The utility of model system research for applied evolutionary questions

For this month’s Evolutionary Application’s research highlights, I look at recent work exemplifying the use of model systems in addressing questions of applied interest:

“Our ability to apply evolutionary theory is necessarily limited by our understanding of natural systems. Unfortunately, given the finite amount of researcher time and funding, we are faced with a trade-off between studying many systems shallowly and studying fewer systems in more depth. The handful of ‘model systems’ that have emerged thus far act as workhorses across disciplines, allowing for a more complete understanding of each system in fields from molecular genetics and evolution, to development, to physiology, to whole organism biology and ecology. However, given how few such model systems exist, it remains to determine how generalizable they are to less well-studied species and the natural world as a whole.

Among the model systems offering a wealth of insight across fields are the yeast Saccharomyces cerevisiae, the nematode Caenorhabditis elegans, the fly Drosophila melanogaster, and the plant Arabidopsis thaliana. These systems, among others, became models due in part to their ease of use in the lab, relative ubiquity, short generation time, and, in part, due to chance. The breadth of knowledge gained from the study of these and other model systems has driven progress across fields and has allowed for multidisciplinary research and cross talk among researchers that may not have otherwise come together. More recently, there has been a renewed interest in examining these systems back in the field and expanding the work to closely related species to determine whether our knowledge from the laboratory is broadly applicable in nature.

In terms of real life application, the model yeast Saccharomyces cerevisiae has the clearest relevance given its use in fermentation processes, the evolutionary history of which has been recently reviewed by Dashko et al. (2014). However, the tremendous toolbox of genetic techniques that is available has also made S. cerevisiae a central player in answering more basic questions in biology. Recent work by Serero et al. (2014) used a range of molecular approaches to examine mutation accumulation of both wild type and mutator strains of S. cerevisiae (i.e., strains with deficiencies in so-called ‘genome maintenance’ genes). By comparing the genome-wide mutational landscape across mutator types, they demonstrate strain-specific and complex effects of mutagenesis on chromosomal structure, mutations, and aneuploidy. The study emphasizes the tremendous diversity of the mutational landscape and provides an approach for examining genomic variation during clonal evolution, as occurs for example during tumor development in cancer.

The nematode Caenorhabditis elegans has acted as powerful model system for studying evolution due in part to the ability of researchers to freeze and resurrect individuals. A recent review by Gray and Cutter (2014) highlights the great potential the system still holds in exploring mutational processes, mating system and life-history evolution, and host-pathogen coevolution. For example, a new study by Sikkink et al. (2014) has examined the importance of phenotypic plasticity in adapting to extreme environments using the outcrossing sister species C. remanei. They experimentally selected for worms that were able to better withstand heat shock during development and found both increased tolerance of heat shock and altered phenotypic plasticity when reared in a novel environment, emphasizing both the role evolution can play in shaping plasticity and the importance of plasticity in allowing adaptation to novel and/or extreme environments.

Research on the fruitfly, Drosophila melanogaster, has paved the way for our understanding of genetics but has also been central to addressing questions regarding the impact of symbionts and pathogens on eukaryotic fitness (reviewed in Fauvarque 2014). Recent work by Versace et al. (2014) experimentally evolved populations of D. melanogaster infected with Wolbachia from multiple clades under hot or cold environments for 37 generations to test for changes in symbiont composition across environments. They discovered rapid increase in infection rates across replicate populations and treatments, suggesting a strong fitness advantage to hosts carrying the symbiont, as well as striking shifts in the composition of the Wolbachia community under cold, but not hot, environmental conditions. Studies of Drosophila have also been useful in understanding pest emergence and evolution. For example, a study by Atallah et al. (2014) has compared those Drosophila species that feed primarily on rotting fruit with those species that have switched to live fruit. Through morphological comparisons of the pest D. suzukii with its close relatives, the authors propose an evolutionary model to explain the modification of the ovipositor that allows puncture of susceptible fruits, demonstrating the utility of an evolutionary framework for addressing questions of pest emergence and management.

