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Veldic et al. Translational Psychiatry (2019) 9:149
https://doi.org/10.1038/s41398-019-0483-9
T
ranslational Psychiatry
ARTICLE Open Access
Genetic variant in SLC1A2 is associated with
elevated anterior cingulate cortex
glutamate and lifetime history of rapid
cycling
Marin Veldic
1
, Vincent Millischer
2,3
, John D. Port
4
, Ada Man-Choi Ho
5
,Yun-FangJia
5
,JenniferR.Geske
6
,
Joanna M. Biernacka
1,6
,LenaBacklund
2,3
, Susan L. McElroy
7
, David J. Bond
8
, J. Carlos Villaescusa
2,3
,
Michelle Skime
1
, Doo-Sup Choi
1,5
, Catharina Lavebratt
2,3
, Martin Schalling
2,3
and Mark A. Frye
1
Abstract
Glutamatergic dysregulation is implicated in the neurobiology of mood disorders. This study investigated the
relationship between the anterior cingulate cortex (AC) glutamate, as measured by proton magnetic resonance
spectroscopy (
1
H-MRS), and single-nucleotide polymorphisms (SNPs) from four genes (GLUL,SLC1A3,SLC1A2, and
SLC1A7) that regulate the extracellular glutamate in 26 depressed patients with major depressive disorder (MDD;
n=15) and bipolar disorder (BD; n=11). Two SNPs (rs3812778 and rs3829280), in perfect linkage disequilibrium, in the
3′untranslated region of the EAAT2 gene SLC1A2, were associated with AC glutamate, with minor allele carriers having
significantly higher glutamate levels (p< 0.001) in comparison with common allele homozygotes. In silico analysis
revealed an association of minor allele carriers of rs3812778/rs382920 with an upregulation of the astrocytic marker
CD44 localized downstream of SLC1A2 on chromosome 11. Finally, we tested the disease relevance of these SNPs in a
large group of depressed patients [MDD (n=458); BD (n=1473)] and found that minor allele carriers had a
significantly higher risk for rapid cycling (p=0.006). Further work is encouraged to delineate the functional impact of
excitatory amino acid transporter genetic variation on CD44 associated physiology and glutamatergic
neurotransmission, specifically glutamate–glutamine cycling, and its contribution to subphenotypes of mood
disorders.
Introduction
There is increasing recognition that glutamatergic dys-
regulation is implicated in the neurobiology of mood dis-
orders. The evidence base spans animal studies,
1
postmortem
2
, imaging
3–5
, and pharmacological studies
3–5
,
as well as the latest genome-wide association studies in
major depressive disorder (MDD)
6
and bipolar disorder
(BD)
7
.
Most of the glutamate functional neuroimaging work in
mood disorders has focused on the prefrontal and cingu-
late cortices, recognizing the anterior cingulate cortex (AC)
as a regulator of emotional and cognitive behavior
8
.Mag-
netic resonance spectroscopy (MRS) is a functional brain
imaging method uniquely positioned to investigate gluta-
matergic biochemical mechanism of action
9,10
. Previous
work indicates that glutamate, glutamine, or the composite
glutamate/glutamine levels in depression may differ by
diagnostic subtype
3,11,12
. While brain regions, magnet
strength, glutamate MRS sequence, and post-processing
© The Author(s) 2019
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Correspondence: Marin Veldic (Veldic.Marin@mayo.edu)
1
Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
2
Department of Molecular Medicine and Surgery (MMK), Karolinska Institutet,
Stockholm, Sweden
Full list of author information is available at the end of the article.
These authors contributed equally: Marin Veldic, Vincent Millischer
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methods differ, available MR spectroscopic evidence to
date suggests that glutamate levels are increased in BD and
reduced in MDD
11–13
.
In this study, in a mixed population of patients with
MDD and BD, we evaluated the relationship between
1
H-
MRS glutamate in the AC and single-nucleotide poly-
morphisms (SNPs) of astrocyte-specific genes, GLUL,
SLC1A3, and SLC1A2, encoding for glutamine synthetase
(GS), excitatory amino acid transporter (EAAT) 1 and
EAAT2, respectively, which are known to regulate
synaptic or extracellular glutamate levels in the astrocyte.
We also included SLC1A7, which encodes for EAAT5,
and is co-expressed with SLC1A2
14
(Fig. 1b). Positive hits
were followed up in silico. Finally, based on our previous
work on rapid cycling (RC)
15,16
, including genetic findings
linking RC to glutamate physiology, we investigated the
association between positive hits and this phenotype in a
large group of unipolar and bipolar depressed patients.
