Inflammatory gene expression in monocytes
of patients with schizophrenia: overlap and
difference with bipolar disorder. A study in
naturalistically treated patients
Roosmarijn C. Drexhage1, Leonie van der Heul-Nieuwenhuijsen1, Roos C. Padmos1,
Nico van Beveren2, Dan Cohen3,4, Marjan A. Versnel1, Willem A. Nolen5
and Hemmo A. Drexhage1
1Department of Immunology, Erasmus MC, Rotterdam, The Netherlands
2Department of Psychiatry, Erasmus MC, Rotterdam, The Netherlands
3Department of Epidemiology, University Medical Center, Groningen, The Netherlands
4GGZ-NHN, Heerhugowaard, The Netherlands
5Department of Psychiatry, University Medical Center, Groningen, The Netherlands
Accumulating evidence indicates an activated inflammatory response system as a vulnerability factor for
schizophrenia (SZ) and bipolar disorder (BD). We aimed to detect a specific inflammatory monocyte gene
expression signature in SZ and compare such signature with our recently described inflammatory
monocyte gene signature in BD. A quantitative-polymerase chain reaction (Q-PCR) case-control gene
expression study was performed on monocytes of 27 SZ patients and compared to outcomes collected in
56 BD patients (all patients naturalistically treated). For Q-PCR we used nine ‘SZ specific genes’ (found in
whole genome analysis), the 19 BD signature genes (previously found by us) and six recently described
autoimmune diabetes inflammatory monocyte genes. Monocytes of SZ patients had (similar to those of
BD patients) a high inflammatory set point composed of three subsets of strongly correlating genes
characterized by different sets of transcription/MAPK regulating factors. Subset 1A, characterized by
ATF3 and DUSP2, and subset 1B, characterized by EGR3 and MXD1, were shared between BD and SZ
patients (up-regulated in 67% and 51%, and 34% and 41%, respectively). Subset 2, characterized by
PTPN7 and NAB2 was up-regulated in the monocytes of 62% BD, but down-regulated in the monocytes of
48% of SZ patients. Our approach shows that monocytes of SZ and BD patients overlap, but also differ
in inflammatory gene expression. Our approach opens new avenues for nosological classifications of
psychoses based on the inflammatory state of patients, enabling selection of those patients who might
benefit from an anti-inflammatory treatment.
Received 8 February 2010; Reviewed 22 April 2010; Revised 11 May 2010; Accepted 8 June 2010;
First published online 15 July 2010
Key words: Bipolar disorder, inflammation, Kraepelinian dichotomy, monocytes, schizophrenia.
We (Padmos et al. 2008a) recently described a sensitive
quantitative polymerase chain reaction (Q-PCR) assay
system to detect the pro-inflammatory state of cir-
culating monocytes of naturastically treated patients
with bipolar disorder (BD) patients and detected in
the monocytes a coherent, mutually correlating set of
19 aberrantly expressed inflammatory genes (‘a gene
signature or fingerprint’), supporting the concept of
an activated inflammatory response system (IRS) in
mood disorders (Smith & Maes, 1995).
Since the concept of an activated IRS also extends to
schizophrenia (SZ) (Smith & Maes, 1995), we hy-
pothesized that the same or a similar abnormal in-
flammatory gene fingerprint could also be found in
monocytes of patients with SZ and we decided to test
Address for correspondence: R. C. Drexhage, M.D., Department of
Immunology, Erasmus MC, PO Box 2040, 3000CA Rotterdam,
Tel.: 0031 10 7044091Fax: 0031 10 7044731
International Journal of Neuropsychopharmacology (2010), 13, 1369–1381. Copyright f CINP 2010
for the 19 aberrantly expressed ‘bipolar signature
genes’ in the circulating monocytes of naturalistically
treated SZ patients. In addition, we searched for new
‘schizophrenia inflammatory genes’ using Affymetrix
whole genome expression profiling (Affymetrix, USA)
on monocytes of SZ patients and selected those genes
involved in inflammation (yielding 15 ‘new’ genes, for
details see Results section).
Autoimmune diabetes, thyroiditis and gastritis
are about three times more prevalent in BD (Padmos
et al. 2004), whereas autoimmune thyrotoxicosis and
Sjogren’s disease are more prevalent in SZ (Eaton et al.
2006). Given our recently reported overlap in mono-
cyte gene expression signatures between BD and auto-
immune diabetes, we additionally included in our
analysis six ‘specific autoimmune diabetes signature
monocyte genes (Padmos et al. 2008b)’ to be complete.
