151
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Good BH, Hallatschek O. Effective models and the search for quantitative principles in microbial evolution. Curr Opin Microbiol 2018; 45:203-212. [PMID: 30530175 PMCID: PMC6599682 DOI: 10.1016/j.mib.2018.11.005] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 08/17/2018] [Accepted: 11/15/2018] [Indexed: 12/14/2022]
Abstract
Microbes evolve rapidly. Yet they do so in idiosyncratic ways, which depend on the specific mutations that are beneficial or deleterious in a given situation. At the same time, some population-level patterns of adaptation are strikingly similar across different microbial systems, suggesting that there may also be simple, quantitative principles that unite these diverse scenarios. We review the search for simple principles in microbial evolution, ranging from the biophysical level to emergent evolutionary dynamics. A key theme has been the use of effective models, which coarse-grain over molecular and cellular details to obtain a simpler description in terms of a few effective parameters. Collectively, these theoretical approaches provide a set of quantitative principles that facilitate understanding, prediction, and potentially control of evolutionary phenomena, though formidable challenges remain due to the ecological complexity of natural populations.
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Affiliation(s)
- Benjamin H Good
- Department of Physics, University of California, Berkeley, United States; Department of Bioengineering, University of California, Berkeley, United States.
| | - Oskar Hallatschek
- Department of Physics, University of California, Berkeley, United States; Department of Integrative Biology, University of California, Berkeley, United States
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152
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Edwards KF, Kremer CT, Miller ET, Osmond MM, Litchman E, Klausmeier CA. Evolutionarily stable communities: a framework for understanding the role of trait evolution in the maintenance of diversity. Ecol Lett 2018; 21:1853-1868. [PMID: 30272831 DOI: 10.1111/ele.13142] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Revised: 04/16/2018] [Accepted: 07/23/2018] [Indexed: 01/15/2023]
Abstract
Biological diversity depends on the interplay between evolutionary diversification and ecological mechanisms allowing species to coexist. Current research increasingly integrates ecology and evolution over a range of timescales, but our common conceptual framework for understanding species coexistence requires better incorporation of evolutionary processes. Here, we focus on the idea of evolutionarily stable communities (ESCs), which are theoretical endpoints of evolution in a community context. We use ESCs as a unifying framework to highlight some important but under-appreciated theoretical results, and we review empirical research relevant to these theoretical predictions. We explain how, in addition to generating diversity, evolution can also limit diversity by reducing the effectiveness of coexistence mechanisms. The coevolving traits of competing species may either diverge or converge, depending on whether the number of species in the community is low (undersaturated) or high (oversaturated) relative to the ESC. Competition in oversaturated communities can lead to extinction or neutrally coexisting, ecologically equivalent species. It is critical to consider trait evolution when investigating fundamental ecological questions like the strength of different coexistence mechanisms, the feasibility of ecologically equivalent species, and the interpretation of different patterns of trait dispersion.
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Affiliation(s)
- Kyle F Edwards
- Department of Oceanography, University of Hawai'i at Manoa, Honolulu, HI, 96822, USA
| | - Colin T Kremer
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, 06520, USA.,Kellogg Biological Station, Michigan State University, Hickory Corners, MI, 49060, USA.,Program in Ecology, Evolutionary Biology, & Behavior, Michigan State University, East Lansing, MI, 48824, USA
| | - Elizabeth T Miller
- Institute of Ecology and Evolution, University of Oregon, Eugene, OR, 97403, USA
| | - Matthew M Osmond
- Department of Zoology, University of British Columbia, Vancouver, BC, V6T 1Z4, USA
| | - Elena Litchman
- Kellogg Biological Station, Michigan State University, Hickory Corners, MI, 49060, USA.,Program in Ecology, Evolutionary Biology, & Behavior, Michigan State University, East Lansing, MI, 48824, USA.,Department of Integrative Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Christopher A Klausmeier
- Kellogg Biological Station, Michigan State University, Hickory Corners, MI, 49060, USA.,Program in Ecology, Evolutionary Biology, & Behavior, Michigan State University, East Lansing, MI, 48824, USA.,Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA
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153
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Passagem-Santos D, Zacarias S, Perfeito L. Power law fitness landscapes and their ability to predict fitness. Heredity (Edinb) 2018; 121:482-498. [PMID: 30190560 PMCID: PMC6180038 DOI: 10.1038/s41437-018-0143-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 08/15/2018] [Accepted: 08/16/2018] [Indexed: 12/19/2022] Open
Abstract
Whether or not evolution by natural selection is predictable depends on the existence of general patterns shaping the way mutations interact with the genetic background. This interaction, also known as epistasis, has been observed during adaptation (macroscopic epistasis) and in individual mutations (microscopic epistasis). Interestingly, a consistent negative correlation between the fitness effect of beneficial mutations and background fitness (known as diminishing returns epistasis) has been observed across different species and conditions. We tested whether the adaptation pattern of an additional species, Schizosaccharomyces pombe, followed the same trend. We used strains that differed by the presence of large karyotype differences and observed the same pattern of fitness convergence. Using these data along with published datasets, we measured the ability of different models to describe adaptation rates. We found that a phenotype-fitness landscape shaped like a power law is able to correctly predict adaptation dynamics in a variety of species and conditions. Furthermore we show that this model can provide a link between the observed macroscopic and microscopic epistasis. It may be very useful in the development of algorithms able to predict the adaptation of microorganisms from measures of the current phenotypes. Overall, our results suggest that even though adaptation quickly slows down, populations adapting to lab conditions may be quite far from a fitness peak.
