The role of epigenetics in the neurobiology of stress and psychiatric diseases

Epigenetics refers to potentially heritable, experience-dependent molecular alterations to regulation of genetic function, or to numerous proteins or transcriptional regulators that alter the expression of genes, without altering the underlying DNA code itself – in other words, “nuclear inheritance which is not based on differences in DNA sequence"(1).

Figure 1: (a) Structure of eukaryotic chromatin.
(b) Known post-translational modifications. From Sun (2013).

Since the genome contains a massive amount of genetic information which must be condensed into a small space, nuclear DNA is found bound to small, basic, lysine-rich proteins called histones which serve to package the DNA into tightly compact units called nucleosomes. The histone octamer consists of two of each of the four core histone proteins H2A, H2B, H3 and H4, as well as H1 linker histones. Each of these have protruding N-termini tails which are subject to various post-translational modifications such as acetylation, methylation and phosphorylation. These modifications recruit various proteins, including histone modifiers, transcription complexes etc., which alter the chromatin, thereby dynamically altering its function(2). 

There are hundreds of such modifications which act together in complex combinations. Some of the most common are acetylation, methylation and phosphorylation. Acetylation is thought to activate gene transcription by neutralising the histone molecule’s positive charge, thereby disrupting its interaction with the negatively charged DNA, resulting in a remodelling of the chromatin into a more “relaxed” structure which is more accessible to transcription factors(3). Acetyl groups are added to lysine by a group of enzymes called histone acetyltransferases (HATs), and removed by histone deacetylases (HDACs)4. Similarly, methylation involves the addition of methyl groups to lysine residues. Addition of a methyl group by a histone methyltransferase (HMT) generates a docking site which recruits transcriptional regulators to specific gene loci4. This can either activate or repress transcription depending on the specific residue involved. DNA itself can also be methylated, repressing expression by interfering with the binding of transcription factors(4). Conversely, histone phosphorylation is generally associated with transcriptional activation.

These epigenetic modifications, along with plasticity, play a crucial role in many aspects of stress pathology including hypothalamo–pituitary–adrenocortical (HPA) axis function, reactivity to glucocorticoids in psychiatric diseases such as PTSD and anxiety, and the disease pathology of depression(5). Thus, epigenetics is a mechanism by which life experiences transduce genetic changes which, combined with existing genetic predispositions to psychiatric diseases such as depression, addiction and schizophrenia, can alter an individuals vulnerability to expressing the disease phenotype.

There are two main categories of animal models used to study depression. The first involves placing the animal under acute stress, such as the forced swim and tail suspension tests; the second involves exposing the animal to chronic stressful stimuli – such as in the chronic mild stress (CMS), chronic unpredictable stress (CUS) or chronic social defeat models.

The CMS model involves continuously exposing mice to a range of mild stressors, such as food/water deprivation, temperature reductions, restraint stress etc., which over a period of weeks leads to gradual reduction of preference for sucrose – an indication of anhedonia, a hallmark symptom of depression – which is reversed by chronic treatment with antidepressant drugs(6). The CUS model is similar except that the stressors are delivered randomly, at unpredictable times.

In the social defeat model, mice are repeatedly confronted with an aggressive male mouse for a period of 30 days, placing them under psycho-emotional stress and leading to the development of anxious-depressive symptoms as determined by behavioural tests(7). Defeated mice display social avoidance when subsequently confronted with an unfamiliar mouse – a characteristic symptom of depression – as compared to controls(8). Mice placed under chronic stress also show anhedonic symptoms such as a decrease in reward behaviours such as preference for sucrose(8). These depressive-like symptoms are also seen in the offspring of defeated mice(9), and neurodevelopmental deficits in mice bred from stressed mothers have even been characterised in up to third generation mice(10). Male mice exposed to social defeat or maternal separation – separating pups from their mother, another early life stress model which leads to depression-like behaviours – produce offspring which are also more vulnerable to stress(11). This stress-vulnerability in turn may be epigenetically transmitted to their offspring by changes in DNA methylations states, and thus changes in expression of, certain genes in their germ cells(12).

