Dynamic proteomic and metabonomic analysis reveal dysfunction and subclinical injury in rat liver during restraint stress

Biochimica et Biophysica Acta 1794 (2009) 1751–1765

Contents lists available at ScienceDirect

Biochimica et Biophysica Acta j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / b b a p a p

Dynamic proteomic and metabonomic analysis reveal dysfunction and subclinical injury in rat liver during restraint stress Ming Chen ⁎,1, Yongqing Wang 1, Yun Zhao 1, Liqun Wang, Jingbo Gong, Lei Wu, Xiujie Gao, Zhihua Yang, Lingjia Qian ⁎ Key Laboratory of Stress Medicine, Tianjing Institute of Hygiene and Environmental Medicine, 1 Dali Road, Heping District, Tianjing 300050, PR China

a r t i c l e

i n f o

Article history: Received 28 April 2009 Received in revised form 5 August 2009 Accepted 6 August 2009 Available online 18 August 2009 Keywords: Restraint stress Rat liver DIGE 1 H-NMR Metabonomics Proteomics

a b s t r a c t Stress is a risk factor for many diseases. In this study, we used fluorescence difference gel electrophoresis combined MALDI-TOF/TOF and 1H-NMR to monitor the intracellular processes in rat liver at proteomic and metabonomic levels when a rat was treated with restraint stress for 8 weeks. Dynamic changes in 42 proteins and 32 chemical groups were monitored and identified. These proteins and chemical groups were implicated in glycolysis, the tricarboxylic acid cycle, fatty acid oxidation, and the urea cycle. To verify the DIGE result, three proteins including DJ-1, Blvrb and AdoHycase were validated by Western blot. Furthermore, some metabolites related to diseases such as lactate, fatty acid, glucose and homocysteine, were observed to be increasing during 8 weeks of restraint stress. Our data indicated that subclinical hepatic injury occurs during restraint stress, including inhibition of glycolysis and gluconeogenesis in the liver, and dysfunction of fatty acid β-oxidation. The results suggest a comprehensive map that addresses how functional proteins act on metabolites to produce energy and process materials in rat liver as it responds to restraint stress. Further functional study on these dynamic change proteins and metabolites may lead to better understanding of the mechanisms of stress-induced diseases. ©2009 Elsevier B.V. All rights reserved.

1. Introduction Numerous studies have established stress, a concept describing the effects of environmental or psychosocial factors on physical or mental well-being, as a risk factor for many diseases [1–9]. For instance, stress plays a major role in immunological diseases and immune-related disease processes. In addition, inflammation, infection, autoimmune processes, and perhaps even the onset and development of malignant tumors may occasionally be associated with the stress phenomenon [3]. Stress has also been shown to be important in vascular hypertension [4], and it may serve as a risk factor, induce blood pressure spikes, or increase an already elevated blood pressure [5,6]. Stress may even cause or contribute to the clinical onset of liver disease in certain cases. In animals, the liver is one of the important organs, which plays many key roles in life processes, such as detoxification, metabolism of fat, protein and sugar, production of blood and bile etc. Some early clinical reports suggested that stress might affect the initiation, course and outcome of liver diseases. For example, Hirose et al. revealed that emotional stress significantly decreased hepatic blood flow [7]. To animal models, Iwai et al. reported that electric foot⁎ Corresponding authors. Tel.: +86 22 84655001; fax: +86 22 23314818. E-mail addresses: [email protected] (M. Chen), [email protected] (L. Qian). 1 These authors contributed equally to this work. 1570-9639/$ – see front matter ©2009 Elsevier B.V. All rights reserved. doi:10.1016/j.bbapap.2009.08.012

shock stress exacerbated liver injury in rats treated with carbon tetrachloride [8]. Further research showed that this stress paradigm could aggravate α-galactosylceramide-induced hepatitis [9]. Thus, growing evidences have shown that stress can have an effect on liver disease. However, the biological changes in the liver during the process of stress remain largely unexplored. Therefore, we undertook to study liver responses at the proteomic and metabonomic levels during stress treatment. To our knowledge, there has been no previous report about this process. 2-D DIGE (two-dimensional differential in-gel electrophoresis) is a recent improvement of the technique based on 2-DE. In 2-D DIGE, protein samples to be compared are labeled covalently with fluorescent cyanine dyes, Cy2, Cy3, and Cy5, respectively. The labeled proteins are mixed, loaded, and separated in a single gel of 2-DE. Gel images with identical protein spot patterns are acquired by scanning the gel at specific wavelengths for each dye. Spot intensities can be compared directly between samples and quantified using image analysis software. Recently, 2-D DIGE has been widely used, with high reproducibility and reliability, for identifying disease markers and proteins involved in biological responses [10,11]. Metabonomics, another tool in systems biology, gives a window on the biochemistry of whole systems, and provides crucial information on drug toxicity, disease processes, and gene and protein functions [12]. Therefore, proteomics and metabonomics provide powerful tools for global analysis and monitoring of proteins and metabolites as they change during biological processes.

1752

M. Chen et al. / Biochimica et Biophysica Acta 1794 (2009) 1751–1765

However, data emerging from any single -omics approach can only provide crude indications of gene or protein function. It has been proposed that these limitations can be overcome by integrating data obtained from two or more distinct approaches [13]. Such integration should not only improve functional annotations but also help to formulate biological hypotheses. Many recent reports have described the application of an integrated strategy in disease biomarker and toxicology studies [14–17]. In this paper, we have used a combination of proteomics and metabonomics to study the biological process in rat liver during restraint stress. Rat liver proteins and metabolites were detected using 2-D DIGE and 1H-NMR, respectively. Our aim was to monitor rat liver protein expression and metabolism to garner clues about the mechanisms of stress-induced diseases. 2. Materials and methods 2.1. Reagents Cy2, Cy3, and Cy5 were purchased from GE Healthcare (Uppsala, Sweden). DMF was purchased from Aldrich (Poole, Dorset, UK). Thiourea and urea were purchased from Fluka (Buchs, Switzerland). DTT, agarose, glycerol, bromophenol blue, CHAPS, mineral oil, acrylamide, Bis, Tris base, glycine, SDS, iodoacetamide, TEMED and immobile DryStrip gels (24 cm, pH 3–10 NL) were purchased from Bio-Rad (Hercules, CA, USA). ACN and methanol were purchased from Fisher (Fair Lawn, New Jersey, USA). TFA was purchased from Merck (Schuchardt, Hohenbrunn, Germany). SBD-F and TECP were purchased from Sigma (St. Louis, MO, USA). Protease inhibitor cocktail was purchased from Roche (Penzberg, Germany). Sequencing grade trypsin was from Promega (Madison, WI). All buffers were prepared with Milli-Q water (Millipore, Bedford, MA, USA). 2.2. Experimental animal model Thirty adult male Wistar rats weighing 200 g–250 g were divided randomly into control and five stress groups (1w, 2w, 4w, 6w, and 8w), and five rats were treated in each group. All rats were housed in a pathogen-free environment at room temperature (22–25 °C) and maintained on rat chow and tap water ad libitum before restraint stress. The restraint stress animal model was induced according to the method of Galea et al [18], with slight modifications. Briefly, individual rats in the stressed group were placed in a specially built size-manipulable cabin for 6 h/day (from 9:00 am to 15:00 pm) for 1, 2, 4, 6, and 8 weeks respectively. Control rats were not disturbed during this period. The animal model experiments were repeated at least three times. Rats were sacrificed 24 h after the last day of restraint. Livers were rapidly excised, immersed in ice-cold PBS buffer, and washed three times. After washing, the livers were cut into large pieces, weighed, and minced into 1 mm3–2 mm3 pieces, and immediately frozen in liquid nitrogen. Blood samples were collected on ice from rat heart into evacuated tubes containing EDTA as an anticoagulant. Plasma was separated within 30 min in a refrigerated centrifuge at 4 °C, and stored at −80 °C until analysis. All of the investigations conformed to the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health. The ethics committees of the Tianjing Institute of Hygiene and Environmental Medicine reviewed and approved our experimental procedures. 2.3. Samples for proteomic analysis Rat liver samples were ground into a fine powder in liquid nitrogen and homogenized in lysis buffer (7 M urea, 2 M thiourea, 4% CHAPS, 30 mM Tris, pH 8.5, one complete proteinase inhibitor cocktail tablet per 50 mL lysis buffer). For improved cell lysis, the solution was

sonicated on ice for 1 min (with 1 s pulse-on and 2 s pulse-off to prevent overheating). The sample was incubated for 30 min at room temperature with repeated vortexing. Unbroken cells or connective tissue were removed from the homogenate by centrifugation at 25,000 ×g for 30 min at 20 °C. The supernatant was stored at −80 °C. Protein concentration was determined with the Bradford assay kit (Bio-Rad, Hercules, CA, USA) using albumin diluted in lysis buffer as standard.

