Download Gene expression changes in schizophrenia

Gene expression changes in schizophrenia
PhD Thesis
Károly Mirnics, MD
Semmelweis University School of PhD Studies
PhD School of Neurosciences
PhD Advisor: Faludi Gábor, MD, DSc
Official Reviewers: Raskó István MD, DSc
Judit Mária Molnár MD, PhD
Head of Examination Board: Mária Sasvári, Msc, DSc
Examination Board Members: Kornélia Tekes, PharmD, PhD
Erika Szádóczky, MD, DSc
Budapest
2010
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1. TABLE OF CONTENTS
1. Table of Contents
2
2. List of Abbreviations
3
3. Introduction
4
4. Aims
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5. Methods
14
6. Results
18
7. Discussion
35
8. Conclusions
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9. Summary
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10. Bibliography
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11. Candidate‘s publications related to thesis
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12. Candidate‘s publications unrelated to thesis
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13. Acknowledgements
90
14. Appendices 1-6 (manuscripts)
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2. LIST OF ABBREVIATIONS
ALR – Average log2 ratio
BA – Brodman Area
CCK – Cholecystokinin
CL – Confidence level
CNS – Central nervous system
CNT or CONT – Control
CR – Calretinin
D1 and D2 – Dopamine receptors 1 and 2
DLPFC – Dorsolateral prefrontal cortex
DNA – Deoxyribonucleic acid
EST – Expressed sequence tag
GABA – Gamma-aminobutyric acid
GAD1, GAD67 – Glutamic acid decarboxylase
GAT1 – GABA transporter 1
IHC – Immunohistochemistry
ISH – In situ hybridization
IR – Immunoreactivity
NPY – Neuropeptide Y
PFC – Prefrontal cortex
PSYN – Presynaptic secretory machinery
PV, PARV – Parvalbumin
qPCR – Quantitative polymerase chain reaction
RGS4 – Regulator of G-protein signaling 4
RMA – Robust multi-array analysis
RNA – Ribonucleic acid
SCH or SCZ – Schizophrenia
SD – Standard deviation
SST – Somatostatin
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3. INTRODUCTION
Schizophrenia – etiology, epidemiology, genetics and epigenetic changes
Schizophrenia is a complex and devastating brain disorder that affects 1% of the
population and ranks as one of the most costly disorders to afflict humans (Carpenter
and Buchanan, 1994; Hyman, 2000). This disorder typically has its clinical onset in late
adolescence or early adulthood, presenting as a constellation of both positive
(delusions, hallucinations, and thought disorganization) and negative (impaired
motivation and decreased emotional expression) symptoms (Lewis and Lieberman,
2000). Alterations in cognitive processes, such as attention and working memory,
however, may be present prior to the onset of the clinical syndrome and appear to
represent core features of the illness. In addition, many individuals with schizophrenia
experience difficulties with depression and substance abuse, factors that contributes to
the 10-15% lifetime incidence of suicide in this disorder (Lewis and Lieberman, 2000).
The etiology of schizophrenia remains elusive but appears to be multifaceted,
with genetic, nutritional, environmental and developmental factors all implicated
(Andreasen, 1996; Pulver, 2000; Tsuang et al., 2001; Weinberger, 1995). In terms of
pathophysiology, a convergence of observations from clinical, neuroimaging and
postmortem studies have implicated the prefrontal cortex (PFC) and superior temporal
gyrus (STG) as major loci of dysfunction in schizophrenia (Andreasen et al., 1997;
Bertolino et al., 2000; Harrison, 1999; McCarley et al., 1999; Selemon et al., 1995;
Weinberger et al., 1986). Abnormal PFC function probably contributes to many of the
cognitive disturbances in schizophrenia and appears to be related to altered synaptic
structure and/or function in this cortical region. In contrast, changes in STG are likely
to be associated with the positive symptoms of this disease (for reviews, see (Keshavan
et al., 1998; Shapleske et al., 1999)).
Schizophrenia is characterized by complex changes across different brain regions
Schizophrenia is a neurodevelopmental disorder that does not appear to be
characterized by prominent cellular loss. The functional abnormalities associated with
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schizophrenia involve both cortical and subcortical regions and affect multiple
neurotransmitter systems (Lewis, 2000).
Modest, but consistent changes in brain volume have been observed both in
posmortem and imaging studies. Some of these changes were also present in firstbreak, unmedicated subjects (Keshavan et al., 1998). Several studies of schizophrenia
demonstrated significant volume reductions across brain regions including the temporal
lobe, hippocampus, dorsolateral prefrontal cortex and mediodorsal thalamus (Gilbert et
al., 2001; Pakkenberg, 1992; Pierri et al., 2001). Furthermore, significant volume
increases in the third ventricle and anterior and temporal horns of the lateral ventricles
have been observed by several groups of investigators (Hirayasu et al., 1998; Kwon et
al., 1999). However, a number of studies failed to replicate the reported differences
(Highley et al., 2001; Kulynych et al., 1996).
Evidence from converging studies suggests a broadly distributed deficit of
white matter across multiple brain areas. Deficits in corpus callosum and
interhemispheric transfer (Frodl et al., 2001), prefrontal white matter and ultrastructural
signs of apoptosis/necrosis in oligodendrocytes have been observed (Uranova et al.,
2001), which may be related to the decreased expression of oligodendrocyte markers in
a recent microarray screen (Hakak et al., 2001). Recently, an increase in HLA-DR
immunoreactive microglia in frontal and temporal cortex of chronic schizophrenics has
been also reported (Radewicz et al., 2000).
PFC-related changes in schizophrenia
A convergence of observations from clinical, neuroimaging and postmortem
studies have implicated the dorsal prefrontal cortex (PFC) as a major locus of
dysfunction in schizophrenia (Andreasen et al., 1997; Bertolino et al., 2000; Selemon et
al., 1995; Weinberger et al., 1986). In subjects with schizophrenia, reductions in gray
matter volume in the dorsal PFC have been observed in neuroimaging studies
(Goldstein et al., 1999; Sanfilipo et al., 2000), and these volumetric changes are
associated with an increase in cell packing density (Lewis, 2000; Selemon et al., 1995;
Selemon et al., 1998), but no change in total neuron number in the PFC (Pakkenberg,
1993; Pierri et al., 2001). Although the size of some PFC neuronal populations appears
to be reduced in schizophrenia (Pierri et al., 2001; Rajkowska et al., 1998), these
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findings are also likely to reflect a decrease in the number of axon terminals, distal
dendrites and dendritic spines (Selemon and Goldman-Rakic, 1999). Consistent with
this interpretation, both the levels of synaptophysin, a presynaptic terminal protein
(Glantz and Lewis, 1997; Karson et al., 1996; Perrone-Bizzozero et al., 1996), and the
density of dendritic spines (Garey et al., 1998; Glantz and Lewis, 2000) are decreased
in the PFC of subjects with schizophrenia.
Changes in gene expression have also been observed in the PFC of subjects
with schizophrenia. In particular, alterations in
gene products
related to
neurotransmission (Akbarian et al., 1995; Akbarian et al., 1996; Garey et al., 1998;
Glantz and Lewis, 2000; Meador-Woodruff et al., 1997; Volk et al., 2000) or second
messenger systems (Dean et al., 1997; Hudson et al., 1999; Shimon et al., 1998) have
been observed. However, each of these studies have focused on only one or a few gene
products at a time, without the ability to investigate the simultaneous expression of
large numbers of genes. Consequently, complex gene expression patterns in the PFC of
subjects with schizophrenia are just beginning to be uncovered.
Within the dorsolateral PFC alterations in the structure and function of both
cytoarchitectonic regions have been associated with schizophrenia (BA9 and BA46)
(Glantz and Lewis, 2000; Melchitzky et al., 1999; Pierri et al., 1999; Shenton et al.,
2001). In BA46 of subjects with schizophrenia, Selemon et al. (Selemon et al., 1998)
report an increased cellular density that is comparable in magnitude to that seen in PFC
BA9. Furthermore, this study reports that the increased density was also present in
Layer II, what was not observed in studies of BA9. In addition, Glantz et al. (Glantz
and Lewis, 1997) found that synaptophysin immunoreactivity was decreased across all
layers in both BA9 and BA46. Despite studies that show common deficits across BA9
and 46 in schizophrenia, these two regions are different in organization and
connectivity in both humans and primates. Melchitzky at al. (Melchitzky et al., 1999)
found that parvalbumin immunorective (PV-IR) terminals originating form the
mediodorsal thalamus exclusively gave rise to different type of synapses across the
middle layers of BA9 and BA46. Finally, Perlstein et al (Perlstein et al., 2001) found
that patients with schizophrenia showed a deficit in physiological activation of the right
BA9 and BA46 and those patients with greater dorsolateral prefrontal cortex
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dysfunction performed more poorly on the "n-back" task. These combined findings
make the right BA46 of PFC an appealing target for microarray analysis.
Fronto-temporal dissociation in schizophrenia
While the deficits in working memory have been associated with PFC
dysfunction, some of the auditory psychotic symptoms may arise from STG
dysfunction (reviewed by (Stephane et al., 2001)), while deficits in cognitive processes
have been linked to both regions. These two areas are heavily interconnected and the
prefrontal and temporal cortical deficits may be closely related (Goldman-Rakic, 1999).
Woodruf et al. (Woodruff et al., 1997) report that the most prominent abnormality in
patients was dissociation between prefrontal and superior temporal gyrus volumes (P <
0.01), supporting a hypothesis of a relative 'fronto-temporal dissociation' in
schizophrenia that may have a developmental origin. In addition, Wible and coworkers
(Wible et al., 2001) found that prefrontal volumes were also associated both with the
severity of negative symptoms and the volume of temporal lobe regions. These same
results were also observed previously in schizophrenic subjects with mostly positive
symptoms, emphasizing the importance of temporal-prefrontal coordination in the
symptomathology of schizophrenia (Bullmore et al., 1998).
The genomic revolution
In the last decades, the scientific community finished the draft of the human
genome (Lander et al., 2001; Olivier et al., 2001) and we entered the post-genomic era
(Husi and Grant, 2001), initiating the exploration of functional genomics and
proteomics (Dongre et al., 2001; Stoll et al., 2002). Students learn about transcriptome
profiling and conditional genetic ablations (Carninci et al., 2001), while gene cloning
and transfection have become routine procedures in many neuroscience laboratories.
Northern hybridizations are giving way to microarray studies; PCR has evolved into a
real-time quantitative assay (Brail et al., 1999; Freeman et al., 1999), while masssequencing of nucleic acids has led to development of SAGE (Velculescu et al., 1995).
These high throughput methods are rapidly changing the way we plan and perform
experiments. We are living in an unprecedented scientific revolution, where the most
optimistic deadlines turn out to be too conservative: the Human Genome Project
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produced a draft of the whole human genome ahead of time (Lander et al., 2001;
Olivier et al., 2001); most of the single gene diseases have been identified, and gene
therapy holds the promise of creating healthy babies that would have died from
inherited disorders (Barranger and Novelli, 2001; Pfeifer and Verma, 2001).
DNA microarrays – use and platforms
DNA microarrays are a crucial part of this technical revolution (DeRisi et al.,
1996; Lockhart et al., 1996; Schena et al., 1995). In recent years, the advent of a variety
of new methods for quantifying messenger RNA (mRNA), the transcription product of
genomic DNA, has made it possible to assess tissue levels of virtually every transcript
expressed in the brain. Available techniques range from in situ hybridization, which is
ideal for determining the cellular specificity and precise level of expression of
individual transcripts, to DNA microarrays which can, in theory, simultaneously survey
the relative expression level of all mRNAs (i.e., the transcriptome) in a tissue sample.
Thus far, microarrays have been successfully applied in comparative genomic
hybridization (Cheung et al., 1998; Cheung and Nelson, 1998; Geschwind et al., 1998;
Gunthard et al., 1998; Kozal et al., 1996), novel gene discovery (Geschwind et al.,
2001; Schena et al., 1996; Tanaka et al., 2000)polymorphysm analysis and gene
expression profiling (DeRisi et al., 1996; Lockhart et al., 1996; Schena et al., 1995).
These studies often are carried out in conjunction with conventional methods for
assessment of transcriptome differences, including in situ hybridization (Mirnics et al.,
2000; Mirnics et al., 2001e), suppression subtractive hybridization (Yang et al., 1999)
and representational difference analysis (Geschwind et al., 2001).
Currently, two widely accepted microarray types are used for gene expression
analysis: oligonucleotide and cDNA microarrays (Cheung et al., 1999; DeRisi et al.,
1996; Lockhart et al., 1996; Lockhart and Winzeler, 2000; Lockhart and Barlow,
2001a; Lockhart and Barlow, 2001b; Schena et al., 1995), though both have
weaknesses (Geschwind, 2000; Mirnics, 2001; Mirnics et al., 2001a; Mirnics, 2002;
Watson et al., 2000). The major manufacturer of oligonucleotide arrays is Affymetrix
(for a technology review, see (Lockhart and Barlow, 2001a; Lockhart and Barlow,
2001b)), offering dozens of distinct microarrays. There are a number of advantages of
the Affymetrix "GeneChip " technology, including the presence of multiple
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oligonucleotide features for assessing the presence of each gene, quantitative results
and good nominal sensitivity. Furthermore, data comparison may be performed post
hoc across many microarrays simultaneously.
cDNA microarrays immobilize longer fragments of genes onto a solid support
(Luo and Geschwind, 2001; Marcotte et al., 2001; Mirnics et al., 2001a). These
microarrays are readily customized, their production is not proprietary, and the printing
and scanning equipment, as well as the software tools for data analysis, are of modest
cost and available from many sources. For comparative gene expression studies,
fluorescence-tagged nucleotides (e.g. Cyanine 3-Cy3 and Cyanine 5-Cy5) or
fluorochrome-couplers are incorporated into the cDNA during a standard reverse
transcription reaction. Such labeled samples are combined and hybridized onto the
same microarray. Each microarray probe will bind its complementary, fluorescently
labeled cDNA species with a distinct excitation/emission wavelength, and the amount
of signal for each fluorochrome can be quantified independently for each microarray
spot.
Oligonucleotide arrays and cDNA arrays both perform well and generate
excellent datasets, yet both share a number of limitations (Hollingshead et al., 2005;
Mirnics, 2001; Mirnics et al., 2001b; Mirnics, 2002; Mirnics, 2003; Mirnics et al.,
2005; Mirnics, 2006).
As an alternative, membrane based cDNA ―macro‖ arrays
remain a viable choice for analysis of gene expression (Becker, 2001; Bertucci et al.,
1999).
