Implication of SSAT by Gene Expression and Genetic Variation in Suicide and Major Depression

ArticleinArchives of General Psychiatry 63(1):35-48 · January 2006with80 Reads
Impact Factor: 14.48 · DOI: 10.1001/archpsyc.63.1.35 · Source: PubMed
Abstract

A large body of evidence suggests that predisposition to suicide, an important public health problem, is mediated to a certain extent by neurobiological factors. To investigate patterns of expression in suicide with and without major depression and to identify new molecular targets that may play a role in the neurobiology of these conditions. Brain gene expression analysis was performed using the Affymetrix HG-U133 chipset in the orbital cortex (Brodmann area [BA] 11), the dorsolateral prefrontal cortex (BA8/9), and motor cortex (BA4). Subsequent studies were carried out in independent samples from adjacent areas to validate positive findings, confirm their relevance at the protein level, and investigate possible effects of genetic variation. We investigated 12 psychiatrically normal control subjects and 24 suicide victims, including 16 with and 8 without major depression, in the brain gene expression analysis, validation, and protein studies. The genetic studies included 181 suicide completers and 80 psychiatrically normal controls. All subjects investigated were male and of French Canadian origin. Gene expression measures from microarray, semiquantitative reverse transcription-polymerase chain reaction, immunohistochemistry, and Western blot analyses. Twenty-six genes were selected because of the consistency of their expression pattern (fold change, >1.3 in either direction [P<or=.01] in at least 2 regions). The spermine/spermidine N(1)-acetyltransferase gene (SSAT) was successfully validated by reverse transcription-polymerase chain reaction, immunohistochemistry, and Western blot analyses. A variant located in the SSAT polyamine-responsive element regulatory region (SSAT342A/C) demonstrated a significant effect of genotype on SSAT brain expression levels (F(1) = 5.34; P = .02). Further investigation of this variant in an independent sample of 181 male suicide completers and 80 male controls showed a higher frequency of the SSAT342C allele among suicide cases (odds ratio, 2.7; 95% confidence interval, 1.4-5.3; P = .005), suggesting that this allele may increase predisposition to suicide. These data suggest a role for SSAT, the rate-limiting enzyme in the catabolism of polyamines, in suicide and depression and a role for the SSAT342 locus in the regulation of SSAT gene expression.

Full-text

Available from: Adolfo Sequeira
ORIGINAL ARTICLE
Implication of SSAT by Gene Expression
and Genetic Variation in Suicide
and Major Depression
Adolfo Sequeira, MSc; Fuad G. Gwadry, PhD; Jarlath M. H. ffrench-Mullen, PhD; Lilian Canetti; Yves Gingras, MSc;
Robert A. Casero, Jr, PhD; Guy Rouleau, MD, PhD; Chawki Benkelfat, MD; Gustavo Turecki, MD, PhD
Context: A large body of evidence suggests that predis-
position to suicide, an important public health prob-
lem, is mediated to a certain extent by neurobiological
factors.
Objective: To investigate patterns of expression in sui-
cide with and without major depression and to identify
new molecular targets that may play a role in the neu-
robiology of these conditions.
Design: Brain gene expression analysis was performed
using the Affymetrix HG-U133 chipset in the orbital cor-
tex (Brodmann area [BA] 11), the dorsolateral prefron-
tal cortex (BA8/9), and motor cortex (BA4). Subsequent
studies were carried out in independent samples from ad-
jacent areas to validate positive findings, confirm their
relevance at the protein level, and investigate possible ef-
fects of genetic variation.
Subjects: We investigated 12 psychiatrically normal con-
trol subjects and 24 suicide victims, including 16 with
and 8 without major depression, in the brain gene ex-
pression analysis, validation, and protein studies. The ge-
netic studies included 181 suicide completers and 80 psy-
chiatrically normal controls. All subjects investigated were
male and of French Canadian origin.
Main Outcome Measures: Gene expression mea-
sures from microarray, semiquantitative reverse tran-
scription–polymerase chain reaction, immunohistochem-
istry, and Western blot analyses.
Results: Twenty-six genes were selected because of the
consistency of their expression pattern (fold change, 1.3
in either direction [P.01] in at least 2 regions). The
spermine/spermidine N
1
-acetyltransferase gene (SSAT)
was successfully validated by reverse transcription–
polymerase chain reaction, immunohistochemistry, and
Western blot analyses. A variant located in the SSAT poly-
amine-responsive element regulatory region
(SSAT342A/C) demonstrated a significant effect of geno-
type on SSAT brain expression levels (F
1
=5.34; P=.02).
Further investigation of this variant in an independent
sample of 181 male suicide completers and 80 male con-
trols showed a higher frequency of the SSAT342C allele
among suicide cases (odds ratio, 2.7; 95% confidence in-
terval, 1.4-5.3; P= .005), suggesting that this allele may
increase predisposition to suicide.
Conclusions: These data suggest a role for SSAT, the rate-
limiting enzyme in the catabolism of polyamines, in sui-
cide and depression and a role for the SSAT342 locus in
the regulation of SSAT gene expression.
Arch Gen Psychiatry. 2006;63:35-48
S
UICIDE IS A MAJOR PUBLIC
health problem, and in many
countries it is the leading
cause of death for men
younger than 35 years.
1
Dur-
ing the past few decades, it has become in-
creasingly clear that individuals who com-
mit suicide have a certain biological
predisposition, part of which is given by
genes.
2
Psychopathology, particularly ma-
jor depressive disorder, is commonly as-
sociated with suicide, but the genetic li-
ability to suicide is likely independent from
the liability to psychiatric disorders.
3-6
A
growing effort has been in place to iden-
tify biological markers for suicide and de-
pression,
7
but most of the studies have fo-
cused on components of the serotonergic
2
and noradrenergic systems.
2
However, it
is clear that additional systems play a role
in the neurobiology of these conditions.
This study aimed to identify new mo-
lecular targets that may play a role in the
neurobiology of suicide with and with-
out major depression. To identify poten-
tial risk factors, a gene expression study
was initially conducted as a screening strat-
egy. This was conducted in the following
3 brain cortical regions: Brodmann area
(BA) 4, BA8/9, and BA11. Postmortem and
Author Affiliations: McGill
Group for Suicide Studies,
Douglas Hospital, McGill
University, Montreal, Quebec
(Messrs Sequeira and Gingras,
Ms Canetti, and Drs Rouleau,
Benkelfat, and Turecki); Gene
Logic Inc, Gaithersburg, Md
(Drs Gwadry and
ffrench-Mullen); and The
Sidney Kimmel Comprehensive
Cancer Center, The Johns
Hopkins School of Medicine,
Baltimore, Md (Dr Casero).
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neuroimaging studies during the past decades have im-
plicated BA8/9 (dorsolateral prefrontal cortex) and BA11
(orbital cortex) in suicide and depression.
8,9
Because of
the common motor function alterations in depressed pa-
tients,
10,11
BA4 (motor cortex), primarily a motor region
linked to motor deficits,
12
was also investigated. The brain
gene expression screening study identified the gene that
codes for spermine/spermidine N
1
-acetyltransferase
(SSAT) as an interesting target, which was further inves-
tigated using a series of complementary strategies. Herein
we present evidence suggesting that polyamines, and par-
ticularly SSAT, may play a role in the predisposition to
suicide and major depression.
METHODS
SUBJECTS
Gene Expression Studies
All subjects were male and of French Canadian origin, a ho-
mogeneous population with a founder effect.
13
Cases and con-
trol subjects were matched on the basis of age and postmor-
tem interval (PMI). All subjects died suddenly without a
prolonged agonal state or protracted medical illness. Brain
samples were obtained from the Quebec Suicide Brain Bank,
Montreal, and were collected with PMIs of less than 36 hours
at autopsy. We sampled BA4, BA8/9, and BA11 at 4°C and snap-
froze the samples in liquid nitrogen before storage at −80°C.
This study was approved by our local institutional review board,
and informed consent was obtained from next of kin.
All subjects were psychiatrically characterized by psycho-
logical autopsies, which are validated methods to reconstruct
psychiatric history by means of extensive proxy-based inter-
views, as outlined elsewhere.
3
Briefly, psychological autopsies
were performed for Axis I of the DSM-IV as assessed by the Struc-
tured Clinical Interview for DSM-IV, which was administered
by trained clinicians with an average of 2 informants per fam-
ily. After the interview, we reviewed the coroner’s notes and
all relevant medical records and wrote a case report for the pur-
pose of a best-estimate diagnosis. Best-consensus DSM-IV Axis
I diagnoses were made by a panel of psychiatrists after analy-
sis of the case reports. The sample consisted of 16 depressed
suicide completers (depressed suicide group) who died dur-
ing an episode of major depression, 18 suicide completers (sui-
cide group) with no lifetime history of major depression, and
12 matched controls with no history of suicidal behavior or a
major psychiatric diagnosis.
Validation and Protein Studies
Samples from all subjects included in the microarray screen-
ing were used for the semiquantitative reverse transcription–
polymerase chain reaction (RT-PCR) and Western blot experi-
ments. Three subjects per group and per region (n =27) were
included in the immunohistochemistry studies. Subjects were
selected with the researchers blinded to the microarray re-
sults, and selections were based on the quality of the tissue for
immunohistochemistry experiments.
Genetic Variation Studies
The population-based gene association study was conducted
in a larger sample consisting of all subjects included in the mi-
croarray studies and an independent sample totaling 181 male
suicide completers and 80 male controls, all of whom were of
French Canadian origin. Suicide completers were consecu-
tively collected from the Montreal Central Morgue, Montreal,
Quebec, and controls were psychiatrically normal subjects ac-
cording to Diagnostic Interview Schedule assessments that were
drawn from the Quebec general population and were matched
by age, sex, and ethnic origin.
MICROARRAY ANALYSIS
Extractions of RNA used in the present study had a minimum
A260/A280 ratio of more than 1.9. The samples were further
checked for evidence of degradation and integrity. Samples had
a minimum 28S/18S ratio of more than 1.6 (2100 Bioanalyzer;
Agilent Technologies, Palo Alto, Calif ). We used the Human
Genome U133 set, which consists of 2 GeneChip arrays with
45 000 probe sets representing more than 39 000 transcripts de-
rived from approximately 33 000 well-substantiated human
genes (available at: http://www.affymetrix.com).
GeneChip analysis was performed with Microarray Analy-
sis Suite version 5.0, Data Mining Tool 2.0, and Microarray da-
tabase software (available at: http://www.affymetrix.com). All
of the genes represented on the GeneChip were globally nor-
malized and scaled to a signal intensity of 100.
Various microarray RNA integrity indicators were used in
this study (
Table 1 and Table 2) to filter samples for quality
for final analysis. Principal component analysis (PCA) was used
Table 1. Quality Control Parameters for Brain Sample Microarrays*
Area RawQ Scale Factor % of Present Calls -Actin 5/3 Ratio GAPDH 5/3 Ratio
U133A, Mean ± SEM
BA4 2.47 ± 0.06 1.18 ± 0.09 40.83 ± 0.64 0.50 ± 0.03 0.75 ± 0.04
BA8/9 2.51 ± 0.09 1.19 ± 0.08 42.21 ± 0.87 0.46 ± 0.03 0.75 ± 0.04
BA11 2.64 ± 0.13 1.34 ± 0.11 41.33 ± 0.98 0.52 ± 0.03 0.74 ± 0.03
U133B, Mean ± SEM
BA4 2.50 ± 0.09 2.73 ± 0.20 28.43 ± 0.68 0.49 ± 0.03 0.67 ± 0.03
BA8/9 2.36 ± 0.08 3.15 ± 0.29 28.14 ± 0.82 0.47 ± 0.03 0.68 ± 0.04
BA11 2.60 ± 0.11 2.87 ± 0.28 27.94 ± 0.83 0.52 ± 0.03 0.67 ± 0.03
Abbreviations: BA, Brodmann area; GAPDH, glyceraldehyde-3-phosphate dehydrogenase.
*Samples were obtained from BA4 (6 controls; 6 suicide completers; and 7 depressed suicide completers), BA8/9 (6 controls; 6 suicide completers; and
7 depressed suicide completers), and BA11 (6 controls; 5 suicide completers; and 8 depressed suicide completers). The lower percentage of present calls in the
B chip compared with the A chip is owing to the fact that the B chip contains primary probe sets representing expressed sequence tag clusters. As a result
overall signal intensities on the B chip are lower, which is reflected by higher scaling factors. RNA quality control parameters (including -actin and GAPDH signal
ratios) are consistent across chips. Values were derived from results of Microarray Analysis Suite version 5.0 analysis (available at: http://www.affymetrix.com).
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to identify outlier arrays quickly. Microarray quality control pa-
rameters included the following: noise (RawQ), consistent num-
ber of genes detected as present across arrays, consistent scale
factors, and consistent -actin and glyceraldehyde-3-phos-
phate dehydrogenase 5/3 signal ratios. Outlier subjects were
excluded on a region basis without any subject being ex-
cluded from all the regions. Similar numbers of subjects were
included in the final analysis across the 3 regions (for BA4: 7
controls, 5 suicide completers, and 10 depressed suicide com-
pleters; for BA8/9: 6 controls, 6 suicide completers, and 7 de-
pressed suicide completers; and for BA11: 6 controls, 5 sui-
cide completers, and 8 depressed suicide completers).
DATA ANALYSIS
We selected genes for analysis on the basis of “present calls”
by Microarray Analysis Suite 5.0. In the present study, for a gene
to be included, it had to be present (detectable) in at least 75%
of the subjects in at least 1 of the 3 groups to reduce the chances
of false-positive findings. Expression data were analyzed us-
ing Genesis (GeneLogic, Gaithersburg, Md) and AVADIS soft-
ware (Strand Genomics, Redwood City, Calif ). Gene expres-
sion values were floored to 1 and then log
2
-transformed.
One-way analysis of variance was performed for each gene
to identify statistically significant gene expression changes. To
identify differences between depressed and nondepressed sui-
cide completers, statistically significant genes were subjected
to a post hoc test for the contrasts of depressed suicide com-
pleters vs controls, suicide completers vs controls, and de-
pressed suicide completers vs suicide completers. In all, 2 cri-
teria were used to determine whether a gene was differentially
expressed. A gene had to have a 1-way analysis of variance P
value of less than or equal to .01. Second, for a given contrast,
a gene had to have a fold change (FC)–P value combination of
1.3 FC in either direction and P.01.
Cluster analysis was performed using average-linkage hi-
erarchical cluster analysis with a correlation metric. Both ex-
pression patterns in individuals and genes were clustered. We
performed PCA on the basis of the initial gene sets and on the
selected genes (according to our significance criteria). The PCA
based on the initial gene set did not discriminate the 3 groups;
the PCA based on the selected genes did.
SEMIQUANTITATIVE RT-PCR
We performed RT in a total volume of 40 µL with 2 µg of total
messenger RNA using M-MLV RT (Gibco BRL Life Technolo-
gies, Burlington, Ontario) and oligo(deoxythymidine)16 prim-
ers. We performed PCR amplification using AmpliTaq Gold (Ap-
plied Biosystems, Foster City, Calif ). Messenger RNA–
specific primers were designed using Primer3 (available at: http:
//www-genome.wi.mit.edu/cgi-bin/primer/primer3_www
.cgi) to avoid amplification of contaminating genomic DNA.
The PCR products were visualized using ethidium bromide stain-
ing after electrophoresis in a 3% agarose gel. Images were digi-
talized and analyzed using Gene Tools software (Syngene, Cam-
bridge, England). New samples collected from adjacent tissue
from all of the subjects included in the microarray expression
studies were used in this analysis (16 depressed suicide com-
pleters, 8 suicide completers, and 12 controls).
IMMUNOHISTOCHEMISTRY AND
WESTERN BLOT ANALYSIS
Sections from tissue adjacent to that used for the microarray
experiment were used for immunohistochemistry. Three sub-
jects per group and per region (n=27) were included in the
analysis, and on average 3 slides per subject were examined.
Immunohistochemical labeling was performed using standard
protocols. In brief, frozen samples were sectioned at 10 µm,
air dried at room temperature, fixed in acetone, and con-
served at −80°C. Before the incubation with the primary anti-
body, slides were acclimated at room temperature for 15 min-
utes and incubated with Tris-buffered saline solution for 10
minutes and with normal rabbit antiserum (Santa Cruz Bio-
technology Inc, Santa Cruz, Calif) to avoid nonspecific bind-
ing. The SSAT antibody was diluted at 1:75. For the staining,
the LSAB2 system peroxidase (Dako Corp, Carpinteria, Calif)
was used according to the manufacturer’s indications. The sec-
tions were evaluated by 2 of us (A.S. and L.C.) who were blinded
to phenotype and brain region. The immunopositive cells were
counted with ImageJ software (version 1.29x; National Insti-
tutes of Health, Bethesda, Md).
Western blot analyses were carried out on additional samples
adjacent to the previous dissections from all the subjects used
in the microarray experiments (16 depressed suicide com-
pleters, 8 suicide completers, and 12 controls). Briefly, 50 µg
of total protein was loaded in 4% to 20% precast gels (Tris-
Glycine; Invitrogen Corp, Carlsbad, Calif ) and transferred onto
nitrocellulose membranes. Membranes were blocked with 6%
milk in Tris-buffered saline with Tween and hybridized to the
anti-SSAT polyclonal primary antibody (1:1000) overnight at
4°C and then to a peroxidase-conjugated secondary antibody
(1:5000; Santa Cruz Biotechnology Inc). The proteins were vi-
sualized by means of chemiluminescence (Bio-Rad Laborato-
ries, Hercules, Calif). For standardization and comparisons,
the membranes were also hybridized to a primary anti–-actin
antibody (1:5000; Sigma-Aldrich Corp, St Louis, Mo). Films
were digitalized, and the bands were counted with Gene Tools
software.
GENOTYPING
Genomic DNA was extracted from blood or from frozen brain
tissue samples using standard procedures.
14
A description of
the PCR method used for amplification can be found else-
where.
15
Genotyping was performed using the SNaPshot
16
pro-
cedure and the ABI 3100 genetic analyzer (Applied Biosys-
tems) following the manufacturer’s instructions. Genotypes were
automatically generated using GeneScan 1.0 and Genotyper 1.0
(Applied Biosystems). Four single nucleotide polymorphisms
were genotyped, 2 in the coding sequence (SSAT460 and
SSAT495) and 2 located a few nucleotides away from the poly-
amine responsive element motif and within the regulatory re-
gion (SSAT342 and SSAT624). The SSAT342A/C genotypes were
Table 2. Summary of the Quality Control Parameters
for Brain Sample Microarrays*
Area
RNA QC,
Mean ± SEM
R
2
Pearson
Correlation†
BA4 0.59 0.04 0.062
BA8/9 0.59 0.03 0.025
BA11 0.61 0.03 0.004
Abbreviations: BA, Brodmann area; RNA QC, RNA quality control
parameter (determined by the average of the 5/3 signal ratios of -actin
and glyceraldehyde-3-phosphate dehydrogenase across U133A and U133B
chips).
*Samples were obtained from BA4 (6 controls; 6 suicide completers; and
7 depressed suicide completers), BA8/9 (6 controls; 6 suicide completers;
and 7 depressed suicide completers), and BA11 (6 controls; 5 suicide
completers; and 8 depressed suicide completers). Values were derived from
results of Microarray Analysis Suite version 5.0 analysis (available at: http:
//www.affymetrix.com).
R
2
Pearson correlation between the postmortem interval and the RNA QC.
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confirmed by MspI (New England Biolabs, Mississauga, On-
tario) digestion followed by visualization by ethidium bro-
mide staining after electrophoresis in a 2.5% agarose gel.
RESULTS
Analysis of demographic parameters showed no signifi-
cant difference in terms of age and PMI between the
groups (
Table 3). Consistent with previous re-
ports,
17-19
analysis of PMI on RNA quality control pa-
rameters showed no significant effect in our sample
(
Figure 1).
As an initial evaluation of the discriminatory ability
of the microarray gene expression assay, we compared
cortical samples (BA4, BA8/9, and BA11) from normal
controls with samples obtained from the cerebellar tis-
sue of the same individuals (n=9). We performed PCA
on the basis of approximately 22 000 genes on the HG-
U133A chip (
Figure 2). Spatial separation on the first
component demonstrates the sensitivity and power of the
1.0
0.6
0.8
0.4
0.2
0.0
10 15 20 25 30 35 40
PMI, h
RNA QC
BA11 (R
2
=
0.004)
BA4 (R
2
=
0.06)
BA8/9 (R
2
=
0.02)
Figure 1. Quality control (QC) parameters for RNA. The relationships
between RNA QC (determined by the average of the 5/3 signal ratios of
-actin and glyceraldehyde-3-phosphate dehydrogenase across U133A and
U133B chips) and the postmortem interval (PMI) in Brodmann area (BA) 4,
BA11, and BA8/9 show no effect of the PMI on the QC measurements.
Table 3. Demographic Characteristics of Subjects Included in the Microarray Expression, Validation, and Protein Studies
Group/Sex/Age, y pH PMI, h Cause of Death DSM-IV (6-mo Diagnosis) Toxicological Findings
C/M/55 6.75 24 MVC
C/M/51 6.83 15 MVC Alcohol dependence Alcohol
C/M/27 6.55 20.5 Cardiac arrest
C/M/21 6.42 24 Cardiac arrest Social phobia
C/M/46 6.42 19.5 Myocardial infarction
C/M/31 6.67 29.5 MVC
C/M/41 6.00 24 Myocardial infarction
C/M/28 6.32 27 MVC
C/M/30 6.37 30 Cardiac arrest
C/M/47 6.49 12 Cardiac arrest Alcohol abuse
C/M/19 6.55 32 MVC
C/M/31 5.95 24 Cardiac arrest Alcohol dependence
DSC/M/40 6.84 23 Hanging MDD, alcohol dependence
DSC/M/28 6.21 20 Hanging MDD, alcohol dependence Alcohol
DSC/M/24 6.71 20 Hanging MDD
DSC/M/39 7.28 19 Overdose MDD
DSC/M/18 6.81 27 Hanging MDD
DSC/M/40 5.50 22 Hanging MDD
DSC/M/26 6.00 34 Hanging MDD Cocaine
DSC/M/49 6.96 32 Hanging MDD, alcohol abuse
DSC/M/22 6.67 11.5 Hanging MDD, alcohol dependence Alcohol, cocaine
DSC/M/39 6.57 25.5 Hanging MDD
DSC/M/35 6.60 31 Hanging MDD, alcohol dependence
DSC/M/45 6.57 20.5 Self-inflicted gunshot MDD, pathological gambling
DSC/M/42 6.40 21 Drowning MDD
DSC/M/53 6.30 29 Hanging MDD, alcohol dependence
DSC/M/19 6.17 29.5 Hanging MDD
DSC/M/26 6.35 21.5 Carbon monoxide MDD, alcohol abuse,
cocaine dependence
Cocaine
SC/M/42 6.10 27 Carbon monoxide
SC/M/33 6.68 18 Hanging
SC/M/51 6.12 21 Self-inflicted gunshot Alcohol dependence
SC/M/36 6.54 25 Hanging
SC/M/29 6.15 26.5 Hanging
SC/M/31 6.27 32.5 Hanging
SC/M/21 6.59 21 Asphyxiation OCD, alcohol dependence Alcohol
SC/M/38 6.00 23 Hanging Alcohol dependence,
cocaine dependence
Alcohol
Abbreviations: C, control subject; DSC, depressed suicide completer; MDD, major depressive disorder; MVC, motor vehicle crash; OCD, obsessive-compulsive
disorder; PMI, postmortem interval; SC, suicide completer.
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microarray experiment design to detect the subtle gene
expression pattern changes between the cortical and cer-
ebellar tissue due to their respective functional and neu-
roanatomical particularities.
To reduce the number of comparisons and the chances
of false-positive findings, statistical comparisons were per-
formed on selected genes instead of the total number of
genes present in the chipset. The Venn diagrams in
Figure 3 summarize the number of differentially ex-
pressed genes observed per comparison using as criteria
P.01 and FC1.3 and the extent of overlap of genes
differentially expressed between the different brain re-
gions. The common genes differentially expressed in BA4,
BA8/9, and BA11 between the 2 suicide groups vs the con-
trols are represented in the intersection of the Venn dia-
grams. A total of 26 genes were common between 2 or 3
regions, and a total of 200 common genes were differ-
entially expressed between the groups across brain re-
gions (Figure 3). As shown in
Table 4, the FCs for the
2 comparisons, depressed suicide completers vs con-
trols and suicide completers vs controls, are highly con-
sistent and going in the same direction for all the genes
in all the regions.
The PCA performed with differentially expressed genes
in each region showed good separation on the first com-
ponent between the 3 groups (
Figure 4C for BA11,
Figure 5C for BA4, and Figure 6C for BA8/9). The spa-
tial discrimination observed demonstrates that the dif-
ference between the groups of subjects is based on genes
that are differentially coregulated between the groups.
In BA11, 375 genes from an initial set of 14 864 se-
lected genes were identified as being differentially ex-
pressed (Figure 3). Most of these genes (275/375) were
misregulated in the depressed suicide completers com-
pared with the controls as shown by the Venn diagrams
(Figure 3). Cluster analysis showed mainly 4 clusters of
genes with similar expression as seen in the cluster im-
age map in Figure 4 A and B. Genes in cluster 1 (Figure 4B)
were on average overexpressed in both suicide groups
when compared with the controls but showed no differ-
ences between the 2 suicide groups, suggesting that
genes in this cluster are coregulated in the same manner
in suicide completers indistinct of the diagnosis. Cluster
2 genes were on average down-regulated in the de-
pressed suicide completers vs the suicide completers and
1000
1000
500
0
–500
600
400
200
0
–200
–400
–600
–800
600
400
200
0
–200
–400
–600
–800
0
500
–500
–500
–500 0 500 1000
z
x
y
0
500
1000
Cerebellum
BA4 BA8/9 BA11
Figure 2. Results of principal component analysis. Analysis was based on
the approximately 22 000 genes on the HG-U133A chip from control samples
in 3 cortical regions (Brodmann area [BA] 11, BA4, and BA8/9) and the
cerebellum. The first 3 components accounted for 31.7% of the total
variance. Components 1, 2, and 3 accounted for 14.1%, 10.7%, and 7.0%
of the variances, respectively.
SC vs DSC (50)
DSC vs C (99)SC vs C (40)
BA4
16
10
46
31
17
17
9
3
8
2
4
SC vs DSC (173)
DSC vs C (58)SC vs C (140)
BA8/9
SC vs DSC (101)
DSC vs C (275)SC vs C (84)
BA11
16
12
132
77
8
11
12
17
17
20
27
26
24
50
9
17
7
50
17
6
5
4
51
42
BA4 (162)
BA8/9 (282)BA11 (375)
Between
Regions
142
142 260
134
3
6
Figure 3. Venn diagrams show the number of genes identified as
differentially expressed and the overlap of genes between the different
comparisons in the motor cortex (Brodmann area [BA] 4), the dorsolateral
prefrontal cortex (BA8/9), and the orbital cortex (BA11). Each circle
represents a single contrast. Upward and downward arrows indicate the
number of up- and down-regulated genes. The numbers of genes that are
differentially expressed in a single contrast are shown in parentheses. The
intersections of the circles indicate the number of genes common between
contrasts. The between-region Venn diagram shows the differentially
expressed genes in BA4, BA8/9, and BA11. The numbers of genes that are
differentially expressed in a single brain region are shown in parentheses.
The intersections of the circles indicate the number of genes differentially
expressed between brain regions. C indicates control subjects;
DSC, depressed suicide completers; and SC, suicide completers.
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Table 4. Common Genes Between Groups in BA4, BA8/9, and BA11
Gene Title
Gene
Symbol Location
Fold Change
vs Control Subjects
DSC Group SC Group
BA4
Emilin and multimerin-domain containing protein 1 EMU1 22q12.2 −1.3 −1.6
Solute carrier family 35, member E1 SLC35E1 19p13.11 −1.5 −1.6
Calpain, small subunit 1 CAPNS1 19q13.13 1.5 1.5
Myeloid leukemia factor 2 MLF2 12p13 1.4 1.4
Leucine-rich repeat (in FLII) interacting protein 1 LRRFIP1 2q37.3 1.5 1.5
Helicase with zinc finger domain HELZ 17q24.3 1.3 1.4
Nuclear receptor subfamily 3, group C, member 1 (glucocorticoid receptor) NR3C1 5q31 1.4 1.5
Trans-Golgi network protein 2 TGOLN2 2p11.2 1.4 1.3
Hippocalcin-like 1 HPCAL1 2p25.1 1.3 1.5
Chromosome 9 open reading frame 5 C9orf5 9q31 1.4 1.5
BA8/9
Arrestin, beta 1 ARRB1 11q13 −1.4 −1.3
Protein phosphatase 1K (PP2C domain containing) PPM1K 4q22.1 −1.7 −1.8
Phosphoinositide-3-kinase, class 2, alpha polypeptide PIK3C2A 11p15.5-p14 −1.7 −2.2
Homo sapiens transcribed sequences −2.1 −2.6
GDP dissociation inhibitor 2 GDI2 10p15 1.7 2.1
Zinc finger protein 6 (CMPX1) ZNF6 Xq13-q21.1 1.9 1.8
Lysosomal-associated protein transmembrane 4 beta LAPTM4B 8q22.1 1.5 1.5
Hepatoma-derived growth factor, related protein 3 HDGFRP3 15q11.2 1.4 1.5
Protocadherin 19 PCDH19 Xq13.3 1.6 1.6
BA11
Homo sapiens, clone IMAGE:4812754, messenger RNA −1.7 −1.5
Homo sapiens transcribed sequences −1.3 −1.4
Adenosine triphosphatase, H
transporting, lysosomal 9 kDa, V0 subunit e ATP6V0E 5q35.2 −1.5 −1.3
Complement component 4A C4A 6p21.3 −1.6 −1.7
CDC42 effector protein (rho GTPase binding) 4 CDC42EP4 17q24-q25 −1.6 −1.5
Centromere protein B, 80 kDa CENPB 20p13 −1.5 −1.6
Chemokine-like factor super family 6 CKLFSF6 3p22.3 −1.4 −1.5
Cathepsin H CTSH 15q24-q25 −1.9 −1.7
Dentin sialophosphoprotein DSPP 4q21.3 −1.9 −1.9
Hypothetical protein FLJ22672 FLJ22672 1q23.1 −1.9 −1.4
Glutathione S-transferase M1 GSTM1 1p13.3 −1.4 −1.4
Glutathione S-transferase M2 (muscle) GSTM2 1p13.3 −1.4 −1.5
Myeloid/lymphoid or mixed-lineage leukemia 4 MLL4 19q13.1 −1.6 −1.6
N-myc down-regulated family member 2 NDRG2 14q11.2 −1.6 −1.5
Retinol binding protein 1, cellular RBP1 3q23 −1.8 −1.7
Spermidine/spermine N
1
-acetyltransferase SSAT Xp22.1 −1.8 −1.4
Stearoyl-CoA desaturase (delta 9 desaturase) SCD 10q23-q24 −1.9 −1.4
Selenoprotein O SELO 22q13.33 −1.3 −1.4
Transforming growth factor receptor II (70/80 kDa) TGFBR2 3p22 −1.6 −1.4
Homo sapiens hypothetical protein LOC284591 1p36.33 1.4 1.6
Amyotrophic lateral sclerosis 2 (juvenile) chromosome region ALS2CR9 2q33 1.9 1.6
NUAK family, SNF1-like kinase, 1 NUAK1 12q23.3 1.4 1.5
Adenosine triphosphatase, H
transporting, lysosomal 13 kDa, V1 subunit G
Isoform 2
ATP6V1G2 6p21.3 1.4 1.3
BA11-associated protein 2 BAIAP2 17q25 1.4 1.4
Beclin 1 (coiled-coil, myosinlike BCL2 interacting protein) BECN1 17q21 1.3 1.3
Cornichon homolog 3 (Drosophila) CNIH3 1q42.12 1.5 1.7
KIAA0379 protein KIAA0379 3p25.1 1.5 1.4
Hypothetical protein LOC285812 LOC285812 1.5 1.5
RAS-associated protein Rap1 LOC51277 2p24.1 1.5 1.7
Protein associated with MYC PAM 13q22 1.5 1.5
Protein tyrosine phosphatase, receptor type, T PTPRT 20q12-q13 1.5 1.3
Raft linking protein RAFTLIN 3p25.1 1.3 1.4
Semaphorin 4F SEMA4F 2p13.1 1.4 1.4
Synapsin II SYN2 3p25 1.3 1.4
Synaptotagmin XIII SYT13 11p12-p11 1.3 1.4
T-cell activation leucine repeat-rich protein TA-LRRP 1p22.2 1.5 1.3
Abbreviations: BA, Brodmann area; CoA, coenzyme A; DSC, depressed suicide completer; H
, hydrogen proton; SC, suicide completer.
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A
B
D
C
Individuals (Cluster Order)
Genes (Cluster Order)
DSC
DSC
DSC
DSC
DSC
DSC
DSC
DSC
SC
SC
SC
SC
SC
C
C
C
C
C
C
–2 0
Intensity
2
2
4
1
3
–2
–6
C SC DSC
–8
20
10
0
10
20
0
–10
6
4
2
0
8
4
2
0
–2
–4
–6
–10
6040–40–60 200–20
z
x
y
60
40
20
–20
–40
–60
0
Cluster 1:192 Rows Cluster 2:121 Rows
Cluster 3:35 Rows Cluster 4:27 Rows
DNA Binding
Magnesium Ion Binding
Cation Transporter Activity
Ion Channel Activity
Calcium Ion Binding
Alpha-Type Channel Activity
Hydrolase Activity
Transferring Phosphorus-Containing Groups
ATP Binding
Adenyl Nucleotide Binding
Purine Nucleotide Binding
0 5 10 15 20 25 30
Frequency, %
Molecular Functioning
Cell-Cell Signaling
Phosphate Metabolism
Neurogenesis
Protein Modification
Metal Ion Transport
Intracellular Signaling Cascade
Organogenesis
Nucleotide and Nucleic Acid Metabolism
Protein Metabolism
Cation Transport
Signal Transduction
Ion Transport
0 5 10 15 20 25 30 35 40
Frequency, %
Biological Process
Cluster 4
Cluster 3
Cluster 2
Cluster 1
Figure 4. Hierarchical cluster analysis of the 375 genes identified as being differentially expressed in Brodmann area (BA) 11. Both expression patterns in
individuals and genes were clustered. A, Clustered image map. The color and intensity indicate direction and level of change. Blue spectrum colors indicate
down-regulated expression; red spectrum colors, up-regulated expression. C indicates controls; DSC, depressed suicide completers; and SC, suicide completers.
B, Cluster set plots display the average expression profile (light green line) of all genes in each of the clusters, along with the minimum and maximum deviation
around the mean (black vertical lines). C, Principal component analysis based on the differentially expressed genes. The first 3 components accounted for 63.3%
of the total variance (components 1, 2, and 3 accounted for 41.9%, 15.9%, and 5.5% of the variance, respectively). The DSC, SC, and C groups show separation
on the first component. D, Graphical representation of the percentage of the most common gene ontology terms within each of the clusters in BA11. ATP indicates
adenosine triphosphate.
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A
B
D
C
Genes (Cluster Order)
C
C
C
C
C
C
C
SC
SC
SC
SC
SC
DSC
DSC
DSC
DSC
DSC
DSC
DSC
DSC
DSC
DSC
–2 0
Intensity
2
14
2
3
C SC DSC
5
0
–5
–10
–15
–15
–10
–5
0
5
3
2
1
0
–1
–2
–3
z
x
y
20
10
01020–10
3
2
1
0
–1
–2
–3
0
–10
Cluster 1:61 Rows Cluster 2:45 Rows
Cluster 4:21 RowsCluster 3:36 Rows
Frequency, %
Molecular Functioning
Frequency, %
Biological Process
Cluster 4
Cluster 3
Cluster 2
Cluster 1
RNA Binding
Peptidase Activity
Hydrolase Activity, Acting on Ester Bonds
Calcium Ion Binding
Transferring Phosphorus-Containing Groups
Phosphotransferase Activity
Purine Nucleotide Binding
ATP Binding
Adenyl Nucleotide Binding
DNA Binding
0 5 10 15 20 25 3530
Phosphate Metabolism
Protein Modification
Catabolism
Macromolecule Biosynthesis
Cell Cycle
Biosynthesis
Protein Metabolism
Signal Transduction
Regulation of Transcription
Nucleic Acid Metabolism
0 5 10 15 20 25 4030 35
Individuals (Cluster Order)
Figure 5. Motor cortex (Brodmann area [BA] 4) analysis. A, Clustered image map of the hierarchical cluster analysis of the 163 genes identified as being
differentially expressed genes in BA4. Both expression patterns in individuals and genes were clustered. The color and intensity indicate direction and level of
change. Blue spectrum colors indicate down-regulated expression; red spectrum colors, up-regulated expression. Most of the genes (99 or approximately 60%)
were misregulated in the depressed suicide completers (DSC) in relation to controls (C). Of these, 63 were up-regulated and 36 were down-regulated in relation to
the C group. SC indicates suicide completers. B, Cluster set plots display the average expression profile (light green line) of all genes in each of the clusters, along
with the minimum and maximum deviation around the mean (black vertical lines). C, Principal component analysis based on the differentially expressed genes.
The first 3 components accounted for 61.2% of the total variance. Components 1, 2, and 3 accounted for 36.4%, 20.0%, and 4.9% of the variance, respectively.
The DSC, SC, and C groups show separation on the first component. D, Graphical representation of the percentage of the most common gene ontology terms
within each of the clusters. ATP indicates adenosine triphosphate.
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A
B
D
Genes (Cluster Order)
C
C
C
C
C
C
SC
SC
SC
SC
SC
SC
DSC
DSC
DSC
DSC
DSC
DSC
DSC
–2 0
Intensity
2
1
C
C SC DSC
–10
0
10
20
20
10
0
–10
z
x
y
0
20
40
–20
4
4
2
0
–2
–4
2
0
–2
–4
0
–20
20
40
Frequency, %
Molecular Functiong
Hydrolase Activity
Guanyl Nucleotide Binding
GTPase Activity
GTP Binding
Phosphotransferase Activity
Oxidoreductase Activity
Cysteine-type Endopeptidase Activity
Calcium Ion Binding
Transferring Phosphorus-Containing Groups
Hydrolase Activity
ATP Binding
Adenyl Nucleotide Binding
RNA Binding
Peptidase Activity
Hydrolase Activity, Acting on Acid Anhydrides
Purine Nucleotide Binding
DNA Binding
0 5 10 15 20
Frequency, %
Biological Process
Cluster 3
Cluster 2
Cluster 1
Macromolecule Catabolism
Organogenesis
Catabolism
Intracellular Signaling Cascade
Regulation of Transcription
Macromolecule Biosynthesis
Biosynthesis
Nucleotide and Nucleic Acid Metabolism
Signal Transduction
Protein Metabolism
0 5 10 15 20
Individuals (Cluster Order)
Cluster 1:151 Rows Cluster 2:114 Rows
Cluster 3:17 Rows
3
2
Figure 6. Dorsolateral prefrontal cortex (Brodmann area [BA] 8/9) analysis. A, Clustered image map of the hierarchical cluster analysis of the 282 genes identified
as being differentially expressed genes in the BA8/9. Both expression patterns in individuals and genes were clustered. The color and intensity indicate direction
and level of change. Blue spectrum colors indicate down-regulated expression; red spectrum colors, up-regulated expression. Most of the genes were
misregulated in suicide completers (SC) in relation to controls (C) (140 or approximately 50%) and depressed suicide completers (DSC) (75 or approximately
25%). B, Cluster set plots display the average expression profile (light green line) of all genes in each of the clusters, along with the minimum and maximum
deviation around the mean (black vertical lines). C, Principal component analysis based on the differentially expressed genes. The first 3 components accounted
for 65.6% of the total variance. Components 1, 2, and 3 accounted for 43.6%, 17.2%, and 4.7% of the variance, respectively. The DSC, SC, and C groups show
separation on the first component. D, Graphical representation of the percentage of the most common gene ontology terms within each of the clusters.
ATP indicates adenosine triphosphate; GTP, guanosine triphosphate.
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the controls, with no major differences between the sui-
cide completers and the controls, suggesting that these
genes may be associated with depression. The SSAT gene
implicated in the stress response was present in this clus-
ter. A graphical representation of SSAT levels in BA11 is
shown in
Figure 7. Clusters 3 and 4 were much smaller
and showed an opposite pattern from each other. Gene
ontology analysis based on these clusters showed clear
differences in terms of the most common gene ontology
terms between the clusters (Figure 4D).
In BA4, 162 genes were identified as being differen-
tially expressed from the initially selected 14 034 genes
(Figure 3). Cluster analysis of differentially expressed
genes in BA4 showed 4 main clusters of genes (Figure 5A).
