Cross-sectional host/viral genotype datasets represent an underutilized resource to identify reproducible early pathways of HIV-1 adaptation and identify correlates of protective immunity.
BackgroundThe reproducible nature of HIV-1 escape from HLA-restricted CD8+ T-cell responses allows the identification of HLA-associated viral polymorphisms “at the population level” – that is, via analysis of cross-sectional, linked HLA/HIV-1 genotypes by statistical association. However, elucidating their timing of selection traditionally requires detailed longitudinal studies, which are challenging to undertake on a large scale. We investigate whether the extent and relative timecourse of immune-driven HIV adaptation can be inferred via comparative cross-sectional analysis of independent early and chronic infection cohorts.ResultsSimilarly-powered datasets of linked HLA/HIV-1 genotypes from individuals with early (median < 3 months) and chronic untreated HIV-1 subtype B infection, matched for size (N > 200/dataset), HLA class I and HIV-1 Gag/Pol/Nef diversity, were established. These datasets were first used to define a list of 162 known HLA-associated polymorphisms detectable at the population level in cohorts of the present size and host/viral genetic composition. Of these 162 known HLA-associated polymorphisms, 15% (occurring at 14 Gag, Pol and Nef codons) were already detectable via statistical association in the early infection dataset at p ≤ 0.01 (q < 0.2) – identifying them as the most consistently rapidly escaping sites in HIV-1. Among these were known rapidly-escaping sites (e.g. B*57-Gag-T242N) and others not previously appreciated to be reproducibly rapidly selected (e.g. A*31:01-associated adaptations at Gag codons 397, 401 and 403). Escape prevalence in early infection correlated strongly with first-year escape rates (Pearson’s R = 0.68, p = 0.0001), supporting cross-sectional parameters as reliable indicators of longitudinally-derived measures. Comparative analysis of early and chronic datasets revealed that, on average, the prevalence of HLA-associated polymorphisms more than doubles between these two infection stages in persons harboring the relevant HLA (p < 0.0001, consistent with frequent and reproducible escape), but remains relatively stable in persons lacking the HLA (p = 0.15, consistent with slow reversion). Published HLA-specific Hazard Ratios for progression to AIDS correlated positively with average escape prevalence in early infection (Pearson’s R = 0.53, p = 0.028), consistent with high early within-host HIV-1 adaptation (via rapid escape and/or frequent polymorphism transmission) as a correlate of progression.ConclusionCross-sectional host/viral genotype datasets represent an underutilized resource to identify reproducible early pathways of HIV-1 adaptation and identify correlates of protective immunity.Electronic supplementary materialThe online version of this article (doi:10.1186/s12977-014-0064-1) contains supplementary material, which is available to authorized users.
To evaluate its utility in discriminating different strains, restriction endonuclease analysis was applied to 12 strains of Actinobacillus actinomycetemcomitans (3 serotype a, 5 serotype b, and 4 serotype c strains). DNA isolated from each strain was digested by 12 different restriction endonucleases, and the electrophoretic banding patterns of the resulting DNA fragments were compared. The DNA fragment patterns produced by Sall, XhoI, and XbaI for the 12 A. actinomycetemcomitans strains were simple (<30 bands) and allowed us to recognize easily 10 distinct genomic clonal types. The three serotype a strains exhibited distinctly different clonal types from one another, the five serotype b strains exhibited an additional four distinct clonal types, and the four serotype c strains showed another three different clonal types. The other endonucleases tested were less useful in typing A. actinomycetemcomitans. We conclude that restriction endonuclease analysis is a powerful tool for typing and discerning genetic heterogeneity and homogeneity among A. actinomycetemcomitans strains. It should, therefore, be very useful for epidemiologic studies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.