Metabolomic Profiling to Identify Potential Serum Biomarkers for

ARTICLE pubs.acs.org/jpr

Metabolomic Profiling to Identify Potential Serum Biomarkers for Schizophrenia and Risperidone Action Jiekun Xuan,†,‡ Guihua Pan,§ Yunping Qiu,|| Lun Yang,† Mingming Su,^ Yumin Liu,# Jian Chen,† Guoyin Feng,§ Yiru Fang,§ Wei Jia,|| Qinghe Xing,*,†,z and Lin He*,†,z,‡ †

)

Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China § Shanghai Institute of Mental Health, Shanghai 200030, China Department of Nutrition, University of North Carolina at Greensboro, North Carolina Research Campus, Kannapolis, North Carolina 28081, United States ^ David H. Murdock Research Institute, Kannapolis, North Carolina 28081, United States # School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China z Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China ‡ Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China

bS Supporting Information ABSTRACT: Despite recent advances in understanding the pathophysiology of schizophrenia and the mechanisms of antipsychotic drug action, the development of biomarkers for diagnosis and therapeutic monitoring in schizophrenia remains challenging. Metabolomics provides a powerful approach to discover diagnostic and therapeutic biomarkers by analyzing global changes in an individual’s metabolic profile in response to pathophysiological stimuli or drug intervention. In this study, we performed gas chromatography mass spectrometry based metabolomic profiling in serum of unmedicated schizophrenic patients before and after an 8-week risperidone monotherapy, to detect potential biomarkers associated with schizophrenia and risperidone treatment. Twenty-two marker metabolites contributing to the complete separation of schizophrenic patients from matched healthy controls were identified, with citrate, palmitic acid, myoinositol, and allantoin exhibiting the best combined classification performance. Twenty marker metabolites contributing to the complete separation between posttreatment and pretreatment patients were identified, with myo-inositol, uric acid, and tryptophan showing the maximum combined classification performance. Metabolic pathways including energy metabolism, antioxidant defense systems, neurotransmitter metabolism, fatty acid biosynthesis, and phospholipid metabolism were found to be disturbed in schizophrenic patients and partially normalized following risperidone therapy. Further study of these metabolites may facilitate the development of noninvasive biomarkers and more efficient therapeutic strategies for schizophrenia. KEYWORDS: metabolomics, schizophrenia, atypical antipsychotics, gas chromatography mass spectrometry, potential biomarkers

’ INTRODUCTION Schizophrenia is a multifactorial psychiatric disorder, with an intricate interplay of genetic and environmental factors contributing to the manifestation of a spectrum of symptoms.1 Despite extensive research for over a century, the molecular pathophysiological processes that underlie this complex disorder remain elusive. Besides, lacking reliable molecular diagnostic tools, current diagnosis of schizophrenia solely relies on subjective interpretation of symptoms presented by patients. In the treatment of schizophrenia, atypical antipsychotic drugs (AAPDs) have demonstrated superiority over conventional neuroleptics in r 2011 American Chemical Society

terms of both clinical efficacy and side effects.2 Risperidone, one of the most widely used first-line AAPDs, shows high efficacy in alleviating both positive and negative symptoms.3 However, variability in clinical response to risperidone is observed among individuals like all other medications, which largely influences the effectiveness of clinical care. There is growing interest in identifying the molecular alterations associated with schizophrenia and antipsychotic treatment, Received: July 19, 2011 Published: October 18, 2011 5433

dx.doi.org/10.1021/pr2006796 | J. Proteome Res. 2011, 10, 5433–5443

Journal of Proteome Research which would facilitate the development of objective diagnostic tools and individualized therapeutic strategies.4,5 Biomarkers are indicators of pathogenic processes or responses to therapeutic interventions, which could be utilized to identify at-risk individuals and facilitate early interventions as well as monitor treatment progress and outcome. The development of biomarkers is of great importance to improve diagnostics and therapeutics of schizophrenia. While the complexity of the disorder and the heterogeneity of phenotypic expression in patients have slowed down the progress of biomarker discovery, recent advances in high-throughput profiling technologies have provided new avenues to search for biomarkers on a system level.6,7 Metabolomics is a rapidly evolving field that attempts to capture the metabolic state of organisms by simultaneous and dynamic assessment of the metabolome—the complete repertoire of small molecules in biological samples.8 An individual’s metabolomic profile represents the final product of interactions among various factors, including genetic, physiological and environmental factors that determine disease risk and interventional outcome of drugs.6 Metabolomics provides a powerful approach to quantitatively measure global changes in metabolic profiles of individuals in response to disease or drug exposure via noninvasive analysis of biofluids (e.g., blood and urine). A series of recent studies have proven the feasibility and advantages of utilizing metabolomic tools in the integrated analysis of multiple biochemical pathways disturbed in central nervous system (CNS) disorders, including Huntington’s disease, Parkinson’s disease, motor neuron disease, bipolar disorder, depression and schizophrenia.9 Thus far, several metabolic mechanisms including abnormalities in glucoregulation, mitochondrial function, neurotransmitter synthesis and lipid metabolism have been implicated in the underlying disease process and treatment of schizophrenia.10 14 However, a comprehensive mapping of disturbances in metabolic pathways in schizophrenia and its treatment is still far from complete. To develop potentially useful diagnostic and therapeutic biomarkers or biomarker panels, further studies are needed to validate early findings and identify more sites of perturbations linking the disorder, metabolic pathways and medication effects. In the current study, we employed a gas chromatography mass spectrometry (GC MS)-based metabolomic platform in conjunction with multivariate statistical analysis to determine global alterations in metabolic profiles of schizophrenic patients in comparison with demographically matched healthy controls or during an 8-week period of risperidone monotherapy. Our study intended to identify metabolic biomarker patterns associated with schizophrenia and risperidone treatment.

