Mathematical Models of Serotonin, Histamine, and Depression

*Janet Best, Anna Marie Buchanan, Herman Frederik Nijhout, Parastoo Hashemi and Michael C. Reed*

## **Abstract**

The coauthors have been working together for ten years on serotonin, dopamine, and histamine and their connection to neuropsychiatric illnesses. Hashemi has pioneered many new experimental techniques for measuring serotonin and histamine in real time in the extracellular space in the brain. Best, Reed, and Nijhout have been making mathematical models of brain metabolism to help them interpret Hashemi's data. Hashemi demonstrated that brain histamine inhibits serotonin release, giving a direct mechanism by which inflammation can cause a decrease in brain serotonin and therefore depression. Many new biological phenomena have come out of their joint research including 1) there are two different reuptake mechanisms for serotonin; 2) the effect of the serotonin autoreceptors is not instantaneous and is long-lasting even when the extracellular concentrations have returned to normal; 3) that mathematical models of serotonin metabolism and histamine metabolism can explain Hashemi's experimental data; 4) that variation in serotonin autoreceptors may be one of the causes of serotonin-linked mood disorders. Here we review our work in recent years for biological audiences, medical audiences, and researchers who work on mathematical modeling of biological problems. We discuss the experimental techniques, the creation and investigation of mathematical models, and the consequences for neuropsychiatric diseases.

**Keywords:** serotonin, histamine, depression, mathematical model

## **1. Introduction**

It is worthwhile to begin by reminding ourselves that the question of depression and the brain is so difficult because the brain consists of many different systems that interact with each other. First is the **electrophysiology** of the brain including the biophysics of individual neurons and the behavior of neural networks. Second is the **biochemistry** of the brain, not just cell biochemistry and the structure and function of receptors, but also the fact that many brain neurons do not do one-toone signaling with other neurons. These neurons, like the serotonin (5-HT) neurons of the dorsal raphe nucleus (DRN) have dense projections to other brain regions in which their axons have myriad varicosities that release the transmitter when the neuron fires, thus changing the concentration of the transmitter in the extracellular space of the projection region. In a sense, these neurons project changes in

biochemistry over long distances in the brain. Example are the 5-HT projections from the DRN to the striatum and the dopamine projection from the substantia nigra to the striatum. Third is the **genomics** of the brain, not just the genotypes of individuals but also how gene expression levels vary depending on electrophysiology, biochemistry, and the other systems below. Fourth is the **endocrine** system. The brain is an endocrine organ itself but is also influenced by other endocrine organs such as the ovaries and the adrenal glands. Fifth, the brain is affected by the current status of the **immune system** that affects the release of histamine from mast cells. Sixth, the brain creates **behavior** but behavior affects the endocrine and biochemical systems. And, these six systems operate on a wide range of spatial and temporal scales.

There are four additional difficulties. The brain is not fixed like a machine, but is dynamically changing on short and long time scales based on its challenges and history of challenges. Secondly, direct *in vivo* experimentation on humans is unethical, so one is left with remote sensing (imaging, drug responses, etc.) and extrapolation from animal experiments often performed on tissue slices. Third, there is an exceptional amount of individual variation. For example, it is known that gene expression levels vary by about 25% from person to person [1–3] and of course vary in time; so what does it mean to speak of "the brain?" Finally, not surprisingly, a myriad of homeostatic mechanisms (such as 5-HT1*<sup>B</sup>* autoreceptors on 5-HT varicosities) have evolved so that the brain can keep functioning "normally", despite changing inputs, gene polymorphisms, and enormous biological variation. These mechanisms, whether gene regulatory networks or biochemical regulatory motifs, operate over limited scales and are almost always nonlinear, and this makes guessing the likely results of interventions very difficult.

In this situation where the system is complex and experimentation is difficult, mathematical modeling can provide a useful tool. A model gives voice to our assumptions about how something works. Every biological experiment is designed within the context of a conceptual model and its results cause us to confirm, reject, or alter that model. Conceptual models are always incomplete because biological systems are very complex and incompletely understood. Moreover, and as a purely practical matter, experiments tend to be guided by small conceptual models of only a very small part of a system, with the assumption (or hope) that the remaining details and context do not matter or can be adequately controlled. Mathematical models are formal statements of conceptual models. Like conceptual models, they are typically incomplete and tend to simplify some details of the system. But what they do have, which experimental systems do not, is that they are completely explicit about what is in the model, and what is not. Having a completely defined system has the virtue of allowing one to test whether the assumptions and structure of the model are sufficient to explain the observed results. The purpose of mathematical models is not just to match extant experimental or clinical data, but to provide an *in silico* platform for experimentation and investigation of system behavior. Such experiments are quick and inexpensive and so are particularly useful for testing hypotheses. Of course, to be useful, mathematical models should be based as much as possible on the underlying physiology.

Janet Best is a mathematician at Ohio State, Michael Reed is a mathematician at Duke University and H. Frederik Nijhout is a biologist at Duke. They have been working together on brain metabolism since 2008. They began by creating a large mathematical model of dopamine (DA) synthesis, storage in vesicles, catabolism, release, reuptake and control in synapses and varicosities [4] and a similar model for serotonin [5]. They used these models (and simpler ones) to study many phenomena, including passive and active stabilization of DA in the striatum [6], the role of 5-HT in the striatum [7], and the interaction of DA and 5-HT in the striatum

## *Mathematical Models of Serotonin, Histamine, and Depression DOI: http://dx.doi.org/10.5772/intechopen.96990*

in levodopa therapy for Parkinson's disease [8, 9]. Their papers on brain metabolism are available on the website sites.duke.edu/metabolism.

Parastoo Hashemi is an electrochemist and biomedical engineer at Imperial College London and the University of South Carolina. She was the first experimentalist to be able to measure the time course of 5-HT concentration and histamine concentration in the extracellular space of the brain *in vivo* [10]. In 2013, she contacted Best, Reed, and Nijhout and asked for help interpreting the results of her experiments, and the four us have been actively collaborating since then. All of our joint papers are available on the above website. Our collaboration always begins by active discussion of new experimental results that often change our previous understanding and therefore require changing previous models. The new models then often suggest new experiments to test new hypotheses that come from model experimentation. In this review, there will be many examples of this back and forth between experiment and modeling that we have found to be very productive. Anna Marie Buchanan is a graduate student in the Department of Chemistry and Biochemistry at the University of South Carolina.

In Section 2 we discuss the importance of homeostatic mechanisms in the brain. In Section 3 we discuss our first modeling paper with the Hashemi Lab [11]. That paper changed our understanding of 5-HT1*<sup>b</sup>* autoreceptors and showed that the way we modeled autoreceptors in 2010 [5] was wrong. Section 4 describes our 2017 paper [12] creating a mathematical model for histamine dynamics in the brain and Section 5 discusses our 2020 paper [13] revising and expanding our original 5-HT model. In Section 6 we briefly describe the techniques for measuring 5-HT and histamine in the extracellular space and in Section 7 we describe our ideas and speculations about depression. Lastly, in Section 8 we discuss future work.

## **2. Homeostatic mechanisms**

The extracellular space occupies a significant portion of brain volume and is extremely important. Not only is it the medium by which nutrients in the plasma are delivered to brain cells but it is all one important medium for communication between cells. Thus, it is not surprising that a variety of mechanisms have evolved to control the extracellular concentrations of neurotransmitters in different brain regions within fairly narrow limits. For example, DA is synthesized from tyrosine by tyrosine hydroxylase (TH) and TH shows substrate inhibition as does tryptophan hydroxylase (TPH) that synthesizes 5-HT from tryptophan. And, the concentration of DA in the extracellular space inhibits both synthesis and release of DA via the DA autoreceptors, a kind of end product inhibition. Similar mechanisms exist for 5-HT via the 5-HT autoreceptors. We will discuss the 5-HT autoreceptors in detail later. Our purpose here is to show what this homeostasis looks like and what the consequences are for DA.

The main determinants of the DA concentration in the extracellular space are rate of release from synapses and varicosities and rate of reuptake by the dopamine transporters (DATs). Release is dependent on the rate of synthesis via TH. **Figure 1** shows the concentration of DA in the extracellular space as a function of TH activity and DAT activity, computed by our 2009 mathematical model [4]. The normal steady state of the model is indicated by the large white dot that corresponds to 100% TH and DAT activity. The genes for TH and DAT have many common polymorphisms in the human population. The steady state extracellular DA concentration for combinations of these polymorphisms are shown by the small white circles on the surface. It's quite amazing, but all these points are on the homeostatic (approximately flat) part of the surface. Even though these polymorphisms are

#### **Figure 1.**

*Dependence of extracellular DA on TH and DAT activity. The large white dot shows the extracellular DA concentration when TH and DAT have normal activity, where, for each variable, normal is scaled to 1. The normal steady state is in the middle of a large relatively flat plateau, extracellular DA does not change much as TH and DAT activity vary. The small white dots show the steady states for different combinations of TH and DAT polymorphisms common in the human population. Though these polymorphisms are functional, in that they have large effects on activity, they do not affect extracellular DA very much. This homeostatic effect is created by the dopamine autoreceptors.*

functional, that is they have big effects on the activities of TH and DAT, they do not affect the extracellular concentration of DA very much. This homeostasis is created by the above two mechanisms, substrate inhibition and the autoreceptors. From an evolutionary point of view maybe the fact that the steady states for the polymorphisms are on the flat part of the surface is not surprising. If a polymorphism pushed the steady up the blue cliff in the back (as in cocaine addiction) or off the orange cliff in the right front (as in Parkinson's disease) then that polymorphism would not likely be common in the human population. It's interesting to consider the row of polymorphism steady states nearest the orange cliff. They are on the homeostatic part of the surface, but barely. One could think of them as "predisposed" to low DA diseases. In fact, individuals with this low TH activity polymorphism often show muscle dystonia and other symptoms of low DA [14]. The surface in **Figure 1** was computed assuming variation in TH and DAT, but there are many other variables in the system, for example monoamine oxidase (MAO), and variations in those variables could change the locations of the white dots.

The point is that the existence of homeostatic mechanisms make linear arguments that assume that a large change in one variable automatically results in large changes in downstream variables both simplistic and often wrong. Therefore, it is important to investigate and understand homeostatic mechanisms in the brain and their consequences.

## **3. Revised understanding of serotonin dynamics**

Efforts to understand the serotonergic system and in particular the clearance dynamics of serotonin date back decades, but results were limited by experimental technology. Only recently has the Hashemi Lab been able to measure serotonin concentrations in the extracellular space *in vivo*. With early fast scan cyclic voltammetry (FSCV, see Section 6) experiments, the Wightman lab was able to measure release and clearance of serotonin in electrically stimulated rat brain slices [15]. The data were fit to a simple model for release and Michaelis–Menten reuptake of serotonin. Further experimental innovation enabled Hashemi to evoke the release of serotonin upon stimulation of the medial forebrain bundle (MFB), and measure the release and clearance *in vivo* in rat substantia nigra pars reticulata (SNr). In an early paper, average release and clearance data for five mice was fit with the Wightman model for release and reuptake [10].

Subsequent efforts in mice to elucidate the serotonergic system with its response to antidepressants and autoreceptor antagonists revealed that serotonin responses are actually heterogeneous, and that averaging the responses obscures potentially important phenomena [11]. Furthermore, some of the data could not be fit well with the Wightman model, as the *Km* value appeared to change during the thirty second experiment. These data were the impetus for Hashemi to contact modelers Best, Reed, and Nijhout to suggest collaboration.

The mouse SNr data showed three distinct serotonin responses to a standard MFB stimulation, primarily differentiated by the clearance slopes, motivating our adoption of the terminology fast, slow, and hybrid. All three responses have a rapid rise. Fast responses are characterized by a rapid return to baseline, while slow responses show a more gradual, linear, return to baseline. Hybrid responses have both fast and slow attributes, descending rapidly for a short time and then switching to a slower decay. See **Figure 2**.

