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Combined application of behavior genetics and microarray analysis to identify regional expression themes and gene-behavior associations.

ABSTRACT: In this report we link candidate genes to complex behavioral phenotypes by using a behavior genetics approach. Gene expression signatures were generated for the prefrontal cortex, ventral striatum, temporal lobe, periaqueductal gray, and cerebellum in eight inbred strains from priority group A of the Mouse Phenome Project. Bioinformatic analysis of regionally enriched genes that were conserved across all strains revealed both functional and structural specialization of particular brain regions. For example, genes encoding proteins with demonstrated anti-apoptotic function were over-represented in the cerebellum, whereas genes coding for proteins associated with learning and memory were enriched in the ventral striatum, as defined by the Expression Analysis Systematic Explorer (EASE) application. Association of regional gene expression with behavioral phenotypes was exploited to identify candidate behavioral genes. Phenotypes that were investigated included anxiety, drug-naive and ethanol-induced distance traveled across a grid floor, and seizure susceptibility. Several genes within the glutamatergic signaling pathway (i.e., NMDA/glutamate receptor subunit 2C, calmodulin, solute carrier family 1 member 2, and glutamine synthetase) were identified in a phenotype-dependent and region-specific manner. In addition to supporting evidence in the literature, many of the genes that were identified could be mapped in silico to surrogate behavior-related quantitative trait loci. The approaches and data set described herein serve as a valuable resource to investigate the genetic underpinning of complex behaviors.

SUBMITTER: Letwin NE

PROVIDER: S-EPMC6675305 | biostudies-literature | 2006 May

REPOSITORIES: biostudies-literature

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Combined application of behavior genetics and microarray analysis to identify regional expression themes and gene-behavior associations.

Letwin Noah E NE Kafkafi Neri N Benjamini Yoav Y Mayo Cheryl C Frank Bryan C BC Luu Troung T Lee Norman H NH Elmer Greg I GI

The Journal of neuroscience : the official journal of the Society for Neuroscience 20060501 20

In this report we link candidate genes to complex behavioral phenotypes by using a behavior genetics approach. Gene expression signatures were generated for the prefrontal cortex, ventral striatum, temporal lobe, periaqueductal gray, and cerebellum in eight inbred strains from priority group A of the Mouse Phenome Project. Bioinformatic analysis of regionally enriched genes that were conserved across all strains revealed both functional and structural specialization of particular brain regions. ...[more]

PMID: 16707780

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Project description:BackgroundTo utilize the large volume of gene expression information generated from different microarray experiments, several meta-analysis techniques have been developed. Despite these efforts, there remain significant challenges to effectively increasing the statistical power and decreasing the Type I error rate while pooling the heterogeneous datasets from public resources. The objective of this study is to develop a novel meta-analysis approach, Consistent Differential Expression Pattern (CDEP), to identify genes with common differential expression patterns across different datasets.ResultsWe combined False Discovery Rate (FDR) estimation and the non-parametric RankProd approach to estimate the Type I error rate in each microarray dataset of the meta-analysis. These Type I error rates from all datasets were then used to identify genes with common differential expression patterns. Our simulation study showed that CDEP achieved higher statistical power and maintained low Type I error rate when compared with two recently proposed meta-analysis approaches. We applied CDEP to analyze microarray data from different laboratories that compared transcription profiles between metastatic and primary cancer of different types. Many genes identified as differentially expressed consistently across different cancer types are in pathways related to metastatic behavior, such as ECM-receptor interaction, focal adhesion, and blood vessel development. We also identified novel genes such as AMIGO2, Gem, and CXCL11 that have not been shown to associate with, but may play roles in, metastasis.ConclusionsCDEP is a flexible approach that borrows information from each dataset in a meta-analysis in order to identify genes being differentially expressed consistently. We have shown that CDEP can gain higher statistical power than other existing approaches under a variety of settings considered in the simulation study, suggesting its robustness and insensitivity to data variation commonly associated with microarray experiments.AvailabilityCDEP is implemented in R and freely available at: http://genomebioinfo.musc.edu/CDEP/.Contactzhengw@musc.edu.

