Defining Reference Sequences for Nocardia Species by Similarity and Clustering Analyses of 16S rRNA Gene Sequence Data

International audienceBACKGROUND: The intra- and inter-species genetic diversity of bacteria and the absence of 'reference', or the most representative, sequences of individual species present a significant challenge for sequence-based identification. The aims of this study were to determine the utility, and compare the performance of several clustering and classification algorithms to identify the species of 364 sequences of 16S rRNA gene with a defined species in GenBank, and 110 sequences of 16S rRNA gene with no defined species, all within the genus Nocardia. METHODS: A total of 364 16S rRNA gene sequences of Nocardia species were studied. In addition, 110 16S rRNA gene sequences assigned only to the Nocardia genus level at the time of submission to GenBank were used for machine learning classification experiments. Different clustering algorithms were compared with a novel algorithm or the linear mapping (LM) of the distance matrix. Principal Components Analysis was used for the dimensionality reduction and visualization. RESULTS: The LM algorithm achieved the highest performance and classified the set of 364 16S rRNA sequences into 80 clusters, the majority of which (83.52%) corresponded with the original species. The most representative 16S rRNA sequences for individual Nocardia species have been identified as 'centroids' in respective clusters from which the distances to all other sequences were minimized; 110 16S rRNA gene sequences with identifications recorded only at the genus level were classified using machine learning methods. Simple kNN machine learning demonstrated the highest performance and classified Nocardia species sequences with an accuracy of 92.7% and a mean frequency of 0.578. CONCLUSION: The identification of centroids of 16S rRNA gene sequence clusters using novel distance matrix clustering enables the identification of the most representative sequences for each individual species of Nocardia and allows the quantitation of inter- and intra-species variability

The Role of Gene Duplication and Unconstrained Selective Pressures in the Melanopsin Gene Family Evolution and Vertebrate Circadian Rhythm Regulation

Melanopsin is a photosensitive cell protein involved in regulating circadian rhythms and other non-visual responses to light. The melanopsin gene family is represented by two paralogs,OPN4x and OPN4m, which originated through gene duplication early in the emergence of vertebrates. Here we studied the melanopsin gene family using an integrated gene/protein evolutionary approach, which revealed that the rhabdomeric urbilaterian ancestor had the same amino acid patterns (DRY motif and the Y and E conterions) as extant vertebrate species, suggesting that the mechanism for light detection and regulation is similar to rhabdomeric rhodopsins. Both OPN4m and OPN4x paralogs are found in vertebrate genomic paralogons, suggesting that they diverged following this duplication event about 600 million years ago, when the complex eye emerged in the vertebrate ancestor. Melanopsins generally evolved under negative selection (ω = 0.171) with some minor episodes of positive selection (proportion of sites = 25%) and functional divergence (θI = 0.349 and θII = 0.126). The OPN4m and OPN4x melanopsin paralogs show evidence of spectral divergence at sites likely involved in melanopsin light absorbance (200F, 273S and 276A). Also, following the teleost lineage-specific whole genome duplication (3R) that prompted the teleost fish radiation, type I divergence (θI = 0.181) and positive selection (affecting 11% of sites) contributed to amino acid variability that we related with the photo-activation stability of melanopsin. The melanopsin intracellular regions had unexpectedly high variability in their coupling specificity of G-proteins and we propose that Gq/11 and Gi/o are the two G-proteins most-likely to mediate the melanopsin phototransduction pathway. The selection signatures were mainly observed on retinal-related sites and the third and second intracellular loops, demonstrating the physiological plasticity of the melanopsin protein group. Our results provide new insights on the phototransduction process and additional tools for disentangling and understanding the links between melanopsin gene evolution and the specializations observed in vertebrates, especially in teleost fish

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

Abstract 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.</p

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. © 2005 Sgourakis et al; licensee BioMed Central Ltd

Fluorescence Fluctuation Techniques for the Investigation of Structure-Function Relationships of G-Protein-Coupled Receptors

G-protein-coupled receptors (GPCRs) are seven transmembrane receptors that form the largest superfamily of signaling proteins, and the family members function in a diverse array of metabolic pathways including cardiac function, immune response, neurotransmission, smell, taste, cell differentiation and growth, and vision. It is becoming clear that alteration in the quaternary structure of the GPCR receptor can have a profound impact on signaling capabilities. Biochemical, biophysical, physiological, x-ray crystallographic, and computational methods have been used over the last 40–50 years to study the structure and function of GPCRs. Evidence from these studies confirm that GPCRs can be organized as monomers, dimers, and higher-order oligomers. However, many times, these methods require extraction of the receptor from its native environment and high levels of expression and only provide a snapshot of information. A need arose for techniques that could measure the assembly and disassembly of receptors at few-to-single molecule resolution in their native environment at fast time scales. In the last 20 years, fluorescence fluctuation techniques have filled this need and provided new insight into the dynamics of GPCR organization in the absence and presence of ligands, agonists, and antagonists. In this book chapter, we provide a brief introduction to GPCR structure and function [Section 1]. An overview of the theoretical basis for fluorescence fluctuations techniques (FFTs) and how FFTs can be used to study the oligomeric structure of GPCRs in live and fixed cells is explained [Section 2]. We discuss the advantages and limitations of FFTs [Section 3], and finally, we summarize select case studies on GPCR structure and function revealed by FFT experiments [Section 4]

