Takaaki Horinouchi

Transcriptome analysis of parallel-evolved Escherichia coli strains under ethanol stress.

BackgroundUnderstanding ethanol tolerance in microorganisms is important for the improvement of bioethanol production. Hence, we performed parallel-evolution experiments using Escherichia coli cells under ethanol stress to determine the phenotypic changes necessary for ethanol tolerance.ResultsAfter cultivation of 1,000 generations under 5% ethanol stress, we obtained 6 ethanol-tolerant strains that showed an approximately 2-fold increase in their specific growth rate in comparison with their ancestor. Expression analysis using microarrays revealed that common expression changes occurred during the adaptive evolution to the ethanol stress environment. Biosynthetic pathways of amino acids, including tryptophan, histidine, and branched-chain amino acids, were commonly up-regulated in the tolerant strains, suggesting that activating these pathways is involved in the development of ethanol tolerance. In support of this hypothesis, supplementation of isoleucine, tryptophan, and histidine to the culture medium increased the specific growth rate under ethanol stress. Furthermore, genes related to iron ion metabolism were commonly up-regulated in the tolerant strains, which suggests the change in intracellular redox state during adaptive evolution.ConclusionsThe common phenotypic changes in the ethanol-tolerant strains we identified could provide a fundamental basis for designing ethanol-tolerant strains for industrial purposes.

Prediction of antibiotic resistance by gene expression profiles.

Although many mutations contributing to antibiotic resistance have been identified, the relationship between the mutations and the related phenotypic changes responsible for the resistance has yet to be fully elucidated. To better characterize phenotype–genotype mapping for drug resistance, here we analyse phenotypic and genotypic changes of antibiotic-resistant Escherichia coli strains obtained by laboratory evolution. We demonstrate that the resistances can be quantitatively predicted by the expression changes of a small number of genes. Several candidate mutations contributing to the resistances are identified, while phenotype–genotype mapping is suggested to be complex and includes various mutations that cause similar phenotypic changes. The integration of transcriptome and genome data enables us to extract essential phenotypic changes for drug resistances.

Genome-wide expression analysis of Saccharomyces pastorianus orthologous genes using oligonucleotide microarrays.

The lager brewing yeast, Saccharomyces pastorianus, an allopolyploid species hybrid, contains 2 diverged sub-genomes; one derived from Saccharomyces cerevisiae (Sc-type) and the other from Saccharomyces bayanus (Sb-type). We analyzed the functional roles of these orthologous genes in determining the phenotypic features of S. pastorianus. We used a custom-made oligonucleotide microarray containing probes designed for both Sc-type and Sb-type ORFs for a comprehensive expression analysis of S. pastorianus in a pilot-scale fermentation. We showed a high degree of correlation between the expression levels and the expression changes for a majority of orthologous gene sets during the fermentation process. We screened the functional categories and metabolic pathways where Sc- or Sb-type genes have higher expression levels than the corresponding orthologous genes. Our data showed that, for example, pathways for sulfur metabolism, cellular import, and production of branched amino acids are dominated by Sb-type genes. This comprehensive expression analysis of orthologous genes can provide valuable insights on understanding the phenotype of S. pastorianus.

Phenotypic convergence in bacterial adaptive evolution to ethanol stress

BackgroundBacterial cells have a remarkable ability to adapt to environmental changes, a phenomenon known as adaptive evolution. During adaptive evolution, phenotype and genotype dynamically changes; however, the relationship between these changes and associated constraints is yet to be fully elucidated.ResultsIn this study, we analyzed phenotypic and genotypic changes in Escherichia coli cells during adaptive evolution to ethanol stress. Phenotypic changes were quantified by transcriptome and metabolome analyses and were similar among independently evolved ethanol tolerant populations, which indicate the existence of evolutionary constraints in the dynamics of adaptive evolution. Furthermore, the contribution of identified mutations in one of the tolerant strains was evaluated using site-directed mutagenesis. The result demonstrated that the introduction of all identified mutations cannot fully explain the observed tolerance in the tolerant strain.ConclusionsThe results demonstrated that the convergence of adaptive phenotypic changes and diverse genotypic changes, which suggested that the phenotype–genotype mapping is complex. The integration of transcriptome and genome data provides a quantitative understanding of evolutionary constraints.

