Global Metabolomic Responses of Escherichia coli to Heat Stress

Article pubs.acs.org/jpr

Global Metabolomic Responses of Escherichia coli to Heat Stress Yangfang Ye,†,‡ Limin Zhang,† Fuhua Hao,† Jingtao Zhang,†,§ Yulan Wang,*,† and Huiru Tang*,† †

State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Centre for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, P.R.China ‡ Key Laboratory of Applied Marine Biotechnology (Ningbo University) Ministry of Education, Ningbo 315211, P.R.China S Supporting Information *

ABSTRACT: Microbial metabolomic analysis is essential for understanding responses of microorganisms to heat stress. To understand the comprehensive metabolic responses of Escherichia coli to continuous heat stress, we characterized the metabolomic variations induced by heat stress using NMR spectroscopy in combination with multivariate data analysis. We detected 15 amino acids, 10 nucleotides, 9 aliphatic organic acids, 7 amines, glucose and its derivative glucosylglyceric acid, and methanol in the E. coli extracts. Glucosylglyceric acid was reported for the first time in E. coli. We found that heat stress was an important factor influencing the metabolic state and growth process, mainly via suppressing energy associated metabolism, reducing nucleotide biosynthesis, altering amino acid metabolism and promoting osmotic regulation. Moreover, metabolic perturbation was aggravated during heat stress. However, a sign of recovery to control levels was observed after the removal of heat stress. These findings enhanced our understanding of the metabolic responses of E. coli to heat stress and demonstrated the effectiveness of the NMR-based metabolomics approach to study such a complex system. KEYWORDS: Escherichia coli, heat stress, metabolomics, nuclear magnetic resonance (NMR), multivariate data analysis

■

INTRODUCTION When mammals face threatening environmental conditions, a well-known “fight or flight” response takes place. Such physiological stress response mechanisms are universally present in all living beings, including bacteria. To survive extreme environments, such as high temperature, bacteria must sense the changes and then use an active response at the level of gene expression.1 Escherichia coli, as a famous and valuable model, can be used to investigate the mechanism of how singlecelled organisms respond to heat stress. In E. coli, during the heat stress response, transcription initiation is regulated largely by σ32. The first step of transcription initiation is the binding of σ32 to the core RNA polymerase to form an Eσ32 holo enzyme complex.2 This binding results in the induction of at least 26 genes3 and the expression of more than 30 heat shock proteins (HSPs).4 The majority of the HSPs are either molecular chaperones (e.g., DnaK, DnaJ, GroEL, GroES, ClpB, etc.), which assist in protecting cellular proteins from thermal denaturation and the refolding of denatured proteins into their native states, or proteases (e.g., Lon, ClpP, FstH, etc.) that degrade the aggregated proteins. Apart from the heat shock genes, the expression of some global regulator genes, such as mlc and arcA tends to increase after the temperature increases. The activation of mlc caused ptsG to be down-regulated, which was subsequently followed by the down-regulation of glucose uptake. In accordance with the up-regulation of arcA following heat shock, the expression of TCA cycle and glyoxylate pathway © 2012 American Chemical Society

genes, such as icdA, sucA, aceA, were down-regulated; thus, the TCA cycle and the glyoxylate pathway were repressed. The temperature increase also caused the up-regulation of pflA and ldhA genes and the down-regulation of the adhE gene under microaerobic conditions. As a consequence, the formate and lactate yield increased, while the ethanol yield decreased.5 In addition, the continuous heat stress up-regulated several genes involved in sulfur metabolism, including the cysHIJ, cysND, cysA and cysP genes, while two genes in the cysteine and methionine degradation pathways (metC and tnaA) were down-regulated.6 In light of the genome- and transcriptome-based observations above, studies on the metabolite level have been carried out. For example, Soini et al.7 monitored adenosine nucleotides, glucose and acetate levels in E. coli during batch cultivation and showed that the transient increase of adenosine nucleotides was accompanied by an eventual decrease of the ATP pool, a gradual decrease of glucose concentration, and an increase in acetate level upon an increase in temperature. Notably, the most recent research on the heat stress response of E. coli is based on integrative metabolomic and transcriptomic technologies.8 This research showed that a consistent decrease in the levels of metabolites related to glycolysis, the pentose phosphate pathway (PPP) and the TCA cycle after heat stress application. Meanwhile, various amino acids including isoleucine, threonine, phenylalanine, lysine, alanine, asparagine, Received: January 5, 2012 Published: February 28, 2012 2559

dx.doi.org/10.1021/pr3000128 | J. Proteome Res. 2012, 11, 2559−2566

Journal of Proteome Research

Article

and allowed to grow until the end of the experiment. The samples were collected at designed time intervals (Figure 1).

glutamic acid, and homoserine were accumulated, but methionine level was significantly reduced after heat stress. However, above researches on heat stress are either based on targeted analysis or based on Mass Spectrometry (MS) detection of metabolites, which could be either incomplete or limited to the central carbon metabolism, the mechanism of the heat stress response in E. coli needed to be further characterized and validated using complementary techniques. The combination of 1H nuclear magnetic resonance (NMR) spectroscopy with multivariate data analysis has demonstrated the potential for studying the complex biological system response to chemical and physical perturbation on the metabolic level and for characterizing disease status.9,10 In this work, we exposed E. coli HB101 to heat stress and compared the temporal changes in the metabolic profile to untreated controls using a NMR-based metabolomics strategy to investigate (a) the overall and specific changes in the E. coli metabolome following heat stress and (b) the trend of the metabolic state after the removal of heat stress. The findings from the NMR-based metabolomics investigation are useful to gain novel insights into the nature of the E. coli heat stress response.

■

Figure 1. Sampling time points during batch cultivation of E. coli HB101. The time 0 indicated the first sampling time point in which the culture had been cultivated at 37 °C for 24 h.

The cell cultures were quenched by a brief incubation on ice, followed by centrifugation (6000 rpm, 5 min, 4 °C), the supernatant was decanted. The cell pellet was subsequently washed three times with 1 mL of ice-cold PBS (6000 rpm, 5 min, 4 °C). All samples were snap frozen in liquid nitrogen and stored at −80 °C until further processing. Extraction of Intracellular Metabolites

MATERIALS AND METHODS

All of the frozen samples (with wet weight of ∼0.4 g) were homogenized in 600 μL of an ice-cold acetonitrile buffer extraction solution. To destroy the bacterial cells, the homogenates were snap frozen in liquid nitrogen and subjected to three times of freeze−thaw cycles with rapid and vigorous manual mixing between each cycle. The samples were further sonicated on wet ice for 99 cycles with each cycle consisting of 2-s pulses and 2-s stops. The supernatant was collected by centrifugation for 10 min at 12 000 rpm and 4 °C. The remaining solid residues were further extracted using the same extract solution and intensively homogenized using a vortex. A second supernatant was collected after centrifugation and pooled with the first one. The combined supernatants from the two extractions were condensed by vacuum to remove acetonitrile. The samples were reconstituted in 600 μL of Na+/K+ buffer and a total of 550 μL of the supernatants was pipetted into 5-mm outer diameter NMR tubes (Norell, ST500-7, U.S.A.) for NMR analysis after centrifugation for 10 min at 12 000 rpm and 4 °C.

Chemicals

Analytical grade acetonitrile, NaCl, K2HPO4·3H2O, and NaH2PO4·2H2O were all purchased from Guoyao Chemical Co. Ltd. (Shanghai, China). Peptone and yeast extracts were obtained from Wuhan Jiehui Biotechnology Co. Ltd. (Hubei, China). Phosphate buffered saline (PBS, pH7.2−7.6) was purchased from Wuhan Boster Biological Technology, Ltd. (Hubei, China). Deuterated water (99.9%) and sodium 3trimethylsilyl [2,2,3,3-d4] propionate (TSP) were purchased from Cambridge Isotope Laboratories Inc. (Andover, MA). Na+−K+ buffer (K2HPO4−NaH2PO4, 0.1 M, pH 7.4), containing 10% D2O and 0.005% TSP, was prepared in H2O.11 The extract solution for intracellular metabolites was prepared by mixing equal volumes of acetonitrile and Na+−K+ buffer. Bacterial Strain and Culture Conditions

E. coli HB101, genotype F− mcrB mrr hsdS20(rB− mB−) recA13 leuB6 ara-14 proA2 lacY1 galK2 xyl-5 mtl-1 rpsL20(SmR) glnV44 λ−, was purchased from the China Center for Type Culture Collection (CCTCC). An initial preculture (24 h, 37 °C, shaking at 150 rpm) was carried out in 100-ml Erlenmeyer flasks containing 30 mL of Luria−Bertani (LB) medium and cotton plugs to reduce the differences caused by colony selection. Then, 2 mL of preculture was grown to an initial optical density (OD600) between 1.6 and 1.7 and transferred to 30 mL of fresh LB broth. After a 24-h incubation, the cultures with OD600 of about 2.5 were used for the heat treatment.

NMR Spectroscopic Analysis

All NMR measurements were performed at 298 K using a Bruker Avance III 600 MHz spectrometer operating at 600.13 MHz for 1H resonance frequency, equipped with an inverse detection cryogenic probe (Bruker, Biospin, Germany). For all samples, the 1H NMR spectra were acquired using a first increment of NOESY pulse sequence (recycle delay−90°−t1− 90°−tm−90°−acquisition). Water suppression was achieved with a weak continuous wave irradiation during both the recycle delay (RD, 2 s) and the mixing time (tm, 100 ms). The t1 was set to 6.5 μs and the 90° pulse length was adjusted to approximately 10 μs. Sixty-four transients were collected into 32 k data points for each spectrum with a spectral width of 20 ppm and an acquisition time of 1.36 s. An exponential window function with a line broadening factor of 1 Hz was applied to all free induction decays (FIDs) prior to Fourier transformation (FT). For resonance assignment purposes, a catalog of twodimensional NMR spectra including 1H−1H COSY, 1H−1H TOCSY, 1H J-resolved, 1H−13C HSQC, and 1H−13C HMBC

Experimental Protocol

A total of 102 flasks of LB medium, containing E. coli in the stationary phase, were randomly divided and subjected to two treatments, (1) untreated control and (2) exposure to heat stress (43 °C). One flask of culture was used for each NMR sample and six replicate samples were prepared for each sampling point. The heat stress was performed by transferring the flasks to another rotator (43 °C) and it took 10 min for temperature to reach equilibrium. After the 2.17-h heat stress treatment, all of the E. coli at 43 °C was transferred to 37 °C 2560



