A database for G proteins and their interaction with GPCRs

Single-Nucleotide Polymorphism in Genes Encoding G Protein Subunits GNB3 and GNAQ Increase the Risk of Cardiovascular Morbidity among Patients Undergoing Renal Replacement Therapy

Single-nucleotide polymorphisms in G protein subunits are linked to an increased risk of cardiovascular events among the general population. We assessed the effects of GNB3 c.825C > T, GNAQ -695/-694GC > TT, and GNAS c.393C > T polymorphisms on the risk of cardiovascular events among 454 patients undergoing renal replacement therapy. The patients were followed up for a median of 4.5 years after the initiation of dialysis. Carriers of the TT/TT genotype of GNAQ required stenting because of coronary artery stenosis (p = 0.0009) and developed cardiovascular events involving more than one organ system (p = 0.03) significantly earlier and more frequently than did the GC/TT or GC/GC genotypes. Multivariate analysis found that the TT/TT genotype of GNAQ was an independent risk factor for coronary artery stenosis requiring stent (hazard ratio, 4.5; p = 0.001), cardiovascular events (hazard ratio, 1.93; p = 0.04) and cardiovascular events affecting multiple organs (hazard ratio, 4.9; p = 0.03). In the subgroup of male patients left ventricular dilatation with abnormally increased LVEDD values occurred significantly more frequently in TT genotypes of GNB3 than in CT/CC genotypes (p = 0.007). Our findings suggest that male dialysis patients carrying the TT genotype of GNB3 are at higher risk of left ventricular dilatation and that dialysis patients carrying the TT/TT genotype of GNAQ are prone to coronary artery stenosis and severe cardiovascular events.

GNB3 c.825C>T (rs5443) Polymorphism and Risk of Acute Cardiovascular Events after Renal Allograft Transplant

The c.825C>T single-nucleotide polymorphism (rs5443) of the guanine nucleotide-binding protein subunit β3 (GNB3) results in increased intracellular signal transduction via G-proteins. The present study investigated the effect of the GNB3 c.825C>T polymorphism on cardiovascular events among renal allograft recipients posttransplant. Our retrospective study involved 436 renal allograft recipients who were followed up for up to 8 years after transplant. The GNB3 c.825C>T polymorphism was detected with restriction fragment length polymorphism (RFLP) polymerase chain reaction (PCR). The GNB3 TT genotype was detected in 43 (10%) of 436 recipients. Death due to an acute cardiovascular event occurred more frequently among recipients with the TT genotype (4 [9%]) than among those with the CC/CT genotypes (7 [2%]; p = 0.003). The rates of myocardial infarction (MI)−free survival (p = 0.003) and acute peripheral artery occlusive disease (PAOD)−free survival (p = 0.004) were significantly lower among T-homozygous patients. A multivariate analysis showed that homozygous GNB3 c.825C>T polymorphism exerted only a mild effect for the occurrence of myocardial infarction (relative risk, 2.2; p = 0.065) or acute PAOD (relative risk, 2.4; p = 0.05) after renal transplant. Our results suggest that the homozygous GNB3 T allele exerts noticeable effects on the risk of MI and acute PAOD only in the presence of additional nonheritable risk factors.

Pharmacological and molecular characterization of the A-type muscarinic acetylcholine receptor from Anopheles gambiae

Muscarinic acetylcholine receptors (mAChRs) which are G protein-coupled receptors play key roles in insect physiology. Whereas vertebrate mAChRs are important targets for pharmaceutical drugs, insect mAChRs are under-exploited by the agro-chemical industry. Moreover, insect mAChRs have been less well studied than their vertebrate counterparts. Their critical functions mean that a better knowledge of the insect mAChRs is crucial for the effort to develop a new molecular-level strategy for insect pest management. Almost all insects possess three mAChRs named A, B and C which differ according to their coupling effector systems and their pharmacological profile. The aim of this study was to characterize the A-type mAChR (mAChR-A) from Anopheles gambiae which is the major vector of malaria in order to develop new strategies in pest management. In this paper, we reported that mAChR-A is more expressed in adult mosquitoes than in larvae. Furthermore, using calcium imaging recordings, we found that the An. gambiae mAChR-A expressed in Sf9 cells is activated by specific muscarinic agonists acetylcholine, muscarine and oxotremorine M and blocked by several mAChR antagonists. Moreover, using inhibitors of phosphoinositide pathway such as Gα_q/11 protein blocker, we have shown that an increased intracellular calcium concentration elicited by the acetylcholine application was mediated by PLC/IP3R pathway. As a rise in intracellular calcium concentration could lead to an increase in the insecticide target sensitivity, these results suggest that An. gambiae mAChR-A should not be only considered as a potential target for new molecules but also as a key element to optimize the efficacy of insecticide in vector control.

