Microarray Analysis of Human Brain Disorders

To date, most of the brain microarray experiments have focused on animal models. There is a substantial body of literature on gene expression changes observed in in vivo and in vitro disease models (Grunblatt et al., 2001; Toyooka et al., 2002; Zhou et al., 2003), effects of drug treatment (Kontkanen et al., 2002; Yamada et al., 2001, 2002), addiction research (Ammon et al., 2003; Thibault et al., 2000, 2001; Yuferov et al., 2003), transcriptome changes in the developing brain (Karsten et al., 2003; Kornblum and Geschwind, 2001; Lockhart and Barlow, 2001b; Sandberg et al., 2000), analysis of transgenic animals (D'Agata et al., 2002; Dirks et al., 2003; Tudor et al., 2002), phenotypical/genotypical influences on brain (Tabakoff et al., 2003; Wang et al., 2003), and aging (Blalock et al., 2003). These experiments greatly complement the rapidly growing transcriptome profiling data obtained from postmortem tissue but cannot replace performing experiments on the diseased human brain; the human brain transcriptome is different from the expression pattern seen in the animal brains, including the transcript profile of the nonhuman primates (Caceres et al., 2003).

A. Neurological Disorders

Postmortem microarray research of neurological disorders has been very productive over the last several years. In particular, transcriptome profiling of Rett's syndrome, Alzheimer's disease (AD), and multiple sclerosis (MS) has been at the forefront of these analyses, and the results are providing a fundamentally new view of these disorders. Analysis of the frontal cortex in subjects with Rett's syndrome revealed that mutation of transcriptional repressor methyl-CpG—binding protein-2 (MECP-2) leads to alterations in the mRNA levels NMDA-NR1, MAP-2, and synaptic vesicle proteins (Johnston et al, 2001), as well as increased expression of glial markers (Colantuoni et al., 2001).

Studies of AD also revealed a complex set of multiregional expression changes. Studies in the amygdala and cingulate cortex revealed upregulated transcripts related to chronic inflammation, cell adhesion, cell proliferation, and protein synthesis, whereas signal transduction, energy metabolism, stress response, synaptic vesicle synthesis and function, calcium binding, and cytoskele-ton-related transcripts were downregulated (Loring et al., 2001). Comparing the neurofibrillary tangle—containing hippocampus of AD subjects to the non—tangle-bearing parietal cortex within the same brains revealed a robust increase in calcineurin Aft mRNA in the pyramidal cells of most diseased subjects relative to control brains (Hata et al., 2001). In the analysis of AD brains Pasinetti (2001) found mRNA expression changes suggesting that protein and amino acid metabolism, cytoskeleton integrity, and fatty acid metabolism are involved in early phases of AD dementia. Most notably, this study also suggested that neurotrans-mitter-released transcripts, including synapsin, may be differentially regulated in the brains of cases at high risk for dementia (Ho et al., 2001). Finally, in a GeneChip study of CA1 region Colangelo et al. (2002) found widespread transcriptional alterations, misregulation of RNAs involved in metal ion homeostasis, trophic factor signaling deficits, decreases in neurotrophic support, and activated apoptotic and neuroinflammatory signaling in moderately affected AD hippo-campal CA1. Although most of the AD microarray studies focused on bulk tissue, elegant studies of Ginsberg et al. (1999) successfully identified the expression profiles of neurofibrillary tangles and CA1 tangle—bearing projection neurons (Ginsberg et al., 2000). Furthermore, they reported that in AD subjects, anterior nucleus basalis neurons undergo selective alterations in gene expression, including upregulation of cathepsin D and downregulation of synaptophysin, synaptotagmin, and protein phosphatases (PP1) transcripts (Mufson et al., 2002b). In addition, they were able to correlate postmortem single-cell expression profiles in nucleus basalis (Mufson et al., 2002a,b) with the degree of cognitive impairment seen in the premortem subjects with AD: The initial findings suggest that alterations in neurofilament and tau gene expression occur in NB neurons at early stages of cognitive decline.