Article

Figure 2. Two typical 600 MHz 1H NMR spectra of E. coli extracts in the untreated control and heat stressed group. Resonance assignments are given in Supporting Information Table S1. Key: 1. isoleucine, 2. leucine, 3. valine, 4. α-amino-β-hydroxybutyrate, 5. lactate, 6. threonine, 7. alanine, 8. acetate, 9. lysine, 10. putrescine, 11. N-acetyl alanine, 12. acetamide, 13. methionine, 14. γ-aminobutyrate, 15. β-aminoadipate, 16. glutamate, 17. pyruvate, 18. succinate, 19. 2-oxoglutarate, 20. methylamine, 21. aspartate, 22. dimethylamine, 23. trimethylamine, 24. phosphorylcholine, 25. choline, 26. arginine, 27. betaine, 28. methanol, 29. glycine, 30. β-glucose, 31. α-glucose, 32. glucosylglyceric acid, 33. ribose-5-phosphate, 34. uracil, 35. uridine, 36. cytidine, 37. fumarate, 38. tyrosine, 39. phenylalanine, 40. xanthine, 41. adenosine 2′,3′-cyclic phosphate, 42. hypoxanthine, 43. adenosine, 44. nicotinate.

apodization functions were applied to the FID prior to FT with forward linear prediction using standard functions provided in TOPSPIN.

spectra were acquired for selected samples. In both COSY and TOCSY experiments, 64 transients were collected into 2048 (2k) data points for each of 128 increments with spectral widths of 10.5 ppm in both dimensions. The MLEV-17 was employed as a spin-lock scheme for TOCSY with a mixing time of 80 ms. In J-resolved spectra, 64 transients were collected into 2k data points for each of 50 increments with spectral widths of 10.5 ppm and 60 Hz in F2 (chemical shift) and F1, respectively. For the HSQC experiments, composite pulse broadband 13C decoupling (globally alternating optimized rectangular pulses, GARP) was employed during the acquisition period; 400 transients were collected into 2k data points for each of the 128 increments with a spectral width of 10.5 ppm in 1H and 175 ppm in the 13C dimension. For HMBC experiments, 352 transients were collected into 2k data points for each of the 128 increments with spectral widths of 10.5 and 220 ppm in the 1H and 13C dimensions, respectively; the long-range coupling constant was set to 6 Hz. These data were zero-filled into 2k data points in the evolution dimensions, and appropriate

Data Analysis 1

H NMR spectra were manually corrected for phase and baseline distortion using TOPSPIN (version 2.0, Bruker Biospin) and referenced to the TSP signal (δ 0.0). Data reduction was accomplished by dividing the spectrum from 9.0 to 0.8 ppm into 1.8 Hz regions (bins) using the AMIX package (version 3.8.3, Bruker Biospin). The regions containing the water resonance (δ 5.14−4.67) and the acetonitrile resonance (δ 2.09−2.07) were removed. The spectra were normalized to the total sum of the spectral integrals to compensate for sample concentration differences. The multivariate data analyses of the normalized NMR data sets were carried out using the SIMCAP+ software package (version 11.0, Umetrics, Sweden). Initially, the principal component analysis (PCA) of the NMR spectral data was performed (on mean-centered data) to visualize the general structure of each data set and to identify any 2561



Article

abnormalities within the data set. Subsequently, a supervised multivariate data analytical tool, orthogonal projection to latent structure discriminant analysis (OPLS-DA), was applied to the analysis of 1H NMR spectral data scaled to unit variance.12,13 The interpretation of the model was facilitated by a back-scaled transformation of the loadings,14 with incorporated color-coded correlation coefficients of the metabolites responsible for the differentiation (such as Figure 4). The color plot was obtained with version 7.1 of the MATLAB (The Mathworks Inc., Natwick, USA) environment using an in-house developed script. In effect, each back-scale transformed loading is plotted as a function of the respective chemical shift with a color code that indicates the weights of the discriminatory variables. A hot color (i.e., red) corresponds to the metabolite being highly different between classes, while a cool color (i.e., blue) corresponds to no differences. To check the validity of the model and avoid the overfitting of the PLS model, the assessment of the 6-fold cross-validated scores from the model was used and the cross-validation parameter Q2, indicating the predictability of the model related to its statistical validity, was calculated.15 An additional crossvalidation tool, a permutation test, was performed for each model by randomizing the order of Y variables for a specified number of times (permutation number = 200). The R2 in the permutated plot describes how well the data fit with the derived model, whereas Q2 describes the predictive ability of the derived model and provides a measure of the model quality. If higher Q2 values were obtained from the permutation models than the one from the true model, then the model was deemed to lack predictive ability.16 To illustrate variations caused by heat stress, the relative changes of typical metabolites were also calculated against the levels of the untreated control group in the form of (Ch − Cc)/Cc, where Ch was the respective metabolite concentration in the heat stressed group and Cc was that in the untreated control group.

