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Dwivedi Y, editor. The Neurobiological Basis of Suicide. Boca Raton (FL): CRC Press/Taylor & Francis; 2012.

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The Neurobiological Basis of Suicide.

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Chapter 4Gamma-Aminobutyric Acid Involvement in Depressive Illness Interactions with Corticotropin-Releasing Hormone and Serotonin

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There is little doubt that genetic and experiential factors contribute to the neurochemical processes responsible for the development of major depressive disorder (MDD) (Caspi et al., 2003; Kendler et al., 2005; Millan, 2006). In this regard, MDD is a biochemically heterogeneous disorder, and any of several neurochemical and/or receptor alterations provoked by stressful experiences might contribute to the development of depressive symptoms. Moreover, the effectiveness of antidepressants in attenuating MDD symptoms might be tied to the particular neurochemical alterations elicited by stressors in any given individual, and multitargeting as a strategy for the treatment of depression has received increased attention (Millan, 2006, 2009).

Although considerable evidence had pointed to a role for serotonergic processes in subserving MDD (Pineyro and Blier, 1999), it is clear that attributing MDD uniquely to serotonin (5-HT) is not a sustainable perspective. Considerable evidence has indicated that the nature of the 5-HT changes associated with depression (e.g., in postmortem analyses of depressed individuals that died by suicide) are highly variable (Anisman, 2009; Stockmeier, 2003). Moreover, drug treatments that affect 5-HT processes are effective in only a portion of patients, not all symptoms resolve with treatment, and recurrence rates are exceedingly high (Millan, 2006). Although not dismissing a role for 5-HT in the evolution or maintenance of MDD, it has been maintained that other processes might contribute in this regard. These have included several growth factors and cytokines, such as brain-derived neurotrophic factor (BDNF) (Duman and Monteggia, 2006) and various interleukins (Anisman et al., 2008; Dantzer et al., 2008), corticotropin-releasing hormone (CRH), and other peptides, such as neuromedin B and somatostatin (Merali et al., 2004, 2006; Nemeroff, 1996; Reul and Holsboer, 2002). There has also been a rejuvenation of the view that γ-aminobutyric acid A (GABAA) functioning might contribute to depressive illness (Rupprecht et al., 2006; Sanacora and Saricicek, 2007; Sequeira and Turecki, 2006; Tunnicliff and Malatynska, 2003), possibly by moderating the interplay between CRH and 5-HT (Hayley et al., 2005).

4.1. γ-AMINOBUTYRIC ACID FUNCTIONING WITHIN THE BRAIN

GABA and GABAA receptors are ubiquitous within the central nervous systems, playing a fundamental role in controlling neural inhibition and timing of neural networks (i.e., gating pyramidal cell synchrony) (Traub et al., 1999; Whitington and Traub, 2003; Whittington et al., 1995). GABAA receptors are pentameric protein complexes constructed from protein subunits named α, β, γ, δ, ε, and π that are derived from a repertoire (cassette) of 21 different proteins/genes (Olsen and Sieghart, 2008). Often these subunits exist as subtypes: α has six, β has three, and γ has three. The functionality of GABAA receptors is determined by specific subunit configurations. The stoichiometry of GABAA receptors is thought to be 2α;2β and one of the other subunits (γ, δ, ε, π) is “chosen” to complete the pentamer. Although there are many possible subunit combinations that could potentially make up a GABAA receptor, there are two primary classes that have been broadly defined. One class comprises receptors that have a γ subunit and the other class is one that has a ´ subunit. For insertion into a synaptic site, a GABAA receptor must contain a γ subunit (which most often is the γ2 subtype in the adult CNS).

Synaptic receptors mediate what is usually termed “phasic inhibition,” but those having the δ subunit are not usually located in postsynaptic densities and are important for providing “tonic (extrasynaptic) inhibition.” Importantly, the variability of GABAA receptor structure as defined by other subunits (α and β) seems to control the timing of phasic inhibition. The timing is primarily manifested by the variability of the rate of decay of the synaptic currents. It seems that once GABA is released from the synaptic vesicle it saturates the synaptic receptors, activating them within a millisecond (although there are exceptions to this generalization). Although the GABA concentration declines very quickly (within 2 or 3 ms), the synaptic current does not, as GABA tends to unbind relatively slowly from its receptors (Hutcheon et al., 2000; Maconochie et al., 1994). The rate at which it unbinds is controlled by structure (subunit expression) (Burgard et al., 1999; Hutcheon et al., 2000; Verdoorn, 1994). So, depending on the subunits comprising a given receptor, synaptic currents can last from 5 to 10 ms to hundreds of milliseconds. This variability in duration seems to be important in determining neuronal network synchronization. For example, fast phasic inhibition tends to create fast brain rhythms (gamma oscillations in hippocampus and cortex; Klausberger et al., 2002, 2004), whereas slow inhibition produces slow brain rhythms (delta in renticular nucleus; Bentivoglio et al., 1990; Zhang et al., 1997). Finally, to some extent, extrasynaptic/tonic inhibition (which is essentially active continuously) also seems important for controlling brain rhythms. In this regard, a recent report implicated tonic inhibition in controlling hippocampal gamma rhythms (Mann and Mody, 2010). Given the heterogeneous expression of GABAA receptor subunits regionally and even at the subcellular level, the understanding of the potential complexity of how all these factors control normal and abnormal brain function is daunting. Nevertheless, the implications are relatively easy to appreciate; perturbations in the structure of GABAA receptors have the potential to alter neural network activity and thus alter behavior.

