Adaptive Landscape by Environment Interactions Dictate Evolutionary Dynamics in Models of Drug Resistance
Publication Date
January 25, 2016
Journal
PLOS Computational Biology
Authors
C. Brandon Ogbunugafor, C. Scott Wylie, Ibrahim Diakite, Daniel M. Weinreich, et al
Volume
12
Issue
1
Pages
e1004710
DOI
https://dx.plos.org/10.1371/journal.pcbi.1004710
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004710
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/26808374
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4726534
Europe PMC
http://europepmc.org/abstract/MED/26808374
Web of Science
000369366100037
Scopus
84956767931
Mendeley
http://www.mendeley.com/research/adaptive-landscape-environment-interactions-dictate-evolutionary-dynamics-models-drug-resistance-2
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Mendeley | Further Information

{"title"=>"Adaptive Landscape by Environment Interactions Dictate Evolutionary Dynamics in Models of Drug Resistance", "type"=>"journal", "authors"=>[{"first_name"=>"C. Brandon", "last_name"=>"Ogbunugafor", "scopus_author_id"=>"13105376500"}, {"first_name"=>"C. Scott", "last_name"=>"Wylie", "scopus_author_id"=>"14055219000"}, {"first_name"=>"Ibrahim", "last_name"=>"Diakite", "scopus_author_id"=>"56525141700"}, {"first_name"=>"Daniel M.", "last_name"=>"Weinreich", "scopus_author_id"=>"35270349700"}, {"first_name"=>"Daniel L.", "last_name"=>"Hartl", "scopus_author_id"=>"23990592000"}], "year"=>2016, "source"=>"PLoS Computational Biology", "identifiers"=>{"issn"=>"15537358", "pui"=>"608034646", "sgr"=>"84956767931", "scopus"=>"2-s2.0-84956767931", "pmid"=>"26808374", "doi"=>"10.1371/journal.pcbi.1004710"}, "id"=>"28e03f33-7547-38c2-903b-28ce9ea6a878", "abstract"=>"The adaptive landscape analogy has found practical use in recent years, as many have explored how their understanding can inform therapeutic strategies that subvert the evolution of drug resistance. A major barrier to applications of these concepts is a lack of detail concerning how the environment affects adaptive landscape topography, and consequently, the outcome of drug treatment. Here we combine empirical data, evolutionary theory, and computer simulations towards dissecting adaptive landscape by environment interactions for the evolution of drug resistance in two dimensions-drug concentration and drug type. We do so by studying the resistance mediated by Plasmodium falciparum dihydrofolate reductase (DHFR) to two related inhibitors-pyrimethamine and cycloguanil-across a breadth of drug concentrations. We first examine whether the adaptive landscapes for the two drugs are consistent with common definitions of cross-resistance. We then reconstruct all accessible pathways across the landscape, observing how their structure changes with drug environment. We offer a mechanism for non-linearity in the topography of accessible pathways by calculating of the interaction between mutation effects and drug environment, which reveals rampant patterns of epistasis. We then simulate evolution in several different drug environments to observe how these individual mutation effects (and patterns of epistasis) influence paths taken at evolutionary \"forks in the road\" that dictate adaptive dynamics in silico. In doing so, we reveal how classic metrics like the IC50 and minimal inhibitory concentration (MIC) are dubious proxies for understanding how evolution will occur across drug environments. We also consider how the findings reveal ambiguities in the cross-resistance concept, as subtle differences in adaptive landscape topography between otherwise equivalent drugs can drive drastically different evolutionary outcomes. Summarizing, we discuss the results with regards to their basic contribution to the study of empirical adaptive landscapes, and in terms of how they inform new models for the evolution of drug resistance.", "link"=>"http://www.mendeley.com/research/adaptive-landscape-environment-interactions-dictate-evolutionary-dynamics-models-drug-resistance-2", "reader_count"=>40, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>1, "Researcher"=>10, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>10, "Student > Postgraduate"=>5, "Student > Master"=>3, "Student > Bachelor"=>4, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>1, "Researcher"=>10, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>10, "Student > Postgraduate"=>5, "Student > Master"=>3, "Student > Bachelor"=>4, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>3, "Biochemistry, Genetics and Molecular Biology"=>6, "Agricultural and Biological Sciences"=>24, "Medicine and Dentistry"=>1, "Veterinary Science and Veterinary Medicine"=>1, "Physics and Astronomy"=>1, "Social Sciences"=>1, "Computer Science"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>24}, "Computer Science"=>{"Computer Science"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>6}, "Unspecified"=>{"Unspecified"=>3}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>1}}, "reader_count_by_country"=>{"United