Gene expression DNA microarrays in brain studies
The first successful microarray experiments were performed in the mid-1990s
(DeRisi et al., 1996; Lockhart et al., 1996; Schena et al., 1995) and triggered a flurry of
cancer-related microarray studies. Yet, reports using brain tissue in microarray studies
were absent for years after the initial publications.
Because of the need to use
postmortem tissues for many human brain diseases, obtaining high quality, wellidentified sample tissue was a significant limitation. The phenotypic complexity of
brain tissue also presented a major hurdle in transcript detection and data analysis.
Furthermore, in most brain disease processes, only a fraction of cells is affected and the
transcript changes are "masked" by the transcripts originating from unaffected cells.
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Finally, gene expression changes associated with brain diseases are likely to be
moderate in magnitude and often do not exceed a 2-fold change, even though a 30%
expression change may have a profound physiological effect on brain function. Thus,
many biologically relevant expression changes would probably remain unnoticed in
microarray experiments.
Indeed, the first studies involving microarrays and brain tissue illustrated both
the promise and the limitations of this technology. In a microarray study involving
animals whose tissues were harvested during sleep and wake cycles, Cirelli and Tononi
(Cirelli and Tononi, 1999) observed only a few expression changes. Similarly, in a
landmark study, Sandberg et al. (Sandberg et al., 2000) discovered previously unknown
expression differences across brain regions - yet, the low number of reported
differences belied previously reported differential gene expression patterns using other
methods (Geschwind, 2000).
Microarray technology is still evolving; arrays contain many more gene probes
and can now also assess splice variants, and sensitivity is improving. The value of gene
expression observations, however, will be determined by our ability to place microarray
data into a biological context, and this remains a major challenge (Becker, 2001;
Geschwind, 2001; Miles, 2001; Mirnics, 2001).
Genomics and complex psychiatric disorders
Major depression, bipolar disorder, autism, schizophrenia and other complex
brain diseases rank amongst the most debilitating and costliest disorders afflictions of
humankind (Hyman, 2000). The complexity of these disorders is due to a combination
of the likely polygenic nature of these disorders, and the essential role played by
environmental perturbations in the expressed dysfunction (Hyman, 2000; Pulver, 2000;
Tsuang, 2000; Tsuang et al., 2001; Weinberger, 1995). It is understandable, therefore,
that mutational analysis of single genes in different cohorts often has led to confusing
and non-reproducible results (Horvath and Mirnics, 2009). Advances in the diagnosis,
the application of quantitative neuroanatomical methods to postmortem tissue and
neuroimaging methods have facilitated a greater understanding of the pathophysiology
of psychiatric disorders.
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The development of novel tools of functional genomics enables us to approach
questions addressing the etiology of complex psychiatric disorders from a novel,
hypothesis-free, data-driven angle. Thus, initial application of high-density
microarrays to a particular disease eliminates the need for a preconceived hypothesis;
this will not limit data collection. Transcriptome expression data can be obtained using
a variety of approaches, including completely impartial hierarchical clustering to
informed assessment using biological criteria. In all instances one can ask simply ‖what
are the specific gene expression changes associated with this disorder?‖ One can
formulate relevant biological hypotheses after data acquisition, with imaginative
analysis of the relationships within and across transcriptomes successful in driving
many discovery processes (Brown et al., 2000; Golub et al., 1999; Hastie et al., 2000;
Hastie et al., 2001; Holter et al., 2001; Mirnics, 2008; Raychaudhuri et al., 2001;
Tamayo et al., 1999; Toronen et al., 1999). As eloquently put by Eric Lander of the
Whitehead Institute, microarray-based research is ―… a ‘hypothesis free’ search for
genes, because it requires no notion in advance of how the gene might contribute to the
disease. Instead, the researchers use powerful molecular biological tools to spot
differences between people, or even individual cells, that have the disease and those
that don’t. I’m a big fan of such ignorance-based techniques, because humans have a
lot of ignorance, and we want to play to our strong suit.‖ (Keystone Millenium
Meeting: A Trends Guide 2000).The first datasets from DNA arrays applied to a
nervous system disorders produced a wealth of information. Using cDNA arrays with
over 5,000 genes on tissue from a subject with multiple sclerosis, Whitney et al.
(Whitney et al., 1999), found that in acute lesions, sixty-two genes were differentially
expressed. These genes included the Duffy chemokine receptor, interferon regulatory
factor-2, and tumor necrosis factor alpha receptor-2 among others, most of them not
previously associated with the disease process of multiple sclerosis. Ginsberg and
colleagues have been at the forefront of analyzing the expression profile of single cells
and RNA content of tangles and plaques in Alzheimer‘s disease (Ginsberg et al., 1997;
Ginsberg et al., 1999; Ginsberg et al., 2000; Mirnics, 2008). In their study (Ginsberg et
al., 2000; Mirnics, 2008), transcriptome profiling was carried out on individual normal
and tangle-bearing hippocampal CA1 neurons using cDNA microarrays with >18,000
probes. The expression differences were further quantified by reverse northern blot
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analysis using probes for 120 selected mRNAs on custom-made cDNA arrays. The
authors uncovered a range of AD-related gene expression changes that included
phosphatases/kinases, cytoskeletal proteins, synaptic proteins, glutamate receptors, and
dopamine receptors. In an impressive effort by Lewohl et al. (Lewohl et al., 2000;
Lewohl et al., 2001), transcriptome profiling of postmortem samples of superior frontal
cortex of alcoholics and non-alcoholics was performed on both cDNA and
oligonucleotide arrays. These studies detected the expression of >4,000 genes in the
superior frontal cortex, of which 163 were found to be differentially expressed by
>40% between alcoholics and non-alcoholics, including myelin and cell cycle related
genes.
Microarray studies involving postmortem human material have also reported
unique datasets associated with the pathophysiology of Rett syndrome (Colantuoni et
al., 2001; Johnston et al., 2001), autism (Purcell et al., 2001) and brain tumors (Evans
et al., 2001; Sallinen et al., 2000). These findings, combined with microarray analysis
of animal models of Parkinson‘s Disease (Grunblatt et al., 2001; Mandel et al., 2000),
ischemia (Jin et al., 2001) and drug abuse (Xie et al., 2002) strongly argue that
microarrays will soon become a routinely used analytical method in all expression
profiling experiments involving brain tissue. This process in the future may take the
form of ―gene expression voxelation‖, a procedure invented and first implemented by
Smith and co-workers (Brown et al., 2002). In this procedure, the brain tissue to be
analyzed is divided into small 3D tissue cubes called ‖voxels‖, and each of these voxels
are analyzed for the expression of tens of thousands of genes using a separate
microarray. The obtained data creates a three-dimensional brain map for each of the
genes, and the spatial distribution of expression changes creates unique patterns that
can be analyzed for co-regulation using principal component analysis and other data
mining tools. Furthermore, the expression analysis can be combined with DNA
regulatory sequence and promoter information (Livesey et al., 2000; Oldham et al.,
2008), creating a co-regulational dataset of unparalleled power.
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4. AIMS
The overall aims of the combined studies were defined as follows:
1) Understanding changes in the gene expression patterns in the postmortem
brains of subject with schizophrenia using an unbiased, hypothesis-free, data-driven
approach.
2) Verifying the newly discovered gene expression changes.
3) Investigating the relationships between gene expression changes and genetic
susceptibility to schizophrenia.
4) Deciphering the role of the gene expression changes in relationship to
pathophysiology and clinical presentation of schizophrenia.
Figure 1. Schizophrenia is a multifaceted disturbance. Predisposing genetic factors (SNPs,
CNVs) interact with a wide range of environmental factors, and their combined effects trigger a
complex cascade of pathophysiological processes in the developing brain. The first changes
most likely include gene expression alterations and a variety of neurochemical-metabolic
disturbances, and this is the central topic of our studies. The altered brain homeostasis impacts
on the ―normal‖ wiring of the brain, resulting in inefficient information processing in the
affected individuals. The combined changes ultimately lead to behavioral, cognitive and
emotional deficits, which are the clinical hallmarks of the disease. Figure adapted from Horvath
and Mirnics, Nature Medicine, 15, 488-90, 2009.
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5. METHODS
5.1 Postmortem samples and subjects
For all our studies fresh-frozen human tissue was obtained from the University
of Pittsburgh‘s Center for the Neuroscience of Mental Disorders Brain Bank. Prefrontal
cortex was identified based on histological criteria as previously described (Glantz and
Lewis, 2000; Volk et al., 2000). In all studies subject pairs were completely matched
for sex, race, postmortem interval, and RNA quality. Consensus DSM-IV diagnoses
were made for all subjects using data from clinical records, toxicology studies and
structured interviews with surviving relatives as previously described (Pierri et al.,
1999). For microarray analysis, cryostat sections (30-50 m) were collected into 50 ml
tubes at –24°C under nuclease-free conditions, providing 300-900 mg of wet weight per
specimen. Total RNA was extracted using standardized methods. RNA quality was
assessed via Agilent 2100 Bioanalyzer.
Only samples with an RIN>7.0 were
considered for further analysis.
5.2 DNA Microarrays
The experiments on synaptic (Mirnics et al., 2000), energy metabolism
(Middleton et al., 2002), 14-3-3 (Middleton et al., 2005) and RGS4 (Mirnics et al.,
2001f) expression studies were performed on UniGEM-V high-density cDNA
microarrays (Incyte Pharmaceuticals, Fremont, CA). These cDNA microarrays tested
human gene expression using genes and ESTs from the public domain UniGene
database. Each array compared a sample to a control by cy3/cy5 dual fluorescent
hybridization. Each UniGEM-V array contained over 7,000 unique and sequence
verified cDNA elements mapped to 6,794 UniGene Homo sapiens annotated clusters.
The immune-chaperone (Arion et al., 2007) and GABA gene (Hashimoto et al.,
2008b) expression studies were performed on custom-made oligonucleotide arrays
manufactured by Nimblegen. Our custom-designed Nimblegen DNA microarrays were
composed of 60-mer single-stranded oligonucleotides synthesized by maskless in situ
photolithographic synthesis. Each microarray contained probes against ~1,800 genes.
The expression of each gene was assessed by 5 independent probe sequences and each
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of the probes was printed at 4 technical replicates on a sub-array (Figure 1 of
manuscript (Arion et al., 2007)). Each microarray consisted of 5 sub-arrays, resulting in
a total of 100 measurements/gene/array (5 probes x 4 replicate spots x 5 identical subarrays). In addition, the experiment was performed in two technical replicates using
independent cDNA and cRNA synthesis steps. As a result, our dataset consisted of 56
microarrays. The ~1,800 gene expression probes were chosen based on 1) previous
microarray studies conducted in our laboratory, 2) previously published gene
expression and genetic data in schizophrenia, and 3) genes participating in cellular
processes implicated in the pathophysiology of PFC dysfunction in schizophrenia. As a
result, the list of microarray probes was enriched in GABA, glutamate, synaptic, glial,
dopamine, serotonin system and other transcripts. Furthermore, based on previous
microarray studies of subjects with schizophrenia and matched controls, an additional
~400 genes with unaltered expression were chosen to ensure proper normalization
across the microarrays. Normalization was perfomed using gcRMA (Gentleman et al.,
2004) .
5.3 Microarray data analysis
We followed two main strategies in our data analysis: 1) Identifying the
differentially expressed transcripts at the level of individual genes; and 2) Assessing the
differential expression of transcripts grouped into modules related to functional
pathways. In all presented studies, to consider a single gene edifferentially expressed,
it had to fulfill a dual-criteria of magnitude change over baseline (±20%) and statistical
significance of p<0.05. For gene group analyses, the expression distribution of the
whole gene group had to be significantly different between the control and
schizophrenic subjects (p<0.05), and the false discovery estimates (obtained by random
permutation analysis (Unger et al., 2005)) had to suggest that the chance of obtaining a
false-positive finding was q<0.05.
5.4 In situ hybridization
For all our in situ hybridization studies, we designed custom primers for each
of the sequence-verified clones of interest. These primers were used to produce ds
cDNA molecules of 750-950 bp using PCR and normal human brain cDNA template.
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The products of these reactions were ligated into a plasmid vector and transformed into
competent E. coli cells.
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S-labeled sense and anti-sense riboprobes were synthesized
and purified using Riboprobe In Vitro Transcription System (Promega) and RNeasy kit
(Qiagen). Sections (20 m) from the PFC of each subject were cut on a cryostat, and
stored on slides at –80ºC until processing. The methods used for in situ hybridization
were previously published (Campbell et al., 1999), with subject pairs always processed
together. For quantification of the in situ hybridization signal, we used Scion Image
(version 3). The investigators were always blind to the sample category. Uncalibrated
optical density (OD) values were measured from high resolution scans of x-ray film (XOmat AR, Kodak) that had been exposed to the processed slides for 2-3 days prior to
being developed. The mean relative OD was calculated for each section in five nonoverlapping rectangular regions with a width of approximately 500 microns
and adjusted in length to span cortical layers 2-6. The long axis of each rectangular
region was positioned in parallel with the orientation of cortical cells. The OD of the
white matter in the section was subtracted from each of these measurements to give five
relative OD values for each subject. For group comparisons, the mean of these five
values was used for each subject. To evaluate the effect of confounds, for each gene
examined by in situ hybridization, group differences were examined using analysis of
covariance (ANCOVA) with diagnosis as the main effect and sex, age, postmortem
interval, brain pH and tissue storage time as covariates.
5.5 qPCR verification of data
In all our studies cDNA synthesis was performed using two independent reverse
transcription reactions for each sample with High Capacity cDNA Archive Kit®
(Applied Biosystems). For each 100 μl reaction, we used 700 ng of the same total RNA
used for microarray analysis. Priming was performed with random hexamers. For each
sample, amplified product differences were measured with 4 independent replicates
using SYBR Green chemistry-based detection (Mimmack et al., 2004). β-actin was
used as the endogenous reference gene since 1) it has been established as a stable
reference gene in the literature (Chen et al., 2001); 2) it did not display significant
variation in gene expression between autistic and control samples in the microarray
studies and 3) it has been established as a stable reference gene in our previous studies
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of the human postmortem brain (Arion et al., 2006; Arion et al., 2007). The efficiency
for each primer set was assessed prior to qPCR measurements, and a primer set was
considered valid if its efficiency was >80%. The qPCR reactions were carried out on an
ABI Prism 7300 thermal cycler (Applied Biosystems Inc.), quantified using ABI Prism
7300 SDS software (with the auto baseline and auto threshold detection options
selected) and statistically analyzed using a Student‘s paired t-test in Microsoft Excel.
5.6 Hierarchical clustering
In all our manuscripts two-way cluster analyses (genes and samples) were
performed on normalized log2 transformed signal levels across the 28 subjects with
Euclidian distance analysis in Genes@Work developed by IBM (Armonk, New York)
(Lepre et al., 2004).
