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

Applicant Beerenwinkel Niko
Number 127017
Funding scheme Interdisciplinary projects
Research institution Computational Systems Biology Department of Biosystems, D-BSSE ETH Zürich
Institution of higher education ETH Zurich - ETHZ
Main discipline Information Technology
Start/End 01.01.2010 - 31.03.2013
Approved amount 552'066.00
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All Disciplines (3)

Discipline
Information Technology
Molecular Biology
Internal Medicine

Keywords (10)

ultra-deep sequencing; pyrosequencing; genome assembly; graphical models; Bayesian statistics; haplotype inference; HIV-1; drug resistance; virus evolution; antiretroviral therapy

Lay Summary (English)

Lead
Lay summary
Biological species exist as populations of related yet different individuals and the genetic diversity within species plays an important role for their survival. Human immunodeficiency virus (HIV) displays particularly high diversity within infected patients, referred to as a "quasispecies". This diversity drives evolutionary escape of HIV from the host's immune response and is predictive of progression to AIDS. The high genetic heterogeneity of HIV 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, a new generation of high-throughput DNA sequencing technologies has been introduced. Because of their high coverage, this approach is referred to as "ultra-deep" (or "next-generation") sequencing. The new sequencing platforms have the potential to resolve genetic variation in a sample at unprecedented detail by direct sequencing of the mixture of clones. While traditional Sanger sequencing can only infer the consensus sequence of a sample, ultra-deep sequencing generates a very large number of individual reads from the population of interest.We pursue a combined approach of ultra-deep sequencing and computational modeling to infer the genetic diversity of intra-patient HIV 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 populations derived from infected patients. We will characterize the viral quasispecies with respect to interactions among mutations and among individual genetic variants in the context of drug resistance development.The proposed investigations will increase our understanding of the complex mechanisms of viral escape from the selective pressure of antiretroviral drugs. The establishment of the new experimental and computational methodologies will be widely applicable to HIV 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.
Direct link to Lay Summary Last update: 21.02.2013

Responsible applicant and co-applicants

Employees

Associated projects

Number Title Start Funding scheme
146143 Understanding and Predicting the Hepatitis C Epidemic in HIV-infected Patients 01.05.2013 Project funding (special)
146331 HIV-1 whole-genome quasispecies analysis in longitudinal clinical samples 01.04.2013 Interdisciplinary projects
134277 Swiss HIV Cohort Study (SHCS) 01.01.2011 Cohort Studies Large
148522 Swiss HIV Cohort Study (SHCS) 01.01.2014 Cohort Studies Large

Abstract

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.
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