The largest cluster was composed of 61 genes on aver-
age overexpressed in the 2 suicide groups compared with
controls (Figure 5A and B, cluster 1). The second clus-
ter in BA4 consisted of 45 genes generally down-
regulated among the depressed suicide completers com-
pared with the suicide completers and the controls and
mostly implicated in protein metabolism and signal trans-
duction. The SSAT gene was also present in this cluster,
and a graphic representation of its differential expres-
sion is shown in Figure 7. Cluster 3 was composed of 37
genes with average lower expression among suicide com-
pleters compared with depressed suicide completers and
the controls. Cluster 4 was the smallest in BA4, with 21
genes mainly having higher expression in both suicide
groups vs the controls. Gene ontology analysis was also
performed in the BA4 based on the clusters observed and
showed clear differences in terms of the most frequent
terms between the clusters (Figure 5D).
In BA8/9, 282 of an initial set of 14 519 selected genes
were identified as being differentially expressed (Figure 3).
The distribution of differentially expressed genes shows
that most genes were misregulated in the suicide com-
pleters in relation to the controls (140 or approximately
50%) and to the depressed suicide completers (75 or ap-
proximately 25%). Nine genes, of which 5 were up-
regulated and 4 were down-regulated, were common in
the suicide groups in relation to the controls (Figure 3
[Venn diagram]). Cluster analysis of BA8/9 showed a par-
ticular expression pattern, with the 2 major clusters of genes
showing, on average, primarily differences between the sui-
cide completers vs the depressed suicide completers and
controls, suggesting that this region may be more suicide
specific (Figure 6A). Cluster 1 was composed of 151 genes
that were on average less expressed among the suicide com-
pleters compared with the controls. Cluster 2 was the sec-
ond largest cluster in BA8/9 with 114 genes. Genes in this
cluster were also differentially expressed in the suicide com-
pleters compared with the depressed suicide completers
and controls, with the difference that they were overex-
pressed in the suicide completers in this cluster. Cluster
3 was composed of only 17 genes mainly up-regulated in
the 2 suicide groups compared with the controls. Gene on-
tology analysis based on the 3 clusters observed in BA8/9
also showed clear differences, confirming the specificity
of these clusters (Figure 6D).
A total of 26 genes were found to be differentially ex-
pressed in at least 2 of the 3 regions investigated as shown
by the intersections in the Venn diagram (Figure 3 and
Table 5). According to 1-way analysis of variance, one
of these genes, SSAT, was differentially expressed in BA4
and BA11 at the P.001 level and in BA8/9 at the P.05
level (Figure 7). In BA4, SSAT was significantly down-
regulated in the depressed suicide completers and sui-
cide completers in relation to the controls, with FCs of
−1.6 (P=.005) and −1.4 (P=.02), respectively. In BA11,
SSAT was significantly down-regulated in suicide groups
in relation to the controls, with FCs of −1.8 (P=.002) and
−1.4 (P= .005), respectively. Finally, in BA8/9, SSAT was
down-regulated in the depressed suicide completers in
relation to the controls, with an FC of −1.4 (P=.02).
As indicated in Table 3, some of the subjects included
in the study had psychopathology other than major de-
1200
1000
800
600
400
200
0
C
P
=
.02
P
=
.006
SC DSC
BA4
Signal Value
Largest Value
Median
Smallest Value
75th Percentile
25th Percentile
1200
1000
800
600
400
200
0
C
P
=
.02
SC DSC
BA8/9
Signal Value
1200
1000
800
600
400
200
0
C
P
=
.003
P
=
.001
SC DSC
BA11
Signal Value
Figure 7. Graphic representations of the observed changes in
spermidine/spermine N
1
-acetyltransferase gene (SSAT ) expression. Raw
Affymetrix data (Microarray Analysis Suite version 5.0; available at:
http://www.affymetrix.com) illustrate the differential expression of SSAT
in Brodmann area (BA) 4, BA8/9, and BA11. C indicates controls;
DSC, depressed suicide completers; and SC, suicide completers.
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pressive disorder. In addition, several subjects had a his-
tory of substance dependence/abuse, which may act as an
important confounder of the gene expression analyses. An
analysis of covariance controlling for a history of sub-
stance dependence/abuse, presence of psychopathology
other than major depressive disorder, and result of the post-
mortem toxicological screening examination indicated that
the group effect on SSAT expression in BA4 (F
2
=3.89;
P=.04) and BA11 (F
2
=12.2; P=.001) was independent of
the effect of these possible confounders.
Differential expression of SSAT in BA4, BA8/9, and BA11
was confirmed by semiquantitative RT-PCR analyses
(
Figure 8A and C) on independent samples from the same
individuals. Lower expression of SSAT was confirmed in
BA4 for the suicide completers (FC=−1.2) and depressed
suicide completers (FC=−1.3) compared with the con-
trols (F
2
=3.75; P=.04). In BA8/9, SSAT expression was
lower among the suicide completers and depressed sui-
cide completers compared with the controls, with FCs of
−1.5 and −1.4, respectively (F
2
=8.08; P=.002). Similarly,
in BA11, SSAT expression was 1.5-fold lower in the sui-
cide completers and 1.4-fold lower in depressed suicide
completers than in the controls (F
2
=9.20; P=.01).
Altered expression at the transcriptional level does not
necessarily lead to altered protein expression, and SSAT
is known to undergo extensive posttranscriptional regu-
lation.
20,21
Therefore, confirmation of the observed changes
at the protein level was carried out by immunohistochem-
istry analysis in tissue sections prepared from the same brain
regions using an SSAT polyclonal antibody.
22
Figure 9A
illustrates the observed changes in SSAT immunoreactiv-
ity in BA4, BA8/9, and BA11 of a control, a suicide com-
pleter, and a depressed suicide completer. Quantification
of immunopositive cells in a subgroup of subjects (3 sub-
jects per group per region [n=27]) showed a lower SSAT
protein expression in both depressed suicide completers
(FC
2
=1.36; P=.04) and suicide completers (FC
2
=1.35;
P=.005) compared with controls. The changes at the pro-
Table 5. Summary of the Genes Identified as Differentially
Expressed in at Least 2 of the 3 Regions and
Their Chromosomal Location
Gene Title
Gene
Symbol
Chromosomal
Location
Calpain, small subunit 1 CAPNS1 19q13.13
Stearoyl-CoA desaturase
(delta-9-desaturase)
SCD 10q23-q24
Hypothetical protein FLJ20700 FLJ20700 19p13.3
1
-Actinin ACTN1 14q24.1-q24.2
Coatomer protein complex, subunit
alpha
COPA 1q23-q25
Hydroxysteroid dehydrogenase–
like 2
HSDL2 9q32
SET translocation (myeloid
leukemia-associated)
SET 9q34
Spermidine/spermine
N
1
-acetyltransferase
SSAT Xp22.1
Likely ortholog of mouse la related
protein
LARP 5q33.2
Trans-Golgi network protein 2 TGOLN2 2p11.2
Transportin-SR TRN-SR 7q32.3
Nudix (nucleoside diphosphate
linked moiety X)–type motif 3
NUDT3 6p21.2
Citron (rho-interacting,
serine/threonine kinase 21)
CIT 12q24
Homo sapiens Alu repeat (LNX1)
messenger RNA sequence
Spermatogenesis associated,
serine-rich 2
SPATS2 12q13.12
p53 Target zinc finger protein WIG1 3q26.3-q27
Period homologue 3 (Drosophila) PER3 1p36.23
ELK3, ETS-domain protein (SRF
accessory protein 2)
ELK3 12q23
Ankyrin repeat domain 40 ANKRD40 17q21.33
Protocadherin 19 PCDH19 Xq13.3
EPH (ephrin) receptor A4 EPHA4 2q36.1
Cathepsin B CTSB 8p22
Oligodendrocyte transcription
factor 1
OLIG1 21q22.11
Phosphoglucomutase 2–like 1 PGM2L1 11q13.4
Hypothetical protein
DKFZp761L1417
DKFZp761L1417 7q22.1
zd57g10.r1
Soares_fetal_heart_NbHH19W
Homo sapiens cDNA clone
W72833 12q14.1
Abbreviations: SR, serine and arginine; SRF, somatotrophin-releasing
factor.