Materials and methods
Participants for the MRS study
The MRS study was approved by the Mayo Clinic
Institutional Review Board (IRB# 06-006659). Potential
subjects were identified and referred to the study by Mayo
Clinic psychiatrists and psychologists from inpatient and
outpatient services, as well as a general intra campus
newsletter. After obtaining written informed consent, 51
individuals, ages 18–65 were diagnosed using the Struc-
tured Clinical Interview for DSM-IV (SCID)
17
; this diag-
nostic interview was administered by trained raters
directly supervised by the principal investigator (MAF).
The inclusion criteria for this study were a current DSM-
IV diagnosis of a major depressive episode associated with
MDD, BD I, or BD II, based on SCID, and a negative
toxicology screen and pregnancy test. Exclusion criteria
included: inability to speak English or provide informed
consent, current treatment with an antidepressant, history
of active substance abuse within the last 6 months,
abnormal thyroid-stimulating hormone, unstable medical
illness, Young Mania Rating Scale (YMRS)
18
> 12 con-
sistent with hypomania, active suicidal ideation with plan,
current psychosis, and antipsychotic treatment within
4 weeks.
Depressive and manic symptom severity was assessed
with the Hamilton Depression Rating Scale-28 Item
Version (HAM-D28)
19,20
, to assess for atypical neurove-
getative symptoms, and the YMRS respectively. All ratings
were conducted by the principal investigator (MAF) or
inter-rater-reliable assistants.
Participants for the genotyping cohort
The cohort consisted of patients with BD and MDD.
The BD cohort consisted of patients from the Mayo Clinic
Individualized Medicine Biobank for Bipolar Disorder
(IRB# 08-008794)
21
and patients recruited from the Unit
of Affective Disorders, Psychiatry Southwest, Karolinska
University Hospital, Huddinge, Stockholm, Sweden
16
. The
assessment was based on interviews, medical records, and
Fig. 1 Anterior cingulate cortex glutamate levels in common
homozygotes and minor allele carriers for SLC1A2 single-
nucleotide polymorphisms (SNPs) rs3812778/rs3829280. a MRI
location for the pregenual anterior cingulate cortex
1
H-MRS voxel
acquisition. The reference image of an 8-cm
3
voxel (2 × 2 × 2 cm) of
predominantly (prefrontal) gray matter was centered on the frontal
interhemispheric fissure. The posterior margin of the voxel was placed
immediately anterior to the genu of the corpus callosum in an area
corresponding to the pregenual anterior cingulate cortex (Brodmann
area 24a, 24b, and 32). bGlutamate–glutamine cycle and glutamate
neurotransmission in the anterior cingulate cortex. Glutamate exerts
its action on a variety of ionotropic (AMPA, NMDA, Kainate) and
metabotropic (mGLUR 1–8) glutamate receptors. Glutamate is
transported from the synaptic cleft into astrocytes by excitatory amino
acid transporters. In astrocytes, glutamate is converted to glutamine
by the astrocyte-specific enzyme glutamine synthetase and shuttled
to the presynaptic neuron by sodium-coupled neutral amino acid
transporters. In presynaptic neurons, phosphate-activated glutaminase
converts glutamine back to glutamate. Glu glutamate, Gln glutamine,
EAAT excitatory amino acid transporter, SNAT sodium-coupled neutral
amino acid transporter, mGLUR metabotropic glutamate receptors,
AMPA α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid
receptor, NMDA N-methyl-D-aspartate receptor, Kainate kainate
receptor, GS glutamine synthetase, GA glutaminase, Pre-SN
presynaptic neuron Post-SN post-synaptic neuron. cBoxplot
representations (median, 25th and 75th percentile) of glutamate levels
measured by two-dimensional J-resolved averaged PRESS sequence in
a combined group of unipolar and bipolar depressed common
homozygotes and minor allele carriers for SLC1A2 SNPs rs3812778 (G/
A) and rs3829280 (A/T). ***Homozygotes versus minor allele carriers,
p=0.00078 for both SNPs
Veldic et al. Translational Psychiatry (2019) 9:149 Page 2 of 10
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questionnaires and performed by specialized psychiatrists
or by trained psychiatric nurses. Patients with MDD were
selected from the PART study
22
, a longitudinal
population-based study in Stockholm County, Sweden,
utilizing the Major Depression Inventory (MDI)
23
.
Rapid cycling has been identified by the biobank as a
clinical phenotype to further investigate the underlying
genetics and neurobiology
15
. Lifetime history of rapid
cycling (RC) was defined as a self-reported history of
having four or more distinct bipolar mood episodes in a
12-month period, with each episode separated by a return
to baseline mood state for at least 2 months, or a switch to
the opposite mood pole. Manic and hypomanic episodes
were counted as being on the same mood pole.
Further description of cohort including clinical variables
quantified can be found in the Supplementary Information.
