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Computational Modeling of Cyclic Nucleotide Signaling Mechanisms in Cardiac Myocytes

  • Claire Y. Zhao
Chapter
Part of the Cardiac and Vascular Biology book series (Abbreviated title: Card. vasc. biol.)

Abstract

The balanced signaling between the two cyclic nucleotides (cNs), cAMP and cGMP, in the cN signaling system plays a critical role in regulating cardiac contractility. Many therapeutic agents have been developed to selectively inhibit or stimulate proteins in the cN signaling system in the attempt to manage and treat heart diseases. Nonetheless, it has been challenging to obtain a comprehensive, system-level understanding of the signal transduction mechanisms, in part because of the participation of multiple phosphodiesterases (PDEs) in the common task of cN degradation, the complex interactions between the signaling proteins, and the large number of cN regulated targets in the tightly coupled excitation-contraction (EC) coupling process. Multi-scale, biophysically detailed, and experimentally validated computational models are well suited to dissect the underlying mechanisms in these nonlinear and intertwined reaction networks. By precisely defining and quantifying biochemical reactions involved, data-driven and integrative modeling bridge causal gaps across spatiotemporal scale, from the characteristics of individual molecular components to the collective responses of the entire signaling network. Through predictive modeling and in-depth analysis, these computational models are powerful in providing insights into cellular mechanisms, formulating novel hypothesis, and proposing possible future experiments. This review focuses on the development of mechanistic models, the close interplay between modeling and experimentation, and the identification of opportunities for future modeling research in the cardiac myocyte cN signaling system.

Keywords

Cyclic nucleotides Phosphodiesterases Signaling network Electrophysiology Compartmentation Heart failure Cardiac myocytes Computational modeling 

10.1 Introduction

The cyclic nucleotides (cNs), cyclic adenosine-3′, 5′-monophosphate (cAMP), and cyclic guanosine-3′, 5′-monophosphate (cGMP) are second messengers that regulate the response of cardiac myocyte to external and internal stimuli (Antos et al. 2009; Cohen 2002; Graves and Krebs 1999). The signal transduction mechanisms that regulate the cellular dynamics of cAMP and cGMP are together referred to as the cN signaling system (Beavo and Brunton 2002). They are among the earliest identified signal transduction systems (Maurice et al. 2014), with key discoveries recognized by the award of several Nobel Prizes (Nobelprize.org 2014). Today, new therapeutic agents have been developed on the basis of their ability to potently and selectively target the constituent cN signaling components (Conti and Beavo 2007; Lugnier 2006). On the other hand, the precise mechanisms by which the cN signaling system modulates tissue-specific intracellular signaling remain to be established (Lugnier 2006; Fischmeister et al. 2006; Hofmann et al. 2009; Conti and Beavo 2007; Francis et al. 2009). As such, research on cN signal transduction mechanisms continue to expand after over half a century of scientific investigation (Beavo and Brunton 2002).

As information quickly accumulates on the various aspects of cN signaling, it has become increasingly important to obtain a comprehensive and integrated understanding of this intricately interconnected signaling system. As a result, the need for data-driven mechanistic and integrative modeling is growing. Through in-depth interpretation of experimental observations and generation of testable biological hypotheses, computational modeling of the cN signaling system aims to reveal the interactions between signal components within the cN system, to uncover synchronization of the cN system with other biological processes, and to bridge gaps in casual links within and between spatiotemporal scales. This review focuses on the close interplay between experiments and mechanistic models of the cN signaling system and identifies opportunities for future research in this area.

10.1.1 Overview of the cN Signaling System

The principle signaling pathways that form the backbone of the cN signaling system are the β-adrenergic signaling pathway for cAMP regulation, among other G-protein-coupled receptor (GPCR) pathways that also participate in cAMP regulation, and the nitric oxide/cGMP/protein kinase G (NO/cGMP/PKG) and natriuretic peptide/cGMP/PKG (NP/cGMP/PKG) signaling pathways for cGMP regulation (Fig. 10.1). All three pathways show parallel in their overall structure, which warrants overall similarity in their corresponding model equations. Initially, a first messenger molecule, usually a hormone or neurotransmitter, triggers signal transduction (pink background) by binding to a trans-membrane receptor (blue background). Then, a cyclase and multiple families of phosphodiesterases (PDEs) are activated, respectively, for second messenger production and degradation (green background), shaping the dynamics of the cN signals (yellow background). Finally, a kinase is activated by the cNs (gray background), which in turn regulates downstream effectors via phosphorylation of these targets. The information encoded in the cN second messengers can be amplified many folds through the responses of downstream targets, such that cellular responses are able to change dramatically upon minute alternations to the cN signals (Antos et al. 2009; Cohen 2002; Graves and Krebs 1999).
Fig. 10.1

cN signaling system in cardiac myocytes. In cardiac myocytes, the synthesis and degradation of cAMP are regulated by GPCR pathways, primarily the β-adrenergic signaling pathway, and that of cGMP are largely regulated by the NO/cGMP/PKG and NP/cGMP/PKG signaling pathways. The three signaling pathways show similarities in their overall structure with a first messenger (pink background) to activate a receptor (blue background), a cyclase and various PDEs (green background) for the production and degradation of the second messengers respectively (yellow background), and activation of a kinase (gray background) to phosphorylate target downstream. The representation in this schematic illustrates the key components comprising the signaling cascades, but does not account for signaling cross-talk or microdomains/compartments.

10.1.2 cN Signaling Pathways and Cardiac Function

Among the various signaling mechanisms capable of exerting positive inotropic effects in humans, the β-adrenergic signaling pathway is the most powerful through which heart rate and contractility are physiologically regulated and maintained (Dzimiri 1999). Following its discovery in 1957, the development of accessible assays that provide meaningful measurement of cAMP in 1971 brought an explosion of activity and interest in characterizing the β-adrenergic pathway (Beavo and Brunton 2002). Based on this research, the second messenger signaling concept (Sect. 10.1.1 and Fig. 10.1), where extracellular first messengers bind to cell-surface receptors and initiates the production of intracellular second messengers, gradually emerged as a signaling paradigm commonly demployed by the cell (Beavo and Brunton 2002). As shown in Fig. 10.1 (top row), synthesis of cAMP (dark red oval) is primarily governed by the β-adrenergic pathway (red-shaded background) in response to elevated catecholamines (e.g., norepinephrine and epinephrine) (Antos et al. 2009; Cohen 2002; Graves and Krebs 1999). These ligands bind to and activate β-adrenergic receptors (β-ARs), which via a G-protein (Gs)-mediated process activate adenylyl cyclase (AC), the enzyme which catalyzes cAMP synthesis (Saucerman and McCulloch 2006; Bers 2002). Subsequent to degradation by PDEs, the net cAMP signal controls PKA activation (Saucerman et al. 2003), and hence, PKA-mediated phosphorylation of downstream targets that regulate contraction and relaxation of cardiac myocytes (Omori and Kotera 2007; Bers 2002). More specifically, PKA-I and PKA-II are the predominant PKA isoforms in cardiac myocytes (Zhao et al. 2015, 2016b).

Conversely, increased cGMP concentration ([cGMP]) is associated with attenuation of cardiac contractility (Tsai and Kass 2009; Boerrigter et al. 2009; Antos et al. 2009; Massion et al. 2005; Hammond and Balligand 2012). As shown in Fig. 10.1 (middle row), synthesis of cGMP is catalyzed by the intracellular soluble guanylate cyclase (sGC) in response to increased NO concentration ([NO]) (Hammond and Balligand 2012; Tsai and Kass 2009) (Fig. 10.1, middle schematic). In the 1960s, cGMP and the enzymes for its synthesis and degradation were discovered (Kots et al. 2009). On the other hand, it was not until the 1980s that the hormone which stimulated the synthesis of cGMP was discovered, first identified as endothelial-derived relaxant factor (EDRF), and later realized to be NO (Ignarro et al. 1987). Research on the NO/cGMP/PKG pathway in the heart exploded in the 1990s, following the discovery that cardiac myocytes constitutively expressed NO synthase (NOS), an enzyme that catalyzes NO synthesis (Balligand et al. 1993). In addition to NO-derived cGMP, cGMP synthesis in cardiac myocytes can be triggered by NP, both atrial NP (ANP) and brain NP (BNP), through its activation of membrane-bound particular guanylate cyclase (pGC), or more specifically GC-A (Roy et al. 2008) (Fig. 10.1, bottom row). In fact, soon following the discoveries of ANP and BNP in 1981 and 1988 respectively, it became apparent that they exert cardiovascular responses (Kuhn 2004; Nishikimi et al. 2006). Both NO- and NP-derived cGMP activate PKG, more specifically isoform PKG-I for cardiac myocytes (Zhao et al. 2015, 2016b), which then regulate downstream targets via protein phosphorylation (Lee and Kass 2012).