Our model, shown below, employs release and Michaelis–Menten clearance kinetics similar to the Wightman model. However, our model additionally incorporates a second reuptake mechanism, a basal concentration of serotonin, and autoreceptor effects. ½ � *S t*ð Þ denotes the concentration of serotonin in the SNr extracellular space. We assume that ½ � *S t*ð Þ satisfies the differential equation:

#### **Figure 2.**

*Fast, slow, and hybrid responses. The three panels on the left show fast (A), slow (B), and hybrid (C) responses measured in the SNr after stimulation of the MFB [11]. The blue curves are experimental data and the red curves come from a simple mathematical model in which the auto receptor effect was changed as a function of time (green curves in the right panels). The data and the modeling provided the first in vivo evidence of two distinct reuptake mechanisms for 5-HT and also showed that autoreceptor effects are long lasting and continue after 5-HT concentrations have returned to baseline.*

$$\frac{d\mathbf{S}[(t)]}{dt} = \mathbf{R}(t)(\mathbf{1} - A(t)) - a \frac{\mathbf{V}\_{\max \mathbf{1}}[\mathbf{S}(t)]}{K\_{m1} + [\mathbf{S}(t)]} - \beta \frac{\mathbf{V}\_{\max \mathbf{2}}[\mathbf{S}(t)]}{K\_{m2} + [\mathbf{S}(t)]} \tag{1}$$

where *R t*ð Þ is the rate of release and *A t*ð Þ is the fraction of stimulated autoreceptors. *R t*ð Þ represents the neuronal firing in the DRN upon stimulation of the MFB and subsequent release of serotonin in the SNr. Firing rises and decays quickly (but not instantaneously) in response to the stimulation due to the noninstantaneous excitation/relaxation of the MFB-DRN-SNr circuitry. The two Michaelis–Menten reuptake mechanisms have different *Vmax* and *Km* values. *V max* <sup>1</sup> and *Km*<sup>1</sup> correspond to slow responses, while *V max* <sup>2</sup> and *Km*<sup>2</sup> correspond to fast responses. The constants *α* and *β* are the weights of the two reuptake mechanisms. For fast responses *α* ¼ 0 and *β* ¼ 1, for slow responses *α* ¼ 1 and *β* ¼ 0. For hybrid responses, *α* is taken as 1 at all times, while we incorporate *β* in a graded, concentration-dependent manner. When ½ � *S t*ð Þ is >44 nM, *β* is 0.03 and then decays linearly to 0 as ½ � *S t*ð Þ decreases from 44 nM to 39 nM and *β* ¼ 0 when ½ � *S t*ð Þ is < 39 nM, meaning that the reuptake associated with *β* is low affinity and so loses effectiveness at low concentrations. Thus hybrid responses have contributions from both reuptake mechanisms.

**Figure 2** shows the model curves (magenta) superimposed onto the three experimental serotonin response types (black). We found that the following *Vmax* and *Km* values fit well to the experimental data: *V max* <sup>1</sup> = 17.5 nM *s* �1, *Km*<sup>1</sup> <sup>¼</sup> 5 nM and *V max* <sup>2</sup> =780 nM *s* �1, and *Km*<sup>2</sup> <sup>¼</sup> 170 nM, respectively. These values were fixed for all simulations while the choices of *α*, *β* differed as indicated above. These *Km* and *Vmax* values agree remarkably well with high affinity, low efficiency (Uptake 1) and low affinity, high efficiency (Uptake 2) as had been suggested by Snyder and colleagues [16]. Daws and colleagues verified pharmacologically that Uptake 1 is likely to occur primarily via serotonin transporters (SERTs) on serotonergic neurons and Uptake 2 includes other transporters on other cells including the dopamine transporter, the norepinephrine transporter, and the organic cation transporter [17, 18]. Our dataset, reviewed here, was the first endogenous, *in vivo* data to support the concept of these two distinct uptake mechanisms for serotonin. We remark that the Uptake 2 parameters that worked well for us are exactly the parameters used by Shaskan and Wightman to match their experimental data. Note that the Uptake 1 parts of the response curves are quite linear, which shows that the SERTs are saturated.

The *R t*ð Þ and *A t*ð Þ functions for each response are shown in **Figure 2**. We assume that in each case the baseline concentration of 5-HT in the extracellular space is 20 nM. For all three response types, we found that the model fit well with the autoreceptor effect increasing linearly after 12 sec and continuing through the end of the 30 sec experiment. To test our model's suggestion of autoreceptor control experimentally, we treated mice with methiothepin, a non-selective serotonin receptor antagonist with highest affinity for the serotonin autoreceptors [19]. We were able to fit the data with the hybrid model, setting the autoreceptor function *A t*ð Þ to zero. In our previous model [5], the autoreceptor effect was an instantaneous response to the current extracellular serotonin concentration. Modeling this data revealed that the autoreceptor response differs from our earlier model in two important ways: it is not instantaneous, and it lasts well beyond when the extracellular serotonin concentration returns to baseline; see **Figure 2**. These observations motivated us to improve our autoreceptor model, see Sections 4 and 5, although we would also learn that the autoreceptors were not solely responsible for these effects in the data. Note that in Panels A and C the concentration is well below baseline at *t* ¼ 30 and still decreasing. We will come back to this issue in Section 5.

## **4. A model for histamine with new autoreceptors**

Histamine is a small molecule that plays an important role in the immune system [20]. In the brain, histamine is stored in mast cells and other non-neuronal cells (containing roughly half of brain histamine [21, 22]), but it also occurs as a neurotransmitter [23]. The neuronal cell bodies are in the tuberomammillary nucleus of the hypothalamus and these neurons send projections throughout the CNS, in particular to the cerebral cortex, amygdala, basal ganglia, hippocampus, thalamus, retina, and spinal cord [20]. Histamine neurons make few synapses, but release histamine from the cell bodies and from varicosities when the neurons fire. Thus the histamine neural system modulates and controls the histamine concentration in projection regions [23].

Understanding the control of histamine in the extracellular space is important because we have shown that the release of histamine inhibits 5-HT release in the hypothalamus [24]. We stimulated the MFB and measured histamine and 5-HT simultaneously in the extracellular space of the hypothalamus *in vivo* in mice; see **Figure 3**. In Panel (a), the blue curve shows the average histamine curve in the extracellular space for 5 animals. The curve peaks shortly after the 2 second

#### **Figure 3.**

*Histamine inhibits 5-HT. Stimulation of the MFB releases histamine but not 5-HT in the hypothalamus. The blue curve in (A) shows extracellular histamine as a function of time and the maroon curve in (B) shows the corresponding inhibition of 5-HT release. 5-HT does not return to baseline even after histamine has returned to baseline because of the long-lasting effect of the H*<sup>3</sup> *histamine receptors on 5-HT varicosities. The green and orange curves show the histamine and 5-HT responses in the presence of thioperamide, a potent H*<sup>3</sup> *antagonist. Error bars showing SEM (n = 5 SEM) are lighter versions of the respective colors. Horizontal bars at 0 μM and 0 nM indicate the timing of the stimulus. Predictions of a simple mathematical model are shown by the dots.*

stimulation from t = 5 sec to t = 7 sec, and then descends to slightly below baseline by t = 30 sec. Clearance of histamine from the extracellular space is likely due to its recycling via transport back into the cytosol. While such a histamine transporter has not been identified, our unpublished experimental data shows that it is hard to deplete vesicular stores, strongly suggesting that extracellular histamine must be reuptaken into the cytosol. As we will see, the descent below baseline is caused by H3 receptors on the histamine varicosities that inhibit histamine release. Simultaneous average measurement of 5-HT in the extracellular space is shown by the maroon curve in Panel (b). As the histamine curve peaks in Panel (a), the 5-HT curve plunges in Panel (b). As the histamine recovers to baseline in Panel (a), the 5- HT curve in Panel (b) rebounds partway towards baseline and then levels off below baseline. It is known that there are histamine H3 receptors on 5-HT neurons that inhibit 5-HT release [25, 26]. These curves show that the effect is long-lasting. In order to test these ideas, we redid the experiments in the presence of thioperamide, a potent H3 receptor antagonist [27]. Now the histamine curve (green) in Panel (a) goes up higher and descends more slowly. The corresponding orange 5-HT curve in Panel (b) descends even further and rebounds less. Its complicated behavior probably results from two competing influences: histamine concentration is higher but thioperamide also partially blocks the H3 receptors on the 5-HT varicosity. The white dots come from a simple mathematical model in which we adjusted the strengths of H3 receptor effects on both of the varicosities by hand. The fact that we could match these curves by doing that provided further confirmation that the results of the experiments were due to H3 receptors. We note that the scales in Panels (a) and (b) are very different, *μ*M and nM.

These experiments and their interpretation provide a likely mechanism by which the neuroinflammation that occurs in a variety of disorders could cause depression. We therefore concluded that it was important to construct a full model of the synthesis, vesicular storage, release and reuptake of histamine, and control in the extracellular space by histamine autoreceptors [12]. Overall, this model is similar to the model that we constructed for serotonin [5]. In the case of both neurotransmitters, autoreceptors on the surfaces of varicosities inhibit release when the extracellular concentration is high and diminish the inhibition when the extracellular concentration is low; this is clearly a mechanism to stabilize the extracellular concentration. In our original serotonin paper [5], we modeled this inhibition to be instantaneous as a phenomenological response to the current concentration of neurotransmitter in the extracellular space. However, as described in the previous section, our FSCV data and modeling [11] showed that autoreceptor effects are long-lasting and persist even when the concentration in the extracellular space has returned to normal. This is almost certainly because the cellular machinery that creates the inhibition and the decay of that machinery take time. Therefore, in our histamine model we introduced a minimal mathematical model of signal transduction at the G-protein coupled autoreceptor consisting of a G-protein subunit and a regulator of G-protein signaling (RGS) protein.

**Figure 4** shows a schematic of the model. The pink boxes indicate substrates that are variables in the model and the gray ovals contain the acronyms of enzymes and transporters. Histidine in the blood (*bHT*) is transported into the varicosity by the histidine transporter (*HTL*) where it becomes cytosolic histidine (*cHT*) or goes into the histidine pool (*HTpool*). Most of the histidine that enters the cell is used for other processes than making histamine and that is what the *HTpool* represents. *cHT* is converted to cytosolic histamine, *cHA*, by the enzyme histidine decarboxylase, *HTDC*. Some *cHA* is catabolized by the enzyme histamine methyltransferase, *HNMT*, some is transported into the vesicles by the monoamine transporter, *MAT*, and becomes vesicular histamine, *vHA*, and some leaks out of the cytosol into the

*Mathematical Models of Serotonin, Histamine, and Depression DOI: http://dx.doi.org/10.5772/intechopen.96990*

#### **Figure 4.**

*Schematic of the mathematical model for histamine. bHT* and *cHT represent blood histidine and cytosolic histidine, respectively. cHA*, *vHA*, *eHT*, *H*<sup>3</sup> � *bHA, and gHA represent cytosolic histamine, vesicular histamine, extracellular histamine, histamine bound to autoreceptors, and glial histamine, respectively. G*<sup>∗</sup> and *G represent activated and inactivated autoreceptor G-proteins and T*<sup>∗</sup> *and T represent activated and inactivated regulators of G-proteins. Names of enzymes and transporters are as follows: HTL, the histidine transporter; HTDC, histidine decarboxylase; HNMT, histamine methyltransferase; HAT, the putative histamine transporter; H*3*, histamine autoreceptor; HTpool, the histidine pool.*

extracellular space (indicated by the dashed line). *vHA* is released into the extracellular space, at a rate proportional to neuronal firing, where it becomes extracellular histamine, *eHA*. In the extracellular space, *eHA* has several fates. It can be transported back into the cytosol by a putative histamine transporter, *HAT*. It can diffuse away (removal). It can be transported into glial cells where it becomes glial histamine, *gHA*, which then leaks out or is catabolized by *HNMT*. Finally, *eHA* can bind to the H3 histamine autoreceptor. The concentration of histamine bound to the autoreceptor, *bHA*, stimulates the conversion of the G-protein subunit, *G*, to its activated state, *G*<sup>∗</sup> . And, *G*<sup>∗</sup> stimulates the conversion of the RGS protein,*T*, to its activated state, *T*<sup>∗</sup> , in which it facilitates the conversion of *G*<sup>∗</sup> back to *G*. It is the activated G-protein subunit, *G*<sup>∗</sup> , that inhibits release and synthesis of histamine. We remark that we only track *<sup>T</sup>*<sup>∗</sup> and *<sup>G</sup>*<sup>∗</sup> since total G-protein, *<sup>G</sup>* <sup>þ</sup> *<sup>G</sup>*<sup>∗</sup> , is assumed constant, as is *<sup>T</sup>* <sup>þ</sup> *<sup>T</sup>*<sup>∗</sup> .

