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Palmer DS, Turner I, Fidler S, Frater J, Goedhals D, Goulder P, Huang KHG, Oxenius A, Phillips R, Shapiro R, Vuuren CV, McLean AR, McVean G. Mapping the drivers of within-host pathogen evolution using massive data sets. Nat Commun 2019; 10:3017. [PMID: 31289267 PMCID: PMC6616926 DOI: 10.1038/s41467-019-10724-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Accepted: 05/20/2019] [Indexed: 11/09/2022] Open
Abstract
Differences among hosts, resulting from genetic variation in the immune system or heterogeneity in drug treatment, can impact within-host pathogen evolution. Genetic association studies can potentially identify such interactions. However, extensive and correlated genetic population structure in hosts and pathogens presents a substantial risk of confounding analyses. Moreover, the multiple testing burden of interaction scanning can potentially limit power. We present a Bayesian approach for detecting host influences on pathogen evolution that exploits vast existing data sets of pathogen diversity to improve power and control for stratification. The approach models key processes, including recombination and selection, and identifies regions of the pathogen genome affected by host factors. Our simulations and empirical analysis of drug-induced selection on the HIV-1 genome show that the method recovers known associations and has superior precision-recall characteristics compared to other approaches. We build a high-resolution map of HLA-induced selection in the HIV-1 genome, identifying novel epitope-allele combinations.
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Affiliation(s)
- Duncan S Palmer
- Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, UK.
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK.
- Institute for Emerging Infections, The Oxford Martin School, Oxford, OX1 3BD, UK.
| | - Isaac Turner
- Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, UK
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK
| | - Sarah Fidler
- Division of Medicine, Wright Fleming Institute, Imperial College, London, W2 1PG, UK
| | - John Frater
- Institute for Emerging Infections, The Oxford Martin School, Oxford, OX1 3BD, UK
- Nuffield Department of Clinical Medicine, University of Oxford, Peter Medawar Building for Pathogen Research, Oxford, OX1 3SY, UK
- Oxford NIHR Biomedical Research Centre, Oxford, OX3 7LE, UK
| | - Dominique Goedhals
- HIV Pathogenesis Programme, Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban, 4013, South Africa
| | - Philip Goulder
- Division of Infectious Diseases, University of the Free State, and 3 Military Hospital, Bloemfontein, 9300, South Africa
- Department of Paediatrics, University of Oxford, Peter Medawar Building for Pathogen Research, Oxford, OX1 3SY, UK
| | - Kuan-Hsiang Gary Huang
- Nuffield Department of Clinical Medicine, University of Oxford, Peter Medawar Building for Pathogen Research, Oxford, OX1 3SY, UK
- Einstein Medical Center Philadelphia, 5501 Old York Road, PA, 19141, USA
| | - Annette Oxenius
- Institute of Microbiology, Swiss Federal Institute of Technology Zurich, 8093, Zurich, Switzerland
| | - Rodney Phillips
- Institute for Emerging Infections, The Oxford Martin School, Oxford, OX1 3BD, UK
- Nuffield Department of Clinical Medicine, University of Oxford, Peter Medawar Building for Pathogen Research, Oxford, OX1 3SY, UK
- Oxford NIHR Biomedical Research Centre, Oxford, OX3 7LE, UK
- Faculty of Medicine, UNSW Sydney, NSW, 2052, Australia
| | - Roger Shapiro
- Botswana Harvard AIDS Institute Partnership, Gaborone, BO 320, Botswana
- Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, MA, 02215, USA
| | - Cloete van Vuuren
- HIV Pathogenesis Programme, Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban, 4013, South Africa
| | - Angela R McLean
- Institute for Emerging Infections, The Oxford Martin School, Oxford, OX1 3BD, UK
- Zoology Department, University of Oxford, South Parks Road, Oxford, OX1 3PS, UK
| | - Gil McVean
- Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, UK
- Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK
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Abstract
The interplay between immune response and HIV is intensely studied via mathematical modeling, with significant insights but few direct answers. In this short review, we highlight advances and knowledge gaps across different aspects of immunity. In particular, we identify the innate immune response and its role in priming the adaptive response as ripe for modeling. The latter have been the focus of most modeling studies, but we also synthesize key outstanding questions regarding effector mechanisms of cellular immunity and development of broadly neutralizing antibodies. Thus far, most modeling studies aimed to infer general immune mechanisms; we foresee that significant progress will be made next by detailed quantitative fitting of models to data, and prediction of immune responses.
