Adaptive laboratory evolution of β-caryophyllene producing Saccharomyces cerevisiae

Background: β-Caryophyllene is a plant terpenoid with therapeutic and biofuel properties. Production of terpenoids through microbial cells is a potentially sustainable alternative for production. Adaptive laboratory evolution is a complementary technique to metabolic engineering for strain improvement, if the product-of-interest is coupled with growth. Here we use a combination of pathway engineering and adaptive laboratory evolution to improve the production of β-caryophyllene, an extracellular product, by leveraging the antioxidant potential of the compound. Results: Using oxidative stress as selective pressure, we developed an adaptive laboratory evolution that worked to evolve an engineered β-caryophyllene producing yeast strain for improved production within a few generations. This strategy resulted in fourfold increase in production in isolated mutants. Further increasing the flux to β-caryophyllene in the best evolved mutant achieved a titer of 104.7 ± 6.2 mg/L product. Genomic analysis revealed a gain-of-function mutation in the a-factor exporter STE6 was identified to be involved in significantly increased production, likely as a result of increased product export. Conclusion: An optimized selection strategy based on oxidative stress was developed to improve the production of the extracellular product β-caryophyllene in an engineered yeast strain. Application of the selection strategy in adaptive laboratory evolution resulted in mutants with significantly increased production and identification of novel responsible mutations

Visualizing evolution in real-time method for strain engineering

The adaptive landscape for an industrially relevant phenotype is determined by the effects of the genetic determinants on the fitness of the microbial system. Identifying the underlying adaptive landscape for a particular phenotype of interest will greatly enhance our abilities to engineer more robust microbial strains. Visualizing evolution in real-time (VERT) is a recently developed method based on in vitro adaptive evolution that facilitates the identification of fitter mutants throughout the course of evolution. Combined with high-throughput genomic tools, VERT can greatly enhance the mapping of adaptive landscapes of industrially relevant phenotypes in microbial systems, thereby expanding our knowledge on the parameters that can be used for strain engineering

Genome-Scale Consequences of Cofactor Balancing in Engineered Pentose Utilization Pathways in Saccharomyces cerevisiae

Biofuels derived from lignocellulosic biomass offer promising alternative renewable energy sources for transportation fuels. Significant effort has been made to engineer Saccharomyces cerevisiae to efficiently ferment pentose sugars such as D-xylose and L-arabinose into biofuels such as ethanol through heterologous expression of the fungal D-xylose and L-arabinose pathways. However, one of the major bottlenecks in these fungal pathways is that the cofactors are not balanced, which contributes to inefficient utilization of pentose sugars. We utilized a genome-scale model of S. cerevisiae to predict the maximal achievable growth rate for cofactor balanced and imbalanced D-xylose and L-arabinose utilization pathways. Dynamic flux balance analysis (DFBA) was used to simulate batch fermentation of glucose, D-xylose, and L-arabinose. The dynamic models and experimental results are in good agreement for the wild type and for the engineered D-xylose utilization pathway. Cofactor balancing the engineered D-xylose and L-arabinose utilization pathways simulated an increase in ethanol batch production of 24.7% while simultaneously reducing the predicted substrate utilization time by 70%. Furthermore, the effects of cofactor balancing the engineered pentose utilization pathways were evaluated throughout the genome-scale metabolic network. This work not only provides new insights to the global network effects of cofactor balancing but also provides useful guidelines for engineering a recombinant yeast strain with cofactor balanced engineered pathways that efficiently co-utilizes pentose and hexose sugars for biofuels production. Experimental switching of cofactor usage in enzymes has been demonstrated, but is a time-consuming effort. Therefore, systems biology models that can predict the likely outcome of such strain engineering efforts are highly useful for motivating which efforts are likely to be worth the significant time investment

Genetic Determinants for n-Butanol Tolerance in Evolved Escherichia coli Mutants: Cross Adaptation and Antagonistic Pleiotropy between n-Butanol and Other Stressors