The H3 histamine receptor (the autoreceptor in this case) is in the rhodopsin family of G-protein coupled receptors [28]. The binding of an extracellular histamine molecule to the autoreceptor causes the release of a G-protein subunit that stimulates a signaling cascade that results in inhibition of release and synthesis. Most G-protein signals are limited by RGS molecules that stimulate the G-protein subunit to rebind [29]. In our minimal model, *G* represents *G<sup>α</sup>* � *GDP* (the inactive G-protein subunit) and *<sup>G</sup>*<sup>∗</sup> represents *<sup>G</sup><sup>α</sup>* � *GTP* (the signaling G-protein unit). Similarly, *T* represents the inactive RGS protein and *T*<sup>∗</sup> represents the active RGS protein.

In our model, *b*<sup>0</sup> is the total concentration of autoreceptors and *bHA* is the concentration of receptors bound to eHA. Normally, *G* and *G*<sup>∗</sup> are in equilibrium and their sum is constant (*g*0). The concentration of bound autoreceptors (*bHA*)

#### **Figure 5.**

*Autoreceptor variable dynamics in the model after stimulation. Release of histamine causes extracellular histamine to rise and then descend as histamine is transported back into the cytosol and into glial cells (green curve in A). The rise in eHA causes the concentration of bound autoreceptors to rise (red curve in Panel A). The rise in bHA causes activation of G-proteins that inhibit release and synthesis of histamine (blue curve in Panel B). The rise in G*<sup>∗</sup> activates the G-protein regulator, *T*<sup>∗</sup> *(pink curve in Panel B) and T* <sup>∗</sup> *starts to deactivate G*<sup>∗</sup> *. It is this dynamics that causes the H*<sup>3</sup> *receptor effect to be long-lasting.*

drives the equilibrium towards *G*<sup>∗</sup> . Similarly, *T* and *T*<sup>∗</sup> are at equilibrium and their sum is a constant (*t*0). *G*<sup>∗</sup> drives the equilibrium towards *T*<sup>∗</sup> . *T*<sup>∗</sup> , in turn, drives the equilibrium between *G* and *G*<sup>∗</sup> back towards *G*. The concentration of *G*<sup>∗</sup> affects the release of histamine from the vesicular compartment through the function *inhib G*<sup>∗</sup> ð Þ¼ <sup>2</sup>*:*<sup>4015</sup> � ð Þ <sup>2</sup>*:*<sup>45</sup> *<sup>G</sup>*<sup>∗</sup> , and this same function appears in the formula for the velocity of the synthesis reaction (HTDC). Since *<sup>G</sup>*<sup>∗</sup> <sup>¼</sup> *:*6945 at equilibrium, tonically the inhibition is 0.7. As *<sup>G</sup>*<sup>∗</sup> ð Þ*<sup>t</sup>* rises the inhibition gets stronger and if *<sup>G</sup>*<sup>∗</sup> ð Þ*<sup>t</sup>* decreases the inhibition becomes weaker.

The shape of the model prediction for eHA reflects the dynamics of *bHA*, *G*<sup>∗</sup> , and *T*<sup>∗</sup> . These curves are depicted in **Figure 5** along with the graph of *eHA*. As one can see, *eHA* goes up first, followed by an increase in *bHA*, the concentration of bound autoreceptors. This causes a rise in *G*<sup>∗</sup> that in turn causes a rise in *T*<sup>∗</sup> that makes *<sup>G</sup>*<sup>∗</sup> start to decline. The inhibition of release given by the function *inhib G*<sup>∗</sup> ð Þ depends on *G*<sup>∗</sup> as described above. This is the long-lasting autoreceptor effect. The dynamics of *G*<sup>∗</sup> and *T*<sup>∗</sup> plays out over the full 30 seconds and drives the eHA concentration below baseline. This autoreceptor model will be used for H3 receptors on serotonin varicosities in Section 5. Full details of this histamine model can be found in [12].

## **5. The new serotonin model**

In 2010, three of the authors (JB, HFN, MCR) created a mathematical model of serotonin synthesis in varicosities, storage in vesicles, release into the extracellular space, reuptake by serotonin transporters (SERTs), and control by serotonin

## *Mathematical Models of Serotonin, Histamine, and Depression DOI: http://dx.doi.org/10.5772/intechopen.96990*

autoreceptors [5]. In subsequent years, they used the model to study and evaluate various hypotheses about serotonergic function including connections with dopaminergic signaling [8, 30], bursts in the DRN [31], the effects of serotonin on levodopa therapy [9], and serotonin dynamics in the basal ganglia [7]. In 2013, they began the collaboration with Parastoo Hashemi, which led to new insights into serotonergic function [11, 24, 32]. As discussed in Section 3, the experimental results in [11] and later papers revealed that various aspects of the 2010 model were naive and too simplistic. So, in 2020, the authors and collaborators expanded and revised the original model to take account of the new findings that we had learned [13]. Here we will briefly discuss the changes and some of the new results. A schematic diagram of the new model is in **Figure 6**.

In the experiments in the Hashemi Lab, the MFB is stimulated for 2 seconds and the antidromic spikes excite the DRN. The DRN sends bursts of action potentials to projection regions such as the SNr, the pre-frontal cortex (PFC), and the hippocampus. Serotonin rises rapidly in the extracellular space in the projection regions and then typically plunges substantially below basal levels within 30 seconds [11, 13, 33–35]. This almost certainly is because inhibition of release by the autoreceptors continues well after the serotonin concentration in the extracellular space has returned to basal levels. In our 2010 model, extracellular serotonin instantaneously affected release, and the Hashemi experiments showed that this is wrong. Therefore, in our new model [13] we include a biochemical model of the

### **Figure 6.**

*Schematic diagram of the model. The rectangular boxes indicate substrates and blue ellipses contain the acronyms of enzymes or transporters. The names of the most important substrates are: Btrp, blood tryptophan; trp, cytosolic tryptophan; htp, 5-hydroxytryptamine; cht, cytosolic serotonin; vht, vesicular serotonin; eht, extracellular serotonin; hia, 5-hydroxyindoleacetic acid; ght, glial serotonin; eha, extracellular histamine. Names of enzymes and transporters are as follows: Trpin, neutral amino acid transporter; DRR, dihydrobiopterin reductase; TPH, tryptophan hydroxylase; AADC, aromatic amino acid decarboxylase; MAT, vesicular monoamine transporter; SERT, 5-HT reuptake transporter; auto, 5-HT*1*<sup>B</sup> autoreceptors; MAO monoamine oxidase; ALDH, aldehyde dehydrogenase; NET, norepinephrine transporter; DAT, dopamine transporter; OCT, organic cation transporter. Removal means uptake by capillaries or diffusion out of the system.*

cellular dynamics caused by serotonin binding to the autoreceptor, including activated receptor G-proteins and activated regulators of G-proteins. This autoreceptor model is similar to the histamine autoreceptor model discussed in Section 4. In addition, we showed in [24, 36] that histamine in the extracellular space inhibits the release of serotonin from serotonin varicosities. Therefore, in the new model, we also include a biochemical model of a histamine H3 receptor on the serotonin varicosity that changes the dynamics of serotonin release. Both of these biochemical models for receptors are indicated schematically in **Figure 6**. As described in Section 3, in [11] we also showed that there are two different serotonin uptake mechanisms, SERTs that pump serotonin back into the varicosities and another uptake, which we call Uptake 2, that pumps serotonin into glial cells [16, 18, 37]. The kinetics of the two uptakes are quite different and both are included in our new model. We also include the effects of serotonin binding protein (SBP) that binds serotonin tightly in vesicles but releases it quickly when the vesicles open to the extracellular space. We also include leakage of 5-HT from the cytosol of neurons and glial cells into the extracellular space (dashed lines). All details of these changes and the full mathematical model can be found in [13]. We discuss below our new model for release from the vesicles. We also made a systems population model from our deterministic model and will show below how we used it to investigate certain aspects of the serotonin system.

In our model there is a constant basal rate of serotonin release at steady state. The question is how should we model release during the Hashemi Lab experiments where the MFB is stimulated for two seconds? In our previous work using the 2010 model we simply increased the firing rate for the two seconds of stimulation and then dropped it back to the basal rate. This issue is complicated by the existence of serotonin binding protein (SBP) that is attached to the inner wall of vesicles and binds serotonin tightly [38, 39]. We will assume that the dissociation is a first order reaction

$$\text{SBP} - \text{serotonin} \stackrel{b}{\longrightarrow} \text{SBP} + \text{serotonin}.\tag{2}$$

If we start with one unit (nM) of SBP-serotonin being released into the extracellular space at time zero, then *SBP t*ðÞ¼ *<sup>e</sup>*�*bt* and *serotonin t*ðÞ¼ <sup>1</sup> � *<sup>e</sup>*�*bt*. The rate of release of serotonin is the derivative, *be*�*bt:* However, we are stimulating for two seconds, so SBP-serotonin complexes are continuously released into the extracellular space between *t* ¼ 0 and *t* ¼ 2 seconds. Assume that the rate of release is 1 nM/ sec, so in two seconds, 2 nM of the complex are released. What is the rate of appearance, *R t*ð Þ, of free serotonin for *t*≤ 2 and *t*>2?

$$R(t) = \int\_0^t \chi\_{[s,2]} b e^{-b(t-s)} ds \quad \text{for} \quad t \le 2,\tag{3}$$

and

$$R(t) = \int\_0^2 \chi\_{[s,2]} b e^{-b(t-s)} ds \quad \text{for} \quad t > 2. \tag{4}$$

Here *χ*½ � *<sup>s</sup>*,2 is the function that is 1 on the interval ½ � *s*, 2 and zero otherwise. A straightforward calculation shows that:

$$R(t) = \begin{cases} 1 - e^{-bt} & \text{if } t \le 2, \\ e^{-b(t-2)} - e^{-bt} & \text{if } t > 2. \end{cases} \tag{5}$$

## *Mathematical Models of Serotonin, Histamine, and Depression DOI: http://dx.doi.org/10.5772/intechopen.96990*

Thus, for a two second stimulation, the rate of release will be proportional to *fire t*ðÞ¼ basal rate þ *r* � *R t*ð Þ where *r* is the strength of the stimulation. Unfortunately, the dissociation constant *b* (inverse seconds) is not known, but we think it is in the range 0*:*5≤*b*≤2 from our simulations of the Hashemi data, so we take *b* ¼ 1 as our baseline case. The release of serotonin into the extracellular space will also be proportional to *vht* and it will also depend on the inhibition from the serotonin autoreceptors and the histamine H3 receptor. Thus, overall release as a function of time will be

$$
\dot{m}h\dot{u}b\_{ht} \left(\mathbf{G}\_{ht}^{\*}\right) \cdot \dot{m}h\dot{u}b\_{ht} \left(\mathbf{G}\_{ha}^{\*}\right) \cdot \left(\mathbf{b}\text{asal\ rate} + r \cdot R(t)\right) \cdot vht.\tag{6}$$

