Transcriptome analysis of parallel-evolved Escherichia coli strains under ethanol stress
© Horinouchi et al; licensee BioMed Central Ltd. 2010
Received: 26 July 2010
Accepted: 19 October 2010
Published: 19 October 2010
Understanding 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.
After 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.
The common phenotypic changes in the ethanol-tolerant strains we identified could provide a fundamental basis for designing ethanol-tolerant strains for industrial purposes.
Experimental evolution is a powerful tool for the study of the evolution of emergent properties in biological systems. This experimental system enables us to clarify phenotypic and genotypic changes responsible for adaptive evolution [1, 2]. Parallel-evolution experiments can be performed under identical conditions, and they enable us to distinguish which phenotypic and genotypic changes are inevitable for adaptive evolution and which occurred by mere chance. For engineering purposes, the outcomes of such evolution experiments have the potential to provide valuable information for the rational design of useful strains . For example, by long-term cultivation of a microorganism under an environmental stress conditions, we can expect to obtain stress-tolerant strains after cycles of mutation and selection. The mechanisms of stress tolerance can be elucidated by analyzing the phenotype and genotype of the tolerant strains. By elucidating these mechanisms, we can develop strategies to induce this tolerance in other strains, such as industrially used strains. Screening of strains following random mutagenesis has been used to obtain strains with desired phenotypes [4–6]. However, the advantage of long-term experimental evolution in comparison with random mutagenesis and screening is that it enables the enrichment of beneficial phenotypic and genetic changes by iterative selections. Thus, the identification of essential factors for higher fitness is expected to be easier in experimental evolution.
In this study, we performed a series of evolution experiments to analyze ethanol tolerance in Escherichia coli cells. This microorganism is widely used in the production of useful materials, including amino acids, enzymes, biofuels, biopolymers, and others [7–9], and its importance in the production of biofuels from biomass resources has recently increased . In the production of ethanol by this microorganism, ethanol is a major stress factor that interferes with growth and ethanol production. Thus, developing ethanol tolerance in E. coli strains is important for the improvement of ethanol production. In fact, the construction of ethanol tolerant strains of several microorganisms, such as Saccharomyces cerevisiae, were performed for the improvement of ethanol productivity, for example, by changing lipid composition of cell membrane and activation of amino acid biosynthesis pathways [11–13]. The screening and expression analysis of ethanol tolerant E. coli strain was also performed [14, 15], which revealed the expression changes of several genes in the ethanol tolerant strain, such as increased metabolism of glycine and betaine, suggesting that these expression changes are involved in the mechanism of ethanol tolerance. However, in this previous study, the ethanol tolerance was analyzed by using a single clone of tolerant strain, and thus the mechanisms necessary for the ethanol tolerance is obscure. In this study, we analyzed several ethanol-tolerant strains obtained by an independent series of evolution experiments, which enabled us to identify common characteristics among the tolerant strains that should be involved in ethanol tolerance. We performed 6 independent series of evolution experiments under ethanol stress for over about 1,000 generations and obtained ethanol-tolerant strains that exhibited about 2-fold increase in specific growth rate compared to the parent strain. To understand the phenotypic changes in these strains, we performed comprehensive gene expression analysis of these tolerant strains by microarrays, and identified genes and functional categories with significantly up- or down-regulated expression among the tolerant strains. We found that genes involved in the iron ion transport and biosynthesis pathways of some amino acids, including tryptophan, histidine, valine, leucine, and isoleucine, were commonly up-regulated in tolerant strains, which suggests that these gene functions are involved in ethanol tolerance. In support of this hypothesis, we confirmed that the addition of isoleucine, tryptophan, and histidine to the culture medium increased the growth rate of the parent strain under ethanol stress. The comprehensive analysis of several ethanol-tolerant strains of E. coli provides clues to understanding the mechanism of ethanol tolerance.
