Comparison of microarray-based mRNA profiling technologies for identification of psychiatric disease and drug signatures

Gene Expression Profiling in Schizophrenia and Related Mental Disorders

The etiology and pathophysiology of schizophrenia and related mental disorders such as bipolar disorder and major depression remain largely unclear. Recent advances in mRNA profiling techniques made it possible to perform genome-wide gene expression analysis in a hypothesis-free manner. It was thought that this large-scale data mining approach would reveal unknown molecular cascades involved in mental disorders. Contrary to this initial expectation, however, DNA microarray results in psychiatric fields have been notoriously discordant. Here the authors review the findings of DNA microarray analysis, focusing on systematic gene expression changes in schizophrenia, as well as alterations in the expression of specific genes, that have been reported and replicated. The authors also address the probable causes for the discordance among studies, possible ways to solve the problem, and their preferred approach for data interpretation.

Systematic review of genome-wide gene expression studies of bipolar disorder

BACKGROUND

Numerous genome-wide gene expression studies of bipolar disorder (BP) have been carried out. These studies are heterogeneous, underpowered and use overlapping samples. We conducted a systematic review of these studies to synthesize the current findings.

METHODS

We identified all genome-wide gene expression studies on BP in humans. We then carried out a quantitative mega-analysis of studies done with post-mortem brain tissue. We obtained raw data from each study and used standardized procedures to process and analyze the data. We then combined the data and conducted three separate mega-analyses on samples from 1) any region of the brain (9 studies); 2) the prefrontal cortex (PFC) (6 studies); and 3) the hippocampus (2 studies). To minimize heterogeneity across studies, we focused primarily on the most numerous, recent and comprehensive studies.

RESULTS

A total of 30 genome-wide gene expression studies of BP done with blood or brain tissue were identified. We included 10 studies with data on 211 microarrays on 57 unique BP cases and 229 microarrays on 60 unique controls in the quantitative mega-analysis. A total of 382 genes were identified as significantly differentially expressed by the three analyses. Eleven genes survived correction for multiple testing with a q-value < 0.05 in the PFC. Among these were FKBP5 and WFS1, which have been previously implicated in mood disorders. Pathway analyses suggested a role for metallothionein proteins, MAP Kinase phosphotases, and neuropeptides.

CONCLUSION

We provided an up-to-date summary of results from gene expression studies of the brain in BP. Our analyses focused on the highest quality data available and provided results by brain region so that similarities and differences can be examined relative to disease status. The results are available for closer inspection on-line at Metamoodics [http://metamoodics.igm.jhmi.edu/], where investigators can look up any genes of interest and view the current results in their genomic context and in relation to leading findings from other genomic experiments in bipolar disorder.

Gene expression alterations in bipolar disorder postmortem brains

OBJECTIVES

  Bipolar disorder (BD) is a mental illness of unknown neuropathology and has several genetic associations. Antipsychotics are effective for the treatment of acute mania, psychosis, or mixed states in individuals with BD. We aimed to identify gene transcripts differentially expressed in postmortem brains from antipsychotics-exposed individuals with BD (hereafter the 'exposed' group), non-exposed individuals with BD (hereafter the 'non-exposed' group), and controls.

METHODS

  We quantified the abundance of gene transcripts in postmortem brains from seven exposed individuals, seven non-exposed individuals, and 12 controls with the Affymetrix U133P2 GeneChip microarrays and technologies. We applied a q-value of ≤0.005 to identify statistically significant transcripts with mean abundance differences between the exposed, non-exposed and control groups.

RESULTS

  We identified 2191 unique genes with significantly altered expression levels in non-exposed brains compared to those in the control and exposed groups. The expression levels of these genes were not significantly different between exposed brains and controls, suggesting a normalization effect of antipsychotics on the expression of these genes. Gene ontology (GO) enrichment analysis showed significant (Bonferroni p ≤ 0.05) clustering of subgroups of the 2191 genes under many GO terms; notably, the protein products of genes enriched are critical to the function of synapses, affecting, for example, intracellular trafficking and synaptic vesicle biogenesis, transport, release and recycling, as well as organization and stabilization of the node of Ranvier.

CONCLUSIONS

  These results support a hypothesis of synaptic and intercellular communication impairment in BD. The apparent normalization of expression patterns with exposure to antipsychotic medication may represent a physiological process that relates both to etiology and improvement patterns of the disorder.

Expression profiling in neuropsychiatric disorders: emphasis on glutamate receptors in bipolar disorder

Functional genomics and proteomics approaches are being employed to evaluate gene and encoded protein expression changes with the tacit goal to find novel targets for drug discovery. Genome-wide association studies (GWAS) have attempted to identify valid candidate genes through single nucleotide polymorphism (SNP) analysis. Furthermore, microarray analysis of gene expression in brain regions and discrete cell populations has enabled the simultaneous quantitative assessment of relevant genes. The ability to associate gene expression changes with neuropsychiatric disorders, including bipolar disorder (BP), and their response to therapeutic drugs provides a novel means for pharmacotherapeutic interventions. This review summarizes gene and pathway targets that have been identified in GWAS studies and expression profiling of human postmortem brain in BP, with an emphasis on glutamate receptors (GluRs). Although functional genomic assessment of BP is in its infancy, results to date point towards a dysregulation of GluRs that bear some similarity to schizophrenia (SZ), although the pattern is complex, and likely to be more complementary than overlapping. The importance of single population expression profiling of specific neurons and intrinsic circuits is emphasized, as this approach provides informative gene expression profile data that may be underappreciated in regional studies with admixed neuronal and non-neuronal cell types.

Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis

Background

Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer.

Results

By applying a procedure for cross-platform data meta-analysis based on RankProd and GlobalAncova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We use this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions.

Conclusions

We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets. This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data.