MR imaging (MRI) and 1H-MRS acquisition
Imaging and acquisition was completed with a GE 3T
Discovery 750 MRI scanner with 22.1 software and an 8-
channel head coil by a neuroradiologist blinded to the
group allocation throughout the entire study who did not
participate in assessing the outcome. The axial plane was
landmarked in all subjects at the center of the forehead,
1 cm above the eyebrows to standardize head position
from scan to scan. A neuroradiologist reviewed baseline
and posttreatment structural MRI data for potential
exclusionary head and brain pathology.
A FAST 3D SPGR sequence was used to acquire volu-
metric data for cerebrospinal fluid (CSF) correction (axial
acquisition; repetition time [TR] =12.6 ms, echo time
[TE] =5.6 ms, flip angle =15°, voxel dimensions =0.49 ×
0.49 × 1.5 mm). Voxel positioning for the midline anterior
cingulate cortex (MACC) and for the left dorsolateral
prefrontal cortex (LDLPFC) voxels followed a systematic
approach during all scans (Fig. 1a; Supplementary
Information).
Based on the prior literature
24,25
, we chose two different
1H-MRS sequences for our glutamate and glutamine
measurements, each with its own strengths. A TE-
optimized PRESS sequence was used to measure both
glutamate and glutamine (PROBE-P PRESS; TE =80 ms,
TR =2000 ms, no. of excitations =8, no. of acquisitions =
128)
25
. A two-dimensional J-resolved averaged PRESS
sequence was used with the goal of collecting an optimized
measure of glutamate (2DJ PRESS; TE =35–195 ms in
16 steps, TR =2000 ms, excitations =8)
26,27
.
Reconstruction and quantification of spectra
Spectroscopic imaging data were transferred to a Sun
workstation running SAGE-IDL (GE Medical Systems).
The data integrity was verified visually; scans with artifact
were excluded from the study. A quantitative analysis of
brain metabolites was performed using the LC Model
software. Basis sets for both the 3T-PRESS and 3T-2DJ
were provided by the vendor. The lower bound of mea-
surement error for glutamate quantification was a
Cramer–Rao lower bound of 20 or less. For glutamine
quantification, the lower bound measurement error was
relaxed to 30 or less to optimize both limited data and
goodness of fit
28,29
.
The SPGR anatomical data were segmented into gray
matter, white matter, and CSF using a technique modified
from a previous study
30
revised to use the FSL package
from FMRIB Oxford
31
. Briefly, SPGR data were converted
into NIFTI format using mri_convert. The T1 volume was
skull-stripped using BET, then segmented into gray
matter, white matter, and CSF using FAST with default
parameters. The segmented data were then overlaid with
the voxel location using in-house software, and the
number of pixels of each tissue type within the voxel was
counted. These counts were then normalized to the total
number of pixels within the voxel to arrive at the fraction
of each tissue within the 1H-MRS voxel. The tissue
volume-corrected metabolite concentrations, [M]TVC,
were then calculated by taking the measured metabolite
concentration, [M]M, and applying a correction factor as
follows: [M]TVC =[M]M x (1/[1−FCSF]) where FCSF is
fraction of CSF. This generated “absolute”(vs relative to
creatine) metabolite concentrations in “institutional units”
specific to our scanner and technique. These CSF-
corrected metabolite concentrations were used for all
statistical analyses.
AC and DLPFC MRS data acquisition of both TE80 and
2DJ Press, spectra reconstruction and quantification were
successfully completed in 39 individuals (BD: N=18,
UD: N=21); remaining subjects were either screening
failures or MRS was of a poor quality (i.e., inadequate
Cramer–Rao bound, head movements during data
acquisition).
Genetic analysis of the MRS cohort
Of the 39 MRS-examined individuals, 26 subjects con-
sented to a blood draw for genetic analysis. Prior to study
initiation, we designated 16 SNPs located in essential
regulatory elements and coding sequences of GLUL (2
SNPs), SLC1A3 (1 SNP), SLC1A2 (12 SNPs), and SLC1A7
(1 SNP). We amplified genomic DNA regions containing
targeted SNPs and sequenced amplicons using an ABI
3730xl automated sequencer (Applied Biosystems, Foster
City, CA, USA). Sequence variants were then analyzed by
Mutation Surveyor version 2.2 (Softgenetics, PA). One
SLC1A2 SNP, rs12360706, was excluded from the analysis
due to poor sequencing quality (Supplementary Table 1).
Three groups in perfect LD (R
2
=1) could be determined:
(1) rs1043101, rs10768121, rs11033046, rs12361171, and
rs3088168; (2) rs3812778 and rs3829280; (3) rs10742338
and rs2229894, leaving nine independent (R
2
< 0.6) loci.
Veldic et al. Translational Psychiatry (2019) 9:149 Page 3 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved
In silico analyses
LDlink (https://analysistools.nci.nih.gov/LDlink/) was
used to perform proxy search for SNPs in LD with
rs3812778, using populations of European descent.