Finally, research focused on Arabidopsis thaliana plants has offered key insights to the genetics of plant adaptation, plant development, and plant–pathogen interactions. Two recent studies have examined natural populations of Arabidopsis to better understand the plant’s ability to move beyond current range limits. First, Wolfe and Tonsor (2014) took advantage of a natural elevational gradient of temperature and precipitation to examine plant adaptation to increase heat and drought. By exposing 48 lineages from across the natural gradient to increasing temperature and decreasing precipitation, they show that 10 of the 12 traits measured differ across the elevations and that populations from the low elevations are most fit in the face of increased heat and drought. In particular, these plants have faster bolting and earlier fruit ripening than those from high elevations, suggesting adaptation toward avoidance of spring heat and drought. In another study, Griffin and Willi (2014) examined natural populations of another Arabidopsis species, A. lyrata, across North America to test whether self-fertilization is more common at the edge of the species range, where effective population size is likely to be lower. Using population surveys and microsatellite markers, they were able to demonstrate at least seven independent transitions from outcrossing to selfing, all of which occurred at the edge of the species range where diversity was lower. Understanding such evolutionary shifts toward self-fertilization offers important insight to the ability of a species to expand its range and potentially to become invasive.

These recent studies highlight the great potential model systems hold for applying evolutionary theory in large part due to their amenability to laboratory conditions and genetic/genomic tools. Overall, it is clear that the knowledge gained from the study of model systems has been greater than the sum of its parts, but the generalizability of such knowledge to nonmodel systems, especially when it comes to translational research, remains an open question.”

References cited:

Atallah, J., L. Teixeira, R. Salazar, G. Zaragoza, and A. Kopp 2014. The making of a pest: the evolution of a fruit-penetrating ovipositor in Drosophila suzukii and related species. Proceedings of the Royal Society B: Biological Sciences 281:20132840.

Dashko, S., N. Zhou, C. Compagno, and J. Piškur 2014. Why, when and how did yeast evolve alcoholic fermentation? FEMS Yeast Research, doi: 10.1111/1567-1364.12161.

Fauvarque, M. O. 2014. Small flies to tackle big questions: assaying complex bacterial virulence mechanisms using Drosophila melanogaster. Cellular Microbiology 16:824–833.

Gray, J. C., and A. D. Cutter 2014. Mainstreaming Caenorhabditis elegans in experimental evolution. Proceedings of the Royal Society B: Biological Sciences 281:20133055.

Griffin, P. C., and Y. Willi 2014. Evolutionary shifts to self-fertilisation restricted to geographic range margins in North American Arabidopsis lyrata. Ecology Letters 17:484–490.

Serero, A., C. Jubin, S. Loeillet, P. Legoix-Né, and A. G. Nicolas 2014. Mutational landscape of yeast mutator strains. Proceedings of the National Academy of Sciences 111:1897–1902.

Sikkink, K. L., R. M. Reynolds, C. M. Ituarte, W. A. Cresko, and P. C. Phillips 2014. Rapid evolution of phenotypic plasticity and shifting thresholds of genetic assimilation in the nematode Caenorhabditis remanei. G3: Genes – Genomes – Genetics 4:1103–1112.

Versace, E., V. Nolte, R. V. Pandey, R. Tobler, and C. Schlötterer 2014. Experimental evolution reveals habitat-specific fitness dynamics among Wolbachia clades in Drosophila melanogaster. Molecular Ecology 23:802–814.

Wolfe, M. D., and S. J. Tonsor 2014. Adaptation to spring heat and drought in northeastern Spanish Arabidopsis thaliana. New Phytologist 201:323–334.


Predicting the evolutionary response of populations to climate change

For this month’s research highlight in Evolutionary Applications, I chose to focus on recent approaches to studying evolutionary responses to climate change:

“Given the increasingly unpredictable weather patterns associated with global climate change, a key aim of current research is to predict whether and how populations will be able to respond. Major advances are being made through a combination of natural, long-term studies and experimental approaches, for example by experimentally manipulating the environment and measuring phenotypic and/or genetic/genomic change. The first issue of Evolutionary Applications this year (Volume 7, Issue 1), under the lead of guest editors Andrew Hendry and Juha Merilä, brought together perspectives from a number of researchers on the leading edge of climate change research. This included multiple synthetic reviews discussing the challenges of teasing apart evolutionary change from more ‘plastic’ responses to environmental perturbation, as well as reviews of the current empirical evidence for and against key theoretical predictions. Overall, the volume highlights that, although data on the evolutionary potential of populations in response to climate change are accumulating, we are still far from where we hope to be in terms of predictive ability, especially given the complexity of most environments (Merilä and Hendry 2014).