Convergent functional genomic studies of ω-3 fatty acids in stress reactivity, bipolar disorder and alcoholism

Omega-3 fatty acids have been proposed as an adjuvant treatment option in psychiatric disorders. Given their other health benefits and their relative lack of toxicity, teratogenicity and side effects, they may be particularly useful in children and in females of child-bearing age, especially during pregnancy and postpartum. A comprehensive mechanistic understanding of their effects is needed. Here we report translational studies demonstrating the phenotypic normalization and gene expression effects of dietary omega-3 fatty acids, specifically docosahexaenoic acid (DHA), in a stress-reactive knockout mouse model of bipolar disorder and co-morbid alcoholism, using a bioinformatic convergent functional genomics approach integrating animal model and human data to prioritize disease-relevant genes. Additionally, to validate at a behavioral level the novel observed effects on decreasing alcohol consumption, we also tested the effects of DHA in an independent animal model, alcohol-preferring (P) rats, a well-established animal model of alcoholism. Our studies uncover sex differences, brain region-specific effects and blood biomarkers that may underpin the effects of DHA. Of note, DHA modulates some of the same genes targeted by current psychotropic medications, as well as increases myelin-related gene expression. Myelin-related gene expression decrease is a common, if nonspecific, denominator of neuropsychiatric disorders. In conclusion, our work supports the potential utility of omega-3 fatty acids, specifically DHA, for a spectrum of psychiatric disorders such as stress disorders, bipolar disorder, alcoholism and beyond.

Interaction networks of lithium and valproate molecular targets reveal a striking enrichment of apoptosis functional clusters and neurotrophin signaling

The overall neurobiological mechanisms by which lithium and valproate stabilize mood in bipolar disorder patients have yet to be fully defined. The therapeutic efficacy and dissimilar chemical structures of these medications suggest that they perturb both shared and disparate cellular processes. To investigate key pathways and functional clusters involved in the global action of lithium and valproate, we generated interaction networks formed by well-supported drug targets. Striking functional similarities emerged. Intersecting nodes in lithium and valproate networks highlighted a strong enrichment of apoptosis clusters and neurotrophin signaling. Other enriched pathways included MAPK, ErbB, insulin, VEGF, Wnt and long-term potentiation indicating a widespread effect of both drugs on diverse signaling systems. MAPK1/3 and AKT1/2 were the most preponderant nodes across pathways suggesting a central role in mediating pathway interactions. The convergence of biological responses unveils a functional signature for lithium and valproate that could be key modulators of their therapeutic efficacy.

An optimized protocol for microarray validation by quantitative PCR using amplified amino allyl labeled RNA

Background

Validation of microarrays data by quantitative real-time PCR (qPCR) is often limited by the low amount of available RNA. This raised the possibility to perform validation experiments on the amplified amino allyl labeled RNA (AA-aRNA) leftover from microarrays. To test this possibility, we used an ongoing study of our laboratory aiming at identifying new biomarkers of graft rejection by the transcriptomic analysis of blood cells from brain-dead organ donors.

Results

qPCR for ACTB performed on AA-aRNA from 15 donors provided Cq values 8 cycles higher than when original RNA was used (P < 0.001), suggesting a strong inhibition of qPCR performed on AA-aRNA. When expression levels of 5 other genes were measured in AA-aRNA generated from a universal reference RNA, qPCR sensitivity and efficiency were decreased. This prevented the quantification of one low-abundant gene, which was readily quantified in un-amplified and un-labeled RNA. To overcome this limitation, we modified the reverse transcription (RT) protocol that generates cDNA from AA-aRNA as follows: addition of a denaturation step and 2-min incubation at room temperature to improve random primers annealing, a transcription initiation step to improve RT, and a final treatment with RNase H to degrade remaining RNA. Tested on universal reference AA-aRNA, these modifications provided a gain of 3.4 Cq (average from 5 genes, P < 0.001) and an increase of qPCR efficiency (from -1.96 to -2.88; P = 0.02). They also allowed for the detection of a low-abundant gene that was previously undetectable. Tested on AA-aRNA from 15 brain-dead organ donors, RT optimization provided a gain of 2.7 cycles (average from 7 genes, P = 0.004). Finally, qPCR results significantly correlated with microarrays.

Conclusion

We present here an optimized RT protocol for validation of microarrays by qPCR from AA-aRNA. This is particularly valuable in experiments where limited amount of RNA is available.

Transcriptome profiling in neurodegenerative disease

Changes in gene expression and splicing patterns (that occur prior to the onset and during the progression of complex diseases) have become a major focus of neurodegenerative disease research. These signature patterns of gene expression provide clues about the mechanisms involved in the molecular pathogenesis of neurodegenerative disease and may facilitate the discovery of novel therapeutic drugs. With the development of array technologies and the very recent RNA-seq technique, our understanding of the pathogenesis of neurodegenerative disease is expanding exponentially. Here, we review the technologies involved in gene expression and splicing analysis and the related literature on three common neurodegenerative diseases: Alzheimer's disease, Parkinson's disease and Huntington's disease.

Coming to grips with complex disorders: genetic risk prediction in bipolar disorder using panels of genes identified through convergent functional genomics

We previously proposed and provided proof of principle for the use of a complementary approach, convergent functional genomics (CFG), combining gene expression and genetic data, from human and animal model studies, as a way of mining the existing GWAS datasets for signals that are there already, but did not reach significance using a genetics-only approach [Le-Niculescu et al., 2009b]. CFG provides a fit-to-disease prioritization of genes that leads to generalizability in independent cohorts, and counterbalances the fit-to-cohort prioritization inherent in classic genetic-only approaches, which have been plagued by poor reproducibility across cohorts. We have now extended our previous work to include more datasets of GWAS, and more recent evidence from other lines of work. In essence our analysis is the most comprehensive integration of genetics and functional genomics to date in the field of bipolar disorder. Biological pathway analyses identified top canonical pathways, and epistatic interaction testing inside these pathways has identified genes that merit future follow-up as direct interactors (intra-pathway epistasis, INPEP). Moreover, we have put together a panel of best P-value single nucleotide polymorphisms (SNPs), based on the top candidate genes we identified. We have developed a genetic risk prediction score (GRPS) based on our panel, and demonstrate how in two independent test cohorts the GRPS differentiates between subjects with bipolar disorder and normal controls, in both European-American and African-American populations. Lastly, we describe a prototype of how such testing could be used to categorize disease risk in individuals and aid personalized medicine approaches, in psychiatry and beyond.

Can we identify genes for alcohol consumption in samples ascertained for heterogeneous purposes?

BACKGROUND

Previous studies have identified evidence of genetic influence on alcohol use in samples selected to be informative for alcoholism research. However, there are a growing number of genome-wide association studies (GWAS) using samples unselected for alcohol consumption (i.e., selected on other traits and forms of psychopathology), which nevertheless assess consumption as a risk factor. Is it reasonable to expect that genes contributing to variation in alcohol consumption can be identified in such samples?

