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WIREs Syst Biol Med
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Omics‐based approaches to understand mechanosensitive endothelial biology and atherosclerosis

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Atherosclerosis is a multifactorial disease that preferentially occurs in arterial regions exposed to d‐flow can be used to indicate disturbed flow or disturbed blood flow. The mechanisms by which d‐flow induces atherosclerosis involve changes in the transcriptome, methylome, proteome, and metabolome of multiple vascular cells, especially endothelial cells. Initially, we begin with the pathogenesis of atherosclerosis and the changes that occur at multiple levels owing to d‐flow, especially in the endothelium. Also, there are a variety of strategies used for the global profiling of the genome, transcriptome, miRNA‐ome, DNA methylome, and metabolome that are important to define the biological and pathophysiological mechanisms of endothelial dysfunction and atherosclerosis. Finally, systems biology can be used to integrate these ‘omics’ datasets, especially those that derive data based on a single animal model, in order to better understand the pathophysiology of atherosclerosis development in a holistic manner and how this integrative approach could be used to identify novel molecular diagnostics and therapeutic targets to prevent or treat atherosclerosis. WIREs Syst Biol Med 2016, 8:378–401. doi: 10.1002/wsbm.1344 This article is categorized under: Models of Systems Properties and Processes > Organismal Models Biological Mechanisms > Regulatory Biology Translational, Genomic, and Systems Medicine > Therapeutic Methods
D‐flow in human carotids, the aortic arch, and abdominal aorta is transduced through the arterial wall and initiates changes at multiple ‐omics levels in the endothelium. Atherosclerosis tends to develop in regions of d‐flow marked by blue arrows. D‐flow on the endothelial cells (ECs) lining the blood vessel wall leads to changes in the EC transcriptome, methylome, proteome, and metabolome that lead to endothelial dysfunction and atherosclerosis. (Adapted with permission from Ref . Copyright 2014 Annual Reviews.)
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Transcriptomics data, methylomics data, and metabolomics data from a single animal model of d‐flow‐induced atherosclerosis. Following partial carotid ligation, endothelial RNA was collected either 12 or 48 h after ligation and subject to a microarray. (a) Heat maps of single samples pooled from three different left carotid arteries (LCAs) or right carotid arteries (RCAs) show the number of genes affected by flow increase from 12 to 48 h. The Venn diagrams also show the temporal effects of d‐flow on the number of upregulated or downregulated mechanosensitive genes. (Adapted with permission from Ref . Copyright 2010 American Society of Hematology). (b) Endothelial RNA collected 48 h postligation (pooled from three mice) was also analyzed by miRNA array. The heat map shows several miRNAs that are differentially regulated by flow. (Reprinted with permission from Ref . Copyright 2013). (c) Partially ligated animals, endothelial genomic DNA from 20 LCAs and RCAs each was pooled and the methylation status was determined by reduced representation bisulfite sequencing (RRBS). Shown is density heat‐map correlation plot portraying the methylation status at each of 3,232,969 CG sites covered by the RRBS analysis. The numbers indicated in the upper, middle, and lower portions indicate CG sites hypermethylated, not altered significantly, and hypomethylated, respectively, in the partially ligated LCA compared with the RCA. (Adapted with permission from Ref . Copyright 2014 American Society for Clinical Investigation). (d) Blood plasma was collected 7 days postligation and liquid chromatography and mass spectrometry (LC‐MS) was performed in order to determine metabolites significantly altered by disturbed blood flow. Metabolomics analysis‐identified top most significantly different 15 ions (top to bottom) are shown with variable importance in projection (VIP) scores and an expression heat map (green: high, red: low) from Partial Least Squares Discriminant Analysis models. Underlined m/z indicated ions that were matched with known chemicals by Metlin metabolite search. (Reprinted with permission from Ref . Copyright 2015 American Physiological Society)
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Integration of the transcriptomics, methylomics, and metabolomics using datasets from one animal model. Using the partial carotid ligation model, endothelial‐enriched RNA was collected and subjected to mRNA or miRNA microarrays in order to determine mechanosensitive genes and miRNAs. Furthermore, genomic DNA was collected in order to determine the status of methylation in many of these genes. Finally, blood plasma from the model was subjected to mass spectrometry in order to identify metabolites that are differentially expressed in the model. State‐of‐the‐art techniques like mass spectroscopy (MS) could be used to profile the proteome using miniscule amount of proteins from the endothelium exposed to stable or disturbed blood flow. These datasets can be subjected to integrative systems biology to identify meaningful information that can lead to discovery of novel biomarkers and therapeutic candidates.
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Models of Systems Properties and Processes > Organismal Models
Biological Mechanisms > Regulatory Biology
Translational, Genomic, and Systems Medicine > Therapeutic Methods

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