Metabonomic Studies on the Physiological Effects ... - ACS Publications

Metabonomic Studies on the Physiological Effects of Acute and Chronic Psychological Stress in Sprague-Dawley Rats Claire R. Teague,†,#,× Firdaus S. Dhabhar,‡,× Richard H. Barton,†,× Bridgette Beckwith-Hall,† Jonathan Powell,§ Mark Cobain,§ Burton Singer,| Bruce S. McEwen,⊥ John C. Lindon,† Jeremy K. Nicholson,† and Elaine Holmes*,† Department of Biomolecular Medicine, SORA Division, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ, United Kingdom, Department of Psychiatry and Behavioural Sciences, Stanford University School of Medicine, Stanford, California 94305, Unilever Research and Development, Colworth, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom, Office of Population Research, Princeton University, Princeton, New Jersey 08544, and Laboratory of Neuroendocrinology, The Rockefeller University, New York, New York 10021 Received August 15, 2006

The biochemical effects of acute and chronic psychological stress have been investigated in male Sprague-Dawley rats using a combination of 1H NMR spectral analysis of plasma and conventional hematological analyses. Animals were subjected to 35 consecutive days of 6-h sessions of stress, and following a 9 day break, were stressed for a further 6-h period. Plasma samples were collected at 0, 1, 3, and 6 h on days 1, 9, 21, 35, and 44, measured using 600 MHz 1H NMR spectroscopy, and analyzed by Principal Components Analysis. Time-dependent biochemical effects of psychological stress on a range of endogenous metabolites were evident and were correlated with the intensity of the stress response as defined by corticosterone and hematological parameters. Following acute stress, increases in the levels of glucose and ketone bodies, and decreases in the levels of acetate, alanine, isoleucine, lactate, leucine, valine, and lipoproteins, were observed. Chronic stress-induced increases in plasma levels of alanine, lactate (day 9), and leucine, valine, and choline (day 44) and decreases in acetate (day 9) and lipoprotein concentrations were observed. Positive correlations between plasma corticosterone level and glucose and glycerol, and between plasma lipoprotein concentrations and hemoglobin levels, were established using Projection to Latent Structures (PLS) analysis. This study indicates the potential of using NMR-based metabonomic strategies for the characterization of endogenous metabolic perturbations induced by psychological stressors and lifestyle choices. Keywords: metabonomics • acute stress • chronic stress • plasma • NMR • multivariate analysis • metabotype • glucocorticoid • allostasis • hematology

Introduction Many types of physiological variation unavoidably introduce levels of complexity in the interpretation of human studies in disease or lifestyle intervention. Heterogeneity in diet, fitness, stress, and other lifestyle factors can all cause biochemical variation. Stress is defined as a constellation of events, beginning with a stimulus (stressor), which precipitates a reaction in the brain (stress perception), and which subsequently results in the * Address correspondence to: Professor E. Holmes. E-mail: [email protected]. † Imperial College London. # Current address: GlaxoSmithKline, The Frythe, Welwyn, Hertfordshire AL7 6AR, UK. × These authors contributed equally to the work. ‡ Stanford University School of Medicine. § Unilever Research and Development. | Princeton University. ⊥ The Rockefeller University.

2080

Journal of Proteome Research 2007, 6, 2080-2093

Published on Web 05/03/2007

activation of certain physiologic systems in the body (stress response).1 The body’s response to acute and chronic stress is thought to have an effect on ageing, and although old rats can initiate a stress response as efficiently as younger animals, they are less able to rapidly down regulate the response to stress.2 Evidence exists for the adverse effects of stress on human health, including asthma, diabetes, gastrointestinal disorders, and diseases relating to the immune system.3 It has also been shown that chronic stress can induce or accelerate the development of atherosclerosis in mice.4 A typical biochemical response to acute stress is an increase in the level of plasma corticosterone, glucose, insulin, glycerol, and ketone bodies (e.g., acetoacetate, β-hydroxybutyrate, and acetone), whereas the response to chronic stress results in a decreased physical body weight and food intake, increased adrenal weight, and slight reductions in liver glycogen and plasma insulin. For both acute and chronic stress, an overall decrease in plasma triacylglyceride concentrations is common.5 10.1021/pr060412s CCC: $37.00

 2007 American Chemical Society

research articles

Metabonomic of Acute and Chronic Psychological Stress

Whereas the acute response to stressors generally promotes adaptation and survival, a sustained stress response may contribute toward or exacerbate disease as a result of sustained endocrinological imbalance6 and may influence the progression of cardiovascular disease and diabetes.7 The main physiological responses to stress are mediated by the sympathoadrenal system, which can be further separated into the sympathetic nervous system (SNS) and adrenal medulla, and hypothalamic-pituitary-adrenocortical (HPA) axis.6 Activation of the SNS and HPA axes causes the release of catecholamines from nerves and the adrenal medulla and leads to the secretion of corticotrophin from the pituitary gland. Corticotrophin, in turn, mediates the release of cortisol from the adrenal cortex. Cortisol is responsible for most glucocorticoid activity in man, with corticosterone as the major glucocorticoid in rodents. Glucocorticoids are known to interact with the brain and pituitary to form a closed-loop feedback system by inhibiting the subsequent release of corticotrophin releasing factor (CRF) and corticotrophin.8 Cells containing adrenal steroid receptors in the hippocampus that are involved in negative feedback are lost with age, with cortisol apparently contributing to this loss. This suggests that the greater the duration of stress experienced, the greater the loss of these crucial hippocampal cells, with the net loss of the negative feedback mechanism that protects against the harmful effects of sustained stress responses.2 Catecholamines are released from the adrenal medulla in response to acute stress, and as their secretion is part of the ‘fight or flight’ response, they increase the basal metabolic rate, stimulate glycogenolysis, and mobilize free fatty acids.9 The term ‘allostasis’ is used to describe the ability to achieve stability through change.10 Through allostasis, the autonomic nervous system, HPA axis, and cardiovascular, metabolic, and immune systems protect the body by responding to internal and external stress, and the price of this accommodation to stress can be allostatic load, which is defined as the wear and tear resulting from chronic over- or underactivity of allostatic systems.3 The way in which individuals respond to chronic stress differs as a result of diet and lifestyle; for example, the effects of chronic stress may be exacerbated by a rich diet and use of tobacco and alcohol, or reduced with moderate exercise.3 Currently, methods of diagnosing stress include medical interview and questionnaires,11 physiological measures,12 and measurement of hormone levels.13 Conventional biochemical methods of characterizing stress status are limited in that only a small number of markers including cortisol and insulin are routinely measured, and currently, the most practical way to diagnose stress is via a stress-oriented, face-to-face medical interview.14 However, the accuracy of questionnaire data is not always easy to verify. More recently, multiparametric methods that allow determination of the presence and significance of many analytes simultaneously have been developed, operating at the gene expression, protein expression, and metabolite levels, leading to a global overview of the integrated response of an organism to a stressor. Metabonomics is defined as ‘the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification’,15 and deals with detecting, identifying, quantitating, and cataloguing the history of time-related metabolic changes in an integrated biological system. With 1H NMR spectroscopy of biofluids, the simultaneous measurement of a variety of low-molecular-weight metabolites from a range of

intermediate metabolic pathways is possible.16,17 High-resolution 1H NMR spectra of biological samples are extremely complex, comprising thousands of signals, many of which overlap. This makes visual inspection of the data very difficult, and hence, there is a need to deconvolve spectral information. To make optimal use of the information present in the 1H NMR spectrum for classification purposes, computer-based data reduction and pattern recognition methods such as Principal Components Analysis (PCA) are routinely applied.18 Metabonomic approaches have been successful in characterizing metabolic responses to toxicity and disease in mammals. A majority of metabonomic studies performed to date have been used to characterize systemic variation corresponding to overt drug toxicity or disease profiles,16-20 with certain combinations of biomarkers indicating a particular toxicity or disease state, However, this approach has also been used to study the more subtle responses induced by nutritional intervention or to define physiological variation.17,18,21 The work carried out in this study has used 1H NMR-based metabonomics to interpret 1H NMR spectroscopic data of plasma to follow the biochemical variation arising from chronic restraint stress in rats, a combination psychological-physiological stessor. Here, 1H NMR spectra of plasma have been generated from Sprague-Dawley (SD) rats following periods of acute and chronic psychological stress and analyzed using chemometric techniques. To overcome the problem of hypervariable, high-concentration metabolites, and to improve interpretation of the data, a hierarchical blocking approach has also been used in conjunction with PCA. Hierarchical blocking, a method of subdividing metabonomic NMR data sets into smaller blocks prior to further chemometric analysis, has been shown to facilitate separation, interpretability, and outlier detection by means of multivariate projections.22 Hematological parameters measured for each collected blood sample were also analyzed using multivariate techniques and were correlated to NMR spectral data matrices using PLS regression.23 Correlations and predictions were made on hematological parameters using plasma 1H NMR spectral data.