METHODS

An exploratory approach was taken to determine whether linkage analyses for heaviness of alcohol consumption, using a sample collected for heterogeneous purposes, could replicate previous findings. Quantity and frequency measures of consumption were collected in telephone interviews from community samples. These measures, and genotyping, were available for 5,441 individuals (5,067 quasi-independent sibling pairs). For 1,533 of these individuals, data were collected on 2 occasions, about 8.2 years apart, providing 2 datasets that maximize data collected at either a younger or an older age. Analyses were conducted to address the question of whether age and heavier levels of alcohol consumption effects outcome. Linkage results were compared in the younger and older full samples, and with samples in which approximately 10, 20, and 40 of drinkers from the lower end of the distribution of alcohol consumption were dropped.

RESULTS

Linkage peaks varied for the age differentiated samples and for percentage of light drinkers retained. Larger peaks (LOD scores >2.0) were typically found in regions previously identified in linkage studies and/or containing proposed candidate genes for alcoholism including AGT, CARTPT, OPRD1, PIK3R1, and PDYN.

CONCLUSIONS

The results suggest that GWAS assessing alcohol consumption as a covariate for other conditions will have some success in identifying genes contributing to consumption-related variation. However, sample characteristics, such as participant age, and trait distribution, may have substantial effects on the strength of the genetic signal. These results can inform forthcoming GWAS where the same restrictions apply.

Convergent functional genomics of genome-wide association data for bipolar disorder: comprehensive identification of candidate genes, pathways and mechanisms

Given the mounting convergent evidence implicating many more genes in complex disorders such as bipolar disorder than the small number identified unambiguously by the first-generation Genome-Wide Association studies (GWAS) to date, there is a strong need for improvements in methodology. One strategy is to include in the next generation GWAS larger numbers of subjects, and/or to pool independent studies into meta-analyses. We propose and provide proof of principle for the use of a complementary approach, convergent functional genomics (CFG), as a way of mining the existing GWAS datasets for signals that are there already, but did not reach significance using a genetics-only approach. With the CFG approach, the integration of genetics with genomics, of human and animal model data, and of multiple independent lines of evidence converging on the same genes offers a way of extracting signal from noise and prioritizing candidates. In essence our analysis is the most comprehensive integration of genetics and functional genomics to date in the field of bipolar disorder, yielding a series of novel (such as Klf12, Aldh1a1, A2bp1, Ak3l1, Rorb, Rora) and previously known (such as Bdnf, Arntl, Gsk3b, Disc1, Nrg1, Htr2a) candidate genes, blood biomarkers, as well as a comprehensive identification of pathways and mechanisms. These become prime targets for hypothesis driven follow-up studies, new drug development and personalized medicine approaches.

Identifying blood biomarkers for mood disorders using convergent functional genomics

There are to date no objective clinical laboratory blood tests for mood disorders. The current reliance on patient self-report of symptom severity and on the clinicians' impression is a rate-limiting step in effective treatment and new drug development. We propose, and provide proof of principle for, an approach to help identify blood biomarkers for mood state. We measured whole-genome gene expression differences in blood samples from subjects with bipolar disorder that had low mood vs those that had high mood at the time of the blood draw, and separately, changes in gene expression in brain and blood of a mouse pharmacogenomic model. We then integrated our human blood gene expression data with animal model gene expression data, human genetic linkage/association data and human postmortem brain data, an approach called convergent functional genomics, as a Bayesian strategy for cross-validating and prioritizing findings. Topping our list of candidate blood biomarker genes we have five genes involved in myelination (Mbp, Edg2, Mag, Pmp22 and Ugt8), and six genes involved in growth factor signaling (Fgfr1, Fzd3, Erbb3, Igfbp4, Igfbp6 and Ptprm). All of these genes have prior evidence of differential expression in human postmortem brains from mood disorder subjects. A predictive score developed based on a panel of 10 top candidate biomarkers (five for high mood and five for low mood) shows sensitivity and specificity for high mood and low mood states, in two independent cohorts. Our studies suggest that blood biomarkers may offer an unexpectedly informative window into brain functioning and disease state.

Target identification for CNS diseases by transcriptional profiling

Gene expression changes in neuropsychiatric and neurodegenerative disorders, and gene responses to therapeutic drugs, provide new ways to identify central nervous system (CNS) targets for drug discovery. This review summarizes gene and pathway targets replicated in expression profiling of human postmortem brain, animal models, and cell culture studies. Analysis of isolated human neurons implicates targets for Alzheimer's disease and the cognitive decline associated with normal aging and mild cognitive impairment. In addition to tau, amyloid-beta precursor protein, and amyloid-beta peptides (Abeta), these targets include all three high-affinity neurotrophin receptors and the fibroblast growth factor (FGF) system, synapse markers, glutamate receptors (GluRs) and transporters, and dopamine (DA) receptors, particularly the D2 subtype. Gene-based candidates for Parkinson's disease (PD) include the ubiquitin-proteosome system, scavengers of reactive oxygen species, brain-derived neurotrophic factor (BDNF), its receptor, TrkB, and downstream target early growth response 1, Nurr-1, and signaling through protein kinase C and RAS pathways. Increasing variability and decreases in brain mRNA production from middle age to old age suggest that cognitive impairments during normal aging may be addressed by drugs that restore antioxidant, DNA repair, and synaptic functions including those of DA to levels of younger adults. Studies in schizophrenia identify robust decreases in genes for GABA function, including glutamic acid decarboxylase, HINT1, glutamate transport and GluRs, BDNF and TrkB, numerous 14-3-3 protein family members, and decreases in genes for CNS synaptic and metabolic functions, particularly glycolysis and ATP generation. Many of these metabolic genes are increased by insulin and muscarinic agonism, both of which are therapeutic in psychosis. Differential genomic signals are relatively sparse in bipolar disorder, but include deficiencies in the expression of 14-3-3 protein members, implicating these chaperone proteins and the neurotransmitter pathways they support as possible drug targets. Brains from persons with major depressive disorder reveal decreased expression for genes in glutamate transport and metabolism, neurotrophic signaling (eg, FGF, BDNF and VGF), and MAP kinase pathways. Increases in these pathways in the brains of animals exposed to electroconvulsive shock and antidepressant treatments identify neurotrophic and angiogenic growth factors and second messenger stimulation as therapeutic approaches for the treatment of depression.

