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.

Ethanol tolerance and withdrawal severity in high drinking in the dark selectively bred mice.

BACKGROUND: Mouse lines are being selectively bred in replicate for high blood ethanol concentrations (BECs) achieved after limited access of ethanol (EtOH) drinking early in the circadian dark phase. High Drinking in the Dark-1 (HDID-1) mice are in selected generation S21, and the replicate HDID-2 line in generation S14. Tolerance and withdrawal symptoms are 2 of the 7 diagnostic criteria for alcohol dependence. Withdrawal severity has been found in mouse studies to be negatively genetically correlated with EtOH preference drinking. METHODS: To determine other traits genetically correlated with high DID, we compared naïve animals from both lines with the unselected, segregating progenitor stock, HS/Npt. Differences between HDID-1 and HS would imply commonality of genetic influences on DID and these traits. RESULTS: Female HDID-1 and HDID-2 mice tended to develop less tolerance than HS to EtOH hypothermia after their third daily injection. A trend toward greater tolerance was seen in the HDID males. HDID-1, HDID-2, and control HS lines did not differ in the severity of acute or chronic withdrawal from EtOH as indexed by the handling-induced convulsion (HIC). Both HDID-1 and HDID-2 mice tended to have greater HIC scores than HS regardless of drug treatment. CONCLUSIONS: These results show that tolerance to EtOH's hypothermic effects may share some common genetic control with reaching high BECs after DID, a finding consistent with other data regarding genetic contributions to EtOH responses. Withdrawal severity was not negatively genetically correlated with DID, unlike its correlation with preference drinking, underscoring the genetic differences between preference drinking and DID. HDID lines showed greater basal HIC scores than HS, suggestive of greater central nervous system excitability.

Ethanol sensitivity in high drinking in the dark selectively bred mice.

BACKGROUND: Mouse lines are being selectively bred in replicate for high blood ethanol concentrations (BECs) achieved after a short period of ethanol (EtOH) drinking early in the circadian dark phase. High Drinking in the Dark-1 (HDID-1) mice were in selected generation S18, and the replicate HDID-2 line in generation S11. METHODS: To determine other traits genetically correlated with high DID, we compared naïve animals from both lines with the unselected, segregating progenitor stock, HS/Npt. Differences between HDID-1 and HS would imply commonality of genetic influences on DID and these traits. RESULTS: HDID-1 mice showed less basal activity, greater EtOH stimulated activity, and greater sensitivity to EtOH-induced foot slips than HS. They showed lesser sensitivity to acute EtOH hypothermia and longer duration loss of righting reflex than HS. HDID-1 and control HS lines did not differ in sensitivity on 2 measures of intoxication, the balance beam and the accelerating rotarod. None of the acute response results could be explained by differences in EtOH metabolism. HDID-2 differed from HS on some, but not all, of the above responses. CONCLUSIONS: These results show that some EtOH responses share common genetic control with reaching high BECs after DID, a finding consistent with other data regarding genetic contributions to EtOH responses.

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.

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.