scholarly journals Hyperalphalipoproteinemia and Beyond: The Role of HDL in Cardiovascular Diseases

Life ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 581
Author(s):  
Antonina Giammanco ◽  
Davide Noto ◽  
Carlo Maria Barbagallo ◽  
Emilio Nardi ◽  
Rosalia Caldarella ◽  
...  
Keyword(s):  
Cardiovascular Diseases ◽  
Hepatic Lipase ◽  
Association Studies ◽  
Treatment Options ◽  
Scavenger Receptor ◽  
Density Lipoprotein ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Polygenic Inheritance

Hyperalphalipoproteinemia (HALP) is a lipid disorder characterized by elevated plasma high-density lipoprotein cholesterol (HDL-C) levels above the 90th percentile of the distribution of HDL-C values in the general population. Secondary non-genetic factors such as drugs, pregnancy, alcohol intake, and liver diseases might induce HDL increases. Primary forms of HALP are caused by mutations in the genes coding for cholesteryl ester transfer protein (CETP), hepatic lipase (HL), apolipoprotein C-III (apo C-III), scavenger receptor class B type I (SR-BI) and endothelial lipase (EL). However, in the last decades, genome-wide association studies (GWAS) have also suggested a polygenic inheritance of hyperalphalipoproteinemia. Epidemiological studies have suggested that HDL-C is inversely correlated with cardiovascular (CV) risk, but recent Mendelian randomization data have shown a lack of atheroprotective causal effects of HDL-C. This review will focus on primary forms of HALP, the role of polygenic inheritance on HDL-C, associated risk for cardiovascular diseases and possible treatment options.

scholarly journals Role of SR-BI in atherosclerosis, malignancies, and infectious diseases

2018 ◽  
Vol 6 (3) ◽  
pp. 66-70
Author(s):  
Zhe An
Keyword(s):  
Infectious Diseases ◽  
Malignant Tumors ◽  
Scavenger Receptor ◽  
Density Lipoprotein ◽  
Type I ◽  
Primary Role ◽  
Cell Functions ◽  
Class B ◽  
High Affinity Receptor

Abstract Scavenger receptor class B type I (SR-BI) is a high-affinity receptor for high-density lipoprotein (HDL). The primary role of this receptor is the selective uptake of HDLs in the liver through reverse cholesterol transport. SR-BI interacts with HDL to regulate lipid metabolism and affects various vascular cell functions involved in atherosclerosis (As). In addition, SR-BI is involved in the development of malignant tumors and infectious diseases. This article reviews the function and potential therapeutic targets of SR-BI in As, malignancies, and infectious diseases.

The Role of Human and Mouse Hepatic Scavenger Receptor Class B Type I (SR-BI) in the Selective Uptake of Low-Density Lipoprotein−Cholesteryl Esters†

Biochemistry ◽  
2003 ◽  
Vol 42 (24) ◽  
pp. 7527-7538 ◽  
Author(s):  
David Rhainds ◽  
Mathieu Brodeur ◽  
Jany Lapointe ◽  
Daniel Charpentier ◽  
Louise Falstrault ◽  
...  
Keyword(s):  
Low Density Lipoprotein ◽  
Scavenger Receptor ◽  
Density Lipoprotein ◽  
Type I ◽  
Low Density ◽  
Cholesteryl Esters ◽  
Scavenger Receptor Class ◽  
Class B ◽  
Human And Mouse

Scavenger receptor class B type I in high-density lipoprotein metabolism, atherosclerosis and heart disease: lessons from gene-targeted mice

2004 ◽  
Vol 32 (1) ◽  
pp. 116-120 ◽  
Author(s):  
B. Trigatti ◽  
S. Covey ◽  
A. Rizvi
Keyword(s):  
Bone Marrow ◽  
Heart Disease ◽  
Knockout Mice ◽  
Scavenger Receptor ◽  
Density Lipoprotein ◽  
Type I ◽  
Scavenger Receptor Class ◽  
Class B

