Molecular Pathway Reconstruction and Analysis of
Disturbed Gene Expression in Depressed Individuals
Who Died by Suicide
Vladimir Zhurov1, John D. H. Stead6, Zul Merali4,5, Miklos Palkovits2, Gabor Faludi3, Caroline Schild-
Poulter1, Hymie Anisman6, Michael O. Poulter1*
1Molecular Brain Research Group, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada, 2Laboratory for Neuromorphology, Hungarian
Academy of Sciences and Semmelweis University, Budapest, Hungary, 3Semmelweis University Hospital, Budapest, Hungary, 4University of Ottawa Institute of Mental
Health Research, Ottawa, Ontario, Canada, 5Departments of Psychology, Psychiatry and Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada,
6Department of Neuroscience, Carleton University, Ottawa, Ontario, Canada
Molecular mechanisms behind the etiology and pathophysiology of major depressive disorder and suicide remain largely
unknown. Recent molecular studies of expression of serotonin, GABA and CRH receptors in various brain regions have
demonstrated that molecular factors may contribute to the development of depressive disorder and suicide behaviour.
Here, we used microarray analysis to examine the expression of genes in brain tissue (frontopolar cortex) of individuals who
had been diagnosed with major depressive disorder and died by suicide, and those who had died suddenly without a
history of depression. We analyzed the list of differentially expressed genes using pathway analysis, which is an assumption-
free approach to analyze microarray data. Our analysis revealed that the differentially expressed genes formed functional
networks that were implicated in cell to cell signaling related to synapse maturation, neuronal growth and neuronal
complexity. We further validated these data by randomly choosing (100 times) similarly sized gene lists and subjecting these
lists to the same analyses. Random gene lists did not provide highly connected gene networks like those generated by the
differentially expressed list derived from our samples. We also found through correlational analysis that the gene expression
of control participants was more highly coordinated than in the MDD/suicide group. These data suggest that among
depressed individuals who died by suicide, wide ranging perturbations of gene expression exist that are critical for normal
synaptic connectively, morphology and cell to cell communication.
Citation: Zhurov V, Stead JDH, Merali Z, Palkovits M, Faludi G, et al. (2012) Molecular Pathway Reconstruction and Analysis of Disturbed Gene Expression in
Depressed Individuals Who Died by Suicide. PLoS ONE 7(10): e47581. doi:10.1371/journal.pone.0047581
Editor: Vinod K. Yaragudri, Nathan Kline Institute for Psychiatric Research and New York School of Medicine, United States of America
Received May 16, 2012; Accepted September 17, 2012; Published October 22, 2012
Copyright: ? 2012 Zhurov et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was supported by a CIHR grant to MOP, HA and ZM. A NARSAD Independent Researcher Award also supported MOP. HA is a Tier I Canada
Research Chair in Neuroscience. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
In an effort to understand the biological processes associated
with depression and suicide, one viable approach has been the
molecular analysis of brain tissue obtained from depressed
individuals who died by suicide relative to non-depressed controls
who died from causes other than suicide. In this regard, marked
differences have been shown with respect to the expression of
CRH, 5-HT and GABAAreceptor subunits mRNAs and protein
between depressed and non-depressed individuals [1–3].
The use of microarrays as a means of ‘‘gene discovery’’ has
provided novel insights into various groups or subgroups of genes
that may be associated with depression/suicide [4–6]. The
significance or meaningfulness of the altered expression of a gene
has relied upon the researcher understanding the functional
implications of these genes. At another level, ontology lists can be
created that might suggest how a set of genes might operate
together to determine more complex phenotypes. For example, a
gene list that included down-regulated genes that control cell
differentiation might implicate impaired development of a normal
phenotype. Beyond this level of analysis, considerable difficulty
can be encountered in the interpretation of microarray data as the
functional implications of hundreds of gene changes is reliant on
the end user having broad knowledge of all potential protein/
protein interactions that could be altered.
One (potential) solution to this inability to analyze gene sets
rationally has come from the use of software that ‘‘reads’’ vast
amounts of information (e.g., from PubMed) and then constructs
relationship maps that permit the user to identify known or
potential novel processes that may be altered. Following from this
method, in the present study we used microarrays to compare the
mRNA expression of frontopolar cortex, a region implicated in
depression and suicide [7,8], obtained from control and
depressed/suicide subjects. We then implemented a method of
analysis that ‘‘reads’’ the current medical literature, thus permit-
ting the construction and display of relationships between various
biological molecules and processes. This analysis implicated a
number of processes involved in cell to cell adhesion and brain
structural processes that appear to be perturbed in the depressed/
suicide brain. Since this analysis provides evidence for the
PLOS ONE | www.plosone.org1October 2012 | Volume 7 | Issue 10 | e47581
functional interactions between all gene products, it is also able to
point out potential functional ‘‘hubs’’ where one protein may be
central in the functioning of many others. This approach to
understanding the involvement of gene sets or hubs in relation to
pathology has been used in the analyses of cancer [9–11] and to
our knowledge it has not previously been adopted for brain related
disturbances. To be sure, when multiple relations are conducted,
even when premised on the scientific literature, the risk of alpha
error is exceedingly high. However, it should be no greater in
control than in brain tissue obtained from depressed individuals
that died by suicide. Thus, this approach, despite its inherent
limitations with respect to any one gene, provides important clues
regarding network differences that might exist between these
Materials and Methods
Description of Subjects Analysed
Gene expression was analyzed from non-psychiatric control
subjects (N=9) and depressed individuals who died by suicide
(N=10). All subjects were Caucasian Hungarian males. Tissue
samples were obtained at autopsy at the Department of Forensic
Medicine of the Semmelweis University Medical School in
Budapest (as described in ). The suicide and control groups
were of approximately equal age, had similar brain pH, post
mortem interval (PMI) and RNA quality (as measured by RNA
integrity number; RIN) (see Table 1 for summary). Cause of death
is also listed in Table 1.