Results and discussion
Parallel laboratory evolution experiments of E. coli under ethanol stress
Effect of ethanol concentration on the growth of each E. coli strain
Ethanol concentration (%)
Specific growth rate (h-1)
0.357 ± 0.006
0.584 ± 0.005
0.385 ± 0.008
0.510 ± 0.015
0.130 ± 0.005
0.168 ± 0.003
0.350 ± 0.008
0.288 ± 0.007
0.023 ± 0.009
0.058 ± 0.004
0.187 ± 0.006
0.177 ± 0.005
0.045 ± 0.006
0.009 ± 0.009
We confirmed that the phenotype of the evolved strains, i.e., higher growth rate under ethanol stress, was stable after cultivating them in M9 medium that did not contain ethanol for more than 100 generations (144 hours). After 100 generations in M9 medium that did not contain ethanol, the specific growth rate of tolerant strains A and F under 5% ethanol stress were 0.345 ± 0.020 and 0.315 ± 0.012 (h-1), respectively, which were similar to those observed after the adaptive evolution shown in Figure 1(b). Measurements by phase-contrast microscopy revealed that there is no significant morphological change (size, shape) between the wild-type strain W3110 and evolved strains (data not shown).
Transcriptome analysis of evolved strains
Functional categories of genes that significantly contribute to PC1 and PC2.
PC 1 top 5%
Galactitol metabolic process
gatB, gatC, gatY, gatZ
phoB, phoU, pstA, pstB, pstC, pstS
Phosphoenolpyruvate-dependent sugar phosphotransferase system
dhaH, gatB, gatC, manX, manY, manZ
Response to stress
clpB, degP, dnaK, grpE, hslJ, hslU, htpG, ldhA, pstS, uspG, relB, relE, yfiA
Response to heat
dnaK, groL, groS, grpE, hslJ, hslU, htpG, ldhA
degP, dnaK, dsbA, groS, groL, grpE, htpG, ppiA
PC 1 bottom 5%
Cellular amino acid biosynthetic process
argF, argI, aroF, aroM, carA, carB, hisA, hisB, hisC, hisD, hisF, hisG, hisH, hisI, leuL, lysC, metH, pheA, thrL, trpL
Histidine biosynthetic process
hisA, hisB, hisC, hisD, hisF, hisG, hisH, hisI
Arginine biosynthetic process
argF, argI, carA, carB
Tricarboxylic acid cycle
fumA, mqo, sdhA, sdhB, sdhC, sdhD
amtB, argT, betT, emrA, entD, fadL, fiu, kgtP, livF, livG, livH, livJ, livK, livM, modA, modB, ompF, oppA, oppB, oppC, oppD, oppF, proP, putP, rbsB, rfbX, sdhA, sdhB, sdhC, sdhD, tsx, uraA, yhbE
Amino acid transport
putP, argT, livF, livG, livM, livH, livK, livJ,
oppA, oppB, oppC, oppD, oppF
flgC, flgD, flhM
PC 2 top 5%
Cellular amino acid biosynthetic process
argH, aroF, carB, hisB, hisC, hisD, hisH, ilvA, ilvB, ilvC, ilvD, ilvE, ilvM, leuA, leuB, leuC, leuD, trpB, trpC, trpD, trpE, trpL, tyrA
Histidine biosynthetic process
hisB, hisC, hisD, hisH
Tryptophan biosynthetic process
trpB, trpC, trpD, trpE, trpL
Branched-chain family amino acid biosynthetic process
ilvA, ilvB, ilvC, ilvD, ilvE, ilvM, leuA, leuB, leuC, leuD
Iron ion transport
cirA, entA, entB, entC, entD, entE, entF, fecA, fecB, fecI, fecR, fepA, fepB, fepC, fes, fhuE, fiu, mntH
Enterobactin biosynthetic process
entA, entB, entC, entD, entE, entF, ybdB
Iron-sulfur cluster assembly
hscA, iscS, sufA, sufB, sufD
PC 2 bottom 5%
Lipopolysaccharide biosynthetic process
eptB, kdtA, htrL, rfaB, rfaC, rfaF, rfaG, rfaI, rfaP, rfaQ, rfaS, rfaY
dppB, dppC, dppD, dppF
Among genes with high loadings on PC1, which correspond to genes commonly up-regulated in response to ethanol stress, we found that genes related to the galactitol metabolic process are significantly overrepresented. In Fig. S1(a) presented in Additional file 2, we show the expression levels of gat genes with high loadings on PC1 (gatB, gatC, gatY, gatZ). As shown in the figure, these gat genes were commonly up-regulated in response to ethanol stress both for strain P and ethanol-tolerant strains. The gat genes are involved in biofilm formation  and are known to be up-regulated in response to several stresses, such as acid stress [19, 20]. The genes related to phosphate transport (phoB, phoU, pstB, pstA, pstC, pstS) were