4.2. GABA INVOLVEMENT IN ANXIETY AND DEPRESSION

The data supporting a role for GABAergic processes in mediating anxiety-related disorders have come from several lines of research. Among other things, it has been reported that (a) GABA levels in plasma and in CSF were increased in stress situations, (b) stressors influenced GABAA receptor functioning, (c) treatments that increase vulnerability to elevated anxiety and depression-like behaviors, such as early life stressors, also influence GABAA subunit expression, and (d) drugs that affect GABAA activity are effective in attenuating anxiety (reviewed in Anisman et al., 2008). Beyond the GABA–anxiety relationship, there is reason to believe that these processes may also contribute to MDD and may be important for the comorbidity that often occurs between anxiety and depression. In particular, it was reported that depression was accompanied by lower levels of GABA in cerebrospinal fluid (Sanacora and Saricicek, 2007) and based on neuroimaging analyses, GABA was reported to be reduced in the dorsolateral prefrontal and occipital cortex of depressed patients (Bhagwagar et al., 2007; Hasler et al., 2007; Price et al., 2009; Sanacora et al., 1999, 2004) and a reduction in the density and size of calbindin-immunoreactive (CB-IR) GABAergic neurons was reported to be evident in the prefrontal and occipital cortex (Maciag et al., 2010; Rajkowska et al., 2007). Furthermore, it was reported that GABA levels within the PFC were inversely related to severity of depression (Honig et al., 1988), and GABAA receptor subunit expression was likely altered in depressed suicides given that GABAA/BDZ (benzodiazepine)-binding sites were elevated (Cheetham et al., 1988). In fact, mood disorders in female patients were reported to be associated with GABAA α1 and α6 polymorphisms (Yamada et al., 2003).

In addition to these GABA variations, it was reported that the relative density of a primary enzyme for GABA synthesis, glutamic acid decarboxylase (GAD) neuropil, was elevated in the hippocampal region of depressed suicidal patients relative to controls, but such an outcome was not apparent in several cortical regions, such as the orbitofrontal, anterior cingulate, dorsolateral prefrontal, and the entorhinal cortex (Gos et al., 2009), although in other studies variations of GAD were detected in diverse prefrontal cortical regions (Bielau et al., 2007; Fatemi et al., 2005; Gos et al., 2009; Karolewicz et al., 2010). Furthermore, it was reported that drugs that act as antidepressants influence interneuron functioning (Akinci and Johnston, 1993; Brambilla et al., 2003; Krystal et al., 2002; Shiah and Yatham, 1998; Tunnicliff and Malatynska, 2003), raising the possibility that the actions of these agents stem from their GABAergic effects.

Despite these positive findings, there have also been reports indicating that neither GABA levels (Korpi et al., 1988), GABA-related enzymes (Cheetham et al., 1988; Sherif et al., 1991), nor the GABA transporter (Sundman-Eriksson and Allard, 2002) differed between drug-free depressed suicide victims and controls (Arranz et al., 1992; Cross et al., 1988; Sundman et al., 1997). Not surprisingly, there were many differences between the depressed populations assessed across studies, as well as the procedures used to assess GABA functioning. Thus, as in many other analyses of relations between pathology and biological substrates, it is difficult to define what factors might have been fundamental in accounting for the between-study differences that have been reported. These inconsistencies notwithstanding, there does seem to be appreciable support for the view that MDD was accompanied by disturbances in key metabolic enzymes involved in the synthesis of glutamate and GABA as well as proteins involved in membrane expression of GABA and the uptake of glutamate by glial cells.

