Project description:BACKGROUND. Discoveries of genetic causes of dyslipidemia have significantly advanced our understanding of lipoprotein metabolism and its implications in cardiometabolic diseases. However, a significant number of patients with dyslipidemia still have unidentified genetic causes. Leveraging insights from genome-wide association studies, where most common associated variants are located in non-coding regions, we propose that rare non-coding variants located in genomic regulatory regions may be responsible for dyslipidemia. METHODS. Using whole-genome sequencing data from the UK Biobank (n > 200,000), we performed region-based rare-variant association analyses, focusing on liver specific genomic regulatory regions (assessed by transposase-accessible chromatin assay coupled with sequencing (ATAC-seq)), to assess whether the cumulative burden of rare genetic variants (allele frequency < 0.01) in these genomic regulatory regions was associated with LDL-cholesterol levels. To validate our findings, we used an advanced cellular model of human induced pluripotent stem cells (hiPSCs) stably expressing a CRISPR-interference system. This system enables, when combined with guide-RNAs, to target regions of interest and silence regulated genes. RESULTS. We found that the cumulative burden of rare genetic variants in 18 among 53,039 ATAC-seq peaks in human liver, is significantly associated with LDL-cholesterol. We focus our proof-of-concept study on a peak located ~17 kb upstream PCSK9, strongly associated with LDL-cholesterol (p=5.85e-52), apolipoprotein-B (p=3.82e-49) and PCSK9 plasma levels (p=1.15e-67). We showed that this regulatory region is also active in hiPSCs and hiPSC-derived liver organoids. When transfected with guide-RNAs targeting this region, we showed that CRISPRi-hiPSCs present a hypermethylation of the targeted region which in terms leads to a substantial decrease in PCSK9 gene expression (-48%), intracellular protein (-65%) and secretion (-36%) (p<0.001). CONCLUSIONS. This study identifies a non-coding genetic region upstream PCSK9 where rare variants lead to decreased PCSK9 expression and lowered LDL-cholesterol. Such findings advocate for the genetic screening of non-coding regulatory regions in patients with unidentified genetic causes of extreme dyslipidemia and open novel perspectives for genetic regulatory therapeutic intervention.

Project description:McAuley2012 - Whole-body Cholesterol Metabolism Lipid metabolism has a key role to play in human longevity and healthy aging. A whole-body mathematical model of cholesterol metabolism that explores the changes in both the rate of intestinal cholesterol absorption and the hepatic rate of clearance of LDL-C from the plasma, has been presented here. The model showed that of these two mechanisms, changes to the rate of LDL-C removal from the plasma with age had the most significant effect on cholesterol metabolism. The original SBML model file was generated using MathSBML 2.5.1. This model is described in the article: A whole-body mathematical model of cholesterol metabolism and its age-associated dysregulation. Mc Auley MM, Wilkinson DJ, Jones JJ, Kirkwood TT. BMC Syst Biol. 2012 Oct 10;6(1):130. Abstract: BACKGROUND: Global demographic changes have stimulated marked interest in the process of ageing. There has been, and will continue to be, an unrelenting rise in the number of the oldest old ( >85 years of age). Together with an ageing population there comes an increase in the prevalence of age related disease. Of the diseases of ageing, cardiovascular disease (CVD) has by far the highest prevalence. It is regarded that a finely tuned lipid profile may help to prevent CVD as there is a long established relationship between alterations to lipid metabolism and CVD risk. In fact elevated plasma cholesterol, particularly Low Density Lipoprotein Cholesterol (LDL-C) has consistently stood out as a risk factor for having a cardiovascular event. Moreover it is widely acknowledged that LDL-C may rise with age in both sexes in a wide variety of groups. The aim of this work was to use a whole-body mathematical model to investigate why LDL-C rises with age, and to test the hypothesis that mechanistic changes to cholesterol absorption and LDL-C removal from the plasma are responsible for the rise. The whole-body mechanistic nature of the model differs from previous models of cholesterol metabolism which have either focused on intracellular cholesterol homeostasis or have concentrated on an isolated area of lipoprotein dynamics. The model integrates both current and previously published data relating to molecular biology, physiology, ageing and nutrition in an integrated fashion. RESULTS: The model was used to test the hypothesis that alterations to the rate of cholesterol absorption and changes to the rate of removal of LDL-C from the plasma are integral to understanding why LDL-C rises with age. The model demonstrates that increasing the rate of intestinal cholesterol absorption from 50% to 80% by age 65 years can result in an increase of LDL-C by as much as 34mg/dL in a hypothetical male subject. The model also shows that decreasing the rate of hepatic clearance of LDL-C gradually to 50% by age 65 years can result in an increase of LDL-C by as much as 116mg/dL. CONCLUSIONS: Our model clearly demonstrates that of the two putative mechanisms that have been implicated in the dysregulation of cholesterol metabolism with age, alterations to the removal rate of plasma LDL-C has the most significant impact on cholesterol metabolism and small changes to the number of hepatic LDL receptors can result in a significant rise in LDL-C. This first whole-body systems based model of cholesterol balance could potentially be used as a tool to further improve our understanding of whole-body cholesterol metabolism and its dysregulation with age. Furthermore, given further fine tuning the model may help to investigate potential dietary and lifestyle regimes that have the potential to mitigate the effects aging has on cholesterol metabolism. This model is hosted on BioModels Database and identified by: MODEL1206010000 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:The NHLBI "Grand Opportunity" Exome Sequencing Project (GO-ESP), a signature project of the NHLBI Recovery Act investment, was designed to identify genetic variants in coding regions (exons) of the human genome (the "exome") that are associated with heart, lung and blood diseases. These and related diseases that are of high impact to public health and individuals from diverse racial and ethnic groups will be studied. These data may help researchers understand the causes of disease, contributing to better ways to prevent, diagnose, and treat diseases, as well as determine whether to tailor prevention and treatments to specific populations. This could lead to more effective treatments and reduce the likelihood of side effects. GO-ESP is comprised of five collaborative components: 3 cohort consortia - HeartGO, LungGO, and WHISP - and 2 sequencing centers - BroadGO and SeattleGO. In this study, we seek to identify the genetic cause of two monogenic lipid disorders-severe hypercholesterolemia and familial hypoalphalipoproteinemia. Monogenic severe hypercholesterolemia is clinically characterized by elevated total and low-density lipoprotein (LDL) cholesterol levels in plasma. Elevated LDL-cholesterol levels lead to excessive deposition of cholesterol in arterial walls, which eventually results in accelerated atherosclerosis and premature cardiovascular disease (CVD). Monogenic hypercholesterolemia has a prevalence of approximately one in 500 individuals, making it one of the most common inherited disorders. To date, mutations in the LDL receptor (LDLR) ligand-binding domains of APOB and PCSK9 have been shown to cause hypercholesterolemia. While mutations in these genes can explain a large percentage of clinically diagnosed patients, the underlying molecular determinant in a substantial fraction of patients remains unknown. Familial hypoalphalipoproteinemia (low HDL-C) is defined by an HDL-C below the age- and sex- specific 10th percentile. ABCA1, LCAT, and APOA1 are known to cause familial hypoalphalipoproteinemia. We hypothesize that: (1) additional novel genes responsible for Mendelian forms of low HDL-C exist; and (2) the causal gene and mutation(s) in each family may be discovered with exome analysis of just a few affected individuals in each pedigree.

