Cited by 827Microbial Metabolic Engineering and BioproductionEvolution and Genetic DynamicsFungal and yeast genetics research

The DOI matches its Crossref record; 189 of the 189 references it cites were found in a registry or an index; 8 of the 202 sources its claims cite were checked for support.

Adaptive laboratory evolution—Principles and applications for biotechnology

Open access
Dragosits, Martin, Mattanovich, Diethard·2013·Microbial Cell Factories

Abstract

Adaptive laboratory evolution is a frequent method in biological studies to gain insights into the basic mechanisms of molecular evolution and adaptive changes that accumulate in microbial populations during long term selection under specified growth conditions. Although regularly performed for more than 25 years, the advent of transcript and cheap next-generation sequencing technologies has resulted in many recent studies, which successfully applied this technique in order to engineer microbial cells for biotechnological applications. Adaptive laboratory evolution has some major benefits as compared with classical genetic engineering but also some inherent limitations. However, recent studies show how some of the limitations may be overcome in order to successfully incorporate adaptive laboratory evolution in microbial cell factory design. Over the last two decades important insights into nutrient and stress metabolism of relevant model species were acquired, whereas some other aspects such as niche-specific differences of non-conventional cell factories are not completely understood. Altogether the current status and its future perspectives highlight the importance and potential of adaptive laboratory evolution as approach in biotechnological engineering.

Overview

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Written from the paper’s full text.

TL;DR

Adaptive laboratory evolution (ALE) is a long-term selection approach that can improve microbial growth, stress tolerance, and biotechnologically relevant phenotypes, often complementing classical genetic engineering. This review summarizes principles, selection methods, mutation types, trade-offs, and applications across bacteria and yeasts, while noting limitations such as unknown evolved genotypes, trade-offs, and limited data for non-conventional hosts.

Key findings

  • Typical ALE experiments run for about 100–2000 generations over weeks to months; within 100–500 generations, fitness increases of up to 50–100% can be achieved, while an extreme case reached up to 1000% improvement in iron reduction rate for *Geobacter sulfurreducens* after 24 months.
  • Across ALE studies, SNPs accounted for 61% of observed genetic changes, deletions approximately 29%, insertions 7%, and insertion-sequence movements 3%; the Lenski *E. coli* experiment exceeds 50,000 generations.
  • ALE can improve nutrient utilization and stress resistance, but evolved phenotypes often show trade-offs or cross-protection depending on selection conditions; for example, ethanol tolerance in *E. coli* came at the cost of decreased acid resistance, while n-butanol tolerance was weakly compatible with oxidative stress.
  • Large-scale transcriptional rearrangements, gene amplifications, and regulatory mutations are common; epistasis is a major factor, and different evolutionary paths can lead to phenotypic convergence.

Methods

  • This is a review article, not a primary experimental study; it synthesizes principles and applications of ALE for biotechnology.
  • It draws on published ALE experiments with bacteria and yeasts, summarized in Table 1 and Table 2, which list species/strain, selection environment, selection time, and references.
  • The main analysis is a narrative synthesis covering laboratory selection methods, mutation types, nutrient and environmental stress adaptation, evolutionary trade-offs and cross-protection, non-conventional hosts, and future directions.

Limitations

  • The paper states that ALE has inherent limitations: evolved strains have unknown genotypes, risking hitchhiking of unfavorable mutations; selection for one condition often causes trade-offs in others; efficient evolutionary engineering of industrial *S. cerevisiae* may be limited by polyploidy; and computational models cannot yet replace trial-and-error laboratory experiments.
  • The paper states that non-conventional host organisms are understudied, with limited physiological data and niche-specific differences not completely understood.
  • Evident from the text: as a review, it does not perform a systematic search or meta-analysis, and some entries in Tables 1 and 2 report “nd” for selection time, indicating incomplete data in the summarized literature.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1186/1475-2859-12-64

