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

Deciphering single-cell gene expression variability and its role in drug response

The effectiveness of drug treatments is profoundly influenced by individual responses, which are shaped by gene expression variability, particularly within pharmacogenes. Leveraging single-cell RNA sequencing (scRNA-seq) data, our study explores the extent of expression variability among pharmacogenes in a wide array of cell types across eight different human tissues, shedding light on their impact on drug responses. Our findings broaden the established link between variability in pharmacogene expression and drug efficacy to encompass variability at the cellular level. Moreover, we unveil a promising approach to enhance drug efficacy prediction. This is achieved by leveraging a combination of cross-cell and cross-individual pharmacogene expression variation measurements. Our study opens avenues for more precise forecasting of drug performance, facilitating tailored and more effective treatments in the future.

Principal Component Analysis of Alternative Splicing Profiles Revealed by Long-Read ONT Sequencing in Human Liver Tissue and Hepatocyte-Derived HepG2 and Huh7 Cell Lines

The long-read RNA sequencing developed by Oxford Nanopore Technology provides a direct quantification of transcript isoforms. That makes the number of transcript isoforms per gene an intrinsically suitable metric for alternative splicing (AS) profiling in the application to this particular type of RNA sequencing. By using this simple metric and recruiting principal component analysis (PCA) as a tool to visualize the high-dimensional transcriptomic data, we were able to group biospecimens of normal human liver tissue and hepatocyte-derived malignant HepG2 and Huh7 cells into clear clusters in a 2D space. For the transcriptome-wide analysis, the clustering was observed regardless whether all genes were included in analysis or only those expressed in all biospecimens tested. However, in the application to a particular set of genes known as pharmacogenes, which are involved in drug metabolism, the clustering worsened dramatically in the latter case. Based on PCA data, the subsets of genes most contributing to biospecimens' grouping into clusters were selected and subjected to gene ontology analysis that allowed us to determine the top 20 biological processes among which translation and processes related to its regulation dominate. The suggested metrics can be a useful addition to the existing metrics for describing AS profiles, especially in application to transcriptome studies with long-read sequencing.

Polygenic modelling and machine learning approaches in pharmacogenomics: Importance in downstream analysis of genome-wide association study data

Genome-wide association studies (GWAS) have identified genetic variations associated with adverse drug effects in pharmacogenomics (PGx) research. However, interpreting the biological implications of these associations remains a challenge. This review highlights 2 promising post-GWAS methods for PGx. First, we discuss the polygenic architecture of the PGx traits, especially for drug-induced liver injury. Experimental modelling using multiple donors' human primary hepatocytes and human liver organoids demonstrated the polygenic architecture of drug-induced liver injury susceptibility and found biological vulnerability in genetically high-risk tissue donors. Second, we discuss the challenges of interpreting the roles of variants in noncoding regions. Beyond methods involving expression quantitative trait locus analysis and massively parallel reporter assays, we suggest the use of in silico mutagenesis through machine learning methods to understand the roles of variants in transcriptional regulation. This review underscores the importance of these post-GWAS methods in providing critical insights into PGx, potentially facilitating drug development and personalized treatment.

The regulatory genome constrains protein sequence evolution: implications for the search for disease-associated genes

The development of explanatory models of protein sequence evolution has broad implications for our understanding of cellular biology, population history, and disease etiology. Here we analyze the GTEx transcriptome resource to quantify the effect of the transcriptome on protein sequence evolution in a multi-tissue framework. We find substantial variation among the central nervous system tissues in the effect of expression variance on evolutionary rate, with highly variable genes in the cortex showing significantly greater purifying selection than highly variable genes in subcortical regions (Mann-Whitney U p = 1.4 × 10^-4). The remaining tissues cluster in observed expression correlation with evolutionary rate, enabling evolutionary analysis of genes in diverse physiological systems, including digestive, reproductive, and immune systems. Importantly, the tissue in which a gene attains its maximum expression variance significantly varies (p = 5.55 × 10^-284) with evolutionary rate, suggesting a tissue-anchored model of protein sequence evolution. Using a large-scale reference resource, we show that the tissue-anchored model provides a transcriptome-based approach to predicting the primary affected tissue of developmental disorders. Using gradient boosted regression trees to model evolutionary rate under a range of model parameters, selected features explain up to 62% of the variation in evolutionary rate and provide additional support for the tissue model. Finally, we investigate several methodological implications, including the importance of evolutionary-rate-aware gene expression imputation models using genetic data for improved search for disease-associated genes in transcriptome-wide association studies. Collectively, this study presents a comprehensive transcriptome-based analysis of a range of factors that may constrain molecular evolution and proposes a novel framework for the study of gene function and disease mechanism.