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Affiliation(s)
- Jessica M Conway
- Department of Mathematics and Center for Infectious Disease Dynamics, Pennsylvania State University, University Park PA 16802, USA
| | - Ruy M Ribeiro
- Laboratorio de Biomatematica, Faculdade de Medicina da Universidade de Lisboa, Portugal and Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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3
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Zheng L, Lin VC, Mu Y. Exploring Flexibility of Progesterone Receptor Ligand Binding Domain Using Molecular Dynamics. PLoS One 2016; 11:e0165824. [PMID: 27824891 PMCID: PMC5100906 DOI: 10.1371/journal.pone.0165824] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Accepted: 10/18/2016] [Indexed: 12/23/2022] Open
Abstract
Progesterone receptor (PR), a member of nuclear receptor (NR) superfamily, plays a vital role for female reproductive tissue development, differentiation and maintenance. PR ligand, such as progesterone, induces conformation changes in PR ligand binding domain (LBD), thus mediates subsequent gene regulation cascades. PR LBD may adopt different conformations upon an agonist or an antagonist binding. These different conformations would trigger distinct transcription events. Therefore, the dynamics of PR LBD would be of general interest to biologists for a deep understanding of its structure-function relationship. However, no apo-form (non-ligand bound) of PR LBD model has been proposed either by experiments or computational methods so far. In this study, we explored the structural dynamics of PR LBD using molecular dynamics simulations and advanced sampling tools in both ligand-bound and the apo-forms. Resolved by the simulation study, helix 11, helix 12 and loop 895–908 (the loop between these two helices) are quite flexible in antagonistic conformation. Several residues, such as Arg899 and Glu723, could form salt-bridging interaction between helix 11 and helix 3, and are important for the PR LBD dynamics. And we also propose that helix 12 in apo-form PR LBD, not like other NR LBDs, such as human estrogen receptor α (ERα) LBD, may not adopt a totally extended conformation. With the aid of umbrella sampling and metadynamics simulations, several stable conformations of apo-form PR LBD have been sampled, which may work as critical structural models for further large scale virtual screening study to discover novel PR ligands for therapeutic application.
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Affiliation(s)
- Liangzhen Zheng
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore
| | - Valerie Chunling Lin
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore
| | - Yuguang Mu
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore
- * E-mail:
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Chan CHS, Sanders LP, Tanaka MM. Modelling the role of immunity in reversion of viral antigenic sites. J Theor Biol 2015; 392:23-34. [PMID: 26723535 DOI: 10.1016/j.jtbi.2015.12.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2014] [Revised: 12/14/2015] [Accepted: 12/16/2015] [Indexed: 12/22/2022]
Abstract
Antigenic sites in viral pathogens exhibit distinctive evolutionary dynamics due to their role in evading recognition by host immunity. Antigenic selection is known to drive higher rates of non-synonymous substitution; less well understood is why differences are observed between viruses in their propensity to mutate to a novel or previously encountered amino acid. Here, we present a model to explain patterns of antigenic reversion and forward substitution in terms of the epidemiological and molecular processes of the viral population. We develop an analytical three-strain model and extend the analysis to a multi-site model to predict characteristics of observed sequence samples. Our model provides insight into how the balance between selection to escape immunity and to maintain viability is affected by the rate of mutational input. We also show that while low probabilities of reversion may be due to either a low cost of immune escape or slowly decaying host immunity, these two scenarios can be differentiated by the frequency patterns at antigenic sites. Comparison between frequency patterns of human influenza A (H3N2) and human RSV-A suggests that the increased rates of antigenic reversion in RSV-A is due to faster decaying immunity and not higher costs of escape.
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Affiliation(s)
- Carmen H S Chan
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia; Evolution & Ecology Research Centre, University of New South Wales, Sydney, NSW, Australia.
| | - Lloyd P Sanders
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia; Evolution & Ecology Research Centre, University of New South Wales, Sydney, NSW, Australia; Computational Social Science, ETH, Zürich, Switzerland
| | - Mark M Tanaka
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia; Evolution & Ecology Research Centre, University of New South Wales, Sydney, NSW, Australia
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Zanini F, Brodin J, Thebo L, Lanz C, Bratt G, Albert J, Neher RA. Population genomics of intrapatient HIV-1 evolution. eLife 2015; 4:e11282. [PMID: 26652000 PMCID: PMC4718817 DOI: 10.7554/elife.11282] [Citation(s) in RCA: 139] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Accepted: 12/08/2015] [Indexed: 12/18/2022] Open
Abstract
Many microbial populations rapidly adapt to changing environments with multiple variants competing for survival. To quantify such complex evolutionary dynamics in vivo, time resolved and genome wide data including rare variants are essential. We performed whole-genome deep sequencing of HIV-1 populations in 9 untreated patients, with 6-12 longitudinal samples per patient spanning 5-8 years of infection. The data can be accessed and explored via an interactive web application. We show that patterns of minor diversity are reproducible between patients and mirror global HIV-1 diversity, suggesting a universal landscape of fitness costs that control diversity. Reversions towards the ancestral HIV-1 sequence are observed throughout infection and account for almost one third of all sequence changes. Reversion rates depend strongly on conservation. Frequent recombination limits linkage disequilibrium to about 100 bp in most of the genome, but strong hitch-hiking due to short range linkage limits diversity.