also commonly up-regulated in response to ethanol stress [Fig. S1(b) in Additional file 2]. These genes are known to be regulated by the PhoR/PhoB 2-component regulatory system in response to change in extracellular phosphate concentration . PhoR/PhoB system is also known to be involved in acid stress response . In a previous study of isobutanol response network of E. coli, PhoB-regulated genes are up-regulated in response to isobutanol stress presumably due to the stress-induced disruption of quinone membrane interaction . Our results suggest that a similar mechanism is involved in the response to ethanol stress. The manXYZ genes encoding subunits of phosphotransferase system for mannose uptake were also significantly up-regulated in response to ethanol stress for all strains [Fig. S1(c) presented in additonal file 2]. Okouchi et al. have shown that manXYZ genes are related to the response to solvent stress, such as n-hexane, cyclohexane, p-xylene, and toluene , and are highly up-regulated at both the transcript and protein levels under n-butanol stress . Our results indicate that the changes in expression levels of manXYZ are also involved in the response to ethanol stress. Furthermore, we found that genes involved in the category "heat stress response" were significantly up-regulated in the response to ethanol stress, which includes genes encoding chaperon proteins (e.g., groS, groL, grpE, and dnaK). This result is consistent with that in previous studies, in which the heat-shock regulatory gene rpoH and its downstream genes are up-regulated when cells are exposed to ethanol , n-butanol , and isobutanol .
As for the genes with low loadings on PC1, which correspond to genes commonly down-regulated in response to ethanol stress, we found that genes related to histidine and arginine biosynthesis were significantly overrepresented, while the expression levels of genes in other pathways of amino acid biosynthesis were relatively unchanged. In Figs. S1(d) and (e), we show the expression levels of representative genes in these pathways. Although the mechanism for this down-regulation is unclear, our results might suggest that the inactivation of these pathways play a role in response to ethanol stress. Furthermore, we found that genes related to flagella biosynthesis were down-regulated in response to the addition of ethanol. Although most genes in this category were excluded from the statistical analysis shown in Table 2 due to their low expression levels in the presence of ethanol, we confirmed that almost all flagella-related genes were severely down-regulated in response to ethanol stress. We show the expression levels of some representative genes related to this category in Fig. S1(f). The decrease in the activity of flagella biosynthesis under ethanol stress was confirmed by using motility assay on soft agar plate (data not shown), as in the responses to other stresses such as heat stress and osmotic stress .
We also found that genes related to lipopolysaccharide (LPS) biosynthesis were generally down-regulated in the tolerant strains in comparison with strain P, except for tolerant strain C. Fig. S1(i) also shows the expression levels of some representative genes of this pathway. LPS is associated with permeability to hydrophobic molecules and is related to defense against stress . The inactivation of LPS biosynthesis might suggest that a change in the outer membrane occurred during adaptive evolution to the ethanol stress environment. It was reported that the increased levels of unsaturated fatty acids are important for ethanol tolerance in Saccharomyces cerevisiae and E. coli[37, 38]. However, in the tolerant strains we obtained, there are no significant changes in the expression levels of genes related to fatty acid biosynthesis in comparison with the strain P.
