Unbiased Metabolite Profiling of Schizophrenia ... - ACS Publications

Article pubs.acs.org/jpr

Unbiased Metabolite Profiling of Schizophrenia Fibroblasts under Stressful Perturbations Reveals Dysregulation of Plasmalogens and Phosphatidylcholines Joanne H. Huang,†,‡ Hyoungjun Park,§ Jonathan Iaconelli,†,‡ Shaunna S. Berkovitch,†,‡ Bradley Watmuff,†,‡ Donna McPhie,∥ Dost Ö ngür,∥ Bruce M. Cohen,∥ Clary B. Clish,‡ and Rakesh Karmacharya*,†,‡,∥ J. Proteome Res. 2017.16:481-493. Downloaded from pubs.acs.org by OPEN UNIV OF HONG KONG on 01/28/19. For personal use only.

†

Center for Experimental Drugs and Diagnostics, Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts 02114, United States ‡ Chemical Biology Program, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, United States § Institute of Neuroinformatics, ETH Zurich and University of Zurich, CH-8057, Zurich, Switzerland ∥ Schizophrenia and Bipolar Disorder Program, Harvard Medical School and McLean Hospital, Belmont, Massachusetts 02478, United States S Supporting Information *

ABSTRACT: We undertook an unbiased metabolite profiling of fibroblasts from schizophrenia patients and healthy controls to identify metabolites and pathways that are dysregulated in disease, seeking to gain new insights into the disease biology of schizophrenia and to discover potential disease-related biomarkers. We measured polar and nonpolar metabolites in the fibroblasts under normal conditions and under two stressful physiological perturbations: growth in low-glucose media and exposure to the steroid hormone dexamethasone. We found that metabolites that were significantly different between schizophrenia and control subjects showed separation of the two groups by partial least-squares discriminant analysis methods. This separation between schizophrenia and healthy controls was more robust with metabolites identified under the perturbation conditions. The most significant individual metabolite differences were also found in the perturbation experiments. Metabolites that were significantly different between schizophrenia and healthy controls included a number of plasmalogens and phosphatidylcholines. We present these results in the context of previous reports of metabolic profiling of brain tissue and plasma in schizophrenia. These results show the applicability of metabolite profiling under stressful perturbations to reveal cellular pathways that may be involved in disease biology. KEYWORDS: metabolic profiling, schizophrenia, phosphatidylcholine, plasmalogen

■

medications.3 Abnormalities of insulin and IGF-1 levels have also been reported in patients with schizophrenia, including in ̈ patients.4 Elevated plasma levels of IGF-1 antipsychotic-naive have been reported in patients with first-episode psychosis.5 First-degree relatives of schizophrenia patients show differences in insulin and IGF-1 levels and the presence of increased insulin

INTRODUCTION

Many studies suggest the presence of significant dysfunction in cellular metabolism in the disease biology of schizophrenia.1 Patients with schizophrenia have an increased risk for insulin resistance, impaired glucose tolerance, and development of diabetes mellitus.2 Metabolic abnormalities in schizophrenia are often attributed to the use of antipsychotic medications, but insulin resistance and risk for diabetes have been shown to be present even independent of the use of psychotropic © 2016 American Chemical Society

Received: July 6, 2016 Published: November 8, 2016 481

DOI: 10.1021/acs.jproteome.6b00628 J. Proteome Res. 2017, 16, 481−493

Article

Journal of Proteome Research

they are “perturbed” with cellular stress. We discuss these findings in the context of previous reports of differences in these pathways in studies of schizophrenia brains.19

resistance.6 Studies have also shown a correlation between serum insulin levels and psychopathology profiles in patients with schizophrenia.7 IGF-1 levels have been reported to correlate with the presence of negative symptoms of schizophrenia.5 Moreover, metabolic profiling studies of plasma samples from monozygotic twins discordant for schizophrenia have suggested a link between metabolic dysregulation and the disease biology of schizophrenia.8 Altered cerebral metabolism has been described in ̈ patients, schizophrenia patients, including in medication-naive using various techniques. An NMR spectroscopy study found that first-episode patients with schizophrenia had significantly elevated glucose concentrations in the CSF, compared to controls, whereas CSF acetate and lactate concentrations were reduced in these patients.9 Moreover, treatment with antipsychotics normalized the CSF metabolite profile in 50% of the patients.9 Positron emission tomography studies have shown decreased glucose metabolism in various cortical and thalamic areas in patients with schizophrenia.10 A post-mortem study reported a decrease in insulin receptor mediated signaling in the dorsolateral prefrontal cortex in patients with schizophrenia.11 Antipsychotic medications have been the mainstay of treatment for schizophrenia and bipolar disorder since the initial discovery of chlorpromazine in the 1950s.12 It is now clear that antipsychotic medications, especially the newer atypical antipsychotic medications, lead to significant metabolic side effects, specifically to a much higher incidence of diabetes.13 Incidentally, antipsychotic medications with better therapeutic efficacy appear to have the most significant metabolic adverse effects as well.14 Clozapine, which has superior efficacy vis-à-vis other antipsychotics, leads to the most significant adverse metabolic effects.15 In animal studies, clozapine has been shown to modulate specific developmental and behavioral phenotypes though modulation of the insulin/ IGF-1 pathway.16 These studies raise the question of whether mechanisms that underlie the therapeutic actions of antipsychotic medications impinge on signaling pathways involved in cellular metabolism. The investigation of schizophrenia disease biology is hindered by the inability to study live neuronal tissue from patients since brain biopsies for routine research efforts are not feasible for technical and ethical reasons. Investigators have often used accessible peripheral tissues to study cellular abnormalities with the expectation that the strong genetic determination of risk may be reflected as cellular abnormalities in surrogate tissue.17 Studies in fibroblasts and bloods cells in schizophrenia suggest that disease-related cellular phenotypes can be observed in peripheral cells.18 Given the evidence for metabolic dysregulation in schizophrenia and studies showing that some disease-related cellular features can be observed in fibroblasts, we undertook a study to profile the metabolome of fibroblasts from subjects with schizophrenia and age- and sexmatched healthy controls. We undertook experiments to measure relative levels of metabolites in an unbiased profiling approach. We performed experiments under normal growth conditions as well as under two stressful perturbations: growth in low-glucose media and exposure to dexamethasone. We identified metabolites that were differentially regulated in the schizophrenia and healthy control groups under these three different conditions. We report intriguing findings of relative differences in a number of plasmalogens and phosphatidylcholines in fibroblasts from schizophrenia subjects, especially when

■

EXPERIMENTAL PROCEDURES

Human Subjects

Subjects with schizophrenia were recruited from the Schizophrenia and Bipolar Disorder Program at McLean Hospital with Institutional Review Board (IRB) approval. The initial subject recruitment referrals were based on diagnoses by treating psychiatrists. The subject enrollment process included a semistructured interview using the Structured Clinical Interview for DSM Disorders to ascertain the diagnoses. For healthy controls, subjects were chosen who had no previous psychiatric diagnoses, treatments, or first-degree relatives with a major psychotic or affective disorder. Exclusion criteria included subjects who had any co-morbid neurological disorder. Isolation of Fibroblasts

Fibroblasts were obtained by informed consent through punch biopsies performed by a physician, under a protocol approved by the IRB. The biopsy specimen was minced into 0.5 mm pieces and placed in the center of a 6 well plate with 3 mL of fibroblast media (DMEM Gibco 11995-065 containing 25 mM glucose, 10% FBS, 100 U/mL penicillin, 100 μg/mL streptomycin). After a week of incubation in 37 °C and 5% CO2, dense fibroblast outgrowths were treated with 0.05% trypsin and passed through a 70 mm strainer after addition of media to remove large pieces of tissue. Cells were passaged at a 1:3 ratio every 5−7 days until the cells reached 80% confluence and were then frozen and stored in liquid nitrogen until use. Sample Preparation for Polar Metabolites