■

Figure 3. Trajectory derived from PCA of 1H NMR spectra of E. coli normalized on the sum of the spectrum indicated metabolic changes associated with cultural time and heat stress. The smallest square and dot represented the start points of the trajectory derived from PCA of 1 H NMR spectra of E. coli extracts in the untreated control and heat stressed group. The legend symbol got larger over the treatment time.

dominated by heat intervention after 1.17 h of heating (Figure 3). Complete recovery of metabolic profiles was not achieved after cooling down the temperature to 37 °C at 2.17 h. The heat-related metabolic effects were evaluated by OPLSDA comparisons of the 1H NMR profiles of the control and heat stressed E. coli collected at the same time point. For illustrative purpose, cross validated scores plot and corresponding coefficient plots of selective time points were displayed in Figure 4. The pair wise comparative OPLS-DA of the NMR data showed significant intergroup metabolomic differences with good model quality indicated by the R2X and Q2 values (Table 1). The results of the permutation tests further suggested that the models constructed from the spectral data at 1.17, 2.17, 2.33, 2.83, 3.33, and 4.33 h were valid. For this analysis, the metabolite was considered to be significant when the value of coefficient was greater than 0.754, which corresponds to the discrimination significance at the level of p < 0.05. The important altered metabolites associated with heat stress at different time points were summarized in Table 1. The temporal changes of the main metabolites, significantly affected by heat stress, were illustrated in Figure 5. During the whole course of heat stress treatment, the significantly changed metabolites showed close associations with the initial heating to 43 °C and later cooling down to 37 °C. The most remarkable change was the long lasting reduction of the glucose level throughout the experimental period. The levels of leucine, isoleucine, valine, lactate, N-acetyl alanine, dimethylamine and methylamine displayed gradual increases and achieved the maximum levels at 2.17 h, and leveled off at the end of experiment. The concentrations of ribose-5phosphate, adenosine, cytidine, tyrosine, betaine and putrescine were decreased at the early time point and reached to the lowest levels at 2.17 h and increased again during the recovery period. Another group of metabolites, including alanine, αaminoadipate, glutamate, succinate and glucosylglyceric acid showed rapid increase in their concentrations at 1.17 h and gradually decreased at later time points. The level of α-aminoβ-hydroxybutyrate was depleted at 1.17 h, but remarkable accumulation of its level to almost above 200% of that of control E. coli was observed at 2.17 h, followed with depletion at 3.33 h.

RESULTS

H NMR Spectra of the E. coli Extract

1

The typical 1H NMR spectra of the E. coli extracts in the untreated control and heat stressed group are shown in Figure 2. The resonances were assigned to specific metabolites (with both 1H and 13C data tabulated in Supporting Information Table S1) according to the literature data17,18 and the extensive 2D NMR analysis including 1H−1H COSY, 1H−1H TOCSY, 1 H J-resolved, 1H−13C HSQC, and 1H−13C HMBC 2D NMR spectroscopy. A total of 44 metabolites were identified and dominating metabolites noted from NMR spectra included a range of amino acids, organic acids and amines, nucleotides, glucose, and its derivative glucosylglyceric acid that was found for the first time in E. coli extracts (Supporting Information Figure S1). Metabolic Changes Due to Heat stress

The overview of global metabolic alterations during 4.33 h of treatment and effects of heat stress were constructed by principal component analysis of all the data collected. The averaged PCA scores were calculated for the first two PCs (Figure 3). The trajectory illustrated relative stable metabolic profiles of the control E. coli during first 2.83 h of culture and the deviation of metabolic profiles from the stable space occurred between 2.83 and 3.33 h. The metabolic profiles obtained from the heat stressed E. coli followed a globally similar start, but presented a significantly different process 2562



Article

Figure 4. OPLS-DA scores (left) and correlation coefficient plots (right) derived from NMR data for E. coli extracts after treatment for 1.17 (A), 2.17 (B), and 4.33 h (C). Results of the untreated controls (stars, black) and heat stressed groups (circles, red) are shown. Each symbol represents the metabolic profile obtained from each bottle of E. coli culture (see Supporting Information Table S1 for metabolite identification key).

■

DISCUSSION The aim of present study was to investigate the global metabolic responses of E. coli to heat stress by employing an NMR-based metabolomic strategy. We examined endogenous metabolic alterations in E. coli extracts at 0.17, 0.67, 1.17, 2.17, 2.33, 2.83, 3.33, and 4.33 h during the treatments. The results indicated that heat stress perturbed the metabolic status of E. coli involving many related metabolic pathways (Figure 6).

cycle flux was repressed based on transcriptomic and metabolomic observations.5,8,19 In light of these observations, we speculated that the significant accumulation of succinate and 2-oxoglutarate was related to a branched, noncyclic TCA cycle. That is, the cycle was divided into two parts: one, leading to the production of 2-oxoglutarate oxidatively, and the other, leading to the production of succinate reductively.20 In fact, the broken TCA cycle was involved in mixed acid fermentation, which generally takes place in facultative anaerobic E. coli in the absence of oxygen. In addition to the succinate and 2oxoglutarate, acetate and lactate were produced by the mixed acid fermentation (Figure 6). However, this reliance on anaerobiosis would intensify the depletion of glucose, because this type of respiration is less efficient in generating ATP. In this study, the lactate level sharply increased at 2.17 h, which indicated active lactate fermentation. Concomitantly, we also detected the ribose-5-phosphate, which is both a product and an intermediate product of the PPP shunt in which glucose-6-phosphate undergoes oxidative decarboxylation to form ribulose-5-phosphate in the oxidative PPP, and is further reversibly isomerized to ribose-5-phosphate in the nonoxidative phase. Our result implies an active PPP shunt and this shunt was served as an alternative to glycolysis in E. coli HB101. Moreover, ribose-5-phosphate is also the precursor for the synthesis of nucleotides. The sharply decreasing ribose-5-phosphate level at 2.17 h strongly indicated depressed nucleotide biosynthesis, which was consistent with the transcript and metabolic level in the E. coli strain MG1655 at the midlog phase.8 Therefore, it was not surprising that a

