Neuropeptide Y receptor Y2 site-directedmutagenesis

Jasna Pruner

Degree project in biology, Master of science (1 year), 2011Examensarbete i biologi 15 hp till magisterexamen, 2011Biology Education Centre and Neuroscience Department, Unit for Pharmacology, Uppsala UniversitySupervisor: Dan Larhammar

LIST OF ABBREVIATIONS

ARC – Arcuate nucleus CCK – Cholecystokinin, previously named pancreozymin CNS – Central nervous systemCRF – Corticotropin-releasing factordNTP – Deoxynucleotide triphosphateGFP – Green fluorescent proteinGPCR – G-protein-coupled receptorMCH – Melanin concentrating hormoneNPY – Neuropeptide Y PNS – Peripheral nervous systemPOMC – Pro-opiomelanocortinPP – Pancreatic polypeptidePVN – Paraventricular nucleusPYY – Peptide YY

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INTRODUCTION

G-protein-coupled receptor structures were not built in a day

During the past few years, the structural biology of G-protein-coupled-receptors (GPCRs) has seen remarkable advances after many years of efforts by numerous research teams worldwide. The year 2000 represented a milestone in GPCR crystallography when the crystallographic structure of bovine rhodopsin was published (Palczewski et al., 2000). Bovine rhodopsin remained the only high resolution G-protein-coupled receptor structure for several years. This initial breakthrough was followed by the publication of a high resolution crystal structure of the human β2 adrenoreceptor in 2007 (Rasmussen et al., 2007), the turkey β1 adrenoreceptor (Warne et al., 2008), human 2A adenosine receptor (Jaakola et al., 2008) as well as two recently resolved structures of chemokine receptor CXCR4 (Wu et al., 2010) and dopamine receptor D3 (Chien et al., 2010). However, these are the crystal structures of receptors in inactive state. The structures of the activated G protein-coupled receptors were a mystery until the structure of a β2 adrenergic receptor coupled to a stimulatory G protein became available (Rasmussen et al., 2011). These sensational findings will undoubtedly extend our knowledge about elusive G-protein-coupled receptors and offer valuable clues for constructing templates for computational 3D model building of receptors with unresolved structures. The approach of 3D-modelling can undoubtedly cast more light on the complex GPCR structure and increase general understanding of GPCRs' backbone and rotamer sidechain conformations (Kobilka, 2007). A good understanding of the receptors’ overall structure and binding pocket topology is crucial for identifying the correct receptor-ligand interactions. Such knowledge is crucial in pharmacological sciences because it can lead to the discovery of potential drugs. Moreover, the prerequisites for replacing empirical drug discovery with rational drug design can be gradually fulfilled in this manner and ultimately optimize drug target identification for high throughput-screening (Kobilka and Schertler, 2008).

There are still a vast number of constraints in studying G-protein-coupled receptor structures which delay the advances of GPCR structural biology. GPCRs are integral membrane proteins embedded in the lipid bilayer, with α-helical segments spanning the membrane seven times and extracellular and intracellular loops of different lengths among various receptor subtypes. The majority of GPCRs are expressed at low levels in their original tissues (Rosenbaum et al., 2009), which demands construction of appropriate, preferably eukaryotic expression systems. In addition, their surface is relatively hydrophobic. Hence they can only be extracted from the cell membrane by solubilization with detergents compatible with crystallization conditions (Congreve and Marshall, 2009). The notion of GPCRs as rigid, bimodal switches occupying active and inactive state is gradually being replaced with the concept of GPCRs as remarkably versatile signaling molecules. Indeed, GPCRs are rather flexible protein structures sensitive to temperature variations, which hamper the crystallization process. Stabilization by binding an antagonist or inverse agonist brings the receptor to its inactive state and this approach was necessary for success in crystallization of all the previously mentioned receptors. In the absence of a ligand, receptors can encompass the entire range of conformations from an inactive to an activated state and therefore crystallize poorly or not at all. Even if it were possible to extract the receptors in different conformations, it would be challenging to determine the receptor conformation in one state based on crystallographic data obtained from conformationally heterogeneous crystals (Rosenbaum et al., 2009). Luckily, crystallographers have discovered elegant techniques and strategies to circumvent the GPCR stability problem. Point mutations can be introduced in order to increase the thermal stability of a protein, as has been done in the β1 adrenoreceptor crystallization study (Warne et al., 2008). Entire extracellular loops can be replaced with the aim of improving thermal stability upon detergent extraction and therefore increasing the likelihood of crystallization (Jaakola et al., 2008). Despite all these obstacles, the bovine rhodopsin GPCR structure is available in its activated conformation (Scheerer et al., 2008). With the technical advances in this research area, the number of crystallized

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GPCRs will undoubtedly grow in the near future, inspired and supported by the experience of the GPCR crystallography pioneers.

Is the grass greener on the computational modeling side of fence?

