Reliable reconstruction of HIV-1 whole genome haplotypes reveals clonal interference and genetic hitchhiking among immune escape variants

BackgroundFollowing transmission, HIV-1 evolves into a diverse population, and next generation sequencing enables us to detect variants occurring at low frequencies. Studying viral evolution at the level of whole genomes was hitherto not possible because next generation sequencing delivers relatively short reads.ResultsWe here provide a proof of principle that whole HIV-1 genomes can be reliably reconstructed from short reads, and use this to study the selection of immune escape mutations at the level of whole genome haplotypes. Using realistically simulated HIV-1 populations, we demonstrate that reconstruction of complete genome haplotypes is feasible with high fidelity. We do not reconstruct all genetically distinct genomes, but each reconstructed haplotype represents one or more of the quasispecies in the HIV-1 population. We then reconstruct 30 whole genome haplotypes from published short sequence reads sampled longitudinally from a single HIV-1 infected patient. We confirm the reliability of the reconstruction by validating our predicted haplotype genes with single genome amplification sequences, and by comparing haplotype frequencies with observed epitope escape frequencies.ConclusionsPhylogenetic analysis shows that the HIV-1 population undergoes selection driven evolution, with successive replacement of the viral population by novel dominant strains. We demonstrate that immune escape mutants evolve in a dependent manner with various mutations hitchhiking along with others. As a consequence of this clonal interference, selection coefficients have to be estimated for complete haplotypes and not for individual immune escapes.

[1]  Rob J. de Boer,et al.  The Rate of Immune Escape Vanishes When Multiple Immune Responses Control an HIV Infection , 2013, The Journal of Immunology.

[2]  L. M. Mansky,et al.  Lower in vivo mutation rate of human immunodeficiency virus type 1 than that predicted from the fidelity of purified reverse transcriptase , 1995, Journal of virology.

[3]  Thomas Leitner,et al.  Recombination Rate and Selection Strength in HIV Intra-patient Evolution , 2009, PLoS Comput. Biol..

[4]  J. da Silva,et al.  The Dynamics of HIV-1 Adaptation in Early Infection , 2012, Genetics.

[5]  Michael P Busch,et al.  Dynamics of HIV viremia and antibody seroconversion in plasma donors: implications for diagnosis and staging of primary HIV infection , 2003, AIDS.

[6]  Rob J. De Boer,et al.  Estimating Costs and Benefits of CTL Escape Mutations in SIV/HIV Infection , 2006, PLoS Comput. Biol..

[7]  O. Gascuel,et al.  A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. , 2003, Systematic biology.

[8]  Alan S. Perelson,et al.  Fitness Costs and Diversity of the Cytotoxic T Lymphocyte (CTL) Response Determine the Rate of CTL Escape during Acute and Chronic Phases of HIV Infection , 2011, Journal of Virology.

[9]  Julie D Thompson,et al.  Multiple Sequence Alignment Using ClustalW and ClustalX , 2003, Current protocols in bioinformatics.

[10]  Douglas D. Richman,et al.  Viral Dynamics of Acute HIV-1 Infection , 1999, The Journal of experimental medicine.

[11]  Todd M. Allen,et al.  Viral evolution and escape during acute HIV-1 infection. , 2010, The Journal of infectious diseases.

[12]  Volker Roth,et al.  Deep Sequencing of a Genetically Heterogeneous Sample: Local Haplotype Reconstruction and Read Error Correction , 2009, RECOMB.

[13]  Leping Li,et al.  ART: a next-generation sequencing read simulator , 2012, Bioinform..

[14]  Sivan Leviyang,et al.  Computational Inference Methods for Selective Sweeps Arising in Acute HIV Infection , 2013, Genetics.

[15]  Alan S. Perelson,et al.  This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits noncommercial use, distribution, and reproduction in other forums, provided the original authors and source are credited , 2022 .

[16]  R. Lenski,et al.  Diminishing returns from mutation supply rate in asexual populations. , 1999, Science.

[17]  Rebecca Batorsky,et al.  Estimate of effective recombination rate and average selection coefficient for HIV in chronic infection , 2011, Proceedings of the National Academy of Sciences.

[18]  Hui Li,et al.  Impact of immune escape mutations on HIV-1 fitness in the context of the cognate transmitted/founder genome , 2012, Retrovirology.

[19]  Edward C. Holmes,et al.  Is the Quasispecies Concept Relevant to RNA Viruses? , 2002, Journal of Virology.

[20]  Alan S. Perelson,et al.  Inferring HIV Escape Rates from Multi-Locus Genotype Data , 2013, Front. Immunol..

[21]  Rebecca R. Gray,et al.  Multiple independent lineages of HIV-1 persist in breast milk and plasma , 2011, AIDS.

[22]  Marcelo Serrano Zanetti,et al.  CodonPhyML: Fast Maximum Likelihood Phylogeny Estimation under Codon Substitution Models , 2013, Molecular biology and evolution.

[23]  Feng Gao,et al.  Vertical T cell immunodominance and epitope entropy determine HIV-1 escape. , 2012, The Journal of clinical investigation.

[24]  Becca Asquith,et al.  Quantifying the Impact of Human Immunodeficiency Virus-1 Escape From Cytotoxic T-Lymphocytes , 2010, PLoS Comput. Biol..