10.1.3 Overview of Mechanistic Models of cN Signaling Pathways

A widely adapted mechanistic model of the β-adrenergic signaling pathway in the cardiac myocyte was developed by Saucerman et al. (2003). Later models have subsequently included multiple compartments of cAMP, in the attempt to study spatial localization of cAMP and its effect on myocyte contraction (Iancu et al. 2007; Heijman et al. 2011; Bondarenko 2014). Other modeling studies dissected the mechanisms contributing to cAMP localization, often based on experiments with simpler cellular systems, such as HEK cells (Xin et al. 2008; Oliveira et al. 2010). Nonetheless, the majority of studies focused on studying β-adrenergic responses of cardiac myocytes, connecting existing models of the β-adrenergic signaling pathway with those of electrophysiology. These studies have elucidated the effects of β-adrenergic stimulation on myofilament contraction (Negroni et al. 2015) and regulation of ion channels (Terrenoire et al. 2009), calcium (Ca2+) cycling (Yang and Saucerman 2012; Song et al. 2001), and ion homeostasis (Kuzumoto et al. 2008). More recent models explored disease mechanisms in relation to the β-adrenergic pathway, such as the initiation of alternans (Hammer et al. 2015) and early afterdepolarizations (Xie et al. 2013), the consequences of signaling disturbances due to gene mutations (Saucerman et al. 2004; Terrenoire et al. 2005), and the development of cardiac hypertrophy (Yang et al. 2014; Ryall et al. 2012).

Comparatively, fewer modeling works have investigated the NO/cGMP/PKG and NP/cGMP/PKG pathways, especially with respect to cardiac myocytes. Models of NO-derived cGMP regulation have primarily been constructed for non-cardiac cells, such as vascular smooth muscles (Kapela et al. 2008; Yang et al. 2005; Held and Dostmann 2012; Cawley et al. 2007), neuronal cells (Bellamy et al. 2000; Philippides et al. 2000), platelets (Roy and Garthwaite 2006; Mo et al. 2004), and HEK cells (Batchelor et al. 2010). Additional modeling efforts have concentrated on understanding NO, a freely diffusible, free radical gas (Hall and Garthwaite 2009), with regard to its diffusion (Schmidt et al. 1997; Ramamurthi and Lewis 1997; Kar and Kavdia 2011), bio-transport (Chen and Popel 2007; Tsoukias et al. 2004; Buerk 2001; Tsoukias 2008), and synthesis via NOS (Heinzen and Pollack 2003; Chen and Popel 2006). Despite of their non-cardiac origins, these models provide valuable molecular insights to the biochemistry of the cGMP signaling pathways in cardiac myocytes.

Due to the data-intensive nature of their modeling approach, mechanistically detailed, biochemically based models that investigate the effects of simultaneous activations of multiple signaling pathways are relatively rare (Saucerman and McCulloch 2004). Soltis et al. (Soltis and Saucerman 2010) investigated the synergy between β-adrenergic and Ca2+-/calmodulin-dependent protein kinase II (CaMKII) pathways via modeling phosphorylation of their common EC coupling substrates. Using logic-based differential equations, where activation or inhibition reactions are represented by normalized Hill functions and cross-talks are computed with logical AND and OR gates, Ryall et al. modeled 14 established pathways regulating cardiac myocyte growth, including all of the three pathways of the cN signaling system (Ryall et al. 2012). Otherwise, despite increasing realization that signaling is highly integrated (Saucerman and McCulloch 2004), the pathways of the cN signaling system (Fig. 10.1) have primarily been modeled in isolation of each other. Overall, investigation of the cross communication and synergistic effects of simultaneous activation of cN pathways still await incorporation of further mechanistically details.

10.2 Modeling Multiple PDE Interactions in Cardiac Myocytes

Cyclic nucleotide phosphodiesterase isoenzymes (PDEs) degrade cAMP and cGMP. They are ubiquitous in mammalian cells (Beavo 1995; Conti et al. 2014; Francis et al. 2011). Although early research has primarily focused on cN synthesis, recent studies have revealed that PDEs are critical to the regulation of numerous physiological processes, such as cell signal transduction, proliferation, and differentiation, apoptosis, and metabolism (Beavo 1995; Conti et al. 2014; Francis et al. 2011). In the cardiovascular system, distinct PDE isoenzymes regulate contractility and relaxation, cell growth/survival, and cardiac structural remodeling (Miller and Yan 2010; Zaccolo and Movsesian 2007; Omori and Kotera 2007). The molecular diversity of PDEs was recognized shortly after its discovery in 1958, as gel filtration and ion exchange chromatography of tissue extracts revealed multiple peaks of PDE activities (Maurice et al. 2014). The present-day nomenclature of PDEs is based on the PDEs’ biochemical properties, regulation mechanisms, and sensitivity to pharmacological agents, as well as the genes they are products of (Lugnier 2006). Currently, PDEs are classified to Classes I, II, and III, with mammalian PDEs belonging to Class I, which is further organized into 11 structurally related but functionally distinct PDE families, PDEs 1–11 (Francis et al. 2011).

10.2.1 Diverse PDE Families in Cardiac Myocytes

The cytoplasm of cardiac myocytes primarily contains PDEs 1–5, each with its own unique biochemical characteristics and regulatory mechanisms (Kass et al. 2007b; Zhang et al. 2008; Omori and Kotera 2007; Beavo 1995; Francis et al. 2009; Lugnier 2006; Fischmeister et al. 2006; Zaccolo and Movsesian 2007). Recently, PDE9 has also been identified to regulate NP-derived cGMP in cardiac myocytes (Lee et al. 2015), independent of NO-derived cGMP. As such, multiple distinctively regulated PDEs participated in the common task of cN degradation. This gives rise to complex interactions between the PDEs themselves and between the PDEs and cNs. Consequently, it has been challenging to obtain a quantitative understanding of the role of each PDE in modulating intracellular signaling (Lugnier 2006; Conti and Beavo 2007; Fischmeister et al. 2006; Beavo and Brunton 2002; Maurice et al. 2014; Beavo 1995).

The significance of PDEs regulating contraction in an isoform-specific manner is highlighted by the prominent effects resulting from alteration of a specific PDE isoform (Ding et al. 2005a; Abi-Gerges et al. 2009; Mehel et al. 2013; Lehnart et al. 2005; Marín-García 2010). For instance, ablation of specific PDE activities through pharmacological inhibition or gene depletion is observed to promote cardiac apoptosis (Ding et al. 2005b), accelerate development of HF (Lehnart et al. 2005), and increase likelihood of cardiac arrhythmias (Lehnart et al. 2005; Molina et al. 2012). On the other hand, drugs that restore specific PDE activities (Knight and Yan 2012), such as PDE3 activity in ischemic and dilated cardiomyopathies (Yan et al. 2007a) and PDE1 and PDE4 activities in cardiac ischemia (Kostic et al. 1997), have cardio-protective effects. In addition, PDE5 inhibition is shown to be beneficial in various cardiac pathologies, such as heart failure (HF), cardiac hypertrophy, and ventricular arrhythmias (Takimoto 2012; Guazzi 2008; Kass et al. 2007a; Zhang et al. 2008).