Beyond the variations of GABA functioning, it appears that the mRNA expression of GABAA subunits may be either up- or down-regulated in association with MDD, depending on the brain region assessed (Choudary et al., 2005; Merali et al., 2004; Poulter et al., 2010b; Rupprecht et al., 2006; Sequeira and Turecki, 2006). Consistent with these findings, a broad gene expression analysis, supported by semiquantitative reverse transcription polymerase chain reaction (RT-PCR) analyses, revealed that glutamatergic (GLU) and GABAergic-related genes were altered across numerous cortical and subcortical brain regions (being particularly notable within the prefrontal cortex and hippocampus) of suicides with and without major depression and controls (Sequeira et al., 2009). Most of the GLU-related probe sets corresponded to ionotropic N methyl D aspartic acid (NMDA) receptor subunits (GRINA, GRIN2A, and GRINL1A) and 2-amino-3-(5-methyl-3-oxo-1,2-oxazol-4-yl)propanoic acid (AMPA) receptors (GRIA3, GRIA4, GRIA1, and GRIA2). Generally, AMPA receptors were up-regulated among the suicides with major depression relative to the control samples and those from suicides without a history of major depression. Conversely, in portions of the prefrontal cortex, Brodmans Area (BA) 46 and BA47, as well as aspects of the parietal cortex (BA38 and BA20), the glutamate metabotropic 3 receptor (GRM3) was down-regulated among the suicides with and without major depression. Thus, the former effects are likely tied to depression, whereas the latter might be related to suicide itself, rather than the depression associated with it.

Our own research has been consistent with the view that GABAA subunit expression was altered in MDD. As seen in Figure 4.1, in the frontopolar cortex (FPC) of depressed suicides the mRNA expression of the α1, α3, α4, and δ subunits was lower in depressed suicides than in controls as was the expression of CRH Type 1 receptors; the latter was coupled with elevated levels of the peptide itself and thus may have reflected a compensatory down-regulation (Merali et al., 2004).

FIGURE 4.1. Mean (±SEM) expression of mRNA CRH1, CRH2, and CRH binding protein (CRH-BP), as well as mRNA expression of GABAA subunits in the frontopolar cortex of depressed individuals that died by suicide and that of controls (nondepressed individuals that died of causes other than suicide).

FIGURE 4.1

Mean (±SEM) expression of mRNA CRH1, CRH2, and CRH binding protein (CRH-BP), as well as mRNA expression of GABAA subunits in the frontopolar cortex of depressed individuals that died by suicide and that of controls (nondepressed individuals that (more...)

Beyond the frank changes of mRNA expression of GABAA subunits, using RT-qPCR analysis, we also showed relatively subtle changes in their expression patterns (Merali et al., 2004; Poulter et al., 2010a,b). It appeared that the expressional organization of the GABAA gene cassette may be altered among depressed individuals that died by suicide. Specifically, in several brain regions of individuals that died suddenly of causes other than suicide, there was appreciable “coordination” between several GABAA subunit mRNA expressions. Essentially, we found that the variations of subunit mRNA expression from individual to individual were often matched by expression levels of another subunit, so that numerous high positive interrelations existed between subunit mRNA expressions. For example, Figure 4.2A shows the relationships between the α1 subunit and several other subunits in the FPC, and very similar patterns were evident with respect to other subunits as well. In fact, of 21 possible correlations involving the α1, α2, α3, α4, α5, δ, and γ subunits, 18 were statistically significant. In contrast, as shown in Figure 4.2B, the comparable subunit interrelations were not apparent among depressed individuals that died by suicide, as only 3 of the 21 possible correlations were statistically significant. The RIN values and the pH in these samples were acceptable and were comparable in the two conditions, and, in this study, as in our other reports, samples were collected from individuals with brief agonal periods. Moreover, these brains were obtained within a few hours of death, as in Hungary, where the brains were collected (by our collaborators Drs. Miklos Palkovits and Gabor Faludi), the law requires that brains be taken soon after death, and permission from family members to use the tissue for experimental purposes is obtained thereafter. Thus, the findings that we reported cannot be readily attributable to nonspecific effects related to tissue harvesting or contamination.

FIGURE 4.2. Coordinated expression was diminished in the frontopolar cortex of depressed individuals that died by suicide.

FIGURE 4.2

Coordinated expression was diminished in the frontopolar cortex of depressed individuals that died by suicide. In (A), regression graphs show examples of several significant correlations between α1 and other subunits. (B) Regressions between these (more...)

When the reduced subunit interrelations in the FPC of depressed suicides were first observed, we considered this to reflect a dysfunctional profile. That is, we assumed that the norm ought to be one of high interrelations reflecting coordination within this system, and indeed, these high interrelations were not only evident in humans, but were observed in mice as well (Poulter et al., 2010a). Since then we have also observed a similar coordination of subunit mRNA expression in the hippocampus and amygdala of humans; however, in other regions such as the orbital frontal cortex and the paraventricular nucleus of the hypothalamus, the significant interrelations between subunits were low (2–3 significant) among individuals that died of causes other than suicide, whereas in these same regions of depressed suicides, these interrelations were more frequent (Poulter et al., 2010b). Thus, we moved from our original perspective that the reduced interrelations reflect a dysfunctional outcome, and instead we simply view the altered interrelationships as potentially reflecting a reorganization of subunit expression.