Downregulation of Ccnd1 and Hes6 in rat hippocampus after chronic exposure to the antidepressant paroxetine

OBJECTIVE

The mechanism of action of antidepressant drugs is not fully understood. Application of genomic methods enables the identification of biochemical pathways that are regulated by antidepressants, and this may provide novel clues to the molecular and cellular actions of these drugs. The present study examined gene expression profiles in the hippocampus of rats exposed to chronic antidepressant treatment.

METHODS

Animals were treated for 12 days with the selective serotonin reuptake inhibitor paroxetine; then, hippocampal ribonucleic acid was recovered, and changes in gene expression were assessed by microarray analysis.

RESULTS

A total of 160 genes that showed differential expression after paroxetine exposure were identified. Using functional relevance and observed fold change as selection criteria, the expression changes in a subset of these genes were confirmed by quantitative polymerase chain reaction.

CONCLUSION

Of this subset, only two genes, cyclin D1 (Ccnd1) and hairy and enhancer of split 6 (Hes6), showed robust and consistent changes in expression. Both genes were downregulated by paroxetine, and both have been previously implicated in neurogenesis. Further investigation of these two genes may provide new insight into the mechanism of action of antidepressants.

Shared gene expression alterations in schizophrenia and bipolar disorder

BACKGROUND

Schizophrenia and bipolar disorder together affect approximately 2.5% of the world population, and their etiologies are thought to involve multiple genetic variants and environmental influences. The analysis of gene expression patterns in brain may provide a characteristic signature for each disorder.

METHODS

RNA samples from the dorsolateral prefrontal cortex (Brodmann area 46) consisting of individuals with schizophrenia (SZ), bipolar disorder (BPD), and control subjects were tested on the Codelink Human 20K Bioarray platform. Selected transcripts were validated by quantitative real-time polymerase chain reaction (PCR). The strong effects of age, gender, and pH in the analysis of differential gene expression were controlled by analysis of covariance (ANCOVA). Criteria for differential gene expression were 1) a gene was significantly dysregulated in both BPD and SZ compared with control subjects and 2) significant in ANCOVA analysis with samples that have a pH above the median of the sample.

RESULTS

A list of 78 candidate genes passed these two criteria in BPD and SZ and was overrepresented for functional categories of nervous system development, immune system development and response, and cell death. Five dysregulated genes were confirmed with quantitative Q-PCR in both BPD and SZ. Three genes were highly enriched in brain expression (AGXT2L1, SLC1A2, and TU3A). The distribution of AGXT2L1 expression in control subjects versus BPD and SZ was highly significant (Fisher's Exact Test, p < 10(-06)).

CONCLUSIONS

These results suggest a partially shared molecular profile for both disorders and offer a window into discovery of common pathophysiology that might lead to core treatments.

Phenomic, convergent functional genomic, and biomarker studies in a stress-reactive genetic animal model of bipolar disorder and co-morbid alcoholism

We had previously identified the clock gene D-box binding protein (Dbp) as a potential candidate gene for bipolar disorder and for alcoholism, using a Convergent Functional Genomics (CFG) approach. Here we report that mice with a homozygous deletion of DBP have lower locomotor activity, blunted responses to stimulants, and gain less weight over time. In response to a chronic stress paradigm, these mice exhibit a diametric switch in these phenotypes. DBP knockout mice are also activated by sleep deprivation, similar to bipolar patients, and that activation is prevented by treatment with the mood stabilizer drug valproate. Moreover, these mice show increased alcohol intake following exposure to stress. Microarray studies of brain and blood reveal a pattern of gene expression changes that may explain the observed phenotypes. CFG analysis of the gene expression changes identified a series of novel candidate genes and blood biomarkers for bipolar disorder, alcoholism, and stress reactivity.

Antidepressant drugs modulate growth factors in cultured cells

BACKGROUND

Different classes of antidepressant drugs are used as a treatment for depression by activating the catecholinergic system. In addition, depression has been associated with decrease of growth factors, which causes insufficient axonal sprouting and reduced neuronal damage repair. In this study, antidepressant treatments are analyzed in a cell culture system, to study the modulation of growth factors.

RESULTS

We quantified the transcription of several growth factors in three cell lines after application of antidepressant drugs by real time polymerase chain reaction. Antidepressant drugs counteracted against phorbolester-induced deregulation of growth factors in PMA-differentiated neuronal SY5Y cells. We also found indications in a pilot experiment that magnetic stimulation could possibly modify BDNF in the cell culture system.

CONCLUSION

The antidepressant effects antidepressant drugs might be explained by selective modulation of growth factors, which subsequently affects neuronal plasticity.

Constitutional polymorphisms of prostate cancer: prognostic and diagnostic implications

Prostate cancer is the most common cancer diagnosis in men. While often perceived as a slow, indolent malignancy, prostate cancer trails only lung cancer among cancer-related mortality in men. Current diagnosis and treatment algorithms are plagued by overdiagnosis of non-lethal indolent prostate cancer with no proven means to predict, detect, and prevent aggressive lethal prostate cancer in men most at risk. These challenges are particularly concerning for African-American men who demonstrate increased rates of prostate cancer incidence and mortality when compared to other ethnic groups. With the completion of the human genome project, technology and techniques now exist to differentiate cancer from normal tissues based on the expression patterns of thousands of genes assessed simultaneously on a single microarray gene 'chip'. This platform has greatly improved our understanding of genes that regulate tumor behavior once cancer is established. Microarrays can also be utilized in patients without cancer to determine which patients are at high risk for tumor development and in need of rational prevention strategies. Constitutional single nucleotide polymorphisms (SNPs) are one source of genetic variation and may serve as a means to identify these high-risk individuals. SNPs are single nucleotide base pair changes within a gene which occur in one percent or more of the population. SNPs can contribute to a disease state by altering the function of a protein encoded by a gene without affecting gene expression. This review will examine the current understanding of constitutional SNPs associated with prostate cancer carcinogenesis, highlight two current diagnostic array platforms and discuss implications for future prevention and screening programs.

