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dc.contributor.author
Gopalakrishnan, Sathej
dc.contributor.author
Montazeri, Hesam
dc.contributor.author
Menz, Stephan
dc.contributor.author
Beerenwinkel, Niko
dc.contributor.author
Huisinga, Wilhelm
dc.date.accessioned
2019-01-18T12:06:02Z
dc.date.available
2017-06-11T15:02:11Z
dc.date.available
2019-01-18T12:06:02Z
dc.date.issued
2014-11-06
dc.identifier.issn
1553-734X
dc.identifier.issn
1553-7358
dc.identifier.other
10.1371/journal.pcbi.1003886
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/95252
dc.identifier.doi
10.3929/ethz-b-000095252
dc.description.abstract
Despite the success of highly active antiretroviral therapy (HAART) in the management of human immunodeficiency virus (HIV)-1 infection, virological failure due to drug resistance development remains a major challenge. Resistant mutants display reduced drug susceptibilities, but in the absence of drug, they generally have a lower fitness than the wild type, owing to a mutation-incurred cost. The interaction between these fitness costs and drug resistance dictates the appearance of mutants and influences viral suppression and therapeutic success. Assessing in vivo viral fitness is a challenging task and yet one that has significant clinical relevance. Here, we present a new computational modelling approach for estimating viral fitness that relies on common sparse cross-sectional clinical data by combining statistical approaches to learn drug-specific mutational pathways and resistance factors with viral dynamics models to represent the host-virus interaction and actions of drug mechanistically. We estimate in vivo fitness characteristics of mutant genotypes for two antiretroviral drugs, the reverse transcriptase inhibitor zidovudine (ZDV) and the protease inhibitor indinavir (IDV). Well-known features of HIV-1 fitness landscapes are recovered, both in the absence and presence of drugs. We quantify the complex interplay between fitness costs and resistance by computing selective advantages for different mutants. Our approach extends naturally to multiple drugs and we illustrate this by simulating a dual therapy with ZDV and IDV to assess therapy failure. The combined statistical and dynamical modelling approach may help in dissecting the effects of fitness costs and resistance with the ultimate aim of assisting the choice of salvage therapies after treatment failure.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Public Library of Science
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Estimating HIV-1 Fitness Characteristics from Cross-Sectional Genotype Data
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
PLoS Computational Biology
ethz.journal.volume
10
en_US
ethz.journal.issue
11
en_US
ethz.journal.abbreviated
PLOS comput. biol.
ethz.pages.start
e1003886
en_US
ethz.size
14 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.nebis
005410277
ethz.publication.place
San Francisco, CA, USA
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::03790 - Beerenwinkel, Niko / Beerenwinkel, Niko
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::03790 - Beerenwinkel, Niko / Beerenwinkel, Niko
ethz.date.deposited
2017-06-11T15:02:54Z
ethz.source
ECIT
ethz.identifier.importid
imp593652bb7251451882
ethz.ecitpid
pub:149443
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-15T04:35:35Z
ethz.rosetta.lastUpdated
2019-01-18T12:06:09Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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