Insulin secretory in pancreatic beta-cells responses to nutrient stimuli and hormonal modulators include multiple messengers and signaling pathways with complex interdependencies. Here we present a computational model that incorporates recent data on glucose metabolism, plasma membrane potential, G-protein-coupled-receptors (GPCR), cytoplasmic and endoplasmic reticulum calcium dynamics, cAMP and phospholipase C pathways that regulate interactions between second messengers in pancreatic beta-cells. The values of key model parameters were inferred from published experimental data. The model gives a reasonable fit to important aspects of experimentally measured metabolic and second messenger concentrations and provides a framework for analyzing the role of metabolic, hormones and neurotransmitters changes on insulin secretion. Our analysis of the dynamic data provides support for the hypothesis that activation of Ca2+-dependent adenylyl cyclases play a critical role in modulating the effects of glucagon-like peptide 1 (GLP-1), glucose-dependent insulinotropic polypeptide (GIP) and catecholamines. The regulatory properties of adenylyl cyclase isoforms determine fluctuations in cytoplasmic cAMP concentration and reveal a synergistic action of glucose, GLP-1 and GIP on insulin secretion. On the other hand, the regulatory properties of phospholipase C isoforms determine the interaction of glucose, acetylcholine and free fatty acids (FFA) (that act through the FFA receptors) on insulin secretion. We found that a combination of GPCR agonists activating different messenger pathways can stimulate insulin secretion more effectively than a combination of GPCR agonists for a single pathway. This analysis also suggests that the activators of GLP-1, GIP and FFA receptors may have a relatively low risk of hypoglycemia in fasting conditions whereas an activator of muscarinic receptors can increase this risk. This computational analysis demonstrates that study of second messenger pathway interactions will improve understanding of critical regulatory sites, how different GPCRs interact and pharmacological targets for modulating insulin secretion in type 2 diabetes.
Citation: Fridlyand LE, Philipson LH (2016) Pancreatic Beta Cell G-Protein Coupled Receptors and Second Messenger Interactions: A Systems Biology Computational Analysis. PLoS ONE 11(5): e0152869. https://doi.org/10.1371/journal.pone.0152869
Editor: Tao Cai, NIDCR/NIH, UNITED STATES
Received: December 4, 2015; Accepted: March 21, 2016; Published: May 3, 2016
Copyright: © 2016 Fridlyand, Philipson. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper.
Funding: This work was supported by grants from the National Institutes of Health to LHP (DK063493 and DK092616), and by University of Chicago Diabetes Research and Training Center funding from the NIH (DK020595) to LHP. The Virtual Cell is supported by NIH Grant Number P41 GM103313 from the National Institute for General Medical Sciences.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: AC, adenylyl cyclase; ACP, PM bound AC; ACc, soluble cytoplasmic AC; AdR, adrenergic receptors; ER, endoplasmic reticulum; CAM, calmodulin; CM, computational model; CRA, Ca2+ release-activated nonselective cation channel; [Ca2+]c, cytoplasmic free Ca2+ concentration; [Ca2+]ER, Ca2+ concentration inside of endoplasmic reticulum; DAG, diacylglycerol; Epac, guanyl exchange protein directly activated by cAMP; ER, endoplasmic reticulum; GIP, glucose-dependent insulinotropic polypeptide; GLP-1, glucagon-like peptide-1; GIPR, glucose-dependent insulinotropic polypeptide receptor; GLP-1R, glucagon-like peptide-1 receptor; GPCR, G-protein-coupled receptor; GPR40, G-protein-coupled receptor 40; GSIS, glucose-stimulated insulin secretion; FFA, free fatty acid; FFAR1/GPR40, free fatty acid receptor; IP3, inositol 1,4,5-trisphosphate; IP3R, IP3 receptor; KATP channels, ATP-sensitive K+ channels; MR, muscarinic acetylcholine receptor; NALCN, nonselective cation channels; PDE, phosphodiesterase; PI, phosphatidylinositol; PIP, phosphatidylinositol 4-phosphate; PIP2, phosphatidylinositol 4,5-bisphosphate; PKA, protein kinase A; PKD, protein kinase D; PKC, protein kinase C; PLC, phospholipase C; PLCP, G-protein-coupled PLC localized on PM; PLCc, cytoplasmic PLC; PM, plasma membrane; P4P, phosphatidylinositol-4-phosphate; SERCA, sarco-(endo)plasmic reticulum Ca2+-ATPase; SOC, store operated-current; T2D, type 2 diabetes; VGCC, voltage-gated Ca2+ channel; Vp, plasma membrane potential
Insulin release from the pancreatic β-cells must respond acutely to meet the insulin demands of the organism. However, in type 2 diabetes (T2D) pancreatic β-cells fail to compensate for an increase in blood glucose concentration with sufficient insulin secretion, leading to progressive hyperglycemia . T2D is a chronic metabolic illness with dramatic increasing medical and financial costs but prevention and effective treatments remain suboptimal. Numerous studies have been published on the regulation of β-cell function. A general reaction network diagram for the β-cell is shown in Fig 1.
Glucose enters the cell through its glucose transporter, and is phosphorylated and metabolized in the mitochondrion as well free fatty acids (FFA). Glucose and FFA metabolism leads to an increase in the ATP/ADP ratio, closure of the ATP-sensitive potassium (KATP) channels leading to plasma membrane (PM) depolarization that increase a calcium influx through the voltage-gated calcium channels and an increase in cytoplasmic Ca2+. Other transmembrane channels can also regulate PM potential: K+Kr is the voltage gated K+ channel, SOC is the store operating channels, Nab is the Na+ background current, NALCN is the specific nonselective cation channel that can be modulated by acetylcholine through activation of M3 muscarinic receptors (M3R). Increase in cytoplasmic Ca2+ leads to the activation of several calcium dependent enzymes including adenylyl cyclase (AC) and phospholipase C (PLC). Phosphoinositides pathway: phosphatidylinositol-4-phosphate (P4P) is synthesized from phosphatidylinositol (PI) by phosphatidylinositol 4-kinases and its synthesis activates by PKC and Ca2+. Phosphotidylinositol-4,5-bisphosphate (PIP2) in turn is primarily formed from P4P by phosphatidylinositol-4-phosphate 5-kinase I. cAMP pathway: Ca2+/CaM is Ca2+-bound calmodulin, Synthesis and degradation of cAMP are catalyzed by adenylyl cyclase and phosphodiesterase (PDE), respectively. ACc is the soluble AC, ACP is the G-protein controlled AC on plasmalemma that can be activated by stimulatory Gαs type G-protein and Ca2+/CaM and deactivated by inhibitory Gi/o type G-protein. PDE activity can be enhanced by Ca2+/CaM, cAMP activates protein kinase A (PKA) and exchange protein (Epac). The incretins glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) bind to their respective receptors (GLP-1R and GIPR), activate ACP and increase intracellular levels of cAMP. Endogenous catecholamines adrenaline (epinephrine) and noradrenaline (norepinephrine) bound with G-protein-coupled α2A-adrenergic receptors (AdR) on plasma membrane and inhibit ACP. Phospholipase C (PLC) pathway. PLCC is the cytoplasmic calcium activated PLC and PLCP is plasma membrane bound PLC that can be activated by Gq type G-protein. Acetylcholine and FFA can bind to their respective receptors (M3R and FFAR1/GPR40) expressed at the cell surface and trigger a Gaq-mediated activation of PLCP. PLCC and PLCP generate inositol-3-phosphate (IP3) and diacylglycerol (DAG) by hydrolyzing membrane PIP2. DAG activates protein kinase C (PKC). Endoplasmic reticulum (ER): SERCA is the endoplasmic reticulum Ca2+ATPase, IP3R is the IP3 receptor; that can be activated by IP3 and PKA. Solid lines indicate fluxes, and dashed lines indicate inhibitory or stimulatory influences on currents or fluxes.
Glucose is the major physiologic regulator of insulin release. Glucose-stimulated insulin secretion (GSIS) includes an increase in ATP/ADP ratio leading to a closure of ATP sensitive potassium (KATP) channels, plasma membrane (PM) depolarization, opening of voltage-gated calcium channels(VGCC) with corresponding calcium influx and an increased cytosolic Ca2+. The rise in intracellular free calcium concentration ([Ca2+]c) is an important signal in the initiation of β-cell insulin secretion [2–4]. The β-cell has numerous G protein coupled receptors (GPCRs) that can activate or inhibit β-cell insulin secretion . Therefore a better understanding of how activation of GPCRs regulate β-cell function might illuminate approaches to help β-cell compensation and lead to better approaches to treatment of T2D.
Additional regulation of insulin release is provided by circulating metabolic secretagogues and by stimuli such as hormones and neurotransmitters. This permits close regulation of islet hormone secretion. For example, non-metabolic stimulation of insulin release occurs during the first phase of feeding and precedes any increase in blood glucose (termed the "cephalic phase"). This is largely mediated by the release of acetylcholine from nerves innervating pancreatic islets and the cholinergic stimulation of the muscarinic acetylcholine receptors [3, 6–8]. Incretin hormones released from gastrointestinal L-cells in response to food intake also stimulate insulin secretion . On the other hand the neurotransmitters such as noradrenaline inhibit insulin secretion to increase glucose availability during times of stress . These signals are mediated by a variety of GPCRs that have complimentary or antagonistic actions on insulin secretion [5, 11]. Interestingly, signaling networks must convert a large variety of extracellular stimuli onto a limited number of intracellular second messenger pathways. This includes intracellular free Ca2+ concentration and the two main signals of activated GPCRs: cyclic AMP (cAMP) on the one hand and inositol 1,4,5-trisphosphate (IP3) and diacylglycerol (DAG) on the other [3, 12, 13]. Group of third pathways through adhesion class GPCR was also found in islets . However, these pathways were only beginning to be studied in β-cells and there is not enough data to include them here. There is therefore considerable interest in understanding how GPCRs in β-cells integrate second messenger signaling.
Despite the recent increase in our knowledge of β-cell physiology and biochemistry, we still lack a coherent model of how second messengers and their interactions regulate β-cell secretion. The complex nature of the receptors and second messenger interactions makes it extremely difficult to understand how they are regulated and which parameters determine their dynamics. However, systems biology has emerged to provide a systematic approach to integrate the complexity of cellular signaling combining dynamic experiments with a computational biology approach [15–17]. Mathematical modeling has been used extensively and with great success to study metabolic and signaling networks [16, 18, 19]. This approach is useful for describing experimental data, deducing regulatory principles, and understanding complex dynamic phenomena such as oscillations. Integrated complex models of signal transduction pathways were published recently for synaptic input studies , mouse hepatocytes , cardiac myocytes  and liver metabolism . However, this approach has not been as well developed for pancreatic β cells. Indeed only GLP-1 receptor coupled G-protein regulated cAMP signaling has been modeled previously [23–28].
We have previously focused on applying this mathematical modeling approach to mechanisms of glucose sensing of insulin secretion, Ca2+ and cAMP dynamics, electrophysiology events and exocytosis in pancreatic β-cells [23, 25, 29–34]. Here the objective is to evaluate the effect of interactions of metabolites, hormones, GPCRs and second messengers in regulation of insulin secretion in the pancreatic β-cell. For this aim we have constructed an integrated mathematical model of interaction of these components based on the framework developed in our previous models. In this work we focus on those cellular signaling pathways that appear essential for relatively rapid β-cell responses. We have estimated individual reaction rates and model parameters by fitting the theoretical reaction scheme to a variety of key experimental findings published to date in both β-cells and insulinoma cell lines. The model gives a reasonable fit to important aspects of experimentally measured metabolic, plasma membrane potential and second messenger concentrations and provides a framework for analyzing the role of metabolic, hormones and neurotransmitters changes on insulin secretion. This allowed us to refine the model to test hypotheses and conclusions about interacting pathways to design in silico experiments.
We analyzed interactions of clinically relevant GPCR agonists. We included those responding to the neurotransmitters acetylcholine and catecholamines with stimulation and inhibition of insulin secretion, respectively, to the incretin hormones GLP-1 and GIP with potentiation of GSIS, and the agonists for the free fatty acid (FFA) receptor (e.g., FFAR1/GPR40) and determined how combinations of different agonists affect second messenger dynamics in β-cell.
Defective regulation of messenger pathways clearly contributes to T2D in a variety of ways (see below). For this reason new anti-diabetic drugs in use and in development exploit several β-cell stimulating GPCR pathways in order to combat the growing health and economic costs of T2D. However, the effects of agonist combinations on the tightly coupled metabolic and signaling pathways of β-cells as well as on insulin secretion in diabetic states have not been carefully studied. We have attempted to analyze these processes and evaluate how particular impairments in the mechanisms of β-cell regulations sensing can lead to insulin release changes in T2D. Here we also examined the hypothesis that simultaneous stimulation of multiple signaling pathways is potentially is inhibitory for β-cell function and insulin secretion, but instead found that for the most part that combined stimulation can be synergistic.
Models and Methods
Here we will briefly outline the different parts of the regulatory mechanisms in β-cell. Fig 1 shows a schematic diagram of the biochemical steps, Ca2+ handling, channels, G-protein-coupled-receptors and mechanisms of second messenger regulation that were used for our mathematical modeling. Details of modeling are described below in the section “Computational model” (CM).
The cellular metabolic mechanisms leading to insulin secretion in pancreatic β-cells are fairly well understood. Glucose-dependent signal transduction begins with uptake of glucose into β-cells via the glucose facilitative transporter. Glucose molecules are rapidly phosphorylated by glucokinase and converted to pyruvate in the cytosol via the glycolytic pathway, and pyruvate and FFA are oxidized within the mitochondria producing ATP. Blocking KATP channels by an ATP/ADP-dependent mechanism initiates plasma membrane depolarization to the threshold potential for leading to Ca2+ influx through VGCCs and increases free cytosolic calcium concentration ([Ca2+]c). The rise in [Ca2+]c is a key signal in the initiation of -cell insulin secretion (for reviews, see [2, 3, 32]). Other metabolic cofactors such as NADH or NAD(P)H have also been considered as possible coupling candidates as well [32, 35]. We used a simplified coarse-grained mathematical model of these mechanisms for pancreatic β-cells that is based on previous results [31, 32] (see CM).
The effects of FFAs on insulin secretion can also due to simply supplying fuel for cell metabolism  and we include this role for FFAs in our model as an increase in ATP/ADP ratio following FFA challenge (see CM). However, we do not consider this mechanism in detail here, because in physiological conditions FFA concentration in blood is low so that FFA acts mainly through FFA activated GPCRs  and only this signaling FFA effect is considered here.