Expression quantitative trait loci (eQTL) were identified
in the DLPFC using the gene expression database Brain-
Cloud (http://braincloud.jhmi.edu/)
32
, based on RNA
sequencing and genotype data of 412 subjects. The
modeling tested for additive genetic effects on expression,
adjusted for sex, ancestry, and expression heterogeneity. A
SNP-feature pair was considered significant with a false
discovery rate less than 1%. Raw data for the significant
pair were obtained from the website. Furthermore, data
were obtained for CD44 from the UK Brain Expression
Consortium (UKBEC) (http://www.braineac.org/), which
includes microarray data and genetic markers from dif-
ferent brain regions from 134 subjects. Genomic anno-
tations were used from UCSC genome for histone
modifications and DNAseI-sensitive regions
33
. The
development transcriptome dataset summarized to genes
from the BrainSpan project (http://www.brainspan.org/)
34
was used to assess correlations between expression of
CD44 and several genes of interest. This data set contains
RNA-sequencing data from up to sixteen brain regions
from 42 donors across the full course of human brain
development. SNPs were functionally annotated using the
genome-wide annotation of variants (GWAVA) tool,
which supports prioritization of noncoding variants by
integrating various genomic and epigenomic annotations
(https://www.sanger.ac.uk/science/tools/gwava)
35
.
Genetic analyses of the genotyping cohort
DNA samples from peripheral blood collected in Swe-
den and at the Mayo Clinic were genotyped for the SNPs
rs3812778 and rs3829280 in SLC1A2 using TaqMan SNP
genotyping assays on QuantStudio 7 Flex instrument
(Applied Biosystems, Foster City, CA, USA). The geno-
typing was performed by an investigator blinded to the
disease status of the patients. The genotyping efficiency
was 98%.
Statistical analysis
Normality was assessed with quantile–quantile plots,
homogeneity of variance was tested using the
Levene’s test.
Demographic and clinical measures are presented using
descriptive statistics. Comparisons between MDD and BD
groups were made using ttests for continuous measures
and a chi-square test for sex.
Linear regression models were used to test the additive
effect of the minor allele (coded as 0, 1, 2) on midline AC
and LDLPFC glutamate concentration for each SNP, fol-
lowed by a two-sided ttest in a dominant model when the
number of minor allele homozygotes was low (i.e.,
grouping A/G and A/A for rs3812778, and A/T and T/T
for rs3829280). A Bonferroni correction was applied for
36 (nine loci, two regions, two methods) independent
tests (p
cor
). Two-sided ttests were used to test for dif-
ferences in glutamate levels between BD and MDD.
The association between CD44 expression and the
genetic data was tested by two-sided ttest using a
dominant model. In the UKBEC data set, q-values were
used to estimate false discovery rates (FDR). Correlations
between the logarithm of CD44 expression and the
logarithm of the expression of the genes of the
glutamate–glutamine cycle were assessed using Spearman
correlation coefficient.
Differences in genotype between diagnoses, as well as
between RC BD and non-rapid cycling (NRC) were tested
using chi-square, as well as logistic regression to correct
for sex and age. A Bonferroni correction for two inde-
pendent tests was applied (p
cor
).
Statistical analyses were conducted using SAS (version
9.4; Cary, NC) and R programming language.
Results
rs3812778/rs3829280 are associated with AC glutamate
levels
As presented in Table 1, there was no statistically sig-
nificant difference for age (p=0.075), sex (p=1.0), or
mood symptom severity, as measured by HAM-D28 (p=
0.073) between mood disorder subtypes.
The minor alleles of the two SNPs rs3812778 and
rs3829280 (in perfect linkage disequilibrium (LD, r
2
=1)
in the 3′UTR region of SLC1A2 gene) were associated
with elevated 2D JPRESS mean AC glutamate levels
(common allele homozygotes: 105 ± 21 units, minor allele
carriers 135 ± 15 units; p=0.00078, p
cor
=0.028) (Fig. 1c).
No association between glutamate levels and diagnosis
(p=0.68), or depression symptom severity (p=0.75) was
found. There was no association between glutamate levels
and any other SNP. There was also no association
between any SNPs, including rs3812778/rs3829280, when
combined glutamate/glutamine levels were analyzed using
the TE80 method. No association was found in the
LDLPFC (Supplementary Table 1).
rs3812778/rs3829280 are associated with CD44 levels
In silico analyses, using the BrainCloud eQTL-browser,
we found an association between the minor allele of
rs3829280 and higher levels of the SLC1A2 neighboring
gene CD44 mRNA (chr11:35240935-35243200(*)) (Fig.