In one of the first cDNA array studies performed on the human postmortem brain tissue, Whitney et al. (1999) analyzed the expression pattern of more than 5000 genes and compared the gene expression profile of normal white matter to that found in acute lesions from the brain of a single patient with MS. Using a radioactive sample labeling technology, this study identified 62 differentially expressed genes, including the Duffy chemokine receptor, interferon regulatory factor-2, and tumor necrosis factor-a receptor-2 among others. In another microarray analysis of MS lesions Lock et al. (2002) reported increased transcripts of genes encoding interleukin-6 (IL-6) and IL-17, interferon-7, and associated downstream cascades. This study also observed significant expression differences between acute lesions with inflammation versus ''silent'' lesions without inflammation. For example, granulocyte colony-stimulating factor is upregulated in acute but not in chronic MS lesions. Based on the human expression findings, this study also evaluated the amelioration of experimental autoimmune encephalomyelitis in mice, finding that knocking out the immuno-globulin Fc primarily ameliorated changes associated with the chronic form of MS. Most recently, Mycko et al. (2003) found major gene expression differences in a microarray analysis of the regions of pathologically proven different activity of MS lesions. Namely, the lesion margin and lesion center in active lesions reported 57 and 69 differentially expressed genes, whereas the margins and centers of silent lesions showed only 11 and 2 differentially expressed genes. To compare differences between chronic active and silent lesions, the investigators also performed a comparison of the pooled data from both types of lesions. Perhaps not unexpectedly, many of the genes with changed expression encoded proteins that are involved in inflammation/immune response. This microarray analysis has also identified a novel set of genes associated with lesion activity in MS, many of them not previously linked to the disease. Finally, McDonough et al. (2003) have profiled MS cortex rather than white matter and have analyzed non—lesioned cortex and lesioned areas in area 4 of motor cortex. Preliminary results suggest that there is a robust decrease in GABAergic neurotransmission including GAD67 (a synthetic enzyme for the inhibitory neurotransmitter GABA), and GABA receptor a1 and ^3 subunits. Furthermore, initial data suggest that transcript decreases in nuclear encoded mitochondrial genes of the respiratory chain are also strongly associated with MS in the motor cortex.

B. Substance Abuse and Addiction Research

Thanks to the mostly conserved responses to various chemical substances between the human and animal brain tissue, animal model studies of substance abuse and addiction are very informative and greatly outnumber the experiments performed on human brain tissue. Transcriptomic studies in this field are spearheaded by ethanol and cocaine abuse analysis (Ang et al., 2001; Freeman et al., 2001, 2002a,b,c; Thibault et al, 2000; Yuferov et al., 2003), both in disease models and on postmortem tissue (Albertson et al., 2003; Lewohl et al., 2000b, 2001). However, the importance of analysis of human postmortem tissue cannot be overestimated; the human brain is more complex than the rodent brain and differs from it in connectivity, variability of genetic background, interneuron/ projection neuron ratio, and numerous other aspects. This is underlined by an elegant genetic association study on microarrays (Uhl et al., 2001) that marked the ADH, BDNF, and seven other loci linked to vulnerability to nicotine or alcohol abuse. One of the first postmortem microarray expression studies also implicated many novel genes that may be associated with alcohol abuse. Lewohl et al. (2000a) used both cDNA and oligonucleotide platforms to assess the effect of chronic ethanol exposure on the transcriptome of the postmortem human pre-frontal cortex and found a prominent downregulation of myelin-related genes in the experimental samples. Furthermore, in this study, cell cycle genes and several neuronal genes also reported reproducible transcript level changes in the cortices of alcoholics. Using a more complex array platform (Mayfield et al., 2002), in addition to transcript changes in genes encoding myelination proteins, these investigators also found mRNA changes in genes involved in calcium, cyclic adenosine monophosphate (cAMP), and thyroid-signaling pathways.