Despite the presently available data, it remains difficult to predict which of the resolved receptor structures would be the most appropriate template in building the model of a receptor with an unresolved crystal structure (Michino et al., 2009). There are various ways to approach molecular modeling of GPCRs and many algorithms performing with different levels of accuracy on different receptor subtypes. GPCR Dock 2008 is an example of a blind-prediction study (Yarnitzki et al., 2010) that pinpointed the computational techniques behind the most accurate molecular 3D-model. It also demonstrated the challenges in computational molecular modeling. Accommodating ligands into a receptor model and hoping to correctly identify molecular interactions is, according to that study, as unlikely to be successful as looking for a needle in a haystack. The low probability of success is because of the vast number of combinations of rotamer conformations that are possible in the ligand-binding pocket. In a community-wide assessment of G-protein-coupled receptor structure modeling and ligand docking (GPCR Dock 2008), conducted prior to the publication of human adenosine 2A receptor crystal structure, the highest ranked contestant managed to identify 34 out of 75 correct ligand-receptor interactions between the adenosine antagonist and the receptor model (Katritch et al., 2009). Later assessments of the GPCR dock 2008 study emphasized the benefits of using multiple instead of single templates with high sequence similarity as the basis for computational molecular modeling (Yarnitzki et al., 2010). At the initial stage of computational modeling, one may choose to use a single template if the sequence similarity is high, especially in the trans-membrane helix segments (Katritch et al., 2009). Rhodopsin exhibits around 20% sequence identity to the receptors of the same class. Thus, rhodopsin represents a poor template choice in 3D modeling of receptors that are not implicated in vision for instance. If the sequence alignment shows moderate to poor identity, it is a far better option to rely on multiple templates to increase the chances to correctly model helical shifts in the trans-membrane helices. This step is of indispensable value for identifying the position, shape and size of the ligand-binding pocket as accurately as possible because it directly affects the position of the ligand docked into it. During the subsequent steps, one performs ligand docking which enables identification of places of interaction between the ligand and the binding pocket in the receptor model. At the final and most challenging stage, modeling of extracellular loops is performed, and the residues in the loop that can interact with the ligand are identified. The lack of structural similarity among the loops of known structures is most likely the reason why it is difficult to make a correct loop model (Michino et al., 2009). An accurate prediction of GPCR structure and ligand interactions remains a challenge due to receptor-specific structural and topological diversity. As improvements in prediction and docking algorithms are made, the currently available templates will become even more useful in their application to structure-based drug design and targeting, as well as in trans-membrane protein biophysics (Michino at al., 2009, Yarnitzki et al., 2010). To demonstrate this possibility and also how rapidly computer-based methods keep evolving - one research team managed to identify the binding pocket of two resolved beta adrenergic receptors with ligand unguided, molecular dynamics simulation approach (Dror et al., 2011). The benefits of applying bioinformatics in drug discovery will however be futile without molecular biological experiments. Testing correct ligand-receptor interactions is possible with the use of site-directed mutagenesis studies. By substituting amino acids thought to be involved in receptor-ligand interaction and testing the mutant receptor in radioligand binding assays, interactions can be verified with maximum certainty. In this multidisciplinary combination of bioinformatics, pharmacodynamics and molecular biology lies the true potential of rational drug design based on homology modeling.

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G-protein-coupled receptor function and diversity GPCRs form the largest gene superfamily in the human genome (Fredriksson and Schiöth, 2005). Despite the diversity in their primary structure, GPCRs are characterized by mutually shared topological features. The authors proposed new phylogenetic classification of human GPCRs by dividing them into five large groups or clans: the rhodopsin-like receptors, frizzled/taste2 receptors, glutamate receptors, adhesion receptors, and secretin receptors (Fredriksson and Schiöth, 2005). Hence, this classification was named the “GRAFS” system after the initial letters of these five groups. The Rhodopsin superfamily constitutes the largest among these.

Surprisingly, there is no correlation between the ligand type preference of a receptor and its corresponding phylogenetic group. GPCRs bind enormously diverse extracellular messengers, to name some: amines (serotonin, histamine, dopamine, cathecolamines), purines and nucleic acid derivatives (adenosine), lipids, peptides and proteins, odorants, pheromones, calcium, protons, and even photons (Jacoby et al., 2006), making them the key proteins in most cellular signaling pathways. Rhodopsin superfamily alone is capable of binding amines, lipids, nucleotides, proteins and small peptides. Based on their sequences, the rhodopsin superfamily receptors are further subdivided into α, β, γ and δ clusters (Fredriksson and Schiöth, 2005).

GPCRs are ubiquitous on cell membranes of major vertebrate systems – cardiovascular, nervous, sensory, immune, endocrine etc (Dorsam and Gutkind, 2007). In brief, activated GPCRs trigger diverse pathways by binding trimeric G-proteins comprised of alpha (α), beta (β) and gamma (γ) subunits, located on the intracellular side of plasma membrane (Figure 1). The beta-adrenergic receptor type 2 can activate an inhibitory or stimulatory signaling pathway depending on which ligand it binds. Agonist binding leads to a stimulatory response while antagonist or inverse agonist binding leads to an inhibitory response. In each of these examples the different types of G-protein α subunit convey the signal. Various α, β and γ subunits are each encoded by different sets of genes. In addition, distinct G-protein subunits potentiate a variety of cellular responses and modulate cellular activity in slightly different ways which ultimately affects gene expression in the cell. Functional defects in any part of a GPCR’s pathway are usually caused by mutations and result in wide spectrum of diseases. In humans, the mutations in genes encoding GPCRs are correlated to at least 30 common diseases: night blindness, asthma (Johnson and Kirk, 2002, Ober and Hoffjan, 2006), depression, cancer, obesity, diabetes (Worth et al., 2009), etc. About 40% non-olfactory GPCRs have a potential to become drug targets (Lagerström and Schiöth, 2008) with between 30% and 50% of presently available drugs on the market acting on about 100 clinically validated GPCR targets (Hopkins and Groom, 2002, Summers, 2010, Katritch et al., 2010).

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Figure 1. Simplified examples of GPCR activation mechanisms (Rosenbaum et al., 2009). Different intracellular responses and desensitization are illustrated for the beta-adrenergic receptor. Agonist binding leads to the activation of G-protein alpha subunit, association to adenylyl cyclase, cyclic adenosine monophosphate (cAMP) synthesis and accumulation, protein kinase A (PKA) stimulation and ultimately, protein phosphorylation triggering certain biological responses. In response to antagonist or inverse agonist binding, the constitutive activity of the receptor is reduced, MAP kinase signaling pathway is activated and the receptor is internalized by arrestin activity after conveying the signal. cAMP levels are downregulated by the activity of phosphodiesterase (PDE) and internalization of the activated receptor is promoted by phosphorylation by G-protein-coupled receptor kinase (GRK) which couples to arrestin. Arrestin in addition promotes the activation of extracellular signal-regulated kinases (ERK) which promotes the internalization of the receptor through clathrin-coated pits. Protein kinase C (PKC) is involved in receptor desensitization.

