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Updated: Feb 8, 2026

Prediction of HIV-1 Coreceptor Usage Tropism by Sequence Analysis using a Genotypic Approach
Published on: December 1, 2011
The substitution rate of HIV-1 subtypes: a genomic approach
Juan Ángel Patiño-Galindo1, Fernando González-Candelas1
1Unidad Mixta Infección y Salud Pública FISABIO-Salud Pública/Universitat de València, Institute for Integrative Systems Biology (I2SysBio), CIBERESP, c/Catedratico Jose Beltran, 2, 46980 Paterna, Valencia, Spain.
Abstract:
HIV-1M causes most infections in the AIDS pandemic. Its genetic diversity is defined by nine pure subtypes and more than sixty recombinant forms. We have performed a comparative analysis of the evolutionary rate of five pure subtypes (A1, B, C, D, and G) and two circulating recombinant forms (CRF01_AE and CRF02 AG) using data obtained from nearly complete genome coding sequences. Times to the most recent common ancestor (tMRCA) and substitution rates of these HIV genomes, and their genomic partitions, were estimated by Bayesian coalescent analyses. Genomic substitution rate estimates were compared between the HIV-1 datasets analyzed by means of randomization tests. Significant differences in the rate of evolution were found between subtypes, with subtypes C and A1 and CRF01_AE displaying the highest rates. On the other hand, CRF02_AG and subtype D were the slowest evolving types. Using a different molecular clock model for each genomic partition led to more precise tMRCA estimates than when linking the same clock along the HIV genome. Overall, the earliest tMRCA corresponded to subtype A1 (median = 1941, 95% HPD = 1943-55), whereas the most recent tMRCA corresponded to subtype G and CRF01_AE subset 3 (median = 1971, 95% HPD = 1967-75 and median = 1972, 95% HPD = 1970-75, respectively). These results suggest that both biological and epidemiological differences among HIV-1M subtypes are reflected in their evolutionary dynamics. The estimates obtained for tMRCAs and substitution rates provide information that can be used as prior distributions in future Bayesian coalescent analyses of specific HIV-1 subtypes/CRFs and genes.
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