Interestingly, preliminary studies by Albertson et al. (2003) also suggest a profound myelin dysregulation in human cocaine abusers, suggesting a possible common mechanism between some effects of cocaine and alcohol abuse. Furthermore, a cDNA array study by Tang et al. (2003) compared gene and protein expression patterns between cocaine overdose victims and age-matched controls in the ventral tegmental area (VTA) and lateral substantia nigra (lSN). Interestingly, whereas the lSN showed no significant changes in gene expression between the overdose victims and matched controls, VTA analysis revealed significant upregulation of NMDAR1, GluR2, GluR5, and KA2 receptor mRNAs both at the transcript and at the protein level.

C. Psychiatric Disorders

Postmortem brain microarray studies of psychiatric disorders have primarily focused on schizophrenia, although there is increased interest in microarray analysis of postmortem tissue from subjects with major depression, bipolar disorder and autism.

In a study of autism, Purcell et al. (2001) analyzed cerebellar samples using two microarray platforms from 10 individuals with autism and 23 matched controls. The mRNA levels of several genes were significantly increased in autism, including excitatory amino acid transporter-1 and glutamate receptor AMPA-1, which was also verified at a protein level. Based on this study, the authors concluded that subjects with autism might have specific abnormalities in the AMPA-type glutamate receptors and glutamate transporters in the cerebellum.

Although several comprehensive and very promising studies are in progress, perhaps because of the phenotypical and molecular complexity of major depression, microarray analysis of this disorder is still at a preliminary stage. In one such initial microarray study on medication-free depressed patients who died by suicide, Sibille et al. (2002) found that expression of 100-150 genes appears to segregate patients into two distinct molecular subtypes. The observed differences in gene expression were consistent across Brodmann areas 9 and 47 and included gene transcripts encoding genes involved in monoaminergic/glutamatergic syn-aptic neurotransmission and neurotrophin-dependent tyrosine phosphorylation. In a separate preliminary study, Evans et al. (2002) used DNA microarrays to study expression profiles of human postmortem brains from patients diagnosed with major depressive or bipolar disorder in the anterior cingulate cortex, dorsolateral prefrontal cortex, and the cerebellum. The findings suggest that the dorsal lateral prefrontal cortex from patients with a major depressive disorder is characterized by coordinated alterations of gene expression in growth factor pathways.

1. Transcriptome Changes in Schizophrenia

Of all psychiatric disorders, transcriptome changes in schizophrenia are the best characterized. The lessons learned in this transcriptome assessment process are directly applicable to microarray analysis of other psychiatric disorders, allowing us to more carefully design and carry out transcriptome experiments.

Schizophrenia is a complex multigenic brain disorder characterized by a constellation of psychotic, negative, and cognitive features (Carpenter and Buchanan, 1994; Lewis and Lieberman, 2000). In addition to genetic factors, environmental factors influencing brain development greatly contribute to risk for the disease, which has a typical clinical onset around puberty/young adulthood. The prefrontal cortex of subjects with schizophrenia shows both anatomical and physiological changes, and prefrontal dysfunction is thought to underlie at least some of the cognitive deficits in schizophrenia. Not surprisingly, micro-arrays studies have focused mostly on complex expression changes that occur in area 9 of the dorsolateral prefrontal cortex (DLPFC).

a. Existing Microarray Studies: Results. The existing microarray studies of schizophrenia provide a solid foundation and can be further explored in specific hypothesis-driven research. These early studies differed considerably in experimental design, including different approaches vis-a-vis sample harvest, material pooling, RNA amplification and labeling, DNA array platforms, and data analysis approaches. Furthermore, the studied cohorts ranged from elderly chronically hospitalized with end-stage schizophrenia to outpatient cohorts that also included individuals with schizoaffective disorder. Not surprisingly, the molecular profiles obtained in these experiments are somewhat different, though not mutually exclusive.

In our initial study, we preformed a cDNA microarray expression profiling of prefrontal cortex (area 9) from matched pairs of schizophrenic and control subjects (Mirnics et al., 2000). A biological pathway-related analysis revealed that genes encoding proteins involved in presynaptic secretory release (PSYN) were decreased in all subjects with schizophrenia, albeit with specific pattern of altered transcripts differing across subjects. A similar, robust decrease pattern was also observed for transcripts encoding genes involved in GABAergic and glutaminer-gic transmission. Selected microarray observations were verified by ISH. At the ''most changed'' analysis level, N-ethylmaleimide—sensitive factor (NSF) and synapsin II (SYN2) were robustly decreased in all subjects with schizophrenia. Furthermore, we observed expression reduction in GAD67 and AMPA-2 receptors, replicating previously reported literature findings (Eastwood et al., 1995; Volk et al., 2000). None of these changes were observed in monkeys chronically treated with haloperidol, suggesting that the obtained expression changes were part of the disease process, rather than a consequence of treatment with antipsychotic medication.

