scholarly journals Fitness Landscapes Reveal Simple Strategies for Steering Evolution to Minimize Antibiotic Resistance

2016 ◽  
Author(s):  
Maria Smith ◽  
Sarah Cobey
Keyword(s):  
Antibiotic Resistance ◽  
Fitness Landscape ◽  
Adaptive Strategy ◽  
Computational Approach ◽  
Fitness Landscapes ◽  
High Performing ◽  
Slow Adaptation ◽  
Starting Conditions ◽  
Evolution Of Resistance

AbstractThe evolution of antibiotic resistance presents a practical and theoretical challenge: the design of strategies that limit the risk of evolved resistance while effectively treating current patients. Sequentially cycling antibiotics has been proposed as a way to slow the evolution of resistance by reducing the extent of adaptation to a given drug, and clinical trials have demonstrated its effectiveness in some settings. Empirical fitness landscapes in theory allow the sequence of drugs to be refined to maximize tradeoffs between drugs and thereby slow adaptation even further. Using the measured growth rates of 16 genotypes ofEscherichia coliin the presence ofβ-lactam antibiotics, we test an adaptive strategy, based on a Markov chain transition matrix, to select drug sequences that continuously minimize resistance. Cycling is never selected over the long term. Instead, monotherapy with the antibiotic that permits the least growth in its landscape’s absorbing state is rapidly selected from different starting conditions. Analysis of a synthetic fitness landscape shows that cycling drugs that induce sensitivity to one other could, in theory, outperform monotherapy. These results underscore the importance of considering the specific topologies of fitness landscape in determining whether to cycle drugs and suggest a general computational approach to identify high performing, practical strategies to manage resistance.

scholarly journals Adaptive benefits from small mutation supplies in an antibiotic resistance enzyme

2017 ◽  
Vol 114 (48) ◽  
pp. 12773-12778 ◽  
Author(s):  
Merijn L. M. Salverda ◽  
Jeroen Koomen ◽  
Bertha Koopmanschap ◽  
Mark P. Zwart ◽  
J. Arjan G. M. de Visser
Keyword(s):  
Antibiotic Resistance ◽  
High Resistance ◽  
Artificial Selection ◽  
Fitness Landscape ◽  
Adaptive Landscape ◽  
Clinical Settings ◽  
Short Term ◽  
Theory Lead

Populations with large mutation supplies adapt via the “greedy” substitution of the fittest genotype available, leading to fast and repeatable short-term responses. At longer time scales, smaller mutation supplies may in theory lead to larger improvements when distant high-fitness genotypes more readily evolve from lower-fitness intermediates. Here we test for long-term adaptive benefits from small mutation supplies using in vitro evolution of an antibiotic-degrading enzyme in the presence of a novel antibiotic. Consistent with predictions, large mutant libraries cause rapid initial adaptation via the substitution of cohorts of mutations, but show later deceleration and convergence. Smaller libraries show on average smaller initial, but also more variable, improvements, with two lines yielding alleles with exceptionally high resistance levels. These two alleles share three mutations with the large-library alleles, which are known from previous work, but also have unique mutations. Replay evolution experiments and analyses of the adaptive landscape of the enzyme suggest that the benefit resulted from a combination of avoiding mutational cohorts leading to local peaks and chance. Our results demonstrate adaptive benefits from limited mutation supplies on a rugged fitness landscape, which has implications for artificial selection protocols in biotechnology and argues for a better understanding of mutation supplies in clinical settings.

scholarly journals A trimethoprim derivative impedes antibiotic resistance evolution

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Madhu Sudan Manna ◽  
Yusuf Talha Tamer ◽  
Ilona Gaszek ◽  
Nicole Poulides ◽  
Ayesha Ahmed ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Resistant Bacteria ◽  
Gene Encoding ◽  
Antibiotic Resistant ◽  
E Coli ◽  
Resistance Evolution ◽  
Evolutionary Trajectories ◽  
Evolution Of Resistance