Such biological and environmental complexity can be disentangled through the use of experimentally manipulated microcosms, and these studies provide a powerful tool for differentiating correlation from causation and investigating interactive, synergistic effects among multiple factors. Researchers in this field are moving beyond tests of increasing temperature to examine more complex factors, including environmental fluctuations and multiple abiotic or biotic stressors (Jeffs and Leather 2014; Vasseur et al. 2014). Recent work by Jennifer Lau and collaborators manipulated both the atmospheric CO2 concentration and the competitive environment of experimental Arabidopsis thaliana populations. They found that, while populations showed little direct response to increased CO2, there were strong indirect effects of the abiotic environment on the population level response to competitor-mediated selection (Lau et al. 2014). This type of indirect effect of climate change on populations would be overlooked in studies focused solely on climate-specific adaptations. Similarly, the effect of climate change at the community level can often be more striking than effects on a single population or species (Bailey et al. 2014). Sarah Evans and colleagues examined adaptation of soil bacterial communities after a decade of drying and rewetting stress and found that community composition was significantly altered relative to plots experiencing normal precipitation, especially in favor of those taxa exhibiting increased stress tolerance (Evans and Wallenstein 2014). The outcome of adaptation to changing environmental conditions can also differ in very meaningful ways among populations or genotypes. Romain Gallet and coauthors recently evolved replicate populations of Escherichia coli under extreme shifts in pH over 2,000 generations and found adaptation to the new pH in all replicates but evidence for niche width expansion in only two of the four replicates. This latter finding emphasizes the importance of incorporating the evolution of plasticity into predictions regarding climate-mediated adaptation and also highlights the very different evolutionary trajectories that populations may take in response to the same selection pressure (Gallet et al. 2014).

Although these experimental approaches offer important insight to the processes underlying climate-mediated evolutionary change, an understanding of whether a population can respond does not clearly translate into an understanding of whether it will respond. As such, there is still great need for long-term studies from natural populations. Katie Becklin and colleagues have examined changes in leaf physiology across seven conifer species by comparing samples preserved in middens (debris piles) from five time points since the last glacial maximum. Their data suggest physiological adaptations of leaves to climate change, such as decreased stomatal conductance, were influenced by evolutionary history but were not the primary determinant of shifts in community composition (Becklin et al. 2014). Finally, combining both natural and experimental datasets can offer a unique perspective on our ability to predict the effects of climate change. Sean Menke and collaborators have compared the results of long-term experimental data with those from natural populations to demonstrate that, although ant community composition has shifted along an elevational gradient over time in the natural populations, there has been relatively little change over the course of experimental warming (Menke et al. 2014). This work highlights the potential limitations of microcosm studies in isolation, given the biological complexity of populations and communities over space and time. However, these limitations can be overcome in part with biologically meaningful replication of experimental plots. This is nicely demonstrated by a recent study of wetland seed banks from multiple latitudes across two continents in which species diversity of communities from southern latitudes were found to be less affected by experimental warming than those from more northern latitudes (Baldwin et al. 2014).

Together, these studies demonstrate the need to incorporate both biotic and abiotic complexity into models and empirical studies in order to fully understand the resilience of populations and communities in the face of climate change. The impressive range of recent approaches, from experimental manipulation of multiple factors, to seminatural studies of communities spanning a wide range of abiotic conditions, to tests of species-level adaptations from preserved specimens across millions of years, has allowed great strides in our understanding of both if and how populations might respond to the increasingly unpredictable environment.”

Bailey, J. K., M. A. Genung, I. Ware, C. Gorman, M. E. Van Nuland, H. Long, and J. A. Schweitzer 2014. Indirect genetic effects: an evolutionary mechanism linking feedbacks, genotypic diversity and coadaptation in a climate change context. Functional Ecology 28:87–95.

Baldwin, A. H., K. Jensen, and M. Schönfeldt 2014. Warming increases plant biomass and reduces diversity across continents, latitudes, and species migration scenarios in experimental wetland communities. Global change biology 20:835–850.

Becklin, K. M., J. S. Medeiros, K. R. Sale, and J. K. Ward 2014. Evolutionary history underlies plant physiological responses to global change since the last glacial maximum. Ecology Letters, doi: 10.1111/ele.12271.

Evans, S. E., and M. D. Wallenstein 2014. Climate change alters ecological strategies of soil bacteria. Ecology letters 17:155–164.