Tissue harvesting occurred after written informed consent was
obtained from next of kin, which included the request to consult
the medical chart and to conduct neurochemical and/or
biochemical analyses. The ethics committee at Semmelweis and
the Ethics Committees of Carleton University and the University
of Western Ontario approved harvesting and analyses of the tissue
samples. The ethical rules for dissecting human brains vary across
countries. In some of the European countries, as in Hungary, once
death is confirmed by 3 physicians/pathologist, the removal of the
brain may proceed. In the cases of persons who died by suicide or
in traffic accident, pathological sectioning, as ‘‘medicolegal cases’’,
is ordinarily obligatory. These brains may be removed from the
skull as soon as 1–2 hours post mortem, frozen and stored until the
pathological sectioning. The dissection (microdissection) of the
brain can be performed after pathological diagnosis has been
obtained, including tests for HIV, tuberculosis, syphilis, hepatitis,
presence of alcohol and other drugs.
The suicide condition comprised individuals that died by
hanging, drug overdose or jump from height. Medical, psychiatric
and drug history of suicides were obtained through chart review
coupled with interviews with the attending physician/psychiatrist
and family members, as previously described . These interviews
were semi-structured and focused on issues such as previous
psychiatric history, family history of mental illness, and recent
stressful experiences. In each instance a psychiatric diagnosis of
depressive disorder was previously on record. The diagnoses were
conducted and/or confirmed by experienced psychiatrists on the
basis of DSM-IV criteria. Insofar as could be determined, the
participants had not used antidepressant medication for at least
two months prior to death and did not have a history of either
drug or alcohol abuse. Toxicological tests of blood samples
confirmed that drugs or alcohol were not present in cases of death
by hanging or jump from height.
With respect to the control participants, examination of medical
records confirmed the absence of a history of psychiatric illness,
alcohol or drug abuse during the last ten years. Moreover,
interviews with family members indicated that control participants
had never been treated for depression, and did not have a history
of alcohol abuse. Causes of death in control subjects were acute
cardiac failure, myocardial infarction or traffic accident. In all
instances death was sudden and did not involve a prolonged
Tissue Collection, Dissection and Storage
Brains were obtained 1–6 hours after death in Budapest,
Hungary. After removal from the skull, the brains were cut in six
major pieces (four cortical lobes, basal ganglia-diencephalon, and
lower brainstem-cerebellum), rapidly frozen on dry ice, and stored
at 280uC until dissection (which occurred 2 days to 2 months
later). At the time of the dissection, the brain samples were sliced
into 1 to 1.5-mm-thick coronal sections at a temperature of 0–
10uC. Cortical samples were always taken from the right
hemisphere. The frontopolar (FPC) region was cut out of the
sections by a fine microdissecting (Graefe’s) knife. This comprised
Brodmann area 10, dissected at the most polar portion of the
frontal lobe below the intermediate frontal sulcus. The samples
were stored in airtight containers or plastic tubes at 280uC until
use. RNA was extracted using Trizol reagent (Invitrogen,
Carlsbad, California). RNA quality assessment was performed
Table 1. Summary of the attributes of the cohort of subject
used for analysis.
DeathAgeRIN PMIBrain pH
1 AMI 56 7.22 6.62
2 AMI465.54 6.36
3 ACF 675.216.35
4 ACF456.65 6.43
5 ACF49 6.96 6.15
6 ACF 415.226.22
8ACF 738.56 6.96
Average 59.46.5 3.76.51
1 Hang 62 220.127.116.11
2 Hang 42 5.73 6.51
3 Hang455.74 6.77
4 Hang 4766 6.92
5 Hang55 8.54 6.97
6 OD49 8.36 6.59
8Jump71 8.31 6.64
10Hang 578.4 16 6.58
SEM 2.8 0.41.30.07
Abbreviations used myocardial infarction; MCI; Acute cardiovascular failure: ACF
Hang; death by hanging; jump death by jump form a height; over dose; OD.