Meta-analysis of 12 genomic studies in bipolar disorder

Multiple genome-wide expression studies of bipolar disorder have been published. However, a unified picture of the genomic basis for the disease has not yet emerged. Genes identified in one study often fail to be identified in other studies, prompting the question of whether microarray studies in the brain are inherently unreliable. To answer this question, we performed a meta-analysis of 12 microarray studies of bipolar disorder. These studies included >500 individual array samples, on a range of microarray platforms and brain regions. Although we confirmed that individual studies showed some differences in results, clear and striking regulation patterns emerged across the studies. These patterns were found at the individual gene level, at the functional level, and at the broader pathway level. The patterns were generally found to be reproducible across platform and region, and were highly statistically significant. We show that the seeming discordance between the studies was primarily a result of the following factors, which are also typical for other brain array studies: (1) Sample sizes were, in retrospect, too small; (2) criteria were at once too restrictive (generally focusing on fold changes >1.5) and too broad (generally using p < 0.05 or p < 0.01 as criteria for significance); and (3) statistical adjustments were not consistently applied for confounders. In addition to these general conclusions, we also summarize the primary biological findings of the meta-analysis, focusing on areas that confirm previous research and also on novel findings.

Sharing and reusing gene expression profiling data in neuroscience

As public availability of gene expression profiling data increases, it is natural to ask how these data can be used by neuroscientists. Here we review the public availability of high-throughput expression data in neuroscience and how it has been reused, and tools that have been developed to facilitate reuse. There is increasing interest in making expression data reuse a routine part of the neuroscience tool-kit, but there are a number of challenges. Data must become more readily available in public databases; efforts to encourage investigators to make data available are important, as is education on the benefits of public data release. Once released, data must be better-annotated. Techniques and tools for data reuse are also in need of improvement. Integration of expression profiling data with neuroscience-specific resources such as anatomical atlases will further increase the value of expression data.

Mitochondrial involvement in psychiatric disorders

Recent findings of mitochondrial abnormalities in brains from subjects with neurological disorders have led to a renewed search for mitochondrial abnormalities in psychiatric disorders. A growing body of evidence suggests that there is mitochondrial dysfunction in schizophrenia, bipolar disorder, and major depressive disorder, including evidence from electron microscopy, imaging, gene expression, genotyping, and sequencing studies. Specific evidence of dysfunction such as increased common deletion and decreased gene expression in mitochondria in psychiatric illnesses suggests that direct examination of mitochondrial DNA from postmortem brain cells may provide further details of mitochondrial alterations in psychiatric disorders.

Comprehensive gene expression analysis in bipolar disorder

OBJECTIVE

To review recent findings by DNA microarray in bipolar disorder (BD).

METHOD

A literature search was performed.

RESULTS

Comprehensive gene expression analysis in the brain, peripheral blood cells, and olfactory neuroepithelium would be a promising strategy for the research of BD. To date, alterations in glutamate receptors (GR), mitochondria-related genes, chaperone genes, oligodendrocyte genes, and markers of gamma amino butyric acidergic (GABAergic) neurons in postmortem brains are replicated by several different strategies. However, alterations in mitochondria-related genes are associated with agonal factors, sample pH, and effects of drugs. Analysis of blood cells showed altered endoplasmic reticulum stress pathway and other molecular cascades. Analysis of olfactory epithelium showed altered expression of genes associated with apoptosis.

CONCLUSIONS

These findings warrant that comprehensive gene expression analysis by DNA microarray will be useful to identify the molecular cascades responsible for BD.

Association analysis of HSP90B1 with bipolar disorder

Pathophysiological role of endoplasmic reticulum (ER) stress response signaling has been suggested for bipolar disorder. The goal of this study was to test the genetic association between bipolar disorder and an ER chaperone gene, HSP90B1 (GRP94/gp96), which is located on a candidate locus, 12q23.3. We tested the genetic association between bipolar disorder and HSP90B1 by case-control studies in two independent Japanese sample sets and by a transmission disequilibrium test (TDT) in NIMH Genetics initiative bipolar trio samples (NIMH trios). We also performed gene expression analysis of HSP90B1 in lymphoblastoid cells. Among the 11 SNPs tested, rs17034977 showed significant association in both Japanese sample sets. The frequency of the SNP was lower in NIMH samples than in Japanese samples and there was no significant association in NIMH trios. Gene expression analysis of HSP90B1 in lymphoblastoid cells suggested a possible relationship between the associated SNP and mRNA levels. HSP90B1 may have a pathophysiological role in bipolar disorder in the Japanese population, though further study will be needed to understand the underlying functional mechanisms.

Methodological considerations for gene expression profiling of human brain

Gene expression profiles of postmortem brain tissue represent important resources for understanding neuropsychiatric illnesses. The impact(s) of quality covariables on the analysis and results of gene expression studies are important questions. This paper addressed critical variables which might affect gene expression in two brain regions. Four broad groups of quality indicators in gene expression profiling studies (clinical, tissue, RNA, and microarray quality) were identified. These quality control indicators were significantly correlated, however one quality variable did not account for the total variance in microarray gene expression. The data showed that agonal factors and low pH correlated with decreased integrity of extracted RNA in two brain regions. These three parameters also modulated the significance of alterations in mitochondrial-related genes. The average F-ratio summaries across all transcripts showed that RNA degradation from the AffyRNAdeg program accounted for higher variation than all other quality factors. Taken together, these findings confirmed prior studies, which indicated that quality parameters including RNA integrity, agonal factors, and pH are related to differences in gene expression profiles in postmortem brain. Individual candidate genes can be evaluated with these quality parameters in post hoc analysis to help strengthen the relevance to psychiatric disorders. We find that clinical, tissue, RNA, and microarray quality are all useful variables for collection and consideration in study design, analysis, and interpretation of gene expression results in human postmortem studies.

Review of the literature examining the correlation among DNA microarray technologies

DNA microarray technologies are used in a variety of biological disciplines. The diversity of platforms and analytical methods employed has raised concerns over the reliability, reproducibility and correlation of data produced across the different approaches. Initial investigations (years 2000-2003) found discrepancies in the gene expression measures produced by different microarray technologies. Increasing knowledge and control of the factors that result in poor correlation among the technologies has led to much higher levels of correlation among more recent publications (years 2004 to present). Here, we review the studies examining the correlation among microarray technologies. We find that with improvements in the technology (optimization and standardization of methods, including data analysis) and annotation, analysis across platforms yields highly correlated and reproducible results. We suggest several key factors that should be controlled in comparing across technologies, and are good microarray practice in general.

Cross platform microarray analysis for robust identification of differentially expressed genes

BACKGROUND

Microarrays have been widely used for the analysis of gene expression and several commercial platforms are available. The combined use of multiple platforms can overcome the inherent biases of each approach, and may represent an alternative that is complementary to RT-PCR for identification of the more robust changes in gene expression profiles. In this paper, we combined statistical and functional analysis for the cross platform validation of two oligonucleotide-based technologies, Affymetrix (AFFX) and Applied Biosystems (ABI), and for the identification of differentially expressed genes.