10.2.2 Mechanistic Models of PDEs

As a first step in understanding multiple PDE actions, Zhao et al. (2015, 2016b) modeled the cN-mediated molecular mechanisms of PDEs 1–5 in detail (Fig. 10.2a–e). Additionally, a PDE9 model is proposed in this review (Fig. 10.2f). The columns of Fig. 10.2 categorize the PDEs by their selectivity for cNs: PDEs 1, 2, and 3 are dual specific, that is, capable of hydrolyzing both cNs (first column), PDE4 is specific to cAMP (second column), and PDEs 5 and 9 are selective for cGMP (third column). Each of the PDE isoenzymes is modeled as dimers of two identical subunits; for simplicity, each panel of Fig. 10.2 shows only one of the two subunits. All mammalian PDE subunits are made up of a catalytic and a regulatory domain (Conti and Beavo 2007; Lugnier 2006; Francis et al. 2011), denoted by the letters “C” and “R,” respectively. The catalytic domain (oval) contains a conserved active site (semicircular socket) that can bind either cAMP or cGMP (Lugnier 2006; Francis et al. 2011; Conti and Beavo 2007). The regulatory domains differ markedly among PDEs (Francis et al. 2011) and consequently are denoted by symbols of different shapes. In addition, for each PDE, cN catalysis reactions are represented by horizontal transitions, and additional regulatory reactions, if any, are shown as vertical transitions. Reversible and irreversible reactions are denoted by double-headed and single-headed arrows, respectively. Reactions consisting of binding of cNs to PDEs, PDE conformational changes, and cN degradation via breakage of the 3′-cyclic phosphate bond are assumed to reach equilibrium rapidly with respect to the time scale of other signaling reactions. The parameters indicated by capital “K” denote binding affinities, whereas those with lowercase “k” denote rate constants. The final degraded products, 5′-AMP and 5′-GMP, are inactive in cN signaling pathways (Francis et al. 2011).
Fig. 10.2

PDEs in cardiac myocytes. PDE monomer subunits are shown with catalytic domains (ovals, denoted by “C”) and regulatory domains (varied shapes, denoted by “R”) for PDEs 1–5 and 9 in (a–f), respectively. Active sites within catalytic domains that bind cAMP and/or cGMP are represented by semicircular sockets. (ac) Competitive binding of cAMP and cGMP to PDEs 1–3, respectively. (b) The GAF-B regulatory domain of PDE2 is represented by open rectangular socket. Its occupancy by either cNs allosterically activates PDE2 by increasing its catalytic domain’s binding affinity to both cNs. (d) cAMP hydrolysis by PDE4 is regulated by PKA-mediated phosphorylation. (e) PDE5 is allosterically activated by cGMP binding to its GAF-A regulatory domain (open rectangular sockets). PKG-phosphorylated PDE5 has increased catalytic rate and increased cGMP affinity compared to the non-phosphorylated active species. Model adapted from that of Batchelor et al. (2010). (f) PDE9 is specific to degradation of NP-regulated cGMP. Figure adapted from Zhao et al. (2015, 2016b).

For dual-specific PDEs 1–3 (Fig. 10.2a–c respectively), both cNs compete for occupancy of cN-binding domains. The cNs competitively bind to the catalytic domains of PDEs 1 and 3 (Fig. 10.2a and c, respectively) and to both the regulatory (rectangular socket) and catalytic domains (semicircular socket) of PDE2 (Fig. 10.2b). Binding of either cNs to the regulatory domain of PDE2 induces allosteric activation of the enzyme (vertical transitions) by increasing the binding affinity of cNs to the catalytic domain (horizontal transitions) (Zoraghi et al. 2004; Francis et al. 2011; Martinez et al. 2002). The above binding schemes replicate and explain experimentally observed PDE cN hydrolysis rates. For PDEs 1 and 3, cAMP and cGMP hydrolysis are progressively suppressed by increasing levels of the other cN (Yan et al. 1996; He et al. 1998). This is because the other cN replaces the said cN in the catalytic domains of the PDEs. On the other hand, PDE2 hydrolysis rates for both cNs are bimodal against increasing concentrations of the other cN, resembling a dome shape (Prigent et al. 1988; Russell et al. 1973). More specifically, for PDE2 cAMP hydrolysis, although cGMP stimulates cAMP degradation rate upon binding to PDE2 regulatory domain, high cGMP can suppress cAMP hydrolysis by preventing cAMP binding at the PDE2 catalytic site. Consequently, PDE2 cAMP hydrolysis rate increases with increasing cGMP until ~3 μM cGMP, above which the rate decreases until it is suppressed below that measured without cGMP (Zhao et al. 2015). A similar bimodal dome shape is observed for PDE2 cGMP hydrolysis rate against cAMP, but with a much less pronounced cAMP-mediated rate stimulation due to differing cGMP affinity to PDE2 domains compared to cAMP (Zhao et al. 2015).

The rest of the PDEs shows much higher binding affinity to one of the cNs (Fig. 10.2d–f). As shown in Fig. 10.2d, PDE4 only appreciably degrades cAMP, but not cGMP. PKA-mediated phosphorylation of PDE4 induces a conformational change (vertical transitions), allowing the PKA-phosphorylated form to hydrolyze cAMP at a faster rate than the non-phosphorylated form (horizontal transitions). On the other hand, the catalytic domains of PDEs 5 and 9 are much more selective for cGMP. As shown in Fig. 10.2e, PDE5 cGMP catalytic rate can be increased by allosteric binding of cGMP to its regulatory domain (Batchelor et al. 2010) and PDE5 phosphorylation by PKG (MacKenzie et al. 2002; Leroy et al. 2008; Corbin et al. 2000; Rybalkin et al. 2002; Castro et al. 2010; Sette and Conti 1996). PDE9, specifically PDE9A, has recently been shown to regulate NP-derived cGMP in cardiac myocytes (Lee et al. 2015). It has even higher cGMP selectivity than PDE5 but lacks the cGMP/PKG stimulatory regulatory domains found in PDE5 (Soderling et al. 1998) (Fig. 10.2f). It is interesting to note that, unlike the other PDEs, PDE9A is not inhibited by xanthine derivatives, such as 3-isobutyl-1-methylxanthine (IBMX), but a number of selective inhibitors have been developed (Lugnier 2006; Bender and Beavo 2006).

10.2.3 Cross-Talk between cN Signaling Pathways

As shown in Fig. 10.2, the cN cross-talk signaling network is composed of the β-adrenergic pathway (red background), the NO/cGMP/PKG signaling pathway (blue background), and the cross-talk between them (yellow background) (Zhao et al. 2016b). NP, such as ANP, elicits distinct responses in cGMP dynamics than that produced by NO (Castro et al. 2006) and does not affect β-adrenergic-stimulated contractility (Takimoto et al. 2007; Perera et al. 2015). As a result, the NP/cGMP/PKG pathway and PDE9, which is specific to NP-derived cGMP (Lee et al. 2015), are currently not included in the cross-talk network (Fig. 10.2). Individual stimulation of the β-adrenergic and NO/cGMP/PKG pathways exert opposing effects, with the former enhancing cardiac inotropy and lusitropy (Katz 2011; Bers 2002) and the latter attenuating contractility (Tsai and Kass 2009; Boerrigter et al. 2009; Antos et al. 2009; Massion et al. 2005; Hammond and Balligand 2012) and antagonizing β-adrenergic tone (Zaccolo and Movsesian 2007; Stangherlin et al. 2011; Champion et al. 2004; Balligand 1999). As a result, the cN cross-talk signaling network (Fig. 10.1) can act to maintain the delicate balance between the cAMP and cGMP signals required for normal cardiac contraction (Stangherlin et al. 2011; Takimoto et al. 2005a; Zaccolo and Movsesian 2007; Senzaki et al. 2001; Mongillo et al. 2006; Stangherlin and Zaccolo 2012; Weiss et al. 1999; Moalem et al. 2006; Abi-Gerges et al. 2009; Mehel et al. 2013).