Materials and Methods Animal Studies. Young adult, male SD rats, n ) 5, (ca. 150 g at the beginning of the study, Harlan Sprague Dawley, Indianapolis, IN) were housed in hanging wire mesh cages in the accredited animal facilities of the Rockefeller University (New York, NY). The animal room was maintained on a 12 h light/12 h dark cycle from 07:00-19:00. All animals were given rat chow and tap water ad libitum. Animals were exposed to a stressor for 6 h per day for 35 consecutive days and, following a 9 day break, were exposed to the same stressor on day 44 as described by the flowchart in Figure 1. Prior to exposure to the daily stressor, a blood sample was taken as a control point for assessing the response to acute stress. The first 0 h sample taken on day 1 was the control point for evaluation of response to chronic stress. The daily stressor consisted of a randomized sequence of wire mesh restraint, shaking (2 oscillations per second in an animal cage placed on a flat bed oscillatory shaker), and restraint plus shaking. The stress session was started at 10 a.m., and stressors were changed every hour during each 6 h stress session to minimize the gradual diminution of the stress response through habituation or adaptation.24 Blood samples were collected into heparinized microfuge tubes at 0, 1, 3, and 6 h during stress sessions on days 1, 9, 21, 35, and 44, by the tail clip method,25 Journal of Proteome Research • Vol. 6, No. 6, 2007 2081

research articles

Figure 1. Schematic showing general experimental and analytical sampling strategy for PCA- and PLS-oriented modeling of the metabolic consequences of acute and chronic stress conditions.

and plasma was obtained by the centrifugation of blood before immediate freezing and storage at -70 °C. Hematological Parameters. Hematological parameters were measured using standard methods as described by Dr. Firdhaus Dhabhar.24 The following parameters were measured: lymphocyte number, red blood cell and platelet numbers, mean corpuscular volume (MCV), hematocrit, and hemoglobin concentration. Plasma corticosterone was additionally measured by radioimmunoassay using a commercially available RIA kit (Diagnostic Products Corporation, Los Angeles, CA). Statistical comparisons were made between acute stress time points (1, 3, and 6 h) and their time 0 h control points, and between chronic time-collection points (days 9, 21, 35, and 44) and day 1, 0 h collected samples, for all hematological measurements. A two-tailed, unpaired Student’s t test was used to test for significance. Preparation of Samples for 1H NMR Spectroscopy. Plasma samples were centrifuged at 10 000 rpm for 5 min to remove any solid matter. Sodium chloride solution (150 µL) (0.9% w/v, made up in 90% H2O/10% D2O) was added to the plasma sample (volume approximately 50 µL) in a microfuge tube. This was then transferred into 3 mm Wilmad NMR tubes. D2O (deuterium oxide) provides a deuterium field-frequency lock for the NMR spectrometer. 1 H NMR spectra of the plasma samples were acquired on a Bruker DRX 600 MHz spectrometer (600.13 MHz 1H-observation frequency) at a probe temperature of 300 K employing a standard water presaturation pulse sequence. Two-dimensional 1 H NMR,26 Carr-Purcell-Meiboom-Gill (CPMG)27,28 spin-echo and diffusion-edited29 spectra were acquired with water presaturation during both recycle delay and mixing time, using standard sequences. Standard and CPMG 1H NMR experiments collected 64 and 128 free induction decays (FID), respectively, into 32K data points with a spectral width of 12019 Hz, an acquisition time of 1.36 s and a total pulse recycle delay of 3.36 s. For the CPMG experiment, 2nτ ) 80 ms. For diffusion-edited NMR spectra, 128 FIDs were collected into 32K data points, with a spectral width of 12019 Hz, an acquisition time of 0.68 s, and a total pulse recycle delay of 2.68 s. The gradient amplitude was set at 34.0 G cm-1, with a diffusion time of 50 ms. The FIDs were multiplied by an exponential weighting function corresponding 2082

Journal of Proteome Research • Vol. 6, No. 6, 2007

Teague et al.

to a line broadening of 0.3 Hz prior to Fourier transformation (FT), except where 1 Hz was used for diffusion-edited spectra. Metabolite identifications were made by comparison with literature values,17 selected TOCSY 1H-1H 2D NMR experiments (data not shown), and choline was additionally identified by comparison with an authentic standard sample. Standard and diffusion-edited 1H NMR spectral profiles have also been investigated and allowed a clearer interpretation of changes to the lipoprotein profile with stress (data not shown). The TOCSY experiment detects coupled homonuclear spins, and spins residing within the same spin system that do not necessarily share mutual couplings. 1H-1H TOCSY NMR spectra from representative plasma samples were recorded using the DIPSI-2 spin-lock scheme30 using time-proportional phase incrementation (TPPI). A total of 64 transients per increment was collected into 2K data points for 128 increments with a spectral width of 6313 Hz. The TOCSY mixing time was 80 ms, and the spin-lock pulse power was adjusted to be equivalent to 5 kHz bandwidth. The data were zero-filled to 2K for both dimensions, and a shifted sine-bell apodization function was applied to the FID in both dimensions, prior to FT. Pattern Recognition Analysis of NMR Spectral Data of Plasma. All plasma 1H NMR spectra were phased and baselinecorrected within XWINNMR (version 3.1, Bruker Biospin, Rheinstetten, Germany), and the chemical shifts were referenced either to the internal lactate CH3 resonance at δ 1.33 or to the bis-allylic methylene resonance of fatty acyl groups at δ 2.79 for the diffusion-edited spectra. Since the number of animals per group was relatively small, the spectra were reduced to 108 integrated regions of equal width (0.04 ppm) corresponding to the region δ 0.2-4.5 using AMIX (version 2.5, Bruker), except for data used for the heirarchical blocking, which was integrated into 0.01 ppm width regions over the same shift range. Data reduction in this way can diminish the effects of chemical shift and other physicochemical phenomena that can exert a relatively strong influence in data sets where the number of samples per group is small. Integral regions corresponding to chemical shifts from δ 4.5 to 10.0 were excluded from further analysis to remove intensity variations due to presaturation of the water resonance, and because the information content of the aromatic region was minimal and added no value to the subsequent models. These data were collected into Excel (Microsoft, Excel 97, SR-2) data tables, where each row described the integral descriptors for an NMR spectrum. Each row was normalized to unit area so as to reduce the effects of concentration differences. Data were mean-centered by subtraction of the average value of each variable from the data and analyzed without further scaling, or using Pareto scaling (1/xstandard deviation). The resulting data matrices were analyzed by PCA using SIMCA-P (version 8, UMETRICS AB, Box 7960, SE90719 Umeå, Sweden). Scores plots of the principal components were constructed to visualize any inherent clustering of the samples based on acute or chronic psychological stress, and from the values of the PC loadings (which indicated the importance of each variable to the separation) and the NMR spectral regions, and therefore, biomarkers of the acute and chronic stress conditions were identified. Hematological parameter measurements for each collected blood sample were correlated to NMR spectral data, using the NMR spectral data as the X matrix and the hematological parameters as the response or Y matrix.

research articles


Table 1. Summary of Acute and Chronic Stress-Related Changes in Blood Plasma Hematological Parametersa groups

LYM 103/µL

RBC 106/µL

HGB g/dL

HCT %

PLT 105/µL

MCV fL

COR µg/100 mL

Control 0 h, day 1 (n ) 5) Acute 1 h (n ) 5) Acute 3 h (n ) 5) Acute 6 h (n ) 5) Chronic day 9 (n ) 5) Chronic day 21 (n ) 4) Chronic day 35 (n ) 4) Chronic day 44 (n ) 4)

15.0 ( 1.0 8.9 ( 0.7*** 5.8 ( 0.1*** 4.8 ( 1.0*** 14.2 ( 0.6 13.0 ( 0.9 10.8 ( 1.6* 10.2 ( 0.6**

9.7 ( 0.2 10.1 ( 0.3 9.5 ( 0.2 9.2 ( 0.4 11.3 ( 0.2** 10.9 ( 0.3* 12.1 ( 1.4 10.2 ( 0.4

19.0 ( 0.6 19.5 ( 0.3 18.5 ( 0.3 17.9 ( 0.8 20.6 ( 0.1* 19.3 ( 0.6 19.1 ( 0.8 18.6 ( 0.5

57.1 ( 1.7 59.1 ( 1.4 55.2 ( 0.9 53.5 ( 2.3 65.1 ( 1.4** 60.5 ( 1.9 59.2 ( 2.4 58.9 ( 1.9

9.4 ( 0.3 7.7 ( 1.1 7.9 ( 1.4 6.2 ( 1.4 6.1 ( 1.1* 7.8 ( 1.6 8.1 ( 1.3 6.9 ( 2.0

58.7 ( 0.4 58.7 ( 0.4 58.4 ( 0.4 58.3 ( 0.4 57.6 ( 0.4 55.6 ( 0.4** 50.7 ( 5.1 58.0 ( 1.1

7.8 ( 1.9 47.6 ( 3.0*** 32.1 ( 5.8*** 39.7 ( 4.9*** 20.0 ( 4.8* 19.7 ( 4.3* 14.0 ( 0.8* 8.9 ( 3.7

a Data adapted from ref 24. Abbreviations: LYM, lymphocytes; N&M, neutrophils and monocytes; RBC, red blood cells; HCT, hematocrit; PLT, platelets; MCV, mean corpuscular volume; COR, corticosterone. Data were expressed as mean ( SEM. *P < 0.05; **P < 0.01; ***P < 0.005.

Correlations and predictions were made on the hematological measures based on 1H NMR plasma spectral data using 50% of the samples as a training set to build the models. The remaining 50% of the samples were used as a validation set to assess the predictive robustness of the models. To overcome the fact that hypervariable, high-concentration, but irrelevant, metabolites can dominate the data variance and hence principal components models, a hierarchical blocking approach was also used. Processed and reduced spectral data matrices were sorted into descending order according to average variable intensities. Data were grouped into 15 blocks of 25 variables with integral regions of 0.01 ppm, and each block was normalized to unit area. PCA analysis was performed on each block following Pareto scaling. Any strong outliers detected as part of the conventional PCA were removed prior to hierarchical block analysis, and any further outliers detected during PCA of each block were also removed. These smaller groupings generate loadings plots which are far less complicated and therefore simpler to interpret and partially circumvent the bias placed on multivariate models by high concentration metabolites where the dynamic range of metabolite concentrations is large.

Results Hematological Analyses. The stress-related changes in lymphocyte numbers have been published previously,24 and key results are summarized here for acute and chronic psychological stress states in Table 1. 1 H NMR Spectroscopy of Rat Plasma Following Acute and Chronic Stress. Both acute and chronic stress induced perturbations in standard 1D, CPMG spin-echo, and diffusionedited 1H NMR spectra, indicating that both the low MW plasma metabolites and the lipoprotein profiles were altered in response to stress. Since the most marked changes occurred in the CPMG data, results are illustrated for this data set. Representative CPMG 600 MHz 1H NMR spectra of rat plasma pre-stress, and at 1, 3, and 6 h on day 1 following the start of the restraint stress session corresponding to acute stress (Figure 2), and at 0 h on days 1, 9, 21, 35, and 44 (Figure 3) corresponding to chronic stress were shown. Visual inspection of the spectral data indicated that substantial alterations in the plasma metabolite profile consistent with a perturbation of homeostasis had occurred in both cases. Following acute stress, an increase in the intensity of the resonances from glucose, β-hydroxybutyrate, and acetone were observed, and a decrease in the intensity of alanine, lactate, and VLDL/LDL lipoproteins was also evident following acute stress in all animals (NMR assignments are given in Table 2).

These changes to the intensities were identified following visual comparison with the NMR spectra of plasma samples from the control group at 0 h on day 1. The 1H NMR spectra of rat plasma following a chronic time period of stress showed an increase in the spectral intensity of lactate (day 9), alanine, and choline (on day 44), and a decease in the levels of VLDL/LDL lipoproteins on day 44 that was far more marked than those changes observed following acute stress (NMR assignments are given in Table 3). Principal Components Analysis of 1H NMR Spectra. To systematically address the metabolic responses to acute and chronic stress, PC maps were generated for each type of 1H NMR plasma spectrum for all animals at each stress time point. In all cases, greater than 90% of the total variance in the data was described by the first 3 PCs indicating that stress was responsible for the majority of the metabolic variation in the data. Metabolic Response to Acute Stress. Samples were classed by collection time-point for day 1 of the stress. Initial PCA of CPMG spin-echo data showed a separation of control (hour 0) samples and samples representing acute stress (hours 1-6) in PC 1 (Figure 4). Some clustering of samples based on collection time-points was also observed along the second PC. From examination of PC loadings taken from PC analyses of CPMG, standard, and diffusion-edited 1H NMR spectral data, the separation of groups was attributed mainly to relatively higher levels of plasma lipoproteins, lactate, acetate, and alanine in samples taken at hours 0 and 1, and higher levels of plasma glucose, glutamine, and the ketone bodies, β-hydroxybutyrate and acetone, at 3 and 6 h after initiation of a stressor. The decrease in lipoprotein resonances associated with prolonged stress (3-6 h) was manifested more strongly in the PCA plots derived from diffusion-edited data (not shown). Metabolic Response to Chronic Stress. The 0 h samples from days 1, 9, 21, 35, and 44 were used to assess the chronic effects of restraint stress and were color coded by collection day. Separation of the groups according to sample collection day was evident along PCs 1 and 2 (Figure 5), with samples collected on days 9 and 44 showing the greatest difference compared with day 1 samples. The separation of samples was attributed mainly to relatively higher levels of plasma lactate in day 9 samples, lower levels of VLDL/LDL lipoproteins in day 44 samples, and higher levels of plasma choline and glycerol in day 44 samples. Acute and Chronic Stress. The effects of chronic stress on the acute stress response, that is, the metabolic changes occurring from 0 to 6 h on days 9-44, were investigated over days 9-35, and again following the 9-day break from stress, Journal of Proteome Research • Vol. 6, No. 6, 2007 2083

research articles

Teague et al.

Figure 2. The 600 MHz 1H NMR CPMG spin-echo spectra (δ 4.3-0.5) of plasma samples collected from male Sprague-Dawley rats at (A) pre-stress, (B) 1 h, (C) 3 h, and (D) 6 h following onset of restraint stress. Abbreviations: OAC, O-acetylglycoproteins; NAC, N-acetylglycoproteins; LDL, low-density lipoprotein; VLDL, very low-density lipoprotein.

on day 44. PCA models were built for each of these stress days and classed according to collection time points, 0-6 h post onset of stress on each collection day. PCA showed some separation of classes for each collection day (data not shown), and this separation was investigated further to optimize the classification using Projection to Latent Structures Discriminant Analysis (PLS-DA). PLS-DA of the acute stress response on days 9, 21, 35, and 44 (Figure 6) indicated that the degree of separation of the samples according to the collection time-point was greater at the earlier collection days, and that by day 44, there was little separation of the samples due to acute stress. Separation of collection time-points on day 9 (Figure 6A) showed that the acute stress response was apparent at 3 h post-stress initiation, compared with day 1, in which the acute stress response was evident by 1 h (Figure 4). Following examination of the corresponding PLS loading weights plots (data not shown), this separation was attributed to a relatively higher plasma glucose level at 3 h, and higher plasma ketone bodies (β-hydroxybutyrate and acetone) at 6 h post-stress onset. Levels of plasma lactate and lipoproteins were 2084


relatively lower in samples collected at hours 3 and 6. On day 21, separation of the samples was observed along the first and second components of the PLS-DA scores plot (Figure 6B), and this was attributed to relatively higher plasma levels of glucose, ketone bodies, and glutamine at hours 3 and 6, and higher levels of plasma lipoproteins at hour 0, and to a lesser extent, hour 1. By day 35 the samples were clustering much more closely together, and the time-related effects of acute stress were less easy to interpret. Separation of groups (observed in the first 2 components, Figure 6C) was attributed to a relatively higher level of plasma lipoprotein groups at hours 0 and 1, higher levels of lactate at hour 3, and increased levels of glucose and glutamine by 6 h. At day 44, animals had spent 9 days in the absence of any stress. Figure 6D shows the PLS-DA scores map for samples collected on day 44. A separation of hour 0 and 1 samples, from 3 and 6 h samples, was apparent along the first component and was attributed to higher levels of plasma lipoproteins in hour 0 and 1 samples, and higher relative levels of plasma glucose, glutamine, β-hydroxybutyrate, and acetate in samples collected at 3 and 6 h.


research articles

Figure 3. The 600 MHz 1H NMR CPMG spin-echo spectra (δ 4.3-0.5) of plasma samples collected from male Sprague-Dawley rats at time ) 0 h, (A) pre-stress, (B) 9 days, (C) 21 days, (D) 35 days, and (E) 44 days after onset of the restraint stress. Abbreviations: OAC, O-acetylglycoproteins; NAC, N-acetylglycoproteins; LDL, low-density lipoprotein; VLDL, very low-density lipoprotein.

