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On the Deformability of an Empirical Fitness Landscape by Microbial Evolution

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On the Deformability of an Empirical Fitness Landscape by Microbial Evolution On the deformability of an empirical fitness landscape by microbial evolution Djordje Bajica,b,1,2, Jean C. C. Vilaa,b,1, Zachary D. Blountc,d,e, and Alvaro Sáncheza,b,2 aDepartment of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511; bMicrobial Sciences Institute, Yale University West Campus, West Haven, CT 06516; cBEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824; dDepartment of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI 48824; and eDepartment of Biology, Kenyon College, Gambier OH 43022 Edited by Marcus W. Feldman, Stanford University, Stanford, CA, and approved September 20, 2018 (received for review May 16, 2018) A fitness landscape is a map between the genotype and its reproduc- fitness peaks and valleys and their connectivity) plays a pivotal role, tive success in a given environment. The topography of fitness land- as it governs the accessibility of evolutionary trajectories (15–17), the scapes largely governs adaptive dynamics, constraining evolutionary role of population structure on evolution (18), the degree of evo- trajectories and the predictability of evolution. Theory suggests that lutionary convergence among populations (19), the expected role of this topography can be deformed by mutations that produce sub- drift, selection, and sex in the evolutionary process (20, 21), the stantial changes to the environment. Despite its importance, the discovery of evolutionary innovations (22), and the predictability of deformability of fitness landscapes has not been systematically stud- evolution (23), a subject of growing importance for the management – ied beyond abstract models, and little is known about its reach and of pathogens and cancer treatment (24 28). Given the fundamental consequences in empirical systems. Here we have systematically char- role that fitness landscapes play in adaptation, if populations do acterized the deformability of the genome-wide metabolic fitness indeed change the topography of their fitness landscapes as they landscape of the bacterium Escherichia coli. Deformability is quanti- evolve, it is imperative to understand precisely how. Do mutations that alter the environment generally also alter the fitness of all fied by the noncommutativity of epistatic interactions, which we subsequent mutations or only a subset of them? If the latter, where experimentally demonstrate in mutant strains on the path to an evo- are those deformations localized in the genotype space, and how lutionary innovation. Our analysis shows that the deformation of strong are they? All these questions remain open, as the deform- fitness landscapes by metabolic mutations rarely affects evolutionary ability (or “rubberness”) of fitness landscapes has never been sys- trajectories in the short range. However, mutations with large envi- tematically studied in empirical systems at the genomic scale. ronmental effects produce long-range landscape deformations in dis- Substantial experimental evidence suggests that microbial fitness tant regions of the genotype space that affect the fitness of later landscapes are likely to exhibit deformability (29–33), making mi- descendants. Our results therefore suggest that, even in situations crobes an ideal system for addressing this issue. Microbial metab- in which mutations have strong environmental effects, fitness land- olism leads to large-scale environmental construction through the scapes may retain their power to forecast evolution over small mu- uptake and release of metabolites (30, 32). Which nutrients are tational distances despite the potential attenuation of that power taken up, which byproducts are released, and in what amounts, are over longer evolutionary trajectories. Our methods and results pro- vide an avenue for integrating adaptive and eco-evolutionary dy- Significance namics with complex genetics and genomics. Fitness landscapes map the relationship between genotype and fitness landscapes | eco-evolutionary feedbacks | ecologically mediated phenotype, and are a core tool for predicting evolutionary pro- gene interactions | gene × environment × gene interactions | cesses from the emergence of resistant pathogens to cancer. The noncommutative epistasis topography of fitness landscapes is determined by the environ- ment. However, populations can also dynamically modify their hen a new genotype appears in a population its re- environment, for instance by releasing metabolites to it, and thus Wproductive success is largely governed by the environment. they may potentially deform their own adaptive landscape. Using Although the environment is often thought of as an