G-protein-coupled receptors (GPCRs) are membrane proteins that modulate physiology across human tissues in response to extracellular signals. GPCR-mediated signalling can differ because of changes in the sequence1,2 or expression3 of the receptors, leading to signalling bias when comparing diverse physiological systems4. An underexplored source of such bias is the generation of functionally diverse GPCR isoforms with different patterns of expression across different tissues. Here we integrate data from human tissue-level transcriptomes, GPCR sequences and structures, proteomics, single-cell transcriptomics, population-wide genetic association studies and pharmacological experiments. We show how a single GPCR gene can diversify into several isoforms with distinct signalling properties, and how unique isoform combinations expressed in different tissues can generate distinct signalling states. Depending on their structural changes and expression patterns, some of the detected isoforms may influence cellular responses to drugs and represent new targets for developing drugs with improved tissue selectivity. Our findings highlight the need to move from a canonical to a context-specific view of GPCR signalling that considers how combinatorial expression of isoforms in a particular cell type, tissue or organism collectively influences receptor signalling and drug responses.

/pdf/combinatorial-expression-of-gpcr-isoforms-affects-signalling-meoqfh1ag4.pdf

Combinatorial expression of GPCR isoforms affects signalling and drug responses.

Several cytochrome P450 (CYP) CYP3A polymorphisms were associated with reduced enzyme function. We aimed to evaluate the influence of these alleles on the pharmacokinetic parameters (PK) of several CYP3A substrates. We included 251 healthy volunteers who received a single dose of ambrisentan, atorvastatin, imatinib, aripiprazole, fentanyl, amlodipine, donepezil, olanzapine, fesoterodine, or quetiapine. The volunteers were genotyped for CYP3A4 and CYP3A5 polymorphisms by qPCR. To compare the PK across studies, measurements were corrected by the mean of each parameter for every drug and were logarithmically transformed. Neither CYP3A phenotype nor individual CYP3A4 or CYP3A5 polymorphisms were significantly associated with differences in PK. However, regarding the substrates that are exclusively metabolized by CYP3A, we observed a higher normalized AUC (p = 0.099) and a tendency of lower normalized Cl (p = 0.069) in CYP3A4 mutated allele carriers what was associated with diminished drug metabolism capacity. CYP3A4 polymorphisms did not show a pronounced influence on PK of the analysed drugs. If so, their impact could be detectable in a very small percentage of subjects. Although there are few subjects carrying CYP3A4 double mutations, the effect in those might be relevant, especially due to the majority of subjects lacking the CYP3A5 enzyme. In heterozygous subjects, the consequence might be less noticeable due to the high inducible potential of the CYP3A4 enzyme.

https://www.mdpi.com/2227-9059/8/4/94/pdf

Effect of the Most Relevant CYP3A4 and CYP3A5 Polymorphisms on the Pharmacokinetic Parameters of 10 CYP3A Substrates

A challenge of next generation sequencing is read contamination. We use Genotype-Tissue Expression (GTEx) datasets and technical metadata along with RNA-seq datasets from other studies to understand factors that contribute to contamination. Here we report, of 48 analyzed tissues in GTEx, 26 have variant co-expression clusters of four highly expressed and pancreas-enriched genes (PRSS1, PNLIP, CLPS, and/or CELA3A). Fourteen additional highly expressed genes from other tissues also indicate contamination. Sample contamination is strongly associated with a sample being sequenced on the same day as a tissue that natively expresses those genes. Discrepant SNPs across four contaminating genes validate the contamination. Low-level contamination affects ~40% of samples and leads to numerous eQTL assignments in inappropriate tissues among these 18 genes. This type of contamination occurs widely, impacting bulk and single cell (scRNA-seq) data set analysis. In conclusion, highly expressed, tissue-enriched genes basally contaminate GTEx and other datasets impacting analyses.

/pdf/consistent-rna-sequencing-contamination-in-gtex-and-other-3lp5o0rtxo.pdf

Consistent RNA sequencing contamination in GTEx and other data sets.

Gene expression and its regulation can vary substantially across tissue types. In order to generate knowledge about gene expression in human tissues, the Genotype-Tissue Expression (GTEx) program has collected transcriptome data in a wide variety of tissue types from post-mortem donors. However, many tissue types are difficult to access and are not collected in every GTEx individual. Furthermore, in non-GTEx studies, the accessibility of certain tissue types greatly limits the feasibility and scale of studies of multi-tissue expression. In this work, we developed multi-tissue imputation methods to impute gene expression in uncollected or inaccessible tissues. Via simulation studies, we showed that the proposed methods outperform existing imputation methods in multi-tissue expression imputation and that incorporating imputed expression data can improve power to detect phenotype-expression correlations. By analyzing data from nine selected tissue types in the GTEx pilot project, we demonstrated that harnessing expression quantitative trait loci (eQTLs) and tissue-tissue expression-level correlations can aid imputation of transcriptome data from uncollected GTEx tissues. More importantly, we showed that by using GTEx data as a reference, one can impute expression levels in inaccessible tissues in non-GTEx expression studies.