The cross-talk between the β-adrenergic and NO/cGMP/PKG pathways (Fig. 10.3, yellow background) can be interpreted as a network phenomenon arising from the molecular selectivity of PDEs to cAMP and cGMP (Fig. 10.1). As shown in Figure 10.3b, cAMP can stimulate its own degradation through activation of PDEs 2 and 4 (green arrows) in the form of negative feedback (Zhao et al. 2015). On the other hand, the presence of cGMP can potentially increase cAMP concentration ([cAMP]) by inhibiting cAMP hydrolysis rates of PDEs 1 and 3 (red arrows) (Zhao et al. 2015). In addition, depending on its concentration ([cGMP]), cGMP can either inhibit or potentiate [cAMP] by regulating PDE2 cAMP hydrolysis activity (red and green arrows) (Zhao et al. 2015). Similarly, as shown in Figure 10.3c, negative feedback on cGMP is accomplished by cAMP- and cGMP-dependent activation of PDE2 and cGMP-dependent activation of PDE5 (Castro et al. 2010; Kass et al. 2007b; Zhang et al. 2008; Francis et al. 2009). On the other hand, the presence of cAMP can potentially increase cGMP by inhibiting cGMP-degrading activities of PDEs 1 and 3, while either inhibiting or potentiating cGMP by regulating PDE2 cGMP hydrolysis activity depending on cAMP (Kass et al. 2007b; Francis et al. 2009). Consequently, the complex interactions comprising cN cross-talk and the participation of multiple PDEs in the common task of cN degradation make understanding the nature of these regulatory mechanisms challenging (Lugnier 2006; Conti and Beavo 2007; Fischmeister et al. 2006; Beavo and Brunton 2002).
Fig. 10.3

cN cross-talk signaling network in cardiac myocytes. a The cN cross-talk signaling network model is composed of the β-adrenergic pathway (red background), the NO/cGMP/PKG signaling pathway (blue background), and cross-talk between them (yellow background) mediated by PDEs 1–5. In the regulation of cAMP- (b) and cGMP- (c) hydrolysis, cNs exert positive (green arrows) or negative (red arrows) regulation of PDE activities. To avoid crowding the figure, the hydrolysis reactions of cNs are omitted in (b) and (c), which would have been drawn as red arrows originating from each PDE to cAMP in (b) and cGMP in (c). Hydrolysis of cAMP and cGMP are, respectively, represented by ovals of faded red in (b) and faded blue in (c). Figure adapted from Zhao et al. (2016b)

The underlying mechanisms of and interactions between PDEs in this nonlinear, tightly coupled reaction system can be revealed by analysis of computational models of the cN cross-talk network that mechanistically replicates experimentally observed activation-response relationships and temporal dynamics (Zhao et al. 2015, 2016b). Modeling reveals that a reduction in the activity of one PDE is compensated by the remaining PDEs, a behavior referred to as coupling (Zhao et al. 2015, 2016b). This model result indicates that the interpretation of experiments investigating the roles of multiple PDEs by measuring cN in response to application of selective blockers can be confounded by network interactions between the different PDEs. It is also discovered that PDE2 exhibits strong coupling with PDE4 in cAMP hydrolysis and with PDE5 for cGMP hydrolysis (Zhao et al. 2015, 2016b). Such coupling between PDE isoenzymes may be an important mechanism in stabilizing cAMP dynamics in the heart (Abi-Gerges et al. 2009; Marín-García 2010; Yan et al. 2007b), including disease settings where alterations in isoenzyme-specific PDE expression and/or activity have been implicated (Menniti et al. 2006; Maurice et al. 2014; Rahnama'i et al. 2013; Yan et al. 2007b; Marín-García 2010; Ding et al. 2005a; Mehel et al. 2013; Lehnart et al. 2005).

10.3 Modeling Regulation of Cardiac Electrophysiology by the cN Signaling System

The availability of increasingly mechanistic models of cN signal transduction system now opens the door for in-depth investigation of neurohormonal regulation of cardiac electrophysiology. Cardiac electrophysiology is a discipline with a rich history of close interplay between experiments and computational modeling since the 1960s (Winslow et al. 2011). Comparatively, signaling networks remain poorly characterized, in part because they are tightly interwoven with a variety of physiological processes in the cell (Saucerman and McCulloch 2004). The cN signaling system regulates the activity of many proteins involved in shaping cardiac contractility via PKA- and PKG-mediated phosphorylation of these proteins. Functional integration of models of the cN signaling system into a whole-cell myocyte model will help clarify and dissect the way by which cN signaling regulates Ca2+ cycling and AP morphology.

10.3.1 Overview of Models of Cardiac EC Coupling

EC coupling (Fig. 10.4) is the process by which electrical excitation leads to action potential (AP) formation, intracellular calcium (Ca2+) cycling, and mechanical contraction of the myofilaments (Fig. 10.4). Briefly, sodium (Na+) channels activate upon membrane depolarization, leading to the upstroke of AP. The subsequent increase in membrane depolarization allows L-type Ca2+ channels (LTCCs) to open, letting Ca2+ into the cell (I CaL ). As this trigger Ca2+ binds to and prompts the opening of ryanodine receptors (RyRs), Ca2+ from the sarcoplasmic reticulum (SR) Ca2+ store is released into the cytoplasm—a process commonly referred to as Ca2+-induced Ca2+ release (CICR) (Bers 2001). The increased Ca2+ concentration ([Ca2+]) in the cytosol allows it to bind to troponin I (TnI), inducing a conformational change in TnI and initiating contraction through shortening of the myofilaments (Bers 2002). The plateau of the AP is shaped by activation of potassium (K+) channels, where the resulting outward K+ current (I K ) opposes I CaL (Tomaselli and Marbán 1999). As LTCC gradually inactivates, I K dominates over I CaL , driving AP back to rest (Tomaselli and Marbán 1999). Cytosolic Ca2+ declines as Ca2+ is extruded from the cell primarily via Na+/Ca2+ exchanger (NCX) or is pumped back into the SR through the SR Ca2+ (SERCA) pump (Tomaselli and Marbán 1999). As Ca2+ declines, Ca2+ dissociates from the myofilaments, and the myocyte begins to relax (Bers 2002).
Fig. 10.4

Regulation of EC coupling by the cN signaling system in cardiac myocytes. Via PKA- and PKG-mediated phosphorylation of several proteins involved (denoted by red and blue dots, respectively), the cN signaling system regulates the EC coupling process, from Ca2+ cycling (orange lines) to contraction of the myofilaments. Phosphorylation targets shown (dots) are summarized from prior literature (Bers 2002; Takimoto 2012; Saucerman and McCulloch 2006; Tamargo et al. 2010; Feldman et al. 2005).

Many computational models of EC coupling process have been developed in varying biophysical detail, with many in-depth reviews on this topic (Roberts et al. 2012; Winslow et al. 2011; Fink et al. 2011; Trayanova and Rice 2011; Williams et al. 2010; Smith et al. 2007; Richard 2001; Noble and Rudy 2001). Briefly, these models are typically composed of a system of ordinary differential equations (ODEs) and/or stochastic processes that depict the opening and closing of channels, transporters, pumps, and exchangers and the resulting ionic fluxes. Starting from deterministic common-pool models where CICR and Ca2+ cycling are modeled in a single compartment, later models incorporated increasingly detailed temporal and spatial profiles of Ca2+ dynamics by incorporating multiple Ca2+ compartments or even finite element meshes to spatially divide the cell, resulting in improved model behavior when compared to emerging experiments, and consequently provided enhanced mechanistic insights, such as those regarding SR release and interval-force relations. Additionally, new functional components are being developed and integrated into existing whole-cell models in pace with new experimental findings, such as models of EC coupling proteins with increased biophysical details, mitochondrial energetics, and myofilament contractions (Winslow et al. 2016). To meet the computational demands of increasingly detailed and integrated models, a spectrum of techniques to improve algorithmic and computational efficiency has also been developed, such as model reduction methods, numerical algorithms, and parallel computing approaches.

10.3.2 PKA-Mediated Phosphorylation of EC Coupling Proteins

Among the three primary pathways in the cN signaling system (Fig. 10.1), phosphorylation targets of the β-adrenergic pathway, their phosphorylation mechanisms, and the effects of phosphorylation contributing to sympathetic responses are the most extensively studied (Fig. 10.4, red dots). Phosphorylation of phospholamban (PLB) and TnI, respectively, speeds up SR Ca2+ re-uptake and dissociation of Ca2+ from the myofilaments, leading to the lusitropic effects of PKA (Li et al. 2000). Additionally, PKA-mediated phosphorylation of LTCC underlies I CaL potentiation in response to β-adrenergic stimulation; however, research is still ongoing to understand the functional link between LTCC gating changes and phosphorylation of the putative PKA sites (Yue et al. 1990; Harvey and Hell 2013). Under β-adrenergic stimulation, the greatly enhanced Ca2+ transient amplitude due to ICaL potentiation more than offsets the reduction in myofilament Ca2+ sensitivity caused by TnI phosphorylation, which by itself alone would have reduced force (Li et al. 2000). At the same time, the faster SR Ca2+ re-uptake contributes to increased SR Ca2+ content. Together, the aforementioned mechanisms ultimately lead to the inotropic effect of PKA.

