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Plant Virus Adaptation to New Hosts: A Multi-scale Approach. Curr Top Microbiol Immunol 2023; 439:167-196. [PMID: 36592246 DOI: 10.1007/978-3-031-15640-3_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Viruses are studied at each level of biological complexity: from within-cells to ecosystems. The same basic evolutionary forces and principles operate at each level: mutation and recombination, selection, genetic drift, migration, and adaptive trade-offs. Great efforts have been put into understanding each level in great detail, hoping to predict the dynamics of viral population, prevent virus emergence, and manage their spread and virulence. Unfortunately, we are still far from this. To achieve these ambitious goals, we advocate for an integrative perspective of virus evolution. Focusing in plant viruses, we illustrate the pervasiveness of the above-mentioned principles. Beginning at the within-cell level, we describe replication modes, infection bottlenecks, and cellular contagion rates. Next, we move up to the colonization of distal tissues, discussing the fundamental role of random events. Then, we jump beyond the individual host and discuss the link between transmission mode and virulence. Finally, at the community level, we discuss properties of virus-plant infection networks. To close this review we propose the multilayer network theory, in which elements at different layers are connected and submit to their own dynamics that feed across layers, resulting in new emerging properties, as a way to integrate information from the different levels.
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2
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Steiner MC, Novembre J. Population genetic models for the spatial spread of adaptive variants: A review in light of SARS-CoV-2 evolution. PLoS Genet 2022; 18:e1010391. [PMID: 36137003 PMCID: PMC9498967 DOI: 10.1371/journal.pgen.1010391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Theoretical population genetics has long studied the arrival and geographic spread of adaptive variants through the analysis of mathematical models of dispersal and natural selection. These models take on a renewed interest in the context of the COVID-19 pandemic, especially given the consequences that novel adaptive variants have had on the course of the pandemic as they have spread through global populations. Here, we review theoretical models for the spatial spread of adaptive variants and identify areas to be improved in future work, toward a better understanding of variants of concern in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) evolution and other contemporary applications. As we describe, characteristics of pandemics such as COVID-19-such as the impact of long-distance travel patterns and the overdispersion of lineages due to superspreading events-suggest new directions for improving upon existing population genetic models.
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Affiliation(s)
- Margaret C. Steiner
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
| | - John Novembre
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
- Department of Ecology & Evolution, University of Chicago, Chicago, Illinois, United States of America
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3
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Blackstone NW, Blackstone SR, Berg AT. Variation and multilevel selection of SARS-CoV-2. Evolution 2020; 74:2429-2434. [PMID: 32880957 PMCID: PMC7461403 DOI: 10.1111/evo.14080] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 08/10/2020] [Indexed: 01/18/2023]
Abstract
The evolution of SARS‐CoV‐2 remains poorly understood. Theory predicts a group‐structured population with selection acting principally at two levels: the pathogen individuals and the group of pathogens within a single host individual. Rapid replication of individual viruses is selected for, but if this replication debilitates the host before transmission occurs, the entire group of viruses in that host may perish. Thus, rapid transmission can favor more pathogenic strains, while slower transmission can favor less pathogenic strains. Available data suggest that SARS‐CoV‐2 may follow this pattern. Indeed, high population density and other circumstances that favor rapid transmission may also favor more deadly strains. Health care workers, exposed to pathogenic strains of hospitalized patients, may be at greater risk. The low case fatality rate on the Diamond Princess cruise ship may reflect the founder effect—an initial infection with a mild strain. A vaccine made with one strain may confer limited immunity to other strains. Variation among strains may lead to the rapid evolution of resistance to therapeutics. Finally, if less pathogenic strains are largely associated with mild disease, rather than treating all SARS‐CoV‐2 positive individuals equally, priority could be focused on testing and contact tracing the most seriously symptomatic patients.
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Affiliation(s)
- Neil W Blackstone
- Department of Biological Sciences, Northern Illinois University, DeKalb, Illinois, 60115
| | - Sarah R Blackstone
- Department of Health Sciences, James Madison University, Harrisonburg, Virginia, 22807
| | - Anne T Berg
- Epilepsy Center, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois, 60611
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4
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Bansept F, Marrec L, Bitbol AF, Loverdo C. Antibody-mediated crosslinking of gut bacteria hinders the spread of antibiotic resistance. Evolution 2019; 73:1077-1088. [PMID: 30957218 DOI: 10.1111/evo.13730] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Accepted: 03/24/2019] [Indexed: 12/23/2022]
Abstract
The body is home to a diverse microbiota, mainly in the gut. Resistant bacteria are selected by antibiotic treatments, and once resistance becomes widespread in a population of hosts, antibiotics become useless. Here, we develop a multiscale model of the interaction between antibiotic use and resistance spread in a host population, focusing on an important aspect of within-host immunity. Antibodies secreted in the gut enchain bacteria upon division, yielding clonal clusters of bacteria. We demonstrate that immunity-driven bacteria clustering can hinder the spread of a novel resistant bacterial strain in a host population. We quantify this effect both in the case where resistance preexists and in the case where acquiring a new resistance mutation is necessary for the bacteria to spread. We further show that the reduction of spread by clustering can be countered when immune hosts are silent carriers, and are less likely to get treated, and/or have more contacts. We demonstrate the robustness of our findings to including stochastic within-host bacterial growth, a fitness cost of resistance, and its compensation. Our results highlight the importance of interactions between immunity and the spread of antibiotic resistance, and argue in the favor of vaccine-based strategies to combat antibiotic resistance.