Pathway Analysis of Depressed/Suicide Brain
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using Agilent 2100 Bioanalyzer (Agilent Technologies, Santa
Clara, California). Table 1 provides a description of the brain pH,
post mortem interval and RNA integrity number (RIN) for each
sample and cause of death. None of these variables were found to
differ significantly between the controls and depressed individuals
that died by suicide (p.0.05).
We utilized GeneChip Human Genome U133 Plus 2.0 Array
(Affymetrix, Santa Clara, California), which analyzes expression
level of over 47,000 transcripts, including 38,500 well-character-
ized human genes.
MAS5 probe level expression data generation algorithms were
used as implemented in Affymetrix Expression Console software
version 1.1. Expression data were filtered using MAS5 detection
call with threshold of $50% present in both classes . If a gene
was considered to be present it was assigned the value of 1, a
marginal presence was given a value of 0.51 and an absent call was
assigned a value of 0. For a probe set to be considered for
subsequent analysis the sum of call values from each subject had to
exceed 4.59 for the control group (n=9) and 5.10 (n=10) for the
Power analysis and FDR assessment.
Suite (GS) (Partek, St. Louis, Missouri) was used to determine
differentially expressed genes between depressed suicide patients
and non-psychiatric controls using Principal Component Analysis
(PCA) and Analysis of Covariance (ANCOVA). We have used
subject’s age, brain pH, PMI and RIN as covariate factors in
ANCOVA. Effect of these covariate factors was removed from the
data set using batch remove tool of Partek GS. Probe sets which
demonstrated significantly different expression levels between
classes at p,0.01 with Fold Change (FC) .1.3 in either direction
were considered for subsequent analysis.
We performed post-hoc power analyses of the mRNA expression
data at a=0.01, b=0.2 and a Fold Change |FC|=1.3 cut-off
using the interactive power analysis tool for microarrays HCE 3.5
. The analyses showed that sufficient power existed to detect
differentially expressed genes at these cutoffs.
We found traditional FDR control methodologies such as BH
 to be too conservative for our data set after removal of
covariates’ effect. However, we performed an assessment of FDR
using Significance Analysis of Microarrays (SAM)  workflow
which demonstrated that with our p and FC cut-offs FDR is
controlled at approximately 0.01.
The probe sets representing 238 known genes, obtained by
filtering expression data according to criteria described earlier,
were loaded into Pathway Studio software version 6.2 (Ariadne
Genomics, Rockville, Maryland) pathway analysis. Pathway
Studio software builds and displays molecular pathways and
connections of biomedical interest. It allows for interpretation of
experimental results in the context of pathways, gene regulation
networks, and protein interaction maps. When performing
pathway reconstruction in Pathway Studio software it is important
to note that reported relationships are not necessary direct in the
biochemical or protein interaction sense. What is reported is an
implied causal relationship extracted from existing scientific
literature. Gene interaction networks were generated to show
known direct relationships involving differentially expressed genes
in the data set. Small networks of less than four proteins were
To determine whether generated network represent a true
biological difference between two classes, we generated 100
random probe sets chosen from all genes that were considered to
be present on the arrays. The lists of these sets were used to
reconstruct relationship networks and then were assessed for
complexity (the number of proteins and relationships within the
generated networks). A bootstrapping analysis was then performed
to determine whether the networks generated from the differen-
tially expressed gene sets could be considered to be part of the
randomly generated networks. In this regard, a Z score of .3.0
was considered to be statistically significant.
To further analyze differentially expressed genes with known
relationships, we generated gene networks based on Pearson
correlations for both classes using Pathway Studio software. We
used a p-value cut-off of 0.01, Pearson’s correlation (r) cut-off of
0.8, removal of 5% of genes with the most stable expression and
only the largest gene networks were considered for subsequent
To identify the possible function of the gene lists generated by
the Pathway studio analysis, we also performed functional
annotation and clustering GO analysis using DAVID (ver 6.7).
Gene lists that were analyzed were the following: differentially
expressed genes (238 genes); differentially expressed genes involved
in direct relationships (46 genes); differentially expressed genes
with correlated expression in the control group (45 genes);
differentially expressed genes with correlated expression in the
suicide group (21 genes).
Samples for Quantitative PCR (QPCR) analyses were prepared
by reverse transcribing 3 mg of total RNA using Superscript II
reverse transcriptase (Invitrogen Canada, Burlington, ON).
Aliquots of this reaction were then used in simultaneous QPCR
reactions. RNA extraction and QC was performed in the same
way as described for microarray experiment.
For QPCR, SYBR Green detection was used according to the
manufacturer’s protocol (iQ SYBR Green Supermix; Bio-Rad,
Hercules, CA). A Bio-Rad MyiQ real-time thermocycler was used
to collect the data. All of the PCR primer pairs used generated
amplicons between 90 and 120 bp. Primer efficiency was
measured from the serial dilutions of cDNA over the range that
incorporated experimental cDNA amounts using iQ software. All
of the primer pairs had a minimum of 90% efficiency. We choose
10 genes at random over the range of expression of FC 0.5 to 2.5.