One of the first things that we did with our new model was to return to the 2014 data [11] that we discussed in Section 3 to see if our new serotonin model could easily match the average curves of fast, slow, and hybrid in the SNr, with relatively few, understandable changes of parameters. The experimental curves for fast, slow, and hybrid (**Figure 2**) do not look like typical response curves measured in the Hashemi Lab. For example, **Figure 7** shows an average of 17 male responses in the CA2 region of the hippocampus. Typical response curves peak, descend towards baseline, drop below baseline, and then curve back towards baseline, whereas the experimental curves in **Figure 2** keep descending. In thinking about this, we remembered that when the MFB is stimulated not only is 5-HT released in the SNr but histamine is also released. So we were in a good position to see if our new serotonin model, with its H3 receptor, would allow us to match the 2014 SNr data. Unfortunately, we do not have the time course of histamine in the SNr in those experiments, because in 2014 the Hashemi Lab had not yet optimized the techniques to simultaneously measure 5-HT and histamine *in vivo* [24, 36]. So we will take our histamine time course in the extracellular space, *eha*, from the control and model curves in **Figure 5** of [12]. Note how complicated the dynamics of *eht* are. When one stimulates the MFB, serotonin is released into the extracellular space stimulating dynamical changes in the 5-HT1*<sup>B</sup>* autoreceptor variables, *Bht*, *G*<sup>∗</sup> *ht*, *T*<sup>∗</sup> *ht*. However, histamine also increases in the extracellular space stimulating dynamical changes in the H3 receptor variables, *Bha*, *G*<sup>∗</sup> *ha*, *T* <sup>∗</sup> *ha*. Both of the activated G-proteins, *G*<sup>∗</sup> *ht* and *G*<sup>∗</sup> *ha* inhibit serotonin release via the functions *inhib G*<sup>∗</sup> *ht* and *inhibha <sup>G</sup>*<sup>∗</sup> *ht* .

#### **Figure 7.**

*Typical 5-HT response curves. The red dots show the average response of 23 male mice in the CA2 after stimulation of the MFB. The blue curve shows the average response predicted by the new 5-HT model. 5-HT rises rapidly and then descends rapidly as it is taken up by SERTs and Uptake 2. The concentration descend below baseline and then curve back towards baseline. This is the long-lasting autoreceptor effect. The average curve is simple and easy to interpret, but the individual curves show great variation; see Figure 8.*

Furthermore, Uptake 2 is rapid but it probably also depends on the distance of glial cells from the electrodes in the three cases. Nevertheless, it was surprisingly easy to give adjustments for a small number of parameters that distinguish between fast, slow, and hybrid responses (see Figure 5 and Table 5 in [13]). The parameters that we had to change were the *Vmax* of Uptake 2, the cutoff for Uptake 2, the strength of the inhibition by the 5-HT1*<sup>B</sup>* and H3 receptors, and the strength of firing during stimulation ð Þ*r* . It is completely reasonable that these parameters would be different for different electrode placements and different densities of receptors on the neuron. No other parameters were changed.

The model we have been discussing is a differential equations model (ODE); there is one differential equation for each of the pink boxed variables in **Figure 6**. All individuals, whether mouse or human, are different, and the variation is important for understanding experimental results and for precision medicine. We investigate this biological variation by creating a systems population model of the deterministic model given above. It is known that the expression levels of most enzymes can vary by about 25% or more between individuals [1–3]. Therefore, to create a systems population model, we choose new *Vmax* values for each (or a subset) of the enzymes and transporters in **Figure 6** by selecting independently from a uniform distribution between 75% and 125% of the normal value. We then run the model to steady state and record all the concentrations and velocities. That is one virtual person (or mouse). If we do this 1000 times, we obtain a database of virtual individuals that we can analyze using the usual statistical tools. The difference is that all of these individuals have the same set of differential equations; only the coefficients are different. So we can experiment with the model to find the mechanistic reasons for particular statistical phenomena. We will give several examples that show why this approach is useful.

The steady state of *eht* in the ODE model is 60 nM; this should be thought of as the steady state for an average mouse (or an "average" person). We allowed the *Vmax* values of TRPin, TPH, AADC, MAT, MAO, Uptake 2, and SERT to vary by 25% above and below their normal values independently. In addition, we allowed *fire t*ð Þ to vary 25% above and below its normal value and we vary the strength of the 5-HT1*<sup>B</sup>* autoreceptors similarly. Distributions of *eht* in various cases are shown in **Figure 9**. The green bars in Panel B show the distribution of *eht* values with normal tryptophan in the blood. The green bars are similar to distributions measured in the Hashemi Lab. The whole distribution moves left (the yellow bars in Panel B) if blood tryptophan is lowered from its normal values of 96*μ*M to 50*μ*M. In Panel A, we show what the distribution of*eht* would look like with no autoreceptors (orange bars) or autoreceptors that are twice strong. Thus, the systems population model allows one to see the effects of changes on a whole population, not just on an individual. Further, if the underlying ODE model is a good representation of the real physiology, then the variation in the population model should correspond to what is seen in the Lab. This gives another way of testing the validity of the underlying ODE model.

In [13] we used the ODE model to fit the average response curves for male and female mice in the hippocampus. Here we want to discuss the variation in the response curves. Panel A of **Figure 8** shows the responses of the 17 male mice. The experimental responses are measured and graphed for each mouse relative to the baseline level of *eht* that is represented in Panel A by *eht* ¼ 0*:* One can see how large the variation is. The curves peak at different times and at different heights. Most, but not all, of the curves descend below baseline and their shapes are quite different; some continue descending while others reach a minimum and then rebound towards zero. The thick red curve is the mean and the thick black curve is the standard deviation, which is substantial even between 15 seconds and 30 seconds although the stimulation was only between t = 5 sec and t = 7 sec.

*Mathematical Models of Serotonin, Histamine, and Depression DOI: http://dx.doi.org/10.5772/intechopen.96990*

#### **Figure 8.**

*Individual response curves. Panel A shows the time courses of eht in the hippocampus of 17 male mice after two seconds of stimulation at t* ¼ 5 *seconds (Hashemi lab). The thick red and black curves are the time courses of the mean and standard deviation, respectively. The response curve are diverse and have different heights, peaks and shapes. Panel B shows 17 randomly selected response curves in a systems population model of 1000 individuals. The red and black curves are the time courses of the mean and the standard deviation of the 1000 model individuals, respectively. In both the experiments and the model, most (but not all) curves descend below baseline after peaking and then curve up towards the baseline. The mean curves and standard deviation curves are similar in the experiments and in the system population model.*

#### **Figure 9.**

*Distributions of extracellular serotonin. Panel A shows the distribution of eht if there is no autoreceptor effect (pink bars) or if the autoreceptor effect is twice as strong as normal (blue bars). The green bars in panel B show the distribution of eht if the autoreceptor effect is "normal". The green bars are similar to distributions measured in the Hashemi lab. The yellow bars in panel B show the distribution of eht if blood tryptophan is lowered from its normal value of 96μ*M to 50*μM. the distribution of eht moves substantially lower.*

We investigated what variation in the main parameters of the model would be necessary to obtain the variation seen in the experiments. To do this we created a virtual population of 1000 individuals. The following parameters were varied uniformly from 40% below to 40% above their normal values: the *Vmax* values for *V*AADC, *V*CATAB, *V*MAT, *V*SERT, *V*TPH, *V*U2; the slope of *inhib* and *inhibsyn*; *eha*, the concentration of histamine in the extracellular space, and *β* that controls the speed of the autoreceptors. In addition, we varied the parameter *r* in *fire t*ð Þ by 25% and the time of the peak by 20%. Panel B of **Figure 8** shows a random sample of 17 of the 1000 model male curves. The thick red curve is the mean of the 1000 model curves and the thick black curve is the standard deviation. The mean curve matches the experimental mean curve very well. The model standard deviation curve is very close to the experimental standard deviation except that at long times (20 second to 30 seconds) it descends slightly while the experimental standard deviation remains constant. Overall, one can see visually that the 17 model curves and the 17 experimental curves look similar as groups of curves. For each of the 1000 individuals, we record their steady state values as well as the values of all of their parameters so we can use multi-linear regression to find which parameters contributed most to the variation in the response curves. At *t* ¼ 7 *sec* (roughly the time of the peak), the three variables that contributed most, in order, were the strength of fire(t), the timing of the peak in fire(t), and the *Vmax* of the SERTs. At *t* ¼ 15 *sec* (when most of

**Figure 10.**

*Variation of SERT and MAO activity. In the population model, we varied only SERT activity and MAO activity. Each dot is one virtual individual, and the coordinates of each point are the activity of SERT (normal = 1) and the steady state concentration of eHT. The blue dots are individuals that have very low MAO activity and the red dots have very high MAO activity. Blocking the SERTs (changing the activity) has a much greater effect on high MAO activity individuals than on low MAO activity individuals.*

the curves have returned to near baseline), the three parameters that contributed most to the variation in response were the *Vmax* of TPH, the speed of the autoreceptors, and the *Vmax* of MAT.

The population model allows us to approach a quite difficult mathematical question that would be very useful for understanding the biology and possible treatments. Suppose one has two populations of mice, for example male and female or obese and not obese or depressed and not depressed. Each of the two populations will produce a large family of experimental responses and those families of curves may be quite different. How can one estimate which parameters in the model cause the difference in the families? This is a way of using the response *eht* curves to probe the differences inside the neurons.

The expression levels of most enzymes can vary by about 25% or more between individuals [1–3]. This means that the *Vmax* values of all the enzymes and transporters in our model vary by at least 25% and that any population of individuals will express this diversity. This poses large issues for drug discovery and treatment because it means that different individuals will react very differently to drugs, as is well-known [40–42]. Here, we present a simple example that shows how to use variation in a small number of variables to investigate questions about drug efficacy. In **Figure 10** we show results from our systems population model where we varied only two constants, the expression level (*Vmax*) of SERT and the expression level of MAO, from 25–175% of normal. Each dot is an individual in a population of 500. The y-axis is the concentration of *eht*, extracellular serotonin, and the x-axis is the expression level of SERT. The blue dots are the individuals with low MAO activity and the red dots are individuals with high MAO activity. The conclusion is clear. Blocking SERTs with an SSRI (equivalent to lowering the expression level) will have a much greater effect on individuals with high MAO activity than on individuals with low MAO activity. Therefore, the systems population model suggests that it is high MAO individuals that will benefit the most from an SSRI. This shows how population models can be used to target specific questions.

## **6. Real-time** *in vivo* **neurotransmitter measurement techniques**

To better answer physiological questions of the brain, especially about mental illness, it is critical to measure brain chemistry, specifically neurotransmitters.

*Mathematical Models of Serotonin, Histamine, and Depression DOI: http://dx.doi.org/10.5772/intechopen.96990*

Measuring neurochemistry is very challenging because neurotransmission is dynamic, and the brain tissue is very delicate. The earliest brain analysis methods utilized brain biopsies that were homogenized, separated and analyzed via HPLC [43]. These methods are offline and give an overview of whole tissue content, but not dynamic transmission. Microdialysis revolutionized brain analysis by utilizing a probe implanted into the brain, perfused with artificial cerebrospinal fluid [44, 45]. At the distal end of the probe is a semi-permeable membrane with a cut-off point such that analytes of interest can diffuse into the probe down a concentration gradient. The outcoming fluid, the dialysate, is collected and analyzed with a secondary method such as HPLC. The time resolution of this method is typically tens of minutes. Niche, electrochemical methods, such as fast scan cyclic voltammetry (FSCV) and fast scan-controlled adsorption voltammetry (FSCAV) can measure the subsecond temporal profile neurotransmission [33, 46, 47], outlined below.