Development of Generic G Protein Peptidomimetics Able to Stabilize Active State Gs Protein-Coupled Receptors for Application in Drug Discovery

G protein-coupled receptors (GPCRs) represent an important group of membrane proteins that play a central role in modern medicine. Unfortunately, conformational promiscuity hampers full therapeutic exploitation of GPCRs, since the largest population of the receptor will adopt a basal conformation, which subsequently challenges screens for agonist drug discovery programs. Herein, we describe a set of peptidomimetics able to mimic the ability of G proteins in stabilizing the active state of the β₂ adrenergic receptor (β₂ AR) and the dopamine 1 receptor (D1R). During fragment-based screening efforts, these (un)constrained peptide analogues of the α₅ helix in G_s proteins, were able to identify agonism pre-imprinted fragments for the examined GPCRs, and as such, they behave as a generic tool, enabling an engagement in agonist earmarked discovery programs.

Development and validation of whole genome-wide and genic microsatellite markers in oil palm (Elaeis guineensis Jacq.): First microsatellite database (OpSatdb)

The availability of large expressed sequence tag (EST) and whole genome databases of oil palm enabled the development of a data base of microsatellite markers. For this purpose, an EST database consisting of 40,979 EST sequences spanning 27 Mb and a chromosome-wise whole genome databases were downloaded. A total of 3,950 primer pairs were identified and developed from EST sequences. The tri and tetra nucleotide repeat motifs were most prevalent (each 24.75%) followed by di-nucleotide repeat motifs. Whole genome-wide analysis found a total of 245,654 SSR repeats across the 16 chromosomes of oil palm, of which 38,717 were compound microsatellite repeats. A web application, OpSatdb, the first microsatellite database of oil palm, was developed using the PHP and MySQL database ( https://ssr.icar.gov.in/index.php ). It is a simple and systematic web-based search engine for searching SSRs based on repeat motif type, repeat type, and primer details. High synteny was observed between oil palm and rice genomes. The mapping of ESTs having SSRs by Blast2GO resulted in the identification of 19.2% sequences with gene ontology (GO) annotations. Randomly, a set of ten genic SSRs and five genomic SSRs were used for validation and genetic diversity on 100 genotypes belonging to the world oil palm genetic resources. The grouping pattern was observed to be broadly in accordance with the geographical origin of the genotypes. The identified genic and genome-wide SSRs can be effectively useful for various genomic applications of oil palm, such as genetic diversity, linkage map construction, mapping of QTLs, marker-assisted selection, and comparative population studies.

Association of G-protein β3 subunit C825T polymorphism with essential hypertension: evidence from 63 729 subjects

Many studies have reported that G-protein β3 subunit (GNB3) C825T polymorphism is associated with essential hypertension (EH), although this remains subject to debate. Thus, meta-analysis was carried out to clarify this relationship. A total of 75 articles, reporting 81 case-control studies evaluating 28 369 patients and 34 933 control individuals, were assessed. Overall, a significant association was observed in the dominant model (odds ratio (OR)=1.11, 95% CI 1.04-1.19), recessive model (OR=1.09, 95% CI 1.01-1.17), TT vs CC (OR=1.16, 95% CI 1.05-1.28), CT vs CC (OR=1.09, 95% CI 1.02-1.17), and additive model (OR=1.07, 95% CI 1.02-1.13) after pooling all eligible studies. Subgroup analysis by ethnicity and gender demonstrated significantly increased EH only in Caucasians using the dominant (OR=1.22, 95% CI 1.07-1.39; TT vs CC, OR=1.29, 95% CI 1.07-1.54; CT vs CC, OR=1.19, 95% CI 1.05-1.35) and additive (OR=1.16, 95% CI 1.05-1.28) models. In summary, the present meta-analysis indicated the GNB3 C825T polymorphism is related to increased EH exclusively in Caucasians.

Cardiovascular diseases-related GNB3 C825T polymorphism has a significant sex-specific effect on serum soluble E-selectin levels

Background

The C825T polymorphism (rs5443) of the Guanine Nucleotide-Binding protein subunit β3 (GNB3) gene has been associated with obesity, essential hypertension, atherosclerosis, coronary diseases, and cerebrovascular events, but with some sex-specific effects. Its association with inflammatory mediators such as cell adhesion molecules has not been studied, although they are heavily involved in cardiovascular diseases’ (CVDs) processes. The aim of our study was then to investigate a possible sex-specific effect of the GNB3 C825T polymorphism on serum soluble cell adhesion molecules such as E, P and L-selectins (sE, sP and sL-selectins).

Results

Participants were from the STANISLAS Family Study and were free of chronic disease as CVDs or cancer. We included in total 771 subjects aged 6 to 58 years (391 males (50.71%) and 380 females (49.29%)). No significant association of rs5443 was observed in the whole population with serum sE, sP and sL-selectins after adjusting for age, sex, body mass index, systolic blood pressure, anti-inflammatory drugs and hormonal drugs consumption. A significant interaction of rs5443 was observed with sex for sE-selectin (p < 0.001), but not for sP and sL-selectins. After adjusting for covariables, the T allele was significantly associated with an additive increase effect on serum sE-selectin levels in males (β = 5.03 ± 2.18; p = 0.020), while a significant additive decrease effect was observed in females (β =−4.46 ± 2.06; p = 0.030). These associations stayed significant after correction for multiple tests (p = 0.045 in males and in females). The additive phenotypic variance was 21.54% in males versus 1.91% in females.

Conclusions

In our Caucasian population, the GNB3 C825T polymorphism showed a significant sex-specific effect on serum sE-selectin levels, with a disadvantage for males, as increased sE-selectin levels has been associated with CVDs outcomes. The T allele has been previously associated with the same CVDs as increased sE-selectin, but more often in males. The link we observed between this polymorphism and E-selectin is then consistent with previous findings, and helps to better understand the deleterious effect of the GNB3 825 T allele on CVDs outcomes in males. We revealed in this study an important pathway through which the GNB3 gene induces CVDs’ outcomes.

Classification of G proteins and prediction of GPCRs-G proteins coupling specificity using continuous wavelet transform and information theory

The coupling between G protein-coupled receptors (GPCRs) and guanine nucleotide-binding proteins (G proteins) regulates various signal transductions from extracellular space into the cell. However, the coupling mechanism between GPCRs and G proteins is still unknown, and experimental determination of their coupling specificity and function is both expensive and time consuming. Therefore, it is significant to develop a theoretical method to predict the coupling specificity between GPCRs and G proteins as well as their function using their primary sequences. In this study, a novel four-layer predictor (GPCRsG_CWTIT) based on support vector machine (SVM), continuous wavelet transform (CWT) and information theory (IT) is developed to classify G proteins and predict the coupling specificity between GPCRs and G proteins. SVM is used for construction of models. CWT and IT are used to characterize the primary structure of protein. Performance of GPCRsG_CWTIT is evaluated with cross-validation test on various working dataset. The overall accuracy of the G proteins at the levels of class and family is 98.23 and 85.42%, respectively. The accuracy of the coupling specificity prediction varies from 74.60 to 94.30%. These results indicate that the proposed predictor is an effective and feasible tool to predict the coupling specificity between GPCRs and G proteins as well as their functions using only the protein full sequence. The establishment of such an accurate prediction method will facilitate drug discovery by improving the ability to identify and predict protein-protein interactions. GPCRsG_CWTIT and dataset can be acquired freely on request from the authors.

Profiling gene expression induced by protease-activated receptor 2 (PAR2) activation in human kidney cells

Protease-Activated Receptor-2 (PAR2) has been implicated through genetic knockout mice with cytokine regulation and arthritis development. Many studies have associated PAR2 with inflammatory conditions (arthritis, airways inflammation, IBD) and key events in tumor progression (angiogenesis, metastasis), but they have relied heavily on the use of single agonists to identify physiological roles for PAR2. However such probes are now known not to be highly selective for PAR2, and thus precisely what PAR2 does and what mechanisms of downstream regulation are truly affected remain obscure. Effects of PAR2 activation on gene expression in Human Embryonic Kidney cells (HEK293), a commonly studied cell line in PAR2 research, were investigated here by comparing 19,000 human genes for intersecting up- or down-regulation by both trypsin (an endogenous protease that activates PAR2) and a PAR2 activating hexapeptide (2f-LIGRLO-NH(2)). Among 2,500 human genes regulated similarly by both agonists, there were clear associations between PAR2 activation and cellular metabolism (1,000 genes), the cell cycle, the MAPK pathway, HDAC and sirtuin enzymes, inflammatory cytokines, and anti-complement function. PAR-2 activation up-regulated four genes more than 5 fold (DUSP6, WWOX, AREG, SERPINB2) and down-regulated another six genes more than 3 fold (TXNIP, RARG, ITGB4, CTSD, MSC and TM4SF15). Both PAR2 and PAR1 activation resulted in up-regulated expression of several genes (CD44, FOSL1, TNFRSF12A, RAB3A, COPEB, CORO1C, THBS1, SDC4) known to be important in cancer. This is the first widespread profiling of specific activation of PAR2 and provides a valuable platform for better understanding key mechanistic roles of PAR2 in human physiology. Results clearly support the development of both antagonists and agonists of human PAR2 as potential disease modifying therapeutic agents.

