• Skip to primary navigation
  • Skip to main content
  • Skip to footer
  • Pessoal
    • Webmail
    • Área de Docentes
    • Área de Não-Docentes
  • Estudantes
    • Webmail
    • Moodle
    • NetP@
    • Biblioteca
    • Escola Doutoral
    • Serviços Académicos
    • Trabalhar no IHMT

IHMT

Instituto de Higiene e Medicina Tropical

  • O Instituto
    • Missão
    • História
    • Mensagem do Diretor
    • Órgãos de governo
    • Docentes e investigadores
    • Unidades de Ensino e de Investigação
  • Ensino
    • Doutoramentos
    • Mestrados
    • Cursos de Especialização
    • Formação transversal
    • Cursos de Curta Duração
    • Ensino à Distância
    • Apoio ao Desenvolvimento
    • Serviços académicos
  • Investigação
    • Centro GHTM
    • Unidade de Clínica Tropical
    • Unidade de Microbiologia Médica
    • Unidade de Parasitologia Médica
    • Unidade de Saúde Pública Global
    • Serviço de Interesse Comum
    • Biobanco
    • Centro Colaborador da OMS
    • Publicações
  • Serviços e gestão
    • Biblioteca
    • Sistema de Qualidade
    • Estatutos e regulamentos
    • Relatórios
    • Contratos públicos
    • Recursos humanos
      • Concursos e bolsas
      • Contratos
      • Avaliação e Desempenho
        • Processo Eleitoral da Comissão Paritária
      • Mobilidade
  • Doenças Tropicais
    • Consulta do Viajante
    • Glossário
    • Museu
    • Vídeos
    • MosquitoWeb
  • Comunidade
    • Cooperação e Desenvolvimento
    • Formação
    • Parcerias
  • Contactos
  • Português
  • English
Home / Publicações / HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure

HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure

  • Autores: Beheydt G, Bruzzone B, Camacho RJ, De Luca A, Deforche K, Grossman Z, Imbrechts S, Incardona F, Libin P, Pironti A, Rhee SY, Ruiz L, Sangeda RZ, Shafer RW, Sönnerborg A, Theys K, Torti C, Van de Vijver DA, Van Laethem K, Van Wijngaerden E, Vandamme AM, Vercauteren J, Zazzi M
  • Ano de Publicação: 2013
  • Journal: Infection Genetics and Evolution
  • Link: http://www.ncbi.nlm.nih.gov/pubmed/23523594

We previously modeled the in vivo evolution of human immunodeficiency virus-1 (HIV-1) under drug selective pressure from cross-sectional viral sequences. These fitness landscapes (FLs) were made by using first a Bayesian network (BN) to map epistatic substitutions, followed by scaling the fitness landscape based on an HIV evolution simulator trying to evolve the sequences from treatment naïve patients into sequences from patients failing treatment.

In this study, we compared four FLs trained with different sequence populations. Epistatic interactions were learned from three different cross-sectional BNs, trained with sequence from patients experienced with indinavir (BNT), all protease inhibitors (PIs) (BNP) or all PI except indinavir (BND).

Scaling the fitness landscape was done using cross-sectional data from drug naïve and indinavir experienced patients (Fcross using BNT) and using longitudinal sequences from patients failing indinavir (FlongT using BNT, FlongP using BNP, FlongD using BND). Evaluation to predict the failing sequence and therapy outcome was performed on independent sequences of patients on indinavir. Parameters included estimated fitness (LogF), the number of generations (GF) or mutations (MF) to reach the fitness threshold (average fitness when a major resistance mutation appeared), the number of generations (GR) or mutations (MR) to reach a major resistance mutation and compared to genotypic susceptibility score (GSS) from Rega and HIVdb algorithms.

In pairwise FL comparisons we found significant correlation between fitness values for individual sequences, and this correlation improved after correcting for the subtype. Furthermore, FLs could predict the failing sequence under indinavir-containing combinations. At 12 and 48 weeks, all parameters from all FLs and indinavir GSS (both for Rega and HIVdb) were predictive of therapy outcome, except MR for FlongT and FlongP.

The fitness landscapes have similar predictive power for treatment response under indinavir-containing regimen as standard rules-based algorithms, and additionally allow predicting genetic evolution under indinavir selective pressure.

HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure

  • Autores: Beheydt G, Bruzzone B, Camacho RJ, De Luca A, Deforche K, Grossman Z, Imbrechts S, Incardona F, Libin P, Pironti A, Rhee SY, Ruiz L, Sangeda RZ, Shafer RW, Sönnerborg A, Theys K, Torti C, Van de Vijver DA, Van Laethem K, Van Wijngaerden E, Vandamme AM, Vercauteren J, Zazzi M
  • Ano de Publicação: 2013
  • Journal: Infection Genetics and Evolution
  • Link: http://www.ncbi.nlm.nih.gov/pubmed/23523594

We previously modeled the in vivo evolution of human immunodeficiency virus-1 (HIV-1) under drug selective pressure from cross-sectional viral sequences. These fitness landscapes (FLs) were made by using first a Bayesian network (BN) to map epistatic substitutions, followed by scaling the fitness landscape based on an HIV evolution simulator trying to evolve the sequences from treatment naïve patients into sequences from patients failing treatment.

In this study, we compared four FLs trained with different sequence populations. Epistatic interactions were learned from three different cross-sectional BNs, trained with sequence from patients experienced with indinavir (BNT), all protease inhibitors (PIs) (BNP) or all PI except indinavir (BND).

Scaling the fitness landscape was done using cross-sectional data from drug naïve and indinavir experienced patients (Fcross using BNT) and using longitudinal sequences from patients failing indinavir (FlongT using BNT, FlongP using BNP, FlongD using BND). Evaluation to predict the failing sequence and therapy outcome was performed on independent sequences of patients on indinavir. Parameters included estimated fitness (LogF), the number of generations (GF) or mutations (MF) to reach the fitness threshold (average fitness when a major resistance mutation appeared), the number of generations (GR) or mutations (MR) to reach a major resistance mutation and compared to genotypic susceptibility score (GSS) from Rega and HIVdb algorithms.

In pairwise FL comparisons we found significant correlation between fitness values for individual sequences, and this correlation improved after correcting for the subtype. Furthermore, FLs could predict the failing sequence under indinavir-containing combinations. At 12 and 48 weeks, all parameters from all FLs and indinavir GSS (both for Rega and HIVdb) were predictive of therapy outcome, except MR for FlongT and FlongP.

The fitness landscapes have similar predictive power for treatment response under indinavir-containing regimen as standard rules-based algorithms, and additionally allow predicting genetic evolution under indinavir selective pressure.

Footer

INSTITUTO DE HIGIENE E
MEDICINA TROPICAL
UNIVERSIDADE NOVA DE LISBOA
Rua da Junqueira, 100 1349-008 Lisboa
T +351 213 652 600
geral@ihmt.unl.pt

Consulta do Viajante e Medicina Tropical
T +351 213 652 630
T +351 213 652 690
T +351 91 182 37 48
T +351 91 182 44 67
medicina.viagens@ihmt.unl.pt

Ensino
Investigação
Medicina Tropical
Cooperação

Siga-nos

  • Facebook
  • LinkedIn
  • YouTube

Receber a “newsletter”

© Copyright 2023 IHMT-UNL Todos os Direitos Reservados.
  • Universidade Nova de Lisboa
  • Fundação para a Ciência e a Tecnologia

    Project UID/Multi/04413/2013