Furthermore, an expanded study that included the original data set, revealed statistically significant expression alterations in 5 of 71 assessed metabolic pathways (Middleton et al., 2002). Reductions in expression were observed for gene transcripts that are part of the ornithine and polyamine metabolism, mitochon-drial malate shuttle system, transcarboxylic acid cycle, aspartate and alanine metabolism, and ubiquitination pathway. Metabolic genes showing the most decreased expression included cytosolic malate dehydrogenase (MAD), mito-chondrial glutamate-oxaloacetate transaminase type 2, ornithine decarboxylase antizyme inhibitor, and ornithine aminotransferase. Most of these genes that were consistently decreased across subjects with schizophrenia were not similarly decreased in haloperidol-treated monkeys. In contrast, the transcript encoding the cytosolic form of MAD displayed increases in expression in chronic haloperi-dol-treated monkeys. This increase was most prominent in the deep cortical layers, the cortical regions that have the highest concentration of the D2 receptors in the DLPFC. These molecular analyses implicate a highly specific pattern of metabolic alterations in the DLPFC of subjects with schizophrenia and raise the possibility that antipsychotic medications may exert a therapeutic effect, at least in part, by normalizing some of these expression changes.

Fig. 1. Substratification and putative co-regulatory patterns in the prefrontal cortex of subjects with schizophrenia. (A) Two-way hierarchical clustering of complementary DNA (cDNA) microarray data obtained by comparing 10 pairs of subjects with schizophrenic and matched controls. The 4096 expressed genes are clustered in rows; matched subject pairs are clustered in columns. For each gene, Z scores are continuously color coded from red (decreased expression) to green (increased expression) in schizophrenic subjects. Two-way clustering was performed by average linkage based on euclidean distance using cluster. Note that there is a significant substratification of the data set: In addition to

Fig. 1. Substratification and putative co-regulatory patterns in the prefrontal cortex of subjects with schizophrenia. (A) Two-way hierarchical clustering of complementary DNA (cDNA) microarray data obtained by comparing 10 pairs of subjects with schizophrenic and matched controls. The 4096 expressed genes are clustered in rows; matched subject pairs are clustered in columns. For each gene, Z scores are continuously color coded from red (decreased expression) to green (increased expression) in schizophrenic subjects. Two-way clustering was performed by average linkage based on euclidean distance using cluster. Note that there is a significant substratification of the data set: In addition to

In a further analysis of the expanded microarray data set, hierarchical clustering revealed an unexpected result: We observed a molecular substratification of the studied subjects with schizophrenia. In Fig. 1A, of the more than 4000 expressed genes (rows) in 10 array comparisons (columns), in addition to about 300 consistently underexpressed (Fig. 1B) and about 70 overexpressed genes, there are two extensive mirror-image data clusters (Fig. 1C), arguing for a presence of two major subclasses of schizophrenic subjects within the data set. The physiological meaning of these major clusters is not easily interpretable, but it suggests that follow-up analysis should also assess the relationship to this substratification. For example, if this gene expression substratification is a result of specific genetic vulnerability, our power to identify the susceptibility genes will be greatly enhanced if we subdivide and analyze the studied cohort according to a molecular substratification.

