HIV-1 Resistance Evolution K. Theys Rega Institute for Medical Research Introduction Rega Institute for Medical Research Treatment Methods Kristof Theys Design Bayesian Network Fitness Landscape Clinical and Epidemiological Virology Katholieke Universiteit Leuven April, 10th Arevir Meeting, Bonn
Outline HIV-1 Resistance Evolution K. Theys Introduction 1 Introduction Rega Institute for Medical Research Rega Institute for Medical Research Treatment of HIV-1 infection Treatment Methods Design Bayesian Network Understanding HIV evolution under selective pressure of 2 Fitness Landscape HIV therapy General overview of applied techniques Bayesian Network Learning Estimation of Fitness Landscape
Outline HIV-1 Resistance Evolution K. Theys Introduction 1 Introduction Rega Institute for Medical Research Rega Institute for Medical Research Treatment of HIV-1 infection Treatment Methods Design Bayesian Network Understanding HIV evolution under selective pressure of 2 Fitness Landscape HIV therapy General overview of applied techniques Bayesian Network Learning Estimation of Fitness Landscape
Clinical and Epidemiological Virology HIV-1 Resistance Evolution Bioinformatics group K. Theys Annemie Vandamme, Raphael Sangeda, Ana Abecasis, Introduction Pieter Libin, Philippe Lemey and Kristof Theys Rega Institute for Medical Research Treatment Research interests Methods Factors that influence therapy response of HIV infected Design patients Bayesian Network Fitness Landscape viral resistance against HIV drugs Molecular evolution and epidemiology of HIV subtype diversity of HIV tracing the origin of HIV Combined approach enables to study the evolution of HIV during selective pressure of HIV
Rega analysis tools HIV-1 Resistance Evolution K. Theys Rega algorithm Introduction genotypic resistance interpretation system Rega Institute for Medical Research Treatment Rega HIV subtyping tool Methods Sequence analysis tools Design Bayesian Network Alignment Fitness Landscape Translation to amino acids Resistance interpretation All accessible at http://jose.med.kuleuven.be All integrated in RegaDB
Outline HIV-1 Resistance Evolution K. Theys Introduction 1 Introduction Rega Institute for Medical Research Rega Institute for Medical Research Treatment of HIV-1 infection Treatment Methods Design Bayesian Network Understanding HIV evolution under selective pressure of 2 Fitness Landscape HIV therapy General overview of applied techniques Bayesian Network Learning Estimation of Fitness Landscape
Therapy Response HIV-1 Resistance Evolution K. Theys Many factors influence therapy outcome adherence Introduction Rega Institute for dosis Medical Research Treatment drug interactions Methods metabolism - absorption Design Bayesian Network complexity - toxicity Fitness Landscape antiviral resistance Keyrole for antiviral resistance cause and consequence of failure Avoid development of resistance irreversible proces cross-resistance
Selecting the optimal combination Life-long HIV antiviral treatment HIV-1 Resistance Evolution K. Theys Planning successful drug sequencing strategies Introduction Rega Institute for Dual requirements for combination Medical Research Treatment Short term Methods be potent (virological suppression) Design Bayesian Network forgiving (minimally affected by adherene) Fitness Landscape high genetic barrier** to resistance Long term minimally cross-resistance Sequencing strategies most applicable for drug naive
Rega’s objectives HIV-1 Resistance Evolution Optimalisation is necessary for succesful lifelong therapy K. Theys Time to failure of combination therapy Introduction Optimal therapy options after therapy failure Rega Institute for Medical Research Treatment Methods Design Bayesian Network Fitness Landscape
Rega’s methods HIV-1 Resistance Evolution Better understanding how resistance develops and K. Theys evolves under the selective pressure of (combination) therapy. Introduction Rega Institute for Medical Research Influences on resistance evolution Treatment Impact of genetic variability ( inter/intra ) Methods Interactions between mutations Design Bayesian Network Genetic barrier of a drug/combination Fitness Landscape Time to therapy failure Evolutionary distance to resistance Genetic barrier Potency of the combination (activity) Phenotype, adherence, . . .
