Chromatin accessibility during memory and the histone code hypothesis

Although the mechanism of highly coordinated regulation of specific transcripts during

memory consolidation remains a mystery, epigenetic mechanisms are beginning to be

implicated in this process. Histone modifications [51, 52, 156], histone variants [160],

DNA methylation [170], miRNA regulation [107], and nucleosome positioning [76] have

all been implicated in regulation of hippocampus-dependent learning. Histone

acetylation, an activating histone modification, is the best studied epigenetic modification

during memory consolidation. Work from our lab and others has implicated the histone

acetyltransferase CBP as a positive regulator of learning [26, 27] and the class I histone

deacetylase (HDAC) proteins as negative regulators [55, 58, 63]. Thus, it appears that

more histone acetylation during memory consolidation leads to enhanced long-term

memory and less histone acetylation leads to impaired long-term memory. Histone

acetylation is thought to decrease the interaction between the positively charged lysine

residue of the histone and negatively charged DNA backbone, thereby increasing

accessibility of chromatin in the surrounding region [35]. In Chapter 4, we used high-

throughput sequencing to study both the histone acetylation and chromatin accessibility

changes that occur 30 minutes after contextual fear memory.

A pilot ChIP-seq experiment investigating three histone acetylation marks found

that H3K9/14ac, a mark we have previously studied [55], displayed the largest number of

changes at promoters. We followed up this result by studying H3K9/14ac genome-wide

in a large cohort of mice. We also investigated whether changes in chromatin

accessibility, which would be the anticipated result of histone acetylation, occur at the

same time. To our surprise, we found only a modest increase in H3K9/14ac surrounding

the transcription start site of genes after fear conditioning. This small increase was not

large enough to be significant at any one particular gene but could be seen when

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averaging across all genes. However, we found a large increase in chromatin

accessibility surrounding the transcription start site of genes using Sono-seq [82]. This

increase was significant at 3064 regions in the genome. These regions are often found

within gene bodies and are enriched in genes that show alternative splicing after

learning and those implicated in autism, a known cognitive disorder. We believe that

these sites of increased chromatin accessibility represent sites in the genome with active

chromatin reorganization occurring during memory consolidation.

There are a number of potential causes for this increase in chromatin

accessibility after learning. The most obvious explanation would be a shift in nucleosome

position. Less nucleosomes in a region would be expected to increase accessibility to

that region. Therefore, we used MNase-seq to map nucleosome positioning throughout

the genome. We found no difference in nucleosome positioning in response to learning,

indicating that this is not the primary force driving the increase in chromatin accessibility.

Changes in transcription factor occupancy, including CREB, could also be driving this

change in accessibility. However, it is unclear whether increased transcription factor

occupancy would result in increases or decreases in Sono-seq signal. Also, there are

many more differential peaks than genes showing altered gene expression, so the

transcription factor would have to be selectively active at a subset of genes.

Finally, histone modifications besides H3K9/14ac may be responsible for this

increase in accessibility. The histone code hypothesis, put forward in 2000 by Strahl and

Allis [30], states that “distinct histone modifications, on one or more tails, act sequentially

or in combination to form a 'histone code' that is, read by other proteins to bring about

distinct downstream events.” This hypothesis has since been updated to acknowledge

that combinatorial modifications probably do not create a specific “code” but rather a

“language” that is dependent on surrounding context [155]. In either case, small changes

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in any particular histone modification could lead to large changes in downstream function

through combinatorial interactions with other marks, which would match our results.

H3K9/14ac alone may not be significant enough to regulate the accessibility of

chromatin, but instead may act in concert with a large number of other histone

modifications to regulate this accessibility. Hippocampal learning may therefore be

changing histone modifications in such a manner as to increase accessibility and

prepare for transcription to occur.

We propose that the increase in chromatin accessibility 30 minutes after learning

may be a set of combinatorial histone modifications removing a gate that allows

transcription to occur. In this model (Figure 5.1), a number of sites would be “opened”

after learning but only a subset of those sites would be bound by the factors necessary

to drive transcription, regulate alternative splicing, or maintain that “open” state for later

transcription. This leads to the intriguing question of whether there is a specific histone

“memory code” that hippocampal neurons use to regulate activity after a learning event.

This “memory code” could be a storage mechanism for long-term memory, with neurons

exhibiting a particular epigenomic code ready to be rapidly activated during memory

recall. This would be a large departure from the classic view of memory being stored at

particular synapses through strengthening or weakening of connections, which has a

limited number of possibilities at any individual synapse. Given the tremendous array of

possible histone modifications, it seems likely that each neuron will have an individual

code that could regulate the ability of that cell to participate in memory traces.

Although there is still a lot of work needed to test this possibility, there are

technological advances that are going to make this possible in the near future. First, the

cost of sequencing is getting cheaper every year and numerous histone modifications

could be tested and overlaid to look for patterns in response to learning. Second, there

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has been a major advancement in mass spectrometry-based quantification of histone

modifications. This technology will allow users to test the relative abundance of all

histone modifications throughout the genome in response to learning [171]. In addition,

new techniques can quantify combinatorial modifications occurring on the same histone

molecule, truly testing the histone code hypothesis [172, 173]. Future studies will use

these novel technologies to test whether a specific “memory code” exists in response to

a learning event. Novel genome-targeting technologies [174] can then be used to test

whether disrupting this code at particular genes changes the response of these genes to

a learning event.

5.3 Future Directions: The need for sorting technologies for brain epigenomic