2.4. 2D-DIGE and imaging Protein samples from the same treatment group were pooled for 2D-DIGE. Then pooled samples were dissolved in lysis buffer (7 M urea, 2 M thiourea, 4% CHAPS, 30 mM Tris, pH 8.5) to give stock solutions with final concentrations of about 5 mg/mL. Cyanine dyes were reconstituted in 99.8% anhydrous DMF and added to labeling reactions at a ratio of 400 pmol Cy dye to 50 μg protein in different groups following the cross-label rule, according to the manufacturer's guidelines. The internal standard was created by pooling an aliquot of all biological samples in the experiment and labeling it with one of the Cy dyes (usually Cy2). Briefly, 50 μg of lysate was minimally labeled with 400 pmol of Cy2, Cy3, and Cy5, respectively, and incubated on ice for at least 30 min in the dark. The labeling reaction was terminated by adding 1 μL of 10 mM lysine and incubating on ice for at least 15 min in the dark. Two samples labeled with Cy3 and Cy5, respectively, were analyzed on the same gel, together with a pooled sample as an internal standard, labeled with Cy2. The DIGE experimental design is shown in Table S1. Prior to IEF, differentially labeled samples to be separated in the same gel were mixed, and added to an equal volume of 2× sample buffer (7 M urea, 2 M urea, 4% CHAPS, 130 mM DTT, 2% pharmalytes 3–10 NL), and finally 1 brought to a total of 450 μL with additional sample dissolved in rehydration buffer (8 M urea, 2% CHAPS, 0.5% pharmalytes 3–10 NL, 20 mM DTT). 2-DE was performed with Amersham Biosciences (Uppsala, Sweden) IPGphor IEF and Ettan Dalt Twelve electrophoresis units. Pre-cast IPG strips (24 cm, pH 3–10 NL) were used for the separation in the first dimension with a total focusing time of 76 kVh at 15 °C. Prior to SDSPAGE, each strip was equilibrated with 10 mL equilibration buffer A (6 M urea, 50 mM Tris–HCl, pH 8.8, 30% glycerol, 2% SDS, 10 mg/mL DTT) on a rocking table for 15 min, followed by 10 mL equilibration buffer B (6 M urea, 50 mM Tris–HCl, pH 8.8, 30% glycerol, 2% SDS, 25 mg/mL iodoacetamide) for another 15 min. The strips were then loaded and run on 12.5% acrylamide gels. The running parameter was set as a constant power of 15 mA per gel at 15 °C for 1 h, 25 mA per gel at 15 °C for 6 h, followed by 30 mA per gel at 15 °C until the bromophenol blue dye front had run off the bottom of the gels. Labeled proteins were visualized by the Typhoon™ 9410 imager (GE Healthcare). All gels were scanned at 100 nm resolution, and the intensity was adjusted to insure that the maximum volume of each image was within 60,000–90,000. Images were cropped to remove areas extraneous to the gel image using Image Quant V5.2 (Amersham Biosciences, UK) prior to analysis. Gel analysis was performed with DeCyder™ 6.5 (GE Healthcare), an analysis software platform designed specifically for 2-D DIGE. Sets of gels were first analyzed, and spots were counted, using the Differential in-gel Analysis (DIA) mode of the DeCyder (GE Healthcare) software package, followed by a comprehensive Biological Variance Analysis (BVA). Gel spots were filtered according to their t-test and 1-ANOVA values. The gel with the most spots was considered the master gel. Inter-gel matching was performed through the inclusion of the internal standard on each gel. More than 92% of the spots could be matched. Then, all of the eighteen images were classified into six groups according to the experimental condition. The DeCyder BVA module was used for performing comparative cross-gel statistical analyses of all spots, permitting the detection of differentially expressed spots between experimental

M. Chen et al. / Biochimica et Biophysica Acta 1794 (2009) 1751–1765

groups (t-test and 1-ANOVA, p < 0.05). Only spots present in at least 12 out of 18 gel images with p < 0.05 were considered and picked. 2.5. In-gel digestion Gels were fixed and stained with coomassie brilliant blue (CBB). Proteins of interest, as defined by the 2D-DIGE/DeCyder analysis, were excised from the CBB-stained gels for a modified in-gel tryptic digestion procedure. Gel pieces were twice washed in 50% ACN and 25 mM of ammonium bicarbonate, and then reduced with 10 mM DTT at 37 °C, and alkylated in the dark with 50 mM iodoacetamide at room temperature for 1 h. After vacuum drying, the gel pieces were incubated with sequencing grade modified trypsin at a final concentration of 0.01 mg/mL in 25 mM of ammonium bicarbonate for 16 h at 37 °C. Tryptic peptide mixtures were first extracted with 20 μL 5% TFA at 40 °C for 1 h, and then re-extracted with the same volume of 2.5% TFA/50% ACN at 30 °C for another 1 h. The extracted solutions were blended, lyophilized and used for further identification by MS.

1753

homogenates were centrifuged at 5000 ×g for 5 min at 4 °C. The supernatants were collected, lyophilized, and reconstituted in 500 μL D2O prior to NMR analysis. Two milliliters of 75% chloroform/25% methanol were added to the pellets for lipid soluble extracted, and the extraction was followed by a further centrifugation (5000 ×g for 15 min at 4 °C). The lipophilic supernatants were collected then dried under a stream of nitrogen and reconstituted in 500 μL of 75% CDCL3/ 25% CD3OD prior to NMR analysis. The reconstituted solution was transferred to 5 mm (o.d.) NMR tubes together with 0.1% TSP as an internal standard and 1% sodium azide in D2O. All spectra were referenced to TSP at 0 ppm. 1H NMR spectra were acquired on each sample at 599.69 MHz on a Varian INOVA 600 spectrometer at 300 K. One-dimensional pre-saturation pulse sequence was used to achieve satisfactory water suppression in the aqueous extracts. In lipid liver extracts, 1D NMR experiments were carried out using the standard pre-saturation pulse sequence to suppress the CD3OD residual water signal. For each sample, 64 transients were collected into 64 K data points with a relaxation delay of 2 s and a mixing period of 150 ms. A spectral width of 12,004.8 Hz and an acquisition time per scan of 4 s were used.

2.6. Mass spectrometer analysis 2.9. Measurement of homocysteine concentration in rat plasma Peptide extracts were dissolved in 4 μL saturated matrix (CHCA dissolved in a solution of 50% v/v ACN, 0.5% v/v TFA) and 0.6 μL of the mixture was spotted manually onto an ABI MALDI target plate. The spots were allowed to dry and then put into ABI4800 Proteomics Analyzer (Applied Biosystems, Framingham, MA), equipped with a 200 Hz frequency-tripled ND:YAG laser, operating at a wavelength of 355 nm and a repetition rate of 200 Hz in both MS and MS/MS modes. When acquiring MS spectra, the laser intensity was set at 4300, and ions were collected between 700 Da and 4000 Da. All the acquired MS spectra represented signal averaging of 1050 laser shots. When carrying out the MS/MS acquisition, the five most intense peptide spots with S/N exceeding 100 were selected and subjected to subsequent MS/MS analysis. Each MS/MS spectrum was compiled from 3000 shots with the laser intensity set to 5800. The collision energy was set at 1 kV and the collision gas was air. Before acquiring data, the instrument was calibrated in plate mode with tryptic peptides of myoglobin as an internal standard. 2.7. Western blot Ten microgram proteins from the restraint stressed rat liver were separated on 15% polyacrylamide gels and transferred to PVDF membranes (Amersham Pharmacia Biotech). Primary antibodies used were anti-DJ1 monoclonal antibody (diluted 1:1000, abcam, UK), anti-AdoHycase antibody (diluted 1:1000, abcam, UK), antiBlvrb antibody (diluted 1:1000, Santa Cruz Biotechnology, USA) and anti-β-actin antibody (diluted 1:1000, Santa Cruz Biotechnology, USA). After transferring to nitrocellulose membranes, filters were blocked for one hour in blocking buffer [20 mM Tris–HCl, pH 7.5, 137 mM NaCl, 0.05% (v/v) Tween 20 (TBST) containing 5% dried milk], and then incubated with the primary antibodies overnight at 4 °C. The membrane was washed 3 times with TBST and then incubated with horseradish peroxidase-conjugated secondary antibodies for one hour prior to visualization of the bands by ECL reagents (Santa Cruz Biotechnology, USA). All the membranes were exposed on X-ray film and scanned by GS-710 scanner (Bio-Rad). A semiquantitative analysis based on OD was performed by QuantitiOne software (Bio-Rad) and ANOVA test analysis. 2.8. 1H-NMR spectroscopy of water-soluble and lipid soluble liver tissue extracts Liver tissue (250 mg) taken from the lateral lobe of each rat was homogenized in 2 mL of 50% acetonitrile in an ice/water bath. The

Homocysteine (Hcy) was measured by high-performance liquid chromatography, as previously reported by Durand et al [19]. Briefly, 240 μL plasma was mixed with 60 μL of 2.5 mM acetyl-cysteine as internal standard. The Hcy-mixed disulfides or protein-bound Hcy in the plasma were treated with TCEP in order to reduce thiols and release them from plasma proteins. Proteins were then precipitated with 0.6 M cold perchloric acid containing 1 M EDTA, left at room temperature for 10 min after thorough mixing and centrifugation (2000 ×g, 10 min). Subsequently, the thiol-compounds in the samples were derivatized with a thiol-specific fluorogenic reagent, SBD-F (50 μL sample mixed with 10 μL of 1.55 M NaOH, 125 μL of 0.125 M borate buffer containing 4 mM EDTA and 50 μL of l mg/mL solution in the borate buffer of the SBD-F, pH 9.5). Complete derivatization of thiols was performed in a water bath at 60 °C during 60 min. Homocysteine in samples was analyzed using a HPLC system consisting of a Waters 2695 Liquid chromatograph, a Waters 2475 fluorescence detector (excitation 390 nm, emission 470 nm). Separation was carried out using a reverse phase column (Symmetry Shield™ RP18, 3.9 mm × 150 mm, 5 μm). Analysis was performed under isocratic concentration (0.08 mol/L NaAc, 1% methanol in water) at a flow rate of 0.8 mL/min for 15 min. 2.10. Data analysis MS/MS spectra were searched against the IPI rat database (v4.33) by GPS Explorer Software v2.0 (with MASCOT as the database search engine) with peptide and fragment ion mass tolerance of 50 ppm and 0.5 Da, respectively. Carbamidomethylation of cysteines and oxidation of methionines were allowed during the search of peptides. The maximum number of missed cleavages was set to 2, with trypsin as the protease. Known contaminant ions (keratin) were excluded. Protein identification results were manually evaluated. Proteins for which the mass tolerance of matched peptides was randomly distributed and almost at the limit, as well as most sequences of matched peptides with one missed cleavage and obvious interference by self-cleavage fragments of trypsin, were rejected as possible falsepositive hits. The identification of best ion with significant MOWSE scores exceeding 59 (confidence level more than 95%, based on mass/ mass spectrums) and protein score with statistically significant (confidence level more than 95%, based on combined mass and mass/mass spectrums) were confirmed as positive hits. Redundancy of proteins that appeared in the database under different names and accession numbers was eliminated. If more than one protein was

