{"doi":"10.1073/pnas.1808485115","title":"On the deformability of an empirical fitness landscape by microbial evolution","abstract":"<jats:title>Significance</jats:title>\n                  <jats:p>\n                    Fitness landscapes map the relationship between genotype and phenotype, and are a core tool for predicting evolutionary processes from the emergence of resistant pathogens to cancer. The topography of fitness landscapes is determined by the environment. However, populations can also dynamically modify their environment, for instance by releasing metabolites to it, and thus they may potentially deform their own adaptive landscape. Using a combination of genome-scale metabolic simulations and experiments with\n                    <jats:italic>Escherichia coli</jats:italic>\n                    strains from the Lenski laboratory Long-Term Evolution Experiment, we systematically and quantitatively characterize the deformability of an empirical fitness landscape. We show that fitness landscapes retain their power to forecast evolution over short mutational distances but environment building may attenuate this capacity over longer adaptive trajectories.\n                  </jats:p>","journal":"Proceedings of the National Academy of Sciences","year":2018,"id":682881,"datarank":0.6476232170304466,"base_score":4.31748811353631,"endowment":4.31748811353631,"self_citation_contribution":0.6476232170304466,"citation_network_contribution":0.0,"self_endowment_contribution":0.6476232170304466,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":74,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":552319,"name":"Jean C. 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However, populations can also dynamically modify their environment, for instance by releasing metabolites to it, and thus they may potentially deform their own adaptive landscape. Using a combination of genome-scale metabolic simulations and experiments with\n                    <jats:italic>Escherichia coli</jats:italic>\n                    strains from the Lenski laboratory Long-Term Evolution Experiment, we systematically and quantitatively characterize the deformability of an empirical fitness landscape. We show that fitness landscapes retain their power to forecast evolution over short mutational distances but environment building may attenuate this capacity over longer adaptive trajectories.\n                  </jats:p>","is_dataset_classified":null,"base_score":4.31748811353631,"endowment":4.31748811353631,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"30322921","pmcid":"PMC6217403","openalex_id":"https://openalex.org/W2950721626","authors":[],"funders":[{"funder_name":"Human Frontier Science Program","grant_id":"RGY0077/2016","title":null},{"funder_name":"John Templeton Foundation","grant_id":"FQEB #RFP-12-13","title":null},{"funder_name":"NSF | BIO | Division of Environmental Biology","grant_id":"NSF; DEB-1019989","title":null},{"funder_name":"National Science Foundation","grant_id":"1019989","title":"LTREB Renewal: The long-term evolution experiment with Escherichia coli"}],"total_grants":4,"fwci":5.4506,"citation_percentile":0.96415782,"influential_citations":0,"citation_trend":[{"year":2018,"count":1},{"year":2019,"count":6},{"year":2020,"count":10},{"year":2021,"count":17},{"year":2022,"count":8},{"year":2023,"count":14},{"year":2024,"count":7},{"year":2025,"count":5},{"year":2026,"count":6}],"oa_status":"bronze","license":"http://www.pnas.org/site/aboutpnas/licenses.xhtml","oa_locations":[{"url":"https://www.pnas.org/content/pnas/115/44/11286.full.pdf","host_type":"journal"},{"url":"https://www.pnas.org/content/pnas/115/44/11286.full.pdf","host_type":"publisher"},{"url":"http://www.pnas.org/syndication/doi/10.1073/pnas.1808485115","host_type":"publisher"},{"url":"https://pnas.org/doi/pdf/10.1073/pnas.1808485115","host_type":"publisher"},{"url":"https://doi.org/10.1073/pnas.1808485115","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/30322921","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6217403","host_type":"repository"},{"url":"https://doi.org/10.1101/293407","host_type":""},{"url":"https://dx.doi.org/10.1101/293407","host_type":""},{"url":"https://dx.doi.org/10.1073/pnas.1808485115","host_type":""},{"url":"http://dx.doi.org/10.1101/293407","host_type":""}],"fields_of_study":["Evolution and Genetic Dynamics","Evolutionary Game Theory and Cooperation","Gene Regulatory Network Analysis","0301 basic medicine","0303 health sciences","03 medical and health sciences","Escherichia coli","Evolution, Molecular","Genetic Fitness","Genotype","Models, Genetic","Mutation"],"mesh_terms":["Escherichia coli","Genotype","Models, Genetic","Mutation","Evolution, Molecular","Genetic Fitness"],"keywords":["Fitness landscape","Predictability","Genetic Fitness","Adaptive evolution","Evolutionary biology","Energy landscape","Ecology","Biology","Biological evolution","Genetics","Gene","Mathematics","Population","Demography","Fitness Landscapes","Eco-evolutionary Feedbacks","Ecologically Mediated Gene Interactions","Gene × Environment × Gene Interactions","Noncommutative Epistasis","Evolution, Molecular","Genotype","Models, Genetic","Mutation","Escherichia coli"],"sdg_mappings":[{"sdg_number":3,"sdg_label":"3. 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