Effect of chronic ethanol consumption in rhesus macaques on the nucleus accumbens core transcriptome.

The nucleus accumbens core (NAcc) has been repeatedly demonstrated to be a key component of the circuitry associated with excessive ethanol consumption. Previous studies have illustrated that in a nonhuman primate (NHP) model of chronic ethanol consumption, there is significant epigenetic remodeling of the NAcc. In the current study, RNA-Seq was used to examine genome-wide gene expression in eight each of control, low/binge (LD*), and high/very high (HD*) rhesus macaque drinkers. Using an FDR < 0.05, zero genes were significantly differentially expressed (DE) between LD* and controls, six genes between HD* and LD*, and 734 genes between HD* and controls. Focusing on HD* versus control DE genes, the upregulated genes (N = 366) were enriched in genes with annotations associated with signal recognition particle (SRP)-dependent co-translational protein targeting to membrane (FDR < 3 × 10-59 ), structural constituent of ribosome (FDR < 3 × 10-47 ), and ribosomal subunit (FDR < 5 × 10-48 ). Downregulated genes (N = 363) were enriched in annotations associated with behavior (FDR < 2 × 10-4 ), membrane organization (FDR < 1 × 10-4 ), inorganic cation transmembrane transporter activity (FDR < 2 × 10-3 ), synapse part (FDR < 4 × 10-10 ), glutamatergic synapse (FDR < 1 × 10-6 ), and GABAergic synapse (FDR < 6 × 10-4 ). Ingenuity Pathway Analysis (IPA) revealed that EIF2 signaling and mTOR pathways were significantly upregulated in HD* animals (FDR < 3 × 10-33 and <2 × 10-16 , respectively). Overall, the data supported our working hypothesis; excessive consumption would be associated with transcriptional differences in GABA/glutamate-related genes.

Phenotypic and gene expression features associated with variation in chronic ethanol consumption in heterogeneous stock collaborative cross mice.

Of the more than 100 studies that have examined relationships between excessive ethanol consumption and the brain transcriptome, few rodent studies have examined chronic consumption. Heterogeneous stock collaborative cross mice freely consumed ethanol vs. water for 3 months. Transcriptional differences were examined for the central nucleus of the amygdala, a brain region known to impact ethanol preference. Early preference was modestly predictive of final preference and there was significant escalation of preference in females only. Genes significantly correlated with female preference were enriched in annotations for the primary cilium and extracellular matrix. A single module in the gene co-expression network was enriched in genes with an astrocyte annotation. The key hub node was the master regulator, orthodenticle homeobox 2 (Otx2). These data support an important role for the extracellular matrix, primary cilium and astrocytes in ethanol preference and consumption differences among individual female mice of a genetically diverse population.

RNA-Seq Analysis of Genetic and Transcriptome Network Effects of Dual-Trait Selection for Ethanol Preference and Withdrawal Using SOT and NOT Genetic Models.

BACKGROUND: Genetic factors significantly affect alcohol consumption and vulnerability to withdrawal. Furthermore, some genetic models showing predisposition to severe withdrawal are also predisposed to low ethanol (EtOH) consumption and vice versa, even when tested independently in naïve animals. METHODS: Beginning with a C57BL/6J × DBA/2J F2 intercross founder population, animals were simultaneously selectively bred for both high alcohol consumption and low acute withdrawal (SOT line), or vice versa (NOT line). Using randomly chosen fourth selected generation (S4) mice (N = 18-22/sex/line), RNA-Seq was employed to assess genome-wide gene expression in ventral striatum. The MegaMUGA array was used to detect genome-wide genotypic differences. Differential gene expression and the weighted gene co-expression network analysis were implemented as described elsewhere (Genes Brain Behav 16, 2017, 462). RESULTS: The new selection of the SOT and NOT lines was similar to that reported previously (Alcohol Clin Exp Res 38, 2014, 2915). One thousand eight hundred and sixteen transcripts were detected as differentially expressed between the lines. For genes more highly expressed in the SOT line, there was enrichment in genes associated with cell adhesion, synapse organization, and postsynaptic membrane. The genes with a cell adhesion annotation included 23 protocadherins, Mpdz and Dlg2. Genes with a postsynaptic membrane annotation included Gabrb3, Gphn, Grid1, Grin2b, Grin2c, and Grm3. The genes more highly expressed in the NOT line were enriched in a network module (red) with annotations associated with mitochondrial function. Several of these genes were module hub nodes, and these included Nedd8, Guk1, Elof1, Ndufa8, and Atp6v1f. CONCLUSIONS: Marked effects of selection on gene expression were detected. The NOT line was characterized by higher expression of hub nodes associated with mitochondrial function. Genes more highly expressed in the SOT aligned with previous findings, for example, Colville and colleagues (Genes Brain Behav 16, 2017, 462) that both high EtOH preference and consumption are associated with effects on cell adhesion and glutamate synaptic plasticity.

