Unbiased Metabolite Profiling of Schizophrenia Fibroblasts under

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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 Öngür, Bruce M. Cohen, Clary B. Clish, and Rakesh Karmacharya J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00628 • Publication Date (Web): 08 Nov 2016 Downloaded from http://pubs.acs.org on November 8, 2016

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Journal of Proteome Research

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 Öngür ^, Bruce M. Cohen ^, Clary B. Clish #, Rakesh Karmacharya * # ^ 1

* Center for Experimental Drugs and Diagnostics, Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Harvard Medical School and Massachusetts General Hospital, Boston, MA 02114 #

Chemical Biology Program, Broad Institute of Harvard and MIT, Cambridge, MA 02142

&

Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge,

MA 02139 ^ Schizophrenia and Bipolar Disorder Program, Harvard Medical School and McLean Hospital, Belmont, MA 02478

1

To whom correspondence should be addressed: Center for Human Genetic Research,

Harvard Medical School and Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114. Tel.: 617-726-5119; Fax: 617-726-0830; Email: [email protected]

Running title: Perturbational metabolic profiling in schizophrenia

ACS Paragon Plus Environment

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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 diseaserelated 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

schizophrenia

experiments.

and

healthy

Metabolites controls

that

included

were a

significantly

number

of

different

between

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

Abbreviations: CE = cholesteryl ester, CTRL = Control, DAG = diacylglycerol, PC = phosphatidylcholine, PLSDA = partial least squares discriminant analysis, SCZ = schizophrenia.


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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 medications 3. Abnormalities of insulin and IGF-1 levels have also been reported in patients with schizophrenia, including in antipsychotic naïve patients 4. Elevated plasma levels of IGF-1 have been reported in patients with first-episode psychosis 5. Firstdegree relatives of schizophrenia patients show differences in insulin and IGF-1 levels and the presence of increased insulin 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 schizophrenia patients, including in medication-naïve patients, 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, while 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. Position Emission Tomography (PET) studies have shown decreased glucose metabolism in various cortical and thalamic areas in patients with schizophrenia


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.A

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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, and 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 sex-matched healthy controls. We undertook experiments to measure relative levels of metabolites in an unbiased profiling approach. We performed experiments under normal growth


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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 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.

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 semi-structured interview using the Structured Clinical Interview for DSM Disorders (SCID) 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 Institutional Review Board. 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


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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 1:3 ratios every 5–7 days until the cells reached 80% confluency and then were frozen and stored in liquid nitrogen until ready for 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 the differences in passage number of cellular senescence. Fibroblasts were thawed and grown in 6-well cell culture plates (Corning, CLS3506) to 80% confluency. Cells were then incubated for 6 hours in fresh normal growth media, low-glucose, or dexamethasone containing media. For the low-glucose condition experiments, cells were grown in media containing only 5.5 mM glucose for 6 hours, compared to 25 mM under normal growth conditions. For the dexamethasone experiments, cells were cultured in the presence of 1 µM dexamethasone for 6 hours in normal fibroblast media. At the end of 6-hour incubation with normal media, low-glucose media, or media with dexamethasone, cells were washed with cold PBS (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 minutes. Cells were then lifted with cell scrapers and the cell lysate/methanol mixture transferred to 1.5 mL centrifuge tubes on dry ice. Tubes containing the lysate/methanol mixture were centrifuged (9,000 x g, 4°C, 10min) to pellet cell debris and proteins. The supernatants were transferred to new 1.5mL centrifuge tubes on dry ice, while keeping the old 1.5 mL tubes containing pellets for further extraction. Pellets were re-suspended in 100µL 80% methanol (-80°C) and centrifuged (9,000 x g, 4°C, 5min), and the supernatants were pooled in tubes containing the earlier supernatants. The collected supernatants were stored at -80°C.


