Contextual fear conditioning induces differential alternative splicing
Introduction
Contextual fear conditioning requires two waves of transcription and protein synthesis in the hippocampus to form long-term memory (Bourtchouladze et al., 1998, Igaz et al., 2002). Our lab and others have focused on discovering the genes regulated during these transcriptional waves using both candidate gene and genome-wide approaches. Our microarray-based studies have indicated that the first wave of transcription induces the largest change in gene expression 30 min after contextual learning (Peixoto, Wimmer et al., 2015). However, gene regulation is a complex process that has multiple layers of control. Levels of particular mRNA isoforms can be regulated by alternative start sites, differential splicing including exon skipping and intron retention, and alternative poly(A) site selection (Leff et al., 1986, Raj and Blencowe, 2015). Alternative splicing can lead to distinct protein function and interactions (Ellis et al., 2012) or regulate mRNA localization (Ehlers et al., 1998, Jaskolski et al., 2004, Papandrikopoulou et al., 1989), and thus is expected to be particularly important in neurons, which need to traffic mRNA to their long cellular processes.
Most previous research studying genome-wide gene expression in the hippocampus after contextual learning has relied on microarray technology (Barnes et al., 2012, Cavallaro et al., 2002, Keeley et al., 2006, Klur et al., 2009, Levenson et al., 2004, Mei et al., 2005, Peixoto, Wimmer et al., 2015). Although microarrays are a reliable tool to measure changes in gene expression, they are unable to distinguish exon-level effects that are indicative of alternative splicing. RNA-seq provides numerous advantages over microarrays (Peixoto, Risso et al., 2015), including the ability to study exon-level changes in gene expression. Isoform-specific gene expression changes are known to occur after fear conditioning, including upregulation of Bdnf IV, but not other Bdnf isoforms (Lubin et al., 2008, Mizuno et al., 2012), and Homer1a, but not Homer1c (Mahan et al., 2012) in response to strong, three shock training protocols. The different Bdnf isoforms have distinct transcription start sites, while the expression of Homer1 isoforms is controlled by the splicing regulator SRp20 (Wang, Chikina, Pincas, & Sealfon, 2014), which is upregulated after learning (Antunes-Martins, Mizuno, Irvine, Lepicard, & Giese, 2007). These examples indicate that gene regulation after learning is more complex than gene-level differences and can be highly selective for particular isoforms of a gene.
Therefore, we used RNA-seq to study differential alternative splicing 30 min after contextual fear conditioning and 30 min after memory retrieval. Applying Remove Unwanted Variation (RUV), a recently designed normalization algorithm (Peixoto, Risso et al., 2015, Risso et al., 2014), to our data, we discovered 171 bins, corresponding to either an entire exon or any portion of a gene, across 138 genes that showed differential expression after learning independent of changes at the gene-level. After memory retrieval 450 differentially expressed bins corresponding to 311 unique genes were discovered. These bins include retained introns, unique start/end sites, or small RNA not yet spliced out of the polyadenylated mRNA. The differences include Snord14e, a small nucleolar RNA, which our lab has previously shown to be regulated at this time point (Peixoto, Wimmer et al., 2015). Sno-RNAs, which are commonly found within introns of genes, regulate RNA processing and have been implicated in memory consolidation (Rogelj, Hartmann, Yeo, Hunt, & Giese, 2003). In addition, Ania-3, an alternative short form of Homer1 that has not previously been linked to learning, ribosome biogenesis regulator Las1l, and the RNA-binding protein Rbm3 were also regulated by contextual fear conditioning. These findings demonstrate that alternative splicing is regulated by contextual learning on a genome-wide scale and also identify novel candidate isoforms that may be pertinent to memory consolidation.