Thus, using Q-PCR, we validated for this report the
abnormal expression of 34 monocyte activation genes
in 27 patients with SZ [compared to monocytes of
32 age-/gender-matched healthy controls (HC)] and
56 patients with BD [42 patients of the previously re-
ported series (Padmos et al. 2008a) plus 14 new cases,
altogether compared to monocytes of 48 age-/gender-
Patients and methods
Patients with SZ
Patients with SZ were diagnosed according to the
DSM-IV criteria and recruited at the Department
of Psychiatry of the Erasmus Medical Center in
Rotterdam. All patients were in-patients. Patients
were diagnosed with SZ according to the DSM-IV cri-
teria after a Comprehensive Assesment of Symptoms
and History (CASH) interview (Andreasen et al. 1992)
and by consensus between two senior psychiatrists
who were blinded to the results. For patients with
symptoms for <6 months, a final diagnosis was made
after 6 months to comply with the DSM-IV criterion.
All patients were acutely psychotic.
The SZ patients were almost all recent onset cases
and had a median duration of illness of only 0.3 yr
(range 0–3 yr). No cases suffered from any other
severe medical illness (including infections and al-
routine laboratory testing (Hb, Ht, leukocyte count,
blood smear and kindney/liver function) on ad-
For Affymetrix microarray analysis (searching
for aberrantly expressed genes in monocytes) two
monocyte pools of SZ patients were used. Each pool
was compared to a monocyte pool of age- and gender-
matched HC (pools were used for minimizing inter-
individual differences in mRNA expression and to
reduce costs for this expensive methodology). Patient
pool 1 consisted of four male cases, aged 22, 26, 27 and
20 yr, patient pool 2 consisted of three male cases, aged
17, 19 and 27 yr. Pools 1 and 2 were compared to two
HC pools of 2r2 males, aged between 22 and 26 yr.
For Q-PCR (verifying the found genes) 27 ad-
ditional SZ patients diagnosed according to DSM-IV
criteria were recruited at the Department of Psychiatry
of the Erasmus Medical Center in Rotterdam.
All but one of the patients received antipsychotic
medication at the time of blood draw; none of the
patients were drug naive. The demographics, duration
of illness and drug usage of the patients used in
Q-PCR are summarized in Table 1.
For the Q-PCR on SZ patients we used a control
group of 32 HC, who were age-/gender-matched to
the SZ patients. These were recruited from enrolling
laboratory staff, medical staff and medical students
(Table 1). The inclusion criteria for HC were an ab-
sence of any psychiatric and autoimmune disorder
and an absent history of these disorders in first-degree
family members. HC had to be in self-professed good
health and free of any obvious medical illness for at
least 2 wk prior to blood draw, including acute infec-
tions and allergic reactions.
Patients with BD
In total, 56 outpatients with DSM-IV bipolar I or II
disorder were recruited from two studies, i.e. the
Dutch site of the former Stanley Foundation Bipolar
Network (SFBN), an international multi-centre re-
search programme described in detail previously
(Suppes et al. 2001) (n=19 patients) and from an on-
going Dutch twin study on BD described in detail by
Vonk et al. (2007) (n=37). Characteristics of BD
patients are given in Table 1. Diagnosis was also made
by means of the SCID. Present mood state was evalu-
ated via the Young Mania Rating Scale (YMRS) and
the Inventory for Depressive Symptomatology (IDS).
The BD patients did not have another severe medical
illness, verified by medical history assessment.
Since age and gender differed between our BD and
SZ patients (Table 1) we compared outcomes of the BD
group to those of an extra group of 48 HC, who were
age-/gender-matched to the BD patients (Table 1). For
inclusion criteria for HC see earlier.
The Medical Ethical Review Committee of the
University Medical Center Utrecht (BD patients) and
the Medical Ethical Review Committee of the Erasmus
1370 R. C. Drexhage et al.
MC Rotterdam (SZ patients) approved the studies.
Written informed consent was obtained from all par-
ticipants after a complete description of the study had
Blood collection and preparation
Blood (drawn in the morning) was collected in clotting
tubes for serum preparation (stored at x80 xC) and in
sodium-heparin tubes for immune cell preparation.
From the heparinized blood, peripheral blood mono-
nuclear cell (PBMC) suspensions were prepared in
the afternoon by low-density gradient centrifugation,
as previously described in detail (Knijff et al. 2006),
within 8 h to avoid activation of the monocytes (ery-
throphagy). PBMCs were frozen in 10% dimethyl-
sulfoxide and stored in liquid nitrogen. This enabled
us to test patient and control immune cells in the same
series of experiments later.
Isolation of monocytes
CD14-positive monocytes were isolated from frozen
PBMCs by a magnetic cell sorting system (Miltenyi
Biotec, Germany). The purity of monocytes was >95%
(determined by morphological screening after Trypan
Blue staining and fluorescent-activated cell sorting).
