G-Protein Coupled Receptor 83 (GPR83) Signaling Determined by Constitutive and Zinc(II)-Induced Activity

The G-protein coupled receptor 83 (GPR83) is an orphan G-protein coupled receptor for which the natural ligand(s) and signaling pathway(s) remain to be identified. Previous studies suggest a role of GPR83 in the regulation of thermogenesis and the control of circulating adiponectin. The aim of this study was to gain insights into the molecular underpinnings underlying GPR83 signaling. In particular, we aimed to assess the underlying G-protein activated signaling pathway of GPR83 and how this pathway is affected by mutational activation and zinc(II) challenge. Finally, we assessed the capacity of GPR83 for homodimerization. Our results show for the first time that mouse (m) GPR83 has high basal Gq/11 activity without affecting Gi or Gs signaling. Furthermore, we found that, under physiological conditions, zinc(II) (but not calcium(II) and magnesium(II)) potently activates mGPR83, thus identifying zinc(II) as an endogenous molecule with agonistic capability to activate mGPR83. In line with the observation that zinc(II)-ions activate mGPR83, we identified a cluster of ion-binding sensitive amino acids (e.g. His145, His204, Cys207, Glu217) in an activation sensitive receptor region of mGPR83. The occurrence of a constitutive activating mutant and a zinc(II)-binding residue at the N-terminal part corroborate the importance of this region in mGPR83 signal regulation. Finally, our results indicate that mGPR83 forms homodimers, which extend the current knowledge and molecular facets of GPR83 signaling.     

Affinity profiling using the peptide microarray technology: a case study

Little about the reliability of measurements obtained using synthetic peptide microarrays is known. We report results from a study on the quantitative reliability of microarrays manufactured by robot-supported immobilization of presynthesized peptides for different microarray platforms. Technological precision is assessed for inter- and intra-device readout comparisons. Correlations between measured signals and known dissociation constants using a phenomenological model derived from the mass action law are discussed. Special emphasis is on discussing the pitfalls of high-throughput affinity measurements. We show that the quantitative determination of binding affinities is prone to be biased toward a mean affinity of around 10(-7)M, while the classification of peptides into either "binders" or "nonbinders" provides very high prediction accuracy. The experimental requirements needed to obtain reliable binding affinity predictions are discussed.     

Identifying gene-specific variations in biomedical text

The influence of genetic variations on diseases or cellular processes is the main focus of many investigations, and results of biomedical studies are often only accessible through scientific publications. Automatic extraction of this information requires recognition of the gene names and the accompanying allelic variant information. In a previous work, the OSIRIS system for the detection of allelic variation in text based on a query expansion approach was communicated. Challenges associated with this system are the relatively low recall for variation mentions and gene name recognition. To tackle this challenge, we integrate the ProMiner system developed for the recognition and normalization of gene and protein names with a conditional random field (CRF)-based recognition of variation terms in biomedical text. Following the newly developed normalization of variation entities, we can link textual entities to Single Nucleotide Polymorphism database (dbSNP) entries. The performance of this novel approach is evaluated, and improved results in comparison to state-of-the-art systems are reported.