### **6.1 Fast-scan cyclic voltammetry**

Fast-Scan cyclic voltammetry is uniquely suited to measure neurotransmission *in vivo*. Its fast temporal dynamics allows for neurochemical detection on a subsecond timescale, approximately a thousand times faster than traditional cyclic voltammetry. Furthermore, FSCV measurements are performed at microelectrodes, typically carbon fiber microelectrodes (CFMEs). CFMEs have a small probe size (diameter 7 *μ*m) and are biocompatible, creating minimal tissue damage and negligible immune response [48, 49]. Carbon electrodes also drive high sensitivity because their highly negative surface preconcentrates positively charged transmitters such as dopamine, serotonin, norepinephrine and histamine. These transmitters are then readily oxidized at the carbon surface, making it an ideal material for neurochemical measurements. Traditionally, FSCV has been utilized to measure dopamine [50–52]. However recent advances have allowed for the detection of other neurotransmitters, such as serotonin and histamine [24, 36, 53, 54].

Serotonin is measured using a CFME that has been modified by electropolymerization of a thin, uniform layer of Nafion. Nafion, a cation exchange polymer, increases the electrode sensitivity to serotonin while reducing the electrode poisoning effects of serotonin metabolites [54]. For *in vivo* experiments, this electrode is placed in the brain region of interest, such as the hippocampus, prefrontal cortex, or SNr. Because FSCV is a background subtracted technique, serotonin is evoked using an electrical stimulation placed in the MFB. Detection occurs by application of a waveform optimized for serotonin measurements. [12] This waveform has a resting potential of 0.2 V, scans up to 1.0 V, down to 0.1 V, and then back to the resting potential of 0.2 V at a scan rate of 1000 Vs1, applied at a frequency of 10 Hz. The signal is presented in the form of cyclic voltammograms (CVs) that qualify and quantify the substrate. **Figure 11** illustrates the FSCV experiment.

Histamine is particularly difficult to detect *in vivo* using FSCV because it lacks a clear, sharp oxidation peak. The Hashemi Lab developed a waveform that produces a unique electrochemical histamine signal. It has a resting potential of 0.5 V, scans to 0.7 V, up to 1.1 V, and then returns to the resting potential of 0.5 V at a scan rate of 600 Vs1. This waveform simultaneously detects serotonin and histamine release *in vivo* [24, 36].

#### **6.2 Fast-scan controlled adsorption voltammetry**

One limitation of FSCV is that because of the large capacitive current generated by the fast scan rate, it is a background subtracted technique [55]. This means that a

#### **Figure 11.**

*Illustrative representation of an FSCV vs. FSCAV experiment described in-text. A. Shows the stimulation of the MFB to induce the release of serotonin in the CA2 and application of the serotonin waveform [53] to detect the evoked change in serotonin concentrations in the extracellular space over time. B. Depicts the modified waveform application for serotonin FSCAV [33] that negates the need for electrical stimulation to detect ambient concentrations of serotonin in the extracellular space each minute. This figure was created with Biorender.com.*

change must be evoked, often electrically or pharmacologically. To address this issue, Atcherly et al. developed the method of fast-scan controlled adsorption voltammetry (FSCAV) to measure ambient concentrations of dopamine [56, 57]. This technique, illustrated in **Figure 11B**, was later adapted to measure serotonin [33]. FSCAV occurs at the same microelectrodes as FSCV. Serotonin FSCAV is performed in three steps: 1) The minimized adsorption step is implemented by applying the waveform at 100 Hz for 2 seconds. 2) The potential is held at +0.2 V for 10 sec for a period of controlled adsorption. 3) The waveform is reapplied at 100 Hz for 18 seconds. The CVs taken in the 3rd step are subtracted from the 1st step and thus serve as the ambient measurement.

## **7. The chemical basis of neuroinflammation**

The vast majority of mental illnesses are associated with inflammation, especially depression which is highly comorbid with inflammation [58]. Increased levels of proinflammatory cytokines in the interleukin-1 and tumor necrosis factor families are linked to neuroinflammation [59, 60] across many different brain disorders. Chronic neuroinflammatory states have been implicated in neurodegenerative

disorders such as Parkinson's Disease [61, 62], Alzheimer's Disease [63–65], and multiple sclerosis [66, 67], in addition to depression [58, 68] and bipolar disorder [69]. While these associations are clear, what is not known is the mechanism by which inflammation affects neurotransmission. We began to address this question by focusing on serotonin with FSCV and FSCAV. Serotonin is implicated in depression because the vast majority of antidepressants target the serotonin system [70]. Serotonin was first measured *in vivo* using FSCV in 1995 by Jackson et al. [53]. The authors detected serotonin in the rat striatum by forcing dopaminergic terminals to release serotonin following loading with 5-Hydroxytryptophan and dopamine depletion with *α*-methyl-p-tyrosine. More recently, using the same waveform we measured endogenous electrically evoked serotonin in the rat SNr [54]. Studies have since expanded to characterizing serotonin in different brain regions, studying differences in male and female mice, looking at serotonin and histamine comodulation and observing the effects of inflammation on this co-modulation. We discuss our key findings below.

## **7.1 Serotonin dynamics in different brain regions**

We first characterized evoked serotonin release and reuptake in the rat SNr following electrical MFB stimulation [54]. The SNr is of interest for serotonin detection as this area has the most dense serotonergic innervation in the brain and thus serotonin is the primary neurotransmitter released following electrical stimulation [71]. The signals obtained *in vivo* were pharmacologically verified using acute administration of the DAT inhibitor, GBR 12909, and the SSRI citalopram. The signals did not respond to DAT inhibition; however, following SERT inhibition, an increase in max amplitude and a slowing of the reuptake was observed. Serotonin response to varying doses of acute SSRI (1 mg kg1, 10 mg kg1, and 100 mg kg1) was examined [72], with uptake *t*1*=*<sup>2</sup> values increasing with dose concentration. However, no dose dependent trend was observed for max amplitude values. Further investigations of serotonin reuptake mechanisms [11] were performed by mathematical modeling through the development of a Michaelis–Menten kinetic model as previously described in Section 3. The presented model establishes a two uptake mechanism for serotonin, a notion that was described back in the 70s as Uptake 1 and 2 [16]. Uptake 1 refers to the high affinity, low efficiency system characterized by the serotonin transporters (SERTs) and Uptake 2 is serotonin clearance by the low affinity, high efficiency mechanism afforded by the dopamine, norepinephrine, organic cation, and plasma membrane transporters [16, 73].

While FSCV continues to provide insight into fast serotonin release and reuptake dynamics, it is limited by its inability to measure steady-state or ambient concentrations. To address this limitation, FSCAV was developed to detect absolute concentrations of both dopamine [57] and serotonin [33] *in vivo*. This technique (described above) yields fast, selective, and sensitive absolute concentrations of serotonin. Using FSCAV we reported serotonin concentrations of 64.9 2.3 nM in the CA2 [33]. **Figure 12** shows ambient serotonin response to the monoamine oxidase B inhibitor, pargyline, in comparison to the DAT inhibitor, GBR 12909. Ambient serotonin levels increase following pargyline administration, but not following GBR administration, confirming that the signal is serotonin.

We expanded FSCV measurements of serotonin to the medial prefrontal cortex (mPFC) [32], another region associated with depression. Here, we found an interesting phenomenon whereby a double peak response was elicited in layers 1–3 of the mPFC. **Figure 13** shows examples of a single peak response as well as a variety of double peak responses in this brain region. Interestingly, each discrete peak had its own specific reuptake profile, thus we hypothesized that distinct axonal bundles in

#### **Figure 12.**

*The dark blue markers represent the average response before and after pargyline (75 mg/kg, intra-peritoneal (i.p.)) administration and the dark red markers represent the average response before and after administration of GBR 12909 (15 mg/kg, i.p.). drug injection time is denoted by the yellow bar at 0 min. Representative colorplots, CVs, and concentration vs. time curves are inset (top, pargyline; bottom, GBR 12909, α = predrug and β = postdrug). (asterisks above blue markers indicate post hoc test: \*p* <*0.0001.) reprinted with permission from the American Chemical Society.*

the MFB terminate in layer-dependent mPFC domains with specific uptake transporters. A mathematical model confirmed that the double peaks could be explained by diffusion of neurotransmitter to the electrode from two different sources, one close and one further away.

Finally, in this part of our work, we compared the *in vivo* serotonin signals between the SNr, the CA2 region of the hippocampus, and the mPFC [35]. We found that the different responses could be modeled as a function of the percentage of Uptake 1/Uptake 2 transporters with the model predicting the largest concentration of serotonin transporters in the SNr. We verified this notion with confocal microscopy and concluded that FSCV could be a potentially useful tool for chemical imaging of local cytoarchitecture. Interestingly, and counterintuitively, the SNr, with the highest density of serotonin terminals and axons, had the lowest ambient levels of serotonin. We realized that this was because of the high affinity of SERTs (Uptake 1 transporters) in this region that serve to maintain steady state levels lower than the other two regions with fewer SERTs.

#### **7.2 Serotonin dynamics between the sexes**

The prevalence of depression differs between males and females, with women being more likely to suffer from the disorder than men [74–76]. As such, it is important to investigate neurochemical and pharmacodynamic disparities across the sexes. In the hippocampus, we observed no significant differences in the evoked serotonin maximum amplitude or the *t*1*=*<sup>2</sup> of clearance between male and female mice [34]. Furthermore, no differences were detected between the mean signal and *Mathematical Models of Serotonin, Histamine, and Depression DOI: http://dx.doi.org/10.5772/intechopen.96990*

#### **Figure 13.**

*Representation of single and double peaks reported by west et al. 2019 in the mPFC. The average serotonin response is depicted in (A). Varying signals are shown in (B) with a traditional single peak displayed in (i.) and five of the most common types of double peaks shown in (ii.-vi.). The inset contains the CVs of both peaks. The first peak is shown in blue and the second in red. Reprinted with permission from Elsevier.*

the signal in different stages of the female mouse estrous cycle. This suggests that there are no major sex differences in the release or reuptake machinery in drug naive mice. Likewise, no significant differences were detected across sexes in ambient levels of serotonin using FSCAV. Differences in clinical efficacy have been observed following the administration of SSRIs, a class of commonly prescribed antidepressants [77]. Following acute administration of the SSRI, escitalopram, ambient serotonin concentrations increased significantly, however no differences were seen between male and female mice. On the other hand, differences were observed in the evoked serotonin reuptake decay curve. At all four doses given (1, 3, 10 and 30 mg/kg) the female mice had a lower percent change in reuptake compared to the males. We speculated that in female mice, compensatory mechanisms (likely via autoreceptors) exist to counteract hormone-mediated chemical fluxes that may affect serotonin.

## **7.3 Histaminergic transmission and modulation of serotonin**

As outlined above, inflammation (peripheral and brain) is becoming synonymous with the pathophysiology of depression [58]. The monoamine histamine is a major inflammatory mediator in the body [77], associated with allergic reactions. However, less is known about histamine's role in the brain. While traditionally believed to be a neuromodulator in the CNS, recent studies have implicated histamine in neuroinflammatory processes as well [78, 79]. To study fast histaminergic dynamics, we optimized an FSCV waveform to simultaneously detect histamine and serotonin *in vivo* [24, 36]. Histamine oxidation was pharmacologically validated in the posterior hypothalamus following application of tacrine, a histamine Nmethyltransferase inhibitor, and thioperamide, an H3 receptor antagonist. Acute tacrine administration slowed the reuptake of histamine significantly, while thioperamide slowed the reuptake and increased the max amplitude. Upon electrochemical release of histamine, a rapid inhibition of serotonin is observed as shown in **Figure 3**. In this figure, release and reuptake of histamine (a) and serotonin (b) are shown before and after thioperamide (H3 receptor antagonist) administration, where the dots are the result of a simple mathematical model where the receptor and autoreceptor strengths were changed dynamically by hand. Using the new full histamine and serotonin models (Sections 4 and 5) with the chemistry of the autoreceptors and the H3 receptors, we were able to predict the experimental results just by using the release and reuptake curve for histamine in the extracellular space that we previously measured.

