Integrative genomic analysis identified common regulatory networks underlying the correlation between coronary artery disease and plasma lipid levels

Chen, Liuying; Yao, Yinghao; Jin, Changchun; Wu, Shen; Liu, Qiang; Li, Jingjing; Ma, Yunlong; Xu, Yizhou; Zhong, Yigang

doi:10.1186/s12872-019-01271-9

Cited by 12 publications

(9 citation statements)

References 39 publications

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“…First, the GWAS datasets utilized are not the most recently conducted and therefore provides the possibility of not capturing the full array of unknown biology. However, despite this our results are consistent with the biology found more recently including overlapping signals in pathways for chylomicron-mediated lipid transport and lipoprotein metabolism (88) as well as more novel findings such as visual transduction pathways. In addition, one of our key drivers KLKB1, which was not found to be a GWAS hit in the dataset we utilized, has since been found to pass the genome wide significance threshold in more recent larger GWAS and is a hit on apolipoprotein A-IV concentrations, which is a major component of HDL and chylomicron particles important in reverse cholesterol transport (89).…”

Section: Discussionsupporting

confidence: 92%

Gene Networks and Pathways for Plasma Lipid Traits via Multi-tissue Multi-omics Systems Analysis

Zhao

Blencowe²,

Saleem

et al. 2020

Preprint

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15Genome-wide association studies (GWAS) have implicated ~380 genetic loci for plasma lipid regulation. 16However, these loci only explain 17-27% of the trait variance and a comprehensive understanding of the 17 molecular mechanisms has not been achieved. In this study, we utilized an integrative genomics approach 18 leveraging diverse genomic data from human populations to investigate whether genetic variants 19 associated with various plasma lipid traits, namely total cholesterol (TC), high and low density 20 lipoprotein cholesterol (HDL and LDL), and triglycerides (TG), from GWAS were concentrated on 21 specific parts of tissue-specific gene regulatory networks. In addition to the expected lipid metabolism 22 pathways, gene subnetworks involved in 'interferon signaling', 'autoimmune/immune activation', 'visual 23 transduction', and 'protein catabolism' were significantly associated with all lipid traits. Additionally, we 24 detected trait-specific subnetworks, including cadherin-associated subnetworks for LDL, glutathione 25 metabolism for HDL, valine, leucine and isoleucine biosynthesis for TC, and insulin signaling and 26 complement pathways for TG. Finally, utilizing gene-gene relations revealed by tissue-specific gene 27 regulatory networks, we detected both known (e.g. APOH, APOA4, and ABCA1) and novel (e.g. F2 in 28 adipose tissue) key regulator genes in these lipid-associated subnetworks. Knockdown of F2 gene 29 (Coagulation Factor II, Thrombin) in 3T3-L1 adipocytes reduced gene expression of Abcb11, Apoa5, 30Apof, Gc, Fabp, Hrg, Proc, and Cd36, several of which are important in lipoprotein transport and fatty 31 acid uptake, providing evidence for a link between adipose thrombin and plasma lipid regulation. Our 32 results shed light on the complex mechanisms underlying lipid metabolism and highlight potential novel 33 targets for lipid regulation and lipid-associated diseases. 34Factor II 36 37 38 Materials and Methods 90 GWAS of lipid traits 91The experimental design, genotyping, and association analyses of HDL, LDL, TC, and TG were 92 described previously (12). The dataset used in this study is comprised of > 100,000 individuals of 93 European descent (sample size 100,184 for TC, 95,454 for LDLC, 99,900 for HDLC and 96,598 for TG), 94 ascertained in the United States, Europe, or Australia. More than 906,600 SNPs were genotyped using 95Affymetrix Genome-Wide Human SNP Array 6.0. Imputation was further carried out to obtain 96 information for up to 2.6 million SNPs using the HapMap CEU (Utah residents with ancestry from 97 northern and western Europe) panel. SNPs with minor allele frequency (MAF) < 1% were removed. 98

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Section: Discussionsupporting

confidence: 92%

Gene Networks and Pathways for Plasma Lipid Traits via Multi-tissue Multi-omics Systems Analysis

Zhao

Blencowe²,

Saleem

et al. 2020

Preprint

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“…However, the early diagnosis of CAD remains substantially di cult due to lack of inadequate biomarkers. Although several biomarkers, such as Fibrinogen and C-reactive protein, have been reported to be associated with CAD [7][8][9][10], these biomarkers generally lack su cient speci city and signi cantly detectable changes appear mainly in the advanced stages of CAD [11]. Thus, the early diagnosis and intervention of CAD pose a signi cant public health challenge with enormous medical and societal consequences.…”

