A multiscale framework for microbial evolution to identify the emergence of antibiotic resistance

La résistance aux antimicrobiens (pharmacorésistance) est une crise sanitaire qui menace nos moyens pour contrôler les infections bactériennes. Nos progrès dans la médecine dépendent de nos habiletés à combattre les infections avec des antibiotiques. Ainsi, il est nécessaire de comprendre le mécanisme entourant l’évolution de la résistance aux antibiotiques. Prédire les trajectoires évolutives de la pharmacorésistance demeure une tâche ardue et urgente. Actuellement, notre capacité à prédire les voies d’évolution bactérienne vers la pharmacorésistance est limitée. Il implique de combler plusieurs contraintes sur différents niveaux d’organisation biologique – des propriétés moléculaires des protéines, l’aptitude des organismes et à la dynamique des populations microbiennes. Dans ce mémoire, je développe un nouveau modèle multiscalaire pour l’évolution microbienne qui intègre principalement la génétique des populations avec la biophysique. Mon système modèle est la ß-lactamase qui concède une résistance contre une large gamme d’antibiotiques ß-lactamines. Tout d’abord, je détermine le paysage d’aptitude de la ß-lactamase en utilisant le balayage mutationnel profond (DMS), un nouvel outil pour tester expérimentalement l’aptitude d’environ 5000 variantes de la ß-lactamase. Ensuite j’intègre ces données expérimentales dans mon modèle computationnel d’évolution microbienne pour étudier les voies évolutives envers la pharmacorésistance. Dans le premier chapitre, je développe un modèle évolutionniste déterministe combinant la dynamique des populations et les effets biochimiques des mutations pour capturer les effets de la sélection purificatrice avec l’ampicilline. En raison des informations limitées qu’un modèle déterministe peut fournier, dans le deuxième chapitre, je bâtis sur le modèle initial en développant un modèle stochastique de l’évolution microbienne. Ce modèle mis à jour vise à déterminer les mutations qui pourraient être enrichies lors d’un traitement antibiotique. J’étudie également les régimes pour atténuer l’émergence de la résistance. Dans le troisième chapitre, je construis expérimentalement avec le DMS, le paysage d’aptitude de TEM-1 (Temoneira-1), une enzyme de la ß-lactamase pour déterminer son niveau de résistance et sa dépendance envers céfotaxime.Antimicrobial resistance is an emerging health crisis that threatens our ability to control bacterial infections. Advances in medical treatments depend on the ability to fight infections with antibiotics. Thus, there is a need to understand the mechanism surrounding the evolution of antibiotic resistance. Predicting the evolutionary trajectories to drug resistance remains a daunting task and is urgently needed. Currently, our aptitude to predict pathways in bacterial evolution to drug resistance is limited. It entails bridging several constraints on various levels of biological organization—from molecular properties of proteins to organismal fitness, to microbial population dynamics. In this memoir, I develop a new multi-scale framework for microbial evolution that integrates principally population genetics with biophysics. My model system is beta-lactamase that provides broad-spectrum resistance against beta-lactam antibiotics. First, I determine the fitness landscape of ß‐lactamase using deep mutational scanning, a novel tool to experimentally assay the fitness of around 5000 variants of beta-lactamase. Then, I integrate this experimental fitness landscape data into my computational model of microbial evolution to study the evolutionary pathways to drug resistance. In the first chapter, I develop a deterministic evolutionary model combining population dynamics and the biochemical effects of mutations to capture the effects of purifying selection under selection with ampicillin. Due to the limited information that a deterministic model can provide, in the second chapter, I build upon the initial model to develop a stochastic model of microbial evolution. This updated model aims to determine mutations that might be enriched during antibiotic treatment. I investigate the landscape of fitness cost against resistance level. I also investigate drug regimens to alleviate the rise of resistance. In the third chapter, I experimentally determine with DMS the fitness landscape of TEM-1 (Temoneira-1), a ß-lactamase enzyme, to study its resistance level and its dose-dependence for cefotaxime

A New Take on John Maynard Smith's Concept of Protein Space for Understanding Molecular Evolution

Much of the public lacks a proper understanding of Darwinian evolution, a problem that can be addressed with new learning and teaching approaches to be implemented both inside the classroom and in less formal settings. Few analogies have been as successful in communicating the basics of molecular evolution as John Maynard Smith’s protein space analogy (1970), in which he compared protein evolution to the transition between the terms WORD and GENE, changing one letter at a time to yield a different, meaningful word (in his example, the preferred path was WORD → WORE → GORE → GONE → GENE). Using freely available computer science tools (Google Books Ngram Viewer), we offer an update to Maynard Smith’s analogy and explain how it might be developed into an exploratory and pedagogical device for understanding the basics of molecular evolution and, more specifically, the adaptive landscape concept. We explain how the device works through several examples and provide resources that might facilitate its use in multiple settings, ranging from public engagement activities to formal instruction in evolution, population genetics, and computational biology

A Driven Disordered Systems Approach to Biological Evolution in Changing Environments