This leaves us with several very fundamental questions. First, are these interrelations an unimportant epiphenomenon or an artifact of the measurement approach? Second, if these are genuine relationships, then what is their functional significance? Third, why are the relationships between subunits altered with suicide/depression, and why do these relationships decline in some regions of the depressed/suicide brain, but increase in others?

The initial thought concerning the interrelations might be that the genes for subunits might be close to one another on a chromosome, and as a result they might vary together (linkage). Indeed, although the gene for the δ subunit appears on its own on chromosome 1, genes for GABA subunit genes appear in clusters (McKernan and Whiting, 1996). For instance, in humans, genes encoding the α1, α6, β2, and γ2 subunits are clustered on chromosome 5, whereas genes for α2, α4, β1, and γ1 subunits appear on chromosome 4, the genes for the α5, β3, and γ3 subunit appear on chromosome 15, and α3 and ε genes are found on the X-chromosome (McKernan and Whiting, 1996; Steiger and Russek, 2004). However, we found that intercorrelations appear between subunits even if they are not on the same chromosomes, and, further, the intercorrelations vary appreciably across brain regions irrespective of the chromosomes on which they appear (Poulter et al., 2010b). Thus, the correlations cannot be attributed to factors related to certain genes being inherited as clusters.

This brings us to whether high interrelations between GABAA subunits are the norm, and whether the interrelations are disturbed in pathologies other than depression/suicide? There have, however, been few studies that assessed GABAA subunit expression in relation to pathology in humans or in animal models, and fewer still that assessed intercorrelations in relation to pathology. Yet, in studies of epilepsy in humans and in animal models, interrelations between GABA receptor subunits were reported (Brooks-Kayal et al., 1999), indicating that our findings were not anomalous and that coordinated expression of genes for the various subunits likely is the norm.

Accepting this view raises the obvious question concerning the functional significance of the subunit interrelations. It is widely accepted that GABAA receptor subunits control the timing of synaptic inhibition. It is also clear that synaptic inhibition is fundamental for generating nearly all types of brain rhythms including gamma, theta, and fast ripple oscillations. No region of the brain generates only one type of rhythm, and nearly every region has homogeneous expression of GABAA receptors. How the homogenous expression relates to differing brain rhythms and behavior is not completely understood. For example, in the hippocampus, one class of interneurons expresses a high abundance of α1 subunits, whereas high expression of α2 subunits appears in pyramidal cells (Klausberger and Somogyi, 2008). Thus, the timing of inhibition in pyramidal cells is different than in interneurons. Adding to this complexity are observations that synaptic inhibition is heterogeneous even on the same cell. In effect, it seems that the brain has evolved a highly complex mosaic of synaptic timing patterns that permit one region to generate preferred brain rhythms and perhaps restrict the generation of others.

Coordinated expression has been reported for other ion channels that are heteromeric, such as gap junctions and potassium channels, so this patterning is not unique to GABA receptor genes. The question is basically one of defining the biological significance for the presence of coordinated regulation of receptors and ion channels. One possibility is that transcriptionally regulating mRNA abundance of subunits would be a potential mechanism to ensure a proportional abundance of protein, as it would be energetically inefficient to produce excess of one protein over another if the stoichiometry of their assembly is 1:1. In this regard, for heteromeric proteins with variable functionality, such as GABAA receptors, having the correct balance of receptors involved in phasic vs. tonic inhibition may be particularly important. In addition, maintaining a balance of synaptic timing as some receptors (e.g., α1) give rise to fast synaptic currents, whereas others are associated with relatively slow synaptic currents (α5). In effect, maintaining balance between fast and slow synaptic currents might be facilitated by having the expression of one subunit with a particular physiological characteristic being balanced by the concurrent expression of another that acts in opposition to the first.

This raises the question as to how these timing patterns interact to produce a rhythm? GABAA receptor activity is not the only one that regulates neural network functioning. Ordinarily, the activity of neural networks or firing patterns involves several cellular characteristics (channel densities, calcium buffering, and cell morphology) and network parameters (distribution of neurons, as well as the abundance and location of synaptic contacts). Computational models that varied these as well as several other parameters revealed considerable “resiliency” as the timing of neural networks could function within a wide range of circuit parameters (Prinz et al., 2004). Nevertheless, critical interrelationships appeared to exist between certain parameters (e.g., synaptic strength), in that being outside of a particular range of values gave rise to the generation of aberrant rhythms (Prinz et al., 2004). As such, it might be expected that variations in the relative amounts of the subunits (or their coordination) that influence the stoichiometric ratios of subunits that make up GABAA receptors would lead to variations of the duration of inhibitory currents. Whether this reorganization creates a situation that puts the circuit outside some critical range is not known. So, at this point, the answer to the question regarding the impact of this organization (vs. disorganization) is not clear. Nevertheless, it seems that there are processes that control the relative strength of one timing pattern over another. These processes may ensure an appropriate “mix” of synaptic timing patterns that ultimately controls rhythms within a brain area. Importantly, our observations and those of others (Brooks-Kayal et al., 1999) suggest that the coordinated expression of the GABAA receptor gene cassette is plastic. Indeed, we have shown that these patterns were perturbed in mice that had been acutely or chronically stressed and that these responses varied in stressor-sensitive and stressor-resilient strains of mice (Poulter et al., 2010a).