Gallet, R., Y. Latour, B. S. Hughes, and T. Lenormand 2014. The dynamics of niche evolution upon abrupt environmental change. Evolution, doi: 10.1111/evo.12359.

Jeffs, C. T., and S. R. Leather 2014. Effects of extreme, fluctuating temperature events on life history traits of the grain aphid, Sitobion avenae. Entomologia Experimentalis et Applicata 150:240–249.

Lau, J. A., R. G. Shaw, P. B. Reich, and P. Tiffin 2014. Indirect effects drive evolutionary responses to global change. New Phytologist 201:335–343.

Menke, S. B., J. Harte, and R. R. Dunn 2014. Changes in ant community composition caused by 20 years of experimental warming vs. 13 years of natural climate shift. Ecosphere, 5:art6.

Merilä, J., and A. P. Hendry 2014. Climate change, adaptation, and phenotypic plasticity: the problem and the evidence. Evolutionary Applications 7:1–14.

Vasseur, D. A., J. P. DeLong, B. Gilbert, H. S. Greig, C. D. Harley, K. S. McCann, V. Savage, T.D. Tunney, and M. I. O’Connor 2014. Increased temperature variation poses a greater risk to species than climate warming. Proceedings of the Royal Society B: Biological Sciences, 281:20132612.

Predicting evolution

For this month’s research highlights in Evolutionary Applications I thought I’d take a look at some of the recent work addressing the predictability of Evolution.

“Whether evolution is predictable becomes a key question when deciding how to translate and apply evolutionary theory to solve real-world problems. Mutations arise by chance, and therefore, it seems fair to question if we can predict when, where, and how a population will respond to a given selection pressure. One of the many exciting results from Richard Lenski’s long-term experimental evolution of Escherichia coli bacterial populations was the clear demonstration that, given a set of so-called ‘potentiating’ mutations, populations followed the same evolutionary trajectory again and again as the experiment was ‘replayed (Blount et al. 2008). In their new paper, Diana Blank and collaborators have taken a similar approach by experimentally evolving E. coli mutants with major gene deletions to test if and how the mutants are able to regain function (Blank et al. 2014). By measuring both the likelihood of recovery and the type of mutations underlying recovered function, the authors uncover a relationship between regulatory versus structural mutations and metabolic function, demonstrating a clear and predictable link between molecular change and functional innovation.

The ability to predict how a population will respond to selection pressure is further aided by a good understanding of the ‘fitness landscape’ (the relationship between genotype and reproductive success). If the effect of every possible mutation, and interactions among mutations, on an organism’s fitness was known, we would have a much higher probability of successfully predicting the evolutionary trajectory in response to a given selection pressure such as drug treatment or insecticide use. Recent work by Ashley Acevedo and colleagues used next-generation sequencing to reveal the mutation landscape of poliovirus and to explore the fitness change associated with thousands of mutations across the landscape (Acevedo et al. 2014). This work sets the stage for generating and testing predictions regarding the evolution of RNA viruses, taking us a step closer to designing ‘evolution-proof’ treatment strategies.

The predictability of evolution at the phenotypic level can also be observed in natural populations, in particular when multiple populations are evolving in response to similar selection pressure. For example, a recent study by Rüdiger Riesch and coauthors examined change across nine species of New World livebearing fish that independently colonized toxic, sulfur spring environments (Riesch et al. 2014). They discovered that, as predicted by evolutionary theory, each of the 22 populations evolved toward larger offspring size and fewer offspring number after colonization, emphasizing that parallel adaptation to a specific environment can lead to repeatable evolution at the phenotypic level across both populations and species.

Finally, a good working knowledge of the mutational landscape and genotype–phenotype map can be extremely useful when employing adaptive laboratory evolution to generate organisms of applied interest. Artificial selection has been around since prehistoric times, but recent advances in, for example, stress-induced and transposon mutagenesis as well as experimental evolution have allowed for the rapid generation of organisms with desired traits. These techniques have recently been employed to select for acid- and temperature-tolerant strains of hydrogen-producing photosynthetic bacteria (Cai and Wang 2014) improved carotenoid production in yeast (Reyes et al. 2014) and reduced ethanol content of fermenting wine (Tilloy et al. 2014). For example, 200 generations of artificial selection for reduced ethanol and enhanced glycerol yields of Saccharomyces cerevisiae were enough to generate new strains showing little to no decrease in fermentation activity (Tilloy et al. 2014). In comparison with the speed at which mutants can be generated by engineering and mutagenesis (e.g., Cai and Wang 2014), this method may seem old-fashioned. However, Valentin Tilloy and collaborators argue that given the feasibility of classic methods and the public hesitation surrounding genetic modification, artificial selection still holds great promise for the generation of commercially relevant organisms (Tilloy et al. 2014). The success of such ‘directed evolution’ will of course depend on our ability to choose the appropriate selective pressures, as emphasized by Luis Reyes and coauthors who recently evolved yeast strains under oxidative stress with the goal of selecting for increased production of carotenoids with known antioxidant properties (Reyes et al. 2014).