Hypothalamic nuclei, neuropeptides and appetite regulation

Energy homeostasis is the process by which the balance between the energy expenditure in metabolic processes and energy intake is maintained at a constant level over a longer period of time (Woods et al., 1998). There is a tendency in energy homeostasis to maintain the stability of metabolic processes even during adjustments to environmental changes (Bhagavan, 2002). Changes in energy expenditure modulate changes in energy intake through feeding behavior, which affects the amount of energy blocks stored in the body (e.g. adipose tissue). In animals and humans in particular, energy homeostasis is regulated by complex physiological systems and behavior involving genetic, neuroendocrinological, nutritional, physiological, environmental and psychological factors (Anubhuti, 2006, Atkinson, 2008). The overall effect of these factors is perceived by the person as either hunger or satiety. The feedback loops of these systems are mutually intertwined through the activity of numerous signaling peptides secreted in the central

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nervous system (neuropeptide Y, orexin) and peripheral organs: stomach (ghrelin), gut (peptide YY3-

36), pancreas (pancreatic polypeptide, insulin), adipose tissue (leptin). Normal feeding behavior and energy homeostasis is maintained through the activity of these neuroendocrine and peripheral signals. In the literature, different authors (Anubhuti, 2006, Atkinson, 2008, Kennett and Clifton, 2010) emphasize the role of different sets of neuropeptides in the regulation of feeding behavior and appetite control. Generally speaking, neuropeptides involved in feeding control can be either orexigenic (appetite-stimulating) or anorexigenic (appetite-inhibiting) agents. Some of the common human appetite-regulating agents are summarized in Table 1.

Similar to many classical neurotransmitters like noradrenalin and dopamine, the action of neuropeptides and endocrine peptides is mediated by G-protein-coupled receptors expressed on neurons located in the central neuroendocrine integrator of the brain – the hypothalamus. Studies of rodent brains have confirmed that the hypothalamus is the primary center for regulation of food intake. GPCRs mediate relatively slow responses in comparison with ionotropic receptors such as ligand-gated ion channels (Wettschureck and Offermanns, 2005). These neurons can interact with each other and are located in the arcuate nucleus (ARC), paraventricular nucleus (PVN) and lateral hypothalamus (Klok et al., 2007) but they can also be found in other locations in the CNS. For example, the brainstem plays an important role in homeostasis.

In the hypothalamic region, the blood-brain barrier is permeable enough to allow a smooth exchange of regulatory peptides and their interaction with the corresponding receptors in hypothalamic nuclei neurons. One should however bear in mind that the complexity of appetite control regulation goes beyond the circuits of hypothalamic nuclei. In humans, regulation of food intake depends highly on the interaction between pathways in higher cortical areas associated with cognition, emotional behavior, the reward system, and homeostatic brain areas (Batterham et al., 2002, Batterham et al., 2007).

Table 1. Orexigenic and anorexigenic agents implicated in appetite regulation.

Location Orexigenic agents Location Anorexigenic agents

Peripheral organs, Hypothalamus

Ghrelin Gastrointestinal tract Peptide YY, PYY3-36

Hypothalamus Neuropeptide Y (NPY) Gastrointestinal tract Cholecystokinin (CKK)

Hypothalamus Orexin Pancreas Pancreatic polypeptide (PP)

Hypothalamus Agouti-related peptide Adipose tissue, CNS Leptin

Hypothalamus Endocannabinoids Pancreas Insulin

Hypothalamus Galanin Hypothalamus Proopiomelanocortin (POMC)

Hypothalamus Melanin-concentrating hormone (MCH)

Hypothalamus Alpha-melanocyte-stimulating hormone (α-MSH)

Hypothalamus Endogenous opioids Hypothalamus Neurotensin

Hypothalamus Corticotrophin-releasing hormone (CRH)

Blood Satietin

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BACKGROUND

The NPY system: discovery, function, tissue distribution and peptide family

Among the signaling peptides, pancreatic peptide was the first to be isolated in 1974 from chicken pancreas at Lund University (Kimmel et al., 1968). Subsequently, peptide YY has been isolated from porcine intestine extract in 1980 at the Karolinska Institute (Tatemoto, 1982) and named PYY due to the tyrosine residues present at both of its terminals. Shortly following the PYY isolation, another peptide was isolated from porcine brain (Tatemoto et al., 1982) and named neuropeptide Y (NPY). At that time, the exact location of NPY secreting neurons had not yet been determined and the function had not been fully understood. In order to address these issues adequately, Tatemoto and colleagues prepared large quantity of NPY isolated from one ton of porcine brains and distributed smaller portions to research teams all over the world in 1982 (Tatemoto, 2004). During the following years, experiments in rats have confirmed high concentration of NPY in paraventricular nucleus, arcuate nucleus, and several other hypothalamic nuclei (Chronwall et al., 1985) as well as in the peripheral nervous system.

Functional studies have demonstrated the involvement of NPY in circadian rhythms (Albers et al., 1984), food intake (Clark et al., 1984), cardiovascular response, pain, stress, anxiety and depression (Fuxe et al., 1983). The effect of NPY on feeding behavior in particular has sparked great interest. Because the injection of NPY into the hypothalamus of rats resulted in increased appetite (Clark et al., 1984), many researchers recognized the potential of the NPY system in treatment of eating disorders such as for example obesity. A large number of studies have been conducted on this topic over the years. Notable highlights include the experiments with transgenic and NPY receptor knockout mice and functional magnetic resonance imaging studies of the effect of PYY on brain activity (Batterham et al., 2007). This functional study in healthy humans demonstrated the lower caloric intake in response to injection of gut hormone peptide YY3-36 versus saline solution (placebo). This study has also contributed to the understanding of which areas in human brain are involved in appetite control.

Human NPY receptors with focus on Y2 and its ligands

The NPY system has several physiological roles, of which the neuroendocrine component is the focus of the research project. Human NPY is abundant both in the central and peripheral nervous system where it acts on three diverse subfamilies of Y receptors – Y1, Y2 and Y5 (Larhammar and Salaneck, 2004). Y4 belongs to the Y1 subfamily based on sequence similarity. Receptor Y3 is often mentioned in scientific publications, even though its existence has not been verified and it is most likely an artifact (Michel et al., 1998). In addition, there is the y6 receptor, not designated with capital letter as it is a pseudogene in most mammals including humans (Starbäck et al., 2000), and belongs to the same subfamily as Y1 and Y4 sharing 50% amino acid identity. Receptors Y7 and Y8 have been lost in mammals (Larsson et al., 2008).

The Y2 receptor is expressed presynaptically in the hypothalamus, mostly in the arcuate nucleus, where it acts as a self-regulating receptor. The NPY receptors belong to the β-cluster of the rhodopsin group (Fredriksson and Schiöth, 2005). During the 1990's, NPY receptors have been in the spotlight and many have been cloned during this decade. The Y1 receptor was the first to be cloned of all human NPY receptors, and it was found to be a GPCR (Larhammar et al., 1992, Herzog et al., 1992). The Y family of receptors interacts with extensively studied peptides from the NPY peptide family (Larhammar and Salaneck, 2004). This family is comprised of three evolutionarily related peptides exhibiting significant amino acid sequence identity: pancreatic polypeptide (PP), peptide YY and neuropeptide Y (NPY) (Figure 2).

