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ABSTRACT: The current study examined the changes in striatal gene network structure induced by short-term selective breeding from a heterogeneous stock for haloperidol response. Brain (striatum) gene expression data were obtained using the Illumina WG 8.2 array, and the datasets from responding and non-responding selected lines were independently interrogated using a weighted gene coexpression network analysis (WGCNA). We detected several gene modules (groups of coexpressed genes) in each dataset; the membership of the modules was found to be largely concordant, and a consensus network was constructed. Further validation of the network topology showed that using approximately 35 samples is sufficient to reliably infer the transcriptome network. An in-depth analysis showed significant changes in network structure and gene connectivity associated with the selected lines; these changes were validated using a bootstrapping procedure. The most dramatic changes were associated with a gene module richly annotated with neurobehavioral traits. The changes in network connectivity were concentrated in the links between this module and the rest of the network, in addition to changes within the module; this observation is consistent with recent results in protein and metabolic networks. These results suggest that a network-based strategy will help identify the genetic factors associated with haloperidol response.
Genes Brain and Behavior 10/2011; 11(1):29-37. · 3.48 Impact Factor
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ABSTRACT: Background: With the advent of the GeneChip Exon Arrays, it is now possible to extract "exon-level" expression estimates, allowing for detection of alternative splicing events, one of the primary mechanisms of transcript diversity. In the context of (1) a complex trait use case and (2) a human cerebellum vs. heart comparison on previously validated data, we present a transcript-based statistical model and validation framework to allow detection of alternative exon usage (AEU) between different groups. To illustrate the approach, we detect and confirm differences in exon usage in the two of the most widely studied mouse genetic models (the C57BL/6J and DBA/2J inbred strains) and in a human dataset. Results: We developed a computational framework that consists of probe level annotation mapping and statistical modeling to detect putative AEU events, as well as visualization and alignment with known splice events. We show a dramatic improvement (∼25 fold) in the ability to detect these events using the appropriate annotation and statistical model which is actually specified at the transcript level, as compared with the transcript cluster/gene-level annotation used on the array. An additional component of this workflow is a probe index that allows ranking AEU candidates for validation and can aid in identification of false positives due to single nucleotide polymorphisms. Discussion: Our work highlights the importance of concordance between the functional unit interrogated (e.g., gene, transcripts) and the entity (e.g., exon, probeset) within the statistical model. The framework we present is broadly applicable to other platforms (including RNAseq).
Frontiers in Neuroscience 01/2011; 5:69.
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ABSTRACT: C57BL/6J (B6) and DBA/2J (D2) are two of the most commonly used inbred mouse strains in neuroscience research. However, the only currently available mouse genome is based entirely on the B6 strain sequence. Subsequently, oligonucleotide microarray probes are based solely on this B6 reference sequence, making their application for gene expression profiling comparisons across mouse strains dubious due to their allelic sequence differences, including single nucleotide polymorphisms (SNPs). The emergence of next-generation sequencing (NGS) and the RNA-Seq application provides a clear alternative to oligonucleotide arrays for detecting differential gene expression without the problems inherent to hybridization-based technologies. Using RNA-Seq, an average of 22 million short sequencing reads were generated per sample for 21 samples (10 B6 and 11 D2), and these reads were aligned to the mouse reference genome, allowing 16,183 Ensembl genes to be queried in striatum for both strains. To determine differential expression, 'digital mRNA counting' is applied based on reads that map to exons. The current study compares RNA-Seq (Illumina GA IIx) with two microarray platforms (Illumina MouseRef-8 v2.0 and Affymetrix MOE 430 2.0) to detect differential striatal gene expression between the B6 and D2 inbred mouse strains. We show that by using stringent data processing requirements differential expression as determined by RNA-Seq is concordant with both the Affymetrix and Illumina platforms in more instances than it is concordant with only a single platform, and that instances of discordance with respect to direction of fold change were rare. Finally, we show that additional information is gained from RNA-Seq compared to hybridization-based techniques as RNA-Seq detects more genes than either microarray platform. The majority of genes differentially expressed in RNA-Seq were only detected as present in RNA-Seq, which is important for studies with smaller effect sizes where the sensitivity of hybridization-based techniques could bias interpretation.