As previously reported, positive vs. negative selection
of immune cells did not influence gene expression
profiles (Lyons et al. 2007).
Affymetrix whole genome gene expression profiling
RNA was isolated from purified monocytes using
RNAeasy columns according to the manufacturer’s
instructions (Qiagen, USA) and as previously de-
scribed (Staal et al. 2004). Fragmented cRNA was hy-
bridized to U95Av2 microarrays according to the
manufacturer’s instructions (Affymetrix). For all ex-
periments, the 5k/3k ratios of GAPDH were f2
RNA was isolated from monocytes as described
earlier. To obtain cDNA for Q-PCR, 1 mg RNA was
reversed-transcribed using the cDNA high-capacity
cDNA Reverse Transcription kit (Applied Biosystems,
USA). Q-PCR was performed as previously described
in detail by Staal et al. (2004) and in the legend of
Scanned microarray images were analysed using
Affymetrix Microarray Suite 4.2 software. Further
analysis was performed using RMA software, modifi-
cation by de Ridder (de Ridder et al. 2006) and
Ingenuity Systems (www.ingenuity.com) software.
Statistical analysis was performed using the SPSS 15.0
package for Windows (SPSS Inc., USA). Data were
tested for normal distribution using the Kolmogorov–
Smirnov test. Depending on the distribution pattern
and the total number of subjects, parametric (normal
distribution and o50 subjects) or non-parametric tests
Table 1. Characteristics of schizophrenia and bipolar patients
and their respective healthy controls used for Q-PCR
Duration illness (yr)
Age of onset
Dutch twin study
Dutch site SFBN
Duration illness (yr)
Age of onset (yr)
Antipsychotics and lithium
SFBN, Stanley Foundation Bipolar Network.
bThe bipolar patients did not have a history of drug or
alcohol dependency for at least 6 months; this was not known
for the schizophrenia patients.
cData on 42 of these 56 patients have been published
previously (Padmos et al. 2008a).
Monocyte fingerprint in psychiatric disorders1371
Table 2. Q-PCR analysis of monocytes of bipolar (BD) patients [n=56, 42 from a
previous study (Padmos et al. 2008a) plus 14 new cases) and schizophrenia (SZ)
patients (n=27) compared to healthy control (HC) values (HC SZ: n=32; HC BD:
n=48), set at 1-fold.
Schizophrenia Bipolar disorder
Genes selected by whole genome screening in this study in schizophrenia
Genes selected in a previous study on bipolar patients (Padmos et al. 2008a)
Genes selected in previous study on autoimmune diabetes (Padmos et al. 2008b)
Q-PCR was performed with Taqman Universal PCR mastermix (Applied
Biosystems, USA). All Taqman probes and consensus primers were pre-formulated
and designed by Applied Biosystems (Assays on Demand, see Supplementary
Table 1, online). PCR conditions were 2 min at 50 xC, 10 min at 95 xC, followed by
40 cycles of 15 s at 95 xC, and finally 1 min at 60 xC. PCR amplification of the reference
gene ABL was performed for each sample to allow normalization between the
samples. ABL was chosen as a reference gene because it was previously shown that
ABL was the most consistently expressed reference gene in haematopoietic cells
(Beillard et al. 2003). The quantitative value obtained from Q-PCR is a cycle threshold
(Ct). The fold change values between different groups were determined from
normalized Ctvalues (Ctgene – Cthousekeeping gene), by the DDCtmethod
1372R. C. Drexhage et al.
(skewed distribution or <50 subjects) were used.
Bonferroni correction for multiple testing was used for
the Affymetrix data (since this was a non-hypothesis-
driven approach). Correction for multiple testing was
not used for the analysis of the Q-PCR data, because
we focused on the effect of specific genes found in
the Affymetrix analysis. The specific tests used are
mentioned in the table notes and figure legends.
Whole genome expression profiling of potential
inflammatory biomarker genes in monocytes of
Affymetrix microarray analysis was performed to
search for aberrantly expressed genes in monocytes on
two monocyte pools of naturalistically treated SZ
patients. Each pool was compared to a monocyte pool
of age- and gender-matched HC (pools were used
for minimizing inter-individual differences in mRNA
expression and to reduce costs for this expensive
methodology). All raw data obtained by Affymetrix
analysis are available as MIAMExpress submission
We analysed the data using a modified RMA
analysis (de Ridder et al. 2006) and considered for
Ingenuity analysis genes which were >2-fold statisti-
cally differentially expressed (p<0.01, corrected for
multiple testing) between SZ patients and HC. This
resulted in 298 discriminating genes (185 up-regulated
and 113 down-regulated). Major pathways found in
Ingenuity analysis were pathways involved in in-
flammatory and immune mediated disease. To select
for genes which could serve as potential biomarkers
for the ‘schizophrenia inflammatory condition’, we
took the top genes from the up and down list, which
were statistically >3.5-fold significantly differentially
expressed between SZ and HC with the purpose of
only identifying strongly discriminating genes. This
resulted in 22 overexpressed genes. None of the genes
was >3.5-fold lower expressed [the first gene of the
list of the lower-expressed genes was CCR2, which
was 2.9-fold lower expressed, but this gene had
already been selected in our previous bipolar study
(Padmos et al. 2008a). Because we were searching for
regulators and biomarkers of inflammation, out of
these 22 aberrantly expressed genes we selected only
genes clearly involved in inflammation. This resulted
in 14 selected aberrantly expressed genes for SZ, and it
is of note that five of these genes had previously been
found overexpressed in BD patients, i.e. PDE4B, IL1,
PTGS2/COX2, CCL20 and CXCL2 (Padmos et al.