Gender-Specific Effects of Selection for Drinking in the Dark on the Network Roles of Coding and Noncoding RNAs.

BACKGROUND: Transcriptional differences between heterogeneous stock mice and high drinking-in-the-dark selected mouse lines have previously been described based on microarray technology coupled with network-based analysis. The network changes were reproducible in 2 independent selections and largely confined to 2 distinct network modules; in contrast, differential expression appeared more specific to each selected line. This study extends these results by utilizing RNA-Seq technology, allowing evaluation of the relationship between genetic risk and transcription of noncoding RNA (ncRNA); we additionally evaluate sex-specific transcriptional effects of selection. METHODS: Naïve mice (N = 24/group and sex) were utilized for gene expression analysis in the ventral striatum; the transcriptome was sequenced with the Illumina HiSeq platform. Differential gene expression and the weighted gene co-expression network analysis were implemented largely as described elsewhere, resulting in the identification of genes that change expression level or (co)variance structure. RESULTS: Across both sexes, we detect selection effects on the extracellular matrix and synaptic signaling, although the identity of individual genes varies. A majority of nc RNAs cluster in a single module of relatively low density in both the male and female network. The most strongly differentially expressed transcript in both sexes was Gm22513, a small nuclear RNA with unknown function. Associated with selection, we also found a number of network hubs that change edge strength and connectivity. At the individual gene level, there are many sex-specific effects; however, at the annotation level, results are more concordant. CONCLUSIONS: In addition to demonstrating sex-specific effects of selection on the transcriptome, the data point to the involvement of extracellular matrix genes as being associated with the binge drinking phenotype.

On the relationships in rhesus macaques between chronic ethanol consumption and the brain transcriptome.

This is the first description of the relationship between chronic ethanol self-administration and the brain transcriptome in a non-human primate (rhesus macaque). Thirty-one male animals self-administered ethanol on a daily basis for over 12 months. Gene transcription was quantified with RNA-Seq in the central nucleus of the amygdala (CeA) and cortical Area 32. We constructed coexpression and cosplicing networks, and we identified areas of preservation and areas of differentiation between regions and network types. Correlations between intake and transcription included largely distinct gene sets and annotation categories across brain regions and between expression and splicing; positive and negative correlations were also associated with distinct annotation groups. Membrane, synaptic and splicing annotation categories were over-represented in the modules (gene clusters) enriched in positive correlations (CeA); our cosplicing analysis further identified the genes affected only at the exon inclusion level. In the CeA coexpression network, we identified Rab6b, Cdk18 and Igsf21 among the intake-correlated hubs, while in the Area 32, we identified a distinct hub set that included Ppp3r1 and Myeov2. Overall, the data illustrate that excessive ethanol self-administration is associated with broad expression and splicing mechanisms that involve membrane and synapse genes.

Selection for drinking in the dark alters brain gene coexpression networks.

BACKGROUND: Heterogeneous stock (HS/NPT) mice have been used to create lines selectively bred in replicate for elevated drinking in the dark (DID). Both selected lines routinely reach a blood ethanol (EtOH) concentration (BEC) of 1.00 mg/ml or greater at the end of the 4-hour period of access in Day 2. The mechanisms through which genetic differences influence DID are currently unclear. Therefore, the current study examines the transcriptome, the first stage at which genetic variability affects neurobiology. Rather than focusing solely on differential expression (DE), we also examine changes in the ways that gene transcripts collectively interact with each other, as revealed by changes in coexpression patterns. METHODS: Naïve mice (N = 48/group) were genotyped using the Mouse Universal Genotyping Array, which provided 3,683 informative markers. Quantitative trait locus (QTL) analysis used a marker-by-marker strategy with the threshold for a significant logarithm of odds (LOD) set at 10.6. Gene expression in the ventral striatum was measured using the Illumina Mouse 8.2 array. Differential gene expression and the weighted gene coexpression network analysis (WGCNA) were implemented largely as described elsewhere. RESULTS: Significant QTLs for elevated BECs after DID were detected on chromosomes 4, 14, and 16; the latter 2 were associated with gene-poor regions. None of the QTLs overlapped with known QTLs for EtOH preference drinking. Ninety-four transcripts were detected as being differentially expressed in both selected lines versus HS controls; there was no overlap with known preference genes. The WGCNA revealed 2 modules as showing significant effects of both selections on intramodular connectivity. A number of genes known to be associated with EtOH phenotypes (e.g., Gabrg1, Glra2, Grik1, Npy2r, and Nts) showed significant changes in connectivity. CONCLUSIONS: We found marked and consistent effects of selection on coexpression patterns; DE changes were more modest and less concordant. The QTLs and differentially expressed genes detected here are distinct from the preference phenotype. This is consistent with behavioral data and suggests that the DID and preference phenotypes are markedly different genetically.