These genes were as follows: b2 microglobulin, B2M,
calreticulin, CALR, caveolin 1, CAV1, caveolin 2, CAV2, coronin
2A, CORO2A, glutamine synthetase, GLUL, lumican, LUM,
neuronal cell adhesion molecule, NRCAM, prion protein, PRNP,
sorting nexin 2, SNX2, RNA polymerase II polypeptide A,
POLR2A. Primers that amplify RNA polymerase II mRNA were
used as a reference gene to normalize the data. (PCR primer
sequences can be found in Supplemental data).
In order to determine whether the gene expression of two
groups was different, principal component analysis (PCA) was
performed on the complete P/A call-filtered MAS5 data set (as
implemented in the Partek Genomics Suite). The first three
principal components of this data set explained 46.4% of the
variability between control and depressed/suicide classes and
demonstrated a separation of classes, albeit with some overlap
(Figure 1). This analysis also showed higher variability of gene
expression in the control than in the depressed suicide group. At
fold change (FC) of 1.3, we found 340 differentially expressed
probe sets, p,0.01. Figure 2 shows a ‘heat map’’ representation of
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the differentially expressed genes (DEGs) found in this analysis.
These 340 probe sets corresponded to 238 annotated genes. Gene
ontology analysis of the 238 DEG showed that it was enriched
with genes involved in intracellular protein transport, synaptic
transmission and cell-cell signaling, p’s ,0.01 (Table 2, Table S1).
To assay whether differentially expressed probe sets represented
a true biological difference between two groups, we generated a
number of probe set lists at different p and FC cut-offs and
compared them to randomly generated probe set lists of the same
size using DAVID (Database for Annotation, Visualization and
Integrated Discovery) functional annotation analysis . We
generated nine differentially expressed probe set lists at combina-
tions of p equal 0.001, 0.01 and 0.05 and FC equal 1.3, 1.4 and 1.5
cut-offs. A number of random probe set lists of the same size were
then generated from the complete probe set list. Functional
annotation analysis was conducted using DAVID and frequency
distributions of GO enrichment scores were compared. The
frequency plot of the MAS5 generated gene set was not the same
as a similar plot where a random gene list was generated (Figure 3).
Importantly, the random list had maximum -log p values of 3.8,
whereas, the maximum -log p value obtained from our dataset was
5.8. Thus our differentially expressed gene list cannot be
considered to be a random outcome.
This list was further validated by performing QPCR analysis on
a subset of genes. This analysis demonstrated high agreement
between microarray and QPCR results (Figure 4). The fold change
found in microarray experiment was positively correlated with the
fold change found in the QPCR experiment (r=0.85, p,0.01).
Pathway and GO Analysis
The potential biological differences that may occur between
differentially expressed genes (DEGs) was determined by perform-
ing an analysis that interprets how the DEGs are known to interact
in biological pathways, gene regulation networks, and protein
interaction maps. This analysis, termed pathway reconstruction,
computationally finds how/if gene products have previously
reported functional relationships based on what is known from
existing scientific literature (see Methods for complete description).
This analysis generated 7 pathways utilizing 59 proteins that had
165 functional relationships. However 6 of the 7 pathways
involved 4 or fewer proteins, and were thus not considered
further. The seventh network comprised 46 proteins with 157
known direct relationships (Figure 5).
The shape of each symbol representing a gene indicates the
putative function of the gene product. In addition the figure
indicates, by colour, whether the gene was up-regulated (red) or
Figure 1. PCA demonstrates separation of control and depressed suicide subject groups. PCA of microarray expression data based on
complete data set. Red nodes represent control subjects, blue nodes - depressed suicide victims. Variable shading indicates distance from a viewer in
Pathway Analysis of Depressed/Suicide Brain
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down-regulated (blue). Functional annotation and enrichment
analysis of these 46 proteins demonstrated that this pathway was
enriched (p,0.01) with gene products involved in cell differenti-
ation, neurogenesis and axon growth (Table 3). Interestingly, five
gene products appeared as network ‘‘hubs’’ as they were
functionally implicated with at least 20 other proteins (see
Figure 5 and Table 4). Each of these hubs is a gene that has an
established functionality in the central nervous system.
In order to further validate this assessment a pathway analysis
was performed where random 238 probes sets were selected from
all genes shown to be present. This was done 100 times. For each
of these randomly chosen gene lists the same analysis was
performed as that used for the gene list found to be differentially
expressed. As in the MAS5 generated data set, determinations
were made of 1) the total number of networks and the number of
proteins and relationships in all pathways; 2) the largest network
generated, and 3) proteins and relationships per network. These
simulations did not produce networks of similar size nor
complexity. This is summarized in Table 5 where we compare
the average complexity of these simulations in comparison to the
values we found in the MAS 5 generated data set. For each
parameter the data set was significantly different from the
simulations (p values for each ,0.01).