’ EXPERIMENTAL SECTION Subjects

Unmedicated Chinese Han patients meeting DSM-IV criteria for schizophrenia were recruited from the outpatient clinic of the Shanghai Mental Health Center, all of whom satisfied the following criteria: (1) aged 18 65 years; (2) had no diabetes mellitus, hyperlipidemia or other severe physical diseases; (3) had no additional psychoses including alcoholism or other substance abuse disorders. Standard informed consents reviewed and approved by the Shanghai Ethical Committee of Human Genetic Resources were obtained from all the participants after the procedure had been fully explained. As controls, healthy volunteers who matched to the patients in age, gender and ethnicity were enrolled in the study. We excluded subjects who

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had a history of psychiatric disorders, diabetes mellitus, hyperlipidemia or the same in their first degree relatives. Clinical Assessment and Intervention

All patients were hospitalized and receiving risperidone monotherapy for a period of eight weeks. The drug dosage was gradually increased to the target dose of 4 mg/day within the first week. Drug safety was assessed every two weeks by routine physical and laboratory examinations, and the Treatment Emergent Symptom Scale (TESS). The Positive and Negative Syndrome Scale (PANSS) was used to evaluate psychopathology on days 0, 28, and 56. During the trial, all participants received a standard diet provided by the hospital and abstained from alcohol and smoking. According to the percentage reduction in PANSS total scores from baseline after the 8-week treatment, patients were classified into responders (25% or greater reduction) and nonresponders (less than 25% reduction). GC MS Sample Preparation, Derivatization and Spectral Acquisition

All blood samples were collected from subjects after an overnight fast. After blood coagulation at room temperature for 30 min, sera were separated by centrifugation at 3000 g for 10 min at 4 C and immediately stored at 80 C until use. Serum metabolites were subjected to trimethylsilyl derivatization according to an established procedure.15 Briefly, a 100-μL aliquot of serum was added with two internal standard solutions (10 μL of L-2-chlorophenylalanine in water, 0.3 mg/mL; 10 μL of heptadecanoic acid in methanol, 1 mg/mL) and thoroughly vortexed. The solution was then extracted by vortex-mixing with 300 μL of methanol/ chloroform (3:1, v/v) for 30s. After storing for 10 min at 20 C, each sample was centrifuged at 12000 g for 10 min. A 300-μL aliquot of supernatant was subsequently transferred into a GC vial and vacuum-dried at room temperature. The dried residue was added with 80 μL of methoxymaine (15 mg/mL in pyridine), and after placing at 30 C for 90 min, the solution was derivatized with 80 μL of BSTFA (1% TMCS) at 70 C for 60 min. Each 1 μL aliquot of the derived extracts was injected into a Perkin-Elmer gas chromatography coupled to a TurboMassAutosystem XL mass spectrometer (Perkin-Elmer Inc., Waltham, MA) in splitless mode. Metabolite separation was performed on a DB-5MS capillary column coated with 5% diphenyl cross-linked 95% dimethylpolysiloxane (30 m 250 μm i.d., 0.25 μm film thickness; Agilent J&W Scientific, Folsom, CA). Helium carrier gas was used with a flow rate of 1.0 mL/min. The injection temperature and the interface temperature were both set to 260 C, and the ion source temperature was held at 200 C. GC temperature program consisted of an initial isothermal heating at 80 C for 2 min after injection, followed by 10 C/min oven temperature ramps to 140 C, 4 C/min to 240 C, and 10 C/ min to 280 C, and a final 13 min maintenance at 280 C. MS measurements were implemented with electron impact ionization (70 eV) in the full scan mode (m/z 30 600). Data Analysis and Biomarker Identification

All the GC MS raw files were converted to CDF format via DataBridge (Perkin-Elmer Inc., Waltham, MA). Automated peak detection, peak matching and retention time alignment were subsequently carried out on CDF files using a deconvolution software package (XCMS) (http://masspec.scripps.edu/xcms/ xcms.php) with default settings except for xcmsSet (full width at half-maximum: fwhm = 4; S/N cutoff value: snthresh = 8, max = 20) and group (bw = 5).16 The XCMS report table was loaded

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Journal of Proteome Research

Table 1. Demographic and Clinical Characteristics of Study Participantsa index

patients

controls

No. of subjects

18

18

Age (years) Gender (male/female)

41.3 ( 16.1 10/8

41.0 ( 15.0 10/8

Age of onset (years)

26.5 ( 7.9

Duration of illness (years)

14.8 ( 13.8

Pretreatment Weight (kg)

60.3 ( 12.8

BMI (kg/m2)

22.1 ( 3.8

PANSS total

68.8 ( 19.0

PANSS positive PANSS negative

21.1 ( 6.8 17.1 ( 7.4

Posttreatment Weight (kg)

60.6 ( 12.9

BMI (kg/m2)

