Identifying the Causal SNP(s) Determining Dalcetrapib Responses

Introduction: Le dalcétrapib est un inhibiteur de la protéine de transfert des esters de cholestérol (CETP) qui augmente le niveau du cholestérol-HDL. Des études d’association pangénomiques ont révélé une association entre les polymorphismes du gène adénylate cyclase de type 9 (ADCY9) et les réponses au dalcétrapib. Le but de cette étude était d’identifier le polymorphisme nucléotidique (SNP) causal, ce qui pourrait mener à comprendre le mécanisme moléculaire modifiant les effets du dalcétrapib sur les bénéfices cardiovasculaires. Méthodes: Des essais d’EMSA (electrophoretic mobility shift assay) ont été réalisés afin d’analyser les effets modificateurs de douze SNPs candidats sur la liaison de protéines nucléaires, provenant de cellules monocytaires THP-1. Ensuite, des essais de transfections avec un gène rapporteur ont été utilisées pour évaluer l’effet transcriptionnel de ces SNPs. La liaison des protéines au SNP rs12920508 a par la suite été étudiée par des chromatographies d’affinité d’ADN suivies par des spectrométries de masse et par MC-EMSA (multiplexed competitor EMSA). Résultats: Sept sur douze SNPs ont démontré une liaison spécifique à un allèle qui n’a pas été influencée par l’exposition des cellules au dalcétrapib. Le résultat des transfections de vecteurs rapporteurs dans les cellules THP-1 a montré que les constructions plasmidiques portant les variants rs1967309 et rs12920508 augmentaient l’activité transcriptionnelle. Onze protéines ont été identifiées comme des candidats potentiels pouvant se lier à la région du SNP rs12920508. De plus, la région contenant les deux variants rs1967309 et rs12920509 a présenté une activité transcriptionnelle accrue et significativement plus élevée pour l’haplotype délétère. Conclusion: Le polymorphisme rs1967309 semble causer la majorité des effets fonctionnels dans la lignée cellulaire THP-1. Cependant, une interaction avec le SNP rs12920508 ou la présence de la région de ce SNP pourrait être nécessaire pour l’activité optimale de rs1967309. Des travaux supplémentaires sont nécessaires pour élucider le lien entre le SNP potentiellement causal et les réponses cardiovasculaires induites par le dalcétrapib.Introduction: Dalcetrapib is a cholesteryl ester transfer protein (CETP) inhibitor that increases the circulating level of HDL-cholesterol. Genome-wide association studies have revealed an association between polymorphisms found in the adenylate cyclase type 9 (ADCY9) gene and responses to dalcetrapib, including its cardiovascular benefits. The purpose of this study was to identify the causal single nucleotide polymorphisms (SNP) which could lead to understand the molecular mechanisms altering dalcetrapib effects on cardiovascular outcomes. Methods: Electrophoretic mobility shift assays (EMSA) were performed to analyze the allele-specific effects of the best causal SNP candidates on binding with nuclear proteins obtained from a THP-1 monocytic cell line. Afterwards, a dual luciferase reporter assay was used to assess the effect of selected genetic variants on gene transcription. Protein binding to SNP rs12920508 was investigated by DNA-affinity chromatography followed by mass spectrometry and multiplexed competitor EMSA. Results: Seven out of 12 SNPs demonstrated allele-specific protein binding, which was not influenced by dalcetrapib exposure of the cells. Results from dual luciferase reporter assay showed that plasmid constructs bearing variants rs12920508 and rs1967309 increased transcriptional activity when transfected into THP-1 undifferentiated monocytic cells. Eleven proteins were identified as potential candidates binding to region of SNP rs12920508. Additionally, region containing both SNPs rs1967309 and rs12920508 displayed increased transcriptional activity with significantly higher activity for deleterious haplotype. Conclusion: Polymorphism rs1967309 seems to be causing most functional effects in the THP-1 monocytic cell line. However, an interaction with rs12920508 or presence of the DNA region of this SNP may be necessary for optimal activity of rs1967309. Further work is required to elucidate the link between potentially causal SNPs and cardiovascular responses induced by dalcetrapib

The how and why of lncRNA function: An innate immune perspective.

Next-generation sequencing has provided a more complete picture of the composition of the human transcriptome indicating that much of the "blueprint" is a vastness of poorly understood non-protein-coding transcripts. This includes a newly identified class of genes called long noncoding RNAs (lncRNAs). The lack of sequence conservation for lncRNAs across species meant that their biological importance was initially met with some skepticism. LncRNAs mediate their functions through interactions with proteins, RNA, DNA, or a combination of these. Their functions can often be dictated by their localization, sequence, and/or secondary structure. Here we provide a review of the approaches typically adopted to study the complexity of these genes with an emphasis on recent discoveries within the innate immune field. Finally, we discuss the challenges, as well as the emergence of new technologies that will continue to move this field forward and provide greater insight into the biological importance of this class of genes. This article is part of a Special Issue entitled: ncRNA in control of gene expression edited by Kotb Abdelmohsen