GPCRs, G-proteins, effectors and their interactions: human-gpDB, a database employing visualization tools and data integration techniques

G-protein coupled receptors (GPCRs) are a major family of membrane receptors in eukaryotic cells. They play a crucial role in the communication of a cell with the environment. Ligands bind to GPCRs on the outside of the cell, activating them by causing a conformational change, and allowing them to bind to G-proteins. Through their interaction with G-proteins, several effector molecules are activated leading to many kinds of cellular and physiological responses. The great importance of GPCRs and their corresponding signal transduction pathways is indicated by the fact that they take part in many diverse disease processes and that a large part of efforts towards drug development today is focused on them. We present Human-gpDB, a database which currently holds information about 713 human GPCRs, 36 human G-proteins and 99 human effectors. The collection of information about the interactions between these molecules was done manually and the current version of Human-gpDB holds information for about 1663 connections between GPCRs and G-proteins and 1618 connections between G-proteins and effectors. Major advantages of Human-gpDB are the integration of several external data sources and the support of advanced visualization techniques. Human-gpDB is a simple, yet a powerful tool for researchers in the life sciences field as it integrates an up-to-date, carefully curated collection of human GPCRs, G-proteins, effectors and their interactions. The database may be a reference guide for medical and pharmaceutical research, especially in the areas of understanding human diseases and chemical and drug discovery. Database URLs: http://schneider.embl.de/human_gpdb; http://bioinformatics.biol.uoa.gr/human_gpdb/

Early effects of FOLFOX treatment of colorectal tumour in an animal model: assessment of changes in gene expression and FDG kinetics

PURPOSE

The very early chemotherapeutic effects of the FOLFOX (fluorouracil, folinic acid, oxaliplatin) protocol were assessed in mice implanted with a human colorectal cell line. The aim of this study was to identify changes in gene expression patterns and to detect combinations of PET parameters that may be helpful in identifying treated tumours early after chemotherapy using dynamic PET studies.

METHODS

A human colorectal cell line (HCT 116) was used in nude mice. Dynamic PET studies were performed in untreated (n = 13) and treated (n = 12) animals. The data were assessed using compartmental and noncompartmental analysis. The removed tumour specimens were assessed by gene array analysis to obtain quantitative information on gene expression.

RESULTS

One chemotherapeutic treatment using the FOLFOX protocol resulted in an upregulation of 2,078 gene probes by more than 25%, while 2,254 probes were downregulated following treatment. The gene array data demonstrated primarily an enhancement of genes related to apoptosis. In particular, the apoptosis antigen 1 (APO-1), p21 and the G protein-coupled receptor 87 (G-87) were 2.6- to 3.3-fold upregulated as compared to the expression in untreated animals. There was a 100% separation of untreated and treated animals on the basis of these three genes. The SUV and the FDG kinetic parameters obtained by compartmental and noncompartmental fitting were not significantly different when individual parameters were compared between groups. However, classification analysis of the combination of the PET parameters VB, K1, k3, and influx revealed an overall accuracy of 84%. We were able to identify 91.7% (11/12) of the treated animals and 76.9% (10/13) of the untreated animals correctly using the classification analysis of PET data.

CONCLUSION

Even one chemotherapeutic treatment using FOLFOX has an impact on gene expression and significantly modulates FDG kinetics. Quantitative assessment of the tracer kinetics and the application of classification analysis to the data are promising tools to identify those tumours that demonstrate a chemotherapeutic effect very early following treatment.

Whole proteome identification of plant candidate G-protein coupled receptors in Arabidopsis, rice, and poplar: computational prediction and in-vivo protein coupling

Computational prediction and in vivo protein coupling experiments identify candidate plant G-protein coupled receptors in Arabidopsis, rice and poplar.