22.2 ( 3.8

PANSS total

48.8 ( 12.2c

PANSS positive

11.7 ( 3.8c

PANSS negative

12.8 ( 5.0b

Data expressed as mean ( S.D. b p < 0.05. c p < 0.01, post- vs pretreatment. a

)

capacity using a Support Vector Machine (SVM).18 A radial basis function (RBF) kernel was used in the SVM classifier, since this kernel function (K(xi, xj) = exp( γ xi xj 2), γ > 0) could map the input data into a higher dimensional space for classification when the relationships between class labels and feature vectors are nonlinear. To avoid a bias caused by using the same data set to determine the optimal classifier and evaluate its performance, a double cross-validation was used instead of a single crossvalidation for performance estimation.19 In brief, the samples were randomly partitioned into N subsets of two cases each. In the outer cross-validation, one of the N subsets was held out as test data and the remaining N 1 subsets were used as training data. To optimize classifier parameters, an inner cross-validation was carried out where the best performance was determined. For each fold of the outer cross-validation, the training data was further split into N 1 subsets, one of which was held out as inner test data and the remaining N 2 subsets were used as inner training data. The classifier with the best performing parameters selected from this inner loop of cross-validation was then trained on the outer training data and applied to the outer test data. This procedure will provide an almost unbiased performance estimate since each time the classifier was evaluated on a test set that was not used for learning. The area under the receiver operating characteristic curve (AUC) was used as a measure of classification performance. A modified implementation of LIBSVM (version 3.1) was carried out for SVM calculations and the R-package ROC (version 1.16.0) was used to plot ROC curves.20,21 The discriminatory power of each marker metabolite was ranked and visualized using heat maps. )

into MATLAB 7.0 (The MathWorks Inc., Natick, MA), where peak intensities were normalized to the overall MS abundance in order to reduce systematic biases within the experiment. Internal standards and all known artifact peaks (e.g., peaks caused by noise, column bleed and TMS derivatization) were manually identified and removed from the report table. To ensure the drug or its metabolites were not included in the data matrix, we performed GC MS analysis of blank solution (methanol), risperidone (0.1 mg/mL methanol solution) and 9-hydroxyrisperidone (0.1 mg/mL methanol solution) in parallel. As shown in Figure S1 (Supporting Information), there were no significant differences in the spectra and no peaks of risperidone or its metabolite 9-hydroxyrisperidone were observed, indicating that they were undetectable by our GC MS protocol. The resulting three-dimensional data matrix that contained sample names (observations), RT-m/z pairs (features) and normalized peak areas (variables) was imported into the Simca-P 11.5 soft Sweden) for statistical analysis. ware (Umetrics, Umea, Prior to multivariate analysis, data were mean-centered and unit-variance scaled to remove the offsets and adjust the importance of high and low abundance metabolites to an equal level. Principal component analysis (PCA) was first utilized to get an overview of systematic variations and general clustering among all observations. However, PCA usually does not achieve the maximum separation between samples. To identify differential metabolites that account for the separation between groups, partial least-squares discriminant analysis (PLS-DA) was applied to construct classification models based on specified sample classes. An internal 5-fold cross-validation was carried out to estimate the performance of PLS-DA models. The calculated R2Y(cum) estimates the goodness of fit of the model that represents the fraction of explained Y-variation, and Q2(cum) estimates the ability of prediction. Excellent models are obtained when the cumulative values of R2Y and Q2 are above 0.8.17 In addition to cross-validation, model validation was also performed by 200 times permutation tests. The VIP (variable importance in the projection) value is a weighted sum of squares of the PLS weights, reflecting the relative contribution of each X variable to the model. The variables with VIP > 1 were considered to be influential for the separation of samples in the scores plots generated from PLS-DA analysis. In parallel, univariate statistical analysis was performed using SPSS 16.0 (SPSS Inc., Chicago, IL, USA) to validate those major contributing variables from the PLS-DA models. The Mann Whitney U-test was used for comparisons between two independent groups (i.e., control and patient groups, or responder and nonresponder groups), while the Wilcoxon signed-rank test was employed for comparisons between two related groups (i.e., pre- and posttreatment groups). All tests were two-tailed and the critical p-value was set at 0.05. Ultimately, differential metabolic features associated with schizophrenia and risperidone treatment were obtained based on the cutoff points of both VIP values from a 5-fold cross-validated PLS-DA model and critical p-values from univariate analysis. In addition, the corresponding fold change was calculated to show the degree of variation in metabolite levels between groups. Biomarker identification was performed in Turbomass 5.3.0 software (PerkinElmer Inc., Waltham, MA) with the commercial compound libraries including NIST, NBS and Wiley.

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’ RESULTS Demographic and Clinical Characteristics

Classification Performance of Putative Biomarkers

Each marker metabolite and different combinations of these potential biomarkers were further evaluated for their classification

The enrolled patients and controls were well-matched in terms of age, gender and ethnicity (Table 1). To eliminate potential confounding factors, the diet, smoking and physical exercise 5435



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Figure 1. Visualization of pathological and pharmacological effects on individual metabolic profiles. Typical GC MS total ion current (TIC) chromatograms of serum samples from (A) the control group, (B) the pretreatment patient group, and (C) the posttreatment patient group. The differential metabolites are labeled as a x. a = lactate; b = glycerol; c = glycine; d = aspartate; e = allantoin; f = α-ketoglutarate; g = N-acetylaspartate; h = phenylalanine; i = citrate; j = erythrose; k = glucose; l = tyrosine; m = glucuronic acid; n = palmitic acid; o = myo-inositol; p = uric acid; q = tryptophan; r = linoleic acid; s = oleic acid; t = stearic acid; u = lactobionic acid; v = γ-tocopherol; w = 1,3-bisphosphoglycerate; x = cholesterol. (D) A 3D PLS-DA scores plot of GC MS spectral data (7 components, R2X(cum) = 0.381, R2Y(cum) = 0.989, Q2(cum) = 0.966), indicating clusters of controls, schizophrenic patients at baseline (SCZ) and schizophrenic patients following the 8-week risperidone treatment (SCZ 8w).

