Evaluation of eight different bioinformatics tools to predict viral tropism in different human immunodeficiency virus type 1 subtypes
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 p...
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| Format: | info:eu-repo/semantics/article |
| Language: | eng |
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American Society for Microbiology
2017
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10171/43161 |
| _version_ | 1793400154242416640 |
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| author | Garrido, C. (Carolina) Roulet, V. (Vanessa) Chueca, N. (Natalia) Poveda, E. (Eva) Aguilera, A. (Antonio) Skrabal, K. (Katharina) Zahonero, N. (Natalia) Carlos-Chillerón, S. (Silvia) Garcia, F. (Federico) Faudon, J. L. (Jean Louis) Soriano, V. (Vincent) Mendoza, C. (Carmen) de |
| author_facet | Garrido, C. (Carolina) Roulet, V. (Vanessa) Chueca, N. (Natalia) Poveda, E. (Eva) Aguilera, A. (Antonio) Skrabal, K. (Katharina) Zahonero, N. (Natalia) Carlos-Chillerón, S. (Silvia) Garcia, F. (Federico) Faudon, J. L. (Jean Louis) Soriano, V. (Vincent) Mendoza, C. (Carmen) de |
| author_sort | Garrido, C. (Carolina) |
| collection | DSpace |
| description | 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 [PSSMX4R5], and PSSMsinsi). 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 PSSMX4R5, 83.8% for
PSSMsinsi, and 71.3% for geno2pheno. When clade B viruses were tested, the best concordances were seen
for PSSMX4R5 (91.4%), PSSMsinsi (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 PSSMsinsi (38.4% versus 100%,
respectively), SVMwetcat (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. |
| format | info:eu-repo/semantics/article |
| id | oai:dadun.unav.edu:10171-43161 |
| institution | Universidad de Navarra |
| language | eng |
| publishDate | 2017 |
| publisher | American Society for Microbiology |
| record_format | dspace |
| spelling | oai:dadun.unav.edu:10171-431612024-02-09T07:22:49Z Evaluation of eight different bioinformatics tools to predict viral tropism in different human immunodeficiency virus type 1 subtypes Garrido, C. (Carolina) Roulet, V. (Vanessa) Chueca, N. (Natalia) Poveda, E. (Eva) Aguilera, A. (Antonio) Skrabal, K. (Katharina) Zahonero, N. (Natalia) Carlos-Chillerón, S. (Silvia) Garcia, F. (Federico) Faudon, J. L. (Jean Louis) Soriano, V. (Vincent) Mendoza, C. (Carmen) de Materias Investigacion::Ciencias de la Salud::Microbiología y biología molecular 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 [PSSMX4R5], and PSSMsinsi). 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 PSSMX4R5, 83.8% for PSSMsinsi, and 71.3% for geno2pheno. When clade B viruses were tested, the best concordances were seen for PSSMX4R5 (91.4%), PSSMsinsi (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 PSSMsinsi (38.4% versus 100%, respectively), SVMwetcat (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. 2017-03-29T07:52:51Z 2017-03-29T07:52:51Z 2008 info:eu-repo/semantics/article https://hdl.handle.net/10171/43161 eng info:eu-repo/semantics/openAccess application/pdf American Society for Microbiology |
| spellingShingle | Materias Investigacion::Ciencias de la Salud::Microbiología y biología molecular Garrido, C. (Carolina) Roulet, V. (Vanessa) Chueca, N. (Natalia) Poveda, E. (Eva) Aguilera, A. (Antonio) Skrabal, K. (Katharina) Zahonero, N. (Natalia) Carlos-Chillerón, S. (Silvia) Garcia, F. (Federico) Faudon, J. L. (Jean Louis) Soriano, V. (Vincent) Mendoza, C. (Carmen) de Evaluation of eight different bioinformatics tools to predict viral tropism in different human immunodeficiency virus type 1 subtypes |
| title | Evaluation of eight different bioinformatics tools to predict viral tropism in different human immunodeficiency virus type 1 subtypes |
| title_full | Evaluation of eight different bioinformatics tools to predict viral tropism in different human immunodeficiency virus type 1 subtypes |
| title_fullStr | Evaluation of eight different bioinformatics tools to predict viral tropism in different human immunodeficiency virus type 1 subtypes |
| title_full_unstemmed | Evaluation of eight different bioinformatics tools to predict viral tropism in different human immunodeficiency virus type 1 subtypes |
| title_short | Evaluation of eight different bioinformatics tools to predict viral tropism in different human immunodeficiency virus type 1 subtypes |
| title_sort | evaluation of eight different bioinformatics tools to predict viral tropism in different human immunodeficiency virus type 1 subtypes |
| topic | Materias Investigacion::Ciencias de la Salud::Microbiología y biología molecular |
| url | https://hdl.handle.net/10171/43161 |
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