Brain gene expression differences related to ethanol preference in the collaborative cross founder strains.

The collaborative cross (CC) founder strains include five classical inbred laboratory strains [129S1/SvlmJ (S129), A/J (AJ), C57BL/6J (B6), NOD/ShiLtJ (NOD), and NZO/HILtJ (NZO)] and three wild-derived strains [CAST/EiJ (CAST), PWK/PhJ (PWK), and WSB/EiJ (WSB)]. These strains encompass 89% of the genetic diversity available in Mus musculus and ∼10-20 times more genetic diversity than found in Homo sapiens. For more than 60 years the B6 strain has been widely used as a genetic model for high ethanol preference and consumption. However, another of the CC founder strains, PWK, has been identified as a high ethanol preference/high consumption strain. The current study determined how the transcriptomes of the B6 and PWK strains differed from the 6 low preference CC strains across 3 nodes of the brain addiction circuit. RNA-Seq data were collected from the central nucleus of the amygdala (CeA), the nucleus accumbens core (NAcc) and the prelimbic cortex (PrL). Differential expression (DE) analysis was performed in each of these brain regions for all 28 possible pairwise comparisons of the CC founder strains. Unique genes for each strain were identified by selecting for genes that differed significantly [false discovery rate (FDR) < 0.05] from all other strains in the same direction. B6 was identified as the most distinct classical inbred laboratory strain, having the highest number of total differently expressed genes (DEGs) and DEGs with high log fold change, and unique genes compared to other CC strains. Less than 50 unique DEGs were identified in common between B6 and PWK within all three brain regions, indicating the strains potentially represent two distinct genetic signatures for risk for high ethanol-preference. 338 DEGs were found to be commonly different between B6, PWK and the average expression of the remaining CC strains within all three regions. The commonly different up-expressed genes were significantly enriched (FDR < 0.001) among genes associated with neuroimmune function. These data compliment findings showing that neuroimmune signaling is key to understanding alcohol use disorder (AUD) and support use of these 8 strains and the highly heterogeneous mouse populations derived from them to identify alcohol-related brain mechanisms and treatment targets.

Effects of estradiol supplementation on the brain transcriptome of old rhesus macaques maintained on an obesogenic diet.

Obesity, the cessation of ovarian steroids with menopause, and age are risk factors for mood disorders, dementia, and Alzheimer's disease (AD). However, immediate hormone therapy (HT) after menopause may have beneficial effects in different brain regions involved in memory and cognition. To more closely replicate the age, endocrine, and metabolic environment of obese postmenopausal women, either on or off HT, middle-aged female rhesus macaques were ovariectomized/hysterectomized (OvH) and maintained on a high-fat, high-sugar, obesogenic Western-style diet (WSD) for 30 months; half of the animals received HT immediately after OvH and half served as placebo controls. RNAseq of the occipital (OC) and prefrontal cortex (PFC), hippocampus (HIP), and amygdala (AMG) identified 293, 379, 505, and 4993 differentially expressed genes (DEGs), respectively. Pathway enrichment analysis identified an activation of neuroinflammation in OC and HIP, but an inhibition in the AMG with HT. Synaptogenesis, circadian rhythm, mitochondrial dysfunction, mTOR, glutamate, serotonin, GABA, dopamine, epinephrine/norepinephrine, glucocorticoid receptor signaling, neuronal NOS, and amyloid processing were exclusively enriched in AMG. As compared to the placebo control group, most of these signaling pathways are downregulated after HT, suggesting a protective effect of HT in OvH females under a WSD. Overall, our results suggest that a chronic obesogenic diet may induce a wide range of alterations in multiple signaling pathways that are linked to age-associated brain pathology and dementia. In these individuals, HT seems to have a protective effect against neuroinflammation, amyloid beta depositions, and tau tangle formation.

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

BACKGROUND: 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). RESULTS: 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. CONCLUSIONS: 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.

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

BACKGROUND: Allelic 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. RESULTS: We 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. CONCLUSION: Comparisons 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.

Short-term selective breeding for high and low prepulse inhibition of the acoustic startle response; pharmacological characterization and QTL mapping in the selected lines.

Selective breeding offers several important advantages over using inbred strain panels in detecting genetically correlated traits to the selection phenotype. The purpose of the current study was to selectively breed for prepulse inhibition (PPI) of the acoustic startle response (ASR), to pharmacologically and behaviorally characterize the selected lines and to use the lines for quantitative trait loci (QTL) mapping. Starting with heterogeneous stock mice formed by crossing the C57BL/6J, DBA/2J, BALB/cJ and LP/J inbred strains and using a short-term selective breeding strategy, animals were selected for High and Low PPI. The selection phenotype was the 80 dB prepulse tone (15 dB above the background noise). After five generations of selection, the High and Low lines differed significantly (78.1 +/- 3.1 vs. 45.2 +/- 3.9 [percent inhibition], p < 0.00001). The effects of haloperidol and MK-801 on PPI were not different between the High and Low lines. However, at the highest dose tested (10 mg/kg), the High line was more sensitive than the Low line to the disruptive PPI effects of methamphetamine. The lines did not differ in terms of basal activity or methamphetamine-induced changes in locomotor activity. The High and Low lines were genotyped using a panel of 768 SNPs. Significant QTLs (LOD > 10) were detected on chromosomes 11 and 16 that appeared similar to those detected previously [Hitzemann, R., Bell, J., Rasmussen, E., McCaughran, J. Mapping the genes for the acoustic startle response (ASR) and prepulse inhibition of the ASR in the BXD recombinant inbred series: effect of high-frequency hearing loss and cochlear pathology. In: Willott JF, editor. Handbook of mouse auditory research: From behavior to molecular biology. New York: CRC Press; 2001, p. 441-455.; Petryshen, T. L, Kirby, A., Hammer, R.P. Jr, Purcell, S., O'Leary, S.B., Singer, J.B., et al. Two quantitative trait loci for prepulse inhibition of startle identified on mouse chromosome 16 using chromosome substitution strains. Genetics 2005; 171: 1895-1904.]. Overall, the current study illustrates that the heritability of PPI is sufficient for shortterm selective breeding and that the lines which are developed can be used to characterize the factors associated with the regulation of PPI.

Regional Differences and Similarities in the Brain Transcriptome for Mice Selected for Ethanol Preference From HS-CC Founders.

The high genetic complexity found in heterogeneous stock (HS-CC) mice, together with selective breeding, can be used to detect new pathways and mechanisms associated with ethanol preference and excessive ethanol consumption. We predicted that these pathways would provide new targets for therapeutic manipulation. Previously (Colville et al., 2017), we observed that preference selection strongly affected the accumbens shell (SH) genes associated with synaptic function and in particular genes associated with synaptic tethering. Here we expand our analyses to include substantially larger sample sizes and samples from two additional components of the "addiction circuit," the central nucleus of the amygdala (CeA) and the prelimbic cortex (PL). At the level of differential expression (DE), the majority of affected genes are region-specific; only in the CeA did the DE genes show a significant enrichment in GO annotation categories, e.g., neuron part. In all three brain regions the differentially variable genes were significantly enriched in a single network module characterized by genes associated with cell-to-cell signaling. The data point to glutamate plasticity as being a key feature of selection for ethanol preference. In this context the expression of Dlg2 which encodes for PSD-93 appears to have a key role. It was also observed that the expression of the clustered protocadherins was strongly associated with preference selection.

Cross-species molecular dissection across alcohol behavioral domains.