These recent studies highlight that our ever-increasing understanding of evolutionary trajectories along the fitness landscape, uncovered by the genotype–phenotype map, is allowing both for an increased ability to predict the outcome of evolution and for the improved application of directed evolution in generating desirable phenotypes. Furthermore, the use of next-generation sequencing to generate single nucleotide fitness landscapes will offer a resolution that was once only dreamed of (Wright 1932).”

Acevedo, A., L. Brodsky, and R. Andino 2014. Mutational and fitness landscapes of an RNA virus revealed through population sequencing. Nature 505:686–690.

Blank, D., L. Wolf, M. Ackermann, and O. K. Silander. 2014. The predictability of molecular evolution during functional innovation. Proceedings of the National Academy of Sciences USA, doi: 10.1073/pnas.1318797111.

Blount, Z. D., C. Z. Borland, and R. E. Lenski 2008. Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. Proceedings of the National Academy of Sciences USA 105:7899–7906.

Cai, J., and G. Wang 2014. Photo-biological hydrogen production by an acid tolerant mutant of Rhodovulum sulfidophilum P5 generated by transposon mutagenesis. Bioresource technology 154:254–259.

Reyes, L. H., J. M. Gomez, and K. C. Kao 2014. Improving carotenoids production in yeast via adaptive laboratory evolution. Metabolic engineering 21:26–33.

Riesch, R., M. Plath, I. Schlupp, M. Tobler, and R. Brian Langerhans 2014. Colonisation of toxic environments drives predictable life-history evolution in livebearing fishes (Poeciliidae). Ecology letters 17:65–71.

Tilloy, V., A. Ortiz-Julien, and S. Dequin. 2014. Reducing ethanol and improving glycerol yield by adaptive evolution of Saccharomyces cerevisiae wine yeast under hyperosmotic conditions. Applied and Environmental Microbiology, doi: 10.1128/AEM.03710-13.

Wright, S. 1932. The roles of mutation, inbreeding, crossbreeding and selection in evolution. Proceedings of the Sixth International Congress on Genetics 1:356–366.

What’s been keeping me busy?

What a whirlwind of a few months it’s been! Stay tuned, as we have some great new data rolling out and a couple of new review/synthesis papers. My lack of blogging recently (which I hope to remedy) is also the result of too many papers to review, lots of travel to and from (with the added complication of being disconnected from London by a washed out train track) – including a recent trip to Lisbon to discuss grapevine trunk disease, and various other projects. One such project which I have thoroughly enjoyed is starting a new “research highlights” section for Evolutionary Applications.

In my opinion, Evol Apps is a great journal that was founded by my good friend, Michelle Tseng just after she finished her PhD in Curt Lively’s lab at Indiana University. At the time, there was no good venue for application of evolutionary theory and no journal aimed at exemplifying how very applicable it can be to everyday problems! I am very happy to be involved with the journal, especially now that it has moved to open access.

The only downside of my taking the time every month to put together a research highlight is that it will no doubt take away from ability to update this blog as much as I would like to (although I will still try). Therefore, I’ve decided to share my first research highlight below – see digital, PDF version here.

“This past year was full of noteworthy evolutionary applications, including a strong focus on the use of combination therapy to slow or stop the spread of drug resistance. In particular, the application of evolutionary theory to resistance of cancers was a hot issue, with a special issue of Evolutionary Applications (volume 6, issue 1), a Nature Reviews Cancer piece about the importance of life-history trade-offs to our understanding of cancer progression (Aktipis et al. 2013), a Nature review article on genomic instability of cancer (Burrell et al. 2013), a Jacques Monod conference on ‘Ecological and evolutionary perspectives in cancer,’ and the 2nd International Biannual Evolution and Cancer Conference. This blossoming interest is uncovering the roles of evolutionary processes in shaping the development, spread, and virulence of cancers, but is also highlighting the current gap in our ability to translate evolutionary theory into successful treatment. An elegant example of the great potential of this approach is the recent work by Shi and coauthors identifying two core resistance pathways in metastatic melanomas (Shi et al. 2014). The researchers found that drug resistance of these cancers occurred most often via two distinct ‘drug escape pathways,’ explaining 70% and 22% of resistance among disease-progressive tissues. These data act as strong justification for the use of combination treatment to target both BRAF inhibitor resistance pathways simultaneously to block these common routes of drug escape.