The scavenger receptor class B type I (SR-BI) is a multi-ligand receptor that can mediate the binding and bi-directional lipid transfer between high-density lipoproteins (HDLs) and cells. It is expressed in a variety of tissues, including the liver, and in macrophages in atherosclerotic plaques. The physiological role of SR-BI has been tested in vivo by the genetic manipulation of SR-BI levels in mice. Mice lacking SR-BI exhibit impaired hepatic-selective HDL cholesterol uptake and increased atherosclerosis, suggesting that SR-BI is required for hepatic reverse cholesterol transport and normally protects against atherosclerosis. Surprisingly, elimination of SR-BI in apolipoprotein E knockout mice results in rapid development of occlusive coronary artery disease, accompanied by spontaneous myocardial infarction, reduced heart function and early death, which points to a role for SR-BI in protection against coronary heart disease. The in vivo role of macrophage SR-BI has been less clear. We have used bone-marrow transplantation to demonstrate that bone-marrow-derived SR-BI also normally protects against atherosclerosis in low-density lipoprotein receptor knockout mice. These results suggest that SR-BI may have multiple protective effects against atherosclerosis in different tissues.

MicroRNAs as Biomarker for Breast Cancer

2020 ◽  
Vol 20 (10) ◽  
pp. 1597-1610 ◽  
Author(s):  
Taru Aggarwal ◽  
Ridhima Wadhwa ◽  
Riya Gupta ◽  
Keshav Raj Paudel ◽  
Trudi Collet ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Association Studies ◽  
Large Family ◽  
Breast Cancer Diagnosis ◽  
Cell Multiplication ◽  
Cell Physiology ◽  
Gene Transcript ◽  
Genome Wide Association Studies ◽  
Non Coding Rnas

Regardless of advances in detection and treatment, breast cancer affects about 1.5 million women all over the world. Since the last decade, genome-wide association studies (GWAS) have been extensively conducted for breast cancer to define the role of miRNA as a tool for diagnosis, prognosis and therapeutics. MicroRNAs are small, non-coding RNAs that are associated with the regulation of key cellular processes such as cell multiplication, differentiation, and death. They cause a disturbance in the cell physiology by interfering directly with the translation and stability of a targeted gene transcript. MicroRNAs (miRNAs) constitute a large family of non-coding RNAs, which regulate target gene expression and protein levels that affect several human diseases and are suggested as the novel markers or therapeutic targets, including breast cancer. MicroRNA (miRNA) alterations are not only associated with metastasis, tumor genesis but also used as biomarkers for breast cancer diagnosis or prognosis. These are explained in detail in the following review. This review will also provide an impetus to study the role of microRNAs in breast cancer.

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.

scholarly journals A comprehensive evaluation of methods for Mendelian randomization using realistic simulations and an analysis of 38 biomarkers for risk of type 2 diabetes

2021 ◽  
Author(s):  
Guanghao Qi ◽  
Nilanjan Chatterjee
Keyword(s):  
Type 2 Diabetes ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Real Data ◽  
Causal Effects ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Simulation Studies ◽  
Sample Sizes

Abstract Background Previous studies have often evaluated methods for Mendelian randomization (MR) analysis based on simulations that do not adequately reflect the data-generating mechanisms in genome-wide association studies (GWAS) and there are often discrepancies in the performance of MR methods in simulations and real data sets. Methods We use a simulation framework that generates data on full GWAS for two traits under a realistic model for effect-size distribution coherent with the heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank and performed down sampling to investigate trends in estimates of causal effects of these biomarkers on the risk of type 2 diabetes (T2D). Results Simulation studies show that weighted mode and MRMix are the only two methods that maintain the correct type I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS whereas the opposite is true for smaller sample sizes. Among the other methods, random-effect IVW (inverse-variance weighted method), MR-Robust and MR-RAPS (robust adjust profile score) tend to perform best in maintaining a low mean-squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real-data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on the risk of T2D across the different methods and estimates from many methods trended in one direction with increasing sample size with patterns similar to those observed in simulation studies. Conclusion The relative performance of different MR methods depends heavily on the sample sizes of the underlying GWAS, the proportion of valid instruments and the validity of the InSIDE assumption. Down-sampling analysis can be used in large GWAS for the possible detection of bias in the MR methods.

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