10.25338/B8QS4F
Harris, Rayna
0000-0002-7943-5650
University of California, Davis
Kao, Hsin-Yi
University of Michigan
Alarcon, Juan Marcos
SUNY Downstate Medical Center
Fenton, André
New York University
Hofmann, Hans
0000-0002-3335-330X
The University of Texas at Austin
Dataset for the transcriptome analysis of hippocampal subfields identifies
gene expression profiles associated with long-term active place avoidance
memory
Dryad
dataset
2020
National Institute of Neurological Disorders and Stroke
https://ror.org/01s5ya894
NS091830
National Science Foundation
https://ror.org/021nxhr62
IOS-1501704
National Institute of Mental Health
https://ror.org/04xeg9z08
5R25MH059472-18
2020-02-10T00:00:00Z
2020-02-10T00:00:00Z
en
https://zenodo.org/record/1068356#.Xjo85BNKjYI
189590 bytes
3
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
All animal care and use comply with the Public Health Service Policy on
Humane Care and Use of Laboratory Animals and were approved by the New
York University Animal Welfare Committee and the Marine Biological
Laboratory IACUC. Male C57BL/6J mice were housed at the Marine Biological
Laboratory on a 12:12 (light: dark) cycle with continuous access to food
and water in home cages with up to five littermates. 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).