Several groups will collect tissue from specific brain regions. The Sequencing Core (Core C) will perform RNA-Seq on these brain regions. 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.
We are working on creating a portal provides gene expression and eQTL data for different rat tissues. The pilot version of this portal is here: ratgtex.org
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.
P50 Project 2 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. In the renewal we choose to work on three new brain regions: posterior ventral tegmental area, nucleus accumbens core, and prelimbic cortex. The main cell types in these regions are : glutamatergic in prelimbic cortex, median spiny neurons in nucleus accumbens core and dopaminergic in posterior ventral tegmental area. 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 | N | Prior experience | Status |
|---|---|---|---|---|---|
| R01 EY021200: Genetic Modulators of Glaucoma | Monica Jablonsky, UTHSC | Eye | 50 | Collected | |
| Pilot: Creating the dataset for TWAS in HS rats | Francesca Telese, UCSD | Brain: whole hemisphere | 375 | Naive | In progress |
| U01DA044468: Genomic analysis of avoidance learning in addiction | Tom Jhou, Medical University of South Carolina | Brain: rostromedial tegmental nucleus | 80 | Extreme high and low phenotypes in cocaine-avoidance and punishment resistance | In progress |
| P50 DA037844 Y5-10, Project 2: Socially-acquired nicotine self-administration | Hao Chen, UTHSC | Brain: prelimbic cortex | 200 | Naive | In progress |
| U01DA046077: Identification of Genetic Features of Delay Discounting Using a Heterogeneous Stock Rat Model | Suzanne Mitchell, Robert Hitzemann, OSHU | Brain: prelimbic cortex | 200 | Behavior testing: Delay discounting | In progress |
| P50 DA037844 Y1-5, Project 2: Socially-acquired nicotine self-administration | Hao Chen, UTHSC | Brain: prelimbic cortex | 88 | Naive | Collected |
| U01DA044468; Genomic analysis of avoidance learning in addiction | Tom Jhou, Medical University of South Carolina | Brain: prelimbic cortex | 80 | Extreme high and low phenotypes in cocaine-avoidance and punishment resistance | In progress |
| P50 DA037844 Y5-10, Project 2: Socially-acquired nicotine self-administration | Hao Chen, UTHSC | Brain: posterior ventral tegmental area | 200 | Naive | In progress |
| P50 DA037844 Y5-10, Project 1: Neurogenetic Substrates of Cocaine Addiction | Paul Meyer, U at Buffalo | Brain: posterior ventral tegmental area | 180 | Exposure to cocaine | In progress |
| P50 DA037844 Y1-5, Project 2: Socially-acquired nicotine self-administration | Hao Chen, UTHSC | Brain: orbitofrontal cortex | 88 | Naive | Collected |
| P50 DA037844 Y5-10, Project 2: Socially-acquired nicotine self-administration | Hao Chen, UTHSC | Brain: nucleus accumbens core | 200 | Naive | In progress |
| U01DA046077: Identification of Genetic Features of Delay Discounting Using a Heterogeneous Stock Rat Model | Suzanne Mitchell, Robert Hitzemann, OSHU | Brain: nucleus accumbens core | 200 | Behavior testing: Delay discounting | In progress |
| P50 DA037844 Y5-10, Project 1: Neurogenetic Substrates of Cocaine Addiction | Paul Meyer, U at Buffalo | Brain: nucleus accumbens core | 180 | Exposure to cocaine | In progress |
| P50 DA037844 Y1-5, Project 2: Socially-acquired nicotine self-administration | Hao Chen, UTHSC | Brain: nucleus accumbens core | 88 | Naive | Collected |
| 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 | Exposure to oxycodone | Collected |
| P50 DA037844 Y1-5, Project 2: Socially-acquired nicotine self-administration | Hao Chen, UTHSC | Brain: lateral habenula | 88 | Naive | Collected |
| U01DA044468; Genomic analysis of avoidance learning in addiction | Tom Jhou, Medical University of South Carolina | Brain: lateral habenula | 80 | Extreme high and low phenotypes in cocaine-avoidance and punishment resistance | In progress |
| P50 DA037844 Y5-10, Project 1: Neurogenetic Substrates of Cocaine Addiction | Paul Meyer, U at Buffalo | Brain: insular cortex | 180 | Exposure to cocaine | In progress |
| P50 DA037844 Y1-5, Project 2: Socially-acquired nicotine self-administration | Hao Chen, UTHSC | Brain: infralimbic cortex | 88 | Naive | Collected |
| R01DK120667: Systems genetics to identify neuronal genes for diet-induced obesity | Leah Solberg Woods, WFU | Brain: hypothalamus | 500 | High fat and Low fat diets | In progress |
| R01DK120667: Systems genetics to identify neuronal genes for diet-induced obesity | Leah Solberg Woods, WFU | Brain: hippocampus | 500 | High fat and Low fat diets | In progress |
| U01DA044468; Genomic analysis of avoidance learning in addiction | Tom Jhou, Medical University of South Carolina | Brain: bed nucleus of the stria terminalis | 80 | Extreme high and low phenotypes in cocaine-avoidance and punishment resistance | In progress |
| U01DA046077: Identification of Genetic Features of Delay Discounting Using a Heterogeneous Stock Rat Model | Suzanne Mitchell, Robert Hitzemann, OSHU | Brain: basolateral amygdala | 200 | Behavior testing: Delay discounting | In progress |