The experimental DEGs list generated values that were up to
36 Z-scores from the means generated from the random lists
(p,0.01). Specifically, we found that the randomly generated
probe lists produced a range of 2–15 network sets (median=8).
The total number of proteins and relationships in these networks
ranged from 25–73 and 20–144, respectively, with approximately
46 proteins and 66 relationships on average. Mean connectivity of
the largest networks from 100 random probe set lists was 1.7
relationships per entity, whereas the connectivity within the
network generated from our experimental data was twice as high
(3.4 relationships per entity). The biggest individual network
generated from the random gene lists had only 73 proteins with
only 95 relationships. In all cases, the number of relationships in
the biggest networks generated for each simulation was consider-
ably less than those generated from the experimentally derived
DEG list (shown in Figure 5). To further illustrate this difference,
Figure 6 shows a plot of the number of proteins in the largest
Figure 2. Heat Map and dendrogram of all Control and depressed/suicide samples show both up and down-regulation and
clustering of 238 differentially expressed genes. Relative Expression values were normalized across all samples within each data set. Rows
represent probes while columns represent individual samples. Grey bars indicate no difference in expression, whereas blue and red indicate more and
less expression, respectively.
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network for each simulation versus the number of relations within
each of these networks and the corresponding value from the
network. The number of proteins and relationships from the
experimentally derived data is graphed as well. As can be seen,
these values do not lie within the distribution of the random gene
list values. Thus, it is highly unlikely that the network generated
from the biological data occurred by chance.
Correlative Gene Expression Analysis
As prior reports using QPCR analysis demonstrated a high
degree of correlation of gene expression of GABAAreceptor sub-
units in control brain relative to depressed suicide brain [1,17], we
analyzed whether a similar profile would be apparent in a much
larger set of differentially expressed genes (i.e., in the 238 DEG in
our data set). As doing analysis of all present genes in both cohorts
independent of whether they were differentially expressed or not is
not feasible/interpretable (due to high a error associated with so
many comparisons), we limited the analysis to those genes that
were differentially expressed, treating the two groups separately. In
effect this analysis asks if the altered gene expression is
accompanied by a loss of coordinated expression as well. Pearson’s
correlation-based gene networks generated from the expression
data (the 340 differentially expressed probe sets) revealed 45 genes
with 134 relationships having a correlation coefficient r .0.8
(positive or negative) at p,0.01 (Figure 7). Examination of the
same gene list in the depressed/suicide cohort showed far fewer
such relationships; only 21 genes with 80 correlations were
identified (Figure 8). GO analysis of these genes again showed the
cellular processes involved in synaptic transmission and cell to cell
adhesion were enriched in this list (see Table 6 and Table S2).
The present findings revealed that in the frontopolar cortex of
depressed individuals that died by suicide, networks of gene
products exist that appear to be dysregulated relative to the non-
depressed cohort that died quickly of causes other than suicide. We
used two types of analysis to provide insight into how the biology
of the depressed suicide brain might be different from normal
controls. The first, pathway analysis, provided the gene networks
shown in Figure 5 which was based on the ‘‘reading’’ of the
scientific literature by the software. The second set of networks
(Figures 7–8) was created by an analysis of gene expression
Table 2. Summary of GO cluster analysis results for the lists of
all differentially expressed genes.
Differentially Expressed Genes
cellular homeostasis and signaling158 4.8E205
cellular localization27 1.3E204
transmission of nerve impulse15 1.7E204
localization of protein 602.2E204
homeostatic process22 6.4E204
establishment of cellular localization 537.3E204
establishment of localization in cell23 1.3E203
cell-cell signaling18 1.8E203
Enrichment score is a –Log10 of a geometric mean of individual p reported for
individual GO terms within cluster. Only the top two clusters that exceed
enrichment score cut-off of 2 (corresponds to geometric mean of p=0.01) are
listed. The listed terms that are present in cluster are the most representative of
the main themes of all GO terms included in cluster.
Figure 3. Functional annotation analysis demonstrates that
differentially expressed probe sets represent true biological
difference between control and depressed suicide subject
groups. Comparison of functional annotation analysis of differentially
expressed probe sets generated at different p and FC cut-offs and
randomly generated probe sets lists of the same size. Note the shift of
the experimental curve to the right.
Figure 4. Relationship between Fold Change (FC) reported in
microarray and QPCR experiments. Pearson’s correlation r=0.86
and p,0.01. Circles represent individual genes. If more than one probe
set was present in microarray dataset for a particular gene, an average
FC was used for this gene as a ‘‘MAS5 FC’’.
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correlations in controls and in depressed individuals that died by
suicide. These analyses indicated agreement in the biological
processes that were implicated as being different in the depressed
suicide brain. The potential biological processes that have been
implicated for these networks are listed in Table 6 (and Table S3).
Although the functional relevance of a few processes are obviously
Figure 5. Gene relationship network generated for differentially expressed genes based on known direct relationships. For detailed
description of various types of relationships see Table S1.