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HomologyNPY Y P S K P D N P G E D A P A E D L A R Y Y S A L R H Y I N L I T R Q R Y-NH2 100%__PYY Y P A K P E A P G E D A S P E E L S R Y Y A S L R H Y L N L V T R Q R Y-NH2 60%__PP A P L E P V Y P G D D A T P E Q M A Q Y A A E L R R Y I N M L T R P R Y-NH2 50%__

Figure 2. The sequence alignment of NPY, PYY and PP. The similarity between NPY and PYY is relatively high in comparison with similarity between NPY and PP. The conserved residues are indicated with red.

There are several features unique to this family of peptides. All are 36 amino acids long with amidated C-terminus and a similar spatial conformation known as the PP-fold (Larhammar 1996). The peptides possess no biological activity unless amidated. Interestingly, the NPY receptors bind the same set of peptides, albeit with different affinities, despite the relatively low amino acid sequence identity (30%). The NMR solution structures of some of the peptides are available in the Protein Database, PDB (http://www.pdb.org/pdb/home/home.do). The 3D structures of Y receptor agonists human NPY, human PYY and human PYY3-36 with PDB identification numbers 1RON, 2DEZ and 2DF0, respectively, are presented in Figure 3.

NPY hPYY hPYY3-36

Figure 3. The Y receptor agonists’ 3D-structure. NPY, human PYY and human PYY3-36 visualized in PyMol software package. The amidated carboxyl terminus tyrosine residues are indicated with red.

The Y2 receptor requires recognition of the carboxy-terminal segment for ligand binding. According to International Union of Basic and Clinical Pharmacology Database Y2 binds NPY and PYY-related agonists with high affinity, the gut hormone PYY3-36 with the highest affinity among all Y receptor subtypes (Beck-Sickinger et al., 2010). Moreover, Y2 is the only Y receptor to bind the peptidomimetic antagonist BIIE0246 (Figure 4).

Figure 4. The chemical structure of BIIE0246, the synthetic antagonist of human NPY receptor Y2.

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Activation of Y2 receptors in response to PYY3-36 resulted in decreased food intake in wild-type mice, but not in Y2 knockout mice (Batterham et al., 2002). On the other hand, inactivation of the gene Y2 receptor of adult mice by injecting Cre recombinase expressing adenovirus into the hypothalamus resulted in transiently increased food intake but decreased body weight (Sainsbury et al., 2002). After several weeks, the average weight and normal appetite had been restored in experimental mice, suggesting the involvement of some closely related compensatory mechanisms behind the knocked out Y2 receptor’s function in the hypothalamus. Experiments performed on obese and normal weight volunteers have shown that injection of PYY3–36 considerably reduces the subjective feeling of hunger, resulting in reduced food consumption at the experimental lunch (Batterham et al., 2003).

Undoubtedly, the Y2 receptor could be an attractive drug target for appetite suppressing drugs together with other NPY receptors if the long-term effects of PYY3-36 are confirmed. The Y2 receptor studies may as well provide additional clues in explaining the cause behind appetite disorders. Identifying the correct Y2 receptor's binding site would produce valuable data which would help develop drugs that mimic the effect of endogenous ligands such as PYY3-36.

Background and previous mutagenesis studies of Y1 and Y2 receptors

Investigation into whether the ligand-receptor interactions inside the binding pocket of the model are correct can be accomplished by introducing amino acid mutations into a receptor by site-directed mutagenesis, followed by saturation and competition binding assays to check if the amino acid replacement affected ligand's affinity to the mutagenized receptor. The Michaelis-Menten law of enzyme kinetics applies to receptor-ligand interactions (Bylund and Yamamura, 1990). The binding assay can confirm if the ligand affinity has been altered, as measured in saturation and competition assays, that is, if the binding positions indeed play a crucial role in the receptor-ligand interaction. As a control, equilibrium dissociation constant Kd of a radioligand with known pharmacological properties for the wild-type receptor is determined and compared to that of a mutant, whereas the higher value indicates decreased ligand affinity. Other relevant parameters obtained from binding studies are the Bmax value, defined as the maximum amount of a tracer radioligand which binds specifically to the receptor, EC50 (effective concentration 50%) indicating competitor ligand potency towards the receptor. Based on these data, the dissociation constant Ki of unlabeled ligands can be estimated (Foreman and Johansen, 2002). Once the Ki and Kd are obtained, they serve to refine the receptor models.

Both the amino acid substitutions and their respective positions tested prior to this degree project and the positions which are the focus of this report are specified in accordance with GPCR numbering system established by Ballesteros and Weinstein (Ballesteros and Weinstein, 1995). The first number next to amino acid symbol indicates the transmembrane region where the residue is located. The second number designates the amino acid position in relation to the most conserved amino acid assigned with number 50. The amino acids positions crucial for accommodating the ligand into the binding pocket along with those that are the focus of this report are shown in Figure 5.

So far, only a few similar mutagenesis studies have been published and those are for human Y1 (hY1) receptor, but several discrepancies exist among these (Du et al., 1997; Kanno et al., 2001; Sautel et al., 1995; Sautel et al., 1996; Sjodin et al., 2006; Walker et al., 1994). Only three publications were dedicated to the human Y2 (hY2) receptor (Åkerberg et al., 2010; Berglund et al., 2002; Merten et al., 2007). The Y2 receptor 3D model (Åkerberg et al., 2010) revealed features that were unknown in the previous model based on bovine rhodopsin (Sautel et al., 1995). In the previous Y2 model, positions Y2.64, Q5.24 and D6.59 were postulated as crucial points of receptor-ligand interaction. However, mutagenesis studies of Q5.24 and D6.59 did not confirm the interaction

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between these residues (Åkerberg et al., 2010). According to the new hA2AR-based model, the different residues in the binding pocket surround and interact with the amidated tyrosine 36 located at the peptides' carboxyl terminus. Several previously unknown features can be observed in the model. For instance, threonine T2.61 forms a hydrogen bond with the highly conserved glutamine Q3.32 which contributes to ligand-receptor interaction deeper inside the binding pocket than previously thought. The hydrophobic residues tyrosine Y3.30, tyrosine Y5.38 and leucine L6.51 directly interact with the amidated tyrosine (Figure 5). The previously proposed binding pocket forming residues (Sautel et al., 1995) seem too far apart to stabilize receptor-ligand interaction (Åkerberg et al., 2010). Moreover, the corresponding positions of ligand binding residues in hY1 receptor are not involved in ligand binding in hY2.