Beta-arrestins in cancer : linking pro-tumorigenic extracellular activated signaling with the tumor suppressor p53 pathway

The IGF-1R is an important player in cancer development that maintains the malignant phenotype by inducing cell proliferation, survival, transformation, motility and invasiveness. Activation of IGF-R results in its Mdm2-dependent ubiquitination and degradation followed by MAPK signaling. IGF-1R ubiquitination by Mdm2 is mediated by scaffolding protein β- arrestin 1. The tumor suppressor p53 pathway is activated in damaged cells causing growth arrest and if necessary, apoptosis and senescence. In normal conditions p53 is inactivated by Mdm2. Activation of extracellular pro-survival signaling has been shown to inhibit p53 activity through β-arrestin 1. Thus the same Mdm2/β-arrestin 1 system regulates two important pathways involved in cancer. The aim of this thesis was to investigate in detail the IGF-1R/β-arrestin/Mdm2/p53 axis and explore the potential use of its components as anti-cancer therapeutic targets. In Paper I we analysed the molecular interplay between p53 and IGF-1R through Mdm2. We tested the effect of p53/Mdm2 disruption on IGF-1R using a panel of melanoma cells and the p53-rectivator Nutlin-3. Disruption of the p53/Mdm2 interaction by Nutlin-3 increased the IGF-1R/Mdm2 interaction, followed by IGF-1R degradation and MAPK activation. This resulted in reduced cell proliferation and invasion and had a two-step effect on cell migration, demonstrating that modulation of the p53/Mdm2/IGF-1R axis is a potential anti-cancer therapeutic strategy. In paper II we focused on the role of β-arrestin isoforms in the p53/Mdm2/IGF-1R axis. By modulating levels of β-arrestin 1 or 2 we identified opposing roles of isoforms on IGF-1R degradation, signaling and p53 pathway. We revealed a higher affinity of ligand-free IGF-1R for β-arrestin 2, and ligand occupied receptor - for β-arrestin 1. Antagonism between isoforms was also observed on biological effects with β-arrestin 2 causing cell cycle arrest and inhibiting IGF-1 response and cell viability, and β-arrestin 1 acting in the opposite direction. Thus we identified the β-arrestin 1/2 system as a second potential drug target within the p53/Mdm2/IGF-1R axis. In paper III we studied the possibility of co-targeting the p53/Mdm2/IGF-1R and the MAPK pathway in melanoma cell lines. We combined MEK inhibitors with 1) balanced IGF-1R inhibition by siRNA; 2) biased IGF-1R inhibition by Nutlin-3, inducing transient MAPK; and 3) biased IGF-1R inhibition by antibody CP, inducing prolonged MAPK. We identified strong synergy between Nutlin-3 and MEK inhibitors. This combination of specific biased IGF-1R inhibition with MEK inhibitors is the first rational anti-cancer strategy identified in this thesis. In paper IV we investigated the possibility of co-targeting the β-arrestin system with the DNA-damage inducing drug dacarbazine. By modulating the level of β-arrestin isoforms in melanoma cell lines we demonstrated that both β-arrestin 1 inhibition and β-arrestin 2 overexpression synergize with dacarbazine. This study revealed the second rational anticancer strategy of this project. To sum up, our findings demonstrate that the p53/Mdm2/IGF-1R axis is a potential target for anti-cancer therapy. However, optimal effects can be achieved only through accurate modulation of multiple pathways regulated by the axis

A novel olfactory receptor gene family in teleost fish:phylogenomics, cellular localization and comparison with other olfactory receptor gene families

