Extended exposure to low doses of azacitidine induces differentiation of leukemic stem cells through activation of myeloperoxidase.

Oral azacitidine (oral-Aza) treatment results in longer median overall survival (OS) (24.7 vs. 14.8 months in placebo) in patients with acute myeloid leukemia (AML) in remission after intensive chemotherapy. The dosing schedule of oral-Aza (14 days/28-day cycle) allows for low exposure of Aza for an extended duration thereby facilitating a sustained therapeutic effect. However, the underlying mechanisms supporting the clinical impact of oral-Aza in maintenance therapy remain to be fully understood. In this preclinical work, we explore the mechanistic basis of oral-Aza/extended exposure to Aza through in vitro and in vivo modeling. In cell lines, extended exposure to Aza results in sustained DNMT1 loss, leading to durable hypomethylation, and gene expression changes. In mouse models, extended exposure to Aza, preferentially targets immature leukemic cells. In leukemic stem cell (LSC) models, the extended dose of Aza induces differentiation and depletes CD34+CD38- LSC. Mechanistically, LSC differentiation is driven in part by increased myeloperoxidase (MPO) expression. Inhibition of MPO activity either by using an MPO-specific inhibitor or blocking oxidative stress, a known mechanism of MPO, partly reverses the differentiation of LSC. Overall, our preclinical work reveals novel mechanistic insights into oral-Aza and its ability to target LSC.

Effect of tissue-grouped regulatory variants associated to type 2 diabetes in related secondary outcomes.

Genome-wide association studies have identified over five hundred loci that contribute to variation in type 2 diabetes (T2D), an established risk factor for many diseases. However, the mechanisms and extent through which these loci contribute to subsequent outcomes remain elusive. We hypothesized that combinations of T2D-associated variants acting on tissue-specific regulatory elements might account for greater risk for tissue-specific outcomes, leading to diversity in T2D disease progression. We searched for T2D-associated variants acting on regulatory elements and expression quantitative trait loci (eQTLs) in nine tissues. We used T2D tissue-grouped variant sets as genetic instruments to conduct 2-Sample Mendelian Randomization (MR) in ten related outcomes whose risk is increased by T2D using the FinnGen cohort. We performed PheWAS analysis to investigate whether the T2D tissue-grouped variant sets had specific predicted disease signatures. We identified an average of 176 variants acting in nine tissues implicated in T2D, and an average of 30 variants acting on regulatory elements that are unique to the nine tissues of interest. In 2-Sample MR analyses, all subsets of regulatory variants acting in different tissues were associated with increased risk of the ten secondary outcomes studied on similar levels. No tissue-grouped variant set was associated with an outcome significantly more than other tissue-grouped variant sets. We did not identify different disease progression profiles based on tissue-specific regulatory and transcriptome information. Bigger sample sizes and other layers of regulatory information in critical tissues may help identify subsets of T2D variants that are implicated in certain secondary outcomes, uncovering system-specific disease progression.

Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention.

Although physical activity and sedentary behavior are moderately heritable, little is known about the mechanisms that influence these traits. Combining data for up to 703,901 individuals from 51 studies in a multi-ancestry meta-analysis of genome-wide association studies yields 99 loci that associate with self-reported moderate-to-vigorous intensity physical activity during leisure time (MVPA), leisure screen time (LST) and/or sedentary behavior at work. Loci associated with LST are enriched for genes whose expression in skeletal muscle is altered by resistance training. A missense variant in ACTN3 makes the alpha-actinin-3 filaments more flexible, resulting in lower maximal force in isolated type IIA muscle fibers, and possibly protection from exercise-induced muscle damage. Finally, Mendelian randomization analyses show that beneficial effects of lower LST and higher MVPA on several risk factors and diseases are mediated or confounded by body mass index (BMI). Our results provide insights into physical activity mechanisms and its role in disease prevention.

Genome-wide association analysis in dilated cardiomyopathy reveals two new players in systolic heart failure on chromosomes 3p25.1 and 22q11.23.