Furthermore, the offspring of rats which exhibit high levels of licking/grooming (LG) show significantly less anxiety/depression-like symptoms than those from mothers which exhibit low levels of LG behaviour(13), and are less reactive to stress in later life(14). Analysis of neuroendocrine levels of the offspring of low LG rats vs. high LG rats reveals distinct differences in HPA axis function and glucocorticoid mRNA expression(15). Additionally, persistent differences in DNA methylation at certain regions of the genome are observed between low LG and high LG rats throughout later life(14), and CUS mice have been shown to have increased activity of the histone deacetylase HDAC2(16). Moreover, female rodents raised by less nurturing mothers in turn show less nurturing behaviour towards their own offspring, as well as higher anxiety levels(17) – a phenomenon also seen in humans.

However, not all mice exhibit the same vulnerability to such stress-induced epigenetic changes. About a third of mice display a resilience to developing depression-like behaviours after exposure to the same stressors, and these mice show differences in levels of specific epigenetic changes – including DNA methylation and histone acetylation/methylation – in specific brain regions, particularly those involved in reward pathways(11). By blocking specific epigenetic modifications, these mouse models can be used to effectively study the importance of these modifications in the development of stress-induced pathologies, with the aim of developing more effective treatments for psychiatric diseases such as depression.

Depression is the result of a disorder of many different circuits within the brain, and as such shows a high level of heterogeneity. Many epigenetic changes have been identified in animal models which lead to the expression of depression-like behaviours in rodents, or resilience to the development of such behaviours. There is increasing evidence that epigenetic changes are also important in the pathogenesis of depression in humans, with similar mechanisms being identified in post-mortem brain samples of depressed humans(2). Depression is known to be heritable – genetics has been estimated to account for ~40% of the risk(18). This is, however, considerably lower than the heritability of other psychiatric diseases such as schizophrenia(2), which combined with the high discordance rate between monozygotic twins (50%)(19), suggests that other factors – such as epigenetics – are involved. In fact, monozygotic twins show significantly increased differences in levels of DNA methylation and histone acetylation as they grow older(19). Moreover, it has been suggested that epigenetic changes may be passed down to the offspring of affected individuals, causing changes in HPA axis function, vasopressin and serotonergic systems and increasing their vulnerability to depression(9,20). Mice subjected to unpredictable maternal separation (MS) who showed increased depression-like symptoms indicated by increased time spent floating in the forced swim test (a measure of helplessness) showed significant improvement when administered antidepressant desipramine vs. saline, as expected. However, interestingly, the depression-like symptoms were also observed in the offspring of MS mice who were reared normally, as well as third-generation mice(12) – suggesting epigenetic changes, to some extent, may be heritable.

Epigenetic changes (methylation) have been identified in glucocorticoid genes important for HPA axis function in the hippocampi of human victims of childhood trauma compared to those with normal childhoods(21,22), and decreased expression of brain-derived neurotrophic factor (BDNF) has been observed in post-mortem brain samples of depressed patients(23) compared to healthy controls – significant since changes in BDNF expression are required for the development of social avoidance induced by social defeat, and knockdown of BDNF expression in the nucleus accumbens (NAc) blocks this by the same mechanism as chronic treatment with antidepressants(8). Furthermore, adaptations within mesolimbic dopamine pathways, of which the NAc is a key component, have been identified as underlying the susceptibility or resilience of mice to develop depressive symptoms as a result of social defeat, specifically anhedonia(24). Susceptibility to depression induced by chronic social defeat has been associated with decreased levels of several histone methyltransferases (HMTs) – including G9a – in the NAc of susceptible mice, while these HMTs are upregulated in resilient mice(25). Additionally, artificial knockdown of G9a was associated with increased susceptibility to depression-like behaviours. Global increases in methylation of H3K4 residues has also been identified as a mechanism of non-selective antidepressant drugs (monoamine oxidase inhibitors), as a result of inhibition of H3K4 demethylation in the nucleosome, leading to alterations in gene expression(26). More recently, decreased expression of another histone demethylase in the NAc, JMJD2, has also been observed in mice subjected to chronic social defeat, and blocking of histone demethylase using inhibitors resulted in depression-like phenotype even in the absence of stress exposure, suggesting that activation of these enzymes may be an effective target for pharmacological treatment of depression(27).