Energy Associated Metabolism

Our data showed a lasting significant drop of the glucose level throughout the heat stress period. Previous studies based on the genomic, transcriptional and proteomic levels showed that energy was needed to respond to the heat stress.4,5 For example, proteases are ATP-dependent, suggesting that ATP is necessary to degrade the aggregated proteins. It has been reported that the initial response of E. coli to heat shock includes the transient increase and lasting decrease of ATP concentration. Concomitantly, a long lasting need for exogenous glucose was detected in E. coli.7 It is well-known that glucose is the preferred carbon and energy source for E. coli, so it is likely that glucose functions as an energy resource. Glycolysis has been indicated as the potential main pathway of glucose consumption (Figure 6). Pyruvate, located at the key point between glycolysis and the tricarboxylic acid (TCA) cycle, was detected in our study. Then, it was converted into acetyl-CoA by decarboxylation and entered the TCA cycle under aerobic conditions. However, high temperature can lead to the reduction of dissolved oxygen in the media.7 The TCA 2563



Article

Table 1. Correlation Coefficients from OPLS-DA of Metabolic Profiles of E. coli Extracts after Treatment for 1.17 h, 2.17 h, 2.33 h, 2.83 h, 3.33 h, and 4.33 ha 1.17 h

2.17 h

2.33 h

2.83 h

3.33 h

4.33 h

R2X = 0.522

R2X = 0.647

R2X = 0.586

R2X = 0.643

R2X = 0.683

R2X = 0.590

metabolites

δ (ppm)

Q2 = 0.837

Q2 = 0.979

Q2 = 0.883

Q2 = 0.805

Q2 = 0.790

Q2 = 0.532

isoleucine leucine valine α-amino-β-hydroxybutyrate lactate alanine acetate lysine putrescine N-acetyl alanine acetamide γ-aminobutyrate α-aminoadipate glutamate succinate 2-oxoglutarate methylamine dimethylamine choline betaine methanol glucose glucosylglyceric acid ribose-5-phosphate uridine cytidine tyrosine xanthine adenosine

0.94(t) 0.98(d) 1.04(d) 1.06(d) 1.33(d) 1.48(d) 1.92(s) 3.03(t) 3.06(t) 2.03(s) 2.01(s) 3.12(t) 2.25(t) 2.36(dt) 2.41(s) 2.45(t) 2.61(s) 2.73(s) 3.23(s) 3.27(s) 3.36(s) 4.67(d) 5.23(d) 5.62(m) 7.88(d) 7.85(d) 6.90(d) 7.91(s) 8.34(s)

0.20 0.60 0.18 −0.82 0.21 0.77 −0.43 0.85 −0.22 0.49 −0.69 0.81 0.91 0.88 0.75 0.88 −0.41 0.35 0.12 0.65 0.60 −0.88 0.46 −0.43 −0.84 −0.86 0.63 −0.45 −0.60

0.88 0.86 0.93 0.94 0.96 −0.28 −0.65 −0.50 −0.90 0.91 0.26 −0.67 0.01 −0.19 −0.12 0.10 0.10 0.93 −0.23 −0.97 0.57 −0.84 −0.49 −0.75 0.02 −0.96 −0.88 −0.37 −0.93

0.82 0.86 0.83 0.80 0.74 0.22 −0.25 0.68 −0.89 0.92 0.25 −0.48 0.91 0.76 0.09 0.47 −0.27 0.85 0.62 −0.79 0.57 −0.77 0.34 −0.81 −0.17 −0.91 −0.40 −0.42 −0.86

−0.52 −0.65 −0.03 −0.36 −0.52 −0.64 −0.60 −0.16 −0.87 −0.54 −0.40 0.37 −0.73 0.17 −0.41 0.24 −0.89 −0.49 0.57 0.78 −0.49 0.66 0.07 −0.91 0.73 0.83 0.95 −0.88 0.17

0.78 0.85 −0.89 −0.84 0.65 −0.85 −0.11 0.85 0.88 −0.83 0.97 −0.46 −0.60 −0.81 0.11 −0.12 0.95 0.75 −0.81 0.97 0.76 −0.84 −0.81 0.76 0.58 0.06 0.76 0.20 0.04

0.38 0.38 −0.50 −0.03 −0.09 −0.81 −0.85 0.39 0.64 −0.50 −0.03 0.57 −0.90 −0.52 −0.77 −0.07 −0.63 0.31 −0.62 0.83 0.32 −0.43 −0.82 0.87 0.37 0.73 0.72 −0.13 0.22

a

A positive value indicates an increase in the concentration of metabolites, and a negative value indicates a decrease in the concentration of metabolites.