Background

The classic paradigm of heterotrimeric G-protein signaling describes a heptahelical, membrane-spanning G-protein coupled receptor that physically interacts with an intracellular Gα subunit of the G-protein heterotrimer to transduce signals. G-protein coupled receptors comprise the largest protein superfamily in metazoa and are physiologically important as they sense highly diverse stimuli and play key roles in human disease. The heterotrimeric G-protein signaling mechanism is conserved across metazoa, and also readily identifiable in plants, but the low sequence conservation of G-protein coupled receptors hampers the identification of novel ones. Using diverse computational methods, we performed whole-proteome analyses of the three dominant model plant species, the herbaceous dicot Arabidopsis thaliana (mouse-eared cress), the monocot Oryza sativa (rice), and the woody dicot Populus trichocarpa (poplar), to identify plant protein sequences most likely to be GPCRs.

Results

Our stringent bioinformatic pipeline allowed the high confidence identification of candidate G-protein coupled receptors within the Arabidopsis, Oryza, and Populus proteomes. We extended these computational results through actual wet-bench experiments where we tested over half of our highest ranking Arabidopsis candidate G-protein coupled receptors for the ability to physically couple with GPA1, the sole Gα in Arabidopsis. We found that seven out of eight tested candidate G-protein coupled receptors do in fact interact with GPA1. We show through G-protein coupled receptor classification and molecular evolutionary analyses that both individual G-protein coupled receptor candidates and candidate G-protein coupled receptor families are conserved across plant species and that, in some cases, this conservation extends to metazoans.

Conclusion

Our computational and wet-bench results provide the first step toward understanding the diversity, conservation, and functional roles of plant candidate G-protein coupled receptors.

Crystal structure of squid rhodopsin with intracellularly extended cytoplasmic region

G-protein-coupled receptors play a key step in cellular signal transduction cascades by transducing various extracellular signals via G-proteins. Rhodopsin is a prototypical G-protein-coupled receptor involved in the retinal visual signaling cascade. We determined the structure of squid rhodopsin at 3.7A resolution, which transduces signals through the G(q) protein to the phosphoinositol cascade. The structure showed seven transmembrane helices and an amphipathic helix H8 has similar geometry to structures from bovine rhodopsin, coupling to G(t), and human beta(2)-adrenergic receptor, coupling to G(s). Notably, squid rhodopsin contains a well structured cytoplasmic region involved in the interaction with G-proteins, and this region is flexible or disordered in bovine rhodopsin and human beta(2)-adrenergic receptor. The transmembrane helices 5 and 6 are longer and extrude into the cytoplasm. The distal C-terminal tail contains a short hydrophilic alpha-helix CH after the palmitoylated cysteine residues. The residues in the distal C-terminal tail interact with the neighboring residues in the second cytoplasmic loop, the extruded transmembrane helices 5 and 6, and the short helix H8. Additionally, the Tyr-111, Asn-87, and Asn-185 residues are located within hydrogen-bonding distances from the nitrogen atom of the Schiff base.

gpDB: a database of GPCRs, G-proteins, effectors and their interactions

gpDB is a publicly accessible, relational database, containing information about G-proteins, G-protein coupled receptors (GPCRs) and effectors, as well as information concerning known interactions between these molecules. The sequences are classified according to a hierarchy of different classes, families and subfamilies based on literature search. The main innovation besides the classification of G-proteins, GPCRs and effectors is the relational model of the database, describing the known coupling specificity of GPCRs to their respective alpha subunits of G-proteins, and also the specific interaction between G-proteins and their effectors, a unique feature not available in any other database.

AVAILABILITY

http://bioinformatics.biol.uoa.gr/gpDB CONTACT: shamodr@biol.uoa.gr

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Whole proteome identification of plant candidate G-protein coupled receptors in Arabidopsis, rice, and poplar: computational prediction and in-vivo protein coupling

BACKGROUND

The classic paradigm of heterotrimeric G-protein signaling describes a heptahelical, membrane-spanning G-protein coupled receptor that physically interacts with an intracellular G alpha subunit of the G-protein heterotrimer to transduce signals. G-protein coupled receptors comprise the largest protein superfamily in metazoa and are physiologically important as they sense highly diverse stimuli and play key roles in human disease. The heterotrimeric G-protein signaling mechanism is conserved across metazoa, and also readily identifiable in plants, but the low sequence conservation of G-protein coupled receptors hampers the identification of novel ones. Using diverse computational methods, we performed whole-proteome analyses of the three dominant model plant species, the herbaceous dicot Arabidopsis thaliana (mouse-eared cress), the monocot Oryza sativa (rice), and the woody dicot Populus trichocarpa (poplar), to identify plant protein sequences most likely to be GPCRs.