Channels and regulation of plasma membrane potential
Plasma membrane potential (Vp) regulates Ca2+ influx through VGCCs in β-cells. On other hand Vp is regulated by ion channels and pumps. A schematic diagram of the principle β-cell channels (Fig 1) includes the more completely characterized channels and pumps. Additional actions such as activation of ion channels by second messengers also have been described [38, 39] and considered in our model (see CM).
β-cells display spike activity in response to glucose, an effect that has been modeled previously [29, 30, 40]. However, changes in PM potential and their corresponding voltage-dependent currents are considerably faster during spikes (milliseconds) than the changes in second messenger concentrations (minutes). For this reason, we excluded fast changes of PM potential here and for simplicity we did not describe ionic spike activity, modeling the time-dependent gating variables for currents as the stationary voltage dependences.
We have included the ATP-sensitive potassium (KATP) channel and the voltage gated K+ (K+Kr) channel. Messengers can affect K+ channel dynamics in various ways. We have taken into account that protein kinase A (PKA) activation and a decrease in phosphatidylinositol 4,5-bisphosphate (PIP2) concentration can lead to block of KATP channels [38, 41, 42] (see CM for details). Several studies suggest that voltage-gated K+ channels might be also regulated by PIP2 [43, 44]. However, Kv2.1 channels, that are the main voltage-gated K+ channels in β-cells [2, 4] are not sensitive to PIP2 depletion . For this reason we excluded the influence of PIP2 on voltage-gated K+ channels.
Ca2+ channels and pumps.
PM depolarization potentiates glucose-induced Ca2+ influx through voltage-gated Ca2+ channel (VDCCs). Depletion of intracellular Ca2+ stores in β-cells activates a special Ca2+-release activated current (or store operating current (SOC)). While the detailed mechanism for coupling of ER Ca2+ store depletion with these PM channels activation is uncertain, it likely involves translocation of STIM1 and STIM2 proteins from the ER calcium stores to the PM where they interact with ORAI1 and related proteins associated with cation influx channels [45, 46]. Plasma membrane Ca2+ pumps provide an outward Ca2+ current.
Voltage-gated Na+ current is likely inactivated in mouse β-cells  due to the relatively high resting Vp, so it is not considered, although it may play a role in human beta cells (see ). Nonselective PM cation channels permeable to Na+ and Ca2+ have been described in pancreatic β-cells, and the transient receptor potential ion channels were proposed for this role [48, 49]. Specific nonselective cation channels (NALCN) are expressed in pancreatic islets and can also be modulated by acetylcholine through activation of M3 muscarinic receptors [39, 50]. We modeled these currents as Na+ background current (INab) through specific channels on PM (see CM).
Receptors, G-proteins and messenger regulation
Regulating signals from hormones and neurotransmitters are mediated by a variety of G-protein-coupled-receptors (GPCRs) that bind ligands from the extracellular space. GPCRs couple to stimulatory and inhibitory membrane-bound heterotrimeric G-proteins, mediating regulation of insulin secretion by the second messenger pathways. Activation of most GPCRs occurs in a similar time frame, but downstream effects may decay with a slower time course and may also effect gene expression.
Two main types of activation were found for different receptors: 1. Collision coupling, where a ligand binds to the free receptor and then the ligand-receptor complex ‘‘collides” with the free G-protein. 2. Pre-coupling, where stable receptor/G-protein complexes pre-exist and a ligand can bind with these complexes. There is accumulating evidence for both collision coupling and pre-coupling of GPCRs [51, 52].
Although there are many different GPCRs for the specific type of receptors there is a shared mechanism of activation. Following interaction with activated receptor the heterotrimeric G-proteins catalyze the exchange of GDP for GTP on the α-subunit. This event triggers conformational and/or dissociation events between the α-subunit and βγ-subunit. Both Gα-GDP and Gβγ subunits then activate (or inhibit) downstream signaling molecules (enzymes, kinases and ion channels) and thereby elicit cellular responses. The activation cycle is terminated by the Gα intrinsic GTPase activity which allows GTP hydrolysis and the reassociation of Gα-GDP with Gβγ subunits so to restore the inactive basal state. Then the G-protein system initiates a new cycle (see CM).
Desensitization is an important mechanism of regulation of receptor activity. At the receptor level several processes of desensitization have been shown to play a role in limiting signal duration and intensity . Desensitization of GPCRs occurs with three phases: the phosphorylation of the receptor bound to ligand (activated receptor) by G-protein-coupled receptor kinases, sequestration/internalization and down-regulation or return to the surface . We will consider the processes of desensitization for each receptor. However, we do not consider how specific processes result in desensitization, such as the SUMOylation of the GLP-1 receptor in response to glucose we have previously described  or phosphorylation . We also did not consider synthesis, degradation or irreversible translocation of receptors in the model. The multiple dephosphorylation/recycling steps were shortened into one single reaction (for details see CM).
Constitutive receptor activity has been observed to occur in many different GPCRs (“tonic receptor activation”) and can be explained by different mechanisms [58–60]. On other hand, islet α-cells can also secrete GLP-1  and it may be that the neurotransmitter acetylcholine can be secreted by human alpha cells . FFAs that activate FFAR1/GPR40 are present in blood and vary following meals. All these data provide reason enough to suggest that some activation of GPCRs takes place even without an additional GPCR agonist. For simplification, we have employed low concentrations of receptor ligands in modeling of experiments even where no specific GPCR agonists were used (for details see CM).
Signal routing to cAMP involves multiple GPCRs that regulate activity of several isoforms of AC and phosphodiesterases leading to production and hydrolysis of cAMP (Fig 1). cAMP activates both PKA and the cAMP-regulated guanine nucleotide exchange factor (Epac). We previously developed a computational model of the pancreatic β-cell cAMP pathway [23, 25] that was used in the construction of the general model here (see CM).
Incretin hormones and receptors in β-cells.
Glucagon-like-peptide-1 (GLP-1) is one potent incretin hormone coupled primarily to the specific αs-G-protein-coupled receptor (GLP-1R). The GLP-1R belongs to the class B family of GPCRs. The GLP-1R couples to the G-protein complex and facilitates the release of the activated Gαs subunit of the complex, which activates plasma membrane-bound adenylyl cyclase (ACP) to produce cAMP [12, 63, 64]. Following agonist-induced receptor activation, GLP-1R is probably internalized . Small ubiquitin-related modifier protein (SUMO) can covalently modify GLP-1R and thereby desensitize GLP-1 signaling .
In islets, GLP-1R is expressed predominantly on β-cells . A mathematical model for GLP-1R signal transduction was published recently . However, this model lacks components such as a complex of G-protein with ACp. To address this we used our general model for GPCR effects (for details see CM).
Another important incretin hormone is glucose-dependent insulinotropic polypeptide or gastric inhibitory polypeptide (GIP). GIP is a 42-amino-acid hormone, secreted from the enteroendocrine K cells and one of the major mediators in the regulation of nutrient-dependent insulin release from the pancreas . Hyperglycemic clamp studies or GIP infusion established that GIP was insulinotropic at physiological concentrations . GIP exerts its effects through binding to a specific αs-G-protein-coupled receptor (GIPR) that also belongs to the B-family class of GPCRs of the glucagon–secretin family of peptides [68, 69].
The GIPR undergoes rapid and reversible homologous desensitization following binding of GIP [68, 70]. Regulator of G-protein signaling-2, G-protein receptor kinase 2, and β-arrestin 1 all have been implicated in GIPR desensitization .
Interestingly, class B GPCRs are characterized by a common topology of the ligand-receptor complex, and by their ability to couple multiple G-proteins. Biochemical and structural studies have led to a model of class B1 GPCR activation by peptide hormones including GLP-1 and GIP, referred to as the “two-step” mechanism . This corresponds to collision coupling, where an agonist binds to the free receptor and then the agonist-receptor complex ‘‘collides” with the free G-protein. Corresponding models were developed for GLP-1R and GIPR (see CM).
Adrenergic agonists, the endogenous catecholamines adrenaline (epinephrine) and noradrenaline (norepinephrine), as well as agonists such as clonidine, all inhibit insulin secretion [10, 73, 74]. G-protein-coupled α2A-adrenergic receptors (AdR) are responsible for these effects for adrenaline/noradrenaline, and they are mediated by the pertussis toxin-sensitive heterotrimeric Gi and Go proteins, that inhibit AC [10, 73]. The mRNAs encoding the α2A adrenoceptor are expressed at relatively high levels in human islets .
Co-immunoprecipitation studies showed pre-coupling of a2A-adrenergic receptors with Gi/o proteins . However, no evidence for a2A-adrenergic receptor and Gi-protein precoupling was found in resonance energy transfer studies and it was suggested that a2A-adrenergic receptors and G-proteins interact by rapid collision coupling . For this reason we also used the collision coupling mechanism for α2A-adrenergic receptor (see CM). Desensitization mechanisms have not been specifically studied in pancreatic β-cells for these receptors, however, these receptors recycle rapidly in COS-7 and HEK-293 cells .
We modeled the processes of desensitization of GLP-1R, GIPR and AdR as a simplified mechanism that includes only one step for the active state (where a receptor is bound with ligand) pass off or recirculation of GPCRs (see CM), because these mechanisms are not well studied.
Adenylyl cyclases and phosphodiesterases.
Activated adenylcyclase (AC) synthesizes cAMP from the substrate Mg2+ATP. Rodent and human β-cells and insulinoma cell lines express different AC isoforms, including the Ca2+ and calmodulin-activated isoforms AC1, AC3, and AC8 [78–81]. Interestingly, among the cloned and characterized members of the mammalian AC family, two isoforms, AC1 and AC8, are synergistically activated by Ca2+-calmodulin and Gsα [82, 83]. Both receptors for GIP and GLP-1 activate the α subunit (Gsα) of the G-protein leading to an activation of AC in pancreatic cell lines and isolated pancreatic islets [67, 84]. The activation of AC by the α subunit (Gsα) of the G-protein may be associated with formation of their complex with PM bound AC (ACp) [85, 86].
GLP-1 resulting in increments in Gsa has been proposed to regulate calmodulin-activated AC8 that is central to GLP-1 signaling in rodent and human β-cells [78, 79, 87]. Our previous kinetic analysis supports this conclusion . Similar interactions with AC8 was suggested for GIPR . Catecholamines inhibit AC, however it is not known which isoform of AC in the β-cell is inhibited . Activation of Gi by catecholamines may block ACP mediated cAMP synthesis thus preventing the augmentation of insulin release stimulated by GLP-1 and GIP . Acute activation of Gi/o-coupled receptors leads to inhibition of AC8 isoform . Therefore we suggest that this Gi binds to the same AC isoform as G-proteins activated by GLP-1 or GIP, but AC bound with Gi has no catalytic activity. We found that these mechanisms of interaction between G-proteins and target enzymes (represented for ACp in Fig 2) can play an important role in the regulation of messenger pathways (see Results and discussion).
Activating G-protein can bind with ACp and their complex can accelerate cAMP production. ACp complex bounded with inhibiting G-protein cannot catalyze cAMP production.
Some soluble cytoplasmic adenylate cyclases (ACc) can also contribute to cAMP synthesis that are not controlled by G-proteins and forskolin but rather by calcium, ATP and bicarbonate . Therefore alongside G-protein activated or inhibited ACp we modeled a cytoplasmic ACc that is insensitive to G-protein and can be activated by glucose and/or Ca2+ (see for detals CM).
Some resting cAMP concentration exists in the absence of agonists (see below). Mechanisms of AC activation were not studied in this case for β–cells. We suggest that AC activity in this case can be a consequence of basal activity of ACc and constitutive G-protein dependent receptor for ACP that is observed in other GPCRs.
Phosphodiesterases (PDEs) hydrolyze cAMP and cyclic GMP. There are eleven known PDE families that differ in primary structures, responses to specific effectors and sensitivities to specific inhibitors, cellular expression and intracellular location and may modulate distinct regulatory pathways within the cell [90, 91]. In β-cells, it has been suggested that several PDE isoforms (1C, 3B, 4, 8B, and 10A) are involved in regulation of insulin secretion [91, 92]. In our model we consider both Ca2+ bound calmodulin activated and constitutive PDE activity as in a previous model  (see CM).
PKA and Epac.
The two primary downstream effectors of cAMP are PKA and the cAMP-regulated guanine nucleotide exchange factors (Epac) that play a critical role in insulin release [64, 93]. PKA is a holoenzyme composed of catalytic and regulatory subunits. The catalytic subunits release when cAMP binds to the regulatory subunits and then act to phosphorylate downstream substrates. Epac is similarly activated by cAMP [64, 93]. We have used a simple mathematical model of these events . There are multiple phosphorylation targets of PKA and Epac that are related to their effects on exocytosis. PKA and Epac pathways can have different physiological relevance based on differences in cAMP binding constants and downstream targets . However, in the present model, downstream targets of PKA and Epac include only PKA blocking of KATP channels (see Eq 13, CM) and regulation of the IP3 receptors in the ER (see CM).
Numerous phosphoinositides are now accepted as independent signaling molecules . We will consider here phosphoinositides for β-cell physiology (see CM). Phosphatidylinositol-4-phosphate (P4P) is synthesized from phosphatidylinositol (PI) by phosphatidylinositol 4-kinases (PI4Ks) and its synthesis activates by PKC and Ca2+ . Phosphotidylinositol-4,5-bisphosphate (PIP2) in turn is primarily formed from P4P by phosphatidylinositol-4-phosphate 5-kinase I  (Fig 1).
Despite being a small component of the plasma membrane, PIP2 has many diverse and critical roles in β-cell physiology. PIP2 serves as a precursor for the messenger molecules inositol-1,4,5-trisphosphate (IP3) and diacylglycerol (DAG) generated following activation of PLC. Membrane content of phospholipids, in particular PIP2, can also determine the sensitivity of the KATP channels. Particularly, the KATP channels became more resistant to ATP-induced closure as the membrane content of PIP2 was increased [98, 99]. A decrease in the PM PIP2 concentration may lead to KATP channels closure and corresponding PM depolarization . We have developed a mathematical model for phosphoinositide dynamics where PIP2 can activate KATP channels (see Eq 10 in CM) and serves as a precursor for IP3 and DAG (see CM).
Phospholipase C pathway
An important component of islet cell signal transduction and the control of islet function is innervation. Importantly, the modulators of insulin secretion include acetylcholine released from pancreatic parasympathetic nerve endings or possibly from alpha cells that activates phospholipase C (PLC) [3, 62, 100]. In general, over 50 hormone receptors can couple to the specific PLC-coupled G-proteins and some receptor tyrosine kinases can also stimulate PLCs that catalyze the formation of DAG and IP3 from PIP2 [101–103].
