Evolution in the light of fitness landscape theory

Fragata, Inês; Blanckaert, Alexandre; Louro, Marco António Dias; Liberles, David A.; Bank, Claudia

doi:10.1016/j.tree.2018.10.009

Cited by 146 publications

(177 citation statements)

References 99 publications

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“…We extracted samples from each condition at multiple time points and used Illumina sequencing to estimate the frequency of each Hsp90 variant over time. We assessed the selection coefficient of each Hsp90 variant from the change in frequency relative to wildtype Hsp90 using a previously developed Bayesian MCMC method [33,34].…”

Section: Resultsmentioning

confidence: 99%

Comprehensive fitness maps of Hsp90 show widespread environmental dependence

Flynn

Rossouw

Cote-Hammarlof

et al. 2019

Preprint

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Gene-environment interactions have long been theorized to influence molecular evolution. However, the environmental dependence of most mutations remains unknown. Using deep mutational scanning, we engineered budding yeast with all 44,604 single codon changes encoding 14,160 amino acid variants in Hsp90 and quantified growth effects under standard laboratory conditions and under five stress conditions (elevated temperature, nitrogen starvation, elevated salinity, high ethanol concentration, and oxidative stress caused by diamide). To our knowledge these are the largest comprehensive fitness maps of point mutant growth effects that have been determined. The growth effects of many variants differed between each of the conditions, indicating that environmental conditions can have a large impact on the evolution of Hsp90. Multiple variants provided growth advantages relative to wildtype Hsp90 under individual conditions, however these variants tended to exhibit growth defects in other environments. The diversity of Hsp90 sequences observed in extant eukaryotes preferentially contain amino acid variants that supported robust growth under all tested conditions. Thus, rather than favoring substitutions in individual conditions, the long-term selective pressure on Hsp90 may have been that of fluctuating environments, leading to robustness under a variety of conditions.

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Section: Resultsmentioning

confidence: 99%

Comprehensive fitness maps of Hsp90 show widespread environmental dependence

Flynn

Rossouw

Cote-Hammarlof

et al. 2019

Preprint

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“…Birth depends on the availability of empty space and resources to synthesize building blocks, whereas death depends on the ability to survive under a variety of different conditions and the potential accumulation (and avoidance) of toxicity. The resulting survival of the fittest (referred to as "competitive fitness" by Fragata et al, 2018) is an emergent phenomenon of eco-evolutionary dynamics [36].…”

Section: Model Overviewmentioning

confidence: 99%

Virtual Microbes evolve multiple mechanisms to the same end: anticipating a serial transfer protocol

Dijk

Meijer

Cuypers

et al. 2019

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1Experimental evolution of microbes often involves a serial transfer protocol with repeated dilutions and transfers to 2 fresh media to start a new growth cycle. Here we study how in silico evolved Virtual Microbe "wild types" (WTs) 3 adapt to such a protocol, study the generic evolutionary features, and investigate how these features depend on prior 4 evolution. All WTs adopt a balance of growth and survival, therewith anticipating the regularity of the serial 5 transfer. We find that this anticipation can happen by means of a single lineage, or by coexisting lineages that 6 specialise on either the growth phase or the stationary phase. Parallel experiments of the same WT show similar 7 trajectories with respect to growth and yield, and similar biases towards diversification. In summary, all our in silico 8 WTs show the same anticipation effects -fitting the periodicity of serial transfer protocol -but prior adaptations 9 determines what solution is found by subsequent evolution. 10 15 adapt to such a simple protocol, we might one day be able to predict evolution in the lab and -ideally -also in 16 nature. Indeed, a lot of evolution in the lab seems remarkably reproducible, where microbes show parallel adaptations 17 both on the level of the phenotype as well as the genotype [4][5][6][7][8][9][10][11]. However, there also seems to be strong potential 18 for divergent evolution, leading to diversity both between and within replicate populations [12][13][14]. Diversification 19 events within populations often entail cross-feeding interactions [12, 13,[15][16][17][18], where species emerge that grow on 20 metabolic by-products. These cross-feeding interactions are increasingly well understood with the help of metabolic 21 modeling and digital evolution [19, 20]. A recent metagenomic study has revealed even more coexisting lineages in the 22 LTEE than were previously reported [21]. It is however not yet clear whether all these polymorphisms are the result 23 of uni-directional cross-feeding interactions, or if other mechanisms could drive coexistence in a simple experiment 24 such as a serial transfer protocol. 25Prior to being subjected to lab conditions, the microbes used in the aforementioned experimental studies all 26 evolved for billions of years in natural environments, experiencing harshly fluctuating and -more often than not -27 unfavorable conditions. While a serial transfer protocol such as that of the LTEE at a first glance selects mostly for 28 higher growth rates when resources are abundant (i.e. during the log phase), there is also selection to survive when 29 resources are depleted and the population no longer grows (i.e. during the stationary phase). In fact, given the 30 1/31 unpredictable conditions found in nature, some of the ancestors of Escherichia coli might have survived precisely 31 because they diverted resources away from growth. Indeed, E. coli does exactly this during the stationary phase by 32 means of the stringent response, regulating up to one third of all genes during starvation [2...

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“…Darwinian evolution is the motion of populations in the space of all possible heritable types graded by their reproductive value, the fitness landscape [1][2][3]. In Wright's vivid words, the interaction of selection and variation enables populations to "continually find their way from lower to higher peaks" [4], thereby providing a universal mechanism for open-ended evolution [5].…”

Section: Introductionmentioning

confidence: 99%

Effective potential reveals evolutionary trajectories in complex fitness landscapes

Smerlak¹

2019

Preprint

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Growing efforts to measure fitness landscapes in molecular and microbial systems are premised on a tight relationship between landscape topography and evolutionary trajectories. This relationship, however, is far from being straightforward: depending on their mutation rate, Darwinian populations can climb the closest fitness peak (survival of the fittest), settle in lower regions with higher mutational robustness (survival of the flattest), or fail to adapt altogether (error catastrophes). These bifurcations highlight that evolution does not necessarily drive populations "from lower peak to higher peak", as Wright imagined. The problem therefore remains: how exactly does a complex landscape topography constrain evolution, and can we predict where it will go next?Here I introduce a generalization of quasispecies theory which identifies metastable evolutionary states as minima of an effective potential. From this representation I derive a coarse-grained, Markov state model of evolution, which in turn forms a basis for evolutionary predictions across a wide range of mutation rates. Because the effective potential is related to the ground state of a quantum Hamiltonian, my approach could stimulate fruitful interactions between evolutionary dynamics and quantum many-body theory. SIGNIFICANCE STATEMENTThe course of evolution is determined by the relationship between heritable types and their adaptive values, the fitness landscape. Thanks to the explosive development of sequencing technologies, fitness landscapes have now been measured in a diversity of systems from molecules to micro-organisms. How can we turn these data into evolutionary predictions? I show that preferred evolutionary trajectories are revealed when the effects of selection and mutations are blended in a single effective evolutionary force. With this reformulation, the dynamics of selection and mutation becomes Markovian, bringing a wealth of classical visualization and analysis tools to bear on evolutionary dynamics. Among these is a coarse-graining of evolutionary dynamics along its metastable states which greatly reduces the complexity of the prediction problem.

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Evolution in the light of fitness landscape theory

Cited by 146 publications

References 99 publications

Comprehensive fitness maps of Hsp90 show widespread environmental dependence

Comprehensive fitness maps of Hsp90 show widespread environmental dependence

Virtual Microbes evolve multiple mechanisms to the same end: anticipating a serial transfer protocol

Effective potential reveals evolutionary trajectories in complex fitness landscapes

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