RESULTS

Our stringent bioinformatic pipeline allowed the high confidence identification of candidate G-protein coupled receptors within the Arabidopsis, Oryza, and Populus proteomes. We extended these computational results through actual wet-bench experiments where we tested over half of our highest ranking Arabidopsis candidate G-protein coupled receptors for the ability to physically couple with GPA1, the sole G alpha in Arabidopsis. We found that seven out of eight tested candidate G-protein coupled receptors do in fact interact with GPA1. We show through G-protein coupled receptor classification and molecular evolutionary analyses that both individual G-protein coupled receptor candidates and candidate G-protein coupled receptor families are conserved across plant species and that, in some cases, this conservation extends to metazoans.

CONCLUSION

Our computational and wet-bench results provide the first step toward understanding the diversity, conservation, and functional roles of plant candidate G-protein coupled receptors.

Computational models of neuronal biophysics and the characterization of potential neuropharmacological targets

The identification and characterization of potential pharmacological targets in neurology and psychiatry is a fundamental problem at the intersection between medicinal chemistry and the neurosciences. Exciting new techniques in proteomics and genomics have fostered rapid progress, opening numerous questions as to the functional consequences of ligand binding at the systems level. Psycho- and neuro-active drugs typically work in nerve cells by affecting one or more aspects of electrophysiological activity. Thus, an integrated understanding of neuropharmacological agents requires bridging the gap between their molecular mechanisms and the biophysical determinants of neuronal function. Computational neuroscience and bioinformatics can play a major role in this functional connection. Robust quantitative models exist describing all major active membrane properties under endogenous and exogenous chemical control. These include voltage-dependent ionic channels (sodium, potassium, calcium, etc.), synaptic receptor channels (e.g. glutamatergic, GABAergic, cholinergic), and G protein coupled signaling pathways (protein kinases, phosphatases, and other enzymatic cascades). This brief review of neuromolecular medicine from the computational perspective provides compelling examples of how simulations can elucidate, explain, and predict the effect of chemical agonists, antagonists, and modulators in the nervous system.

Computational prediction of the coupling specificity of g protein-coupled receptors

G protein-coupled receptors (GPCRs) represent one of the most important categories of membrane proteins that play important roles in signaling pathways. GPCRs transduce the extracellular stimuli into intracellular second messengers via their coupling to specific class of heterotrimeric GTP-binding proteins (G proteins) and the subsequent regulation of a diverse variety of effectors. Understanding the coupling specificity of GPCRs is critical for further comprehending their function, and is of tremendous clinical significance because GPCRs are the most successful drug targets. This minireview addresses the computational approaches that have been created for the prediction of coupling specificity of GPCRs and highlights the perspective of bioinformatics strategies that may be used to tackle this important task. In addition, some of the important resources of this field are also provided.

Ligand screening system using fusion proteins of G protein-coupled receptors with G protein alpha subunits

G protein-coupled receptors (GPCRs) constitute one of the largest families of genes in the human genome, and are the largest targets for drug development. Although a large number of GPCR genes have recently been identified, ligands have not yet been identified for many of them. Various assay systems have been employed to identify ligands for orphan GPCRs, but there is still no simple and general method to screen for ligands of such GPCRs, particularly of G(i)-coupled receptors. We have examined whether fusion proteins of GPCRs with G protein alpha subunit (Galpha) could be utilized for ligand screening and showed that the fusion proteins provide an effective method for the purpose. This article focuses on the followings: (1) characterization of GPCR genes and GPCRs, (2) identification of ligands for orphan GPCRs, (3) characterization of GPCR-Galpha fusion proteins, and (4) identification of ligands for orphan GPCRs using GPCR-Galpha fusion proteins.

Prediction of GPCR-G protein coupling specificity using features of sequences and biological functions

Understanding the coupling specificity between G protein-coupled receptors (GPCRs) and specific classes of G proteins is important for further elucidation of receptor functions within a cell. Increasing information on GPCR sequences and the G protein family would facilitate prediction of the coupling properties of GPCRs. In this study, we describe a novel approach for predicting the coupling specificity between GPCRs and G proteins. This method uses not only GPCR sequences but also the functional knowledge generated by natural language processing, and can achieve 92.2% prediction accuracy by using the C4.5 algorithm. Furthermore, rules related to GPCR-G protein coupling are generated. The combination of sequence analysis and text mining improves the prediction accuracy for GPCR-G protein coupling specificity, and also provides clues for understanding GPCR signaling.