Cites

189 of 189 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Figure 1 Adaptive laboratory evolution (ALE). ALE can be performed in the laboratory by (a) sequential serial passages in shake flasks, where nutrients will not be limited and certain growth parameters can heavily fluctuate. (b) Alternatively, chemostat cultures can be applied, where one nutritional component is typically limited and cell density can be much higher than in shake flasks. Additionally, cell density and environmental conditions can be kept constant and more complex cultivation strategies can be implemented. (c) The increase of fitness during laboratory evolution experiments is fast in the first stage but generally slows down during prolonged selection, whereas the number of mutations is steadily increasing; however network complexity leads to a decreasing beneficial effect of additional mutations. [36,67]. (d) Mutations that are usually identified in ALE studies. Single nucleotide polymorphisms (SNPs), smaller insertions and deletions (indels) and larger deletions and insertions contribute to genetic and gene regulatory changes and fitness changes during the selection for improved phenotypes.
Figure 1 Adaptive laboratory evolution (ALE). ALE can be performed in the laboratory by (a) sequential serial passages in shake flasks, where nutrients will not be limited and certain growth parameters can heavily fluctuate. (b) Alternatively, chemostat cultures can be applied, where one nutritional component is typically limited and cell density can be much higher than in shake flasks. Additionally, cell density and environmental conditions can be kept constant and more complex cultivation strategies can be implemented. (c) The increase of fitness during laboratory evolution experiments is fast in the first stage but generally slows down during prolonged selection, whereas the number of mutations is steadily increasing; however network complexity leads to a decreasing beneficial effect of additional mutations. [36,67]. (d) Mutations that are usually identified in ALE studies. Single nucleotide polymorphisms (SNPs), smaller insertions and deletions (indels) and larger deletions and insertions contribute to genetic and gene regulatory changes and fitness changes during the selection for improved phenotypes.CC BY · PubMed Central
Figure 2 Species-specific differences in gene regulatory networks. Species-specific network properties allow for anticipatory behavior of the environment and consequently species-specific fitness trade-offs, in case that environmental stresses do not occur as in their natural order. Ultimately, these network properties may lead to distinct trade-offs among non-conventional host organisms during laboratory evolution.
Figure 2 Species-specific differences in gene regulatory networks. Species-specific network properties allow for anticipatory behavior of the environment and consequently species-specific fitness trade-offs, in case that environmental stresses do not occur as in their natural order. Ultimately, these network properties may lead to distinct trade-offs among non-conventional host organisms during laboratory evolution.CC BY · PubMed Central
Figure 3 Adaptive laboratory evolution for microbial biotechnology. After laboratory evolution, clone analyses and selection, a suitable clone can be directly used for the desired process. Alternatively, the identification of the genetic basis of the improved phenotype can be combined with genetic engineering. The fitness increase tends to slow down during ALE, due to inherent properties of biological networks and molecular evolution. In order to allow for efficient strain engineering, laboratory evolution may be combined with classical genetic engineering tools (e.g. transposon libraries, over-expression libraries and genome shuffling). Short sequential rounds of artificial selection and in vitro genetic manipulation can be applied in order to obtain the desired phenotype more efficiently. Novel genetic circuits and synthetic elements for product formation and complex microbial behavior can be introduced into the ancestral or evolved cell factory.
Figure 3 Adaptive laboratory evolution for microbial biotechnology. After laboratory evolution, clone analyses and selection, a suitable clone can be directly used for the desired process. Alternatively, the identification of the genetic basis of the improved phenotype can be combined with genetic engineering. The fitness increase tends to slow down during ALE, due to inherent properties of biological networks and molecular evolution. In order to allow for efficient strain engineering, laboratory evolution may be combined with classical genetic engineering tools (e.g. transposon libraries, over-expression libraries and genome shuffling). Short sequential rounds of artificial selection and in vitro genetic manipulation can be applied in order to obtain the desired phenotype more efficiently. Novel genetic circuits and synthetic elements for product formation and complex microbial behavior can be introduced into the ancestral or evolved cell factory.CC BY · PubMed Central

Verification

Claims the paper makes

131 Not checked yet · 13 Own interpretation · 7 No basis given · 2 Couldn’t confirm · 2 Own result · 1 Partly supported · 1 Not supported · accuracy not yet measured

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Show all 79 claims

  • It may state two separate claims as one. 67
  • Probably not a substantial claim. 20
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  • The paper relies on this without support.

    Mutations are the basis underlying genetic change and the selection of improved phenotypes in nature as well as in the laboratory.

    “Mutations are the basis underlying genetic change and the selection of improved phenotypes in nature as well as in the laboratory.”
  • The paper relies on this without support.

    Microbial cells evolved mechanisms to successfully cope with stressful conditions in order to maintain cellular homeostasis.

    “Microbial cells evolved mechanisms to successfully cope with such stressful conditions in order to maintain cellular homeostasis.”
  • The paper relies on this without support.