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Affiliation(s)
- Fabio Zanini
- Evolutionary Dynamics and Biophysics, Max Planck Institute for Developmental Biology, Tübingen, Germany
| | - Johanna Brodin
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm, Sweden
| | - Lina Thebo
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm, Sweden
| | - Christa Lanz
- Evolutionary Dynamics and Biophysics, Max Planck Institute for Developmental Biology, Tübingen, Germany
| | - Göran Bratt
- Department of Clinical Science and Education, Stockholm South General Hospital, Stockholm, Sweden
| | - Jan Albert
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm, Sweden
- Department of Clinical Microbiology, Karolinska University Hospital, Stockholm, Sweden
| | - Richard A Neher
- Evolutionary Dynamics and Biophysics, Max Planck Institute for Developmental Biology, Tübingen, Germany
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Hartfield M, Alizon S. Within-host stochastic emergence dynamics of immune-escape mutants. PLoS Comput Biol 2015; 11:e1004149. [PMID: 25785434 PMCID: PMC4365036 DOI: 10.1371/journal.pcbi.1004149] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2014] [Accepted: 01/22/2015] [Indexed: 12/28/2022] Open
Abstract
Predicting the emergence of new pathogenic strains is a key goal of evolutionary epidemiology. However, the majority of existing studies have focussed on emergence at the population level, and not within a host. In particular, the coexistence of pre-existing and mutated strains triggers a heightened immune response due to the larger total pathogen population; this feedback can smother mutated strains before they reach an ample size and establish. Here, we extend previous work for measuring emergence probabilities in non-equilibrium populations, to within-host models of acute infections. We create a mathematical model to investigate the emergence probability of a fitter strain if it mutates from a self-limiting strain that is guaranteed to go extinct in the long-term. We show that ongoing immune cell proliferation during the initial stages of infection causes a drastic reduction in the probability of emergence of mutated strains; we further outline how this effect can be accurately measured. Further analysis of the model shows that, in the short-term, mutant strains that enlarge their replication rate due to evolving an increased growth rate are more favoured than strains that suffer a lower immune-mediated death rate (‘immune tolerance’), as the latter does not completely evade ongoing immune proliferation due to inter-parasitic competition. We end by discussing the model in relation to within-host evolution of human pathogens (including HIV, hepatitis C virus, and cancer), and how ongoing immune growth can affect their evolutionary dynamics. The ongoing evolution of infectious diseases provides a constant health threat. This evolution can either result in the production of new pathogens, or new strains of existing pathogens that escape prevailing drug treatments or immune responses. The latter process, also known as immune escape, is a predominant reason for the persistence of several viruses, including HIV and hepatitis C virus (HCV), in their human host. As a consequence, the within-host emergence of new strains has been the intense focus of modelling studies. However, existing models have neglected important feedbacks that affects this emergence probability. Specifically, once a mutated pathogen arises that spreads more quickly than the initial (resident) strain, it potentially triggers a heightened immune response that can eliminate the mutated strain before it spreads. Our study outlines novel mathematical modelling techniques that accurately quantify how ongoing immune growth reduces the emergence probability of mutated pathogenic strains over the course of an infection. Analysis of this model suggests that, in order to enlarge its emergence probability, it is evolutionary beneficial for a mutated strain to increase its growth rate rather than tolerate immunity by having a lower immune-mediated death-rate. Our model can be readily applied to existing within-host data, as demonstrated with application to HIV, HCV, and cancer dynamics.
