Dataset Information

ALEdb 1.0: a database of mutations from adaptive laboratory evolution experimentation.

ABSTRACT: Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover causal mutations that confer desired phenotypic functions. ALE not only represents a controllable experimental approach to systematically discover genotype-phenotype relationships, but also allows for the revelation of the series of genetic alterations required to acquire the new phenotype. Numerous ALE studies have been published, providing a strong impetus for developing databases to warehouse experimental evolution information and make it retrievable for large-scale analysis. Here, the first step towards establishing this resource is presented: ALEdb (http://aledb.org). This initial release contains over 11 000 mutations that have been discovered from eleven ALE publications. ALEdb (i) is a web-based platform that comprehensively reports on ALE acquired mutations and their conditions, (ii) reports key mutations using previously established trends, (iii) enables a search-driven workflow to enhance user mutation functional analysis through mutation cross-reference, (iv) allows exporting of mutation query results for custom analysis, (v) includes a bibliome describing the databased experiment publications and (vi) contains experimental evolution mutations from multiple model organisms. Thus, ALEdb is an informative platform which will become increasingly revealing as the number of reported ALE experiments and identified mutations continue to expand.

SUBMITTER: Phaneuf PV

PROVIDER: S-EPMC6323966 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

ALEdb 1.0: a database of mutations from adaptive laboratory evolution experimentation.

Phaneuf Patrick V PV Gosting Dennis D Palsson Bernhard O BO Feist Adam M AM

Nucleic acids research 20190101 D1

Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover causal mutations that confer desired phenotypic functions. ALE not only represents a controllable experimental approach to systematically discover genotype-phenotype relationships, but also allows for the revelation of the series of genetic alterations required to acquire the new phenotype. Numerous ALE studies have been published, providing a strong impetus for developing databases to warehouse experimental ...[more]

PMID: 30357390

Similar Datasets

Project description:It has been shown recently in a number of in vitro laboratory evolution experiments that under repetitive antibiotic exposure, bacterial populations can adapt quickly to the treatment condition by becoming tolerant and/or resistant to the drug. The repeated killing and regrowth cycles hasten the selection for tolerant/resistant mutants with survival advantages. Due to the random nature of mutagenesis and the large target size of tolerance mutations, this dynamic evolutionary process appears to be highly unpredictable, generating distinct mutants even under identical, well-controlled laboratory conditions. Here, we utilized an adaptive laboratory evolution (ALE) experiment to hunt for novel tolerance and resistance mutations by subjecting multiple lineages of methicillin-resistant Staphylococcus aureus (MRSA) to repetitive daptomycin treatment. By sequencing multiple isolates along the course of evolution, we obtained three tolerant mutants that have different tolerance levels and identified novel daptomycin resistance mutations in the mprF gene. In addition, we found that tolerance/resistance development is more rapid if the population is treated in the exponential phase than if it is treated in the stationary phase, which is likely attributable to the more effective killing of growing cells by the antibiotic. Through competition assays, we found that whether or not the resistant mutants can take over the population heavily depends on the relative survival advantages conferred by the tolerance and resistance mutations. This study reports novel daptomycin resistance and tolerance mutations and offers new insights into the dynamics of the development of tolerance and resistance in bacterial populations under antibiotic exposure. IMPORTANCE Although the phenotype of increased tolerance and/or resistance was commonly observed in evolved populations from typical adaptive laboratory evolution (ALE) experiments, a wide variety of mutations that underlie those phenotypes have been discovered. Therefore, performing ALE experiments in multiple populations in parallel would serve the purpose of mining for different tolerant/resistant mutants and would be useful to explore the diverse population dynamics of evolution. In this study, we performed in vitro evolution in a clinically relevant methicillin-resistant Staphylococcus aureus (MRSA) pathogen, using a lethal concentration of a drug that is frequently used in the clinic, daptomycin. Using this strategy, we obtained three distinct daptomycin-tolerant mutants and identified six daptomycin resistance mutations in different locations on the mprF gene, collectively adding to our current knowledge of this important pathogen. In addition, we found out that in most cases, the daptomycin-resistant mutant outcompetes other susceptible and tolerant mutants and becomes established in the final population. Follow-up competition experiments offered an explanation; the resistant mutant cannot invade populations of tolerant mutants that confer higher survival advantages than itself. In summary, we demonstrated an experimental strategy to explore the landscape and dynamics of the evolution of tolerance and resistance in MRSA toward daptomycin and made observations that will guide future ALE experiments.

