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Nazziwa J, Andrews SM, Hou MM, Bruhn CAW, Garcia-Knight MA, Slyker J, Hill S, Lohman Payne B, Moringas D, Lemey P, John-Stewart G, Rowland-Jones SL, Esbjörnsson J. Higher HIV-1 evolutionary rate is associated with cytotoxic T lymphocyte escape mutations in infants. J Virol 2024; 98:e0007224. [PMID: 38814066 PMCID: PMC11265422 DOI: 10.1128/jvi.00072-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 04/20/2024] [Indexed: 05/31/2024] Open
Abstract
Escape from cytotoxic T lymphocyte (CTL) responses toward HIV-1 Gag and Nef has been associated with reduced control of HIV-1 replication in adults. However, less is known about CTL-driven immune selection in infants as longitudinal studies of infants are limited. Here, 1,210 gag and 1,264 nef sequences longitudinally collected within 15 months after birth from 14 HIV-1 perinatally infected infants and their mothers were analyzed. The number of transmitted founder (T/F) viruses and associations between virus evolution, selection, CTL escape, and disease progression were determined. The analyses indicated that a paraphyletic-monophyletic relationship between the mother-infant sequences was common (80%), and that the HIV-1 infection was established by a single T/F virus in 10 of the 12 analyzed infants (83%). Furthermore, most HIV-1 CTL escape mutations among infants were transmitted from the mothers and did not revert during the first year of infection. Still, immune-driven selection was observed at approximately 3 months after HIV-1 infection in infants. Moreover, virus populations with CTL escape mutations in gag evolved faster than those without, independently of disease progression rate. These findings expand the current knowledge of HIV-1 transmission, evolution, and CTL escape in infant HIV-1 infection and are relevant for the development of immune-directed interventions in infants.IMPORTANCEDespite increased coverage in antiretroviral therapy for the prevention of perinatal transmission, paediatric HIV-1 infection remains a significant public health concern, especially in areas of high HIV-1 prevalence. Understanding HIV-1 transmission and the subsequent virus adaptation from the mother to the infant's host environment, as well as the viral factors that affect disease outcome, is important for the development of early immune-directed interventions for infants. This study advances our understanding of vertical HIV-1 transmission, and how infant immune selection pressure is shaping the intra-host evolutionary dynamics of HIV-1.
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Affiliation(s)
- Jamirah Nazziwa
- Department of Translational Medicine, Lund University, Lund, Sweden
- Lund University Virus Centre, Lund University, Lund, Sweden
| | - Sophie M. Andrews
- Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
| | - Mimi M. Hou
- Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
| | | | - Miguel A. Garcia-Knight
- Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, California, USA
| | - Jennifer Slyker
- Department of Global Health, University of Washington, Seattle, Washington, USA
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Sarah Hill
- Department of Pathobiology and Population Sciences, Royal Veterinary College, London, United Kingdom
| | - Barbara Lohman Payne
- Department of Paediatrics and Child Health, University of Nairobi, Nairobi, Kenya
- Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Dorothy Moringas
- Department of Paediatrics and Child Health, University of Nairobi, Nairobi, Kenya
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Grace John-Stewart
- Department of Global Health, University of Washington, Seattle, Washington, USA
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
- Department of Medicine, University of Washington, Seattle, Washington, USA
- Department of Pediatrics, University of Washington, Seattle, Washington, USA
- Global Center for Integrated Health of Women, Adolescents and Children (Global WACh), University of Washington, Seattle, Washington, USA
| | | | - Joakim Esbjörnsson
- Department of Translational Medicine, Lund University, Lund, Sweden
- Lund University Virus Centre, Lund University, Lund, Sweden
- Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
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Gupta MK, Vadde R. Next-generation development and application of codon model in evolution. Front Genet 2023; 14:1091575. [PMID: 36777719 PMCID: PMC9911445 DOI: 10.3389/fgene.2023.1091575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/17/2023] [Indexed: 01/28/2023] Open
Abstract
To date, numerous nucleotide, amino acid, and codon substitution models have been developed to estimate the evolutionary history of any sequence/organism in a more comprehensive way. Out of these three, the codon substitution model is the most powerful. These models have been utilized extensively to detect selective pressure on a protein, codon usage bias, ancestral reconstruction and phylogenetic reconstruction. However, due to more computational demanding, in comparison to nucleotide and amino acid substitution models, only a few studies have employed the codon substitution model to understand the heterogeneity of the evolutionary process in a genome-scale analysis. Hence, there is always a question of how to develop more robust but less computationally demanding codon substitution models to get more accurate results. In this review article, the authors attempted to understand the basis of the development of different types of codon-substitution models and how this information can be utilized to develop more robust but less computationally demanding codon substitution models. The codon substitution model enables to detect selection regime under which any gene or gene region is evolving, codon usage bias in any organism or tissue-specific region and phylogenetic relationship between different lineages more accurately than nucleotide and amino acid substitution models. Thus, in the near future, these codon models can be utilized in the field of conservation, breeding and medicine.