AIMS: Our objective was to better understand the genetic bases of dilated cardiomyopathy (DCM), a leading cause of systolic heart failure. METHODS AND RESULTS: We conducted the largest genome-wide association study performed so far in DCM, with 2719 cases and 4440 controls in the discovery population. We identified and replicated two new DCM-associated loci on chromosome 3p25.1 [lead single-nucleotide polymorphism (SNP) rs62232870, P = 8.7 × 10-11 and 7.7 × 10-4 in the discovery and replication steps, respectively] and chromosome 22q11.23 (lead SNP rs7284877, P = 3.3 × 10-8 and 1.4 × 10-3 in the discovery and replication steps, respectively), while confirming two previously identified DCM loci on chromosomes 10 and 1, BAG3 and HSPB7. A genetic risk score constructed from the number of risk alleles at these four DCM loci revealed a 3-fold increased risk of DCM for individuals with 8 risk alleles compared to individuals with 5 risk alleles (median of the referral population). In silico annotation and functional 4C-sequencing analyses on iPSC-derived cardiomyocytes identify SLC6A6 as the most likely DCM gene at the 3p25.1 locus. This gene encodes a taurine transporter whose involvement in myocardial dysfunction and DCM is supported by numerous observations in humans and animals. At the 22q11.23 locus, in silico and data mining annotations, and to a lesser extent functional analysis, strongly suggest SMARCB1 as the candidate culprit gene. CONCLUSION: This study provides a better understanding of the genetic architecture of DCM and sheds light on novel biological pathways underlying heart failure.

A comparison of two workflows for regulome and transcriptome-based prioritization of genetic variants associated with myocardial mass.

A typical task arising from main effect analyses in a Genome Wide Association Study (GWAS) is to identify single nucleotide polymorphisms (SNPs), in linkage disequilibrium with the observed signals, that are likely causal variants and the affected genes. The affected genes may not be those closest to associating SNPs. Functional genomics data from relevant tissues are believed to be helpful in selecting likely causal SNPs and interpreting implicated biological mechanisms, ultimately facilitating prevention and treatment in the case of a disease trait. These data are typically used post GWAS analyses to fine-map the statistically significant signals identified agnostically by testing all SNPs and applying a multiple testing correction. The number of tested SNPs is typically in the millions, so the multiple testing burden is high. Motivated by this, in this study we investigated an alternative workflow, which consists in utilizing the available functional genomics data as a first step to reduce the number of SNPs tested for association. We analyzed GWAS on electrocardiographic QRS duration using these two workflows. The alternative workflow identified more SNPs, including some residing in loci not discovered with the typical workflow. Moreover, the latter are corroborated by other reports on QRS duration. This indicates the potential value of incorporating functional genomics information at the onset in GWAS analyses.

Integrative Functional Annotation of 52 Genetic Loci Influencing Myocardial Mass Identifies Candidate Regulatory Variants and Target Genes.

BACKGROUND: Regulatory elements may be involved in the mechanisms by which 52 loci influence myocardial mass, reflected by abnormal amplitude and duration of the QRS complex on the ECG. Functional annotation thus far did not take into account how these elements are affected in disease context. METHODS: We generated maps of regulatory elements on hypertrophic cardiomyopathy patients (ChIP-seq N=14 and RNA-seq N=11) and nondiseased hearts (ChIP-seq N=4 and RNA-seq N=11). We tested enrichment of QRS-associated loci on elements differentially acetylated and directly regulating differentially expressed genes between hypertrophic cardiomyopathy patients and controls. We further performed functional annotation on QRS-associated loci using these maps of differentially active regulatory elements. RESULTS: Regions differentially affected in disease showed a stronger enrichment ( P=8.6×10-5) for QRS-associated variants than those not showing differential activity ( P=0.01). Promoters of genes differentially regulated between hypertrophic cardiomyopathy patients and controls showed more enrichment ( P=0.001) than differentially acetylated enhancers ( P=0.8) and super-enhancers ( P=0.025). We also identified 74 potential causal variants overlapping these differential regulatory elements. Eighteen of the genes mapped confirmed previous findings, now also pinpointing the potentially affected regulatory elements and candidate causal variants. Fourteen new genes were also mapped. CONCLUSIONS: Our results suggest differentially active regulatory elements between hypertrophic cardiomyopathy patients and controls can offer more insights into the mechanisms of QRS-associated loci than elements not affected by disease.