The manuscript for publication of the most recent mutagenesis data of six additional positions in hY2 is currently being prepared (Xu et al 2011., manuscript in preparation). In brief, two different docking solutions on three-dimensional model based on the structure of the adenosine 2A will be presented. Two docking solutions have highlighted following positions as important for various aspects of ligand-receptor binding: T2.61, Q3.32, H7.39, Y3.30, Y5.38 and L6.51. The positions T2.61, Q3.32 and H7.39 were chosen due of their hypothesized importance in the stability of the receptor. The amino acid residues Y3.30, Y5.38 and L6.51 were chosen based on their hypothetical role in building the hydrophobic binding pocket. The peptide ligands pPYY and hPYY3-36 both displayed reduced affinity to the mutants T2.61A, Q3.32H, Y5.38A, L6.51A and double mutants Y3.30L+Y5.38L, Y5.38L+L6.51A, Y3.30L+L6.51A. Additionally, pPYY3-36 also showed reduced affinity for Q3.32E, Y3.30A, H7.39Q, and Y5.38L. The reduced affinity of the Y2-selective non-peptide antagonist BIIE0246 was observed for Q3.32E and H7.39Q which is in agreement with the peptide result. Interestingly, BIIE0246 also showed increased affinity to some of the same mutants, i.e. T2.61A, Q3.32H and Y3.30L. The reciprocal double mutant Q3.32H+H7.39Q completely lost binding even though its expression was confirmed on confocal microscope. The main conclusion that can be drawn from these results is that the points of interaction significantly differ between the natural peptide agonists and the much smaller non-peptide antagonist in the receptor, even though the design of antagonist structure was based on the peptide structure. Overall, the binding results after mutagenesis were in good agreement with the modeling and docking procedure. The derived mutagenesis data was necessary for further refinement of the human Y2 receptor model.

Additional amino acid substitutions arose as mutagenesis candidates, and those mutations are the subject of this report. Amino acid substitutions which need to be introduced to human Y2 receptor in focus of this report are: Y1.39A, Q6.55L, Q3.32A, Q3.32L, H7.39A and H7.39L. Initial test binding assays performed for Q6.55L, Q3.32A, Q3.32L, H7.39A and H7.39L have so far shown promising results.

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Figure 5. The ‘snake’ plot of hY2R. The plot shows six amino acid residues believed to be involved in ligand binding - T2.61, Q3.32, H7.39, Y3.30, Y5.38, L6.51. Amino acid residues indicated in green were mutagenized during the course of this degree project. The most conserved residues in trans-membrane regions are indicated in gray. The colors of the transmembrane regions in this image correspond to the colors in the 3D-model.

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RESULTS

Selection of positions for mutagenesis

Position Y1.39 has never been studied before but is conserved and might be interacting with Y36 on the peptide agonist (image not shown). The position Q6.55 was chosen as a good candidate for more intimate pharmacological characterization because it is conserved in both the Y2 and Y1 receptor (Fällmar et al. 2011). According to the previously published results, substitution of glutamine at this position to alanine resulted in increased affinity for PYY3-36. The same substitution in Y1 results in reduced affinity for PYY3-36. In order to elucidate this and produce data that will help to further refine the receptor model, this position was selected to be substituted to leucine. The amidated C-terminus of the dipeptide fragment (Figure 6A) and full hNPY docking solution (Figure 6B) forms polar interactions with residues Q3.32 and H7.39, which happen to be completely conserved within the Y receptor family. In order to test the stability of receptor-ligand contacts, both of these positions were selected to be substituted to leucine and alanine, which are more dramatic mutations than previously done in this site-directed mutagenesis study.

Figure 6. Modelling of interactions with the Y2 receptor (Xu et al, manuscript in preparation). (A) GOLD docking solution of the acetylated C-terminal dipeptide fragment of hNPY (CH3C(O)-R35-Y36-NH2, in magenta) in the A2A-based hY2 receptor model. Side chains of the six hY2 positions investigated by site-directed mutagenesis (Thr2.61, Tyr3.30, Gln3.32, Tyr5.38, Leu6.51 and His7.39), as well as four previously mutated residues (Tyr2.64, Gln6.55, Asp6.59 and Tyr7.31) (Åkerberg et al., 2010; Merten et al., 2007 and Fällmar et al., 2011), are shown in sticks. The transmembrane helices of the hY2 model are shown in anti-clockwise order (TM1, dark blue – TM7, red). (B) Docking solution obtained by HADDOCK for the full hNPY peptide (magenta) in the A2A-based hY2 receptor model. The C-terminal dipeptide is explicitly shown in magenta sticks.

After the positions were selected, the primers were designed and PCR based site-directed mutagenesis was performed for all six selected mutants. The bacterial transformation with PCR products was successful for Q6.55L, Q3.32A, H7.39L and H7.39A and resulted in many colonies on an antibiotic selective plate. Only a single colony was obtained by transformation with Y 1.39A and none for Q3.32L. One colony was picked from available plates and inoculated into a fresh culture which was used to purify the plasmid carrying mutant receptor gene. While waiting for sequence confirmation, a new primer was designed for Q3.32L and the PCR was repeated for Y1.39A in order to provide more transformed colonies. The colonies were obtained for Q3.32L and their plasmid

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purified. Plasmid DNA yields obtained from Miniprep purification of the corresponding mutants estimated on NanoDrop are given in Table 2. Table 2. Plasmid yields obtained from Miniprep purification of different mutants and their respective purities

Mutation Yield [ng/μl] A260 260/230 260/280

Y1.39A 268.29 5.36 2.08 1.92

Q3.32A 464.96 9.29 1.85 1.85

Q6..55L 665.46 13.31 1.62 1.75

H7.39A 369.09 7.38 2.01 1.90

H7.39L 577.12 11.54 1.92 1.83

Q3.32L 577.84 11.55 2.17 1.87

Upon confirmation of correctly introduced mutation from the sequencing results, Maxiprep purification was performed for Q3.32A, Q3.32L, Q6.55L, H7.39L, H7.39A on several occasions as well as for WT plasmid DNA which was meant to be used as a transfection and test binding control. Plasmid DNA yields obtained from Maxiprep purification of the corresponding mutants estimated on NanoDrop are given in Table 3.