1754

M. Chen et al. / Biochimica et Biophysica Acta 1794 (2009) 1751–1765

identified in one spot, the single protein member with the highest protein score was singled out from the multi-protein family. All free induction decay (FID) data were Fourier transformed into 1 H-NMR spectra. All of the 1H-NMR spectra of liver tissue extracts were phase- and baseline-corrected and then data-reduced to equal

width (0.02 ppm), corresponding to the region of δ9.4–δ0.2, using the VNMR 6.1C software package (Varian Inc, Palo Alto, CA). For the NMR spectra of aqueous soluble liver tissue extracts, the region of δ5.1–δ4.7 was removed to eliminate the artifacts of the residual water resonance. For the aqueous liver extracts, the region δ2.07 (residual

Fig. 1. DIGE gel image of liver proteins from rat after 2 weeks and 6 weeks of stress. (A) Blue (Cy2) image of proteins from the internal standard; (B) green (Cy3) image of proteins from stressed rat at 6 weeks; (C) red (Cy5) image of proteins from stressed rat at 2 weeks; and (D) the overlay image shows white spots containing proteins that have equal expression levels in the three samples. The spots having significance (p < 0.05) are marked with arrowheads and numbers.

M. Chen et al. / Biochimica et Biophysica Acta 1794 (2009) 1751–1765

1755

acetonitrile signal) was also removed from all spectra, while for the lipophilic extracts, the region δ3.1–δ3.4 (residual methanol and deuterated methanol) was removed from all spectra. Prior to PCA, all spectra data sets were imported into the SIMCA-P 10.0 software package (Umetrics AB, Umea, Sweden). Separately, these data were mean-centered and pareto-scaled. Pareto scaling gives each variable a variance numerically equal to its standard deviation. Score plots from the first two PCs were used to visualize the separation of groups, and the values of the PC loadings reflected the NMR spectral regions that were altered as a function of stress time. Mean data were calculated for each integrated region for each time point. Plots of PC1 and PC2 on these data represent the metabolic changes with the time of stress.

6 weeks of restraint stress, respectively (Table 1). Most of the change in expression over time occurred after 4 weeks of restraint stress. DeCyder analysis showed protein changes in rat liver over stress time (Table S2). The DeCyder DIA module provides 3D simulations of the protein spots, which allowed the comparison of spot intensity among many images. Each protein spot was analyzed for its relative amount of protein. The changes in protein spots in six experimental groups were determined by BVA according to the ratios of Log sample/standard. Sixty-three protein spots exhibited significant upor down-regulated expression over time (see examples in Fig. 2). Some proteins, such as fructose-1,6-bisphosphatase and biliverdin reductase, were up-regulated during the eight weeks of stress, while others, such as cathepsin D precursor and catalase, were downregulated (Fig. 2 and Supplemental data 1).

3. Results

3.2. Identification of differentially expressed proteins

3.1. Analysis of differentially expressed proteins

These 63 differentially expressed protein spots were excised from the 2-DE gel, digested by trypsin, and analyzed by MALDI-TOF-TOF MS. Detailed data on the 42 proteins that were identified are given in Table 2. Among the identified proteins, 33 proteins had more than 2 peptide hits, and 9 were identified with a single peptide MS/MS (results are given in Supplemental data 1). All of the 42 identified proteins were functionally categorized based on universal Gene Ontology annotation terms, using the GOfact tool [20]. Each protein was linked to at least one GO annotation category, totaling to 38 biological processes, 30 cellular components, and 40 molecular function proteins. Fig. 3 shows the expected overrepresentation in the cellular component category, of GO terms related to mitochondrion (43.3%), nucleus (16.7%), and cytosol (13.3%). In the molecular function category, 3 GO terms in the binding and catalytic activity groups were enriched. Catalytic protein was the major subcategory (28 proteins), including hydrolase activity protein, transferase activity protein and peptidase activity proteins. As expected, hydrolase activity (6 proteins) was enriched with high significance (p < 2.5 E-6). In the biological process category, 6 GO terms were enriched, and 3 of them were related to metabolism, which showed the most significant enrichment (p < 5.2 E-3). Twentynine proteins were annotated, with 12 belonging to protein/amino acid metabolism, 5 to lipid metabolism, 3 to carbohydrate metabolism, and 4 to nucleic acid catabolism (Table 2).

A goal of this study was to measure the changes in the rat liver proteome during restraint stress. For this purpose, 2-D DIGE expression profiles of rat livers were generated at 0-, 1-, 2-, 4-, 6-, and 8-week time points in the course of 8 weeks of restraint stress (Fig. 1). Up to 1476–1650 different spots (1560 ± 60, n = 6 Cy2 images) were detected on six gels. DIGE analyses indicated 63 spots that exhibited statistically significant dynamic expression changes across all of six experimental time points (1-ANOVA, p < 0.05). The number of protein spots with greater than 1.3-, 1.5-, 1.7-, and 2.0-fold differences in abundance over time following stress stimulation are shown in Table 1. There were 16 protein spots with greater than 1.3fold average changes in abundance after 1 week of chronic stress. Of these 16 spots, 11 spots were up-regulated, and 5 were downregulated. At the 1.7-fold difference cut off there were 3 up-regulated spots; and at the 2-fold cut off, there was 1. The number of significantly changed spots increased with the increase of stress time. For example, there were 7, 19, and 12 protein spots with greater than 2-fold average changes in protein abundance after 2, 4, and

Table 1 Number of protein spots with a significant change in stressed rat liver over time. Stress time (week)

Abundance ratiosa

Total no. of spots

No. of upregulated

No. of downregulated

3.3. Analysis of differential metabolites

1

≥1.3 ≥1.5 ≥1.7 ≥2.0 ≥1.3 ≥1.5 ≥1.7 ≥2.0 ≥1.3 ≥1.5 ≥1.7 ≥2.0 ≥1.3 ≥1.5 ≥1.7 ≥2.0 ≥1.3 ≥1.5 ≥1.7 ≥2.0

16 10 3 1 31 24 14 7 45 35 30 19 45 33 20 12 37 24 18 8

11 7 3 1 17 14 8 5 22 17 14 10 28 21 13 9 15 10 7 5

5 3 0 0 14 10 6 2 23 18 16 9 17 12 7 3 22 14 11 3

To study the biochemical change, the water and lipid soluble extraction of liver tissues were analyzed by NMR. Samples were collected from 0 to 8 weeks during the restraint stress process. The effect of restraint stress on the rat liver NMR spectral profile is clearly illustrated in the PC plot shown in Fig. 4A. Spectra from rats stressed for 0, 1, 4, 6, and 8 weeks were clearly separated. This plot emphasizes the large degree of variation in rat liver metabolism under stress conditions over time. The samples from the six-week time point appear to be close to the control group in PC1. The chemical shift contains information on molecular structure and can be used to discriminate 1H-NMR spectra of various molecules. Therefore, the chemical components were assigned to the spectra on the basis of the published data. Fig. 4B and C shows the 600 MHz 1H-NMR spectra of the liver in restraint stressed rat. Thirty-two chemical groups were assigned from the stressed rat liver and classified as lipid metabolism intermediates, amino acid metabolism intermediates, glycolysis intermediates, nucleic metabolism intermediates, and TCA cycle intermediates (Table S3). The relative quantity of the chemical group was estimated according to the peak area of each chemical shift. Among the 32 chemical groups, 13 increased (including N + (CH3)3, CH2C = C, CH2CO, CH2OPO− 2 , γCH2, H1, CH3, α,βCH2, acetate, inosine, uridine, N + (CH3)3, TMAO-betaine), 12 decreased (including C18CH3,

2

4

6

8

a Ratios between the number of spots at each time point and time zero were calculated from the average standardized abundance of two spots. Only averages with t-test values < 0.05 were included. For each time point of the stress treatment, the abundance ratio cut offs were set to 1.3, 1.5, 1.7, and 2.0. The total number of matched protein spots with abundances greater than the cut off value is shown. Zero week treatment was set as control.

1756

M. Chen et al. / Biochimica et Biophysica Acta 1794 (2009) 1751–1765

CH3, (CH2)n, CH2OCOR, CH= CH, γ CH3, βCH3, tyrosine, C1H, uracil, 5′ GMP, nicotinurate), and 7 remained unchanged during stress, compared with the chemical groups levels of the control group. For

example, CH2CO and CH2OPO− 2 have several fold increases after 4 weeks of stress. Other molecules, such as bile acid and glycogen, decreased at least 2-fold during the eight weeks of stress (Fig. 4D and Table S3).