Figure 5. Time dependence of individual metabolite variations. The values of Y axis were calculated against the levels of the untreated control group in the form of (Ch − Cc)/Cc, where Ch was the respective metabolite concentration in the heat stressed group and Cc was that in the untreated control group. The error bars represent standard deviation. MA: methylamine. DMA: dimethylamine. AHB: α-amino-β-hydroxybutyrate. GA: glucosylglyceric acid. RP: ribose-5-phosphate. Glucose: the combined α-glucose and β-glucose.

conditions favor a higher growth rate for E. coli.22 Thus, a decrease in putrescine levels revealed an inhibited growth of E. coli because of heat stress in our work. Subsequently, a sharp decline of nucleotide derivatives, such as cytidine and adenosine, resulted from reduced rate in the synthesis of DNA, RNA, and protein.23 Additionally, it was possible that putrescine served as a good carbon and nitrogen source, likely in Pseudomonas aeroginosa,24 to be converted into γ-aminobutyrate (GABA) and succinate entering the TCA cycle to support of cell growth (Figure 6). Furthermore, putrescine catabolism is possibly related to the defense against oxidative

massive decrease in the levels of nucleotides, including uridine, cytidine and adenosine, was observed during the heat stress period. Growth Associated Metabolism

Putrescine is a compound that indicates the bacterial growth state. As the major polyamine in E. coli cells,21 it plays a necessary role in cell division.22 The transcript levels of potABD and speB, which encode components of the spermidine/ putrescine ATP-dependent importer and an enzyme of the putrescine biosynthetic pathway, are upregulated when 2564



Article

amino acids is to maintain the normal osmolarity of the cytoplasm. They also help retain cellular water, prevent subcellular structure from collapse, and increase the global thermostability of proteins.25,31 For example, glutamate is an osmoregulatory solute in E. coli.32 This amino acid protects the enzymes from the loss of activity during heat stress by activating the chaperone ClpB, resulting in an increased efficiency of chaperone-mediated protein disaggregation in E. coli.33 Glutamate also serves as a primary precursor for stressrelated metabolites, such as γ-aminobutyrate and polyamines, via arginine. It is possible that the thermoprotective effect of glutamate depends on its function as the precursor for the stress-related biosynthesis of metabolites. Although lysine was not regarded as a stress-related metabolite, its catabolism was highly affected by stress since lysine could be converted to glutamate and other stress-related metabolites, such as γaminobutyrate and putrescine, in response to stress.34 Consistent with notion, we also observed a marked elevation of leucine, isoleucine, valine, glutamate, alanine, and lysine levels. Supporting evidence also comes from the observation of increased levels of a range of amino acids under heat condition (45 °C) during the mid-log phase of E. coli growth.8 As for the origin of these amino acids, it is likely that the increased breakdown of misfolded or abnormal proteins or the degradation of some proteins for the synthesis of new proteins that are important for survival during heat stress, which contributed to the presence of high levels of amino acids.8,35

Figure 6. Metabolic pathways affected by heat stress during the 43 °C treatment. Metabolites colored in red, green, or black represent a higher, lower, or similar level in heat stressed E. coli extracts compared with control E. coli. Metabolites in italic black were not detected.

damage which is caused by heat stress,25 which could also lead to the reduction in the level of putrescine. Evidence showed that the puu genes are included in putrescine catabolism and directly participate in this defense.6 Tkachenko et al. reported putrescine protecting cells against oxidative damage potentially by the regulation of the expression of the OxyR regulon genes.21 However, the perturbation of energy metabolism and the inhibition of growth because of heat stress are recoverable, which is illustrated in Figure 5. Nearly all the metabolites were rebalanced toward the levels of control, with exception of the levels of alanine, acetate, α-aminoadipate, succinate, betaine, ribose-5-phosphate, cytidine, and glucosylglyceric acid, after the removal of heat stress. For example, the glucose level showed no significant difference in heat stressed strains compared to the controls at time point of 2.83 and 4.33 h, while putrescine, cytidine, ribose-5-phosphate levels had lasting elevation at later time point, which strongly suggests that E. coli is revived after heat stress. A previous study also showed that a similar recovery of the growth, and metabolite pools in a new steady state might be reestablished.7

■

CONCLUSION The time-resolved analysis of the metabolomic responses of E. coli to heat stress was investigated to understand metabolic perturbation and the subsequent trend of returning to cellular homeostasis. Heat stress was the obvious factor influencing the metabolic trajectory and growth pattern of E. coli and was mainly associated with a disturbed energy metabolism and depressed growth, accompanied by altered amino acid metabolism and active osmotic regulation. This study has highlighted the benefits of NMR-based metabolomics strategy to gain new insight into the response mechanisms of E. coli to heat stress.