Systems Metabolic Engineering: The Creation of Microbial Cell Factories by Rational Metabolic Design and Evolution

It is widely acknowledged that in order to establish sustainable societies, production processes should shift from petrochemical-based processes to bioprocesses. Because bioconversion technologies, in which biomass resources are converted to valuable materials, are preferable to processes dependent on fossil resources, the former should be further developed. The following two approaches can be adopted to improve cellular properties and obtain high productivity and production yield of target products: (1) optimization of cellular metabolic pathways involved in various bioprocesses and (2) creation of stress-tolerant cells that can be active even under severe stress conditions in the bioprocesses. Recent progress in omics analyses has facilitated the analysis of microorganisms based on bioinformatics data for molecular breeding and bioprocess development. Systems metabolic engineering is a new area of study, and it has been defined as a methodology in which metabolic engineering and systems biology are integrated to upgrade the designability of industrially useful microorganisms. This chapter discusses multi-omics analyses and rational design methods for molecular breeding. The first is an example of the rational design of metabolic networks for target production by flux balance analysis using genome-scale metabolic models. Recent progress in the development of genome-scale metabolic models and the application of these models to the design of desirable metabolic networks is also described in this example. The second is an example of evolution engineering with omics analyses for the creation of stress-tolerant microorganisms. Long-term culture experiments to obtain the desired phenotypes and omics analyses to identify the phenotypic changes are described here.

Development of an Automated Culture System for Laboratory Evolution

Laboratory evolution provides phenotype–genotype mappings and quantitative analysis of selective pressures, giving important insights about evolutionary dynamics. Moreover, parallel laboratory evolution clarifies which phenotypic and genotypic changes are inevitable for adaptive evolution. Such parallel experiments, however, remain labor-intensive. In this study, to facilitate massive parallel laboratory evolution, we developed an automated culture system that can maintain hundreds of independent culture series in exponential growth phase under various culture conditions. We demonstrate the performance of this automated culture system using the laboratory evolution of Escherichia coli under various stressors.

Time-programmable drug dosing allows the manipulation, suppression and reversal of antibiotic drug resistance in vitro

Multi-drug strategies have been attempted to prolong the efficacy of existing antibiotics, but with limited success. Here we show that the evolution of multi-drug-resistant Escherichia coli can be manipulated in vitro by administering pairs of antibiotics and switching between them in ON/OFF manner. Using a multiplexed cell culture system, we find that switching between certain combinations of antibiotics completely suppresses the development of resistance to one of the antibiotics. Using this data, we develop a simple deterministic model, which allows us to predict the fate of multi-drug evolution in this system. Furthermore, we are able to reverse established drug resistance based on the model prediction by modulating antibiotic selection stresses. Our results support the idea that the development of antibiotic resistance may be potentially controlled via continuous switching of drugs.

Improvement of isopropanol tolerance of Escherichia coli using adaptive laboratory evolution and omics technologies

Isopropanol (IPA) is the secondary alcohol that can be dehydrated to yield propylene. To produce IPA using microorganisms, a significant issue is that the toxicity of IPA causes retardation or inhibition of cell growth, decreasing the yield. One possible strategy to overcome this problem is to improve IPA tolerance of production organisms. For the understanding of tolerance to IPA, we performed parallel adaptive laboratory evolution (ALE) of Escherichia coli under IPA stress. To identify the genotypic change during ALE, we performed genome re-sequencing analyses of obtained tolerant strains. To verify which mutations were contributed to IPA tolerance, we constructed the mutant strains and quantify the IPA tolerance of the constructed mutants. From these analyses, we found that five mutations (relA, marC, proQ, yfgO, and rraA) provided the increase of IPA tolerance. To understand the phenotypic change during ALE, we performed transcriptome analysis of tolerant strains. From transcriptome analysis, we found that expression levels of genes related to biosynthetic pathways of amino acids, iron ion homeostasis, and energy metabolisms were changed in the tolerant strains. Results from these experiments provide fundamental bases for designing IPA tolerant strains for industrial purposes.

Suppression of antibiotic resistance acquisition by combined use of antibiotics

We analyzed the effect of combinatorial use of antibiotics with a trade-off relationship of resistance, i.e., resistance acquisition to one drug causes susceptibility to the other drug, and vice versa, on the evolution of antibiotic resistance. We demonstrated that this combinatorial use of antibiotics significantly suppressed the acquisition of resistance.

Application of adaptive laboratory evolution to overcome a flux limitation in an Escherichia coli production strain

Gene deletion strategies using flux balance analysis (FBA) have improved the growth‐coupled production of various compounds. However, the productivities were often below the expectation because the cells failed to adapt to these genetic perturbations. Here, we demonstrate the productivity of the succinate of the designed gene deletion strain was improved by adaptive laboratory evolution (ALE). Although FBA predicted deletions of adhE‐pykAF‐gldA‐pflB lead to produce succinate from glycerol with a yield of 0.45 C‐mol/C‐mol, the knockout mutant did not produce only 0.08 C‐mol/Cmol, experimentally. After the ALE experiments, the highest succinate yield of an evolved strain reached to the expected value. Genome sequencing analysis revealed all evolved strains possessed novel mutations in ppc of I829S or R849S. In vitro enzymatic assay and metabolic profiling analysis revealed that these mutations desensitizing an allosteric inhibition by L‐aspartate and improved the flux through Ppc, while the activity of Ppc in the unevolved strain was tightly regulated by L‐aspartate. These result demonstrated that the evolved strains achieved the improvement of succinate production by expanding the flux space of Ppc, realizing the predicted metabolic state by FBA.