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Sample preparation for lipids At the end of 6-hour incubation with normal media, low-glucose media, or media with dexamethasone, cells were washed once with cold PBS (Mg2+-/Ca2+-) to initiate metabolite extraction. 800 µL of cold isopropranolol (4°C) was added to the plate and cells were immediately lifted with cell scrapers. The cell lysate/isopropranolol mixtures were transferred to 1.5mL centrifuge tubes on ice, and incubated at 4°C for 1 hour, 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 (9,000 x g, 4°C, 10min). 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 x 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


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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 isotope-labeled 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 x 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 minute followed by a linear gradient to 40% mobile phase B (acetonitrile with 0.1% formic acid) over 10 minutes. 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: sheath gas 40, sweep gas 2, spray voltage 3.5 kV, capillary temperature 350°C, S-lens RF 40, heater temperature 300°C, microscans 1, automatic gain control target 1e6, and maximum ion time 250 ms. Metabolite identities were confirmed using mass and retention time matching to authentic reference standards. Lipid analyzed using a Shimadzu Nexera X2 UHPLC (Shimadzu Corp.; Marlborough, MA) coupled to an Exactive Plus orbitrap mass spectrometer (Thermo Fisher Scientific; Waltham, MA). Lipid extracts (100 uL) were dried using a nitrogen evaporator and resuspended in 100 uL of isopropanol containing 1,2-dilauroyl-snglycero-3-phosphocholine as an internal standard (Avanti Polar Lipids; Alabaster, AL). Samples (10 µL) were injected onto a 100 x 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 vol/vol/vol 10mM ammonium acetate/methanol/formic acid) for 1 minute followed by a linear gradient to 80% mobile-phase B (99.9:0.1 vol/vol methanol/formic acid) over 2 minutes, a linear gradient to 100% mobile phase B over 7 minutes, then 3 minutes 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: 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 1e6, and maximum ion time


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100 ms. Identities of 149 lipids were determined based on comparison to reference plasma extracts and were denoted by total number of carbons in the lipid acyl chain(s) and total number of double bonds in the lipid acyl chain(s). MultiQuant 2.1 (SCIEX; Framingham MA) was used peak detection and integration of negative ion mode targeted data (110 metabolites) while 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 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 3.0

21

was used to analyze the data. Univariate analyses were undertaken to identify group differences among annotated metabolites between the two groups that each contained ten biological replicates.

Results Fibroblasts were cultured from ten subjects who met criteria for the DSM-IV-TR diagnosis of schizophrenia as well as ten 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 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


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that particular experimental well before analysis. These data were used to generate a metabolite signature and we employed a partial least squares discriminant analysis (PLS-DA) approach to model the separation of the two groups (fig. 1). To analyze the metabolite data for fibroblasts grown 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 a high number of phosphatidylcholines – six out of 13 significant metabolites in the list were phosphatidylcholines, including the highestranked metabolite C30:0 PC. The list also includes α-glycerophosphocholine 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 (fig. 2).

Peturbational profiling We analyzed the data similarly for metabolites collected for experiments where fibroblasts were exposed to two different perturbations for 6 hours – growth under low-glucose conditions and exposure to 1 µM dexamethasone. PLS-DA models generated with these metabolite signatures again showed a clear separation of the schizophrenia samples from healthy controls under both perturbation conditions, with clearer separation between the groups compared to normal growth conditions (fig. 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.


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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, 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 while the phosphatidylcholines and plasmalogens in the list were again present at lower levels in the schizophrenia group compared to the healthy control group (fig. 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 (fig. 5).

There were two metabolites that were consistently different between the schizophrenia and healthy control groups under all thee conditions. C40:7 PC plasmalogen and C30:1 PC were lower in schizophrenia cells at baseline and under the perturbations 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


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schizophrenia and healthy control groups under both stressful 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 17b, 23

being to be used to explore the biology of various psychiatric disorders have suggested the possibility of identifying biomarkers for schizophrenia major depressive disorder

26

and PTSD

induced metabolic changes as well

. Such studies

24

, bipolar disorder

25

,

27

, and have also been used to study medication-

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 normal growth conditions (tables 2-4). We report on 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 (fig. 1,3).


12

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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 plasmalogens and phosphatidylcholines dynamics 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 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 (DHA) and arachidonic acid – these mediators are released from sn-2 position of the glycerol backbone by phospholipases as part of a deacylation-reacylation cycle termed “lipid remodeling” reported dysregulation of plasmalogens in schizophrenia

31

. A number of studies have

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 is intriguing in this regard.