Table 3. Plasmid yields obtained from Maxiprep purification of different mutants and their respective purities

Mutation Yield [ng/μl] A260 260/230 260/280

WT 1493.20 29.86 2.33 1.93

Q3.32L 1780.20 35.60 2.29 1.92

Q3.32A 316.61 6.33 2.28 1.85

Q6..55L 453.71 13.31 2.28 1.85

H7.39A 2101.90 42.04 2.31 1.92

H7.39L 987.80 19.75 2.35 1.94

So far, HEK 293 cells were transfected with purified Q6.55L, Q6.55N, Q3.32A, H7.39A, H7.39L and wild-type plasmids. Receptor preparation was made from the cells harvested upon 48 h after transfection, which provided optimal receptor abundance. The initial test binding of the receptor preparation of Q6.55L, Q6.55N, H7.39A, H7.39L diluted four, eight and sixteen times resulted in loss of binding of Q6.55L, H7.39A, H7.39L judging by the similar number of counts measured for non-specific versus total binding at all dilutions (Figure 7). Q6.55N however did show some binding, as indicated by slightly higher number of counts in total binding than non-specific at all membrane dilutions (Figure 7). This was confirmed in the repeated test binding where the lesser dilution factors were used (Figure 8). In addition, the binding test of Q3.32A was performed in this run, and showed low binding of this mutant receptor. Repeated test binding for this mutant has shown loss of binding as well (Figure 9). In addition, different plasmid DNA used for transfection of the cells resulted in similar numbers of counts at all dilutions of Q3.32A receptor preparation. All mutant receptors have shown reduced binding comparing to the wild-type (Figure 10).

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Figure 7. Test binding results performed for mutant receptors Q6.55L, Q6.55N, H7.39A, and H7.39L diluted four, eight and sixteen times.

Figure 8. Repeated test binding results performed for mutant receptors Q6.55L and Q6.55N and preliminary test binding of Q3.32A, performed for undiluted receptor preparation and preparations diluted twice and four times.

Figure 9. Repeated test binding results performed for Q3.32A mutant receptor preparation previously transfected with different plasmid DNA amounts. Receptor preparation was diluted four, eight and sixteen times in this run.

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Figure 10. Test binding results performed for wild-type receptor preparation previously transfected with different plasmid DNA amounts. Receptor preparation was diluted four, eight and sixteen times in this run.

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DISCUSSION

This degree project is part of a research project aiming to determine the properties of the Y2 receptor ligand-binding network by integrated approach combining computational modeling and site-directed mutagenesis. Plasmid DNA carrying mutant human NPY Y2 receptors coding region was successfully generated for mutations Q3.32A, Q3.32L, Q6.55L, H7.39L and H7.39A which were confirmed by sequencing. Their respective purities and yields were within the desired limits.

Previously, it has been demonstrated that substituting polar glutamine with nonpolar alanine at the position 6.55 resulted in increased binding of pNPY, [125I]pPYY and hPYY3-36 (Fällmar et al., 2011). The explanation offered to this unusual mutant receptor binding property was that this amino acid exchange resulted in a conformational change of the receptor which indirectly enhances the ligand–receptor interaction. This remains intriguing since the substitution to leucine resulted in binding loss while substitution to asparagine resulted in reduced binding and it will be further investigated in saturation and competition binding assays during the continuation of this project.

The binding loss in H7.39A and H7.39L can be explained by receptor instability caused by substituting electrically charged group (histidine) with hydrophobic side chains (alanine and leucine) at this conserved position. Since the plasmid purities were within expected limits (Table 3), and different plasmid concentrations used for transfection did not result in significantly high variation in count numbers in the test binding (Figures 9 and 10), the binding loss in Q3.32A, H7.39A and H7.39L can only be attributed to the effect of mutations on receptors' stability which causes loss of their binding properties.

Previous experiments at the position Q3.32, have shown that substituting the polar, uncharged glutamine with the positively charged histidine side chain resulted in 3- to 4-fold reduced affinity for the radioligand [125I]pPYY and also in slightly increased affinity of the antagonist BIIE0246, comparing to the wild-type receptor. It is common among peptide-binding GPCRs that the binding site of the non-peptidic antagonists does not fully overlap with that of the natural agonists (Fong et al., 1993, Gether et al., 1993, Schambye et al., 1994, Wang et al., 1994). A careful look into the orientation of side chains and the proposed binding mode of the amidated C-terminus resembles the role of the analogous pair of conserved residues in the adrenergic receptors (D3.32 and N7.39), which are crucial in the binding of the charged amino group of both agonists and antagonists (Rosenbaum et al., 2007; Warne et al., 2011). According to Figures 8 and 9, the test binding of Q3.32A diluted four times gave rise to about 600 cpm and 1500 cpm, respectively. The discrepancy in different test binding runs is expected to exist due to different transfection efficiency in different batches of receptor preparation. The binding loss observed for these mutant receptors indicate not only that these residues are involved in the direct interaction with peptide ligands but also that the docking simulation of the full peptide agonist is very likely correct. Overall, the preliminary results of the positions investigated during this degree project are consistent with the docking solution for the full hNPY peptide (Figure 6B) in the Adenosine 2A-based hY2 receptor model.

The receptor positions described in this report will remain the subject of further investigation. Test binding results will be repeated in order to confirm current findings and the GFP expression will be controlled in order to rule out the possibility of binding loss caused by the loss of receptor expres-sion on the cell membrane. HEK 293 cells remain to be transfected with the plasmid-carrying gene for Q3.32L receptor mutant. Test binding of such cells is expected to result in the binding loss, as did receptor Q3.32A. A new site-directed mutagenesis will be attempted for Y1.39A with a new primer pair and altered PCR cycling conditions.