[25]  Christopher Quince,et al.  Benchmarking of viral haplotype reconstruction programmes: an overview of the capacities and limitations of currently available programmes , 2014, Briefings Bioinform..

[26]  Hans Wolf,et al.  Identification and characterization of conserved and variable regions in the envelope gene of HTLV-III/LAV, the retrovirus of AIDS , 1986, Cell.

[27]  Alan S. Perelson,et al.  Transmission of Single HIV-1 Genomes and Dynamics of Early Immune Escape Revealed by Ultra-Deep Sequencing , 2010, PloS one.

[28]  M. Lässig,et al.  A predictive fitness model for influenza , 2014, Nature.

[29]  Persephone Borrow,et al.  The immune response during acute HIV-1 infection: clues for vaccine development , 2009, Nature Reviews Immunology.

[30]  Hui Li,et al.  Identification and characterization of transmitted and early founder virus envelopes in primary HIV-1 infection , 2008, Proceedings of the National Academy of Sciences.

[31]  B. Korber,et al.  Deciphering Human Immunodeficiency Virus Type 1 Transmission and Early Envelope Diversification by Single-Genome Amplification and Sequencing , 2008, Journal of Virology.

[32]  D. Huson,et al.  Application of phylogenetic networks in evolutionary studies. , 2006, Molecular biology and evolution.

[33]  R. Gibbs,et al.  Evolution of human immunodeficiency virus type 1 nucleotide sequence diversity among close contacts. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[34]  M. Lässig,et al.  Clonal Interference in the Evolution of Influenza , 2012, Genetics.

[35]  Niko Beerenwinkel,et al.  Error correction of next-generation sequencing data and reliable estimation of HIV quasispecies , 2010, Nucleic acids research.

[36]  Alan S. Perelson,et al.  The first T cell response to transmitted/founder virus contributes to the control of acute viremia in HIV-1 infection , 2009, The Journal of experimental medicine.

[37]  Nicholas Eriksson,et al.  ShoRAH: estimating the genetic diversity of a mixed sample from next-generation sequencing data , 2011, BMC Bioinformatics.

[38]  Michael M. Desai,et al.  Pervasive Genetic Hitchhiking and Clonal Interference in 40 Evolving Yeast Populations , 2013, Nature.

[39]  A. Halpern,et al.  A computer program designed to screen rapidly for HIV type 1 intersubtype recombinant sequences. , 1995, AIDS research and human retroviruses.

[40]  Trey Ideker,et al.  Cytoscape 2.8: new features for data integration and network visualization , 2010, Bioinform..

[41]  A. Perelson,et al.  Genetic identity, biological phenotype, and evolutionary pathways of transmitted/founder viruses in acute and early HIV-1 infection , 2009, The Journal of experimental medicine.

[42]  Li Yin,et al.  Empirical validation of viral quasispecies assembly algorithms: state-of-the-art and challenges , 2013, Scientific Reports.

[43]  Sergei L. Kosakovsky Pond,et al.  Not so different after all: a comparison of methods for detecting amino acid sites under selection. , 2005, Molecular biology and evolution.

[44]  Christian L. Althaus,et al.  Dynamics of Immune Escape during HIV/SIV Infection , 2008, PLoS Comput. Biol..

[45]  Julio A. Rozas Liras,et al.  DnaSP v 5 : a software for comprehensive analysis of DNA polymorphism data , 2009 .

[46]  Wilco Keulen,et al.  Estimating Relative Fitness in Viral Competition Experiments , 2000, Journal of Virology.

[47]  O. Pybus,et al.  Unifying the Epidemiological and Evolutionary Dynamics of Pathogens , 2004, Science.

[48]  Richard A Neher,et al.  Mathematical modeling of escape of HIV from cytotoxic T lymphocyte responses , 2012, Journal of statistical mechanics.

[49]  Daniel I. S. Rosenbloom,et al.  Evolutionary dynamics of HIV at multiple spatial and temporal scales , 2012, Journal of Molecular Medicine.

[50]  Philip J. R. Goulder,et al.  HIV-1 Viral Escape in Infancy Followed by Emergence of a Variant-Specific CTL Response1 , 2005, The Journal of Immunology.

[51]  Mattia C. F. Prosperi,et al.  QuRe: software for viral quasispecies reconstruction from next-generation sequencing data , 2012, Bioinform..

[52]  M. Malim,et al.  HIV-1 Sequence Variation Drift, Shift, and Attenuation , 2001, Cell.

[53]  Huldrych F. Günthard,et al.  Whole Genome Deep Sequencing of HIV-1 Reveals the Impact of Early Minor Variants Upon Immune Recognition During Acute Infection , 2012, PLoS pathogens.

[54]  Pablo Librado,et al.  DnaSP v5: a software for comprehensive analysis of DNA polymorphism data , 2009, Bioinform..

[55]  Volker Roth,et al.  Probabilistic Inference of Viral Quasispecies Subject to Recombination , 2012, RECOMB.

[56]  Sergei L. Kosakovsky Pond,et al.  Datamonkey 2010: a suite of phylogenetic analysis tools for evolutionary biology , 2010, Bioinform..

[57]  S. Elena,et al.  Clonal interference and the evolution of RNA viruses. , 1999, Science.

[58]  J Gillis,et al.  SIVΔnef vaccination mobilizes systemic and mucosal natural killer cells in Mamu A*01+ macaques , 2012, Retrovirology.