Relative SSAT mRNA Level, % of β-Actin
A
B
C
SSAT (235 bp)
β-Actin (157 bp)
C SC DSC
C
SSAT 342 A 20 (0.25) 20 (0.11)
C 60 (0.75)
(OR
=
2.68; 95% CI
=
1.35-5.33; P
=
.005)
161 (0.89)
SC
200
50
100
150
0
CSCDSC
Group
C SC DSC
Group
C SC DSC
Group
BA4 BA8/9 BA11
Figure 8. Spermine/spermidine N
1
-acetyltransferase gene (SSAT ) and
-actin polymerase chain reaction (PCR) products after reverse transcription
and PCR amplification. A, Agarose gel stained by ethidium bromide. Bands
illustrated as an example are from Brodmann area (BA) 4. C indicates
controls; DSC, depressed suicide completers; and SC, suicide completers.
B, Distribution of the SSAT342A/C locus in a sample of SC (n=181) and
matched controls (n=80). CI indicates confidence interval; OR, odds ratio.
C, Graphical representation of the relative SSAT messenger RNA (mRNA)
levels in C, SC, and DSC groups (percentage of -actin). The
semiquantitative analysis by reverse transcription–PCR of SSAT mRNA levels
was performed in the motor cortex (BA4), dorsolateral prefrontal cortex
(BA8/9), and orbital cortex (BA11). Asterisks indicate significant differences
compared with the C group.
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tein level were also confirmed by quantification of SSAT
by Western blot analysis in adjacent samples from the same
regions from all subjects initially selected for the micro-
array analysis (16 depressed suicide completers, 8 sui-
cide completers, and 12 controls). Significant alteration
of SSAT protein levels was observed between the groups
in BA4 (F
2
=4.47; P=.02), BA8/9 (F
2
=4.80; P=.02), and
BA11 (F
2
=4.08; P=.03), with lower expression among the
suicide groups, particularly the depressed suicide com-
pleters, compared with the controls (Figure 9B). Thus, the
microarray evidence, confirmed by the semiquantitative
RT-PCR results, reflect relevant changes at the protein level
and suggests a possible role of SSAT in the pathophysiol-
ogy of suicide and depression.
It was recently shown that SSAT expression is closely
regulated by a cis-acting polyamine-responsive element
located in the promoter region of this TATA-less gene.
23
We studied the genetic variation at 4 loci in the SSAT
gene and investigated their influence on SSAT expres-
sion. Because SSAT is an X-linked locus not located in
the pseudoautosomal region, male subjects are hemizy-
gous, and because all brains were from male donors, we
could investigate the direct relationship between SSAT
allelic variants and the altered expression of SSAT. The
only polymorphic locus, SSAT342A/C, which is located
in the polyamine-responsive element–regulatory re-
gion, showed a significant effect on SSAT expression lev-
els in BA4, BA8/9, and BA11 (F
1
=5.34; P=.02), with sub-
jects having the SSAT342A variant showing more
expression. Because we observed lower SSAT expres-
sion levels in the brains of suicide completers, we hy-
pothesized that suicide completers from the general popu-
lation would have less frequency of SSAT342A. To test
this hypothesis, a sample of 181 French Canadian male
suicide completers and 80 French Canadian male con-
trols from the general population underwent genotyp-
ing. This analysis (Figure 8B) showed a protective role
of the SSAT342A variant because not having this variant
significantly increased the risk of committing suicide (odds
ratio, 2.7; 95% confidence interval, 1.4-5.3; P=.005). Thus,
the SSAT342A variant, which is associated with a higher
level of expression of SSAT in BA4 and BA11, is found
significantly less frequently among suicide completers
compared with controls, suggesting that this locus may
play a role in suicide predisposition through a regula-
tory influence on SSAT expression in the brain.
COMMENT
Using microarray brain expression analysis as a screen-
ing tool in a group of suicide completers with and with-
out major depression and a group of controls, we have
identified SSAT as a gene that is differentially expressed
in BA4, BA8/9, and BA11. Differential expression of SSAT
was confirmed by semiquantitative RT-PCR, immuno-
histochemistry analysis, and Western blot findings. Analy-
sis of the genetic variation at the SSAT342A/C locus in
the vicinity of the polyamine-responsive element lo-
cated in the promoter of the SSAT gene demonstrated an
effect of the genotype on gene expression, with the A al-
lele associated with higher levels of SSAT expression. Con-
versely, the evaluation of the SSAT342 polymorphism in
an independent sample of 183 suicide completers and 80
controls showed a lower frequency of the A allele among
the suicide completers, suggesting a protective role against
suicide and depression. The main conclusions of our study
concerning SSAT are based on the consistency of the sig-
nificance in different brain regions (BA4, BA8/9, and
BA11), the validation of these differences using alterna-
tive (RT-PCR) and complementary methods (immuno-
histochemistry and Western blot), and the observation
that variation at the promoter region influences levels of
expression and may play a role in predisposition to sui-
cide and depression.
Spermine/spermidine N
1
-acetyltransferase is the rate-
limiting enzyme in the catabolism of polyamines
24
(sper-
midine and spermine) and is implicated in the poly-
amine stress response.
25,26
Polyamines, especially
spermine, are stored in synaptic vesicles and released by
depolarizationlike neurotransmitters.
27
Evidence sug-
gests the implication of polyamines in mood disorders,
such as the observation that lithium prevents the stress-
induced polyamine response in rats.
25,28,29
In addition,
spermidine and spermine block the serotonin trans-
porter transient current in a manner similar to fluox-
A
C
BA4
BA8/9
BA11
SC DSC
200
50
100
150
0
C SC DSC
Group
C SC DSC
Group
C SC DSC
Group
BA4 BA8/9 BA11
Immunoreactivity of SSAT Over β-Actin
B
Figure 9. Confirmation of the spermine/spermidine N
1
-acetyltransferase
gene (SSAT) expression differences at the protein level. A, Immunohisto-
chemical staining photomicrographs of adjacent brain sections using an
anti-SSAT polyclonal antibody (1:75) in the motor cortex (Brodmann area
[BA] 4), the dorsolateral prefrontal cortex (BA8/9), and the orbital cortex
(BA11) of a control subject (C), a suicide completer (SC), and a suicide
completer with major depression (DSC). Arrows indicate examples of
positive immunohistochemical staining. B, Western blot analysis using an
anti-SSAT polyclonal antibody (1:1000) in the motor cortex (BA4),
dorsolateral prefrontal cortex (BA8/9), and orbital cortex (BA11). Asterisks
indicate significant differences in comparisons with the control group.
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©2006 American Medical Association. All rights reserved.
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Page 12
etine hydrochloride and cocaine.
30
Finally, glutamater-
gic neurotransmission is closely controlled by intracellular
levels of polyamines, with spermine and spermidine being
specific modulators of N-methyl-
D-aspartate and AMPA
(-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid)
receptor activity.
31
Consequently, significant down-
regulation of SSAT would be expected to disrupt poly-
amine homeostasis, resulting in regional increases in lev-
els of spermine, spermidine, or both. Considering the
multiple processes in which polyamines of the central ner-
vous system have been implicated, changes in poly-
amine levels may produce profound effects.
Some of the most important limitations of this study
include a possible confounding effect of comorbidity with
substance dependence/abuse and limited power of the mi-
croarray expression study. In our sample, as expected,
several subjects had a history of substance dependence/
abuse (alcohol and cocaine). This was the case in both
suicide groups, and to a lesser degree, in the control group
(alcohol). However, after controlling for the presence of
these factors and other relevant potential confounders,
the group effect on SSAT expression remained signifi-
cant in BA4 and BA11, suggesting that the results ob-
served in the gene expression screening, at least with SSAT,
are not a consequence of these other factors.
Although the sample used in the microarray study is
of limited power, and multiple testing may lead to a high
rate of false discoveries, the following should be taken into
account when interpreting our major findings. (1) We used
a number of procedures to avoid false-positive results. For
instance, we filtered out genes not present in at least 75%
of the subjects per group. This procedure reduces signifi-
cantly the number of comparisons by decreasing probe sets
being tested (approximately 15 000 probe sets instead of
the approximately 44 000). In addition, the criteria we used
to determine whether a gene was differentially expressed
combined the FC and P value criteria. (2) Our brain ex-
pression studies were used only as a first step of a screen-
ing procedure to identify potential targets of interest. Sev-
eral levels of internal consistency were used to select the
target of further study. (3) The findings implicating SSAT
are based on different levels of observation, including (a)
consistency between different gene probes signals, (b) con-
sistency between brain regions, (c) validation using an
alternative method (RT-PCR), (d) confirmation at the
protein level using 2 complementary methods (immuno-
histochemistry and Western blot), and (e) genetic evi-
dence suggesting that variation at the promoter region may
influence levels of expression.