2008a), pointing to a strong overlap of inflammatory
set points between monocytes of BD and SZ. In sum,
nine new ‘schizophrenia specific’ up-regulated genes
(MXD1, F3, MAFF, EGR3, THBS, SERPINB2/PAI-2,
RGC32, EREG, CXCL3) were finally selected and we
included these nine new genes together with the 19
aberrantly expressed ‘bipolar signature genes’ and the
six ‘autoimmune diabetes signature genes’ in the
validating Q-PCR analysis of the 27 SZ patients, 56 BD
patients [42 patients of the previously reported study
of Padmos et al. (2008a) plus 14 new cases] and their 32
and 48 matched HC respectively.
Q-PCR analysis of monocytes of SZ and BD patients
Table 2 (and Supplementary Table 1, available online)
show that of the 34 genes tested, the mRNA expression
levels of 25 genes were significantly different (p<0.05
by ANCOVA, corrected for age and gender) in the
monocytes of the 27 SZ patients compared to HC,
while in the monocytes of the 56 BD patients 27 genes
were significantly differently expressed. Data obtained
in Q-PCR on the mRNA expression levels of the
various genes in the patient groups correlated very
strongly to those obtained in the above-described
Affymetrix analysis (SZ: r=0.708; BD: r=0.663,
(2xDDCt, User Bulletin 2, Applied Biosystems, USA). To correct for inter-assay variance, we set the mean of the studied genes
found in the healthy control groups in the same assay for each gene to 1 (SDCt: HC=0, 2x0=1). The fold change values of the
genes in patients’ monocytes were expressed relative to this set mean of 1.
Data are expressed relative to this HC value. HC SCZ: n=32; HC BD: n=48.
aValues >1: patients have a higher expression than control group.
bValues <1: patients have a lower expression than control group.
cP tested by univariate ANCOVA vs. control subjects; age and gender are included in this model.
Purity of monocytes was >90% (as determined by morphology on each sample) and >92% as determined by fluorescent
assisted cell sorting analysis. Yield of monocytes was 28% (¡10%) in the patient groups group and 21% (¡8%) for the HC
group (n.s.) of the Ficoll-isolated peripheral blood mononuclear cells.
The DCtvalues are available in Supplementary Table 1 (online).
(Table 2 footnote continued)
Monocyte fingerprint in psychiatric disorders1373
CCL2 CCL7 CDC42
05 101520 25
05 1015 2025
Fig. 1. Heat map of gene correlation. Correlation of expression of the various genes; data represent Spearman’s correlation coefficients, tested on the relative mRNA expression of the
genes in 83 individuals: 56 bipolar patients, 27 schizophrenia patients. Significant positive correlations (p<0.05) are given by the red scale (darkest red are correlations >0.60),
significant negative correlations are given by the green scale. White fields are not significant. (a) The correlations of all tested genes to each other are shown. (b) Three sets of MAPK
regulators/transcription factors have been extracted from panel (a), namely DUSP2/ATF3, MXD1/EGR3 and PTPN7/NAB2 and correlations to the other genes are shown. Note that
(1) DUSP2/ATF3 correlate strongest to sub-cluster 1A genes (and weaker to the other subsets of genes), (2) MXD1/EGR3 correlates strongest to sub-cluster 1B genes and many of the
sub-cluster 1A genes (but weaker to DUSP2/ATF3) and (3) that PTPN7/NAB2 correlates strongest to sub-cluster 2 genes. (c) Dendrograms of schizophrenia and bipolar disorder.
R. C. Drexhage et al.
There were no differences in mRNA gene ex-
pression for the different groups of HC (Supplemen-
tary Table 1).
The aberrantly expressed genes were mostly shared
between the two disorders but also in part not shared.
Of the nine ‘schizophrenia specific’ genes, eight were
confirmed by Q-PCR in SZ (RGC32 appeared not to be
higher expressed); while in BD patients five of these
genes were significantly higher expressed (MAFF, F3,
EREG, CXCL3, RGC32).
