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Identification and optimization of ethanol tolerance mechanisms in Escherichia coli by means of experimental evolution.

Fossil fuel reserves are declining while energy demands continue to increase. Therefore, solving the energy issue poses one of the greatest challenges of the 21st century. Over the past decades, multiple efforts have been undertaken to facilitate the switch from fossil fuels to renewable biofuels. Especially bioethanol has gained much attention. It is one of the biofuels that is already widely integrated in our society. Ethanol is produced in microbial fermentation processes by microorganisms such as yeast or Escherichia coli that convert sugar into ethanol. However, one of the major obstacles in obtaining high yields is the toxicity of ethanol itself. When production proceeds and ethanol titers rise, further production of ethanol is gradually inhibited. Therefore, understanding and increasing ethanol tolerance in ethanol-producing organisms is needed to further improve the production process and increase the competitiveness of bioethanol over fossil fuels. Despite its industrial relevance, ethanol tolerance in E. coli remains poorly understood. Therefore, this work focuses on several aspects of ethanol tolerance in E. coli ranging from the evolutionary dynamics driving adaptation to high ethanol stress to the genetic architecture underlying high ethanol tolerance and the physiological effects of ethanol on E. coli.

Ethanol tolerance is a complex phenotype which requires multiple mutations as it is established by a network of cooperating genes and pathways. Moreover, ethanol is highly toxic for E. coli imposing near-lethal stress conditions. To study ethanol tolerance we exploited the power of experimental evolution and natural selection. Our work provides the first insight into the evolutionary dynamics underlying adaptation to such complex and near-lethal stress conditions. Surprisingly, we found that only lines with an increased mutation rate are able to acquire high ethanol tolerance (up to 8.5%). Moreover we found evidence that hypermutation rapidly provides enough genetic variation in the population under stress to enable adaptation and avoid extinction. Strikingly, we discovered an enormous flexibility in the mutation rate of populations adapting to increasing ethanol percentages. Specifically, mutation rates rise when exposed to increased ethanol stress and decline again once the population is adapted to that percentage. This cycle of increase and decrease in mutation rate was observed multiple times showing an enormous genetic potential to adapt the mutation rate in E. coli. In addition, we identified cellular mortality as the driving force underlying these quick changes in the mutation rate. Taken together, our findings shed new light on the evolutionary dynamics under stress. Moreover, they can have implication in other relevant situations as well such as cells exposed to antibiotics or cancer cells treated with chemotherapy. Hypermutation provides a way to survive these lethal conditions and must therefore be considered in future therapy strategies.

While hypermutation was essential to obtain high ethanol tolerant strains, it also severely increased the complexity of the mutational dataset resulting from sequencing these lines. To cope with this complexity, we developed IAMBEE (Identification of Adaptive Mutations in Bacterial Evolution Experiments). This tool uses both the change in frequency of mutations during a selective sweep that corresponds to a significant phenotypic change and a score that reflects the effect of the mutation at the protein level in a network analysis to prioritize pathways that are involved in the phenotype. Using this tool we identified and confirmed the role of various pathways in high ethanol tolerance. For example, changes in fatty acid biosynthesis and changes in multi-drug efflux pump as well as changes in transcription and translation fidelity are causal for increased ethanol tolerance. The results of this part of our work could serve as a basis for future strain improvement efforts. To fully understand the mechanistic implications of the identified mutations in ethanol tolerance, we already reconstructed them in a clean background with an in-house developed scarless genome engineering method.
  
Finally, we identified several components underlying ethanol-induced mutagenesis. More specifically, ethanol induces oxidative stress, possibly by lipid peroxidation. The reactive oxygen species react with the DNA and result in lesions that in turn activate the SOS-response. This leads to overexpression of SOS-inducible error-prone polymerases that mutagenically repair damage in the DNA, resulting in mutations. More experiments are needed to confirm this mechanism, but these results might have implication for ethanol consumption in beverages and related health implications.

In conclusion, this thesis contributes to the understanding of adaptation to extreme stress conditions, which might lead to the development of anti-evolution therapy strategies and the development of fast strain optimization methods based on hypermutation. IAMBEE is applicable for other complex mutational datasets as well and our CRISPR-based method might be extremely useful for others working with E. coli and even with different organisms for which a similar approach can be applied. Finally, the identified mutations might serve as a basis for further strain improvements for the bioethanol industry.

Date:1 Oct 2012 →  24 Aug 2017
Keywords:Mutagenesis, Escherichia coli, Bio-ethanol, Experimental evolution, Ethanol tolerance
Disciplines:Biomaterials engineering, Biological system engineering, Biomechanical engineering, Other (bio)medical engineering, Environmental engineering and biotechnology, Industrial biotechnology, Other biotechnology, bio-engineering and biosystem engineering, Microbiology, Systems biology, Laboratory medicine, Genetics, Molecular and cell biology, Scientific computing, Bioinformatics and computational biology, Public health care, Public health services
Project type:PhD project