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difficult to reconcile with known brain functions (myeloid
leukocyte differentiation for example), the overwhelming majority
are involved in regulatory or developmental process, synaptic
communication and cell to cell interactions.
These conclusions are based on a computational analysis which
relies on identifying functional relationships that have been
established to various degrees of certainty in the biomedical
literature. The analysis enables the effective access of 21 million
PubMed abstracts and 61 full text journals that cover mammalian
biology, a task that is obviously not possible by conventional
reading of the scientific literature. Although this analysis, at face
value appears useful and exciting, there is, simply by sheer number
of possible interactions analyzed, the question of whether or not
this analysis is valid. First, some proteins have many functions and
will therefore link with many others. Enzymes may have many
targets or substrates and so relationships that are reported by this
analysis may not be valid within a certain cell type that does not
express a target molecule. The analysis is only as good as the
present day knowledge of the protein interactions that are
reported. There is also no ‘‘quality control’’ for the validity of
the data contained in the publications. These limitations make the
GO analysis particularly important as it identifies the overall
implications of the gene interactions that are being reported.
These caveats notwithstanding, our data/analysis is remarkably
consistent in that each GO analysis generated similar functional
groups, those involved in cell structure and communication. As
well, we showed that the pathway analysis of a random selection of
the same number of genes did not provide apparently biologically
relevant networks. This conclusion is based on the pathway
analysis of the 100 random probe set lists of the same size where
we reconstructed relationship networks and then assessed number
of proteins and relationships in these networks. A statistical
analysis showed that random networks identified by the pathway
analysis were significantly smaller and less connected than the
networks generated from the experimentally derived DEG list.
This indicates that the interrelations found through the pathway
analysis likely represent true biological differences between
controls and depressed suicide samples. The function of this
network is implicated in cell to cell communication as many of the
processes suggested are involved in cell adhesion, cell morphology
and synapse formation. A similar approach, commonly referred to
as ‘‘bootstrapping’’ is a standard statistical procedure in other
studies  but to our knowledge has not been done before in an
analysis of this kind.
The pathway analysis also identified five genes that appeared as
‘‘network hubs’’ with connectivity higher than 20: RAC1,
CTNNB1, STAT3, EP300 and PTK2. These genes are of interest
as they represent the central points in cellular machinery that have
relationships with a variety of other proteins. For example, there
are more than 1500 relationships currently known for each of the
Table 3. Summary of GO cluster analysis results for the lists of differentially expressed genes with known direct relationships.
Genes with direct relationships
GO terms present in cluster
Enrichment score p value
range Genes present in cluster Gene Count
positive regulation of cellular and
biological process, system
4.0 1E204,p,0.01 CALR, CTNNB1, EP300, FMR1, HDAC7, ID2, IL6R, KDR, LEPR, MEF2D,
NRCAM, NRP2, PLAGL1, PTK2, RAC1, RTN4, SOD2, STAT3, TFE3, TIMP2,
TNFSF10, VCAM1, XRCC5
regulation of cellular and biological
3.9 1E204,p,0.001 ARG2, C1D, CALR, CCNC, CREM, CTNNB1, DAD1, DBNL, EP300, GDI1,
HDAC7, HMGB1, ID2, IL6R, KDR, LEPR, LRRFIP1, MEF2D, NCOR2, NR2C1,
NRCAM, PLAGL1, PRDX4, PTK2, RAB7A, RAC1, RTN4, SOD2, STAT3,
TFE3, TIMP2, TNFSF10, TXN, VCAM1, XRCC5
regulation of cell differentiation and
3.9 1E206.,p,0.01 CALR, CTNNB1, HDAC7, ID2, IL6R, NRCAM, PTK2, RTN4, SOD2, TFE3,
neurogenesis, cell differentiation,
developmental process, cell development
3.7 1E204,p,0.01ARG2, CALR, CREM, CTNNB1, DAD1, EP300, FMR1, ID2, IL6R, KDR,
LEPR, MEF2D, NRCAM, NRP2, PTK2, RAC1, RTN4, SOD2, STAT3,
TIMP2, VCAM1, XRCC5
regulation of cell differentiation,
neurogenesis, regulation of axiogenesis
3.3 1E204,p,0.06CALR, CTNNB1, EP300, HDAC7, HMGB1, ID2, IL6R, KDR, NRCAM, NRP2,
PTK2, RAC1, RTN4, SOD2, STAT3, TFE3, TIMP2, XRCC5
Enrichment score is a –Log10 of a geometric mean of individual p reported for individual GO terms within cluster. Only top 5 clusters that exceed enrichment score cut-
off of 2 (corresponds to geometric mean of p=0.01) are listed. The listed terms present in cluster are the most representative of the main themes of all GO terms
included in cluster.
Table 4. Summary of the known functions of genes that were found to be ‘‘hubs’’ in the pathway analysis network of DEGs’.