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Affiliation(s)
- Florence Bansept
- Sorbonne Université, CNRS, Laboratoire Jean Perrin (UMR 8237),, F-75005 Paris, France
| | - Loïc Marrec
- Sorbonne Université, CNRS, Laboratoire Jean Perrin (UMR 8237),, F-75005 Paris, France
| | - Anne-Florence Bitbol
- Sorbonne Université, CNRS, Laboratoire Jean Perrin (UMR 8237),, F-75005 Paris, France
| | - Claude Loverdo
- Sorbonne Université, CNRS, Laboratoire Jean Perrin (UMR 8237),, F-75005 Paris, France
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5
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Ke R, Li H, Wang S, Ding W, Ribeiro RM, Giorgi EE, Bhattacharya T, Barnard RJO, Hahn BH, Shaw GM, Perelson AS. Superinfection and cure of infected cells as mechanisms for hepatitis C virus adaptation and persistence. Proc Natl Acad Sci U S A 2018; 115:E7139-E7148. [PMID: 29987026 PMCID: PMC6065014 DOI: 10.1073/pnas.1805267115] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
RNA viruses exist as a genetically diverse quasispecies with extraordinary ability to adapt to abrupt changes in the host environment. However, the molecular mechanisms that contribute to their rapid adaptation and persistence in vivo are not well studied. Here, we probe hepatitis C virus (HCV) persistence by analyzing clinical samples taken from subjects who were treated with a second-generation HCV protease inhibitor. Frequent longitudinal viral load determinations and large-scale single-genome sequence analyses revealed rapid antiviral resistance development, and surprisingly, dynamic turnover of dominant drug-resistant mutant populations long after treatment cessation. We fitted mathematical models to both the viral load and the viral sequencing data, and the results provided strong support for the critical roles that superinfection and cure of infected cells play in facilitating the rapid turnover and persistence of viral populations. More broadly, our results highlight the importance of considering viral dynamics and competition at the intracellular level in understanding rapid viral adaptation. Thus, we propose a theoretical framework integrating viral and molecular mechanisms to explain rapid viral evolution, resistance, and persistence despite antiviral treatment and host immune responses.
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Affiliation(s)
- Ruian Ke
- Department of Mathematics, North Carolina State University, Raleigh, NC 27695
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Hui Li
- Department of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Department of Microbiology, University of Pennsylvania, Philadelphia, PA 19104
| | - Shuyi Wang
- Department of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Department of Microbiology, University of Pennsylvania, Philadelphia, PA 19104
| | - Wenge Ding
- Department of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Department of Microbiology, University of Pennsylvania, Philadelphia, PA 19104
| | - Ruy M Ribeiro
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545
- Laboratory of Biomathematics, Faculty of Medicine, University of Lisbon, 1600-276 Lisbon, Portugal
| | - Elena E Giorgi
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Tanmoy Bhattacharya
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545
- Santa Fe Institute, Santa Fe, NM 87501
| | | | - Beatrice H Hahn
- Department of Medicine, University of Pennsylvania, Philadelphia, PA 19104;
- Department of Microbiology, University of Pennsylvania, Philadelphia, PA 19104
| | - George M Shaw
- Department of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Department of Microbiology, University of Pennsylvania, Philadelphia, PA 19104
| | - Alan S Perelson
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545;
- Santa Fe Institute, Santa Fe, NM 87501
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6
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Dobrovolny HM, Beauchemin CAA. Modelling the emergence of influenza drug resistance: The roles of surface proteins, the immune response and antiviral mechanisms. PLoS One 2017; 12:e0180582. [PMID: 28700622 PMCID: PMC5503263 DOI: 10.1371/journal.pone.0180582] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Accepted: 06/16/2017] [Indexed: 12/16/2022] Open
Abstract
The emergence of influenza drug resistance has become of particular interest as current planning for an influenza pandemic involves using massive amounts of antiviral drugs. We use semi-stochastic simulations to examine the emergence of drug resistant mutants during the course of a single infection within a patient in the presence and absence of antiviral therapy. We specifically examine three factors and their effect on the emergence of drug-resistant mutants: antiviral mechanism, the immune response, and surface proteins. We find that adamantanes, because they act at the start of the replication cycle to prevent infection, are less likely to produce drug-resistant mutants than NAIs, which act at the end of the replication cycle. A mismatch between surface proteins and internal RNA results in drug-resistant mutants being less likely to emerge, and emerging later in the infection because the mismatch gives antivirals a second chance to prevent propagation of the mutation. The immune response subdues slow growing infections, further reducing the probability that a drug resistant mutant will emerge and yield a drug-resistant infection. These findings improve our understanding of the factors that contribute to the emergence of drug resistance during the course of a single influenza infection.
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Affiliation(s)
- Hana M. Dobrovolny
- Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, United States of America
- Department of Physics, Ryerson University, Toronto, ON, Canada
| | - Catherine A. A. Beauchemin
- Department of Physics, Ryerson University, Toronto, ON, Canada
- Interdisciplinary Theoretical Science (iTHES) Research Group at RIKEN, Wako, Japan
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7
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Ke R, Loverdo C, Qi H, Sun R, Lloyd-Smith JO. Rational Design and Adaptive Management of Combination Therapies for Hepatitis C Virus Infection. PLoS Comput Biol 2015; 11:e1004040. [PMID: 26125950 PMCID: PMC4488346 DOI: 10.1371/journal.pcbi.1004040] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2014] [Accepted: 11/13/2014] [Indexed: 12/17/2022] Open
Abstract
Recent discoveries of direct acting antivirals against Hepatitis C virus (HCV) have raised hopes of effective treatment via combination therapies. Yet rapid evolution and high diversity of HCV populations, combined with the reality of suboptimal treatment adherence, make drug resistance a clinical and public health concern. We develop a general model incorporating viral dynamics and pharmacokinetics/ pharmacodynamics to assess how suboptimal adherence affects resistance development and clinical outcomes. We derive design principles and adaptive treatment strategies, identifying a high-risk period when missing doses is particularly risky for de novo resistance, and quantifying the number of additional doses needed to compensate when doses are missed. Using data from large-scale resistance assays, we demonstrate that the risk of resistance can be reduced substantially by applying these principles to a combination therapy of daclatasvir and asunaprevir. By providing a mechanistic framework to link patient characteristics to the risk of resistance, these findings show the potential of rational treatment design.
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Affiliation(s)
- Ruian Ke
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America
- * E-mail: (RK); (JOLS)
| | - Claude Loverdo
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America
- CNRS/UPMC Univ Paris 06, UMR 8237, Laboratoire Jean Perrin LJP, Paris, France
| | - Hangfei Qi
- Department of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Ren Sun
- Department of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, California, United States of America
- The Molecular Biology Institute, University of California, Los Angeles, Los Angeles, California, United States of America
- Department of Infectious Diseases, Novartis Institutes for BioMedical Research, Emeryville, California, United States of America
- Zhejiang University, Hangzhou, China
| | - James O. Lloyd-Smith
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail: (RK); (JOLS)
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8
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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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9
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Five challenges in evolution and infectious diseases. Epidemics 2015; 10:40-4. [DOI: 10.1016/j.epidem.2014.12.003] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2014] [Revised: 12/09/2014] [Accepted: 12/10/2014] [Indexed: 01/09/2023] Open
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10
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Gog JR, Pellis L, Wood JLN, McLean AR, Arinaminpathy N, Lloyd-Smith JO. Seven challenges in modeling pathogen dynamics within-host and across scales. Epidemics 2014; 10:45-8. [PMID: 25843382 DOI: 10.1016/j.epidem.2014.09.009] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2014] [Revised: 09/19/2014] [Accepted: 09/21/2014] [Indexed: 01/18/2023] Open
Abstract
The population dynamics of infectious disease is a mature field in terms of theory and to some extent, application. However for microparasites, the theory and application of models of the dynamics within a single infected host is still an open field. Further, connecting across the scales--from cellular to host level, to population level--has potential to vastly improve our understanding of pathogen dynamics and evolution. Here, we highlight seven challenges in the following areas: transmission bottlenecks, heterogeneity within host, dynamic fitness landscapes within hosts, making use of next-generation sequencing data, capturing superinfection and when and how to model more than two scales.