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The Y2 receptor model based on CxCR4 refined with the mutagenesis data is currently in prepara-tion and we will begin performing molecular dynamics simulations on the finalized model. Eventu-ally, we expect the ligand binding to hY2 to be fully simulated.

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MATERIAL AND METHODS

PCR Site-directed mutagenesis of Y2 receptor genes

After determination of positions critical for the ligand binding based on the 3D model and ligand docking, the sense and antisense mutagenesis primers were designed with automated web based software PrimerX available at http://www.bioinformatics.org/primerx/. The primers were designed with optimal codon usage compatible with human expression system. The primer pairs used in PCR site directed mutagenesis are summarized in Table 4.

Table 4. The primer pairs and plasmids used in site-directed mutagensis.

Template Primer Primer oligonucleotide sequence

WT hY2 Y2Y1.39A F 5'-GTTCTCATATTGGCCGCCTGCTCCATCATCTTG-3'

WT hY2 Y2Y1.39A R 5'-CAAGATGATGGAGCAGGCGGCCAATATGAGAAC-3'

WT hY2 Y2Q6.55L F 5'-CTCCATGCCTTCCTGCTTGCCGTTGAC-3'

WT hY2 Y2Q6.55L R 5'-GTCAACGGCAAGCAGGAAGGCATGGAG-3'

WT hY2 Y2Q3.32A F 5'-GTGCCCTATGCCGCAGGCCTGGCAGTACAAGTATC-3'

WT hY2 Y2Q3.32A R 5'-GATACTTGTACTGCCAGGCCTGCGGCATAGGGCAC-3'

WT hY2 Y2Q3.32L F 5'-CTGGTGCCCTATGCCTTAGGCCTGGCAGTACAAG-3'

WT hY2 Y2Q3.32L R 5'-CTTGTACTGCCAGGCCTAAGGCATAGGGCACCAG-3'

WT hY2 Y2Q3.32L F 5'-CCCTATGCCCTGGGCCTGGCAGTACAAGT-3'

WT hY2 Y2Q3.32L R 5'-ACTTGTACTGCCAGGCCCAGGGCATAGGG -3'

WT hY2 Y2H7.39A F 5'-CATCTTCACAGTGTTCGCCATTATCGCCATGTGC-3'

WT hY2 Y2H7.39A R 5'-GCACATGGCGATAATGGCGAACACTGTGAAGATG-3'

WT hY2 Y2H7.39L F 5'-CTTCACAGTGTTCCTCATTATCGCCATGTG-3'

WT hY2 Y2H7.39L R 5'-CACATGGCGATAATGAGGAACACTGTGAAG-3' The receptor's coding region sequence which was used as a template in this study was following:

atgggtccaataggtgcagaggctgatgagaaccagacagtggaagagatgaaggtggaacaatacgggccacaaacaactcctagaggtgaactggtccctgaccctgagccagagcttatagatagtaccaagctgattgaggtacaagttgttctcatattggcctactgctccatcatcttgcttggggtaattggcaactccttggtgatccatgtggtgatcaaattcaagagcatgcgcacagtaaccaactttttcattgccaatctggctgtggcagatcttttggtgaacactctgtgtctaccgttcactcttacctataccttaatgggggagtggaaaatgggtcctgtcctgtgccacctggtgccctatgcccagggcctggcagtacaagtatccacaatcaccttgacagtaattgccctggaccggcacaggtgcatcgtctaccacctagagagcaagatctccaagcgaatcagcttcctgattattggcttggcctggggcatcagtgccctgctggcaagtcccctggccatcttccgggagtattcgctgattgagatcattccggactttgagattgtggcctgtactgaaaagtggcctggcgaggagaagagcatctatggcactgtctatagtctttcttccttgttgatcttgtatgttttgcctctgggcattatatcattttcctacactcgcatttggagtaaattgaagaaccatgtcagtcctggagctgcaaatgaccactaccatcagcgaaggcaaaaaaccaccaaaatgctggtgtgtgtggtggtggtgtttgcggtcagctggctgcctctccatgccttccagcttgccgttgacattgacagccaggtcctggacctgaaggagtacaaactcatcttcacagtgttccacattatcgccatgtgctccacttttgccaatccccttctctatggctggatgaacagcaactacagaaaggctttcctctcggccttccgctgtgagcagcggttggatgccattcactctgaggtgtccgtgacattcaaggctaaaaagaacctggaggtcagaaagaacagtggccccaatgactctttcacagaggctaccaatgtctaa

Expression vector pcDNA-DEST47 (Invitrogen) compatible with expression in mammalian cell lines, carrying wild type hY2 gene, ampicillin resistance gene and a green fluorescent protein (GFP) gene was used as template for mutagenesis. QuikChange II XL Site-Directed Mutagenesis Kit (Stratagene) was used to create a point mutation and generate mutant hY2 receptor gene. Each reac-tion mixture contained 20.5 μl ddH2O, 2.5 μl reaction buffer, 1 μl plasmid (50 ng/μl), 0.5 μl of re-verse and forward primers, 0.5 μl dNTP mix and 0.5 μl PfuUltra HF DNA polymerase, added in that order. PCR cycling parameters for were: 1 min at 95°C, followed by 18 cycles of 50 sec at 95°C, 50 sec at 60°C, 9 min at 68°C finishing with 7 min at 68°C. In order to remove mutation-free DNA, the parental plasmid has been digested by adding 1 μl Dpn I restriction enzyme (Stratagene) to each reaction tube and incubating at 37°C for 1 h.

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Transformation of the bacterial cells

The One Shot® TOP10 Competent EndA- E. coli strain (Invitrogen, Carlsbad, CA) was methyltransferase deficient and therefore appropriate for growing the unmethylated plasmids suited for mammalian cell transfection. The transformation of pre-chilled cells was performed by 30 s long heat-shock at 42°C with 2 μl of plasmid and immediately transferred on ice for 2 minutes. 250 μl of S.O.C medium was added to the tubes and incubated with shaking for one hour. 50 μl of the cell solution was streaked on LB-agar plate with added Ampicillin (100 μg/ml) and cultured overnight at 37°C, enabling the selection of transformed colonies during the following day. Selected colony carrying the cloned plasmid was inoculated into the LB-medium with 0,2% glucose and ampicillin 50 μg/ml and grown overnight at 37°C with 200 rpm shaking, until reaching the exponential growth phase. The medium has been centrifuged at 8000 x g for 20 min and the supernatant discarded.