Outline HIV-1 Resistance Evolution K. Theys Introduction 1 Introduction Rega Institute for Medical Research Rega Institute for Medical Research Treatment of HIV-1 infection Treatment Methods Design Bayesian Network Understanding HIV evolution under selective pressure of 2 Fitness Landscape HIV therapy General overview of applied techniques Bayesian Network Learning Estimation of Fitness Landscape
Applied techniques and applications HIV-1 Resistance Bayesian network learning for resistance development Evolution K. Theys role of mutations and polymorphisms in treatment failure Introduction influence of subtype diversity on resistance Rega Institute for Medical Research improve genotypic interpretation systems Treatment Methods Estimation of fitness landscapes Design Bayesian Network model of HIV evolution under the selective pressure of Fitness Landscape treatment predict evolution of HIV Calculate genetic barrier define the genotypic correlates which influence genetic barrier Prediction of therapy response genotypic predictors based on estimated fitness
Outline HIV-1 Resistance Evolution K. Theys Introduction 1 Introduction Rega Institute for Medical Research Rega Institute for Medical Research Treatment of HIV-1 infection Treatment Methods Design Bayesian Network Understanding HIV evolution under selective pressure of 2 Fitness Landscape HIV therapy General overview of applied techniques Bayesian Network Learning Estimation of Fitness Landscape
Bayesian Network Analysis HIV-1 Resistance Evolution Probabilistic Graphical Models K. Theys Finds minimal set of direct dependencies that together explain most observed correlation Introduction Rega Institute for Medical Research Represent direct dependencies in a directed graph Treatment Methods A Bayesian Network refactors the JPD of multivariable Design Bayesian Network data in product of CPDs: Fitness Landscape n � P ( A 1 , . . . , A n ) = P ( A i | parents ( A i )) i by assuming conditional independencies Refactoring of JPD P ( 30 N , 71 V , 88 D , 90 M ) = P ( 71 V | 30 N ) xP ( 88 D | 30 N ) xP ( 30 N ) xP ( 90 M )
Bayesian Network learning On amino acid sequence data HIV-1 Resistance Evolution A Bayesian Network can be “learned“ from data K. Theys Can be used as a blue-print for amino acid interactions Introduction for an emperical fitness function Rega Institute for Medical Research Treatment Can be interpreted semantically Methods Design mutations connected to drug node Bayesian Network Fitness Landscape presence is directly influenced by drug major mutation mutations connected to each other presence is influenced by other mutation minor mutation in resistance pathway mutations connected to polymorphisms presence is influenced by natural variation
Bayesian Network of Nelfinavir resistance HIV-1 Resistance Evolution K. Theys Introduction Rega Institute for Medical Research Treatment Methods Design Bayesian Network Fitness Landscape
Outline HIV-1 Resistance Evolution K. Theys Introduction 1 Introduction Rega Institute for Medical Research Rega Institute for Medical Research Treatment of HIV-1 infection Treatment Methods Design Bayesian Network Understanding HIV evolution under selective pressure of 2 Fitness Landscape HIV therapy General overview of applied techniques Bayesian Network Learning Estimation of Fitness Landscape
Darwinian Fitness (during treatment) HIV-1 Resistance Evolution K. Theys Defines the ability to replicate in a given environment Introduction dependent on virus (replication capacity) Rega Institute for Medical Research dependent on environment (CTL response, therapy, ...) Treatment Methods Fitness is the driving force behind evolution Design Bayesian Network Selective pressure of therapy influences relative fitness Fitness Landscape of population Shift in quasispecies distribution and selection of resistant strains Viral Resistance is the Outcome of Viral Replication, Mutation and Selection
Estimating a fitness function . . . to imagining a fitness landscape HIV-1 Resistance Evolution K. Theys Introduction Rega Institute for Medical Research Treatment Methods Design Bayesian Network Fitness Landscape
Fitness Landscape Objective HIV-1 Resistance Evolution A model for HIV evolution during treatment K. Theys Mutation, fitness and selection Introduction Rega Institute for Main components Medical Research Treatment An estimated in vivo fitness landscape Methods Design A mathematical model of evolution Bayesian Network Fitness Landscape Concept Observing evolution provides information on fitness High correlation between prevalence and selective advantage Simulated evolution over the landscape predict evolution genetic barrier
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