PKA is reported to phosphorylate RyRs, but results are mixed with decrease, increase, and lack of change in RyR open probability (Reiken et al. 2003; Bers 2006). Results are similarly varied concerning whether RyR phosphorylation alters the intrinsic responsiveness of SR Ca2+ release that triggers Ca2+entry from LTCC. This is in part because of the challenges in isolation of intrinsic RyR effects from the highly interwoven CICR process (Bers 2002; Bers 2006). Besides RyRs, PKA phosphorylation of the corresponding channels has reported to modulate the kinetics of K+ currents, I Ks (Chen and Kass 2011; Kurokawa et al. 2009) and I Kr (Harmati et al. 2011), as well as chloride (Cl) currents, ICl(Ca) (Guo et al. 2008) and I CFTR (Hwang et al. 1993). PKA phosphorylation of phospholemman (PLM) is reported to increase NaK pump Na+ sensitivity and activation (Despa et al. 2008; Despa et al. 2005) and act as a feed-forward regulator of Ca2+ (Yang and Saucerman 2012). Furthermore, protein phosphatase inhibitor 1 (I1) phosphorylation by PKA enhances its inhibition of PP1 and may thereby augment β-adrenergic signaling (Gupta et al. 1996). Finally, PKA phosphorylates myosin-binding protein C (MyBPC) in addition to TnI at the myofilaments, but the precise contractile effects of this phosphorylation are still much debated (Marston and de Tombe 2008; Solaro and Kobayashi 2011).

10.3.3 PKG-Mediated Phosphorylation of EC Coupling Proteins

Many physiological effects modulated by cGMP, such as attenuating contractility, accelerating relaxation, and improving myocyte stiffness, are likely to be mediated by direct PKG phosphorylation of the respective proteins (Takimoto 2012) (Fig. 10.4, blue dots). On the other hand, much less is known of PKG-mediated phosphorylation compared to that of PKA. Studies have demonstrated PKG phosphorylation sites on LTCC (Jiang et al. 2000; Yang et al. 2007). Activation of the NO/cGMP/PKG pathway reduces LTCC open probability (Tohse and Sperelakis 1991; Fiedler et al. 2002; Tohse et al. 1995; Schröder et al. 2003) through prolonged channel closed time with no effects on channel open time or single-channel conductance (Tohse and Sperelakis 1991; Fiedler et al. 2002). Additionally, PKG phosphorylation of myofilament on TnI and titin sites has been shown to induce negative inotropic effects and accelerate relaxation (Shah et al. 1994; Krüger et al. 2009; Lee et al. 2010). Furthermore, PLB can be phosphorylated by PKG in vitro, which may serve to reverse the inhibition of PLB upon SERCA and thus increase SR Ca2+ uptake and produce positive inotropic and lusitropic effects in mammalian hearts (Huggins et al. 1989; Pierkes et al. 2002). Finally, PKG phosphorylation of transient receptor potential canonical 6 (TRPC6), a receptor-operated Ca2+ channel, suppresses its conductance and results in inhibition of calcineurin (CaN)-NFAT signaling (Koitabashi et al. 2010).

10.3.4 Effects of Dual Phosphorylation by PKA and PKG

As shown in Fig. 10.4 (red and blue dots), several proteins shaping EC coupling can be phosphorylated by both PKA and PKG, including LTCC, TnI, and PLB. While some phosphorylation sites are shared by both PKA and PKG, other targets possess distinct PKA and PKG phosphorylation sites. For instance, serine 16 on PLB is a common PKA and PKG phosphorylation site, and LTCC has both common and distinct sites (Colyer 1998; Wegener et al. 1989). Consequently, some of these EC coupling proteins may be simultaneously phosphorylated by both kinases, and each of them may theoretically undergo four distinct phosphorylation states: non-, PKA-, PKG-, and PKA- and PKG phosphorylated. In most cases, the behavior of the target protein under each phosphorylation state, especially under dual phosphorylation, awaits further investigation. Comparatively, more efforts has been focused on LTCC. The cN cross-talk signaling network is shown to exert both stimulatory and inhibitory regulatory actions on LTCCs via PKA- and PKG-mediated phosphorylation (Zhao et al. 2016a). Suppression of pre-stimulated I CaL via NO/cGMP/PKG pathway activation is consistently observed by various groups (Méry et al. 1991; Sumii and Sperelakis 1995; Levi et al. 1994; Shirayama and Pappano 1996; Wahler and Dollinger 1995; Mubagwa et al. 1993; Imai et al. 2001; Levi et al. 1989; Abi-Gerges et al. 2001; Ziolo et al. 2003). On the other hand, under basal, non-stimulated conditions, NO/cGMP/PKG pathway activation has yielded complex results on I CaL , including increased (Kumar et al. 1997), decreased (Grushin et al. 2008; Ziolo et al. 2001), or unchanged currents (Levi et al. 1994; Abi-Gerges et al. 2001). At the single-channel level, it is known that PKA increases LTCC currents by promoting more channels to gate in a high-activity gating mode (Yue et al. 1990). On the other hand, further studies are required to elucidate the impact of PKA and PKG phosphorylation on LTCC current kinetics, distribution of gating modes, gating characteristics of each gating mode, and Ca2+-dependent inactivation (CDI) (Catterall 2000; Josephson et al. 2010).

10.4 Modeling Spatially Resolved cN Signaling in Cardiac Myocytes

The compartmentation hypothesis is proposed to explain the context-dependent specificity of cN signaling, which often manifests as distinct physiological responses via manipulation of the same signaling molecule. In the late 1970s, data emerged demonstrating distinct cardiac functions caused by cAMP activated by different hormone receptors, indicating that cAMP selectively activates its downstream targets (Hayes et al. 1979; Brunton et al. 1979). On the other hand, our understanding of the subcellular formation, localization, and dynamics of cN signals has lagged far behind that of Ca2+ (Rich et al. 2014). Traditionally, activation of the cN signaling system is measured by monitoring downstream physiological events or by membrane-based biochemical assays (Beavo and Brunton 2002). The former method is unable to resolve signaling events from downstream regulatory events, and the latter alters the native structure of the cell. As a result, these techniques do not permit detailed kinetic or spatial assessment of signaling events, especially in the micron-second spatial-temporal scale. With advancements in techniques for real-time and spatiotemporally resolved recording of the cN signaling system (Lohse et al. 2008; Herget et al. 2008; Sprenger and Nikolaev 2013; Rich et al. 2014), it is now possible to quantify the dynamics and spatial distribution of cN signaling in cardiac myocytes. Leveraging upon these experimental insights, spatially resolved computational modeling can help in understanding the diversification of cN signals in subcellular microdomains and communication between the proposed cellular compartments (Saucerman et al. 2014).