RESULTS

In this study, we analysed differentially expressed genes after treatment of an ovarian carcinoma cell line with a cell cycle inhibitor. Treated versus control RNA was analysed for expression of 16425 genes represented on both platforms. We assessed reproducibility between replicates for each platform using CAT plots, and we found it high for both, with better scores for AFFX. We then applied integrative correlation analysis to assess reproducibility of gene expression patterns across studies, bypassing the need for normalizing expression measurements across platforms. We identified 930 genes as differentially expressed on AFFX and 908 on ABI, with approximately 80% common to both platforms. Despite the different absolute values, the range of intensities of the differentially expressed genes detected by each platform was similar. ABI showed a slightly higher dynamic range in FC values, which might be associated with its detection system. 62/66 genes identified as differentially expressed by Microarray were confirmed by RT-PCR.

CONCLUSION

In this study we present a cross-platform validation of two oligonucleotide-based technologies, AFFX and ABI. We found good reproducibility between replicates, and showed that both platforms can be used to select differentially expressed genes with substantial agreement. Pathway analysis of the affected functions identified themes well in agreement with those expected for a cell cycle inhibitor, suggesting that this procedure is appropriate to facilitate the identification of biologically relevant signatures associated with compound treatment. The high rate of confirmation found for both common and platform-specific genes suggests that the combination of platforms may overcome biases related to probe design and technical features, thereby accelerating the identification of trustworthy differentially expressed genes.

Molecular characterization of human multiple myeloma cell lines by integrative genomics: Insights into the biology of the disease

To investigate the patterns of genetic lesions in a panel of 23 human multiple myeloma cell lines (HMCLs), we made a genomic integrative analysis involving FISH, and both gene expression and genome-wide profiling approaches. The expression profiles of the genes targeted by the main IGH translocations showed that the WHSC1/MMSET gene involved in t(4;14)(p16;q32) was expressed at different levels in all of the HMCLs, and that the expression of the MAF gene was not restricted to the HMCLs carrying t(14;16)(q32;q23). Supervised analyses identified a limited number of genes specifically associated with t(4;14) and involved in different biological processes. The signature related to MAF/MAFB expression included the known MAF target genes CCND2 and ITGB7, as well as genes controlling cell shape and cell adhesion. Genome-wide DNA profiling allowed the identification of a gain on chromosome arm 1q in 88% of the analyzed cell lines, together with recurrent gains on 8q, 18q, 7q, and 20q; the most frequent deletions affected 1p, 13q, 17p, and 14q; and almost all of the cell lines presented LOH on chromosome 13. Two hundred and twenty-two genes were found to be simultaneously overexpressed and amplified in our panel, including the BCL2 locus at 18q21.33. Our data further support the evidence of the genomic complexity of multiple myeloma and reinforce the role of an integrated genomic approach in improving our understanding of the molecular pathogenesis of the disease. This article contains Supplementary Material available at http://www.interscience.wiley.com/jpages/1045-2257/suppmat.

Altered expression of hippocampal dentate granule neuron genes in a mouse model of human 22q11 deletion syndrome

Hemizygous deletion of a 3 Mb region of 22q11.2 is found in 1/4000 humans and produces 22q11 deletion syndrome (22q11DS). Up to 35% of 22q11DS patients develop schizophrenia, making it the second highest risk factor for schizophrenia. A mouse model for 22q11DS, the Df1/+ mouse, carries a hemizygous deletion in a region syntenic with the human deletion. Df1/+ mice are mostly viable but display deficits in prepulse inhibition and learning and memory, two common traits of schizophrenia thought to result, at least in part, from defects in hippocampal neurons. We used oligonucleotide microarrays and QRT-PCR to evaluate gene expression changes in hippocampal dentate granule neurons of Df1/+ mice versus wild-type littermates (n=12/group). The expression of only 287 genes changed with p value significance below 0.05 by microarray, yet 12 of the 21 Df1 region genes represented on the array showed highly significantly reduced expression compared to wild-type controls (33% on average, p values from 10(-3) to 10(-7)). Variants in two of these genes, COMT and PRODH, have been linked with schizophrenia. Overlap of the 287 genes with the reportedly reduced expression of mitochondrial, ubiquitin/proteasome, and synaptic plasticity genes in schizophrenia dentate granule neurons, was not significant. However, modest increases in expression of mitochondrial electron transport genes were observed in the Df1/+ mice. This perhaps indicates a compensation for mitochondrial dysfunction caused by the strongly reduced expression of the Df1 region-encoded mitochondrial enzymes proline dehydrogenase (Prodh) and thioredoxin reductase 2 (Txnrd2).

Gene array profiles of alcohol and aldehyde metabolizing enzymes in brains of C57BL/6 and DBA/2 mice

BACKGROUND

Differences in ethanol metabolizing enzymes expressed in brain have been suggested to contribute to the significant differences in ethanol (alcohol) preference between inbred C57BL/6 and DBA/2 mouse strains.

METHODS

We have utilized 2 different platforms of oligonucleotide microarray technology (CodeLink UniSet I BioArray from G.E. Healthcare and MG U74A v2.0 from Affymetrix) to simultaneously assess expression of alcohol and acetaldehyde metabolizing enzymes in the whole brain of naïve (no exposure to alcohol) C57BL/6 and DBA/2 mice.

RESULTS

There were no significant differences between the 2 strains of mice in gene expression intensity for alcohol dehydrogenases (ADH), catalase, and a number of the cytochrome P450 family of genes, which can be involved in ethanol catabolism. However, significantly higher expression of mRNA for aldehyde dehydrogenase 2 (ALDH2), an isoform mainly responsible for the catabolism of acetaldehyde, was observed in whole brains of DBA/2 mice with both platforms. Aldehyde dehydrogenase 2 protein was also higher in DBA/2 brain. Expression of aldehyde dehydrogenase 1A1 (ALDH1A1) mRNA was found to be higher in brains of DBA/2 mice, when measured with the CodeLink platform, but not when measured with Affymetrix arrays or quantitative reverse transcriptase-real-time polymerase chain reaction (qRT-PCR). The ALDH1A1 protein, however, reflected the results obtained with the CodeLink arrays and was higher in DBA/2 brain, compared with brains of C57BL/6 mice. In contrast, the expression intensity for the aldehyde dehydrogenase 7A1 (ALDH7A1) mRNA and protein was significantly higher in C57BL/6 mice than DBA/2 mice. These expression differences are consistent with more rapid metabolism of acetaldehyde in brains of DBA/2 mice.