Decoding the Non-coding Genome: Novel Technologies for the Characterization of Non-coding Elements and Variation

One of the key frontiers in genomics research is decoding the function of non-coding sequence and variation. Non-coding sequence, once thought to be junk DNA, is now known to regulate gene expression in a tissue-specific manner, and is frequently found to be mutated in cases of complex human disease. Despite their importance in human disease, non-coding regions are vastly understudied compared to protein coding regions. This is in part due to the abundance of non-coding sequences currently predicted to comprise 98.8% of the genome compared to protein coding regions, which make up only 1.2%. To complicate things further, most of this sequence is non-functional. A non-coding mutation may lead to a change in gene expression or a difference in human phenotype, yet it could show no change in gene expression at all. Therefore, there is considerable demand for novel computational and experimental tools focused on identifying functional non-coding sequences, and prioritizing variation associated with gene expression regulation and human disease. The focus of the work in this dissertation is the development of novel tools to identify functional non-coding regulatory sequences, and to prioritize the variation that falls within these sequences. I will introduce the following computational tools, the SNP Effect Matrix Pipeline (SEMpl) and the SNP Effect Matrix Pipeline with Methylation (SEMplMe). These methods integrate data from genome-wide annotations of functional elements, such as sites of transcription factor protein binding (ChIP-seq), open chromatin (DNase-seq), and DNA methylation (WGBS), to generate predictions of the consequences of nucleotide and methylation changes to binding affinity in transcription factor binding sites. As transcription factor binding sites are the building blocks of larger regulatory sequences, such as regulatory elements, functional alterations caused by the introduction of a variant or DNA methylation may lead to aberrant gene expression. SEMpl and SEMplMe are easy to use tools to help researchers prioritize the hundreds of putative regulatory variants that emerge from high-throughput studies, such as genome-wide association studies. This will greatly increase the rate at which non-coding variation can be experimentally validated. I will also introduce experimental tools focused on identifying larger blocks of regulatory non-coding sequence: cis-regulatory elements. Cis-regulatory elements are sequences that are able to alter or drive gene expression. Currently, a large body of in- formation exists for regulatory elements that are associated with an increase in gene expression, known as positive regulatory elements. However, regulatory elements associated with a decrease in gene expression, also known as negative regulatory elements, are comparatively understudied. To help fill this gap in knowledge between positive and negative regulatory elements, I helped develop two novel methodologies that are able to invert negative regulation into a positive reporter signal. The observed positive output allows negative regulatory elements to be characterized in a spatio-temporal manner in vivo in whole animals. This advancement will allow negative regulatory elements to be studied in a manner similar to what has already been achieved for positive regulatory elements for the first time. Together, the studies in this dissertation investigate non-coding regulatory sequence genome-wide through the development of novel tools which prioritize regulatory variation and identify and characterize regulatory elements.PHDGenetics and Genomics PhDUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/166151/1/ssnishi_1.pd

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

UGT pharmacogenomics in drug metabolism and diseases

Glucuronidation, mediated by UDP-glucuronosyltransferase enzymes (UGTs), is a major phase II biotransformation pathway and, complementary to phase I metabolism and membrane transport, one of the most important cellular defense mechanism responsible for the inactivation of therapeutic drugs, other xenobiotics and numerous endogenous molecules. Individual variability in UGT enzymatic pathways is significant and may have profound pharmacological and toxicological implications. Several genetic and genomic processes are underlying this variability and are discussed in the context of drug metabolism and diseases such as cancer

Translating lung function genome-wide association study (GWAS) findings: new insights for lung biology

Chronic respiratory diseases are a major cause of worldwide mortality and morbidity. Although hereditary severe deficiency of α1 antitrypsin (A1AD) has been established to cause emphysema, A1AD accounts for only ∼1% of Chronic Obstructive Pulmonary Disease (COPD) cases. Genome-wide association studies (GWAS) have been successful at detecting multiple loci harboring variants predicting the variation in lung function measures and risk of COPD. However, GWAS are incapable of distinguishing causal from noncausal variants. Several approaches can be used for functional translation of genetic findings. These approaches have the scope to identify underlying alleles and pathways that are important in lung function and COPD. Computational methods aim at effective functional variant prediction by combining experimentally generated regulatory information with associated region of the human genome. Classically, GWAS association follow-up concentrated on manipulation of a single gene. However association data has identified genetic variants in >50 loci predicting disease risk or lung function. Therefore there is a clear precedent for experiments that interrogate multiple candidate genes in parallel, which is now possible with genome editing technology. Gene expression profiling can be used for effective discovery of biological pathways underpinning gene function. This information may be used for informed decisions about cellular assays post genetic manipulation. Investigating respiratory phenotypes in human lung tissue and specific gene knockout mice is a valuable in vivo approach that can complement in vitro work. Herein, we review state-of-the-art in silico, in vivo, and in vitro approaches that may be used to accelerate functional translation of genetic findings

Inducible expression quantitative trait locus analysis of the MUC5AC gene in asthma in urban populations of children

BACKGROUND: Mucus plugging can worsen asthma control, lead to reduced lung function and fatal exacerbations. MUC5AC is the secretory mucin implicated in mucus plugging, and MUC5AC gene expression has been associated with development of airway obstruction and asthma exacerbations in urban children with asthma. However, the genetic determinants of MUC5AC expression are not established. OBJECTIVE: To assess single-nucleotide polymorphisms (SNPs) that influence MUC5AC expression and relate to pulmonary functions in childhood asthma. METHODS: We used RNA-sequencing data from upper airway samples and performed cis-expression quantitative trait loci (eQTL) and allele specific expression (ASE) analyses in two cohorts of predominantly Black and Hispanic urban children, a high asthma-risk birth cohort and an exacerbation-prone asthma cohort. We further investigated inducible MUC5AC eQTLs during incipient asthma exacerbations. We tested significant eQTLs SNPs for associations with lung function measurements and investigated their functional consequences in DNA regulatory databases. RESULTS: We identified two independent groups of SNPs in the MUC5AC gene that were significantly associated with MUC5AC expression. Moreover, these SNPs showed stronger eQTL associations with MUC5AC expression during asthma exacerbations, consistent with inducible expression. SNPs in one group also showed significant association with decreased pulmonary functions. These SNPs included multiple EGR1 transcription factor binding sites suggesting a mechanism of effect. CONCLUSIONS: These findings demonstrate the applicability of organ specific RNA-sequencing data to determine genetic factors contributing to a key disease pathway. Specifically, they suggest important genetic variations that may underlie propensity to mucus plugging in asthma and could be important in targeted asthma phenotyping and disease management strategies

The Pharmacoepigenomics Informatics Pipeline and H-GREEN Hi-C Compiler: Discovering Pharmacogenomic Variants and Pathways with the Epigenome and Spatial Genome

Over the last decade, biomedical science has been transformed by the epigenome and spatial genome, but the discipline of pharmacogenomics, the study of the genetic underpinnings of pharmacological phenotypes like drug response and adverse events, has not. Scientists have begun to use omics atlases of increasing depth, and inferences relating to the bidirectional causal relationship between the spatial epigenome and gene expression, as a foundational underpinning for genetics research. The epigenome and spatial genome are increasingly used to discover causative regulatory variants in the significance regions of genome-wide association studies, for the discovery of the biological mechanisms underlying these phenotypes and the design of genetic tests to predict them. Such variants often have more predictive power than coding variants, but in the area of pharmacogenomics, such advances have been radically underapplied. The majority of pharmacogenomics tests are designed manually on the basis of mechanistic work with coding variants in candidate genes, and where genome wide approaches are used, they are typically not interpreted with the epigenome. This work describes a series of analyses of pharmacogenomics association studies with the tools and datasets of the epigenome and spatial genome, undertaken with the intent of discovering causative regulatory variants to enable new genetic tests. It describes the potent regulatory variants discovered thereby to have a putative causative and predictive role in a number of medically important phenotypes, including analgesia and the treatment of depression, bipolar disorder, and traumatic brain injury with opiates, anxiolytics, antidepressants, lithium, and valproate, and in particular the tendency for such variants to cluster into spatially interacting, conceptually unified pathways which offer mechanistic insight into these phenotypes. It describes the Pharmacoepigenomics Informatics Pipeline (PIP), an integrative multiple omics variant discovery pipeline designed to make this kind of analysis easier and cheaper to perform, more reproducible, and amenable to the addition of advanced features. It described the successes of the PIP in rediscovering manually discovered gene networks for lithium response, as well as discovering a previously unknown genetic basis for warfarin response in anticoagulation therapy. It describes the H-GREEN Hi-C compiler, which was designed to analyze spatial genome data and discover the distant target genes of such regulatory variants, and its success in discovering spatial contacts not detectable by preceding methods and using them to build spatial contact networks that unite disparate TADs with phenotypic relationships. It describes a potential featureset of a future pipeline, using the latest epigenome research and the lessons of the previous pipeline. It describes my thinking about how to use the output of a multiple omics variant pipeline to design genetic tests that also incorporate clinical data. And it concludes by describing a long term vision for a comprehensive pharmacophenomic atlas, to be constructed by applying a variant pipeline and machine learning test design system, such as is described, to thousands of phenotypes in parallel. Scientists struggled to assay genotypes for the better part of a century, and in the last twenty years, succeeded. The struggle to predict phenotypes on the basis of the genotypes we assay remains ongoing. The use of multiple omics variant pipelines and machine learning models with omics atlases, genetic association, and medical records data will be an increasingly significant part of that struggle for the foreseeable future.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145835/1/ariallyn_1.pd