Hierarchical Blocking Analysis. Hierarchical blocking analysis applied to the CPMG spin-echo spectral data allowed more detailed interpretation of the spectral regions responsible for classifying stress status. Acute Stress: The hierarchical blocking analysis results are summarized in Table 4 and indicate which metabolites were identified at each collection time-point, for each block (block 1 containing the largest NMR peaks). These results confirmed those using the original data set and, in addition, identified lysine as being relatively higher in control animals compared with acutely stressed rats, and increased citrate concentrations in samples collected at 1 h following the onset of stress. Stress-induced separation was less convincing as the intensity of the variables became lower, with the last 5 data blocks (blocks 11-15) containing either noise or unresolved, and therefore latent, signals. Chronic Stress: PCA performed on mean-centered and hierarchically blocked data for hour 0 samples collected on days 1-44 (Table 5) was also consistent with the results from the unblocked data but, in addition, highlighted a relative increase

in plasma alanine and a decrease in acetate with progressing chronic stress. Projection to Latent Structures Analysis of 1H NMR Spectra and Hematological Data. To determine any relationship between changes in hematological parameters and the 1H NMR spectroscopic data, PLS models were built with data from samples collected from all animals at all time-points. All measured hematological parameters were incorporated into the PLS model. Pareto and unit-variance scaling were applied to the X- (NMR) and Y- (hematological parameters) data, respectively. To determine any correlations between the NMR data and the hematological measures, samples were classed purely on the basis of their hematological data, and not on collection time-points. The weights map for this PLS model (data not shown) indicated that the corticosterone level was associated with an increase in plasma glucose and glycerol, and this was accompanied by a separation along the first component of the samples obtained at 0 and 1 hours and samples obtained 3 and 6 hours after stress induction. Plasma lipoproteins were Journal of Proteome Research • Vol. 6, No. 6, 2007 2085

research articles

Teague et al.

Table 2. Summary of Acute Stress-Related Relative Metabolite Changesa average fold change from control at collection time points: metabolite

Acetate Acetone Alanine Citrate** Glucose

Glutamine Isoleucine

Lactate Leucine Lipoprotein*

Lysine**

N-acetylglycoproteins* O-acetylglycoproteins* Valine

β-hydroxybutyrate

1

δ H

1h

1.93 (s) 2.23 (s) 1.48 (d) 3.79 (q) 2.54(AB) 2.61(AB) 3.24 (dd) 3.40 (dd) 3.41 (dd) 3.47 (ddd) 3.49 (t) 3.53 (t) 3.71 (t) 3.72 (dd) 3.74 (m) 3.84 (m) 3.84 (ddd) 3.90 (dd) 4.64 (d) 5.23 (d) 2.14 (m) 2.46 (m) 3.77 (t) 0.94 (t) 1.01 (d) 1.26 (m) 1.48 (m) 1.98 (m) 3.68 (d) 1.33 (d) 4.12 (q) 0.96 (d) 1.71 (m) 3.73 (t) 0.86 (t) 1.29 (m) 1.57 (m) 1.72 (m) 2.00 (m) 2.25 (m) 2.77 (m) 5.30 (m) 1.48 (m) 1.73 (q) 1.91 (m) 3.03 (m) 3.76 (t) 2.04 (s) 2.14 (s) 0.99 (d) 1.04 (d) 2.28 (m) 3.62 (d) 1.20 (d) 2.31 (dd) 2.41 (dd) 4.16 (m)

0.4 ( 0.05 1.3 ( 0.3 0.6 ( 0.05

moiety

CH3 CH3 CH3 CH 1/ CH 2 2 1/ CH 2 2 β-C2H β-C4H r-C2H β-C5H β-C3H r-C2H r-CH3 β-C6H′ r-C6H′ r-C6H r-C5H β-CH1 R-CH1 β-CH2 γ-CH2 R-CH CH3 β-CH3 R-CH2(ii) R-CH2(i) β-CH R-CH CH3 CH CH3 CH2 + γ CH R-CH CH3 CH2 CH2 CH2CO CH2 CH2CHd CH2 CdC CH2 CO CdCCH2 CdC dCH γCH2 δCH2 βCH2 CH2 RCH NHCOCH3 OHCOCH3 CH3 CH3 β-CH R-CH CH3 CH2(ii) CH2(i) CH

3h

0.5 ( 0.1 1.8 ( 0.3 0.7 ( 0.05

6h

0.5 ( 0.1 1.2 ( 0.2 0.8 ( 0.1

1.2 ( 0.25

1.15 ( 0.2

1.1 ( 0.15

1.5 ( 0.1

1.4 ( 0.1

1.4 ( 0.1

0.8 ( 0.05

1.3 ( 0.1

1.2 ( 0.2

0.8 ( 0.05

1.0 ( 0.1

0.8 ( 0.1

0.7 ( 0.05

0.6 ( 0.05

0.6 ( 0.05

0.7 ( 0.05

0.9 ( 0.05

0.8 ( 0.1

1.0 ( 0.05

0.9 ( 0.05

0.8 ( 0.05

0.5 ( 0.05

0.8 ( 0.05

0.7 ( 0.05

0.9 ( 0.05 0.8 ( 0.05

0.9 ( 0.05 1.1 ( 0.05

0.8 ( 0.1 1.1 ( 0.1

0.6 ( 0.05

1.0 ( 0.1

0.7 ( 0.1

1.1 ( 0.1

2.0 ( 0.1

1.3 ( 0.1

a Data taken from integration of normalized spectral regions with 0.01 ppm width ( SEM. * ) Integration taken from diffusion edited 1H NMR spectra. ** ) Integration taken from data normalized to total intensity of appropriate block from logical blocking. Bold italic type indicates which spectral regions were used for the integration.

positively associated with red blood cell numbers, hematocrit, and hemoglobin levels. Higher lymphocyte numbers were related to higher plasma lactate levels, both of which were higher overall in hour 0 samples than in samples collected at hours 1, 3, and 6, and also on day 9 compared with days 1, 21, 35, and 44. Predictions were made for each of the hematological parameters based on the 1H NMR spectral data for the data 2086


set as a whole, and separate PLS models were created for each hematological variable. For each model, the data were split into a training and test set, each comprising half of the data. Scatter graphs are shown in Figure 7 to illustrate the correlation between observed clinical variables and those predicted from the PLS model based on 1H NMR spectra. Pearson coefficients indicated that a relatively weak correlation existed between each of the hematological measures and the NMR

research articles

Metabonomic of Acute and Chronic Psychological Stress Table 3. Summary of Chronic Stress-Related Relative Metabolite Changesa

fold change from control at 0 h on: metabolite

Alanine** Acetate** Choline Glucose

Glycerol Isoleucine

Lactate Leucine Lipoprotein*

N-acetylglycoproteins* Pyruvate** Valine

moiety

CH3 CH CH3 CH3 CH2NH CH2OH β-C2H β-C4H r-C2H β-C5H β-C3H r-C2H r-CH3 β-C6H′ r-C6H′ r-C6H r-C5H β-CH1 R-CH1 CH2 CH2 CH CH3 β-CH3 R-CH2(ii) R-CH2(i) β-CH R-CH CH3 CH CH3 CH2 + γ CH R-CH CH3 CH2 CH2 CH2CO CH2 CH2CHd CH2 CdC CH2 CO CdCCH2 CdC dCH NHCOCH3 CH3 CH3 CH3 β-CH R-CH