States"=>1, "France"=>2}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2634211"], "description"=>"<p>Adaptive landscapes for <i>Plasmodium falciparum</i> across environments are organized into their accessible trajectories across several drug concentrations of pyrimethamine and cycloguanil. We define an accessible pathway as having an increasing fitness along the steps of a pathway. The five panels correspond to PYR (A-C, in blue) and CYC (D-F, in red). The <i>y</i>-axis is growth rate. The <i>x</i>-axis denotes Hamming distance (0 = wild type ancestor, 1 = single mutants, 2 = double mutant, 3 = triple mutant, 4 = quadruple mutant), and the <i>z</i>-axis corresponds to the 24 different possible pathways between the wild type ancestor (0000) and maximal resistant allele (1111) (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004710#pcbi.1004710.s005\" target=\"_blank\">S3 Table</a> for pathways a-x, corresponding to each individual pathway). Note how trajectories differ as a function of drug concentration (top to bottom) and drug type (left to right). Growth rates are in terms of time<sup>-1</sup>. Concentrations: no drug (A,D), 100 uM (B, E), and 10,000 uM (C,F). Note how both the number and topography of accessible pathways vary across drug environments.</p>", "links"=>[], "tags"=>["drug environments", "mic", "evolution", "Drug Resistance", "Plasmodium falciparum Dihydrofolate Reductase", "adaptive landscapes", "ic", "adaptive landscape analogy", "drug environment", "DHFR", "mutation effects", "adaptive landscape topography", "Environment Interactions Dictate Evolutionary Dynamics"], "article_id"=>1642273, "categories"=>["Biological Sciences"], "users"=>["C. Brandon Ogbunugafor", "C. Scott Wylie", "Ibrahim Diakite", "Daniel M. Weinreich", "Daniel L. Hartl"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004710.g003", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_An_illustration_of_how_the_structure_of_accessible_adaptive_trajectories_differs_between_drug_environments_/1642273", "title"=>"An illustration of how the structure of accessible adaptive trajectories differs between drug environments.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-28 12:41:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/2634214"], "description"=>"<p>Each color represents the effect of a particular mutation. Small (unconnected) symbols represent individual mutational effects in a particular genetic background; large symbols (connected by lines) represent the average mutational effects over all genetic backgrounds. The scatter of the smaller symbols around the large ones indicates that individual effects are dependent on genetic background, the signature of epistasis. (A) and (B) represent the calculations for pyrimethamine (absolute and scaled, respectively), with (C) and (D) representing the calculations for cycloguanil. For 4B and 4D, units are in relative effects of mutation, a property determined by the absolute fitness effect divided by the growth rate of the ancestor (0000). (B) and (D) are also marked with circles and labels that identify especially large epistatic interactions (labeled in terms of the mutational events that produce the notable fitness effect) at particular drug concentrations. Due to the large differences in scaled effects for the two drugs, the <i>y</i>-axes for B and D have different ranges. Though epistasis can be observed here, the standard deviation in fitness effects was measured directly and plotted in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004710#pcbi.1004710.s002\" target=\"_blank\">S2 Fig</a>. In 4D, the 0011→0111 mutation effect is slightly offset (vertically) so that it can be distinguished from the 0101→0111 mutation effect.</p>", "links"=>[], "tags"=>["drug environments", "mic", "evolution", "Drug Resistance", "Plasmodium falciparum Dihydrofolate Reductase", "adaptive landscapes", "ic", "adaptive landscape analogy", "drug environment", "DHFR", "mutation effects", "adaptive landscape topography", "Environment Interactions Dictate Evolutionary Dynamics"], "article_id"=>1642276, "categories"=>["Biological Sciences"], "users"=>["C. Brandon Ogbunugafor", "C. Scott Wylie", "Ibrahim Diakite", "Daniel M. Weinreich", "Daniel L. Hartl"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004710.g004", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_effect_of_mutations_changes_as_a_function_of_drug_environment_and_genetic_background_with_prominent_epistasis_/1642276", "title"=>"The effect of mutations changes as a function of drug environment and genetic background, with prominent epistasis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-28 12:41:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/2634215"], "description"=>"<p>These are illustrative examples of the most preferred pathways for evolution at each of the simulated PYR and CYC environments. Panels correspond to several simulation scenarios: (A) no drug, (B) low PYR (1.0 uM), (C) intermediate PYR (100 uM), (D) high PYR (10,000 uM), (E) low CYC (1.0 uM), (F) intermediate CYC (100 uM), and (G) high CYC (10,000 uM). Here we observe 5 different fixing alleles across the 7 different drug environments. This is indicative of how the preferred adaptive trajectory is strongly dependent on drug environment. Not only do pathways differ, but the mean fixation times for the fixing allele also differs significantly across environment: <i>F</i> = 763.36, df = 5,564, <i>P</i> < 0.0000001; <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004710#pcbi.1004710.s008\" target=\"_blank\">S6 Table</a>).