This review summarizes the proceedings of a symposium presented at the "Alcoholism and Stress: A Framework for Future Treatment Strategies" conference held in Volterra, Italy on May 9-12, 2017. Psychiatric diseases, including alcohol-use disorders (AUDs), are influenced through complex interactions of genes, neurobiological pathways, and environmental influences. A better understanding of the common neurobiological mechanisms underlying an AUD necessitates an integrative approach, involving a systematic assessment of diverse species and phenotype measures. As part of the World Congress on Stress and Alcoholism, this symposium provided a detailed account of current strategies to identify mechanisms underlying the development and progression of AUDs. Dr. Sean Farris discussed the integration and organization of transcriptome and postmortem human brain data to identify brain regional- and cell type-specific differences related to excessive alcohol consumption that are conserved across species. Dr. Brien Riley presented the results of a genome-wide association study of DSM-IV alcohol dependence; although replication of genetic associations with alcohol phenotypes in humans remains challenging, model organism studies show that COL6A3, KLF12, and RYR3 affect behavioral responses to ethanol, and provide substantial evidence for their role in human alcohol-related traits. Dr. Rob Williams expanded upon the systematic characterization of extensive genetic-genomic resources for quantifying and clarifying phenotypes across species that are relevant to precision medicine in human disease. The symposium concluded with Dr. Robert Hitzemann's description of transcriptome studies in a mouse model selectively bred for high alcohol ("binge-like") consumption and a non-human primate model of long-term alcohol consumption. Together, the different components of this session provided an overview of systems-based approaches that are pioneering the experimental prioritization and validation of novel genes and gene networks linked with a range of behavioral phenotypes associated with stress and AUDs.

Alignment of the transcriptome with individual variation in animals selectively bred for High Drinking-In-the-Dark (HDID).

Among animals at risk for excessive ethanol consumption such as the HDID selected mouse lines, there is considerable individual variation in the amount of ethanol consumed and the associated blood ethanol concentrations (BECs). For the HDID lines, this variation occurs even though the residual genetic variation associated with the DID phenotype has been largely exhausted and thus is most likely associated with epigenetic factors. Here we focus on the question of whether the genes associated with individual variation in HDID-1 mice are different from those associated with selection (risk) (Iancu et al., 2013). Thirty-three HDID-1 mice were phenotyped for their BECs at the end of a standard DID trial, were sacrificed 3 weeks later, and RNA-Seq was used to analyze the striatal transcriptome. The data obtained illustrate that there is considerable overlap of the risk and variation gene sets, both focused on the fine-tuning of synaptic plasticity.

Cosplicing network analysis of mammalian brain RNA-Seq data utilizing WGCNA and Mantel correlations.

Across species and tissues and especially in the mammalian brain, production of gene isoforms is widespread. While gene expression coordination has been previously described as a scale-free coexpression network, the properties of transcriptome-wide isoform production coordination have been less studied. Here we evaluate the system-level properties of cosplicing in mouse, macaque, and human brain gene expression data using a novel network inference procedure. Genes are represented as vectors/lists of exon counts and distance measures sensitive to exon inclusion rates quantifies differences across samples. For all gene pairs, distance matrices are correlated across samples, resulting in cosplicing or cotranscriptional network matrices. We show that networks including cosplicing information are scale-free and distinct from coexpression. In the networks capturing cosplicing we find a set of novel hubs with unique characteristics distinguishing them from coexpression hubs: heavy representation in neurobiological functional pathways, strong overlap with markers of neurons and neuroglia, long coding lengths, and high number of both exons and annotated transcripts. Further, the cosplicing hubs are enriched in genes associated with autism spectrum disorders. Cosplicing hub homologs across eukaryotes show dramatically increasing intronic lengths but stable coding region lengths. Shared transcription factor binding sites increase coexpression but not cosplicing; the reverse is true for splicing-factor binding sites. Genes with protein-protein interactions have strong coexpression and cosplicing. Additional factors affecting the networks include shared microRNA binding sites, spatial colocalization within the striatum, and sharing a chromosomal folding domain. Cosplicing network patterns remain relatively stable across species.

Coexpression and cosplicing network approaches for the study of mammalian brain transcriptomes.

Next-generation sequencing experiments have demonstrated great potential for transcriptome profiling. While transcriptome sequencing greatly increases the level of biological detail, system-level analysis of these high-dimensional datasets is becoming essential. We illustrate gene network approaches to the analysis of transcriptional data, with particular focus on the advantage of RNA-Seq technology compared to microarray platforms. We introduce a novel methodology for constructing cosplicing networks, based on distance measures combined with matrix correlations. We find that the cosplicing network is distinct and complementary to the coexpression network, although it shares the scale-free properties. In the cosplicing network, we find a set of novel hubs that have unique characteristics distinguishing them from coexpression hubs: they are heavily represented in neurobiological functional pathways and have strong overlap with markers of neurons and neuroglia, long-coding lengths, and high number of both exons and annotated transcripts. We also find that gene networks are plastic in the face of genetic and environmental pressures.

Introduction to sequencing the brain transcriptome.

High-throughput next-generation sequencing is now entering its second decade. However, it was not until 2008 that the first report of sequencing the brain transcriptome appeared (Mortazavi, Williams, Mccue, Schaeffer, & Wold, 2008). These authors compared short-read RNA-Seq data for mouse whole brain with microarray results for the same sample and noted both the advantages and disadvantages of the RNA-Seq approach. While RNA-Seq provided exon level resolution, the majority of the reads were provided by a small proportion of highly expressed genes and the data analysis was exceedingly complex. Over the past 6 years, there have been substantial improvements in both RNA-Seq technology and data analysis. This volume contains 11 chapters that detail various aspects of sequencing the brain transcriptome. Some of the chapters are very methods driven, while others focus on the use of RNA-Seq to study such diverse areas as development, schizophrenia, and drug abuse. This chapter briefly reviews the transition from microarrays to RNA-Seq as the preferred method for analyzing the brain transcriptome. 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, and can be used to extract genotype information, e.g., nonsynonymous coding single nucleotide polymorphisms. RNA-Seq embraces the complexity of the brain transcriptome and provides a mechanism to understand the underlying regulatory code; the potential to inform the brain-behavior-disease relationships is substantial.

Differential network analysis reveals genetic effects on catalepsy modules.

We performed short-term bi-directional selective breeding for haloperidol-induced catalepsy, starting from three mouse populations of increasingly complex genetic structure: an F2 intercross, a heterogeneous stock (HS) formed by crossing four inbred strains (HS4) and a heterogeneous stock (HS-CC) formed from the inbred strain founders of the Collaborative Cross (CC). All three selections were successful, with large differences in haloperidol response emerging within three generations. Using a custom differential network analysis procedure, we found that gene coexpression patterns changed significantly; importantly, a number of these changes were concordant across genetic backgrounds. In contrast, absolute gene-expression changes were modest and not concordant across genetic backgrounds, in spite of the large and similar phenotypic differences. By inferring strain contributions from the parental lines, we are able to identify significant differences in allelic content between the selected lines concurrent with large changes in transcript connectivity. Importantly, this observation implies that genetic polymorphisms can affect transcript and module connectivity without large changes in absolute expression levels. We conclude that, in this case, selective breeding acts at the subnetwork level, with the same modules but not the same transcripts affected across the three selections.

Detection of expression quantitative trait Loci in complex mouse crosses: impact and alleviation of data quality and complex population substructure.

Complex Mus musculus crosses, e.g., heterogeneous stock (HS), provide increased resolution for quantitative trait loci detection. However, increased genetic complexity challenges detection methods, with discordant results due to low data quality or complex genetic architecture. We quantified the impact of theses factors across three mouse crosses and two different detection methods, identifying procedures that greatly improve detection quality. Importantly, HS populations have complex genetic architectures not fully captured by the whole genome kinship matrix, calling for incorporating chromosome specific relatedness information. We analyze three increasingly complex crosses, using gene expression levels as quantitative traits. The three crosses were an F(2) intercross, a HS formed by crossing four inbred strains (HS4), and a HS (HS-CC) derived from the eight lines found in the collaborative cross. Brain (striatum) gene expression and genotype data were obtained using the Illumina platform. We found large disparities between methods, with concordance varying as genetic complexity increased; this problem was more acute for probes with distant regulatory elements (trans). A suite of data filtering steps resulted in substantial increases in reproducibility. Genetic relatedness between samples generated overabundance of detected eQTLs; an adjustment procedure that includes the kinship matrix attenuates this problem. However, we find that relatedness between individuals is not evenly distributed across the genome; information from distinct chromosomes results in relatedness structure different from the whole genome kinship matrix. Shared polymorphisms from distinct chromosomes collectively affect expression levels, confounding eQTL detection. We suggest that considering chromosome specific relatedness can result in improved eQTL detection.

Computational detection of alternative exon usage.

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).