PLC pathway receptors.
Pancreatic β cells express various Gq-coupled receptors, including the muscarinic receptors, FFAR1/GPR40, GPR120 and different P2Y receptor subtypes, that can regulate PLC activity . However, here we included only what seem to be the most important for the β-cell: muscarinic acetylcholine receptor (MR) and FFAR1/GPR40. Others of this class seem to be expressed at lower levels [5, 104].
Muscarinic acetylcholine receptor (MR).
Acetylcholine binds to MRs in the β-cell membrane coupled to Gαq/11 to activate PLC . mRNA encoding the type 3 MR is the most abundant in human islets but other isofroms are expressed in β-cells .
Initially, it was thought that MR and G-proteins interact freely with each other on the cell membrane . However, type 3 MRs can pre-couple with their preferential classes of G-proteins  and here we used a pre-coupled model (see CM). Agonist-bound MR are phosphorylated by G-protein-coupled receptor kinases that leads to receptor internalization and recycling (or down regulation) .
Free fatty acid receptor (FFAR1/GPR40).
The class A G-protein-coupled receptor GPR40, now also known as free fatty acid receptor 1 (FFAR1) is predominantly expressed in mouse, rat and human pancreatic β-cells and plays a major part in fatty acid amplification of GSIS . FFAR1/GPR40 is activated by medium- to long-chain fatty acids and it is predominantly coupled to Gαq that typically signals through PLC-mediated hydrolysis of membrane phospholipids and is coupled to the formation of IP3 and DAG [5, 106]. A collision coupling mechanism was suggested as the predominant mechanism for FFAR1/GPR40 (see ). Interestingly, investigations of β cell-specific inactivation of the genes encoding the Gαq/11-coupled receptor have shown that glucose does not directly activate this receptor . In stably transfected HEK-293 cells, FFAR1/GPR40 receptors underwent rapid agonist-induced internalization . However, in the absence of specific data we used a general mechaism for modeling the processes of desensitization for MR and FFAR1/GPR40 in β-cells (see CM).
Phospholipase C (PLC).
At least thirteen different PLC family members have been identified in various mammalian tissues and the consensus is that PIP2 is the major substrate of PLC yielding DAG and IP3 [110, 111]. Mouse and rat islets have been reported to express members of the three major phospholipase (PLC) subfamilies; PLC-β1,—β2,—β3,—β4, PLC-γ1, PLC-δ1 and PLC-δ2 [112, 113]. These enzymes appear to be activated by different mechanisms. It is likely that PLC-β isoforms, activated by carbachol, are localized on the PM and are regulated by the Gαq subfamily of G-proteins . On other hand PLCδ1 is localized in the cytoplasm and regulated by nutrients including glucose . PLC-γ1 is entirely cytoplasmic in MIN6 cells . Interestingly, PLC-γ is directly phosphorylated and activated by tyrosine kinase . Therefore we modeled the two main PLC forms in β-cells as a nutrient (glucose) activated cytoplasmic PLC form (PLCC) and as a G-protein-coupled PLC that is activated by specific Gq-type G-proteins in the PM (PLCP) (Fig 1).
PLC- β and Gαq can form a complex mediating PLC activity . We therefore suggested that the PLCP can be activated only when this G-protein binds with its corresponding PLC (see CM). Interestingly, all the PLC isoforms discussed here can be activated by Ca2+ [110, 111]. However, the cytoplasmic PLC isoform activated by glucose is more strongly controlled by Ca2+ availability than the isoform that is activated by agonists [115, 117]. We thus considered the constant for [Ca2+]c activation for PLCp to be higher (0.4 μM) in comparison with the PLCC (0.2 μM) (see CM).
IP3 and DAG handling.
The dynamic intracellular concentrations of IP3 and DAG are determined by the rates of synthesis and degradation. We used our simple model  for modeling IP3 synthesis and degradation. The DAG model was developed similarly to the IP3 model (see CM). However, DAG remains bound within the PM and it was simulated as a concentration closely associated with the PM.
Regulation of PKC activation.
DAG promotes membrane recruitment and activation of protein kinase C (PKC), an enzyme linked to the regulation of many cellular processes . Several PKC isoenzymes are expressed in pancreatic β-cells and it is known that PKC phosphorylates and activates the components of the exocytotic machinery in β-cell that is important for the regulation of insulin secretion. However, the precise role of PKC-family proteins in β-cell physiology is still controversial and the phosphorylation targets that mediate the enhancement of exocytosis by PKC are not well known . For this reason in our quantitative model of messenger interactions we consider only activation of P4P synthesis from PI by activated PKC (see Fig 1).
DAG can also activate PDK1 in mouse islets  and this kinase is also responsible for stimulating insulin release . However, activated PDK1 does not seem to directly regulate the processes that we consider here and its regulation is not further considered.
Regulation of Ca2+ dynamics.
Calcium is a ubiquitous second messenger that regulates many biological processes, such as cell signaling and transport, membrane excitability and substance secretion. In the β-cell Ca2+ homeostasis is controlled by transmembrane ion flux and involves coordinated interplay between multiple ion transport mechanisms and organelles. Ca2+ enters the β-cells primarily through voltage-gated Ca2+ channels. Additionally, Ca2+ store-operated current (SOC) increases [Ca2]c (see CM). The plasma membrane of β-cells contains P-type ATPases that pump Ca2+ out of the cytosol to the extracellular environment [122, 123].
Ca2+ enters the endoplasmic reticulum (ER) via P-type ATPases (SERCA pumps, primarily SERCA2 and 3 in β-cells) using ATP and leaves predominantly through intracellular ER Ca2+ channels. The type III inositol 1,4,5 triphosphate receptor (IP3R) is the main calcium release channel in the β-cell ER. When IP3 binds to IP3R, Ca2+ passes from the lumen of ER into the cytosol [3, 122–124]. An additional regulatory control of IP3R is possible through phosphorylation of IP3R or through the associated proteins that bind to the regulatory domain. PKA activation leads to an opening of IP3R [125–127] (Fig 1). We have included models of IP3R in our general model (see CM).
Developing and simulation an integrated computational system of β-cell GPCR signal transduction and metabolic signaling
We were able to construct an integrated kinetic mathematical model of second messenger pathways. The model is described by mass action kinetics and is formulated as a system of ordinary differential equations (see CM). To handle the model equations from a numerical viewpoint, we need to know the dimensions and ranges of both variables and parameters so as to confine output values within physiological limits. However, precise determination of the model coefficients is limited due to a lack of adequate experimental data. Therefore, the model parameters were taken from the literature when possible and evaluated manually to best reproduce the experimental results that were found in available literature. All parameters and constants were fitted to be in their physiological ranges.
Equations, parameters, coefficients and outputs are represented below in the “Computational Model” and contain all the information necessary to carry out the simulations presented in this paper. They are pointed out in the text as a simulation at basal levels, and all simulations used the same set of parameters except where noted in the text or in the corresponding figure legends. Our simulations give time-dependent changes of cellular parameters. This allows a comparison with the corresponding experimental time-dependent data. To calculate persistent cellular parameters, the model was allowed to run up to steady-state values without changes in coefficients.
The model consists of a system of nonlinear ordinary differential equations describing the time rate of change in parameters. We developed the mathematical submodels of regulation process in the β-cell using web-based modeling resources of “The National Resource for Cell Analysis and Modeling (NRCAM)” This system was solved using the Virtual Cell simulation framework (University of Connecticut Health Center). The entire model and simulated results is publicly available for direct simulation on the website ‘‘Virtual Cell” (www.nrcam.uchc.edu) in ‘‘Math-Model Database” on the ‘‘math workspace” in the library ‘‘Fridlyand” with name ‘‘Messengers interaction”. Free registration and corresponding instructions are available on that website. It is easy to copy, change and simulate this model with other parameters directly on the Virtual Cell platform on their website. Visualization and graphical analysis were performed using “Excel”.
Results and Discussion
In this section we use computational simulations to critically review and analyze the experimental observations of intracellular regulatory mechanisms. Increases in the intracellular concentrations of Ca2+, cAMP and DAG contribute to increased insulin secretion [3, 93, 123]. This allows us to compare data on insulin secretion with simulated results of second messenger pathway activation dynamics.
Simulation of glucose action (Fig 3, left part)
At low glucose concentrations (defined here as 3 mM) the simulated resting PM potential is approximately -62 mV and [Ca2+]c is approximately 0.1 μM, corresponding to experimental data [122, 128]. The constitutive concentrations of activators for each specific G-protein coupled receptors were chosen to obtain 1/10 activity of the corresponding PM bound enzymes.
(A) ATP/ADP and PM potentials (Vp); (B) Free cytoplasmic Ca2+ concentration ([Ca2+]c) and free Ca2+ concentration in ER ([Ca2+]ER); (C) Cytoplasmic cAMP and relative activated PKA (PKAa). (D) IP3 and DAG concentrations. (E) Concentrations of P4P and PIP2 on PM. Initially all coefficients were on basal level (see Tables in CM). Low glucose concentration (3 mM) was simulated initially (left part). An increased extracellular glucose level at 1000 sec (from 3 to 8 mM) stimulates ATP/ADP increase that blocks KATP channels. This inhibition allows the PM depolarization (Fig 3A) that activates the voltage-gated Ca2+ channels and increases cytoplasmic Ca2+. This activate SERCA and increase Ca2+ in the endoplasmic reticulum ([Ca2+]ER). Increased [Ca2+]c activates also PLC and correspondently increases IP3 and DAG, decreases PIP2 and increases P4P concentrations. Increase in GLP-1 (from 3.1e-7 μM to 6.2e-4 μM) with corresponding ACP activation was simulated at 4000 sec. This leads to a fast increase in cAMP concentration and PKA activation (Fig 3C). Rise in PKA activity increases Ca2+ discharge from the ER through the inositol 1,4,5 triphosphate receptor (IP3R) and decreases [Ca2+]ER (Fig 3B).
A simulation of increasing extracellular glucose would produce a rise in the ATP/ADP ratio that blocks KATP channels. This inhibition would allow the PM depolarization that activates the voltage-gated Ca2+ channels and increases cytoplasmic Ca2+ (Fig 3). Increased [Ca2+]c activates constitutive PM bound and G-protein independent PLC activity and thereby increases IP3 and DAG concentrations. DAG activates PKC. PKC and Ca2+ also activate phosphatidylinositol 4-kinases leading to an increase in P4P concentration and corresponding acceleration of IP3 and DAG production. cAMP dynamics is considered later.
Increased [Ca2+]c accelerates Ca2+ entry into the ER by SERCA pumps that can increase [Ca2+]ER. Simulated increase in [Ca2+]ER (Fig 3B) corresponds to the experimental data that have shown an increased [Ca2+]ER with increased glucose [129–131]. The half-time for increased [Ca2+]ER after increased glucose was about 10 min  matched by our simulations (Fig 3B).
In at least some islets from patients with T2D, althought data is extrememly limited, there seems to be a limited glucose-stimulated ATP production in β-cells so that glucose is not fully capable of closing KATP channels in order to stimulate Ca2+ influx [132–134]. Under these pathophysiological conditions, glucose alone fails to generate the critically important cytosolic Ca2 + signal that initiates insulin exocytosis. By facilitating glucose-dependent KATP channel closure, sulfonylureas can restore the Ca2 + signal, thereby allowing glucose-stimulated insulin secretion (GSIS) to occur . We were able to simulate this mechanism. For example, we simulated the decrease of the glucose dependent saturated ATP/ADP ratio that leads to decreased PM potential and a failure to increase [Ca2 +]c in GSIS. Additional closure of KATP channels can depolarize the PM and restore increased [Ca2+]c in GSIS (Fig 4).
(A) KATP channel current (IKATP), (B) PM potential (Vp); (C) [Ca2+]c. T2D conditions were simulated by a decrease of the coefficient that is responsible for glucose dependent saturated ATP/ADP ratio (ATDm, Eq 4). ATDm was decreased from 32 (basal level) to 15. In this case PM membrane potential does not increase up to the nessesary threshold and does not lead to [Ca2+]c increase as glucose is increased from 3 mM to 8 mM at 1000 sec. For simulation of KATP channel blocking the maximal conductance (gmKATP, Eq 9) was decreased from 24 nS (basal level) to 9 nS at 3000 sec. This induces additional PM depolarization and fast [Ca2+]c increase.
cAMP pathway regulation
Cellular cAMP level is determined by the relative activities of AC and phosphodiesterases (PDEs). The association of cAMP effectors with signaling complexes and PM bound AC (ACp) that regulate cAMP concentration (Fig 2) explains the differential signaling initiated by members of the Gs-and Gi-protein receptor families.
Glucose stimulation alone has led to either unchanged  or insignificantly increased cAMP levels [137–139]. Our simulation (Fig 3C) explains the insignificant increase in cAMP levels due to low activity of G-protein dependent PM bound AC (ACP) when the specific hormone is absent, closely reflecting the corresponding experimental data. For example, according to  cAMP concentration in rat β-cells corresponds to 2.6 fmol 103 cells-1 at low glucose levels (1.4 mM) and 3.6 fmol 103 cells-1 at high glucose (20 mM). We converted these units in 1.81 μM and 2.51 μM for cAMP concentration in cell using volume of single β-cell. Our simulated cAMP concentrations were 1.83 μM at low and 2.104 μM at high glucose similar to the experimental data (Fig 3C). In this case cAMP dynamics are determined by constitutively active G-proteins that activate ACP and soluble AC (ACc) (both can be activated by Ca2+/CaM) and Ca2+-dependent PDE isoforms. The insignificant increase in cAMP concentration may result from offsetting events: calcium-stimulated phosphodiesterase activity may offset increased AC activity.
Even a low cAMP level may be necessary for insulin secretion because decreasing cAMP inhibits GSIS. For example phosphodiesterase 3β (PDE3B) negatively regulates insulin secretion during GSIS through its cAMP-hydrolyzing activity .
GIP is secreted in the same time and concentration frame as GLP-1 and these concentrations are sufficient to activate GLP-1R or GIPR in humans [141, 142]. GLP-1 and GIP receptor activation on β-cells leads to increased cAMP and intracellular Ca2+ that result in exocytosis of insulin-granules as a late response to oral glucose (20–120 min) . GLP-1 and GIP have insulinotropic action in healthy humans [141, 144, 145].