</p>", "links"=>[], "tags"=>["drug environments", "mic", "evolution", "Drug Resistance", "Plasmodium falciparum Dihydrofolate Reductase", "adaptive landscapes", "ic", "adaptive landscape analogy", "drug environment", "DHFR", "mutation effects", "adaptive landscape topography", "Environment Interactions Dictate Evolutionary Dynamics"], "article_id"=>1642277, "categories"=>["Biological Sciences"], "users"=>["C. Brandon Ogbunugafor", "C. Scott Wylie", "Ibrahim Diakite", "Daniel M. Weinreich", "Daniel L. Hartl"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004710.g005", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulations_of_evolution_in_pyrimethamine_PYR_and_cycloguanil_CYC_reveal_differences_in_evolutionary_dynamics_across_environments_/1642277", "title"=>"Simulations of evolution in pyrimethamine (PYR) and cycloguanil (CYC) reveal differences in evolutionary dynamics across environments.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-28 12:41:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/2634217", "https://ndownloader.figshare.com/files/2634218", "https://ndownloader.figshare.com/files/2634219", "https://ndownloader.figshare.com/files/2634220", "https://ndownloader.figshare.com/files/2634221", "https://ndownloader.figshare.com/files/2634222", "https://ndownloader.figshare.com/files/2634223", "https://ndownloader.figshare.com/files/2634224", "https://ndownloader.figshare.com/files/2634225"], "description"=>"<div><p>The adaptive landscape analogy has found practical use in recent years, as many have explored how their understanding can inform therapeutic strategies that subvert the evolution of drug resistance. A major barrier to applications of these concepts is a lack of detail concerning how the environment affects adaptive landscape topography, and consequently, the outcome of drug treatment. Here we combine empirical data, evolutionary theory, and computer simulations towards dissecting adaptive landscape by environment interactions for the evolution of drug resistance in two dimensions—drug concentration and drug type. We do so by studying the resistance mediated by <i>Plasmodium falciparum</i> dihydrofolate reductase (DHFR) to two related inhibitors—pyrimethamine and cycloguanil—across a breadth of drug concentrations. We first examine whether the adaptive landscapes for the two drugs are consistent with common definitions of cross-resistance. We then reconstruct all accessible pathways across the landscape, observing how their structure changes with drug environment. We offer a mechanism for non-linearity in the topography of accessible pathways by calculating of the interaction between mutation effects and drug environment, which reveals rampant patterns of epistasis. We then simulate evolution in several different drug environments to observe how these individual mutation effects (and patterns of epistasis) influence paths taken at evolutionary “forks in the road” that dictate adaptive dynamics <i>in silico</i>. In doing so, we reveal how classic metrics like the IC<sub>50</sub> and minimal inhibitory concentration (MIC) are dubious proxies for understanding how evolution will occur across drug environments. We also consider how the findings reveal ambiguities in the cross-resistance concept, as subtle differences in adaptive landscape topography between otherwise equivalent drugs can drive drastically different evolutionary outcomes. Summarizing, we discuss the results with regards to their basic contribution to the study of empirical adaptive landscapes, and in terms of how they inform new models for the evolution of drug resistance.</p></div>", "links"=>[], "tags"=>["drug environments", "mic", "evolution", "Drug Resistance", "Plasmodium falciparum Dihydrofolate Reductase", "adaptive landscapes", "ic", "adaptive landscape analogy", "drug environment", "DHFR", "mutation effects", "adaptive landscape topography", "Environment Interactions Dictate Evolutionary Dynamics"], "article_id"=>1642279, "categories"=>["Biological Sciences"], "users"=>["C. Brandon Ogbunugafor", "C. Scott Wylie", "Ibrahim Diakite", "Daniel M. Weinreich", "Daniel