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HIV-1 envelope sequence-based diversity measures for identifying recent infections.

Alexis Kafando1, Eric Fournier2, Bouchra Serhir2

  • 1Département de microbiologie, infectiologie et immunologie, Faculté de médecine, Université de Montréal, Montréal, Québec, Canada.

Plos One
|December 29, 2017
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Summary

Identifying recent HIV-1 infections is key for public health. Shannon entropy effectively distinguishes recent from chronic HIV-1 infections using env gene sequences, aiding incidence monitoring.

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Area of Science:

  • Virology
  • Epidemiology
  • Bioinformatics

Background:

  • Accurate identification of recent HIV-1 infections is vital for effective public health strategies and incidence surveillance.
  • Sequence-based methods offer potential for distinguishing recent from chronic HIV-1 infections.

Purpose of the Study:

  • To evaluate and compare four sequence-based diversity measures for identifying recent HIV-1 infections.
  • To assess the performance of percent diversity, percent complexity, Shannon entropy, and number of haplotypes.

Main Methods:

  • Analyzed 249 HIV-1 env gene sequences from diagnostic samples using next-generation sequencing.
  • Compared four diversity measures: percent diversity, percent complexity, Shannon entropy, and number of haplotypes.
  • Utilized frequency distribution curves, median/interquartile range, and ROC analysis with AUC.

Main Results:

  • Percent diversity, number of haplotypes, and Shannon entropy showed potential in discriminating recent from chronic infections (p<0.0001).
  • Shannon entropy achieved satisfactory accuracy in identifying recent infections within specific env segments (gp120 C2_1, C2_3, and V3) based on AUC values.
  • AUC values for Shannon entropy ranged from 0.805 to 0.812 for the identified segments.

Conclusions:

  • Shannon entropy is a valuable tool for predicting HIV-1 infection recency.
  • This measure can aid in more accurate HIV-1 incidence monitoring and public health interventions.