As part of Research Project 2, Dr. Hao Chen will collect specific brain regions. The Sequencing Core (Core C) will perform RNA-Seq on these brain regions. In this research project we will use RNA-Seq data to quantify gene expression in each brain region and use these data to map eQTLs. We will also analyze allele-specific expression (ASE) as another indicator of regulatory variation.
The abundance of a gene transcript is a quantitative trait and can be mapped to genetic loci. We anticipate many of our behavioral QTL will overlap with multiple genes, while we assume only one of them is the causal gene for the trait. In these cases, the presence of an eQTL for one particular gene in the QTL region (i.e., a cis-eQTL) suggests that genetic variants regulate both the trait and the expression of that gene. It is very likely that this genetic variation regulates the behavioral phenotype by changing the expression of this particular gene (as opposed to other genes within the QTL).
We use naïve animals to generate these eQTL data because we assume this difference in gene expression causes the variation in behavioral phenotype among the HS population. By using treatment naïve brains, these data will also be useful for for other projects on genetic analysis in HS rats. Because the brain is a highly heterogeneous tissue with tightly regulated gene expression patterns, it is important to obtain brain samples with high anatomical precision, while maintaining RNA quality. We developed a dissection method using 50 µm cryosections, which produces RNA with high quality.
We already collected data (n=88 rats, 440 RNA-seq samples) from the nucleus accumbens core, lateral habenula, infralimbic cortex, prelimbic cortex, and orbitofrontal cortex; all of which play key roles in many of the addiction-related behaviors. We choose to work on five new brain regions in this renewal prioritized by the current research projects: posterior ventral tegmental area (pVTA), medial habenula (mHb), ventral hippocampus (vHIP), insular cortex (IC), and lateral hypothalamus (LH). Among these brain regions, pVTA contains the cell bodies of the mesolimbic dopamine
neurons projecting to accumbens and prefrontal cortex and is critical for both nicotine and cocaine IVSA (Diana, 2011); mHb is one of the few regions has strong expression of Chrna3 and Chrnb4, both implicated in smoking (Frahm et al.,2011); IC integrates multimodal sensory information and is involved in the reinstatement of drug seeking (Arguello et al., 2017); smokers with lesion in the IC experience no difficulty in smoking cessation (Naqvi et al., 2007); LH contains orexin neurons and is involved in escalation of cocaine intake (James and Aston-Jones, 2017). vHIP is involved in
context induced reinstatement of multiple drugs (Marchant et al., 2015). Measuring more brain regions will allow us to detect more eQTL because gene expression is highly heterogeneous in the brain and many genes are only expressed in a few limited locations.
Samples collected from HS rats that are available for eQTL mapping
| Project | Investigator | Tissue | Number of samples | |
|---|---|---|---|---|
| 1 | P50 DA037844 Y5-10, Project 1: Neurogenetic Substrates of Cocaine Addiction | Paul Meyer, U at Buffalo | Brain: nucleus accumbens core | 180 |
| 2 | P50 DA037844 Y5-10, Project 1: Neurogenetic Substrates of Cocaine Addiction | Paul Meyer, U at Buffalo | Brain: insular cortex | 180 |
| 3 | P50 DA037844 Y5-10, Project 1: Neurogenetic Substrates of Cocaine Addiction | Paul Meyer, U at Buffalo | Brain: posterior VTA | 180 |
| 4 | P50 DA037844 Y1-5, Project 2: Socially-acquired nicotine self-administration | Hao Chen, UTHSC | Brain: nucleus accumbens core | 88 |
| 5 | P50 DA037844 Y1-5, Project 2: Socially-acquired nicotine self-administration | Hao Chen, UTHSC | Brain: lateral habenula | 88 |
| 6 | P50 DA037844 Y1-5, Project 2: Socially-acquired nicotine self-administration | Hao Chen, UTHSC | Brain: orbitofrontal cortex | 88 |
| 7 | P50 DA037844 Y1-5, Project 2: Socially-acquired nicotine self-administration | Hao Chen, UTHSC | Brain: infralimbic cortex | 88 |
| 8 | P50 DA037844 Y1-5, Project 2: Socially-acquired nicotine self-administration | Hao Chen, UTHSC | Brain: prelimbic cortex | 88 |
| 9 | P50 DA037844 Y5-10, Project 2: Socially-acquired nicotine self-administration | Hao Chen, UTHSC | Brain: posterior ventral tegmental area | 100 |
| 10 | P50 DA037844 Y5-10, Project 2: Socially-acquired nicotine self-administration | Hao Chen, UTHSC | Brain: medial habenula | 100 |
| 11 | P50 DA037844 Y5-10, Project 2: Socially-acquired nicotine self-administration | Hao Chen, UTHSC | Brain: ventral hippocampus | 100 |
| 12 | P50 DA037844 Y5-10, Project 2: Socially-acquired nicotine self-administration | Hao Chen, UTHSC | Brain: insular cortex | 100 |
| 13 | P50 DA037844 Y5-10, Project 2: Socially-acquired nicotine self-administration | Hao Chen, UTHSC | Brain: lateral hypothalamus | 100 |
| 14 | U01 DA050239: Single-cell Resolution analysis of chromatin accessibility and gene expression changes in a model of drug addiction | Francesca Telese, UCSD | Brain: nucleus accumbens | 100 |
| 15 | Pilot: Creating the dataset for TWAS in HS rats | Francesca Telese, UCSD | Brain: whole hemisphere | 375 |
| 16 | U01DA046077: Identification of Genetic Features of Delay Discounting Using a Heterogeneous Stock Rat Model | Suzanne Mitchell, OSHU | Brain: nucleus accumbens core | 200 |
| 17 | U01DA046077: Identification of Genetic Features of Delay Discounting Using a Heterogeneous Stock Rat Model | Suzanne Mitchell, OSHU | Brain: basolateral amygdala | 200 |
| 18 | U01DA046077: Identification of Genetic Features of Delay Discounting Using a Heterogeneous Stock Rat Model | Suzanne Mitchell, OSHU | Brain: infralimbic cortex | 200 |
| 19 | R01 EY021200: Genetic Modulators of Glaucoma | Monica Jablonsky, UTHSC | Eye | 50 |