Similar approaches are being applied to tackle the current antibiotic crisis, as has been reviewed in a recent Nature Reviews Genetics article (Palmer and Kishony 2013). The authors point out the importance of using optimal combinations of antibiotics, ideally chosen based on negative cross-resistance and antagonistic interactions among resistance mechanisms. However, a recent paper by Pena-Miller and colleagues, combining experimental and genomic techniques with mathematical modeling, highlights that caution must be employed when employing combination treatments, as the use of synergistic antibiotics can in fact increase pathogen load under suboptimal treatment durations or subinhibitory drug concentrations (Pena-Miller et al. 2013). A similar warning has now been raised for combination treatment of malaria due to the discovery of high association among resistance alleles in multiple regions of China (Ding et al. 2013). Furthermore, a clear understanding of epistasis among resistance mutations is key to predicting the spread of multidrug-resistant bacteria, as has been nicely demonstrated by Borrell et al. (2013). They found that combinations of drug resistance mutations in Mycobacterium smegmatis showing a competitive fitness advantage in vitro were also the most frequently observed combinations found in multidrug-resistant clinical isolates of human tuberculosis in South Africa.

The application of evolutionary theory to development of combined treatments is certainly not limited to human disease. Experimental evolution of the green chlorophyte, Chlamydomonas reinhardtii, was used by Lagator and coauthors to demonstrate the utility of combined herbicides for slowing the evolution of resistance in agricultural settings (Lagator et al. 2013). However, their data come with a similar word of warning: using these combinations at low doses can select for both increased rates of resistance and a more general resistance mechanism relative to single herbicide treatment.

This small subsample of recent research published in Evolutionary Applications and other high-profile journals on combination treatment emphasizes the power of applying evolutionary theory to treatment design, both in terms of creating more ‘evolution-proof’ combinations and in predicting potential negative consequences of this approach. We look forward to future research on the topic and continued monitoring of treatments designed in the light of evolutionary theory.”

References cited:

Aktipis, C. A., A. M. Boddy, R. A. Gatenby, J. S. Brown, and C. C. Maley 2013. Life history trade-offs in cancer evolution. Nature Reviews Cancer 13:883–892.

Borrell, S., Y. Teo, F. Giardina, E. M. Streicher, M. Klopper, J. Feldmann, … S. Gagneux 2013. Epistasis between antibiotic resistance mutations drives the evolution of extensively drug-resistant tuberculosis. Evolution, Medicine, and Public Health 2013:65–74.

Burrell, R. A., N. McGranahan, J. Bartek, and C. Swanton 2013. The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501:338–345.

Ding, S., R. Ye, D. Zhang, X. Sun, H. Zhou, T. F. McCutchan, and W. Pan. 2013. Anti-folate combination therapies and their effect on the development of drug resistance in Plasmodium vivax. Scientific Reports 3:????–????.

Lagator, M., T. Vogwill, A. Mead, N. Colegrave, and P. Neve 2013. Herbicide mixtures at high doses slow the evolution of resistance in experimentally evolving populations of Chlamydomonas reinhardtii. New Phytologist 198:938–945.

Palmer, A. C., and R. Kishony 2013. Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance. Nature Reviews Genetics 14:243–248.

Pena-Miller, R., D. Laehnemann, G. Jansen, A. Fuentes-Hernandez, P. Rosenstiel, H. Schulenburg, and R. Beardmore 2013. When the most potent combination of antibiotics selects for the greatest bacterial load: the smile-frown transition. PLoS biology 11:e1001540.

Shi, H., W. Hugo, X. Kong, A. Hong, R. C. Koya, G. Moriceau, … R. S. Lo 2014. Acquired resistance and clonal evolution in melanoma during BRAF inhibitor therapy. Cancer Discovery 4:80–93.