Dataset for the transcriptome analysis of hippocampal subfields identifies gene expression profiles associated with long-term active place avoidance memory
Harris, Rayna et al. (2020), Dataset for the transcriptome analysis of hippocampal subfields identifies gene expression profiles associated with long-term active place avoidance memory, Dryad, Dataset, https://doi.org/10.25338/B8QS4F
The hippocampus plays a critical role in storing and retrieving spatial information. By targeting the dorsal hippocampus and manipulating specific “candidate” molecules using pharmacological and genetic manipulations, we have previously discovered that long-term active place avoidance memory requires transient activation of particular molecules in dorsal hippocampus. These molecules include amongst others, the persistent kinases Ca-calmodulin kinase II (CaMKII) and the atypical protein kinase C isoform PKC iota/lambda for acquisition of the conditioned behavior, whereas persistent activation of the other atypical PKC, protein kinase M zeta (PKM zeta) is necessary for maintaining the memory for at least a month. It nonetheless remains unclear what other molecules and their interactions maintain active place avoidance long-term memory, and the candidate molecule approach is both impractical and inadequate to identify new candidates since there are so many to survey. Here we use a complementary approach to identify candidates by transcriptional profiling of hippocampus subregions after formation of the long-term active place avoidance memory. Interestingly, 24-h after conditioning and soon after expressing memory retention, immediate early genes were upregulated in the dentate gyrus but not Ammon’s horn of the memory expressing group. In addition to determining what genes are differentially regulated during memory maintenance, we performed an integrative, unbiased survey of the genes with expression levels that covary with behavioral measures of active place avoidance memory persistence. Gene Ontology analysis of the most differentially expressed genes shows that active place avoidance memory is associated with activation of transcription and synaptic differentiation in dentate gyrus but not CA3 or CA1, whereas hypothesis-driven candidate molecule analyses identified insignificant changes in the expression of many LTP-associated molecules in the various hippocampal subfields, nor did they covary with active place avoidance memory expression, ruling out strong transcriptional regulation but not translational regulation, which was not investigated. These findings and the data set establish an unbiased resource to screen for molecules and evaluate hypotheses for the molecular components of a hippocampus-dependent, long-term active place avoidance memory.
To examine spatial learning and memory, we used a well-established active place avoidance paradigm. Littermates were randomly assigned to one of our treatment groups (standard-trained, n=8; standard-yoked, n=8; conflict-trained, n=9; conflict-yoked, n=9. All mice were exposed to nine 10-min trials in the active place avoidance arena. Mice were placed on an elevated circular 40-cm diameter arena made of parallel bars that rotated at 1 rpm. The arena wall was transparent and thus contained the mouse on the arena while allowing it to observe the environment. The location of the mouse in the arena was determined from an overhead digital video camera interfaced to a PC-controlled tracking system (Tracker, Bio-Signal Group Inc., Acton, MA). Trained mice in the active place avoidance task are conditioned to avoid the location of mild shocks (constant current 0.2 mA, 500 ms, 60 Hz) that can be localized by visual cues in the environment. Yoked-control mice are delivered the identical sequence of shocks that was received by a particular trained mouse, the difference being that for the yoked mice, the shocks cannot be avoided or localized to a portion of the environment. Mice are allowed to become familiar with walking on the rotating arena during a pretraining trial with no shock. Then each mouse received three training trails separated by a 2-h inter-trial interval. The mice were returned to their home cage overnight. The next day, each mouse received a “Retest trial” with the shock in the same location as before. For the next three training trials, the shock zone remains in the same place for standard-trained animals but is relocated 180° for the conflict-trained mice. The next day, all mice receive a memory “Retention trial” with the shock off to evaluate the strength of the conditioned avoidance.
A day after the last training session, and 30 minutes after the retention session without shock, mice were anesthetized with 2% (vol/vol) isoflurane for 2 minutes and decapitated. Transverse 300 μm brain slices were cut using a vibratome (model VT1000 S, Leica Biosystems, Buffalo Grove, IL) and incubated at 36°C for 30 min and then at room temperature for 60-90 min in oxygenated artificial cerebrospinal fluid (aCSF in mM: 125 NaCl, 2.5 KCl, 1 MgSO4, 2 CaCl2, 25 NaHCO3, 1.25 NaH2PO4, and 25 Glucose). Slices were cut in half so that one hemisphere could be used for RNA-seq and one for ex vivo slide physiology.
For RNA-sequencing, the DG, CA3, CA1 subfields were micro-dissected using a 0.25 mm punch (Electron Microscopy Systems) and a Zeiss dissecting scope. RNA was isolated using the Maxwell 16 LEV RNA Isolation Kit (Promega). RNA libraries were prepared by the Genomic Sequencing and Analysis Facility at the University of Texas at Austin and sequenced on the Illumina HiSeq platform. Reads were processed on the Stampede Cluster at the Texas Advanced Computing Facility. Quality of raw and filtered reads was checked using the program FASTQC (Wingett and Andrews, 2018) and visualized using MultiQC (Ewels et al., 2016). We obtained 6.9 million ± 6.3 million reads per sample. Next, we used Kallisto to pseudo-align raw reads to a mouse references transcriptome (Gencode version 7), which yielded 2.6 million ± 2.1 million reads per sample. Mapping efficiency was about 42%. Transcript counts from Kallisto were imported into R and aggregated to yield gene counts using the gene identifier from the Gencode transcriptome. DESeq2 was used to normalize and quantify gene expression with a false discovery corrected (FDR) p-value < 0.1. ShinyGo was used to identify Gene Ontology terms associated with genes that are correlated with PC1. All genes associated with particular GO terms were identified using the Gene Ontology Browser . We compared the GO terms for candidate genes, differentially expressed genes, and a list of genes identified as important for long-term potentiation. We relied on the R packages ggplot2, cowplot, and corrr for data visualization.
Spatial behavior was evaluated by automatically computing (TrackAnalysis software (Bio-Signal Group Corp., Acton, MA) 26 measures that characterize a mouse’s use of space during the trial. All statistical analyses were performed using R version 3.6.0 (2019-04-26) -- "Planting of a Tree”, relying heavily on the software from the tidyverse library. Principal component analysis (PCA) was conducted to reduce the dimensionality of the data. One- and two-way ANOVAs were used to identify group differences in behavioral measures across one or multiple trials, respectively. For statistical analysis of gene expression, we used DESeq2 to normalize and quantify gene counts with a false discovery corrected (FDR) p-value < 0.1. DESeq2 models evaluated gene expression differences either between the four behavioral treatment groups (standard-trained, standard-yoked, conflict-trained, and conflict-yoked) or between the combined memory-trained and combined yoked-control groups.
Raw sequence data and differential gene expression data are available in NCBI's Gene Expression Omnibus Database (accession: GSE99765).
National Institute of Neurological Disorders and Stroke, Award: NS091830
National Science Foundation, Award: IOS-1501704
National Institute of Mental Health, Award: 5R25MH059472-18