Fibroblasts were controlled for passage number between the different lines to ensure that variations observed are not due to differences in the passage number or cellular senescence. Fibroblasts were thawed and grown in 6-well cell culture plates (Corning, CLS3506) to 80% confluence. Cells were then incubated for 6 h in fresh normal growth media, low-glucose, or dexamethasone containing media. For the low-glucose condition experiments, cells were grown in media containing only 1 mM glucose for 6 h, compared to 25 mM under normal growth conditions. For the dexamethasone experiments, cells were cultured in the presence of 1 μM dexamethasone for 6 h in normal fibroblast media. At the end of the 6 h incubation with normal media, low-glucose media, or media with dexamethasone, cells were washed with cold PBS (without Mg2+/Ca2+) to initiate metabolite extraction. After aspirating the PBS, 800 μL of cold 80% methanol (−80 °C) was immediately added to the plates and incubated at −80 °C for 15 min. Cells were then lifted with cell scrapers, and the cell lysate/methanol mixture was transferred to 1.5 mL centrifuge tubes on dry ice. Tubes containing the lysate/methanol mixture were centrifuged (9000g, 4 °C, 10 min) to pellet cell debris and proteins. The supernatants were transferred to new 1.5 mL centrifuge tubes on dry ice, and the old 1.5 mL tubes containing pellets were kept for further extraction. Pellets were resuspended in 100 μL of 80% methanol (−80 °C) and centrifuged (9000g, 4 °C, 5 min), and the supernatants were pooled in tubes containing the earlier supernatants. The collected supernatants were stored at −80 °C. 482


Article

Journal of Proteome Research Sample Preparation for Lipids

internal standard (Avanti Polar Lipids, Alabaster, AL). Samples (10 μL) were injected onto a 100 × 2.1 mm, 1.7 μm ACQUITY BEH C8 column (Waters, Milford, MA) that was eluted isocratically with 80% mobile phase A (95:5:0.1 v/v/v 10 mM ammonium acetate/methanol/formic acid) for 1 min followed by a linear gradient to 80% mobile phase B (99.9:0.1 v/v methanol/formic acid) over 2 min, a linear gradient to 100% mobile phase B over 7 min, and then 3 min at 100% mobile phase B. Positive ion mode MS data were acquired using full scan analysis over 200−1100 m/z at 70 000 resolution and 3 Hz data acquisition rate. Other MS settings were as follows: sheath gas, 50; in-source CID, 5 eV; sweep gas, 5; spray voltage, 3 kV; capillary temperature, 300 °C; S-lens RF, 60; heater temperature, 300 °C; microscans, 1; automatic gain control target, 1 × 106; and maximum ion time, 100 ms. Identities of 149 lipids were determined based on comparison to reference plasma extracts and were denoted by the total number of carbons in the lipid acyl chain(s) and the total number of double bonds in the lipid acyl chain(s). MultiQuant 2.1 (SCIEX, Framingham, MA) was used for peak detection and integration of negative ion mode targeted data (110 metabolites), and TraceFinder 3.1 (Thermo Fisher Scientific, Waltham, MA) was used for detection and integration of known polar metabolite peaks (86 metabolites). Both MultiQuant and TraceFinder results were visually inspected for the quality of peak integration and to confirm metabolite identities against reference standards. Nontargeted processing of positive ion mode polar metabolite and lipid data was done using Progenesis CoMet 2.0 (Nonlinear Dynamics, Newcastle upon Tyne, UK) for unbiased detection and integration of peaks. A total of 11 993 LC-MS peaks were detected, of which 344 were annotated, known metabolites (Table S1). MetaboAnalyst 3.021 was used to analyze the data. Univariate analyses were undertaken to identify group differences among annotated metabolites between the two groups that each contained 10 biological replicates.

At the end of the 6 h incubation with normal media, lowglucose media, or media with dexamethasone, cells were washed once with cold PBS (without Mg2+/Ca2+) to initiate metabolite extraction. Cold isopropranol (800 μL, 4 °C) was added to the plate, and cells were immediately lifted with cell scrapers. The cell lysate/isopropranol mixtures were transferred to 1.5 mL centrifuge tubes on ice and incubated at 4 °C for 1 h while covered with aluminum foil to avoid exposure to light. Tubes containing the lysate mixtures were then vortexed and centrifuged to remove cell debris and proteins (9000g, 4 °C, 10 min). Supernatants were collected and stored at −80 °C. Metabolite Profiling

Polar metabolites and lipids in cell extracts were profiled using a combination of three liquid chromatography tandem mass spectrometry (LC-MS) methods. Targeted, negative ion mode polar metabolite profiling data were acquired using an ACQUITY UPLC (Waters Corp., Milford, MA) coupled to a 5500 QTRAP triple quadrupole mass spectrometer (SCIEX, Framingham, MA) as described previously.20 Briefly, cell extracts (10 μL) were injected directly onto a 150 × 2.0 mm Luna NH2 column (Phenomenex, Torrance, CA) that was eluted at a flow rate of 400 μL/min with initial conditions of 10% mobile phase A [20 mM ammonium acetate and 20 mM ammonium hydroxide (Sigma-Aldrich, St. Louis, MO) in water (VWR)] and 90% mobile phase B [10 mM ammonium hydroxide in 75:25 v/v acetonitrile/methanol (VWR)] followed by a 10 min linear gradient to 100% mobile phase A. The ion spray voltage was −4.5 kV, and the source temperature was 500 °C. Multiple reaction monitoring settings were determined using authentic reference standards as previously described.20 Nontargeted, positive ionization mode polar metabolite LC−MS data were conducted using Shimadzu Nexera X2 U-HPLC (Shimadzu Corp., Marlborough, MA) coupled to a Q Exactive hybrid quadrupole orbitrap mass spectrometer (Thermo Fisher Scientific, Waltham, MA). Cell extracts (100 μL) were dried using a nitrogen evaporator and resuspended in water (10 μL) and 90 μL of 74.9:24.9:0.2 v/v/v acetonitrile/methanol/formic acid containing stable isotopelabeled internal standards (valine-d8, Sigma-Aldrich, St. Louis, MO, and phenylalanine-d8, Cambridge Isotope Laboratories, Andover, MA). Samples (10 μL) were injected onto a 150 × 2 mm, 3 μm Atlantis HILIC column (Waters, Milford, MA), and the column was eluted isocratically at a flow rate of 250 μL/min with 5% mobile phase A (10 mM ammonium formate and 0.1% formic acid in water) for 0.5 min followed by a linear gradient to 40% mobile phase B (acetonitrile with 0.1% formic acid) over 10 min. MS analyses were carried out using electrospray ionization in the positive ion mode using full scan analysis over 70−800 m/z at 70 000 resolution and 3 Hz data acquisition rate. Other MS settings were as follows: sheath gas, 40; sweep gas, 2; spray voltage, 3.5 kV; capillary temperature, 350 °C; Slens RF, 40; heater temperature, 300 °C; microscans, 1; automatic gain control target, 1 ×106; and maximum ion time, 250 ms. Metabolite identities were confirmed using mass and retention time matching to authentic reference standards. Lipids were analyzed using a Shimadzu Nexera X2 U-HPLC (Shimadzu Corp., Marlborough, MA) coupled to an Exactive Plus orbitrap mass spectrometer (Thermo Fisher Scientific, Waltham, MA). Lipid extracts (100 μL) were dried using a nitrogen evaporator and resuspended in 100 μL of isopropanol containing 1,2-dilauroyl-sn-glycero-3-phosphocholine as an

■

RESULTS Fibroblasts were cultured from 10 subjects who met criteria for the DSM-IV-TR diagnosis of schizophrenia as well as 10 healthy controls matched for sex and age (Table 1). The two groups consisted of 6 pairs of male subjects and 4 pairs of female subjects individually matched for age. The average age at time of sample collection was 39.1 (SEM ± 3.63) years for the Table 1. Demographic Information and Medication History for Subjects in the Study healthy subjects

483

schizophrenia subjects

age

sex

age

sex

23 24 31 31 37 41 44 49

M M M F M M F M

21 27 32 32 36 40 46 50

M M M M F M F F

52 55

F F

50 57

M F

antipsychotic medication history clozapine quetiapine clozapine, risperidone, haloperidol risperidone aripiprazole clozapine, haloperidol clozapine, haloperidol, olanzapine, quetiapine, perphenazine, thiothixene clozapine DOI: 10.1021/acs.jproteome.6b00628 J. Proteome Res. 2017, 16, 481−493

Article

Journal of Proteome Research schizophrenia subjects and 38.7 (SEM ± 3.60) for healthy controls. All schizophrenia subjects were treated with antipsychotic medications, with half of the group treated with clozapine. None of the subjects had any diagnoses of neurological or cardiovascular disease. We measured relative levels of 11 993 LC−MS peaks, including 344 annotated metabolites, as described above. We normalized the raw data for each metabolite to the total metabolites for that particular experimental well before analysis. These data were used to generate a metabolite signature, and we employed a partial least-squares discriminant analysis (PLSDA) approach to model the separation of the two groups (Figure 1). To analyze the metabolite data for fibroblasts grown

Table 2. Annotated Metabolites That Significantly Distinguish (p < 0.05, Unpaired t-Test) Schizophrenia and Healthy Control Subjects under Normal Growth Conditions and the Effect Size of Each (Cohen’s d) metabolite

p value

effect size (d)