As well as methylation, histone acetylation in the NAc has also been identified as an important epigenetic factor. Chronic social defeat causes a persistent increase in H3 acetylation in mice, which is associated with reduced levels of histone deacetylase 2 (HDAC2) in the NAc; reduced levels of HDAC2 has also been observed in post-mortem NAc samples from depressed humans(28). Additionally, another histone deacetylase, HDAC5, is associated with resilience, and chronic antidepressant treatment effectively increased expression of HDAC5, resulting in a decreased susceptibility to depression(29). This effect is however specific to the NAc, as HDAC5 administered virally into the hippocampus reversed the drug’s antidepressant effect(30). Thus, specific epigenetic modifications can have varying effects dependent on the brain regions involved.

Changes in expression of transcription factors have also been implicated. ΔFosB, which persists for long periods of times once expressed, can act as both an activator of transcription and a repressor(31). ΔFosB has been shown to be required for the commonly prescribed antidepressant fluoxetine to reverse depression-like behaviours induced by social defeat in mice, similarly to BDNF(32).

As well as being important in the aetiology of depression, the transcription factor ΔFosB has also been shown to act as a “molecular switch” for addiction(33). Following repeated administration of many different drugs of abuse, increased levels of ΔFosB is seen in the NAc, as well as in the dorsal striatum, another region involved in reward pathways. ΔFosB is thought to initiate and maintain changes in gene expression long after drug administration has ceased, perhaps underlying the risk of relapse. Overexpression of the transcription factor causes increased sensitivity to the drug – associated with increased likelihood of addiction – as well as increased drug-seeking behaviour(33). Once initiated, ΔFosB represses the expression of G9a in the NAc, causing increased dendritic spine plasticity of NAc neurons – a hallmark feature of addiction(34,35). Similar to in depression, this epigenetic reduction of G9a induced by cocaine exposure causes a global reduction in H3K9 methylation, which is thought to contribute to the potentiated behavioural responses to cocaine(4), and the subsequent establishment of addiction(36). When G9a is artificially overexpressed in the NAc, cocaine-addicted mice become less susceptible to stress compared to wild-type addicted mice in which G9a is reduced(25). Thus, G9a effectively serves as a molecular marker of resilience. This also suggests overlapping epigenetic mechanisms in the increased susceptibility to stress caused by depression and addiction.

FosB, as well as c-fos genes in the striatum, have been found to show an increased acetylation at histone H4 subunits(37). Activation of these genes by acetylation of the promoter regions, induced by chronic exposure to cocaine, appears to play a long-term role in addiction – indeed, these genes show increased acetylation for days to weeks after the last drug exposure(4). It is thought that chronic cocaine exposure achieves this by increasing the phosphorylation of HDAC5 causing it to be removed from the nucleus, thus allowing histone H4 to be acetylated by HATs at these genes(29). This is supported by evidence that knockdown of the HAT, or overexpression of HDACs, results in a reduced sensitivity to cocaine(29).

Schizophrenia is another psychiatric disease which has been shown to be regulated by epigenetic mechanisms. A common mouse model of schizophrenia is the methylazoxymethanol (MAM) model, in which H3K9 acetylation is decreased due to an increase of histone deacetylase 2 (HDAC2), leading to neurobehavioral deficits characteristic of schizophrenia. Recent experiments using this model have shown that valproic acid (VPA), a short chain fatty acid often prescribed for its mood-stabilising effects, prevents this increase in HDAC2, inducing increased acetylation at specific gene promoters implicated in the pathology of schizophrenia(38,39). Another drug commonly prescribed in the treatment of schizophrenia is the antipsychotic clozapine – a D2 receptor antagonist. Clozapine, however, shows higher therapeutic efficacy compared to other D2 receptor antagonists(40), and it is thought that this may be in part due to its additional ability to upregulate methylation of H3K4 residues at the Gad1 gene promoter, which is crucial in the synthesis of GABA(41). Reduced expression of Gad1 in the prefrontal cortex has been identified in the brains of some schizophrenic patients(42); thus, methylation of H3K4 residues at the promoter region of this gene may explain the enhanced efficacy of clozapine in the treatment of psychosis/schizophrenia.

As well as DNA/histone modifications, RNA can also be subject to epigenetic changes which can play a role in disease pathology. A recent study using RNA sequencing found a significant down-regulation of the long non-coding RNA (lncRNA) Gomafu, which is thought to play a role in anxiety as well as the expression of schizophrenia-related genes(43).