habits were also controlled during the clinical trial. Two patients dropped out due to concurrent somatic illness (n = 1) or poor response (n = 1). No significant changes from baseline in weight and body mass index (BMI) were observed at the end of risperidone treatment. As shown in Table S1 (Supporting Information), there was a significant difference in age and a trend difference in illness duration between responders and nonresponders, suggesting that early diagnosis and treatment of patients may result in improved therapeutic outcome. No significant discrepancies were found between the two groups in other baseline features. Metabolomic Profiling of Serum Samples

The typical GC MS total ion current (TIC) chromatograms of serum samples from the control group, and the pretreatment patient group and the posttreatment patient group are shown in Figure 1A, B, C. Visual inspection of the GC MS spectra revealed significant differences between the three groups. For instance, a decrease of uric acid was observed in the pretreatment patient group as compared to the control group, and a decrease of

myo-inositol was observed in the posttreatment group as compared to the pretreatment group. However, visual analysis is not applicable to examine the overall differentiation of metabolomic profiles between groups, since data interpretation can be complicated by interindividual variation. Therefore, multivariate analysis was performed to reduce the data to a low dimensional space where discrimination of metabolomic profiles between sample classes can be modeled. A total of 3923 features were initially generated by processing the mass spectrometry data using XCMS, and after removing internal standards and artifact peaks, 3805 features were remained and used for subsequent multivariate analysis. The PCA model derived from GC MS spectra of all the samples revealed the general structure of the full data set, in which five principal components cumulatively accounted for 36.0% of the data variation (Figure S2, Supporting Information). PLS-DA was further performed to maximize the separation between samples. The supervised models were validated with 200 times permutation tests (Figure S3A C, Supporting Information). As presented in Figure 1D, three distinct clusters 5436



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A clear separation was established in the PLS-DA model (Figure 2B), indicating a significant impact of risperidone treatment on the global metabolism. The metabolic alterations in responders and nonresponders were also evaluated, respectively. Those significantly altered metabolites in responders are more likely to be associated with the therapeutic effects of risperidone and could be potentially used as biomarkers for therapeutic response monitoring. The differential metabolites accountable for the pharmacological effects of risperidone were summarized in Table 3. PLS-DA was also applied to capture variations in pretreatment metabolic profiles of schizophrenic patients which may influence individuals’ metabolic responses to medication and consequently lead to interindividual differences in treatment outcome. As shown in the scores plot (Figure S4, Supporting Information), risperidone responders were distinctly separated from nonresponders at baseline. Notably, the significant difference in age between responders and nonresponders indicates that age may be a contributor to the discrimination of the two groups. Age-related antipsychotic drug sensitivity has been suggested by literature.22 Thus, it is possible that age-related variations in pretreatment metabolic phenotype could contribute to intersubject variability in therapeutic response to risperidone. The differential metabolites could be taken into consideration as potential predictive biomarkers of risperidone response (Table S2, Supporting Information). Biomarker Combinations with Best Classification Performance

Figure 2. PLS-DA scores plot indicating discrimination between (A) controls and schizophrenic patients at baseline (4 components, R2X(cum) = 0.305, R2Y(cum) = 0.995, Q2(cum) = 0.923), and (B) pretreatment and posttreatment patients (4 components, R2X(cum) = 0.33, R2Y(cum) = 0.997, Q2(cum) = 0.988).

were identified in a three-dimensional PLS-DA scores plot. Clear separation was detected between controls and schizophrenic patients at baseline in the first PLS-DA component, while the second PLS-DA component showed differences in schizophrenic patients before and after the 8-week risperidone treatment, suggesting that metabolic perturbations under pathological conditions and pharmacological intervention were evident in the patients. The PLS-DA model derived from GC MS spectra of baseline serum samples from schizophrenic patients and healthy controls was employed to explore intrinsic differences in metabolic physiology. As presented in Figure 2A, PLS-DA scores plot showed a division of schizophrenia and control samples into two distinct clusters with a clear separation detected in PLS-DA component 1. Among all the differential variables selected according to the VIP values from the PLS-DA model (VIP > 1) and the p-values from the Mann Whitney U test (p < 0.05), 22 metabolites were identified as potential biomarkers for diagnosis of schizophrenia, mainly including amino acids, fatty acids, organic acids, carbohydrates, etc. (Table 2). To evaluate the physiological responses to risperidone treatment, PLS-DA was performed to discriminate between the preand posttreatment metabolic profiles of schizophrenic patients.

Heat maps were created to illustrate the discriminatory power of each potential biomarker (Figure 3A,B). Two groups of marker metabolites were ranked in order of AUC values, respectively. Since a complex disease or drug action involves systematic deregulation of biochemical pathways, a set of multiple biomarkers rather than a single biomarker could have more power for discrimination and provide more clinically useful information. Therefore, metabolites with AUC > 0.85 were used to build different biomarker combinations whose discrimination performances were evaluated. For discrimination between schizophrenic patients and controls, a set of four top biomarkers, including citrate, palmitic acid, myo-inositol and allantoin provided the maximum classification performance with AUC = 0.958 (Figure 3A). For discrimination between patients before and after risperidone treatment, a combination of three top biomarkers, including myoinositol, uric acid and tryptophan, showed the best classification performance with AUC = 0.949 (Figure 3B).