2a, p=0.00010). These findings were strengthened with
data from the UK Brain Expression Consortium, where
significant associations were identified between the minor
allele of rs3812778/rs3829280 and higher levels of the
full-length transcript of CD44 in the cerebellar cortex,
putamen, and substantia nigra, as well as in the average of
Veldic et al. Translational Psychiatry (2019) 9:149 Page 4 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved
all measured brain regions (Fig. 2b). Significant associa-
tions were also found for other CD44 transcripts (Sup-
plementary Table 2). When searching for potential
surrounding functional SNPs, we found six SNPs in per-
fect LD (R
2
=1) with rs3812778/rs3829280: rs10836358,
rs67384276, rs56193087, rs1570216, rs4508184,
rs12360706. Analysis with GWAVA, a tool for functional
annotation of noncoding sequence, revealed high values
across all prediction scores (> 0.6) for rs1570216, indi-
cating high probability for functionality for this SNP lying
in the 3′-UTR of SLC1A2 in a genomic area sensitive for
DNaseI also rich in H3K27 acetylation and H3K4
monomethylation (Supplementary Table 3).
CD44 strongly correlates with astrocytic markers
Given the important role CD44 plays in brain devel-
opment
36
, the correlation between CD44 expression and
the expression of genes of the glutamate–glutamine cycle
were assessed across several brain regions pre- and
postnatally using data from the BrainSpan project
34
(Fig.
3a). Strong positive correlations between CD44 and genes
typically expressed in astrocytes (GLUL, SLC1A3,
SLC1A2, and SLC38A3) were seen both pre- and post-
natally, while no or negative correlations were observed
for genes typically expressed in neurons (GLS, SLC1A1,
SLC1A6, SLC38A1, SLC17A7, SLC17A6, SLC17A8, and
SLC1A7). Furthermore, CD44 also very strongly corre-
lated with typical astrocytic markers like AQP4, S100b,
and GFAP (Fig. 3b).
rs3812778/rs3829280 and rapid cycling (RC) prevalence
We then performed an exploratory investigation to
check for disease relevance. Similar to the genetic
spectroscopic study, the follow-up cohort was com-
posed of both MDD and BD individuals of Swedish and
Caucasian American origin. Demographic character-
istics can be found in Table 1. rs3812778/rs3829280
were in Hardy–Weinberg equilibrium, and the minor
allele frequencies (MAF) of both SNPs in the whole
cohort were 13%, corresponding to those in European
populations
37
.
There was no significant difference in the percentage of
minor allele carriers of rs3812778/rs3829280 in BD vs
MDD participants (odds ratio (OR): 1.05 [95% confidence
Fig. 2 Expression of CD44 in different brain regions. Boxplot representations (median, 25th and 75th percentile) of CD44 expression stratified by
rs3812778/rs3829280 aCD44 (chr11:35240935-35243200(*)) in the dorsolateral prefrontal cortex as measured by RNA sequencing (data from
BrainCloud), (b)CD44 (Affimetrix transcript t3326635) in ten different brain regions, measured by microarray (data from the UK Brain Expression
Consortium). ***p=0.00010, *p< 0.05
Table 1 Subject demographics
MR spectroscopy—genotyping Genotyping
BD MDD Rapid cycling BD Non-rapid cycling BD MDD
Number of participants
1
11 15 638 835 458
Age (mean ± SD) 33 (11.3) 35.2 (13.3) 41.2 (14.2) 47.2 (15.4) 51.8 (11.9)
Sex (female/male) 8/3 11/4 403/235 463/372 330/128
HAM-D (mean, SD) 36.3 (9.1) 29.9 (7.7) NA NA NA
BD bipolar depression, MDD major depressive disorder, NA not applicable
1
All participants were of Swedish or Caucasian American origin
Veldic et al. Translational Psychiatry (2019) 9:149 Page 5 of 10
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interval (CI): 0.84–1.32], p=0.647). While the percentage
of minor allele carriers was comparable between MDD
(21.9% [95% CI: 19.1–24.9]) and non-RC (NRC) BD
(21.8% [95% CI: 17.9–25.8]), RC BD participants had a
significantly higher percentage of minor allele carriers in
comparison with the MDD+NRC BD group (26.9% [95%
CI: 23.5–30.5]) (age- and sex-adjusted OR: 1.38 [95% CI:
1.09–1.73], p=0.006, p
cor
=0.012; unadjusted OR: 1.32
[95% CI: 1.05–1.64], p=0.015, p
cor
=0.03).
Focusing only on patients with BD, a similar effect could
be observed between RC BD and NRC BD (age- and sex-
adjusted OR: 1.37 [95% CI: 1.07–1.76], p=0.012, p
cor
=
0.024; unadjusted OR: 1.31 [95% CI: 1.03–1.67], p=0.028,
p
cor
=0.056; Fig. 4). The lifetime history of rapid cycling
was higher (58%) in the American sites (tertiary referral
clinic) than at the Swedish site (a primary referral site,
28%). The model was therefore corrected for site, without
significantly affecting the outcome (age, sex, and site-
adjusted OR: 1.40 [95% CI: 1.08–1.82], p=0.011, p
cor
=
0.22).
Discussion
Here, we report a significant association between the
minor alleles of rs3812778/rs3829280, two SNPs in per-
fect linkage disequilibrium in the 3′UTR of the EAAT2
gene SLC1A2, and 2DJ glutamate levels in the AC. After
being released from presynaptic nerve terminals, the
extracellular glutamate is cleared by a family of excitatory
amino acid transporters (EAAT1-5)
38
(Fig. 1b). Astrocytes
play a major role in glutamate homeostasis in the
Fig. 3 Correlations between CD44 expression and genes of the glutamate/glutamine cycle across several brain regions pre- and
postnatally. a Correlations between CD44 expression (log) and the genes of the glutamate/glutamine cycle (log) across several brain regions in
prenatal brains (N
donors
=20, N
datapoints
=237) and postnatal brains (N
donors
=22, N
datapoints
=287), reported as Spearman’s correlation coefficients
(error bars 95% CI). bSpearman correlation between the expression of CD44 and the astrocytic marker GFAP in pre- and postnatal brains
Veldic et al. Translational Psychiatry (2019) 9:149 Page 6 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved
neocortex, with EAAT2, the most abundant glutamate
transporter in the forebrain, responsible for up to 95% of
glutamate clearance in the mammalian brain, mainly
being expressed on astrocytic plasma membranes.
39,40
Glutamate levels in the AC have been associated with
mood disorders. There has been previous speculation that
glutamate levels may distinguish BD (i.e., increased glu-
tamate) from MDD (i.e., decreased glutamate)
12,13,41
.
However, glutamate levels in the brain are also regulated
by genetic variations in molecules, such as EAAT and
enzymes responsible for glutamate–glutamine conversion
and glutamine–glutamate conversion which have not
been studied comparatively in different types of mood
disorders. For instance, Ongur et al.
42
showed that a
specific haplotype of four SNPs within GLS1, the gene
encoding for the enzyme glutaminase generating gluta-
mate from glutamine, was significantly associated with
glutamine/glutamate in the parietooccipital cortex and
rs956572 in a mixed group of healthy controls and
patients with bipolar disorder and schizophrenia
42
.A
second example is the work that identified a SNP in B-cell
lymphoma 2 (Bcl-2) shown to be associated with
increased anterior cingulate cortical glutamate solely in
euthymic bipolar I disorder
43
.
In silico analysis showed that the minor allele of
rs3812778/rs3829280 was associated with increased levels
of CD44 mRNA. CD44 is situated downstream of SLC1A2
on chromosome 11 (Supplementary Fig. 1a) and codes for
a transmembrane glycoprotein acting as a receptor for
hyaluronan, a key component of the extracellular matrix
in the brain. It is implicated in cell-matrix binding, sig-
naling, and cell migration
44
, as well as in the activation
and the resolution of inflammatory processes
45
and plays
important roles in physiology (e.g., organogenesis) and
pathology (e.g., cancer and metastasis)
44
. In the CNS,
CD44 is mainly expressed on glial cells, in particular
astrocytes, but expression has also been shown on neu-
rons
36
, in neural stem cells, astrocyte, and oligoden-
drocyte precursor cells at early postnatal stages
46
.CD44
has been implicated in many physiological CNS functions,
such as neural development, axon guidance, and astrocyte
differentiation
36
. In humans, CD44 has been suggested as
a candidate gene associated with BD using convergent
functional genomics
47,48
. Furthermore, CD44 has been
identified in a brain GWAS study as a possible risk gene
for suicidal behavior
49
and the CD44 ligand hyaluronic
acid was reported to be elevated in the CSF of suicide
attempters, correlating with blood-brain barrier perme-
ability, a hallmark of neuroinflammation
50
. Higher levels
of CD44 were also reported in the white matter of patients
with multiple sclerosis
51
and astrocytes of patients with
Alzheimer’s disease
52
.CD44 has also been associated with
disorders of the CNS in animal models: while CD44
deficiency is protective against cerebral ischemia injury in
mice
53
,CD44 levels have also been shown to be changed
by omega-3 fatty acid treatment in female, but not male
mice a stress-reactive knockout animal model of bipolar
disorder and co-morbid alcoholism
54
. Finally, an invol-
vement of CD44 in synaptic transmission has been sug-
gested with Matzke et al. showing that CD44-deficient
mice had markedly reduced glutamatergic synaptic exci-
tation
55
. Taken together, this evidence points toward a
central role of CD44 in CNS functions, and it can there-
fore be hypothesized that a disturbance in CD44 signaling
can lead to a change in glutamate turnover.
The strong positive correlations between CD44 and
astrocytic markers, as well as with the genes of the
glutamine–glutamate cycle expressed in astrocytes could
be indicative of an effect of rs3812778/rs3829280 on
astrocyte numbers. The changes in glutamate metabolism
could therefore also be explained by changed levels of
astrocytic glutamate transporters, including SLC1A2.
Therefore, we cannot exclude that rs3812778/rs3829280
also affect the expression of SLC1A2, but that this effect
cannot be detected in brain homogenates. Indeed,
SLC1A2 is highly regulated with various transcription
factor-binding sites, as well as regulatory elements in the
UTRs
1,56
. EAAT2 has been implicated in the pathophy-
siology of several disorders of the CNS, including Par-
kinson’s disease, epilepsy, amyotrophic lateral sclerosis,
Alzheimer’s disease, addiction, schizophrenia, as well as
MDD and BD.
1
On the molecular level, there is strong
evidence of downregulation of EAAT2 in diverse brain
regions in MDD
2,57
. Early stress impact on the gray matter
has been shown to be influenced by a functional poly-
morphism in EAAT2 in BD in the hippocampus, a brain
region with greater atrophy in BD versus MDD
58
; this
Fig. 4 Percentage of rs3812778 and rs3829280 minor allele
carriers in patients with rapid cycling BD, non-rapid cycling BD
and major depressive disorder. Bar graph representation of the
percentage (error bars: 95% CI) of minor allele carriers (rs3812778: A/G
and G/G; rs3829289: A/T and T/T) in the different diagnostic groups.
*Patients with rapid cycling BD versus patients with non-rapid cycling
BD, chi-square test, p=0.028;
#
patients with rapid-cycling BD versus
combined patients with non-rapid cycling BD and MDD, chi-square
test, p=0.015. RC BD rapid-cycling BD, NRC BD non-rapid cycling BD,
MDD major depressive disorder
Veldic et al. Translational Psychiatry (2019) 9:149 Page 7 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved
gene-by-environment interaction in the hippocampus has
not been described in MDD
59
. Also, epigenetically
mediated effects of early-life stress and addiction on
EAAT2 expression regulation may play an important role
in determining glutamate clearance rates and subsequent
in vivo glutamate measurements
60
. Finally, EAAT2 also
affects synaptic transmission, as blocking it with dihy-
drokainate, a specific inhibitor for EAAT2, leads to
extended N-methyl-D-aspartate (NMDA)-receptor-
mediated excitatory post-synaptic currents
61
.
The regulatory potential of rs3812778/rs3829280 is
supported by the fact that these SNPs are in perfect LD
with rs1570216, also situated in the 3′UTR of SLC1A2,in
a region sensitive for DNaseI, with high H3K27 acetyla-
tion and H3K4 monomethylation, pointing toward an
active regulatory area. The results from GWAVA, an
annotation tool for noncoding variants that integrates
various genomic and epigenomic variables, also point
toward the functionality of rs1570216
35
.
Our group has previously shown associations between
RC and rs2230912, a genetic variation in P2RX7
16
,
encoding for P2X purinoreceptor 7, a ligand-gated non-
selective cation channel, which has also been implicated
in modulating glutamatergic signaling
62
. Reporting a
novel association between the minor alleles of rs3812778/
rs3829280 and an increased risk for RC, we decided to test
whether we could find an interaction of the two genetic
variants in our cohort and included rs2230912 in our
model. However, we did not see an interactive effect
between both SNPs, and the effect of rs3812778/
rs3829280 on RC was not changed by this additional
variable (Supplementary Table 4).
Summarizing our findings, we hypothesize that the
minor alleles of rs3812778/rs3829280 are associated with
an upregulation of CD44, possibly indicative of an
increase in astrocyte numbers in the brain which in
combination with excitatory amino acid transporter
modulation, is associated with an increased glutamate
recycling resulting in dysregulated glutamatergic neuro-
transmission, associated with an increased risk of RC
(Supplementary Fig. 1b). This hypothesis is supported by
findings from Michael et al. who has shown that elevated
glutamate/glutamine in the DLPFC of BD II patients is
associated with RC
63
. To what extent anti-glutamatergic
mood-stabilizing anticonvulsants such as lamotrigine,
which has an evidence base in treating rapid cycling
bipolar II disorder, could impact this interaction remains
to be investigated
64
. One can therefore question whether
the currently reported differences seen in functional
imaging between BD and MDD, are being driven by
currently established diagnostic criteria (i.e., presence of
absence of a history of hypo/mania) or rather by clinical
subphenotypes like the presence of RC
63
, psychosis
65
,or
melancholic vs non-melancholic depression subtypes
3
.