In addition to the expression changes common to all tolerant strains as discussed above, there were expression changes specifically occurred in each tolerant strain. For example, under ethanol stress condition, genes involved in the methionine biosynthesis pathway were significantly down-regulated in strain A and strain C in comparison with other strains [Fig. S1(j)]. Note that, strain A and strain C exhibited relatively higher growth rates under ethanol than the other tolerant strains except for strain B, and thus the down-regulation of methionine related genes in these two strains might be responsible for their higher growth rates. The development of ethanol tolerance by the down-regulation of methionine related genes might be possible when increasing production of some metabolites which share the same precursor with methionine, such as those derived from oxaloacetate, is responsible for the ethanol tolerance. Such analysis of specific expression changes in each tolerant strain might be helpful to illustrate the mechanisms of ethanol tolerance in more details.
Effect of amino acids and iron ion supplementation on ethanol tolerance of E. coli
In this study, a series of evolution experiments was performed to investigate the adaptive evolution of E. coli under conditions of ethanol stress. We obtained 6 ethanol-tolerant strains through independent long-term culture experiments. These strains showed an approximately 2-fold increase in specific growth rate under 5% ethanol stress. Comprehensive gene expression analysis of the tolerant strains revealed that common changes in expression levels occurred among the tolerant strains we obtained, which strongly suggests that these phenotypic changes are involved in the development of ethanol tolerance. We found that genes related to iron ion metabolism were commonly up-regulated in the tolerant strains, which suggests that a change in the redox state occurs during adaptive evolution. We also found that the genes related to biosynthetic pathways of tryptophan, histidine, valine, leucine, and isoleucine were commonly up-regulated in the tolerant strains. The activation of these amino acid biosynthesis pathways is speculated to be responsible for the ethanol stress tolerance we observed, and this hypothesis was partially supported by the finding that supplementation of isoleucine, tryptophan, and histidine into the medium increases the specific growth rate under an ethanol stress environment. These findings should be a starting point of understanding the molecular mechanisms involved in the ethanol stress tolerance in E. coli, and thus can be fundamental knowledge for designing ethanol-tolerant E. coli cells for the improvement of ethanol productivity in the industry.
The common expression changes observed in ethanol tolerant strains A-F showed little overlap with previous studies about ethanol tolerance in E. coli cells [15, 39]. Among the common expression changes in the tolerant strains we identified, only the up-regulations of enterobactin biosynthesis genes, which are involved in iron ion metabolism, were already reported as those related to ethanol tolerance . In Ref. , genes and their functions related to ethanol tolerance in E. coli were screened by using a comprehensive transposon mutant library and an overexpression library. In this study, in addition to enterobactin biosynthesis, genes related to osmoregulation and cell-wall biogenesis were found to be involved in the ethanol tolerance. In another previous study about ethanol tolerance in E. coli, ethanol-tolerant strains were obtained by using serial transfer culture experiments. Microarray expression analysis of the ethanol tolerant strain revealed that genes regulated by FNR, which mediates the transition from aerobic to anaerobic growth, were significantly down-regulated and aromatic amino acid biosynthesis (aroF, aroG, aroL, and tyrA) and glycine metabolism (gcvT, gcvP, and lpdA) are up-regulated in comparison with the control strain. In our data, the expression changes of genes in these previously screened categories were not observed except for those related to enterobactin. These differences can be due to the differences in the methodology for the screening and the environmental condition used for the experiments. For example, in the previous studies Luria-Bertani medium was used for the cultivation of E. coli cells, while we used the synthetic medium without amino acids.
Whole-genome resequencing analysis of the tolerant strains will provide information on the mutations that caused the observed phenotypic changes during adaptive evolution. Our preliminary results of whole-genome resequencing analysis showed that there were little overlaps among identified mutations in the tolerant strains we obtained, indicating that no cross-contamination occurred during the parallel-evolution experiments (data not shown). By integrating phenotypic analysis results and the genome data, we expect that more details on the mechanism of ethanol tolerance of E. coli cells will be clarified in future studies.