C30:0 PC putrescine C34:1 PC C40:7 PC plasmalogen 5-adenosylhomocysteine C34:1 PC plasmalogen-B alpha-glycerophosphate alpha-glycerophosphocholine C30:1 PC alanine C36:1 PC C36:0 PC C34:0 PC

0.019252 0.021574 0.021782 0.026792 0.027156 0.03084 0.031379 0.03446 0.035258 0.036789 0.038929 0.048732 0.049623

−1.149598281 −1.125220857 −1.123160053 −1.078409635 −1.119138725 −1.047657955 1.075813744 1.037820492 −1.018116716 −1.008671867 −0.996068162 −0.945419167 −0.94128777

separation of the schizophrenia samples from healthy controls under both perturbation conditions, with clearer separation between the groups compared to normal growth conditions (Figure 3). The lists of annotated metabolites were different than the ones under normal growth conditions, and the highest-ranked metabolites had smaller p values in the perturbation experiments. We identified 14 metabolites that were statistically significantly different between the two groups (p < 0.05) under low-glucose conditions (Table 3). The metabolite that was most significantly different under low-glucose conditions was lactate, followed by choline. The list also includes three phosphatidylcholines, three plasmalogens, and two diacylglycerols, as well as lactose, pantothenate, cotinine, and ornithine. We compared the levels of these individual metabolite hits between cells from schizophrenia and healthy controls. The top three hits in the list, lactate, choline, and lactose, were present at higher levels in the schizophrenia group compared to the healthy control group, whereas the phosphatidylcholines and plasmalogens in the list were again present at lower levels in the schizophrenia group compared to the healthy control group (Figure 4). In the experiment where the cells were exposed to dexamethasone, we identified 12 metabolites that were different between the two groups at statistically significant levels (p < 0.05) (Table 4). The metabolite that was most significantly different between the groups was alanine, followed by C14:0 cholesteryl ester. The list also includes four phosphatidylcholines, three plasmalogens, uracil, cotinine, and α-glycerophosphocholine. While levels of uracil, cotinine, and α-glycerophosphocholine were higher in the schizophrenia group compared to the healthy control group, the phosphatidylcholines and plasmalogens in the list, as well as alanine, were lower in the schizophrenia group compared to the healthy control group (Figure 5). There were two metabolites that were consistently different between the schizophrenia and healthy control groups under all three conditions. C40:7 PC plasmalogen and C30:1 PC were lower in schizophrenia cells at baseline and under the perturbation conditions. There were three metabolites, cotinine, C36:5 PC plasmalogen-A, and C40:10 PC, that were not different between the groups under normal growth conditions but were different between the schizophrenia and

Figure 1. Separation of schizophrenia patients and healthy controls in a partial least-squares discriminant analysis (PLS-DA) model with metabolites that were significantly different between the two groups. O refers to schizophrenia subjects, and X refers to healthy control subjects.

under normal growth conditions, we undertook univariate analyses to identify group differences among the metabolites between schizophrenia cells and healthy control cells. Under normal growth conditions, there were 13 annotated metabolites that were different between the two groups at statistically significant levels, at a significance threshold of p < 0.05 by univariate analysis (Table 2). The list is notable for its high number of phosphatidylcholines: 6 six out of 13 significant metabolites in the list were phosphatidylcholines, including the highest-ranked metabolite, C30:0 PC. The list also includes αglycerophosphocholine and two plasmalogens. The other metabolites in the list included putrescine, 5-adenosylhomocysteine, α-glycerophosphate, and alanine. We found that the range of values for these metabolites in the schizophrenia group was generally tighter than in the healthy control group and the phosphatidylcholines and plasmalogens in the list were present at lower levels in the schizophrenia group compared to the healthy control group (Figure 2). Peturbational Profiling

We analyzed the data similarly for metabolites collected for experiments where fibroblasts were exposed to two different perturbations for 6 h: growth under low-glucose conditions and exposure to 1 μM dexamethasone. PLS-DA models generated with these metabolite signatures again showed a clear 484


Article


Figure 2. Distribution of normalized relative metabolite levels in schizophrenia patients and control subjects for the annotated metabolites that were significantly different between the two groups when grown under normal conditions. * denotes significance at p < 0.05 (unpaired t-test).

■

healthy control groups under both stress perturbations. Alanine, C34:1 PC, and α-glycerophosphocholine, which were significantly different between the groups under normal growth conditions, were again significantly different in the presence of dexamethasone, but they were not significantly different under low-glucose conditions.

DISCUSSION

Recent investigation of human disease biology using metabolite profiling of patient tissue has led to new insights into various human diseases.22 Metabolite profiling of patient tissue is also being to be used to explore the biology of various psychiatric disorders.17b,23 Such studies have suggested the possibility of 485


Article


Table 3. Annotated Metabolites That Significantly Distinguish (p < 0.05, Unpaired t-Test) Schizophrenia and Healthy Control Subjects under Low-Glucose Conditions and the Effect Size of Each (Cohen’s d) metabolite

p value

effect size (d)

lactate choline lactose pantothenate C40:7 PC plasmalogen C30:1 PC cotinine C40:9 PC C36:5 PC plasmalogen-A C34:2 DAG ornithine C36:2 DAG C42:11 PE plasmalogen C40:10 PC

0.009624 0.015182 0.016483 0.019694 0.02812 0.029025 0.029482 0.040596 0.041412 0.042471 0.04372 0.048494 0.048545 0.04965

1.295231468 1.199984097 1.18261275 −1.144751616 −1.06787139 −1.060947094 1.057531517 −0.986682166 −0.982218218 −0.976540634 0.970014786 −0.946530676 −0.946290022 −0.941163673

normal growth conditions (Tables 2−4). We report a number of metabolites that were present at significantly different relative levels between schizophrenia patients and healthy control subjects under normal conditions as well as metabolites that were significantly different between the two groups under the perturbations. We found that these metabolite profiles led to a clear separation of schizophrenia subjects from control subjects (Figures 1 and 3). Two metabolite classes, plasmalogens and phosphatidylcholines, stood out as being consistently different between the two groups. A subset of plasmalogens and phosphatidylcholines were significantly less abundant in the cells from schizophrenia subjects compared to the healthy control subjects. While some of those differences were present under normal growth conditions, differences in levels for others were apparent only under stressful perturbations. These results are of special interest given recent findings of differences in the dynamics of plasmalogens and phosphatidylcholines in schizophrenia. Plasmalogens are complex structural glycerophospholipids that play important roles in brain development.30 They are major components of membranes and are important for various functions of the membrane, including modulating the fluidity of the plasma membrane and membrane fusion for neurotransmitter release.31 Plasmalogens have a vinyl-ether linked fatty alcohol at the sn-1 position of the glycerol backbone along with a phosphoethanolamine or phosphocholine at sn-3, and they serve as reservoirs for fatty acid mediators including docosahexaenoic acid and arachidonic acid; these mediators are released from the sn-2 position of the glycerol backbone by phospholipases as part of a deacylation−reacylation cycle termed “lipid remodeling”.31 A number of studies have reported dysregulation of plasmalogens in schizophrenia.19b An initial study described lower levels of circulating plasmalogens in patients with schizophrenia.32 In another targeted lipidomic analysis, plasmalogen levels were again found to be reduced in the plasma of schizophrenia subjects.19a Our findings, in the context of an unbiased metabolite profiling study, of reduced levels of a number of plasmalogens in schizophrenia fibroblasts are intriguing in this regard. Dysregulation in phosphatidylcholines has been described in multiple studies of schizophrenia, as has the effect of antipsychotic medications in affecting their levels.33 Mass

Figure 3. Separation of schizophrenia patients and controls with perturbational profiling. PLS-DA models are shown for metabolites that were significantly different between the two groups for the lowglucose experiment (A) and the dexamethasone experiment (B). O refers to schizophrenia subjects, and X refers to healthy control subjects.

identifying biomarkers for schizophrenia,24 bipolar disorder,25 major depressive disorder,26 and PTSD27 and have also been used to study medication-induced metabolic changes.23c,24f,28 We report our findings from an unbiased metabolic profiling study of fibroblasts from schizophrenia patients and healthy controls, under normal growth conditions as well under physiologic perturbations. Our experimental approach aimed to see whether stressful conditions may expose disease-related vulnerabilities in patient cells that may not be evident under normal growth conditions, as a way to recreate a gene− environment interaction in vitro. 25e,29 We found that perturbation resulted in the identification of specific metabolites that were significantly different between the two groups, and the highest ranked metabolites had lower, more statistically significant p values than metabolites that were different under 486