■

Amino Acid Metabolism and Osmotic Regulation

ASSOCIATED CONTENT

S Supporting Information *

Heat stress induced depleted level of betaine, which is believed to be the most active naturally occurring osmoprotectant molecule for E. coli.26 Our results indicated that heat stress caused osmotic stress in E. coli (Figures 4 and 5). Numerous studies suggested that betaine provides thermoprotection by preventing the heat-related denaturation of citrate synthase in E. coli.27 It is also reported that glutamate and betaine displayed an associated thermoprotective effect to allow protein disaggregation and refolding under high temperatures in the E. coli cell.28 Choline is a chemical chaperone that protects citrate synthase from thermodenaturation27 and can also be metabolized to produce betaine, providing osmoprotective effect. A ubiquitous response of bacteria to depleted level of betaine associated with osmotic stress is to accumulate high concentrations of alternative organic osmolytes.29 A number of osmolytes, such as amines and amino acids,30 responded to continuous heat stress. Under the condition of depleted level of naturally occurring osmolytes, the function of a number of

Two-dimensional NMR spectra of glucosylglyceric acid and 1H NMR chemical shifts and signal assignments of low-molecularweight metabolites. This material is available free of charge via the Internet at http://pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected] (Y.W.); [email protected]. cn (H.T.). Telephone: +86-(0)27-87197143 (Y.W.); +86-(0) 27-87198430 (H.T.). Fax: +86-(0)27-87199291 (Y.W.); +86(0)27-87199291 (H.T.). Present Address §

Qingdao Institute of BioEnergy and Bioprocess Technology, Chinece Academy of Sciences Notes

The authors declare no competing financial interest. 2565



■

ACKNOWLEDGMENTS

■

REFERENCES

Article

(16) Westerhuis, J. A.; Hoefsloot, J. C.; Smit, S.; Vis, D. J.; Smilde, A. K.; van Velzen, E. J.; van Duijnhoven, J. P. M; van Dorsten, F. A. Assessment of PLS-DA cross validation. Metabolomics 2008, 4, 81−89. (17) Fan, T. W. M. Metabolite profiling by one- and two-dimensional NMR analysis of complex mixtures. Prog. Nucl. Magn. Reson. Spectrosc. 1996, 28, 161−219. (18) Fan, T. W. M.; Lane, A. N. Structure-based profiling of metabolites and iotopomers by NMR. Prog. Nucl. Magn. Reson. Spectrosc. 2008, 52, 69−117. (19) Wittmann, C.; Weber, J.; Betiku, E.; Krömer, J.; Böhm, D.; Rinas, U. Response of fluxome and metabolome to temperatureinduced recombinant protein synthesis in Escherichia coli. J. Biotechnol. 2007, 132, 375−384. (20) Rosenqvist, H.; Kasula, H.; Reunanen, O.; Nurmikko, V. The 4aminobutyrate pathway and 2-oxoglutarate dehydrogenase in Escherichia coli. Acta Chem. Scand. 1973, 27, 3091−3100. (21) Tkachenko, A. G.; Nesterova, L. Y.; Pshenichnov, M. P. Role of putrescine in the regulation of the expression of the oxidative stress defense genes of Escherichia coli. Microbiology 2001, 70, 133−137. (22) Gutierrez-Ríos, R. M.; Freyre-Gonzalez, J. A.; Resendis, O.; Collado-Vides, J.; Saier, M.; Gosset, G. Identification of regulatory network topological units coordinating the genome-wide transcriptional response to glucose in Escherichia coli. BMC Microbiol. 2007, 8, 53−71. (23) Tabor, C. W.; Tabor, H. Polyamines in microorganisms. Microbiol. Rev. 1985, 49, 81−99. (24) Chou, H. T.; Kwon, D. H.; Hegazy, M.; Lu, C. D. Transcriptome analysis of agmatine and putrescine catabolism in Pseudomonas aeruginosa PAO1. J. Bacteriol. 2008, 190, 1966−1975. (25) Privalle, C. T.; Fridovich, I. Induction of superoxide dismutase in Escherichia coli by heat shock. Proc. Natl. Acad. Sci. U.S.A. 1987, 84, 2723−2726. (26) Chambers, S. T.; Kunin, C. M.; Miller, D.; Hamada, A. Dimethylthetin can substitute for glycine betaine as an osmoprotectant molecule for Escherichia coli. J. Bacteriol. 1987, 169, 4845−4847. (27) Caldas, T.; Demont-Caulet, N.; Ghazi, A.; Richarme, G. Thermoprotection by glycinebetaine and choline. Microbiology 1999, 145, 2543−2548. (28) Diamant, S.; Eliahu, N.; Rosenthal, D.; Goloubinoff, P. Chemical chaperones regulate molecular chaperones in vitro and in cells under combined salt and heat stresses. J. Biol. Chem. 2001, 276, 39586−39591. (29) Cayley, S.; Lewis, B. A.; Record, M. T. Jr Origins of the osmoprotective properties of betaine and proline in Escherichia coli K12. J. Bacteriol. 1992, 174, 1586−1595. (30) Poolman, B.; Glaasker, E. Regulation of compatible solute accumulation in bacteria. Mol. Microbiol. 1998, 29, 397−407. (31) Neelon, K.; Schreier, H. J.; Meekins, H.; Robinson, P. M.; Roberts, M. F. Compatible solute effects on thermostability of glutamine synthetase and aspartate transcarbamoylase from Methanococcus jannaschii. Biochim. Biophys. Acta 2005, 1753, 164−173. (32) MacMillan, S. V.; Alexander, D. A.; Culham, D. E.; Kunte, H. J.; Marshall, E. V.; Rochon, D.; Wood, J. M. The ion coupling and organic substrate specificities of somoregulatory transporter ProP in Escherichia coli. Biochim. Biophys. Acta 1999, 1420, 30−44. (33) Diamant, S.; Rosenthal, D.; Azem, A.; Eliahu, N.; Ben-Zvi, A. P.; Goloubinoff, P. Dicarboxylic amino acids and glycine-betaine regulate chaperone-mediated protein-disaggregation under stress. Mol. Microbiol. 2003, 49, 401−410. (34) Galili, G.; Tang, G. L.; Zhu, X. H.; Gakiere, B. Lysine catabolism: a stress and development super-regulated metabolic pathway. Curr. Opin. Plant Biol. 2001, 4, 261−266. (35) Malmendal, A.; Overgaard, J.; Bundy, J. G.; Sørensen, J. G.; Nielsen, N. C.; Loeschche, V.; Holmstrup, M. Metabolomic profiling of heat shock: hardening and recovery of homeostasis in Drosophila. Am. J. Physiol.: Regul., Integr. Comp. Physiol. 2006, 291, R205−R212.

