 Phylogenetic Dependency Networks: Inferring Patterns of CTL Escape and Codon Covariation in HIV-1 GagJonathan M Carlson, Zabrina L Brumme, Christine M Rousseau, Chanson J Brumme, Philippa Matthews, Carl Kadie, James I Mullins, Bruce D Walker, P Richard Harrigan, Philip J R Goulder and David Heckerman PLOS Computational Biology, 2008, vol. 4, issue 11, 1-23 Abstract: HIV avoids elimination by cytotoxic T-lymphocytes (CTLs) through the evolution of escape mutations. Although there is mounting evidence that these escape pathways are broadly consistent among individuals with similar human leukocyte antigen (HLA) class I alleles, previous population-based studies have been limited by the inability to simultaneously account for HIV codon covariation, linkage disequilibrium among HLA alleles, and the confounding effects of HIV phylogeny when attempting to identify HLA-associated viral evolution. We have developed a statistical model of evolution, called a phylogenetic dependency network, that accounts for these three sources of confounding and identifies the primary sources of selection pressure acting on each HIV codon. Using synthetic data, we demonstrate the utility of this approach for identifying sites of HLA-mediated selection pressure and codon evolution as well as the deleterious effects of failing to account for all three sources of confounding. We then apply our approach to a large, clinically-derived dataset of Gag p17 and p24 sequences from a multicenter cohort of 1144 HIV-infected individuals from British Columbia, Canada (predominantly HIV-1 clade B) and Durban, South Africa (predominantly HIV-1 clade C). The resulting phylogenetic dependency network is dense, containing 149 associations between HLA alleles and HIV codons and 1386 associations among HIV codons. These associations include the complete reconstruction of several recently defined escape and compensatory mutation pathways and agree with emerging data on patterns of epitope targeting. The phylogenetic dependency network adds to the growing body of literature suggesting that sites of escape, order of escape, and compensatory mutations are largely consistent even across different clades, although we also identify several differences between clades. As recent case studies have demonstrated, understanding both the complexity and the consistency of immune escape has important implications for CTL-based vaccine design. Phylogenetic dependency networks represent a major step toward systematically expanding our understanding of CTL escape to diverse populations and whole viral genes.Author Summary: One of the enduring challenges facing HIV vaccine design is the remarkable rate of viral mutation and adaptation that limits the ability of the immune system to mount a lasting effective response. This rapid rate of mutation leads to extensive within- and between-host viral diversity that makes creation of a broadly reactive vaccine difficult. A first step in overcoming this challenge is to identify consistent patterns in viral adaptation. Recently, several studies have analyzed large groups of HIV-infected individuals and looked for correlations between HIV polymorphisms and the HLA class I alleles that restrict the cellular immune response. Here, we point out a limitation of previous approaches: correlations among HLA alleles and HIV codons lead to statistical confounding if not taken into consideration. In response, we develop two statistical models of evolution that explicitly represent stochastic selection pressure from multiple sources. After validating these models on synthetic data, we analyze the patterns of immune escape in a multicenter cohort of over 1000 individuals. Our results identify a dense network of interactions between HLA alleles and HIV codons, as well as among HIV codons, reflecting both a complexity and a promising consistency in the way that HIV adapts to the human immune response. Date: 2008 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1000225 DOI: 10.1371/journal.pcbi.1000225 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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