Finally, we decided to test a hypothesis that at least some of the gene transcripts consistently decreased across all subjects with schizophrenia (cluster in Fig. 1B) may share a common underlying molecular drive. To test this, we performed a batch retrieval of 2kB/gene from the E^Reírieve database for the 50 most changed genes in our study. This region presumably contains both the promoter site and multiple gene transcription regulatory elements. After obtaining these sequences, they were reformatted with a custom made perl script and imported into a SRMS database (Silico Informatics Systems, Santa Clara, California). This was followed by a motif analysis on the MEME server (Bailey and Elkan, 1995), and the obtained results were once again inserted into the SRMS database. Graphical visualization of the motifs, patterns, and sequences was performed in SRMS. Interestingly, the genes with consistently decreased expression shared multiple DNA motifs (Fig. 1D) that may represent common regulatory sequences. Even when corrected for multiple comparisons, the occurrence of multiple motifs was highly significant (p < .0001).

about 200 genes showing a consistent downregulation across all comparisons (denoted by red vertical bar) and about 50 genes reporting a consistent upregulation (vertical green bar), we observed two major clusters of genes that show an inverted expression pattern (vertical blue bars). (B) To visualize the individual genes and their expression ratio z scores, part of the cluster denoting consistently decreased genes is enlarged. (C) Pairwise expression z scores for the two clusters showing inverted gene expression patterns are plotted on the y-axis; x-axis denotes matched schizophrenia—control subject pairs. Note that for this subset of genes the expression patterns are inverted. (D) Lines represent 5' DNA region of several genes selected from the cluster of Fig. 1B. Colored boxes denote common motifs in the promoter region. Note that the same conserved motifs occur in multiple genes, suggesting a putative common co-regulation of these transcripts. (E) Targeted assessment of coregulation of the functionally related N-ethylmaleimide sensitive factor (NSF)7-SNAP and NSF are highly co-regulated patterns across the 10 comparisons, whereas a-SNAP shows a less correlated downregulation pattern. (See Color Insert.)

To assess gene expression patterns in the cerebellum, prefrontal cortex, and middle temporal gyrus of subjects with schizophrenia, Vawter et al. (2001) used a pooled experimental design in conjunction with membrane-based cDNA micro-arrays containing about 1100 brain-biased probes. In the cerebellum and PFC of drug-treated subjects, 21 genes showed differential expression, compared to only 5 genes for drug-naive patients. Differentially expressed gene products were related to synaptic signaling and proteolytic functions, some of which also showed differential expression in the middle temporal gyrus (tyrosine-3-monooxygenase/ tryptophan 5-monooxygenase activation protein, eta polypeptide; sialyl transfer-ase; proteasome subunit, alpha type 1; ubiquitin carboxyl-terminal esterase L1; and solute carrier family 10, member 1). In a separate study, using the same DNA array platform and multiple pools of RNA from subjects with schizophrenia and matched controls (BA9 PFC), Vawter et al. (2002) found three genes that showed consistently decreased expression in schizophrenia by both z-ratio differences and decreased normalized numerical ratios. These were histidine triad nucleotide-binding protein (HINT), ubiquitin-conjugating enzyme E2N (UBE2N) and glutamate receptor, ionotropic, AMPA-2 (GRIA2). Interestingly, the results confirmed many of the gene expression decreases we reported in our initial study. These genes included multiple gene products belonging to the presynaptic secretory, glutamatergic, and GABAergic pathways.

Using a groupwise experimental design and Afifymetrix GeneChip oligonucleotide arrays with about 6000 probe sets, Hakak et al. (2001) performed a transcriptome analysis of the PFC of subjects with elderly, hospitalized subjects with schizophrenia, and matched controls. Most notably the results identified a set of oligodendrocyte- and myelination-related genes that were underexpressed in the diseased subjects. In a further linear discriminant analysis (Schadt et al., 2001), the 35 myelination-related genes (including the most decreased MAL, MAG, transferrin, gelsolin, and Her-3 transcripts) perfectly separated out the schizophrenic subjects from the matched controls.