Fig. 2. DeCyder output showing several of the identified proteins. Enlarged regions of matched protein spots (blue circles) and the three-dimensional fluorescence intensity profiles of the individual spots are shown in the left panels. Graphical representations of all matched spots for a particular protein are shown in the right panel, with changes in expression over time. Values are the standardized log of abundance. The two points at each time point on the graph represent single values from different gels, and lines are plotted using the averaged values. The protein points labeled with Cy3 and Cy5 from the same gel are connected with a broken line.

M. Chen et al. / Biochimica et Biophysica Acta 1794 (2009) 1751–1765

1757

Fig. 2 (continued).

3.4. Effect of restraint stress on the concentration of homocysteine in rat plasma

in the control group (Fig. 5). These results indicate that restraint stress may lead to homocysteine accumulation in rat plasma.

Fig. 5 shows the concentration of plasma homocysteine over stress time. There was no obvious change in the stressed rats' homocysteine concentration in the first two weeks compared with control (3.04 ± 0.65 μmol/L vs. 2.62 ± 0.47 μmol/L, n = 5, p > 0.05). After 4 weeks of restraint stress, homocysteine concentration was increased by 2.5fold, compared with the control group (7.86 ± 1.37 μmol/L vs. 2.67 ± 0.51 μmol/L, n = 5). In groups stressed for 4, 6, and 8 weeks, homocysteine concentration was significantly (p < 0.01) higher than

3.5. Protein validation by Western blot In DIGE and MS/MS results, we indentified 42 proteins have dynamic change during the accumulation of homocysteine in rat plasma. To verify DIGE result, samples from three independent experiments were further analyzed by Western blot. The specificity of the antibodies against DJ-1, AdoHycase and Blvrb had been testified by Western blot (Fig. 6). As shown in Fig. 6, the continuous up-

1758

M. Chen et al. / Biochimica et Biophysica Acta 1794 (2009) 1751–1765

Table 2 Proteins identified by 2-D DIGE and MALDI-TOF-TOF. Appearanceb 1-ANOVA p-value c

p-value IDd

Mr (Da )

PI

Coverage Scoref (%)e

Peptide hits g

Peptides identified

18 (18)

0.0076

1.30E-10

47842

7.63

30%

145

2

18 (18)

2.6E-06

6.40E-47

41844

8.09

44%

508

5

1416 IPI00207217 Enoyl-CoA hydratase

18 (18)

0.001

4.00E-38

31496

8.39

44%

420

5

K.AQDTAELFFEDVR.L K.GFYYLMQELPQER.L K.DFTATDLTEFAAR.A R.YALQSQQR.W R.VGVPTETGALTLNR.L K.VPPETIDSVIVGNVMQSSSDAAYLAR.H K.TNVSGGAIALGHPLGGSGSR.I K.NSSVGLIQLNRPK.A K.FLSHWDHITR.I K.SLAMEMVLTGDR.I K.LFYSTFATDDR.R K.AQFGQPEILLGTIPGAGGTQR.L

Carbohydrate metabolism 1045 IPI00194045 Isocitrate dehydrogenase [NADP]

15 (18)

0.0042

1.00E-32

46705

6.53

51%

366

3

998

IPI00231745 Fructose-1, 6-bisphosphatase 1

18 (18)

0.032

5.00E-35

39584

5.54

69%

389

4

1440 IPI00231767 Triosephosphate isomerase

18 (18)

0.0042

3.20E-30

26832

6.89

65%

341

3

967

18 (18)

0.015

8.00E-16

47116

6.64

47%

197

2

18(18)

0.0092

3.2E-11

38957

6.2

34%

151

2

18 (18)

0.011

6.4E-35

56115

6.77

57%

388

5

1629 IPI00231757 Proteasome subunit alpha type-2

18 (18)

8.80E-05

3.20E-17

25910

6.92

53%

211

3

716

IPI00421528 Proteasome 26S subunit, ATPase 3

15 (18)

0.014

2E-27

49518

5.09

50%

313

3

226

IPI00392830 77 kDa protein

12 (18)

0.01

0.03

76896

5.25

14%

61

1

K.GVLFASGQNLAR.Q K.DEIPYLR.K R.TLIEFLLR.F R.MPLFEHYTR.Q R.LILADALCYAHTFNPK.V K.HIGLVYSGMGPDYR.V K.SILYDER.S K.LAQQYYLVYQEPIPTAQLVQR.V K.DSYLILETLPTEYDSR.V R.KIEFPMPNEEAR.A K.MNVSPDVNYEELAR.C K.HFSVEGQLEFR.A

2.60E-04

9.60E-06

57883

8.44

25%

96

1

K.AISFVGSNQAGEYIFER.G

0.018

5.00E-26

31277

5.98

57%

299

4

18 (18)

8.80E-04

5.00E-12

19751

5.78

92%

159

2

15 (18)

0.012

5E-25

56318

5.19

46%

289

3

R.GPAHHLLLGER.V K.YGLLVGGAECHR.Y K.LYAEGDIPVPHAR.R K.YGLLVGGAECHRYDLGGLVMVK.D R.SFPDFPIPGVLFR.D R.DISPLLKDPDSFR.A K.AHGGYSVFAGVGER.T R.VALTGLTVAEYFR.D K.VALVYGQMNEPPGAR.A

Amino acid and derivative metabolism 214 IPI00207941 Dimethylglycine dehydrogenase

18 (18)

0.023

6.4E-66

95987

6.91

67%

698

5

863

IPI00211127 Argininosuccinate synthase

18 (18)

0.0094

3.20E-06

46467

7.63

32%

101

2

1034 IPI00210920 Aspartate aminotransferase

15 (18)

0.028

2.5E-22

47284

9.13

52%

262

3

823

IPI00324633 Glutamate dehydrogenase 1

18 (18)

0.0074

2.5E-38

61377

8.05

34%

422

4

462

IPI00475676 Delta-1-pyrroline-5carboxylate dehydrogenase

18 (18)

9.70E-04

2.00E-08

61830

7.14

20%

123

1

Spot no.a

IPI no.

Protein description

Lipid metabolism 798 IPI00211225 Long-chain specific acyl-CoA dehydrogenase 1032 IPI00201413 3-ketoacyl-CoA thiolase, mitochondrial

IPI00764333 Succinate coenzyme A ligase 1353 IPI00194324 Pyruvate dehydrogenase E1 component subunit beta Protein metabolism 452 IPI00471530 Cytosol aminopeptidase

Nucleobase\nucleoside\nucleotide and nucleic acid metabolism 15 (18) 869 IPI00205018 Methylmalonatesemialdehyde dehydrogenase 1360 IPI00371243 Nicotinate-nucleotide 18 (18) pyrophosphorylase

1504 IPI00555297 Adenine phosphoribosyltransferase 756 IPI00551812 ATP synthase subunit beta

K.LILPYVELDLHSYDLGIENR.D R.LVTGWVKPIIIGR.H K.GQETSTNPIASIFAWSR.G K.DFDPAINEYIQR.K K.SRPSLPLPQSR.A K.FPPDNSAPYGAR.Y K.KFPPDNSAPYGAR.Y K.FFVGGNWK.M K.DLGATWVVLGHSER.R K.LPADTEVVCAPPTAYIDFAR.Q R.ETYLAILMDR.S K.INFDDNAEFR.Q R.IMEGPAFNFLDAPAVR.V K.TYYMSAGLQPVPIVFR.G

R.IVNAAGFWAR.E R.ISYTGELGWELYHR.R R.NITDELGVLGVAGPYAR.R R.LEEETGQVVGFHQPGSIR.L K.NYPATIIQEPLVLTEPTR.T R.MPEFYNR.F K.GQVYILGR.E R.FVTVQTISGTGALR.V R.DAGMQLQGYR.Y K.ILIRPLYSNPPLNGAR.I K.TFVVQGFGNVGLHSMR.Y K.HGGTIPVVPTAEFQDR.I R.DSNYHLLMSVQESLER.K K.IIAEGANGPTTPEADKIFLER.N K.VANEPILAFTQGSPER.D

M. Chen et al. / Biochimica et Biophysica Acta 1794 (2009) 1751–1765

1759

Table 2 (continued) Appearanceb 1-ANOVA p-value c

p-value IDd

Mr (Da )

PI

Coverage Scoref (%)e

Peptide hits g

Peptides identified

15 (18) 18 (18)

0.019 0.0002

6.40E-07 3.2E-52

68686 59719

6.09 7.07

35% 48%

108 561

1 5

1511 IPI00231107 Parathymosin 598 IPI00197770 Mitochondrial aldehyde dehydrogenase

18 (18) 15 (18)

0.0093 0.027

0.00016 2.5E-38

11552 56453

4.15 6.63

44% 50%

84 422

1 4

323

IPI00212622 Gastrin-binding protein

15 (18)

0.015

5.00E-08

82613

9.16

32%

119

3

516

IPI00567316 60 kDa heat shock protein

18 (18)

0.037

1.3E-39

60917

5.91

48%

435

4

169

IPI00471584 HSP90-beta

18 (18)

0.013

1.00E-13

83229

4.97

33%

176

5

1509 IPI00212523 DJ-1

18 (18)