Large-scale analysis of human gene expression variability associates highly variable drug targets with lower drug effectiveness and safety

Motivation

The effectiveness of drugs tends to vary between patients. One of the well-known reasons for this phenomenon is genetic polymorphisms in drug target genes among patients. Here, we propose that differences in expression levels of drug target genes across individuals can also contribute to this phenomenon.

Results

To explore this hypothesis, we analyzed the expression variability of protein-coding genes, and particularly drug target genes, across individuals. For this, we developed a novel variability measure, termed local coefficient of variation (LCV), which ranks the expression variability of each gene relative to genes with similar expression levels. Unlike commonly used methods, LCV neutralizes expression levels biases without imposing any distribution over the variation and is robust to data incompleteness. Application of LCV to RNA-sequencing profiles of 19 human tissues and to target genes of 1076 approved drugs revealed that drug target genes were significantly more variable than protein-coding genes. Analysis of 113 drugs with available effectiveness scores showed that drugs targeting highly variable genes tended to be less effective in the population. Furthermore, comparison of approved drugs to drugs that were withdrawn from the market showed that withdrawn drugs targeted significantly more variable genes than approved drugs. Last, upon analyzing gender differences we found that the variability of drug target genes was similar between men and women. Altogether, our results suggest that expression variability of drug target genes could contribute to the variable responsiveness and effectiveness of drugs, and is worth considering during drug treatment and development.

Availability and implementation

LCV is available as a python script in GitHub (https://github.com/eyalsim/LCV).

Supplementary information

Supplementary data are available at Bioinformatics online.

Molecular characterisation of ILRUN, a novel inhibitor of proinflammatory and antimicrobial cytokines

Regulation of type-I interferon (IFN) production is essential to the balance between antimicrobial defence and autoimmune disorders. The human protein-coding gene ILRUN (inflammation and lipid regulator with UBA-like and NBR1-like domains, previously C6orf106) was recently characterised as an inhibitor of antiviral and proinflammatory cytokine (interferon-alpha/beta and tumor necrosis factor alpha) transcription. Currently there is a paucity of information about the molecular characteristics of ILRUN, despite it being associated with several diseases including virus infection, coronary artery disease, obesity and cancer. Here, we characterise ILRUN as a highly phylogenetically conserved protein containing UBA-like and a NBR1-like domains that are both essential for inhibition of type-I interferon and tumor necrosis factor alpha) transcription in human cells. We also solved the crystal structure of the NBR1-like domain, providing insights into its potential role in ILRUN function. This study provides critical information for future investigations into the role of ILRUN in health and disease.

Consistent RNA sequencing contamination in GTEx and other data sets

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.

psichomics: graphical application for alternative splicing quantification and analysis

Alternative pre-mRNA splicing generates functionally distinct transcripts from the same gene and is involved in the control of multiple cellular processes, with its dysregulation being associated with a variety of pathologies. The advent of next-generation sequencing has enabled global studies of alternative splicing in different physiological and disease contexts. However, current bioinformatics tools for alternative splicing analysis from RNA-seq data are not user-friendly, disregard available exon-exon junction quantification or have limited downstream analysis features. To overcome such limitations, we have developed psichomics, an R package with an intuitive graphical interface for alternative splicing quantification and downstream dimensionality reduction, differential splicing and gene expression and survival analyses based on The Cancer Genome Atlas, the Genotype-Tissue Expression project, the Sequence Read Archive project and user-provided data. These integrative analyses can also incorporate clinical and molecular sample-associated features. We successfully used psichomics in a laptop to reveal alternative splicing signatures specific to stage I breast cancer and associated novel putative prognostic factors.