We modeled the actions of incretin hormones and the resulting activation of ACP that increases cAMP synthesis and PKA activation. To do this we set up a simulation with GLP-1 and GIP levels 20 times higher than the Michaelis constant for GLP-1 or GIP receptor binding (coefficient KGLP1, or KGIP, Eqs 46 and 47). As illustrated in Figs 1 and 3 simulation of increased cAMP leads to dual activation of the PKA and Epac branches of the cAMP pathway. Indeed it was found that GLP-1 and GIP lead to additional glucose-dependent closure of KATP channels and an increase in Ca2+ release from ER leading to [Ca2+]c increase, that accelerates Ca2+ dependent insulin secretion [64, 68, 93, 146]. We were able to simulate increased [Ca2+]c (and a corresponding fall in [Ca2+]ER) by GLP-1R activation as a consequence of IP3R stimulation through PKA activation (Fig 3).
However, in low glucose concentrations treatment of pancreatic β-cells with agents that increase cAMP- alone has little or no effect on insulin secretion. Only a combination of GLP-1 or GIP and high glucose potentiates GSIS [84, 147]. This important feature reduces the chance of producing hypoglycemia in patients with T2D with GLP-1 agonists . Our computational model agrees with this behavior (see Fig 5, left part). At low glucose when [Ca2+]c is low, activation of GLP-1R significantly increases ACP (Fig 5B), however this does not lead to increased cAMP because increased [Ca2+]c is required for ACp activation. On the other hand the model predicts that a facilitation of cAMP production by ACP by GLP-1R agonists can be stimulated if glucose increases the concentration of Ca2+-dependent calmodulin that can activate PM bound AC in β-cells (see Figs 3 and 5). A small effect of GLP-1 on increased cAMP at low glucose was also simulated in our previous model of AC activation and of cAMP dynamics  becouse this effect can be explained by the sensitivity of ACP to [Ca2+]c changes rather than by the properties of GLP-1R. (A similar simulation of GLP-1 effects via AC activation was reported ). This and our previous analysis  provides support for a pivotal role of Ca2+/CaM-dependent AC activation (possibly the AC8 isoform) in GLP-1 effects. The potentiating effect of cAMP on insulin secretion requires synergism between the cAMP pathway and the Ca2+ signal. Our model of cAMP dynamic regulation may also help to explain the mechanism responsible for the pattern of cAMP changes that take place during phases changes and oscillations in intracellular Ca2+ .
(A) Cytoplasmic [Ca2+]c and cAMP dynamics. (B) ACGLP, ACGIP and ACAR are the concentrations of complexes of AC on PM (ACP) bound with corresponding G-proteins activated by GLP-1R, GIPR and α2A adrenergic receptors. (C) VACp is G-protein and Ca2+ dependent AC activity on PM, VACc is the glucose and Ca2+-activated soluble AC activity that is independent on G-protein. At low glucose concentrations (3 mM) (left part) GLP-1 was increased from basal level (3.1e-7 μM) to 6.2e-4 μM at 1000 sec. Increased extracellular glucose level was simulated at 3000 sec (from 3 mM to 8 mM). Than GIP administration was modeled as the increased GIP from basal level (1.37e-6 μM) to 3.42e-3 μM at 5000 sec.
This model also provides an explanation for the observed clinical interaction between GLP-1R agonists and KATP channel blockers. Several human clinical trials have demonstrated that patients using GLP-1R agonists and KATP channel blockers therapies have a greater incidence of hypoglycaemia than those not using KATP channel blockers . KATP channel blockers have also been shown to abrogate the glucose dependence of GLP-1R agonist activity in the rat pancreas .
Although the mechanism of this proposed uncoupling event has not been fully elucidated experimentally, our simulation suggests that KATP channel blockers may lead to modest PM depolarization and increased Ca2+ concentration even in low glucose (not shown) leading to activation of Ca2+ dependent AC on PM and an increased cAMP (and corresponding insulin secretion) in the presence of GLP-1R agonists. Thus, block of KATP channels independently of glucose may allow GLP-1R agonists to bypass their inherent glucose requirement by stimulating the downstream effects ordinarily associated with increased glucose. Our simulation shows also that similar behavior may also occur for GIPR activators in the presence of KATP channel blockers (not shown).
GLP-1 and GIP interaction.
In rodent and healthy human subjects the insulinotropic effectiveness of GLP-1 and GIP may be additive . We were able to simulate an additional increase in cAMP concentration following GIPR activation with GLP-1 administration (Fig 5B). This synergetic GIP effect takes place because GLP-1R cannot fully activate ACp and GIPR administration leads to additional ACp activation in our model, i.e. a sum of ACp activated by GLP-1R (ACGLP) and GIPR (ACGIP) was higher than ACGLP with GLP-1 activation only.
However, investigations in mice have shown a less protracted action of GIP vs GLP-1 that would suggest a more prolonged activation of the GLP-1 receptor than that of GIPR . One hypothesis, is that GIP provokes accelerated desensitization of the GIPR . Ligand-induced desensitization and trafficking of GPCRs have been implicated as critical mechanisms for modulating response duration in vivo . A naturally occurring variant of the GIP receptor underwent enhanced agonist-induced desensitization in adipocytes, which impaired GIP control of adipose insulin sensitivity . We have exploited our mathematical model to examine the influence of receptor desensitization in cAMP dynamics. According to results of our modeling an increase in the rate constant for desensitization and/or a decrease of rate of return to surface for GIPR can actually decrease ACp activity bound with GIPR to compare with AC bound with GLP-1R (not shown). However, we were not able to find any data where a signaling desensitization in response to these two hormones has been compared under the same experimental setting in pancreatic β-cells.
Incretin hormones and T2D.
GLP-1 may be able restore a Ca2+ signal by facilitating glucose-dependent KATP channel closure and by enhancing SOC allowing normal GSIS . We were able to simulate this mechanism (Fig 6). In Fig 4 we suggested that in T2D glucose leads to an insufficiently increased ATP/ADP ratio (as a consequence of some unspecified dysfunction). We simulate the initial T2D conditions in Fig 6 (as well as in Fig 4) as decreased glucose dependent saturated ATP/ADP ratio that leads to a inability to increase [Ca2+]c during GSIS. However, GLP-1 administration then leads to additional depolarization and [Ca2+]c increase in our model (Fig 6). This additional depolarization is a consequence of increased ISOC due to decreased [Ca2+]ER after IP3R activation by PKAa, that also leads to increased Ca2+ flux from ER to cytosol. PKA activation also decreases the conductance of KATP.
T2D conditions were simulated by decreasing the glucose dependent saturated ATP/ADP ratio (ATDm, Eq 4). ATDm was decreased from 32 (basal level) to 15 as in Fig 4. In this case PM membrane potential is decreased and does not lead to [Ca2+]c increase as glucose increases from 3 mM to 8 mM at 1000 sec as well as in Fig 4. GLP-1 administration was simulated at 3000 sec ([GLP-1] was increased from 3.1e-7 μM to 6.2e-4 μM. This induces cAMP increase with additional PM depolarization and Ca2+ influx into cell. Corresponding PKA activation also increases Ca2+ flow from ER. These effects lead to significant [Ca2+]c and cAMP increase.
Patients with T2D can have preserved GLP-1 and GIP secretion [142, 153]. However, the insulinotropic effect of incretins is diminished in T2D patients, due in part to reduced expression of incretin receptors as a consequence of perhaps glucotoxicity or lipotoxicity. For example, GLP-1R and GIPR levels were decreased in islets from mouse and rat model of diabetes and from T2D patients [57, 154, 155]. Elevated non-esterified fatty acids lead to a decreased GLP-1R expression and downregulation of GLP-1 receptor signaling in β-cell lines and mouse islets from db/db mice . On other hand the transgenic expression of GLP-1R was able to restore GLP-1R-dependent stimulation of cAMP in isolated islets from mouse and in insulinoma cell lines [156, 157]. It was also shown that at high glucose levels GLP1-R can also be covalently modified in mouse islets by small ubiquitin-related modifier protein (SUMO) reducing its ability to be stimulated by GLP-1R agonist . We have exploited our model to analyze these results. Proportional loss of cell surface GLP-1R and GIPR leads to a decreased cAMP and Ca2+ level even in the presence of high GLP-1 and GIP stimulation because the total receptor number is a limiting factor for activation of G-protein bound AC in our model. For example, a decrease in the total GLP-1R by 2-fold leads to a decrease of cAMP from 10.5 to 4.5 μM in a simulation of GLP-1 administration (if the calculations were made similar to Fig 3).
In summary, the results in this section suggest that the concentration of incretin hormone receptors on the PM can be a limiting factor in incretin function in rodent, normal human and T2D patients.
Catecholamines (epinephrine and norepinephrine), the agonists of alpha adrenergic receptors, inhibit insulin secretion by decreasing cAMP content in β-cell as a result of inhibition of AC activity . Our simulation also shows a catecholamine inhibition of cAMP concentration that can reduce GSIS (Fig 7A). This is a consequence of block of PM bound AC (ACP) activity because this AC isoform following catecholamine-receptor binding cannot produce cAMP (see Fig 7B). ACP plays a role in maintaining low levels of cAMP at GSIS in our model even without GPCR agonists activating the cAMP pathway. This is a consequence of constitutive activity of GPCRs that activate ACp. Even low cAMP levels may be necessary for insulin secretion (see above). Therefore, activation of catecholamine receptors decreasing ACp activity further suppresses GSIS even though cAMP content may already be low.
(A) Cytoplasmic cAMP and [Ca2+]c dynamics. (B) ACGLP, ACGIP and ACAR are the concentrations of complexes of AC on PM (ACP) bound with corresponding G-proteins activated by GLP-1R, GIPR and α2A adrenergic receptors. (C) VACp is G-protein and Ca2+ dependent AC activity on PM. The initial simulation was with low glucose (3 mM) as in Fig 3. Increase of glucose (8 mM at 1000 sec) induced changes of intracellular parameters. Catecholamine concentration (AR3) was increased from basal level (0.002 μM) to 3 μM at 3000 sec. Than GLP-1 was increased from basal level (3.1e-7 μM) to 6.2e-4 μM at 5000 sec.
Subthreshold α2-adrenergic activation with clonidine counteracts GLP-1 potentiation of GSIS . Our simulation also shows that activated adrenergic receptors can block an increased cAMP concentration by incretin hormones because a significant part of PM bound AC is bound with Gi-proteins activated by catecholamine receptors. It blocks the ability of ACP to be activated by G αs-protein-coupled receptors. In this case the increase in cAMP concentration after simulation of GLP-1 was reduced (~6 μM, Fig 7A) in comparison with simulation in the absence of α2-adrenergic activation (~15 μM, Fig 3C).
α2A receptor antagonists have been proposed as T2D therapeutic agents . We used our model to evaluate this possibility. Our simulation showed that a decrease in the number of α2A receptors does not lead to a significantly increased cAMP concentration during GSIS, when we assume that α2A receptor number is small (to reflect basal levels of this receptor accepted in our model). For example, with glucose stimulation (as in Fig 3) the increase in cAMP concentration is insignificant (from 2.104 to 2.106 μM) if the α2A receptor content (ReARt in Eq 48) is lowered 10-fold. This happens because constitutive catecholamine activity blocks only a small fraction of the PM bound AC in the absence of specific activators (see Fig 5B). Block of this fraction cannot significantly effect activation of the remaining fraction of ACP by incretins (not shown). Consequently, α2A receptor antagonists are unlikely to be useful in T2D if the activity of α2A receptors is insignificant.
However, T2D diabetes risk can be associated with increased expression of α2A receptors and a concomitant reduction in insulin secretion . Our simulation of cAMP dynamics with an increased α2A receptor expression level (even without their agonists being present) gives a result that is indeed similar to α2-adrenergic activation shown in Fig 7, i.e. to significant decrease in cAMP. This shows that increased α2A receptor expression should lead to decreased cAMP production during GSIS without and with the incretin hormones (under physiological conditions). ACP activity is reduced since a significant part of PM bound AC may be inhibited (bound with Gi-proteins) and cannot be activated during GSIS or by incretin receptors. In this case α2A receptor antagonists might be useful in increase insulin secretion. It does seem that this effect is limited to situations where this is significantly increased expression of α2A receptors over control situations.
Regulation of AC and PDE activity.
Regulation of intracellular cAMP concentration during GSIS is also possible using AC activators such as forskolin or a PDE inhibitor (e.g., IBMX) . For example, β-cell PDE3B can be activated by insulin, IGF-1 and leptin that inhibit insulin secretion . Interestingly, PDE4 inhibitors may increase insulin secretion in humans .
Increased cAMP by AC activation or PDE inhibition causes various events, including Ca2+ mobilization from internal Ca2+ stores and activation of non-selective cation channels [80, 84]. In our model, activation of AC or PDE inhibition increased cAMP concentration, similar to the above for GLP-1 or GIP stimulation (see below for a special case of expression of different PDE isoforms). Then increased cAMP leads to PKA activation and block of KATP channels, activation of Ca2+ release from ER and additional PM depolarization (see Figs 1 and 6). Notice that dual activation of the PKA and Epac branches of the cAMP signaling mechanism can act directly on the exocytotic machinery [68, 146].
PLC pathway regulation
PLC activation increases insulin secretion in mouse and rat islets [3, 100, 115]. Drug-mediated, chronic, and selective activation of β-cell Gq signaling greatly improves β-cell function and glucose homeostasis in mice .
Constitutuve PLC activity occurs at low glucose levels. Increased glucose can activate the PLC pathway and significantly increase IP3 and DAG concentration in conditions when PLC pathway GPCRs are not activated [95, 124, 163]. We simulated this process (Fig 3D) and found that the increases in IP3 and DAG following glucose stimulation occur through cytoplasmic Ca2+ activation of both G-protein dependent and independent forms of PLC. In this case the degradation rate for IP3 or DAG does not increase with increased [Ca2+]c. This is in contrast to cAMP degradation where Ca2+ activates phosphodiesterases. Additionally, PLC activation during GSIS and the corresponding decrease in PIP2 concentration can simulate KATP channels closure and increased Ca2+ influx through voltage-activated Ca2+ channels (see Figs 1 and 3E).
FFAR1/GPR40 agonists enhance insulin secretion. They markedly enhance cytoplasmic and mitochondrial Ca2+ in insulinoma cells and increased IP3 and ATP levels in islets normal and diabetic rats . GSIS (i.e. at high glucose level) was also augmented in pancreatic β-cells in normal and diabetic mice that overexpressed FFAR1/GPR40 [165, 166].