4.60E-04

8.00E-53

19961

6.32

89%

567

5

K.LGEYGFQNAVLVR.Y R.GPLLVQDVVFTDEMAHFDR.E K.NFTDVHPDYGAR.V R.LAQEDPDYGLR.D K.GAGAFGYFEVTHDITR.Y R.LGPNYLQIPVNCPYR.A R.TAEEEDEADPKR.Q R.VVGNPFDSR.T K.AAQAAFQLGSPWR.R K.TIPIDGDFFSYTR.H R.TFVQEDVYDEFVER.S K.TVLGVPEVLLGILPGAGGTQR.L K.NLNSEIDNILVNLR.L K.YESAYGTQFTPCQLLR.D K.GANPVEIRR.G R.AAVEEGIVLGGGCALLR.C K.LVQDVANNTNEEAGDGTTTATVLAR. R.TVIIEQSWGSPK.V K.IDIIPNPQER.T R.ALLFIPR.R R.RAPFDLFENK.K K.HFSVEGQLEFR.A R.GVVDSEDLPLNISR.E K.GAEEMETVIPVDIMR.R K.MMNGSHYSYSESR.V K.GAEEMETVIPVDIMR.R R.AGIKVTVAGLAGKDPVQCSR.D K.GLIAAICAGPTALLAHEVGFGCK.V

Cell homeostasis 235 IPI00196656 Ba1-667

18 (18)

0.026

5.00E-56

107379 8.35

34%

599

5

626

18 (18)

0.0015

2.50E-23

48730

5.05

33%

272

3

Development 1057 IPI00389571 Keratin, type II cytoskeletal 8

18 (18)

0.0005

1.3e-042

53985

5.83

55%

465

5

K.LALDIEIATYR.K R.LEGLTDEINFLR.Q R.ATLEAAIADAEQRGELAVK.D R.AQYEEIANR.S R.LQAEIDALKGQR.A

Unclassified 821 IPI00212104 Predicted 42 kDa protein

18 (18)

0.0032

8.00E-29

41942

5.77

52%

327

4

761 949

15 (18) 18 (18)

0.04 0.024

2.00E-10 1.00E-26

27731 46406

4.71 8.03

20% 42%

143 306

1 4

18 (18)

0.031

4.20E-06

27670

7.6

43%

100

1

R.YEFEQQR.Y R.VAAQAVEDVLNIAR.R R.ALNEVFIGESLSSR.A K.NVEHIIDSLRDEGIEVR.L K.NQNINLENNLGEVEAR.Y R.ASAAVGLSYGAHSNLCINQIVR.N R.AFNADFR.R.QYVYNVAR.A K.GVYVLMSGLDLER.L R.GIHVEVPGAEAENLGPLQVAR.V

18 (18)

4.70E-05

4E-37

22083

6.29

78%

410

4

824 IPI00476295 Adenosylhomocysteinase 1157 IPI00325765 Aflatoxin B1 aldehyde reductase member 2

18 (18) 18 (18)

0.0086 0.01

3.2E-06 1.30E-13

47507 40649

6.07 8.35

46% 43%

101 175

1 3

1487 IPI00205036 Hemoglobin alpha 2 chain

15 (18)

0.0069

2E-45

15275

8.45

68%

493

4

992 IPI00212731 Cathepsin D precursor 1484 IPI00287835 Hemoglobin subunit alpha-1/2

18(18) 18 (18)

2.30E-05 5.90E-04

0.0044 1E-38

44652 15319

6.66 7.82

16% 71%

70 426

1 3

Spot no.a

IPI no.

Protein description

Response to stimulus 376 IPI00191737 Serum albumin precursor 477 IPI00231742 Catalase

IPI00365929 Protein disulfide isomerase A6 precursor

IPI00370348 28 kDa protein IPI00193716 Isovaleryl-CoA. dehydrogenase, mitochondrial precursor

1630 IPI00364321 Electron transfer flavoprotein subunit beta 1489 IPI00392676 Predicted biliverdin reductase B

K.DCTGNFCLFR.S R.LYLGHSYVTAIR.N R.KPVDQYEDCYLAR.I K.KPVTEFATCHLAQAPNHVVVSR.K K.LPEGTTYEEYLGAEYLQAVGNIR.K R.TGEAIVDAALSALR.Q K.GSFSEQGINEFLR.E R.GSTAPVGGGSFPNITPR.E

K.IAIFGATGR.T K.HDLGHFMLR.C R.LPSEGPQPAHVVVGDVLQAGDVDK.T K.YVAVMPPHIGDQPLTGAYTVTLDGR.G K.SKFDNLYGCR.E R.QVETELLPCLR.Y R.FFGNSWSETYR.N R.WMYHHSQLQGTR.G K.IGGHGGEYGEEALQR.M K.LRVDPVNFK.F K.TYFSHIDVSPGSAQVK.A K.AADHVEDLPGALSTLSDLHAHK.L K.NIFSFYLNR.D K.IGGHGGEYGEEALQR.M K.TYFSHIDVSPGSAQVK.A K.AADHVEDLPGALSTLSDLHAHK.L

1760

M. Chen et al. / Biochimica et Biophysica Acta 1794 (2009) 1751–1765

regulation of Blvrb was observed during the restraint stress time point, and AdoHycase levels increased and peaked at 4 weeks of stress treatment, and then lowered, but are still above control levels at 6 weeks of stress, before returning to approximately control levels after 8 weeks of stress. While DJ-1 protein was up-regulated during the first week of stress treatment, and then down-regulated. It can be seen that the DJ-1 level down-regulated nearly 2-fold than control at 4 weeks of stress treatment. These results were consistent with the DIGE results as shown in Supplemental data 1. 4. Discussion 4.1. Stress responsive proteins

Fig. 3. Gene ontology annotation of identified proteins. Identified proteins were submitted for gene ontology analysis using GOfact, a freely available web-based program from the laboratory of systems biology at the Beijing Institute of Radiation Medicine (http://www.hupo.org.cn). Identified proteins were categorized based on their (A) subcellular locations; (B) biological processes; and (C) molecular functions. Piechart values represent the number of proteins found in the given category for all submitted proteins with GO annotations.

Expression levels of eight identified proteins, including aldehyde dehydrogenase (ALDH), parathymosin (ParaT), DJ-1, gastrin-binding protein (GBP), catalase, serum albumin precursor, Hsp60, and Hsp90 correlated to the stress response (Table 2). ALDH is a group of enzymes that catalyze the oxidation of aliphatic and aromatic aldehydes to the corresponding acids via a pyridine nucleotide dependent reaction. ALDH was defined as a stress response protein by Han et al, who reported that ALDH was up-regulated in E. coli under four different types of stress [21]. In our study, we found that ALDH was down-regulated during the first 4 weeks of stress, and then up-regulated (Supplemental data 1). We observed that ParaT was down-regulated during the process of restraint stress (Supplemental data 1). ParaT is expressed in all cell types and is widely distributed in mammalian tissues, with the highest concentrations found in the liver, heart, and kidney [22,23]. Early studies identified ParaT as a zinc-binding protein that interacts with several enzymes involved in carbohydrate metabolism [24,25]. In other reports by Okamoto and Isohashi, ParaT was found to inhibit the binding of the activated glucocorticoid receptor to nuclei, suggesting its involvement in the regulation of glucocorticoid action [26,27]. We also observed an increase in glucocorticoid (GC) in stressed rat liver (data not shown). Therefore, our results suggest that ParaT might participate in regulation of the GC pathway, which plays a key role in the process of restraint stress. In our study, we found that DJ-1 was up-regulated during the first 2 weeks of stress, and then down-regulated (Supplemental data 1). This down-regulation was further validated by Western blot (Fig. 6). DJ-1, originally identified as an oncogene product, is a protein with various functions in cellular transformation, oxidative stress response (mitochondrial) and transcriptional regulation, and its oxidative state at cysteine residues determines activities of DJ-1. Fan et al. reported that DJ-1 exerts its cytoprotection through inhibiting the p53-Baxcaspase pathway. Over-expression of DJ-1 decreases the expression of Bax and inhibits caspase activation, whereas knockdown of DJ-1 increases Bax protein levels and accelerates caspase-3 activation and cell death induced by UV exposure [28]. Mo et al. reported that DJ-1 protects against UV-induced cell death also through the suppression of the MEKK1-SEK1-JNK1 signaling pathway [29]. Many publications have reported that heat shock proteins are induced by stress [30–32]. Two heat shock proteins, Hsp60 and Hsp90, show decreasing expression during the first 4 weeks of stress treatment, and then increase (Fig. 2 and Supplemental data 1). Hsp60 is a mitochondrial

Notes to Table 2 a Spot no. is the unique number of the position where the spot is displayed in the master gel. b Appearance means the number of gel maps in which one certain spot could be detected (there were 18 gel maps in total in our study). c p-value of 1-ANOVA test. d p-value ID is the best expectation value of identified protein as calculated by Mascot. e Amino acid sequence coverage for the identified protein. f Score is the protein score based on combined mass and mass/mass spectrums. g Peptide hits is the unique number of MS/MS spectrums which match to the trypsin peptide.