Plasmid purification and sequencing

Plasmid was purified by using the column-based purification kit E.Z.N.A.®

Plasmid Miniprep Kit I (Omega Bio-Tek) according to the manufacturer's recommendation. The kit was supplied with re-suspension buffer, RNase A, lysis buffer, precipitation buffer, equilibration buffer, wash buffer, elu-tion buffer and TE buffer, all of unspecified composition. After adding the RNase A buffer, bacteria were pelleted by centrifugation at 10000x g for one minute, washed with resuspension buffer, cent-rifuged at 13000x g and washed again several times at the same speed. Upon centrifugation, the su-pernatant was applied to the previously equilibrated mini-column, and the DNA was eluted with 30 μl purified water after several washing steps in between. The concentration and purity of the plas-mid have been controlled with Nanodrop spectrophotometer. The purified plasmid has been placed at -20°C. The desired mutations on the plasmids carrying hY2 gene have been confirmed by se-quencing. Upon confirmed sequence, large amount or plasmid derived from 200 μl overnight liquid culture was purified by using PureLinkTM HiPure Plasmid DNA Purification kit (Invitrogen). The kit was supplied with Resuspension Buffer (50 mM Tris-HCl, pH 8.0, 10 mM EDTA RNase A 20 mg/ml), Lysis Buffer (0.2 M NaOH, 1% SDS), Precipitation Buffer (3.1 M Potassium acetate, pH 5.5), Equilibration Buffer (0.1 M Sodium acetate, pH 5.0, 0.6 M NaCl, 0.15% Triton® X-100), Wash Buffer (0.1 M Sodium acetate, pH 5.0, 825 mM NaCl), Elution Buffer (100 mM Tris-HCl, pH 8.5, 1.25 M NaCl) and TE Buffer (10 mM Tris-HCl, pH 8.0, 0.1 mM EDTA). In brief, the cells were harvested by centrifugation at 4000x g for 10 minutes and the medium removed. 10 ml of the Re-suspension Buffer was used to homogenize the cell pellet and immediately after lysed by incubating the sample with 10 ml of Lysis Buffer for no more than 5 minutes. Upon adding 10 ml Precipitation Buffer, the sample was gently mixed and then centrifuged at 14000x g for 10 minutes at room tem-perature. The supernatant was loaded onto the column previously equilibrated with Equilibration Buffer and allowed to flow by gravity. Thereafterthe column has been washed with 60 ml Wash Buffer, and the DNA eluted with 15 ml Elution Buffer water. The plasmid was precipitated with 10.5 ml Isopropanol and centrifuged at 14000xg for 30 minutes at 4oC. The pellet was resuspended in 70 % ethanol and centrifuged for additional 10 minutes at 14000xg and 4oC. Air-dried pellet was re-suspended with 500 μl MiliQ water, its yield and quality were estimated on NanoDrop instru-ment.

Transfection for transient expression of the mutant receptors and the test binding of membrane preparations

Human embryonic kidney (HEK 293) cell line grown to 90-95% confluence, were transfected with purified plasmids carrying mutant genes, dissolved in Lipofectamine 2000 transfection reagent (Invitrogen). In order to optimize the transfection, some samples were treated with three different

19

amounts of plasmid. In other words, the standard amount of Lipofectamine (1 μl) was used with 2 μg, 4 μg and 8 μg of wild type and Q3.32A plasmid. The plasmids for which the correct sequence has been confirmed were: Q6.55L, Q3.32A, Q3.32L, H7.39A, H7.39L. Plasmid for Q6.55N was available from before and used for comparison with Q6.55L. The transfected cells were incubated in serum-free medium (Opti-MEM, Invitrogen) with added fetal blood serum, Ampicillin, Penicillin and Streptomycin ensuring optimal growth environment and reduced risk of bacterial contamination. As 48 h long incubation provided sufficient abundance of the receptor on transiently transfected cells, they were harvested from the bottom of the plate, centrifuged, resuspended in the binding buffer (25 mM HEPES buffer, pH=7.4 with 2.5 mM CaCl2 and 1 mM MgCl2) and homogenized. The cell membrane samples were aliquoted into microfuge tubes in 200 μl portions and stored at -20°C. In order to determine the right dilution of membrane preparation, test binding of the receptor preparation was performed for previously confirmed mutants Q6.55L, Q6.55N, Q3.32A, H7.39A, H7.39L, whereas the plasmid for Q6.55N was available from previous studies of this position. Homogenized receptor preparations were resuspended in the Binding Buffer (25 mM HEPES buffer, pH=7.4 with 2.5 mM CaCl2 and 1 mM MgCl2) and diluted four, eight and sixteen times with the Binding Buffer with added Bacitracin. Test binding was performed in triplicates for both the total and non-specific binding in total amount of 100 μl per well. The porcine peptide YY mono-iodinated with iodine isotope 125 ([125I]pPYY) with specific activity of 2200 Ci/mmol (Perkin Elmer) was used as radioligand at the concentration of 70 pM in all test binding runs. Human PYY has been used as the displacing agent for the non-specific binding at the concentration of 250 nM. The standard incubation time of 3h has been used to bring the reaction to the steady state in all test bindings. Upon finished incubation, receptor bound ligand was transferred to printed glass filter (Wallac Oy) presoaked in 0.3% polyethyleneimine buffer with 96-cell harvester (Tomtec). The unbound ligand was rinsed with ice-cold 50 mM Tris buffer (pH=7.4) for 10 seconds. The filters have been thoroughly dried at 50°C and encased with MeltiLex scintillator sheets (Wallac Oy). Their associated radioactivity was detected and measured in counts per minute (cpm) using scintillation counter.

ACKNOWLEDGEMENTS

I am thankful to my supervisor Dan Larhammar for the amazing opportunity to work on Y2 receptor mutagenesis, all the stimulating discussions and his contagious passion for G-protein-coupled receptors. My gratitude goes to Bo Xu for the good advice, patience and all our successfully performed experiments. Rashidur Rahman and Björn Edlund deserve my many thanks for their hard and good work during the experiments we performed. I would hereby like to acknowledge my study councilor Eva Damm for her vital encouragement and support on many occasions. I am thankful to my husband Milan who had supported me throughout the course of my studies in all the ways imaginable.

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