Supposing our suggestion that coordinated subunits influence electrical rhythmicity, it would be expected that a stimulus that normally generates a gamma rhythm, for example, would not do so as readily in response to sufficiently intense challenges. This could mean that the rhythm would not be generated at all or the strength of the oscillation (higher or lower) would be inadequate or inappropriate. Altered brain synchrony has been documented for many neurological disorders including epilepsy and sleep disorders, and altered EEG rhythms have been documented in those with MDD (Howland and Thase, 1991; Koyama and Yamashita, 1992; Pollock and Schneider, 1990). Coordinated GABAA receptor subunit expression may be a mechanism that ensures that normal brain electrical activity occurs and disturbances in these patterns may alter brain activity in such a way that abnormal cognitive function is created.

From this perspective, a certain degree of coordination between subunits would be advantageous for appropriate neural activity being maintained. Deviations from this coordinated pattern, in the form of elevated or reduced subunit interrelations across brain regions, might be viewed as engendering dysregulated neuronal firing patterns. In this regard, following our initial report of disturbed coordination of subunit expression in the FPC of individuals who died by suicide (Merali et al., 2004), we observed that this pattern was also apparent in the hippocampus and amygdala, but in some brain regions (orbital frontal cortex and paraventricular nucleus of the hypothalamus) coordinated expression of the same subunits was appreciably increased in depressed/suicides relative to nondepressed controls (Poulter et al., 2010b).

Studies of neuronal changes in postmortem tissue in humans do not permit analyses of the processes “causally” related to these outcomes, as experimental manipulations obviously cannot be undertaken. Thus, we assessed GABAA subunit gene expression and coordination in brain regions of mice that were highly stressor reactive (BALB/cByJ mice) relative to that evident in the stress-resilient C57BL/6ByJ strain (Poulter et al., 2010a). We observed that in most brain regions examined an acute stressor moderately increased the expression of these subunits, and these effects were still more pronounced following a chronic stressor, especially within the hippocampus. This was very different from the profile evident in the cortical regions of the depressed suicides where the subunit expression was diminished relative to controls, although in the mouse strains a high degree of coordinated expression of the subunits was apparent, just as they were in humans.

Interestingly, different profiles of GABAA subunit coordination were apparent in the highly stressor-reactive BALB/cByJ mice relative to that evident in the stress-resilient C57BL/6ByJ strain (the profiles were highly similar in other brain regions). Importantly, in these strains acute and chronic stressors differentially influenced subunit coordination. Unfortunately, the observed outcomes, while very pronounced, were exceptionally complex. In some regions the stressors reduced subunit organization, but in other regions organization appeared to be increased. Moreover, whereas some effects of acute stressors were exaggerated following chronic stressors, depending on the strain and the brain region examined, an apparent normalization of the coordinated gene expression occurred following a chronic stressor regimen (Poulter et al., 2010a). At this point, it is difficult to make definitive conclusions concerning the relationship between the stressor-provoked subunit organization and particular behavioral changes. But it might turn out to be meaningful that this organization is tied to stressor reactivity and is altered (increased or decreased) by acute and chronic stressor experiences. Nevertheless, we suggest that analyses that involve coordinated expression of factors that influence neuronal firing, in addition to analyses of the extent of subunit gene expression per se, may provide fundamental information regarding brain–behavior relations.