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154
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Predicting the evolution of Escherichia coli by a data-driven approach. Nat Commun 2018; 9:3562. [PMID: 30177705 PMCID: PMC6120903 DOI: 10.1038/s41467-018-05807-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Accepted: 06/12/2018] [Indexed: 12/31/2022] Open
Abstract
A tantalizing question in evolutionary biology is whether evolution can be predicted from past experiences. To address this question, we created a coherent compendium of more than 15,000 mutation events for the bacterium Escherichia coli under 178 distinct environmental settings. Compendium analysis provides a comprehensive view of the explored environments, mutation hotspots and mutation co-occurrence. While the mutations shared across all replicates decrease with the number of replicates, our results argue that the pairwise overlapping ratio remains the same, regardless of the number of replicates. An ensemble of predictors trained on the mutation compendium and tested in forward validation over 35 evolution replicates achieves a 49.2 ± 5.8% (mean ± std) precision and 34.5 ± 5.7% recall in predicting mutation targets. This work demonstrates how integrated datasets can be harnessed to create predictive models of evolution at a gene level and elucidate the effect of evolutionary processes in well-defined environments. How reproducible evolutionary processes are remains an important question in evolutionary biology. Here, the authors compile a compendium of more than 15,000 mutation events for Escherichia coli under 178 distinct environmental settings, and develop an ensemble of predictors to predict evolution at a gene level.
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155
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Abstract
The causes and consequences of spatiotemporal variation in mutation rates remain to be explored in nearly all organisms. Here we examine relationships between local mutation rates and replication timing in three bacterial species whose genomes have multiple chromosomes: Vibrio fischeri, Vibrio cholerae, and Burkholderia cenocepacia. Following five mutation accumulation experiments with these bacteria conducted in the near absence of natural selection, the genomes of clones from each lineage were sequenced and analyzed to identify variation in mutation rates and spectra. In lineages lacking mismatch repair, base substitution mutation rates vary in a mirrored wave-like pattern on opposing replichores of the large chromosomes of V. fischeri and V. cholerae, where concurrently replicated regions experience similar base substitution mutation rates. The base substitution mutation rates on the small chromosome are less variable in both species but occur at similar rates to those in the concurrently replicated regions of the large chromosome. Neither nucleotide composition nor frequency of nucleotide motifs differed among regions experiencing high and low base substitution rates, which along with the inferred ~800-kb wave period suggests that the source of the periodicity is not sequence specific but rather a systematic process related to the cell cycle. These results support the notion that base substitution mutation rates are likely to vary systematically across many bacterial genomes, which exposes certain genes to elevated deleterious mutational load. That mutation rates vary within bacterial genomes is well known, but the detailed study of these biases has been made possible only recently with contemporary sequencing methods. We applied these methods to understand how bacterial genomes with multiple chromosomes, like those of Vibrio and Burkholderia, might experience heterogeneous mutation rates because of their unusual replication and the greater genetic diversity found on smaller chromosomes. This study captured thousands of mutations and revealed wave-like rate variation that is synchronized with replication timing and not explained by sequence context. The scale of this rate variation over hundreds of kilobases of DNA strongly suggests that a temporally regulated cellular process may generate wave-like variation in mutation risk. These findings add to our understanding of how mutation risk is distributed across bacterial and likely also eukaryotic genomes, owing to their highly conserved replication and repair machinery.