Limitations
An important limitation of our study is the small
sample size of the MRS study, and replication in a larger
sample is warranted. Furthermore, we only analyzed a
small number of genes. Examining additional genes
known to be implicated in depression and involved in
either the glutamate/glutamine cycle (e.g., GLS1), reg-
ulation of neuronal plasticity and cellular resilience (e.g.,
BCL2), or purinergic signaling (e.g., P2RX7) may provide
a better understanding of the underlying neurobiology of
these glutamate-level alterations
66–68
. In addition, no
experimental evidence proves that the glutamine/gluta-
mate ratio directly reflects synaptic neurotransmission of
glutamate. However, J-resolved MRS sequence is opti-
mized for glutamate detection. This sequence attempts to
address some major challenges, including resolving
glutamine and glutamate signal from underlying macro-
molecule resonances as well as those from glutamate-
conjugate compounds, such as glutathione. Moreover,
studying the glutamine/glutamate ratio has been fruitful,
and several lines of evidence reviewed above indicate that
changes in glutamine/glutamate correlate with and thus
are a measure of changes in glutamatergic activity
42
.
Another limitation is heterogeneity of diagnostic assess-
ment between BD and MDD. BD assessment was based
on interviews, medical records review, and ques-
tionnaires in a clinical sample while MDD cases in the
genotyping cohort were selected in a random population
cohort and defined by MDI, However, validation studies
for the use of MDI in making DSM-IV-based diagnosis of
depression have been performed in population-based
settings
23
, clinical settings
69
, and outpatient settings
70
.In
addition, a population-based sample may reduce the
effect of confounders, such as propensity toward help
seeking. Also, given that the nature of our cohorts, the
sample size of our genotype cohorts was fixed. However,
power calculations showed that, given our sample size
and the allelic frequencies, we would be able to detect,
with a power of 80%, an effect size corresponding to an
OR of 1.36 for RC versus NRC and 1.4 for the compar-
ison of MDD versus BD. Finally, inter-rater reliability
assessment was not conducted between Swedish and
American sites.
Conclusion
This study is the first to associate spectroscopic findings
with gene variants in molecules central to glutamate
processing in mood disorders. Future studies combining
neuroimaging, genotyping, epigenetic, and possibly other
quantifiable diagnostic measurements, with deep clinical
phenotyping may provide enough elements to construct
nosological categories, and invest in developing biological
psychiatric phenotypes that can contribute to diagnostic
classification and treatment intervention
3,11,71
.
Veldic et al. Translational Psychiatry (2019) 9:149 Page 8 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Acknowledgements
We thank Inger Romer Ek, MSc, for the phenotyping of the bipolar patients. as
well as all the patients who participated in this study.
Author details
1
Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA.
2
Department of Molecular Medicine and Surgery (MMK), Karolinska Institutet,
Stockholm, Sweden.
3
Neurogenetics Unit, Center for Molecular Medicine,
Karolinska University Hospital, Stockholm, Sweden.
4
Department of Radiology,
Mayo Clinic, Rochester, MN, USA.
5
Department of Molecular Pharmacology &
Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.
6
Department of
Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
7
Lindner Center of
Hope, University of Cincinnati, Cincinnati, OH, USA.
8
Department of Psychiatry,
University of Minnesota, Minneapolis, MN, USA
Authors' contributions
Project conception and design: M.V., V.M., J.D.P., M.S., D.S.C., C.L., and M.A.F.;
patient recruitment: L.B., C.L., M.S., M.A.F., and S.L.M.; MRS experiments: J.D.P.;
genotyping of patients: V.M., C.L., M.C.H., and Y.F.J.; in silico analyses: V.M. andJ.
C.V.; the data analysis: M.V., V.M., J.R.G., J.M.B., and C.L.; paper writing: M.V., V.M.,
J.D.P., D.S.C., C.L., M.S., and M.A.F.; revision of the manuscript: all authors.
Competing interests
This work was supported by the National Institute of Mental Health
RO1MH079261, National Alliance for Research in Depression and
Schizophrenia (NARSAD) Independent Investigator Award, the Marriott
Foundation and Mayo Clinic Genomics of Addition to Dr. Frye, and by the
Mayo Foundation for Medical Education and Research as well as the J. Willard
and Alice S. Marriott Foundation grant to Dr. Veldic. The project was supported
by grants from the Karolinska Institutet, the KI-Mayo Collaboration (MV, CL, VM),
the Swedish Research Council (2016-02653 (MS); 2014-10171 (CL)), the Swedish
Brain Foundation (FO2017-0129 (CL); FO2018-0141 (CL)) and grants from the
regional agreement on medical training and clinical research (ALF) between
the Stockholm County Council and the Karolinska Institutet (SLL20170292 (CL)).
Dr. Choi is a scientific advisory board member to Peptron Inc. Dr. Frye is a
consultant (for Mayo Clinic) to Janssen, Mitsubishi Tanabe Pharma Corporation,
Myriad, Sunovion, and Teva Pharmaceuticals. None of this funding contributed
to work carried out in this study.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Supplementary information accompanies this paper at (https://doi.org/
10.1038/s41398-019-0483-9).
Received: 7 December 2018 Revised: 7 March 2019 Accepted: 10 April 2019
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