Taste and pheromone perception in the fruit fly Drosophila melanogaster

Taste is an essential sense for detection of nutrient-rich food and avoidance of toxic substances. The Drosophila melanogaster gustatory system provides an excellent model to study taste perception and taste-elicited behaviors. "The fly" is unique in the animal kingdom with regard to available experimental tools, which include a wide repertoire of molecular-genetic analyses (i.e., efficient production of transgenics and gene knockouts), elegant behavioral assays, and the possibility to conduct electrophysiological investigations. In addition, fruit flies, like humans, recognize sugars as a food source, but avoid bitter tasting substances that are often toxic to insects and mammals alike. This paper will present recent research progress in the field of taste and contact pheromone perception in the fruit fly. First, we shall describe the anatomical properties of the Drosophila gustatory system and survey the family of taste receptors to provide an appropriate background. We shall then review taste and pheromone perception mainly from a molecular genetic perspective that includes behavioral, electrophysiological and imaging analyses of wild type flies and flies with genetically manipulated taste cells. Finally, we shall provide an outlook of taste research in this elegant model system for the next few years.

The GNB3 C825T polymorphism and essential hypertension: a meta-analysis of 34 studies including 14,094 cases and 17,760 controls

OBJECTIVES

The C825T single nucleotide polymorphism of the G-protein beta3 (GNB3) has been implicated in susceptibility to essential hypertension, through the expression of an alternatively spliced truncated variant. In an effort to clarify earlier inconclusive results, we performed a meta-analysis of population-based case-control genetic association studies.

METHODS

Random-effects methods were applied on summary data in order to combine the results of the individual studies.

RESULTS

We identified in total 34 studies, including 14,094 hypertensive cases and 17,760 controls. The TT versus CC + CT contrast yielded an overall odds ratio (OR) of 1.08 [95% confidence interval (CI): 1.01, 1.15], the contrast of TT + CT versus CC, an OR of 1.17 (95% CI: 1.06, 1.29), whereas that of the T allele versus C allele yielded a non-significant OR of 1.05 (95% CI: 0.98, 1.13). There was moderate evidence for a publication bias in the latter two contrasts, which was eliminated after excluding studies not in Hardy-Weinberg equilibrium and those performed on non-normal populations (those with a diagnosis of diabetes, obesity and myocardial infarction). Subgroup analyses revealed that non-significant estimates arose from studies on Asian populations, as opposed to the Caucasian ones. Furthermore, the frequency of the T allele was lower in Caucasians and these populations were found to inhabit higher latitudes.

CONCLUSIONS

The findings are in agreement with a recently proposed causal model for systolic blood pressure, which correlates it with the T allele and the absolute latitude. Further studies are needed in order to fully address questions about the aetiological mechanism of the particular association, as well as to study the effect in populations of African descent.

RINGdb: an integrated database for G protein-coupled receptors and regulators of G protein signaling

Background

Many marketed therapeutic agents have been developed to modulate the function of G protein-coupled receptors (GPCRs). The regulators of G-protein signaling (RGS proteins) are also being examined as potential drug targets. To facilitate clinical and pharmacological research, we have developed a novel integrated biological database called RINGdb to provide comprehensive and organized RGS protein and GPCR information.

Results

RINGdb contains information on mutations, tissue distributions, protein-protein interactions, diseases/disorders and other features, which has been automatically collected from the Internet and manually extracted from the literature. In addition, RINGdb offers various user-friendly query functions to answer different questions about RGS proteins and GPCRs such as their possible contribution to disease processes, the putative direct or indirect relationship between RGS proteins and GPCRs. RINGdb also integrates organized database cross-references to allow users direct access to detailed information. The database is now available at .

Conclusion

RINGdb is the only integrated database on the Internet to provide comprehensive RGS protein and GPCR information. This knowledgebase will be useful for clinical research, drug discovery and GPCR signaling pathway research.