Epigenetic mechanisms are thus increasingly becoming recognised as important factors in the pathology of many psychiatric diseases including depression, addiction and schizophrenia. Drugs which target key enzymes which modify histones/chromatin, such as histone deacetylase inhibitors, may prove effective in the treatment of depression – an area in which novel drug targets are lacking.

However, there are some difficulties when studying epigenetic mechanisms. For example, although depression-like behaviours have been shown to be transmitted across generations, if in vitro fertilisation (IVF) is used in such mice models – i.e. artificially impregnating female mice with sperm from socially defeated mice – susceptibility for depression-like behaviours is not significantly increased in the offspring(44). It would be interesting to investigate why this is – perhaps pre-natal environment plays a key role in the transmission of epigenetic changes induced in the parent mice. More studies using IVF would help to elucidate this.

Additionally, due to the high diversity of the human genome, a great number of subjects will need to be studied in order to obtain accurate data. Genome-wide association studies (GWAS) could be useful. Additionally, epigenetic changes in RNA could be far more important than hitherto recognised. The use of RNA sequencing, similar to Spadaro et al (43), to identify the relevance of long non-coding RNAs (lncRNAs) in the regulation of epigenetic processes relevant in the development of psychiatric disorders could also pave the way for the development of novel drug targets.

Furthermore, researchers often study epigenetic modifications by altering the expression, or levels, of certain enzymes such as HATs, HDACs, or HMTs. However these enzymes are non-specific in that they affect thousands of genes. Thus, it is vital to establish reliable methods to target one specific type of modification at a specific gene promoter, in specific cell types – perhaps combined with GWAS – in order to produce more useful and reliable data.

A final important factor to consider in mouse models is that in some cases, the stress imposed on the adult mice may cause them to interact with their pups differently, which in turn may cause the behavioural changes seen in the pups – rather than epigenetic mechanisms underlying the behavioural differences being transmitted genetically. While these behavioural changes may be epigenetic in nature, it is important to distinguish these from those inherited directly from the parent.

The importance of epigenetic factors in the pathology of psychiatric and neurological diseases is only recently being discovered, and continues to be a key area of study in modern neuroscience research.

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Advances in Brain Imaging: A Shift Towards Functional Connectivity

Numerous imaging techniques have been utilised to study the structure and function of the brain, including functional magnetic resonance imaging (fMRI), positron-emission tomography (PET) and X-ray computed tomography (CT). fMRI is a form of blood-oxygen-level dependent (BOLD) contrast imaging which measures the relative proportions of oxyhemoglobin and deoxyhemoglobin – based on the assumption that oxygenated blood flows to active neurons at a greater rate than inactive neurons (the hemodynamic response) – while PET detects gamma rays emitted by a positron-emitting radiotracer introduced into the body and CT combines many X-ray images to construct a 3D representation of the brain. fMRI has typically been used to map brain regions which become activated while the subject is engaged in a particular task, however more recently it has been realised that since brain regions often work in networks (e.g. the default mode network), understanding cognitive processes and behaviours at a neurobiological level requires analysis of the functional connectivity between brain regions constituting functional networks (Menon, 2011). The brain is a unique organ in that, despite its fixed anatomy, local interactions are dynamically modulated to allow a vast functional repertoire (Park & Friston, 2013). Thus, projects such as the Human Connectome Project have begun to map the functional connectivity of the brain using resting-state and task-based fMRI, while recent advances in neuroimaging technology have enabled mental processes and behaviours to be correlated with specific brain structures with greater accuracy and reliability. 

Tong & Pratte (2012) discuss the advances in brain imaging, presenting a wealth of information gathered on a range of mental processes using novel fMRI techniques such as multivoxel pattern analysis. The authors discuss how new techniques allow (to some extent) the ability to “mind read” which of two previously viewed images a subject was imagining, and go as far as to say: “As these methodologies continue to advance, it will become increasingly important to consider the ethical implications of this technology”. This is a bold statement emphasising the fast-evolving nature of brain imaging technology. The authors also review how numerous studies over the past decade have succeeded in decoding various top-down mental processes, such as feature-based attention (Kamitani & Tong, 2005), imagination (Reddy et al., 2010), episodic memory (Rissman & Wagner, 2012) and numerical processing (Knops et al., 2009). For example, Kamitano & Tong (2005) found, using statistical algorithms on fMRI data, that specific fMRI signals in the visual cortex (V1) could reliably predict which of eight stimulus orientations the subject was attending to, indicating that the visual cortex encodes detailed orientation information which can reliably predict subjective perception and suggesting direct orientation mapping at a neurobiological level. Later neuroimaging research by Tong et al. (2012) found that the correlation between such orientation-selective activity patterns and the quality of the sensory input could be directly predicted by the average BOLD amplitude in the brain region of interest using multivariate pattern analysis, offering a reliable model of fMRI pattern classification. This is a prime example of how both brain imaging technology and the way in which researchers use it has advanced over recent years, allowing unprecedented revelations in the neurobiology of specific brain functions.