## **7.4 Serotonin and histamine in inflammation models**

The inhibition of serotonin by histamine fueled our interest in the comodulation of these analytes in inflammation models. In recent work, we found that upon acute lipopolysaccaride (LPS) induced inflammation, ambient serotonin levels rapidly decreased as a function of increased histamine. Escitalopram was much less capable of increasing the serotonin levels under this inflammation state. We found that this was because escitalopram (and other common antidepressants) inhibit histamine reuptake. This inhibition raises histamine, which depresses serotonin release, counteracting the effect of the antidepressant on the SERTs. Only with the dual strategy of inhibiting serotonin reuptake (by an SSRI) and inhibiting histamine synthesis were we able to return the serotonin to pre-inflammation control levels. We are now actively studying serotonin/histamine co-modulation in other inflammation/depression models in mice including chronic stress and neurodegeneration.

## **8. Future outlook**

Our *in vivo* studies have allowed us to measure and compare and contrast serotonin in different brain regions, to study serotonin dynamics in male and female mice, to investigate serotonin and histamine co-modulation and to ask how this modulation changes under inflammation. This program has provided invaluable information about the dynamics of these two modulators in health and pathophysiology in mice. Our future goals are to apply our findings to *ex vivo* models that more closely mimic human inflammation as a path towards depression diagnosis and treatment. We are exploring a variety of stem cell models, derived from humans, as model systems for personalized diagnostic and drug screening platforms. The continuing, active collaboration and innovation between the

experimentalists and the mathematical modelers, as has been the case in the last seven years, will drive novel discoveries in our future program.

## **Acknowledgements**

The authors would like to thank Brenna Park for helpful edits and making **Figure 11**. Partial support for this research came from NIH through R01MH106563-01A1 (PH, JB, HFN, MCR) and 1R21MH109959-01A1 (PH, JB, HFN, MCR) and from the NSF through support for the Mathematical Biosciences Institute, DMS-1440386.

## **Abbreviations**


## **Author details**

Janet Best<sup>1</sup> \*†, Anna Marie Buchanan2†, Herman Frederik Nijhout<sup>3</sup> , Parastoo Hashemi2,4 and Michael C. Reed<sup>3</sup>

1 The Ohio State University, Columbus, OH, USA

2 University of South Carolina, Columbia, SC, USA

3 Duke University, Durham, NC, USA

4 Imperial College, London, UK

\*Address all correspondence to: best.82@osu.edu

† These authors contributed equally.

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Mathematical Models of Serotonin, Histamine, and Depression DOI: http://dx.doi.org/10.5772/intechopen.96990*

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## **Chapter 2**

## Serotonin, Sleep and Depression: A Hypothesis

*Vladimir M. Kovalzon*

## **Abstract**

For most cases of endogenous depression (major depression), the hypothesis of monoamine deficiency, despite a number of limitations it faces, is still considered the most acceptable explanation. The main difficulty faced by this hypothesis is the *reason* for the decrease in the level of cerebral monoamines (primarily serotonin) during depression. It is assumed either increased activity of the MAO enzyme, which metabolizes serotonin, or a mutation with the loss of function of the gene of the Tph-2 enzyme, which synthesizes serotonin, as possible causes. In this review, a third cause is proposed, which can explain a number of cases of «spontaneous» onset of depressive symptoms in apparently healthy people, as well as links the hypotheses of "monoamine deficiency" and "disturbances in circadian rhythms." It is assumed that the formation of endogenous depression is due to a combination of two factors: a reduced "basal" level of cerebral serotonin and excessively long pre-morning periods of REM sleep, during which the release of cerebral monoamines stops altogether. As a possible way to of non-drug treatment of depression, not deprivation, but fragmentation of this phase of sleep is suggested, that is much easier for patients to tolerate.

**Keywords:** serotonin, sleep, rem sleep, depression, monoamine hypothesis

## **1. Introduction**

A hypothesis is put forward according to which two factors play an important role in the formation of a number of cases of so-called "endogenous" (major) depression. First, the initially lowered (but within the reaction norm) level of cerebral serotonin, reflecting the gene polymorphisms of the human population. Second, the excessively long pre-morning periods of REM sleep associated with the "pressure of civilization" on the natural structure of the human wakefulness-sleep cycle, during which the release of cerebral monoamines stops altogether. It is a combination of these two factors that can lead to the emotional imbalance seen in depression.

## **2. Serotonin and sleep**

Serotonin (5-HT) is one of the oldest and most important mediators in the central nervous system, participating in a wide range of behavioral, physiological and pathological processes. The history of its study goes back about 70 years, nevertheless, serotonin remains one of the most mysterious neurotransmitters.

As is known, the largest accumulation of serotonergic neurons in the brain is observed in the dorsal raphe nuclei (DRN) and the pons varolii (zones B6 and B7 according to Dahlström & Fuxe [1]). The total number of such cells in human brain is relatively small - about one hundred thousand. The serotonergic system has two characteristics: first, the unusually numerous ramifications of its axons (up to a million bifurcations of a single axon). Secondly, the extraordinary variety of types (at least 7) and subtypes (at least 14; some researchers even count more than 20) of their receptors, among which there are both membrane depolarizing (subtypes 5-HT2A-C, 5-HT3, 5-HT6, 5-HT7) and hyperpolarizing it (5-HT1A,B). Due to the abundant "treelike" branching, several hundred thousands of serotonergic neurons of the brain stem innervate tens of billions of other neurons in the human brain: practically all the nerve cells of the neocortex, hippocampus, striatum, and hypothalamus, other parts of the brain as well as motor neurons of the spinal cord [1, 2]. Only the upper olive complex (part of the auditory system) and the optic chiasm are devoid of serotonin afferents [3]. And due to the receptor diversity, ligands of serotonin receptors can effect both activating and inhibitory processes on the brain and behavior in general.

The role of serotonin transmission in the regulation of wakefulness and sleep was first identified by the work of Michel Jouvet and his laboratory in Lyon, France. In these experiments of the classic of world somnology, performed on cats using primitive technologies of the 60s - early 70s of the last century, the following was shown. Intracerebral administration of serotonin, or electro-stimulation of the DRN or the median raphe nucleus (MnRN), where most 5-HT neurons in the brain are located, induces a short period of paradoxical (REM) sleep, followed by prolonged deep slow-wave sleep (NREM). If, on the contrary, the level of cerebral serotonin is reduced by systemic administration of parachlorophenylalanine (PCPA), which blocks serotonin synthesis, or by destruction of the MnRN, both phases of sleep are sharply reduced. This insomnia lasts at least 10 days. In this case, effect of PCPA is eliminated by the administration of the precursor of serotonin - 5-hydroxytryptophan. These and other early experiments served as the basis for Michel Jouvet's hypothesis about serotonin as "somnotonin" (as it was then called by the Swiss somnologist Werner Koella), the main factor in slow wave sleep [4]. However, further experiments performed in the same laboratory of Jouvet with electrical stimulation and reversible shutdown of DRN neurons caused by local tissue cooling to 10 °C, pointed, on the contrary, to serotonin as a factor of wakefulness. Eventually, it was proved that this hypothesis of Jouvet was wrong - in particular, insomnia caused by suppression of serotonergic neurotransmission was associated with a disorder of thermoregulation, a drop in body temperature, which led to an increase in the motor activity of cats to warm up [5]. And the insomnia that occurs in experimental rats and cats as a result of the administration of PCPA, as it turned out, is the result of a sharp increase in sensitivity to the surrounding animal stimuli, and not a disorder of the regulation of the wakefulness-sleep cycle [6].

Summing up the results of many years of research, a disciple of Michel Jouvet, Raymond Cespuglio, suggested that serotonin may be involved in the regulation of the wakefulness-sleep cycle in two different ways: in wakefulness serotonin is realized on the presynaptic membrane of the 5-HT neurons and promotes the formation and accumulation in target cells hypnogenic neuropeptides: vasointestinal polypeptide (VIP), corticotropin-like intermediate lobe peptide (CLIP), substance P (SP); in the subsequent period of sleep, under the influence of these peptides, dendritic (nonsynaptic) realization of serotonin in the nuclei of the raphe occurs and its binding to the 5-HT1B autoreceptors, as a result of which the synaptic release of serotonin is weakened and stopped [3]. However, this hypothesis also has not received convincing experimental confirmation [6].

Experiments with extracellular registration have shown that most serotonergic neurons are very active in the waking state, and during the transition to sleep and further into deep NREM sleep, they progressively slow down their activity and completely "silence" immediately before the transition to REM sleep. Thorough studies of the activity of not only large and medium-sized, but also small cells of the dorsal raphe of the model mouse brain in the wake–sleep cycle, carried out by Jouvet's disciple Kazuya Sakai, revealed a high anatomical, neurochemical and functional heterogeneity of these neurons. The majority of neurons in this area (52%) are indeed serotonergic (5-HT/DR), and almost all of them (48%) are active only in wakefulness, but a significant part (25% of all cells) are active in sleep, and judging by the spike shape, 19% of them are GABAergic, and only 6% are serotonergic [7]. Apparently, serotonin neurons are mainly responsible for maintaining calm (relaxed) wakefulness; thus, according to some data, they are most active during food consumption and reduce the frequency of impulses with increased behavioral activation [5].

Agonists of all serotonin receptors stimulate wakefulness and suppress NREM and REM sleep when administered systemically or intraventricularly. In this case, the activation of wakefulness occurs by depolarizing the histaminergic tuberomammillary neurons of the posterior hypothalamus, as well as GABA/parvalbumin-containing neurons of the basal forebrain region, which project into the hippocampus and neocortex. Suppression of NREM is carried out mainly by inhibition of neurons in the "sleep center" VLPO, mediated by the 5-HT1A receptor [5]. And the suppression of REM sleep occurs due to inhibition of cholinergic REM-on neurons of the pons [5].

With direct microinjection of inhibitory receptor 5-HT1A agonists into the dorsal raphe nuclei, an increase in REM sleep occurs, whereas similar injections of inhibitory autoreceptor 5-HT1B agonists and activating 5-HT2A/C, 5-HT3 and 5-HT7 receptors suppress REM sleep, which is consistent with the concept of the need for inhibition of 5-HT neurons to trigger REM sleep [8]. Systemic administration of non-selective antagonists of the 5-HT2A/C receptors, selective antagonists or reversible agonists of the 5-HT2A receptor in laboratory rats and mice, healthy subjects and patients with primary or comorbid insomnia causes an increase in NREM sleep, which, again, is consistent with the idea of the participation of 5-HT neurons in maintaining wakefulness [6].

Thus, according to the results of neural and pharmacological studies, serotonin seemed finally established as the status of a wakefulness mediator along with other monoamines (norepinephrine, dopamine, histamine), as well as acetylcholine and glutamate. The main source of serotonin - the DRN – were introduced on diagrams as one of the clusters of the reticular ascending activating system [9, 10]. It has also been shown to play an important role in the negative regulation of REM sleep: without turning off serotonin transmission, neither initiation nor maintenance of REM sleep is possible [6].

In this case, selective shutdown of serotonergic transmission should suppress wakefulness by increasing NREM sleep. Such a methodological opportunity appeared with the introduction of molecular genetic and other newest innovative techniques into neurophysiology. It was found that the brain has its own special isoform of the enzyme tryptophan hydroxylase - Tph2, which converts the amino acid tryptophan, which is supplied to the body with protein food, into 5-hydroxytryptophan, a precursor of serotonin, and encoded by a separate gene. This discovery made it possible to create knockout mice for this gene, in which the content of cerebral serotonin does not exceed 4% of its content in the brain of control mice (that is, practically absent). Figuratively describing the phenotype of such mice, which grew up "without serotonin in their brain", can be named as "evil dwarfs."