’ DISCUSSION Metabolomic profiling has been suggested as an efficient means for the identification and quantification of metabolites from biochemical pathways that are altered in response to disease or therapeutic intervention.23 The present study demonstrates the feasibility and effectiveness of utilizing GC MS-based metabolomic profiling to capture metabolic markers that represent biochemical changes in schizophrenia or upon risperidone treatment. Understanding the biological significance of these metabolites may provide further insights into the mechanisms underlying the pathophysiology of schizophrenia and risperidone action, and facilitate the discovery of biomarkers for diagnosis, treatment monitoring and response prediction in schizophrenia. Compared with matched controls, schizophrenic patients showed significantly higher baseline serum levels of glucose and lactate, and lower levels of 1,3-bisphosphoglycerate (1,3BPG). 5437



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Table 2. Differential Metabolites Accountable for the Discrimination between Schizophrenic Patients and Healthy Controls at Baseline schizophrenia vs control no.

metabolites

a

VIP

Pb

FCc

pathway (KEGG)

1

Glucose

2.00

0.0342

1.50

Glycolysis

2

1,3-Bisphosphoglycerate

2.46

0.0315

1.61

Glycolysis

3 4

Lactate Citrate

2.06 1.89

0.0359 0.0087

2.32 2.66

Glycolysis TCA Cycle

5

α-Ketoglutarate

1.52

0.0342

1.52

TCA Cycle

6

Allantoin

2.14

0.0298

3.50

Uric acid metabolism

7

Uric acid

1.65

0.0247

2.73

Purine metabolism

8

γ-Tocopherol

1.33

0.0248

1.53

Vitamin E metabolism

9

N-Acetylaspartate

1.91

0.0293

1.96

Alanine, aspartate and glutamate metabolism

10

Aspartate

1.96

0.0021

2.20

Alanine, aspartate and glutamate metabolism

11 12

Glycine Tryptophan

1.62 2.21

0.0232 0.0071

1.80 2.07

Glycine, serine and threonine metabolism Tryptophan Metabolism

13

Myo-inositol

1.82

0.0235

1.46

Inositol phosphate metabolism

14

Glucuronic acid

1.30

0.0033

1.81

Inositol phosphate metabolism

15

Linoleic acid

2.39

0.0124

2.69

Fatty acid metabolism

16

Oleic acid

1.89

0.0146

2.52


17

Stearic acid

2.33

0.0171

1.81


18

Palmitic acid

1.66

0.0037

1.77


19 20

Glycerol Cholesterol

2.38 2.17

0.0046 0.0290

1.42 1.43

Glycerolipid metabolism Steroid biosynthesis

21

Lactobionic acid

1.83

0.0315

1.83

Others

22

Erythrose

1.49

0.0402

1.49

Others

a

Variable importance in the projection (VIP) values were obtained from cross-validated PLS-DA models with a threshold of 1. b P-values were calculated from two-tailed Mann Whitney U-test with a threshold of 0.05. c Fold change was calculated as the ratio of the mean metabolite levels between two groups. A positive value of fold change indicates a relatively higher concentration of metabolites while a negative value of fold change indicates a relatively lower concentration in schizophrenic patients as compared to healthy controls.

Blood-borne glucose is the major substrate for oxidative energy metabolism in the brain.24 Abnormalities in peripheral glucose metabolism have been shown to be linked with metabolic syndrome in schizophrenic patients for decades.25 The notion that such abnormalities are intrinsic to schizophrenia is supported by evidence of genetic linkage between the disease and genes related to glycolysis.26 Altered expression of glycolytic enzymes in both peripheral and central tissues of schizophrenic patients has also been reported.11,27 Lactate is the end-product of glucose metabolism under anaerobic conditions. Our observation of decreased levels of the glycolytic intermediate 1,3BPG and increased levels of lactate may suggest an impairment of glycolysis and a shift toward the less-efficient anaerobic respiration for energy production. Not surprisingly, markedly lower levels of citrate and α-ketoglutarate, two intermediates of the TCA cycle, were observed in schizophrenic patients. The depletion of these two metabolites may indicate a reduction in TCA cycle activity and a deficit in mitochondrial respiration. Overall, significant differences of those metabolites all point toward the possibility of alterations in glycolytic activity and mitochondrial function accompanied by an increase in anaerobic energy metabolism in schizophrenic patients. Serum levels of two antioxidants, uric acid and γ-tocopherol, were significantly lower in schizophrenic patients than in controls. Uric acid, the end-product of purine metabolism, is a major antioxidant that accounts for approximately 60% of the free radical scavenging activity in human blood.28 γ-Tocopherol, one

of the major forms of vitamin E, also possesses potent antioxidant properties.29 The reduction in serum antioxidants may indicate a defect in the antioxidant defense system (AODS), which has been suggested as a cause of oxidative stress in schizophrenia.30 In the presence of oxidative stress, reactive oxygen species (ROS) can contribute to the generation of allantoin from uric acid.31 It has been suggested that allantoin is a useful marker of oxidative stress.32,33 Thus, our observation that serum levels of allantoin were markedly higher in schizophrenic patients may indicate an increase in oxidative stress. The decreased serum levels of uric acid and γ-tocopherol in the patients were normalized after 8 weeks of risperidone treatment. Interestingly, the uric acid elevation only occurred in risperidone responders but not in nonresponders, suggesting that uric acid may be a useful biomarker for monitoring therapeutic efficacy. Peripheral amino acids play a pivotal role in neurotransmitter availability in the brain. Serotonin (5-hydroxytryptamine, 5-HT) is a crucial neurotransmitter involved in the regulation of diverse psychological and physiological functions, and disturbances of 5-HT function have been implicated in the pathophysiology of schizophrenia.34 Brain 5-HT content is dependent on the availability of its amino acid precursor, tryptophan.35 In line with earlier reports,36,37 our finding of decreased levels of serum tryptophan in schizophrenic patients implies a reduction in tryptophan availability which could lead to 5-HT dysfunction in the CNS. AAPDs with potent 5-HT2A receptor antagonism, such as risperidone, may exert regulatory effects on 5-HT release and in turn tryptophan 5438