Of the 19 ‘bipolar signature genes’ 16 were statisti-
cally significantly up-regulated in SZ, while for three
genes statistical significance was not reached (CCR2,
NAB2, EMP1). Confirming our previous data, we
found almost all 19 ‘bipolar signature genes’ (apart
from CCR2) statistically significantly overexpressed in
this extended set of BD patients.
Of the six ‘autoimmune diabetes signature genes’,
five were not aberrantly expressed in SZ, while PTPN7
Interestingly, in the BD sample 3/6 genes were up-
regulated, including PTPN7.
Cluster analysis and identification of sub-clusters in
the pro-inflammatory signature
To study their mutually inter-dependent state in
expression, we subsequently performed a cluster
analysis on the Q-PCR data. The heat map and
dendrograms of this analysis are given in Fig. 1a. In
sum, expression levels of virtually all genes correlated
to each other, yet two sub-clusters of mutually very
strongly correlating genes (correlation coefficient
>0.60) could clearly be identified (two major red
blocks in the figure), each predominantly correlating
to a different set of transcription/MAPK regulating
factors, i.e. ATF3/DUSP2 and PTPN7/NAB2, respect-
ively. These two sets of transcription factors were
mutually, but not strongly, correlated (Fig. 1b).
The first sub-cluster correlating to ATF3 and DUSP2
consisted predominantly of various well-known in-
flammatory compounds, such as the pro-inflamma-
tory cytokines IL1, IL6 and TNF, the inflammatory
compounds PTGS2/COX2 and PTX3, various inflam-
matory chemokines (CCL20, CXCL2, CXCL3) and
PDE4B (Fig. 1b).
The second sub-cluster (further indicated as sub-
cluster 2) correlating to PTPN7 and NAB2 consisted
predominantly of various adhesion/motility/chemo-
tactic factors, such as EMP1, CDC42, CCL2 and CCL7
In careful analysis (Fig. 1b, Supplementary Fig. 1,
online), sub-cluster 1 contained a further sub-cluster
Table 3. Fold change values of all genes grouped by cluster
The quantitative value obtained from Q-PCR is a cycle
threshold (Ct). The fold change values between different
groups were determined from the normalized Ctvalues
(Ctgene – Cthousekeeping gene), via the DD Ctmethod
(User Bulletin, Applied Biosystems). The fold change of HC
was set to 1.
Data are expressed relative to this HC value. HC SCZ:
n=32; HC BD: n=48.
Values >1: patients have a higher expression than control
group. Boxes indicate significantly up-regulated.
Values <1: patients have a lower expression than
control group. Grey shaded box indicates significantly
p tested by univariate ANCOVA vs. control subjects; age
and gender are included in this model.
Monocyte fingerprint in psychiatric disorders1375
consisting of the transcription factors EGR3 and
MXD1, which were mutually strongly correlating to
the transcription factors MAFF and F3, but more
weakly to ATF3/DUSP2 and PTPN7/NAB2 (Fig. 1b,
this sub-sub cluster is further indicated as sub-cluster
1B, the other remaining set being sub-cluster 1A).
The expression of the three sub-clusters in SZ and BD
Tables 3 and 4 show that sub-cluster 1A is expressed in
the monocytes of both SZ and BD patients. If one de-
fines sub-cluster 1A positivity as positive for ATF3
and/or DUSP2 (i.e. an expression level higher than the
mean¡1 S.D. of the HC values) 67% of BD and 52% of
SZ patients are positive vs. 24% and 24% of their
matched HC, respectively (Table 4).
With regard to sub-cluster 1B, Tables 3 and 4 show
that SZ patients and BD patients are positive, but in
lower proportions. Using the same type of definition
as for sub-cluster 1A (but now for EGR3 and/or
MXD1), it appeared that SZ patients were significantly
positive for 41% vs. 34% of BD patients (vs. 13% and
22% of their HC, Table 4).
With regard to sub-cluster 2, Tables 3 and 4 show
that only BD patients show an up-regulation of these
genes. Interestingly, SZ patients show a significant
down-regulation of two transcription factors belong-
ing to this sub-cluster, i.e. PTPN7 and NAB2 (signifi-
cant for PTPN7), while MAPK6 is up-regulated (yet
not to the same extent as in BD). It is noteworthy that
the adhesion/motility factors EMP1 and STX1A are
down-regulated too. If one defines sub-cluster 2 posi-
tivity as positive for PTPN7 and/or NAB2 (see above
for definition), even a significantly reduced expression
in SZ vs. HC can be seen (7% vs. 21%), while in BD
there is a significant increased expression (62% vs.