Biological evolution of a population is governed by the fitness landscape, which is a map from genotype to fitness. However, a fitness landscape depends on the organisms environment, and evolution in changing environments is still poorly understood. We study a particular model of antibiotic resistance evolution in bacteria where the antibiotic concentration is an environmental parameter and the fitness landscapes incorporate tradeoffs between adaptation to low and high antibiotic concentration. With evolutionary dynamics that follow fitness gradients, the evolution of the system under slowly changing antibiotic concentration resembles the athermal dynamics of disordered physical systems under external drives. Exploiting this resemblance, we show that our model can be described as a system with interacting hysteretic elements. As in the case of the driven disordered systems, adaptive evolution under antibiotic concentration cycling is found to exhibit hysteresis loops and memory formation. We derive a number of analytical results for quasistatic concentration changes. We also perform numerical simulations to study how these effects are modified under driving protocols in which the concentration is changed in discrete steps. Our approach provides a general framework for studying motifs of evolutionary dynamics in biological systems in a changing environment

Predictable Properties of Fitness Landscapes Induced by Adaptational Tradeoffs

Fitness effects of mutations depend on environmental parameters. For example, mutations that increase fitness of bacteria at high antibiotic concentration often decrease fitness in the absence of antibiotic, exemplifying a tradeoff between adaptation to environmental extremes. We develop a mathematical model for fitness landscapes generated by such tradeoffs, based on experiments that determine the antibiotic dose-response curves of Escherichia coli strains, and previous observations on antibiotic resistance mutations. Our model generates a succession of landscapes with predictable properties as antibiotic concentration is varied. The landscape is nearly smooth at low and high concentrations, but the tradeoff induces a high ruggedness at intermediate antibiotic concentrations. Despite this high ruggedness, however, all the fitness maxima in the landscapes are evolutionarily accessible from the wild type. This implies that selection for antibiotic resistance in multiple mutational steps is relatively facile despite the complexity of the underlying landscape

Drug-induced resistance evolution necessitates less aggressive treatment

Increasing body of experimental evidence suggests that anticancer and antimicrobial therapies may themselves promote the acquisition of drug resistance by increasing mutability. The successful control of evolving populations requires that such biological costs of control are identified, quantified and included to the evolutionarily informed treatment protocol. Here we identify, characterise and exploit a trade-off between decreasing the target population size and generating a surplus of treatment-induced rescue mutations. We show that the probability of cure is maximized at an intermediate dosage, below the drug concentration yielding maximal population decay, suggesting that treatment outcomes may in some cases be substantially improved by less aggressive treatment strategies. We also provide a general analytical relationship that implicitly links growth rate, pharmacodynamics and dose-dependent mutation rate to an optimal control law. Our results highlight the important, but often neglected, role of fundamental eco-evolutionary costs of control. These costs can often lead to situations, where decreasing the cumulative drug dosage may be preferable even when the objective of the treatment is elimination, and not containment. Taken together, our results thus add to the ongoing criticism of the standard practice of administering aggressive, high-dose therapies and motivate further experimental and clinical investigation of the mutagenicity and other hidden collateral costs of therapies. Author summary Evolution of drug resistance to anticancer and antimicrobial therapies is widespread among cancer and pathogen cell populations. Classical theory posits strictly that genetic and phenotypic variation is generated in evolving populations independently of the selection pressure. However, recent experimental findings among antimicrobial agents, traditional cytotoxic chemotherapies and targeted cancer therapies suggest that treatment not only imposes selection but can also affect the rate of adaptation by increasing mutability. Here we analyse a model with drug-induced increase in mutation rate and explore its consequences for treatment optimisation. We argue that the true biological cost of treatment is not limited to the harmful side-effects, but instead realises even more profoundly by fundamentally changing the underlying eco-evolutionary dynamics within the microenvironment. Using the concept of evolutionary rescue, we formulate the treatment as an optimal control problem and solve the optimal elimination strategy, which minimises the probability of evolutionary rescue. We show that aggressive elimination strategies, which aim at eradication as fast as possible and which represent the current standard of care, can be detrimental even with modest drug-induced increases (fold changePeer reviewe

White Paper 2: Origins, (Co)Evolution, Diversity & Synthesis Of Life

Publicado en Madrid, 185 p. ; 17 cm.How life appeared on Earth and how then it diversified into the different and currently existing forms of life are the unanswered questions that will be discussed this volume. These questions delve into the deep past of our planet, where biology intermingles with geology and chemistry, to explore the origin of life and understand its evolution, since “nothing makes sense in biology except in the light of evolution” (Dobzhansky, 1964). The eight challenges that compose this volume summarize our current knowledge and future research directions touching different aspects of the study of evolution, which can be considered a fundamental discipline of Life Science. The volume discusses recent theories on how the first molecules arouse, became organized and acquired their structure, enabling the first forms of life. It also attempts to explain how this life has changed over time, giving rise, from very similar molecular bases, to an immense biological diversity, and to understand what is the hylogenetic relationship among all the different life forms. The volume further analyzes human evolution, its relationship with the environment and its implications on human health and society. Closing the circle, the volume discusses the possibility of designing new biological machines, thus creating a cell prototype from its components and whether this knowledge can be applied to improve our ecosystem. With an effective coordination among its three main areas of knowledge, the CSIC can become an international benchmark for research in this field