external a combination of genome-scale metabolic simulations and ex- driver of natural selection, it can also be shaped by the evolving periments with Escherichia coli strains from the Lenski laboratory population itself, for instance through its metabolic activity or Long-Term Evolution Experiment, we systematically and quanti- through interactions with the abiotic habitat or other species (1– tatively characterize the deformability of an empirical fitness 3). These population-driven environmental changes can in turn landscape. We show that fitness landscapes retain their power to modify the fitness effects of future mutations, closing in an eco- forecast evolution over short mutational distances but environ- evolutionary feedback loop (4). Eco-evolutionary feedbacks are ment building may attenuate this capacity over longer adaptive well documented in natural (5) and experimental (6) pop- trajectories. ulations, and at all scales of biological organization: from the cellular scale [e.g., in the evolution of cancer (7) and microbial Author contributions: D.B., J.C.C.V., and A.S. designed research; D.B., J.C.C.V., Z.D.B., and populations (8)] to the organismal scale in animal (9) and plant A.S. performed research; D.B., J.C.C.V., Z.D.B., and A.S. contributed new reagents/analytic evolution (10). Given the growing evidence that evolutionary and tools; D.B., J.C.C.V., Z.D.B., and A.S. analyzed data; and D.B., J.C.C.V., Z.D.B., and A.S. wrote the paper. ecological processes, including niche construction, occur on similar timescales, there is a critical need to understand the ge- The authors declare no conflict of interest. nomic bases of these eco-evolutionary feedbacks (11). This article is a PNAS Direct Submission. The “map” between each genotype and its adaptive value in a Published under the PNAS license. given environment is known as the “fitness landscape” (12). Because Data deposition: All data in this paper have been deposited in GitHub, https://github.com/ populations actively modify their environment, new mutations can, vilacelestin/Bajicetal2018. in principle, have environmental as well as fitness effects. Thus, 1D.B. and J.C.C.V. contributed equally to this work. evolving populations may dynamically reshape (“deform”) the fit- 2To whom correspondence may be addressed. Email: [email protected] or alvaro. ness landscapes on which they are adapting (13, 14). Although they [email protected]. are often used only metaphorically to depict or visualize adaptation, This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. fitness landscapes are a major determinant of evolution. In partic- 1073/pnas.1808485115/-/DCSupplemental. ular, the topography of a fitness landscape (i.e., the location of Published online October 15, 2018. 11286–11291 | PNAS | October 30, 2018 | vol. 115 | no. 44 www.pnas.org/cgi/doi/10.1073/pnas.1808485115 Downloaded by guest on September 25, 2021 all governed by the structure of the metabolic network and byproducts of the other (Fig. 1F). The result is a gene-by- therefore by the genotype (34, 35).Asaresult,newmutationsthat environment-by-gene (G × E × G) interaction in which the mag- change the metabolic network can also change the patterns of nitude of epistasis may depend on the order in which mutations metabolic uptake and secretion, altering the environment and occur. In other words, epistasis becomes noncommutative. The potentially also altering the fitness of future mutations (32). value of that noncommutative fitness shift (δ) characterizes the Microbial physiology and growth can be explicitly simulated using deformation of a two-step mutational trajectory (Fig. 1F). genome-scale metabolic models (36–38). Due to their excellent Noncommutative epistasis and fitness intransitivity are closely predictive capabilities (36) and utility for easily and rapidly screening related but not identical concepts (SI Appendix, Fig. S3) (45). In its millions of genotypes, these models have been successfully used to simplest, qualitative formulation, “intransitivity” refers to situa- systematically explore the genotype space (39). Recent advances tions in which the fitness of three mutants (A, B, C) in pairwise in dynamic metabolic modeling make it possible to explicitly simu- competition are nonhierarchical (i.e., A invades B, B invades C, late the growth of microbial communities and their environmental and C invades A). A less stringent, quantitative definition of in- feedbacks with evolution (40, 41), making genome-wide dynamic transitivity has been applied when the relative fitness between a metabolic modeling of microbial genotypes a promising method to mutant and its ancestor cannot be predicted by the sum of cu- examine the deformability of fitness landscapes (Fig. 1A). mulative fitness gains along a mutational trajectory (45). This Here,
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