Genome sequencing analysis of blood cells identifies germline haplotypes strongly associated with drug resistance in osteosarcoma patients

BACKGROUND

Osteosarcoma is the most common malignant bone tumor in children. Survival remains poor among histologically poor responders, and there is a need to identify them at diagnosis to avoid delivering ineffective therapy. Genetic variation contributes to a wide range of response and toxicity related to chemotherapy. The aim of this study is to use sequencing of blood cells to identify germline haplotypes strongly associated with drug resistance in osteosarcoma patients.

METHODS

We used sequencing data from two patient datasets, from Inova Hospital and the NCI TARGET. We explored the effect of mutation hotspots, in the form of haplotypes, associated with relapse outcome. We then mapped the single nucleotide polymorphisms (SNPs) in these haplotypes to genes and pathways. We also performed a targeted analysis of mutations in Drug Metabolizing Enzymes and Transporter (DMET) genes associated with tumor necrosis and survival.

RESULTS

We found intronic and intergenic hotspot regions from 26 genes common to both the TARGET and INOVA datasets significantly associated with relapse outcome. Among significant results were mutations in genes belonging to AKR enzyme family, cell-cell adhesion biological process and the PI3K pathways; as well as variants in SLC22 family associated with both tumor necrosis and overall survival. The SNPs from our results were confirmed using Sanger sequencing. Our results included known as well as novel SNPs and haplotypes in genes associated with drug resistance.

CONCLUSION

We show that combining next generation sequencing data from multiple datasets and defined clinical data can better identify relevant pathway associations and clinically actionable variants, as well as provide insights into drug response mechanisms.

Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data

Up to half of all patients do not respond to pharmacological treatment as intended. A substantial fraction of these inter-individual differences is due to heritable factors and a growing number of associations between genetic variations and drug response phenotypes have been identified. Importantly, the rapid progress in Next Generation Sequencing technologies in recent years unveiled the true complexity of the genetic landscape in pharmacogenes with tens of thousands of rare genetic variants. As each individual was found to harbor numerous such rare variants they are anticipated to be important contributors to the genetically encoded inter-individual variability in drug effects. The fundamental challenge however is their functional interpretation due to the sheer scale of the problem that renders systematic experimental characterization of these variants currently unfeasible. Here, we review concepts and important progress in the development of computational prediction methods that allow to evaluate the effect of amino acid sequence alterations in drug metabolizing enzymes and transporters. In addition, we discuss recent advances in the interpretation of functional effects of non-coding variants, such as variations in splice sites, regulatory regions and miRNA binding sites. We anticipate that these methodologies will provide a useful toolkit to facilitate the integration of the vast extent of rare genetic variability into drug response predictions in a precision medicine framework.

Pharmacogenomics and big genomic data: from lab to clinic and back again

The field of pharmacogenomics is an area of great potential for near-term human health impacts from the big genomic data revolution. Pharmacogenomics research momentum is building with numerous hypotheses currently being investigated through the integration of molecular profiles of different cell lines and large genomic data sets containing information on cellular and human responses to therapies. Additionally, the results of previous pharmacogenetic research efforts have been formulated into clinical guidelines that are beginning to impact how healthcare is conducted on the level of the individual patient. This trend will only continue with the recent release of new datasets containing linked genotype and electronic medical record data. This review discusses key resources available for pharmacogenomics and pharmacogenetics research and highlights recent work within the field.

Post-transcriptional Regulation of UGT2B10 Hepatic Expression and Activity by Alternative Splicing