Fitness landscapes for predicting evolution between environments

Prediction of evolution is an ambitious undertaking that would consolidate knowledge from all fields of biology for the benefit of global health and biodiversity. Although prediction has been a foundational goal of population genetics theory, this goal is obstructed by the common simplifying assumptions of absent or weak genetic interactions (G⇥G), gene-by-environment interac tions (G⇥E), and higher-order epistasis-by-environment inter actions (G⇥G⇥E). This thesis examines the challenges posed by genetic and environmental interactions to the goal of predict ing evolution. Fitness landscapes models are brought to bear on data from both wild populations and laboratory conditions in order to investigate the predictability of two pressing issues: species-level biodiversity and antibiotic resistance evolution

Host-parasite coevolution promotes innovation through deformations in fitness landscapes

During the struggle for survival, populations occasionally evolve new functions that give them access to untapped ecological opportunities. Theory suggests that coevolution between species can promote the evolution of such innovations by deforming fitness landscapes in ways that open new adaptive pathways. We directly tested this idea by using high- throughput gene editing- phenotyping technology (MAGE- Seq) to measure the fitness landscape of a virus, bacteriophage λ, as it coevolved with its host, the bacterium Escherichia coli. An analysis of the empirical fitness landscape revealed mutation- by- mutation- by- host- genotype interactions that demonstrate coevolution modified the contours of λ’s landscape. Computer simulations of λ’s evolution on a static versus shifting fitness landscape showed that the changes in contours increased λ’s chances of evolving the ability to use a new host receptor. By coupling sequencing and pairwise competition experiments, we demonstrated that the first mutation λ evolved en route to the innovation would only evolve in the presence of the ancestral host, whereas later steps in λ’s evolution required the shift to a resistant host. When time- shift replays of the coevo-lution experiment were run where host evolution was artificially accelerated, λ did not innovate to use the new receptor. This study provides direct evidence for the role of coevolution in driving evolutionary novelty and provides a quantitative framework for predicting evolution in coevolving ecological communities

Resource Consumption, Sustainability, and Cancer

abstract: Preserving a system’s viability in the presence of diversity erosion is critical if the goal is to sustainably support biodiversity. Reduction in population heterogeneity, whether inter- or intraspecies, may increase population fragility, either decreasing its ability to adapt effectively to environmental changes or facilitating the survival and success of ordinarily rare phenotypes. The latter may result in over-representation of individuals who may participate in resource utilization patterns that can lead to over-exploitation, exhaustion, and, ultimately, collapse of both the resource and the population that depends on it. Here, we aim to identify regimes that can signal whether a consumer–resource system is capable of supporting viable degrees of heterogeneity. The framework used here is an expansion of a previously introduced consumer–resource type system of a population of individuals classified by their resource consumption. Application of the Reduction Theorem to the system enables us to evaluate the health of the system through tracking both the mean value of the parameter of resource (over)consumption, and the population variance, as both change over time. The article concludes with a discussion that highlights applicability of the proposed system to investigation of systems that are affected by particularly devastating overly adapted populations, namely cancerous cells. Potential intervention approaches for system management are discussed in the context of cancer therapies.This is the authors' final accepted manuscript. The final publication is available at http://dx.doi.org/10.1007/s11538-014-9983-

The Impact of Collateral Evolution on Optimal Dosing Strategies and Evolution on Paired Fitness Landscapes

Drug resistance is an ever-growing threat to successful treatment of bacterial, cancer and viral infections. As pathogens and cancers continue to find evolutionary solutions to the drugs we treat them with, scientists have begun to focus on more evolutionary-based therapies such as drug cycling. These therapies aim to constrain or control evolution in a particular way such that intractable resistance never evolves. In the same vein, recent work has revealed collateral sensitivity as a promising avenue to guide evolution away from untreatable resistance states. Collateral evolution occurs when a population evolves resistance to the selecting drug and this mechanism of resistance confers "collateral" effects to different drugs it is not exposed to. In this work we show how collateral profiles might be used to slow the acquisition to resistance in a simplified laboratory-based evolution experiment. We demonstrate that intuitive cycling protocols often fail over long time periods, whereas mathematically optimized protocols maintain long-term sensitivity at the cost of transient periods of high resistance. We then extend this work to include nonantibiotic stressors such as pH, salt and food preservatives. This extension highlights that more work is necessary to understand the role these common environments have on the development of multidrug resistance. Finally, using the well-known fitness landscape paradigm, we explore how collateral effects influence the evolutionary dynamics of a pair of landscapes with tunable correlations. We show that alternating evolution in highly correlated environments can lead to higher mean fitness than evolution in either landscape alone, while alternating between two anti-correlated landscapes results in a lower mean fitness. We demonstrate this is due to the location and number of shared maxima between the two correlated landscapes, which change as a function of ruggedness (epistasis) and paired landscape correlation. Taken together, these results begin to answer many of the important questions required to translate collateral sensitivity into clinical treatments.PHDBiophysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163088/1/jamaltas_1.pd