Faster HIV-1 disease progression among Brazilian individuals recently infected with CXCR4-utilizing strains
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Updated: May 25, 2026
Prediction of HIV-1 Coreceptor Usage (Tropism) by Sequence Analysis using a Genotypic Approach
Published on: December 1, 2011
Maria Cecilia Araripe Sucupira1, Sabri Sanabani, Rodrigo M Cortes
1Infectious Diseases Division, Federal University of Sao Paulo, Sao Paulo, Brazil.
Introduction:
Primary HIV infection is usually caused by R5 viruses, and there is an association between the emergence of CCXR4-utilizing strains and faster disease progression. We characterized HIV-1 from a cohort of recently infected individuals in Brazil, predicted the virus's co-receptor use based on the env genotype and attempted to correlate virus profiles with disease progression.
Methods:
A total of 72 recently infected HIV patients were recruited based on the Serologic Testing Algorithm for Recent HIV Seroconversion and were followed every three to four months for up to 78 weeks. The HIV-1 V3 region was characterized by sequencing nine to twelve weeks after enrollment. Disease progression was characterized by CD4+ T-cell count decline to levels consistently below 350 cells/µL.
Results:
Twelve out of 72 individuals (17%) were predicted to harbor CXCR4-utilizing strains; a baseline CD4<350 was more frequent among these individuals (p = 0.03). Fifty-seven individuals that were predicted to have CCR5-utilizing viruses and 10 individuals having CXCR4-utilizing strains presented with baseline CD4>350; after 78 weeks, 33 individuals with CCR5 strains and one individual with CXCR4 strains had CD4>350 (p = 0.001). There was no association between CD4 decline and demographic characteristics or HIV-1 subtype.
Conclusions:
Our findings confirm the presence of strains with higher in vitro pathogenicity during early HIV infection, suggesting that even among recently infected individuals, rapid progression may be a consequence of the early emergence of CXCR4-utilizing strains. Characterizing the HIV-1 V3 region by sequencing may be useful in predicting disease progression and guiding treatment initiation decisions.
