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Network-Based Identification of Adaptive Pathways in Evolved Ethanol-Tolerant Bacterial Populations

(2017) MOLECULAR BIOLOGY AND EVOLUTION. 34(11). p.2927-2943
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Bioinformatics: from nucleotids to networks (N2N)
Abstract
Efficient production of ethanol for use as a renewable fuel requires organisms with a high level of ethanol tolerance. However, this trait is complex and increased tolerance therefore requires mutations in multiple genes and pathways. Here, we use experimental evolution for a system-level analysis of adaptation of Escherichia coli to high ethanol stress. As adaptation to extreme stress often results in complex mutational data sets consisting of both causal and noncausal passenger mutations, identifying the true adaptive mutations in these settings is not trivial. Therefore, we developed a novel method named IAMBEE (Identification of Adaptive Mutations in Bacterial Evolution Experiments). IAMBEE exploits the temporal profile of the acquisition of mutations during evolution in combination with the functional implications of each mutation at the protein level. These data are mapped to a genome-wide interaction network to search for adaptive mutations at the level of pathways. The 16 evolved populations in our data set together harbored 2,286 mutated genes with 4,470 unique mutations. Analysis by IAMBEE significantly reduced this number and resulted in identification of 90 mutated genes and 345 unique mutations that are most likely to be adaptive. Moreover, IAMBEE not only enabled the identification of previously known pathways involved in ethanol tolerance, but also identified novel systems such as the AcrAB-TolC efflux pump and fatty acids biosynthesis and even allowed to gain insight into the temporal profile of adaptation to ethanol stress. Furthermore, this method offers a solid framework for identifying the molecular underpinnings of other complex traits as well.
Keywords
experimental evolution, biological networks, ethanol tolerance, bacteria, hypermutation, gene prioritization, ORGANIC-SOLVENT TOLERANCE, GENOME-WIDE ASSOCIATION, ESCHERICHIA-COLI, EXPERIMENTAL EVOLUTION, SACCHAROMYCES-CEREVISIAE, SOMATIC MUTATIONS, MEMBRANE-FLUIDITY, LIPID-COMPOSITION, COMPLEX TRAIT, CANCER

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Citation

Please use this url to cite or link to this publication:

Chicago
Swings, Toon, Bram Weytjens, Thomas Schalck, Camille Bonte, Natalie Verstraeten, Jan Michiels, and Kathleen Marchal. 2017. “Network-Based Identification of Adaptive Pathways in Evolved Ethanol-Tolerant Bacterial Populations.” Molecular Biology and Evolution 34 (11): 2927–2943.
APA
Swings, T., Weytjens, B., Schalck, T., Bonte, C., Verstraeten, N., Michiels, J., & Marchal, K. (2017). Network-Based Identification of Adaptive Pathways in Evolved Ethanol-Tolerant Bacterial Populations. MOLECULAR BIOLOGY AND EVOLUTION, 34(11), 2927–2943.
Vancouver
1.
Swings T, Weytjens B, Schalck T, Bonte C, Verstraeten N, Michiels J, et al. Network-Based Identification of Adaptive Pathways in Evolved Ethanol-Tolerant Bacterial Populations. MOLECULAR BIOLOGY AND EVOLUTION. 2017;34(11):2927–43.
MLA
Swings, Toon, Bram Weytjens, Thomas Schalck, et al. “Network-Based Identification of Adaptive Pathways in Evolved Ethanol-Tolerant Bacterial Populations.” MOLECULAR BIOLOGY AND EVOLUTION 34.11 (2017): 2927–2943. Print.
@article{8546635,
  abstract     = {Efficient production of ethanol for use as a renewable fuel requires organisms with a high level of ethanol tolerance. However, this trait is complex and increased tolerance therefore requires mutations in multiple genes and pathways. Here, we use experimental evolution for a system-level analysis of adaptation of Escherichia coli to high ethanol stress. As adaptation to extreme stress often results in complex mutational data sets consisting of both causal and noncausal passenger mutations, identifying the true adaptive mutations in these settings is not trivial. Therefore, we developed a novel method named IAMBEE (Identification of Adaptive Mutations in Bacterial Evolution Experiments). IAMBEE exploits the temporal profile of the acquisition of mutations during evolution in combination with the functional implications of each mutation at the protein level. These data are mapped to a genome-wide interaction network to search for adaptive mutations at the level of pathways. The 16 evolved populations in our data set together harbored 2,286 mutated genes with 4,470 unique mutations. Analysis by IAMBEE significantly reduced this number and resulted in identification of 90 mutated genes and 345 unique mutations that are most likely to be adaptive. Moreover, IAMBEE not only enabled the identification of previously known pathways involved in ethanol tolerance, but also identified novel systems such as the AcrAB-TolC efflux pump and fatty acids biosynthesis and even allowed to gain insight into the temporal profile of adaptation to ethanol stress. Furthermore, this method offers a solid framework for identifying the molecular underpinnings of other complex traits as well.},
  author       = {Swings, Toon and Weytjens, Bram and Schalck, Thomas and Bonte, Camille and Verstraeten, Natalie and Michiels, Jan and Marchal, Kathleen},
  issn         = {0737-4038},
  journal      = {MOLECULAR BIOLOGY AND EVOLUTION},
  keyword      = {experimental evolution,biological networks,ethanol tolerance,bacteria,hypermutation,gene prioritization,ORGANIC-SOLVENT TOLERANCE,GENOME-WIDE ASSOCIATION,ESCHERICHIA-COLI,EXPERIMENTAL EVOLUTION,SACCHAROMYCES-CEREVISIAE,SOMATIC MUTATIONS,MEMBRANE-FLUIDITY,LIPID-COMPOSITION,COMPLEX TRAIT,CANCER},
  language     = {eng},
  number       = {11},
  pages        = {2927--2943},
  title        = {Network-Based Identification of Adaptive Pathways in Evolved Ethanol-Tolerant Bacterial Populations},
  url          = {http://dx.doi.org/10.1093/molbev/msx228},
  volume       = {34},
  year         = {2017},
}

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