Mimmack et al. (2002), using different cohorts, also performed a DNA array analysis of the PFC of subjects with schizophrenia. In this study the investigators employed a custom-made cDNA array platform with 300 gene probes, which were selected based on their likeliness to be involved in the pathophysiology of schizophrenia. This study, which was cross-validated across three independent cohorts of subjects, found that several members of the apolipoprotein L (apoL) family showed increased mRNA levels in subjects with schizophrenia. Importantly, the apoL proteins belong to the group of high-density lipoproteins, with all six apoL genes located in proximity to each other on chromosome 22q12, a confirmed susceptibility locus for schizophrenia. The same laboratory performed an indexing-based differential display PCR study and GeneChip analysis of BA9 PFC of subjects with schizophrenia, bipolar disorder, and matched controls (Tkachev et al., 2003). These oligonucleotide arrays contained more than

20,000 unique gene probe sets, providing an in-depth view into the transcriptome changes associated with these two diseases. Results of differential display and quantitative PCR analysis, as well as the microarray study, showed a reduction of key oligodendrocyte-related and myelin-related transcripts in subjects with schizophrenia and bipolar disorder. Importantly, the expression changes for both disorders showed a high degree of overlap.

In an elegant analysis of entorhinal cortex layer II stellate neurons from postmortem samples of schizophrenic and age-matched control brains, Hemby et al. (2002) found marked differences in expression of various G-protein-coupled receptor signaling transcripts, glutamate receptor subunits, synaptic proteins, and other transcripts. In a secondary screen of these entorhinal cortex layer II stellate neurons, schizophrenia-associated decreases were observed in levels of G-protein subunit i(alpha)1, glutamate receptor 3, NMDA receptor 1, synaptophysin, SNAP23, and SNAP25.

b. Existing Microarray Studies: Common Findings. At the simplest level of data analysis, these studies identified the ''genes with most changed expression." Although the findings are somewhat diverse (mostly as a result of specificities in experimental design), it is also important to point out that there are consistent findings between these expression studies. Genes that reported expression changes in the PFC of subjects with schizophrenia across multiple studies (Mirnics et al, 2000, 2001c; Vawter et al., 2001, 2002; Petryshen et al., 2003; Pongrac et al., 2002) (performed on multiple cohorts in different laboratories) include several neural genes such as regulator of G-protein signaling 4 (RGS4), neuroserpine, AMPA-2 receptor, GAD67, AF1q, NSF, 14-3-3 isoforms, MAD-1, as well as multiple oligodendrocyte-related genes (proteolipid protein 1, ErbB3, transferrin, myelin-associated glycoprotein, and gelsolin (Hakak et al., 2001; Hof et al., 2002; Mimmack et al., 2002; Pongrac et al., 2002; Tkachev et al., 2003). These molecules paint a complex molecular picture of schizophrenia, one that involves molecular disturbances in various cell types.

When all the results from the schizophrenia transcriptome studies are combined, it appears that the affected transcripts in schizophrenia are associated with the processes of synaptic release, cell signaling, second messenger systems, energy metabolism, protein turnover, and myelination. These changes are distributed in a complex pattern across different cell populations, including projection neurons, interneurons, and oligodendrocytes. How are these changes orchestrated? Although we can generate informed and testable hypotheses about co-regulations of some transcripts (e.g., BDNF-TRKB-GAD67-parvalbumin (Hashimoto et al., 2003) or 0LIG1-S0X10-PLP1-Her-3-MBP-M0G-MAG (Tkachev et al., 2003), the connection between other systems is not obvious, and in its complexity, it will exceed the power of transcriptome analysis methods, thereby emphasizing the need for follow-up experiments in various biological models.

c. Existing Microarrays Studies: Differences. Although a significant portion of the variable findings can be explained by the different methodological approaches, the diversity of the molecular phenotypes subsumed under schizophrenia may hold a key to others. In this context, all studied cohorts may be biased to preferentially include different molecular subphenotypes of subjects with schizophrenia. For example, chronically hospitalized subjects who responded poorly to antipsychotic medications may have a more severe (or even different) molecular phenotype than subjects with schizophrenia who were living in the community and responded well to treatment. This explanation is consistent with the current view of the genetics of schizophrenia, which suggests that there are a large number of susceptibility genes, each of which has a relatively small effect (Pulver, 2000; Tsuang, 2000). As a result, the potential combination of the genetic susceptibility factors is huge, and this can undoubtedly result in a broad spectrum of transcriptome phenotypes of schizophrenia.