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Bletsa M, Suchard MA, Ji X, Gryseels S, Vrancken B, Baele G, Worobey M, Lemey P. Divergence dating using mixed effects clock modelling: An application to HIV-1. Virus Evol 2019; 5:vez036. [PMID: 31720009 PMCID: PMC6830409 DOI: 10.1093/ve/vez036] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
The need to estimate divergence times in evolutionary histories in the presence of various sources of substitution rate variation has stimulated a rich development of relaxed molecular clock models. Viral evolutionary studies frequently adopt an uncorrelated clock model as a generic relaxed molecular clock process, but this may impose considerable estimation bias if discrete rate variation exists among clades or lineages. For HIV-1 group M, rate variation among subtypes has been shown to result in inconsistencies in time to the most recent common ancestor estimation. Although this calls into question the adequacy of available molecular dating methods, no solution to this problem has been offered so far. Here, we investigate the use of mixed effects molecular clock models, which combine both fixed and random effects in the evolutionary rate, to estimate divergence times. Using simulation, we demonstrate that this model outperforms existing molecular clock models in a Bayesian framework for estimating time-measured phylogenies in the presence of mixed sources of rate variation, while also maintaining good performance in simpler scenarios. By analysing a comprehensive HIV-1 group M complete genome data set we confirm considerable rate variation among subtypes that is not adequately modelled by uncorrelated relaxed clock models. The mixed effects clock model can accommodate this rate variation and results in a time to the most recent common ancestor of HIV-1 group M of 1920 (1915-25), which is only slightly earlier than the uncorrelated relaxed clock estimate for the same data set. The use of complete genome data appears to have a more profound impact than the molecular clock model because it reduces the credible intervals by 50 per cent relative to similar estimates based on short envelope gene sequences.
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Affiliation(s)
- Magda Bletsa
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven – University of Leuven, Leuven, Belgium
| | - Marc A Suchard
- Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | - Xiang Ji
- Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA
| | - Sophie Gryseels
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven – University of Leuven, Leuven, Belgium
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
| | - Bram Vrancken
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven – University of Leuven, Leuven, Belgium
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven – University of Leuven, Leuven, Belgium
| | - Michael Worobey
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven – University of Leuven, Leuven, Belgium
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Raghwani J, Redd AD, Longosz AF, Wu CH, Serwadda D, Martens C, Kagaayi J, Sewankambo N, Porcella SF, Grabowski MK, Quinn TC, Eller MA, Eller LA, Wabwire-Mangen F, Robb ML, Fraser C, Lythgoe KA. Evolution of HIV-1 within untreated individuals and at the population scale in Uganda. PLoS Pathog 2018; 14:e1007167. [PMID: 30052678 PMCID: PMC6082572 DOI: 10.1371/journal.ppat.1007167] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Revised: 08/08/2018] [Accepted: 06/20/2018] [Indexed: 12/15/2022] Open
Abstract
HIV-1 undergoes multiple rounds of error-prone replication between transmission events, resulting in diverse viral populations within and among individuals. In addition, the virus experiences different selective pressures at multiple levels: during the course of infection, at transmission, and among individuals. Disentangling how these evolutionary forces shape the evolution of the virus at the population scale is important for understanding pathogenesis, how drug- and immune-escape variants are likely to spread in populations, and the development of preventive vaccines. To address this, we deep-sequenced two regions of the HIV-1 genome (p24 and gp41) from 34 longitudinally-sampled untreated individuals from Rakai District in Uganda, infected with subtypes A, D, and inter-subtype recombinants. This dataset substantially increases the availability of HIV-1 sequence data that spans multiple years of untreated infection, in particular for different geographical regions and viral subtypes. In line with previous studies, we estimated an approximately five-fold faster rate of evolution at the within-host compared to the population scale for both synonymous and nonsynonymous substitutions, and for all subtypes. We determined the extent to which this mismatch in evolutionary rates can be explained by the evolution of the virus towards population-level consensus, or the transmission of viruses similar to those that establish infection within individuals. Our findings indicate that both processes are likely to be important.