We simulated the effect of FFAR1/GPR40 activation via Gαq-proteins leading to PLC (PLCp) pathway activation (Fig 8). However, this effect was insignificant for Ca2+ and IP3 at low glucose levels and only increased glucose levels (that can increase [Ca2+]c) can significantly activate PLCp (Fig 8D). This leads to an additional rise in IP3 and DAG concentrations. Increased IP3 also results in decreased [Ca2+]ER and an increase in [Ca2+]c as a result of IP3-dependent release of Ca2+ from the ER. Decreased [Ca2+]ER also opens store-operated channels (SOC) accelerating Ca2+ and possibly other cations into the cytoplasm through these channels. PLCp activation decreases PIP2 that can also lead to additional KATP channels inhibition (see Fig 8E). All these processes enhance PM depolarization, increasing Ca2+ influx (Fig 8A). Formation of DAG also activates PKC, resulting in increased efficiency of Ca2+ on exocytosis during GSIS (Fig 1).
Two protocols were stimulated: 1. (---) glucose was increase from 3 mM to 8 mM at 3000 sec, than acetylcholine, M3R activator, (AM3 concentration in model) was added at 5000 sec (AM3 was increased from basal level 0.0022 μM to 2.2 μM) and 2. (- - - -) FFAR1/GPR40 agonist was added at 1000 sec (AR7 was increased from basal level 0.0506 μM to 82 μM), than glucose (3000 sec) and acetylcholine were added (5000 sec). (A) Free cytoplasmic Ca2+ concentration ([Ca2+]c. (B) Free Ca2+ concentration in ER ([Ca2+]ER); (C) IP3 concentration. (D) VPLP is the activity of PM bound PLC (Eq 78). (E) Concentrations of PIP2 on PM.
These simulations reflect experimental data. Unbound FFAs in plasma in the physiological range can activate FFAR1/GPR40 in the presence of glucose and eventually amplifies GSIS in short-term exposure [106, 167]. FFA action on FFAR1/GPR40 caused PLC activation and a rise in [Ca2+]c both in primary mouse and rat β-cells and in INS-1 cells with increased glucose [168–170]. Palmitate also markedly reduced the calcium storage capacity of the ER measured by thapsigargin-induced Ca2+ release, suggesting that ER calcium stores may participate in raising [Ca2+]c in isolated mouse islets . These events are consistent with a decrease in [Ca2+]ER following FFAR1/GPR40 activation in our simulation (Fig 8B).
We evaluated the effect of FFAR1/GPR40 potentiation at low glucose in more detail to evaluate possible enhanced risks of hyperglycemia. At low glucose concentrations, activating FFAR1/GPR40 does not increase [Ca2+]c in pancreatic rat β-cells  nor does it stimulate insulin secretion in mouse islets . Interestingly, mouse pancreatic β-cells expressing increased FFAR1/GPR40 do not show increased insulin secretion in low glucose . PLC activity is unaffected in our model (Fig 8D left part), because increased cytosolic Ca2+ is required to increase G-protein dependent PLC activity. This is an advantageous feature that should reduce the chance of producing hyperinsulinemia and hypoglycemia in T2D patients given FFAR1/GPR40 agonists. Indeed, selective pharmacological activation of FFAR1/GPR40 by TAK-875 significantly improved glycemic control in T2D patients with only a modest risk of hypoglycemia . This result resembles our analysis of GSIS stimulated by GLP-1, where GLP-1 action alone (i.e. at low glucose) has little or no effect on insulin secretion. Only a combination of GLP-1 and high glucose potentiates insulin secretion. We explained this effect also through the dependence of increased [Ca2+]c to increase G-protein dependent AC activity.
We can also predict that using FFAR1/GPR40 agonists simultaneously with KATP channel blockers can lead to increased incidence of hypoglycemia similar to employing GLP-1R agonists and KATP channel blockers together. Both of these effects are associated with an increase in [Ca2+]c due to sulfonylureas or similar KATP inhbiting drugs that can activate PLC or AC even in low glucose levels. Indeed, FFAR1/GPR40 activation increased insulin secretion in the presence of sulfonylurea under low glucose conditions through enhancement of PKC signaling in insulinoma cells .
Activation of FFAR1/GPR40 may be a viable therapeutic approach for treatment of type 2 diabetes . Our simulations suggest that FFAR1/GPR40 should increase insulin secretion in the presence of elevated glucose, because FFAR1/GPR40 agonists should increase [Ca2+]c and DAG during GSIS, supporting the evidence that these agonists may be useful as novel insulin secretagogues with low risk of hypoglycemia. However, the simulations also indicate that using FFAR1/GPR40 activators together with sulfonylureas could increase the incidence of insulin secretion overshoot and thereby increase hypoglycemia, as it was discussed for incretin hormones.
Activation of muscarinic receptors.
Cholinergic muscarinic agonists, including the endogenous neurotransmitter acetylcholine and its synthetic nonhydrolyzable analog carbachol, increase [Ca2+]c and GSIS in normal β-cells . Stimulation of the MR activates Gαq-proteins leading to PLCp activation in our model (Fig 1) Modeling of MR activation is shown in Fig 8 (right) for high glucose levels for two cases, with and without preliminary FFAR1/GPR40 activation. This simulation (without preliminary FFAR1/GPR40 activation) corresponds to experimental evidence that at high glucose level, stimulation with the muscarinic receptor agonists (for example, carbachol), induces a rapid and sustained PLC activation [124, 174], depolarizes PM and enhances glucose-induced electrical activity . Several studies have also suggested that acetylcholine can regulate [Ca2+]c dynamics leading to [Ca2+]c increase and [Ca2+]ER decrease [129, 176]. Our model also takes into account a suggestion that the depolarizing effects of the muscarinic receptor agonists can be attributed partially to a store-operated current (SOC) that activates following IP3-dependent release of Ca2+ from the ER.
β-cell specific inactivation of genes encoding Gαq/11 in mice leads to decreased PM depolarization with increased glucose . According to our simulations this result seems to be a consequence of decreased SOC because (1) [Ca2+]ER does not decrease significantly without IP3 and (2) KATP channels remain open because PIP2 concentration does not decrease markedly without PLC activation.
The effect of glucose and carbachol on GSIS is at least additive in rat islets . Based on our model this effect can be explained by significant independence of PLC pathway that increases IP3 and DAG (DAG content changes proportionally IP3 in our model) (Fig 8C) and glucose activation of GSIS through PM depolarization and [Ca2+]c increase (Fig 8A).
Moderate levels of acetylcholine have little effect on PM depolarization or on sustained cytosolic Ca2+ in mouse islets at low glucose [124, 176–178]. We simulated activation of muscarinic receptors (MR) at low glucose levels. In this case, activation of MR evoked only a small increase in [Ca2+]c similar to modeling of FFAR1/GPR40 activation (for example, see Fig 8, left) if conductivity of specific nonselective cation channels (NALCN) (that can be activated by MR) was insignificant (not shown). This can be explained as well as for FFAR1/GPR40 activation (see Fig 8) so that increased cytosolic Ca2+ is necessary for activation of membrane bound PLC.
A different scenario was found for rat or human islets. Acetylcholine increased [Ca2+]c in isolated islets from Wistar rats at low glucose (2.8 mM)  and in human β–cells . These results can be explained by activation of the MR dependent NALCN channels that lead to transient PM depolarization and increased [Ca2+]c even with the simulation conducted in low glucose (Fig 9). Likewise, our simulation of electrophysiological events in human β-cells showed that activation of NALCN channels can lead to additional PM depolarization and increased insulin secretion at high glucose. This can also decrease the threshold for glucose-induced spike activity and also lead to increased [Ca2+]c .
(A) PM potential (Vp); (B) [Ca2+]c and [Ca2+]ER; (C) Cytoplasmic IP3 concentration and the activity of PM bound PLC (VPLP). For simulation of NALCN channel activity the maximal conductance (gmNM3, Eq 25, CM) was initially increased from 0 nS (basal level), to 35 nS. Addition of acetylcholine that opens NALCN channel was simulated at 1000 sec at the arrow (AM3 was increased from basal level 0.0022 μM to 2.2 μM). All simulations were performed at low glucose (3 mM).
Acetylcholine activates Na+ influx by an activation of NALCN permeable to Na+ , i.e. an increased Na+ inward current through NALCN leading to PM depolarization (see [30, 50]) that corresponds to mechanisms considered in our model, where opening of NALCN lead to increase in Na+ influx (see Eq 25, CM).
Activation of MR dependent NALCN channels in low glucose could be a mechanism by which acetylcholine increases human insulin secretion during first phase of feeding where an increase in insulin secretion precedes an increase in blood glucose. This increase, termed "cephalic phase", is thought to be largely mediated by the release of acetylcholine from nerves innervating pancreatic islets or from the alpha cells [6, 62, 180]. In this case activation of MRs may increase the capability for insulin secretion even while still in sub-threshold glucose levels.
Zawalich and colleagues  showed that the failure of PLC activation resulted in impairment of insulin secretion under sustained glucose exposure in rats. It was also found that mice that selectively lacked MRs in pancreatic β-cells showed decreased insulin release and impaired glucose tolerance. Consistent with this concept, transgenic mice that selectively overexpressed type 3 MRs in pancreatic β-cells exhibited increased insulin release and improved glucose tolerance . Our simulation with a decreased number of MRs leads to decreased [Ca2+]c and DAG concentration at high glucose because constitutive MR activity takes part in IP3 and DAG production. For example, our simulation with 10x reduced MR abundance (to compare with the example in Fig 3) shows a significant decrease in [Ca2+]c (from 272 to 250 nM) and DAG concentration (from 1131 to 926 #/μm-2) at high glucose. The "cephalic phase" should also be blocked in these conditions. This should lead to decreased insulin secretion. These experimental data and our simulations suggest that optimal acetylcholine receptor stimulation is essential for β-cell insulin secretion.
Approaches aimed at enhancing signaling through β-cell MRs (or downstream) might become therapeutically useful for the treatment of T2D (see [104, 182]). However, our consideration shows that MR activation could conceivably lead to hypoglycemia at relatively low glucose concentrations as a consequence of NALCN channel activation leading to PM depolarization and increased [Ca2+]c.
FFAR1/GPR40 and muscarinic receptor interactions.
The temporal interplay between FFAR1/GPR40 and muscarinic receptors was investigated . Palmitic acid at physiological levels could acutely inhibit subsequent acetylcholine-stimulated insulin secretion (through activation of FFAR1/GPR40) and the rise of intracellular Ca2+ production in isolated mouse islets during GSIS. According to these authors the mechanism of such effect could be a Ca2+ release from the ER during FFAR1/GPR40 activation that prevents subsequent acetylcholine induced Ca2+ release. Our simulation shows that this effect can take place, i.e. FFAR1/GPR40 activation can prevent additional [Ca2+]ER decrease and Ca2+ release at acetylcholine administration (Fig 8A and 8B). However, there is also competition at the level of PLC such that activation of FFAR1/GPR40 leads to G-proteins binding to PLCp that decreases the ability of PLCp to bind with G-proteins, activated by acetylcholine (Fig 8D). Consequently, simultaneous use of activators for FFAR1/GPR40 and the muscarinic receptor may not lead to significant additive effects on insulin secretion.
Role of phosphoinositides
The rate of phosphoinositide metabolism is increased in glucose-stimulated islets. This effect is due to PLC-mediated hydrolysis of PIP2 and leads to a decrease in PIP2 content and an increase in P4P . Increased glucose leads also to activation of PLC and decreased PIP2. An increase in P4P in our model is a consequence of PKC and Ca2+ mediated activation of phosphatidylinositol 4-kinase (see Fig 1 and Fig 3E).
We modeled block of KATP channels due to decreased PIP2 (see Eq 10, CM). This can cause additional PM depolarization and an elevated level of [Ca2+]c thogether increasing the insulin secretion rate. These simulations reflect data that shows disrupting the interaction between KATP channels and PIP2 by overexpressing Kir6.2 in mutants with decreased sensitivity to PIP2 causes persistent membrane depolarization and elevated basal level insulin secretion . Additionally, PLC activation can also simulate closure of KATP channel as a consequence of a decreased PIP2 concentration [41, 42].
Glucose-induced P4P elevation required voltage-gated Ca2+ entry and was mimicked by membrane-depolarizing stimuli. P4P elevation was also sensitive to PKC inhibition and mimicked by phorbol ester stimulation . We were able to simulate these results because P4P synthesis is activated by PKC (see Eq 74 in CM and Figs 1 and 8E). Our model confirms that PIP2 levels in β cells are regulated for proper insulin exocytosis [96, 183].
Interaction of pathways
Activation of the GLP-1R, GIPR, MR and FFAR1/GPR40 receptors following meal ingestion could be simultaneous, very closely timed or even sequential. For this reason it is important to evaluate cross talk between GPCR agonists, comparing and combining those that function via the Gαs pathway (e.g., GLP-1 and GIP) with those that function via the Gαq/11 pathway (e.g. acetylcholine and FFA or FFAR1/GPR40 agonists).
Interestingly, activation of the cAMP pathway can activate the PLC pathway. For example, GLP-1 can activate PKC through Ca2+-dependent activation of PLC in the insulin-secreting rat β-cell line INS-1 . Indeed, according to our calculations an activation of the cAMP pathway (such as GLP-1 addition) leads to PKA activation and corresponding closure of the KATP channels and opening of IP3R leading to an significant increase in [Ca2+]c (from 0.272 to 0.302 μM) (Fig 10A). This activates Ca2+-dependent PLC leading to an increase of IP3 (and DAG) concentrations (see Fig 10B).
(A) [Ca2+]c and [Ca2+]ER; (B) Cytoplasmic cAMP and IP3 concentrations; (C) Concentrations of P4P and PIP2 on PM. Glucose (8 mM) induced changes of intracellular parameters were initially simulated as in Fig 3 up to steady state (left part). GLP-1 was simulated at 1000 sec as an increased GLP-1 from basal level (3.1e-7 μM) to 6.2e-4 μM. After that FFAR1/GPR40 activation was simulated at 4000 sec as increased AR7 from basal level (0.0506 μM) to 82 μM.
On other hand, if FFAR1/GPR40 activators are employed first, to activate the PLC pathway, this does not lead to an increase in cAMP concentration (see Fig 11B). In this case FFAR1/GPR40 activation significantly increases [Ca2+]c, IP3 and DAG but it does not effect cAMP concentration. According to our model (that was constructed mainly from mouse islets data) cAMP concentrationis not increased because this would occur only by activation of PM bound AC by specific G-protein coupled receptors (such as GLP-1R or GIPR). Indeed agonist activation of the Gq/11 receptor (i.e. an activation of PLC pathway) has no significant effect on intracellular cAMP level in mouse islets [186, 187].