Predicting the coupling specificity of GPCRs to G-proteins by support vector machines

G-protein coupled receptors (GPCRs) represent one of the most important classes of drug targets for pharmaceutical industry and play important roles in cellular signal transduction. Predicting the coupling specificity of GPCRs to G-proteins is vital for further understanding the mechanism of signal transduction and the function of the receptors within a cell, which can provide new clues for pharmaceutical research and development. In this study, the features of amino acid compositions and physiochemical properties of the full-length GPCR sequences have been analyzed and extracted. Based on these features, classifiers have been developed to predict the coupling specificity of GPCRs to G-proteins using support vector machines. The testing results show that this method could obtain better prediction accuracy.

Predicting G-protein coupled receptors-G-protein coupling specificity based on autocross-covariance transform

Determining G-protein coupled receptors (GPCRs) coupling specificity is very important for further understanding the functions of receptors. A successful method in this area will benefit both basic research and drug discovery practice. Previously published methods rely on the transmembrane topology prediction at training step, even at prediction step. However, the transmembrane topology predicted by even the best algorithm is not of high accuracy. In this study, we developed a new method, autocross-covariance (ACC) transform based support vector machine (SVM), to predict coupling specificity between GPCRs and G-proteins. The primary amino acid sequences are translated into vectors based on the principal physicochemical properties of the amino acids and the data are transformed into a uniform matrix by applying ACC transform. SVMs for nonpromiscuous coupled GPCRs and promiscuous coupled GPCRs were trained and validated by jackknife test and the results thus obtained are very promising. All classifiers were also evaluated by the test datasets with good performance. Besides the high prediction accuracy, the most important feature of this method is that it does not require any transmembrane topology prediction at either training or prediction step but only the primary sequences of proteins. The results indicate that this relatively simple method is applicable. Academic users can freely download the prediction program at http://www.scucic.net/group/database/Service.asp.

Prediction of the coupling specificity of GPCRs to four families of G-proteins using hidden Markov models and artificial neural networks

MOTIVATION

G-protein coupled receptors are a major class of eukaryotic cell-surface receptors. A very important aspect of their function is the specific interaction (coupling) with members of four G-protein families. A single GPCR may interact with members of more than one G-protein families (promiscuous coupling). To date all published methods that predict the coupling specificity of GPCRs are restricted to three main coupling groups G(i/o), G(q/11) and G(s), not including G(12/13)-coupled or other promiscuous receptors.

RESULTS

We present a method that combines hidden Markov models and a feed-forward artificial neural network to overcome these limitations, while producing the most accurate predictions currently available. Using an up-to-date curated dataset, our method yields a 94% correct classification rate in a 5-fold cross-validation test. The method predicts also promiscuous coupling preferences, including coupling to G(12/13), whereas unlike other methods avoids overpredictions (false positives) when non-GPCR sequences are encountered.

AVAILABILITY

A webserver for academic users is available at http://bioinformatics.biol.uoa.gr/PRED-COUPLE2

A method for the prediction of GPCRs coupling specificity to G-proteins using refined profile Hidden Markov Models

BACKGROUND

G- Protein coupled receptors (GPCRs) comprise the largest group of eukaryotic cell surface receptors with great pharmacological interest. A broad range of native ligands interact and activate GPCRs, leading to signal transduction within cells. Most of these responses are mediated through the interaction of GPCRs with heterotrimeric GTP-binding proteins (G-proteins). Due to the information explosion in biological sequence databases, the development of software algorithms that could predict properties of GPCRs is important. Experimental data reported in the literature suggest that heterotrimeric G-proteins interact with parts of the activated receptor at the transmembrane helix-intracellular loop interface. Utilizing this information and membrane topology information, we have developed an intensive exploratory approach to generate a refined library of statistical models (Hidden Markov Models) that predict the coupling preference of GPCRs to heterotrimeric G-proteins. The method predicts the coupling preferences of GPCRs to Gs, Gi/o and Gq/11, but not G12/13 subfamilies.

RESULTS

Using a dataset of 282 GPCR sequences of known coupling preference to G-proteins and adopting a five-fold cross-validation procedure, the method yielded an 89.7% correct classification rate. In a validation set comprised of all receptor sequences that are species homologues to GPCRs with known coupling preferences, excluding the sequences used to train the models, our method yields a correct classification rate of 91.0%. Furthermore, promiscuous coupling properties were correctly predicted for 6 of the 24 GPCRs that are known to interact with more than one subfamily of G-proteins.

CONCLUSION

Our method demonstrates high correct classification rate. Unlike previously published methods performing the same task, it does not require any transmembrane topology prediction in a preceding step. A web-server for the prediction of GPCRs coupling specificity to G-proteins available for non-commercial users is located at http://bioinformatics.biol.uoa.gr/PRED-COUPLE.