Contextual fear conditioning induces differential alternative splicing

https://doi.org/10.1016/j.nlm.2016.07.018Get rights and content

Highlights

  • A number of genes show alternative splicing during learning.

  • Homer1 isoform Ania-3 is regulated by fear conditioning.

  • Differential isoform usage can vary with shock only, context only, or fear conditioning.

Abstract

The process of memory consolidation requires transcription and translation to form long-term memories. Significant effort has been dedicated to understanding changes in hippocampal gene expression after contextual fear conditioning. However, alternative splicing by differential transcript regulation during this time period has received less attention. Here, we use RNA-seq to determine exon-level changes in expression after contextual fear conditioning and retrieval. Our work reveals that a short variant of Homer1, Ania-3, is regulated by contextual fear conditioning. The ribosome biogenesis regulator Las1l, small nucleolar RNA Snord14e, and the RNA-binding protein Rbm3 also change specific transcript usage after fear conditioning. The changes in Ania-3 and Las1l are specific to either the new context or the context-shock association, while the changes in Rbm3 occur after context or shock only. Our analysis revealed novel transcript regulation of previously undetected changes after learning, revealing the importance of high throughput sequencing approaches in the study of gene expression changes after learning.

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.

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    1

    These authors made equivalent contributions to the paper.

    2

    Current address: Sr. Research Scientist, Ibis Biosciences, Carlsbad, CA, USA.

    3

    Current address: Assistant Professor, Washington State University Spokane, Spokane, WA, USA.

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