Uncovering Drug-Responsive Regulatory Elements

Luizon, Marcelo R.; Ahituv, Nadav

doi:10.2217/pgs.15.121

Cited by 20 publications

(15 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Enhancers have been identified as potential determinants of drug response [53,54]. Here, we used ChIP-seq for H3K27ac to identify metformin-responsive associated enhancers and found putative enhancer sequences near genes related to metformin action on gluconeogenesis.…”

Section: Discussionmentioning

confidence: 99%

Genomic Characterization of Metformin Hepatic Response

et al. 2016

Self Cite

View full text Add to dashboard Cite

Metformin is used as a first-line therapy for type 2 diabetes (T2D) and prescribed for numerous other diseases. However, its mechanism of action in the liver has yet to be characterized in a systematic manner. To comprehensively identify genes and regulatory elements associated with metformin treatment, we carried out RNA-seq and ChIP-seq (H3K27ac, H3K27me3) on primary human hepatocytes from the same donor treated with vehicle control, metformin or metformin and compound C, an AMP-activated protein kinase (AMPK) inhibitor (allowing to identify AMPK-independent pathways). We identified thousands of metformin responsive AMPK-dependent and AMPK-independent differentially expressed genes and regulatory elements. We functionally validated several elements for metformin-induced promoter and enhancer activity. These include an enhancer in an ataxia telangiectasia mutated (ATM) intron that has SNPs in linkage disequilibrium with a metformin treatment response GWAS lead SNP (rs11212617) that showed increased enhancer activity for the associated haplotype. Expression quantitative trait locus (eQTL) liver analysis and CRISPR activation suggest that this enhancer could be regulating ATM, which has a known role in AMPK activation, and potentially also EXPH5 and DDX10, its neighboring genes. Using ChIP-seq and siRNA knockdown, we further show that activating transcription factor 3 (ATF3), our top metformin upregulated AMPK-dependent gene, could have an important role in gluconeogenesis repression. Our findings provide a genome-wide representation of metformin hepatic response, highlight important sequences that could be associated with interindividual variability in glycemic response to metformin and identify novel T2D treatment candidates.

show abstract

Section: Discussionmentioning

confidence: 99%

Genomic Characterization of Metformin Hepatic Response

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…We expect that epigenomics research focused on the regulatory role of GWAS associated SNPs located in non-coding regions might provide a deeper understanding of pathways involved in the response to antihypertensive drugs. Notably, more than 96% of the associated SNPs from GWAS in pharmacogenomics reside in non-coding regions, which suggest these common variants are regulatory ( Luizon and Ahituv, 2015 ). In addition, gene–gene interactions among common regulatory variants can have strong effects in drug response phenotypes ( Sadee et al, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

“…Gene–gene interactions among common regulatory variants can have strong effects in drug response phenotypes ( Sadee et al, 2014 ). Notably, findings from 108 GWAS in pharmacogenomics revealed that 96.4% of the 928 associated single nucleotide polymorphisms (SNPs) are located in non-coding regions ( Luizon and Ahituv, 2015 ). These findings suggest that most of common variants associated with variability in drug response are regulatory.…”

Section: Introductionmentioning

confidence: 99%

“…However, most of the SNPs used in GWAS arrays were selected as informative genetic markers with higher allele frequencies and more likely to have greater power to detect association. In addition, the associated SNPs in GWAS are not necessarily the causal variant responsible for the phenotype, but may be in high linkage disequilibrium with the causal variant ( Luizon and Ahituv, 2015 ). Notably, the failure to replicate findings from pharmacogenetic studies, particularly in the analysis of single-locus, has prompted the search for novel statistical methods to analyze the data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pharmacogenomics of Hypertension and Preeclampsia: Focus on Gene–Gene Interactions

2018

View full text Add to dashboard Cite

Hypertension is a leading cause of cardiovascular mortality, but only about half of patients on antihypertensive therapy achieve blood pressure control. Preeclampsia is defined as pregnancy-induced hypertension and proteinuria, and is associated with increased maternal and perinatal mortality and morbidity. Similarly, a large number of patients with preeclampsia are non-responsive to antihypertensive therapy. Pharmacogenomics may help to guide the personalized treatment for non-responsive hypertensive patients. There is evidence for the association of genetic variants with variable response to the most commonly used antihypertensive drugs. However, further replication is needed to confirm these associations in different populations. The failure to replicate findings from single-locus association studies has prompted the search for novel statistical methods for data analysis, which are required to detect the complex effects from multiple genes to drug response phenotypes. Notably, gene–gene interaction analyses have been applied to pharmacogenetic studies, including antihypertensive drug response. In this perspective article, we present advances of considering the interactions among genetic polymorphisms of different candidate genes within pathways relevant to antihypertensive drug response, and we highlight recent findings related to gene–gene interactions on pharmacogenetics of hypertension and preeclampsia. Finally, we discuss the future directions that are needed to unravel additional genes and variants involved in the responsiveness to antihypertensive drugs.

show abstract

“…Similar to human disease, genetic variation in gene regulatory elements can have a significant effect on drug response. Notably, 96.4% of genetic polymorphisms identified in GWAS of pharmacogenomic traits were in non-coding regions 6 . High-throughput and massively parallel methods were described to decode and characterize the impact of these variants 7 .…”

Section: Functional Studies In Pharmacogenomics (Wang Ahituv)mentioning

confidence: 99%

Genomewide Association Studies in Pharmacogenomics: Meeting Report of the NIH Pharmacogenomics Research Network‐RIKEN (PGRN‐RIKEN) Collaboration

Yee

Momozawa

Kamatani

et al. 2016

Clin Pharma and Therapeutics

View full text Add to dashboard Cite

Genomewide association studies (GWAS) have resulted in the identification of many heritable genetic factors that underlie risk for human disease or variation in physiologic traits. In contrast, there are fewer GWAS of drug response phenotypes, despite extensive unexplained interindividual variability. To address this urgent need, the NIH Pharmacogenomics Research Network (PGRN) and the Center for Integrative Medical Sciences (IMS) at RIKEN support a collaboration, PGRN-RIKEN, with the goal of accelerating GWAS of drug response phenotypes.

show abstract

Uncovering Drug-Responsive Regulatory Elements

Cited by 20 publications

References 64 publications

Genomic Characterization of Metformin Hepatic Response

Genomic Characterization of Metformin Hepatic Response

Pharmacogenomics of Hypertension and Preeclampsia: Focus on Gene–Gene Interactions

Genomewide Association Studies in Pharmacogenomics: Meeting Report of the NIH Pharmacogenomics Research Network‐RIKEN (PGRN‐RIKEN) Collaboration

Contact Info

Product

Resources

About