(A) [Ca2+]c and [Ca2+]ER; (B) Cytoplasmic cAMP and IP3 concentrations; (C) Concentrations of P4P and PIP2 on PM. Glucose (8 mM) induced changes of intracellular parameters were initially simulated as in Fig 3 up to steady state (left part). FFAR1/GPR40 activator was simulated at 1000 sec as an increased AR7 from basal level (0.0506 μM) to 82 μM. After than GLP-1 action was simulated at 3000 sec (GLP-1 increased from basal level (3.1e-7 μM) to 6.2e-4 μM).
However, the effect of activation of the PLC pathway on the cAMP pathway may be species dependent. For example, a different behavior was found in rat β-cells. In isolated rat islets the MR agonist carbamylcholine chloride (CCh) evoked a concentration-dependent increase in cAMP generation with a maximum at least 4.5-fold above control even at basal 2.8 mmol/l glucose. However, in this case activation of the PLC pathway may only partially activate the cAMP pathway. This was “partial” because forskolin and GLP-1 increased cAMP accumulation 10-fold and 23-fold, respectively , i.e. G-protein dependent AC activity plays a significant role in rat β-cells. Interestingly, acetylcholine acts predominately through activation of the cAMP/PKA pathway alone (no increase in the IP3 pathway) to enhance Ca2+-stimulated insulin release islets in the Goto-Kakizaki β-cell, a spontaneous rat model of T2D  although this finding has not been replicated.
We used our model to propose a hypothesis that can explain these experimental data. For example, our simulation showed that increased cAMP following activation of the PLC pathway may be due to the presence in rat of mainly PDE forms that are not activated by [Ca2+]c (Fig 12). In this case increased [Ca2+]c following acetylcholine MR activation (for example by activation of NALCN channels at low glucose accelerates the rate of cAMP production but not degradation, because the maximal activity in Ca2+/CaM activated PDE was decreased, leading to a significant increase in cAMP. cAMP increases 4.8 fold following simulation of the MR receptor activation and 24-fold with additional GLP-1 administration, compared to cAMP levels without agonists (Fig 12C), that corresponds to experimental data . We could not find any information about the behavior of cAMP and IP3 in human β-cells related to possible interactions of the cAMP and PLC pathways. This question demands further investigation. However, our theoretical analysis shows the possible mechanisms and consequences of potential interactions.
(A) PM potentials (Vp); (B) [Ca2+]c and [Ca2+]ER; (C) Cytoplasmic cAMP and IP3 concentrations. For simulation of NALCN channel activity the maximal conductance (gmNM3, Eq 25, CM) was initially increased from 0 nS (basal level) to 35 nS. PDE activity (Vcpde, Eq 66) was decreased from 1.4 μmol s-1 (basal level) to 0.3 μmol s-1. All simulations were performed at low glucose level (3 mM).
In general, according to these simulations, simultaneous activation of the cAMP and PLC pathways should increase GSIS. This follows increased second messengers such as [Ca2+]c, cAMP and DAG that can activate an insulin secretion. In other words, receptor agonists for cAMP and PLC pathways can act cooperatively (for example see Figs 10 and 11). Indeed, the combination of GLP-1 and acetylcholine increased insulin release compared with each compound alone during an acute test in the β-cell line BRIN BD11  and in the isolated perfused rat pancreas . FFAR1/GPR40 and GLP-1 receptor agonists markedly increased GSIS and their combination led to GSIS that was significantly increased in comparison with each compound alone . These data are in accord with our simulations. Interestingly, FFAR1/GPR40 and GLP-1 receptor agonists as well as their combination did not increase insulin secretion at low glucose in islets isolated from normal rats . This could be the result of a inadequate activation of AC and PLC at low [Ca2+]c.
Limitations and Perspectives
Interpretation of the effects of G-protein blockers and activators on β-cells is difficult because a change of any messenger potentially alters several aspects of the messenger dynamics of other messenger pathways. For this reason, using a mathematical modeling approach opens up significant new possibilities and allows a better understanding of potential messenger interactions. Our model represents significant improvements in a number of areas of β-cell computational simulation. It was possible to roughly determine the contribution of each messenger pathway to [Ca2+]c and insulin secretion dynamics under specific conditions. Our simulations confirm that the specific messenger dynamics interactions can lead to an appropriate increase in insulin secretion.
The model we developed here must also be considered in the context of known limitations. Some limitations are unavoidable because of the limited current knowledge of basic biochemical and physiological data, the extensive variability among experimental data, the considerable variation in experimental conditions, and the potentially deleterious effects inherent in experiments with β-cells. For example, the actual variations in the number of receptors, G-proteins and target enzymes during signaling as well as the mechanisms of their interactions are not known.
These limitations required some formulations from other models based on animal experiments to be used. Therefore it would be unreasonable to expect the model to accurately reproduce all previously observed experimental results. However, we were able to show that the model we developed can reproduce a number of experimental observations ranging from G-protein coupled receptors to Ca2+ and insulin secretion dynamics. It can be used as the groundwork for in silico examination of the effects of agents that modulate insulin secretion via regulation of GPCRs. As other receptors are further characterized they can be readily incorporated into this basic model. This creates opportunities for in silico studies of possible functional influence of different agents on β–cell functional properties that it is difficult to evaluate at this time.
The data used to fit the parameters of our model were taken primarily from mouse islets and cell lines. For this reason the simulations of the experimental results obtained with rat and human β-cells can present difficulties. Some differences between species were stressed above. However, more data is clearly needed to analyze the human cells and islets.
Other limitations are related to mechanisms of insulin secretion. Insulin is secreted by exocytosis of large dense-core vesicles. Regulation of insulin–containing granule secretion in β-cells is a complex function of cytoplasmic Ca2+, cAMP and the PLC pathways. Several mathematical models were developed for describing exocytosis of insulin granules (see for review [25, 192]). However, our knowledge of the molecular regulation of exocytosis in β-cells remains fragmentary. A detailed consideration and modeling of the effects of additional regulatory pathways on the rate of exocytosis of insulin–containing granules is needed.
The multiple receptors expressed by β-cell pass their signals through a common set of downstream effectors distinguished by multiple isoforms with different specificities and activities. The coupling among these pathways causes interactions among the signals sent by the different classes of receptors. We have developed a computational integrated model of the G-protein signal transduction system from the receptors to the intracellular second-messengers: Ca2+, PIP2, cAMP, DAG and IP3. The model includes detailed descriptions of interactions between G-protein-coupled-receptors, [Ca2+]c, Ca2+-bound calmodulin (Ca2+/CaM), adenylyl cyclase, phosphodiesterase and PLC pathway regulation. We used this model to predict the signal processing of several G-protein-coupled-receptors.
By combining experimental data on G-protein and second messenger interactions with mathematical modeling, our systems approach is likely yield a detailed analysis of metabolic and signaling pathway interactions in the β-cell and help elucidate key regulatory mechanisms involved in regulation of insulin secretion. The simulations provide a way to compare the time-course and consequence of changes in expression of mechanisms that regulate cytoplasmic Ca2+ relative to the activation of cAMP and PLC pathways and to evaluate hypotheses on regulatory mechanisms of insulin secretion.
The most important results of our system biology computational analysis are:
- The concentration of incretin hormone receptors in the PM can be a key limiting factor in mechanisms leading to activation of insulin secretion by incretins.
- There can considerable variability in the effects of acetylcholine and GLP-1, GIP or FFAR1/GPR40 agonists on insulin secretion. Our theoretical analysis shows that activators of GLP-1, GIP and FFAR1/GPR40 receptors may have a low risk of hypoglycemia because they need a concomitant increase in [Ca2+]c for activation whereas an activator of muscarinic receptors can lead to hypoglycemia. This is a consequence of an activation of specific channels, leading to depolarization of the PM and [Ca2+]c increase even at lower blood glucose levels.
- G-coupled receptor activators can have additive effects on insulin secretion if they act on different messenger pathways (i.e. cAMP and PLC pathway in our case). The effectors may not have any additive interactions if they act on the same pathway (for example the pair GLP-1 and GIP or FFAR1/GPR40 activators and acetylcholine).
- The modeling of messenger pathway interactions is important for determining the pharmacological targets for improving insulin secretion in T2D. The results of the model applications can guide clinical approaches to improvement of β-cell function by combinations of stimuli via GPCR agonists, helping to minimize side effects and maximize clinical benefit.
Computational Model (CM)
The purpose of this section is to develop the mathematical formalism for a β-cell model of receptors and second messenger interactions. A schematic diagram of the biochemical steps, channels, receptors, Ca2+ handling and messenger pathways in β-cell is presented in Fig 1. We will highlight some basic assumptions and general properties of the model reflecting the main mechanisms describing in text. We assume concentration of most species lies in the range that allows us to avoid the need to use complex stochastic algorithms that would be necessary if regulatory processes are dependent on minute quantities of molecules. The β-cell is modeled as a small spherical cell. Modeling  has shown that in such a cell, for example, cAMP is distributed rapidly and uniformly throughout the cytoplasm, even following GLP-1 receptor activation, in time intervals that we used in our simulations (minutes). On this base we suggested that species are distributed uniformly on PM, in cytoplasm or ER of β-cells in our models. Certainly some molecular events are critically dependent on localized gradients such as possible activation of insulin granule release by Ca2+ near specific Ca2+ channels , however these potentially important events are not considered in our model.
The present model is created as systems of ordinary differential equations with nonlinear terms. Numerical integration was carried out using standard numerical methods. Three compartments: plasma membrane, cytoplasm and ER were used. The units, except where indicated otherwise: cytosolic and ER species and ligand concentrations are represented by concentration in (μM) while membrane-bound species are given as area densities (number per unit of membrane area) in (# μm−2), time in seconds (s), voltage in millivolts (mV), current in femtoamperes (fA), conductance in picosiemens (pS), capacitance in femtofarads (fF) (per cell).
Experimental data are often represented only for concentration per volume and it is often nessesary to calculate a concentration on PM surface. Then the coefficient (fNc) to convert the concentration from # μm-2 to μM for β-cell can be calculated as (1) where Sc is the cell surface, Vc is the volume of the cell cytoplasm (in liter) and Nav is Avogadro’s constant. fNc = 0.00211 μM μm-2 if the data from Table 1 were used.
Lifetimes are often only accessible from experimental articles. Assuming the first-order processes, the detected lifetimes (t1/2) can be converted to rate constants according to equation k = ln2/t1/2 = 0.693/ t1/2.
The data used to fit the computational model in our study were taken primarily from isolated rodent β-cells and cell lines. Some parameters were taken from the literature. Remaining parameters were experimentally unreachable and we optimized their values by simulated the model and compare with experimental data (Tables 1–6). For simplicity, we use the concentrations the components inside a cell without brackets. Basal initial conditions were denoted as (-in).
The influence of glucose (Glu) and FFA on GSIS was modeled as the dynamical changes in [ATP]/[ADP] ratio that determines KATP channel opening. We assume that the nucleotide ratio satisfies the first-order kinetic (see ). (2) where (3) (4) (5) (6) and (7) where ADPf and ATP are concentrations of free ADP and ATP in cytoplasm, Glu and FFA are the concentrations of glucose and free fatty acid in surrounding medium. ATDo is the steady-state ATP/ADP ratio. Glui is the initial Glu concentration. Glu1 is the change in Glu in time (t1).
The dependence of ATDo on extracellular concentration of glucose and FFA (Eq 4) was suggested as a Hill function with saturated value (ATDm), KGF is the coefficient for half-saturated glucose and FFA concentrations, hgla is the the Hill coefficient, kGFr is the coefficient for a conversion from FFA to glucose concentration, tATD is the time constant (Table 2).
The coefficients KGF and hgla were taken from work  where these data were evaluated for human β-cell. The scaling coefficient kGFr was evaluated using data for FFAR1/GPR40 knockout mice because FFA can also activate FFAR1/GPR40 receptor affecting on GSIS. According to Fig 5A from  insulin secretion at 8.3 mM glucose and 0.5 mM palmitate is about the same as for 12 mM glucose in FFAR1/GPR40 knockout mice islets. In this case kGFr = 7.4 (example calculated for palmitate concentration).
Membrane potential, channels and pumps
Plasma membrane potential.
Increased extracellular glucose promotes membrane depolarization. Several ionic channels regulate this process. A diagram of the main β-cell specific channels considered here is presented in Fig 1. We used our models of electrophysiological processes in the β-cell [29, 30, 33] as a framework. However, only the most important currents were taken into account to achieve the desired granularity. For simplification Boltzman-type equations in steady-state were employed for activation (di) and inactivation functions (fi) to avoid a simulation of spike activity.
We modeled the electrophysiological events for one cell. The plasma membrane (PM) potential in a single β-cell can be described with the following current balance differential equation (8) where Vp is the PM potential, t is the time, Cm is the whole-cell membrane capacitance, IKATP is the ATP-sensitive K+ channel current, IKr is the voltage-dependent K+ current, ICa is the high-voltage-activated Ca2+ current, ISOC is the store-operated current activated by decrease of Ca2+ in ER, IPCa is the plasma membrane Ca2+-pump current, INab is the Na+ background current, that can be activated also by muscarinic acetylcholine M3 receptor.
ATP-sensitive K+ channels current (IKATP).
We adopted a kinetic model [29, 33] for whole-cell ATP-sensitive K+ channels current (IKATP). However, a dependence of this current on PIP2 and PKA activation was also found and we introduced the corresponding factors. The influence of PIP2 was represented as Michaelis-Menten function (fKPI, Eq 10) where PIP2 increase can enchance IKATP.
Both SUR1 and Kir6.2 subunits contain consensus PKA phophorylation sites and, for example, GLP-1 dependent activation of PKA leads to phosphorylation of SUR1 subunits lowering their affinity for ADP [205, 206]. For simplicity we introduced this dependence as a function (fKPKa, Eq 13) where an activated PKA (PKAa) decreases the calculated [MgADPf]c leading to additional IKATP decrease. (9) where (10) (11) (12) (13) (14) where gmKATP is is the maximum conductance for IKATP, gmKATPi is the initial conductance, gmKATP1 is the increase in conductance in time (t9). EK is the reversal potential for K+ current, PIP2 is the PIP2 content on PM, KKPI2 is the activation constant, OKATP is the fraction of open KATP channels, kdd, ktd and ktt are the dissotiation coefficients, MgADPf is free Mg-bound ADP, ATPf and ADPf are free ATP and ADP concentrations in cytoplasm; kKPKa is the inhibition constant.
