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 Evolution Rapidly Optimizes Stability and Aggregation in Lattice Proteins Despite Pervasive Landscape Valleys and Mazes

Genetics ◽  
2020 ◽  
Vol 214 (4) ◽  
pp. 1047-1057 ◽  
Author(s):  
Jason Bertram ◽  
Joanna Masel
Keyword(s):  
Landscape Scale ◽  
High Dimensionality ◽  
Competing Interactions ◽  
Aggregation Propensity ◽  
Landscape Models ◽  
High Fitness ◽  
Indirect Path ◽  
Genetic Sequences ◽  
Evolutionary Consequences ◽  
Lattice Proteins

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 theoretical arguments have proposed that extremely long, winding adaptive paths may be required to reach even 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 explore the prevalence, scale, and evolutionary consequences of landscape mazes in a biophysically grounded computational model of protein evolution that captures the “frustration” between “stability” and aggregation propensity. Our stability-aggregation landscape exhibits extensive sign epistasis and local peaks galore. Although this frequently obstructs adaptive ascent to high fitness and virtually eliminates reproducibility of evolutionary outcomes, many adaptive paths do successfully complete the ascent from low to high fitness, with hydrophobicity a critical mediator of success. These successful paths exhibit maze-like properties on a global landscape scale, in which taking an indirect path helps to avoid low-fitness local peaks. This delicate balance of “hard but possible” adaptation could occur more broadly in other biological settings where competing interactions and frustration are important.

scholarly journals A two-level, dynamic fitness landscape of hepatitis C virus revealed by self-organized haplotype maps

2021 ◽  
Author(s):  
Soledad Delgado ◽  
Celia Perales ◽  
Carlos García-Crespo ◽  
María Eugenia Soria ◽  
Isabel Gallego ◽  
...  
Keyword(s):  
Cell Culture ◽  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Deep Sequencing ◽  
Fitness Landscape ◽  
Lower Number ◽  
Fitness Landscapes ◽  
Self Organized ◽  
High Fitness ◽  
Culture Environment

ABSTRACTFitness landscapes reflect the adaptive potential of viruses. There is no information on how fitness peaks evolve when a virus replicates extensively in a controlled cell culture environment. Here we report the construction of Self-Organized Maps (SOMs), based on deep sequencing reads of three amplicons of the NS5A-NS5B-coding region of hepatitis C virus (HCV). A two-dimensional neural network was constructed and organized according to sequence relatedness. The third dimension of the fitness profile was given by the haplotype frequencies at each neuron. Fitness maps were derived for 44 HCV populations that share a common ancestor that was passaged up to 210 times in human hepatoma Huh-7.5 cells. As the virus increased its adaptation to the cells, the number of fitness peaks expanded, and their distribution shifted in sequence space. The landscape consisted of an extended basal platform, and a lower number of protruding higher fitness peaks. The function that relates fitness level and peak abundance corresponds a power law, a relationship observed with other complex natural phenomena. The dense basal platform may serve as spring-board to attain high fitness peaks. The study documents a highly dynamic, double-layer fitness landscape of HCV when evolving in a monotonous cell culture environment. This information may help interpreting HCV fitness landscapes in complex in vivo environments.IMPORTANCEThe study provides for the first time the fitness landscape of a virus in the course of its adaptation to a cell culture environment, in absence of external selective constraints. The deep sequencing-based self-organized maps document a two-layer fitness distribution with an ample basal platform, and a lower number of protruding, high fitness peaks. This landscape structure offers potential benefits for virus resilience to mutational inputs.

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 A family of fitness landscapes modeled through gene regulatory networks

2021 ◽  
Author(s):  
Chia-Hung Yang ◽  
Samuel V. Scarpino
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Evolutionary Dynamics ◽  
Fitness Landscape ◽  
Fitness Function ◽  
Fitness Landscapes ◽  
Relative Fitness ◽  
Landscape Models ◽  
Fitness Value ◽  
Gene Regulatory

AbstractOver 100 years, Fitness landscapes have been a powerful metaphor for understanding the evolution of biological systems. These landscapes describe how genotypes are connected to each other and are related according to relative fitness. Despite the high dimensionality of such real-world landscapes, empirical studies are often limited in their ability to quantify the fitness of different genotypes beyond point mutations, while theoretical works attempt statistical/mechanistic models to reason the overall landscape structure. However, most classical fitness landscape models overlook an instinctive constraint that genotypes leading to the same phenotype almost certainly share the same fitness value, since the information of genotype-phenotype mapping is rarely incorporated. Here, we investigate fitness landscape models through the lens of Gene Regulatory Networks (GRNs), where the regulatory products are computed from multiple genes and collectively treated as the phenotypes. With the assumption that regulatory mediators/products exhibit binary states, we prove topographical features of GRN fitness landscape models such as accessibility and connectivity insensitive to the choice of the fitness function. Furthermore, using graph theory, we deduce a mesoscopic structure underlying GRN fitness landscape models that retains necessary information for evolutionary dynamics with minimal complexity. We also propose an algorithm to construct such a mesoscopic backbone which is more efficient than the brute-force approach. Combined, this work provides mathematical implications for fitness landscape models with high-dimensional genotype-phenotype mapping, yielding the potential to elucidate empirical landscapes and their resulting evolutionary processes in a manner complementary to existing computational studies.

scholarly journals Single cell fitness landscapes induced by genetic and pharmacologic perturbations in cancer

2020 ◽  
Author(s):  
Sohrab Salehi ◽  
Farhia Kabeer ◽  
Nicholas Ceglia ◽  
Mirela Andronescu ◽  
Marc Williams ◽  
...  
Keyword(s):  
Single Cell ◽  
Fitness Landscape ◽  
Clonal Selection ◽  
Selection Process ◽  
Treated Patient ◽  
Fitness Landscapes ◽  
Growth Potential ◽  
Response To Treatment ◽  
Dependent Manner ◽  
High Fitness