4.3. EPIGENETIC REGULATION

Epigenetics broadly defines two processes that alter or control DNA structure and ultimately the degree that DNA is transcribed. The first process is the chemical modification of DNA by the methylation of cytosines that are paired with guanines (CpGs) (D’Alessio and Szyf, 2006). This occurs through the activity of a family of enzymes called DNA methyl transferases (DNMT). There are three kinds of DNMTs that methylate DNA, namely DNMT-1, -3A, and -3B (type 2 was improperly named based on sequence homology and seems to methylate RNA; Schaefer and Lyko, 2010; Szyf and Detich, 2001). The methylation of DNA attracts proteins that recognize methylated CpGs (meCpGs) that, in turn, attract other proteins (histones) that tend to wind and condense the DNA. Condensed DNA is unable to be transcribed and hence the gene expression is prevented. The other process occurs through “histone rearrangements,” which is controlled by their covalent modification. Histones, as already mentioned, are proteins that bind and wrap DNA. Depending on how they are positioned on DNA they can either enhance or repress transcription (Miller et al., 2008; Roth and Sweatt, 2009; Roth et al., 2009). This occurs through their acetylation and/or methylation by specific enzymes. These covalent modifications are reversible and so there is a dynamic regulation of the degree to which DNA is exposed to transcription factors and RNA polymerase by histone positioning. Importantly, DNA methylation and histone rearrangements also act in concert with one another. It should also be mentioned that chromatin structure is under the control of noncoding RNA, which are small untranslated RNA molecules that bind to chromatin and control its interaction with DNA (Malecova and Morris, 2010). Although epigenetic mechanisms often involve covalent modifications (methylation and acetylation), they are reversible and all these mechanisms are surprisingly “well tuned” to environmental and experiential stimuli. Thus, the view developed that the genome is under constant epigenetic surveillance, permitting cells to respond to their environment as the need arises. It is particularly exciting that this surveillance occurs in brain (D’Alessio and Szyf, 2006; Lubin et al., 2008; McGowan et al., 2009; Miller et al., 2008; Mullins-Sweatt et al., 2009; Poulter et al., 2008; Roth and Sweatt, 2009; Roth et al., 2009; Sweatt, 2009; Szyf et al., 2008; Weaver et al., 2004, 2005) and may contribute to the processes associated with mental illness.

Despite the allure of this relatively new line of research in studying brain–behavior relations, which is admittedly still in its infancy, several fundamental questions need to be addressed, some of which are not unlike those that have plagued research that involve microarrays used to assess the relationships between discrete behaviors and variations associated with thousands of genes concurrently. Specifically, there is good reason to believe that a very large number of genes encounter epigenetic changes, and thus one must wonder how meaningful this is when any one of these changes is tied to a particular phenotype. Studies that involve analyses of phenotypes in relation to genome wide scans have dealt with this through the use of a very large number of participants in an effort to deal with the possibility of spurious findings (i.e., alpha errors). Analyses that involve epigenetic changes, such as our own research involving GABAA subunit promotors (Poulter et al., 2008), or those that involve glucocorticoid receptor promoters (McGowan et al., 2008) might thus be victims of this very problem. This said, the findings that have been reported might turn out to provide valuable information concerning the processes associated with stress and depression.

Based on studies in animals, it was reported that early life negative experiences and chronic stressors can induce DNA methylation and histone acetylation (Weaver et al., 2004, 2006). As such experiences can precipitate MDD, it prompted us to ask whether epigenetic mechanisms may be at play in the brain samples obtained from MDD/suicide completers. Indeed, we found that DNMT-3B expression was increased in both males and females and that this was associated with the hypermethylation of the GABAA receptor α1 subunit promoter. Furthermore, the expression of DNMT-3B mRNA was negatively correlated to the expression of α1 subunit mRNA (Poulter et al., 2008). These data suggest that the DNA methylation of the promoter may be responsible for altered expression of the α1 subunit that we had previously reported to be related to depression/suicide. Similar data have also been reported in an analogous cohort of brain samples where the RNA polymerase promoter was shown to be hypermethylated (McGowan et al., 2008). Unlike our data where only a few sites were specifically methylated on the α1 promoter, this study indicated that the hyper-methylation showed no specificity as all sites assessed seemed to be more or less similarly affected. The exact mechanism by which the α1 subunit is down-regulated by these processes is not clear. We suggested several possibilities in this regard. One was that the specific methylation sites are within transcription factor–binding regions and when methylated cannot be occupied by the transcription factor. The other possibility is that the methylation sites block the binding/activation of adjacent transcription factor–binding sites. Indeed, one hypermethylated site was next to a putative responsi element binding (CREB) regulatory site and so we speculated that mRNA expression might be reduced if a methyl-binding domain protein occluded CREB binding. This interaction has been found to occur on the BDNF gene (Levenson et al., 2006), but there is no reason to believe that this would not occur on GABA genes as well.

Hypermethylation might not be the only factor that accounts for the down-regulation of GABAA receptor subunits in the suicide MDD brains, as we found that the α3 subunit was decreased in expression, although it has few or no CpGs in its proximal promoter region. Alternatively, global alterations of chromatin structure may occur that create “broad” control signals that lead to the exposure of numerous promoter sequences to transcription factors/RNA polymerase. This would “globally” up-regulate gene expression, which may be an efficient way to control coordinated expression of GABAA subunits discussed earlier. By contrast, methylation events may be more dependent on the absence or presence of CpGs within the promoter regions. Thus, methylation may affect only a few genes, shutting down their activity, thereby reducing the coordinated expression patterns. As indicated earlier, conclusions derived from epigenetic studies in postmortem human brain might be subject to difficulties related to the potentially large number of other concurrent changes that might have occurred. Nonetheless, it does seem that brain plasticity in both animal and humans might be influenced by epigenetic processes and the possibility that these processes contribute to pathology warrants further investigation.