Dysregulation in phosphatidylcholines have been described in multiple studies of schizophrenia, as have the effect of antipsychotic medications in affecting their levels

33

. Mass spectrometry-

based studies of postmortem brains in schizophrenia revealed abnormalities in a number of phosphatidylcholines, both in the gray matter and in the white matter 19d. Phosphorus 31 nuclear


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

schizophrenia subjects compared to controls

in the hippocampus of

34

. Investigation of postmortem brain tissue in

schizophrenia using matrix-assisted laser desorption/ionization imaging mass spectrometry showed abnormal distributions of phosphatidylcholine in the frontal cortex

35

. Another

postmortem 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 only different 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, had been shown to be significantly different between postmortem brains of schizophrenia subjects and healthy controls, both in the gray matter and in the 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

. α-

glycerophosphorocholine, another metabolite which was significantly different between the group under normal growth conditions, had been shown to be different in studies of brain tissues as well 19c.


14

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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 been hypothesized to play a role in schizophrenia, 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 had been shown to be accompanied by more severe psychosis symptoms in hospitalized patients with schizophrenia and increase in alanine levels were 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 levels of cholesteryl esters in fibroblasts from schizophrenia but there are no previous studies that have identified specific cholesteryl esters that were different 43.


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The metabolite that was most significantly different between the two groups under low-glucose conditions was lactate. Postmortem studies in schizophrenia show elevated levels of lactate in the brain

9, 44

. Interestingly, measurement in cerebrospinal fluid (CSF) show 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 low-glucose 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 for 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 had shown differences in phosphoinositide turnover and it had been reported that elevated DAG levels correlated with improved outcomes after a period of three years

51

. In our study, we found lower levels of C34:2 DAG and C36:2 DAG in schizophrenia

subjects under low-glucose conditions.


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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 tissue 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 schizophrenia subjects were not medication naïve, the results could have been confounded by the effect of the medications. The fibroblasts had been 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 towards 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 (CNS) 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 total transcriptome expressed in postmortem 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 in peripheral cells

17a, 29, 55

. Nonetheless, it is

important to follow up these results in human neuronal cells, derived from patient induced


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pluripotent stem cells (iPSCs) to see which group-related metabolite differences observed in the fibroblasts are present in neuronal cells as well 56.

Conclusion

In summary, we undertook unbiased metabolite profiling fibroblasts from schizophrenia subjects and matched health 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 is 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 postmortem brains and cerebrospinal fluid, as well as a number of novel metabolites that had not yet been implicated in schizophrenia disease biology.

Acknowledgements

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, Phyllis & Jerome Lyle Rappaport Foundation and from Steve Willis and Elissa Freud.

Disclosures The authors of this paper do not have any commercial associations that might pose a conflict of interest in connection with this manuscript.


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Supporting Information Table S1. Annotated metabolites. Details of the annotated metabolites, including the negative ion and positive ion polar metabolites as well as the positive ion lipids, including the method used for measurement, HMDB ID and retention times. For the positive ion polar metabolites and positive ion lipids, m/z values are provided. For the negative ion polar metabolites, information is included for the parent ion, product ion, declustering potential (DP) and collision energy (CE).


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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.; Klosterk‚àö‚àÇtter, 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 D-lactate 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 2009, 172 (1), 44-8. 48. (a) Th‚Äö√†√∂¬¨¬©berge, 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. N-acetylaspartate and choline in first episode schizophrenia: a 1H magnetic resonance spectroscopy study at 4.0 Tesla. Psychiatry Res 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 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, 1 (7797), 262-3.


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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. 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 B Neuropsychiatr Genet 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. The American journal of pathology 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. Molecular and cellular neurosciences 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. Molecular and cellular neurosciences 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 chemical biology 2015, 10 (3), 883-90.


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TABLE 1. Demographic information and medication history for subjects in the study. Healthy subjects

Schizophrenia subjects

Age 23 24 31 31 37 41 44

Sex M M M F M M F

Age 21 27 32 32 36 40 46

Sex M M M M F M F

49 52 55

M F F

50 50 57

F M F

Antipsychotic Medication History Clozapine Quetiapine Clozapine, risperidone, haloperidol Risperidone Aripiprazole Clozapine, haloperidol Clozapine, haloperidol, olanzapine, quetiapine, perphenazine, thiothixene Clozapine


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Page 28 of 37

TABLE 2. Annotated metabolites that significantly distinguish (P < 0.05, unpaired t-test) and effect size (Cohen’s d) for schizophrenia and healthy control subjects under normal growth conditions.