Article


Figure 4. Relative metabolite levels under low-glucose conditions. Relative metabolite levels between schizophrenia patients and healthy controls subjects are shown for annotated metabolites that were significantly different between the two groups. * denotes significance at p < 0.05 (unpaired ttest).

nuclear magnetic resonance spectroscopy (MRS) studies in schizophrenia subjects have shown differences in glycerophosphorylcholine in the prefrontal cortex in schizophrenia and

spectrometry-based studies of post-mortem brains in schizophrenia revealed abnormalities in a number of phosphatidylcholines, in both the gray and white matter.19d 31Phosphorus 487


Article


been hypothesized to play a role in schizophrenia, but there are no known reports of differences in 5-adenosylhomocysteine in schizophrenia.39 There are no previous reports of differences in α-glycerophosphate in schizophrenia. While relative levels of alanine were lower in schizophrenia fibroblasts under normal growth conditions, alanine was the most significantly different metabolite between the groups when the fibroblasts were cultured in the presence of dexamethasone. Lower plasma levels of alanine have been shown to be accompanied by more severe psychosis symptoms in hospitalized patients with schizophrenia, and increases in alanine levels have been shown to correlate with symptom improvement.40 D-Alanine is an endogenous agonist at the NMDA-glycine site and has been hypothesized to have beneficial effects on schizophrenia.41 In a double-blind, placebo-controlled trial, addition of D-alanine to patients’ antipsychotic medication regimen led to significant reduction in the Clinical Global Impression Scale and Positive and Negative Syndrome Scale (PANSS) total scores.42 The next metabolite that was significantly different between the two groups in the presence of dexamethasone was the cholesteryl ester C14:0 CE, which was lower in the schizophrenia fibroblasts. Previous studies have reported lower levels of total cholesteryl esters in fibroblasts from schizophrenia, but there are no previous studies that have identified specific cholesteryl esters that were different.43 The metabolite that was most significantly different between the two groups under low-glucose conditions was lactate. Postmortem studies in schizophrenia have shown elevated levels of lactate in the brain.9,44 Interestingly, measurement in cerebrospinal fluid (CSF) has shown lower levels of lactate in first-episode schizophrenia and prodromal patients while showing elevated levels in chronic patients.9,45 Serum lactate levels have been shown to be elevated in schizophrenia compared to healthy controls.46 Similar to these results, we found that lactate levels in schizophrenia fibroblasts under lowglucose conditions were significantly elevated compared to levels in healthy controls. These results also mirror MRS studies that show the presence of aberrant bioenergetics in the brains of patients with schizophrenia.47 Among other metabolites that were significantly different under low-glucose conditions were choline, lactose, pantothenate, ornithine, and two diacylglycerols (C34:2 DAG and C36:2 DAG). Choline levels, which were elevated in schizophrenia fibroblasts under low-glucose conditions, have been described to be aberrant in schizophrenia and associated with longer durations of untreated psychosis in MRS studies in first-episode schizophrenia.48 While pantothenate has been hypothesized to play a role in the pathophysiology of schizophrenia, there have been no studies to date that have shown any differences in levels of pantothenate in schizophrenia subjects.49 Studies of plasma from schizophrenia patients have reported elevated levels of ornithine, consistent with our findings in the fibroblasts.50 Early studies in platelets of schizophrenia subjects have shown differences in phosphoinositide turnover, and it was reported that elevated DAG levels correlated with improved outcomes after a period of 3 years.51 In our study, we found lower levels of C34:2 DAG and C36:2 DAG in schizophrenia subjects under low-glucose conditions. We are encouraged by these intriguing findings in the context of an unbiased profiling study, which has resulted in the identification of a number of metabolites that are consistent with previous studies in brain samples and other peripheral

Table 4. Annotated Metabolites That Significantly Distinguish (p < 0.05, Unpaired t-Test) Schizophrenia and Healthy Control Subjects in the Presence of Dexamethasone and the Effect Size of Each (Cohen’s d) metabolite

p value

effect size (d)

alanine C14:0 CE C40:7 PC plasmalogen C36:5 PC plasmalogen-A C40:10 PC uracil C30:1 PC C32:1 PC C34:1 PC cotinine α-glycerophosphocholine C40:7 PE plasmalogen

0.005073 0.019224 0.019595 0.022688 0.030644 0.030771 0.031239 0.036382 0.038294 0.041624 0.044928 0.045808

−1.426596596 −1.149914784 −1.145826457 −1.11439437 −1.049059294 1.101509612 −1.044835448 −1.011148696 −0.999740567 0.981071222 0.963857371 −0.959468221

have also shown that phosphatidylcholine levels in the cerebral cortex correlated with symptoms of psychosis while levels of cortical glycerophosphocholine correlated with executive functioning.19c Another MRS study that included twin pairs discordant for schizophrenia again showed differences in phosphocholines and glycerophosphocholines in the hippocampus of schizophrenia subjects compared to controls.34 Investigation of post-mortem brain tissue in schizophrenia using matrix-assisted laser desorption/ionization imaging mass spectrometry showed abnormal distributions of phosphatidylcholine in the frontal cortex.35 Another post-mortem study reported decreased levels of phosphatidylcholine in thalamic tissue from schizophrenia subjects.36 In our comparison of metabolite profiles of fibroblasts from schizophrenia subjects and healthy controls, phosphatidylcholines were highly represented in the list of metabolites that were significantly different between the two groups (Tables 2−4). While C30:1 PC was consistently lower in the schizophrenia fibroblasts under normal growth conditions and under perturbation, there were others that were different only under specific conditions. A number of these phosphatidylcholines are of interest in the context of previous findings. C34:1 PC and C36:1 PC, which were among the phosphatidylcholines identified in our study, have been shown to be significantly different between post-mortem brains of schizophrenia subjects and healthy controls, in both the gray and white matter.19d C30:0 PC, which was the highest ranked metabolite under normal growth conditions, was shown to be significantly different between schizophrenia subjects and healthy controls in the gray matter but not in the white matter.19d αGlycerophosphorocholine, another metabolite that was significantly different between the groups under normal growth conditions, has been shown to be different in studies of brain tissues as well.19c Among other metabolites that were significantly different between the two groups under normal growth conditions were putrescine, 5-adenosylhomocysteine, and α-glycerophosphate. Putrescine belongs to the broader polyamine family, and polyamines and trace amines have putative roles in the central nervous system and the mechanisms of antipsychotic medications.37 While differences in total polyamines and spermidine have been described in fibroblasts from schizophrenia subjects, there have been no previous reports of differences in putrescine levels.38 Similarly, methylation has 488


Article


Figure 5. Relative metabolite levels with exposure to dexamethasone. Relative metabolite levels between schizophrenia patients and control subjects are shown for annotated metabolites that were significantly different between the two groups. * denotes significance at p < 0.05 (unpaired t-test).

tissues as well as novel metabolites that may have a role in disease biology, especially for cellular functioning under stressful conditions. Nonetheless, these findings are preliminary, and there are a number of caveats and limitations to our study. One limitation is the sample size, which was limited by the cost of the metabolic profiling experiments. We identified a number of metabolites that were different between the groups at a significance threshold of p < 0.05, but these were not significant after adjusting for multiple testing. Our findings now provide the impetus for targeted studies to validate the top metabolites identified in these profiling experiments. Another caveat relates to the patient population studied. Since the ̈ the results schizophrenia subjects were not medication naive, could have been confounded by the effect of the medications. The fibroblasts were grown and passaged in fresh media for multiple passages before the metabolic profiling experiments,

which makes such a scenario less likely. Another caveat to this study is the fact that we have used peripheral cells in a profiling study of a psychiatric disorder that affects the brain.52 With an eye toward finding potential biomarkers, we worked with easily accessible tissue. In light of the strong genetic determination of risk for schizophrenia, we also hypothesized that dysregulation in metabolic pathways associated with disease in the central nervous system may also be present in peripheral tissues such as fibroblasts.53 Analysis of whole-genome biomarker expression in blood and brain samples shows that about 22% of the total transcriptome expressed in post-mortem brain is expressed at a similar level and pattern in blood elements.54 Studies comparing gene expression patterns, epigenetic differences and subcellular organelles in bipolar disorder and schizophrenia have found disease-related changes that are present in both the brain and peripheral cells.17a,29,55 489


