HIV-1 whole-genome quasispecies analysis by ultra-deep sequencing and computational haplotype inference to determine the mechanisms of drug resistance development

HIV-1 whole-genome quasispecies analysis by ultra-deep sequencing and computational haplotype inference to determine the mechanisms of drug resistance development For many biological species, the traditional view of "one organism, one genome" is insufficient to explain their behavior. The genetic diversity within these species plays an important role for their survival in a given environment. In HIV-1 infection, a diverse population of viruses is maintained in each individual host, facilitating the evolutionary escape of HIV-1 from the host's immune response. The high genetic heterogeneity of HIV-1 quasispecies constitutes a major obstacle in the development of an effective vaccine and it limits therapeutic options due to drug resistant mutants. In recent years, the next generation of high-throughput DNA sequencing technologies has been introduced. Because of their high coverage based on sequencing many short reads in parallel, this approach is referred to as ultra-deep sequencing. The new sequencing platforms have the potential to resolve the genetic variation in a sample at unprecedented detail by direct sequencing of the mixture of clones. While traditional Sanger sequencing can only infer a consensus sequence of a sample, ultra-deep sequencing generates a very large number of individual reads from the population of interest. Beyond the standard usage of deep sequencing, we propose a combined approach of ultra-deep sequencing and computational modeling to infer the genetic diversity of intra-host HIV-1 populations. In this joint effort between physician scientists, molecular biologists, computational biologists, and computer scientists, our main goal is to reconstruct the individual full-length haplotypes of HIV-1 populations derived from infected patients and to characterize these quasispecies with respect to interactions among mutations and among individual variants in the context of drug resistance development. To achieve this goal, we will (1) develop and optimize an experimental methodology for ultra-deep sequencing of full-length HIV-1 virus populations, (2) devise computational and statistical methods for haplotype reconstruction from a set of short, error-prone, observed sequence reads, and (3) analyze 100 pre- and post-treatment patient samples with this approach in order to determine the mechanisms driving the evolution of drug resistance. The comprehensiveness of this study due to the whole-genome and population-wide approach is a unique feature that is possible only with this interdisciplinary approach. The proposed investigations will increase our understanding of the complex mechanisms of viral escape from the pressure of antiretroviral drugs. The establishment of the new experimental and computational methodologies will be widely applicable to HIV-1 infected patients in Switzerland and beyond. They will further increase the scientific value of the Swiss HIV Cohort Study, a large long-term Swiss collaboration, and they may considerably increase quality of patient care in the future.