Project description:BACKGROUND:Long non-coding RNAs (lncRNAs) have previously been emerged as key players in a series of biological processes. Dysregulation of lncRNA is correlated to human diseases including neurological disorders. Here, we developed a multi-step bioinformatics analysis to study the functions of a particular Down syndrome-associated gene DSCR9 including the lncRNAs. The method is named correlation-interaction-network (COIN), based on which a pipeline is implemented. Co-expression gene network analysis and biological network analysis results are presented. METHODS:We identified the regulation function of DSCR9, a lncRNA transcribed from the Down syndrome critical region (DSCR) of chromosome 21, by analyzing its co-expression genes from over 1700 sets and nearly 60,000 public Affymetrix human U133-Plus 2 transcriptional profiling microarrays. After proper evaluations, a threshold is chosen to filter the data and get satisfactory results. Microarray data resource is from EBI database and protein-protein interaction (PPI) network information is incorporated from the most complete network databases. PPI integration strategy guarantees complete information regarding DSCR9. Enrichment analysis is performed to identify significantly correlated pathways. RESULTS:We found that the most significant pathways associated with the top DSCR9 co-expressed genes were shown to be involved in neuro-active ligand-receptor interaction (GLP1R, HTR4, P2RX2, UCN3, and UTS2R), calcium signaling pathway (CACNA1F, CACNG4, HTR4, P2RX2, and SLC8A3), neuronal system (KCNJ5 and SYN1) by the KEGG, and GO analysis. The A549 and U251 cell lines with stable DSCR9 overexpression were constructed. We validated 10 DSCR9 co-expression genes by qPCR in both cell lines with over 70% accuracy. CONCLUSIONS:DSCR9 was highly correlated with genes that were known as important factors in the developments and functions of nervous system, indicating that DSCR9 may regulate neurological proteins regarding Down syndrome and other neurological-related diseases. The pipeline can be properly adjusted to other applications.

Project description:Meningiomas are the most common primary brain tumors bearing in a minority of cases an aggressive phenotype. Although meningiomas are stratified according to their histology and clinical behavior, the underlying molecular genetics predicting aggressiveness are not thoroughly understood. We performed whole transcript expression profiling in 10 grade I and four grade II meningiomas, three of which invaded the brain. Microarray expression analysis identified deleted in colorectal cancer (DCC) as a differentially expressed gene (DEG) enabling us to cluster meningiomas into DCC low expression (3 grade I and 3 grade II tumors), DCC medium expression (2 grade I and 1 grade II tumors), and DCC high expression (5 grade I tumors) groups. Comparison between the DCC low expression and DCC high expression groups resulted in 416 DEGs (p-value<0.05; fold change>2). The most significantly downregulated genes in the DCC low expression group comprised DCC, phosphodiesterase 1C (PDE1C), calmodulin-dependent 70kDa olfactomedin 2 (OLFM2), glutathione S-transferase mu 5 (GSTM5), phosphotyrosine interaction domain containing 1 (PID1), sema domain, transmembrane domain (TM) and cytoplasmic domain, (semaphorin) 6D (SEMA6D), and indolethylamine N-methyltransferase (INMT). The most significantly upregulated genes comprised chromosome 5 open reading frame 63 (C5orf63), homeodomain interacting protein kinase 2 (HIPK2), and basic helix-loop-helix family, member e40 (BHLHE40). Biofunctional analysis identified as predicted top upstream regulators beta-estradiol, TGFB1, Tgf beta complex, LY294002, and dexamethasone and as predicted top regulator effectors NFkB, PIK3R1, and CREBBP. The microarray expression data served also for a comparison between meningiomas from female and male patients and for a comparison between brain invasive and non-invasive meningiomas resulting in a number of significant DEGs and related biofunctions. In conclusion, based on its expression levels, DCC may constitute a valid biomarker to identify those benign meningiomas at risk for progression.

Dataset Information

Combined application of behavior genetics and microarray analysis to identify regional expression themes and gene-behavior associations.

Publications

Combined application of behavior genetics and microarray analysis to identify regional expression themes and gene-behavior associations.

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