Article


U.; Robinson, D. G.; Schooler, N. R.; Brunette, M. F.; Mueser, K. T.; Rosenheck, R. A.; Marcy, P.; Addington, J.; Estroff, S. E.; Robinson, J.; Penn, D. L.; Azrin, S.; Goldstein, A.; Severe, J.; Heinssen, R.; Kane, J. M. Cardiometabolic risk in patients with first-episode schizophrenia spectrum disorders: baseline results from the RAISE-ETP study. JAMA Psychiatry 2014, 71 (12), 1350−63. (4) (a) Venkatasubramanian, G.; Chittiprol, S.; Neelakantachar, N.; Naveen, M. N.; Thirthall, J.; Gangadhar, B. N.; Shetty, K. T. Insulin and insulin-like growth factor-1 abnormalities in antipsychotic-naive schizophrenia. Am. J. Psychiatry 2007, 164 (10), 1557−60. (b) Guest, P. C.; Wang, L.; Harris, L. W.; Burling, K.; Levin, Y.; Ernst, A.; Wayland, M. T.; Umrania, Y.; Herberth, M.; Koethe, D.; van Beveren, J. M.; Rothermundt, M.; McAllister, G.; Leweke, F. M.; Steiner, J.; Bahn, S. Increased levels of circulating insulin-related peptides in firstonset, antipsychotic na,àö√òve schizophrenia patients. Mol. Psychiatry 2010, 15 (2), 118−9. (5) Palomino, A.; González-Pinto, A.; Martinez-Cengotitabengoa, M.; Ruiz de Azua, S.; Alberich, S.; Mosquera, F.; Matute, C. Relationship between negative symptoms and plasma levels of insulin-like growth factor 1 in first-episode schizophrenia and bipolar disorder patients. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 2013, 44, 29−33. (6) Yang, J.; Liu, X.; Wang, L.; Lv, H.; Yu, J.; Xun, Z.; Yang, G. Abnormality of glycometabolism related factors in non-psychotic offspring of schizophrenic patients. Psychiatry Res. 2012, 198 (2), 183− 6. (7) Fan, X.; Liu, E.; Pristach, C.; Goff, D. C.; Henderson, D. C. Higher fasting serum insulin levels are associated with a better psychopathology profile in acutely ill non-diabetic inpatients with schizophrenia. Schizophrenia research 2006, 86 (1−3), 30−5. (8) 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), 756−60. (9) 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 medicine 2006, 3 (8), e327. (10) (a) Clark, C.; Kopala, L.; Li, D. K.; Hurwitz, T. Regional cerebral glucose metabolism in never-medicated patients with schizophrenia. Can. J. Psychiatry 2001, 46 (4), 340−5. (b) Chen, R. Y.; Chen, E.; Ho, W. Y. A five-year longitudinal study of the regional cerebral metabolic changes of a schizophrenic patient from the first episode using Tc-99m HMPAO SPECT. European archives of psychiatry and clinical neuroscience 2000, 250 (2), 69−72. (11) Zhao, Z.; Ksiezak-Reding, H.; Riggio, S.; Haroutunian, V.; Pasinetti, G. M. Insulin receptor deficits in schizophrenia and in cellular and animal models of insulin receptor dysfunction. Schizophrenia research 2006, 84 (1), 1−14. (12) Gardner, D. M.; Baldessarini, R. J.; Waraich, P. Modern antipsychotic drugs: a critical overview. CMAJ. 2005, 172 (13), 1703− 11. (13) Newcomer, J. W. Antipsychotic medications: metabolic and cardiovascular risk. J. Clin Psychiatry 2007, 68 (Suppl 4), 8−13. (14) (a) Girgis, R. R.; Javitch, J. A.; Lieberman, J. A. Antipsychotic drug mechanisms: links between therapeutic effects, metabolic side effects and the insulin signaling pathway. Mol. Psychiatry 2008, 13 (10), 918−29. (b) Lieberman, J. A.; Stroup, T. S.; McEvoy, J. P.; Swartz, M. S.; Rosenheck, R. A.; Perkins, D. O.; Keefe, R. S.; Davis, S. M.; Davis, C. E.; Lebowitz, B. D.; Severe, J.; Hsiao, J. K. Effectiveness of antipsychotic drugs in patients with chronic schizophrenia. N. Engl. J. Med. 2005, 353 (12), 1209−23. (15) (a) Wirshing, D. A.; Wirshing, W. C.; Kysar, L.; Berisford, M. A.; Goldstein, D.; Pashdag, J.; Mintz, J.; Marder, S. R. Novel antipsychotics: comparison of weight gain liabilities. J. Clin. Psychiatry 1999, 60 (6), 358−63. (b) Lieberman, J. A. Metabolic changes associated with antipsychotic use. Prim Care Companion J. Clin Psychiatry 2004, 6 (Suppl 2), 8−13.

Nonetheless, it is important to follow up these results in human neuronal cells, derived from patient induced pluripotent stem cells (iPSCs) to see which group-related metabolite differences observed in the fibroblasts are present in neuronal cells as well.56

■

CONCLUSIONS In summary, we undertook unbiased metabolite profiling fibroblasts from schizophrenia subjects and matched healthy control subjects, under basal conditions as well as in the presence of two stressful cellular perturbations, with the hypothesis that the complex genetic vulnerability of schizophrenia subjects will result in relative differences in metabolites in pathways that impinge on disease biology. We found by PLSDA analyses that metabolite profiles lead to a clear separation of schizophrenia and control cells, with clearer separation in the presence of stressful perturbations. Furthermore, our unbiased profiling study led to the identification of a number of metabolites that had been previously described to be aberrant in schizophrenia based on studies of post-mortem brains and cerebrospinal fluid, as well as a number of novel metabolites that have not yet been implicated in schizophrenia disease biology.

■

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jproteome.6b00628. Description of Table S1 (PDF) Annotated metabolites (Table S1) (XLSX)

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel.: 617-726-5119. Fax: 617-726-0830. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS This study was carried out with funding from the Ryan Licht Sang Bipolar Foundation, the National Institute of Mental Health, the Doris Duke Charitable Foundation, the Phyllis & Jerome Lyle Rappaport Foundation, and Steve Willis and Elissa Freud.

■

ABBREVIATIONS CE, cholesteryl ester; CTRL, control; DAG, diacylglycerol; PC, phosphatidylcholine; PLSDA, partial least-squares discriminant analysis; SCZ, schizophrenia

■

REFERENCES

(1) (a) Dwyer, D. S.; Lu, X. H.; III, A. M. Neuronal glucose metabolism and schizophrenia: therapeutic prospects? Expert Rev. Neurother. 2003, 3 (1), 29−40. (b) Kaidanovich-Beilin, O.; Cha, D. S.; McIntyre, R. S. Crosstalk between metabolic and neuropsychiatric disorders. F1000 Biol. Rep 2012, 4, 14. (2) Meyer, J. M.; Stahl, S. M. The metabolic syndrome and schizophrenia. Acta Psychiatr. Scand. 2009, 119 (1), 4−14. (3) (a) FREEMAN, H. Resistance to insulin in mentally disturbed soldiers. Arch. Neurol. Psychiatry 1946, 56 (1), 74−8. (b) Correll, C. 490


