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

Colville, Alexandre; Iancu, Ovidiu D.; Lockwood, Denesa R.; Darakjian, Priscila; McWeeney, Shannon K.; Searles, Robert P.; Zheng, Christina; Hitzemann, Robert

doi:10.3389/fgene.2018.00300

Cited by 17 publications

(46 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the current study, DV was the least informative parameter. This differs markedly from the results in Colville and colleagues (2017, 2018) where a large number of genes exhibited sizable changes in variance associated with selection for EtOH preference. However, a key difference is that these studies used HS‐collaborative cross (HS‐CC) mice as the founders rather than B6xD2 F 2 s as founders.…”

Section: Discussioncontrasting

confidence: 99%

“…Consistent with previous observations (Colville et al, 2017, 2018), selection had strong effects on DW; 1,717 genes were significantly affected (Table ). Forty‐three genes showed a difference in relative intramodular connectivity of ≥0.5, and for 34 of these genes, connectivity was higher in the NOT line.…”

Section: Resultssupporting

confidence: 91%

“…The genes in Table were entered into a consensus module network analysis, which parses the data from each selected line into network modules (Colville et al, 2017, 2018). The list of genes was culled to remove those genes that contribute the least to network connectivity (the bottom 10%).…”

Section: Resultsmentioning

confidence: 99%

“…We recognize that there are numerous approaches to analyzing genome‐wide RNA‐Seq surveys of the brain transcriptome. The approach we have taken here is one that we have previously found useful (Colville et al, 2017, 2018; HItzemann et al, 2019). The focus is on 3 parameters (DE, DV, and DW), which can be generated completely independent of the network analyses.…”

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

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

Kozell

Lockwood

Darakjian

et al. 2020

Alcoholism Clin & Exp Res

Self Cite

View full text Add to dashboard Cite

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 na€ ıve animals.Methods: Beginning with a C57BL/6J 9 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.

show abstract

Section: Discussioncontrasting

confidence: 99%

Section: Resultssupporting

confidence: 91%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

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

Kozell

Lockwood

Darakjian

et al. 2020

Alcoholism Clin & Exp Res

Self Cite

View full text Add to dashboard Cite

show abstract

“…stock in past experiments [83][84][85][86]. The DO population is recommended for new selection experiments.…”

Section: Other Applications Of Diversity Micementioning

confidence: 99%

High-Diversity Mouse Populations for Complex Traits

et al. 2019

View full text Add to dashboard Cite

Contemporary mouse genetic reference populations are a powerful platform to discover complex disease mechanisms. Advanced high-diversity mouse populations include the Collaborative Cross (CC) strains, Diversity Outbred (DO) stock, and their isogenic founder strains. When used in systems genetics and integrative genomics analyses, these populations efficiently harnesses known genetic variation for precise and contextualized identification of complex disease mechanisms. Extensive genetic, genomic, and phenotypic data are already available for these high-diversity mouse populations and a growing suite of data analysis tools have been developed to support research on diverse mice. This integrated resource can be used to discover and evaluate disease mechanisms relevant across species. The Challenge of Complex Disease Complex diseases present compelling biomedical challenges that can be studied using human and nonhuman animal genetics. Although advances in human genetics have identified loci for many heritable complex diseases [1-8], there are several well-known limitations of genomewide association studies (GWASs). (i) For many human disease loci, the biological mechanism of action is unknown. (ii) When little is known about a locus, the path from genetic association to clinically actionable targets is unclear. (iii) Disease process or developmental trajectory is not always obvious from a causal genetic variant. (iv) GWAS results may not generalize across human subpopulations. (v) Medical records and participant phenotyping is often incomplete, imprecise, and retrospective, whereas model organism phenotyping can include in-depth, prospective, and standardized measures. (vi) Sample size requirements are formidable in human GWASs. (vii) Power is insufficient to study interacting genetic loci. (viii) Studies of genetic interaction with development, environment, and sex are largely intractable. Further, heterogeneous diseases like psychiatric disorders manifest with overlapping symptoms, intertwined disease trajectories [9], and complex genetic regulation [9,10]. In summary, the SNP-to-disease association model underlying GWASs cannot readily capture complex disease biology without further biological context. These challenges are often tractable with discovery genetics in model organisms.

show abstract

Modeling Brain Gene Expression in Alcohol Use Disorder with Genetic Animal Models

Hitzemann,

Ozburn,

Lockwood

et al. 2023

Current Topics in Behavioral Neurosciences

View full text Add to dashboard Cite

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

Cited by 17 publications

References 57 publications

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

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

High-Diversity Mouse Populations for Complex Traits

Modeling Brain Gene Expression in Alcohol Use Disorder with Genetic Animal Models

Contact Info

Product

Resources

About