    Efficiency of nutrient source utilization is an important aspect of microbial growth and for industrial purposes.

    “Efficiency of nutrient source utilization is an important aspect of microbial growth and for industrial purposes it may be governed by factors such as substrate cost or increased bioconversion rates.”
  • The paper relies on this without support.

    In industrial processes microbial cells may be challenged with non-natural stresses that may result in poor and non-competitive process performance.

    “In contrast to natural environments, in industrial processes microbial cells may be challenged with non-natural stresses that may result in poor and non-competitive process performance.”
  • The paper relies on this without support.

    The total number of generations is commonly used to estimate the emergence of adaptive mutations.

    “the total number of generations is commonly used to estimate the emergence of adaptive mutations.”
  • The paper relies on this without support.

    The evolution of novel functions and pathways can be hardly observed in a laboratory evolution experiment, even with microbial cells and their fast growth rates.

    “The evolution of novel functions and pathways can be hardly observed in a laboratory evolution experiment, even with microbial cells and their fast growth rates.”
  • The paper relies on this without support.

    rpoS plays a crucial role towards survival in high osmotic conditions.

    “Furthermore, it is known that rpoS plays a crucial role towards survival in high osmotic conditions.”
  • The paper relies on this without support.

    Mesophilic organisms such as E. coli and S. cerevisiae have inherent properties that limit their use in certain applications, e.g. high temperature processes.

    “Mesophilic organisms such as E. coli and S. cerevisiae have inherent properties that limit their use in certain applications, e.g. high temperature processes.”
  • The paper relies on this without support.

    Limited data on the physiology of non-conventional organisms still represent a significant obstacle towards rational host cell engineering.

    “Currently, limited data on the physiology of such organisms still represent a significant obstacle towards rational host cell engineering.”
  • The paper relies on this without support.

    Enormous computational power is necessary to simulate living cells, even on a non-evolutionary scale.

    “Enormous computational power is necessary to simulate living cells, even on a non-evolutionary scale.”
  • The paper relies on this without support.

    Even for well-studied organisms such as E. coli and S. cerevisiae, approximately 30% of genes have unknown function.

    “Additionally, even for well-studied organisms such as E. coli and S. cerevisiae, there is still a significant number (approximately 30%) of genes with unknown function.”
  • The paper relies on this without support.

    The complex interactions of living organisms with their environment are not well understood.

    “Furthermore, the complex interactions of living organisms with their environment are not well understood.”
  • The paper relies on this without support.

    Our understanding of the effect of many environmental stressors on cells, their precise action on certain cellular components, the mode of stress sensing, cellular signal transduction and cross-talk between signal transduction pathways is still limited.

    “Specifically, our understanding of the effect of many environmental stressors on cells, their precise action on certain cellular components, the mode of stress sensing, cellular signal transduction and cross-talk between signal transduction pathways is still limited.”
  • The authors state this as an assumption.

    Evolution is constrained to work on available blueprints.

    “As François Jacob pointed out: “Evolution is a tinkerer”, and as such evolution is constrained to work on available blueprints.”
  • The paper relies on this without support.

    Genetic engineers have the possibility to rapidly construct novel circuits.

    “However, genetic engineers have the possibility to rapidly construct novel circuits.”
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Metadata

Authors
Dragosits, M., & Mattanovich, D.
Affiliations
BOKU University (AT)
Corresponding author
Dragosits, Martin
Venue
Microbial Cell Factories
Published
2013
Volume
12
Issue
1
Pages
64
ISSN
1475-2859 (ISSN-L)
OpenAlex
W2115941371
Cited by
827
References
188
Citations by year
2026: 80 · 2025: 85 · 2024: 99 · 2023: 69 · 2022: 63 · 2021: 80 · 2020: 84 · 2019: 67 · 2018: 66 · 2017: 52 · 2016: 36 · 2015: 23 · 2014: 21 · 2013: 2
FWCI
2.57
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Microbial Cell Factories (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • BOK:ePub (Universitätsbibliothek der Universität f Bodenkultur Wien) (submittedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
Keywords
adaptive laboratory evolution, microbial engineering, molecular evolution, nutrient metabolism, stress metabolism, comparative genomics, transcriptomics, next-generation sequencing
MeSH terms
Biotechnology, Evolution, Molecular, Gene Regulatory Networks

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