Article


patients with severe mental illness. Front Biosci (Elite Ed) 2011, 3, 308−14. (c) Cai, H. L.; Li, H. D.; Yan, X. Z.; Sun, B.; Zhang, Q.; Yan, M.; Zhang, W. Y.; Jiang, P.; Zhu, R. H.; Liu, Y. P.; Fang, P. F.; Xu, P.; Yuan, H. Y.; Zhang, X. H.; Hu, L.; Yang, W.; Ye, H. S. Metabolomic analysis of biochemical changes in the plasma and urine of first-episode neuroleptic-naive schizophrenia patients after treatment with risperidone. J. Proteome Res. 2012, 11 (8), 4338−50. (d) Sethi, S.; Brietzke, E. Omics-Based Biomarkers: Application Of Metabolomics In Neuropsychiatric Disorders. Int. J. Neuropsychopharmacol. 2016, 19, pyv096. (e) Turck, C. W.; Filiou, M. D. What Have Mass Spectrometry-Based Proteomics and Metabolomics (not) Taught Us about Psychiatric Disorders? Molecular Neuropsychiatry 2015, 1, 69− 75. (24) (a) Perkins, D. O.; Jeffries, C. D.; Addington, J.; Bearden, C. E.; Cadenhead, K. S.; Cannon, T. D.; Cornblatt, B. A.; Mathalon, D. H.; McGlashan, T. H.; Seidman, L. J.; Tsuang, M. T.; Walker, E. F.; Woods, S. W.; Heinssen, R. Towards a psychosis risk blood diagnostic for persons experiencing high-risk symptoms: preliminary results from the NAPLS project. Schizophr Bull. 2015, 41 (2), 419−28. (b) Chan, M. K.; Krebs, M. O.; Cox, D.; Guest, P. C.; Yolken, R. H.; Rahmoune, H.; Rothermundt, M.; Steiner, J.; Leweke, F. M.; van Beveren, N. J.; Niebuhr, D. W.; Weber, N. S.; Cowan, D. N.; Suarez-Pinilla, P.; Crespo-Facorro, B.; Mam-Lam-Fook, C.; Bourgin, J.; Wenstrup, R. J.; Kaldate, R. R.; Cooper, J. D.; Bahn, S. Development of a blood-based molecular biomarker test for identification of schizophrenia before disease onset. Transl. Psychiatry 2015, 5, e601. (c) Tomasik, J.; Schwarz, E.; Guest, P. C.; Bahn, S. Blood test for schizophrenia. European archives of psychiatry and clinical neuroscience 2012, 262 (Suppl 2), 79−83. (d) Koike, S.; Bundo, M.; Iwamoto, K.; Suga, M.; Kuwabara, H.; Ohashi, Y.; Shinoda, K.; Takano, Y.; Iwashiro, N.; Satomura, Y.; Nagai, T.; Natsubori, T.; Tada, M.; Yamasue, H.; Kasai, K. A snapshot of plasma metabolites in first-episode schizophrenia: a capillary electrophoresis time-of-flight mass spectrometry study. Transl. Psychiatry 2014, 4, e379. (e) Ores̆ic̆, M.; Tang, J.; SeppänenLaakso, T.; Mattila, I.; Saarni, S. E.; Saarni, S. I.; Lönnqvist, J.; SysiAho, M.; Hyötyläinen, T.; Perälä, J.; Suvisaari, J. Metabolome in schizophrenia and other psychotic disorders: a general populationbased study. Genome Med. 2011, 3 (3), 19. (f) Xuan, J.; Pan, G.; Qiu, Y.; Yang, L.; Su, M.; Liu, Y.; Chen, J.; Feng, G.; Fang, Y.; Jia, W.; Xing, Q.; He, L. Metabolomic profiling to identify potential serum biomarkers for schizophrenia and risperidone action. J. Proteome Res. 2011, 10 (12), 5433−43. (25) (a) Aberg, K. A.; McClay, J. L.; Nerella, S.; Clark, S.; Kumar, G.; Chen, W.; Khachane, A. N.; Xie, L.; Hudson, A.; Gao, G.; Harada, A.; Hultman, C. M.; Sullivan, P. F.; Magnusson, P. K.; van den Oord, E. J. Methylome-wide association study of schizophrenia: identifying blood biomarker signatures of environmental insults. JAMA Psychiatry 2014, 71 (3), 255−64. (b) Chen, J. J.; Huang, H.; Zhao, L. B.; Zhou, D. Z.; Yang, Y. T.; Zheng, P.; Yang, D. Y.; He, P.; Zhou, J. J.; Fang, L.; Xie, P. Sex-specific urinary biomarkers for diagnosing bipolar disorder. PLoS One 2014, 9 (12), e115221. (c) Chen, J. J.; Liu, Z.; Fan, S. H.; Yang, D. Y.; Zheng, P.; Shao, W. H.; Qi, Z. G.; Xu, X. J.; Li, Q.; Mu, J.; Yang, Y. T.; Xie, P. Combined application of NMR- and GC-MS-based metabonomics yields a superior urinary biomarker panel for bipolar disorder. Sci. Rep. 2014, 4, 5855. (d) Xu, X. J.; Zheng, P.; Ren, G. P.; Liu, M. L.; Mu, J.; Guo, J.; Cao, D.; Liu, Z.; Meng, H. Q.; Xie, P. 2,4Dihydroxypyrimidine is a potential urinary metabolite biomarker for diagnosing bipolar disorder. Mol. BioSyst. 2014, 10 (4), 813−9. (e) Huang, J. H.; Berkovitch, S. S.; Iaconelli, J.; Watmuff, B.; Park, H.; Chattopadhyay, S.; McPhie, D.; Ö ngür, D.; Cohen, B. M.; Clish, C. B.; Karmacharya, R. Perturbational Profiling of Metabolites in Patient Fibroblasts Implicates α-Aminoadipate as a Potential Biomarker for Bipolar Disorder. Mol. Neuropsychiatry 2016, 2 (2), 97−106. (26) (a) Zheng, P.; Chen, J. J.; Huang, T.; Wang, M. J.; Wang, Y.; Dong, M. X.; Huang, Y. J.; Zhou, L. K.; Xie, P. A novel urinary metabolite signature for diagnosing major depressive disorder. J. Proteome Res. 2013, 12 (12), 5904−11. (b) Zheng, P.; Gao, H. C.; Li, Q.; Shao, W. H.; Zhang, M. L.; Cheng, K.; Yang, d. Y.; Fan, S. H.; Chen, L.; Fang, L.; Xie, P. Plasma metabonomics as a novel diagnostic

(16) (a) Karmacharya, R.; Sliwoski, G. R.; Lundy, M. Y.; Suckow, R. F.; Cohen, B. M.; Buttner, E. A. Clozapine interaction with phosphatidyl inositol 3-kinase (PI3K)/insulin-signaling pathway in Caenorhabditis elegans. Neuropsychopharmacology 2009, 34 (8), 1968−78. (b) Weeks, K. R.; Dwyer, D. S.; Aamodt, E. J. Antipsychotic drugs activate the C. elegans akt pathway via the DAF-2 insulin/IGF-1 receptor. ACS Chem. Neurosci. 2010, 1 (6), 463−73. (17) (a) Harris, L. W.; Pietsch, S.; Cheng, T. M.; Schwarz, E.; Guest, P. C.; Bahn, S. Comparison of peripheral and central schizophrenia biomarker profiles. PLoS One 2012, 7 (10), e46368. (b) Schwarz, E.; Guest, P. C.; Rahmoune, H.; Harris, L. W.; Wang, L.; Leweke, F. M.; Rothermundt, M.; Bogerts, B.; Koethe, D.; Kranaster, L.; Ohrmann, P.; Suslow, T.; McAllister, G.; Spain, M.; Barnes, A.; van Beveren, N. J.; Baron-Cohen, S.; Steiner, J.; Torrey, F. E.; Yolken, R. H.; Bahn, S. Identification of a biological signature for schizophrenia in serum. Mol. Psychiatry 2012, 17 (5), 494−502. (c) Wang, L.; Lockstone, H. E.; Guest, P. C.; Levin, Y.; Palotás, A.; Pietsch, S.; Schwarz, E.; Rahmoune, H.; Harris, L. W.; Ma, D.; Bahn, S. Expression profiling of fibroblasts identifies cell cycle abnormalities in schizophrenia. J. Proteome Res. 2010, 9 (1), 521−7. (18) Wang, L.; Lockstone, H. E.; Guest, P. C.; Levin, Y.; Palotás, A.; Pietsch, S.; Schwarz, E.; Rahmoune, H.; Harris, L. W.; Ma, D.; Bahn, S. Expression profiling of fibroblasts identifies cell cycle abnormalities in schizophrenia. J. Proteome Res. 2010, 9 (1), 521−7. (19) (a) Wood, P. L.; Filiou, M. D.; Otte, D. M.; Zimmer, A.; Turck, C. W. Lipidomics reveals dysfunctional glycosynapses in schizophrenia and the G72/G30 transgenic mouse. Schizophrenia research 2014, 159 (2−3), 365−9. (b) Wood, P. L.; Holderman, N. R. Dysfunctional glycosynapses in schizophrenia: disease and regional specificity. Schizophrenia research 2015, 166 (1−3), 235−7. (c) Weber-Fahr, W.; Englisch, S.; Esser, A.; Tunc-Skarka, N.; Meyer-Lindenberg, A.; Ende, G.; Zink, M. Altered phospholipid metabolism in schizophrenia: a phosphorus 31 nuclear magnetic resonance spectroscopy study. Psychiatry Res., Neuroimaging 2013, 214 (3), 365−73. (d) Schwarz, E.; Prabakaran, S.; Whitfield, P.; Major, H.; Leweke, F. M.; Koethe, D.; McKenna, P.; Bahn, S. High throughput lipidomic profiling of schizophrenia and bipolar disorder brain tissue reveals alterations of free fatty acids, phosphatidylcholines, and ceramides. J. Proteome Res. 2008, 7 (10), 4266−77. (20) Townsend, M. K.; Clish, C. B.; Kraft, P.; Wu, C.; Souza, A. L.; Deik, A. A.; Tworoger, S. S.; Wolpin, B. M. Reproducibility of metabolomic profiles among men and women in 2 large cohort studies. Clin. Chem. 2013, 59 (11), 1657−67. (21) Xia, J.; Sinelnikov, I. V.; Han, B.; Wishart, D. S. MetaboAnalyst 3.0–making metabolomics more meaningful. Nucleic Acids Res. 2015, 43 (W1), W251−7. (22) (a) Lewis, G. D.; Wei, R.; Liu, E.; Yang, E.; Shi, X.; Martinovic, M.; Farrell, L.; Asnani, A.; Cyrille, M.; Ramanathan, A.; Shaham, O.; Berriz, G.; Lowry, P. A.; Palacios, I. F.; Tasan, M.; Roth, F. P.; Min, J.; Baumgartner, C.; Keshishian, H.; Addona, T.; Mootha, V. K.; Rosenzweig, A.; Carr, S. A.; Fifer, M. A.; Sabatine, M. S.; Gerszten, R. E. Metabolite profiling of blood from individuals undergoing planned myocardial infarction reveals early markers of myocardial injury. J. Clin. Invest. 2008, 118 (10), 3503−12. (b) Shaham, O.; Wei, R.; Wang, T. J.; Ricciardi, C.; Lewis, G. D.; Vasan, R. S.; Carr, S. A.; Thadhani, R.; Gerszten, R. E.; Mootha, V. K. Metabolic profiling of the human response to a glucose challenge reveals distinct axes of insulin sensitivity. Mol. Syst. Biol. 2008, 4, 214. (c) Shaham, O.; Slate, N. G.; Goldberger, O.; Xu, Q.; Ramanathan, A.; Souza, A. L.; Clish, C. B.; Sims, K. B.; Mootha, V. K. A plasma signature of human mitochondrial disease revealed through metabolic profiling of spent media from cultured muscle cells. Proc. Natl. Acad. Sci. U. S. A. 2010, 107 (4), 1571−5. (23) (a) Bot, M.; Chan, M. K.; Jansen, R.; Lamers, F.; Vogelzangs, N.; Steiner, J.; Leweke, F. M.; Rothermundt, M.; Cooper, J.; Bahn, S.; Penninx, B. W. Serum proteomic profiling of major depressive disorder. Transl. Psychiatry 2015, 5, e599. (b) Schwarz, E.; Whitfield, P.; Nahnsen, S.; Wang, L.; Major, H.; Leweke, F. M.; Koethe, D.; Lio, P.; Bahn, S. Alterations of primary fatty acid amides in serum of 491


