The Location of Missense Variants in the Human GIP Gene Is Indicative for Natural Selection.

The intestinal hormone, glucose-dependent insulinotropic polypeptide (GIP), is involved in important physiological functions, including postprandial blood glucose homeostasis, bone remodeling, and lipid metabolism. While mutations leading to physiological changes can be identified in large-scale sequencing, no systematic investigation of GIP missense variants has been performed. Here, we identified 168 naturally occurring missense variants in the human GIP genes from three independent cohorts comprising ~720,000 individuals. We examined amino acid changing variants scattered across the pre-pro-GIP peptide using in silico effect predictions, which revealed that the sequence of the fully processed GIP hormone is more protected against mutations than the rest of the precursor protein. Thus, we observed a highly species-orthologous and population-specific conservation of the GIP peptide sequence, suggestive of evolutionary constraints to preserve the GIP peptide sequence. Elucidating the mutational landscape of GIP variants and how they affect the structural and functional architecture of GIP can aid future biological characterization and clinical translation.

P2X2 receptor subunit interfaces are missense variant hotspots, where mutations tend to increase apparent ATP affinity.

BACKGROUND AND PURPOSE: P2X receptors are trimeric ligand-gated ion channels that open a cation-selective pore in response to ATP binding to their large extracellular domain. The seven known P2X subtypes can assemble as homotrimeric or heterotrimeric complexes and contribute to numerous physiological functions, including nociception, inflammation and hearing. The overall structure of P2X receptors is well established, but little is known about the range and prevalence of human genetic variations and the functional implications of specific domains. EXPERIMENTAL APPROACH: Here, we examine the impact of P2X2 receptor inter-subunit interface missense variants identified in the human population or by structural predictions. We test both single and double mutants through electrophysiological and biochemical approaches. KEY RESULTS: We demonstrate that predicted extracellular domain inter-subunit interfaces display a higher-than-expected density of missense variations and that the majority of mutations that disrupt putative inter-subunit interactions result in channels with higher apparent ATP affinity. Lastly, we show that double mutants at the subunit interface show significant energetic coupling, especially if located in close proximity. CONCLUSION AND IMPLICATIONS: We provide the first structural mapping of the mutational distribution across the human population in a ligand-gated ion channel and show that the density of missense mutations is constrained between protein domains, indicating evolutionary selection at the domain level. Our data may indicate that, unlike other ligand-gated ion channels, P2X2 receptors have evolved an intrinsically high threshold for activation, possibly to allow for additional modulation or as a cellular protection mechanism against overstimulation.

Utilizing drug-target-event relationships to unveil safety patterns in pharmacovigilance.

INTRODUCTION: Signal detection is the most pivotal activity of signal management to guarantee that drugs maintain a positive risk-benefit ratio during their lifetime on the market. Signal detection is based on the systematic evaluation of available data sources, which have recently been extended in order to improve timely and comprehensive signal detection of drug safety problems. AREAS COVERED: In recent years, attempts have been made to incorporate pharmacological data for the prediction of safety signals. Previous studies have shown that data on the pharmacological targets of drugs are predictive of post-marketing adverse events. However, current approaches limit such predictions to adverse events expected from the interaction of a drug with the main pharmacological target and do not take off-target interactions into consideration. EXPERT OPINION: The authors propose the application of predictive modeling techniques utilizing pharmacological data from public databases for predicting drug-target-event relationships deriving from main- and off-target binding and from which potential safety signals can be deduced. Additionally, they provide an operative procedure for the identification of clinically relevant subgroups for predicted safety signals.

Primary Care Prescription Drug Use and Related Actionable Drug-Gene Interactions in the Danish Population.

Pharmacogenetics (PGx) aims to improve drug therapy using the individual patients' genetic make-up. Little is known about the potential impact of PGx on the population level, possibly hindering implementation of PGx in clinical care. Therefore, we investigated how many patients use actionable PGx drugs, have actionable genotypes or phenotypes and which patients could benefit the most of PGx testing. We included PGx recommendations from two international PGx consortia (Clinical Pharmacogenetics Implementation Consortium (CPIC) and Dutch Pharmacogenetics Working Group (DPWG)). Using data from publically accessible sales information drawn from the Danish Register of Medicinal Product Statistics (MEDSTAT), we identified the number of users of actionable prescription PGx drugs among the total Danish population in 2017. We estimated actionable genotypes or phenotypes based on reported frequencies from literature. We identified 49 drug-gene interactions related to 41 unique prescription drugs. The estimated median frequency of actionable genotypes or phenotypes among prescription drug users was 25% (interquartile range 7-26%). Six of 41 drugs were used more than twice as much in women. Actionable PGx drugs were most frequently used by 45-79 year old patients (62%), followed by 25-44 year old patients (18%). Almost half of the actionable PGx drugs (19/41) were psychotropics (i.e., antidepressants, antipsychotics, or psychostimulants). PGx testing can have a substantial impact on the population, as one in four prescription drug users has an actionable genotype or phenotype and could thus benefit from PGx testing. We advocate for prospective panel-based PGx testing at the time of the first PGx drug prescription ("as needed"), with PGx results ready prior to start of the first, and all future, therapies.

LEADS-PEP: A Benchmark Data Set for Assessment of Peptide Docking Performance.

With increasing interest in peptide-based therapeutics also the application of computational approaches such as peptide docking has gained more and more attention. In order to assess the suitability of docking programs for peptide placement and to support the development of peptide-specific docking tools, an independently constructed benchmark data set is urgently needed. Here we present the LEADS-PEP benchmark data set for assessing peptide docking performance. Using a rational and unbiased workflow, 53 protein-peptide complexes with peptide lengths ranging from 3 to 12 residues were selected. The data set is publicly accessible at www.leads-x.org . In a second step we evaluated several small molecule docking programs for their potential to reproduce peptide conformations as present in LEADS-PEP. While most tested programs were capable to generate native-like binding modes of small peptides, only Surflex-Dock and AutoDock Vina performed reasonably well for peptides consisting of more than five residues. Rescoring of docking poses with scoring functions ChemPLP, ChemScore, and ASP further increased the number of top-ranked near-native conformations. Our results suggest that small molecule docking programs are a good and fast alternative to specialized peptide docking programs.