Genome-wide association meta-analysis of 30,000 samples identifies seven novel loci for quantitative ECG traits.

Genome-wide association studies (GWAS) of quantitative electrocardiographic (ECG) traits in large consortia have identified more than 130 loci associated with QT interval, QRS duration, PR interval, and heart rate (RR interval). In the current study, we meta-analyzed genome-wide association results from 30,000 mostly Dutch samples on four ECG traits: PR interval, QRS duration, QT interval, and RR interval. SNP genotype data was imputed using the Genome of the Netherlands reference panel encompassing 19 million SNPs, including millions of rare SNPs (minor allele frequency < 5%). In addition to many known loci, we identified seven novel locus-trait associations: KCND3, NR3C1, and PLN for PR interval, KCNE1, SGIP1, and NFKB1 for QT interval, and ATP2A2 for QRS duration, of which six were successfully replicated. At these seven loci, we performed conditional analyses and annotated significant SNPs (in exons and regulatory regions), demonstrating involvement of cardiac-related pathways and regulation of nearby genes.

An oviduct-on-a-chip provides an enhanced in vitro environment for zygote genome reprogramming.

Worldwide over 5 million children have been conceived using assisted reproductive technology, and research has concentrated on increasing the likelihood of ongoing pregnancy. However, studies using animal models have indicated undesirable effects of in vitro embryo culture on offspring development and health. In vivo, the oviduct hosts a period in which the early embryo undergoes complete reprogramming of its (epi)genome in preparation for the reacquisition of (epi)genetic marks. We designed an oviduct-on-a-chip platform to better investigate the mechanisms related to (epi)genetic reprogramming and the degree to which they differ between in vitro and in vivo embryos. The device supports more physiological (in vivo-like) zygote genetic reprogramming than conventional IVF. This approach will be instrumental in identifying and investigating factors critical to fertilization and pre-implantation development, which could improve the quality and (epi)genetic integrity of IVF zygotes with likely relevance for early embryonic and later fetal development.

Druggability of Coronary Artery Disease Risk Loci.

BACKGROUND: Genome-wide association studies have identified multiple loci associated with coronary artery disease and myocardial infarction, but only a few of these loci are current targets for on-market medications. To identify drugs suitable for repurposing and their targets, we created 2 unique pipelines integrating public data on 49 coronary artery disease/myocardial infarction-genome-wide association studies loci, drug-gene interactions, side effects, and chemical interactions. METHODS: We first used publicly available genome-wide association studies results on all phenotypes to predict relevant side effects, identified drug-gene interactions, and prioritized candidates for repurposing among existing drugs. Second, we prioritized gene product targets by calculating a druggability score to estimate how accessible pockets of coronary artery disease/myocardial infarction-associated gene products are, then used again the genome-wide association studies results to predict side effects, excluded loci with widespread cross-tissue expression to avoid housekeeping and genes involved in vital processes and accordingly ranked the remaining gene products. RESULTS: These pipelines ultimately led to 3 suggestions for drug repurposing: pentolinium, adenosine triphosphate, and riociguat (to target CHRNB4, ACSS2, and GUCY1A3, respectively); and 3 proteins for drug development: LMOD1 (leiomodin 1), HIP1 (huntingtin-interacting protein 1), and PPP2R3A (protein phosphatase 2, regulatory subunit b-double prime, α). Most current therapies for coronary artery disease/myocardial infarction treatment were also rediscovered. CONCLUSIONS: Integration of genomic and pharmacological data may prove beneficial for drug repurposing and development, as evidence from our pipelines suggests.