PLoS ONE 01/2011; 6(3):e17820. · 4.09 Impact Factor
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ABSTRACT: The current study focused on the extent genetic diversity within a species (Mus musculus) affects gene co-expression network structure. To examine this issue, we have created a new mouse resource, a heterogeneous stock (HS) formed from the same eight inbred strains that have been used to create the collaborative cross (CC). The eight inbred strains capture > 90% of the genetic diversity available within the species. For contrast with the HS-CC, a C57BL/6J (B6) × DBA/2J (D2) F2 intercross and the HS4, derived from crossing the B6, D2, BALB/cJ and LP/J strains, were used. Brain (striatum) gene expression data were obtained using the Illumina Mouse WG 6.1 array, and the data sets were interrogated using a weighted gene co-expression network analysis (WGCNA).
Genes reliably detected as expressed were similar in all three data sets as was the variability of expression. As measured by the WGCNA, the modular structure of the transcriptome networks was also preserved both on the basis of module assignment and from the perspective of the topological overlap maps. Details of the HS-CC gene modules are provided; essentially identical results were obtained for the HS4 and F2 modules. Gene ontology annotation of the modules revealed a significant overrepresentation in some modules for neuronal processes, e.g., central nervous system development. Integration with known protein-protein interactions data indicated significant enrichment among co-expressed genes. We also noted significant overlap with markers of central nervous system cell types (neurons, oligodendrocytes and astrocytes). Using the Allen Brain Atlas, we found evidence of spatial co-localization within the striatum for several modules. Finally, for some modules it was possible to detect an enrichment of transcription binding sites. The binding site for Wt1, which is associated with neurodegeneration, was the most significantly overrepresented.
Despite the marked differences in genetic diversity, the transcriptome structure was remarkably similar for the F2, HS4 and HS-CC. These data suggest that it should be possible to integrate network data from simple and complex crosses. A careful examination of the HS-CC transcriptome revealed the expected structure for striatal gene expression. Importantly, we demonstrate the integration of anatomical and network expression data.
BMC Genomics 10/2010; 11:585. · 4.07 Impact Factor
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ABSTRACT: Evidence for genetic linkage to alcohol and other substance dependence phenotypes in areas of the human and mouse genome have now been reported with some consistency across studies. However, the question remains as to whether the genes that underlie the alcohol-related behaviors seen in mice are the same as those that underlie the behaviors observed in human alcoholics. The aims of the current set of analyses were to identify a small set of alcohol-related phenotypes in human and in mouse by which to compare quantitative trait locus (QTL) data between the species using syntenic mapping. These analyses identified that QTLs for alcohol consumption and acute and chronic alcohol withdrawal on distal mouse chromosome 1 are syntenic to a region on human chromosome 1q where a number of studies have identified QTLs for alcohol-related phenotypes. Additionally, a QTL on human chromosome 15 for alcohol dependence severity/withdrawal identified in two human studies was found to be largely syntenic with a region on mouse chromosome 9, where two groups have found QTLs for alcohol preference. In both of these cases, while the QTLs were found to be syntenic, the exact phenotypes between humans and mice did not necessarily overlap. These studies demonstrate how this technique might be useful in the search for genes underlying alcohol-related phenotypes in multiple species. However, these findings also suggest that trying to match exact phenotypes in humans and mice may not be necessary or even optimal for determining whether similar genes influence a range of alcohol-related behaviors between the two species.
Addiction Biology 04/2010; 15(2):185-99. · 4.83 Impact Factor
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ABSTRACT: Here, we map a quantitative trait locus (QTL) with a large effect on predisposition to barbiturate (pentobarbital) withdrawal to a 0.44 Mb interval of mouse chromosome 1 syntenic with human 1q23.2. We report a detailed analysis of the genes within this interval and show that it contains 15 known and predicted genes, 12 of which demonstrate validated genotype-dependent transcript expression and/or nonsynonymous coding sequence variation that may underlie the influence of the QTL on withdrawal. These candidates are involved in diverse cellular functions including intracellular trafficking, potassium conductance and spatial buffering, and multimolecular complex dynamics, and indicate both established and novel aspects of neurobiological response to sedative-hypnotics. This work represents a substantial advancement toward identification of the gene(s) that underlie the phenotypic effects of the QTL. We identify Kcnj9 as a particularly promising candidate and report the development of a Kcnj9-null mutant model that exhibits significantly less severe withdrawal from pentobarbital as well as other sedative-hypnotics (zolpidem and ethanol) versus wild-type littermates. Reduced expression of Kcnj9, which encodes GIRK3 (Kir3.3), is associated with less severe sedative-hypnotic withdrawal. A multitude of QTLs for a variety of complex traits, including diverse responses to sedative-hypnotics, have been detected on distal chromosome 1 in mice, and as many as four QTLs on human chromosome 1q have been implicated in human studies of alcohol dependence. Thus, our results will be primary to additional efforts to identify genes involved in a wide variety of behavioral responses to sedative-hypnotics and may directly facilitate progress in human genetics.