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6. RESULTS
6.1. Presynaptic gene expression changes in the PFC of subjects with
schizophrenia
(APPENDIX 1. - Mirnics, K., Middleton, F.A., Marquez, A., Lewis, D.A., Levitt, P.,
2000. Molecular characterization of schizophrenia viewed by microarray analysis of
gene expression in prefrontal cortex. Neuron. 28, 53-67.)
We analyzed global gene expression in PFC area 9 from six matched pairs of
schizophrenic and control subjects. Of all genes and expressed sequence tags (ESTs)
interrogated by the six microarrays, an average of 3735 (SD = 1345) gene transcripts
per array were detectable in the pairwise comparisons. Of these transcripts, 4.8% were
judged to be differentially expressed within a subject pair based on the stringent
criterion of having ≥ 1.9 -fold difference (99% confidence level [CL]) between subjects
in fluorescent signal intensity. The observed differences for any subject pair, in general,
were comparably distributed in both directions: 2.6% of the genes were expressed at
higher levels in schizophrenic subjects than in the matched controls, whereas 2.2%
were expressed at lower levels in the schizophrenic subjects.
The changes in schizophrenic subjects were assessed by gene expression
profiling for 250 gene groups related to metabolic pathways, enzymes, functional
pathways, or brain-specific functions. More than 98% of the gene groups, when
compared to the expression pattern of all detectable transcripts, were not significantly
different (p > 0.05) between the schizophrenic and control subjects, establishing that
other changes that we did detect are not due simply to human subject variability. This
observation also is in agreement with previous findings that total mRNA levels in
schizophrenic subjects are comparable to those in the unaffected human population.
However, several gene groups exhibited significantly changed expression in
schizophrenic subjects, both within individual pairs and across pairs (presynaptic
secretory machinery, GABA transmission, glutamate transmission, energy metabolism,
growth factors, and receptors).
In particular, transcript levels were significantly decreased in the schizophrenic
subjects for a group of genes that were functionally related to the presynaptic secretory
machinery (PSYN) (Figure 2B). Expression of genes involved in glutamate and GABA
18
neurotransmission (Figure 2C and Figure 2D) were also decreased, supporting
previous studies that have documented changes of individual genes within these latter
groups in the PFC of schizophrenic subjects. Across the six microarray comparisons, of
the 62 genes represented in the PSYN group, on average 41 PSYN genes products were
detectable in the subject pairs. Schizophrenic subjects differed in the number of genes
in the PSYN group that were decreased over the 95% confidence level. For each of the
six comparisons, between 6 and 26 genes were changed (13.3%–56.9% of expressed
PSYN genes). Furthermore, when combined, out of the total of 246 PSYN group
observations, 17.9% of genes showed a decrease at the 99% CL (≤−1.9), and 29.3%
showed a significant decrease at the 95% CL (≤−1.6) in the schizophrenic subjects
across the six pairwise comparisons. This shift in the distribution of the PSYN genes
was present in all subjects, and it was highly significant for both the individual
comparisons and at the level of the combined data (p < 0.0001). This highly skewed
distribution of the PSYN gene group was not due to individual variability between
subjects, because individual random variability must occur in both directions. At the
99% CL, 2.2% of all genes were underexpressed in the schizophrenic subjects,
suggesting that less than one PSYN gene per subject would be decreased due to the to
individual variability (41 expressed genes × 0.022 = 1 gene for a single comparison or
246 × 0.022 = 5.5 genes across all six pairs). Furthermore, in a random distribution, at
least six PSYN genes would be expected to show increased expression across the six
schizophrenic subjects (246 × 0.028). However, this was not the case for the genes in
the PSYN group. Over the 99% CL across the six schizophrenic subjects, only one
(0.4%) PSYN gene reported an increase, and 44 (17.9%) PSYN transcripts were
decreased. In addition, at the 95% CL we observed only 2 (0.8%) increased and 72
(29.3%) decreased PSYN genes across all the schizophrenic subjects.
The hierarchical data analysis revealed an unexpected aspect of altered gene
expression. The specific members of the PSYN gene group showing decreased
expression, and the relative magnitude of individual transcript changes, differed across
the pairwise comparisons, revealing different patterns of decreased gene expression in
the schizophrenic subjects. We also found that these results were not related to
confounding factor such as medication history, sample bias or substance abuse.
19
Figure 2. Gene Expression Differences in PFC Area 9 for Six Pairs of
Schizophrenic and Control Subjects as Revealed by Microarray Analysis. For
each gene group, all expressed genes were classified into signal intensity
difference intervals (0.1 bins) according to their Cy5/Cy3 signal ratio. Transcripts
in a ―1‖ bin had identical Cy5 versus Cy3 signal intensities. Positive values (to the
right) on the x axis denote higher Cy5 signal in schizophrenic subjects (S > C),
negative values (to the left) correspond to higher Cy3 signal intensity in the
control subjects (C > S). The y axis reports the percentage of expressed genes
across the six subject pairs per bin for each gene group. In all panels, the white
bars (all genes) denote distribution of all expressed genes across the six PFC
pairwise comparisons (n = 22,408).(A) Hybridization of two aliquots of the same
cortical mRNA from one control subject (all genes control). One aliquot of
control cortical mRNA was labeled with Cy3, the other with Cy5, and the
combined sample was hybridized onto a single UniGEM-V cDNA microarray. At
the 99% confidence level (≥ 1.9 ), only four of the 4844 expressed genes (0.08%)
reported a false positive Cy5 bias, and two genes (0.04%) reported a false positive
Cy3 bias. In contrast with this control experiment, 4.8% of all expressed genes in
the six combined PFC comparisons showed a differential expression over the 99%
confidence level. Of all expressed genes in the six combined PFC comparisons,
95% of the expressed genes reported Cy3/Cy5 signal intensity ratios ≤ 1.6 . B-D
denote distribution of expressed genes for transcripts related to PSYN group (B),
glutamate system (C), GABA system (D). Figure from (Mirnics et al., 2000).
6.2. Assessment of the expression of genes belonging to various metabolic
pathways in the PFC of subjects with schizophrenia
20
(APPENDIX 2. - Middleton, F.A., Mirnics, K., Pierri, J.N., Lewis, D.A., Levitt, P.,
2002. Gene expression profiling reveals alterations of specific metabolic pathways in
schizophrenia. J Neurosci. 22, 2718-29.)
In this study, we wished to determine whether transcript levels in more than 70
different gene groups involved in cellular metabolism, which could impact the quality
of neuronal communication, were altered in subjects with schizophrenia and whether
the effects on these gene groups were interrelated. Most of the metabolism gene groups
that we analyzed did not display significant differences in transcript levels between
schizophrenic and control subjects (manuscript Figure 1). However, five gene groups
did display significant alterations (p < 0.05) in transcript levels in five or more of the
10 array comparisons. These included the malate shuttle, transcarboxylic acid (TCA)
cycle, ornithine-polyamine, aspartate-alanine, and ubiquitin metabolism groups. Several
other gene groups also displayed significantly decreased expression in fewer than five
array comparisons. No metabolic gene groups, however, showed significant increases in
expression in more than two array comparisons. When analyzed across all subjects with
schizophrenia, the mean expression levels of each of the five most affected gene groups
were consistently and significantly decreased compared with matched controls
(manuscript Figure 1-2). In addition, analysis of the mean pairwise Z score distributions
for these gene groups revealed two distinct and highly correlated patterns of decreased
expression in the subjects with schizophrenia. The first of these patterns was present in
seven of 10 array comparisons with primary intercorrelations ranging from 0.77 to
0.99. The second pattern was present in two array comparisons (630c/566s and
604c/581s; r = 0.68). Only one array comparison failed to demonstrate significant
similarity with other array comparisons (558c/317s). To examine whether there were
interactions among the effects on different metabolic gene groups, we next computed a
correlation matrix using the
log of the p value for each gene group and performed a
varimax PCA on this matrix. PCA permits one to search for significant relationships
between multiple data sets and reduces the complexity of these relationships to a small
number of factors that best describe the variance of the data. The PCA we performed
produced eight factors that described >99.9% of the variance of the correlation matrix
for the 71 gene groups. The degree to which the effects on different gene groups are
related to each factor is provided by the oblique factor weights for each gene group,
21
which are the correlation of each variable with each factor. Examination of the oblique
factor weights in our PCA analysis (Figure 3) revealed that the effects on the malate
shuttle, TCA cycle, and ubiquitin groups were highly correlated (Factor 2). Factor 3, in
contrast, more accurately described the effects on aspartate-alanine and ornithinepolyamine metabolism. This analysis also revealed that a number of gene groups with
decreases in expression in fewer than five schizophrenic subjects had effects that were
correlated with those of the more significantly affected gene groups. For example, the
tyrosine and cysteine metabolism gene groups exhibited significant decreases in
expression in three schizophrenic subjects, with the effects on all 10 subjects highly
correlated with factor 2 (oblique factor weights 0.977 and 0.827, respectively).
Likewise, expression of glycolysis genes was also significantly decreased in three
schizophrenic subjects, with effects that were highly correlated with factor 3 (oblique
factor weight 0.805). Overlap in gene membership between different metabolic groups
is one possible explanation for the apparent similarity of statistical effects on different
gene groups; that is, a few genes whose changes were robust might impact multiple
gene groups. To further probe this issue, we examined the overlap in gene membership
among the most consistently affected gene groups. Interestingly, of the four genes
comprising the malate shuttle group that were present on the array, two genes were part
of the TCA group (n = 13 genes) and the other two genes were part of the aspartatealanine group (n = 16 genes). Each of these four shared genes was significantly
decreased in most schizophrenic subjects compared with controls. The ornithinepolyamine group (n = 18 genes) also shared two different genes with the aspartatealanine group. These particular genes, however, were not significantly affected in
schizophrenic subjects. The ubiquitin group (n = 47 genes) did not share any genes with
the other metabolic gene groups. These observations, combined with our analysis of the
statistical effects on different gene groups, indicate that simply sharing one or a few
genes is insufficient to explain the effects we described. In many cases, groups with a
high degree of overlap in membership do not have similar statistical effects (e.g.,
tyrosine and phenylalanine gene groups; ornithine-polyamine and urea cycle gene
groups). Conversely, many of the gene groups with the highest correlated effects do not
have any genes in common (e.g., ubiquitin and tyrosine; ubiquitin and malate shuttle).
22
Although small overlaps in group membership do not appear to produce correlated
effects on different gene groups, they do establish real biological links between them.
Of the five different metabolic cascades we identified as significantly affected in five or
more array comparisons, we were able to establish biological links between four of
these groups at the single gene level, with the lone exception being the ubiquitin gene
group. These relationships may have important biological significance (see Discussion).
The transcripts encoding MAD1, OAT, GOT2, and OAZIN were selected for additional
analysis using in situ hybridization. In situ hybridization analysis confirmed the
microarray finding that expression of each of these four genes was significantly
decreased (p < 0.05) in the PFC of the subjects with schizophrenia (Figure 4 in
manuscript). Moreover, there was no interaction between these decreases and other
subject characteristics, such as brain pH, PMI, or tissue storage time. The decreased
expression was present in the majority of the 10 subject pairs for each gene.
Figure 3. Correlations and connections between affected gene groups. A, To
estimate the mathematical relationships between the effects on different gene groups, the
normalized p values from were used to calculate a correlation matrix and perform a PCA.
The PCA revealed strong relationships between the effects on the malate shuttle, TCA
(citrate) cycle, and ubiquitin metabolism gene groups (Factor 2) and between aspartatealanine metabolism and ornithine-polyamine metabolism (Factor 3). Variance
proportions for factors 1-8 were 0.308, 0.233, 0.169, 0.091, 0.069, 0.055, 0.054, and
0.020, respectively. Figure 3a from (Middleton et al., 2002).
23
6.3. Altered expression of the 14-3-3 gene family in schizophrenia
(APPENDIX 3. - Middleton, F.A., Peng, L., Lewis, D.A., Levitt, P., Mirnics, K., 2005.
Altered expression of 14-3-3 genes in the prefrontal cortex of subjects with
schizophrenia. Neuropsychopharmacology. 30, 974-83.)
The explored microarray data set (Middleton et al., 2002; Mirnics et al., 2000) also
contained other promising leads, including expression changes in the transcripts
encoding the 14-3-3 protein family members. First we examined the expression levels
of all seven 14-3-3 genes in the PFC of 10 subjects with schizophrenia and their
matched controls (manuscript Table 1) using either cDNA microarrays or ISH.
According to the cDNA arrays, the majority of 14-3-3 genes exhibited moderate to
severe decreases in expression in schizophrenia, which were significant at the level of
the
entire
family
across
all
10
comparisons
(p<0.021)
(Figure
4).
Figure 4. Microarray analysis of 14-3-3 gene expression. The transcript levels of
six 14-3-3 genes were examined using high-density cDNA microarrays on postmortem samples of area 9 from 10 subjects with schizophrenia and their matched
controls. (a) This analysis revealed a consistent shift toward reduced expression of
most 14-3-3 transcripts. The fluorescent signal intensity for each matched subject pair
is plotted on a log scale. The dashed line indicates equal expression, whereas the solid
line indicates two-fold changes in expression. (b) The differences reported by the
microarray were converted into Z scores, which revealed that the most significantly
affected transcripts were 14-3-3 beta and zeta, followed by eta and theta. The mean Z
score for each transcript is indicated by the solid bar. Notably, only the beta and zeta
genes had mean Z scores less than -1.96 (the two-tailed cutoff for significance) across
the 10 subject pairs. Adapted from Figure 1 in (Middleton et al., 2005).
Having established that the expression of at least some 14-3-3 genes was significantly
altered in subjects with schizophrenia, we wished to determine whether this family
effect was related to the presynaptic and metabolic gene group effects we reported
24
previously (see (Middleton et al., 2002; Mirnics et al., 2000)). This analysis indicated
that the 14-3-3 gene group effect was most correlated with the presynaptic gene group
(PSYN) and Aspartate/Alanine gene group (Asp/Ala). The similarities among these
groups were evident in at least three of the four eigenvector dimensions. Using PCR,
we confirmed the robust expression of all seven 14-3-3 genes in area 9 of the human
PFC. Although most of the 14-3-3 genes were highly expressed in the brain, 14-3-3
sigma appeared to be less abundant by this analysis. However, as we had virtually no
data available on the cellular distribution of the 14-3-3 isoforms, we needed to establish
the anatomical distribution of the 14-3-3 family transcripts in the human cortex. ISH
was the method of choice in this regard: it could simultaneously provide a quantitative
validation of gene expression changes and assess the anatomical distribution of the
transcript in the studied tissue, yet it would not be dependent on putative tissue harvest
biases, RNA isolation, reverse transcription, or amplification procedures. Having first
established the specificity of each probe, and the lack of any apparent intra-areal effect,
we next sought to confirm the patterns of expression differences reported by the
microarrays for three of the 14-3-3 genes (beta, zeta, and eta) and also to examine the
potential expression abnormalities in the single 14-3-3 gene that was not represented on
the array (14-3-3 gamma). MANCOVA confirmed the significant decreased expression
in schizophrenia of two genes: beta -31.9% (F=5.74, p=0.048) and zeta -18.2%
(F=16.98, p=0.005). Sheffe's post hoc test confirmed the significance of the 14-3-3 beta
and zeta findings (p=0.0094 and 0.0007, respectively). To examine the potential for
antipsychotic medication to influence our results, we examined 14-3-3 gene expression
in four PFC areas 9 and 46 from male cynomolgus monkeys treated chronically with
haloperidol decanoate and benzotropine and matched control animals. We examined
the expression pattern of the two most robustly affected 14-3-3 genes in the human
studies (14-3-3 beta and zeta) in three to four sections from each animal. There was no
difference in 14-3-3 zeta expression, while 14-3-3 beta increased 28% (p<0.05). This
increase is in direct contrast to the decrease seen in the subjects with schizophrenia, and
appeared to occur primarily in pyramidal cell layers.
6.4. Increased expression of immune and chaperone genes in schizophrenia
25
(APPENDIX 4. - Arion, D., Unger, T., Lewis, D.A., Levitt, P., Mirnics, K., 2007.
Molecular evidence for increased expression of genes related to immune and
chaperone function in the prefrontal cortex in schizophrenia. Biol Psychiatry. 62, 71121.)
In this study, a Nimblegen custom microarray analysis allowed us to compare
the expression levels of > 1800 different genes, including > 400 control genes between
SCZ and CTR samples from area 9 of human postmortem PFC. The combination of
four independent analytical strategies reduced the dataset to 67 transcripts reporting
significant differential expression between PFC-harvested samples from SCZ and CTR
(|ALR| > .263; 1.2-fold and p < .05). Of these 67 genes, 22 genes (33%) were found
upregulated in SCZ samples compared with CTR samples with a mean ALR value of
.68 (1.6-fold; Table 2A and Table 2B in manuscript) and 45 (67%) were found
downregulated with a mean ALR value of .58 (1.5-fold; Table 2B). The 67
differentially expressed genes between SCZ and CTR samples represented a
heterogenic group of transcripts, some of which have been previously associated with
the pathophysiological processes in schizophrenia. Downregulation of transferrin (TF),
synapsin 2 (SYN2), synaptojanin 1 (SYNJ1), regulator of G-protein signaling 4
(RGS4),
mitogen-activated
protein
kinase
1
(MAPK1),
glutamic-oxaloacetic
transaminase 1 (GOT1), potassium channel, subfamily K, member 1 (KCNK1), and
mu-crystallin (CRYM) have all been previously reported to be downregulated in
subjects with schizophrenia(Aston et al., 2004; Hakak et al., 2001; Iwamoto and Kato,
2006; Middleton et al., 2002; Mirnics et al., 2000; Pongrac et al., 2002; Vawter et al.,
2002). Finally, this experimental series also confirmed and extended previous findings
of altered GABAergic system transcriptome in schizophrenia (Lewis et al., 2005b).
The analyses revealed an unexpected upregulation of genes related to immune function
and chaperone system. Of the 19 fully annotated transcripts that reported increased
expression in subjects with schizophrenia, 10 (> 50%) have been linked to functions in
immune/chaperone systems (Table 2A and Table 2B in manuscript). For example, in
subjects with schizophrenia, > 50% expression upregulation was observed for the
transcripts encoding 28kDa heat shock protein (HSPB1), 70kDa heat shock protein 1B
(HSPA1B), 70kDa heat shock protein 1A (HSPA1A), metallothionein 2A (MT2A),
interferon
induced
transmembrane
protein
26
1
(IFITM1),
interferon
induced
transmembrane protein 3 (IFITM3), CD14 antigen (CD14), chitinase 3-like1 (CHI3L1),
serine-cysteine protease inhibitor A3 (SERPINA3), and paired immunoglobulin-like
receptor beta (PILRB). These transcripts and their protein products are known to
exhibit increased expression in response to cellular stress and/or immune stimulation
(Chung et al., 2003; Chung et al., 2004; Gosslau and Rensing, 2000; Kohler et al.,
2003; Lewin et al., 1991; Mosser et al., 2000; Mousseau et al., 2000; Penkowa and
Hidalgo, 2001; Potter et al., 2001; Seidberg et al., 2003; Shiratori et al., 2004; Smith et
al., 2006; Takekawa and Saito, 1998; van Bilsen et al., 2004; Xia et al., 2006).
Figure 5. Hierarchical clustering
of gene expression changes.
Normalized log2 intensities were
clustered by Genes@Work in two
dimensions (horizontal: genes;
vertical: samples) on the basis of
Euclidian distance. Each colored
pixel represents a single gene
expression value in a single
subject. The color intensity is
proportional to its relative
expression level (green:
underexpressed; red:
overexpressed). Note that a subset
of subjects with schizophrenia (9
of 14, vertical clustering on the
left) shows a commonly altered
expression pattern that is distinct
from the rest of the schizophrenic
and control subjects. Expressed
sequence tags (ESTs) and
unannotated probesets were
removed from the dataset, whereas
γ-aminobutyric acid (GABA)ergic transcripts are denoted with
***. Adapted from Figure 3 in
(Middleton et al., 2005).
A two-way unsupervised
clustering (genes and subjects) of
the annotated gene probe signal
intensities resulted in the separation of the samples in two different clusters (Figure 5).
27
Of the 14 SCZ subjects, 9 clustered together, and these subjects clearly were major
contributors to the statistical significance across the dataset. In contrast, the remaining 5
SCZ subjects seemed to be indistinguishable from the control subjects, suggesting a
molecular sub-stratification within the SCZ samples. The clustering also yielded an
interesting separation along the dimension of expressed genes; here, the
immune/chaperone-related transcripts (MT2A, IFITM1, IFITM3, CHI3L1, SERPINA3,
CD14, TNC, GADD45G, HSPB1, HSPA1B, and HSPA1A) strongly clustered together
as a distinct sub-group, suggesting a potential co-regulation of these molecules.
The reported expression findings did not seem to be a result of administration of
chronic antipsychotic medication: subjects with schizophrenia that were not taking
medication at time of death showed similar expression changes to subjects that were
taking medication at the time of death. o validate the microarray findings, we selected
12 genes for real-time qPCR analysis. Six of these transcripts were overexpressed
(SERPINA3, CHI3L1, HSPB1, MT2A, IFITM1, IFITM3), whereas six other genes
were underexpressed (DIRAS2, CRYM, TF, MOG, MAPK1, RGS4) in the SCZ
samples. The agreement between the ΔΔCt of qPCR and DNA microarray ALR
datasets was striking: the differential expression for the 12 investigated genes was
correlated at r = .93 (p < .001) between the two methods (manuscript Figure 4).
The clustered data strongly suggested that the upregulated immune- and HSPrelated transcripts might represent a robust, selectively regulated gene expression
network. To test this, we performed an expression level cross-correlation of five
immune genes with three HSP family members. This correlational analysis was
performed across the whole dataset (28 human PFC samples) for the five probes/gene
(manuscript Figure 5). The data revealed that the expression levels for the five immune
markers tested (SERPINA3, IFITM1, IFITM3, CHI3L1, and CD14) were highly
correlated within the human PFC (max r = .73–.93, all p < .01). Similarly, the three
HSP family members (HSPA1B, HSPB1, and HSPA1A) showed a high within-group
correlation (max r = .85–.99, all p < .01). In contrast, only a weak-to-moderate
correlation was observed between the members of the chaperone and immune genes,
suggesting that the modulation of the changed immune-related genes and altered
chaperone-related transcripts might occur independently and not as a direct adaptive
relationship between these two systems.
28
6.5. GABA-ergic expression changes in schizophrenia
(APPENDIX 5. - Hashimoto, T., Arion, D., Unger, T., Maldonado-Aviles, J.G., Morris,
H.M., Volk, D.W., Mirnics, K., Lewis, D.A., 2008. Alterations in GABA-related
transcriptome in the dorsolateral prefrontal cortex of subjects with schizophrenia. Mol
Psychiatry. 13, 147-61.)
The investigated GABA-related transcripts fell into three categories. The first
category involved transcripts encoding presynaptic molecules that regulate GABA
neurotransmission, namely GAD67 and GAT1. The second category consisted of
transcripts encoding neuropeptides, including SST, neuropeptide Y (NPY) and CCK,
each of which is expressed and used as a neuromodulator by a subset of GABA
neurons. The third category corresponded to different GABAA receptor subunits,
including α1, α4, β3, γ2 and δ. SST mRNA exhibited the most robust decrease in the
schizophrenia group with four probes meeting the criteria and one of them detecting
ALR=-0.67, which corresponds to a 1.6-fold decrease in mRNA level. For each of
these 10 GABA-related transcripts, expression levels were decreased in the
schizophrenia subject in at least 10 of the 14 subject pairs. In order to verify the
decreased expression in schizophrenia of six GABA-related transcripts representing the
three categories, the expression levels of GAD67, SST, NPY, CCK, GABAA α1 and
GABAA δ mRNAs were quantified by real-time qPCR in the same 14 subject pairs. We
found significant mean decreases in the expression of GAD67 (12%, t26=-1.8, P=0.049),
SST (44%, t26=-3.3, P=0.003), NPY (28%, t26=-2.9, P=0.007), GABAA α1 (42%, t26=8.6, P<0.001) and GABAA δ (25%, t26=-2.6, P=0.010) mRNAs in the schizophrenia
group (Figure 3 or manuscript). For each of these mRNAs, the pattern of pair-wise
expression changes in –ddCT (equivalent to log2 ratio of schizophrenia to control
levels) were highly correlated across the 14 subject pairs for each of these five
transcripts (r=0.71, P=0.005 for GAD67 mRNA; r=0.79, P=0.001 for SST mRNA;
r=0.71, P=0.005 for NPY mRNA; r=0.53, P=0.053 for GABAA α1 mRNA and r=0.58,
P=0.029 for GABAA δ mRNA) (Figure 6). For CCK mRNA, in contrast to the
microarray findings, qPCR showed a trend for an increase in subjects with
schizophrenia (t26=1.4, P=0.095). As a further step of verification, and in order to
adjudicate the conflicting findings between the microarray and qPCR analyses for CCK
29
mRNA, we conducted quantitative in situ hybridization for SST and CCK mRNAs
using 23 subject pairs including 13 pairs (except for pair 7) used in the microarray and
qPCR analyses. The expression levels of SST and CCK mRNAs, as measured in the
full thickness of the gray matter, were significantly decreased by 36% (F1,20=15.4,
P=0.001) and 15% (F1,20=8.4, P=0.009), respectively, in the schizophrenia group
compared to the control group (manuscript Figure 5). The percentage expression
changes measured by in situ hybridization and qPCR were highly correlated for GAD67
(r=0.89, P<0.001) and SST (r=0.90, P<0.001) mRNAs across the 13 subject pairs
(manuscript Figure 4).
Figure 6. Correlations among expression changes assessed by DNA microarray,
qPCR and in situ hybridization. For GAD67 (left column) and SST (right column)
mRNAs, log2-transformed within-pair differences in mRNA levels determined by
microarray are plotted against –ddC T determined by qPCR (upper row) and percentage
within-pair differences determined by in situ hybridization are plotted against those
calculated from –ddC Ts (lower row). Adapted from (Middleton et al., 2005).
In order to assess the relationships among the expression of 10 GABA-related
transcripts with decreased expression levels in schizophrenia, we performed a
30
hierarchical cluster analysis of their expression data obtained by microarray across all
28 subjects. A dendrogram generated by this analysis indicated two major groups. The
first group included GAD67, GAT1, SST, NPY and CCK, each of which is expressed
by either all or subsets of GABA neurons. The second group consisted of four GABAA
receptor subunits, including α1, α4, γ2 and δ. GABAA receptor β3 subunits did not
cluster with any of the other transcripts. As the cluster analysis suggested strong coregulation of transcripts within each of the two groups, we next assessed the
relationships among their expression changes in schizophrenia by performing
secondary correlation analyses across the 14 subject pairs. In the first group, after
Bonferroni's correction, we observed significantly correlated expression changes for
GAD67 and SST (r=0.71, P<0.005), GAD67 and NPY (r=0.72, P<0.004), GAD67 and
CCK (r=0.84, P<0.001) and SST and NPY (r=0.81, P<0.001). Among GABAA
receptor subunits, there were significant correlations between α1 and γ2 (r=0.84,
P<0.001), α1 and δ (r=0.81, P<0.001) and γ2 and δ (r=0.85, P<0.001).
In order to evaluate the potential effect of long-term exposure to typical or
atypical antipsychotic medications, we examined mRNAs representative of the three
categories of changed GABA-related transcripts (that is GAD67, SST and GABAA α1)
in the DLPFC of monkeys chronically exposed to placebo, haloperidol or olanzapine
(n=6 per group) (Dorph-Petersen et al., 2005). The expression patterns of GAD67, SST
and GABAA α1 mRNAs, as revealed by in situ hybridization, did not differ across the
groups of monkeys (manuscript Figure 6).
6.6. RGS4 – a gene critically involved in schizophrenia?
(APPENDIX 6. - Mirnics, K., Middleton, F.A., Stanwood, G.D., Lewis, D.A., Levitt, P.,
2001. Disease-specific changes in regulator of G-protein signaling 4 (RGS4)
expression in schizophrenia. Mol Psychiatry. 6, 293-301.)
From the UniGem-V cDNA microarray dataset (Middleton et al., 2002;
Middleton et al., 2005; Mirnics et al., 2000; Pongrac et al., 2004), one strong single
gene finding emerged. Of the 4.8% of differentially expressed genes, regulator of Gprotein signaling (RGS4) transcript was the only gene decreased at the 99% CL in all
schizophrenic subjects. Across the six microarray comparisons these decreases ranged
from 50% to 84% (manuscript Figure 1).
31
To verify the microarray findings for the decrease in RGS4 expression, we
performed in situ hybridization on the PFC from ten subject pairs of schizohreniccontrol subjects. In control subjects, RGS4 labeling was heavy in the PFC (manuscript
Figure 2A), mimicking previously described labeling in the rat (Gold et al., 1997). The
labeling of large and small cells was most prominent in layers III and V, with sparse
labeling in the intervening granular layer IV. White matter labeling was virtually
indistinguishable from off-section background. Based on optical density analysis, 9/10
subject pairs exhibited a 10.2% to 74.3% decrease in PFC RGS4 expression
(manuscript Figure 2B).