Imputing Gene Expression in Uncollected Tissues Within and Beyond GTEx

What is known and objective Statins are widely used worldwide in the prevention and treatment of coronary atherosclerotic heart disease and ischaemic stroke. However, in clinical application, statins have shown great individual differences in terms of the efficacy and safety, some of which are related to genetic factors. The purpose of this article was to summarize the recent advances about the correlation between gene polymorphisms and the efficacy/safety of statins. Methods We searched the databases including PharmGKB and PubMed (published before June 2019) using the keywords such as 'statin', 'gene polymorphism' and 'SNP' and obtained more than 100 articles. In this review, we described the clinical studies of genetic variants associated with both the efficacy and adverse reactions of statins. We also clarified the importance of taking pharmacogenetic variation into account to improve the clinical application of statins. Results and discussion The available data were collected and analysed to present the polymorphisms of candidate genes encoding the most promising proteins including SLCO1B1 (encoding uptake transporters); ABCB1, ABCC2, ABCG2 (encoding effluent transporter); APOE, APOA5 (encoding apolipoprotein); genes encoding cytochrome P450 enzyme system; KIF6, HMGCR, LDLR, LPA, PCSK9, COQ2, CETP, etc These genes were proved to be related to the pharmacodynamics and pharmacokinetics of statins, thus affecting the efficacy and safety. What is new and conclusion In this paper, the correlation between gene polymorphisms and the efficacy/safety of statins was summarized. The authors reached a consensus that the variants of the genes encoding uptake and effluent transporters have the most effect on the efficacy/safety of statins. It pointed out that it is desirable to do genetic testing of these transporter genes to reduce the incidence of myopathy or to achieve better outcomes before patients use statins, especially in the regions with high frequency of risk allele.

Pharmacogenetics of statins treatment: Efficacy and safety.

Variation in the expression level and activity of genes involved in drug disposition and action ('pharmacogenes') can affect drug response and toxicity, especially when in tissues of pharmacological importance. Previous studies have relied primarily on microarrays to understand gene expression differences, or have focused on a single tissue or small number of samples. The goal of this study was to use RNA-sequencing (RNA-seq) to determine the expression levels and alternative splicing of 389 Pharmacogenomics Research Network pharmacogenes across four tissues (liver, kidney, heart and adipose) and lymphoblastoid cell lines, which are used widely in pharmacogenomics studies. Analysis of RNA-seq data from 139 different individuals across the 5 tissues (20-45 individuals per tissue type) revealed substantial variation in both expression levels and splicing across samples and tissue types. Comparison with GTEx data yielded a consistent picture. This in-depth exploration also revealed 183 splicing events in pharmacogenes that were previously not annotated. Overall, this study serves as a rich resource for the research community to inform biomarker and drug discovery and use.

/pdf/transcriptomic-variation-of-pharmacogenes-in-multiple-human-f3a7hs0v9c.pdf

Transcriptomic variation of pharmacogenes in multiple human tissues and lymphoblastoid cell lines

Our objective was to evaluate the associations of genetic variants affecting simvastatin (SV) and simvastatin acid (SVA) metabolism [the gene encoding cytochrome P450, family 3, subfamily A, polypeptide 4 (CYP3A4)*22 and the gene encoding cytochrome P450, family 3, subfamily A, polypeptide 5 (CYP3A5)*3] and transport [the gene encoding solute carrier organic anion transporter family member 1B1 (SLCO1B1) T521C] with 12-hour plasma SV and SVA concentrations. The variants were genotyped, and the concentrations were quantified by high performance liquid chromatography-tandem mass spectrometry in 646 participants of the Cholesterol and Pharmacogenetics clinical trial of 40 mg/d SV for 6 weeks. The genetic variants were tested for association with 12-hour plasma SV, SVA, or the SVA/SV ratio using general linear models. CYP3A5*3 was not significantly associated with 12-hour plasma SV or SVA concentration. CYP3A4*1/*22 participants had 58% higher 12-hour plasma SV concentration compared with CYP3A4*1/*1 participants (P = 0.006). SLCO1B1 521T/C and 521C/C participants had 71% (P 1) SVA/SV ratio groups (P = 0.001). In conclusion, CYP3A4*22 and SLCO1B1 521C were significantly associated with increased 12-hour plasma SV and SVA concentrations, respectively. CYP3A5*3 was not significantly associated with 12-hour plasma SV or SVA concentrations. The combination of CYP3A4*22 and SLCO1B1 521C was significantly associated with SVA/SV ratio, which may translate into different clinical SV risk/benefit profiles.

Individual and Combined Associations of Genetic Variants in CYP3A4, CYP3A5, and SLCO1B1 With Simvastatin and Simvastatin Acid Plasma Concentrations.

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Transcriptomic variation of pharmacogenes in multiple human tissues and lymphoblastoid cell lines

Individual and Combined Associations of Genetic Variants in CYP3A4, CYP3A5, and SLCO1B1 With Simvastatin and Simvastatin Acid Plasma Concentrations.