Section: Introductionmentioning

confidence: 99%

Alternations in gut microbiota and host transcriptome of patients with coronary artery disease

Chen,

Mou,

et al. 2023

Preprint

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Background Coronary artery disease (CAD) is a widespread heart condition caused by atherosclerosis and influences millions of people worldwide. Early detection of CAD is challenging due to the lack of specific biomarkers. The gut microbiota and host-microbiota interactions have been well documented to affect human health. However, investigation that reveals the role of gut microbes in CAD is still limited. This study aims to uncover the synergistic effects of host genes and gut microbes associated with CAD through integrative genomic analyses. Results Herein, we collected 54 fecal and 54 blood samples from CAD patients and matched controls, and performed amplicon and transcriptomic sequencing on these samples, respectively. By comparing CAD patients with health controls, we found that dysregulated gut microbes were significantly associated with CAD. By leveraging the Random Forest method, we found that 10 bacteria biomarkers can distinguish CAD patients from health controls with a high performance (AUC = 0.939). We observed that there existed prominent associations of gut microbes with several clinical indices relevant to heart functions. Integration analysis revealed that CAD-relevant gut microbe genus Fusicatenibacter was associated with expression of CAD-risk genes, such as GBP2, MLKL, and CPR65. In addition, the upregulation of immune-related pathways in CAD patients were identified to be primarily associated with higher abundance of genus Blautia, Eubacterium, Fusicatenibacter, and Monoglobus. Conclusions Our results highlight that dysregulated gut microbes contribute risk to CAD by interacting with host genes. These identified microbes and interacted risk genes may have high potentials as biomarkers for CAD.

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“…Since the release of the open source Mergeomics R package ( https://bioconductor.org/packages/release/bioc/html/Mergeomics.html ) ( 21 ) and web server in 2016 ( 22 ), this tool has been used to model a diverse set of diseases including cardiometabolic disorders such as non-alcoholic fatty liver disease ( 23 ), cardiovascular disease ( 24–26 ) and type 2 diabetes ( 27 ), autoimmunity including psoriasis ( 28 ) and rheumatoid arthritis ( 29 ), alcohol dependence ( 30 ), brain injury ( 31 ), Sjogren's syndrome ( 32 ) and environmental contributions to disease ( 33–35 ). Importantly, multiple validations of molecular predictions from Mergeomics with in silico , in vitro and in vivo studies highlight the validity and causal nature of the disease network predictions ( 23 , 27–28 , 31 , 35–40 ).…”

Section: Introductionmentioning

confidence: 99%

Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics

Ding

Blencowe

Nghiem

et al. 2021

Nucleic Acids Research

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The Mergeomics web server is a flexible online tool for multi-omics data integration to derive biological pathways, networks, and key drivers important to disease pathogenesis and is based on the open source Mergeomics R package. The web server takes summary statistics of multi-omics disease association studies (GWAS, EWAS, TWAS, PWAS, etc.) as input and features four functions: Marker Dependency Filtering (MDF) to correct for known dependency between omics markers, Marker Set Enrichment Analysis (MSEA) to detect disease relevant biological processes, Meta-MSEA to examine the consistency of biological processes informed by various omics datasets, and Key Driver Analysis (KDA) to identify essential regulators of disease-associated pathways and networks. The web server has been extensively updated and streamlined in version 2.0 including an overhauled user interface, improved tutorials and results interpretation for each analytical step, inclusion of numerous disease GWAS, functional genomics datasets, and molecular networks to allow for comprehensive omics integrations, increased functionality to decrease user workload, and increased flexibility to cater to user-specific needs. Finally, we have incorporated our newly developed drug repositioning pipeline PharmOmics for prediction of potential drugs targeting disease processes that were identified by Mergeomics. Mergeomics is freely accessible at http://mergeomics.research.idre.ucla.edu and does not require login.

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Integrative genomic analysis identified common regulatory networks underlying the correlation between coronary artery disease and plasma lipid levels

Cited by 12 publications

References 39 publications

Gene Networks and Pathways for Plasma Lipid Traits via Multi-tissue Multi-omics Systems Analysis

Gene Networks and Pathways for Plasma Lipid Traits via Multi-tissue Multi-omics Systems Analysis

Alternations in gut microbiota and host transcriptome of patients with coronary artery disease

Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics

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