Across all ten subject pairs, the mean level of RGS4
expression was significantly decreased (-34.4%) in the schizophrenic subjects
(F1,15=6.95; p=0.019). This decrease was comparable in schizophrenic subjects with
and
without
any
history
of
alcohol
32
abuse
(t-test;
p=0.125).
Figure 7. PFC RGS4 expression levels are decreased in nine of ten subjects with
schizophrenia (a) Emulsion-dipped in situ hybridization micrographs of PFC tissue
sections from a schizophrenic (622s) and matched control (685c) subject viewed under
darkfield illumination. Roman numbers denote cortical layers. Note the strong labeling
across all cortical layers except lamina IV, and the diminished labeling is absent. Scale
bar = 400 mum. (b) The in situ hybridization data from 10 PFC pairwise comparisons
were quantified using film densitometry. The x axis represents subject classes, the y axis
reports average film optical density from three repeated hybridizations, measured across
all layers. Lines connecting symbols indicate a matched subject pair. Hash marks indicate
group means. Note that in 10 PFC pairwise comparisons, nine schizophrenic subjects
showed RGS4 transcript reduction (mean = -34.5%; F1,15 = 6.95; P = 0.019). (c)
Subjects with MDD exhibit comparable RGS4 expression levels as their matched control
subjects (mean = +3.5%; F1,15 = 0.27; P 0.61). Adapted from Figure 2 in (Mirnics et al.,
2001f)
We next examined whether the decreased expression of RGS4 transcript was
associated with another psychiatric disorder. In situ hybridization analysis of PFC area
9 in ten subjects with MDD revealed no difference in mean levels of RGS4 (+3.5%)
expression (F1,15=0.27; p=0.61) compared to matched controls ( manuscript Figure 2C).
In our sample of 10 subjects with schizophrenia, two were not receiving
antipsychotic medications at the time of death (622s and 537s). Both of these subjects
still showed decreased expression of RGS4. Using an animal model (Pierri et al., 1999),
we probed the potential of haloperidol to directly modulate RGS4 transcript levels
(manuscript Figure 4). In situ hybridization analysis in four matched pairs of
chronically treated and control cynomolgus monkeys showed no difference in PFC
RGS4 expression (mean=+5.3%; p=0.26). Similarly, a cDNA microarray comparison in
one monkey pair confirmed that haloperidol treatment did not alter RGS4 gene
expression in the frontal cortex (0% change).
Next, to examine whether there is a more widespread cortical deficiency in
RGS4 transcript, we assessed by in situ hybridization RGS4 expression in visual (VC)
and motor (MC) cortex from the same 10 pairs of control and schizophrenic subjects. In
VC, RGS4 showed heavy labeling in the gray matter, with a very prominent bi-laminar
pattern in the supragranular and infragranular layers (mansucript Figure 5A), but sparse
labeling in layer IV. Combined RGS4 expression in the supragranular and infragranular
layers of VC was decreased by 32.8% (F1,15=8.24; p=0.012)(manuscript Figure 5B). In
MC, RGS4 expression was concentrated over cell-rich layers II-III and V-VI of both
33
control and schizophrenic subjects (manuscript Figure 6A). Layer IV is very attenuated
in MC, and exhibited virtually no RGS4 labeling. The mean RGS4 expression
(manuscript Figure 6B) was decreased by 34.2% across the 10 schizophrenic subjects
(F1,15=10.18; p=0.006). Within the subjects with schizophrenia RGS4 expression was
consistently decreased across the PFC, VC and MC (manuscipt Figure 7). Factor
analysis of the pairwise differences in RGS4 gene expression across the three different
cortical areas for all 9 common schizophrenic and control subject pairs revealed that
over 84% of the total variance in expression was accounted for by diagnosis (variance
proportion
=
0.848,
eigenvalue
34
=
2.544,
p
=
0.001).
7. DISCUSSION
Presynaptic gene expression changes and a synaptic-neurodevelopmental model of
schizophrenia
In light of the observed presynaptic expression deficits in schizophrenia, we
propose a model suggesting that different sequence mutations or polymorphisms in
genes related to synaptic communication, combined with other factors, might lead to
the shared clinical manifestations of schizophrenia (Mirnics et al., 2001c). The basic
tenets of this model have four components:
1. The etiology of schizophrenia involves a polygenic pattern of inheritance, resulting
in altered function of proteins that control the ‗mechanics‘ of synaptic transmission.
2. The heterogeneity in expression defects within the PSYN group reflects distinct
adaptive capacities of different populations of neurons. The alterations in RGS4
expression could reflect a primary gene defect or a postsynaptic adaptation in an effort
to enhance neurotransmission, which thus compensates for the deficits in PSYN gene
function.
3. Deficits in PSYN and RGS4 gene expression might affect the extended postnatal
developmental process of synapse formation and pruning (Bourgeois et al., 1994;
Huttenlocher, 1979), which ultimately provides a link to the neurodevelopmental
timecourse of schizophrenia (Weinberger, 1995). In the model (Figure 8), impaired
synaptic transmission in subjects with schizophrenia is present from early life (probably
in a subset of synapses), but the exuberant synaptic connections that form during the
third trimester of gestation and early childhood compensate for a functional synaptic
impairment. These exuberant synapses are pruned by late adolescence, uncovering the
existing synaptic impairment. Inadequate synaptic activity might lead to overpruning
and manifestations of the symptoms of schizophrenia.
4. Independent of their possible roles in causing the disease, the deficits in PSYN and
RGS4 expression have physiological and behavioral consequences relevant to the
pathophysiology of schizophrenia, and could help explain many of the cognitive
deficits that arise from prefrontal cortex dysfunction in schizophrenics. Knockout
studies in mice, in which specific members of the PSYN group have been deleted,
reveal the complexity of physiological deficits that occur because of presynaptic
dysfunction (Jahn and Sudhof, 1999; Rosahl et al., 1995; Schluter et al., 1999; Verhage
35
et al., 2000), suggesting that the experimental data support the synapticneurodevelopmental model of schizophrenia that we propose.
36
Figure 8. A synaptic–neurodevelopmental model of schizophrenia. This model
proposes an altered development of cortical circuits as a result of disruption in SYN and
regulator of G-protein signaling 4 (RGS4) function. During childhood, an over-abundance
of excitatory and inhibitory synapses is produced in the cerebral cortex (Bourgeois et al.,
1994; Huttenlocher, 1979). As a result of molecular defects and altered SYN or RGS4
gene function, synaptic drive is decreased (fewer +, indicating excitatory signaling and −,
indicating inhibitory signaling) in subjects who will develop schizophrenia. Although
core complex formation, release or recycling of individual vesicles (SYN vesicles shown
by green circles), or both, can be impaired, the physiological deficiency remains clinically
asymptomatic until synaptic pruning ends. Before pre-adolescence, the initial
overproduction of synapses compensates for functional deficits, thus retaining circuit
function above the disease threshold. During the period of normal pruning, from
adolescence through to puberty, altered synaptic function might contribute to abnormal
decreases of specific classes of synapse (for example, on basilar dendrites). In addition,
continued decrease in synaptic drive might result in further loss of both inhibitory and
excitatory synapses. As a result, the physiological synaptic ‗buffer‘ is lost, resulting in the
subthreshold state for normal circuit function. Then, the decreased number of synapses is
not able to compensate for impaired synapse function, and the clinical manifestations of
the disease emerge. In an attempt to compensate for inefficient presynaptic release during
development, postsynaptic changes might follow, including (but not limited to)
downregulation of RGS4. This could result in a compensatory increase in duration of
signaling through G-protein-coupled receptors. However, these adaptational mechanisms
might not be sufficiently effective (or even desirable), and, as a result of the impaired
synaptic release (and consequent lack of normal synaptic drive), overpruning of the
presynaptic neuropil could occur. Adapted from Figure 4 in (Mirnics et al., 2001c).
Metabolic alterations in schizophrenia at the molecular level
Our analysis has revealed a consistent and significant decrease in the expression
of genes encoding proteins involved in the mitochondrial malate shuttle, the
transcarboxylic acid cycle, aspartate and alanine metabolism, ornithine and polyamine
metabolism, and ubiquitin metabolism. Furthermore, because of the important
relationship that exists between cellular metabolism and synaptic activity in the brain,
these findings converge with our studies demonstrating reduced expression of gene
groups involved in presynaptic function and the reduced expression of RGS4, a protein
involved in postsynaptic signaling. Together, the data suggest that deficits in neuronal
communication may contribute to the core pathophysiology of schizophrenia.
At the chromosomal level, many of the individual metabolic transcripts we identified as
abnormally expressed in subjects with schizophrenia are located on cytogenetic loci that
are directly linked or associated with the disorder, including 1q32-44, 5q11-13, 8p2221, 17q21, and 22q11-13 (Thaker and Carpenter, 2001). In addition, previous reports
37
suggest that mitochondrial genes are expressed at abnormal levels in schizophrenia
(Marchbanks et al., 1995; Maurer et al., 2001; Mulcrone et al., 1995; Prince et al.,
1999; Whatley et al., 1996). Thus, it is possible that some metabolic-related genes may
prove to be bona fide susceptibility genes. However, whether these transcriptional
changes in metabolic gene groups reflect primary or secondary changes, they clearly
have the potential to alter neuronal metabolism and activity, thereby contributing to
defects in neuronal communication.
Our findings indicate that a number of biologically related and mitochondriadependent processes are affected in schizophrenia, with the altered expression of the
malate shuttle genes that is perhaps the most intriguing feature of the dataset. In a study
published over 35 years ago, serum malate dehydrogenase activity was reported to be
significantly diminished (~25%) in 50 subjects with schizophrenia compared with
10 controls (Burlina and Visentin, 1965). These findings are consistent with our data on
the decreased expression of MAD1 in schizophrenia. The potential biological
consequences of a decrease in malate dehydrogenase activity, and a general decrease in
the activity of the malate shuttle, are quite significant. Namely, one of the most
important functions of the malate shuttle is to transfer hydrogen ions [in the form of
reduced nicotinamide adenine dinucleotide (NADH)] from the cytoplasm into the
mitochondria. Therefore, schizophrenia may be associated with increased [H+]reducing equivalents in the cytosol. Increases in cytosolic [H+] are known to decrease
the activity of the major rate-limiting enzyme of glycolysis, 6-phosphofructokinase.
Thus, decreased malate shuttle activity in the PFC of subjects with schizophrenia could
produce secondary effects on the rate of glycolysis, perhaps contributing to the reduced
glucose use observed in the PFC of these subjects while they are engaged in cognitive
tasks (Andreasen et al., 1992; Berman et al., 1986; Buchsbaum et al., 1992; Weinberger
et al., 1986).
In addition, the malate shuttle system also acts in concert with a malate-citrate
exchange system that is part of the TCA cycle and serves as an entry point for fatty acid
synthesis. In fact, the malate shuttle system and the TCA system both contain the gene
for MAD1. If the malate shuttle activity is reduced and the activity of the malate-citrate
exchange system is reduced as well, one might expect to find a loss in cytosolic citrate
and decreased activity of other TCA proteins. In our data, we found a reduction in the
38
expression of at least three other TCA genes in subjects with schizophrenia: isocitrate
dehydrogenase 3, ATP citrate lyase, and dihydrolipoamide dehydrogenase. Together,
these findings suggest that TCA metabolism is significantly affected in schizophrenia.
Given the role that TCA metabolism plays in fatty acid synthesis, these findings may
help explain the reductions in markers of fatty acid metabolism that have been reported
in several studies of subjects with schizophrenia (Fenton et al., 2000; Keshavan et al.,
2000; Pettegrew et al., 1991; Stanley et al., 2000; Yao et al., 2002; Yao et al., 2000).
Finally, decreased malate shuttle activity could directly alter cytosolic levels of
aspartate and glutamate, given the role that the malate shuttle plays in the exchange of
cytosolic malate for mitochondrial -ketoglutarate and then (after transamination of ketoglutarate into glutamate) the exchange of cytosolic glutamate for mictochondrial
aspartate. Alterations in cytosolic aspartate and glutamate levels could affect not only
the metabolism of these molecules but also ornithine-polyamine metabolism – also
present in our dataset.
In a previous study, we found that reduced expression of transcripts encoding
synaptic proteins was a common feature of subjects with schizophrenia (Mirnics et al.,
2000; Mirnics et al., 2001c). Interestingly, the vast majority of measurable metabolic
flux in the brain occurs at synapses (Nudo and Masterton, 1986; Sokoloff et al., 1977).
Indeed, many of the processes that are essential to synaptic vesicle docking and release
are energy dependent and require high levels of ATP production. In our previous study,
two of the most consistently affected genes within the presynaptic group (Netylmalemide-sensitive factor and vacuolar ATPase) were ATPases that use the energy
provided by synaptically localized mitochondria to help maintain a readily releasable
pool of synaptic vesicles. Together with our present results, these findings indicate that
neurons within the PFC of schizophrenic subjects will likely have difficulty meeting the
normal metabolic demands placed on them by neural activity, leading to phenotypic
manifestations of the disease.
Altered transcript expression of the 14-3-3 gene family in schizophrenia
Our microarray studies of the PFC in subjects with schizophrenia revealed three
principal findings regarding the expression of 14-3-3 gene family members: 1) of the
six 14-3-3 genes represented on the arrays (beta, eta, epsilon, sigma, theta, and zeta),
39
five were detectable in all 10 pair-wise comparisons, with one (sigma) detectable in
7/10 comparisons; 2) all of the 14-3-3 family members except for sigma were
decreased in most comparisons; 3) at the group level, these genes were significantly
changed in all 10 arrays (p<0.021), representing the single most-changed gene group
we have identified to date. The expression changes reported on the microarrays were
confirmed by ISH, which revealed decreases for zeta and beta>eta>gamma, supporting
the microarray findings.
As the 14-3-3 family of proteins plays an integral role in regulating many
aspects of cellular function in the brain, including signal transduction, neurotransmitter
metabolism, and mitochondrial function (there are at least 100 different binding
partners for these proteins that have been identified), it is difficult to precisely define
the appropriate context in which to view 14-3-3 gene alterations in schizophrenia. Of
the more than 300 functionally defined gene groups that we have studied in the same
cohort of subjects, we find compelling evidence that the magnitude and statistical
significance of the 14-3-3 gene family effect closely parallels (and is highly correlated
with) the changes in presynaptic secretory function and Aspartate/Alanine metabolism
gene groups. Thus, we tentatively suggest that at least in schizophrenia, the most
critical functions, that an effect of the 14-3-3 gene family might exert, are those related
to neurotransmission and neurotransmitter metabolism. Indeed, the 14-3-3 gene familiy
was originally characterized by the descriptive title of tyrosine monooxygenase/tryptophan monooxygenase-activating proteins. The decreased synthesis of multiple
14-3-3 transcripts should produce in the cell a decrease in the amount of dopamine
available for neurotransmission—an event that could influence both excitatory and
inhibitory neurotransmission via D1 and D2 receptor classes. The decreased aspartate
metabolism could reduce the amount of excitatory neurotransmitters (including both
aspartate and glutamate) available for release, as well as the bioenergetic properties of
mitochondria localized to the synapse. The decrease in the presynaptic secretory
machinery group genes that also occurs in these subjects suggests that the substrates for
neurotransmission as well as the machinery for neurotransmission are collectively and
profoundly altered in schizophrenia.