Tong & Pratte (2012) also highlight methodological issues encountered in brain imaging research. For example, when analysing fMRI data collected while subjects watched humorous events in a video, it transpired that the ventricles were the most statistically informative brain region in predicting a subjects’ urge to laugh – even though it is highly unlikely that the ventricles themselves play a functional role in the cognitive processing of humorous events (Tong & Pratte, 2012). This error is dubbed the “fallacy of reverse inference” (Poldrack, 2006), and is eloquently demonstrated by neuroscientist James Fallon’s conclusion that he must be a psychopath based on PET images of his own brain (Fallon, 2013). Another problem faced is that brain regions are often associated with multiple mental processes, and regions implicated in particular functions tend to vary with experimental variables such as task demands and the specific baseline condition used to identify them (Rabinovich et al., 2012), and even between individual subjects (Kelly et al., 2012). For instance, the hippocampus is known to be involved in the recall of episodic memories, but has also been implicated in the imagining of future events; however, patients with hippocampal damage in some conditions retain unimpaired imagination, suggesting that the hippocampus may not be necessary for it (Martin et al., 2011). Furthermore, there is the issue of causation versus correlation. For example, a famous MRI study by Maguire et al. (2006) found that London taxi drivers had greater grey matter volume in the mid-posterior (but not anterior) hippocampus compared to London bus drivers, who are not required to learn the colossal amount of information which constitutes “the knowledge”. However, the results do not indicate whether these differences are a direct result of the learning of “the knowledge”, or whether subjects showing this particular neuroanatomy are predisposed to becoming London taxi drivers.

Bennett et al. (2009) highlighted some of the common issues faced when analysing fMRI data (e.g. the multiple comparisons problem) by providing evidence that fMRI scans of a dead salmon apparently showed statistically significant brain activity according to commonly-used statistical tests. Subsequent fMRI studies swiftly adopted the corrected comparison methods proposed (Bennett et al., 2009). Additionally, while identifying functional connectivity has recently been recognised as more important than identifying individual brain regions (Menon, 2011), there are limits to how much fMRI can reveal about connectivity. For example, fMRI studies demonstrate that activation of the prefrontal cortex during cognitive evaluation of threatening facial expressions is associated with an attenuated response of the amygdala, apparently indicating a functional network for emotional regulation (Hariri et al., 2003). However, fMRI data does not necessarily show that the amygdala was inhibited by the prefrontal cortex; this is merely inferred. In fact, such negative correlations are often incorrectly interpreted as “inhibitory interactions” (Kelly et al., 2012). Thus, novel techniques / computational models have been developed attempting to overcome these issues.

One such model is the state-space multivariate dynamical systems (MDS) model (Ryali et al., 2011), which takes into account inherent regional differences in the hemodynamic response and focuses on changes in latent signals rather than BOLD signals – which themselves do not necessarily measure the underlying neural activity. More recently optogenetic fMRI (ofMRI) – which combines optogenetic control of neural circuits with fMRI, enabling more direct investigation of connectivity in vivo (Lee, 2012) – when combined with the MDS model was found to be reliable method for identifying functional interactions between brain regions (Ryali et al., 2016).

Figure 1: ofMRI: optically-driven local excitation in defined rodent neocortical cells drives positive BOLD. a. Experimental schematic: transduced cells (triangles) and blue light delivery shown in M1 motor cortex. b. ChR2-EYFP expression in M1. c. ofMRI hemodynamic response during 6 consecutive epochs of optical stimulation. d. BOLD activation is observed at the site of optical stimulation. Adapted from Lee (2012).