Gene symbolGene name Role
CTNNB1 catenin, beta 1 nervous system development, neuroprotection
EP300E1A binding protein p300neuronal differentiation
PTK2 protein tyrosine kinase 2 neuronal migration, neuronal plasticity
RAC1 rho family, small GTP binding proteinNeuronal development,myelination
STAT3signal transducer and activator of transcription 3neuronal survival and regeneration, leptin signalling
Pathway Analysis of Depressed/Suicide Brain
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five aforementioned proteins. Importantly, each of these has an
established role in nervous system processes, having been
implicated in nervous system development, neuronal migration
and differentiation, neuroprotection and neuronal plasticity
It is also interesting that 11 transcription factors were identified
that regulate at least two genes from the initial network. Of these,
8 have well established roles in nervous system functioning.
Specifically, SP1, SP3 and RELA are involved in neurite growth,
myelination and neuron survival [19–22], and TCF4 is important
for nervous system development, axon morphogenesis and
oligodendrocyte differentiation . FOXO3  regulates
neural progenitor stem cell proliferation as well as the induction
of genomic death responses upon its’ translocation from the
cytosol to the nucleus in response to excitotoxic stimuli .
MeCP2 is one of the central factors involved in gene regulation
through differential CpG methylation in various tissues and
organs, including the nervous system . Finally, CREB1 and
EGR1 have well established roles in synaptic plasticity, learning
and memory [27–30].
We also found 7 genes that were under control of at least 2
transcription factors implicated in the direct relationship network
in Figure 3. Of these, 5 have a well-established role in nervous
system functioning. FAS is involved in neuronal development and
degeneration [31,32], CCND1 is important for neuronal cells
proliferation [33,34], and GFAP and ERBB2 are involved in glia
and Schwann cell function, as well as synapse formation and
maturation [35,36]. Furthermore, VEGFA is involved in angio-
genesis and is also implicated in development of amyotrophic
lateral sclerosis (ALS) [37,38]. How any of these factors contribute
to the underlying depressive behavior is unclear, but the wide
ranging effects on the expression of these transcription factors
suggest broad disturbances in gene function.
Networks Identified through Correlation Analyses
As we have previously shown in a much more limited analysis,
gene expression in MDD/suicide brain seems to be much less
coordinated than in control samples . In the correlation
analyses done here we found 43 correlated genes in the control
(non-depressed) class, forming two distinct sub-networks with 134
correlated relationships. In the depressed/suicide condition, by
contrast, only 21 proteins were significantly correlated to one
another. The loss of coordinated expression seemed to be more
profound among genes that were up-regulated in MDD/suicide
group. Specifically, in the depressed/suicide PC networks, 12
DEG’s were up-regulated in the depressed/suicide class and 33
were down-regulated (approx. 1:3). In MDD/suicide PC network,
in contrast, only 2 DEG’s were up-regulated and 19 were down-
regulated (approx. 1:10). This suggests that the down-regulation is
‘‘concerted’’, whereas genes that were up-regulated in expression
appear to do so in a more ‘‘random’’ manner. Overall this analysis
suggests that in depressed/suicide individuals there is a wide
ranging loss of organized expression among genes that are
important for determining the wiring neural networks.
A number of genes were down-regulated in depressed/suicide
tissue, but were not present in the network (Figure 5) that
nonetheless would be predicted to have wide ranging effects on
synaptic function. One of these, GNAS (stimulatory alpha subunit
of G protein), is involved in many neurotransmitter signaling
cascades, including 5-HT and dopamine receptors. Another is
RTN4 or neurite outgrowth inhibitor, a regulator of apoptosis and
was implicated in neurodevelopmental processes [39–41]. As well,
the expression of synaptosomal-associated protein 25 (SNAP25),
which is involved in synaptic vesicle membrane docking and fusion
and regulation of neurotransmitter release was reduced. The
down-regulation of this protein has also been implicated in several
psychiatric disorders . In addition, SPARCL1 or hevin is a
putative extracellular matrix glycoprotein that binds calcium and
plays an important role in the developing nervous system .
Finally, GLUL or glutamate-ammonia ligase clears L-glutamate,
the major neurotransmitter in the central nervous system, from
Table 5. Summary of the attributes of the randomly generated gene networks and the networks that were generated from the
MAS5 DEG list.
Parameter Average for 100 generated lists ± SEM MAS 5 generated listZp
Proteins in the largest network 29.161.4 46 12.5
Relationships in the largest network53.763.2157 32.6
Proteins per network7.060.48.4 3.6
Relationships per network11.060.923.6 14.0
In comparison to parameters from the randomly generated networks, each parameter from the Mas 5 DEG generated Z-scores that were significantly different from the
average values that were generated by 100 simulations.
Figure 6. Comparison of complexity of relationship networks
generated for the DEG’s list and 100 random probe set lists of
the same size. Total number of entities and relationships present in
the largest network generated for a list were used to construct the
graph. Circles represent random probe set lists, yellow square
represents the average for 100 random list networks and the red
diamond represents the DEG list. The red triangle shows the value of
entities versus relationships found from our gene list. The Z score for
this value in relation to average (yellow square) was 12.5 (p,0.01).