1

δ H

1.48 (d) 3.79 (q) 1.93 (s) 3.21 (s) 3.52 (m) 4.07 (m) 3.24 (dd) 3.40 (dd) 3.41 (dd) 3.47 (ddd) 3.49 (t) 3.53 (t) 3.71 (t) 3.72 (dd) 3.74 (m) 3.84 (m) 3.84 (ddd) 3.90 (dd) 4.64 (d) 5.23 (d) 3.56 (dd) 3.65 (dd) 3.79 (dd) 0.94 (t) 1.01 (d) 1.26 (m) 1.48 (m) 1.98 (m) 3.68 (d) 1.33 (d) 4.12 (q) 0.96 (d) 1.71 (m) 3.73 (t) 0.86 (t) 1.29 (m) 1.57 (m) 1.72 (m) 2.00 (m) 2.25 (m) 2.77 (m) 5.30 (m) 2.04 (s) 2.38 (s) 0.99 (d) 1.04 (d) 2.28 (m) 3.62 (d)

day 9

day 21

day 35

day 44

1.3 ( 0.05

1.2 ( 0.1

1.2 ( 0.1

1.3 ( 0.05

0.55 ( 0.15

0.6 ( 0.15

0.6 ( 0.15

0.8 ( 0.2

1.1 ( 0.05

1.1 ( 0.1

0.9 ( 0.05

1.7 ( 0.05

0.7 ( 0.05

1.0 ( 0.05

1.0 ( 0.05

1.1 ( 0.05

1.1 ( 0.2

1.0 ( 0.1

1.0 ( 0.1

1.4 ( 0.1

1.1 ( 0.05

1.0 ( 0.05

1.0 ( 0.05

1.1 ( 0.05

1.3 ( 0.1

0.9 ( 0.05

0.8 ( 0.05

0.9 ( 0.1

1.0 ( 0.05

1.0 ( 0.05

1.0 ( 0.05

1.1 ( 0.05

1.0 ( 0.05

1.0 ( 0.05

1.0 ( 0.1

0.8 ( 0.05

1.0 ( 0.1 1.2 ( 0.1

1.0 ( 0.05 0.9 ( 0.1

1.0 ( 0.05 0.9 ( 0.1

0.9 ( 0.05 0.75 ( 0.1

1.1 ( 0.05

1.0 ( 0.05

1.0 ( 0.05

1.2 ( 0.05

a Data taken from integration of normalized spectral regions with 0.01 ppm width ( SEM. * ) Integration taken from diffusion edited 1H NMR spectra. ** ) Integration taken from data normalized to total intensity of appropriate block from logical blocking. Bold italic type indicates which spectral regions were used for the integration.

spectral data, with the exception of mean corpuscular volume and platelet count, for which no significant correlation was observed. The best correlations between clinical and NMR data existed for plasma lymphocyte numbers (r ) 0.74) and corticosterone concentration (r ) 0.62). Pearson coefficients and predictions for each hematological measurement are summarized in Table 6. Accurate prediction of red blood cells, hemoglobin, and hematocrit values based on 1H NMR plasma spectra were possible with 86, 93, and 84% of test-set samples, respectively, correctly predicted to within 10% of the measured values. Semiquantitative predictions were possible for lymphocytes and corticosterone.

Discussion Although most metabonomic studies to date have concentrated on the profiling of drug toxicity, the importance of

understanding physiological variation relating to diet and lifestyle must not be underestimated, and this study has shown that the time-dependent variability in plasma following psychological stress can be readily visualized using 1H NMR spectroscopy-based metabonomic approaches. The high degree of overlap from the various metabolite signals in 1H NMR spectra of biofluids can make comparisons difficult, particularly in biofluids such as blood plasma where homeostatic regulation only allows for relatively subtle changes in metabolite concentrations. However, the use of chemometric techniques along with two-dimensional NMR experiments allows the identification and characterization of metabolites that correlate with physiological (or pathological) variation. In this study, chemometric analysis of spectral data from plasma samples grouped according to collection time-point, allowed the identification of key discriminatory metabolites Journal of Proteome Research • Vol. 6, No. 6, 2007 2087

research articles

Figure 4. PCA scores map for data derived from the CPMG spinecho 1H NMR spectra of plasma obtained from acutely stressed animals on day 1 of the restraint stress, and separated based on the collection time-point.

Figure 5. PCA scores map for data derived from the CPMG spinecho 1H NMR spectra of plasma obtained from chronically stressed animals at hour 0 of the study, and separated based on the length of the chronic stress.

relating to both acute and chronic stress states. In addition, it was possible to show how the acute stress response altered with repeated stress over a chronic time-period. Metabolic Changes in Plasma after Acute and Chronic Stress. This work has shown a number of substantial metabolic changes to the plasma of rats following a period of stress. Here, acute stress was shown to significantly increase the levels of circulating corticosterone. A typical stress response involves an increased hypothalamic-pituitary-adrenal (HPA) axis activity, which involves the release of corticotrophin-releasing hormone (CRH) from the hypothalamus. CRH acts on the adenohypophysis, causing release of corticotrophin (ACTH) which then 2088


Teague et al.

acts on the adrenal cortex inducing increased secretion of corticosteroid hormones.14 The chemometric analysis of samples collected on day 1 showed relative increases in plasma levels of glucose, ketone bodies (acetone and β-hydroxybutyrate), glutamine (by 3 h) and citrate, and decreases in lactate, lipoproteins, acetate, alanine, isoleucine, leucine, valine, and lysine. The increased glycemia is likely to be at least partially the result of hepatic glycogen mobilization induced by catecholamines, an induced gluconeogenesis via glucocorticoid action, or a combination of both processes. The observed increase in plasma ketone bodies, oxidation products of long chain fatty acids, is well-documented following acute stress and starvation.5,31 Catecholamines stimulate the hormone-sensitive lipase in white adipose tissue, which leads to triglyceride mobilization, and its oxidation in the liver leads to the observed increase of ketone bodies in the plasma. In this study, a decrease in plasma lipoprotein levels following acute stress was observed, in agreement with published data.5 Levels of plasma glutamine showed an initial decline followed by an increase at 3 h following the onset of the stress. As glutamine is the most abundant extracellular amino acid in vivo, and can act as a substrate for gluconeogenesis when blood glucose is low, the relative decrease at 1 h is not surprising. The relative changes in plasma glutamine following stress indicates an increased demand for this amino acid during acute stress and therefore its increased synthesis. The increase in plasma levels of citrate observed following acute stress also suggests the incorporation of glucogenic precursors into the TCA cycle in order to increase blood glucose. Lactate and alanine form the major raw materials for gluconeogenesis and are produced by active skeletal muscle and erythrocytes; therefore, the relative decrease in these metabolites indicates a stimulation of gluconeogenic pathways following acute stress. Significant decreases in the relative amounts of the branched chain amino acids (valine, leucine, and isoleucine) were observed following acute stress, and stress-related decreases in plasma amino acid concentrations have been previously reported. Indeed, a number of amino acids including both leucine and isoleucine have been shown to decrease in concentration following foot shock in rats,32 and plasma levels of valine, arginine, and tryptophan were also decreased in rats following a period of restraint stress.33 The stress-related decrease in these amino acids may indicate their uptake into the TCA cycle in order to increase blood glucose or to produce ketone bodies via acetyl-CoA. The decreases in plasma lysine seen here following acute stress have been previously observed following transportation stress in pigs,34 and prolonged dietary lysine inadequacy has been shown to increase stress-induced anxiety in experimental animals.35 The PCA analysis of samples depicting the chronic stress state (day 1 compared with days 9-35), and the metabolic profile following a 9 day break from the restraint stress, on day 44, showed a time-dependent separation. By day 9 of chronic stress, increases in plasma lactate, alanine, and pyruvate were apparent, along with relatively decreased plasma glucose and acetate. As the experiment proceeded, some of these observed metabolite changes appeared to move back to control levels, or change direction. By day 44, marked increases in plasma choline and glycerol, and a marked decrease in plasma lipoproteins, were observed. Adaptation to Chronic Stress. The stressor used in the present study meant that animals experienced a degree of


research articles

Figure 6. PLS-DA scores maps derived from CPMG spin-echo 1H NMR spectral data, showing the separation of samples based on collection time-point (samples collected 0 - 6 h post-stress initiation), on days (A) 9, (B) 21, (C) 35, and (D) 44.