M. Chen et al. / Biochimica et Biophysica Acta 1794 (2009) 1751–1765

1761

Fig. 4. Effect of restraint stress on metabolism in rat liver. (A) Principle Component Analysis scores plot from 1H NMR spectra of aqueous soluble liver tissue extracts from stressed rat at (red ) 0 weeks; (green ) 1 week; (blue ) 2 weeks; (yellow ) 4 weeks; (purple )6 weeks; and (black ▼) 8 weeks. This plot emphasizes the large degree of variation in rat liver metabolism under stress conditions over time. (B) 600 MHz 1H NMR spectra in low chemical shift regions (δ0–5.5) of water-soluble liver tissue extracts and (C) 1H NMR spectra of lipid soluble liver tissue extract in stressed rats. Metabolites were assigned according to the chemical shift value. (D) The dynamic change of partial metabolites in rat liver during 8 weeks of restraint stress (more information can be seen in Table S3).

chaperone, typically held responsible for refolding and transporting proteins from the cytoplasm into the mitochondrial matrix. In addition to its role as a heat shock protein, Hsp60 plays an important role in the transport and maintenance of mitochondrial proteins, and the transmission and replication of mitochondrial DNA [31,32]. Therefore, we think that the dramatic change in DJ-1 and heat shock proteins in rat liver may relate to the dysfunction of mitochondria under stress conditions. 4.2. Homocysteine accumulated in restraint stressed rat plasma Homocysteine is a sulfur amino acid and a normal intermediate in the methionine metabolism pathway. This pathway involves the formation of S-adenosylmethionine (SAM), which subsequently transfers a methyl group to methyl acceptor molecules, and forms adenosylhomocysteine, which is subsequently converted to homocysteine. Homocysteine is then either converted back to methionine by remethylation, or further metabolized to cysteine via the transsulfuration pathway. When excess homocysteine is produced in the body and not readily converted into methionine or cysteine, it is excreted from the tightly regulated intracellular environment into the

blood as cytotoxin. It is the role of the liver to remove excess homocysteine from the blood. In many individuals with liver disease, homocysteine levels can rise beyond normal levels (i.e. hyperhomocysteinemia) and lead to adverse health outcomes [33,34]. Usually, homocysteine in rat plasma was diet-induced, i.e. by feeding highmethionine diet [35,36]. However, it has been proved that restraint stress has significant influence on the adrenal cortex and then lead to vasculature and metabolism system damage in rats [37–39]. Furthermore, de Souza et al. and de Oliveira et al. reported their results after a rat was treated using different stress manipulations, including restraint stress, swimming and cold, and found that not all stress manipulations could increase homocysteine concentrations in rat plasma, and restraint stress was the only type of stress that altered homocysteine concentrations in rat [40,41]. So, their papers indicate the specific relationship between restraint stress and the increase of homocysteine levels. In our study, we can see that homocysteine concentration was increased more than 3-fold after 8 weeks of restraint stress (seen Fig. 5). These results indicate that restraint stress may lead to homocysteine accumulation in rat plasma. Two proteins related to homocysteine metabolism were identified in rat liver. Adenosylhomocysteinase (AdoHycase, SAHH) is an enzyme

1762

M. Chen et al. / Biochimica et Biophysica Acta 1794 (2009) 1751–1765

Fig. 5. Effect of restraint stress on homocysteine concentration in rat plasma. Homocysteine (Hcy) was measured by high-performance liquid chromatography. Chromatogram of plasma sample from rat stressed for 0, 2, 4, 6 and 8 weeks; Data are presented as mean±SEM, and analyzed by t-test.

then down-regulated (Fig. 6). Another protein correlated with homocysteine metabolism is biliverdin reductase (Blvrb), which is a serine/ threonine kinase that catalyzes the reduction of biliverdin to produce bilirubin. Domain analysis shows that this protein has an AdoHycase super-family domain (data not shown). In contrast to AdoHycase, Blvrb was up-regulated continuously (Fig. 6). We noted that the weekly increase in homocysteine levels does not correlate with the AdoHycase Western blot data (Fig. 6) which shows that AdoHycase levels peak at week 4, and return to approximately control levels at week 8, whereas the homocysteine level increases throughout the 8 weeks of stress treatment (Fig. 5). This may be due, in part, to cumulative build up of homocysteine. On the other hand, we suppose that Blvrb might involve in homocysteine metabolism. It is reported that homocysteine can be induced by smoking, alcohol consumption, low consumption of fruits and vegetables, high intake of methionine-containing proteins, cystathionine β-synthase (CBS) or/and methionine synthase deficiencies [44–46]. Our data indicated that homocysteine can be induced by restraint stress (Fig. 5). For many years, the nutritional disorder or deficiency of CBS in hyperhomocysteinemia has been the focus of research [47–49]. Our results show that homocysteine can be elevated along with the up-regulation of AdoHycase and Blvrb under stress conditions. 4.3. Carbohydrate metabolism in rat liver during restraint stress

that catalyzes the hydrolysis of S-adenosylhomocysteine (AdoHyc) to form adenosine and homocysteine. In recent years, elevated homocysteine was considered an independent risk factor for many diseases, including cardiovascular disease, diabetes, gastric ulcer, cancer, and Parkinson's disease [42,43]. In the present study, we found that AdoHycase was up-regulated during the first 4 weeks of stress, and

In this study, we identified 4 proteins that are related to carbohydrate metabolism, including fructose-1,6-bisphosphatase 1 (Fbp1), triosephosphate isomerase (Tpi1), isocitrate dehydrogenase (Idh) and pyruvate dehydrogenase E1(Pdhb). Fbp1 catalyzes hydrolysis of fructose-1,6-diphosphate to fructose-6-phosphate, which is the reverse reaction of rate-determining step in glycolysis [17]. The

Fig. 6. Western blot validation of 3 proteins. Western blot of AdoHycase (Left top), Blvrb (Left middle) and DJ-1, β-actin as an internal standard. The change ratio of proteins is normalized by β-actin based on OD value of protein bands. Graphical representations of AdoHycase (Right top), Blvrb (Right middle) and DJ-1 (Right bottom) are shown in the right panel with changes in expression over time. The result from the Western blot was consistent with the DIGE result (generated by DeCyder, see Supplemental data 1).

M. Chen et al. / Biochimica et Biophysica Acta 1794 (2009) 1751–1765

up-regulation of Fbp1 might inhibit glycolysis. Tpi1, the other enzyme involved in the glycolysis, is also up-regulated. Interestingly, this enzyme converts dihydroxyacetone phosphate to glyceraldehyde 3phosphate. Of the two, only glyceraldehyde 3-phosphate can take participation in glycolysis. Therefore, we suggest that the increased expression of Tpi1 is a remedy for inhibited glycolysis in rat liver under stress. All in all, glycolysis was partially inhibited under the stress of our experiments. Evidence for this result is also provided by the increase in liver glucose and decrease in glycogen levels detected by 1H-NMR (Fig. 4D and Table S3). Lactate, a potential source of endogenous molecular toxins, is the end product of glycolysis [50]. Jing et al. reported that increased lactate is a key biomarker related to lipodystrophy [51]. Higher levels of lactate were observed in stressed rat liver compared to control in our study (Fig. 4D and Table S3). The increase in the lactate level also suggested that gluconeogenesis was inhibited, and changes in carbohydrate and energy metabolism occurred. The effects of various stresses have been well documented in the rat, including diet/water deprivation, heat shock, sleep deprivation and acclimatization [52–55]. It has been reported that perturbations of liver metabolism by liver toxins can increase the rate of glycogenolysis [56,57]. Combining these results with our own, we speculate that inhibition of glycolysis and gluconeogenesis in liver may be a response common to the various stress conditions. We also observed a distinct change in TCA-related enzymes (isocitrate dehydrogenase and pyruvate dehydrogenase E1). Pyruvate dehydrogenase E1 is a key entry point for carbon in the TCA cycle. Isocitrate dehydrogenase converts isocitrate to oxalosuccinate and αketoglutarate during the TCA cycle. It has been reported that increased stress load is connected to an increased energy requirement, and leads to metabolic readjustment. Hoffmann et al. reported that heat shock

1763

stress can induce TCA cycle-related enzymes in the E. coli expression system [58]. In our study, we found that isocitrate dehydrogenase and pyruvate dehydrogenase E1 (two key enzymes in TCA) show dramatic changes. These two enzymes increased continuously during the first 4 weeks of stress, and then decreased (Supplemental data 1). Support for this result is provided by the same tendency of succinate and acetate in stressed rat liver at the metabonomic level (Fig. 4D and Table S3). Kil et al. reported that the primary function of mitochondrial NADP+ dependent isocitrate dehydrogenase is the control of cellular defenses against oxidative damage through the supply of NADPH, which is needed for the generation of glutathione [59]. Although glutathione was not detected in our 1H-NMR analysis, the relationship between stress and oxidative damage has been reported. So, we can propose that the TCA cycle may be activated in the early response to stress conditions, due to requirements for energy and redox balance. 4.4. Lipid metabolism in rat liver during restraint stress DIGE analyses suggested that expression of three pivotal proteins involved in fatty acid β-oxidation were affected by stress treatment, including long-chain specific acyl-CoA dehydrogenase (Acadl), 3ketoacyl-CoA thiolase (Acaa2, mitochondrial) and enoyl-CoA hydratase (Echs1). Acadl, the enzyme that catalyzes the first reaction in the β-oxidation of fatty acids, had a tendency to decrease during the stress period (Supplemental data 1). Another enzyme, Acaa2, which converts β-ketoacyl-CoA to acetyl CoA, was also down-regulated. On the other hand, 1H-NMR analyses indicated that the levels of saturated fatty acyl (Lipid CH2CO) and unsaturated fatty acyl (Lipid CH2C = C) were elevated. Triglycerides, considered as storage for excess fatty

Fig. 7. The summarized dynamic change at proteomic and metabonomic levels in stressed rat liver cell. Metabolites and proteins in red and blue represent an increasing and decreasing concentration in the course of 8 weeks of stress, respectively. Metabolites and proteins in black means they were not detected in our experiment.

