Hippocampal Gene Expression in bred High Responder (bHR) vs. bred Low Responder (bLR) Rats: Illumina Microarray Data from Generation F15

The strong pattern of comorbidity amongst psychiatric disorders is believed to be generated by a spectrum of latent liability, arising from a complex interplay of genetic risk and environmental factors, such as stress and childhood adversity. At one end of this spectrum are internalizing disorders, which are associated with neuroticism, anxiety, and depression. At the other end of the spectrum are externalizing disorders, which are associated with risk-taking and novelty-seeking, as seen in mania, substance abuse, and impulse-control disorders. We model the genetic contributions underlying both extremes of this spectrum by selectively breeding rats that react differently to a novel environment. “Bred high responder” (bHR) rats are highly exploratory with a disinhibited, novelty-seeking temperament, including hyperactivity, aggression, and drug-seeking. “Bred low responder” (bLR) rats are highly-inhibited, exhibiting reduced locomotor activity and anxious and depressive-like behavior. These behavioral propensities are robust and stable, beginning early in development similar to temperament in humans. This Illumina (RatRef-12v1 Beadchip) microarray study examined gene expression in the hippocampus in generation F15 male bHR rats and bLR rats at age postnatal day 14 (P14, n=6 per group). Overall Design: This microarray study examined gene expression in the hippocampus in generation F15 male bHR rats and bLR rats at age postnatal day 14 (P14, n=6 per group). Sacrifice & RNA Extraction: The rats were sacrificed at postnatal day 14 (P14) via rapid decapitation followed by immediate brain extraction. The whole hippocampus was rapidly dissected on ice, fast-frozen at –40°C, and stored at –80°C before processing. TRIzol reagent (Invitrogen, Calsbad, CA) was used to extract total RNA, followed by purification using RNeasy RNA purification columns (Qiagen, Valencia, CA). The RNA was divided into two aliquots earmarked for transcriptional profiling using different microarray platforms. The quality and concentration of the RNA was determined using an Agilent bioanalyzer (Palo Alto, CA) and wavelength absorbance (260/280 nm ratio) by Nanodrop. Microarray: One aliquot from each sample was transcriptionally profiled using Illumina (RatRef-12v1 Beadchip) microarray according to standard manufacturer’s procedures. Microarray Data Preprocessing: To extract the AVG_Signal for all probes for each of the samples, we used the read.idat() function (R package limma version 3.28.21; Ritchie et al. 2015, Nucleic Acids Res. 43: e47). We performed a log2 transformation and then quantile normalized the data using the normalize.quantiles() function in the R package preprocessCore (version 1.34.0; Bolstad 2016 https://github.com/bmbolstad/preprocessCore). During quality control, a potential outlier was noted during principal components analysis (PCA). However, due to the strong sample-sample correlations among all samples (>0.98), removing the outlier was deemed unnecessary. The full analysis code is available at: https://github.com/isabellie4/PhenotypeProject and https://github.com/hagenaue/bHRbLR_MetaAnalysisProject.