The detoxification enzyme UDP-glucuronosyltransferase UGT2B10 is specialized in the N-linked glucuronidation of many drugs and xenobiotics. Preferred substrates possess tertiary aliphatic amines and heterocyclic amines, such as tobacco carcinogens and several antidepressants and antipsychotics. We hypothesized that alternative splicing (AS) constitutes a means to regulate steady-state levels of UGT2B10 and enzyme activity. We established the transcriptome of UGT2B10 in normal and tumoral tissues of multiple individuals. The highest expression was in the liver, where 10 AS transcripts represented 50% of the UGT2B10 transcriptome in 50 normal livers and 44 hepatocellular carcinomas. One abundant class of transcripts involves a novel exonic sequence and leads to two alternative (alt.) variants with novel in-frame C termini of 10 or 65 amino acids. Their hepatic expression was highly variable among individuals, correlated with canonical transcript levels, and was 3.5-fold higher in tumors. Evidence for their translation in liver tissues was acquired by mass spectrometry. In cell models, they colocalized with the enzyme and influenced the conjugation of amitriptyline and levomedetomidine by repressing or activating the enzyme (40%-70%; P < 0.01) in a cell context-specific manner. A high turnover rate for the alt. proteins, regulated by the proteasome, was observed in contrast to the more stable UGT2B10 enzyme. Moreover, a drug-induced remodeling of UGT2B10 splicing was demonstrated in the HepaRG hepatic cell model, which favored alt. variants expression over the canonical transcript. Our findings support a significant contribution of AS in the regulation of UGT2B10 expression in the liver with an impact on enzyme activity.

Quantitative profiling of the UGT transcriptome in human drug-metabolizing tissues

Alternative splicing as a mean to control gene expression and diversify function is suspected to considerably influence drug response and clearance. We report the quantitative expression profiles of the human UGT genes including alternatively spliced variants not previously annotated established by deep RNA-sequencing in tissues of pharmacological importance. We reveal a comprehensive quantification of the alternative UGT transcriptome that differ across tissues and among individuals. Alternative transcripts that comprise novel in-frame sequences associated or not with truncations of the 5'- and/or 3'- termini, significantly contribute to the total expression levels of each UGT1 and UGT2 gene averaging 21% in normal tissues, with expression of UGT2 variants surpassing those of UGT1. Quantitative data expose preferential tissue expression patterns and remodeling in favor of alternative variants upon tumorigenesis. These complex alternative splicing programs have the strong potential to contribute to interindividual variability in drug metabolism in addition to diversify the UGT proteome.

Imputing Gene Expression in Uncollected Tissues Within and Beyond GTEx

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.

The CYP2C19 Intron 2 Branch Point SNP is the Ancestral Polymorphism Contributing to the Poor Metabolizer Phenotype in Livers with CYP2C19*35 and CYP2C19*2 Alleles

CYP2C19 rs12769205 alters an intron 2 branch point adenine leading to an alternative mRNA in human liver with complete inclusion of intron 2 (exon 2B). rs12769205 changes the mRNA reading frame, introduces 87 amino acids, and leads to a premature stop codon. The 1000 Genomes project (http://browser.1000genomes.org/index.html) indicated rs12769205 is in linkage disequilibrium with rs4244285 on CYP2C19*2, but found alone on CYP2C19*35 in Blacks. Minigenes containing rs12769205 transfected into HepG2 cells demonstrated this single nucleotide polymorphism (SNP) alone leads to exon 2B and decreases CYP2C19 canonical mRNA. A residual amount of CYP2C19 protein was detectable by quantitative proteomics with tandem mass spectrometry in CYP2C19*2/*2 and *1/*35 liver microsomes with an exon 2 probe. However, an exon 4 probe, downstream from rs12769205, but upstream of rs4244285, failed to detect CYP2C19 protein in livers homozygous for rs12769205, demonstrating rs12769205 alone can lead to complete loss of CYP2C19 protein. CYP2C19 genotypes and mephenytoin phenotype were compared in 104 Ethiopians. Poor metabolism of mephenytoin was seen in persons homozygous for both rs12769205 and rs4244285 (CYP2C19*2/*2), but with little effect on mephenytoin disposition of CYP2C19*1/*2, CYP2C19*1/*3, or CYP2C19*1/*35 heterozygous alleles. Extended haplotype homozygosity tests of the HapMap Yorubans (YRI) showed both haplotypes carrying rs12769205 (CYP2C19*35 and CYP2C19*2) are under significant natural selection, with CYP2C19*35 having a higher relative extended haplotype homozygosity score. The phylogenetic tree of the YRI CYP2C19 haplotypes revealed rs12769205 arose first on CYP2C19*35 and that rs4244285 was added later, creating CYP2C19*2. In conclusion, rs12769205 is the ancestral polymorphism leading to aberrant splicing of CYP2C19*35 and CYP2C19*2 alleles in liver.