32%) (Table 4). Conversely, if sub-cluster 2 is defined
as a ‘negative’ signature (Table 4, last column) a
higher proportion of SZ patients is positive for such
reduced expression (48%).
Relation of monocyte inflammatory gene expression
to medication use, disease duration and disease
To test for the influence of lithium and antipsychotics,
we turned to the group of BD patients, since almost all
patients with SZ were on antipsychotics; only one was
not, but had used an antipsychotic in the past. Of the
56 BD patients, 32 were on lithium, eight were on anti-
psychotics, five used both and at the time of blood
draw 11 were not on lithium or an antipsychotic but
had used this medication in the past (>6 months ago).
Table 5a shows the effects of the medications in this
BD group. Use of lithium and antipsychotics either
alone or in combination resulted in a significant de-
crease of PDE4B, but not of other genes (although
there was a near significant trend for a decreasing ef-
fect of lithium and antipsychotics on other important
signature 1A genes such as IL1 and TNF).
With regard to sub-cluster 2 genes there was a
near significant increasing trend of the use of anti-
psychotics in BD patients on genes such as PTPN7,
NAB2 and STX1A (data not shown). This increasing
effect of antipsychotics on sub-cluster 2 genes was also
reflected in the ‘lifetime cumulative antipsychotic
drugusage in haloperidol
positively and significantly correlated to important
sub-cluster 2 genes as PTPN7 (r=0.55), HSPA1A
(r=0.61) and EMP1 (r=0.62).
Table 4. The prevalence of sub-clusters in bipolar patients, schizophrenia patients and healthy controls
Cluster 1ACluster 1BCluster 2Cluster 2
ap<0.05 vs. healthy controls.
bp<0.05 vs. bipolar disorder.
Positive is defined as an mRNA expression >1 S.D. away of the mean level found in the healthy controls. Signature is defined on
the transcription factors positive. p values are obtained from x2test.
1376 R. C. Drexhage et al.
Duration of illness
The patients with SZ were in general recent onset
cases and had a median duration of illness of only
y2 months (Table 1). Effects of duration of illness
were not noticeable.
The BD patients had a median duration of illness of
16 yr (range 3.5–40 yr, Table 1); in this latter group
there was a weak, although significant correlation be-
tween disease duration and the expression of some of
the signature genes: we found a weak positive cor-
relation for IL6, PTX3, CCL2 and EMP1 (r=0.30–0.40)
and a weak negative correlation for HSPA1A (r=
x0.30), indicating a slightly stronger expression of
part of the inflammatory fingerprint over years.
Disease quality and severity
In the SZ patients disease quality and severity (as ex-
pressed in the various PANSS scales) did not correlate
to any of the various gene expression levels.
With regard to BD, we previously
(Padmos et al. 2008a) that the actual mood status of the
patients tested was to some extent related to the in-
flammatory gene expression. During a manic episode,
the mRNA expression of 2/19 genes were significantly
increased in monocytes of manic vs. euthymic BD
patients; during depressive episodes 6/19 genes. In
the presently extended series of 56 BD patients we
largely confirmed this observation and now found
1/34 genes during mania (n=7 patients) and 6/34
genes during depression (n=9 patients) raised [in
comparison to euthymic patients (n=40 patients),
Table 5b]. Interestingly these were all cluster 2 genes.
Although active disease thus is related to a higher ex-
pression of many of the cluster 2 signature genes (in
depressive phases more than in manic phases), it must
be noted that virtually all of the cluster 2 genes were
still significantly higher in euthymic BD patients
compared to HC.
Inflammatory gene expression at the protein level
We previously reported on the IL1b, IL6, TNFa, CCL7
and CCL2 levels in the serum of these sets of BD and
SZ patients (Drexhage et al. 2008; Padmos et al. 2008a),
and found that only IL1b was increased in BD (com-
pared to HC), while IL1b, IL6, TNFa and CCL2 were all
increased in SZ patients (compared to HC).
With regard to PTX3, we were able in this study to
measure serum levels and found these increased in BD
and SZ patients (Fig. 2). The figure shows that in BD
Table 5. Correlations of aberrant gene expression
(a) With medication use in bipolar patients
Medication GenespB 95% CI
x4.736 to x19.867
x6.555 to x26.671
x5.468 to x28.817
Linear regression with lithium, antipsychotics and both medications were included in the model. The values of patients on the
indicated drug are set to 1 in the model. B, Regression coefficient.
(b) With the mood status of bipolar patients
Depressive (n=9) vs. Euthymic (n=40)Manic (n=7) vs. Euthymic (n=40)
B 95% CIpB 95% CIp
x44.81 to 90.68
x14.30 to 31.83
x6.78 to 6.22
x0.49 to 4.99
0.36 to 4.22
x1.58 to 9.24
Determination of the influence of mood on mRNA expression of molecules via ANCOVA analysis.