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Table 3. Differential Metabolites Accountable for the Discrimination between Posttreatment and Pretreatment Metabolic Profiles in All Patients, in Treatment Responders and in Treatment Nonresponders after an 8-Week Risperidone Monotherapy total (post vs pretreatment) no.

metabolites

a

b

VIP

P

responder (post vs pretreatment)

c

b

P

FC

nonresponder (post vs pretreatment)

c

Pb

FC

FCc

1

Glucose

1.90

0.0105

1.48

0.0313

1.28

0.0313

2

1,3-Bisphosphoglycerate

1.60

0.0085

1.48

0.0469

1.42

0.0156

1.90 1.49

3 4

Lactate Uric acid

1.66 1.94

0.0313 0.0392

1.63 1.81

0.0081 0.0156

1.51 2.33

0.0469 -

1.79 1.46

5

γ-Tocopherol

1.94

0.0295

1.63

0.0234

1.96

0.0469

6

Aspartate

2.09

0.0295

2.17

0.0313

2.56

-

-

7

Glycine

1.48

0.0419

1.97

-

-

-

-

8

Tryptophan

1.91

0.0067

1.87

0.0156

2.40

-

-

9

Phenylalanine

1.84

0.0295

2.13

0.0156

2.97

-

-

10

Tyrosine

1.85

0.0166

2.28

0.0156

2.99

-

-

11 12

Myo-inositol Glucuronic acid

1.68 1.67

0.0247 0.0337

1.49 1.70

-

-

-

-

13

Linoleic acid

2.02

0.0234

2.02

0.0234

2.96

-

-

14

Oleic acid

1.70

0.0156

1.77

0.0295

2.33

-

-

15

Stearic acid

1.97

0.0295

1.91

0.0156

2.83

-

-

16

Palmitic acid

1.93

0.0295

2.01

-

-

0.0156

3.93

17

Glycerol

1.33

0.0295

1.38

-

-

0.0313

1.43

18

Cholesterol

1.65

0.0166

1.31

0.0313

1.35

0.0156

1.29

19 20

Lactobionic acid Erythrose

1.63 1.59

0.0398 0.0107

1.63 1.84

0.0295

1.76

0.0295

2.41

a

Variable importance in the projection (VIP) values were obtained from cross-validated PLS-DA models with a threshold of 1. b P-values were calculated from two-tailed Wilcoxon signed-rank test with a threshold of 0.05. c Fold change was calculated as the ratio of the mean metabolite levels between two groups. A positive value of fold change indicates a relatively higher concentration of metabolites while a negative value of fold change indicates a relatively lower concentration in posttreatment samples as compared to pretreatment samples. The symbol “-” represents statistically nonsignificant values (VIP < 1 or P > 0.05).

metabolism.38 Alfredsson and Wiesel39 found a correlation between an increase of serum tryptophan and clinical improvement during antipsychotic treatment. In this study, we observed that tryptophan levels were elevated after risperidone treatment and this elevation was only found in the posttreatment sera of responders, suggesting that tryptophan is a potential biomarker for monitoring treatment efficacy. Tyrosine, a precursor of the neurotransmitter dopamine, shares a common transport system across the blood-brain barrier with tryptophan and other large neutral amino acids.40 Although we did not find significant alterations in serum levels of tyrosine in schizophrenic patients, the brain availability of tyrosine could be abnormal due to competition with reduced concentrations of tryptophan. The potent serotonergic antagonism of AAPDs has been suggested to contribute to their capability to increase dopamine release and balance out the dopaminergic blockade effect in the brain, which may explain the improved efficacy against negative symptoms and reduced EPS liability of these drugs.41 43 The elevations in serum concentrations of tyrosine and its precursor phenylalanine in responders after treatment may imply up-regulated dopamine levels associated with the antipsychotic effects of risperidone. N-acetylaspartate (NAA) is a marker of neuronal integrity and viability.44 It has been suggested to function as an organic osmolyte in neurons, a source of acetate for myelin lipid synthesis in oligodendrocytes, and a precursor of the peptide neurotransmitter N-acetylaspartylglutamate.44 NAA is primarily synthesized from aspartate and acetyl-coenzyme A in neuronal mitochondria.

