Abstract 056: Integrative Genomic Analysis Unravels Novel Pathways And Key Regulatory Networks In Blood Pressure

Hypertension ◽  
2014 ◽  
Vol 64 (suppl_1) ◽  
Author(s):  
Yuqi Zhao ◽  
Xingyi Shi ◽  
Ville-Petteri Mäkinen ◽  
Qingying Meng ◽  
Xia Yang
Keyword(s):  
Blood Pressure ◽  
Endothelial Cells ◽  
Cardiovascular Diseases ◽  
Regulatory Networks ◽  
Association Studies ◽  
Data Driven ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Aortic Endothelial Cells ◽  
Aortic Endothelial

Blood pressure (BP) is a highly heritable trait and hypertension is a major risk factor for cardiovascular diseases. Recent genome-wide association studies (GWAS) have implicated a number of susceptibility loci for systolic (SBP) and diastolic (DBP) blood pressure. However, these loci together only explain 1% of the BP variability and the underlying mechanisms remain elusive. In this study, we utilized an integrative genomics approach that leveraged multiple genetic and genomic datasets including 1) GWAS from CHARGE (The Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium for DBP and SBP, 2) expression quantitative trait loci (eQTLs) from genetics of gene expression studies of human tissues related to hypertension (such as peripheral blood, whole blood, human aortic endothelial cells, and adipose tissue), 3) knowledge-driven biological pathways, and 4) data-driven regulatory gene networks. The integration of these diverse data sources enabled tissue-specific investigations on whether the genetic variants associated with BP concentrated on specific parts of gene regulatory networks, termed as “subnetworks”, and whether novel key regulators in the subnetworks could be identified based on data-driven network topology. We identified 10 and 8 subnetworks for DBP and SBP respectively. Among these, subnetworks associated with ion homeostasis, ALK in cardiac myocytes, B cell receptor signaling, and Regulation of Insulin Secretion, were shared between DBP and SBP. More interestingly, we detected tissue-specific pathways, for example, L1CAM interactions in aortic endothelial cells and Leukocyte transendothelial migration in adipose. Among the trait-specific subnetworks, those involved in megakaryocyte development and cytoskeleton remodeling were found to be SBP-specific while GAB1 signalosome subnetwork was DBP-specific. Finally, by utilizing the gene-gene relationships revealed by the network architecture, we detected key regulator genes, both known (e.g. COL1A1, KL, and OSR1) and novel (e.g. EFEMP1, and CRABP2), in these blood pressure subnetworks. Our results shed lights on the complex mechanisms underlying blood pressure and highlight potential novel targets for hypertension and cardiovascular diseases.

Identification of Phosphorylation Associated SNPs for Blood Pressure, Coronary Artery Disease and Stroke from Genome-wide Association Studies

2019 ◽  
Vol 19 (10) ◽  
pp. 731-738
Author(s):  
Xingchen Wang ◽  
Xingbo Mo ◽  
Huan Zhang ◽  
Yonghong Zhang ◽  
Yueping Shen
Keyword(s):  
Blood Pressure ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Cardiovascular Diseases ◽  
Association Studies ◽  
Serum Levels ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Artery Disease

Purpose: Phosphorylation-related SNP (phosSNP) is a non-synonymous SNP that might influence protein phosphorylation status. The aim of this study was to assess the effect of phosSNPs on blood pressure (BP), coronary artery disease (CAD) and ischemic stroke (IS). Methods: We examined the association of phosSNPs with BP, CAD and IS in shared data from genome-wide association studies (GWAS) and tested if the disease loci were enriched with phosSNPs. Furthermore, we performed quantitative trait locus analysis to find out if the identified phosSNPs have impacts on gene expression, protein and metabolite levels. Results: We found numerous phosSNPs for systolic BP (count=148), diastolic BP (count=206), CAD (count=20) and IS (count=4). The most significant phosSNPs for SBP, DBP, CAD and IS were rs1801131 in MTHFR, rs3184504 in SH2B3, rs35212307 in WDR12 and rs3184504 in SH2B3, respectively. Our analyses revealed that the associated SNPs identified by the original GWAS were significantly enriched with phosSNPs and many well-known genes predisposing to cardiovascular diseases contain significant phosSNPs. We found that BP, CAD and IS shared for phosSNPs in loci that contain functional genes involve in cardiovascular diseases, e.g., rs11556924 (ZC3HC1), rs1971819 (ICA1L), rs3184504 (SH2B3), rs3739998 (JCAD), rs903160 (SMG6). Four phosSNPs in ADAMTS7 were significantly associated with CAD, including the known functional SNP rs3825807. Moreover, the identified phosSNPs seemed to have the potential to affect transcription regulation and serum levels of numerous cardiovascular diseases-related proteins and metabolites. Conclusion: The findings suggested that phosSNPs may play important roles in BP regulation and the pathological mechanisms of CAD and IS.

