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- Kozielewicz, P, et al.
(författare)
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Overexpression of Orphan Receptor GPR61 Increases cAMP Levels upon Forskolin Stimulation in HEK293 Cells: in vitro and in silico Validation of 5-(Nonyloxy)Tryptamine as a Low-Affinity Inverse Agonist
- 2019
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Ingår i: Pharmacology. - : S. Karger AG. - 1423-0313 .- 0031-7012. ; 104:5-6, s. 377-382
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Tidskriftsartikel (refereegranskat)abstract
- GPR61 is an orphan receptor that belongs to Class A of G-protein-coupled receptors. It has been reported that GPR61 has a constitutive activity and couples to Gα<sub>s</sub>. In the present study, we characterized GPR61 function and ligand binding by experimental and molecular docking studies. We demonstrated that heterologous expression of GPR61 in HEK293 cells enhanced the cAMP synthesis response to forskolin, whereas the basal cAMP synthesis was unaffected. 5-(Nonyloxy)tryptamine inhibited forskolin-stimulated cAMP production in GPR61-expressing HEK293 cells. These studies highlight that the intrinsic activity of this receptor is only measurable following its synergy with Gα<sub>s</sub>.
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- Romagnoni, A, et al.
(författare)
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
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Tidskriftsartikel (refereegranskat)abstract
- Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
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