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156
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de Visser JAGM, Elena SF, Fragata I, Matuszewski S. The utility of fitness landscapes and big data for predicting evolution. Heredity (Edinb) 2018; 121:401-405. [PMID: 30127530 PMCID: PMC6180140 DOI: 10.1038/s41437-018-0128-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 07/13/2018] [Accepted: 07/13/2018] [Indexed: 11/25/2022] Open
Affiliation(s)
| | - Santiago F Elena
- Instituto de Biología Molecular y Celular de Plantas (IBMCP), Consejo Superior de Investigaciones Científicas-Universitat Politècnica de València, València, Spain. .,Instituto de Biología Integrativa de Sistemas (I2SysBio), Consejo Superior de Investigaciones Científicas-Universitat de València, València, Spain. .,The Santa Fe Institute, Santa Fe, NM, 87501, USA.
| | - Inês Fragata
- Instituto Gulbenkian de Ciência, Oeiras, Portugal.
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157
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Pellestor F, Gatinois V. Chromothripsis, a credible chromosomal mechanism in evolutionary process. Chromosoma 2018; 128:1-6. [DOI: 10.1007/s00412-018-0679-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Revised: 07/31/2018] [Accepted: 08/02/2018] [Indexed: 01/17/2023]
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158
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Turner CB, Marshall CW, Cooper VS. Parallel genetic adaptation across environments differing in mode of growth or resource availability. Evol Lett 2018; 2:355-367. [PMID: 30283687 PMCID: PMC6121802 DOI: 10.1002/evl3.75] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Revised: 06/18/2018] [Accepted: 07/06/2018] [Indexed: 01/13/2023] Open
Abstract
Evolution experiments have demonstrated high levels of genetic parallelism between populations evolving in identical environments. However, natural populations evolve in complex environments that can vary in many ways, likely sharing some characteristics but not others. Here, we ask whether shared selection pressures drive parallel evolution across distinct environments. We addressed this question in experimentally evolved populations founded from a clone of the bacterium Burkholderia cenocepacia. These populations evolved for 90 days (approximately 600 generations) under all combinations of high or low carbon availability and selection for either planktonic or biofilm modes of growth. Populations that evolved in environments with shared selection pressures (either level of carbon availability or mode of growth) were more genetically similar to each other than populations from environments that shared neither characteristic. However, not all shared selection pressures led to parallel evolution. Genetic parallelism between low-carbon biofilm and low-carbon planktonic populations was very low despite shared selection for growth under low-carbon conditions, suggesting that evolution in low-carbon environments may generate stronger trade-offs between biofilm and planktonic modes of growth. For all environments, a population's fitness in a particular environment was positively correlated with the genetic similarity between that population and the populations that evolved in that particular environment. Although genetic similarity was low between low-carbon environments, overall, evolution in similar environments led to higher levels of genetic parallelism and that genetic parallelism, in turn, was correlated with fitness in a particular environment.
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Affiliation(s)
- Caroline B. Turner
- Microbiology and Molecular GeneticsUniversity of PittsburghPittsburghPennsylvania
| | | | - Vaughn S. Cooper
- Microbiology and Molecular GeneticsUniversity of PittsburghPittsburghPennsylvania
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159
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Du X, King AA, Woods RJ, Pascual M. Evolution-informed forecasting of seasonal influenza A (H3N2). Sci Transl Med 2018; 9:9/413/eaan5325. [PMID: 29070700 DOI: 10.1126/scitranslmed.aan5325] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 05/26/2017] [Accepted: 08/16/2017] [Indexed: 12/13/2022]
Abstract
Interpandemic or seasonal influenza A, currently subtypes H3N2 and H1N1, exacts an enormous annual burden both in terms of human health and economic impact. Incidence prediction ahead of season remains a challenge largely because of the virus' antigenic evolution. We propose a forecasting approach that incorporates evolutionary change into a mechanistic epidemiological model. The proposed models are simple enough that their parameters can be estimated from retrospective surveillance data. These models link amino acid sequences of hemagglutinin epitopes with a transmission model for seasonal H3N2 influenza, also informed by H1N1 levels. With a monthly time series of H3N2 incidence in the United States for more than 10 years, we demonstrate the feasibility of skillful prediction for total cases ahead of season, with a tendency to underpredict monthly peak epidemic size, and an accurate real-time forecast for the 2016/2017 influenza season.
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Affiliation(s)
- Xiangjun Du
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA
| | - Aaron A King
- Departments of Ecology and Evolutionary Biology and Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Robert J Woods
- University of Michigan Health System, University of Michigan, Ann Arbor, MI 48109, USA
| | - Mercedes Pascual
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA. .,Santa Fe Institute, Santa Fe, NM 87501, USA
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160
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Cavagna A, Giardina I, Mora T, Walczak AM. Physical constraints in biological collective behaviour. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.coisb.2018.03.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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161
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Williams MJ, Werner B, Heide T, Curtis C, Barnes CP, Sottoriva A, Graham TA. Quantification of subclonal selection in cancer from bulk sequencing data. Nat Genet 2018; 50:895-903. [PMID: 29808029 PMCID: PMC6475346 DOI: 10.1038/s41588-018-0128-6] [Citation(s) in RCA: 154] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Accepted: 03/23/2018] [Indexed: 12/11/2022]
Abstract
Subclonal architectures are prevalent across cancer types. However, the temporal evolutionary dynamics that produce tumor subclones remain unknown. Here we measure clone dynamics in human cancers by using computational modeling of subclonal selection and theoretical population genetics applied to high-throughput sequencing data. Our method determined the detectable subclonal architecture of tumor samples and simultaneously measured the selective advantage and time of appearance of each subclone. We demonstrate the accuracy of our approach and the extent to which evolutionary dynamics are recorded in the genome. Application of our method to high-depth sequencing data from breast, gastric, blood, colon and lung cancer samples, as well as metastatic deposits, showed that detectable subclones under selection, when present, consistently emerged early during tumor growth and had a large fitness advantage (>20%). Our quantitative framework provides new insight into the evolutionary trajectories of human cancers and facilitates predictive measurements in individual tumors from widely available sequencing data.
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Affiliation(s)
- Marc J Williams
- Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Mary University of London, London, UK
- Department of Cell and Developmental Biology, University College London, London, UK
- Centre for Mathematics and Physics in the Life Sciences and Experimental Biology (CoMPLEX), University College London, London, UK
| | - Benjamin Werner
- Evolutionary Genomics & Modelling Lab, Centre for Evolution and Cancer, Institute of Cancer Research, London, UK
| | - Timon Heide
- Evolutionary Genomics & Modelling Lab, Centre for Evolution and Cancer, Institute of Cancer Research, London, UK
| | - Christina Curtis
- Departments of Medicine and Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Chris P Barnes
- Department of Cell and Developmental Biology, University College London, London, UK.
- UCL Genetics Institute, University College London, London, UK.
| | - Andrea Sottoriva
- Evolutionary Genomics & Modelling Lab, Centre for Evolution and Cancer, Institute of Cancer Research, London, UK.
| | - Trevor A Graham
- Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Mary University of London, London, UK.
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162
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Willemsen A, Carrasco JL, Elena SF, Zwart MP. Going, going, gone: predicting the fate of genomic insertions in plant RNA viruses. Heredity (Edinb) 2018; 121:499-509. [PMID: 29743566 DOI: 10.1038/s41437-018-0086-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 03/28/2018] [Accepted: 03/29/2018] [Indexed: 11/09/2022] Open
Abstract
Horizontal gene transfer is common among viruses, while they also have highly compact genomes and tend to lose artificial genomic insertions rapidly. Understanding the stability of genomic insertions in viral genomes is therefore relevant for explaining and predicting their evolutionary patterns. Here, we revisit a large body of experimental research on a plant RNA virus, tobacco etch potyvirus (TEV), to identify the patterns underlying the stability of a range of homologous and heterologous insertions in the viral genome. We obtained a wide range of estimates for the recombination rate-the rate at which deletions removing the insertion occur-and these appeared to be independent of the type of insertion and its location. Of the factors we considered, recombination rate was the best predictor of insertion stability, although we could not identify the specific sequence characteristics that would help predict insertion instability. We also considered experimentally the possibility that functional insertions lead to higher mutational robustness through increased redundancy. However, our observations suggest that both functional and non-functional increases in genome size decreased the mutational robustness. Our results therefore demonstrate the importance of recombination rates for predicting the long-term stability and evolution of viral RNA genomes and suggest that there are unexpected drawbacks to increases in genome size for mutational robustness.
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Affiliation(s)
- Anouk Willemsen
- Laboratory MIVEGEC (UMR CNRS 5290, IRD 224, UM), National Center for Scientific Research (CNRS), Montpellier, France
| | - José L Carrasco
- Instituto de Biología Molecular y Celular de Plantas (IBMCP), Consejo Superior de Investigaciones Científicas-Universitat Politècnica de València, València, Spain
| | - Santiago F Elena
- Instituto de Biología Molecular y Celular de Plantas (IBMCP), Consejo Superior de Investigaciones Científicas-Universitat Politècnica de València, València, Spain.,Instituto de Biología Integrativa de Sistemas (I2SysBio), Consejo Superior de Investigaciones Científicas-Universitat de València, Paterna, Spain.,The Santa Fe Institute, Santa Fe, NM, 87501, USA
| | - Mark P Zwart
- Microbial Ecology Department, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands. .,Laboratory of Genetics, Wageningen University, Wageningen, The Netherlands.
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163
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Chaturvedi S, Lucas LK, Nice CC, Fordyce JA, Forister ML, Gompert Z. The predictability of genomic changes underlying a recent host shift in Melissa blue butterflies. Mol Ecol 2018; 27:2651-2666. [DOI: 10.1111/mec.14578] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Revised: 02/05/2018] [Accepted: 02/09/2018] [Indexed: 01/06/2023]
Affiliation(s)
- Samridhi Chaturvedi
- Department of Biology Utah State University Logan Utah
- Ecology Center Utah State University Logan Utah
| | | | | | | | | | - Zachariah Gompert
- Department of Biology Utah State University Logan Utah
- Ecology Center Utah State University Logan Utah
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164
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Nosil P, Villoutreix R, de Carvalho CF, Farkas TE, Soria-Carrasco V, Feder JL, Crespi BJ, Gompert Z. Natural selection and the predictability of evolution in Timema stick insects. Science 2018; 359:765-770. [PMID: 29449486 DOI: 10.1126/science.aap9125] [Citation(s) in RCA: 109] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2017] [Accepted: 12/21/2017] [Indexed: 01/03/2023]
Abstract
Predicting evolution remains difficult. We studied the evolution of cryptic body coloration and pattern in a stick insect using 25 years of field data, experiments, and genomics. We found that evolution is more difficult to predict when it involves a balance between multiple selective factors and uncertainty in environmental conditions than when it involves feedback loops that cause consistent back-and-forth fluctuations. Specifically, changes in color-morph frequencies are modestly predictable through time (r2 = 0.14) and driven by complex selective regimes and yearly fluctuations in climate. In contrast, temporal changes in pattern-morph frequencies are highly predictable due to negative frequency-dependent selection (r2 = 0.86). For both traits, however, natural selection drives evolution around a dynamic equilibrium, providing some predictability to the process.
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Affiliation(s)
- Patrik Nosil
- Department of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, UK.
| | - Romain Villoutreix
- Department of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, UK
| | | | - Timothy E Farkas
- Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT 06369, USA
| | - Víctor Soria-Carrasco
- Department of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Jeffrey L Feder
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Bernard J Crespi
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Zach Gompert
- Department of Biology, Utah State University, Logan, UT 84322, USA.
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165
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Abstract
The deterministic force of natural selection and stochastic influence of drift shape RNA virus evolution. New deep-sequencing and microfluidics technologies allow us to quantify the effect of mutations and trace the evolution of viral populations with single-genome and single-nucleotide resolution. Such experiments can reveal the topography of the genotype-fitness landscapes that shape the path of viral evolution. By combining historical analyses, like phylogenetic approaches, with high-throughput and high-resolution evolutionary experiments, we can observe parallel patterns of evolution that drive important phenotypic transitions. These developments provide a framework for quantifying and anticipating potential evolutionary events. Here, we examine emerging technologies that can map the selective landscapes of viruses, focusing on their application to pathogenic viruses. We identify areas where these technologies can bolster our ability to study the evolution of viruses and to anticipate and possibly intervene in evolutionary events and prevent viral disease.
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Affiliation(s)
- Patrick T Dolan
- Department of Biology, Stanford University, E200 Clark Center, 318 Campus Drive, Stanford, CA 94305, USA; Department of Microbiology and Immunology, University of California, San Francisco, 600 16th Street, GH-S572, UCSF Box 2280, San Francisco, CA 94143-2280, USA
| | - Zachary J Whitfield
- Department of Microbiology and Immunology, University of California, San Francisco, 600 16th Street, GH-S572, UCSF Box 2280, San Francisco, CA 94143-2280, USA
| | - Raul Andino
- Department of Microbiology and Immunology, University of California, San Francisco, 600 16th Street, GH-S572, UCSF Box 2280, San Francisco, CA 94143-2280, USA.
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166
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Badyaev AV, Morrison ES. Emergent buffering balances evolvability and robustness in the evolution of phenotypic flexibility. Evolution 2018; 72:647-662. [DOI: 10.1111/evo.13441] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 01/20/2018] [Indexed: 12/25/2022]
Affiliation(s)
- Alexander V. Badyaev
- Department of Ecology and Evolutionary Biology University of Arizona Tucson Arizona 85721
| | - Erin S. Morrison
- Department of Ecology and Evolutionary Biology University of Arizona Tucson Arizona 85721
- Sackler Institute for Comparative Genomics American Museum of Natural History New York New York 10024
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167
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Morris DH, Gostic KM, Pompei S, Bedford T, Łuksza M, Neher RA, Grenfell BT, Lässig M, McCauley JW. Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology. Trends Microbiol 2018; 26:102-118. [PMID: 29097090 PMCID: PMC5830126 DOI: 10.1016/j.tim.2017.09.004] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 09/06/2017] [Accepted: 09/19/2017] [Indexed: 01/16/2023]
Abstract
Seasonal influenza is controlled through vaccination campaigns. Evolution of influenza virus antigens means that vaccines must be updated to match novel strains, and vaccine effectiveness depends on the ability of scientists to predict nearly a year in advance which influenza variants will dominate in upcoming seasons. In this review, we highlight a promising new surveillance tool: predictive models. Based on data-sharing and close collaboration between the World Health Organization and academic scientists, these models use surveillance data to make quantitative predictions regarding influenza evolution. Predictive models demonstrate the potential of applied evolutionary biology to improve public health and disease control. We review the state of influenza predictive modeling and discuss next steps and recommendations to ensure that these models deliver upon their considerable biomedical promise.
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Affiliation(s)
- Dylan H Morris
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
| | - Katelyn M Gostic
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA
| | - Simone Pompei
- Institute for Theoretical Physics, University of Cologne, Cologne, Germany
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Marta Łuksza
- Institute for Advanced Study, Princeton, NJ, USA
| | - Richard A Neher
- Biozentrum, University of Basel and Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Michael Lässig
- Institute for Theoretical Physics, University of Cologne, Cologne, Germany
| | - John W McCauley
- Worldwide Influenza Centre, Francis Crick Institute, London, UK
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168
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R Nené N, Mustonen V, J R Illingworth C. Evaluating genetic drift in time-series evolutionary analysis. J Theor Biol 2018; 437:51-57. [PMID: 28958783 PMCID: PMC5703635 DOI: 10.1016/j.jtbi.2017.09.021] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Revised: 06/20/2017] [Accepted: 09/18/2017] [Indexed: 11/15/2022]
Abstract
The Wright-Fisher model is the most popular population model for describing the behaviour of evolutionary systems with a finite population size. Approximations have commonly been used but the model itself has rarely been tested against time-resolved genomic data. Here, we evaluate the extent to which it can be inferred as the correct model under a likelihood framework. Given genome-wide data from an evolutionary experiment, we validate the Wright-Fisher drift model as the better option for describing evolutionary trajectories in a finite population. This was found by evaluating its performance against a Gaussian model of allele frequency propagation. However, we note a range of circumstances under which standard Wright-Fisher drift cannot be correctly identified.
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Affiliation(s)
- Nuno R Nené
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Ville Mustonen
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Department of Biosciences, Department of Computer Science, Institute of Biotechnology, University of Helsinki, Helsinki 00014, Finland
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169
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Abstract
The five most pervasive anthropogenic threats to biodiversity are over-exploitation, habitat changes, climate change, invasive species, and pollution. Since all of these threats can affect intraspecific biodiversity—including genetic variation within populations—humans have the potential to induce contemporary microevolution in wild populations. We highlight recent empirical studies that have explored the effects of these anthropogenic threats to intraspecific biodiversity in the wild. We conclude that it is critical that we move towards a predictive framework that integrates a better understanding of contemporary microevolution to multiple threats to forecast the fate of natural populations in a changing world.
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170
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Abstract
The recent discovery of a new class of massive chromosomal rearrangements, occurring during one unique cellular event and baptized "chromothripsis," deeply modifies our perception on the genesis of complex genomic rearrangements, but also it raises the question of the potential driving role of chromothripsis in species evolution. The occurrence of chromothripsis appears to be in good agreement with macroevolution models proposed as a complement to phyletic gradualism. The emergence of this unexpected phenomenon may help to demonstrate the contribution of chromosome rearrangements to speciation process.
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Affiliation(s)
- Franck Pellestor
- Unit of Chromosomal Genetics, Department of Medical Genetics, Arnaud de Villeneuve Hospital, Montpellier CHRU, Montpellier, France. .,INSERM U1183 Unit "Genome and Stem Cell Plasticity in Development and Ageing", Institute for Regenerative Medicine and Biotherapy, St Eloi Hospital, Montpellier, France.
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171
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.Rainey PB, Remigi P, Farr AD, Lind PA. Darwin was right: where now for experimental evolution? Curr Opin Genet Dev 2017; 47:102-109. [DOI: 10.1016/j.gde.2017.09.003] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Revised: 09/13/2017] [Accepted: 09/14/2017] [Indexed: 01/02/2023]
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172
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Lahiri S, Nghe P, Tans SJ, Rosinberg ML, Lacoste D. Information-theoretic analysis of the directional influence between cellular processes. PLoS One 2017; 12:e0187431. [PMID: 29121044 PMCID: PMC5679622 DOI: 10.1371/journal.pone.0187431] [Citation(s) in RCA: 7] [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: 06/04/2017] [Accepted: 10/19/2017] [Indexed: 11/25/2022] Open
Abstract
Inferring the directionality of interactions between cellular processes is a major challenge in systems biology. Time-lagged correlations allow to discriminate between alternative models, but they still rely on assumed underlying interactions. Here, we use the transfer entropy (TE), an information-theoretic quantity that quantifies the directional influence between fluctuating variables in a model-free way. We present a theoretical approach to compute the transfer entropy, even when the noise has an extrinsic component or in the presence of feedback. We re-analyze the experimental data from Kiviet et al. (2014) where fluctuations in gene expression of metabolic enzymes and growth rate have been measured in single cells of E. coli. We confirm the formerly detected modes between growth and gene expression, while prescribing more stringent conditions on the structure of noise sources. We furthermore point out practical requirements in terms of length of time series and sampling time which must be satisfied in order to infer optimally transfer entropy from times series of fluctuations.
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Affiliation(s)
- Sourabh Lahiri
- Gulliver laboratory, PSL Research University, ESPCI, 10 rue de Vauquelin, 75231 Paris Cedex 05, France
| | - Philippe Nghe
- Laboratory of Biochemistry, PSL Research University, ESPCI, 10 rue de Vauquelin, 75231 Paris Cedex 05, France
| | - Sander J. Tans
- FOM Institute AMOLF, Science Park, 104, 1098 XG Amsterdam, the Netherlands
| | - Martin Luc Rosinberg
- Laboratoire de Physique Théorique de la Matière Condensée, Université Pierre et Marie Curie, CNRS UMR 7600, 4 place Jussieu, 75252 Paris Cedex 05, France
| | - David Lacoste
- Gulliver laboratory, PSL Research University, ESPCI, 10 rue de Vauquelin, 75231 Paris Cedex 05, France
- * E-mail:
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173
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Abstract
A long-standing goal in evolutionary biology is predicting evolution. Here, we show that the architecture of macromolecules fundamentally limits evolutionary predictability. Under physiological conditions, macromolecules, like proteins, flip between multiple structures, forming an ensemble of structures. A mutation affects all of these structures in slightly different ways, redistributing the relative probabilities of structures in the ensemble. As a result, mutations that follow the first mutation have a different effect than they would if introduced before. This implies that knowing the effects of every mutation in an ancestor would be insufficient to predict evolutionary trajectories past the first few steps, leading to profound unpredictability in evolution. We, therefore, conclude that detailed evolutionary predictions are not possible given the chemistry of macromolecules. Evolutionary prediction is of deep practical and philosophical importance. Here we show, using a simple computational protein model, that protein evolution remains unpredictable, even if one knows the effects of all mutations in an ancestral protein background. We performed a virtual deep mutational scan—revealing the individual and pairwise epistatic effects of every mutation to our model protein—and then used this information to predict evolutionary trajectories. Our predictions were poor. This is a consequence of statistical thermodynamics. Proteins exist as ensembles of similar conformations. The effect of a mutation depends on the relative probabilities of conformations in the ensemble, which in turn, depend on the exact amino acid sequence of the protein. Accumulating substitutions alter the relative probabilities of conformations, thereby changing the effects of future mutations. This manifests itself as subtle but pervasive high-order epistasis. Uncertainty in the effect of each mutation accumulates and undermines prediction. Because conformational ensembles are an inevitable feature of proteins, this is likely universal.
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174
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van Dijk T, Hwang S, Krug J, de Visser JAGM, Zwart MP. Mutation supply and the repeatability of selection for antibiotic resistance. Phys Biol 2017; 14:055005. [PMID: 28699625 DOI: 10.1088/1478-3975/aa7f36] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Whether evolution can be predicted is a key question in evolutionary biology. Here we set out to better understand the repeatability of evolution, which is a necessary condition for predictability. We explored experimentally the effect of mutation supply and the strength of selective pressure on the repeatability of selection from standing genetic variation. Different sizes of mutant libraries of antibiotic resistance gene TEM-1 β-lactamase in Escherichia coli, generated by error-prone PCR, were subjected to different antibiotic concentrations. We determined whether populations went extinct or survived, and sequenced the TEM gene of the surviving populations. The distribution of mutations per allele in our mutant libraries followed a Poisson distribution. Extinction patterns could be explained by a simple stochastic model that assumed the sampling of beneficial mutations was key for survival. In most surviving populations, alleles containing at least one known large-effect beneficial mutation were present. These genotype data also support a model which only invokes sampling effects to describe the occurrence of alleles containing large-effect driver mutations. Hence, evolution is largely predictable given cursory knowledge of mutational fitness effects, the mutation rate and population size. There were no clear trends in the repeatability of selected mutants when we considered all mutations present. However, when only known large-effect mutations were considered, the outcome of selection is less repeatable for large libraries, in contrast to expectations. We show experimentally that alleles carrying multiple mutations selected from large libraries confer higher resistance levels relative to alleles with only a known large-effect mutation, suggesting that the scarcity of high-resistance alleles carrying multiple mutations may contribute to the decrease in repeatability at large library sizes.
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Affiliation(s)
- Thomas van Dijk
- Laboratory of Genetics, Wageningen University, Wageningen, Netherlands. These authors contributed equally
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175
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Xue KS, Stevens-Ayers T, Campbell AP, Englund JA, Pergam SA, Boeckh M, Bloom JD. Parallel evolution of influenza across multiple spatiotemporal scales. eLife 2017; 6:e26875. [PMID: 28653624 PMCID: PMC5487208 DOI: 10.7554/elife.26875] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Accepted: 05/28/2017] [Indexed: 01/13/2023] Open
Abstract
Viral variants that arise in the global influenza population begin as de novo mutations in single infected hosts, but the evolutionary dynamics that transform within-host variation to global genetic diversity are poorly understood. Here, we demonstrate that influenza evolution within infected humans recapitulates many evolutionary dynamics observed at the global scale. We deep-sequence longitudinal samples from four immunocompromised patients with long-term H3N2 influenza infections. We find parallel evolution across three scales: within individual patients, in different patients in our study, and in the global influenza population. In hemagglutinin, a small set of mutations arises independently in multiple patients. These same mutations emerge repeatedly within single patients and compete with one another, providing a vivid clinical example of clonal interference. Many of these recurrent within-host mutations also reach a high global frequency in the decade following the patient infections. Our results demonstrate surprising concordance in evolutionary dynamics across multiple spatiotemporal scales.
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Affiliation(s)
- Katherine S Xue
- Department of Genome Sciences, University of Washington, Seattle, United States
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, United States
| | - Terry Stevens-Ayers
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, United States
| | - Angela P Campbell
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, United States
| | - Janet A Englund
- Seattle Children’s Research Institute, Seattle, United States
- Department of Pediatrics, University of Washington, Seattle, United States
| | - Steven A Pergam
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, United States
- Department of Medicine, University of Washington, Seattle, United States
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, United States
| | - Michael Boeckh
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, United States
- Department of Medicine, University of Washington, Seattle, United States
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, United States
| | - Jesse D Bloom
- Department of Genome Sciences, University of Washington, Seattle, United States
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, United States
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176
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Calvignac-Spencer S, Lenz TL. The One Past Health workshop: connecting ancient DNA and zoonosis research. Bioessays 2017; 39. [DOI: 10.1002/bies.201700075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Affiliation(s)
| | - Tobias L. Lenz
- Research Group for Evolutionary Immunogenomics; Department of Evolutionary Ecology; Max Planck Institute for Evolutionary Biology; Plön Germany
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