Tumour fitness landscapes underpin selection in cancer, impacting etiology, evolution and response to treatment. Progress in defining fitness landscapes has been impeded by a lack of timeseries perturbation experiments over realistic intervals at single cell resolution. We studied the nature of clonal dynamics induced by genetic and pharmacologic perturbation with a quantitative fitness model developed to ascribe quantitative selective coefficients to individual cancer clones, enable prediction of clone-specific growth potential, and forecast competitive clonal dynamics over time. We applied the model to serial single cell genome (>60,000 cells) and transcriptome (>58,000 cells) experiments ranging from 10 months to 2.5 years in duration. We found that genetic perturbation of TP53 in epithelial cell lines induces multiple forms of copy number alteration that confer increased fitness to clonal populations with measurable consequences on gene expression. In patient derived xenografts, predicted selective coefficients accurately forecasted clonal competition dynamics, that were validated with timeseries sampling of experimentally engineered mixtures of low and high fitness clones. In cisplatin-treated patient derived xenografts, the fitness landscape was inverted in a time-dependent manner, whereby a drug resistant clone emerged from a phylogenetic lineage of low fitness clones, and high fitness clones were eradicated. Moreover, clonal selection mediated reversible drug response early in the selection process, whereas late dynamics in genomically fixed clones were associated with transcriptional plasticity on a fixed clonal genotype. Together, our findings outline causal mechanisms with implication for interpreting how mutations and multi-faceted drug resistance mechanisms shape the etiology and cellular fitness of human cancers.

Working With Resistance and Encouraging Buy-In to New Programs

2011 ◽  
Vol 16 (1) ◽  
pp. 18-21
Author(s):  
Sara Joffe
Keyword(s):  
Long Term Care ◽  
Term Care ◽  
Optimal Outcomes ◽  
Speech Language Pathologist

In order to best meet the needs of older residents in long-term care settings, clinicians often develop programs designed to streamline and improve care. However, many individuals are reluctant to embrace change. This article will discuss strategies that the speech-language pathologist (SLP) can use to assess and address the source of resistance to new programs and thereby facilitate optimal outcomes.

scholarly journals The role of hydrological transience in peatland pattern formation

2013 ◽  
Vol 1 (1) ◽  
pp. 29-43 ◽  
Author(s):  
P. J. Morris ◽  
A. J. Baird ◽  
L. R. Belyea
Keyword(s):  
Steady State ◽  
Hydraulic Properties ◽  
Three Dimensional ◽  
Structural Complexity ◽  
Plant Litter ◽  
Future Climate Change ◽  
Litter Production ◽  
Landscape Models ◽  
Water Tables

Abstract. The sloping flanks of peatlands are commonly patterned with non-random, contour-parallel stripes of distinct micro-habitats such as hummocks, lawns and hollows. Patterning seems to be governed by feedbacks among peatland hydrological processes, plant micro-succession, plant litter production and peat decomposition. An improved understanding of peatland patterning may provide important insights into broader aspects of the long-term development of peatlands and their likely response to future climate change. We recreated a cellular simulation model from the literature, as well as three subtle variants of the model, to explore the controls on peatland patterning. Our models each consist of three submodels, which simulate: peatland water tables in a gridded landscape, micro-habitat dynamics in response to water-table depths, and changes in peat hydraulic properties. We found that the strength and nature of simulated patterning was highly dependent on the degree to which water tables had reached a steady state in response to hydrological inputs. Contrary to previous studies, we found that under a true steady state the models predict largely unpatterned landscapes that cycle rapidly between contrasting dry and wet states, dominated by hummocks and hollows, respectively. Realistic patterning only developed when simulated water tables were still transient. Literal interpretation of the degree of hydrological transience required for patterning suggests that the model should be discarded; however, the transient water tables appear to have inadvertently replicated an ecological memory effect that may be important to peatland patterning. Recently buried peat layers may remain hydrologically active despite no longer reflecting current vegetation patterns, thereby highlighting the potential importance of three-dimensional structural complexity in peatlands to understanding the two-dimensional surface-patterning phenomenon. The models were highly sensitive to the assumed values of peat hydraulic properties, which we take to indicate that the models are missing an important negative feedback between peat decomposition and changes in peat hydraulic properties. Understanding peatland patterning likely requires the unification of cellular landscape models such as ours with cohort-based models of long-term peatland development.

scholarly journals The Role of Ultra-widefield Retinal Imaging as a Standard Assessment Tool in the Cataract Practice

2017 ◽  
Vol 10 (01) ◽  
pp. 31
Author(s):  
Kerry K Assil ◽  
V Nicholas Batra ◽  
◽  
Keyword(s):  
Cataract Surgery ◽  
Assessment Tool ◽  
Comprehensive Evaluation ◽  
Retinal Imaging ◽  
High Expectations ◽  
Optimal Outcomes ◽  
Standard Assessment ◽  
Retinal Pathology ◽  
Quality Of Vision

Comprehensive evaluation of retinal health prior to and following cataract surgery is critical to supporting optimal outcomes. In addition to the importance of identifying retinal pathology that may prevent or delay cataract surgery, continuous advances in refractive intraocular lens technology and cataract surgical technique, coupled with increasingly high expectations regarding visual outcomes among younger patients, make the consideration of long-term quality of vision paramount in the cataract assessment. Optos® ultrawidefield retinal imaging supports this clinical objective by providing imaging standardization in a streamlined, patient-friendly exam process, supporting robust documentation that facilitates mapping of disease progression, and offering potential economic advantages in a resourceconstrained environment.

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.

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