CONCLUSIONS

The use of 2 different microarray platforms provides important cross-validation of many results, and some discrepancies can be resolved with qRT-PCR and immunoblotting. The expression differences that were validated may affect alcohol/aldehyde metabolism in brain and/or alcohol preference in the 2 strains of mice.

Multiple dysregulated pathways in nasopharyngeal carcinoma revealed by gene expression profiling

Gene expression profiling was conducted using primary human nasopharyngeal carcinoma (NPC) biopsy samples to improve the understanding of the molecular pathways defining NPC and to identify novel potential therapeutic targets. RNA samples were extracted from 36 patients suspected to have NPC and hybridized onto the Affymetrix U133A chip. NPC was diagnosed in 19 patients, 11 had lymphoid hyperplasia (LH), and 6 were "normal" biopsies. Clinical stages for these NPC patients ranged from I-IV, including one M1. All NPC patients (except the M1) were treated with curative intent, which included radiotherapy alone (4 patients), or combined with chemotherapy (14 patients). Unsupervised clustering demonstrated a distinct NPC expression pattern, compared to normal biopsies. Subsequent Significance Analysis of Microarrays (SAM) derived from 14 NPC and 6 normal samples discovered 1,089 differentially regulated genes. Pathway analyses revealed novel insights into the mechanisms leading to NPC, whereby upregulation of NFkappaB2 and survivin play central roles in increasing resistance to apoptosis, and changes in integrin and WNT/beta-catenin signaling leading to uncontrolled proliferation. The role of survivin in resisting apoptosis in NPC was confirmed by RNA interference. Our data provide novel insights into the development and progression of NPC, and suggest survivin as a novel therapeutic target for NPC.

Candidate genes, pathways and mechanisms for alcoholism: an expanded convergent functional genomics approach

We describe a comprehensive translational approach for identifying candidate genes for alcoholism. The approach relies on the cross-matching of animal model brain gene expression data with human genetic linkage data, as well as human tissue data and biological roles data, an approach termed convergent functional genomics. An analysis of three animal model paradigms, based on inbred alcohol-preferring (iP) and alcohol-non-preferring (iNP) rats, and their response to treatments with alcohol, was used. A comprehensive analysis of microarray gene expression data from five key brain regions (frontal cortex, amygdala, caudate-putamen, nucleus accumbens and hippocampus) was carried out. The Bayesian-like integration of multiple independent lines of evidence, each by itself lacking sufficient discriminatory power, led to the identification of high probability candidate genes, pathways and mechanisms for alcoholism. These data reveal that alcohol has pleiotropic effects on multiple systems, which may explain the diverse neuropsychiatric and medical pathology in alcoholism. Some of the pathways identified suggest avenues for pharmacotherapy of alcoholism with existing agents, such as angiotensin-converting enzyme (ACE) inhibitors. Experiments we carried out in alcohol-preferring rats with an ACE inhibitor show a marked modulation of alcohol intake. Other pathways are new potential targets for drug development. The emergent overall picture is that physical and physiological robustness may permit alcohol-preferring individuals to withstand the aversive effects of alcohol. In conjunction with a higher reactivity to its rewarding effects, they may able to ingest enough of this nonspecific drug for a strong hedonic and addictive effect to occur.

Systems biology in drug safety and metabolism: integration of microarray, real-time PCR and enzyme approaches

The last decade has seen a rapid expansion in the field of functional genomics, due mainly to the global gene expression profiling capabilities provided by techniques, such as microarray analysis. Application of these technologies in fields as diverse as plant research, to public health and environmental sciences, forensic science and drug research, shows the versatility of these tools and the promise they hold for revolutionizing research in the life sciences. In drug discovery, attempts have been made to use functional genomics in target identification and validation, lead selection and optimization, and in preclinical studies to predict clinical outcome. These studies have provided a plethora of data and undoubtedly expanded our understanding of genetic alterations in diseased and non-diseased states, but the benefits that these technologies hold have not yet been fully realized. This review discusses how a comprehensive approach to gene regulation studies, a 'systems biology' approach, is being applied in a drug development setting to address mechanism-based questions and issues raised by regulatory authorities.

Mitochondrial-related gene expression changes are sensitive to agonal-pH state: implications for brain disorders

Mitochondrial defects in gene expression have been implicated in the pathophysiology of bipolar disorder and schizophrenia. We have now contrasted control brains with low pH versus high pH and showed that 28% of genes in mitochondrial-related pathways meet criteria for differential expression. A majority of genes in the mitochondrial, chaperone and proteasome pathways of nuclear DNA-encoded gene expression were decreased with decreased brain pH, whereas a majority of genes in the apoptotic and reactive oxygen stress pathways showed an increased gene expression with a decreased brain pH. There was a significant increase in mitochondrial DNA copy number and mitochondrial DNA gene expression with increased agonal duration. To minimize effects of agonal-pH state on mood disorder comparisons, two classic approaches were used, removing all subjects with low pH and agonal factors from analysis, or grouping low and high pH as a separate variable. Three groups of potential candidate genes emerged that may be mood disorder related: (a) genes that showed no sensitivity to pH but were differentially expressed in bipolar disorder or major depressive disorder; (b) genes that were altered by agonal-pH in one direction but altered in mood disorder in the opposite direction to agonal-pH and (c) genes with agonal-pH sensitivity that displayed the same direction of changes in mood disorder. Genes from these categories such as NR4A1 and HSPA2 were confirmed with Q-PCR. The interpretation of postmortem brain studies involving broad mitochondrial gene expression and related pathway alterations must be monitored against the strong effect of agonal-pH state. Genes with the least sensitivity to agonal-pH could present a starting point for candidate gene search in neuropsychiatric disorders.

An online database for brain disease research

Background

The Stanley Medical Research Institute online genomics database (SMRIDB) is a comprehensive web-based system for understanding the genetic effects of human brain disease (i.e. bipolar, schizophrenia, and depression). This database contains fully annotated clinical metadata and gene expression patterns generated within 12 controlled studies across 6 different microarray platforms.

Description

A thorough collection of gene expression summaries are provided, inclusive of patient demographics, disease subclasses, regulated biological pathways, and functional classifications.

