title: Assessing predicted HIV-1 replicative capacity in a clinical setting.
abstract: HIV-1 replicative capacity (RC) provides a measure of within-host fitness and is determined in the context of phenotypic drug resistance testing. However it is unclear how these in-vitro measurements relate to in-vivo processes. Here we assess RCs in a clinical setting by combining a previously published machine-learning tool, which predicts RC values from partial pol sequences with genotypic and clinical data from the Swiss HIV Cohort Study. The machine-learning tool is based on a training set consisting of 65000 RC measurements paired with their corresponding partial pol sequences. We find that predicted RC values (pRCs) correlate significantly with the virus load measured in 2073 infected but drug naïve individuals. Furthermore, we find that, for 53 pairs of sequences, each pair sampled in the same infected individual, the pRC was significantly higher for the sequence sampled later in the infection and that the increase in pRC was also significantly correlated with the increase in plasma viral load and with the length of the time-interval between the sampling points. These findings indicate that selection within a patient favors the evolution of higher replicative capacities and that these in-vitro fitness measures are indicative of in-vivo HIV virus load.
authors.affiliation: ETH Zürich, Institute of Integrative Biology, Zürich, Switzerland. rkouyos@princeton.edu
authors.forename: Roger D
authors.initials: RD
authors.lastname: Kouyos
authors.forename: Viktor
authors.initials: V
authors.lastname: von Wyl
authors.forename: Trevor
authors.initials: T
authors.lastname: Hinkley
authors.forename: Christos J
authors.initials: CJ
authors.lastname: Petropoulos
authors.forename: Mojgan
authors.initials: M
authors.lastname: Haddad
authors.forename: Jeannette M
authors.initials: JM
authors.lastname: Whitcomb
authors.forename: Jürg
authors.initials: J
authors.lastname: Böni
authors.forename: Sabine
authors.initials: S
authors.lastname: Yerly
authors.forename: Cristina
authors.initials: C
authors.lastname: Cellerai
authors.forename: Thomas
authors.initials: T
authors.lastname: Klimkait
authors.forename: Huldrych F
authors.initials: HF
authors.lastname: Günthard
authors.forename: Sebastian
authors.initials: S
authors.lastname: Bonhoeffer
mesh_terms: D000328:Adult; D019380:Anti-HIV Agents / Q000494:pharmacology; D001185:Artificial Intelligence; D001483:Base Sequence; D003198:Computer Simulation; D024882:Drug Resistance, Viral / Q000235:genetics; D005260:Female; D015751:Genes, pol; D005838:Genotype; D015658:HIV Infections / Q000821:virology*; D015497:HIV-1 / Q000187:drug effects / Q000235:genetics / Q000502:physiology*; D006801:Humans; D008297:Male; D008826:Microbial Sensitivity Tests; D019562:Viral Load; D014766:Viremia; D014779:Virus Replication / Q000235:genetics
chemical_list: D019380:Anti-HIV Agents
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