Cross-tolerance and antagonistic pleiotropy have been observed between different complex phenotypes in microbial systems. These relationships between adaptive landscapes are important for the design of industrially relevant strains, which are generally subjected to multiple stressors. In our previous work, we evolved Escherichia coli for enhanced tolerance to the biofuel n-butanol and discovered a molecular mechanism of n-butanol tolerance that also conferred tolerance to the cationic antimicrobial peptide polymyxin B in one specific lineage (green fluorescent protein [GFP] labeled) in the evolved population. In this work, we aim to identify additional mechanisms of n-butanol tolerance in an independent lineage (yellow fluorescent protein [YFP] labeled) from the same evolved population and to further explore potential cross-tolerance and antagonistic pleiotropy between n-butanol tolerance and other industrially relevant stressors. Analysis of the transcriptome data of the YFP-labeled mutants allowed us to discover additional membrane-related and osmotic stress-related genes that confer n-butanol tolerance in E. coli. Interestingly, the n-butanol resistance mechanisms conferred by the membrane-related genes appear to be specific to n-butanol and are in many cases antagonistic with isobutanol and ethanol. Furthermore, the YFP-labeled mutants showed cross-tolerance between n-butanol and osmotic stress, while the GFP-labeled mutants showed antagonistic pleiotropy between n-butanol and osmotic stress tolerance

Evolved Osmotolerant Escherichia coli Mutants Frequently Exhibit Defective N-Acetylglucosamine Catabolism and Point Mutations in Cell Shape-Regulating Protein MreB

Biocatalyst robustness toward stresses imposed during fermentation is important for efficient bio-based production. Osmotic stress, imposed by high osmolyte concentrations or dense populations, can significantly impact growth and productivity. In order to better understand the osmotic stress tolerance phenotype, we evolved sexual (capable of in situ DNA exchange) and asexual Escherichia coli strains under sodium chloride (NaCl) stress. All isolates had significantly improved growth under selection and could grow in up to 0.80 M (47 g/liter) NaCl, a concentration that completely inhibits the growth of the unevolved parental strains. Whole genome resequencing revealed frequent mutations in genes controlling N-acetylglucosamine catabolism (nagC, nagA), cell shape (mrdA, mreB), osmoprotectant uptake (proV), and motility (fimA). Possible epistatic interactions between nagC, nagA, fimA, and proV deletions were also detected when reconstructed as defined mutations. Biofilm formation under osmotic stress was found to be decreased in most mutant isolates, coupled with perturbations in indole secretion. Transcriptional analysis also revealed significant changes in ompACGL porin expression and increased transcription of sulfonate uptake systems in the evolved mutants. These findings expand our current knowledge of the osmotic stress phenotype and will be useful for the rational engineering of osmotic tolerance into industrial strains in the future

Computational identification of adaptive mutants using the VERT system

<p/> <p>Background</p> <p>Evolutionary dynamics of microbial organisms can now be visualized using the Visualizing Evolution in Real Time (VERT) system, in which several isogenic strains expressing different fluorescent proteins compete during adaptive evolution and are tracked using fluorescent cell sorting to construct a population history over time. Mutations conferring enhanced growth rates can be detected by observing changes in the fluorescent population proportions.</p> <p>Results</p> <p>Using data obtained from several VERT experiments, we construct a hidden Markov-derived model to detect these adaptive events in VERT experiments without external intervention beyond initial training. Analysis of annotated data revealed that the model achieves consensus with human annotation for 85-93% of the data points when detecting adaptive events. A method to determine the optimal time point to isolate adaptive mutants is also introduced.</p> <p>Conclusions</p> <p>The developed model offers a new way to monitor adaptive evolution experiments without the need for external intervention, thereby simplifying adaptive evolution efforts relying on population tracking. Future efforts to construct a fully automated system to isolate adaptive mutants may find the algorithm a useful tool.</p