Pathway Analysis of Depressed/Suicide Brain
PLOS ONE | www.plosone.org9 October 2012 | Volume 7 | Issue 10 | e47581
neuronal synapses . Overall these changes implicate a
profound disturbance in excitatory amino transmission irrespective
of any other changes in gene expression within the networks
identified. The fact that they appear to be altered in expression but
are not represented in the largest network identified (Figure 5)
suggests a wider disturbance in the gene expression than was
identified by pathway analysis.
One of the limitations of this study is the small n of both the
control and sample groups. This was deliberate as we choose to
analyze a small number of well-matched samples (similar age RIN
Figure 7. Pearson’s correlation based gene network for differentially expressed genes for the control cohort. Red nodes represent
genes up-regulated in the depressed suicide group, blue nodes represent genes down-regulated in the depressed suicide. Network contains 45
genes connected by 134 correlation relationships higher than 0.8.
Pathway Analysis of Depressed/Suicide Brain
PLOS ONE | www.plosone.org10 October 2012 | Volume 7 | Issue 10 | e47581
sex etc) rather than a larger more variable cohort. For the
correlation analysis, where hundreds of correlations were com-
pared, the a error may be potentially high, however, this ought to
be comparable in both groups. Thus our results showing many
fewer correlations in depressed/suicide cannot be attributed to the
number of genes compared. We should also note that some genes
that we and others have found to be down-regulated (BDNF,
GABRA1 [1,45] ) were not found in these analyses (although they
Figure 8. Coordinated gene expression is greatly reduced in suicide/MDD cohort. Pearson’s correlation-based gene network for
differentially expressed genes for depressed suicide class. Red nodes represent genes up-regulated in depressed suicide class, blue nodes represent
genes down-regulated in depressed suicide class, graphs - correlation of gene expression. Network contains 21 genes connected by 80 correlation
relationships higher than 0.8.
Pathway Analysis of Depressed/Suicide Brain
PLOS ONE | www.plosone.org11 October 2012 | Volume 7 | Issue 10 | e47581
were near the cut-off used here). This is likely attributed to the fact
that although microarray analysis is very reproducible is not very
sensitive in detecting small changes in gene expression. This fact
can be observed in Figure 2 where a 1.3 fold change in expression
detected on the microarray correlated to more than 2 cycles of
change in the QPCR data. Thus the differences reported here are
likely the largest differences in gene expression between these two
groups. Finally, our cohort is also confounded by the fact that the
samples come from those who also committed suicide. Thus our
findings may be relevant to suicidality and/or depression. Recent
studies by Turecki and others have shown that there may be gene
expression patterns that are associated with suicidality [6,46] albeit
with some overlap in those associated MDD. This overlap is a
significant confound in ‘‘teasing out’’ the gene expression that is
unique to these two psychiatric states. Studies of gene expression in
those with MDD who did not die from suicide found two highly
dysregulated genes including stresscopin, a neuropeptide involved
in stress responses and Forkhead box D3 (FOXD3), a transcription
factor as well as factors related to synapse formation [47,48]. The
finding of another Forkhead box transcription factor is similar to
the data reported here where we found FOXO3 and many factors
related to synaptogenesis/maintenance. This suggests that our
data speak more to depressive syndrome rather suicidality.
In summary, the present findings indicate that among depressed
individuals that died by suicide, profound alterations exist in the
expression of genes that control synaptic function, cell adhesion
and cytoarchitecture. They also extend and support our observa-
tion [8,17] that coordinated gene expression is apparently
disturbed in the MDD/suicide samples in comparison to normal
controls. Interestingly, we also found that in mice acute and
chronic stressors can also alter coordinated gene expression of the
GABAAreceptor gene cassette . As stressful events may be a
precipitating factor in the development of MDD, it might be
important to identify the biochemical and/or epigenetic processes
that disturb normal gene expression. These data also provide a
number of new targets for interventions that could help treat
List (p, ,0.01 FC . .1.3).
GO Analysis of Differentially Expressed Gene
GO analysis of DEGS from pathway analysis
GO analysis of genes correlated in suicide
We would also like to thank Dr Robert Hegele and David Carter from the
London Regional Genomics Centre at the Robarts Research Institute for
help in the analyses and a critical reading of this manuscript.
Conceived and designed the experiments: MOP HA CSP ZM. Performed
the experiments: VZ CSP JS GF MP. Analyzed the data: VZ JS HA MOP.
Contributed reagents/materials/analysis tools: GF MP. Wrote the paper:
MOP VZ HA CSP.
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regulation of development7 5.8E203
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cellular homeostasis 331.2E202
synaptic vesicle docking during exocytosis2 1.2E202
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secretion by cell4 1.4E202
Correlated in suicide group
GO Term Gene count
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regulation of synapse structure and activity2 2.9E202
regulation of developmental process4 4.5E202
synaptic transmission3 4.9E202
regulation of developmental growth25.5E202
nitrogen compound biosynthetic process35.7E202
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