Two-way Bayesian hierarchical phylogenetic models: An application to the co-evolution of gp120 and gp41 during and after enfuvirtide treatment.

Christina M R Kitchen, Jing Kroll, Daniel R Kuritzkes, Erik Bloomquist, Steven G Deeks, Marc A Suchard
Author Information
  1. Christina M R Kitchen: Department of Biostatistics, University of California, Los Angeles, CA, United States.

Abstract

Enfuvirtide (ENF) is a fusion inhibitor that prevents the entry of HIV virions into target cells. Studying the characteristics of viral evolution during treatment and after a treatment interruption can lend insight into the mechanisms of viral evolution and fitness. Although interruption of anti-HIV therapy often results in rapid emergence of an archived "wild-type" virus population, previous work from our group indicates that when only ENF is interrupted, viral gp41 continues to evolve forward and resistance mutations are lost due to back-mutation and remodeling of the envelope protein. To examine the co-evolution of gp120 and gp41 during ENF interruption we extend the Bayesian Hierarchical Phylogenetic model (HPM). Current HPMs enforce conditional independence across all outcomes while biologically all gene regions within a patient should return the same tree unless recombination confers an evolutionary selective advantage. A two-way-interaction HPM is proposed that provides middle ground between these two extremes and allows us to test for differences in evolutionary pressures across gene regions in multiple patients simultaneously. When the model is applied to a well-characterized cohort of HIV-infected patients interrupting ENF we find that across patients, the virus continued to evolve forward in both gene regions. Overall, the hypothesis of independence over dependence between the gene regions is supported. Models that allow for the examination of co-evolution over time will be increasingly important as more therapeutic classes are developed, each of which may impact other through novel and complex mechanisms.

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Grants

  1. R37 AI055357/NIAID NIH HHS
  2. P30 AI060354-01/NIAID NIH HHS
  3. R21 AI055273/NIAID NIH HHS
  4. K24 RR016482/NCRR NIH HHS
  5. P30 AI060354-019003/NIAID NIH HHS
  6. P30 MH062246-01/NIMH NIH HHS
  7. P30 AI028697-18/NIAID NIH HHS
  8. R01 AI058845-02S1/NIAID NIH HHS
  9. M01 RR000083/NCRR NIH HHS
  10. P30 AI060354/NIAID NIH HHS
  11. R01 AI058845/NIAID NIH HHS
  12. P30 MH062246/NIMH NIH HHS
  13. M01 RR000083-37/NCRR NIH HHS
  14. R01 AI052745/NIAID NIH HHS
  15. K24 RR016482-01/NCRR NIH HHS
  16. R01 AI052745-04/NIAID NIH HHS
  17. R01 AI052745-01/NIAID NIH HHS
  18. P30 AI028697/NIAID NIH HHS
  19. R01 AI055357/NIAID NIH HHS
  20. M01 RR000083-37S10452/NCRR NIH HHS
  21. R21 AI055273-01/NIAID NIH HHS
  22. R01 AI055357-02/NIAID NIH HHS

Word Cloud

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