Strain and culture conditions in the evolution experiments
E. coli strain W3110 was used as the wild-type strain in this study. The W3110 strain was obtained from National BioResource Project (National Institute of Genetics, Japan). In the evolution experiments, the cells were cultured in 10 mL of M9 minimal medium (2.0 mM MgSO4·7H2O, 0.1 mM CaCl2, 0.5 g/L NaCl, 3.0 g/L KH2PO4, 17.1 g/L NaHPO4·12H2O, 1.0 g/L NH4Cl, 4.0 g/L glucose; pH 7.0)  with or without 5% (v/v) ethanol at the final concentration. Cell culture was performed at 30 °C with shaking at 150 strokes min-1 using water bath shakers (Personal-11, Taitec Co., Saitama, Japan). We diluted the cells into a fresh medium every 24 hours. The cells were maintained in the exponential growth phase by adjusting the initial cell concentration of each dilution to a final cell concentration of less than 0.05 as measured by optical density at 600 nm (OD600). The specific growth rate was calculated based on the initial and final cell concentrations of the daily dilution. We confirmed that this calculation of the specific growth rate using 2 time points was accurate (the average absolute deviation is less than 3%) by measuring the specific growth rates of strain P and the evolved strains using OD600 values of more than 5 time points. In all evolution experiments, the cells were grown under microaerobic conditions in test tubes with screw cap. The cells after the evolution experiments were stored as glycerol stocks at -80 °C and used for further analysis.
Phenotype assays of evolved strains
The evolved E. coli strains, strain P (parent strain) and the wild-type strain W3110 were inoculated from the glycerol stock to M9 medium and cultured for the preculture. After 8 or 9 generations, cells were inoculated in 10 mL of M9 medium with varying ethanol concentrations (0, 5, 6, 6.5, and 7%). The other conditions were identical to those in the evolution experiments. The experiments in cultures with varying ethanol concentrations were performed 3 times independently. For the evaluation of amino acids and iron ion supplementation, cell growth was analyzed using a biophotorecorder (Toyo Rikakikai CO., Ltd, Tokyo, Japan) with 5 mL of M9 medium with or without ethanol. The supplementation of tryptophan, histidine, valine, leucine, isoleucine were investigated in the final concentrations ranging from 0.1 mM to 10 mM, and the effect of iron ion supplementation was evaluated by adding FeSO4 into the medium in the final concentration ranging 1 μM to 4 μM.
For transcriptome analysis, a custom-designed tilling microarray of E. coli W3110 in Affymetrix platform was used, which contains approximately 1.5 million perfect-match 21-bp probes for the E. coli genome and the corresponding approximately 4.5 million single-base mismatch probes [Ono et al., manuscript in preparation]. For the sample preparation, each strain was inoculated from the frozen stock into 10 mL of M9 medium for the preculture. Five-microliter aliquots of the preculture medium cells were inoculated into 10 mL of M9 medium without or with 5% (v/v) ethanol and cultured for 5 generations (without ethanol) or 10 generations (with ethanol). The cells in the exponential growth phase were harvested by centrifugation and stored at -80 °C before RNA extraction. Total RNA was isolated and purified from cells using an RNeasy mini kit with on-column DNA digestion (Qiagen, Hilden, Germany). Synthesis of cDNA, fragmentation and end-terminus biotin labeling were carried out in accordance with the Affymetrix protocols. Hybridization, washing, staining, and scanning were carried out according to the Expression Analysis Technical Manual (provided by Affymetrix). We used the same equipments as shown in a previous study .
Thus, the observation that this p value is small enough in real data indicates that genes related to functional category A is significantly overrepresented in the genes having the top 5% highest or lowest loading factors.
We thank Kumi Tanabe for technical assistance. This work was supported by Grants-in-Aid for Scientific Research 21360401 (to H. S.), 20700270 (to C. F.), 21780071 (to T. Hi.), 21700324 (to N. O.), respectively, and the "Global COE (Centers of Excellence) program" in Osaka University from the Ministry of Education, Culture, Sports, Science and Technology, Japan.
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