A Hypertension Gene: Are We There Yet?

2011 ◽  
Vol 14 (4) ◽  
pp. 295-304 ◽  
Author(s):  
Samantha J. Lupton ◽  
Christine L. Chiu ◽  
Joanne M. Lind
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Diseases ◽  
Association Studies ◽  
Single Gene ◽  
Pressure Control ◽  
Genome Wide Association Studies ◽  
Candidate Gene Association ◽  
Genome Wide ◽  
Common Genetic Variants ◽  
Treatment Of Hypertension

Cardiovascular diseases are the leading cause of death worldwide. Essential hypertension is a major risk factor for the development of other cardiovascular diseases and is caused by a combination of environmental and genetic factors, with up to 50% of blood pressure variance currently attributed to an individual's genetic makeup. By studying genes that cause monogenic forms of hypertension and pathways relevant to blood pressure control, a number of polymorphisms have been identified that increase an individual's risk of developing high blood pressure. We report on candidate gene association studies and genome-wide association studies that have been performed to date in the field of hypertension research. It is becoming clear that for the majority of people there is no single gene polymorphism that causes hypertension, but rather a number of common genetic variants, each having a small effect. Using pharmacogenomics to personalize the treatment of hypertension holds promise for achieving and sustaining normotensive pressures quickly, while minimizing the risk of adverse reactions and unwanted side-effects. This will decrease the risk of stroke and myocardial infarction in individuals and lead to a reduced burden of disease upon society as a whole.

scholarly journals Molecular Biology Networks and Key Gene Regulators for Inflammatory Biomarkers Shared by Breast Cancer Development: Multi-Omics Systems Analysis

Biomolecules ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1379
Author(s):  
Su Yon Jung ◽  
Jeanette C. Papp ◽  
Matteo Pellegrini ◽  
Herbert Yu ◽  
Eric M. Sobel
Keyword(s):  
Breast Cancer ◽  
Regulatory Networks ◽  
Inflammatory Diseases ◽  
Association Studies ◽  
Full Range ◽  
Gene Interaction ◽  
Interferon Γ ◽  
Inflammatory Biomarkers ◽  
Genome Wide Association Studies ◽  
Tissue Specific

As key inflammatory biomarkers C-reactive protein (CRP) and interleukin-6 (IL6) play an important role in the pathogenesis of non-inflammatory diseases, including specific cancers, such as breast cancer (BC). Previous genome-wide association studies (GWASs) have neither explained the large proportion of genetic heritability nor provided comprehensive understanding of the underlying regulatory mechanisms. We adopted an integrative genomic network approach by incorporating our previous GWAS data for CRP and IL6 with multi-omics datasets, such as whole-blood expression quantitative loci, molecular biologic pathways, and gene regulatory networks to capture the full range of genetic functionalities associated with CRP/IL6 and tissue-specific key drivers (KDs) in gene subnetworks. We applied another systematic genomics approach for BC development to detect shared gene sets in enriched subnetworks across BC and CRP/IL6. We detected the topmost significant common pathways across CRP/IL6 (e.g., immune regulatory; chemokines and their receptors; interferon γ, JAK-STAT, and ERBB4 signaling), several of which overlapped with BC pathways. Further, in gene–gene interaction networks enriched by those topmost pathways, we identified KDs—both well-established (e.g., JAK1/2/3, STAT3) and novel (e.g., CXCR3, CD3D, CD3G, STAT6)—in a tissue-specific manner, for mechanisms shared in regulating CRP/IL6 and BC risk. Our study may provide robust, comprehensive insights into the mechanisms of CRP/IL6 regulation and highlight potential novel genetic targets as preventive and therapeutic strategies for associated disorders, such as BC.