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Affiliation(s)
- Jayna Raghwani
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Department of Zoology, Peter Medawar Building, University of Oxford, Oxford, United Kingdom
| | - Andrew D. Redd
- Laboratory of Immunoregulation, Division of Intramural Research, NIAID, NIH, Baltimore MD, United States of America
- Department of Medicine, Johns Hopkins Medical Institute, Johns Hopkins University, Baltimore MD, United States of America
| | - Andrew F. Longosz
- Laboratory of Immunoregulation, Division of Intramural Research, NIAID, NIH, Baltimore MD, United States of America
| | - Chieh-Hsi Wu
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - David Serwadda
- Rakai Health Sciences Program, Kalisizo, Uganda
- School of Public Health, Makerere University, Kampala, Uganda
| | - Craig Martens
- Genomics Unit, RTS, RTB, Rocky Mountain Laboratories, Division of Intramural Research, NIAID, NIH, Hamilton MT, United States of America
| | | | - Nelson Sewankambo
- Rakai Health Sciences Program, Kalisizo, Uganda
- School of Medicine, Makerere University, Kampala, Uganda
| | - Stephen F. Porcella
- Genomics Unit, RTS, RTB, Rocky Mountain Laboratories, Division of Intramural Research, NIAID, NIH, Hamilton MT, United States of America
| | - Mary K. Grabowski
- Department of Pathology, Johns Hopkins Medical Institute, Johns Hopkins University, Baltimore, MD, United States of America
| | - Thomas C. Quinn
- Laboratory of Immunoregulation, Division of Intramural Research, NIAID, NIH, Baltimore MD, United States of America
- Department of Medicine, Johns Hopkins Medical Institute, Johns Hopkins University, Baltimore MD, United States of America
| | - Michael A. Eller
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States of America
| | - Leigh Anne Eller
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States of America
| | - Fred Wabwire-Mangen
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States of America
| | - Merlin L. Robb
- U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States of America
| | - Christophe Fraser
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Katrina A. Lythgoe
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Department of Zoology, Peter Medawar Building, University of Oxford, Oxford, United Kingdom
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Suchard MA, Lemey P, Baele G, Ayres DL, Drummond AJ, Rambaut A. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol 2018; 4:vey016. [PMID: 29942656 PMCID: PMC6007674 DOI: 10.1093/ve/vey016] [Citation(s) in RCA: 1821] [Impact Index Per Article: 303.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
The Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package has become a primary tool for Bayesian phylogenetic and phylodynamic inference from genetic sequence data. BEAST unifies molecular phylogenetic reconstruction with complex discrete and continuous trait evolution, divergence-time dating, and coalescent demographic models in an efficient statistical inference engine using Markov chain Monte Carlo integration. A convenient, cross-platform, graphical user interface allows the flexible construction of complex evolutionary analyses.
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Affiliation(s)
- Marc A Suchard
- Department of Biomathematics, David Geffen School of MedicineUniversity of California, Los Angeles, 621 Charles E. Young Dr., South, Los Angeles, CA, 90095 USA
- Department of Biostatistics, Fielding School of Public HealthUniversity of California, Los Angeles, 650 Charles E, Young Dr., South, Los Angeles, CA, 90095 USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Dr., South, Los Angeles, CA, 90095 USA
| | - Philippe Lemey
- Department of Microbiology and Immunology, Rega Institute, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Guy Baele
- Department of Microbiology and Immunology, Rega Institute, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Daniel L Ayres
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, 125 Biomolecular Science Bldg #296, College Park, MD 20742 USA
| | - Alexei J Drummond
- Department of Computer Science, University of Auckland, 303/38 Princes St., Auckland, 1010 NZ
- Centre for Computational Evolution, University of Auckland, 303/38 Princes St., Auckland, 1010 NZ
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Ashworth Laboratories, Edinburgh, EH9 3FL UK
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