physical stress.24,36 In actively contracting skeletal muscle, which is most likely to occur in early acute stress, the rate of glycolytic pyruvate production far exceeds that of uptake requirements for the TCA cycle, and hence, most of the pyruvate formed under these conditions is reduced to lactate, which flows via the blood to the liver, where it is converted into glucose. The increase in alanine under chronic stress, even where lowered lactate is observed, is thus likely to be the result of protein catabolism stimulated by corticosterone, with a possible contribution from generally reduced levels of gluconeogenesis following the habituation to chronic stress. In general, as the animals adapted to the stressor, corticosterone secretion diminished, and the glucocorticoid-related antagonism of the insulin response appeared to diminish, allowing uptake of glucose, with an observed decrease of the abnormally high glucose in the blood. As the duration of the period over which the animals were chronically stressed increased, time-related separation of the acute stress samples became less marked (Figure 6). Although the animals used in the study were all adults, over a 44 day duration study, some changes relating to age may have been apparent. Since a weight-matched control group of animals, which had not been

subjected to daily stress, was not included in this study, this effect cannot be ruled out. However, since many of the metabolic parameters showed diminishing perturbation after the first 9 days, suggesting habituation, the observed metabolic changes are unlikely to be related to age. Recovery from Chronic Stress. It should be recalled that the individual perception of stress is important in determining the magnitude and direction of metabolic response, along with the ability to habituate to repeated stress exposures (e.g., for humans, the experience of repeated public speaking is not as stressful as on the first occasion). In perceptual terms, it may even be hypothesized that the disappearance of a “familiar stressor” might well serve as a “meta-stressor” for some subsequent time period. Following the 9 day break from the stressor, intended to allow the animals to begin to recover from the effects of the allostatic load, the stress protocol and sampling was repeated. Levels of plasma choline and glycerol were increased, along with a significant decrease in plasma levels of lipoproteins. Under conditions of stress, lipolysis is known to increase, despite hyperinsulinaemia, via the actions of glucocorticoids antagonizing the antilipolytic action of insulin. Corticosterone Journal of Proteome Research • Vol. 6, No. 6, 2007 2089

research articles

Teague et al.

Table 4. Summary of Metabolite Changes Identified by Logical Blocking Analysis for Samples Collected on Day 1

Table 5. Summary of Metabolite Changes Identified by Logical Blocking Analysis for Samples Collected at Hour 0

Table 6. Summary of Correlations of Hematological Variables with 1H NMR Spectral Data and Predicted Hematological Variables for Samples in the Test Seta clinical variable

Pearson coefficient (r)* training set n ) 43

LYM RBC HGB HCT COR

0.74 0.50 0.53 0.51 0.62

% predicted within (10% of observed value test set n ) 43 33 86 93 84 19

a *Taken from training set. Abbreviations: LYM, lymphocytes; RBC, red blood cells; HGB, hemoglobin; HCT, hematocrit; COR, corticosterone.

is a lipolysis-stimulating hormone, and inhibits glucose transport, leading to diminished fatty acid re-esterification in adipose tissue, for which glucose is essential. It is less clearly understood why increased lipolysis should occur predomi2090


nantly on day 44, following a break from the stress, whereas no significant decrease in lipoproteins were observed following chronic stress. This effect may simply be a result of transitional high-turnover lipoprotein metabolism, which has not yet achieved an adjusted homeostatic steady-state for input component metabolites. The effects of chronic stress on plasma lipoprotein triglyceride profiles have been widely studied, with contradictory results ranging from no observed variation,37 to decreased,5 or even increased levels.38 Also on day 44, plasma choline levels were significantly increased. This increase in plasma choline may simply reflect an overall increase in plasma membrane turnover and/or increased specific phosphatidylcholine breakdown. In other studies, altered blood choline homeostasis has been reported as a function of general aging, with plasma choline content significantly elevated in elderly controls and Alzheimer patients when compared to the young.39 It is also well-documented that chronic stress can have a negative impact on cardiovascular disease,40,41 and though the mechanisms by which this may occur are less-clearly understood, they are likely to include stress-related induced changes in generalized lipoprotein- and phospholipid-related metabolism.5 It has been shown that exposure to stress in mice decreases the blood-brain barrier permeability to systemically administered doses of pyridostigmine bromide, a reversible inhibitor of acetylcholinesterase,42 but that blood-brain barrier choline uptake changes minimally with aging in the rat.43 This published data supports the hypothesis that the elevated plasma choline levels observed at day 44 may be indicative of a stress-induced decreased permeability of the blood-brain barrier. Acute Stress Superimposed on Chronic Stress. Changes in the metabolic profile of the plasma where acute stress followed chronic stress have been shown, and further PCA analysis was carried out on samples exhibiting acute stress (at 0-6 h) with progressing chronic stress (i.e., from day 9 to 35). Separation of the samples according to collection time-point (0-6 h following onset) became less obvious with advancing chronic stress, an indication that the response to chronic stress was becoming less pronounced, and that animals were adapting to the stress. This adaptation to the stress was validated by the observation that plasma corticosterone concentrations showed a smaller differential with acute stress as chronic stress progressed. It is significant that the collection time-point at which the most acute stress response occurred was apparent, that is, when samples mapped away from control samples. This was shifted from 1 h on day 1, to 3 h by day 9, and this effect was mirrored in the plasma corticosterone concentrations. The acute stress response on day 44 showed a greater degree of variation than on all other collection days, highlighting the interindividual variation in recovery time from the effects of chronic stress. PLS Regression Models. PLS regression models were built to correlate the hematological data with significantly related regions of the 1H NMR spectra, with the aim of improving the understanding of any hematological-metabolite relationships. The PLS model incorporating all samples was validated by predicting the values of hematological measures from a second PLS model using the same data but divided into a training set and a validation set. Analysis of the PLS model indicated that lymphocyte numbers were correlated with a higher level of plasma lactate and lipoproteins, and lower levels of plasma glucose. Semiquantitative prediction of lymphocyte


research articles

Figure 7. Scatter graphs showing the correlation between observed hematological variables and those predicted from the PLS model. The quoted r values are the Pearson product moment correlation coefficients, describing the extent of correlation between observed hematological parameters and NMR data for training-set samples.

numbers was possible based on the 1H NMR spectral data. Counts of red blood cells, hemoglobin, hematocrit, and platelet levels all showed a positive correlation with plasma lipoproteins and were able to be generally predicted from the 1 H NMR spectral data taken from all samples in the investigation. There is evidence that hemoconcentration may be one mechanism by which psychological stress can cause elevations in serum lipids. This hypothesis states that a rise in serum lipid and lipoprotein levels reflects a filtration of fluid out of the intravascular space rather than an increase in the synthesis of these compounds per se.44 This may be a simple result of stress-

induced diuresis, perhaps mediated by induced blood-pressure elevation. Plasma corticosterone was positively associated with plasma glucose and glycerol, and negatively correlated with levels of plasma lactate, and a semiquantitative prediction of corticosterone levels was possible based on the 1H NMR spectral data. Although the animal numbers employed in this study were small at n ) 5, the time dependency makes the analysis stronger, and the coherent responses of the animals to the stress, as investigated using PLS modeling, gives a greater confidence to the results. Journal of Proteome Research • Vol. 6, No. 6, 2007 2091