The values of patients with a euthymic mood are set to 1. B, Regression coefficient.
Monocyte fingerprint in psychiatric disorders1377
and SZ PTX3 serum levels are significantly raised over
HC levels, i.e. y2.5-fold in BD and y1.5-fold in SZ.
When we correlated serum PTX3 protein levels to
monocyte gene expression levels we found a signifi-
cant positive correlation, although weakly (r=0.184,
factors other than monocyte gene expression also de-
termine serum PTX3 levels.
The outcomes of our study show that circulating
monocytes are set at a high inflammatory gene ex-
pression set-point in both SZ and BD. On the protein
level these high gene expression set-points were
(weakly, but significantly) reflected in high serum
levels of pro-inflammatory cytokines and compounds.
Although monocytes of BD and SZ patients clearly
overlapped in signature gene expression sets 1A and
1B, they also differed with regard to signature gene
expression set 2. We found the MAP kinase-regulating
factors PTPN7 and NAB2 up-regulated in monocytes
of BD patients, but down-regulated in monocytes of
SZ patients. Our immune biomarker approach thus
made a distinction between BD and SZ possible.
Although thus supporting the dichotomy between BD
and SZ as introduced in 1899 by Kraepelin (1899), our
immune data also lend support to the recently ex-
pressed view by geneticists (Bramon & Sham, 2001;
Lichtenstein et al. 2009; Owen & Craddock, 2009;
Thomson et al. 2005) that BD and SZ are strongly
overlapping entities sharing the same vulnerability
genes, since we found monocytes of considerable
proportions of BD and SZ patients to share an up-
regulated pro-inflammatory gene sub-cluster 1A,
composed mainly of a network of well known pro-
inflammatory cytokines and compounds such as IL1,
IL6, TNF, PTGS2/COX2 and PTX3, many of which
have previously been found up-regulated mainly at
the protein level in mood disorders and SZ (Drexhage
et al. 2008; Padmos et al. 2008a).
In addition, we found monocytes, particularly of SZ
patients, to be set at a further and higher inflammatory
set-point, due in particular to an extra up-regulation of
the transcription factors/regulators EGR3, MXD1,
MAFF and F3. These transcription factors/regulators
of monocytes, but also play a role in the regulation
of theinflammatory set-point
macrophages (Ayer & Eisenman, 1993; Blank, 2008;
Carter & Tourtellotte, 2007; Collins et al. 2008; Hurlin
& Huang, 2006; Motohashi et al. 1997). Our data,
which show a strong correlation of EGR3, MXD1,
MAFF and F3 to the inflammatory cytokines and
compounds to which ATF3 and DUSP2 were also
correlating, supports such a view of involvement of
these transcription factors in inflammation. Interest-
ingly, the expression of EGR3, MXD1, MAFF and F3
were particularly correlated to the up-regulation of
the adipogenic and vascular pathology factors THBS
and SERPINB2/PAI-2, and it is here of note that the
incidence of the metabolic syndrome is increased in
BD and SZ (Birkenaes et al. 2007).
Our study has limitations. First, patients with SZ
were predominantly male and young in the 20-yr age
group and virtually all had recent onset disease and
were on antipsychotic medication; the BD patients
were predominantly female and in the 40-yr age
group, had mainly longstanding disease and many
used lithium and/or antipsychotics. All patients were
naturalistically treated and none of our patients was
‘treatment naive’. It could be argued that age and
gender simply explained the differences between SZ
Bipolar disorderSchizophrenia Healthy control
p < 0.01
p < 0.01
Fig. 2. Serum PTX3 levels in bipolar disorder (BD) patients,
schizophrenia (SZ) patients and healthy controls (HC).
Box plots of PTX3 are given. The serum PTX3 level was
determined via an in-house ELISA (M7M) on the serum of
58 BD patients, 181 SZ patients and 188 HC, some of whom
were also used for Q-PCR according to the manufacturer’s
protocol (for patient details see Drexhage et al. 2008). The box
indicates the lower and upper quartiles. The line within the
box represents the median. The whiskers extend to the 2.5
and 97.5 percentiles. The outliers are characterized by the
filled dots. ANCOVA was used for statistical evaluation.
Age and gender were included in the statistical model, other
confounding factors such as adiposity could, however, not be
investigated, since we were not informed of the adiposity in
the majority of the cases tested here. The figure shows that in
BD and SZ, PTX3 serum levels are significantly raised over
HC levels, i.e. y2.5-fold in BD and y1.5-fold in SZ.