From lipid locus to drug target through human genomics.

In the last decade, over 175 genetic loci have robustly been associated to levels of major circulating blood lipids. Most loci are specific to one or two lipids, whereas some (SUGP1, ZPR1, TRIB1, HERPUD1, and FADS1) are associated to all. While exposing the polygenic architecture of circulating lipids and the underpinnings of dyslipidaemia, these genome-wide association studies (GWAS) have provided further evidence of the critical role that lipids play in coronary heart disease (CHD) risk, as indicated by the 2.7-fold enrichment for macrophage gene expression in atherosclerotic plaques and the association of 25 loci (such as PCSK9, APOB, ABCG5-G8, KCNK5, LPL, HMGCR, NPC1L1, CETP, TRIB1, ABO, PMAIP1-MC4R, and LDLR) with CHD. These GWAS also confirmed known and commonly used therapeutic targets, including HMGCR (statins), PCSK9 (antibodies), and NPC1L1 (ezetimibe). As we head into the post-GWAS era, we offer suggestions for how to move forward beyond genetic risk loci, towards refining the biology behind the associations and identifying causal genes and therapeutic targets. Deep phenotyping through lipidomics and metabolomics will refine and increase the resolution to find causal and druggable targets, and studies aimed at demonstrating gene transcriptional and regulatory effects of lipid associated loci will further aid in identifying these targets. Thus, we argue the need for deeply phenotyped, large genetic association studies to reduce costs and failures and increase the efficiency of the drug discovery pipeline. We conjecture that in the next decade a paradigm shift will tip the balance towards a data-driven approach to therapeutic target development and the application of precision medicine where human genomics takes centre stage.

Integrative Bioinformatics Approaches for Identification of Drug Targets in Hypertension.

High blood pressure or hypertension is an established risk factor for a myriad of cardiovascular diseases. Genome-wide association studies have successfully found over nine hundred loci that contribute to blood pressure. However, the mechanisms through which these loci contribute to disease are still relatively undetermined as less than 10% of hypertension-associated variants are located in coding regions. Phenotypic cell-type specificity analyses and expression quantitative trait loci show predominant vascular and cardiac tissue involvement for blood pressure-associated variants. Maps of chromosomal conformation and expression quantitative trait loci (eQTL) in critical tissues identified 2,424 genes interacting with blood pressure-associated loci, of which 517 are druggable. Integrating genome, regulome and transcriptome information in relevant cell-types could help to functionally annotate blood pressure associated loci and identify drug targets.

Impact of carotid atherosclerosis loci on cardiovascular events.

BACKGROUND: Genome-wide association studies (GWAS) have identified six single-nucleotide polymorphisms (SNPs) related to carotid intima media thickness (cIMT) or plaque. However, whether these loci relate to other vascular diseases and subsequent vascular events is unclear. METHODS AND RESULTS: We tested six SNPs (rs4888378, rs11781551, rs445925, rs6601530, rs17398575 and rs1878406) for association with subclinical atherosclerotic measures (cIMT, plaque presence and ankle-brachial index), as well as ischemic stroke, abdominal aortic aneurysm, peripheral or coronary artery disease (CAD) in the Second Manifestations of ARTerial disease (SMART) cohort. Four SNPs were associated with cIMT and two with plaque (p < 0.05). One SNP was also significantly associated to CAD (rs1878406, OR = 1.24, 95% CI = 1.08-1.42, p = 2 × 10(-3)). A genetic risk score (GRS) based on the cIMT-related SNPs was associated to increased risk of cIMT itself (p = 1 × 10(-3)), but not to other secondary outcomes or vascular events during follow-up (p = 0.86). CONCLUSIONS: In addition to replicating previously published associations for cIMT, we confirmed a nominally significant effect between the GRS and cIMT.