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Affiliation(s)
- Matthew Hartfield
- Laboratoire MIVEGEC (UMR CNRS 5290, IRD 224, UM1, UM2), 911 avenue Agropolis, Montpellier, France
- * E-mail:
| | - Samuel Alizon
- Laboratoire MIVEGEC (UMR CNRS 5290, IRD 224, UM1, UM2), 911 avenue Agropolis, Montpellier, France
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Roberts HE, Hurst J, Robinson N, Brown H, Flanagan P, Vass L, Fidler S, Weber J, Babiker A, Phillips RE, McLean AR, Frater J. Structured observations reveal slow HIV-1 CTL escape. PLoS Genet 2015; 11:e1004914. [PMID: 25642847 PMCID: PMC4333731 DOI: 10.1371/journal.pgen.1004914] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2014] [Accepted: 11/23/2014] [Indexed: 01/11/2023] Open
Abstract
The existence of viral variants that escape from the selection pressures imposed by cytotoxic T-lymphocytes (CTLs) in HIV-1 infection is well documented, but it is unclear when they arise, with reported measures of the time to escape in individuals ranging from days to years. A study of participants enrolled in the SPARTAC (Short Pulse Anti-Retroviral Therapy at HIV Seroconversion) clinical trial allowed direct observation of the evolution of CTL escape variants in 125 adults with primary HIV-1 infection observed for up to three years. Patient HLA-type, longitudinal CD8+ T-cell responses measured by IFN-γ ELISpot and longitudinal HIV-1 gag, pol, and nef sequence data were used to study the timing and prevalence of CTL escape in the participants whilst untreated. Results showed that sequence variation within CTL epitopes at the first time point (within six months of the estimated date of seroconversion) was consistent with most mutations being transmitted in the infecting viral strain rather than with escape arising within the first few weeks of infection. Escape arose throughout the first three years of infection, but slowly and steadily. Approximately one third of patients did not drive any new escape in an HLA-restricted epitope in just under two years. Patients driving several escape mutations during these two years were rare and the median and modal numbers of new escape events in each patient were one and zero respectively. Survival analysis of time to escape found that possession of a protective HLA type significantly reduced time to first escape in a patient (p = 0.01), and epitopes escaped faster in the face of a measurable CD8+ ELISpot response (p = 0.001). However, even in an HLA matched host who mounted a measurable, specific, CD8+ response the average time before the targeted epitope evolved an escape mutation was longer than two years.
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Affiliation(s)
- Hannah E. Roberts
- The Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, Oxford University, Oxford, United Kingdom
| | - Jacob Hurst
- The Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, Oxford University, Oxford, United Kingdom
- The Institute for Emerging Infections, The Oxford Martin School, Oxford, Oxford United Kingdom
| | - Nicola Robinson
- The Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, Oxford University, Oxford, United Kingdom
- Oxford NIHR Comprehensive Biomedical Research Centre, Oxford, United Kingdom
| | - Helen Brown
- The Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, Oxford University, Oxford, United Kingdom
- Oxford NIHR Comprehensive Biomedical Research Centre, Oxford, United Kingdom
| | - Peter Flanagan
- The Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, Oxford University, Oxford, United Kingdom
| | - Laura Vass
- The Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, Oxford University, Oxford, United Kingdom
| | - Sarah Fidler
- Division of Medicine, Wright Fleming Institute, Imperial College, London, United Kingdom
| | - Jonathan Weber
- Division of Medicine, Wright Fleming Institute, Imperial College, London, United Kingdom
| | - Abdel Babiker
- Medical Research Council Clinical Trials Unit, London, United Kingdom
| | - Rodney E. Phillips
- The Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, Oxford University, Oxford, United Kingdom
- The Institute for Emerging Infections, The Oxford Martin School, Oxford, Oxford United Kingdom
- Oxford NIHR Comprehensive Biomedical Research Centre, Oxford, United Kingdom
- * E-mail:
| | - Angela R. McLean
- The Institute for Emerging Infections, The Oxford Martin School, Oxford, Oxford United Kingdom
- Department of Zoology, Oxford University, Oxford, United Kingdom
| | - John Frater
- The Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, Oxford University, Oxford, United Kingdom
- The Institute for Emerging Infections, The Oxford Martin School, Oxford, Oxford United Kingdom
- Oxford NIHR Comprehensive Biomedical Research Centre, Oxford, United Kingdom
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Hartfield M, Murall CL, Alizon S. Clinical applications of pathogen phylogenies. Trends Mol Med 2014; 20:394-404. [DOI: 10.1016/j.molmed.2014.04.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2014] [Revised: 04/02/2014] [Accepted: 04/03/2014] [Indexed: 12/16/2022]
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Leviyang S. Constructing lower-bounds for CTL escape rates in early SIV infection. J Theor Biol 2014; 352:82-91. [PMID: 24603063 DOI: 10.1016/j.jtbi.2014.02.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2013] [Revised: 01/15/2014] [Accepted: 02/17/2014] [Indexed: 12/30/2022]
Abstract
Intrahost human and simian immunodeficiency virus (HIV and SIV) evolution is marked by repeated viral escape from cytotoxic T-lymphocyte (CTL) response. Typically, the first such CTL escape starts around the time of peak viral load and completes within one or two weeks. Many authors have developed methods to quantify CTL escape rates, but existing methods depend on sampling at two or more timepoints. Since many datasets capture the dynamics of the first CTL escape at a single timepoint, we develop inference methods applicable to single timepoint datasets. To account for model uncertainty, we construct estimators which serve as lower bounds for the escape rate. These lower-bound estimators allow for statistically meaningful comparison of escape rates across different times and different compartments. We apply our methods to two SIV datasets, showing that escape rates are relatively high during the initial days of the first CTL escape and drop to lower levels as the escape proceeds.
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Affiliation(s)
- Sivan Leviyang
- Georgetown University, Department of Mathematics and Statistics, United States
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