Another advancement in statistical analysis being increasingly employed in fMRI studies is the shift from the identification of regions of interest (ROI) to the “parcellation” of whole brain resting-state fMRI data into spatially coherent regions of homogenous functional connectivity (e.g. by cluster analysis) (Craddock et al., 2012). This type of analysis offers several advantages, including providing a measure of the stability of resting-state functional networks between individual subject data and across grouped subjects data (Bellec et al., 2010). Variations between subjects in these intrinsic functional connectivity based “parcellations” does not appear to be correlated with structural variations; rather, they follow intrinsic variations in functional connectivity evoked by specific tasks (Mennes et al., 2010). Thus, studies are beginning to examine links between these task-evoked variations in resting-state functional connectivity and specific behaviours/mental processes (Kelly et al., 2012; Adelstein et al., 2011). Such novel methods of statistical analysis hold promise in elucidating stable functional networks across individuals, with the aim of explaining cognitive functions at a neurobiological level.

Furthermore, recent advances in MRI imaging techniques allow full brain scans to be completed up to 2-3 times faster. This is a significant advantage since it allows for greater statistical definition of neuronal networks (i.e clearer identification of functionally relevant networks), as well as enhanced visualisations of structural connections in the brain such as white matter tracts (Feinberg & Setsompop, 2013). Similarly, the development of high-field strength MRI scanners is expected to dramatically advance our understanding of the pathology of multiple sclerosis (MS) (Filippi, 2014). Advances in brain imaging techniques and how they are used is also benefitting the study of many other pathological behaviours such as those of autism or schizophrenia. Rather than focusing on pathology of individual brain regions, studies are increasingly focusing on aberrant interactions between specific distributed neural networks as well as more general disturbances in functional connectivity (Menon, 2011). For example, one study found that schizophrenic subjects show less integrated but more diverse functional connectivity during task-based behavioural measures compared to controls, even suggesting a possible advantage of the “schizophrenia connectome” (Lynall et al., 2010). Recent neuroimaging studies of autism spectrum disorder (ASD) have also shifted the focus to identifying abnormal connectivity (Vissers et al., 2012), with some studies reporting “underconnectivity” in frontal regions and others reporting “overconnectivity” – likely as a result of the specific methodological variables/analysis type in each study (Nair et al., 2014). Studies are beginning to examine the development of normal functional connectivity in the brain throughout adolescence and the effect of genes and the environment in the development of abnormal connectivity (Blakemore, 2012). However, while these advances are promising, there remain some limitations; for example, different mental diseases often affect the same resting-state networks, bringing into question the specificity of the findings of neuroimaging studies (Barkhof et al., 2014).

Figure 2: EEG waveform representations to spatial/map representations (and analyses).
From Michel & Murray (2012)

Recent advances in signal analysis have also allowed electroencephalography (EEG) to be used as an efficient brain imaging technique, particularly useful in the investigation of abnormal temporal dynamics in functional networks at the millisecond range. Michel & Murray (2012) argue that the full potential of EEG has been underestimated, proposing that proper analysis of the electric field potentials from each electrode can provide spatio-temporal information which may be useful as an adjunct to fMRI studies. Diffusion tensor imaging (DTI) – a form of MRI which tracks the diffusion of water molecules along white matter tracts – has also been used effectively in a recent connectome analysis study of major depressive disorder (MDD), finding reduced structural connectivity in regions constituting the default mode network as well as the frontal cortex, thalamus and caudate regions, thought to be involved in emotional and cognitive processing (Korgaonkar et al., 2014). However, structural connectivity does not necessarily predict functional connectivity.

With regard to connectome projects, Ohno et al. (2016) review recent advances in the acquisition and analysis of large “connectomic” data sets, while concluding that the combination of different imaging modalities and further advances in the (automated) analysis of data may revolutionise our understanding of the structural and functional connectome, elucidating neural mechanisms underlying not only basic sensory/motor functions and disease but also higher mental processes such as consciousness and intelligence.

Recent brain imaging studies have shown a marked paradigm shift from attempting to localise mental processes/behaviours to individual brain regions to identifying whole-brain structural and functional connectivity patterns which may become aberrant in various neurological/psychiatric disorders. As brain imaging technologies continue to advance, researchers continue to develop novel methods to exploit them to their full potential, allowing unprecedented advances in the neurobiology of specific cognitive functions and behaviours.

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