research articles Relationship to Other Effects of Acute and Chronic Stress. Effects of acute and chronic stress differ in a number of other ways besides those described in the present study. In the immune system, whereas acute stress increases delayed-type hypersensitivity, chronic stress has the opposite effect to reduce delayed-type hypersensitivity responses.45 In the brain, acute stress is known to enhance memory processes that depend on the hippocampus, whereas chronic stress causes remodeling of neural connections in the hippocampus, prefrontal cortex, and amygdala and leads to impaired memory and enhanced fear, anxiety, and aggression.46 Chronic Stress and Aging. It is known that stress hormones play a major role in the aging process2 and that allostatic load may be an important factor in differences in the rate of aging.47 The variation of allostatic states and allostatic load as a result of differences in individual lifestyle, major life events, and socioeconomic status is a highly individual matter, also dependent on genotype, early experiences, and the types of experience through life.48 Thus, daily life experiences and, significantly, the experience thereof, will differentiate metabolic time-progression. Chronic stress can have long-term effects on susceptibility to disease, and for example, the sustained elevation of glucocorticoids causes an increase in insulin secretion to counteract induced insulin insensitivity. This long-term elevation of insulin favors hyperlipidemia and can accelerate atherogenesis, and can precipitate or exacerbate diabetes.7,49 It has been shown in non-human primates that the incidence of atherosclerosis is increased among the dominant males of unstable social hierarchies, and in socially subordinate females.50 In humans a lack of control in employment increases the risk of coronary heart disease, and job strain results in an increase in ambulatory blood pressure.51,52 The sustained stress of being a caregiver for autistic children accelerates the shortening of telomeres in circulating white blood cells, a biological index of aging.53 A lifetime of greater emotionality in novel environments and greater stress reactivity has been shown to result in earlier mortality in rats.54 Early life experiences, including prenatal stress and the quality and quantity of maternal care, are likely contributors to the stress reactivity,55,56 and greater stress hormone reactivity has been associated with accelerated decline of cognitive processes that are dependent on the hippocampus, a stress-vulnerable region of the brain.57-60 Thus, allostatic load depends on early life events as well as on the quality and quantity of real-time stressors throughout the lifespan. In conclusion, allostatic load is important to predict later vulnerability to disease, with the hope of encouraging early interventions to delay or prevent diseases later in life; thus, the development of methods to measure allostatic load are ongoing. Here it has been shown that NMR spectroscopy-based metabonomics demonstrates a novel approach to studying the extended metabolic effects of allostatic load, and may help in determining some of the many ‘pre-disease’ pathways that increase in prevalence in association with age and with stress. Abbreviations: CRF, corticotrophin-releasing factor; FT, Fourier transformation; HPA, hypothalamic-pituitary-adrenocortical; LDL, low-density lipoprotein; NMR, nuclear magnetic resonance; PCA, principal components analysis; PLS, projection to latent structures; PLS-DA, projection to latent structures discriminant analysis; SD, Sprague-Dawley; SNS, sympathetic nervous system; TCA, tricarboxylic acid cycle; VLDL, very lowdensity lipoprotein. 2092


Teague et al.

Acknowledgment. We thank Unilever for financial support (C. Teague and B. Beckwith-Hall). The experiments and hematological and endocrine analyses described here were supported by the John D. & Catherine T. MacArthur Foundation (F.S.D. and B.S.M.). R. H. Barton thanks the Wellcome Trust for funding under the BAIR project. References (1) Dhabhar, F. S.; McEwen, B. S.; Spencer, R. L. Adaptation to prolonged or repeated stress-comparison between rat strains showing intrinsic differences in reactivity to acute stress. Neuroendocrinology 1997, 65, 360-368. (2) Sapolsky, R. M.; Krey, L. C.; McEwen, B. S. The neuroendocrinology of stress and aging: the glucocorticoid cascade hypothesis. Endocr. Rev. 1986, 7, 284-301. (3) McEwen, B. S.; Stellar, E. Stress and the individual. Mechanisms leading to disease. Arch. Int. Med. 1993, 153, 2093-2101. (4) Kumari, M.; Grahame-Clarke, C.; Shanks, N.; Marmot, M.; Lightman, S.; Vallance, P. Chronic stress accelerates atherosclerosis in the apolipoprotein E deficient mouse. Stress 2003, 6, 297299. (5) Ricart-Jane´, D.; Rodriguez-Sureda, V.; Benavides, A.; PeinadoOnsurbe, J.; Lopez-Tejero, M. D.; Llobera, M. Immobilization stress alters intermediate metabolism and circulating lipoproteins in the rat. Metabolism 2002, 51, 925-931. (6) VanItallie, T. B. Stress: a risk factor for serious illness. Metabolism 2002, 51 (6 Suppl. 1), 40-45. (7) Brindley, D. N.; Rolland, Y. Possible connections between stress, diabetes, obesity, hypertension and altered lipoprotein metabolism that may result in atherosclerosis. Clin. Sci. 1989, 77, 453461. (8) Keller-Wood, M. E.; Dallman, M. F. Corticosteroid inhibition of ACTH secretion. Endocr. Rev. 1984, 5, 1-24. (9) Bray, J. J.; Cragg, P. A.; Macknight, A. D. C.; Mills, R. G. The Adrenal Glands. In Lecture Notes on Human Physiology, 4th ed.; Blackwell Science: Oxford, 1999; pp 255-260. (10) Sterling, P.; Eyer, J. Allostasis: A New Paradigm To Explain Arousal Pathology.In Handbook of Life Stress, Cognition and Health; John Wiley & Sons: New York, 1988; pp 629-649. (11) Wright, R. J.; Finn, P.; Contreras, J. P.; Cohen, S.; Wright, R. O.; Staudenmayer, J.; Wand, M.; Perkins, D.; Weiss, S. T.; Gold, D. R. Chronic caregiver stress and IgE expression, allergen-induced proliferation, and cytokine profiles in a birth cohort predisposed to atopy. J. Allergy Clin. Immunol. 2004, 113, 1051-1057. (12) Matthews, K. A.; Katholi, C. R.; McCreath, H.; Whooley, M. A.; Williams, D. R.; Zhu, S.; Markovitz, J. H. Blood pressure reactivity to psychological stress predicts hypertension in the CARDIA study. Circulation 2004, 110, 74-78. (13) Uedo, N.; Ishikawa, H.; Morimoto, K.; Ishihara, R.; Narahara, H.; Akedo, I.; Ioka, T.; Kaji, I.; Fukuda, S. Reduction in salivary cortisol level by music therapy during colonoscopic examination. Hepatogastroenterology 2004, 51, 451-453. (14) Noble, R. E. Diagnosis of stress. Metabolism 2002, 51 (6 Suppl. 1), 37-39. (15) Nicholson, J. K.; Lindon, J. C.; Holmes, E. ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 1999, 29, 11811189. (16) Nicholson, J. K.; Wilson, I. D. High resolution nuclear magnetic resonance spectroscopy of biological samples as an aid to drug development. Prog. Drug Res. 1987, 31, 427-479. (17) Lindon, J. C.; Nicholson, J. K.; Everett, J. R. NMR spectroscopy of biofluids. Annu. Rep. NMR Spectrosc. 1999, 38, 1-88. (18) Holmes, E.; Nicholls, A. W.; Lindon, J. C.; Connor, S. C.; Connelly, J. C.; Haselden, J. N.; Damment, S. J.; Spraul, M.; Neidig, P.; Nicholson, J. K. Chemometric models for toxicity classification based on NMR spectra of biofluids. Chem. Res. Toxicol. 2000, 13, 471-478. (19) Lindon, J. C.; Nicholson, J. K.; Holmes, E; Antti, H.; Bollard, M. E.; Keun, H; et. al. Contemporary issues in toxicology. The role of metabonomics in toxicology and its evaluation by the COMET project. Toxicol. Appl. Pharmacol. 2003, 187, 137-46. (20) Sanins, S. M.; Nicholson, J. K.; Elcombe, C.; Timbrell, J. A. Hepatotoxin-induced hypertaurinuria: a proton NMR study. Arch. Toxicol. 1990, 64, 407-411.

