ArticleScience advances2016
Environmental changes bridge evolutionary valleys.
Article in Science advances, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed, 105 citations in OpenAlex.
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- Arabidopsis acclimation to daily environmental fluctuations converts a defense response regulator into a susceptibility factor toward Sclerotinia.The New phytologist · 2026Article
- Evolutionary dynamics under phenotypic uncertainty.bioRxiv : the preprint server for biology · 2026Article
- Decoding the substrate specificity landscape of a promiscuous enzyme through multi-substrate mutational scanning.Nature communications · 2026Article
- Evolutionary pathways in epistatic mechanical networks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Using TraDIS as a complementary approach to long term evolution for mapping adaptive mutations in Escherichia coli.PLoS genetics · 2026Article
- Dynamic signature of activity-stability tradeoff in lactamase evolution.Nature communications · 2026Article
- Bioengineering hybrid artificial life.Frontiers in bioinformatics · 2025Review
- Have you tried turning it off and on again? Oscillating selection to enhance fitness-landscape traversal in adaptive laboratory evolution experiments.Metabolic engineering communications · 2023Review
- Evolvability-enhancing mutations in the fitness landscapes of an RNA and a protein.Nature communications · 2023Article
- Evolutionary rescue of resistant mutants is governed by a balance between radial expansion and selection in compact populations.Nature communications · 2022Article
- Path probability selection in nature and path integral.Scientific reports · 2022Article
- On the incongruence of genotype-phenotype and fitness landscapes.PLoS computational biology · 2022Article
- Genomic epidemiological models describe pathogen evolution across fitness valleys.Science advances · 2022Article
- Host-parasite coevolution promotes innovation through deformations in fitness landscapes.eLife · 2022Article
- Environmental selection and epistasis in an empirical phenotype-environment-fitness landscape.Nature ecology & evolution · 2022Article
- Dynamic RNA Fitness Landscapes of a Group I Ribozyme during Changes to the Experimental Environment.Molecular biology and evolution · 2022Article
- In vivo hypermutation and continuous evolution.Nature reviews. Methods primers · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
In the basic fitness landscape metaphor for molecular evolution, evolutionary pathways are presumed to follow uphill steps of increasing fitness. How evolution can cross fitness valleys is an open question. One possibility is that environmental changes alter the fitness landscape such that low-fitness sequences reside on a hill in alternate environments. We experimentally test this hypothesis on the antibiotic resistance gene TEM-15 β-lactamase by comparing four evolutionary strategies shaped by environmental changes. The strategy that included initial steps of selecting for low antibiotic resistance (negative selection) produced superior alleles compared with the other three strategies. We comprehensively examined possible evolutionary pathways leading to one such high-fitness allele and found that an initially deleterious mutation is key to the allele's evolutionary history. This mutation is an initial gateway to an otherwise relatively inaccessible area of sequence space and participates in higher-order, positive epistasis with a number of neutral to slightly beneficial mutations. The ability of negative selection and environmental changes to provide access to novel fitness peaks has important implications for natural evolutionary mechanisms and applied directed evolution.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.