Article

Journal of Proteome Research approach for major depressive disorder. J. Proteome Res. 2012, 11 (3), 1741−8. (27) Karabatsiakis, A.; Hamuni, G.; Wilker, S.; Kolassa, S.; Renu, D.; Kadereit, S.; Schauer, M.; Hennessy, T.; Kolassa, I. T. Metabolite profiling in posttraumatic stress disorder. J. Mol. Psychiatry 2015, 3 (1), 2. (28) (a) Sussulini, A.; Prando, A.; Maretto, D. A.; Poppi, R. J.; Tasic, L.; Banzato, C. E.; Arruda, M. A. Metabolic profiling of human blood serum from treated patients with bipolar disorder employing 1H NMR spectroscopy and chemometrics. Anal. Chem. 2009, 81 (23), 9755−63. (b) Ma, D.; Guest, P. C.; Bahn, S. Metabonomic studies of schizophrenia and psychotropic medications: focus on alterations in CNS energy homeostasis. Bioanalysis 2009, 1 (9), 1615−26. (29) Naydenov, A. V.; MacDonald, M. L.; Ongur, D.; Konradi, C. Differences in lymphocyte electron transport gene expression levels between subjects with bipolar disorder and normal controls in response to glucose deprivation stress. Arch. Gen. Psychiatry 2007, 64 (5), 555−64. (30) (a) Niemoller, T. D.; Bazan, N. G. Docosahexaenoic acid neurolipidomics. Prostaglandins Other Lipid Mediators 2010, 91 (3−4), 85−9. (b) Aubourg, P.; Wanders, R. Peroxisomal disorders. Handb Clin Neurol 2013, 113, 1593−609. (31) Wood, P. L. Lipidomics of Alzheimer’s disease: current status. Alzheimer's Res. Ther. 2012, 4 (1), 5. (32) Kaddurah-Daouk, R.; McEvoy, J.; Baillie, R.; Zhu, H.; Yao, J.; Nimgaonkar, V. L.; Buckley, P. F.; Keshavan, M. S.; Georgiades, A.; Nasrallah, H. A. Impaired plasmalogens in patients with schizophrenia. Psychiatry Res. 2012, 198 (3), 347−52. (33) McEvoy, J.; Baillie, R. A.; Zhu, H.; Buckley, P.; Keshavan, M. S.; Nasrallah, H. A.; Dougherty, G. G.; Yao, J. K.; Kaddurah-Daouk, R. Lipidomics reveals early metabolic changes in subjects with schizophrenia: effects of atypical antipsychotics. PLoS One 2013, 8 (7), e68717. (34) (a) Lutkenhoff, E. S.; van Erp, T. G.; Thomas, M. A.; Therman, S.; Manninen, M.; Huttunen, M. O.; Kaprio, J.; Lönnqvist, J.; O’Neill, J.; Cannon, T. D. Proton MRS in twin pairs discordant for schizophrenia. Mol. Psychiatry 2010, 15 (3), 308−18. (b) Shirayama, Y.; Yano, T.; Takahashi, K.; Takahashi, S.; Ogino, T. In vivo 31P NMR spectroscopy shows an increase in glycerophosphorylcholine concentration without alterations in mitochondrial function in the prefrontal cortex of medicated schizophrenic patients at rest. European journal of neuroscience 2004, 20 (3), 749−56. (35) Matsumoto, J.; Sugiura, Y.; Yuki, D.; Hayasaka, T.; Goto-Inoue, N.; Zaima, N.; Kunii, Y.; Wada, A.; Yang, Q.; Nishiura, K.; Akatsu, H.; Hori, A.; Hashizume, Y.; Yamamoto, T.; Ikemoto, K.; Setou, M.; Niwa, S. Abnormal phospholipids distribution in the prefrontal cortex from a patient with schizophrenia revealed by matrix-assisted laser desorption/ionization imaging mass spectrometry. Anal. Bioanal. Chem. 2011, 400 (7), 1933−43. (36) Schmitt, A.; Wilczek, K.; Blennow, K.; Maras, A.; Jatzko, A.; Petroianu, G.; Braus, D. F.; Gattaz, W. F. Altered thalamic membrane phospholipids in schizophrenia: a postmortem study. Biol. Psychiatry 2004, 56 (1), 41−5. (37) (a) Bernstein, H. G.; Müller, M. The cellular localization of the L-ornithine decarboxylase/polyamine system in normal and diseased central nervous systems. Prog. Neurobiol. 1999, 57 (5), 485−505. (b) Karmacharya, R.; Lynn, S. K.; Demarco, S.; Ortiz, A.; Wang, X.; Lundy, M. Y.; Xie, Z.; Cohen, B. M.; Miller, G. M.; Buttner, E. A. Behavioral effects of clozapine: involvement of trace amine pathways in C. elegans and M. musculus. Brain Res. 2011, 1393, 91−9. (38) Ramchand, C. N.; Das, I.; Gliddon, A.; Hirsch, S. R. Role of polyamines in the membrane pathology of schizophrenia. A study using fibroblasts from schizophrenic patients and normal controls. Schizophrenia research 1994, 13 (3), 249−53. (39) Baldessarini, R. J.; Stramentinoli, G.; Lipinski, J. F. Methylation hypothesis. Arch. Gen. Psychiatry 1979, 36 (3), 303−7. (40) Hatano, T.; Ohnuma, T.; Sakai, Y.; Shibata, N.; Maeshima, H.; Hanzawa, R.; Suzuki, T.; Arai, H. Plasma alanine levels increase in patients with schizophrenia as their clinical symptoms improve-Results