Correlating serum PTX3 protein levels to monocyte gene
expression levels we found a significant positive correlation,
although only weakly (r=0.184, p=0.05, Spearman’s
1378 R. C. Drexhage et al.
and BD and that medication is the causal factor for the
high inflammatory gene expression level in monocytes
of psychiatric patients.
With regard to age and gender, it must be noted that
we compared the patient findings with those of age-
and gender-matched control groups and that the
monocyte inflammatory state in these control groups
of different age and gender did not differ (Supple-
mentary Table 1).
With regard to the effects of medication it must be
noted that effects of lithium and antipsychotics are
generally anti-inflammatory in character (Drzyzga
et al. 2006; Pollmacher et al. 2000; Rybakowski, 2000)
and the data presented here, as well as our previous
data (Padmos et al. 2008a), support such an immune
suppressive action and show that, if anything, these
medications do not induce but rather correct the ab-
normal inflammatory set-point of patient monocytes
(see Table 5a). However, we can not totally rule out an
important effect of illicit drugs on the induction of the
specific characteristics of the SZ monocyte signature,
since our study group of SZ patients was not con-
trolled for this variable (the BD patients were, see
Another limitation is that in our experimental de-
sign we made a selection of aberrantly expressed
genes by selecting, in whole genome analysis, only
highly over- and under-expressed genes (y3.5-fold),
which were clearly involved in inflammation and in-
flammatory processes. Although this approach proved
to be fruitful in detecting the three fingerprint patterns
described here (which also made a distinction between
BD and SZ possible), we may have missed important
causal genes for the inflammatory set-points, since our
assumption that the sheer expression level of genes is
important for the inflammatory state, is naive. Clearly,
further studies of additional genes that are less ab-
errantly expressed but are critically involved in in-
flammation and/or have previously been described as
aberrant in psychiatric disorders are clearly needed to
see whether they are essential components of the in-
flammatory monocyte gene fingerprints.
The criteria used in psychiatry for validating noso-
logical categories have usually been restricted to clini-
cal features, outcome and family history (Craddock
& Owen, 2007). Kraepelin used these tools in for-
mulating his ideas, leading to his dichotomous classi-
fication between SZ and BD. Given that the main goal
of modern psychiatry is to provide effective treatment,
the view has been expressed that the ultimate vali-
dator for a diagnostic system must be treatment re-
sponse based on a detailed knowledge of pathogenesis
(Owen & Craddock, 2009).
Our study, using powerful new research genomic
tools, precisely provides such new immune biological
validators, which probably not only play a role in
the immune pathogenesis of SZ and BD but are also
potential treatment targets. There are several reports
indicating that pharmacological interferences with
some of the up-regulated inflammatory signature
genes found, i.e. interference with PTGS2/COX-2,
PDE4B and TNF, may alleviate signs and symptoms of
SZ and depression (Akhondzadeh et al. 2007; Martina
et al. 2006; Muller et al. 2004, 2006; Myint et al. 2007;
Nery et al. 2008; Tyring et al. 2006; Zhu et al. 2001) and
it can thus be envisaged that in particular patients
positive for monocyte cluster 1 genes would benefit
from treatment with COX-2, PDE4 and TNF inhibitors.
In conclusion, we here describe the first steps in an
immune molecular dissecting approach on inflamma-
tory monocytes, which has already led to the identifi-
cation of three coherent sets of putative immune
biomarker genes, opening new avenues for nosologi-
cal distinctions in psychiatric disease based on in-
flammation. Our approach could also lead to putative
sub-classification of patients with psychotic or BD,
who could possibly benefit from adjunctive anti-
inflammatory treatment targeting important finger-
Supplementary material accompanies this paper on
the Journal’s website (http://journals.cambridge.org/
This study was funded in part by EU-FP7-HEALTH-
F2-2008-222963 ‘MOODINFLAME’, Hersenstichting
funding organizations had no further role in the study
design; collection, analysis and interpretation of data;
the writing of the report; and the decision to submit
the paper for publication.
W. A. Nolen has received grants from The
Netherlands Organization for Health Research and
Development, the European Union, the Stanley
Medical Research Institute, AstraZeneca, Eli Lilly,
GlaxoSmithKline and Wyeth.
We thank Alberto Mantovani for kindly providing
the antibodies for the PTX3 ELISA. We thank Harm de
Wit and Annemarie Wijkhuijs for their excellent tech-
nical assistance, Caspar Looman for statistical advice
and Tar van Os for designing the figures. Dr Chris
McCully helped us with the style and grammar of the
Monocyte fingerprint in psychiatric disorders 1379
Statement of Interest
W. A. Nolen has received honoraria/speaker’s fees
from AstraZeneca, Eli Lilly, Pfizer, Servier and Wyeth,
and has served on advisory boards for AstraZeneca,
Cyberonics, Pfizer and Servier.
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