Aspartate is derived from the TCA cycle and involved in energy production as an integral component of the malate shuttle. A previous study has reported reduced expression of genes involved in the TCA cycle, the malate shuttle system and aspartate metabolism, which could account for abnormal levels of aspartate and NAA in schizophrenia.45 Reductions of NAA have been repeatedly observed in subjects with schizophrenia.46 The reduced serum levels of NAA and aspartate may imply deficits in neuronal activity and mitochondrial function in schizophrenic patients. Glycine functions as a coagonist along with glutamate or aspartate in stimulation of N-methyl D-aspartate (NMDA) receptors.47 The low levels of glycine may point to hypofunction of NMDA receptor-mediated neurotransmission in the patients, which represents a key pathogenic mechanism of schizophrenia.48 Risperidone exhibits very low affinity for NMDA receptors. However, there is evidence suggesting that risperidone could reduce NMDA receptor binding while increasing non-NMDA receptor levels in the brain.49,50 The decreases in serum levels of glycine and aspartate after chronic risperidone treatment may reflect the modulatory effects of the drug on NMDA receptor function which are likely to contribute uniquely to its therapeutic efficacy and low EPS liability. Myo-inositol is the most abundant inositol stereoisomer. As an important constituent of the phosphatidylinositol second messenger system (PI-cycle), myo-inositol is required for the synthesis of membrane inositol phospholipids and related cellular signal transduction.51,52 Abnormalities in myo-inositol concentration and 5439



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Figure 3. Visualization of discriminatory power across individual biomarker candidates and maximum classification performances of combined biomarker sets. Heat maps are used to show the discriminatory capacity of each metabolite estimated with AUC. Color corresponds to AUC values as indicated in the palette. Red and blue represent high and low values, respectively. ROC curves illustrate the best combined discrimination performances for distinguishing between (A) schizophrenic patients and healthy controls and (B) posttreatment and pretreatment patient groups.

PI-cycle functioning in neurons have been implicated in the pathophysiology of psychiatric disorders.53 It has been suggested that antipsychotic drugs may act by dampening PI-cycle hyperactivity in schizophrenic brains.54 In our study, the serum levels of myo-inositol were increased in schizophrenic patients and normalized by risperidone treatment, suggesting the involvement of myo-inositol in the pathophysiology and treatment of schizophrenia and a role of myo-inositol as a biomarker for diagnosis and treatment monitoring in schizophrenic patients. Altered serum fatty acid composition with lower levels of saturated (palmitic acid, C16:0; stearic acid, C18:0), monounsaturated (oleic acid, C18:1n-9) and polyunsaturated (linoleic acid, C18:2n-6) fatty acids were observed in schizophrenic patients, suggesting an association of schizophrenia with abnormalities in fatty acid metabolism. Linoleic acid is a precursor of the principal omega-6 polyunsaturated fatty acid arachidonic acid, which

mediates neuronal signal transduction as a second messenger and induces neurotransmission.55 Reductions of plasma linoleic acid in subjects with schizophrenia have been repeatedly reported, supporting the notion of abnormal arachidonic acid metabolism in schizophrenia.56 There is evidence suggesting that atypical antipsychotics could exert antioxidant effects and reduce lipid peroxidation,57 which may contribute to the elevation of linoleic acid levels after risperidone treatment. It has also been suggested that antipsychotic medications produce modulatory effects on fatty acid biosynthesis, resulting in alteration of lipid homeostasis.58 The modulation of the ratio of unsaturated to saturated fatty acids that could lead to altered fluidity and function of cell membranes in responders following treatment may be related to the therapeutic effects of risperidone. Schizophrenic patients treated with AAPDs are prone to developing body weight gain/obesity, diabetes and dyslipidemia.59 5440



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Figure 4. Schematic representation of the major metabolic pathways illustrating the metabolic effects of schizophrenia and risperidone treatment. Redcolored symbols represent significant up-regulations of metabolites in schizophrenic patients compared with controls or after risperidone treatment, while blue-colored symbols indicate down-regulations.

There is evidence suggesting that AAPDs may induce an accumulation of lipid and cholesterol in hepatocytes and adipocytes via stimulating lipogenesis and cholesterogenesis while inhibiting lipolysis.60 62 Lipolysis is a catabolic process responsible for the breakdown of triacylglycerols. Impairment in lipolysis can be suggested from the reduction of glycerol release into the circulation.63 Abnormal intracellular lipid accumulation is speculated to account for reductions in insulin sensitivity and insulinstimulated glucose transport activity, which may partly explain insulin resistance and glucose intolerance in obesity and diabetes.64,65 In this study, the elevations in serum levels of glucose and cholesterol, and the reductions in serum levels of glycerol could be partly related to risperidone-induced metabolic side effects in schizophrenic patients.

’ CONCLUSIONS Taken together, metabolic markers identified from the metabolomic analysis suggest disturbances of energy metabolism, antioxidant defense systems, neurotransmitter metabolism, fatty acid biosynthesis and phospholipid metabolism in schizophrenic patients, which could be partially normalized by risperidone therapy (summarized in Figure 4). Moreover, significant baseline differences in metabolomic profiles between risperidone responders and nonresponders suggest that an individual’s pretreatment metabolic phenotype may have a notable impact on the posttreatment outcome. The blood profiling approach shows its potential to

identify noninvasive biomarkers for diagnostics and therapeutics of schizophrenia. Further refinement and validation of these biomarkers in larger cohorts of patients would be of considerable interest.