In a recent study, Sibille et al
32
performed a microar-
ray analysis comparing expression patterns in BA9 and
BA47 of depressed suicide completers vs psychiatrically
normal controls who were matched on the basis of sex,
age, PMI, and race. They observed no evidence of differ-
ences in gene expression that correlated with major de-
pression and suicide. Many differences between the 2 stud-
ies could explain the discrepant results obtained. First,
in our study, we included male subjects only, and as dem-
onstrated by the same group,
33
prefrontal cortex gene ex-
pression has a strong sex-related component, probably
increasing the gene expression variability if male and
female subjects are combined in the study. Second, all
subjects included in our study were of French Canadian
origin, a population with a well-known and well-charac-
terized founder effect.
13
It is possible that by investigat-
ing subjects from this young (approximately 12 genera-
tions) and isolated population, we reduced the total
variability in gene expression patterns in our study. Fi-
nally, another significant difference is that our analysis
was performed using the Human Genome U133 set, which
consists of 2 GeneChip arrays with approximately 45 000
probe sets, whereas the analysis that was performed in
the study by Sibille et al
32
used only the U133A GeneChip,
which contains approximately half the probe sets (22 000
probe sets) of the U133 set.
In this study, we simultaneously screened the expres-
sion levels of genes using microarray analysis in post-
mortem cortical regions from suicide completers with and
without major depression vs a group of controls. We iden-
tified SSAT as a candidate mediating risk for suicide. This
effect appears to be moderated, to a certain extent, by the
presence of major depressive disorder. However, our study
design does not allow us to completely separate the effect
of suicide from the underlying psychopathology. Such
resolution could be obtained by an investigation of con-
trols with depression who were not suicide completers.
However, collecting such a sample is operationally chal-
lenging given the mean age of the suicide completers. Con-
firmation of our results and further investigation of the
role of SSAT and other polyamine-metabolizing en-
zymes in the neurobiology of suicide and major depres-
sive disorder is warranted.
Submitted for Publication: October 13, 2004; final re-
vision received February 17, 2005; accepted June 15, 2005.
Correspondence: Gustavo Turecki, MD, PhD, McGill
Group for Suicide Studies, Douglas Hospital, McGill Uni-
versity, 6875 LaSalle Blvd, Montreal, Quebec, Canada H4H
1R3 (gustavo.turecki@mcgill.ca).
Author Contributions: Mr Sequeira and Dr Gwadry con-
tributed equally to this study.
Funding/Support: This study was supported in part by
grants MOP-38078 and MOP-53321 from the Canadian
Institutes of Health Research, Ottawa, Ontario.
Acknowledgment: We thank the Bureau du Coroner du
Que´bec, Montreal, for their support; and W. H. Zheng,
PhD, and Amanda Li for their technical support.
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    • "Хотя функции ПА в мозге пока в значительной мере остаются загадкой [38, 51, 119, 184, 185, 189, 197, 198, 208, 212, 277,[284][285][286][287][288][289] , сейчас становится очевидным, что ПА связаны с глиальными клетками. Измененный метаболизм ПА может лежать в основе расстройств мозга [44, 94], в том числе депрессии с суицидальнoй тенденцией [95]. Истощение запаса эндогенного СПД/СПМ при определенной диете [96, 290] или при генной активации деградирующего СПМ фермента приводит к потере ПА и ухудшении резистентности нейронов к патологическим факторам [97, 98]. "
    Full-text · Article · Jan 2016 · Biologicheskie membrany
    0Comments 0Citations
    • "Hypoxia is also unlikely to account for increase expression in the HC, since only sudden death controls were included. Despite accumulating evidence implicating SAT1 in depression and/ or suicide (Sequeira et al., 2006; Fiori et al., 2009 Fiori et al., , 2010 Guipponi et al., 2009; Klempan et al., 2009b; Turecki, 2010a,b, 2011; Le-Niculescu et al., 2013; Lopez et al., 2014 ) including the current report , the mechanistic relationship between SAT1 expression, polyamine contents with depression and suicide remains unclear. Constitutively lower brain SAT1 expression in MDD could either be a result of, or directly contribute to, maladaptive polyamine stress response (PSR) during chronic stress (Gilad and Gilad, 2003 ). "
    [Show abstract] [Hide abstract] ABSTRACT: Low brain expression of the spermidine/spermine N-1 acetyltransferase (SAT1) gene, the rate-limiting enzyme involved in catabolism of polyamines that mediate the polyamine stress response (PSR), has been reported in depressed suicides. However, it is unknown whether this effect is associated with depression or with suicide and whether all or only specific isoforms expressed by SAT1, such as the primary 171 amino acid protein-encoding transcript (SSAT), or an alternative splice variant (SSATX) that is involved in SAT1 regulated unproductive splicing and transcription (RUST), are involved. We applied next generation sequencing (RNA-seq) to assess gene-level, isoform-level, and exon-level SAT1 expression differences between healthy controls (HC, N=29), DSM-IV major depressive disorder suicides (MDD-S, N=21) and MDD non-suicides (MDD, N=9) in the dorsal lateral prefrontal cortex (Brodmann Area 9, BA9) of medication-free individuals postmortem. Using small RNA-seq, we also examined miRNA species putatively involved in SAT1 post-transcriptional regulation. A DSM-IV diagnosis was made by structured interview. Toxicology and history ruled out recent psychotropic medication. At the gene-level, we found low SAT1 expression in both MDD-S (vs. HC, p=0.002) and MDD (vs. HC, p=0.002). At the isoform-level, reductions in MDD-S (vs. HC) were most pronounced in four transcripts including SSAT and SSATX, while reductions in MDD (vs. HC) was pronounced in three transcripts, one of which was reduced in MDD relative to MDD-S (all p<0.1 FDR corrected). We did not observe evidence for differential exon-usage (i.e. splicing) nor differences in miRNA expression. Results replicate the finding of low SAT1 brain expression in depressed suicides in an independent sample and implicate low SAT1 brain expression in MDD independent of suicide. Low expression of both SSAT and SATX isoforms suggest shared transcriptional mechanisms involved in RUST may account for low SAT1 brain expression in depressed suicides. Future studies are required to understand the functions and regulation of SAT1 isoforms, and how they relate to the pathogenesis of MDD and suicide. Copyright © 2015. Published by Elsevier Inc.
    Full-text · Article · May 2015 · Neurobiology of Disease
    0Comments 2Citations
    • "In their analysis for transcripts, about 200 candidate genes were identified as dysregulated when MDE patients were compared with controls (including the regulator of endothelin 1 (EDN1), ELK3 ETS-domain protein (ELK3), progestin and adipoQ receptor family member VI (PAQR6), protein phosphatase Mg2+/Mn2+ dependent 1K (PPM1K), and G-protein signaling 7 binding protein (RGS7BP) genes). The EDN1, ELK3, PAQR6, PPM1K, and RGS7BP genes have also been previously identified as dysregulated in brain tissues with MDD (Guilloux et al., 2012; Sequeira et al., 2006 Sequeira et al., , 2007). Belzeaux and colleagues also tested the hypothesis that gene expression profile (including mRNA and miRNA expression) can predict response to antidepressants with MDE patients (Belzeaux et al., 2012). "
    [Show abstract] [Hide abstract] ABSTRACT: Major depressive disorder (MDD) is a serious health concern worldwide. Currently there are no predictive tests for the effectiveness of any particular antidepressant in an individual patient. Thus, doctors must prescribe antidepressants based on educated guesses. With the recent advent of scientific research, genome-wide gene expression microarray studies are widely utilized to analyze hundreds of thousands of biomarkers by high-throughput technologies. In addition to the candidate-gene approach, the genome-wide approach has recently been employed to investigate the determinants of MDD as well as antidepressant response to therapy. In this review, we mainly focused on gene expression studies with genome-wide approaches using RNA derived from peripheral blood cells. Furthermore, we reviewed their limitations and future directions with respect to the genome-wide gene expression profiling in MDD pathogenesis as well as in antidepressant therapy. Copyright © 2015. Published by Elsevier Inc.
    Full-text · Article · Feb 2015 · Progress in Neuro-Psychopharmacology and Biological Psychiatry
    0Comments 3Citations
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