We acknowledge the financial supports from the Ministry of Science and Technology of China (2012CB934000 and 2010CB912501), National Natural Science Foundation of China (20825520, 20775087, 20921004, 31100032 and 21175149) and Chinese Academy of Sciences (KJCX2-YWW11). Financial supports from Zhejiang Provincial Natural Science Foundation of China (No.Y3090046), University National Oceanographic Public Welfare Project (201205029) and Academic Discipline Project of Ningbo University (xkl11089) are also acknowledged.

(1) Boor, K. J. Bacterial stress responses: what doesn’t kill them can make them stronger. PLoS Biol. 2006, 4, 18−20. (2) Zhao, K.; Liu, M. Z.; Burgess, R. R. The global transcriptional response of Escherichia coli to induced σ32 protein involves σ32 regulon activation followed by inactivation and degradation of σ32 in vivo. J. Biol. Chem. 2005, 280, 17758−17768. (3) Chuang, S. E.; Blattner, F. R. Characterization of twenty six new heat shock genes of Escherichia coli. J. Bacteriol. 1993, 175, 5242−5252. (4) Nonaka, G.; Blankschien, M.; Herman, C.; Gross, C. A.; Rhodius, V. A. Regulon and promoter analysis of the E. coli heat-shock factor, σ32, reveals a multifaceted cellular response to heat shock. Gene. Dev. 2006, 20, 1776−1789. (5) Hasan, C. M.; Shimizu, K. Effect of temperature up-shift on fermentation and metabolic characteristics in view of gene expressions in Escherichia coli. Microb. Cell. Fact. 2008, 7, 35. (6) Gunasekera, T. S.; Csonka, L. N.; Paliy, O. Genome-wide transcriptional responses of Escherichia coli K-12 to continuous osmotic and heat stresses. J. Bacteriol. 2008, 190, 3712−3720. (7) Soini, J.; Falschlehner, C.; Mayer, C.; Böhm, D.; Weinel, S.; Oanula, J.; Vasala, A.; Neubauer, P. Transient increase of ATP as a response to temperature up-shift in Escherichia coli. Microb. Cell. Fact. 2005, 4, 9−17. (8) Jozefczuk, S.; Klie, S.; Catchpole, G.; Szymanshi, J.; CuadrosInostroza, A.; Steinhauser, D.; Selbig, J.; Willmitzer, L. Metabolomic and transcriptomic stress response of Escherichia coli. Mol. Syst. Biol. 2010, 6, 364−380. (9) Zhang, L. M.; Ye, Y. F.; An, Y. P.; Tian, Y.; Wang, Y. L.; Tang, H. R. Systems responses of rats to aflatoxin B1 exposure revealed with metabonomic changes in multiple biological matrices. J. Proteome Res. 2011, 10, 614−623. (10) Zhang, X. Y.; Wang, Y. L.; Hao, F. H.; Zhou, X. H.; Han, X. Y.; Tang, H. R.; Ji, L. N. Human serum metabonomic analysis reveals progression axes for glucose intolerance and insulin resistance statuses. J. Proteome Res. 2009, 8, 5188−5195. (11) Xiao, C. N.; Hao, F. H.; Qin, X. R.; Wang, Y. L.; Tang, H. R. An optimized buffer system for NMR-based urinary metabonomics with effective pH control, chemical shift consistency and dilution minimization. Analyst 2009, 134, 916−925. (12) Trygg, J.; Wold, S. Orthogonal projections to latent structures (O-PLS). J. Chemometr. 2002, 16, 119−128. (13) Vandenberg, R. A.; Hoefsloot, H. C.; Westerhuis, J.; Smilde, J. A.; Vanderwerf, A. K. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 2006, 7, 142−156. (14) Cloarec, O.; Dumas, M. E.; Trygg, J.; Craig, A.; Barton, R. H.; Lindon, J. C.; Nicholson, J. K.; Holmes, E. Evaluation of the orthogonal projection on latent structure model limitations caused by chemical shift variability and improved visualization of biomarker changes in 1H NMR spectroscopic metabonomic studies. Anal. Chem. 2005, 77, 517−526. (15) Trygg, J.; Holmes, E.; Lundstedt, T. Chemometrics in metabonomics. J. Proteome Res. 2007, 6, 469−479. 2566


Global Metabolomic Responses of Escherichia coli to Heat Stress

Recommend Documents