Effects of Dipyrone on Prostaglandin Production by Human Platelets and Cultured Bovine Aortic Endothelial Cells

1983 ◽  
Vol 49 (02) ◽  
pp. 132-137 ◽  
Author(s):  
A Eldor ◽  
G Polliack ◽  
I Vlodavsky ◽  
M Levy
Keyword(s):  
Endothelial Cells ◽  
Arachidonic Acid ◽  
Competitive Inhibition ◽  
Inhibitory Effect ◽  
Human Platelets ◽  
Ionophore A23187 ◽  
Aortic Endothelial Cells ◽  
Bovine Aortic Endothelial Cells ◽  
Aortic Endothelial

SummaryDipyrone and its metabolites 4-methylaminoantipyrine, 4-aminoantipyrine, 4-acetylaminoantipyrine and 4-formylaminoan- tipyrine inhibited the formation of thromboxane A2 (TXA2) during in vitro platelet aggregation induced by ADP, epinephrine, collagen, ionophore A23187 and arachidonic acid. Inhibition occurred after a short incubation (30–40 sec) and depended on the concentration of the drug or its metabolites and the aggregating agents. The minimal inhibitory concentration of dipyrone needed to completely block aggregation varied between individual donors, and related directly to the inherent capacity of their platelets to synthesize TXA2.Incubation of dipyrone with cultured bovine aortic endothelial cells resulted in a time and dose dependent inhibition of the release of prostacyclin (PGI2) into the culture medium. However, inhibition was abolished when the drug was removed from the culture, or when the cells were stimulated to produce PGI2 with either arachidonic acid or ionophore A23187.These results indicate that dipyrone exerts its inhibitory effect on prostaglandins synthesis by platelets or endothelial cells through a competitive inhibition of the cyclooxygenase system.

Genetic Interactions Effects of Cardiovascular Disorder Using Computational Models: A Review

2020 ◽  
Vol 9 (3) ◽  
pp. 177-191
Author(s):  
Sridharan Priya ◽  
Radha K. Manavalan
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Cardiovascular Diseases ◽  
Computational Models ◽  
Genetic Interaction ◽  
Association Studies ◽  
Genetic Interactions ◽  
Computational Techniques ◽  
Genome Wide Association Studies ◽  
Artery Disease

Background: The diseases in the heart and blood vessels such as heart attack, Coronary Artery Disease, Myocardial Infarction (MI), High Blood Pressure, and Obesity, are generally referred to as Cardiovascular Diseases (CVD). The risk factors of CVD include gender, age, cholesterol/ LDL, family history, hypertension, smoking, and genetic and environmental factors. Genome- Wide Association Studies (GWAS) focus on identifying the genetic interactions and genetic architectures of CVD. Objective: Genetic interactions or Epistasis infer the interactions between two or more genes where one gene masks the traits of another gene and increases the susceptibility of CVD. To identify the Epistasis relationship through biological or laboratory methods needs an enormous workforce and more cost. Hence, this paper presents the review of various statistical and Machine learning approaches so far proposed to detect genetic interaction effects for the identification of various Cardiovascular diseases such as Coronary Artery Disease (CAD), MI, Hypertension, HDL and Lipid phenotypes data, and Body Mass Index dataset. Conclusion: This study reveals that various computational models identified the candidate genes such as AGT, PAI-1, ACE, PTPN22, MTHR, FAM107B, ZNF107, PON1, PON2, GTF2E1, ADGRB3, and FTO, which play a major role in genetic interactions for the causes of CVDs. The benefits, limitations, and issues of the various computational techniques for the evolution of epistasis responsible for cardiovascular diseases are exhibited.

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