4.4. INTERRELATIONS BETWEEN GABA, CRH, AND 5-HT

We have attempted to make a case for GABAA involvement in depressive illness, but this should not be misconstrued as other potential processes being excluded. To the contrary, we have argued elsewhere that GABA functioning in the context of depression and anxiety ought to be considered in relation to both CRH and 5-HT functioning at hypothalamic and at extrahypothalamic sites (Anisman et al., 2008).

A detailed review of CRH and 5-HT involvement in depression/anxiety is not within the scope of the present chapter (but see reviews in Anisman et al., 2008; Holsboer, 2003; Nemeroff and Vale, 2005). Suffice it that there is considerable biochemical and pharmacological evidence supporting CRH involvement in depressive illness, including our own findings that CRH levels were elevated and CRH1 receptor mRNA expression was reduced within several aspects of the frontal cortex of depressed individuals that died by suicide (Merali et al., 2004). Postmortem analyses of depressed suicides have also revealed altered 5-HT receptor binding and variations of the expression of 5-HT receptor subtypes within the frontal cortex and/or hippocampus (Arango et al., 2003; Bhagwagar et al., 2006; Mintun et al., 2004; Stockmeier, 2003). As well, controls and depressed suicides differed with respect to the expression of several 5-HT receptor mRNAs, including 5-HT1A, 5-HT1B, and p11 (a protein involved in 5-HT receptor membrane expression; Anisman et al., 2008; Svenningsson and Greengard, 2007; Svenningsson et al., 2006). To be sure, the data supporting 5-HT involvement in MDD have not been unequivocal, as there have been reports indicating that depression/suicide was not associated with particular 5-HT receptor variations (Lowther et al., 1997; Rosel et al., 1997) and DNA micro-array analyses indicated few molecular genetic differences within the dorsolateral and ventral prefrontal cortex of suicides and controls (Sibille et al., 2004). Perhaps, given that (a) the wide array of symptoms that characterize depressive illness vary across individuals, (b) depressive subtypes exist, (c) depression may be treatment-responsive or treatment-nonresponsive, and (d) depressed suicides are not necessarily reflective of depression per se, it should not be surprising that such studies have yielded inconsistent results. As described earlier, MDD is likely a biochemically heterogeneous illness, and as such it might be productive to think of the illness in terms of CRH, 5-HT, and GABA acting together, in some fashion, to influence depressive disorders. Thus, although it is highly likely that 5-HT plays some role in MDD, it seems that other factors/processes contribute in this regard, possibly by interacting with factors such as CRH. It does appear, after all, that 5-HT acting agents may be effective in managing MDD (Pineyro and Blier, 1999) especially when combined with treatments that target other processes (Millan, 2009).

Of the candidates that might be operating in conjunction with 5-HT, there is considerable evidence pointing to interactions with CRH. For instance, it was reported that stressor-provoked CRH release may be fundamental in promoting hippocampal 5-HT changes (Linthorst et al., 2002). As well, CRH appears to regulate a subpopulation of raphe neurons that promote 5-HT release at terminal regions within the PFC (Kirby et al., 2000; Valentino et al., 2001). In fact, CRH administered to the DRN influenced forebrain 5-HT release (Price and Lucki, 2001), and chronic treatment with a CRH1 antagonist, NBI 30775 (which has antidepressant actions), altered hippocampal 5-HT functioning (Oshima et al., 2003). Moreover, in genetically engineered mice with altered CRH or CRH receptors, the activity of 5-HT was increased as were signs of anxiety (Penalva et al., 2002; van Gaalen et al., 2002a,b).

An additional process by which CRH and 5-HT might interact was recently proposed to involve a multistep mechanism in which activation of CRH1 receptors increased 5-HT2 signaling by increasing the number of 5-HT2 receptors on the cell surface. Specifically, after activation, CRH1 receptors are internalized to endosomes where they dimerize with 5-HT2c receptors. This facilitates the recycling of 5-HT2c receptor from endosomes to the cell surface when the dimer is recycled. Thus, the availability of the 5-HT2c receptor at the cell surface is increased, which has the effect of altering anxiety-related behaviors elicited by selective 5-HT2c acting drugs. There is no reason to dismiss the possibility that CRH might, through this same process, affect symptoms of MDD (Magalhaes et al., 2010).

Beyond the interrelations between CRH and 5-HT, reciprocal innervation also appears to occur between GABAA and 5-HT activity within the PFC and hippocampus, and might contribute to MDD (Brambilla et al., 2003). It was suggested, in this regard, that 5-HT1A receptors influence GABAA receptor expression (Sibille et al., 2000), hence regulating GABAergic inhibitory transmission. Moreover, it was proposed that chronic SSRI treatments may actually have their therapeutic effects through actions on GABA processes within limbic brain regions (Zhong and Yan, 2004), which could come about through several different mechanisms. This said, it was reported that the activity of 5-HT neurons within the dorsal raphe nucleus seemed to be regulated by GABAA and 5-HT1A inhibitory receptors (Boothman et al., 2006; Cremers et al., 2007; Judge et al., 2006), which would affect forebrain 5-HT release. As well, other 5-HT receptor subtypes may interact with GABAA functioning, and it was reported that 5-HT2c antagonists augmented the acute effect of SSRIs on hippocampal 5-HT release, an outcome that was modifiable by GABA manipulations (Cremers et al., 2007).