’ ASSOCIATED CONTENT

bS

Supporting Information Figure S1 displays GC MS chromatograms of risperidone and 9-hydroxyrisperidone standards, Figure S2 depicts the PCA overview of all serum samples, Figure S3 presents the permutation tests for the PLS-DA models, Table S1 shows the baseline demographics and clinical features of risperidone responders and nonresponders, Table S2 lists the differentially expressed baseline metabolites between the two groups, and Figure S4 presents the corresponding PLS-DA scores plot. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*Dr. Lin He, Bio-X Center, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China, e-mail: [email protected]. Dr. Qinghe Xing, Institutes of Biomedical Sciences, Fudan University, 138 Yixueyuan Road, Shanghai 200032, China, e-mail: xingqinghef@ gmail.com. Tel/Fax: 00 86 21 62822491. 5441



’ ACKNOWLEDGMENT This work was supported by the S973 Program (2006CB910601), the National Natural Science Foundation of China (30700203), the Shanghai Leading Academic Discipline Project (B205), and the Shanghai Municipal Commission for Science and Technology. We are grateful to all the participants and medical staff working on the project. ’ REFERENCES (1) Sawa, A.; Snyder, S. H. Schizophrenia: diverse approaches to a complex disease. Science 2002, 296 (5568), 692–695. (2) Buckley, P. F. Broad therapeutic uses of atypical antipsychotic medications. Biol. Psychiatry 2001, 50 (11), 912–924. (3) Marder, S. R.; Meibach, R. C. Risperidone in the treatment of schizophrenia. Am. J. Psychiatry 1994, 151 (6), 825–835. (4) St€ober, G.; Ben-Shachar, D.; Cardon, M.; Falkai, P.; Fonteh, A. N.; Gawlik, M.; Glenthoj, B. Y.; Grunblatt, E.; Jablensky, A.; Kim, Y. K.; Kornhuber, J.; McNeil, T. F.; Muller, N.; Oranje, B.; Saito, T.; Saoud, M.; Schmitt, A.; Schwartz, M.; Thome, J.; Uzbekov, M.; Durany, N.; Riederer, P. Schizophrenia: from the brain to peripheral markers. A consensus paper of the WFSBP task force on biological markers. World J. Biol. Psychiatry 2009, 10 (2), 127–155. (5) Thomas, E. A. Molecular profiling of antipsychotic drug function: convergent mechanisms in the pathology and treatment of psychiatric disorders. Mol. Neurobiol. 2006, 34 (2), 109–128. (6) Nicholson, J. K. Global systems biology, personalized medicine and molecular epidemiology. Mol. Syst. Biol. 2006, 2, 52. (7) Gramolini, A. O.; Peterman, S. M.; Kislinger, T. Mass spectrometry-based proteomics: a useful tool for biomarker discovery? Clin. Pharmacol. Ther. 2008, 83 (5), 758–760. (8) Holmes, E.; Wilson, I. D.; Nicholson, J. K. Metabolic Phenotyping in Health and Disease. Cell 2008, 134 (5), 714–717. (9) Quinones, M. P.; Kaddurah-Daouk, R. Metabolomics tools for identifying biomarkers for neuropsychiatric diseases. Neurobiol. Dis. 2009, 35 (2), 165–176. (10) Holmes, E.; Tsang, T. M.; Huang, J. T.; Leweke, F. M.; Koethe, D.; Gerth, C. W.; Nolden, B. M.; Gross, S.; Schreiber, D.; Nicholson, J. K.; Bahn, S. Metabolic profiling of CSF: evidence that early intervention may impact on disease progression and outcome in schizophrenia. PLoS Med. 2006, 3 (8), e327. (11) Prabakaran, S.; Swatton, J. E.; Ryan, M. M.; Huffaker, S. J.; Huang, J. T.; Griffin, J. L.; Wayland, M.; Freeman, T.; Dudbridge, F.; Lilley, K. S.; Karp, N. A.; Hester, S.; Tkachev, D.; Mimmack, M. L.; Yolken, R. H.; Webster, M. J.; Torrey, E. F.; Bahn, S. Mitochondrial dysfunction in schizophrenia: evidence for compromised brain metabolism and oxidative stress. Mol. Psychiatry 2004, 9 (7), 684–697; 643. (12) Yao, J. K.; Dougherty, G. G., Jr.; Reddy, R. D.; Keshavan, M. S.; Montrose, D. M.; Matson, W. R.; Rozen, S.; Krishnan, R. R.; McEvoy, J.; Kaddurah-Daouk, R. Altered interactions of tryptophan metabolites in first-episode neuroleptic-naive patients with schizophrenia. Mol. Psychiatry 2010, 15 (9), 938–953. (13) Khaitovich, P.; Lockstone, H. E.; Wayland, M. T.; Tsang, T. M.; Jayatilaka, S. D.; Guo, A. J.; Zhou, J.; Somel, M.; Harris, L. W.; Holmes, E.; P€a€abo, S.; Bahn, S. Metabolic changes in schizophrenia and human brain evolution. Genome Biol. 2008, 9 (8), R124. (14) Tsang, T. M.; Huang, J. T.; Holmes, E.; Bahn, S. Metabolic profiling of plasma from discordant schizophrenia twins: correlation between lipid signals and global functioning in female schizophrenia patients. J. Proteome Res. 2006, 5 (4), 756–760. (15) Li, H.; Xie, Z.; Lin, J.; Song, H.; Wang, Q.; Wang, K.; Su, M.; Qiu, Y.; Zhao, T.; Song, K.; Wang, X.; Zhou, M.; Liu, P.; Zhao, G.; Zhang, Q.; Jia, W. Transcriptomic and metabonomic profiling of obesity-prone and obesity-resistant rats under high fat diet. J. Proteome Res. 2008, 7 (11), 4775–4783. (16) Smith, C. A.; Want, E. J.; O’Maille, G.; Abagyan, R.; Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling

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Metabolomic Profiling to Identify Potential Serum Biomarkers for

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