Section snippets
Subjects
C57Bl/6J mice were maintained under standard conditions with food and water available ad libitum. Adult male mice 2 months of age were kept on a 12-h light/12-h dark cycle with lights on at 7AM. All behavioral and biochemical experiments were performed during the light cycle with training starting at 10AM (ZT3). All animal experiments were approved by the Institutional Animal Care and Use Committee of the University of Pennsylvania and were consistent with National Institutes of Health
Results
RNA-seq has the advantage of distinguishing exon-level reads that are difficult to identify by any other method, and therefore it is an ideal technique to study alternative splicing. We used RNA-seq to study gene expression in the hippocampus 30 min after contextual fear conditioning, a time point our lab has previously determined to show robust expression changes after fear conditioning (Peixoto, Wimmer et al., 2015). We used GSNAP (Wu & Nacu, 2010) to align reads to the mm9 mouse genome and
Discussion
In this study, we provide the first evidence of genome-wide regulation of alternative splicing after learning in the hippocampus. Using bin counts produced by HTSeq and the limma Bioconductor package, we compared bins representing a unique piece of a gene against expression of that entire gene to create a list of bin-level changes. We were able to detect significant gene expression changes at 171 bins occurring in response to contextual fear conditioning at 138 genes. The exact number of
Conflict of interest
None.
Acknowledgments
This research was supported by NIH R01MH087463 to T.A.
References (44)
- et al.
Tissue-specific alternative splicing remodels protein–protein interaction networks
Molecular Cell
(2012) - et al.
Cytoplasmic Rbfox1 regulates the expression of synaptic and autism-related genes
Neuron
(2016) - et al.
Distinct gene expression profiles in hippocampus and amygdala after fear conditioning
Brain Research Bulletin
(2005) - et al.
Object-location training elicits an overlapping but temporally distinct transcriptional profile from contextual fear conditioning
Neurobiology of Learning and Memory
(2014) - et al.
Alternative splicing in the mammalian nervous system: Recent insights into mechanisms and functional roles
Neuron
(2015) - et al.
HTSeq — A Python framework to work with high-throughput sequencing data
Bioinformatics
(2015) - et al.
Detecting differential usage of exons from RNA-seq data
Genome Research
(2012) - et al.
Sex-dependent up-regulation of two splicing factors, Psf and Srp20, during hippocampal memory formation
Learning & Memory
(2007) - et al.
Quantitatively and qualitatively different cellular processes are engaged in CA1 during the consolidation and reconsolidation of contextual fear memory
Hippocampus
(2012) - et al.
Synaptic activity-induced conversion of intronic to exonic sequence in Homer 1 immediate early gene expression
Journal of Neuroscience
(2002)
Different training procedures recruit either one or two critical periods for contextual memory consolidation, each of which requires protein synthesis and PKA
Learning & Memory
Memory-specific temporal profiles of gene expression in the hippocampus
Proceedings of the National Academy of Sciences of the United States of America
Splice variant-specific interaction of the NMDA receptor subunit NR1 with neuronal intermediate filaments
Journal of Neuroscience
NR4A nuclear receptors support memory enhancement by histone deacetylase inhibitors
The Journal of Clinical Investigation
Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists
Nucleic Acids Research
Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
Nature Protocols
Amygdala regulation of immediate-early gene expression in the hippocampus induced by contextual fear conditioning
Journal of Neuroscience
Two time periods of hippocampal mRNA synthesis are required for memory consolidation of fear-motivated learning
Journal of Neuroscience
Subunit composition and alternative splicing regulate membrane delivery of kainate receptors
Journal of Neuroscience
Differential transcriptional response to nonassociative and associative components of classical fear conditioning in the amygdala and hippocampus
Learning & Memory
Hippocampal-dependent spatial memory functions might be lateralized in rats: An approach combining gene expression profiling and reversible inactivation
Hippocampus
Voom: Precision weights unlock linear model analysis tools for RNA-seq read counts
Genome Biology
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- 1
These authors made equivalent contributions to the paper.
- 2
Current address: Sr. Research Scientist, Ibis Biosciences, Carlsbad, CA, USA.
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Current address: Assistant Professor, Washington State University Spokane, Spokane, WA, USA.