Metabolite

P-value

Effect size (d)

C30:0 PC

0.019252

-1.149598281

putrescine

0.021574

-1.125220857

C34:1 PC

0.021782

-1.123160053

C40:7 PC plasmalogen

0.026792

-1.078409635

5-adenosylhomocysteine

0.027156

-1.119138725

C34:1 PC plasmalogen-B

0.03084

-1.047657955

alpha-glycerophosphate

0.031379

1.075813744

alpha-glycerophosphocholine

0.03446

1.037820492

C30:1 PC

0.035258

-1.018116716

alanine

0.036789

-1.008671867

C36:1 PC

0.038929

-0.996068162

C36:0 PC

0.048732

-0.945419167

C34:0 PC

0.049623

-0.94128777


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TABLE 3. Annotated metabolites that significantly distinguish (P < 0.05, unpaired t-test) and effect size (Cohen’s d) for schizophrenia and healthy control subjects under low-glucose conditions.

Low glucose condition Metabolite

P-value

Effect size (d)

lactate

0.009624

1.295231468

choline

0.015182

1.199984097

lactose

0.016483

1.18261275

pantothenate

0.019694 -1.144751616


0.02812

C30:1 PC

0.029025 -1.060947094

cotinine

0.029482

C40:9 PC

0.040596 -0.986682166

C36:5 PC plasmalogen-A

0.041412 -0.982218218

C34:2 DAG

0.042471 -0.976540634

ornithine

0.04372

C36:2 DAG

0.048494 -0.946530676

C42:11 PE plasmalogen

0.048545 -0.946290022

C40:10 PC

0.04965

-1.06787139

1.057531517

0.970014786

-0.941163673


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Page 30 of 37

TABLE 4. Annotated metabolites that significantly distinguish (P < 0.05, unpaired t-test) and effect size (Cohen’s d) for schizophrenia and healthy control subjects in the presence of dexamethasone.

Dexamethasone exposure Metabolite

P-value

Effect size (d)

alanine

0.005073

-1.426596596

C14:0 CE

0.019224

-1.149914784


0.019595

-1.145826457

C36:5 PC plasmalogen-A

0.022688

-1.11439437

C40:10 PC

0.030644

-1.049059294

uracil

0.030771

1.101509612

C30:1 PC

0.031239

-1.044835448

C32:1 PC

0.036382

-1.011148696

C34:1 PC

0.038294

-0.999740567

cotinine

0.041624

0.981071222

alpha-glycerophosphocholine

0.044928

0.963857371

C40:7 PE plasmalogen

0.045808

-0.959468221


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Figure Legends

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 to healthy control subjects.

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 ttest).

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

FIGURE 4. Relative metabolite levels under low-glucose conditions. Differences in relative metabolite levels between schizophrenia patients and healthy controls subjects for annotated metabolites significantly different between the two groups. * denotes significance at p < 0.05 (unpaired t-test).

FIGURE 5. Relative metabolite levels with exposure to dexamethasone. Differences relative in metabolite levels between schizophrenia patients and control subjects for annotated metabolites significantly different between the two groups. * denotes significance at p < 0.05 (unpaired ttest).


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FIGURE 1


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Page 33 of 37

Rela%ve'abundance'

FIGURE 2

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*'

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Rela1ve"abundance"

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


CTRL''

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33


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FIGURE 3 A. Low glucose

B. Dexamethasone


34

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Rela%ve'abundance'

FIGURE 4 Choline' *'

Lactate' *'

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*'

*'

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Lactose' *'*'

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Pantothenate'

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


''''SCZ' C36:2'DAG' *'

CTRL''

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35


FIGURE 5

C14:0'CE'

*'

*'

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Alanine'

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C32:1'PC'

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*'

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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''''SCZ'''


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36

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For TOC only


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Unbiased Metabolite Profiling of Schizophrenia Fibroblasts under

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