1764

M. Chen et al. / Biochimica et Biophysica Acta 1794 (2009) 1751–1765

acid in the liver, were decreased (Table S3). This suggests that fatty acid was accumulated in the liver. Saturated and unsaturated fatty acids differ significantly in their contributions to lipotoxicity. Previous studies in Chinese hamster ovary cells, cardiac myocytes, breast cancer cell lines, and hematopoietic precursor cell lines all suggested that lipotoxicity from the accumulation of long-chain fatty acids is specific to, or made much worse by, saturated fatty acids [60–64]. These data predict that increased hepatic saturated fatty acids may promote liver damage in stressed rat, and perturbation in fatty acid oxidation and metabolic systems may decrease energy usage in the liver, leading to lipid storage in the liver cells. Bile acids serve many important physiological functions, including cholesterol homeostasis, lipid absorption, and generation of bile flow, all of which help to excrete and recirculate drugs, vitamins, and endogenous and exogenous toxins [65]. Many diseases, such as hepatobiliary and intestinal diseases, will affect the concentration of bile acids. Therefore, bile acid has long been considered to be a marker for liver injury [66]. In our study, we found that bile acid in liver tissue was decreased by 1.5- to 6-fold during the 8 weeks of stress treatment (Fig. 4D and Table S3). This is in contrast with the findings in a study of human hepatocellular carcinoma, where an increase in the level of bile acid was observed [51]. The authors propose that the increase occurred with bile duct obstruction, which is common in liver impairments caused by propagation and invasion of hepatocellular carcinoma (HCC) cells in the bile duct. In our study, we suggest that lower levels of bile acids in liver tissue indicate that a subclinical hepatic injury may be occurring. In summary, using “-omics” strategies, we found that a combination of information from protein and metabolite levels provides an integrated picture of the response to restraint stress in rat liver. We identified 42 proteins and 32 chemical groups, implicated in glycolysis, TCA cycle, fatty acid oxidation, and the urea cycle. Our data suggests that subclinical hepatic injury may be occurring, including inhibition of glycolysis and gluconeogenesis, and dysfunction of fatty acid β-oxidation. Homocysteine, a putative risk factor of many diseases, could be elevated by increased adenosylhomocysteinase and biliverdin reductase activity during stress. Our data comprehensively mapped rat liver responses to restraint stress, showing changes in the interactions of proteins and metabolites that produce energy and process materials in liver cell (summarized in Fig. 7). Acknowledgements The authors thank Wenfeng Yu, Yujuan Yan and Xianzhong Yan for their assistance with the DIGE and 1H-NMR experiment. We also thank two anonymous reviewers for their patient review and critical reading of our manuscript. This research was supported in part by grants from the National High Technologies R&D Program of China (2006AA02Z4C08) and the Key Project for Application and Basic Research of Tianjing Municipality (06YFZJC02400). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.bbapap.2009.08.012. References [1] B.S. McEwen, Protective and damaging effects of stress mediators, N. Engl. J. Med. 338 (1998) 171–179. [2] B.S. McEwen, E. Stellar, Stress and the individual mechanisms leading to disease, Arch. Intern. Med. 153 (1993) 2093–2101. [3] T. Esch, G.B. Stefano, G.L. Fricchione, H. Benson, Stress-related diseases—a potential role for nitric oxide, Med. Sci. Monit. 8 (2002) RA103–RA118. [4] T. Esch, G.B. Stefano, G.L. Fricchione, H. Benson, Stress in cardiovascular diseases, Med. Sci. Monit. 8 (2002) RA93–RA101.

[5] S.R. Wenneberg, R.H. Schneider, K.G. Walton, C.R. Maclean, D.K. Levitsky, J.W. Salerno, R.K. Wallace, J.V. Mandarino, M.V. Rainforth, R. Waziri, A controlled study of the effects of the transcendental meditation program on cardiovascular reactivity and ambulatory blood pressure, Int. J. Neurosci. 89 (1997) 15–28. [6] S. Lutgendorf, H. Logan, H.L. Kirchner, N. Rothrock, S. Svengalis, K. Iverson, D. Lubaroff, Effects of relaxation and stress on the capsaicin-induced local inflammatory response, Psychosom. Med. 62 (2000) 524–534. [7] S. Hirose, C. Hirayama, Y. Ikemi, The influence of emotional stress on the liver blood flow, Kyushu J. Med. Sci. 12 (1961) 319–323. [8] M. Iwai, S. Saheki, Y. Ohta, T. Shimazu, Foot-shock stress accelerates carbon tetrachloride-induced liver injury in rats: implication of the sympathetic nervous system, Biomed. Res. (Tokyo) 7 (1986) 145–154. [9] Y. Chida, N. Sudo, J. Sonoda, H. Sogawa, C. Kubo, Electric foot-shock stress-induced exacerbation of α-galactosylceramide-triggered apoptosis in the mouse liver, Hepatology 39 (2004) 1131–1140. [10] N.S. Tannu, S.E. Hemby, Two-dimensional fluorescence difference gel electrophoresis for comparative proteomics profiling, Natl. Protoc. 1 (2006) 1732–1742. [11] B. Kersten, G.K. Agrawal, H. Iwahashi, R. Rakwal, Plant phosphoproteomics: a long road ahead, Proteomics 6 (2006) 5517–5528. [12] J.K. Nicholson, I.D. Wilson, Opinion: understanding ‘global’ systems biology: metabonomics and the continuum of metabolism, Nat. Rev. Drug Discov. 8 (2003) 668–676. [13] M.A. Vidal, Biological atlas of functional maps, Cell 104 (2001) 333–339. [14] A. Craig, J. Sidaway, E. Holmes, T. Orton, D. Jackson, R. Rowlinson, J. Nickson, R. Tonge, I. Wilson, J. Nicholson, Systems toxicology: integrated genomic, proteomic and metabonomic analysis of methapyrilene induced hepatotoxicity in the rat, J. Proteome Res. 5 (2006) 1586–1601. [15] H. Li, Z. Xie, J. Lin, H. Song, Q. Wang, K. Wang, M. Su, Y. Qiu, T. Zhao, K. Song, X. Wang, M. Zhou, P. Liu, G. Zhao, Q. Zhang, W. Jia, Transcriptomic and metabonomic profiling of obesity-prone and obesity-resistant rats under high fat diet, J. Proteome Res. 7 (2008) 4775–4783. [16] J. Griffin, J. Scott, J. Nicholson, The influence of pharmacogenetics on fatty liver disease in the wistar and kyoto rats: a combined transcriptomic and metabonomic study, J. Proteome Res. 6 (2007) 54–61. [17] F. Matsuzaki, M. Shimizu, H. Wariishi, Proteomic and metabolomic analyses of the white-rot fungus Phanerochaete chrysosporium exposed to exogenous benzoic acid, J. Proteome Res. 7 (2008) 2342–2350. [18] L.A. Galea, B.S. Mcewen, P. Tanapat, T. Deak, R.L. Spencer, F.S. Dhabhar, Sex differences in dendritic atrophy of CA3 pyramidal neurons in response to chronic restraint stress, Neurosci. 81 (1997) 689–697. [19] P. Durand, L.J. Fortin, S. Lussier-Cacan, J. Davignon, D. Blache, Hyperhomocysteinemia induced by folic acid deficiency and methionine load—applications of a modified HPLC method, Clin. Chim. Acta 252 (1996) 83–93. [20] D. Li, J. Li, S. Ouyang, S. Wu, J. Wang, X. Xu, Y. Zhu, F. He, An integrated strategy for functional analysis in large-scale proteomic research by gene ontology, Prog. Biochem. Biophys. 32 (2005) 1026–1029. [21] K.Y. Han, J.S. Park, H.S. Seo, K.Y. Ahn, J. Lee, Multiple stressor-induced proteome responses of Escherichia coli BL21(DE3), J. Proteome Res. 7 (2008) 1891–1903. [22] M. Clinton, M. Frangou-Lazaridis, C. Panneerselvam, B.L. Horecker, Prothymosin alpha and parathymosin: mRNA and polypeptide levels in rodent tissues, Arch. Biochem. Biophys. 269 (1989) 256–263. [23] M. Frangou-Lazaridis, M. Clinton, G.J. Goodall, B.L. Horecker, Prothymosin alpha and parathymosin: amino acid sequences deduced from the cloned rat spleen cDNAs, Arch. Biochem. Biophys. 263 (1988) 305–310. [24] I.A. Brand, A. Heinickel, Key enzymes of carbohydrate metabolism as targets of the 115-kDa Zn(2+)-binding protein (parathymosin), J. Biol. Chem. 266 (1991) 20984–20989. [25] H.I. Trompeter, I.A. Brand, H.D. Soling, The primary sequence of the PFK-1 inactivating zinc-binding protein as deduced from cDNA sequencing Identity of the zinc-binding protein with rat parathymosin, FEBS Lett. 253 (1989) 63–66. [26] K. Okamoto, F. Isohashi, Purification and primary structure of a macromoleculartranslocation inhibitor II of glucocorticoid-receptor binding to nuclei from rat liver inhibitor II is the 115-kDa Zn2 + -binding protein (parathymosin), Eur. J. Biochem. 267 (2000) 155–162. [27] G. Martic, Z. Karetsou, K. Kefala, A.S. Politou, C.R. Clapier, T. Straub, T. Papamarcaki, Parathymosin affects the binding of linker histone H1 to nucleosomes and remodels chromatin structure, J. Biol. Chem. 280 (2005) 16143–16150. [28] J. Fan, H. Ren, E. Fei, N. Jia, Z. Ying, P. Jiang, M. Wu, G. Wang, Sumoylation is critical for DJ-1 to repress p53 transcriptional activity, FEBS Lett. 582 (2008) 1151–1156. [29] J.S. Mo, M.Y. Kim, E.J. Ann, J.A. Hong, H.S. Park, DJ-1 modulates UV-induced oxidative stress signaling through the suppression of MEKK1 and cell death, Cell Death Differ. 15 (2008) 1030–1041. [30] M.S. Marber, C.H. Mestril, M.R. Sayen, D.M. Yellon, W.H. Dillmann, Overexpression of the rat inducible 70-kD heat stress protein in a transgenic mouse increases the resistance of the heart to ischemic injury, J. Clin. Invest. 95 (1995) 1446–1456. [31] H. Itoh, A. Komatsuda, H. Ohtani, H. Wakui, H. Imai, K. Sawada, M. Otaka, M. Ogura, A. Suzuki, F. Hamada, Mammalian HSP60 is quickly sorted into the mitochondria under conditions of dehydration, Eur. J. Biochem. 269 (2002) 5931–5938. [32] B.A. Kaufman, J.E. Kolesar, P.S. Perlman, R.A. Butow, A function for the mitochondrial chaperonin Hsp60 in the structure and transmission of mitochondrial DNA nucleoids in Saccharomyces, J. Cell Biol. 163 (2003) 457–461. [33] G. Borgia, I. Gentile, G. Fortunato, F. Borrelli, S. Borelli, M. de Caterina, M.D. Di Taranto, M. Simone, F. Borgia, C. Viola, L. Reynaud, R. Cerini, L. Sacchetti, Homocysteine levels and sustained virological response to PEGylated-interferon alpha2b plus ribavirin therapy for chronic hepatitis C: a prospective study, Liver Int. 29 (2008) 248–252.