Project description:The occurrence of mutations is a cornerstone of the evolutionary theory of adaptation, capitalizing on the rare chance that a mutation confers a fitness benefit. Natural selection is increasingly being leveraged in laboratory settings for industrial and basic science applications. Despite increasing deployment, there are no standardized procedures available for designing and performing adaptive laboratory evolution (ALE) experiments. Thus, there is a need to optimize the experimental design, specifically for determining when to consider an experiment complete and for balancing outcomes with available resources (i.e., laboratory supplies, personnel, and time). To design and to better understand ALE experiments, a simulator, ALEsim, was developed, validated, and applied to the optimization of ALE experiments. The effects of various passage sizes were experimentally determined and subsequently evaluated with ALEsim, to explain differences in experimental outcomes. Furthermore, a beneficial mutation rate of 10-6.9 to 10-8.4 mutations per cell division was derived. A retrospective analysis of ALE experiments revealed that passage sizes typically employed in serial passage batch culture ALE experiments led to inefficient production and fixation of beneficial mutations. ALEsim and the results described here will aid in the design of ALE experiments to fit the exact needs of a project while taking into account the resources required and will lower the barriers to entry for this experimental technique.IMPORTANCE ALE is a widely used scientific technique to increase scientific understanding, as well as to create industrially relevant organisms. The manner in which ALE experiments are conducted is highly manual and uniform, with little optimization for efficiency. Such inefficiencies result in suboptimal experiments that can take multiple months to complete. With the availability of automation and computer simulations, we can now perform these experiments in an optimized fashion and can design experiments to generate greater fitness in an accelerated time frame, thereby pushing the limits of what adaptive laboratory evolution can achieve.

Project description:BackgroundAdaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover mutations that confer phenotypic functions of interest. However, the task of finding and understanding all beneficial mutations of an ALE experiment remains an open challenge for the field. To provide for better results than traditional methods of ALE mutation analysis, this work applied enrichment methods to mutations described by a multiscale annotation framework and a consolidated set of ALE experiment conditions. A total of 25,321 unique genome annotations from various sources were leveraged to describe multiple scales of mutated features in a set of 35 Escherichia coli based ALE experiments. These experiments totalled 208 independent evolutions and 2641 mutations. Additionally, mutated features were statistically associated across a total of 43 unique experimental conditions to aid in deconvoluting mutation selection pressures.ResultsIdentifying potentially beneficial, or key, mutations was enhanced by seeking coding and non-coding genome features significantly enriched by mutations across multiple ALE replicates and scales of genome annotations. The median proportion of ALE experiment key mutations increased from 62%, with only small coding and non-coding features, to 71% with larger aggregate features. Understanding key mutations was enhanced by considering the functions of broader annotation types and the significantly associated conditions for key mutated features. The approaches developed here were used to find and characterize novel key mutations in two ALE experiments: one previously unpublished with Escherichia coli grown on glycerol as a carbon source and one previously published with Escherichia coli tolerized to high concentrations of L-serine.ConclusionsThe emergent adaptive strategies represented by sets of ALE mutations became more clear upon observing the aggregation of mutated features across small to large scale genome annotations. The clarification of mutation selection pressures among the many experimental conditions also helped bring these strategies to light. This work demonstrates how multiscale genome annotation frameworks and data-driven methods can help better characterize ALE mutations, and thus help elucidate the genotype-to-phenotype relationship of the studied organism.

Dataset Information

ALEdb 1.0: a database of mutations from adaptive laboratory evolution experimentation.

Publications

ALEdb 1.0: a database of mutations from adaptive laboratory evolution experimentation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets