Nicole A.R. Walter

Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays.

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.

Mpdz is a quantitative trait gene for drug withdrawal seizures

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 genes product (multiple PDZ domain protein) affects drug dependence, withdrawal and relapse.

A strategy for the integration of QTL, gene expression, and sequence analyses.

Although hundreds if not thousands of quantitative trait loci (QTL) have been described for a wide variety of complex traits, only a very small number of these QTLs have been reduced to quantitative trait genes (QTGs) and quantitative trait nucleotides (QTNs). A strategy, Multiple Cross Mapping (MCM), is described for detecting QTGs and QTNs that is based on leveraging the information contained within the haplotype structure of the mouse genome. As described in the current report, the strategy utilizes the six F2 intercrosses that can be formed from the C57BL/6J (B6), DBA/2J (D2), BALB/cJ (C), and LP/J (LP) inbred mouse strains. Focusing on the phenotype of basal locomotor activity, it was found that in all three B6 intercrosses, a QTL was detected on distal Chromosome (Chr) 1; no QTL was detected in the other three intercrosses, and thus, it was assumed that at the QTL, the C, D2, and LP strains had functionally identical alleles. These intercross data were used to form a simple algorithm for interrogating microsatellite, single nucleotide polymorphism (SNP), brain gene expression, and sequence databases. The results obtained point to Kcnj9 (which has a markedly lower expression in the B6 strain) as being the likely QTG. Further, it is suggested that the lower expression in the B6 strain results from a polymorphism in the 5′-UTR that disrupts the binding of at least three transcription factors. Overall, the method described should be widely applicable to the analysis of QTLs.

Gene-based sequence-tagged-sites (STSs) as the basis for a human gene map.

Using our data set of 3,143 single pass sequences from human brain cDNA libraries, we have developed a strategy in which gene–based sequence–tagged–sites (STSs), derived from 3′untranslated regions of human cDNAs, are rapidly assigned to megabase–insert yeast artificial chromosomes and somatic cell hybrids to generate regional gene mapping data. Employing this approach, we have mapped 318 cDNAs, representing 308 human genes. Ninety–two of these mapped to regions implicated in human genetic diseases, identifying them as candidate genes. Extension of this strategy has the potential to result in virtually every human gene having, at its 3′ end, its own associated STS, with each STS in turn specifying both a corresponding genomic clone and a specific regional location in the genome.

SNPs matter: impact on detection of differential expression

As described in other articles in this Special Section, 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.

Genetic diversity and striatal gene networks: focus on the heterogeneous stock-collaborative cross (HS-CC) mouse

BackgroundThe 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).ResultsGenes 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.ConclusionsDespite 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.

A comparison of selected quantitative trait loci associated with alcohol use phenotypes in humans and mouse models

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.

Mapping a Barbiturate Withdrawal Locus to a 0.44 Mb Interval and Analysis of a Novel Null Mutant Identify a Role for Kcnj9 (GIRK3) in Withdrawal from Pentobarbital, Zolpidem, and Ethanol

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.

Genes, Behavior, and Next-Generation RNA Sequencing

Advances in next-generation sequencing suggest that RNA-Seq is poised to supplant microarray-based approaches for transcriptome analysis. This article briefly reviews the use of microarrays in the brain-behavior context and then illustrates why RNA-Seq is a superior strategy. Compared with microarrays, RNA-Seq has a greater dynamic range, detects both coding and noncoding RNAs, is superior for gene network construction, detects alternative spliced transcripts, detects allele specific expression and can be used to extract genotype information, e.g. nonsynonymous coding single nucleotide polymorphisms. Examples of where RNA-Seq has been used to assess brain gene expression are provided. Despite the advantages of RNA-Seq, some disadvantages remain. These include the high cost of RNA-Seq and the computational complexities associated with data analysis. RNA-Seq embraces the complexity of the transcriptome and provides a mechanism to understand the underlying regulatory code; the potential to inform the brain-behavior relationship is substantial.

High throughput sequencing in mice: a platform comparison identifies a preponderance of cryptic SNPs

BackgroundAllelic variation is the cornerstone of genetically determined differences in gene expression, gene product structure, physiology, and behavior. However, allelic variation, particularly cryptic (unknown or not annotated) variation, is problematic for follow up analyses. Polymorphisms result in a high incidence of false positive and false negative results in hybridization based analyses and hinder the identification of the true variation underlying genetically determined differences in physiology and behavior. Given the proliferation of mouse genetic models (e.g., knockout models, selectively bred lines, heterogeneous stocks derived from standard inbred strains and wild mice) and the wealth of gene expression microarray and phenotypic studies using genetic models, the impact of naturally-occurring polymorphisms on these data is critical. With the advent of next-generation, high-throughput sequencing, we are now in a position to determine to what extent polymorphisms are currently cryptic in such models and their impact on downstream analyses.ResultsWe sequenced the two most commonly used inbred mouse strains, DBA/2J and C57BL/6J, across a region of chromosome 1 (171.6 – 174.6 megabases) using two next generation high-throughput sequencing platforms: Applied Biosystems (SOLiD) and Illumina (Genome Analyzer). Using the same templates on both platforms, we compared realignments and single nucleotide polymorphism (SNP) detection with an 80 fold average read depth across platforms and samples. While public datasets currently annotate 4,527 SNPs between the two strains in this interval, thorough high-throughput sequencing identified a total of 11,824 SNPs in the interval, including 7,663 new SNPs. Furthermore, we confirmed 40 missense SNPs and discovered 36 new missense SNPs.ConclusionComparisons utilizing even two of the best characterized mouse genetic models, DBA/2J and C57BL/6J, indicate that more than half of naturally-occurring SNPs remain cryptic. The magnitude of this problem is compounded when using more divergent or poorly annotated genetic models. This warrants full genomic sequencing of the mouse strains used as genetic models.