10.4.1 Compartmentation of cN Signaling

Figure 10.5 summarizes the proposed localization of cN signaling proteins with cardiac myocyte membrane ultrastructures (yellow), EC coupling proteins (green), and functional signaling axes (red) identified in literature. The vertical columns of Fig. 10.5 represent possible microdomains and/or signaling hubs. As shown, the sarcolemma compartment contains AC and pGC due to their membrane association (Laflamme and Becker 1999; Kuhn 2003), as well as PDE2 (Simmons and Hartzell 1988), PDE3 (Weishaar et al. 1987), PDE4 (Baillie et al. 2002; Mika et al. 2014), and PKA, primarily PKA-II (Di Benedetto et al. 2008). Enrichments of β2-AR (Nikolaev et al. 2006; Nikolaev et al. 2010) and AC (Laflamme and Becker 1999) are also found in T-tubular membranes. Recently, caveolae have emerged as a structural hub for localized signaling (Calaghan et al. 2008). Through their association with caveolin, a protein marker for caveolae, β2-AR, AC, PKA, and PP2A (Balijepalli et al. 2006; Rybin et al. 2000), as well as eNOS (Calaghan et al. 2008), are shown to reside in this microdomain. PDEs 2 (Mongillo et al. 2006; Fischmeister et al. 2006), 3 (Ahmad et al. 2015), 4 (Lehnart et al. 2005), and 9 (Lee et al. 2015) are reported to associate with the SR membrane. Besides the above, most signaling proteins have a fraction that is diffused throughout the cytosol (Fig. 10.5) (Omori and Kotera 2007; Fischmeister et al. 2006).
Fig. 10.5

Spatial organization of cN signaling components in cardiac myocytes. Key cN signaling components (rows) localized within potential microdomains/compartments (columns) in cardiac myocytes are identified by color-filled cells, with different colors identifying localization with myocyte ultrastructures (yellow), macromolecular signaling complexes (green), and functional signaling pathways (red).

Evidence also emerged for the existence of macromolecular signaling complexes, or signalosomes, in structuring localized cN signaling by organizing signaling components into spatial and/or functional aggregates (Fig. 10.5, green). For instance, subpopulations of LTCC is believed to be differentially distributed and regulated within the cardiac myocyte (Balijepalli et al. 2006; Christ et al. 2009; Shaw and Colecraft 2013), with tethered PP2A and PP2B (Xu et al. 2010) and distinct patterns of PDE regulation (Leroy et al. 2008; Wang et al. 2009; Benitah et al. 2010; Warrier et al. 2007). In addition, PDE4D is found to be an integral component of the RyR2 complex at the SR membrane, which contributes to localized control of Ca2+ release and SR Ca2+ store depletion (Lehnart et al. 2005; Beca et al. 2011). It is also found in association with the KCNQ1/KCNE1 K+ channel, responsible for localized I Ks regulation (Terrenoire et al. 2009). PDE3A1 has also recently been shown to participate in the SERCA/PLB/AKAP signalosome, where phosphorylation by PKA promotes its targeting to the signalosome and where it may modulate cAMP in a highly localized manner (Ahmad et al. 2015). In addition, via localization in lipid rafts, compartmentalized PDE2 activity is shown to attenuate cAMP signals via a NO/cGMP-dependent pathway (Mongillo et al. 2006). Finally, the sarcomere is also shown to be co-localized with a variety of PDEs, with PDEs 1–4 all showing a Z- and/or M-line enrichment (Mika et al. 2012; Hammond and Balligand 2012; Fischmeister et al. 2006).

The functional segregation of the cN signaling system has gradually been shaped in literature (Fig. 10.5, red). Specific targeting of cAMP is first to be recognized (Hayes et al. 1979; Brunton et al. 1979). In addition to cAMP microdomains created by signalosomes (Fig. 10.5, green), cAMP synthesized by β1-AR stimulation creates a gradient that propagated throughout the cell, whereas that by β2-AR stimulation does not markedly diffuse (Nikolaev et al. 2006). More recently, NP-derived cGMP is shown to trigger different responses than that of NO-derived cGMP (Kuhn 2015; Hammond and Balligand 2012) and is proposed to reside in distinct subcellular compartments (Castro et al. 2006; Lee et al. 2015; Piggott et al. 2006). The NP-derived cGMP pool is also shown to be primarily regulated by PDEs 2, 5, and 9 (Castro et al. 2006; Kass et al. 2007a; Lee et al. 2015), whereas PDEs 1, 2, 3, and 5 all contribute to degrading the NO-derived pool (Castro et al. 2006).

10.4.2 PDE Localization as a Mechanism Underlying cN Compartmentation

Localized cN degradation by PDEs is by far the most recognized mechanism of cN compartmentation, among others such as localized synthesis, physical barriers, cAMP buffering, cell shape, and cAMP export (Saucerman et al. 2014; Mika et al. 2012; Ziolo et al. 2008; Fischmeister et al. 2006; Vandecasteele et al. 2006). As shown in the rows of Fig. 10.5, PDEs are localized with many anatomical and functional structures of the cardiac myocytes. Compartmentation of PDE4 isoforms is mediated by their unique N-terminal domains (Houslay and Adams 2003) that can bind anchor/scaffold proteins, myomegalin (Verde et al. 2001), mAKAP (Dodge et al. 2001), and β-arrestins (Perry et al. 2002), which enables their targeting to specific cellular membranes. On the other hand, mechanisms of localization of other PDE isoforms await further investigation (Fischmeister et al. 2006; Omori and Kotera 2007). In addition, precise measurements of the concentrations of the PDEs in each proposed compartment are lacking, together with that of other residing signaling proteins.

10.5 Modeling cN Signaling System in Heart Failure

Disease progression of the hypertrophied or remodeled heart inevitably proceeds to heart failure (HF) (Tsai and Kass 2009). At the cellular level, HF is characterized by impaired inotropic signaling, Ca2+ mismanagement, and altered myofilament function (Mudd and Kass 2008; Marín-García 2010). Prior research has concentrated on the remodeling of ion currents in HF (Tomaselli and Marbán 1999; Aiba and Tomaselli 2010; Janse 2004; Nattel et al. 2007), but recently remodeling of the cN signaling system begins to receive more attention as a possible contributing factor (Maurice et al. 2014; Kass 2012; Marín-García 2010; Mudd and Kass 2008; Bender and Beavo 2006; Beavo and Brunton 2002; Wollert and Drexler 2002). Prominent remodeling of the components comprising the cN signaling network has been observed, including changes in expression levels and spatial reorganization of signaling proteins, that causes altered and/or untargeted cN signals and aberrant phosphorylation of target proteins (Marín-García 2010; Saucerman and McCulloch 2006; Wollert and Drexler 2002; Perera and Nikolaev 2013; Lohse et al. 2003). These perturbations disturb the delicate balance between the cNs and may ultimately lead to cardiac dysfunction (Marín-García 2010; Saucerman and McCulloch 2006; Wollert and Drexler 2002; Perera and Nikolaev 2013; Lohse et al. 2003). The construction of computational models for disease conditions can be viewed as sensitivity analyses of baseline models of normal physiology, with specific parameters perturbed as informed by experimental finding. These disease models will help understand and compare the effects of observed molecular changes, dissect the underlying mechanisms of physiological changes, and establish causal links between pathophysiological changes observed at varying spatiotemporal scales.

10.5.1 Changes in cN Synthesis in the Failing Heart

The changes affecting cN synthesis are summarized in Table 10.1 below. Studies show down-regulation of β1-ARs in myocardial membranes (Bristow et al. 1982) and a decrease in functional coupling of the remaining β1-ARs and β2-ARs to the Gas-AC system in HF (Bristow et al. 1989). This uncoupling is likely to be exacerbated by enhanced expression of β-adrenergic receptor kinase (β-ARK) (Ungerer et al. 1993). Together with decreased expression of AC (Damy et al. 2004) and increased tonic inhibition by Gi proteins (Neumann et al. 1988; Böhm et al. 1990), these changes lead to attenuation of the systolic tension response to β-agonist (Lamba and Abraham 2000). In addition, the synthesis and secretion of ANP and BNP by ventricular myocytes are increased with the development of HF, leading to elevated plasma levels (Yasue et al. 1994), which are useful biomarkers indicative of cardiac pathology (McKie and Burnett 2005). The reduction of GC-A (Tsutamoto et al. 1992) and/or its decreased responsiveness to ANP (Kuhn et al. 2002) have been associated with the blunted vasodilatory and diuretic response to ANP in HF (Riegger et al. 1988). Regarding the NO/cGMP/PKG pathway, it has been reported that reduced expression and uncoupling of eNOS in hypertrophic heart decrease NO bioavailability and promote the production of reactive oxygen species (ROS) (Takimoto et al. 2005b; Loyer et al. 2008). Furthermore, decreased cGMP production from sGC upon NO stimulation is observed in mice with transverse aortic constriction (TAC)-induced cardiac hypertrophy and dysfunction (Tsai et al. 2012).
Table 10.1

Remodeling of cN synthesis in the failing heart

Component

Changes

HF model

Species

Ref.