Our observations of altered expression of the 14-3-3 gene group may also be of
interest in relationship to the disturbances in markers of GABA neurotransmission in
40
the PFC of subjects with schizophrenia. The 14-3-3 proteins (including the zeta and eta
forms) have been shown to associate and co-localize with the C-terminus of presynaptic
GABA(B)R1 receptors in rat brain preparations and tissue cultured cells (Couve et al.,
2001). Furthermore, in the presence of 14-3-3, the authors reported a reduction in the
dimerization of GABA(B)R1 and R2 subunits. The coupling of GABA(B)R2 to R1
permits surface expression of GABA(B)R1 and the appearance of potassium and
calcium currents (Jones et al., 2000). Thus, it is possible that the reduction in 14-3-3
gene expression may represent a compensatory mechanism for overcoming a reduction
in GABA signaling that is present in the PFC of subjects with schizophrenia.
Immune-chaperone dysfunction in schizophrenia
Detailed analysis of the custom DNA microarray data from a cohort of 14
matched pairs of CTR and SCZ brain samples from area 9 of human PFC revealed
unexpected and strongly correlated upregulation of a subset of genes involved in
immune/chaperone function. The immune/chaperone signature was primarily present in
a subset of subjects with schizophrenia. We believe that the chaperone and immune
changes are of common origin and causally interrelated, and they will be discussed in
this context.
The neuroimmune hypothesis of schizophrenia has been debated for decades
(Giovannoni and Baker, 2003; Hanson and Gottesman, 2005; Jones et al., 2005; Muller
et al., 2000; Muller and Schwarz, 2006; Patterson, 2002; Rothermundt et al., 2001;
Strous and Shoenfeld, 2006), albeit replication across different cohorts of patients has
been elusive (Rothermundt et al., 2001). Nevertheless, the combined evidence suggests
an infective-immune predisposition to schizophrenia, and that this predisposition is
likely to interact with genetic susceptibility for developing the disease. In this context,
the changes related to immune/chaperone functions can represent either a response to
an ongoing infective-immune challenge or a long-lasting signature of an immune
system challenge that may have acted during brain development, which in the human
extends in a lengthy fashion from 1st trimester through puberty. Most of the studies of
schizophrenia to date suggest that the observed neuroimmune changes are a longlasting consequence of a previous infective-immune challenge (for review see (Nawa
and Takei, 2006; Sperner-Unterweger, 2005)). Here, we addressed this more directly by
41
determining that there is no change in the transcript levels of OAS1 and INFγ, two
critical markers of acute immune response (Rothwell and Hopkins, 1995; Rothwell et
al., 1996). Thus, we also favor the interpretation of a developmentally-based, long-term
alteration in the transcriptome of genes related to immune/chaperone function, although
one must be aware that certain pre-mortem life stressors and adverse socio-economic
conditions, which are highly prevalent in patients with schizophrenia, may also
contribute to some of the observed expression changes.
Long-term consequences of early immune challenge are not unprecedented.
Studies in rodents that undergo prenatal or perinatal exposure to immune challenges
(such as maternal exposure to polyriboinosinic-polyribocytidilic acid - poly(I:C), a
synthetic cytokine inducer), high levels of pro-inflammatory cytokines or viral
infections develop post-adolescent behavioral deficits that are similar in nature to
clinical manifestations in schizophrenia (Ashdown et al., 2006; Meyer et al., 2005;
Tohmi et al., 2004; Zuckerman and Weiner, 2005). In the mouse, prenatal exposure in
mid-pregnancy to poly(I:C) reduces the number of reelin positive cells in hippocampus
(Meyer et al., 2006). Furthermore, poly(I:C) administration also causes increased
dopamine turnover, prepulse inhibition deficits and cognitive impairments in the adult
offspring, and the latter is improved by administration of clozapine (Ozawa et al.,
2006). Finally, in a rat model, poly(I:C) administration during pregnancy also produced
long-lasting pathophysiological changes that are also observed in schizophrenia,
including dopaminergic hyperfunction and loss of latent inhibition (Zuckerman et al.,
2003).
In view of these data, we propose that the transcriptome signature of altered
genes related to immune/chaperone function may be a consequence of early life TNF-α,
IL-1, IL-6 and/or INFγ brain activation. In this proposed mechanism, the elevated proinflammatory cytokine levels during late embryonic development or perinatal period
could not only impair normal differentiation and/or refinement of neural connectivity,
but also leave behind a specific immune/chaperone signature primarily consisting of
altered IFITM, SERPINA3 and HSP transcript increases.
How does elevation of these immune/chaperone system molecules contribute to
the symptoms of schizophrenia? We speculate that this immune/chaperone signature
extends beyond a correlation with an early environmental insult and may actively
42
contribute to the clinical features of the illness. Many immune/chaperone genes are
known to be essential for the normal functioning of the CNS, and immune function
genes are capable of altering cognitive performance (Heyser et al., 1997; Hoffman et
al., 1998; Wilson et al., 2002; Ziv et al., 2006). The causality between the
immune/chaperone gene expression changes and altered cognitive performance in
schizophrenic patients needs to be addressed in comprehensive clinical studies and in
animal models.
GABA-system related expression changes in schizophrenia
Our observation of reduced levels of GAD67 and GAT1 mRNAs in the DLPFC
of the same subjects with schizophrenia and the correlation between these changes
across pairs (r=0.62, P<0.018), are consistent with previous studies indicating that both
the synthesis and presynaptic reuptake of GABA are reduced in the subset of GABA
neurons that express PV (Hashimoto et al., 2003; Lewis et al., 1999; Lewis et al.,
2005a; Volk et al., 2001). Because a primary reduction in GAT1 does not induce
changes in the levels of GAD (Jensen et al., 2003), the downregulation of GAT1, which
prolongs the activity of synaptically released GABA (Overstreet and Westbrook, 2003),
is likely to be a compensatory response to decreased GABA synthesis in these neurons
(Lewis et al., 2005a).
The highly significant correlations among the gene expression changes for
GAD67, SST and NPY suggest that GAD67 mRNA expression is also decreased in
another subset of GABA neurons that express both SST and NPY. In the cortex, SST is
expressed by the majority of calbindin-containing GABA neurons, a separate
population from those that express PV or CR (Conde et al., 1994; Gonzalez-Albo et al.,
2001; Kubota et al., 1994), and a subset of SST-containing neurons largely overlaps
with the majority of NPY-containing neurons (Hendry et al., 1984; Kubota et al.,
1994). The localization of SST- and NPY-containing neurons predominantly in layers
II and V (Hendry et al., 1984; Kubota et al., 1994) may account for the deficits in
GAD67 mRNA expression in these layers, which could not be explained by the
expression deficits in PV-containing neurons(Volk et al., 2001). Because SST- and
NPY-containing neurons selectively target distal dendrites of pyramidal neurons
(Gonchar et al., 2002; Hendry et al., 1984; Kawaguchi and Kubota, 1996; Kubota et al.,
43
1994),
these
coordinated
gene
expression
changes
suggest
that
GABA
neurotransmission is altered at the dendritic domain of pyramidal neurons in the
DLPFC of subjects with schizophrenia. Furthermore, given the functions of SST and
NPY as inhibitory neuromodulators (Baraban and Tallent, 2004), their gene expression
deficits indicate the presence of additional mechanisms affecting inhibitory regulation
of DLPFC circuitry in schizophrenia.
CCK is heavily expressed in GABA neurons that do not contain PV or SST
(Kawaguchi and Kondo, 2002; Lund and Lewis, 1993) and is expressed at low levels in
some pyramidal neurons (Schiffmann and Vanderhaeghen, 1991). The clustering of
CCK with GAD67 and their highly correlated expression changes suggest a deficit in
GABA synthesis in CCK-containing GABA neurons. The axon terminals of CCKpositive basket neurons converge with those from PV-containing neurons on the
perisomatic domain of pyramidal neurons (Kawaguchi and Kondo, 2002). Thus,
alterations in GABA regulation on this domain of pyramidal neurons appear to involve
at least two subpopulations of GABA neurons in the DLPFC of subjects with
schizophrenia.
GABAA receptors containing the α1 and γ2 subunits are enriched in
postsynaptic sites where they mediate phasic inhibition (Mangan et al., 2005; Wei et
al., 2003). In contrast, GABAA receptors containing the δ subunit, which is often
coassembled with the α4 subunit in the forebrain, are selectively localized to
extrasynaptic sites (Farrant and Nusser, 2005; Mangan et al., 2005; Wei et al., 2003).
These extrasynaptic receptors, which have a high sensitivity to GABA and thus can be
activated by ambient GABA molecules in extracellular space, mediate tonic inhibition
which reduces the effects of synaptic inputs over time (Farrant and Nusser, 2005;
Hendry et al., 1994). Given the predominant localization of the α1, γ2 and δ subunits to
dendrites (Fritschy and Mohler, 1995; Hendry et al., 1994), the highly correlated
expression deficits for these transcripts suggest coordinated downregulation of GABAA
receptors mediating phasic and tonic inhibition in the dendritic domain of DLPFC
pyramidal neurons in schizophrenia.
Our findings provide a clearer picture of the nature of altered GABA
neurotransmission in the DLPFC of subjects with schizophrenia, which is summarized
in Figure 9. Previous studies demonstrated mRNA expression deficits in PV-
44
containing, but not in CR-containing, GABA neurons in the DLPFC of subjects with
schizophrenia (Hashimoto et al., 2003; Volk et al., 2001). These changes were
associated with the downregulation of GAT1 in the presynaptic terminals of PVcontaining chandelier neurons (Pierri et al., 1999) and the upregulation of GABAA
receptor α2 subunit in the postsynaptic axon initial segments of pyramidal neurons
(Volk et al., 2002). Our current study suggests alterations in GABA neurotransmission
provided by two additional subpopulations of DLPFC GABA neurons: SST- and NPYcontaining neurons and CCK-containing basket neurons, which predominately target
the distal dendrites and cell bodies of pyramidal neurons, respectively. Furthermore,
gene expression deficits for α1 and γ2 GABAA receptor subunits and for δ and α4
subunits suggest decreased synaptic (phasic) and extrasynaptic (tonic) inhibition,
respectively, in pyramidal neuron dendrites (Figure 9, upper enlarged square). GABAmediated regulation at the dendritic domain of pyramidal neurons is important for the
selection and integration of excitatory inputs from different cortical and subcortical
areas, whereas GABA inputs at the perisomatic domain, including the axon initial
segment and cell body, are critical for control of the timing and synchronization of
pyramidal neuron firing (Markram et al., 2004; Somogyi and Klausberger, 2005).
Therefore, the findings suggest altered GABA-mediated regulation of both inputs to
and outputs from DLPFC pyramidal neurons in subjects with schizophrenia. These
alterations are certain to affect information processing in DLPFC circuitry and thus are
likely to be major contributors to working memory impairments in schizophrenia.
45
Figure 9. Schematic summary of alterations in GABA-mediated circuitry in the
DLPFC of subjects with schizophrenia. Altered GABA neurotransmission by PVcontaining neurons (green) is indicated by gene expression deficits in these neurons
and associated changes in their synapses, a decrease in GAT1 expression in their
terminals and an upregulation of GABAA receptor α2 subunit at the axon initial
segments of pyramidal neurons (lower enlarged square). Decreased expression of
both SST and NPY mRNAs indicates alterations in SST and/or NPY-containing
neurons (blue) that target the distal dendrites of pyramidal neurons. These changes
appear to be accompanied by a downregulation of GABAA receptor subunits,
including the α1 and γ2 subunits present in receptors that mediate synaptic (phasic)
inhibition and the α4 and δ subunits present in receptors that mediate extrasynaptic
(tonic) inhibition (upper enlarged square), in dendrites of pyramidal neurons.
Decreased CCK mRNA levels indicate an alteration of CCK-containing large basket
neurons (purple) that represent a separate source of perisomatic inhibition from PVcontaining neurons. Gene expression in CR-containing GABA neurons (red) does not
seem to be altered. Other neurons, such as PV-containing basket neurons, are not
shown because the nature of their involvement in schizophrenia is unclear. G, generic
GABA neuron; P, pyramidal neuron; I–IV, layers of the DLPFC. Adapted from
Figure 7 in (Hashimoto et al., 2008a).
46
8. CONCLUSIONS
Major conclusions:
1) Postmortem human brain tissue can be analyzed by high throughput gene
expression profiling, and this analysis
gives us unique insight into the
pathophysiological disturbances that occur in the PFC of patients with schizophrenia
(Mirnics et al., 2004; Mirnics et al., 2006). This is especially important as none of the
existing animal models are able to fully mimic the ongoing pathophysiological
conditions in brain of diseased individuals (Insel, 2007).
2) Schizophrenia is characterized by complex gene expression changes in the
human PFC (Horvath and Mirnics, 2009; Mirnics et al., 2004; Mirnics and Pevsner,
2004; Mirnics et al., 2006). These expression changes appear to be primarily related to
the disease process itself, and not a result of medication, sample bias, or quality of
postmortem tissue. We uncovered systemic gene expression deficits related to
presynaptic markers (Mirnics et al., 2000; Mirnics et al., 2001c), energy metabolism
genes (Middleton et al., 2002), 14-3-3 family proteins (Middleton et al., 2005), and
GABA-system transcripts (Hashimoto et al., 2003; Hashimoto et al., 2008a; Hashimoto
et al., 2008b). Furthermore, we reported a strong, sustained and complex induction of
immune system and chaperone mRNAs, suggesting that schizophrenia has in
important, ongoing and potentially progressive neuroinflammatory component (Arion
et al., 2007). Many of these findings have been also replicated by other investigators,
using independent sample cohorts.
3) The molecular variability between subjects with schizophrenia is tremendous
(Mirnics and Lewis, 2001; Mirnics et al., 2001c; Mirnics et al., 2001d; Mirnics, 2002;
Mirnics, 2006), and this is very likely reflected in the vastly different clinical
presentations of patients. Yet, the various genetic disturbances are likely to converge
and have a sustained effect on well-defined biological functions, resulting in common
gene expression patterns – even when the genetic susceptibility to disease is quite
different (Mirnics et al., 2001c; Mirnics et al., 2004; Mirnics et al., 2006).
4)
It appears that important relationships may exist between altered gene
expression and genetic susceptibility to psychiatric disorders (Mirnics and Pevsner,
2004; Mirnics et al., 2006). For example, RGS4 (Chen et al., 2004; Chowdari et al.,
47
2002; Mirnics et al., 2001f; Morris et al., 2004; Williams et al., 2004), GAD67
(Addington et al., 2005; Volk et al., 2000), DTNBP1(Straub et al., 2002; Weickert et
al., 2004), NRG1 (Hashimoto et al., 2004; Petryshen et al., 2005; Stefansson et al.,
2002) and GABRAB2 (Ishikawa et al., 2004; Lo et al., 2004) show expression
alterations in the postmortem brain of subjects with schizophrenia, and these genes
have been also implicated as putative, heritable schizophrenia susceptibility genes.
Thus, we propose that for some genes, altered expression in the postmortem human
brain may have a dual origin: polymorphisms in the candidate genes themselves or
upstream genetic-environmental factors that converge to alter their expression level.
Based on the currently available data, we suggest the hypothesis that certain gene
products, which function as ―molecular hubs‖, commonly show altered expression in
psychiatric disorders and confer genetic susceptibility for one or more diseases
(Mirnics and Pevsner, 2004; Mirnics et al., 2006; Winden et al., 2009).
5) It appears that RGS4 is one of the genes critically involved in the
pathophysiology of schizophrenia. First, RGS4 mRNA and protein expression are both
reduced in the PFC of subjects with schizophrenia (Erdely et al., 2006; Mirnics et al.,
2001f). Second, specific genetic variants in the promoter region of RGS4 appear to
predispose to developing schizophrenia (Chen et al., 2004; Chowdari et al., 2002;
Lipska et al., 2006; Morris et al., 2004; Williams et al., 2004). Third, genetic variants of
RGS4 predict brain structure (Prasad et al., 2005), PFC function and connectivity
(Buckholtz et al., 2007; Stefanis et al., 2008), and cognitive performance (Buckholtz et
al., 2007; Prasad et al., 2009). Finally, the same RGS4 SNP variant is predictive of
certain treatment responses (Campbell et al., 2008; Lane et al., 2008). These combined
data suggest that RGS4 is an appealing, knowledge-based therapeutic target for
developing novel medications for treatment of schizophrenia.
Minor conclusions:
1) The outcome high-throughput gene expression profiling experiments (e.g.
DNA microarray studies) greatly depends on the platform used and analytical
employed analytical procedures (Hollingshead et al., 2005). The inconsistency between
various studies can be, at least partially, explained by these methodological differences.
48
2) Negative data in the DNA microarray experiments should not be interpreted
as an ―absence of gene expression change‖ (Mirnics, 2002; Mirnics, 2006; Mirnics et
al., 2006; Mirnics, 2008). Due to the complexity of array, probes and hybridization
conditions some ―real‖, biologically important expression changes remain hidden from
us in these experiments. This applies is in particular to genes that have a low level of
expression in the studied tissue.
3) All DNA microarray data should be considered preliminary, and if possible,
replicated on a new, independent cohort with a different method (e.g. qPCR) (Mirnics,
2002; Mirnics, 2006; Mirnics et al., 2006; Mirnics, 2008).
4) Appropriate subject matching is essential for a meaningful experimental
outcome. Sex, postmortem interval, drug abuse, smoking, age, race and many other
factors have strong influence on gene expression measurements (Mirnics, 2002;
Mirnics, 2006; Mirnics et al., 2006; Mirnics, 2008).
5) Using DNA polymer microarray probes improve sensitivity of transcriptome
profiling experiments (Hollingshead et al., 2006).
6) DNA microarray transcriptome profiling is a bulk assessment method. It does
not provide information about where the expression change occurs. Is gene X reduced
in both projection neurons and interneurons? Is it reduced in all cortical laminae? To
answer these questions, one must resort to a secondary verification, such as in situ
hybridization (Mirnics, 2002; Mirnics, 2006; Mirnics et al., 2006; Mirnics et al., 2008).
7) Some of the DNA microarray-uncovered gene expression changes are
characteristic of more than one human brain disorder (Mirnics, 2001; Mirnics et al.,
2004; Mirnics et al., 2006). Yet, the same gene expression change, present is various
disorders, still might to be essential for the specific pathophysiology – it might occur in
a different brain region, different cell type or the change can have a different timecourse.
8) We found across our studies that in the adult human PFC >90% of mRNA
expression changes also result in changes at the protein level. However, we have no
way of knowing if there are meaningful protein level changes that are not defined by
mRNA changes. This important question would need to be addressed by combined
proteome–transcriptome analyses in the future.
49
9) Analysis of any DNA microarray dataset should be always performed both at
the individual gene level and at a pathway level, using appropriate false discovery
estimates (Lazarov et al., 2005; Unger et al., 2005).
9. SUMMARY
Schizophrenia is a complex and devastating brain disorder that affects 1% of the
population and ranks as one of the most costly disorders to afflict humans. The etiology
of schizophrenia remains elusive but appears to be multifaceted, with genetic,
nutritional, environmental and developmental factors all implicated. The development
of novel tools of functional genomics enables us to approach questions addressing the
etiology of complex psychiatric disorders from a novel, hypothesis-free, data-driven
angle. As the prefrontal cortex dysfunction is a hallmark of the disease, using various
DNA microarray platforms we performed a transcriptome profiling of the human
postmortem prefrontal cortex of subjects with schizophrenia and matched controls.
Data were validated by real-time quantitative PCR and in situ hybridization. Our results
show that 1) transcripts encoding proteins involved in the regulation of presynaptic
function were decreased in all subjects with schizophrenia, in a subject-specific pattern
(Mirnics, K., et al., 2000. Neuron. 28, 53-67); 2) there is a highly specific pattern of
metabolic alterations in the PFC of subjects with schizophrenia (Middleton, F. A., et al.,
2002, J Neurosci. 22, 2718-29); 3) the majority of 14-3-3 gene family exhibited
statistically significant decreases in expression in schizophrenia (Middleton, F. A., et
al., 2005. Neuropsychopharmacology. 30, 974-83); 4) there was a systemic repression
of GABA transcripts in diseased subjects (Hashimoto, T., et al., 2008. Mol Psychiatry.
13, 147-61) and 5) expression of genes related to immune and chaperone function was
increased in individuals with the disease (Arion, D., et al., 2007. Biol Psychiatry. 62,
711-21). Furthermore, we established RGS4 as a gene critically involved in the
pathophysiology of schizophrenia (Mirnics, K., et al., 2001. Mol Psychiatry. 6, 293-301
and Chowdari, K. V., et al., 2002. Hum Mol Genet. 11, 1373-80). These data suggest
that schizophrenia is a coordinated dysfunction of multiple transcriptional networks,
and allows identification of convergence points within these disrupted networks
50
(„disease-related molecular hubs‖). We believe that this novel information will
ultimately lead to identification of new, knowledge-based therapeutic targets for
schizophrenia.
51
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11. CANDIDATE’S PUBLICATIONS RELATED TO THESIS
Arion D, Unger T, Lewis DA, Levitt P, Mirnics K. (2007) Molecular evidence for
increased expression of genes related to immune and chaperone function in the
prefrontal cortex in schizophrenia. Biol Psychiatry, 62: 711-721.
Arion D, Unger T, Lewis DA, Mirnics K. (2007) Molecular markers distinguishing
supragranular and infragranular layers in the human prefrontal cortex. Eur J Neurosci,
25: 1843-1854.
Arion D, Horvath S, Lewis DA, Mirnics K. (2009) Infragranular gene expression
disturbances in the prefrontal cortex in schizophrenia: signature of altered neural
development? Neurobiol Dis, 37: 738-746.
Chowdari KV, Mirnics K, Semwal P, Wood J, Lawrence E, Bhatia T, Deshpande SN,
B KT, Ferrell RE, Middleton FA, Devlin B, Levitt P, Lewis DA, Nimgaonkar VL.
(2002) Association and linkage analyses of RGS4 polymorphisms in schizophrenia.
Hum Mol Genet, 11: 1373-1380.
Chowdari KV, Bamne M, Wood J, Talkowski ME, Mirnics K, Levitt P, Lewis DA,
Nimgaonkar VL. (2008) Linkage disequilibrium patterns and functional analysis of
RGS4 polymorphisms in relation to schizophrenia. Schizophr Bull, 34: 118-126.
Garbett K, Ebert PJ, Mitchell A, Lintas C, Manzi B, Mirnics K, Persico AM. (2008)
Immune transcriptome alterations in the temporal cortex of subjects with autism.
Neurobiol Dis, 30: 303-311.
Garbett K, Gal-Chis R, Gaszner G, Lewis DA, Mirnics K. (2008) Transcriptome
alterations in the prefrontal cortex of subjects with schizophrenia who committed
suicide. Neuropsychopharmacol Hung, 10: 9-14.
Garbett KA, Horvath S, Ebert PJ, Schmidt MJ, Lwin K, Mitchell A, Levitt P, Mirnics
K. (2010) Novel animal models for studying complex brain disorders: BAC-driven
miRNA-mediated
in
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gene
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Mol
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Ginsberg SD, Mirnics K. (2006) Functional genomic methodologies. Prog Brain Res,
158: 15-40.
Glorioso C, Sabatini M, Unger T, Hashimoto T, Monteggia LM, Lewis DA, Mirnics K.
(2006) Specificity and timing of neocortical transcriptome changes in response to
BDNF gene ablation during embryogenesis or adulthood. Mol Psychiatry, 11: 633-648.
Hashimoto T, Volk DW, Eggan SM, Mirnics K, Pierri JN, Sun Z, Sampson AR, Lewis
DA. (2003) Gene expression deficits in a subclass of GABA neurons in the prefrontal
cortex of subjects with schizophrenia. J Neurosci, 23: 6315-6326.
Hashimoto T, Arion D, Unger T, Maldonado-Aviles JG, Morris HM, Volk DW,
Mirnics K, Lewis DA. (2008) Alterations in GABA-related transcriptome in the
dorsolateral prefrontal cortex of subjects with schizophrenia. Mol Psychiatry, 13: 147161.
Hashimoto T, Bazmi HH, Mirnics K, Wu Q, Sampson AR, Lewis DA. (2008)
Conserved regional patterns of GABA-related transcript expression in the neocortex of
subjects with schizophrenia. Am J Psychiatry, 165: 479-489.
Hollingshead D, Lewis DA, Mirnics K. (2005) Platform influence on DNA microarray
data in postmortem brain research. Neurobiol Dis, 18: 649-655.
Hollingshead D, Korade Z, Lewis DA, Levitt P, Mirnics K. (2006) DNA self-polymers
as microarray probes improve assay sensitivity. J Neurosci Methods, 151: 216-223.
Horvath S, Mirnics K. (2009) Breaking the gene barrier in schizophrenia. Nat Med, 15:
488-490.
Lazarov O, Robinson J, Tang YP, Hairston IS, Korade-Mirnics Z, Lee VM, Hersh LB,
Sapolsky RM, Mirnics K, Sisodia SS. (2005) Environmental enrichment reduces Abeta
levels and amyloid deposition in transgenic mice. Cell, 120: 701-713.
Levitt P, Chowdari KV, Nimgaonkar VL, Mirnics K.(2003). Methods and systems for
facilitating the diagnosis and treatment of schizophrenia., Vol., ed.^eds. Vanderbilt
University, USA.
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Levitt P, Ebert P, Mirnics K, Nimgaonkar VL, Lewis DA. (2006) Making the case for a
candidate vulnerability gene in schizophrenia: Convergent evidence for regulator of Gprotein signaling 4 (RGS4). Biol Psychiatry, 60: 534-537.
Lewis DA, Mirnics K, Levitt P.(2004). Transcriptomes in Schizophrenia: Assessing
Altered Gene Expression with Microarrays. In: Neurodevelopment and Schizophrenia.
Vol., Keshavan MS, Kennedy JL, Murray RM, ed.^eds. Cambridge University Press,
Cambridge, UK, pp. 210-223.
Lewis DA, Levitt P, Mirnics K. (2005) Gene expression changes in schizophrenia:
How do they arise? What do they mean? Clin Neurosci Res, 5: 15-21.
Lewis DA, Mirnics K.(2005). Genes Dysregulated in Schizophrenia and Schizophrenia
Treatment Methods. Vol., ed.^eds. University of Pittsburgh, USA.
Lewis DA, Mirnics K. (2006) Transcriptome alterations in schizophrenia: disturbing
the functional architecture of the dorsolateral prefrontal cortex. Prog Brain Res, 158:
141-152.
Middleton FA, Mirnics K, Pierri JN, Lewis DA, Levitt P. (2002) Gene expression
profiling reveals alterations of specific metabolic pathways in schizophrenia. J
Neurosci, 22: 2718-2729.
Middleton FA, Peng L, Lewis DA, Levitt P, Mirnics K. (2005) Altered expression of
14-3-3
genes
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the
prefrontal
cortex
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subjects
with
schizophrenia.
Neuropsychopharmacology, 30: 974-983.
Mirnics K, Middleton FA, Marquez A, Lewis DA, Levitt P. (2000) Molecular
characterization of schizophrenia viewed by microarray analysis of gene expression in
prefrontal cortex. Neuron, 28: 53-67.
Mirnics K. (2001) Microarrays in brain research: the good, the bad and the ugly. Nat
Rev Neurosci, 2: 444-447.
Mirnics K.(2001). Gene Expression Microarrays in Brain Research: It is all about
design! In: DNA Microarray Workshop Syllabus, Geschwind DH, ed. Society for
Neuroscience, Rockville, MD, pp. 56-67.
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Mirnics K, Lewis DA. (2001) Genes and subtypes of schizophrenia. Trends Mol Med,
7: 281-283.
Mirnics K, Lewis DA, Levitt P.(2001). DNA Mircroarray Studies of Human Brain
Disorders. In: Methods in Neurogenetics. Moldin S, Chin H, eds. CRC Press, LLC,
Boca Raton, pp. 188-217.
Mirnics K, Middleton FA, Lewis DA, Levitt P. (2001) Delineating novel signature
patterns of altered gene expression in schizophrenia using gene microarrays.
ScientificWorldJournal, 1: 114-116.
Mirnics K, Middleton FA, Lewis DA, Levitt P. (2001) The human genome: gene
expression profiling and schizophrenia. Am J Psychiatry, 158: 1384.
Mirnics K, Middleton FA, Lewis DA, Levitt P. (2001) Analysis of complex brain
disorders with gene expression microarrays: schizophrenia as a disease of the synapse.
