Super-conserved receptors expressed in the brain: biology and medicinal chemistry efforts

Development of Ligands for the Super Conserved Orphan G Protein-Coupled Receptor GPR27 with Improved Efficacy and Potency

The orphan G protein-coupled receptor GPR27 appears to play a role in insulin production, secretion, lipid metabolism, neuronal plasticity, and l-lactate homeostasis. However, investigations on the function of GPR27 are impaired by the lack of potent and efficacious agonists. We describe herein the development of di- and trisubstituted benzamide derivatives 4a-e, 7a-z, and 7aa-ai, which display GPR27-specific activity in a β-arrestin 2 recruitment-based assay. Highlighted compounds are PT-91 (7p: pEC50 6.15; Emax 100%) and 7ab (pEC50 6.56; Emax 99%). A putative binding mode was revealed by the docking studies of 7p and 7ab with a GPR27 homology model. The novel active compounds exhibited no GPR27-mediated activation of G proteins, indicating that the receptor may possess an atypical profile. Compound 7p displays high metabolic stability and brain exposure in mice. Thus, 7p represents a novel tool to investigate the elusive pharmacology of GPR27 and assess its potential as a drug target.

Characterizing the SREB G protein-coupled receptor family in fish: Brain gene expression and genomic differences in upstream transcription factor binding sites

The SREB (Super-conserved Receptors Expressed in Brain) family of orphan G protein-coupled receptors is highly conserved in vertebrates and consists of three members: SREB1 (orphan designation GPR27), SREB2 (GPR85), and SREB3 (GPR173). SREBs are associated with processes ranging from neuronal plasticity to reproductive control. Relatively little is known about similarities across the entire family, or how mammalian gene expression patterns compare to non-mammalian vertebrates. In fish, this system may be particularly complex, as some species have gained a fourth member (SREB3B) while others have lost genes. To better understand the system, the present study aimed to: 1) use qPCR to characterize sreb and related gene expression patterns in the brains of three fish species with different systems, and 2) identify possible differences in transcriptional regulation among the receptors, using upstream transcription factor binding sites across 70 ray-finned fish genomes. Overall, regional patterns of sreb expression were abundant in forebrain-related areas. However, some species-specific patterns were detected, such as abundant expression of receptors in zebrafish (Danio rerio) hypothalamic-containing sections, and divergence between sreb3a and sreb3b in pufferfish (Dichotomyctere nigroviridis). In addition, a gene possibly related to the system (dkk3a) was spatially correlated with the receptors in all three species. Genomic regions upstream of sreb2 and sreb3b, but largely not sreb1 or sreb3a, contained many highly conserved transcription factor binding sites. These results provide novel information about expression differences and transcriptional regulation across fish that may inform future research to better understand these receptors.

Integrative gene expression analysis for the diagnosis of Parkinson's disease using machine learning and explainable AI

Parkinson's disease (PD) is a progressive neurodegenerative disorder. Various symptoms and diagnostic tests are used in combination for the diagnosis of PD; however, accurate diagnosis at early stages is difficult. Blood-based markers can support physicians in the early diagnosis and treatment of PD. In this study, we used Machine Learning (ML) based methods for the diagnosis of PD by integrating gene expression data from different sources and applying explainable artificial intelligence (XAI) techniques to find the significant set of gene features contributing to diagnosis. We utilized the Least Absolute Shrinkage and Selection Operator (LASSO), and Ridge regression for the feature selection process. We utilized state-of-the-art ML techniques for the classification of PD cases and healthy controls. Logistic regression and Support Vector Machine showed the highest diagnostic accuracy. SHapley Additive exPlanations (SHAP) based global interpretable model-agnostic XAI method was utilized for the interpretation of the Support Vector Machine model. A set of significant biomarkers that contributed to the diagnosis of PD were identified. Some of these genes are associated with other neurodegenerative diseases. Our results suggest that the utilization of XAI can be useful in making early therapeutic decisions for the treatment of PD. The integration of datasets from different sources made this model robust. We believe that this research article will be of interest to clinicians as well as computational biologists in translational research.