To determine KKPI2 (Eq 10) we suggest that fKPI dependence corresponds to PIP2 dose-dependent activation of KATP channels (in absence GST-Syn-1A) where EC50 values for PIP2 was calculated as 2.38±0.81 μM  or 1125 #/μm-2 in our definition (see Eq 1).
Voltage-gated K+ current (IKr).
Different voltage gated K+ currents (IKr) were also registrated in β-cell. For example, delayed rectifier K+ current is main voltage-dependent K+ current in rodents . This current activates at membrane potential near -30 mV and then increased with the applied voltage, an inactivation was negligible during at least 200 ms depolarization . The Hodgkin-Huxley-type with stationary dependences for delayed rectifier K+ current and some corresponding coefficients were used from our previous mouse and human β-cell model to describe IKr [29, 30]. (15) where (16) (17) gmKr is the maximum conductance for IKr, VdKr is the half-activation potential, kdKr is the slope of half-activation potential.
Ca2+ current (ICa).
ICa was modeled as L-type Ca2+ channel with one inactivation gating variables  without time dependent parameters (18) where (19) (20) where gmCa is the maximum conductance for ICa, ECa is the reversal potential for Ca2+ current, VdCa is the half-activation potential, kdCa is the slope of half-activation potential, kdCap is the coefficient of the constitutive channel activity, VfCa is the half-inactivation potential, kfCa is the slope of half-inactivation potential.
Store-operated Ca2+ current (ISOC).
[Ca2+]ER decrease activates the Ca2+ release-activated Ca2+ current through PM. Interestingly, in human Jurkat leukaemic T cells expressing an ER-targeted Ca2+ indicator, SOC channel activation follows the function of [Ca2+]ER, reaching half-maximum at ~200 μM with a Hill coefficient of ~4 . We use these dependence and coefficients in our model (Table 2). We consider ISOC as a voltage-independent Ca2+ inward current (21) where (22) gmSOC is the maximum whole-cell conductance, fSOC is the [Ca2+]ER dependent function, KNS is the [Ca2+]ER inhibition constant.
Plasma membrane Ca2+ pump current (IPCa).
Ca2+ pumps provide an outward current and also contribute to Vp. The corresponding equation was adapted from previous model  (23) where PmCa is the maximum IPCa current, KpCa is the [Ca2+]c activation constant.
Na+ background current (INab).
The model contains a voltage-independent Na+ background current. This current can depolarize the resting PM potential and modifies mouse and human β-cell electrical activity (see for details [29, 30, 33]). We have also included the specific NALCN channels, that activatable by the muscarinic acetylcholine M3 receptor. Than (24) where (25) ENa is the reversal potential for Na+, gmNb is the permanent conductance for INab, gmNM3 is the maximum conductivity of NALCN channels and LRG6 is the M3 receptor bound with ligand and G-protein (see below).
Receptors, G-proteins and target enzymes
Plasma membrane receptors and G proteins usually possess a high number of distinct binding domains inducing the formation of large multiprotein signaling complexes. In quantitative models of GPCR signaling that incorporate these varied states, parameter values are often uncharacterized or varied over large ranges, making identification of important parameters and signaling outcomes difficult . However, despite their diversity, signaling pathways in β-cells employ a set of common components that we have used for modeling. Fig 13 shows the reaction scheme for the minimal model of a receptor cascade. We modeled two different proposals about ligand-receptor interactions that were suggested in literature: 1. Collision coupling model, where a ligand binds to the free receptor and then the ligand-receptor complex ‘‘collides” with the free G-protein (Fig 13A). 2. Pre-coupling model where stable receptor/G-protein complex exists in the absence of ligand and ligand bounds with this complex (Fig 13B).
Rnd is the desensitized receptor, Rn is the free receptor, Ln is the ligand, LRn is the receptor bound with ligand, LRGnGDP is the receptor bound with ligand and G protein, GnGDP is the G proteins consisting of subunits α, β and γ. In this ground state the α-subunit is bound to GDP. GαnGTP is the GTP-bound α subunit of G protein, Gβγn is the β and γ subunit of G protein, GαnGDP is GDP-bound α subunit of G protein, GαnGTPEn is the complex of the α-subunit carries the GTP and effectors enzyme (En). Efn is the enzyme that is not bound with G protein. The constants can be identified by their subcripts, where n the forward transition and nr is the reverse transition. For reversible reactions (double arrows), forward reactions are in the direction of association. (A) Collision coupling model. (B). Pre-coupling of receptor and G-protein model.
In the collision coupling model the initial steps in GPCR signaling take place in the plasma membrane and involve the binding of a ligand (Ln) to a receptor that rapidly shifts an inactive receptor (Rn) to an active state (LnRn). In the following steps the activated receptor couples to heterotrimeric G proteins (GαβγnGDP). In pre-coupling model the initial steps in GPCR signaling take place also in the plasma membrane. In this case a ligand binds with a receptor/G-protein complex.
The following steps are identical for both models. Heterotrimeric G proteins coupled with receptor activates through the release of a bound guanosine diphosphate (GDP) and the capture of guanosine triphosphate (GTP) on the G protein α-subunit. A series of intra and intermolecular events in the heterotrimer Gαβγ leads to a dissociation of this complex resulting in active Gα·GTP and Gβγ subunits.
Although both Gα·GTP and Gβγ can then activate different signaling pathways and effector proteins, we focus only on the reactions downstream of Gα·GTP activation since it is principal process for receptors, that we considerate in this article. In this case, as laterally diffusing Gα·GTP subunits bind isoforms of inactive (presumably freely diffusing) target enzymes (Efn) on the inner leaflet of the plasma membrane and forming a complex (GαnGTP En) (coefficient k4n), that activates or inhibits biochemical processes. Activated G proteins (GαnGTP) deactivate through their own GTPase activity (coefficient k5n) (or much faster when bound to Efn, coefficient k6n). This allows GαnGDP to recombine with Gβγn (coefficient k7n) starting a new cycle.
Cells can fine-tune their excitability by changing the susceptibility of receptors, e.g. switch between active (LnRn−in collision coupling model or LnRnGnGDP in pre-coupling model) forms and desensitized form (Rnd) by regulating the number of receptors via internalization. We have modeled process of desensitization of activated receptor (rate constant—k9n). Internalized receptors can then undergo activation and return to the surface in our model (rate constant—k8n).
Receptor dynamics and G protein cascade.
Our model of receptor dynamics is based on several earlier studies [209, 210]. However, we were not able to find a model that matches well to our aims. The majority of these models do not include a G-protein bound with target enzyme. For this reason we constructed a minimal model of the receptor signaling transduction and corresponding target enzymes activations.
Several suggestions were made. Our model assumes that initial concentration of ligand is sufficiently high so that it is not significantly depleted by binding to the receptors and therefore the ligands can be presumed as a fixed concentration. The process of receptors and G-proteins synthesis is assumed to compensate for intracellular receptor and G protein degradation, so that the total area density of receptors and G proteins in different states (free, active, and internalized) remains constant.
We have assumed that the processes occur in the plasma membrane and all the species, except for the agonist, are membrane bound and are diffuse freely. We simplify the model by ignoring the processes, such as the nucleotide exchange on G-proteins that are not bound to the receptor or bound to the receptor that is not bound to the ligand and others. Receptor desensitization was included as a process that can deactivate the receptor-ligand complex. The multiple deactivation and reactivation steps were shortened in single irreversible reactions (see Fig 13).
We modeled two different models of ligand-receptor interactions: 1. Collision coupling model and 2. Pre-coupling model (see Fig 13). Based on the above assumptions and using the law of mass action the collision coupling model suggests that ligands, receptors and G-proteins collide and binds to the corresponding enzyme transiently to produce target enzyme activation. For simplicity, we use concentrations on PM without brackets. Than a collision coupling model is represented as a system of ordinary differential equations: (26) (27) (28) The following conservation constrain also holds (Rnt) (29) or (30) where [Ln] is the free ligand concentration; KL is the Michaelis-Menten constant, Rn, LRn, LRGn and Rnd are, respectively, the area density of free, bound with ligand, bound with ligand and G-protein and internalized (desensitized) receptors and Rnt is the total receptor area density in # μm-2. (Definition LnRnGn instead LnRnGnGDP was used for simplification). k1n, k2n, k3n, k8n and k9n are the forward rate constants and k2nr is the backward rate constant (see Fig 13A).
The first binding step reflects a simple bimolecular interaction between the ligand (Ln) and the receptor (Rn) (coefficient k1n) that is strictly dependent on ligand concentration. According to mass action kinetics, we have assumed that GαnGTPEn are produced at a rate proportional to the area density of GTP-bound α subunit of G-protein, GαnGTP, and the inactive target protein area density (Efn) (coefficient k4n). (31) (32) (33) where k4n - k7n are the forward rate constants.
We suggest that the content of the endogenous G-proteins (Gnt) and target enzymes (Ent) remain constant (34) or (35) (36) (37) or (38) where GnGDP, LRGn, GαnGTP, GαnGDP and GαnGTPEn are the area density of free, bound with ligand and receptor, active, inactive and bound with the target enzyme G-proteins, respectively. Gnt is the total area density of Gn proteins per unit of PM, both activated and inactivated. Efn is the free target enzyme and Ent is the total area density of the target enzyme.
Pre-coupling mechanism of ligand-receptor interaction suggestes that the stable receptor/G-protein complex exists in the absence of ligand and ligand bounds with this complex (see Fig 13B). Equations for a pre-coupling model can also be obtained similarly Eqs 26–30 as a system of ordinary differential equations: (39) (40) (41) The following conservation constrains also hold (42) (43) (44) or (45)
Several suggestions were accepted in order to determine of coefficients of both models:
Kinetics of ligand binding are usually fast relative to the other processes in the model (see ). The fast speed observed for receptor activation in response to saturated concentrations of small agonist molecules (coefficient k1n, Fig 13A) has half-life (t1/2) ranges between ≈ 40 and 100 ms in the case of α2A- and β1-adrenergic receptors, muscarinic M1- and M2-receptors  (k = ln2/t1/2). We used a coefficient of receptor ligand binding (k1n ≈ 7 μM-1 s-1) for the collision coupled model that corresponds to the half-life about 100 ms.
Coefficient for binding of LRn with target G protein in the collision coupling model (k2n in Fig 13A) depends on the area density of moleculs on PM and was evaluated as 1 #-1μm2 s-1 . Coefficient for receptor–G-protein unbinding (k2nr) was calculated for collision coupling model as 0.68 s-1 and for pre-coupling model as 6.8 s-1 .
Binding of GαnGTP with target enzyme Efn (coefficient k4n in Fig 13A and 13B) was evaluated by  as 1 #-1μm2 s-1. Deactivation of GαnGTP through its own GTPase activity (coefficient k5n Fig 13A and 13B) was evaluated by  as 0.026 s-1. Dissociation of complex of G protein and target enzyme (coefficient k6n Fig 13A and 13B) was evaluated by us as 0.4 s-1. The recombination of α and βγ subunits (coefficient k7n in Fig 13A) is thought not to be limiting and was evaluated as 1 μm2 s-1 [216, 218]. Typical rate constants for desensitization of activated receptor (k9n) and its return to the surface (k8n) in our model was taken from model for slow GLP-1 receptor kinetics  (k8n = 5 10−5 s-1 and k9n = 0.000283 s-1). We used these parameters and coefficients if no additional experimental data can be found in literature for some receptors, corresponding G proteins or target enzymes.
Tonic receptor effects are simulated by including the presence of low concentrations of corresponding receptor ligands even when the experiments were simulated without specific agonists.
We used the collision coupling model for this receptor. The processes were described similar to Eqs 26–40) (46) GLP1 was used instead [Ln], GLP1i is the initial GLP-1 concentration. GLP11 is the increase in GLP1 in time (t2), KGLP1 was used instead KL and R1X instead the corresponding coefficients (see Table 3). ACGLP is the PM bound AC activated by GLP-1 receptor. The parameters and coefficients can be identified by their subscripts, where 1 was used instead n (Table 3).
Specific concentrations and coefficients: According  the amount of GLP-1R (ReGLt) in one INS-1 cell is about 2,000, i.e. it can be evaluated as about 2 # μm-2 (if cell surface is about 1000 μm-2). Half saturation (log10M = -10.5) for GLP1 was measured as an increase in cAMP production in the pancreatic β-cell line (Fig 5 from ), that we used for evaluation of KGLP1 (0.000031 μM, Table 3). Other coefficients were chosen as general coefficients.
Regulated mechanisms for GIP receptor are assumed to be similar to those of GLP-1R. (47) GIP was used instead [Ln], KGIP instead KL and R2X instead the corresponding coefficients (see Table 3). ACGIP is the PM bound ACp activated by GIP receptor. GIPi is the initial GIP concentration. GIP1 is the increase in GIP in time (t3). The parameters and coefficients can be identified by their subscripts, where 2 was used instead n (Table 3).
Specific concentrations and coefficients: INS-1 cells expressed an average of 2443μ400 GIP receptors (G2t) on the cell surface or ~ 2.5 # μm-2 . EC50 for GIP was evaluated as 171 pM for activation cAMP production , i.e. KGIP = 0.000171 μM. Other coefficients were chosen as the general coefficients.
α2A-adrenergic receptor (R3).
A model of α2A adrenergic receptor activation in β-cell was constructed as a collision coupling model. In this case an active GαGTP messenger (GαT3) released from stimulated receptor binds to the enzyme ACpf. (48) AR3 was used instead [Ln], KAR3 instead KL and R3X instead the corresponding coefficients (see Table 3). AR3i is the initial AR3 concentration. AR31 is the increase in AR3 in time (t4), ACAR is the PM bound AC activated by α2A-adrenergic receptor. The parameters and coefficients can be identified by their subscripts, where 3 was used instead n (Table 3).
Specific concentrations and coefficients: Noradrenaline (norepinephrine) was shown to be a potent inhibitor of GSIS from rat pancreatic islets, with half-maximal inhibition of the secretory response to 20 mM-glucose occurring at approx. 0.3 μM , and we accepted this value for the coefficient KAR3. Other coefficients were chosen as the general coefficients.
Basal concentrations of GLP-1R, GIPR and AdR, that determinate constitutive receptor activity, were taken in such manner that 0.1 ACP was bound for each receptor (see Eq 59).
M3 muscarinic receptor (R6).