Conclusion

The combination of database content, structure, and query speed offers researchers an efficient tool for data mining of brain disease complete with information such as: cross-platform comparisons, biomarkers elucidation for target discovery, and lifestyle/demographic associations to brain diseases.

Long versus short oligonucleotide microarrays for the study of gene expression in nonhuman primates

The high degree of sequence similarity between human and nonhuman primate (NHP) genomic DNA suggests that human genome sequence-based DNA microarrays may be used effectively to study gene expression in NHP disease models. In the present study, two distinct commercially available human genome microarray platforms, the Affymetrix HG U133A GeneChip System utilizing Human Genome U133A GeneChips and the Applied Biosystems Expression Array System utilizing the Human Genome Survey Microarray, were used to identify and characterize gene expression changes in the anterior cerebellum of a macaque monkey model of human alcoholism. The Affymetrix microarray consists of eleven short oligonucleotide probe sets for each gene while the Applied Biosystems Microarray uses a single long oligonucleotide per gene. Cross-mapping of probes revealed a total of 11,542 genes that are represented on both microarray platforms. Absolute measures of gene expression ("present calls") from the cerebellum RNA samples were 65-70% (Applied Biosystems Expression Array System) and 27-30% (AffymetrixGeneChip System) among these common gene targets. Analysis of variance (ANOVA; p<0.05; >1.2 fold change; detected on at least 50% of the arrays) indicated 932 and 515 differentially expressed genes for the Applied Biosystems and Affymetrix microarrays, respectively. Significance analysis of microarrays (SAM) identified 255 significant genes at 5% false discovery rate (FDR) for the Applied Biosystems data set and five significant genes at 60% FDR (minimum FDR) for the Affymetrix data set. TaqMan assay-based real-time PCR validation of a number of differentially-expressed genes yielded results that agreed well with the array data in the majority of comparisons. This study demonstrates that human sequence-based DNA arrays can be used effectively to detect differential gene expression in an NHP disease model and provides evidence that the use of this long oligonucleotide-based microarray platform may be more suitable for cross-species gene expression studies than a short oligonucleotide-based system.

Experimental comparison and cross-validation of the Affymetrix and Illumina gene expression analysis platforms

The growth in popularity of RNA expression microarrays has been accompanied by concerns about the reliability of the data especially when comparing between different platforms. Here, we present an evaluation of the reproducibility of microarray results using two platforms, Affymetrix GeneChips and Illumina BeadArrays. The study design is based on a dilution series of two human tissues (blood and placenta), tested in duplicate on each platform. The results of a comparison between the platforms indicate very high agreement, particularly for genes which are predicted to be differentially expressed between the two tissues. Agreement was strongly correlated with the level of expression of a gene. Concordance was also improved when probes on the two platforms could be identified as being likely to target the same set of transcripts of a given gene. These results shed light on the causes or failures of agreement across microarray platforms. The set of probes we found to be most highly reproducible can be used by others to help increase confidence in analyses of other data sets using these platforms.

Deficient hippocampal neuron expression of proteasome, ubiquitin, and mitochondrial genes in multiple schizophrenia cohorts

BACKGROUND

Hippocampal dentate granule neurons are altered in schizophrenia, but it is unknown if their gene expressions change in schizophrenia or other psychiatric diseases.

METHODS

Laser-captured dentate granule neurons from two groups of schizophrenia and control cases and from major depression and bipolar disease cases were examined for alterations in gene expression using complementary DNA (cDNA) microarrays and reverse transcription polymerase chain reaction (RT-PCR).

RESULTS

Compared with 24 control cases, the 22 schizophrenia patients in both groups revealed decreases in clusters of genes that encode for protein turnover (proteasome subunits and ubiquitin), mitochondrial oxidative energy metabolism (isocitrate, lactate, malate, nicotinamide adenine dinucleotide [NADH], and succinate dehydrogenases; cytochrome C oxidase; adenosine triphosphate [ATP] synthase), and genes associated with neurite outgrowth, cytoskeletal proteins, and synapse plasticity. These changes were not obtained in 9 bipolar cases or 10 major depression cases and were not associated with age, sex, brain weight, body weight, postmortem interval, or drug history. Brain pH contributed to the variance of some genes but was mostly independent of the disease effect.

CONCLUSIONS

Decreases in hippocampal neuron gene expression are consistent with brain imaging and microarray studies of the frontal cortex in schizophrenia. A mitochondrial and ubiquitin-proteasome hypofunctioning of dentate granule neurons may contribute to the deficits of schizophrenia.

Evaluation of the similarity of gene expression data estimated with SAGE and Affymetrix GeneChips

Background

Serial Analysis of Gene Expression (SAGE) and microarrays have found awidespread application, but much ambiguity exists regarding the evaluation of these technologies. Cross-platform utilization of gene expression data from the SAGE and microarray technology could reduce the need for duplicate experiments and facilitate a more extensive exchange of data within the research community. This requires a measure for the correspondence of the different gene expression platforms. To date, a number of cross-platform evaluations (including a few studies using SAGE and Affymetrix GeneChips) have been conducted showing a variable, but overall low, concordance. This study evaluates these overall measures and introduces the between-ratio difference as a concordance measure pergene.

Results

In this study, gene expression measurements of Unigene clusters represented by both Affymetrix GeneChips HG-U133A and SAGE were compared using two independent RNA samples. After matching of the data sets the final comparison contains a small data set of 1094 unique Unigene clusters, which is unbiased with respect to expression level. Different overall correlation approaches, like Up/Down classification, contingency tables and correlation coefficients were used to compare both platforms. In addition, we introduce a novel approach to compare two platforms based on the calculation of differences between expression ratios observed in each platform for each individual transcript. This approach results in a concordance measure per gene (with statistical probability value), as opposed to the commonly used overall concordance measures between platforms.

Conclusion

We can conclude that intra-platform correlations are generally good, but that overall agreement between the two platforms is modest. This might be due to the binomially distributed sampling variation in SAGE tag counts, SAGE annotation errors and the intensity variation between probe sets of a single gene in Affymetrix GeneChips. We cannot identify or advice which platform performs better since both have their (dis)-advantages. Therefore it is strongly recommended to perform follow-up studies of interesting genes using additional techniques. The newly introduced between-ratio difference is a filtering-independent measure for between-platform concordance. Moreover, the between-ratio difference per gene can be used to detect transcripts with similar regulation on both platforms.