GSE212000
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, A/J, C57BL/6J (B6), NOD/ShiLtJ, and NZO/HILtJ) and three wild-derived strains (CAST/EiJ, PWK/PhJ (PWK) and WSB/EiJ) The strains encompass 89% of the genetic diversity available in Mus musculus and about 10 to 20 times more genetic diversity than found in Homo sapiens. For more than 60 years the B6 strain has been used as the model strain for high ethanol preference/high consumption. However, another one of the CC founder strains, PWK, was 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, the nucleus accumbens core and the prelimbic cortex. 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 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. 238 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 the observation that neuroimmune signaling is key to understanding alcohol use disorder and 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. Overall design: Mice were obtained from Jackson Laboratory at 5 weeks of age and allowed to habituate at the VAPORHCS VMU facility for three weeks. At 8 weeks of age, naive mice were euthanized by cervical location and brains removed and immediately frozen on dry ice. Brain regions of interest were dissected from frozen brains using biopsy punch methods and atlas landmarks. The brain regions are the prelimbic cortex (Prl), the nucleus accumbens core (NacC), and the central nucleus of the amygdala (CeA). The eight strains of mice used are: C57BL/6J (B6), A/J (A), 129S1/SvImJ (129), NOD/LtJ (NOD), NZO/HILtJ (NZO), CAST/EiJ (CAST), PWK/PhJ (PWK) and WSB/EiJ (WSB). From each strain of mice, three males and three females were used, for a total of 48 animals and 144 samples. Dissected brain tissue was submitted to the GPSR at OHSU for RNA isolation using QIASymphony robot and standard Qiagen kits/reagents. A total of 144 libraries were multiplexed 2 per lane, balanced for sex, family and strain, yielding between 12 and 41 million total paired-end reads per sample. Sequence alignment was based on the mm10 version of the mouse genome (Ensembl_Mouse_GRCm39_GCA_000001635.9). FastQC (Andrews, 2010) was used for quality checks on the raw sequence data and revealed one batch with significantly lower read counts. This batch was re-sequenced for quality control, bringing the mean counts up to the numbers found in the other two batches. Sequenced data were aligned using the STAR aligner (Dobin et al., 2013), allowing for a maximum of three mismatches per 100 bp read. On average, around 80% of the reads were uniquely aligned. Read counts were also obtained through the STAR aligner. Reads were aligned to the Mus_musculus.GRCm39.105 mouse annotation to generate counts at the gene level. Gene expression data were imported into the R application environment.