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Affiliation(s)
- Julia R Gog
- Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA; Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, University of Cambridge, Cambridge CB3 0WA, United Kingdom.
| | - Lorenzo Pellis
- Warwick Infectious Disease Epidemiology Research Centre (WIDER) and Warwick Mathematics Institute, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - James L N Wood
- Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA; Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, Cambridge CB3 0ES, United Kingdom
| | - Angela R McLean
- Department of Zoology, Oxford Martin School, University of Oxford, South Parks Road, Oxford OX1 3PS, United Kingdom
| | - Nimalan Arinaminpathy
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom
| | - James O Lloyd-Smith
- Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA; Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095, USA
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11
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Loverdo C, Lloyd-Smith JO. Evolutionary invasion and escape in the presence of deleterious mutations. PLoS One 2013; 8:e68179. [PMID: 23874532 PMCID: PMC3714272 DOI: 10.1371/journal.pone.0068179] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2013] [Accepted: 05/26/2013] [Indexed: 12/25/2022] Open
Abstract
Replicators such as parasites invading a new host species, species invading a new ecological niche, or cancer cells invading a new tissue often must mutate to adapt to a new environment. It is often argued that a higher mutation rate will favor evolutionary invasion and escape from extinction. However, most mutations are deleterious, and even lethal. We study the probability that the lineage will survive and invade successfully as a function of the mutation rate when both the initial strain and an adaptive mutant strain are threatened by lethal mutations. We show that mutations are beneficial, i.e. a non-zero mutation rate increases survival compared to the limit of no mutations, if in the no-mutation limit the survival probability of the initial strain is smaller than the average survival probability of the strains which are one mutation away. The mutation rate that maximizes survival depends on the characteristics of both the initial strain and the adaptive mutant, but if one strain is closer to the threshold governing survival then its properties will have greater influence. These conclusions are robust for more realistic or mechanistic depictions of the fitness landscapes such as a more detailed viral life history, or non-lethal deleterious mutations.
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Affiliation(s)
- Claude Loverdo
- Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, California, United States of America
| | - James O. Lloyd-Smith
- Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, California, United States of America
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
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12
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Loverdo C, Lloyd-Smith JO. Intergenerational phenotypic mixing in viral evolution. Evolution 2013; 67:1815-22. [PMID: 23730772 PMCID: PMC3676872 DOI: 10.1111/evo.12048] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2012] [Accepted: 12/25/2012] [Indexed: 01/12/2023]
Abstract
Viral particles (virions) are made of genomic material packaged with proteins, drawn from the pool of proteins in the parent cell. It is well known that when virion concentrations are high, cells can be coinfected with multiple viral strains that can complement each other. Viral genomes can then interact with proteins derived from different strains, in a phenomenon known as phenotypic mixing. But phenotypic mixing is actually far more common: viruses mutate very often, and each time a mutation occurs, the parent cell contains different types of viral genomes. Due to phenotypic mixing, changes in viral phenotypes can be shifted by a generation from the mutations that cause them. In the regime of evolutionary invasion and escape, when mutations are crucial for the virus to survive, this timing can have a large influence on the probability of emergence of an adapted strain. Modeling the dynamics of viral evolution in these contexts thus requires attention to the mutational mechanism and the determinants of fitness.
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Affiliation(s)
- Claude Loverdo
- Department of Ecology and Evolutionary Biology, University of California-Los Angeles, CA 90095, USA.
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13
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Park M, Loverdo C, Schreiber SJ, Lloyd-Smith JO. Multiple scales of selection influence the evolutionary emergence of novel pathogens. Philos Trans R Soc Lond B Biol Sci 2013; 368:20120333. [PMID: 23382433 DOI: 10.1098/rstb.2012.0333] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
When pathogens encounter a novel environment, such as a new host species or treatment with an antimicrobial drug, their fitness may be reduced so that adaptation is necessary to avoid extinction. Evolutionary emergence is the process by which new pathogen strains arise in response to such selective pressures. Theoretical studies over the last decade have clarified some determinants of emergence risk, but have neglected the influence of fitness on evolutionary rates and have not accounted for the multiple scales at which pathogens must compete successfully. We present a cross-scale theory for evolutionary emergence, which embeds a mechanistic model of within-host selection into a stochastic model for emergence at the population scale. We explore how fitness landscapes at within-host and between-host scales can interact to influence the probability that a pathogen lineage will emerge successfully. Results show that positive correlations between fitnesses across scales can greatly facilitate emergence, while cross-scale conflicts in selection can lead to evolutionary dead ends. The local genotype space of the initial strain of a pathogen can have disproportionate influence on emergence probability. Our cross-scale model represents a step towards integrating laboratory experiments with field surveillance data to create a rational framework to assess emergence risk.
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Affiliation(s)
- Miran Park
- Department of Ecology and Evolutionary Biology, University of California, 610 Charles E. Young Dr. South, Los Angeles, CA 90095, USA.
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14
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Alexander HK, Bonhoeffer S. Pre-existence and emergence of drug resistance in a generalized model of intra-host viral dynamics. Epidemics 2012; 4:187-202. [DOI: 10.1016/j.epidem.2012.10.001] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2012] [Revised: 10/15/2012] [Accepted: 10/16/2012] [Indexed: 11/30/2022] Open
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