Just as communication relevant to depression occurs between 5-HT and GABA, it appears that CRH and GABAA functioning within the hypothalamus (and in limbic neural circuits) may be related. For example, it was shown that the basal expression of transcripts encoding several subunits of the GABAA receptor was present within CRH neurons fundamental in the stress response (Cullinan, 2000; Cullinan and Wolfe, 2000). Moreover, in vivo, CRH and arginine vasopressin (AVP) gene expression was increased by bicuculline methiodide, a GABAA antagonist (Cole and Sawchenko, 2002), although in vitro application of the GABAA antagonist altered AVP mRNA, without affecting CRH expression (Bali and Kovacs, 2003).

In addition to these hypothalamic changes, it was reported that CRH was uniquely expressed in GAD-positive interneurons in rat cortex and that GABAA receptor expression was altered upon chronic stressor exposure (Cullinan and Wolfe, 2000; Yan et al., 1998). Importantly, transcripts encoding several GABAA receptor subunits were altered within CRH neurons that are ordinarily responsive to stressors (Cullinan, 2000; Cullinan and Wolfe, 2000), and pharmacological treatments that ordinarily influence GABA functioning affected CRH mRNA expression within limbic sites (Cullinan and Wolfe, 2000; Gilmor et al., 2003; Skelton et al., 2000; Stout et al., 2001).

It seems that connections exist between CRH and 5-HT, CRH and GABA, and between 5-HT and GABA functioning. Thus, it should not be surprising that three-way interrelationships exist between CRH, 5-HT, and GABA neuronal functioning. In fact, studies using dual immunoelectron microscopy to assess synaptic contacts indicated that CRH has both direct and indirect effects on dorsal raphe 5-HT neurons, with GABA serving as a mediator in this regard (Waselus et al., 2005). This directional process is, to be sure, not the only one that might exist, as CRH variations may instigate 5-HT receptor changes, which then influence frontal cortical GABAA functioning (Tan et al., 2004).

In addition to these connections, it seems that the GABAA-mediated inhibition of dorsal raphe 5-HT neurons was potentiated by the progesterone metabolite, allopregnanolone (Kaura et al., 2007). Further to this point, GABAA δ subunit expression may be influenced by progesterone, and the stressor-sensitive neurosteroid, 3α,5α-tetrahydrodeoxycorticosterone (THDOC) (Reddy, 2003), might mediate this effect (Maguire and Mody, 2007). It should also be mentioned that among male mice long-term social isolation reduced responsiveness to GABA mimetic agents, an effect that was attributed to down-regulated biosynthesis of neurosteroids and decreased α12 and γ2 subunits coupled with an increase of α4 and α5 subunits. These effects were reversed by selective serotonin reuptake inhibitors (SSRI; fluoxetine and norfluoxetine) when administered systemically at nmol/kg doses (Girdler and Klatzkin, 2007; Matsumoto et al., 2007). These data raise the possibility that stressor effects on depressive symptoms, and the sex differences that exist, may involve interactions between GABAA subunits and THDOC and/or allopregnanolone (Birzniece et al., 2006), and as such support the view that treatments targeting neuroactive steroids and the GABAA receptor may be fruitful in the treatment of depression.

4.5. CONCLUDING COMMENTS

As indicated earlier, GABA is the most ubiquitous neurotransmitter in the CNS and GABA-containing interneurons, acting as an inhibitory neurotransmitter, are essential in regulating hyperexcitability as well as the synchronization and shaping of cortical neuronal activity (Gelman and Marín, 2010). As such, it should come as no surprise that GABA functioning might be involved in some fashion. In the present report, we offer three basic take-home messages: (a) Following almost a decade of GABAA functioning being largely overlooked in the analysis of MDD, there has, for good reason, been a resurgence of interest in the possible role of this neurotransmitter in depressive illness. (b) GABA likely works in collaboration with other neurotransmitters, notably 5-HT and CRH, in affecting depressive illness, and these actions are moderated by neurosteroids. (c) The coordination in the appearance of the subunits that comprise GABAA receptors may be fundamental in the timing and synchronization of neuronal activity and might thus influence depressive illness. These suggestions are clearly not independent of one another, and it is likely that the interactions between the systems, as well as the within system regulation that occurs, are modifiable by stressors that promote depressive illness.

ACKNOWLEDGMENT

The research from the authors was supported by grants from the Canadian Institutes of Health Research. H.A. is a Canada Research Chair in Neuroscience.

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