Rapid Microbiological Testing: Monitoring the Development of Bacterial Stress

The ability to respond to adverse environments effectively along with the ability to reproduce are sine qua non conditions for all sustainable cellular forms of life. Given the availability of an appropriate sensing modality, the ubiquity and immediacy of the stress response could form the basis for a new approach for rapid biological testing. We have found that measuring the dielectric permittivity of a cellular suspension, an easily measurable electronic property, is an effective way to monitor the response of bacterial cells to adverse conditions continuously. The dielectric permittivity of susceptible and resistant strains of Escherichia coli and Staphylococcus aureus, treated with gentamicin and vancomycin, were measured directly using differential impedance sensing methods and expressed as the Normalized Impedance Response (NIR). These same strains were also heat-shocked and chemically stressed with Triton X-100 or H2O2. The NIR profiles obtained for antibiotic-treated susceptible organisms showed a strong and continuous decrease in value. In addition, the intensity of the NIR value decrease for susceptible cells varied in proportion to the amount of antibiotic added. Qualitatively similar profiles were found for the chemically treated and heat-shocked bacteria. In contrast, antibiotic-resistant cells showed no change in the NIR values in the presence of the drug to which it is resistant. The data presented here show that changes in the dielectric permittivity of a cell suspension are directly correlated with the development of a stress response as well as bacterial recovery from stressful conditions. The availability of a practical sensing modality capable of monitoring changes in the dielectric properties of stressed cells could have wide applications in areas ranging from the detection of bacterial infections in clinical specimens to antibiotic susceptibility testing and drug discovery

Determination of Real-Time Efflux Phenotypes in Escherichia coli AcrB Binding Pocket Phenylalanine Mutants Using a 1,2′-Dinaphthylamine Efflux Assay

To evaluate the importance of phenylalanine residues for substrate transport in the Escherichia coli efflux pump protein AcrB, we subjected Phe-to-Ala binding pocket mutants to a real-time efflux assay with the novel near-infrared lipophilic membrane probe 1,2′-dinaphthylamine (1,2′-DNA). All mutations, with the exception of F617A, led to considerable retardation of efflux. F610A was the point mutation with the most pronounced impact, followed by F628A, F615A, F136A, and F178A. This is the first study to demonstrate the importance of single phenylalanine residues within the AcrB binding pocket for real-time substrate transport

Novel Quantitative Real-Time LCR for the Sensitive Detection of SNP Frequencies in Pooled DNA: Method Development, Evaluation and Application

BACKGROUND: Single nucleotide polymorphisms (SNP) have proven to be powerful genetic markers for genetic applications in medicine, life science and agriculture. A variety of methods exist for SNP detection but few can quantify SNP frequencies when the mutated DNA molecules correspond to a small fraction of the wild-type DNA. Furthermore, there is no generally accepted gold standard for SNP quantification, and, in general, currently applied methods give inconsistent results in selected cohorts. In the present study we sought to develop a novel method for accurate detection and quantification of SNP in DNA pooled samples. METHODS: The development and evaluation of a novel Ligase Chain Reaction (LCR) protocol that uses a DNA-specific fluorescent dye to allow quantitative real-time analysis is described. Different reaction components and thermocycling parameters affecting the efficiency and specificity of LCR were examined. Several protocols, including gap-LCR modifications, were evaluated using plasmid standard and genomic DNA pools. A protocol of choice was identified and applied for the quantification of a polymorphism at codon 136 of the ovine PRNP gene that is associated with susceptibility to a transmissible spongiform encephalopathy in sheep. CONCLUSIONS: The real-time LCR protocol developed in the present study showed high sensitivity, accuracy, reproducibility and a wide dynamic range of SNP quantification in different DNA pools. The limits of detection and quantification of SNP frequencies were 0.085% and 0.35%, respectively. SIGNIFICANCE: The proposed real-time LCR protocol is applicable when sensitive detection and accurate quantification of low copy number mutations in DNA pools is needed. Examples include oncogenes and tumour suppressor genes, infectious diseases, pathogenic bacteria, fungal species, viral mutants, drug resistance resulting from point mutations, and genetically modified organisms in food