M. Chen et al. / Biochimica et Biophysica Acta 1794 (2009) 1751–1765 [34] A.M. Troen, M. Shea-Budgell, B. Shukitt-Hale, D.E. Smith, J. Selhub, I.H. Rosenberg, B-vitamin deficiency causes hyperhomocysteinemia and vascular cognitive impairment in mice, Proc. Natl. Acad. Sci. USA 105 (2008) 12474–12479. [35] H. Morita, H. Kurihara, S. Yoshida, Y. Saito, T. Shindo, Y. Oh-Hashi, Y. Kurihara, Y. Yazaki, R. Nagai, Diet-induced hyperhomocysteinemia exacerbates neointima formation in rat carotid arteries after balloon injury, Circulation 103 (2001) 133–139. [36] C. Woo, Y. Siow, OK, homocysteine activates cAMP-response element binding protein in HepG2 through cAMP/PKA signaling pathway, Arterioscler. Thromb. Vasc. Biol. 25 (2006) 1043–1050. [37] M. Blake, D. Buckley, A. Buckley, Dopaminergic regulation of heat shock protein-70 expression in adrenal gland and aorta, Endocrinology 132 (1993) 1063–1070. [38] M. Blake, A. Buckley, D. Buckley, K. LaVoi, T. Bartlett, Neural and endocrine mechanisms of cocaine-induced 70-kDa heat shock protein expression in aorta and adrenal gland, J. Pharmacol. Exp. Ther. 268 (1994) 522–529. [39] S. McDougall, J.R. Paull, R. Widdop, A. Lawrence, Restraint stress: differential cardiovascular responses in Wistar-Kyoto and spontaneously hypertensive rats, Hypertension 35 (2000) 126–129. [40] F. de Souzza, M. Rodrigues, S. Tufik, J. Nobrega, V. D'Almeida, Acute stressorselective effects on homocysteine metabolism and oxidative stress parameters in female rats, Pharmacol. Biochem. Behav. 85 (2006) 400–407. [41] A. de Oliveira, D. Suchecki, S. Cohen, V. D'Almeida, Acute stressor-selective effect on total plasma homocysteine concentration in rats, Pharmacol. Biochem. Behav. 77 (2004) 269–273. [42] A. Rasola, P. Bernardi, The mitochondrial permeability transition pore and its involvement in cell death and in disease pathogenesis, Apoptosis 12 (2007) 815–833. [43] G.Z. Feuerstein, P.R. Young, Apoptosis in cardiac diseases: stress- and mitogenactivated signaling pathways, Cardiovasc. Res. 45 (2000) 560–569. [44] S. Tsiara, M. Elisaf, D.P. Mikhailidis, Influence of smoking on predictors of vascular disease, Angiology 54 (2003) 507–530. [45] S.R. Craddick, P.J. Elmer, E. Obarzanek, W.M. Vollmer, L.P. Svetkey, M.C. Swain, The DASH diet and blood pressure, Curr. Atheroscler. Rep. 5 (2003) 484–491. [46] K.J. Levers, L.A. Kluijtmans, H.J. Blom, Genetics of hyperhomocysteinaemia in cardiovascular disease, Ann. Clin. Biochem. 40 (2003) 46–59. [47] S. Kim, K. Lee, Y. Shin, S. Choi, E. Do, S. Kim, B. Chun, M. Lee, K. Shin, Stress-induced decrease of granule cell proliferation in adult rat hippocampus: assessment of granule cell proliferation using high doses of bromodeoxyuridine before and after restraint stress, Mol. Cells 19 (2005) 74–80. [48] V. Cavalca, G. Cighetti, F. Bamonti, A. Loaldi, L. Bortone, C. Novembrino, M. De Franceschi, R. Belardinelli, M.D. Guazzi, Oxidative stress and homocysteine in coronary artery disease, Clin. Chem. 47 (2001) 887–892. [49] R.J. Windle, Y.M. Kershaw, N. Shanks, S.A. Wood, S.L. Lightman, C.D. Ingram, Oxytocin attenuates stress-induced c-fos mRNA expression in specific forebrain regions associated with modulation of hypothalamo-pituitary-adrenal activity, J. Neurosci. 24 (2004) 2974–2982. [50] C.V. Dang, G.L. Semenza, Oncogenic alterations of metabolism, Trends Biochem. Sci. 24 (1999) 68–72.

1765

[51] N. Jiang, X. Yan, W. Zhou, Q. Zhang, H. Chen, Y. Zhang, X. Zhang, NMR-based metabonomic investigations into the metabolic profile of the senescenceaccelerated mouse, J. Proteome Res. 7 (2008) 3678–3686. [52] A.N. Phipps, J. Stewart, B. Wright, I.D. Wilson, Effect of diet on the urinary excretion of hippuric acid and other dietary-derived aromatics in rat A complex interaction between diet, gut microflora and substrate specificity, Xenobiotica 28 (1998) 527–537. [53] P. Clausing, M. Gottschalk, Effects of drinking water acidification, restriction of water supply and individual caging on parameters of toxicological studies in rats, Z. Versuchstierkd. 32 (1989) 129–134. [54] M.H. Harrison, Effects of acute heat stress on tissue carbohydrate in fasted rats, Aviat. Space Environ. Med. 47 (1976) 165–167. [55] A. Rechtschaffen, B.M. Bergmann, C.A. Everson, C.A. Kushida, M.A. Gilliland, Sleep deprivation in the rat: X integration and discussion of the findings, Sleep 25 (2002) 68–87. [56] H. Wu, X. Zhang, X. Li, Y. Wu, F. Pei, Acute biochemical effects of La(NO3)3 on liver and kidney tissues by magic-angle spinning 1H nuclear magnetic resonance spectroscopy and pattern recognition, Anal. Biochem. 339 (2005) 242–248. [57] N.J. Waters, E. Holmes, C.J. Waterfield, R.D. Farrant, J.K. Nicholson, NMR and pattern recognition studies on liver extracts and intact livers from rats treated with alpha-naphthylisothiocyanate, Biochem. Pharmacol. 64 (2002) 67–77. [58] F. Hoffmann, J. Weber, U. Rinas, Metabolic adaptation of Escherichia coli during temperature-induced recombinant protein production: 1. Readjustment of metabolic enzyme synthesis, Biotechnol. Bioeng. 80 (2002) 313–319. [59] I.S. Kil, J.W. Park, Regulation of mitochondrial NADP+-dependent isocitrate dehydrogenase activity by glutathionylation, J. Biol. Chem. 280 (2005) 10846–10854. [60] S. Hardy, Y. Langelier, M. Prentki, Oleate activates phosphatidylinositol 3-kinase and promotes proliferation and reduces apoptosis of MDA-MB-231 breast cancer cells, whereas palmitate has opposite effects, Cancer Res. 60 (2000) 6353–6358. [61] L.L. Listenberger, D.S. Ory, J.E. Schaffer, Palmitate-induced apoptosis can occur through a ceramide-independent pathway, J. Biol. Chem. 276 (2001) 14890–14895. [62] J.E. de Vries, M.M. Vork, T.H. Roemen, Y.F. de Jong, J.P. Cleutjens, G.J. van der Vusse, M. van Bilsen, Saturated but not mono-unsaturated fatty acids induce apoptotic cell death in neonatal rat ventricular myocytes, J. Lipid Res. 38 (1997) 1384–1394. [63] K. Maedler, G.A. Spinas, D. Dyntar, W. Moritz, N. Kaiser, M.Y. Donath, Distinct effects of saturated and monounsaturated fatty acids on beta-cell turnover and function, Diabetes 50 (2001) 69–76. [64] M.B. Paumen, Y. Ishida, M. Muramatsu, M. Yamamoto, T. Honjo, Inhibition of carnitine palmitoyltransferase I augments sphingolipid synthesis and palmitateinduced apoptosis, J. Biol. Chem. 272 (1997) 3324–3329. [65] S. Perwaiz, B. Tuchweber, D. Mignault, T. Gilat, I.M. Yousef, Determination of bile acids in biological fluids by liquid chromatography-electrospray tandem mass spectrometry, J. Lipid Res. 42 (2001) 114–119. [66] M.J. Nunes, M.E. de Paiva, Pereira Bastos de Siqueira, Increased serum bile acids as a possible biomarker of hepatotoxicity in Brazilian workers exposed to solvents in car repainting shops, Biomarkers 10 (2005) 456–463.