Project description:Inherited variants in the LDL receptor (LDLR) gene are the most common cause of familial hypercholesterolemia (FH), significantly increasing coronary artery disease risk. Early identification of pathogenic LDLR variants enables prompt intervention with lipid-lowering therapies; however, most LDLR variants observed in the population have uncertain or absent clinical classifications, limiting the clinical utility of genetic testing for definitive FH diagnosis, cascade testing of at-risk relatives, and timely lipid-lowering intervention. We developed an innovative, activity-normalized prime editing screening pipeline to measure the impact of 5,184 LDLR coding variants on LDL-cholesterol (LDL-C) uptake. Through pairing a genotypic outcome reporter with every prime editing guide RNA (pegRNA), we adjust phenotypic measurements to account for variable editing efficiency. Further, we use a statistical estimation approach that leverages measurements for all missense variants at a given position to denoise the resulting scores. We show that prime editing-mediated reporter editing correlates with endogenous variant installation frequency, allowing activity normalization to improve imputation of LDLR variant effect. Our optimized prime editing assay identifies a broad, continuous spectrum of variant functional effects. We achieve robust separation of pathogenic vs. benign ClinVar variants and concordance between experimentally derived functional scores and LDL-C levels measured in UK Biobank participants. Further, we calibrate the strength of this functional evidence to align with the ACMG/AMP variant interpretation guidelines. By integrating additional sources of evidence, a majority of currently unclassified rare LDLR variants appear to meet computational evidence thresholds for reclassification and can be prioritized for expert review. We use the broad coverage of this screen to gain insight into how apolipoproteins bind to LDLR. In particular, we identify and characterize rare LDLR variants that enhance LDL-C uptake through increased interaction with apolipoprotein B. Finally, we compare prime editing-based functional scores with those derived from recent base editing and cDNA-based LDLR variant screens, and find that all approaches show robust correlation with clinically observed LDL-C levels and computational scores, while prime editing identifies splice-altering coding variants that are not modeled by cDNA screening. Altogether, our approach demonstrates the power of prime editing to significantly improve understanding of how variants in LDLR impact function and contribute to FH.

			Action	DRS
	SRR5894820.fastq.gz	Fastqsanger.gz
	SRR5894830.fastq.gz	Fastqsanger.gz
	SRR5894840.fastq.gz	Fastqsanger.gz
	SRR5894821.fastq.gz	Fastqsanger.gz
	SRR5894831.fastq.gz	Fastqsanger.gz

Dataset Information

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets