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.

Dual-trait selection for ethanol consumption and withdrawal: genetic and transcriptional network effects.

BACKGROUND: Data from C57BL/6J (B6) × DBA/2J (D2) F2 intercrosses (B6xD2 F2 ), standard and recombinant inbred strains, and heterogeneous stock mice indicate that a reciprocal (or inverse) genetic relationship exists between alcohol consumption and withdrawal severity. Furthermore, some genetic studies have detected reciprocal quantitative trait loci (QTLs) for these traits. We used a novel mouse model developed by simultaneous selection for both high alcohol consumption/low withdrawal and low alcohol consumption/high withdrawal and analyzed the gene expression and genome-wide genotypic differences. METHODS: Randomly chosen third selected generation (S3 ) mice (N = 24/sex/line), bred from a B6xD2 F2 , were genotyped using the Mouse Universal Genotyping Array, which provided 2,760 informative markers. QTL analysis used a marker-by-marker strategy with the threshold for a significant log of the odds (LOD) set at 10. Gene expression in the ventral striatum was measured using the Illumina Mouse 8.2 array. Differential gene expression and the weighted gene co-expression network analysis (WGCNA) were implemented. RESULTS: Significant QTLs for consumption/withdrawal were detected on chromosomes (Chr) 2, 4, 9, and 12. A suggestive QTL mapped to Chr 6. Some of the QTLs overlapped with known QTLs mapped for 1 of the traits individually. One thousand seven hundred and forty-five transcripts were detected as being differentially expressed between the lines; there was some overlap with known withdrawal genes (e.g., Mpdz) located within QTL regions. WGCNA revealed several modules of co-expressed genes showing significant effects in both differential expression and intramodular connectivity; a module richly annotated with kinase-related annotations was most affected. CONCLUSIONS: Marked effects of selection on expression and network structure were detected. QTLs overlapping with differentially expressed genes on Chr 2 (distal) and 4 suggest that these are cis-eQTLs (Chr 2: Kif3b, Kcnq2; Chr 4: Mpdz, Snapc3). Other QTLs identified were on Chr 2 (proximal), 9, and 12. Network results point to involvement of kinase-related mechanisms and outline the need for further efforts such as interrogation of noncoding RNAs.

Utilizing RNA-Seq data for de novo coexpression network inference.

MOTIVATION: RNA-Seq experiments have shown great potential for transcriptome profiling. While sequencing increases the level of biological detail, integrative data analysis is also important. One avenue is the construction of coexpression networks. Because the capacity of RNA-Seq data for network construction has not been previously evaluated, we constructed a coexpression network using striatal samples, derived its network properties and compared it with microarray-based networks. RESULTS: The RNA-Seq coexpression network displayed scale-free, hierarchical network structure. We detected transcripts groups (modules) with correlated profiles; modules overlap distinct ontology categories. Neuroanatomical data from the Allen Brain Atlas reveal several modules with spatial colocalization. The network was compared with microarray-derived networks; correlations from RNA-Seq data were higher, likely because greater sensitivity and dynamic range. Higher correlations result in higher network connectivity, heterogeneity and centrality. For transcripts present across platforms, network structure appeared largely preserved. From this study, we present the first RNA-Seq data de novo network inference.

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.

Gamma oscillations in the auditory cortex of awake rats.

Numerous reports of human electrophysiology have demonstrated gamma (30-150 Hz) frequency oscillations in the auditory cortex during listening. However, only a small number of studies in non-human animals have provided evidence for gamma oscillations during listening. In this report, multi-site recordings from primary auditory cortex (A1) were carried out using a 16-channel microelectrode array in awake rats as they passively listened to tones. We addressed two fundamental questions: (i) Is passive listening associated with an increase in gamma oscillation in A1? And, if so: (ii) Are A1 gamma oscillations during passive listening coherent within local networks and/or over long distances? All sites within A1 showed a short-latency burst of activity in the low-gamma (30-70 Hz) and high-gamma (90-150 Hz) bands in the local field potential (LFP). Additionally, 53% of sites within A1 also showed longer-latency bursts of gamma oscillation that occurred episodically for up to 350 ms after tone onset, but these varied both in latency and in occurrence across trials. There was significant coherence in the low-gamma band between spike activity and the LFP recorded with the same electrode. However, neither LFPs nor the spike activity between sites spaced at least 300 µm apart showed coherent activity in the gamma band. The experiments demonstrated that gamma oscillations are present, but not uniformly expressed, throughout A1 during passive listening and that there is strong local coherence in the spatiotemporal organization of gamma activity.

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.

Regional Analysis of the Brain Transcriptome in Mice Bred for High and Low Methamphetamine Consumption.

Transcriptome profiling can broadly characterize drug effects and risk for addiction in the absence of drug exposure. Modern large-scale molecular methods, including RNA-sequencing (RNA-Seq), have been extensively applied to alcohol-related disease traits, but rarely to risk for methamphetamine (MA) addiction. We used RNA-Seq data from selectively bred mice with high or low risk for voluntary MA intake to construct coexpression and cosplicing networks for differential risk. Three brain reward circuitry regions were explored, the nucleus accumbens (NAc), prefrontal cortex (PFC), and ventral midbrain (VMB). With respect to differential gene expression and wiring, the VMB was more strongly affected than either the PFC or NAc. Coexpression network connectivity was higher in the low MA drinking line than in the high MA drinking line in the VMB, oppositely affected in the NAc, and little impacted in the PFC. Gene modules protected from the effects of selection may help to eliminate certain mechanisms from significant involvement in risk for MA intake. One such module was enriched in genes with dopamine-associated annotations. Overall, the data suggest that mitochondrial function and glutamate-mediated synaptic plasticity have key roles in the outcomes of selective breeding for high versus low levels of MA intake.

Network-Based Predictors of Progression in Head and Neck Squamous Cell Carcinoma.

The heterogeneity in head and neck squamous cell carcinoma (HNSCC) has made reliable stratification extremely challenging. Behavioral risk factors such as smoking and alcohol consumption contribute to this heterogeneity. To help elucidate potential mechanisms of progression in HNSCC, we focused on elucidating patterns of gene interactions associated with tumor progression. We performed de-novo gene co-expression network inference utilizing 229 patient samples from The Cancer Genome Atlas (TCGA) previously annotated by Bornstein et al. (2016). Differential network analysis allowed us to contrast progressor and non-progressor cohorts. Beyond standard differential expression (DE) analysis, this approach evaluates changes in gene expression variance (differential variability DV) and changes in covariance, which we denote as differential wiring (DW). The set of affected genes was overlaid onto the co-expression network, identifying 12 modules significantly enriched in DE, DV, and/or DW genes. Additionally, we identified modules correlated with behavioral measures such as alcohol consumption and smoking status. In the module enriched for differentially wired genes, we identified network hubs including IL10RA, DOK2, APBB1IP, UBASH3A, SASH3, CELF2, TRAF3IP3, GIMAP6, MYO1F, NCKAP1L, WAS, FERMT3, SLA, SELPLG, TNFRSF1B, WIPF1, AMICA1, PTPN22; the network centrality and progression specificity of these genes suggest a potential role in tumor evolution mechanisms related to inflammation and microenvironment. The identification of this network-based gene signature could be further developed to guide progression stratification, highlighting how network approaches may help improve clinical research end points and ultimately aid in clinical utility.

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.

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.

Enhanced functional connectivity involving the ventromedial hypothalamus following methamphetamine exposure.

Methamphetamine (MA) consumption causes disruption of many biological rhythms including the sleep-wake cycle. This circadian effect is seen shortly following MA exposure and later in life following developmental MA exposure. MA phase shifts, entrains the circadian clock and can also alter the entraining effect of light by currently unknown mechanisms. We analyzed and compared immunoreactivity of the immediate early gene c-Fos, a marker of neuronal activity, to assess neuronal activation 2 h following MA exposure in the light and dark phases. We used network analyses of correlation patterns derived from global brain immunoreactivity patterns of c-Fos, to infer functional connectivity between brain regions. There were five distinct patterns of neuronal activation. In several brain areas, neuronal activation following exposure to MA was stronger in the light than the dark phase, highlighting the importance of considering circadian periods of increased effects of MA in defining experimental conditions and understanding the mechanisms underlying detrimental effects of MA exposure to brain function. Functional connectivity between the ventromedial hypothalamus (VMH) and other brain areas, including the paraventricular nucleus of the hypothalamus and basolateral and medial amygdala, was enhanced following MA exposure, suggesting a role for the VMH in the effects of MA on the brain.

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.

Role of mGluR4 in acquisition of fear learning and memory.

Group III metabotropic glutamate receptors (mGluRs), which are generally located presynaptically, modulate synaptic transmission by regulating neurotransmitter release. Previously we showed enhanced amygdala-dependent cued fear conditioning in mGluR4(-/-) mice 24 h following training involving two tone-shock pairings. In this study, we assessed the effects of modulating mGluR4 signaling on acquisition and extinction of conditioned fear. mGluR4(-/-) and wild-type female and male mice received 10 tone-shock pairings during training. Compared to wild-type mice, mGluR4(-/-) mice showed enhanced acquisition and extinction of cued fear. Next, we assessed whether acute pharmacological stimulation of mGluR4 with the specific orthosteric mGluR4 agonist LSP1-2111 also affects acquisition and extinction of cued fear. Consistent with the enhanced acquisition of cued fear in mGluR4(-/-), LSP1-2111, at 2.5 and 5 mg/kg, inhibited acquisition of cued fear conditioning in wild-type male mice. The drug's effect on extinction was less clear and only a subtle effect was seen at 5 mg/kg. Finally, analysis of microarray data of amygdala tissues from mGluR4(-/-) versus wild-type and from wild-type mice treated with a mGluR4 agonist versus saline revealed a significant overlap in pattern of gene expression. Together, these data support a role for mGluR4 signaling in acquisition of fear learning and memory. This article is part of a Special Issue entitled 'Metabotropic Glutamate Receptors'.

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.