1
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Lievens EJP, Kühn S, Horas EL, Le Pennec G, Peter S, Petrosky AD, Künzel S, Feulner PGD, Becks L. High parasite diversity maintained after an alga-virus coevolutionary arms race. J Evol Biol 2024; 37:795-806. [PMID: 38699979 DOI: 10.1093/jeb/voae053] [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: 10/01/2023] [Revised: 02/27/2024] [Accepted: 05/02/2024] [Indexed: 05/05/2024]
Abstract
Arms race dynamics are a common outcome of host-parasite coevolution. While they can theoretically be maintained indefinitely, realistic arms races are expected to be finite. Once an arms race has ended, for example due to the evolution of a generalist-resistant host, the system may transition into coevolutionary dynamics that favour long-term diversity. In microbial experiments, host-parasite arms races often transition into a stable coexistence of generalist-resistant hosts, (semi-)susceptible hosts, and parasites. While long-term host diversity is implicit in these cases, parasite diversity is usually overlooked. In this study, we examined parasite diversity after the end of an experimental arms race between a unicellular alga (Chlorella variabilis) and its lytic virus (PBCV-1). First, we isolated virus genotypes from multiple time points from two replicate microcosms. A time-shift experiment confirmed that the virus isolates had escalating host ranges, i.e., that arms races had occurred. We then examined the phenotypic and genetic diversity of virus isolates from the post-arms race phase. Post-arms race virus isolates had diverse host ranges, survival probabilities, and growth rates; they also clustered into distinct genetic groups. Importantly, host range diversity was maintained throughout the post-arms race phase, and the frequency of host range phenotypes fluctuated over time. We hypothesize that this dynamic polymorphism was maintained by a combination of fluctuating selection and demographic stochasticity. Together with previous work in prokaryotic systems, our results link experimental observations of arms races to natural observations of long-term host and parasite diversity.
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Affiliation(s)
- Eva J P Lievens
- Aquatic Ecology and Evolution Group, Department of Biology, University of Konstanz, Konstanz, Germany
| | - Samuel Kühn
- Aquatic Ecology and Evolution Group, Department of Biology, University of Konstanz, Konstanz, Germany
| | - Elena L Horas
- Aquatic Ecology and Evolution Group, Department of Biology, University of Konstanz, Konstanz, Germany
| | - Guénolé Le Pennec
- Department of Fish Ecology and Evolution, EAWAG, Swiss Federal Institute of Aquatic Science and Technology, Kastanienbaum, Switzerland
| | - Sarah Peter
- Aquatic Ecology and Evolution Group, Department of Biology, University of Konstanz, Konstanz, Germany
| | - Azade D Petrosky
- Aquatic Ecology and Evolution Group, Department of Biology, University of Konstanz, Konstanz, Germany
| | - Sven Künzel
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Biology, Plön, Germany
| | - Philine G D Feulner
- Department of Fish Ecology and Evolution, EAWAG, Swiss Federal Institute of Aquatic Science and Technology, Kastanienbaum, Switzerland
- Department of Biology, Institute of Ecology and Evolution, University of Bern, Bern, Switzerland
| | - Lutz Becks
- Aquatic Ecology and Evolution Group, Department of Biology, University of Konstanz, Konstanz, Germany
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2
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Tellier A, Hodgins K, Stephan W, Stukenbrock E. Rapid evolutionary adaptation: Potential and constraints. Mol Ecol 2024; 33:e17350. [PMID: 38591817 DOI: 10.1111/mec.17350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 03/18/2024] [Accepted: 03/28/2024] [Indexed: 04/10/2024]
Affiliation(s)
- Aurélien Tellier
- Population Genetics, Department of Life Science Systems, Technical University of Munich, Freising, Germany
| | - Kathryn Hodgins
- School of Biological Sciences, Monash University, Clayton, Australia
| | - Wolfgang Stephan
- Natural History Museum Berlin and University of Munich, Munich, Germany
| | - Eva Stukenbrock
- Botanical Institute, Christian-Albrechts University, Max Planck Institute for Evolutionary Biology, Plön, Germany
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3
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Hoang KL, Read TD, King KC. Incomplete immunity in a natural animal-microbiota interaction selects for higher pathogen virulence. Curr Biol 2024; 34:1357-1363.e3. [PMID: 38430909 PMCID: PMC10962313 DOI: 10.1016/j.cub.2024.02.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 12/18/2023] [Accepted: 02/07/2024] [Indexed: 03/05/2024]
Abstract
Incomplete immunity in recovered hosts is predicted to favor more virulent pathogens upon re-infection in the population.1 The microbiota colonizing animals can generate a similarly long-lasting, partial immune response, allowing for infection but dampened disease severity.2 We tracked the evolutionary trajectories of a widespread pathogen (Pseudomonas aeruginosa), experimentally passaged through populations of nematodes immune-primed by a natural microbiota member (P. berkeleyensis). This bacterium can induce genes regulated by a mitogen-activated protein kinase (MAPK) signaling pathway effective at conferring protection against pathogen-induced death despite infection.3 Across host populations, this incomplete immunity selected for pathogens more than twice as likely to kill as those evolved in non-primed (i.e., naive) or immune-compromised (mutants with a knockout of the MAPK ortholog) control populations. Despite the higher virulence, pathogen molecular evolution in immune-primed hosts was slow and constrained. In comparison, evolving pathogens in immune-compromised hosts were characterized by substantial genomic differentiation and attenuated virulence. These findings directly attribute the incomplete host immunity induced from microbiota as a significant force shaping the virulence and evolutionary dynamics of novel infectious diseases.
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Affiliation(s)
- Kim L Hoang
- Department of Biology, University of Oxford, 11a Mansfield Road, Oxford OX1 3SZ, UK; Division of Infectious Diseases, Emory University School of Medicine, 1760 Haygood Drive, Atlanta, GA 30322, USA.
| | - Timothy D Read
- Division of Infectious Diseases, Emory University School of Medicine, 1760 Haygood Drive, Atlanta, GA 30322, USA
| | - Kayla C King
- Department of Biology, University of Oxford, 11a Mansfield Road, Oxford OX1 3SZ, UK; Department of Zoology, University of British Columbia, 6270 University Boulevard, Vancouver, BC V6T 1Z4, Canada; Department of Microbiology & Immunology, University of British Columbia, 1365 - 2350 Health Sciences Mall, Vancouver, BC V6T 1Z3, Canada.
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4
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Hu G, Wang Y, Liu X, Strube ML, Wang B, Kovács ÁT. Species and condition shape the mutational spectrum in experimentally evolved biofilms. mSystems 2023; 8:e0054823. [PMID: 37768063 PMCID: PMC10654089 DOI: 10.1128/msystems.00548-23] [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: 05/27/2023] [Accepted: 08/11/2023] [Indexed: 09/29/2023] Open
Abstract
IMPORTANCE Biofilm formation is a vital factor for the survival and adaptation of bacteria in diverse environmental niches. Experimental evolution combined with the advancement of whole-population genome sequencing provides us a powerful tool to understand the genomic dynamic of evolutionary adaptation to different environments, such as during biofilm development. Previous studies described the genetic and phenotypic changes of selected clones from experimentally evolved Bacillus thuringiensis and Bacillus subtilis that were adapted under abiotic and biotic biofilm conditions. However, the full understanding of the dynamic evolutionary landscapes was lacking. Furthermore, the differences and similarities of adaptive mechanisms in B. thuringiensis and B. subtilis were not identified. To overcome these limitations, we performed longitudinal whole-population genome sequencing to study the underlying genetic dynamics at high resolution. Our study provides the first comprehensive mutational landscape of two bacterial species' biofilms that is adapted to an abiotic and biotic surface.
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Affiliation(s)
- Guohai Hu
- China National GeneBank, BGI, Shenzhen, China
- BGI Research, Shenzhen, China
- Bacterial Interactions and Evolution Group, DTU Bioengineering, Technical University of Denmark, Lyngby, Denmark
| | - Yue Wang
- China National GeneBank, BGI, Shenzhen, China
- BGI Research, Shenzhen, China
- BGI Research, Beijing, China
| | - Xin Liu
- China National GeneBank, BGI, Shenzhen, China
- BGI Research, Shenzhen, China
- BGI Research, Beijing, China
| | - Mikael Lenz Strube
- Bacterial Ecophysiology and Biotechnology Group, DTU Bioengineering, Technical University of Denmark, Lyngby, Denmark
| | - Bo Wang
- China National GeneBank, BGI, Shenzhen, China
- BGI Research, Shenzhen, China
- Shenzhen Key Laboratory of Environmental Microbial Genomics and Application, BGI Research, Shenzhen, China
| | - Ákos T. Kovács
- Bacterial Interactions and Evolution Group, DTU Bioengineering, Technical University of Denmark, Lyngby, Denmark
- Institute of Biology, Leiden University, Leiden, The Netherlands
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5
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Venkataram S, Kryazhimskiy S. Evolutionary repeatability of emergent properties of ecological communities. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220047. [PMID: 37004728 PMCID: PMC10067272 DOI: 10.1098/rstb.2022.0047] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 12/07/2022] [Indexed: 04/04/2023] Open
Abstract
Most species belong to ecological communities where their interactions give rise to emergent community-level properties, such as diversity and productivity. Understanding and predicting how these properties change over time has been a major goal in ecology, with important practical implications for sustainability and human health. Less attention has been paid to the fact that community-level properties can also change because member species evolve. Yet, our ability to predict long-term eco-evolutionary dynamics hinges on how repeatably community-level properties change as a result of species evolution. Here, we review studies of evolution of both natural and experimental communities and make the case that community-level properties at least sometimes evolve repeatably. We discuss challenges faced in investigations of evolutionary repeatability. In particular, only a handful of studies enable us to quantify repeatability. We argue that quantifying repeatability at the community level is critical for approaching what we see as three major open questions in the field: (i) Is the observed degree of repeatability surprising? (ii) How is evolutionary repeatability at the community level related to repeatability at the level of traits of member species? (iii) What factors affect repeatability? We outline some theoretical and empirical approaches to addressing these questions. Advances in these directions will not only enrich our basic understanding of evolution and ecology but will also help us predict eco-evolutionary dynamics. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.
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Affiliation(s)
- Sandeep Venkataram
- Department of Ecology, Behavior and Evolution, UC San Diego, La Jolla, CA 92093, USA
| | - Sergey Kryazhimskiy
- Department of Ecology, Behavior and Evolution, UC San Diego, La Jolla, CA 92093, USA
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6
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Alamil M, Thébaud G, Berthier K, Soubeyrand S. Characterizing viral within-host diversity in fast and non-equilibrium demo-genetic dynamics. Front Microbiol 2022; 13:983938. [PMID: 36274731 PMCID: PMC9581327 DOI: 10.3389/fmicb.2022.983938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 09/08/2022] [Indexed: 11/13/2022] Open
Abstract
High-throughput sequencing has opened the route for a deep assessment of within-host genetic diversity that can be used, e.g., to characterize microbial communities and to infer transmission links in infectious disease outbreaks. The performance of such characterizations and inferences cannot be analytically assessed in general and are often grounded on computer-intensive evaluations. Then, being able to simulate within-host genetic diversity across time under various demo-genetic assumptions is paramount to assess the performance of the approaches of interest. In this context, we built an original model that can be simulated to investigate the temporal evolution of genotypes and their frequencies under various demo-genetic assumptions. The model describes the growth and the mutation of genotypes at the nucleotide resolution conditional on an overall within-host viral kinetics, and can be tuned to generate fast non-equilibrium demo-genetic dynamics. We ran simulations of this model and computed classic diversity indices to characterize the temporal variation of within-host genetic diversity (from high-throughput amplicon sequences) of virus populations under three demographic kinetic models of viral infection. Our results highlight how demographic (viral load) and genetic (mutation, selection, or drift) factors drive variations in within-host diversity during the course of an infection. In particular, we observed a non-monotonic relationship between pathogen population size and genetic diversity, and a reduction of the impact of mutation on diversity when a non-specific host immune response is activated. The large variation in the diversity patterns generated in our simulations suggests that the underlying model provides a flexible basis to produce very diverse demo-genetic scenarios and test, for instance, methods for the inference of transmission links during outbreaks.
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Affiliation(s)
- Maryam Alamil
- INRAE, BioSP, Avignon, France
- Department of Mathematics and Computer Science, Alfaisal University, Riyadh, Saudi Arabia
- *Correspondence: Maryam Alamil ;
| | - Gaël Thébaud
- PHIM Plant Health Institute, INRAE, Univ Montpellier, CIRAD, Institut Agro, IRD, Montpellier, France
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7
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Pallikonda HA, Turajlic S. Predicting cancer evolution for patient benefit: Renal cell carcinoma paradigm. Biochim Biophys Acta Rev Cancer 2022; 1877:188759. [PMID: 35835341 DOI: 10.1016/j.bbcan.2022.188759] [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: 03/18/2022] [Revised: 06/20/2022] [Accepted: 07/06/2022] [Indexed: 10/17/2022]
Abstract
Evolutionary features of cancer have important clinical implications, but their evaluation in the clinic is currently limited. Here, we review current approaches to reconstruct tumour subclonal structure and discuss tumour sampling method and experimental design influence. We describe clear-cell renal cell carcinoma (ccRCC) as an exemplar for understanding and predicting cancer evolutionary dynamics. Finally, we discuss how understanding cancer evolution can benefit patients.
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Affiliation(s)
| | - Samra Turajlic
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, United Kingdom; Skin and Renal Units, The Royal Marsden NHS Foundation Trust, London, United Kingdom; Melanoma and Kidney Cancer Team, Institute of Cancer Research, London, United Kingdom.
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8
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McLean KD, Gowler CD, Dziuba MK, Zamani H, Hall SR, Duffy MA. Sexual recombination and temporal gene flow maintain host resistance and genetic diversity. Evol Ecol 2022. [DOI: 10.1007/s10682-022-10193-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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9
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Avecilla G, Chuong JN, Li F, Sherlock G, Gresham D, Ram Y. Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics. PLoS Biol 2022; 20:e3001633. [PMID: 35622868 PMCID: PMC9140244 DOI: 10.1371/journal.pbio.3001633] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 04/14/2022] [Indexed: 11/24/2022] Open
Abstract
The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these 2 parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based likelihood-free inference approaches. We tested the suitability of 2 evolutionary models: a standard Wright-Fisher model and a chemostat model. We evaluated 2 likelihood-free inference algorithms: the well-established Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) algorithm, and the recently developed Neural Posterior Estimation (NPE) algorithm, which applies an artificial neural network to directly estimate the posterior distribution. By systematically evaluating the suitability of different inference methods and models, we show that NPE has several advantages over ABC-SMC and that a Wright-Fisher evolutionary model suffices in most cases. Using our validated inference framework, we estimate the CNV formation rate at the GAP1 locus in the yeast Saccharomyces cerevisiae to be 10-4.7 to 10-4 CNVs per cell division and a fitness coefficient of 0.04 to 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our inference-based estimates using 2 distinct experimental methods-barcode lineage tracking and pairwise fitness assays-which provide independent confirmation of the accuracy of our approach. Our results are consistent with a beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining the outsized importance of CNVs in rapid adaptive evolution. More generally, our study demonstrates the utility of novel neural network-based likelihood-free inference methods for inferring the rates and effects of evolutionary processes from empirical data with possible applications ranging from tumor to viral evolution.
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Affiliation(s)
- Grace Avecilla
- Department of Biology, New York University, New York, New York, United States of America
- Center for Genomics and Systems Biology, New York University, New York, New York, United States of America
| | - Julie N. Chuong
- Department of Biology, New York University, New York, New York, United States of America
- Center for Genomics and Systems Biology, New York University, New York, New York, United States of America
| | - Fangfei Li
- Department of Genetics, Stanford University, California, Stanford, United States of America
| | - Gavin Sherlock
- Department of Genetics, Stanford University, California, Stanford, United States of America
| | - David Gresham
- Department of Biology, New York University, New York, New York, United States of America
- Center for Genomics and Systems Biology, New York University, New York, New York, United States of America
| | - Yoav Ram
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
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10
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DeLong JP, Al-Sammak MA, Al-Ameeli ZT, Dunigan DD, Edwards KF, Fuhrmann JJ, Gleghorn JP, Li H, Haramoto K, Harrison AO, Marston MF, Moore RM, Polson SW, Ferrell BD, Salsbery ME, Schvarcz CR, Shirazi J, Steward GF, Van Etten JL, Wommack KE. Towards an integrative view of virus phenotypes. Nat Rev Microbiol 2021; 20:83-94. [PMID: 34522049 DOI: 10.1038/s41579-021-00612-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/26/2021] [Indexed: 12/25/2022]
Abstract
Understanding how phenotypes emerge from genotypes is a foundational goal in biology. As challenging as this task is when considering cellular life, it is further complicated in the case of viruses. During replication, a virus as a discrete entity (the virion) disappears and manifests itself as a metabolic amalgam between the virus and the host (the virocell). Identifying traits that unambiguously constitute a virus's phenotype is straightforward for the virion, less so for the virocell. Here, we present a framework for categorizing virus phenotypes that encompasses both virion and virocell stages and considers functional and performance traits of viruses in the context of fitness. Such an integrated view of virus phenotype is necessary for comprehensive interpretation of viral genome sequences and will advance our understanding of viral evolution and ecology.
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Affiliation(s)
- John P DeLong
- School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, NE, USA.
| | - Maitham A Al-Sammak
- Tropical Biological Research Unit, College of Science, University of Baghdad, Baghdad, Iraq.,Nebraska Center for Virology, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Zeina T Al-Ameeli
- Nebraska Center for Virology, University of Nebraska-Lincoln, Lincoln, NE, USA.,Medical Technical Institutes, Middle Technical University, Baghdad, Iraq
| | - David D Dunigan
- Nebraska Center for Virology, University of Nebraska-Lincoln, Lincoln, NE, USA.,Department of Plant Pathology, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Kyle F Edwards
- Department of Oceanography, University of Hawai'i at Mānoa, Honolulu, HI, USA
| | - Jeffry J Fuhrmann
- Department of Plant and Soil Sciences, University of Delaware, Newark, DE, USA.,Department of Biological Sciences, University of Delaware, Newark, DE, USA
| | - Jason P Gleghorn
- Department of Biological Sciences, University of Delaware, Newark, DE, USA.,Department of Biomedical Engineering, University of Delaware, Newark, DE, USA
| | - Hanqun Li
- Department of Biological Sciences, University of Delaware, Newark, DE, USA.,Delaware Biotechnology Institute, University of Delaware, Newark, DE, USA
| | - Kona Haramoto
- Department of Plant and Soil Sciences, University of Delaware, Newark, DE, USA.,Delaware Biotechnology Institute, University of Delaware, Newark, DE, USA
| | - Amelia O Harrison
- Department of Plant and Soil Sciences, University of Delaware, Newark, DE, USA.,Delaware Biotechnology Institute, University of Delaware, Newark, DE, USA
| | - Marcia F Marston
- Department of Biology and Marine Biology, Roger Williams University, Bristol, RI, USA
| | - Ryan M Moore
- Delaware Biotechnology Institute, University of Delaware, Newark, DE, USA.,Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA
| | - Shawn W Polson
- Department of Plant and Soil Sciences, University of Delaware, Newark, DE, USA.,Department of Biological Sciences, University of Delaware, Newark, DE, USA.,Delaware Biotechnology Institute, University of Delaware, Newark, DE, USA.,Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA
| | - Barbra D Ferrell
- Delaware Biotechnology Institute, University of Delaware, Newark, DE, USA.,Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA
| | - Miranda E Salsbery
- School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, NE, USA
| | | | - Jasmine Shirazi
- Department of Biomedical Engineering, University of Delaware, Newark, DE, USA
| | - Grieg F Steward
- Department of Oceanography, University of Hawai'i at Mānoa, Honolulu, HI, USA
| | - James L Van Etten
- Nebraska Center for Virology, University of Nebraska-Lincoln, Lincoln, NE, USA.,Department of Plant Pathology, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - K Eric Wommack
- Department of Plant and Soil Sciences, University of Delaware, Newark, DE, USA. .,Department of Biological Sciences, University of Delaware, Newark, DE, USA. .,Delaware Biotechnology Institute, University of Delaware, Newark, DE, USA.
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11
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Schroeder S, Mache C, Kleine-Weber H, Corman VM, Muth D, Richter A, Fatykhova D, Memish ZA, Stanifer ML, Boulant S, Gultom M, Dijkman R, Eggeling S, Hocke A, Hippenstiel S, Thiel V, Pöhlmann S, Wolff T, Müller MA, Drosten C. Functional comparison of MERS-coronavirus lineages reveals increased replicative fitness of the recombinant lineage 5. Nat Commun 2021; 12:5324. [PMID: 34493730 PMCID: PMC8423819 DOI: 10.1038/s41467-021-25519-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 08/05/2021] [Indexed: 01/20/2023] Open
Abstract
Middle East respiratory syndrome coronavirus (MERS-CoV) is enzootic in dromedary camels across the Middle East and Africa. Virus-induced pneumonia in humans results from animal contact, with a potential for limited onward transmission. Phenotypic changes have been suspected after a novel recombinant clade (lineage 5) caused large nosocomial outbreaks in Saudi Arabia and South Korea in 2016. However, there has been no functional assessment. Here we perform a comprehensive in vitro and ex vivo comparison of viruses from parental and recombinant virus lineages (lineage 3, n = 7; lineage 4, n = 8; lineage 5, n = 9 viruses) from Saudi Arabia, isolated immediately before and after the shift toward lineage 5. Replication of lineage 5 viruses is significantly increased. Transcriptional profiling finds reduced induction of immune genes IFNB1, CCL5, and IFNL1 in lung cells infected with lineage 5 strains. Phenotypic differences may be determined by IFN antagonism based on experiments using IFN receptor knock out and signaling inhibition. Additionally, lineage 5 is more resilient against IFN pre-treatment of Calu-3 cells (ca. 10-fold difference in replication). This phenotypic change associated with lineage 5 has remained undiscovered by viral sequence surveillance, but may be a relevant indicator of pandemic potential. MERS-CoV is enzootic in dromedary camels, can spread to humans but undergoes limited onward transmission. Here, Schroeder et al. compare clinical isolates of MERS-CoV in vitro and show that the predominantly circulating recombinant lineage 5 possess a fitness advantage over parental lineage 3 and 4 due to reduced activation of innate immune signaling.
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Affiliation(s)
- Simon Schroeder
- Institute of Virology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Christin Mache
- Unit 17, Influenza and other Respiratory Viruses, Robert Koch Institut, Berlin, Germany
| | - Hannah Kleine-Weber
- Infection Biology Unit, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany
| | - Victor M Corman
- Institute of Virology, Charité-Universitätsmedizin Berlin, Berlin, Germany.,German Centre for Infection Research (DZIF), Berlin, Germany
| | - Doreen Muth
- Institute of Virology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Anja Richter
- Institute of Virology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Diana Fatykhova
- Dept. of Infectious and Respiratory Diseases, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Ziad A Memish
- Research and Innovation Department, King Saud Medical City, Ministry of Health, Riyadh, Saudi Arabia.,College of Medicine, Alfaisal University, Riyadh, Kingdom of Saudi Arabia.,Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Megan L Stanifer
- Department of Infectious Diseases, Molecular Virology, Heidelberg University Hospital, Heidelberg, Germany
| | - Steeve Boulant
- Research Group "Cellular polarity and viral infection", German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Infectious Diseases, Virology, Heidelberg University, Heidelberg, Germany
| | - Mitra Gultom
- Institute of Virology and Immunology (IVI), Bern, Switzerland.,Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, Bern, Switzerland.,Institute for Infectious Diseases, University of Bern, Bern, Switzerland.,Graduate School for Biomedical Science, University of Bern, Bern, Switzerland
| | - Ronald Dijkman
- Institute of Virology and Immunology (IVI), Bern, Switzerland.,Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, Bern, Switzerland.,Institute for Infectious Diseases, University of Bern, Bern, Switzerland
| | - Stephan Eggeling
- Department of Thoracic Surgery, Vivantes Clinics Neukölln, Berlin, Germany
| | - Andreas Hocke
- Dept. of Infectious and Respiratory Diseases, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Stefan Hippenstiel
- Dept. of Infectious and Respiratory Diseases, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Volker Thiel
- Institute of Virology and Immunology (IVI), Bern, Switzerland.,Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Stefan Pöhlmann
- Infection Biology Unit, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany
| | - Thorsten Wolff
- Unit 17, Influenza and other Respiratory Viruses, Robert Koch Institut, Berlin, Germany
| | - Marcel A Müller
- Institute of Virology, Charité-Universitätsmedizin Berlin, Berlin, Germany.,German Centre for Infection Research (DZIF), Berlin, Germany.,Martsinovsky Institute of Medical Parasitology, Tropical and Vector Borne Diseases, Sechenov University, Moscow, Russia
| | - Christian Drosten
- Institute of Virology, Charité-Universitätsmedizin Berlin, Berlin, Germany. .,German Centre for Infection Research (DZIF), Berlin, Germany.
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12
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Blasco-Costa I, Hayward A, Poulin R, Balbuena JA. Next-generation cophylogeny: unravelling eco-evolutionary processes. Trends Ecol Evol 2021; 36:907-918. [PMID: 34243958 DOI: 10.1016/j.tree.2021.06.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 06/09/2021] [Accepted: 06/11/2021] [Indexed: 11/19/2022]
Abstract
A fundamental question in evolutionary biology is how microevolutionary processes translate into species diversification. Cophylogeny provides an appropriate framework to address this for symbiotic associations, but historically has been primarily limited to unveiling patterns. We argue that it is essential to integrate advances from ecology and evolutionary biology into cophylogeny, to gain greater mechanistic insights and transform cophylogeny into a platform to advance understanding of interspecific interactions and diversification more widely. We discuss key directions, such as incorporating trait reconstruction and considering multiple scales of network organization, and highlight recent developments for implementation. A new quantitative framework is proposed to allow integration of relevant information, such as quantitative traits and assessment of the contribution of individual mechanisms to cophylogenetic patterns.
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Affiliation(s)
- Isabel Blasco-Costa
- Department of Invertebrates, Natural History Museum of Geneva, PO Box 6434, CH-1211 Geneva 6, Switzerland; Department of Arctic and Marine Biology, UiT The Arctic University of Norway, Langnes, PO Box 6050, 9037 Tromsø, Norway.
| | - Alexander Hayward
- Centre for Ecology and Conservation, University of Exeter, Penryn Campus, Penryn, Cornwall, Exeter, TR10 9FE, UK
| | - Robert Poulin
- Department of Zoology, University of Otago, PO Box 56, Dunedin, New Zealand
| | - Juan A Balbuena
- Cavanilles Institute of Biodiversity and Evolutionary Biology, University of Valencia, PO Box 22085, 46071 Valencia, Spain
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13
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Märkle H, John S, Cornille A, Fields PD, Tellier A. Novel genomic approaches to study antagonistic coevolution between hosts and parasites. Mol Ecol 2021; 30:3660-3676. [PMID: 34038012 DOI: 10.1111/mec.16001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 05/09/2021] [Accepted: 05/20/2021] [Indexed: 12/13/2022]
Abstract
Host-parasite coevolution is ubiquitous, shaping genetic and phenotypic diversity and the evolutionary trajectory of interacting species. With the advances of high throughput sequencing technologies applicable to model and non-model organisms alike, it is now feasible to study in greater detail (a) the genetic underpinnings of coevolution, (b) the speed and type of dynamics at coevolving loci, and (c) the genomic consequences of coevolution. This review focuses on three recently developed approaches that leverage information from host and parasite full genome data simultaneously to pinpoint coevolving loci and draw inference on the coevolutionary history. First, co-genome-wide association study (co-GWAS) methods allow pinpointing the loci underlying host-parasite interactions. These methods focus on detecting associations between genetic variants and the outcome of experimental infection tests or on correlations between genomes of naturally infected hosts and their infecting parasites. Second, extensions to population genomics methods can detect genes under coevolution and infer the coevolutionary history, such as fitness costs. Third, correlations between host and parasite population size in time are indicative of coevolution, and polymorphism levels across independent spatially distributed populations of hosts and parasites can reveal coevolutionary loci and infer coevolutionary history. We describe the principles of these three approaches and discuss their advantages and limitations based on coevolutionary theory. We present recommendations for their application to various host (prokaryotes, fungi, plants, and animals) and parasite (viruses, bacteria, fungi, and macroparasites) species. We conclude by pointing out methodological and theoretical gaps to be filled to extract maximum information from full genome data and thereby to shed light on the molecular underpinnings of coevolution.
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Affiliation(s)
- Hanna Märkle
- Professorship for Population Genetics, Department of Life Science Systems, School of Life Sciences, Technical University of Munich, Freising, Germany.,Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
| | - Sona John
- Professorship for Population Genetics, Department of Life Science Systems, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Amandine Cornille
- INRAE, CNRS, AgroParisTech, GQE - Le Moulon, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Peter D Fields
- Department of Environmental Sciences, University of Basel, Zoology, Basel, Switzerland
| | - Aurélien Tellier
- Professorship for Population Genetics, Department of Life Science Systems, School of Life Sciences, Technical University of Munich, Freising, Germany
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14
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Measuring Coevolutionary Dynamics in Species-Rich Communities. Trends Ecol Evol 2020; 35:539-550. [DOI: 10.1016/j.tree.2020.02.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 01/24/2020] [Accepted: 02/03/2020] [Indexed: 12/18/2022]
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15
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Živković D, John S, Verin M, Stephan W, Tellier A. Neutral genomic signatures of host-parasite coevolution. BMC Evol Biol 2019; 19:230. [PMID: 31856710 PMCID: PMC6924072 DOI: 10.1186/s12862-019-1556-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 12/09/2019] [Indexed: 12/21/2022] Open
Abstract
Background Coevolution is a selective process of reciprocal adaptation in hosts and parasites or in mutualistic symbionts. Classic population genetics theory predicts the signatures of selection at the interacting loci of both species, but not the neutral genome-wide polymorphism patterns. To bridge this gap, we build an eco-evolutionary model, where neutral genomic changes over time are driven by a single selected locus in hosts and parasites via a simple biallelic gene-for-gene or matching-allele interaction. This coevolutionary process may lead to cyclic changes in the sizes of the interacting populations. Results We investigate if and when these changes can be observed in the site frequency spectrum of neutral polymorphisms from host and parasite full genome data. We show that changes of the host population size are too smooth to be observable in its polymorphism pattern over the course of time. Conversely, the parasite population may undergo a series of strong bottlenecks occurring on a slower relative time scale, which may lead to observable changes in a time series sample. We also extend our results to cases with 1) several parasites per host accelerating relative time, and 2) multiple parasite generations per host generation slowing down rescaled time. Conclusions Our results show that time series sampling of host and parasite populations with full genome data are crucial to understand if and how coevolution occurs. This model provides therefore a framework to interpret and draw inference from genome-wide polymorphism data of interacting species.
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Affiliation(s)
- Daniel Živković
- Section of Population Genetics, Technical University of Munich, Freising, Germany.
| | - Sona John
- Section of Population Genetics, Technical University of Munich, Freising, Germany
| | - Mélissa Verin
- Section of Population Genetics, Technical University of Munich, Freising, Germany.,Department of Mathematics and Statistics, Queen's University, Kingston, Ontario, Canada
| | - Wolfgang Stephan
- Leibniz Institute for Evolution and Biodiversity Science, Berlin, Germany
| | - Aurélien Tellier
- Section of Population Genetics, Technical University of Munich, Freising, Germany.
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16
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Retel C, Kowallik V, Huang W, Werner B, Künzel S, Becks L, Feulner PGD. The feedback between selection and demography shapes genomic diversity during coevolution. SCIENCE ADVANCES 2019; 5:eaax0530. [PMID: 31616788 PMCID: PMC6774728 DOI: 10.1126/sciadv.aax0530] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Accepted: 09/06/2019] [Indexed: 06/10/2023]
Abstract
Species interactions and coevolution are integral to ecological communities, but we lack empirical information on when and how these interactions generate and purge genetic diversity. Using genomic time series data from host-virus experiments, we found that coevolution occurs through consecutive selective sweeps in both species, with temporal consistency across replicates. Sweeps were accompanied by phenotypic change (resistance or infectivity increases) and expansions in population size. In the host, population expansion enabled rapid generation of genetic diversity in accordance with neutral processes. Viral molecular evolution was, in contrast, confined to few genes, all putative targets of selection. This study demonstrates that molecular evolution during species interactions is shaped by both eco-evolutionary feedback dynamics and interspecific differences in how genetic diversity is generated and maintained.
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Affiliation(s)
- Cas Retel
- Department of Fish Ecology and Evolution, Center for Ecology, Evolution and Biogeochemistry, EAWAG, Swiss Federal Institute of Aquatic Science and Technology, Kastanienbaum, Switzerland
- Division of Aquatic Ecology, Institute of Ecology and Evolution, University of Bern, Bern, Switzerland
| | - Vienna Kowallik
- Community Dynamics Group, Department of Evolutionary Ecology, Max Planck Institute for Evolutionary Biology, Plön, Germany
| | - Weini Huang
- Group of Theoretical Biology, The State Key Laboratory of Biocontrol, School of Life Science, Sun Yat-sen University, Guangzhou, China
- Complex Systems and Networks Research Group, School of Mathematical Sciences, Queen Mary University of London, London, UK
| | - Benjamin Werner
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Sven Künzel
- Department Evolutionary Genetics, Max Planck Institute for Evolutionary Biology, Plön, Germany
| | - Lutz Becks
- Community Dynamics Group, Department of Evolutionary Ecology, Max Planck Institute for Evolutionary Biology, Plön, Germany
- Kiel Evolution Center, Biologiezentrum, Kiel, Germany
| | - Philine G. D. Feulner
- Department of Fish Ecology and Evolution, Center for Ecology, Evolution and Biogeochemistry, EAWAG, Swiss Federal Institute of Aquatic Science and Technology, Kastanienbaum, Switzerland
- Division of Aquatic Ecology, Institute of Ecology and Evolution, University of Bern, Bern, Switzerland
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Abstract
How virulence evolves after a virus jumps to a new host species is central to disease emergence. Our current understanding of virulence evolution is based on insights drawn from two perspectives that have developed largely independently: long-standing evolutionary theory based on limited real data examples that often lack a genomic basis, and experimental studies of virulence-determining mutations using cell culture or animal models. A more comprehensive understanding of virulence mutations and their evolution can be achieved by bridging the gap between these two research pathways through the phylogenomic analysis of virus genome sequence data as a guide to experimental study.
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Affiliation(s)
- Jemma L Geoghegan
- Department of Biological Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Edward C Holmes
- Marie Bashir Institute for Infectious Diseases and Biosecurity, Charles Perkins Centre, School of Life and Environmental Sciences and Sydney Medical School, The University of Sydney, Sydney, New South Wales, Australia.
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18
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Park HJ, Pichugin Y, Huang W, Traulsen A. Population size changes and extinction risk of populations driven by mutant interactors. Phys Rev E 2019; 99:022305. [PMID: 30934279 DOI: 10.1103/physreve.99.022305] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Indexed: 11/07/2022]
Abstract
Spontaneous random mutations are an important source of variation in populations. Many evolutionary models consider mutants with a fixed fitness, chosen from a fitness distribution without considering microscopic interactions among the residents and mutants. Here, we go beyond this and consider "mutant interactors," which lead to new interactions between the residents and invading mutants that can affect the population size and the extinction risk of populations. We model microscopic interactions between individuals by using a dynamic interaction matrix, the dimension of which increases with the emergence of a new mutant and decreases with extinction. The new interaction parameters of the mutant follow a probability distribution around the payoff entries of its ancestor. These new interactions can drive the population away from the previous equilibrium and lead to changes in the population size. Thus, the population size is an evolving property rather than an externally controlled variable. We calculate the average population size of our stochastic system over time and quantify the extinction risk of the population by the mean time to extinction.
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Affiliation(s)
- Hye Jin Park
- Department of Evolutionary Theory, Max Planck Institute for Evolutionary Biology, 24306 Plön, Germany
| | - Yuriy Pichugin
- Department of Evolutionary Theory, Max Planck Institute for Evolutionary Biology, 24306 Plön, Germany
| | - Weini Huang
- Complex Systems and Networks Research Group, School of Mathematical Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, United Kingdom.,Group of Theoretical Biology, The State Key Laboratory of Biocontrol, School of Life Science, Sun Yat-sen University, Guangzhou 510060, China
| | - Arne Traulsen
- Department of Evolutionary Theory, Max Planck Institute for Evolutionary Biology, 24306 Plön, Germany
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19
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Ecological and Evolutionary Processes Shaping Viral Genetic Diversity. Viruses 2019; 11:v11030220. [PMID: 30841497 PMCID: PMC6466605 DOI: 10.3390/v11030220] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 02/22/2019] [Accepted: 02/27/2019] [Indexed: 02/07/2023] Open
Abstract
The contemporary genomic diversity of viruses is a result of the continuous and dynamic interaction of past ecological and evolutionary processes. Thus, genome sequences of viruses can be a valuable source of information about these processes. In this review, we first describe the relevant processes shaping viral genomic variation, with a focus on the role of host–virus coevolution and its potential to give rise to eco-evolutionary feedback loops. We further give a brief overview of available methodology designed to extract information about these processes from genomic data. Short generation times and small genomes make viruses ideal model systems to study the joint effect of complex coevolutionary and eco-evolutionary interactions on genetic evolution. This complexity, together with the diverse array of lifetime and reproductive strategies in viruses ask for extensions of existing inference methods, for example by integrating multiple information sources. Such integration can broaden the applicability of genetic inference methods and thus further improve our understanding of the role viruses play in biological communities.
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20
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Theodosiou L, Hiltunen T, Becks L. The role of stressors in altering eco‐evolutionary dynamics. Funct Ecol 2019. [DOI: 10.1111/1365-2435.13263] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Loukas Theodosiou
- Community Dynamics GroupMax Planck Institute for Evolutionary Biology Plön Germany
- Department of Microbial Population BiologyMax Planck Institute for Evolutionary Biology Plön Germany
| | - Teppo Hiltunen
- Department of MicrobiologyUniversity of Helsinki Helsinki Finland
- Department of BiologyUniversity of Turku Turku Finland
| | - Lutz Becks
- Community Dynamics GroupMax Planck Institute for Evolutionary Biology Plön Germany
- Limnology ‐ Aquatic Ecology and Evolution, Limnological InstituteUniversity of Konstanz Konstanz Germany
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21
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Fragata I, Blanckaert A, Dias Louro MA, Liberles DA, Bank C. Evolution in the light of fitness landscape theory. Trends Ecol Evol 2019; 34:69-82. [DOI: 10.1016/j.tree.2018.10.009] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 10/16/2018] [Accepted: 10/17/2018] [Indexed: 01/28/2023]
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22
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Cairns J, Jokela R, Hultman J, Tamminen M, Virta M, Hiltunen T. Construction and Characterization of Synthetic Bacterial Community for Experimental Ecology and Evolution. Front Genet 2018; 9:312. [PMID: 30154827 PMCID: PMC6102323 DOI: 10.3389/fgene.2018.00312] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 07/23/2018] [Indexed: 01/21/2023] Open
Abstract
Experimental microbial ecology and evolution have yielded foundational insights into ecological and evolutionary processes using simple microcosm setups and phenotypic assays with one- or two-species model systems. The fields are now increasingly incorporating more complex systems and exploration of the molecular basis of observations. For this purpose, simplified, manageable and well-defined multispecies model systems are required that can be easily investigated using culturing and high-throughput sequencing approaches, bridging the gap between simpler and more complex synthetic or natural systems. Here we address this need by constructing a completely synthetic 33 bacterial strain community that can be cultured in simple laboratory conditions. We provide whole-genome data for all the strains as well as metadata about genomic features and phenotypic traits that allow resolving individual strains by amplicon sequencing and facilitate a variety of envisioned mechanistic studies. We further show that a large proportion of the strains exhibit coexistence in co-culture over serial transfer for 48 days in the absence of any experimental manipulation to maintain diversity. The constructed bacterial community can be a valuable resource in future experimental work.
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Affiliation(s)
- Johannes Cairns
- Department of Microbiology, University of Helsinki, Helsinki, Finland
| | - Roosa Jokela
- Department of Microbiology, University of Helsinki, Helsinki, Finland
| | - Jenni Hultman
- Department of Microbiology, University of Helsinki, Helsinki, Finland
| | - Manu Tamminen
- Department of Biology, University of Turku, Turku, Finland
| | - Marko Virta
- Department of Microbiology, University of Helsinki, Helsinki, Finland
| | - Teppo Hiltunen
- Department of Microbiology, University of Helsinki, Helsinki, Finland
- Department of Biology, University of Turku, Turku, Finland
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23
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Population size changes and selection drive patterns of parallel evolution in a host-virus system. Nat Commun 2018; 9:1706. [PMID: 29703896 PMCID: PMC5923231 DOI: 10.1038/s41467-018-03990-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Accepted: 03/26/2018] [Indexed: 11/09/2022] Open
Abstract
Predicting the repeatability of evolution remains elusive. Theory and empirical studies suggest that strong selection and large population sizes increase the probability for parallel evolution at the phenotypic and genotypic levels. However, selection and population sizes are not constant, but rather change continuously and directly affect each other even on short time scales. Here, we examine the degree of parallel evolution shaped through eco-evolutionary dynamics in an algal host population coevolving with a virus. We find high degrees of parallelism at the level of population size changes (ecology) and at the phenotypic level between replicated populations. At the genomic level, we find evidence for parallelism, as the same large genomic region was duplicated in all replicated populations, but also substantial novel sequence divergence between replicates. These patterns of genome evolution can be explained by considering population size changes as an important driver of rapid evolution.
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