While for two of four mammalian olfactory receptor families, all of them G protein-coupled receptors, ortholog teleost families have been identified and well-characterized (OR and V2R), two other families (V1R and TAAR) lack to date a systematic study in non-mammalian vertebrates. By data mining I identified a total of six V1R-like genes in five teleost species plus four orthologs in one jawless and one cartilaginous fish species each. In the phylogenetic analysis these ora genes (olfactory receptor, class A-related) form a single clade with three subclades, one of them including the entire mammalian V1R superfamily. The Ora family originates early in the vertebrate lineage, before the separation of the jawless from jawed fish. A similar search was performed also for taar genes in genomes of five teleosts, two basal fish and seven higher vertebrates. Taar genes segregate into three classes and their family size ranges from 18 to 112 genes in teleosts (pufferfish and zebrafish, respectively), while mammalian families contain at most 19 genes (opossum). The TAAR family originated in the common ancestor of bony and cartilaginous fishes, after its divergence from jawless fish. In these and other properties the ora and taar gene families turn out to be at opposite poles of the spectrum of olfactory receptor families. All the six teleost Ora family members are evolutionarily much older than the speciation events in the teleost lineage, while most extant teleost taar genes have emerged late in evolution, well after the split between basal teleosts (zebrafish) and neoteleostei (stickleback, medaka, pufferfish). Taar genes are largely arranged according to phylogenetic proximity in two big clusters (both syntenic to the single sarcopterygian gene cluster), whereas the ora genes are organized as singletons or symmetrical gene pairs. TAAR genes are mostly monoexonic, whereas two ora genes exhibit a highly conserved multi-exonic structure. Furthermore, the ora genes are under strong negative selection (minute dN/dS values), whereas the teleost taar genes display a relaxed pattern of global negative selection and an unprecedented degree of local positive selection. Taken together, the ora gene repertoire is highly conserved across teleosts, in striking contrast to the frequent species-specific expansions observed in mammalian V1Rs. The inverse is observed for the taar gene repertoire, which is rather conserved across mammalian species, but exhibits frequent and large species-specific expansions in teleosts. Thus, the transition from teleosts to tetrapods may parallel a transition in function as well as regulation of both the ora/V1R and TAAR gene families. Consistent with a function as olfactory receptors all zebrafish ora and all analyzed taar genes (except taar1) were expressed in sparse subsets of olfactory receptor neurons. The olfactory epithelium contains three subtypes of olfactory receptor neurons, ciliated, microvillous and crypt cells, the latter so far without known receptors, but with both cilia and microvilli. I found the ora genes to be expressed in the crypt cells, thereby deorphanizing this third type of OSN. Furthermore, the ora genes follow the monogenic rule of expression previously reported for members of other olfactory receptor gene families. Ora genes co-express both Gαi and Gαo, supporting the hypothesis that crypt cells might possess two distinct olfactory signaling pathways, one via their cilia and the other via microvilli

MonoAminergic Receptors in the Stomatogastric Nervous System: Characterization and Localization in Panulirus Interruptus

Neural circuit flexibility is fundamental to the production of adaptable behaviors. Invertebrate models offer relatively simple networks consisting of large, identifiable neurons that are useful for investigating the electrophysiological properties that contribute to circuit output. In particular, central pattern generating circuits within the crustacean stomatogastric nervous system have been well characterized with regard to their synaptic connectivities, cellular properties, and response to modulatory influences. Monoaminergic modulation is essential for the production of adaptable circuit output in most species. Monoamines, such as dopamine and serotonin, signal via metabotropic receptors, which activate intracellular signaling cascades. Many of the neuronal and network targets of monoaminergic modulation in the crustacean stomatogastric nervous system are known, but nothing is known of the signal transduction cascades that mediate the biophysical response. This work represents a thorough characterization of monoaminergic receptors in the crustacean stomatogastric nervous system. We took advantage of the close phylogenetic relationship between crustaceans and insects to clone monoaminergic receptors from the spiny lobster. Using a novel database mining strategy, we were able to identify several uncharacterized monoaminergic receptors in the Panulirus interruptus genome. We cloned one serotonin (5-HT2βPan) and three dopamine receptors (D1αPan, D1βPan, and D2αPan), and characterized them with regard to G protein coupling and signal transduction cascades. We used a heterologous expression system to show that G protein couplings and signaling properties of monoaminergic receptors are strongly conserved among vertebrate and invertebrate species. This work further shows that DAR-G protein couplings in the stomatogastric nervous system are unique for a given receptor subtype, and receptors can couple to multiple signaling pathways, similar to their mammalian homologs. Custom made antibodies were used to localize monoamine receptors in the stomatogastric ganglion, and in identified neurons. Pyloric neurons show unique receptor expression profiles, which supports the idea of receptor expression as an underlying mechanism for cell-type specific effects of a given modulator. Receptors are localized to the synaptic neuropil, but are not expressed in the membrane of large diameter processes or the soma. The localization of dopamine receptors in identified pyloric neurons suggests that they may respond to synaptic, paracrine or neurohormonal dopamine signals. This work also supports the idea that different types of signals can be generated by a single receptor