcia; Vanessa Pirrone; Michael R Nonnemacher; Brian Wigdahl
Journal:  Curr HIV Res       Date:  2014       Impact factor: 1.581

3.  APOBEC3G/F as one possible driving force for co-receptor switch of the human immunodeficiency virus-1.

Authors:  Eva Heger; Alexander Thielen; Ramona Gilles; Martin Obermeier; Thomas Lengauer; Rolf Kaiser; Susanna Trapp
Journal:  Med Microbiol Immunol       Date:  2011-05-15       Impact factor: 3.402

Review 4.  Maraviroc: a review of its use in the management of CCR5-tropic HIV-1 infection.

Authors:  Caroline M Perry
Journal:  Drugs       Date:  2010-06-18       Impact factor: 9.546

5.  Identification of dual-tropic HIV-1 using evolved neural networks.

Authors:  Gary B Fogel; Susanna L Lamers; Enoch S Liu; Marco Salemi; Michael S McGrath
Journal:  Biosystems       Date:  2015-09-28       Impact factor: 1.973

Review 6.  How HIV changes its tropism: evolution and adaptation?

Authors:  Donald E Mosier
Journal:  Curr Opin HIV AIDS       Date:  2009-03       Impact factor: 4.283

7.  Appraising the performance of genotyping tools in the prediction of coreceptor tropism in HIV-1 subtype C viruses.

Authors:  Saleema Crous; Ram Krishna Shrestha; Simon A Travers
Journal:  BMC Infect Dis       Date:  2012-09-02       Impact factor: 3.090

8.  Genotypic tropism testing by massively parallel sequencing: qualitative and quantitative analysis.

Authors:  Martin Däumer; Rolf Kaiser; Rolf Klein; Thomas Lengauer; Bernhard Thiele; Alexander Thielen
Journal:  BMC Med Inform Decis Mak       Date:  2011-05-13       Impact factor: 2.796

9.  Maraviroc in the treatment of HIV infection.

Authors:  Neelanjana Ray
Journal:  Drug Des Devel Ther       Date:  2009-02-06       Impact factor: 4.162

10.  Comparative analysis of cell culture and prediction algorithms for phenotyping of genetically diverse HIV-1 strains from Cameroon.

Authors:  Viswanath Ragupathy; Jiangqin Zhao; Xue Wang; Owen Wood; Sherwin Lee; Sherri Burda; Phillipe Nyambi; Indira Hewlett
Journal:  AIDS Res Ther       Date:  2009-11-25       Impact factor: 2.250

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