They are fertile and females have milk, but they do not care for their offspring, and therefore half of their offspring dies [11, 12]. Disorders of the wakefulness-sleep cycle in these mutants are limited, judging by the results of registration of locomotor activity, to a slight increase in sleep and suppression of wakefulness in daylight (daytime), which seems to correspond with the above hypothesis [13].

At the same time, in another study on genetically modified mice with the homozygous Tph2 mutation (intact neurons, but complete absence of serotonin in the central nervous system) and polysomnographic registration, the following was found. A small (but statistically significant) decrease in the duration of NREM sleep and a corresponding increase in active wakefulness in mutant animals compared with control occurred only when the light was turned on and off. Apparently, the absence of serotonin increases the reactivity of the animal to light stimulation. It was also shown that the sleep of the mutants was less fragmented. No further disturbances in the wake–sleep cycle were identified. In this series of experiments, the absence of serotonin caused only very small changes, not confirming the original hypothesis [14].

However, Tph2 knockout mice cannot serve as an adequate model for studying the role of serotonin in the regulation of the wakefulness-sleep cycle, since it is unclear whether the revealed phenotypic changes are the result of abnormal development, compensation for the lack of serotonin by other transmitters, or, indeed, impaired neurotransmission in adults. To solve this problem, a method was developed to turn off the expression of the Tph2 gene by microinjection of its blocker directly into the tissue of the raphe nuclei of the midbrain and pons in the genetically created mouse strain [15]. By visual analysis of video recordings, it was possible to reveal an increased level of motor activity, especially noticeable in the night (active) phase of the nychthemeron, when in the second half of the night the control individuals experienced a period of decreased activity, called by the authors "siesta". In mice with blocked serotonergic transmission, such periods were absent altogether; they ran almost continuously all night [15]. Thus, according to the results of this study, serotonin itself behaves more like "sleep factor" than "wake factor".

Since 5-HT containing neurons also secrete glutamate and various neuropeptides, the effect of their destruction may be quite different from that of the elimination of serotonin itself. In the work of Japanese authors [16], carried out using polysomnographic recording, neurotoxic destruction of serotonin-containing DR neurons in special genetically engineered mice led to a decrease in REM sleep at night, when its representation is already low. In addition, according to the data of the same authors, in the experimental mice, in comparison with the control ones, the response to the new environment was weakened and the power of the theta rhythm in wakefulness was increased. However, all these effects were so small that they were detected only with the help of statistical tricks. This, however, did not prevent the authors from concluding that their data support the main hypothesis about the role of serotonin as a factor of wakefulness (positive) and REM sleep (negative), presented above.

Finally, in a recently published study led by renowned Boston somnologist Patrick Fuller using a novel method of highly selective chemogenetic activation of serotonergic neurons in the DRN in combination with polysomnography and behavioral tests, no unambiguous results were obtained either [17]. A "compensatory" restoration of NREM sleep, slightly suppressed by the 5-HT neuron activator injection procedure, was shown to return to baseline levels. This effect can hardly be called somnogenic, but it is definitely not activating. In addition, a change in behavior in the open field was found, which the authors interpret as a decrease in the level of anxiety under the influence of the activation of serotonergic neurons in the DRN.

## *Serotonin, Sleep and Depression: A Hypothesis DOI: http://dx.doi.org/10.5772/intechopen.96525*

However, testing in a cruciform elevated maze revealed no changes. The authors refer to a recent study that revealed the existence of two mutually intertwining serotonergic subsystems in the DRN that innervate the orbital frontal cortex and the central amygdala differently. One of these subsystems supports anxiogenic and the other anxiolytic functions. It is possible that the simultaneous activation of both subsystems is associated with the uncertainty of the results obtained in such experiments [17].

As mentioned above, most serotonin-secreting neurons are "silent" during the entire period of REM sleep until the moment of its completion (by awakening or re-entering NREM sleep), and in fact not one single serotonin molecule is released from the presynaptic membrane during this time.

As can be seen from the **Table 1**, the intercellular fluid in wakefulness is saturated mainly with the mediators with depolarizing action on the postsynaptic membrane. During the transition to NREM sleep, all these molecules quickly disappear from the intercellular environment being replaced by the main inhibitory mediator of the brain, GABA, that concentration increases with the deepening of NREM sleep, and the peptide galanin colocolized with GABA. The cerebral biochemical environment in REM sleep is special. High levels of acetylcholine, glutamate and galanin are combined with a complete absence of orexin (hypocretin) and monoamines — serotonin, norepinephrine and histamine, with the exception of dopamine, the concentration of which may sometimes even exceed that in wakefulness. A new mediator appears, the MCH peptide, which mediates the hypothalamo-pontine level of REM sleep regulation. The release of GABA in general is significantly reduced, but remains high in areas of the orexinergic (LHA), histaminergic (TMN), serotonergic (DR) and noradrenergic (LC) neurons localization. In these systems, GABAergic neurons play the role of a "lock" preventing depolarization of these cells during the entire period of REM sleep.


*Abbreviations: W – wake; NREM sleep – non rapid eye movement sleep; REM sleep - rapid eye movement sleep; 5-HT – serotonin; GABA –* γ*-aminobutyric acid; MCH – melanin-concentrating hormone; LC – locus coeruleus; DR – dorsal raphe; TMN – tubero-mammillar nucleus; VTA – ventral tegmental area; SNpc – substantia nigra/pars compacta; vPAG – ventral periaquetuctul gray matter; LDT/PPT – latero-dorsal tegmentum/pedunculo-pontine tegmentum; BF – basal forebrain; PC/PB – preceoruleus/parabrachialis nuclei; LHA – lateral hypothalamic area; VLPO – ventro-lateral preoptic area; MnPO – median preoptic area; PH – posterior hypothalamus;* **↑** *– increase in release;* **↓ -***decrease in release;* **↑↑** *- substantial increase in release;* **↓↓ -** *substantial decrease in release;* **↑/↓ -** *increase or decrease in release dependently of the site of cerebral localization;* **→** *– gradual decrease in release;* ↔ *– release ceased.*

#### **Table 1.**

*A simplified scheme for the secretion of cerebral neurotransmitters in the sleep–wake cycle (data from animal studies).*

Obviously, the level of serotonin (as well as norepinephrine and histamine) at the sites of projection of aminergic neurons (and, possibly, in the brain as a whole) can decrease during this time. However, the periods of REM sleep in all animals are short, and in some species (small rodents, birds, etc.) they are extremely short (from a few seconds to 1 min) [18, 19]. So this decrease cannot be significant, and in the subsequent period of wakefulness, the normal, "basal" level of serotonergic transmission is quickly restored.

The situation is different in humans. In adults, unlike animals, sleep is of a continuous, so-called "monophasic" or "consolidated" nature. This means that an adult living in modern urban conditions is waking all day (16 hours), and the entire daily "quota" of sleep, usually 5 cycles 1.5 hour each, is realized at night "at a time." In this case, the first half of the night sharply differs from the second - and this is another important difference between human sleep and animal sleep (**Figure 1**, upper graph). In the first half of the night, a person implements mainly the need for deep slow wave sleep (NREM), which has accumulated over a long period of wakefulness (stage 3; according to the old classification - stages 3 + 4, "delta sleep" is apparently a state that is critical for the survival of the organism). In the second half of the night, the need for REM sleep is realized, which alternates with periods of superficial NREM sleep (stage 2). At the same time, individual periods of REM

#### **Figure 1.**

*A hypnogram of a healthy human subject (top) and a depressed patient (bottom) [20]. W - wakefulness, REM - REM sleep, S1-S4 - stages of NREM sleep, MT and BM - various types of movements during sleep, EM - rapid eye movements. It can be seen that the patient has fragmented sleep, REM sleep is disinhibited, and deep NREM sleep (stages 3 and 4; according to the new classification, they combined into one), on the contrary, is suppressed.*

## *Serotonin, Sleep and Depression: A Hypothesis DOI: http://dx.doi.org/10.5772/intechopen.96525*

sleep, which in a healthy person occupies about 2 night hours, can last 20, 30, and even 40 minutes in the last sleep cycles [20]. Naturally, such long periods of inactivity of the "serotonin factory" of the brain cannot pass without leaving a trace.

What determines these differences in the structure of human sleep? A newborn baby sleeps around the clock with short sucking breaks, total about 16 hours, 8 of which is occupied by the so-called "activated sleep", which is considered as the precursor of adult REM sleep. A one-year-old child has two periods of daytime sleep, and a four-year-old is allowed to sleep only once a day, after lunch. An eightyear-old is already attending school and cannot sleep during the day, and this daily rhythm (without daytime sleep) is maintained by the majority of the modern urban population for life. Psychophysiological studies of the wakefulness-sleep rhythm carried out at one time in healthy subjects who were transferred to a 24-hour bed rest when isolated from the external environment [21], as well as some observations of ethnographers on the nature of sleep in primitive tribes living in isolation from civilization [22], allow to make the following assumption. By nature, a person has a sleep–wake rhythm with two periods of short naps. With this mode, the duration of night sleep is significantly shortened; a person can get up at dawn (in summer). Sleep becomes less consolidated, sleep cycles can be interspersed with more or less prolonged episodes of wakefulness. The differences between the first and second half of the night are smoothed out. In general, human sleep begins to resemble more animal sleep [21–23]. The monophasic nature of sleep of a modern person, apparently, is associated not so much with biological, genetic factors, as with the "pressure of civilization", distorting, disrupting the natural alternation of wakefulness and sleep. During a 16-hour daytime period of continuous wakefulness, a modern person experiences repeated "intrusions" of sleep mechanisms, realized in the form of episodes of local sleep, microsleep and an increase in the delta index in the EEG [24]. A monophasic diurnal rhythm (without daytime sleep) is acquired by the majority of the modern urban population in childhood and retained for the rest of their lives [25].

Thus, the circadian rhythm of a modern urban person is 16 hours of sleep deprivation, followed by 8 hours of sleep. And the law of "rebound" is as follows: first, delta sleep is restored (stage 3), then REM sleep [26]. On the other hand, superficial sleep is considered an "optional" state, which "can be dispensed with." Consequently, the unusually long pre-morning periods of REM sleep, in which serotonergic transmission can be severely depleted, are mainly due to "civilization pressure" disrupting natural circadian dynamics.

## **3. Serotonin and depression**

Although the role of serotonin in the regulation of the wakefulness-sleep cycle remains not completely defined, its participation in emotional reactions is well known. In popular literature, serotonin is often referred to as the "happiness hormone". Excessive activation of serotonergic transmission in the brain causes movement disorders – part of the so-called "serotonin syndrome", and insufficient activation seems associated with diseases such as depression, schizophrenia, anxiety disorders, etc. [27, 28]. In the 50–60s of the last century, the so-called "catecholaminergic" hypothesis became widespread, linking the occurrence of depression with a lack of noradrenergic transmission. It was replaced by the "serotonin" hypothesis of endogenous depression, which was first published in the Lancet magazine in an article by a psychopharmacologist from Leningrad (USSR, now St. Petersburg, Russia) Izyaslav (Slava) Lapin and his graduate student Gregory Oxenkrug: "Intensification of the central serotoninergic processes as a possible

determinant of the thymoleptic effect" [29]. The article had about 350 citations in the first 18 years (to date, according to Google searches, about 800). This led Eugene Garfield to include it in the "This Week's Citation Classic" section of the Current Contents and to publish a note by Oxenkrug on how the article was created [30].

