ArticleNature ecology & evolution2018
Multi-environment fitness landscapes of a tRNA gene.
Article in Nature ecology & evolution, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
37 citing papers in PubMed, 100 citations in OpenAlex.
- Synthetic Approaches as Empirical Tools for Testing Evolutionary Theory.Genome biology and evolution · 2026Review
- Deconstructing empirical fitness seascapes across scales of granularity.G3 (Bethesda, Md.) · 2026Article
- Spontaneous mutation rate and spectrum are modulated by organismal fitness.Science advances · 2026Article
- Deconstructing empirical fitness seascapes across scales of granularity.bioRxiv : the preprint server for biology · 2026Article
- Adaptive tracking with antagonistic pleiotropy results in seemingly neutral molecular evolution.Nature ecology & evolution · 2025Article
- Repeatability of evolution and genomic predictions of temperature adaptation in seed beetles.Nature ecology & evolution · 2025Article
- The highly rugged yet navigable regulatory landscape of the bacterial transcription factor TetR.Nature communications · 2024Article
- Towards parsimonious generative modeling of RNA families.Nucleic acids research · 2024Article
- Genotype sampling for deep-learning assisted experimental mapping of a combinatorially complete fitness landscape.Bioinformatics (Oxford, England) · 2024Article
- Convergent genetic adaptation ofFrontiers in molecular biosciences · 2024Article
- ACIDES: on-line monitoring of forward genetic screens for protein engineering.Nature communications · 2023Article
- Patterns and evolutionary consequences of pleiotropy.Annual review of ecology, evolution, and systematics · 2023Article
- Evolvability-enhancing mutations in the fitness landscapes of an RNA and a protein.Nature communications · 2023Article
- mutscan-a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data.Genome biology · 2023Article
- Environment-dependent epistasis increases phenotypic diversity in gene regulatory networks.Science advances · 2023Article
- Mutation bias and the predictability of evolution.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Review
- Epistasis decreases with increasing antibiotic pressure but not temperature.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
- Robustness and innovation in synthetic genotype networks.Nature communications · 2023Article
- What Has Genomics Taught An Evolutionary Biologist?Genomics, proteomics & bioinformatics · 2023Review
- gUMI-BEAR, a modular, unsupervised population barcoding method to track variants and evolution at high resolution.PloS one · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
A fitness landscape (FL) describes the genotype-fitness relationship in a given environment. To explain and predict evolution, it is imperative to measure the FL in multiple environments because the natural environment changes frequently. Using a high-throughput method that combines precise gene replacement with next-generation sequencing, we determine the in vivo FL of a yeast tRNA gene comprising over 23,000 genotypes in four environments. Although genotype-by-environment interaction is abundantly detected, its pattern is so simple that we can transform an existing FL to that in a new environment with fitness measures of only a few genotypes in the new environment. Under each environment, we observe prevalent, negatively biased epistasis between mutations. Epistasis-by-environment interaction is also prevalent, but trends in epistasis difference between environments are predictable. Our study thus reveals simple rules underlying seemingly complex FLs, opening the door to understanding and predicting FLs in general.
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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.