AbstractThe antibiotic trimethoprim (TMP) is used to treat a variety of Escherichia coli infections, but its efficacy is limited by the rapid emergence of TMP-resistant bacteria. Previous laboratory evolution experiments have identified resistance-conferring mutations in the gene encoding the TMP target, bacterial dihydrofolate reductase (DHFR), in particular mutation L28R. Here, we show that 4’-desmethyltrimethoprim (4’-DTMP) inhibits both DHFR and its L28R variant, and selects against the emergence of TMP-resistant bacteria that carry the L28R mutation in laboratory experiments. Furthermore, antibiotic-sensitive E. coli populations acquire antibiotic resistance at a substantially slower rate when grown in the presence of 4’-DTMP than in the presence of TMP. We find that 4’-DTMP impedes evolution of resistance by selecting against resistant genotypes with the L28R mutation and diverting genetic trajectories to other resistance-conferring DHFR mutations with catalytic deficiencies. Our results demonstrate how a detailed characterization of resistance-conferring mutations in a target enzyme can help identify potential drugs against antibiotic-resistant bacteria, which may ultimately increase long-term efficacy of antimicrobial therapies by modulating evolutionary trajectories that lead to resistance.

scholarly journals Evolution rapidly optimizes stability and aggregation in lattice proteins despite pervasive landscape valleys and mazes

2019 ◽  
Author(s):  
Jason Bertram ◽  
Joanna Masel
Keyword(s):  
Fitness Landscape ◽  
Fitness Landscapes ◽  
Optimal Outcomes ◽  
Landscape Models ◽  
High Fitness ◽  
Genetic Sequences ◽  
Folded Structures ◽  
Lattice Proteins ◽  
Fold Stability

AbstractFitness landscapes are widely used to visualize the dynamics and long-term outcomes of evolution. The fitness landscapes of genetic sequences are characterized by high dimensionality and “ruggedness” due to sign epistasis. Ascending from low to high fitness on such landscapes can be difficult because adaptive trajectories get stuck at low-fitness local peaks. Compounding matters, recent computational complexity arguments have proposed that extremely long, winding adaptive paths may be required to even reach local peaks, a “maze-like” landscape topography. The extent to which peaks and mazes shape the mode and tempo of evolution is poorly understood due to empirical limitations and the abstractness of many landscape models. We develop a biophysically-grounded computational model of protein evolution based on two novel extensions of the classic hydrophobic-polar lattice model of protein folding. First, rather than just considering fold stability we account for the tradeoff between stability and aggregation propensity. Second, we use a “hydrophobic zipping” algorithm to kinetically generate ensembles of post-translationally folded structures. Our stability-aggregation fitness landscape exhibits extensive sign epistasis and local peaks galore. We confirm the postulated existence of maze-like topography in our biologically-grounded landscape. Although these landscape features frequently obstruct adaptive ascent to high fitness and virtually eliminate reproducibility of evolutionary outcomes, many adaptive paths do successfully complete the long ascent from low to high fitness. This delicate balance of “hard but possible” adaptation could occur more broadly provided that the optimal outcomes possible under a tradeoff are improved by rare constraint-breaking substitutions.

scholarly journals Exploiting evolutionary non-commutativity to prevent the emergence of bacterial antibiotic resistance

2014 ◽  
Author(s):  
Daniel Nichol ◽  
Peter Jeavons ◽  
Alexander G. Fletcher ◽  
Robert A. Bonomo ◽  
Philip K. Maini ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Markov Chain ◽  
Fitness Landscape ◽  
Markov Chain Model ◽  
Drug Application ◽  
Fitness Landscapes ◽  
Chain Model ◽  
Optimal Drug ◽  
Genotype Space ◽  
Emergence Of Resistance

AbstractThe increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. Here, we develop a Markov Chain model of evolution in asexually reproducing populations which is an encoding of the Strong Selection Weak Mutation model of evolution on fitness landscapes. This model associates the global properties of the fitness landscape with the algebraic properties of the Markov Chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies. Utilizing this formalism, we analyse 15 empirical fitness landscapes of E. coli under selection by di.erent beta-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be both hindered and promoted by di.erent sequences of drug application. Further, we derive optimal drug application sequences with which we can probabilistically ‘steer’ the population through genotype space to avoid the emergence of resistance. This suggests a new strategy in the war against antibiotic.therapy.resistant organisms: drug sequencing to shepherd evolution through genotype space to states from which resistance cannot emerge and by which to maximize the chance of successful therapy.Background:The increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. Previous studies of bacterial evolutionary dynamics have shown that populations evolving on fitness landscapes follow predictable paths. In this article, we develop a general mathematical model of evolution and hypothesise that it can be used to understand, and avoid, the emergence of antibiotic resistance.Methods and Findings:We develop a Markov Chain model of evolution in asexually reproducing populations which is an encoding of the Strong Selection Weak Mutation model of evolution on fitness landscapes. This model associates the global properties of the fitness landscape with the algebraic properties of the Markov Chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies. Utilizing this formalism, we analyse 15 empirical fitness landscapes of E. coli under selection by different β-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be both hindered and promoted by different sequences of drug application. We show that resistance to a given antibiotic is promoted in 61.4%, 68.6% and 70.3% of possible orderings of single, pair or triple prior drug administrations, respectively. Further, we derive optimal drug application sequences with which we can probabilistically ‘steer’ the population through genotype space to avoid the emergence of resistance.Conclusions:Our model provides generalisable results of interest to theorists studying evolution as well as providing specific, testable predictions for experimentalists to validate our methods. Further, these results suggest a new strategy in the war against antibiotic-therapy-resistant organisms: drug sequencing to shepherd an evolving population through genotype space to states from which resistance cannot emerge and from which we can maximize the likelihood of successful therapy using existing drugs. While our results are derived using a specific class of antibiotics, the method is generalisable to other situations, including the emergence of resistance to targeted therapy in cancer and how species could change secondary to changing climate or geographical movement.