research articles

Metabonomic of Acute and Chronic Psychological Stress (21) Bollard, M. E.; Stanley, E. G.; Lindon, J. C.; Nicholson, J. K.; Holmes, E. NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition. NMR Biomed. 2005, 18, 143-162. (22) Teague, C. R. NMR spectroscopic and chemometric approaches to investigate metabolic variation in biofluids. Ph.D. Thesis, London University, 2004, Chapter 5, pp 194-275. (23) Wold, S.; Kettaneh, N.; Fride´n, H. Modelling and diagnostics of batch processes and analogous kinetic experiments. Chemom. Intell. Lab. Syst. 1998, 44, 331-340. (24) Dhabhar, F. S.; McEwen, B. S. Acute stress enhances while chronic stress suppresses cell-mediated immunity in vivo: a potential role for leukocyte trafficking. Brain, Behav., Immun. 1997, 11, 286-306. (25) Dhabhar, F. S.; Miller, A. H.; McEwen, B. S.; Spencer, R. L. Effects of stress on immune cell distribution. Dynamics and hormonal mechanisms. J. Immunol. 1995, 154, 5511-5527. (26) Jeener, J.; Meier, B. H.; Bachmann, P.; Ernst, R. R. Investigation of exchange processes by two-dimensional NMR spectroscopy. J. Chem. Phys. 1979, 71, 4546-4553. (27) Carr, H. Y.; Purcell, E. M. Effects of diffusion on free precession in nuclear magnetic resonance experiments. Phys. Rev. 1954, 94, 630-638. (28) Meiboom, S.; Gill, D. Modified spin-echo method for measuring nuclear relaxation times. Rev. Sci. Instrum. 1958, 29, 688-691. (29) Wu, D.; Chen, A.; Johnson, C. S., Jr. An improved diffusionordered spectroscopy experiment incorporating bipolar-gradient pulses. J. Magn. Reson., Ser. A 1995, 115, 260-264. (30) Shaka, A. J.; Lee, C. J.; Pines, A. Iterative schemes for bilinear operations; applications to spin decoupling. J. Magn. Reson. 1988, 77, 274-293. (31) Stryer, L. Biosynthesis of building blocks. In Biochemistry; W. H. Freeman and Company: New York, 1975; pp 776-777. (32) Malyszko, J.; Urano, T.; Yan, D.; Serizawa, K.; Kozima, Y.; Takada, Y.; Takada, A. Foot shock-induced changes in blood and brain serotonin and related substances in rats. Jpn. J. Physiol. 1994, 44, 35-47. (33) Milakofsky, L.; Hare, T. A.; Miller, J. M.; Vogel, W. H. Rat plasma levels of amino acids and related compounds during stress. Life Sci. 1985, 36, 753-761. (34) Srinongkote, S.; Smriga, M.; Nakagawa, K.; Toride, Y. A diet fortified with L-lysine and L-arginine reduces plasma cortisol and blocks anxiogenic response to transportation in pigs. Nutr. Neurosci. 2003, 6, 283-289. (35) Smriga, M.; Kameishi, M.; Uneyama, H.; Torii, K. Dietary L-lysine deficiency increases stress-induced anxiety and fecal excretion in rats. J. Nutr. 2002, 132, 3744-3746. (36) Glavin, G. B.; Pare, W. P.; Sandbak, T.; Bakke, H. K.; Murison, R. Restraint stress in biomedical research: an update. Neurosci. Biobehav. Rev. 1994, 18, 223-249. (37) Rebuffe-Scrive, M.; Walsh, U. A.; McEwen, B.; Rodin, J. Effect of chronic stress and exogenous glucocorticoids on regional fat distribution and metabolism. Physiol. Behav. 1992, 52, 583-590. (38) Hershock, D.; Vogel, W. H. The effects of immobilization stress on serum triglycerides, nonesterified fatty acids, and total cholesterol in male rats after dietary modifications. Life Sci. 1989, 45, 157-165. (39) Sherman, K. A.; Gibson, G. E.; Blass, J. P. Human red blood cell choline uptake with age and Alzheimer’s disease. Neurobiol. Aging 1986, 7, 205-209. (40) Greenwood, D. C.; Muir, K. R.; Packham, C. J.; Madeley, R. J. Coronary heart disease: a review of the role of psychosocial stress and social support. J. Public Health Med. 1996, 18, 221-231. (41) Strike, P. C.; Steptoe, A. Psychosocial factors in the development of coronary artery disease. Prog. Cardiovasc. Dis. 2004, 46, 337347. (42) Sinton, C. M.; Fitch, T. E.; Petty, F.; Haley, R. W. Stressful manipulations that elevate corticosterone reduce blood-brain barrier permeability to pyridostigmine in the rat. Toxicol. Appl. Pharmacol. 2000, 165, 99-105.

(43) Allen, D. D.; Smith, Q. R. Blood-brain barrier choline transport in the senescent rat. Neurosci. Lett. 1999, 277, 198-202. (44) Bachen, E. A.; Muldoon, M. F.; Matthews, K. A.; Manuck, S. B. Effects of hemoconcentration and sympathetic activation on serum lipid responses to brief mental stress. Psychosom. Med. 2002, 64, 587-594. (45) Dhabhar, F.; McEwen, B. Enhancing versus suppressive effects of stress hormones on skin immune function. Proc. Natl. Acad. Sci. U.S.A. 1999, 96, 1059-1064. (46) McEwen, B. S.; Chattarji, S. Molecular mechanisms of neuroplasticity and pharmacological implications: the example of tianeptine. Eur. Neuropsychopharm. 2004, 14, S497-S502. (47) McEwen, B. S.; Seeman, T. Protective and damaging effects of mediators of stress. Elaborating and testing the concepts of allostasis and allostatic load. Ann. N. Y. Acad. Sci. 1999, 896, 3047. (48) McEwen, B. S. Interacting mediators of allostasis and allostatic load: towards an understanding of resilience in aging. Metabolism 2003, 52 (10 Suppl. 2), 10-16. (49) Surwit, R. S.; Seldin, M. F.; Kuhn, C. M.; Cochrane, C.; Feinglos, M. N. Control of expression of insulin resistance and hyperglycemia by different genetic factors in diabetic C57BL/6J mice. Diabetes 1991, 40, 82-87. (50) Shively, C. A.; Clarkson, T. B. Social status and coronary artery atherosclerosis in female monkeys. Arterioscler. Thromb. 1994, 14, 721-726. (51) Bosma, H.; Marmot, M. G.; Hemingway, H.; Nicholson, A. C.; Brunner, E.; Stansfeld, S. A. Low job control and risk of coronary heart disease in Whitehall II (prospective cohort) study. Br. Med. J. 1997, 314, 558-565. (52) Schnall, P. L.; Schwartz, J. E.; Landsbergis, P. A.; Warren, K.; Pickering, T. G. Relation between job strain, alcohol, and ambulatory blood pressure. Hypertension 1992, 19, 488-494. (53) Epel, E. S.; Blackburn, E. H.; Lin, J.; Dhabhar, F. S.; Adler, N. E.; Morrow, J. D.; Cawthon, R. M. Accelerated telomere shortening in response to life stress. Proc. Natl. Acad. Sci. U.S.A. 2004, 101, 17312-17315. (54) Cavigelli, S. A.; McClintock, M. K. Fear of novelty in infant rats predicts adult corticosterone dynamics and an early death. Proc. Natl. Acad. Sci. U.S.A. 2003, 100, 16131-16136. (55) Fride, E.; Dan, Y.; Feldon, J.; Halevy, G.; Weinstock, M. Effects of prenatal stress on vulnerability to stress in prepubertal and adult rats. Physiol. Behav. 1986, 37, 681-687. (56) Caldji, C.; Francis, D.; Sharma, S.; Plotsky, P. M.; Meaney, M. J. The effects of early rearing environment on the development of GABAA and central benzodiazepine receptor levels and noveltyinduced fearfulness in the rat. Neuropsychopharmacology 2000, 22, 219-229. (57) Lupien, S.; DeLeon, M. J.; DeSanti, S.; Convit, A.; Tannenbaum, B. M.; Nair, N. P. V.; McEwen, B. S.; Hauger, R. L.; Meaney, M. J. Longitudinal increase in cortisol during human aging predicts hippocampal atrophy and memory deficits. Proceedings of the Annual Meeting of the Society for Neuroscience, 1996, 22:#740.1, 1889. (58) McEwen, B. S. Stress and hippocampal plasticity. Ann. Rev. Neurosci. 1999, 22, 105-122. (59) Liu, D.; Diorio, J.; Day, J. C.; Francis, D. D.; Meaney, M. J. Maternal care, hippocampal synaptogenesis and cognitive development in rats. Nat. Neurosci. 2000, 3, 799-806. (60) Dhabhar, F. S.; McEwen, B. S.; Spencer, R. L. Stress response, adrenal steroid receptor levels and corticosteroid-binding globulin levelssa comparison between Sprague-Dawley, Fischer 344 and Lewis rats. Brain Res. 1993, 616, 89-98.

PR060412S

Journal of Proteome Research • Vol. 6, No. 6, 2007 2093

Metabonomic Studies on the Physiological Effects ... - ACS Publications

Recommend Documents