d. Expression Changes vs Susceptibility Genes in Schizophrenia. In our initial micro-array study, we found regulator of G-protein signaling 4 (RGS4) as the gene with a most prominent expression reduction across all subjects with schizophrenia (Mirnics et al., 2001c). This finding was of a particular interest to us, because the RGS4 protein limits the duration of signaling from multiple G-protein receptors, including the ones that are the targets of atypical antipsychotic agents. Verification by ISH revealed that this decrease in the RGS4 transcript was present across multiple cortical regions, including the prefrontal, primary motor, and visual cortices. Furthermore, this change was not observed in monkeys treated with chronic antipsychotic medication or in subjects with major depression. These results raised the possibility that expression changes in RGS4 might reflect a primary genetic abnormality and that variants in RGS4 might confer increased risk for schizophrenia. To test this idea, we conducted genetic association and linkage studies (Chowdari et al., 2002) using samples ascertained independently in Pittsburgh and New Delhi and by the NIMH Collaborative Genetics Initiative. Using the transmission disequilibrium test, we observed significant transmission distortion in the Pittsburgh and NIMH samples. Among SNPs spanning approximately 300 kb, significant associations involved four SNPs localized to a 10-kb region at RGS4, although the associated haplotypes differed. Two other recent research groups have obtained results confirming an association between RGS4 SNPs and schizophrenia (Morris et al., 2003; Williams et al., 2003), suggesting that RGS4 represents a novel schizophrenia susceptibility gene.

Conforming RGS4 as a susceptibility gene has important implications by providing proof of principle that microarray-discovered transcriptome changes may identify underlying susceptibility genes. Indeed, further evidence is now emerging that expression studies are providing valuable leads for genetic associations: GAD-67 (one of the genes with the most consistently observed altered expression in schizophrenia) (Addington et al., 2003; Straub et al., 2003) and

GABA-A receptor ß3 (Lo et al., 2003) and 72 (Turunen et al., 2003) subunits (both showing expression decreases in our data set) have been implicated in preliminary genetic studies as putative schizophrenia susceptibility genes.

D. Molecular Similarities between Brain Disorders

Similarities in transcriptome changes across multiple psychiatric disorders are not entirely unexpected in the context of the partially overlapping susceptibility loci. Indeed, the emerging microarray data suggest that certain molecular events may be characteristic for more than one brain disease. This knowledge will be essential to understanding the disease-associated molecular pathophysiology; however, these relationships are not well explored.

Although they are clinically very different diseases, schizophrenia and MS appear to share common deficits in expression of myelination genes, critical GABA system transcripts, mitochondrial genes, and RGS4 expression (Hakak et al., 2001; Lock et al, 2002; McDonough et al., 2003; Middleton et al., 2002; Mirnics et al., 2000; Mycko et al., 2003; Pongrac et al., 2002; Steinman and Zamvil 2003; Tkachev et al., 2003; Whitney et al., 1999). However, these molecular changes occur in different brain regions, thus potentially defining different phenotypical manifestations of the disease. Bipolar disorder and schizophrenia also share a common deficit in the myelination-related genes (Tkachev et al., 2003). Interestingly, similar to bipolar disorder and schizophrenia, cocaine abuse and ethanol abuse may also be associated with deficits in gene transcripts responsible for myelination (Albertson et al, 2003; Lewohl et al., 2000b; Mayfield et al, 2002). Autism, major depression, and schizophrenia share a common mechanism of altered glutaminergic gene expression (Mirnics et al., 2000; Purcell et al., 2001; Sibille et al., 2002). ADD and schizophrenia are both characterized by reduction in synaptic markers (Ho et al., 2001; Mirnics et al., 2000; Pasinetti 2001; Vawter et al., 2002).

In contrast, the absence of similar expression changes may also be somewhat informative vis-a-vis the disease process. Perhaps surprisingly, at the level of expression changes, major depression and bipolar disorder appear to have less in common than expected (Evans et al., 2003; Petryshen et al., 2003; Tomita et al., 2003).

Aspergers Answers Revealed

Aspergers Answers Revealed

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