from the Juntendo University Schizophrenia Projects (JUSP). Psychiatry Res. 2010, 177 (1−2), 27−31. (41) (a) McBain, C. J.; Kleckner, N. W.; Wyrick, S.; Dingledine, R. Structural requirements for activation of the glycine coagonist site of N-methyl-D-aspartate receptors expressed in Xenopus oocytes. Mol. Pharmacol. 1989, 36 (4), 556−65. (b) Tanii, Y.; Nishikawa, T.; Hashimoto, A.; Takahashi, K. Stereoselective antagonism by enantiomers of alanine and serine of phencyclidine-induced hyperactivity, stereotypy and ataxia in the rat. J. Pharmacol Exp Ther 1994, 269 (3), 1040−8. (42) Tsai, G. E.; Yang, P.; Chang, Y. C.; Chong, M. Y. D-alanine added to antipsychotics for the treatment of schizophrenia. Biol. Psychiatry 2006, 59 (3), 230−4. (43) Mahadik, S. P.; Mukherjee, S.; Correnti, E. E.; Kelkar, H. S.; Wakade, C. G.; Costa, R. M.; Scheffer, R. Plasma membrane phospholipid and cholesterol distribution of skin fibroblasts from drug-naive patients at the onset of psychosis. Schizophrenia research 1994, 13 (3), 239−47. (44) Halim, N. D.; Lipska, B. K.; Hyde, T. M.; Deep-Soboslay, A.; Saylor, E. M.; Herman, M. M.; Thakar, J.; Verma, A.; Kleinman, J. E. Increased lactate levels and reduced pH in postmortem brains of schizophrenics: medication confounds. J. Neurosci. Methods 2008, 169 (1), 208−13. (45) (a) Huang, J. T.; Leweke, F. M.; Tsang, T. M.; Koethe, D.; Kranaster, L.; Gerth, C. W.; Gross, S.; Schreiber, D.; Ruhrmann, S.; Schultze-Lutter, F.; Klosterkötter, J.; Holmes, E.; Bahn, S. CSF metabolic and proteomic profiles in patients prodromal for psychosis. PLoS One 2007, 2 (8), e756. (b) Regenold, W. T.; Phatak, P.; Marano, C. M.; Sassan, A.; Conley, R. R.; Kling, M. A. Elevated cerebrospinal fluid lactate concentrations in patients with bipolar disorder and schizophrenia: implications for the mitochondrial dysfunction hypothesis. Biol. Psychiatry 2009, 65 (6), 489−94. (46) Fukushima, T.; Iizuka, H.; Yokota, A.; Suzuki, T.; Ohno, C.; Kono, Y.; Nishikiori, M.; Seki, A.; Ichiba, H.; Watanabe, Y.; Hongo, S.; Utsunomiya, M.; Nakatani, M.; Sadamoto, K.; Yoshio, T. Quantitative analyses of schizophrenia-associated metabolites in serum: serum Dlactate levels are negatively correlated with gamma-glutamylcysteine in medicated schizophrenia patients. PLoS One 2014, 9 (7), e101652. (47) (a) Du, F.; Cooper, A. J.; Thida, T.; Sehovic, S.; Lukas, S. E.; Cohen, B. M.; Zhang, X.; Ongür, D. In vivo evidence for cerebral bioenergetic abnormalities in schizophrenia measured using 31P magnetization transfer spectroscopy. JAMA Psychiatry 2014, 71 (1), 19−27. (b) Ongür, D.; Prescot, A. P.; Jensen, J. E.; Cohen, B. M.; Renshaw, P. F. Creatine abnormalities in schizophrenia and bipolar disorder. Psychiatry Res., Neuroimaging 2009, 172 (1), 44−8. (48) (a) Théberge, J.; Al-Semaan, Y.; Drost, D. J.; Malla, A. K.; Neufeld, R. W.; Bartha, R.; Manchanda, R.; Menon, R.; Densmore, M.; Schaefer, B.; Williamson, P. C. Duration of untreated psychosis vs. Nacetylaspartate and choline in first episode schizophrenia: a 1H magnetic resonance spectroscopy study at 4.0 T. Psychiatry Res., Neuroimaging 2004, 131 (2), 107−14. (b) Kraguljac, N. V.; Reid, M.; White, D.; Jones, R.; den Hollander, J.; Lowman, D.; Lahti, A. C. Neurometabolites in schizophrenia and bipolar disorder - a systematic review and meta-analysis. Psychiatry Res., Neuroimaging 2012, 203 (2− 3), 111−25. (49) (a) Bou Khalil, R. Pantothenate’s possible role in schizophrenia pathogenesis. Clin. Neuropharmacol. 2012, 35 (6), 296. (b) Monro, J. Pantothenic acid in schizophrenia. Lancet 1973, 301 (7797), 262−3. (50) He, Y.; Yu, Z.; Giegling, I.; Xie, L.; Hartmann, A. M.; Prehn, C.; Adamski, J.; Kahn, R.; Li, Y.; Illig, T.; Wang-Sattler, R.; Rujescu, D. Schizophrenia shows a unique metabolomics signature in plasma. Transl. Psychiatry 2012, 2, e149. (51) Kaiya, H.; Nishida, A.; Imai, A.; Nakashima, S.; Nozawa, Y. Accumulation of diacylgylcerol in platelet phosphoinositide turnover in schizophrenia: a biological marker of good prognosis? Biol. Psychiatry 1989, 26 (7), 669−76. (52) Hayashi-Takagi, A.; Vawter, M. P.; Iwamoto, K. Peripheral Biomarkers Revisited: Integrative Profiling of Peripheral Samples for Psychiatric Research. Biol. Psychiatry 2014, 75 (12), 920−928. 492


Article

Journal of Proteome Research (53) Munkholm, K.; Peijs, L.; Vinberg, M.; Kessing, L. V. A composite peripheral blood gene expression measure as a potential diagnostic biomarker in bipolar disorder. Transl. Psychiatry 2015, 5, e614. (54) Rollins, B.; Martin, M. V.; Morgan, L.; Vawter, M. P. Analysis of whole genome biomarker expression in blood and brain. Am. J. Med. Genet., Part B 2010, 153B (4), 919−36. (55) (a) Auta, J.; Smith, R. C.; Dong, E.; Tueting, P.; Sershen, H.; Boules, S.; Lajtha, A.; Davis, J.; Guidotti, A. DNA-methylation gene network dysregulation in peripheral blood lymphocytes of schizophrenia patients. Schizophrenia research 2013, 150 (1), 312−8. (b) Guidotti, A.; Auta, J.; Davis, J. M.; Dong, E.; Gavin, D. P.; Grayson, D. R.; Sharma, R. P.; Smith, R. C.; Tueting, P.; Zhubi, A. Toward the identification of peripheral epigenetic biomarkers of schizophrenia. J. Neurogenet. 2014, 28 (1−2), 41−52. (c) Iwamoto, K.; Bundo, M.; Washizuka, S.; Kakiuchi, C.; Kato, T. Expression of HSPF1 and LIM in the lymphoblastoid cells derived from patients with bipolar disorder and schizophrenia. J. Hum. Genet. 2004, 49 (5), 227−31. (d) Cataldo, A. M.; McPhie, D. L.; Lange, N. T.; Punzell, S.; Elmiligy, S.; Ye, N. Z.; Froimowitz, M. P.; Hassinger, L. C.; Menesale, E. B.; Sargent, L. W.; Logan, D. J.; Carpenter, A. E.; Cohen, B. M. Abnormalities in mitochondrial structure in cells from patients with bipolar disorder. Am. J. Pathol. 2010, 177 (2), 575−85. (e) Konradi, C.; Eaton, M.; MacDonald, M. L.; Walsh, J.; Benes, F. M.; Heckers, S. Molecular evidence for mitochondrial dysfunction in bipolar disorder. Arch. Gen. Psychiatry 2004, 61 (3), 300−8. (56) (a) Watmuff, B.; Berkovitch, S. S.; Huang, J. H.; Iaconelli, J.; Toffel, S.; Karmacharya, R. Disease signatures for schizophrenia and bipolar disorder using patient-derived induced pluripotent stem cells. Mol. Cell. Neurosci. 2016, 73, 96−103. (b) Berkovitch, S. S.; Iaconelli, J.; Karmacharya, R. Patient-Derived iPSCs as a Model for Schizophrenia. Journal of Stem Cell Research & Regenerative Medicine 2015, 2 (1), e001. (c) Karmacharya, R.; Haggarty, S. J. Stem cell models of neuropsychiatric disorders. Mol. Cell. Neurosci. 2016, 73, 1− 2. (d) Iaconelli, J.; Huang, J. H.; Berkovitch, S. S.; Chattopadhyay, S.; Mazitschek, R.; Schreiber, S. L.; Haggarty, S. J.; Karmacharya, R. HDAC6 inhibitors modulate Lys49 acetylation and membrane localization of beta-catenin in human iPSC-derived neuronal cells. ACS Chem. Biol. 2015, 10 (3), 883−90.

493


Unbiased Metabolite Profiling of Schizophrenia ... - ACS Publications

Recommend Documents