Gene expression changes during memory consolidation

Studies of genome-wide gene expression changes after hippocampal learning have

been previously attempted using microarrays [70, 71, 110-112], but there have been few

genes discovered by this method and little overlap between labs. Two recent studies

have used RNA-seq to study gene expression in mutant mice after learning [76, 160].

The advent of next-generation sequencing technology to study gene expression using

RNA-seq provides a number of benefits over these previous studies. RNA-seq produces

better resolution than microarrays, the ability to detect novel transcripts, and the ability to

quantify alternative splicing. In addition, the variance between sequencing runs could

prove to be less substantial than between microarray runs that rely on hybridization.

Therefore, normalizing RNA-seq data in a standard way should produce reproducible

results between labs and between training paradigms.

In Chapter 2, we used RNA-seq to study gene expression at 30 minutes after

contextual fear conditioning. This is a time point at which our lab has observed maximum

gene expression differences after learning [61, 70]. We discovered that standard RNA-

seq normalization procedures are unable to capture the difference between untrained

and trained groups. This leads to a small list of differentially regulated genes that may or

may not be caused by the learning event. Therefore, we applied the recently published

remove unwanted variation (RUV) normalization [114] that is an improved normalization

method for noisy data sets such as the whole hippocampal samples used in our study.

Briefly, this method of normalization includes an additional factor to account for

unwanted variation by using negative control genes that are known not to be altered by

training. RUV allowed us to differentiate between trained and untrained groups, meaning

that any differentially expressed genes were likely the result of the contextual fear

training. We discovered that this normalization method greatly improved the number of

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novel genes detected as different after learning. Importantly, this analysis also increased

the proportion of positive control genes discovered, suggesting that it was functioning as

expected. Because RUV normalization makes fear conditioning the major source of

variation between samples, the list of genes differentially expressed 30 minutes after

learning using RUV normalization will provide a reproducible set of genes showing

changes in response to learning. This method of normalization can be applied to all

RNA-seq studies and will greatly improve detection power and reliability of results in

future studies of brain function.

The regulation of gene expression is a highly complex process that includes

transcription of a primary transcript, 5’ capping, polyadenylation, and splicing into a

mature mRNA. Alternative splicing is a coordinated process by which different

transcripts can be produced from the same gene. Regulation of alternative splicing has

been recognized in circadian function [161], addiction [162], and neurodegeneration

[163]. There are also multiple individual examples of alternative splicing regulating

learning and memory [134, 164-169], indicating this process may be an important

regulatory step in the nervous system. However, no genome-wide studies have been

used to investigate the regulation of alternative splicing during memory consolidation.

Because RNA-seq also provides the ability to study exon-specific events such as those

occurring by differential splicing, we applied RUV normalization to exon-specific analysis

and demonstrated numerous exon-specific expression changes occurring during

memory consolidation. We validated a number of these changes, including Ania-3 (a

poorly studied isoform of Homer1), translational regulator Las1l and RNA-binding protein

Rbm3. We believe that this analysis provides the first description of large-scale

differential exon usage in response to learning. Although transcription of splicing factors

is regulated by fear conditioning [134], it is unclear whether these changes would be

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translated quickly enough to cause the splicing changes observed. It is possible that

histone modification changes could be altering exon usage [135, 137], but more work is

necessary to show these marks change in response to training. Future studies can be

conducted to see if transcripts containing or excluding the identified differential exon lead

to changes in localization or function of the protein.

The major question that remained about the genes discovered by our RNA-seq

analysis was whether these changes depend on the training paradigm used. In other

words, are the genes regulated by contextual fear conditioning the same as those

regulated by other hippocampus-dependent learning tasks? To answer this question, we

used a high-throughput qPCR approach in Chapter 3. The goal of this study was to

compare the targets and temporal profile of gene expression after training for object-

location memory (OLM), a spatial learning task, to that of fear conditioning, a contextual

learning task. We discovered that while gene targets are regulated in a similar manner

after OLM, the temporal dynamics of these gene expression changes differs from that

observed after fear conditioning. A subset of genes regulated 30 minutes after OLM

remain elevated 2 hours after training, while these same genes return to baseline by 2

hours after fear conditioning. Although the stress of a footshock during contextual fear

training may be expected to produce a larger transcriptional response, it appears that

the three training trials used for OLM result in longer lasting transcriptional changes.

Therefore, we hypothesize that a common set of targets are regulated by all forms of

hippocampal learning, but the timing of these changes can differ based on the paradigm

being tested.

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