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Updated: Aug 19, 2026
Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
Published on: December 28, 2010
A reliable phenotype predictor for human immunodeficiency virus type 1 subtype C based on envelope V3 sequences
Mark A Jensen1, Mia Coetzer, Angélique B van 't Wout
1Department of Microbiology, University of Washington, Seattle, WA, USA. mark.jensen@emory.edu
Insights
A new C-PSSM predictor accurately identifies CXCR4 variants in HIV-1 subtype C infections, improving understanding of coreceptor usage and disease progression. This method offers a faster, cheaper alternative for identifying CXCR4 variants in subtype C.
Area of Science:
- Virology
- Bioinformatics
- Genetics
Background:
- Human immunodeficiency virus type 1 (HIV-1) subtype C causes over 50% of global infections.
- CXCR4 coreceptor usage in HIV-1 subtype C is less understood than in subtype B, despite its association with disease progression.
- Accurate prediction of CXCR4 variants is crucial for understanding HIV-1 subtype C pathogenesis.
Purpose of the Study:
- To develop and validate a genotypic prediction method for CXCR4 coreceptor usage in HIV-1 subtype C.
- To compare the performance of the new method against existing prediction strategies.
Main Methods:
- A Position-Specific Scoring Matrix (PSSM) approach, previously successful for subtype B, was adapted for subtype C V3 loop sequences.
- A training set of 279 subtype C sequences with known phenotypes (CCR5+ NSI and CXCR4+ SI) was used to derive the C-PSSM predictor.
- Performance was evaluated using bootstrapping and leave-one-out cross-validation, with a separate validation set.
Main Results:
- The C-PSSM predictor achieved a specificity of 94% and a sensitivity of 75% on the training data.
- The method demonstrated significantly higher sensitivity (75%) compared to other methods (e.g., charged residue method at 47.8%).
- Validation on a unique subtype C set yielded a specificity of 83% and sensitivity of 83%.
Conclusions:
- The developed C-PSSM predictor is a reliable and sensitive tool for predicting CXCR4 coreceptor usage in HIV-1 subtype C.
- This bioinformatic approach offers a rapid and cost-effective means to identify CXCR4 variants, aiding research into subtype C progression.
- The findings suggest that specific genetic sites influencing coreceptor usage may differ between HIV-1 subtypes.
Abstract:
In human immunodeficiency virus type 1 (HIV-1) subtype B infections, the emergence of viruses able to use CXCR4 as a coreceptor is well documented and associated with accelerated CD4 decline and disease progression. However, in HIV-1 subtype C infections, responsible for more than 50% of global infections, CXCR4 usage is less common, even in individuals with advanced disease. A reliable phenotype prediction method based on genetic sequence analysis could provide a rapid and less expensive approach to identify possible CXCR4 variants and thus increase our understanding of subtype C coreceptor usage. For subtype B V3 loop sequences, genotypic predictors have been developed based on position-specific scoring matrices (PSSM). In this study, we apply this methodology to a training set of 279 subtype C sequences of known phenotypes (228 non-syncytium-inducing [NSI] CCR5(+) and 51 SI CXCR4(+) sequences) to derive a C-PSSM predictor. Specificity and sensitivity distributions were estimated by combining data set bootstrapping with leave-one-out cross-validation, with random sampling of single sequences from individuals on each bootstrap iteration. The C-PSSM had an estimated specificity of 94% (confidence interval [CI], 92% to 96%) and a sensitivity of 75% (CI, 68% to 82%), which is significantly more sensitive than predictions based on other methods, including a commonly used method based on the presence of positively charged residues (sensitivity, 47.8%). A specificity of 83% and a sensitivity of 83% were achieved with a validation set of 24 SI and 47 NSI unique subtype C sequences. The C-PSSM performs as well on subtype C V3 loops as existing subtype B-specific methods do on subtype B V3 loops. We present bioinformatic evidence that particular sites may influence coreceptor usage differently, depending on the subtype.
