Krumm, B. | Search bioRxiv

bioRxiv2021

Mechanism of dopamine binding and allosteric modulation of the human D1 dopamine receptor

Dopamine is an essential neurotransmitter, which functions are mediated by five G protein-coupled receptors, dopamine D1 to D5 receptors (D1R-D5R) in mammals. Among them, D1R is the most abundantly expressed dopamine receptor in the CNS and is the central receptor mediating excitatory dopamine signaling in multiple dopaminergic pathways. Dysregulation of D1R signaling has been directly linked to Parkinsons disease (PD), schizophrenia, and drug abuse. Due to its fundamental functions in human diseases, D1R has long been the subject of intensive drug development effort toward the treatment of neuropsychiatric diseases. Here, we report the structures of D1R-Gs complex bound to endogenous agonist dopamine and synthetic agonist SKF81297, both with positive allosteric modulator LY3154207. These structures reveal the basis of dopamine recognition, the binding and potential allosteric regulation of DRD1 PAM LY3154207, and provide structural templates for design of subtype-selective D1R ligand for drug discovery targeting DRD1 for treating various CNS diseases.

molecular biology↗

bioRxiv2017

Genotype-phenotype association mining in bipolar disorder: market research meets complex genetics

Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: applying a data mining procedure to identify yet undetected genotype-phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2,835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted. Two of these - one associated with eating disorder and the other with anxiety - remained significant in an independent dataset after robust correction for multiple testing, showing considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively). Our approach may help detect novel specific genotype-phenotype relationships in BD typically not explored by analyses like GWAS. While we adapted the data mining tool within the context of BD gene discovery, it may facilitate identifying highly specific genotype-phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.

genomics↗