β1-AR

Density ↓ 50%

DCM

Human

Bristow et al. (1982)

 

Number ↓ 60%

DCM

Human

Bristow et al. (1986)

 

Number and mRNA ↓ 50%

DCM and ICM

Human

Ungerer et al. (1993)

β2-AR

Uncoupling from AC

DCM

Human

Bristow et al. (1989)

β-ARK

mRNA ↑ 3 fold; activity ↑

DCM and ICM

Human

Ungerer et al. (1993)

 

Abundance ↑

--

Human

Koch et al. (1995)

Gi

Protein ↑ 40%

DCM

Human

Neumann et al. (1988)

 

Protein ↑ 40%

DCM

Human

Böhm et al. (1990)

AC

Activity ↓ 60%

PI

Canine

Marzo et al. (1991)

 

mRNA ↓ 40%; activity ↓ 40%

PI

Canine

Ishikawa et al. (1994)

 

AC6 mRNA and activities ↓ 35%

MI

Rat

Espinasse et al. (1999)

 

Expression and activity ↓

DCM

Human

Damy et al. (2004)

ANP

Synthesis and secretion by ventricular myocytes ↑

DCM

Human

Yasue et al. (1994)

BNP

Synthesis and secretion by ventricular myocytes ↑

DCM

Human

Yasue et al. (1994)

GC-A

Protein ↓

CHF

Human

Tsutamoto et al. (1992)

 

↓ cGMP production

CHF

Human

Kuhn et al. (2002)

eNOS

Protein ↓

CHF

Human

Loyer et al. (2008)

 

Uncouples

TAC

Mouse

Takimoto et al. (2005b)

sGC

↓ NO-stimulated activity

TAC

Mouse

Tsai et al. (2012)

CHF congestive HF, DCM dilated cardiomyopathy, ICM ischemic cardiomyopathy, MI myocardial infarction, PI pacing induced, TAC transverse aortic constriction, -- various etiologies

10.5.2 Changes in Expressions and Activities of PDEs in the Failing Heart

Mathematical modeling is well suited for revealing the alterations in PDE interactions caused by the isoform-specific changes in PDEs in HF, summarized in Table 10.2. Miller et al. (2009) reported that PDE1A expression was significantly upregulated in hypertrophy and contributes to the reduction of cGMP-PKG signaling. Furthermore, PDE2 upregulation in the failing heart is observed to attenuate β-adrenergic signaling (Mehel et al. 2013), decreased PDE3 activity promotes cardiac myocyte apoptosis (Ding et al. 2005a), and PDE4 downregulation is associated with arrhythmias in cardiac hypertrophy and HF (Lehnart et al. 2005). Most studies report an increase in PDE5 expression and activity in the failing heart (Nagendran et al. 2007; Lu et al. 2010; Pokreisz et al. 2009), which may arise from increased myocardial oxidative stress (Lu et al. 2010) and contributes to adverse ventricular remodeling (Pokreisz et al. 2009). Finally, PDE9A activity is upregulated in hypertrophy and HF, and its inhibition is reported to reverse pre-established heart disease independent of NOS (Lee et al. 2015).
Table 10.2

Remodeling of PDEs in the failing heart

Component

Changes

HF model

Species

Ref.

PDE1

mRNA and protein ↑

Hypertrophy

Rat

Miller et al. (2009)

PDE2

Protein ↑ 3 fold

DCM

Human

Aye et al. (2012)

 

Expression and activity ↑ 2 fold

Advanced HF

Human

Mehel et al. (2013)

PDE3

Expression and activity ↓

Apoptosis

Human

Ding et al. (2005a)

 

Activity ↓

PI

Canine

Sato et al. (1999)

 

mRNA and protein ↓

PI

Canine

Smith et al. (1998)

PDE4

Expression ↓ 40% in RyR2 complex

--

Human

Lehnart et al. (2005), Abi-Gerges et al. (2009)

PDE5

mRNA and protein ↑

Hypertrophy

Human

Nagendran et al. (2007)

 

Expression and activity ↑

DCM and ICM

Human

Pokreisz et al. (2009)

 

Protein ↑ 4.5 fold

CHF

Human

Lu et al. (2010)

PDE9

Protein ↑ 1 fold

DCM and HFpEF

Human

Lee et al. (2015)

CHF congestive HF, DCM dilated cardiomyopathy, HFpEF HF with preserved ejection fraction, ICM ischemic cardiomyopathy, PI pacing induced, TAC transverse aortic constriction, -- various etiologies

10.5.3 Changes in Spatial Localization of cN Signaling in the Failing Heart

Recent studies revealed spatially resolved remodeling of the cN signaling system in the failing heart. For instance, via nanoscale live-cell scanning ion conductance and fluorescence resonance energy transfer (FRET) microscopy techniques, Nikolaev et al. discovered that β2-AR-mediated cAMP synthesis, which is normally limited to T-tubules (Nikolaev et al. 2006), redistributes to outer sarcolemma in HF (Nikolaev et al. 2010), while functional β1-AR remain distributed throughout the cell membrane. Regarding the cGMP pathways in HF, PDE5 is observed to be retargeted to hydrolyze NP-derived instead of NO-derived cGMP (Zhang et al. 2012), together with a loss of Z-band localization (Senzaki et al. 2001). In addition, it has been reported that sGC β1 subunits shift out of caveolin-enriched microdomains in HF that lead to reduced NO-stimulated activity in sGC (Tsai et al. 2012). Furthermore, in cardiomyocytes from hypertrophic hearts, Perera et al. revealed that β-AR-stimulated contractility is amplified by NP/cGMP signaling due to spatial redistribution of PDEs 2 and 3 (Perera et al. 2015).

10.5.4 Changes in Phosphorylation Status of EC Coupling Proteins in the Failing Heart

Prolongation of AP duration (APD) is the electrophysiological hallmark of myocytes isolated from failing hearts regardless of disease etiology (Aiba and Tomaselli 2010). Remodeling of ionic currents, such as downregulation of K+ currents and alternation in Na+ and Ca2+ currents, is a key change in HF and is reviewed in detail by prior works (Aiba and Tomaselli 2010; Janse 2004; Nattel et al. 2007; Tomaselli and Marbán 1999). Models of diseased cellular electrophysiology have been developed primarily based on altering the ionic fluxes of EC coupling proteins, often in isolation of signaling networks (Winslow et al. 1998; Winslow et al. 2001). Changes in the phosphorylation status of the corresponding EC coupling proteins in HF may contribute to electrical remodeling, although further studies are needed to ascertain these results. For instance, in human HF, single-channel studies suggest a reduction in LTCC number with an increase in open probability that may arise from altered channel phosphorylation (Chen et al. 2002; Schröder et al. 1998). Hyper-phosphorylation of RyR by PKA may increase diastolic Ca2+ leak and generate spontaneous Ca2+ waves underlying triggered arrhythmias in HF (Marx et al. 2000). Additionally, evidence suggests that the reduction in I-1 protein and its phosphorylation by PKA in the failing heart lead to increased PP1 activity, which in turn may result in reduced phosphorylation of downstream proteins, such as PLB (El-Armouche et al. 2004). It is also interesting to note that the kinases themselves are remodeled in HF, which may be an underlying cause in the observed alterations in target protein phosphorylation. For instance, protein expressions of the regulatory subunits of PKA, types PKA-RI and PKA-RII, are reported to decrease by ~ 50% (Aye et al. 2012; Zakhary et al. 1999). In addition, interaction between PKA and AKAP SPHKAP is observed to increase by six fold (Aye et al. 2012). Finally, PKGIα protein expression is observed to increase three fold (Aye et al. 2012), but PKGI activity has been reported to decrease in some HF patients (van Heerebeek et al. 2012).