Trends Neurosci, 24: 479-486.
Mirnics K, Middleton FA, Stanwood GD, Lewis DA, Levitt P. (2001) Disease-specific
changes in regulator of G-protein signaling 4 (RGS4) expression in schizophrenia. Mol
Psychiatry, 6: 293-301.
Mirnics K. (2002) Microarrays in brain research: Data quality and limitations. Current
Genomics, 3: 13-19.
Mirnics K. (2003). Analysis of Mental Disorders Using DNA Microarrays. In:
Neuroscience at the Post-genomic Era. Vol., Mallet J, ed.^eds. IPSEN Foundation,
Paris, pp. 25-42.
Mirnics K. (2003). Schizophrenia as a disease of synapse: Gene microarray studies.
Vol. 149, Levitt P, Lewis DA, DeFelipe J, ed.^eds. Instituto Juan March de Estudios e
Investigaciones, Madrid, Spain, pp. 36-37.
Mirnics ZK, Mirnics K, Terrano D, Lewis DA, Sisodia SS, Schor NF. (2003) DNA
microarray profiling of developing PS1-deficient mouse brain reveals complex and
coregulated expression changes. Mol Psychiatry, 8: 863-878.
Mirnics K, Levitt P, Lewis DA. (2004) DNA microarray analysis of postmortem brain
tissue. Int Rev Neurobiol, 60: 153-181.
84
Mirnics K, Pevsner J. (2004) Progress in the use of microarray technology to study the
neurobiology of disease. Nat Neurosci, 7: 434-439.
Mirnics K, Korade Z, Arion D, Lazarov O, Unger T, Macioce M, Sabatini M, Terrano
D, Douglass KC, Schor NF, Sisodia SS. (2005) Presenilin-1-dependent transcriptome
changes. J Neurosci, 25: 1571-1578.
Mirnics K. (2006) Microarrays in brain research: Data quality and limitations revisited.
Current Genomics, 7: 11-19.
Mirnics K, Levitt P, Lewis DA. (2006) Critical appraisal of DNA microarrays in
psychiatric genomics. Biol Psychiatry, 60: 163-176.
Mirnics K. (2008) What is in the brain soup? Nat Neurosci, 11: 1237-1238.
Mirnics K, Norstrom EM, Garbett K, Choi SH, Zhang X, Ebert P, Sisodia SS. (2008)
Molecular signatures of neurodegeneration in the cortex of PS1/PS2 double knockout
mice. Mol Neurodegener, 3: 14.
Pongrac J, Middleton FA, Lewis DA, Levitt P, Mirnics K. (2002) Gene expression
profiling with DNA microarrays: advancing our understanding of psychiatric disorders.
Neurochem Res, 27: 1049-1063.
Pongrac JL, Middleton FA, Peng L, Lewis DA, Levitt P, Mirnics K. (2004) Heat shock
protein 12A shows reduced expression in the prefrontal cortex of subjects with
schizophrenia. Biol Psychiatry, 56: 943-950.
Smith SE, Li J, Garbett K, Mirnics K, Patterson PH. (2007) Maternal immune
activation alters fetal brain development through interleukin-6. J Neurosci, 27: 1069510702.
Talkowski ME, Seltman H, Bassett AS, Brzustowicz LM, Chen X, Chowdari KV,
Collier DA, Cordeiro Q, Corvin AP, Deshpande SN, Egan MF, Gill M, Kendler KS,
Kirov G, Heston LL, Levitt P, Lewis DA, Li T, Mirnics K, Morris DW, Norton N,
O'Donovan MC, Owen MJ, Richard C, Semwal P, Sobell JL, St Clair D, Straub RE,
Thelma BK, Vallada H, Weinberger DR, Williams NM, Wood J, Zhang F, Devlin B,
Nimgaonkar VL. (2006) Evaluation of a susceptibility gene for schizophrenia:
genotype based meta-analysis of RGS4 polymorphisms from thirteen independent
samples. Biol Psychiatry, 60: 152-162.
85
Unger T, Korade Z, Lazarov O, Terrano D, Schor NF, Sisodia SS, Mirnics K. (2005)
Transcriptome differences between the frontal cortex and hippocampus of wild-type
and humanized presenilin-1 transgenic mice. Am J Geriatr Psychiatry, 13: 1041-1051.
Unger T, Korade Z, Lazarov O, Terrano D, Sisodia SS, Mirnics K. (2005) True and
false discovery in DNA microarray experiments: transcriptome changes in the
hippocampus of presenilin 1 mutant mice. Methods, 37: 261-273.
Winden KD, Oldham MC, Mirnics K, Ebert PJ, Swan CH, Levitt P, Rubenstein JL,
Horvath S, Geschwind DH. (2009) The organization of the transcriptional network in
specific neuronal classes. Mol Syst Biol, 5: 291.
86
12. CANDIDATE’S PUBLICATIONS UNRELATED TO THESIS
Arion D, Sabatini M, Unger T, Pastor J, Alonso-Nanclares L, Ballesteros-Yanez I,
Garcia Sola R, Munoz A, Mirnics K, DeFelipe J. (2006) Correlation of transcriptome
profile with electrical activity in temporal lobe epilepsy. Neurobiol Dis, 22: 374-387.
Campbell DB, D'Oronzio R, Garbett K, Ebert PJ, Mirnics K, Levitt P, Persico AM.
(2007) Disruption of cerebral cortex MET signaling in autism spectrum disorder. Ann
Neurol, 62: 243-250.
Chowdari KV, Northup A, Pless L, Wood J, Joo YH, Mirnics K, Lewis DA, Levitt PR,
Bacanu SA, Nimgaonkar VL. (2007) DNA pooling: a comprehensive, multi-stage
association analysis of ACSL6 and SIRT5 polymorphisms in schizophrenia. Genes
Brain Behav, 6: 229-239.
Djakovic-Svajcer K, Banic B, Mirnics K.(1988). Opioid Analgenics in Memory
Consolidation. In: Neuron, Barin and Behavior. Advances in the Biosciences, Vol. 70,
Bajic B, ed. Pergamon Press, New York, pp. 136-141.
Dutta R, McDonough J, Yin X, Peterson J, Chang A, Torres T, Gudz T, Macklin WB,
Lewis DA, Fox RJ, Rudick R, Mirnics K, Trapp BD. (2006) Mitochondrial dysfunction
as a cause of axonal degeneration in multiple sclerosis patients. Ann Neurol, 59: 478489.
Dutta R, McDonough J, Chang A, Swamy L, Siu A, Kidd GJ, Rudick R, Mirnics K,
Trapp BD. (2007) Activation of the ciliary neurotrophic factor (CNTF) signalling
pathway in cortical neurons of multiple sclerosis patients. Brain, 130: 2566-2576.
Hidvegi T, Mirnics K, Hale P, Ewing M, Beckett C, Perlmutter DH. (2007) Regulator
of G Signaling 16 is a marker for the distinct endoplasmic reticulum stress state
associated with aggregated mutant alpha1-antitrypsin Z in the classical form of alpha1antitrypsin deficiency. J Biol Chem, 282: 27769-27780.
Koerber HR, Mirnics K, Brown PB, Mendell LM. (1994) Central sprouting and
functional plasticity of regenerated primary afferents. J Neurosci, 14: 3655-3671.
87
Koerber HR, Mirnics K. (1995) Morphology of functional long-ranging primary
afferent projections in the cat spinal cord. J Neurophysiol, 74: 2336-2348.
Koerber HR, Mirnics K, Mendell LM. (1995) Properties of regenerated primary
afferents and their functional connections. J Neurophysiol, 73: 693-702.
Koerber HR, Mirnics K. (1996) Plasticity of dorsal horn cell receptive fields after
peripheral nerve regeneration. J Neurophysiol, 75: 2255-2267.
Koerber HR, Mirnics K, Kavookjian AM, Light AR. (1999) Ultrastructural analysis of
ectopic synaptic boutons arising from peripherally regenerated primary afferent fibers.
J Neurophysiol, 81: 1636-1644.
Koerber HR, Mirnics K, Lawson JJ. (2006) Synaptic plasticity in the adult spinal dorsal
horn: the appearance of new functional connections following peripheral nerve
regeneration. Exp Neurol, 200: 468-479.
Korade Z, Kenchappa RS, Mirnics K, Carter BD. (2008) NRIF is a Regulator of
Neuronal Cholesterol Biosynthesis Genes. J Mol Neurosci.
Korade Z, Kenworthy AK, Mirnics K. (2009) Molecular consequences of altered
neuronal cholesterol biosynthesis. J Neurosci Res, 87: 866-875.
Krystal JH, Carter CS, ..., Mirnics K, ..., Young EA, McCandless R, authors). (2008) It
is time to take a stand for medical research and against terrorism targeting medical
scientists. Biol Psychiatry, 63: 725-727.
Lintas C, Sacco R, Garbett K, Mirnics K, Militerni R, Bravaccio C, Curatolo P, Manzi
B, Schneider C, Melmed R, Elia M, Pascucci T, Puglisi-Allegra S, Reichelt KL,
Persico AM. (2009) Involvement of the PRKCB1 gene in autistic disorder: significant
genetic association and reduced neocortical gene expression. Mol Psychiatry, 14: 705718.
Mirnics K, Koerber HR. (1995) Prenatal development of rat primary afferent fibers: II.
Central projections. J Comp Neurol, 355: 601-614.
Mirnics K, Koerber HR. (1995) Prenatal development of rat primary afferent fibers: I.
Peripheral projections. J Comp Neurol, 355: 589-600.
88
Mirnics K, Koerber HR. (1997) Properties of individual embryonic primary afferents
and their spinal projections in the rat. J Neurophysiol, 78: 1590-1600.
Mirnics ZK, Caudell E, Gao Y, Kuwahara K, Sakaguchi N, Kurosaki T, Burnside J,
Mirnics K, Corey SJ. (2004) Microarray analysis of Lyn-deficient B cells reveals
germinal center-associated nuclear protein and other genes associated with the
lymphoid germinal center. J Immunol, 172: 4133-4141.
Mirnics ZK, Yan C, Portugal C, Kim TW, Saragovi HU, Sisodia SS, Mirnics K, Schor
NF. (2005) P75 neurotrophin receptor regulates expression of neural cell adhesion
molecule 1. Neurobiol Dis, 20: 969-985.
Sabatini MJ, Ebert P, Lewis DA, Levitt P, Cameron JL, Mirnics K. (2007) Amygdala
gene expression correlates of social behavior in monkeys experiencing maternal
separation. J Neurosci, 27: 3295-3304.
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13. ACKNOWLEDGENTS
I dedicate this thesis to Željka, Marco and Emma.
First and foremost, I have to thank my wife, Dr. Željka Korade, for everything – her
love and support, our wonderful family life, tremendous kids (Emma and Marco),
scientific wisdom, patience and self-sacrifice for advancement of my career. Without
her at my side, it would not be worth anything.
I also wish to thank my PhD advisor, Prof. Dr. Gábor Faludi from the bottom of my
heart – for providing guidance over the years, scientifically challenging me and for
being a wonderful friend. He found a way to link me back to the Hungarian scientific
community, to rekindle my Hungarian identity, to show me my scientific roots.
I am very grateful for the guidance, help and challenge of my committee members.
Next, I am eternally grateful to SOTE PhD School for giving me a chance to formally
become part of the SOTE Alma Mater. I am extremely proud to be a SOTE graduate; it
means a World to me. I also need to thank Tímea Rab, for helping me navigate through
the maze of paperwork with kindness and patience.
I also need to acknowledge my parents, who raised me to love, be curious and have an
open mind. I wish to thank for the guidance of my teachers, mentors and colleagues
who helped me grow and skilled me to believe in myself: Dr. István Görög from
Orebić, Croatia, who taught me dedication and unwavering spirit; Kornelija DjakovićŠvjacer (University of Novi Sad), who showed me how to love science, Dr. David A.
Lewis (U of Pittsburgh), for shaping me into a rigorous scientist and Dr. Miklós
Palkovits (Hungarian Academy of Sciences) for continuously amazing me with his
scientific and social insights and blazing spirit. However, from all my teachers Dr. Pat
Levitt (University of Southern California) had the most influence on my career and
scientific thinking. He has been a tremendous mentor, an admired colleague, and a
father figure to me over the years, and his friendship and wisdom will stay with me till
the rest of my life.
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Finally, I have to thank for all the scientists who worked for me and with me over the
years, and who are too numerous to name individually. They critically shaped my
scientific thinking over the years, and some of the best studies arose from their ideas,
thus my success is their success, too.
Most of the research presented here was performed on postmortem brain tissue. I want
to thank the donor families for their generosity at their time of sorrow and loss –
without their gift none of this research would have been possible.
Over the years our research has received substantial funding from the National Institute
of Mental Health (NIMH), National Institute of Aging (NIA), National Institute for
Neurological Disorders and Stroke (NINDS), National Institute of Child Health and
Human Development (NICHD), National Alliance for Research on Schizophrenia and
Depression (NARSAD), American Foundation for Suicide Prevention (AFSP) and Eli
Lilly and Company. I am thankful to all of them for funding our research.
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APPENDIX 1.
Mirnics K, Middleton FA, Marquez A, Lewis DA, Levitt P.
(2000) Molecular characterization of schizophrenia viewed by
microarray analysis of gene expression in prefrontal cortex.
Neuron, 28: 53-67.
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APPENDIX 2.
Middleton FA, Mirnics K, Pierri JN, Lewis DA, Levitt P. (2002)
Gene expression profiling reveals alterations of specific
metabolic pathways in schizophrenia. J Neurosci, 22: 2718-29.
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APPENDIX 3.
Middleton FA, Peng L, Lewis DA, Levitt P, Mirnics K. (2005)
Altered expression of 14-3-3 genes in the prefrontal cortex of
subjects with schizophrenia. Neuropsychopharmacology, 30:
974-83.
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APPENDIX 4.
Arion D, Unger T, Lewis DA, Levitt P, Mirnics K. (2007)
Molecular evidence for increased expression of genes related to
immune and chaperone function in the prefrontal cortex in
schizophrenia. Biol Psychiatry, 62: 711-21.
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APPENDIX 5.
Hashimoto T, Arion D, Unger T, Maldonado-Aviles JG, Morris
HM, Volk DW, Mirnics K, Lewis DA. (2008) Alterations in
GABA-related transcriptome in the dorsolateral prefrontal cortex
of subjects with schizophrenia. Mol Psychiatry, 13: 147-61.
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APPENDIX 6.
Mirnics K, Middleton FA, Stanwood GD, Lewis DA, Levitt P.
(2001) Disease-specific changes in regulator of G-protein
signaling 4 (RGS4) expression in schizophrenia. Mol Psychiatry,
6: 293-301.
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