We used the pre-coupled model for this receptor. (49) AM3 was used instead [Ln], KAM3 instead KL and R6X instead the corresponding coefficients (see Table 4). PLCM3 is the PM bound PLC activated by M3 muscarinic receptor; PLCpf is the free PLC on PM (see Eq 88). AM3i is the initial AM3 concentration. AM31 is the increase in AM3 in time (t6). The parameters and coefficients can be identified by their subscripts, where 6 or M3 were used instead n.
Specific concentrations and coefficients: acetylcholine and carbachol (MR agonist) have different KAM3. We used the coefficients by Hoffman et al  for acetylcholine in human M3 muscarinic receptor (see Table 4). Agonists such as acetylcholine, carbachol or muscarine activate each receptor construct with half-maximal activation times between 60 and 70ms for human M3 muscarinic receptor  (coefficient R61) (or R61 = 10 s-1).
Receptor deactivation kinetics was found to be slow and independent of agonist concentrations  (coefficient R62r was taken according ). R68 (coef k8n in Fig 13B) was evaluated as the coefficient of M3 receptor dephosphorylation in work . Human M3 receptors were internalized with a half life of 11 min (660 s) for M3 receptors expressed in COS-7 cells and in the presence of 10−3 M carbamylcholine (for receptor activation)  and we determined the coefficient R69 from these data. Other coefficients were chosen as general coefficients (Table 4).
A model for FFAR1/GPR40 was made as for collision coupling model receptors. (50) AR7 was used instead [Ln], KAR7 instead KL and R7X instead the corresponding coefficients (see Table 4). PLCR40 is the PM bound PLC activated by FFAR1/GPR40 receptor. AR7i is the initial AR7 concentration. AR71 is the increase in AR7 in time (t7). The parameters and coefficients can be identified by their subscripts, where 7 or R40 were used instead of n.
Specific concentrations and coefficients: KAR7 for AR7 was evaluated as palmitic acid potency (EC50) to induce [Ca2+]c rise in mouse CHO cells expressing FFAR1/GPR40 . Other coefficients were chosen as the general coefficients.
Basal concentrations of FFAR1/GPR40 and AR7, that determinate constitutive receptor activity, were taken in such manner that 0.1 PLCPt was bound for each receptor (see Eq 79).
Ca2+ binds to calmodulin (CaM) in four steps and generates four species of Ca2+-bound calmodulin: CaCaM, Ca2CaM, Ca3CaM, and Ca4CaM. However, CaM needs to bind at least 3 Ca2+ to be active. We accepted the model for CaM dynamic from  (Table 5). (51) where (52) (53) (54) (55) (56) where k1f–k4f are the forward rate constants and k1b–k4b are the backward rate constants in the four steps of Ca2+ binding to CaM, CaMo is the total amount calmodulin, CaMa is the active form of CaM.
Modeling of cAMP pathway
Activated adenylyl cyclase synthesizes cAMP from the substrate Mg2+ATP. Our recent mathematical model of the cAMP pathway in pancreatic β-cells includes detailed descriptions of interactions between [Ca2+]c, Ca2+-bound calmodulin (Ca2+/CaM), adenylyl cyclase (AC), phosphodiesterase (PDE) and dynamics of cAMP concentration in the cytoplasm . We have extended this model to include a description of cAMP-dependent modulation of PKA and Ecap and an activation of exocytosis processes . In this article we added the equation for cAMP production by soluble Ca2+-activated AC isoform. As cAMP molecules are known to diffuse through the cytosol, we refer to cAMP production and degradation in terms of concentration (in μM) instead of the area density of molecules on PM. The dynamic of intracellular concentration of cAMP is determined by the rates of cAMP synthesis and degradationcan (see ): (57) where VAC is AC activity, VPDE is the PDE activity, kda is the coefficient of PDE-independent cAMP degradation.
AC activities were divided into two functionally distinct categories: (58) where VACp is G-protein and Ca2+ dependent AC activity on PM, VACc is the glucose and Ca2+-activated cytoplasmic AC activity that is independent on G-proteins.
GLP-1 and GIP activate and catecholamines inhibite the same CaM and Ca2+-dependent isoform of AC on PM that bound corresponding G protens for activation. Equation for the area density of this activated AC isoform on PM is based on our previous analysis (see Eqs 46–48) (59) or (60) where (61) (62) ACpt is the total concentration of AC isoform on PM, ACpf is the concentration of free AC isoform on PM, ACGLP, ACGIP and ACAR are the concentrations of ACp bound with corresponding G-proteins activated by GLP-1R, GIPR and α2A adrenergic receptors (see above). ACpa is the area density of active AC molecules that can be stimulated by G-proteins, ACpar is the relative concentration of ACpa. CaM/Ca2+ dependence of ACp activity was accepted similarly to work , (63) where (64) VmCaM is the maximum ACp activity, facca is CaM activation factor, KPCaM is the CaMa activation constant, KNCa is the [Ca2+]c inhibition constant.
It was found that in S49 lymphoma cells, a widely used model system, each cell contains only a few thousand functional copies of the catalytic subunit of AC for binding forskolin (3000 molecules per cell) that can be consider as evaluation of PM bound AC (as 3 # μm-2) and G-protein appears to exist in stoichiometric excess relative to AC . We used this data for evaluation of the content of PM bound AC isoform in β cell (Table 5).
Another isoform is cytoplasmic AC (ACc) that can be activated by ATP and Ca2+. We used a the Michaelis-Menten function for an dependence ACc on ATP and [Ca2+]c concentrations that were found for this AC isoform  (65) where VmACc is the maximum ACc activity, KmAACS is the of ATP activation constant, KmCACS is the [Ca2+]c activation constant, kACS is the coefficient of the constitutive ACc activity.
The equation for the function of PDE was accepted from  in simplificated form. We added also the coefficient (kipde) for a simulation of the specific inhibition. (66) where Vgpde is the activity of Ca2+/CaM-independent PDE, Vcpde is the basal level of Ca2+/CaM dependent PDE activity, Kdpe is the CaMa activation constant, Kpde is the cAMP activation constant, kipde is the the activation or inhibition coefficient. kipdei is the basal coefficient. kipde1 is the change in kipde in time (tpde).
cAMP dependent PKA and Epac activation.
Binding of cAMP to the regulatory units of PKA or Epac results in release of the catalytic units and an activation of PKA or Epac. We used the mathematical models of PKA activation . Relative steady-state level of PKA activation by cAMP (fpca) was described by an empirical Hill-type equation . (67) where (68) (69) where PKAa is the relative concentration of active PKA, PKAi is the relative concentration of inactive PKA, the total relative concentration of active and inactive PKA was accepted as 1, tpka is the time constant; kak is the scaling factor, fpca is cAMP potential factor, where Kpcm is the cAMP activation coefficient  and hpca is the Hill coefficient.
The pathway, that includes Epac activation, was modeled similarly to the PKA dynamics (70) where (71) (72) where EPa is the relative concentration of active Epac, EPi is the relative concentration of inactive Epac, the total relative concentration of active and inactive Epac was accepted as 1; fEcA is the cAMP potential factor, where Kmep is the cAMP activation constant for Epac, hce is the Hill coefficient, tep is the time constant.
Phosphoinositides regulate numerous processes in pancreatic β-cells. We modeled phosphoinositides dynamics using the models and coefficients for sA201 cells [200, 201, 231] because a few quantitative kinetic measurements were made for pancreatic β-cells. According to  P4P synthesis activates by PKCa and Ca2+ that we have also taken into account introducing the specific term (fPI). Than the equation for P4P dynamic can be written as: (73) where (74) where PI is the intracellular pool of phospholipids on PM, PIP2 is the concentration of plasma membrane-bound phosphatidylinositol bisphosphate, kPI is the P4P production coefficient, kPIr is the backward rate constant for P4P, kP4Pr is the backward rate constant for PIP2, kP4P is the the PIP2 production coefficient, KCaPI is the [Ca2+]c activation coefficient, KP4PK is the PKCa activation coefficient.
According to  ATP dose-dependently stimulated PIP2 synthesis has the half-maximally stimulation at 300 μM ATP that significantly lower than ATP concentration in cytoplasm (see ). For this reason we did not take into account ATP concentration dependence at PIP2 synthesis. On the other hand PLC hydrolyzes plasma PIP2 molecules into inositol trisphosphate (IP3) and DAG. The rate of this reaction depends on activity of PLC. (75) where (76) where VPL is the PLC activity (see below, Eq 77), KPIP2 is the PIP2 activation coefficient.
Density of phosphoinositides at the plasma membrane in β-cell remains uncertain. We assumed that free PIP2 is about 5000 # μM-2 in our modeling similarly evaluation for tsA201 cells in work .
PLC signaling pathway
Activation of PLC by M3 muscarinic receptors and FFAR1/GPR40 receptors was modeled above (Eqs 49–50). However, PLC activity is also regulated by G-protein independent (but [Ca2+]c dependent) ways in insulin-secreting cells. We consider two different PLC isoforms: 1. MR and FFAR1/GPR40 activated (PLCp). 2. Ca2+ activated G-protein independent PLC form (PLCc). A schematic of these two PLC isoforms is shown in Fig 1. (77) where (78) (79) (80) (81) where VPL is the total PLC activity, VPLP is the G-protein dependent and VPLC is the G-protein independent (but Ca2+ dependent) PLC activity, kpPL is the coefficient of the PLC constitutive activity. VmPLP is the maximum of VPLP activity, KCaPL is the [Ca2+]c activation constant, VmPLPi is the initial (basal) activity. VmPLP1 is the increase in VmPLC in time (t10), PLCpt is the is the total concentration of PLCp isoform on PM, PLCpa is the concentration of PLCp bound with MR and FFAR1/GPR40 receptors, PLCpf is the concentration of free PLCp, VmPLC is the the maximum VPLC activity, KCCaPL is the [Ca2+]c activation constant.
Endogenous PLC on PM (3 # μm-2) was evaluated for a transformed human kidney cell line (tsA-201) whose dimensions are close to that of the β-cell [200, 216] and we used this value for PLCpt evaluation (Table 4).
The generation of IP3 is determined by the hydrolysis rate of PIP2. Because IP3 molecules diffuse through the cytosol, we refer to IP3 production and degradation in terms of concentration (in μM) instead of the area density of molecules on PM. Different mechanisms seem to be available in the β-cells for degradation of IP3, however, we assume that IP3 is degraded at a rate proportional to the concentration of IP3. Then we used the simplest model of IP3 dynamics from ) but it was modified to consider the processes of IP3 production on PM. (82) where fNc is the coefficient to convert from # μm-2 to μM (Eq 1), kdIP3 is the rate constant of IP3 degradation.
DAG is the product of PLC-catalyzed breakdown of phosphoinositides, stimulates by PLC where it is produced stoichiometrically with IP3. However, DAG is bound with PM. Model of the DAG dynamics was developed similarly to the IP3 model. Coefficients from model  for native tsA201 cells were used (Table 6). (83) where kdDAG is the coefficient of DAG degradation
DAG dependent PKC activation.
Binding of DAG to the regulatory units of PKC results in release of the catalytic units from PKC and its activation. Relative PKC activation by DAG was described similarly to work  as equation: (84) (85) where PKCt = 1
PKCa is the relative concentration of active PKC, PKCi is the relative concentration of inactive PKC, PKCt is the total relative concentration of active and inactive PKC, kPKC and kPKCr are the forward backward rate constants. t1/2 was evaluated as 204 s-1  (kPKCr = 0.0034 s-1).
Cytoplasmic Ca2+ dynamics was modeled at the whole cell level. We included only fluxes through Ca2+ channels on PM, Ca2+ pumps on PM and ER and Ca2+ flux from ER. Then the free cytoplasmic Ca2+ ([Ca2+]c or Cac in computational program) dynamics can be modeled by the following equations: (86) where fcf is the fraction of free Ca2+ in cytoplasm, F is Faraday’s constant, Vc is the effective volume of the cytosolic compartment, and ksg is the coefficient of Ca2+ sequestration rate. Total Ca2+ current on PM include the current through the voltage-dependent Ca2+ channels—ICa (Eq 18); the store-operated current—ISOC (Eq 21) and the PM Ca2+ ATP-ase pumps—IPCa (Eq 23). Jser is the Ca2+ flux from the cytosol into the ER activated by Ca2+ ATP-ases (SERCA) (per cell); Jrel is the Ca2+ flux from the ER into the cytosol (per cell).
Ca2+ dynamics in ER ([Ca2+]Er or CaER in computational program) can be modeled by the following equation: (87) where fer is the is the fraction of free Ca2+ in ER, VER is the effective volume of the ER compartment. A function of the Ca2+ ATP-ase on ER (per cell) was modeled using the usual expression : (88) where PCaEr is the maximum SERCA pump rate, Kser is the [Ca2+]c activation constant.
The total Ca2+ flux from the ER into the cytosol is given by (89) where kmIP is the maximum permeability for IP3-activated channel, PRIP3 is the IP3 receptor channel open probability, kleak is the coefficient of Ca2+ passive leak from the ER through unspecified channels.
The simplest model of IP3 receptor (IP3R) activated by IP3 and Ca2+ was taken from Bertram and Sherman . Additional possibility is an activation of IP3R by PKA. PKA-mediated phosphorylation leads to a direct increase in the sensitivity of the IP3 receptor toward IP3 without shifting its Ca2+ sensitivity. We modeled this effect as a decreased IP3R activation constant with PKA activation (function fIPKA). (90) where
PRIP3 is the channel open probability, KRPCa is the [Ca2+]c activation constant, KIP3 is the IP3 activation constant, dinac is the [Ca2+]c inhibition constant at higher concentrations of Ca2+, kIP3R is the PKAa activation coefficient. The first factor represents an activation by Ca2+, the second an activation by IP3, and the third an inactivation at high concentration of [Ca2+]c.
The model consists of a system of nonlinear ordinary differential equations describing the time rate of change in parameters. Parameter values and initial conditions (Tables 1–6) contain all the information necessary to carry out the simulations. These values were used in all simulations except where indicated otherwise. To calculate the steady-state cellular parameters, the model was allowed to run for at least 1000 s with no external stimulation.
This work was supported by grants from the National Institutes of Health to LHP (DK063493 and DK092616), and by University of Chicago Diabetes Research and Training Center funding from the NIH (DK020595) to LHP. The Virtual Cell is supported by NIH Grant Number P41 GM103313 from the National Institute for General Medical Sciences.
Analyzed the data: LEF LHP. Wrote the paper: LEF LHP. Mathematical simulation: LEF.
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