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Estimating the frequency of superinfection in a large European collaborative HIV database. István Bartha 1 , M. Assel, P. Sloot, M. Zazzi, C. Torti, E. Schülter, A. De Luca, A. Sönnerborg, A.B. Abecasis, A.-M. Vandamme, R. Paredes, D. van de Vijver, V. Müller 1

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estimating the frequency of superinfection in a large european collaborative hiv database
Estimating the frequency of superinfection in a large European collaborative HIV database

István Bartha1, M. Assel, P. Sloot, M. Zazzi, C. Torti, E. Schülter, A. De Luca, A. Sönnerborg, A.B. Abecasis, A.-M. Vandamme, R. Paredes, D. van de Vijver, V. Müller1

1Eötvös Loránd University, Budapest

background
Background
  • Infection of an HIV+ individual - superinfection
  • Transmission of drug resistant strain, accelerate disease progression
  • Few studies on the prevalence of superinfection
  • No routine testing

Is it possible to detect superinfection from routine genotypic data?

slide3
Data
  • Virolab and EuResist collaborative HIV databases (Italy, Spain, Belgium, Sweden, Germany )
  • At least 2 sequences from each patient
  • Most of the patients are treated.

4656 patients

data sequences
Data - sequences
  • Sequences of RT and PR regions: 14196 sequences, average 1kb long
definition of superinfection
Definition of superinfection
  • Last common ancestor of a patient’s sequences is not in the patient
problems
Problems
  • Build a reliable phylogenetic tree in feasible time

rough trees by

RAxML

4656 patients

303 patients

MrBayes

results

  • 1-1 month on a 4500-core cluster
phase i maximum likelihood trees
Phase I. - Maximum Likelihood trees
  • Maximum Likelihood trees with RAxML v.7.2.6 on 14196 sequences starting from randomized Maximum Parsimony trees
  • Initial starting tree affects the final results -> repeated 100 times
  • Fast & rough
phase ii bayesian trees with mrbayes
Phase II. Bayesian trees with MrBayes
  • Each patient in Phase II was analyzed by MrBayes 3.1.2 (modified by Alexandros Stamatakis)
  • Provides estimate of reliability (posterior probabilities of the clades)
phase ii analysis of bayesian trees
Phase II - Analysis of Bayesian trees
  • Remove all branches with weak support
  • Check whether the patient’s sequences form a monophyletic cluster or fail to do so
validating the method
Validating the method
  • How many false negatives?
  • We analyzed 150 rejected patients by Phase I.
validating the method1
Validating the method
  • How many false negatives?
  • We analyzed 150 rejected patients by Phase I.
significant effects
Significant effects
  • Data center has significant effect (p = 0.002792)
  • Follow-up time has significant effect (p = 0.015) : 6% increase in probability of being superinfected per year of follow-up
discussion
Discussion
  • 68 patient (1.4%) identified
  • Unknown number of false negative patients
      • Arbitrary threshold on branch support - independent validation
      • Should run deep analysis on all patient - computer time restrictionsImprove pre-selection
acknowledgment
Acknowledgment

Viktor Müller

Anna Abecasis

Anne-Mieke Vandamme

M. Assel, P. Sloot,

M. Zazzi, C. Torti,

E. Schülter, A. De Luca, A. Sönnerborg,

R. Paredes,

D. van de Vijver

detection of superinfection
Detection of superinfection
  • Overlapping timescales of with-in host and among-host evolution
  • Unable to differentiate between superinfectious and long-time persisted viral strains - this is a general limitation of any distance based method