L. Hartl"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1004710.s001", "https://dx.doi.org/10.1371/journal.pcbi.1004710.s002", "https://dx.doi.org/10.1371/journal.pcbi.1004710.s003", "https://dx.doi.org/10.1371/journal.pcbi.1004710.s004", "https://dx.doi.org/10.1371/journal.pcbi.1004710.s005", "https://dx.doi.org/10.1371/journal.pcbi.1004710.s006", "https://dx.doi.org/10.1371/journal.pcbi.1004710.s007", "https://dx.doi.org/10.1371/journal.pcbi.1004710.s008", "https://dx.doi.org/10.1371/journal.pcbi.1004710.s009"], "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Adaptive_Landscape_by_Environment_Interactions_Dictate_Evolutionary_Dynamics_in_Models_of_Drug_Resistance_/1642279", "title"=>"Adaptive Landscape by Environment Interactions Dictate Evolutionary Dynamics in Models of Drug Resistance", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2016-01-28 12:41:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/2634208"], "description"=>"<p>(A) Basic structure of available pathways between the ancestor (0000) and quadruple mutant (1111). 0000 corresponds to the wild-type DHFR genotype, with 1111 the mutated protein at each of four sites (N51I, C59R, S108N, and I164L). (B, C) Growth curves for the <i>P</i>. <i>falciparum</i> alleles in pyrimethamine and cycloguanil, simulated from previously measured empirical measurements [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004710#pcbi.1004710.ref011\" target=\"_blank\">11</a>,<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004710#pcbi.1004710.ref012\" target=\"_blank\">12</a>]. Growth rates are in terms of time<sup>-1</sup>. The <i>x</i>-axis is in terms Log<sub>10</sub> of the drug concentration (uM). Note the subtle differences between the growth rate curves for the individual alleles across the two related drugs.</p>", "links"=>[], "tags"=>["drug environments", "mic", "evolution", "Drug Resistance", "Plasmodium falciparum Dihydrofolate Reductase", "adaptive landscapes", "ic", "adaptive landscape analogy", "drug environment", "DHFR", "mutation effects", "adaptive landscape topography", "Environment Interactions Dictate Evolutionary Dynamics"], "article_id"=>1642270, "categories"=>["Uncategorised"], "users"=>["C. Brandon Ogbunugafor", "C. Scott Wylie", "Ibrahim Diakite", "Daniel M. Weinreich", "Daniel L. Hartl"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004710.g001", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_P_falciparum_resistance_to_pyrimethamine_PYR_and_cycloguanil_CYC_Basic_structure_of_the_adaptive_landscape_and_growth_rates_across_drug_environment_/1642270", "title"=>"<i>P</i>. <i>falciparum</i> resistance to pyrimethamine (PYR) and cycloguanil (CYC): Basic structure of the adaptive landscape and growth rates across drug environment.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-28 12:41:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/2634209"], "description"=>"<p>(A) Correlations between the IC<sub>50</sub> values for pyrimethamine (<i>y</i>-axis) and cylcoguanil (<i>x</i>-axis). Each point corresponds to a single genotype in the landscape. The landscapes are significantly correlated for both traits, indicating cross-resistance (IC<sub>50</sub>: <i>R</i><sup>2</sup> = 0.74, <i>P</i> = 3.9 X 10<sup>−5</sup>; Error bars on IC<sub>50</sub> values represent standard errors of the published experimental replicates [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004710#pcbi.1004710.ref011\" target=\"_blank\">11</a>,<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004710#pcbi.1004710.ref012\" target=\"_blank\">12</a>]. (B) A graph of the <i>R</i><sup>2</sup> as a function of drug concentration, demonstrating that the landscapes remain correlated across drug concentrations, albeit less so as drug concentration increases. Pearson product-moment correlations were significant (<i>P</i> < 0.05) at all concentrations except the highest in our study (1.0 X 10<sup>5</sup> uM), which was nearly significant (<i>P</i> = 0.067). Individual scatter plots that produce these <i>R</i><sup>2</sup> values, and corresponding <i>P</i> values, can be found in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004710#pcbi.1004710.s001\" target=\"_blank\">S1 Fig</a>.</p>", "links"=>[], "tags"=>["drug environments", "mic", "evolution", "Drug Resistance", "Plasmodium falciparum Dihydrofolate Reductase", "adaptive landscapes", "ic", "adaptive landscape analogy", "drug environment", "DHFR", "mutation effects", "adaptive landscape topography", "Environment Interactions Dictate Evolutionary Dynamics"], "article_id"=>1642271, "categories"=>["Biological Sciences"], "users"=>["C. Brandon Ogbunugafor", "C. Scott Wylie", "Ibrahim Diakite", "Daniel M. Weinreich", "Daniel L. Hartl"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004710.g002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Resistance_to_pyrimethamine_is_correlated_with_resistance_to_cycloguanil_/1642271", "title"=>"Resistance to pyrimethamine is correlated with resistance to cycloguanil.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-28 12:41:48"}

PMC Usage Stats | Further Information

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Relative Metric

{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
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