Frequency selectivity, multistability, and oscillations emerge from models of genetic regulatory systems

1998 ◽  
Vol 274 (2) ◽  
pp. C531-C542 ◽  
Author(s):  
Paul Smolen ◽  
Douglas A. Baxter ◽  
John H. Byrne
Keyword(s):  
Cellular Response ◽  
Cultured Cells ◽  
Computational Approach ◽  
Long Term Memory ◽  
Mrna Levels ◽  
Complex Dynamic ◽  
Dynamic Activity ◽  
Regulatory Systems ◽  
Complex Models

To examine the capability of genetic regulatory systems for complex dynamic activity, we developed simple kinetic models that incorporate known features of these systems. These include autoregulation and stimulus-dependent phosphorylation of transcription factors (TFs), dimerization of TFs, crosstalk, and feedback. The simplest model manifested multiple stable steady states, and brief perturbations could switch the model between these states. Such transitions might explain, for example, how a brief pulse of hormone or neurotransmitter could elicit a long-lasting cellular response. In slightly more complex models, oscillatory regimes were identified. The addition of competition between activating and repressing TFs provided a plausible explanation for optimal stimulus frequencies that give maximal transcription. Such optimal frequencies are suggested by recent experiments comparing training paradigms for long-term memory formation and examining changes in mRNA levels in repetitively stimulated cultured cells. In general, the computational approach illustrated here, combined with appropriate experiments, provides a conceptual framework for investigating the function of genetic regulatory systems.

scholarly journals On the development of instabilities in an annulus and a shell composed of a poro-hyperelastic material

2018 ◽  
Vol 474 (2218) ◽  
pp. 20180239 ◽  
Author(s):  
A. P. S. Selvadurai ◽  
A. P. Suvorov
Keyword(s):  
Soft Tissues ◽  
Mechanical Response ◽  
Numerical Solutions ◽  
Pore Space ◽  
Stability Loss ◽  
Computational Approach ◽  
Hyperelastic Material ◽  
Porous Skeleton ◽  
Internal Pressures

The paper investigates the development of instability in an internally pressurized annulus of a poro-hyperelastic material. The theory of poro-hyperelasticity is proposed as an approach for modelling the mechanical behaviour of highly deformable elastic materials, the pore space of which is saturated with a fluid. The consideration of coupling between the mechanical response of the hyperelastic porous skeleton and the pore fluid is important when applying the developments to soft tissues encountered in biomechanical applications. The paper examines the development of an instability in a poro-hyperelastic annulus subjected to internal pressure. Using a computational approach, numerical solutions are obtained for the internal pressures that promote either short-term or long-term instability in a poro-hyperelastic annulus and a poro-hyperelastic shell. In addition, time-dependent effects of stability loss are examined. The analytical solutions are used to benchmark the accuracy of the computational approach.

scholarly journals On the (un-)predictability of a large intragenic fitness landscape

2016 ◽  
Author(s):  
Claudia Bank ◽  
Sebastian Matuszewski ◽  
Ryan T. Hietpas ◽  
Jeffrey D. Jensen
Keyword(s):  
Amino Acid ◽  
Fitness Landscape ◽  
Fitness Landscapes ◽  
Specific Information ◽  
Theoretical Approaches ◽  
Elevated Salinity ◽  
Theoretical Predictions ◽  
Experimental Approaches ◽  
Deterministic Processes ◽  
Negative Epistasis

