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The complexity of our memory systems is one of the reasons why it is quite a difficult area to study, because it can be difficult to set up experiments where we are absolutely sure which aspects of memory our experimental techniques are actually addressing. But one thing we know for sure is that memory involves long-term changes in gene expression, and in the way neurons make connections with one another. And that again leads to the hypothesis that epigenetic mechanisms may play a role.

In mammals, both DNA methylation and histone modifications play a role in memory and learning. Rodent studies have shown that these changes may be targeted to very specific genes in discrete regions of the brain, as we have come to expect. For example, the DNA methyltransferase proteins DNMT3A and DNMT3B increase in expression in the adult rat hippocampus in a particular learning and memory model. Conversely, treating these rats with a DNA methyltransferase inhibitor such as 5-azacytidine blocks memory formation and affects both the hippocampus and the cortex[225].

A particular histone acetyltransferase (protein which adds acetyl groups to histones) gene is mutated in a human disorder called Rubinstein-Taybi syndrome. Mental retardation is a frequent symptom in this disease. Mice with a mutant version of this gene also have low levels of histone acetylation in the hippocampus, as we would predict. They also have major problems in long-term memory processing in the hippocampus[226]. When these mice were treated with SAHA, the histone deacetylase inhibitor, acetylation levels in the hippocampus went up, and the memory problems improved[227].

SAHA can inhibit many different histone deacetylases, but in the brain some of its targets seem to be more important than others. The two most highly expressed enzymes of this class are HDAC1 and HDAC2. These differ in the ways they are expressed in the brain. HDAC1 is predominantly expressed in neural stem cells, and in a supportive, protective population of non-neurons called glial cells. HDAC2 is predominantly expressed in neuronal cells[228], so it’s unsurprising that this is the histone deacetylase that is most important in learning and memory.

Mice whose neurons over-express Hdac2 have poor long-term memory, even though their short-term memory is fine. Mice whose neurons don’t express any Hdac2 have excellent memories. These data show us that Hdac2 has a negative effect on memory storage. The neurons which over-expressed Hdac2 formed far fewer connections than normal, whereas the opposite was true for the neurons lacking Hdac2. This supports our model of epigenetically-driven changes in gene expression ultimately altering complex networks in the brain. SAHA improves memory in the mice that over-express Hdac2, presumably by dampening down its effects on histone acetylation and gene expression. SAHA also improves memory in normal mice[229].

In fact, increased acetylation levels in the brain seem to be consistently associated with improved memory. Learning and memory both improved in mice kept in conditions known as environmentally enriched. This is a fancy way of saying they had access to two running wheels and the inside of a toilet roll. The histone acetylation levels in the hippocampus and cortex were increased in the mice in the more entertaining surroundings. Even in these mice, the histone acetylation levels and memory skills improved yet further if they were treated with SAHA[230].

We can see a consistent trend emerging. In various different model systems, learning and memory improve when animals are treated with DNA methyltransferase inhibitors, and especially with histone deacetylase inhibitors. As we saw in the last chapter, there are drugs licensed in both these classes, such as 5-azacytidine and SAHA, respectively. It’s very tempting to speculate about taking these anti-cancer drugs and using them in conditions where memory loss is a major clinical problem, such as Alzheimer’s disease. Perhaps we might even use them as general memory enhancers in the wider population.

Unfortunately, there are substantial difficulties in doing this. These drugs have side-effects which can include severe fatigue, nausea and a higher risk of infections. These side-effects are considered acceptable if the alternative is an inevitable and fairly near-term death from cancer. But they might be considered less acceptable for treating the early stages of dementia, when the patient still has a relatively reasonable quality of life. And they would certainly be unacceptable for the general population.

There is an additional problem. Most of these drugs are really bad at getting into the brain. In many of the rodent experiments, the drugs were administered directly into the brain, and often into very defined regions such as the hippocampus. This isn’t a realistic treatment method for humans.

There are a few histone deacetylase inhibitors that do get into the brain. A drug called sodium valproate has been used for decades to treat epilepsy, and clearly must be getting into the brain in order to do this. In recent years, we have realised that this compound is also a histone deacetylase inhibitor. This would be extremely encouraging for trying to use epigenetic drugs in Alzheimer’s disease but unfortunately, sodium valproate only inhibits histone deacetylases very weakly. All the animal data on learning and memory have shown that stronger inhibitors work much better than weak ones at reversing these deficits.

It’s not just in disorders like Alzheimer’s disease that epigenetic therapies could be useful if we manage to develop suitable drugs. Between 5 and 10 per cent of regular users of cocaine become addicted to the drug, suffering from uncontrollable cravings for this stimulant. A similar phenomenon occurs in rodents, if animals are allowed unlimited access to the drug. Addiction to stimulants such as cocaine is a classic example of inappropriate adaptations by memory and reward circuits in the brain. These maladaptations are regulated by long-lasting changes in gene expression. Changes in DNA methylation, and in how methylation is read by MeCP2, underpin this addiction. This happens via a set of poorly understood interactions which include signalling factors, DNA and histone modifying enzymes and readers, and miRNAs. Related pathways also underpin addiction to amphetamines[231][232].

If we return to the starting point of this chapter, it’s clear that there’s a major need to stop children who have suffered early trauma from developing into adults with a substantially higher than normal risk of mental illness. It’s very appealing to think we might be able to use epigenetic drug therapies to improve their life chances. Unfortunately, one of the problems in designing therapies for children who have been abused or neglected is that it’s actually pretty difficult to identify those who will be permanently damaged as adults, and those who will have healthy, happy and fulfilled lives. There are enormous ethical dilemmas around giving drugs to children, when we can’t be sure if an individual child actually needs the treatment. In addition, clinical trials to determine if the drugs actually do any good would need to last for decades, which makes them economically almost a non-starter for any pharmaceutical company.

But we mustn’t end on too negative a note. Here’s a great story about an epigenetic event and behaviour. There is a gene called Grb10 that is involved in various signalling pathways. It’s an imprinted gene, and the brain only expresses the paternally inherited copy. If we switch off this paternal copy, the mouse can’t produce any Grb10 protein, and the animals develop a very odd phenotype. They nibble off the face fur and whiskers of other mice in the same cage. This is a sort of aggressive grooming, a bit like a pecking order in chickens. In addition, if faced with a big mouse that they don’t know, the Grb10 mutant mice don’t back away – they stand their ground[233].

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225

For a useful review of DNA methylation and memory formation, see Day and Sweatt (2010), Nature Neuroscience 13: 1319–1329.

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226

Korzus et al. (2004), Neuron 42: 961–972.

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227

Alarcón et al. (2004), Neuron 42: 947–959.

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228

MacDonald and Roskams (2008), Dev Dyn. 237: 2256–2267.

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229

Guan et al. (2009), Nature 459: 55–60.

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230

Fischer et al. (2007), Nature 447: 178–182.

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231

Im et al. (2010), Nature Neuroscience 13: 1120–1127.

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232

Deng et al. (2010), Nature Neuroscience 13: 1128–1136.

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233

Garfield et al. (2011), Nature 469: 534–538.