Lapin and Oxenkrug were the first to link emotional disorders and sleep disturbances in depression with a common causative factor - impaired serotonin transmission due to changes in the turnover of cerebral serotonin. Reducing serotonergic transmission in the brain through the hypothalamus-pituitary–adrenal cortex axis disinhibits the release of cortisol. Cortisol activates the enzyme tryptophan dioxygenase (TDO), which "shunts" the normal turnover of serotonin and converts it (in the presence of the pro-inflammatory cytokine γ-interferon, which appears in response to stress) into kynurenine. As a result, serotonin is released less and less. Neuroactive kynurenines, in turn, increase anxiety and impair cognitive performance. Subsequently Lapin showed that the metabolism of kynurenine in the absence of vitamin B6 leads to the appearance of diabetogenic derivatives [31]. The impact of Lapin's ideas on the further development of world psychiatry and psychopharmacology was described in detailed reviews by Oxenkrug [32, 33].

Later, other authors developed a "new serotonin hypothesis" [34], according to which an increased level of glucorticoids, systemic inflammatory processes, and neuroimmune activation of microglia stimulate the synthesis of enzymes tryptophan dioxygenase and indoleamine dioxygenase (TDO/IDO) and finally shift the breakdown of tryptophan to the kynurenine pathway. Finally, the initial development of Lapin recently received a new generalization in the form of the so-called "serotonin-kynurenine-inflammatory" hypothesis of the onset of depression (see **Figure 2**) [35, 36]. Based on the latest biochemical and molecular biology studies, these authors show that the metabolites of kynurenine - oxidized kynurenine, quinolinic acid and the cation NAD+ (nicotinamide-adenine-dinucleotide), which have exito- and neurotoxic properties, cause an excessive increase in glutamatergic neurotransmission, suppressing neurogenesis in the *fascia dentata* of the hippocampus, apoptosis and neurodegeneration. The kynurenine pathway of serotonin metabolism occurs in microglia, and the proliferation of microglia has been found in a number of studies using neuroscanning of depressed patients. So, despite the fact that modern theories of the origin of depression concentrate more on neuroinflammatory and neurodegenerative processes [36–39], Lapin's serotonin idea, put forward more than half a century ago has not lost its relevance.

Back in 1960, a reduced (almost 3 times) level of serotonin in the cerebrospinal fluid of depressed patients was confirmed [40]. And selective serotonin reuptake inhibitors (increasing 5-HT concentration in the synaptic cleft) have been widely and successfully used in clinical medicine as antidepressants for more than 30 years [2]. However, the generalizing works of the last two decades have given some authors the basis for a paradoxical conclusion that not suppression, but, on the contrary, the excess of serotonin neurotransmission is the cause (or at least one of the causes) endogenous depression (melancholy), or that serotonin is not involved at all in the pathogenesis of this disease [41–44].

In recent years, researchers have turned their attention not to cerebral serotonin itself, but to its carrier protein (5-HTT) and the gene for this protein. It turned out that people homo- or heterozygous for its short allele are less resistant to stress and are more at risk of developing insomnia and depression than carriers of the long allele [45]. The short allele is associated with a decrease in the number of 5-HTT binding regions on the surface of the presynaptic membrane and, accordingly, in the reuptake of excess serotonin. From this point of view, the disorder of serotonin transmission in some types of depression, in fact, may be associated more with an excess than a lack of serotonin in the synaptic cleft.

*Serotonin, Sleep and Depression: A Hypothesis DOI: http://dx.doi.org/10.5772/intechopen.96525*

#### **Figure 2.**

*Simplified illustration of the kynurenine pathway. Tryptophan (TRP) is predominantly converted into kynurenine (KYN) by the indoleamine 2,3-dioxygenase (IDO) isozymes and tryptophan dioxygenase (TDO). IDO-1 is expressed in various immune cells throughout the body, notably dendritic cells, monocytes, and macrophages. Less is known about the more recently discovered IDO-2 enzyme although it is more selectively expressed in dendritic cells, liver, kidney, and the brain and it does not appear to have a significant effect on peripheral kynurenine concentration. TDO-2 is an alternative nomenclature for TDO. KYN can be metabolized into kynurenic acid (KYNA), which is usually considered to be neuroprotective, by the KAT isozymes. Alternatively, it may be converted into anthranilic acid by kynureninase or 3-hydroxykynurenine (3HK) by kynurenine monooxygenase (KMO). Metabolism down the latter pathway increases under inflammatory conditions. 3HK is a free radical generator while quinolinic acid (QA) is a known neurotoxin and gliotoxin. Thus, metabolites in this pathway are usually considered to be neurotoxic. QA is the endogenous source of nicotinamide and nicotinamide adenine dinucleotide (NAD+) [35].*

Apparently, under the general term "depression" there is several (and maybe even many) diseases of various etiologies [46]. At the same time, the majority of patients respond positively to the intake of selective serotonin reuptake inhibitors (SSRIs). Moreover, almost any drug that inhibits the reuptake of monoamines (primarily serotonin, but partly also norepinephrine and dopamine) has thymoleptic (antidepressant) properties [46]. However, a very long interval (calculated in weeks) from the start of antidepressant administration to the appearance of a therapeutic effect is an indirect indication that the lack of monoaminergic transmission is most likely a secondary, "downward" consequence of some still unknown primary disorders [46]. Nevertheless, for most cases of endogenous depression

(major depression), the hypothesis of monoamine deficiency is still considered the most acceptable [46]. Apparently, the impairment of serotonergic neurotransmission is one of the links in a cascade of molecular biological events that ultimately lead to neuroinflammatory and neurodegenerative changes in certain parts of the brain, as mentioned above.

The main difficulty faced by this hypothesis is the *reason* for the decrease in serotonin levels in the brain during depression. Among the possible reasons, either an increased activity of the MAO enzyme, which metabolizes serotonin, or a mutation with loss of function of the gene of the Tph-2 enzyme, which synthesizes serotonin, is proposed. In this review, we propose a third reason that can explain a number of cases of "spontaneous" onset of depressive symptoms in apparently healthy people, as well as link the hypotheses of "monoamine deficiency" and "circadian rhythm disturbances" [46].

## **4. Sleep and depression**

Depression is one of those relatively few diseases that are characterized by pronounced and rather specific sleep disorders. In addition, these disorders can occur much earlier than the main symptoms (mood disorders, etc.), and therefore serve as important predictors of the disease. Non-specific disorders in depression include difficulty falling asleep, frequent nighttime awakenings, and early morning awakenings. However, there is a more specific violation of the sleep structure: suppression of deep NREM sleep (stage 3) and disinhibition of REM sleep (see **Figure 1**, lower graph). The suppression of deep SWS manifests itself in the loss of stage 4 (according to the old classification), reduction and fragmentation of stage 3, a decrease in the EEG delta index, and lengthening of stage 2. The disinhibition of REM sleep is manifested both quantitatively and qualitatively. Quantitatively, by reducing the latency of the onset of the first REM period down to zero, when sleep can begin with a REM episode, which never happens in a healthy adult; lengthening the first REM period; an increase in the proportion of the total duration of REM sleep in all night sleep. Qualitatively, the disinhibition of REM sleep manifests itself in an increase in the generation of rapid eye movements already in the first REM sleep period. Although similar disorders of REM sleep are observed not in all patients with depression, but only in 50–70% (according to various sources), and similar phenomena of REM sleep "disinhibition" can sometimes be observed in other neuropsychiatric disorders (schizophrenia, manic psychosis), nevertheless for "major" depression, they are much more typical and more pronounced [37].

As noted above, suppression of neurogenesis in adult animals was shown in various experimental models of depression (mice, rats) [47]. Interestingly, in other models associated with an increase in REM sleep (some forms of stress), according to some data, neurogenesis in the hippocampus is also impaired. On the other hand, inhibition of neurogenesis is also observed in sleep deprivation [37].

One of the main arguments in favor of the hypothesis of a causal relationship (rather than just a correlation) between depression and REM sleep is the effects of antidepressants. It is well known that most antidepressants that prevent the natural breakdown of serotonin (and other brain amines): tricyclic drugs, selective serotonin reuptake inhibitors (SSRI), deeply inhibit REM sleep. Especially effective in this regard are MAO inhibitors, which can almost completely eliminate REM sleep for months and years [37–39]. Millions of patients around the world have taken and are taking these drugs. No cases of cognitive impairment were reported; instead, there is some evidence that MAO inhibitors even improve memory! On the contrary,

## *Serotonin, Sleep and Depression: A Hypothesis DOI: http://dx.doi.org/10.5772/intechopen.96525*

the latest generation of benzodiazepines, used as hypnotics and practically do not disturb the duration and distribution of REM sleep, have a pronounced detrimental effect on memory due to the effect of these drugs on the GABA signaling system [48–51].

Now, imagine that in the human population, with its unusually wide gene diversity, there are subjects with initially lowered levels of cerebral serotonin. This may be due to some gene polymorphisms that cause, for example, the synthesis from dietary tryptophan, not serotonin, but kynurenines (as Lapin believed) [29, 31–33]), or a decrease in the formation of tryptophan hydroxylase-2, which synthesizes cerebral serotonin from its precursor, or an increased level of the MAO-A enzyme, which metabolizes serotonin, etc. For such people, long premorning periods of REM sleep become especially dangerous, since they can reduce the level of cerebral serotonin below a certain critical level, the threshold for disruption of general serotonergic transmission and the occurrence of emotional disorders. This approach is confirmed by the subjective reports of patients reporting the appearance of the first feelings of depression even during the experience of morning dreams and reaching their maximum severity immediately upon awakening. However, by the evening (as cerebral serotonin accumulates in the course of a vigorous state), the patient's condition gradually improves, depressive symptoms go away by themselves, and he/she feels completely healthy ... until a new period of sleep comes! [46]. It is clear that against the background of a low, near-threshold level of cerebral serotonin, even immersions in NREM sleep causing a decrease in serotonin release can re-launch pathological processes in the brain.

On the other hand, the release of cerebral serotonin is involved in the inhibition of the glutamatergic/cholinergic center of REM sleep triggering in the pons [5, 9, 10]. Then, the weakening of this inhibition may be associated with a wellstudied increase in the "pressure" of REM sleep in depression, which manifests itself, in particular, in the shortening of the latent period of the first episode of this sleep phase, as mentioned above [37–39]. Moreover, according to some reports, even genetic relatives of such patients, who do not suffer from depression, but, assuringly might have the same gene polymorphism and, as a result, a lowered "basal" level of cerebral serotonin, also have excessively prolonged periods of REM sleep [37]. That is, one can assume that all people who initially have a lowered level of cerebral serotonin, due to this, have an increased "pressure" of REM sleep, which further lowers this level.

It becomes clear why it is not possible to create a more or less adequate experimental model of stress-induced anhedonia (depression) [52]. For this, apparently, it is necessary to adapt the experimental mice to "human conditions": a constant 16-hour sleep deprivation (in the dark period of the day) accompanied by its 8-hour "return" (in the light period). And it is necessary to influence chronic stress also in the dark period against the background of this artificial circadian rhythm. It is possible that in this case the applied impacts will be more effective.

## **5. Conclusion**

Thus, according to the proposed hypothesis, the formation of depression is due to a combination of two factors - a reduced level of cerebral serotonin and the structure of human night sleep with extremely long pre-morning periods of REM sleep. It is known that total sleep deprivation (or selective REM sleep deprivation) is used as an effective but short-term thymoleptic action. According to the proposed approach, fragmentation of REM sleep can be just as effective. If it really turns out

to be effective in alleviating depressive symptoms, then it can be relatively easily automated by giving the patient during REM sleep signals (for example, sound), selected so that they do not wake him up at all, but only wake him up, transferring from REM sleep to the 2nd or 1st stage of NREM sleep. Such a procedure, which is much more easily tolerated by patients, will also be suitable for chronic use.

## **Acknowledgements**

This work was supported by the Russian Science Foundation (project No. 17-15-01433).

## **Author details**

Vladimir M. Kovalzon Severtsov Institute Ecology/Evolution, Russian Academy of Sciences, Moscow, Russia

\*Address all correspondence to: kovalzon@sevin.ru

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Serotonin, Sleep and Depression: A Hypothesis DOI: http://dx.doi.org/10.5772/intechopen.96525*

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## **Chapter 3**