AbstractThe study of fitness landscapes, which aims at mapping genotypes to fitness, is receiving ever-increasing attention. Novel experimental approaches combined with NGS methods enable accurate and extensive studies of the fitness effects of mutations – allowing us to test theoretical predictions and improve our understanding of the shape of the true underlying fitness landscape, and its implications for the predictability and repeatability of evolution.Here, we present a uniquely large multi-allelic fitness landscape comprised of 640 engineered mutants that represent all possible combinations of 13 amino-acid changing mutations at six sites in the heat-shock protein Hsp90 in Saccharomyces cerevisiae under elevated salinity. Despite a prevalent pattern of negative epistasis in the landscape, we find that the global fitness peak is reached via four positively epistatic mutations. Combining traditional and extending recently proposed theoretical and statistical approaches, we quantify features of the global multi-allelic fitness landscape. Using subsets of the data, we demonstrate that extrapolation beyond a known part of the landscape is difficult owing to both local ruggedness and amino-acid specific epistatic hotspots, and that inference is additionally confounded by the non-random choice of mutations for experimental fitness landscapes.Author SummaryThe study of fitness landscapes is fundamentally concerned with understanding the relative roles of stochastic and deterministic processes in adaptive evolution. Here, the authors present a uniquely large and complete multi-allelic intragenic fitness landscape of 640 systematically engineered mutations in yeast Hsp90. Using a combination of traditional and recently proposed theoretical approaches, they study the accessibility of the global fitness peak, and the potential for predictability of the fitness landscape topography. They report local ruggedness of the landscape and the existence of epistatic hotspot mutations, which together make extrapolation and hence predictability inherently difficult, if mutation-specific information is not considered.

scholarly journals The distribution of epistasis on simple fitness landscapes

2018 ◽  
Author(s):  
Christelle Fraïsse ◽  
John J. Welch
Keyword(s):  
Evolutionary Dynamics ◽  
Fitness Landscape ◽  
Null Model ◽  
Large Data ◽  
Fitness Landscapes ◽  
Mutational Bias ◽  
Data Set ◽  
Epistatic Interactions ◽  
Standing Variation ◽  
Mean And Variance

AbstractFitness interactions between mutations can influence a population’s evolution in many different ways. While epistatic effects are difficult to measure precisely, important information about the overall distribution is captured by the mean and variance of log fitnesses for individuals carrying different numbers of mutations. We derive predictions for these quantities from simple fitness landscapes, based on models of optimizing selection on quantitative traits. We also explore extensions to the models, including modular pleiotropy, variable effects sizes, mutational bias, and maladaptation of the wild-type. We illustrate our approach by reanalysing a large data set of mutant effects in a yeast snoRNA. Though characterized by some strong epistatic interactions, these data give a good overall fit to the non-epistatic null model, suggesting that epistasis might have little effect on the evolutionary dynamics in this system. We also show how the amount of epistasis depends on both the underlying fitness landscape, and the distribution of mutations, and so it is expected to vary in consistent ways between new mutations, standing variation, and fixed mutations.

scholarly journals Cheaters shape the evolution of phenotypic heterogeneity in Bacillus subtilis biofilms

2018 ◽  
Author(s):  
Marivic Martin ◽  
Anna Dragoš ◽  
Simon B. Otto ◽  
Daniel Schäfer ◽  
Susanne Brix ◽  
...  
Keyword(s):  
Bacillus Subtilis ◽  
Prolonged Exposure ◽  
Adaptive Strategy ◽  
Phenotypic Heterogeneity ◽  
Structural Proteins ◽  
Cell Level ◽  
General Adaptation ◽  
Matrix Production ◽  
Matrix Components

ABSTRACTBiofilms are closely packed cells held and shielded by extracellular matrix composed of structural proteins and exopolysaccharides (EPS). As matrix components are costly to produce and shared within the population, EPS-deficient cells can act as cheaters by gaining benefits from the cooperative nature of EPS producers. Remarkably, genetically programmed EPS producers can also exhibit phenotypic heterogeneity at single cell level. Previous studies have shown that spatial structure of biofilms limits the spread of cheaters, but the long-term influence of cheating on biofilm evolution is not well understood. Here, we examine the influence of EPS non-producers on evolution of matrix production within the populations of EPS producers in a model biofilm-forming bacterium, Bacillus subtilis. We discovered that general adaptation to biofilm lifestyle leads to an increase in phenotypical heterogeneity of eps expression. Apparently, prolonged exposure to EPS-deficient cheaters, may result in different adaptive strategy, where eps expression increases uniformly within the population. We propose a molecular mechanism behind such adaptive strategy and demonstrate how it can benefit the EPS-producers in the presence of cheaters. This study provides additional insights on how biofilms adapt and respond to stress caused by exploitation in long-term scenario.

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