IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1000225.html
   My bibliography  Save this article

Phylogenetic Dependency Networks: Inferring Patterns of CTL Escape and Codon Covariation in HIV-1 Gag

Author

Listed:
  • Jonathan 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
  • David Heckerman

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.

Suggested Citation

  • Jonathan 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 & David He, 2008. "Phylogenetic Dependency Networks: Inferring Patterns of CTL Escape and Codon Covariation in HIV-1 Gag," PLOS Computational Biology, Public Library of Science, vol. 4(11), pages 1-23, November.
  • Handle: RePEc:plo:pcbi00:1000225
    DOI: 10.1371/journal.pcbi.1000225
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000225
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1000225&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1000225?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Andrew J. McMichael & Sarah L. Rowland-Jones, 2001. "Cellular immune responses to HIV," Nature, Nature, vol. 410(6831), pages 980-987, April.
    2. Jennifer Listgarten & Zabrina Brumme & Carl Kadie & Gao Xiaojiang & Bruce Walker & Mary Carrington & Philip Goulder & David Heckerman, 2008. "Statistical Resolution of Ambiguous HLA Typing Data," PLOS Computational Biology, Public Library of Science, vol. 4(2), pages 1-15, February.
    3. Jeffrey T Leek & John D Storey, 2007. "Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis," PLOS Genetics, Public Library of Science, vol. 3(9), pages 1-12, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Reeves Karyn L. & McKinnon Elizabeth J. & James Ian R., 2012. "Correction for Founder Effects in Host-Viral Association Studies via Principal Components," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(4), pages 1-17, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arjun Bhattacharya & Anastasia N. Freedman & Vennela Avula & Rebeca Harris & Weifang Liu & Calvin Pan & Aldons J. Lusis & Robert M. Joseph & Lisa Smeester & Hadley J. Hartwell & Karl C. K. Kuban & Car, 2022. "Placental genomics mediates genetic associations with complex health traits and disease," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. repec:jss:jstsof:40:i14 is not listed on IDEAS
    3. Won Jun Lee & Sang Cheol Kim & Jung-Ho Yoon & Sang Jun Yoon & Johan Lim & You-Sun Kim & Sung Won Kwon & Jeong Hill Park, 2016. "Meta-Analysis of Tumor Stem-Like Breast Cancer Cells Using Gene Set and Network Analysis," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-20, February.
    4. Emanuele Aliverti & Kristian Lum & James E. Johndrow & David B. Dunson, 2021. "Removing the influence of group variables in high‐dimensional predictive modelling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 791-811, July.
    5. Marron, J.S., 2017. "Big Data in context and robustness against heterogeneity," Econometrics and Statistics, Elsevier, vol. 2(C), pages 73-80.
    6. Seungchul Baek & Yen‐Yi Ho & Yanyuan Ma, 2020. "Using sufficient direction factor model to analyze latent activities associated with breast cancer survival," Biometrics, The International Biometric Society, vol. 76(4), pages 1340-1350, December.
    7. Griffin, Maryclare & Hoff, Peter D., 2019. "Lasso ANOVA decompositions for matrix and tensor data," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 181-194.
    8. Yunfeng Li & Jarrett Morrow & Benjamin Raby & Kelan Tantisira & Scott T Weiss & Wei Huang & Weiliang Qiu, 2017. "Detecting disease-associated genomic outcomes using constrained mixture of Bayesian hierarchical models for paired data," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-16, March.
    9. Nadia Anikeeva & Maria Steblyanko & Leticia Kuri-Cervantes & Marcus Buggert & Michael R. Betts & Yuri Sykulev, 2022. "The immune synapses reveal aberrant functions of CD8 T cells during chronic HIV infection," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    10. Zhaohui Qin & Ben Li & Karen N. Conneely & Hao Wu & Ming Hu & Deepak Ayyala & Yongseok Park & Victor X. Jin & Fangyuan Zhang & Han Zhang & Li Li & Shili Lin, 2016. "Statistical Challenges in Analyzing Methylation and Long-Range Chromosomal Interaction Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(2), pages 284-309, October.
    11. Zemin Zheng & Jinchi Lv & Wei Lin, 2021. "Nonsparse Learning with Latent Variables," Operations Research, INFORMS, vol. 69(1), pages 346-359, January.
    12. Chee Ho H’ng & Shanika L. Amarasinghe & Boya Zhang & Hojin Chang & Xinli Qu & David R. Powell & Alberto Rosello-Diez, 2024. "Compensatory growth and recovery of cartilage cytoarchitecture after transient cell death in fetal mouse limbs," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    13. Mark Reimers, 2010. "Making Informed Choices about Microarray Data Analysis," PLOS Computational Biology, Public Library of Science, vol. 6(5), pages 1-7, May.
    14. Leek Jeffrey T & Storey John D., 2011. "The Joint Null Criterion for Multiple Hypothesis Tests," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-22, June.
    15. Nicoló Fusi & Oliver Stegle & Neil D Lawrence, 2012. "Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies," PLOS Computational Biology, Public Library of Science, vol. 8(1), pages 1-9, January.
    16. Jin Hyun Ju & Sushila A Shenoy & Ronald G Crystal & Jason G Mezey, 2017. "An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci," PLOS Computational Biology, Public Library of Science, vol. 13(5), pages 1-26, May.
    17. Miecznikowski, Jeffrey C. & Gold, David & Shepherd, Lori & Liu, Song, 2011. "Deriving and comparing the distribution for the number of false positives in single step methods to control k-FWER," Statistics & Probability Letters, Elsevier, vol. 81(11), pages 1695-1705, November.
    18. Aline Talhouk & Stefan Kommoss & Robertson Mackenzie & Martin Cheung & Samuel Leung & Derek S Chiu & Steve E Kalloger & David G Huntsman & Stephanie Chen & Maria Intermaggio & Jacek Gronwald & Fong C , 2016. "Single-Patient Molecular Testing with NanoString nCounter Data Using a Reference-Based Strategy for Batch Effect Correction," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-18, April.
    19. Iwami, Shingo & Nakaoka, Shinji & Takeuchi, Yasuhiro, 2008. "Viral diversity limits immune diversity in asymptomatic phase of HIV infection," Theoretical Population Biology, Elsevier, vol. 73(3), pages 332-341.
    20. Yuto Hasegawa & Juhyun Kim & Gianluca Ursini & Yan Jouroukhin & Xiaolei Zhu & Yu Miyahara & Feiyi Xiong & Samskruthi Madireddy & Mizuho Obayashi & Beat Lutz & Akira Sawa & Solange P. Brown & Mikhail V, 2023. "Microglial cannabinoid receptor type 1 mediates social memory deficits in mice produced by adolescent THC exposure and 16p11.2 duplication," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    21. Charlotte Soneson & Sarah Gerster & Mauro Delorenzi, 2014. "Batch Effect Confounding Leads to Strong Bias in Performance Estimates Obtained by Cross-Validation," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-13, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1000225. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.