Journal of Neuroscience 09/2009; 29(37):11662-73. · 7.11 Impact Factor
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ABSTRACT: Microarrays are widely used to evaluate gene expression at the genome scale. However, all too often the importance of data analysis at the level of the individual probe is overlooked. This is a particular problem when trying to detect differences in gene expression levels among genetically unique animals, across inbred animal strains, or among genetically modified animals. Of particular concern is the presence of small modifications in the DNA (i.e., single nucleotide polymorphisms [SNPs]) that occur naturally and differentiate one individual from the next. This article describes the potential impact of SNPs on analyses of gene expression differences and introduces an approach called SNP masking, which implements removal of SNP-affected probes. SNP masking is a valuable and feasible approach that can ameliorate these hybridization problems.
Alcohol research & health: the journal of the National Institute on Alcohol Abuse and Alcoholism 01/2008; 31(3):270-1. · 0.58 Impact Factor
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Nature Methods 10/2007; 4(9):679-80. · 19.28 Impact Factor
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ABSTRACT: Cell adhesion molecules are involved in a diverse array of cellular processes. Recent data suggests that human immunodeficiency virus (HIV-1) co-opts their functions, in particular the properties of the intercellular cell adhesion molecules (ICAMs), to enhance viral infection and transmission. To investigate mechanisms that may underlie the non-progression that occurs in immunodeficiency virus-infected chimpanzees, we amplified the protein coding regions of multiple non-human primate ICAMs 1-5 and two ICAM ligands, leukocyte function-associated antigen-1 (LFA-1) and macrophage antigen 1 (Mac-1). We then employed a phylogenetic tree-based approach to comparative genomics, in order to screen for the presence of adaptive changes. Strong Darwinian positive selection in chimpanzee ICAMs 1, 2 and 3 was observed, most markedly in domains that are critical for the integrity and maintenance of ICAM-1 dimerization. As binding of ligands, including the attachment of virions, is influenced by the state of ICAM 1 dimerization, chimpanzee ICAMs may have evolved to modulate their own dimerization. In concert with previous evidence suggesting an ancient retroviral pandemic as a prominent selective force in chimpanzee evolution, adaptation of chimpanzee ICAMs may have effected a mechanism that explains the lack of immunosuppression observed following HIV-1 or simian immunodeficiency virus (SIVcpz) infection.
Journal of Theoretical Biology 03/2005; 232(3):339-46. · 2.21 Impact Factor
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ABSTRACT: Physiological dependence and associated withdrawal episodes can constitute a powerful motivational force that perpetuates drug use and abuse. Using robust behavioral models of drug physiological dependence in mice, positional cloning, and sequence and expression analyses, we identified an addiction-relevant quantitative trait gene, Mpdz. Our findings provide a framework to define the protein interactions and neural circuit by which this gene's product (multiple PDZ domain protein) affects drug dependence, withdrawal and relapse.
Nature Neuroscience 08/2004; 7(7):699-700. · 15.53 Impact Factor
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ABSTRACT: Cell adhesion molecules are involved in a diverse array of cellular processes. Recent data suggests that human immunodeficiency virus (HIV-1) co-opts their functions, in particular the properties of the intercellular cell adhesion molecules (ICAMs), to enhance viral infection and transmission. To investigate mechanisms that may underlie the non-progression that occurs in immunodeficiency virus-infected chimpanzees, we amplified the protein coding regions of multiple non-human primate ICAMs 1–5 and two ICAM ligands, leukocyte function-associated antigen-1 (LFA-1) and macrophage antigen 1 (Mac-1). We then employed a phylogenetic tree-based approach to comparative genomics, in order to screen for the presence of adaptive changes. Strong Darwinian positive selection in chimpanzee ICAMs 1, 2 and 3 was observed, most markedly in domains that are critical for the integrity and maintenance of ICAM-1 dimerization. As binding of ligands, including the attachment of virions, is influenced by the state of ICAM 1 dimerization, chimpanzee ICAMs may have evolved to modulate their own dimerization. In concert with previous evidence suggesting an ancient retroviral pandemic as a prominent selective force in chimpanzee evolution, adaptation of chimpanzee ICAMs may have effected a mechanism that explains the lack of immunosuppression observed following HIV-1 or simian immunodeficiency virus (SIVcpz) infection.
Journal of Theoretical Biology.