10.6 Multi-Type Data Integration and Fusion via Modeling

Biophysically based and experimentally validated computational modeling is a powerful tool for dissecting the integrated behavior of complex biological systems (Winslow et al. 2012). When coupled with experiments, this approach is particularly insightful for the distillation of critical relationships in complex, intertwined, and often times nonintuitive (patho-)physiological phenomena (Winslow et al. 2012). Models allow for the alteration of internal concentrations and rates not necessarily well controlled or even accessible in experiments, therefore providing a tightly controlled experimental setup for predictions unattainable by current technology. By integrating wide ranges of experimental data obtained via a variety of experimental techniques, mechanistic models provide a common and coherent framework for the data sets incorporated, which would often be otherwise interpreted in isolation. These model-integrated data, in turn, strengthen the predictive power of the models. Model simulation, thus, can be viewed as a form of data mining that seeks to extrapolate new knowledge from existing data sets.

Because of the numerous and fast-accumulating experimental findings in various aspects of the cN signaling system (Sects. 10.210.5), the mechanisms by which cN signaling influence cardiac function have been difficult to conceptualize in a unified manner. Modeling via an integrated systems approach is well suited for comprehensive and in-depth understanding of the regulatory pathways. As an example, the cN cross-talk signaling network model by Zhao et al. (2015, 2016b) fused a multitude of experimental data, effectively leveraging upon the extensive knowledge in published literature (Fig. 10.6), such that biomedical reactions can be represented in mechanistic detail. As shown in Fig. 10.6 (lower level), gel filtration and ion exchange chromatography of tissue extracts and radioimmunoassay with specific PDE inhibitors helped confirm the presence and activity of the specific PDE isoenzyme in cardiac myocytes (Weishaar et al. 1987; Bode et al. 1991; Movsesian et al. 1991; Bethke et al. 1992; Méry et al. 1995). The states of each PDE models (Zhao et al. 2015) were extracted from structural data from X-ray crystallography and amino acid sequencing and electron density maps (Pandit et al. 2009; Wu et al. 2004; Martinez et al. 2002). Quantification of binding affinities is derived from biochemical assays of purified PDEs, with the behavior of cN competition characterized by assays having both cNs in the reaction system (Yan et al. 1996; Prigent et al. 1988; Beavo et al. 1971; Komas et al. 1991; Hambleton et al. 2005; Degerman et al. 1997; He et al. 1998; MacKenzie et al. 2002; Sette and Conti 1996). Furthermore, assays performed on PDE proteins with site-directed mutagenesis or truncated PDE proteins serve to isolate the functional role of each binding site (Beavo 1995; Jäger et al. 2010; Francis et al. 2010). FRET imaging, which revealed instantaneous decay of cN signals upon withdrawal of specific PDE inhibitors (Fischmeister et al. 2006; Castro et al. 2006; Herget et al. 2008) and upon increased PDE activation (Nikolaev et al. 2005), informed that the kinetics of intra-protein reactions of PDEs reach equilibrium rapidly with respect to the time scale of other reactions in the signaling network.
Fig. 10.6

Multi-scale modeling of cN signaling network. Experimental techniques informing models of the cN cross-talk signaling network (Zhao et al. 2015, 2016a, b) are shown at their respective spatiotemporal scales

Radioimmunoassay pin-pointed the cellular-average concentration of cNs under varying extents of pathway stimulation (Kuznetsov et al. 1995; Kameyama et al. 1985; Hohl and Li 1991; Iancu et al. 2008; Katsube et al. 1996; Vila-Petroff et al. 1999; Castro et al. 2006; Castro et al. 2010). Building upon radioimmunoassay (Verde et al. 1999), live-cell cN imaging in cardiac myocytes, via FRET or CNGC channel recordings, performed under various protocols of PDE inhibition, provided insights to the relative activities of the PDEs within the cN network (Rochais et al. 2006; Castro et al. 2006). In addition, live-cell imaging revealed detailed dynamics of cN signals and inferred the kinetics of their synthesis and degradation, which had been inaccessible previously (Rochais et al. 2004; Castro et al. 2006). These experimental insights (Fig. 10.6) quantified the steady-state and dynamic behaviors of the computational models. Going forward, a variety of experimental techniques can help in connecting the gap between cN signaling and electrophysiology. For instance, a model of cN regulation of LTCC can be formulated based on data obtained from single channel (Yue et al. 1990; Klein et al. 2000; Schröder et al. 2003) and patch clamp (Katsube et al. 1996; Kameyama et al. 1985; Grushin et al. 2008; Wahler and Dollinger 1995; Abi-Gerges et al. 2001) recording experiments in control, over-expression, knockout models, and LTCCs with site-directed mutagenesis, as well as insights obtained from consensus site prediction algorithms (Fig. 10.6, top). Theses experimental techniques and approaches to integrate them in a computational model (Fig. 10.6) will be useful for guiding future modeling as well as experimental research.

As demonstrated in Fig. 10.6, integrative modeling unified many physiological quantities across spatiotemporal scale, providing a comprehensive framework for experimental findings to be viewed in perspective of each other. Thus, model analysis can provide in-depth interpretations of experiments which are often confounded by complex compensatory network interactions (Zhao et al. 2015, 2016b, a). For the cN cross-talk signaling network, the PDE models revealed the molecular mechanisms by which the PDEs decode information carried in the cGMP signal in β-adrenergic pathway regulation (Zhao et al. 2015). A precise quantitative definition of cN cross-talk and the ways by which cGMP and cAMP signals influence each other were obtained by analyzing the interaction of individual signaling elements in the context of the network architecture (Zhao et al. 2015, 2016b). PDE interactions arising from dynamic cN cross-talk within the cN cross-talk signaling network were delineated (Zhao et al. 2015, 2016b). The way by which LTCC simultaneously interact with both kinases was hypothesized (Zhao et al. 2016a). As such, integrated computational modeling is well suited for identifying biological mechanisms and predicting downstream consequences.

Conclusion

Therapeutic potential of the cN signaling network is highlighted in literature (Cohen 2002; Amanfu and Saucerman 2011; Boerrigter et al. 2009; Bender and Beavo 2006; Francis et al. 2009; Lugnier 2006); however, the extent to which pathological changes intervene with the normal operations of the entire signaling system, the cardiac cell, and ultimately the whole heart remains to be carefully studied. Leveraging more than 50 years of experimental insights (Sect. 10.1), mechanistic models of the cN signaling systems has expanded over the last decade (Sect. 10.1.3). Analyses of these models have provided quantitative and system-level insights and mechanistic predictions within each and across multiple biophysical hierarchies (Sects. 10.1 and 10.2). Data-driven, multi-scale modeling proves to be a powerful tool that links the characteristics of individual proteins, to the signaling network, and to the interaction between signaling and electrophysiology (Sects. 10.1, 10.2, and 10.6). Further functional integration of these models with electrophysiological models and into higher-dimensional tissue models will continue to advance the understanding of the formation and propagation of Ca2+ signals and AP in the heart (Sect. 10.3). In addition, recent breakthroughs in spatiotemporally resolved live-cell imaging of cNs in cardiac myocytes have begun to provide new insights in cN compartmentation and will offer valuable guidance for the development of future computational models (Sect. 10.4). Spatially resolved modeling is well suited for studying how local and global signaling is orchestrated to achieve coherent cellular communication. As the understanding of normal physiology gradually matures, new frontiers emerge for modeling diseased cardiac myocyte, which will help establish the causal link between altered signaling components, remodeled signal transduction process, and pathophysiological responses (Sect. 10.5). On the other hand, significant challenges remain. Divergent effects of neurohormonal stimulation observed in experiments remain a hurdle to the construction and validation of computation models. Open questions persist in the fundamental biophysics of the cN signaling network, such as precise quantification of the contribution of each signaling protein in each proposed cellular microdomains/compartments and mechanisms and functional consequences of protein phosphorylation. Nonetheless, computational modeling serves to provide in-depth analysis of experimental observations, form new hypothesis, and identify experimental conditions for future studies of the cN signaling system.

Notes

Acknowledgments

This work was supported by Natural Sciences and Engineering Research Council (NSERC) of Canada scholarships, CGS M-377616-2009 and PGSD3-405041-2011, awarded to C.Y.Z.

Compliance with Ethical Standards

Conflict of Interest Statement

The authors declare that they have no conflict of interest.

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringInstitute for Computational Medicine, The Johns Hopkins University School of Medicine and Whiting School of EngineeringBaltimoreUSA
  2. 2.Acute Care Solutions, Philips Research North AmericaCambridgeUSA

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