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W2103237492 abstract "HomeCirculationVol. 121, No. 1Cardiovascular Networks Free AccessResearch ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessResearch ArticlePDF/EPUBCardiovascular NetworksSystems-Based Approaches to Cardiovascular Disease Aldons J. Lusis, PhD and James N. Weiss, MD Aldons J. LusisAldons J. Lusis From the Department of Medicine/Division of Cardiology (A.J.L., J.N.W.), Department of Microbiology, Immunology and Molecular Genetics (A.J.L.), Department of Human Genetics (A.J.L.), and Department of Physiology (J.N.W.), University of California, Los Angeles. Search for more papers by this author and James N. WeissJames N. Weiss From the Department of Medicine/Division of Cardiology (A.J.L., J.N.W.), Department of Microbiology, Immunology and Molecular Genetics (A.J.L.), Department of Human Genetics (A.J.L.), and Department of Physiology (J.N.W.), University of California, Los Angeles. Search for more papers by this author Originally published5 Jan 2010https://doi.org/10.1161/CIRCULATIONAHA.108.847699Circulation. 2010;121:157–170“If there is any area in which a network thinking could trigger a revolution, I believe that biology is it.”—Albert László Barabási1Traditional biological and biochemical studies deal with relatively few components, allowing intuitive reasoning to guide hypotheses and experiments. For example, a popular experimental protocol in cardiovascular research is to examine gene functions through the use of transgenic or gene-targeted mice. Although clearly informative, it is becoming increasingly clear that such studies alone are not sufficient to explain complex processes such as arrhythmias, heart failure, and atherogenesis. Even a basic behavior, the action potential of a cardiomyocyte, requires the coordinated actions of >20 different ion transporters and channels.2 Perturbing proteins individually will help establish their functions, but it will not provide a full understanding of how they function together (quantitatively, temporally, and spatially). For this, a more global analysis, in which the activities of all of the relevant proteins are tracked over time and then integrated into a quantitative mathematical model, is required to provide a deeper level of understanding of cardiomyocyte dynamics.A new branch of biology, called systems biology, seeks to identify the components of complex systems and to model their dynamic interactions.3 The approach arose in large part as a result of new technical and analytical developments. The Human Genome Project provided a biological “parts list,” and technologies such as massively parallel DNA sequencing, expression microarrays, and tandem mass spectrometric analyses of proteins and metabolites have made high-throughput analysis of biological systems feasible. To deal with the data explosion, novel statistical and other mathematical modeling approaches are being developed.Basically, systems-based approaches involve 4 steps. The first is to define the system to be examined (eg, a cardiomyocyte). The second is to identify the components of the system (eg, the set of proteins regulating a property of interest). The third is to determine how the components interact with each other. This can be done experimentally or may be based on the published literature, and the set of components and their interactions is called a network. The fourth is to model the dynamics of the network mathematically (ie, how it changes over time or responds to various perturbations).Given the complexity of the cardiovascular system and of cardiovascular diseases (Figure 1),4,5 systems-based approaches are likely to play an increasingly important role in elucidating the higher-order interactions underlying traits such as atherosclerosis, cardiac hypertrophy, heart failure, and arrhythmias. These approaches should have translational value in giving biological context to the multitude of genetic variants reported to be associated with disease and in providing a framework for the development of pharmacological treatments. Here, we review progress relevant to cardiovascular networks and their dynamics. We organized our review according to the different approaches used to model networks. In each section, we include examples relevant to cardiovascular biology and disease. Download figureDownload PowerPointFigure 1. Graph of the components (clinical traits) and their interactions (connecting lines) in cardiovascular disease. This crude network is based on the results of clinical studies and experimental models. Data derived from refs. 4, 5.General Properties of Biological NetworksOne of the central concepts in systems biology is that networks, rather than classic linear pathways, underlie biological processes. The concept of biological networks arose when classic metabolic pathways were represented as graphs in which the components (ie, metabolites) were called nodes and their interactions (ie, enzymatic steps converting one metabolite to another) were called links or edges. It then became clear that the overall structure of metabolic pathways was much more interconnected and redundant than previously recognized.6 Biological networks occur on many different levels such as genes, transcripts, proteins, metabolites, organelles, cells, organs, organisms, and social systems. In general, they appear to exhibit an architecture described mathematically as “scale free,” in which most nodes have few links but a small fraction of nodes (called hubs) are highly interconnected. This architecture arises naturally in systems that evolve under selective pressures where nodes randomly interact and form links with other nodes. The system grows over time by adding new nodes and links, and new links to already highly connected nodes are favored (preferential attachment, or the principle that “the richer get richer”).7 Scale-free architecture does not require but is fully compatible with a hierarchal modular organization of components,6,8 which is typical for biological systems (eg, the organization of metabolism into various modules as shown in Figure 2A).9–11 Thus, local groups of sparsely linked nodes form modules that are linked to other modules through their corresponding hub nodes. The challenge of systems biology is to construct detailed biological networks for each level, from the gene to the organism, and then to connect the levels by integrating orthogonal data sets. Download figureDownload PowerPointFigure 2. Networks contributing to adiposity in mice. A, A set of curated pathways was shown to be enriched in genes differentially expressed between livers of fat and lean mice in a segregating mouse population.9 Note that these pathways tend to center on the tricarboxylic acid cycle, which is central in energy production. Through the use of systems genetics, a set of genes (labeled 1 through 9) were predicted to be causally related to adiposity in mice.10 Their effects on adiposity were validated with transgenic approaches, and expression array analyses of the transgenic mice showed that the differentially regulated genes were enriched in many of the same tricarboxylic acid–centered pathways (indicated by numbering above pathways).11 B, Systems genetics approaches were used to construct a directed “bayesian” coexpression network model based on global expression array analysis of segregating populations of mice. One subnetwork, or module, was significantly associated with adiposity. Notably, the genes causally involved in adiposity (above) correspond to hubs in the network. Data derived from refs. 9, 11.Networks exhibit several key features that make them well suited for systems evolving in response to selective pressures such as biological systems operating through random mutation and natural selection. For self-organizing systems, properties such as adaptability and robustness are just as important as efficiency, and the redundancy of pathways in a network intuitively lends itself to adaptability and robustness more so than a purely linear pathway. Although redundancy might seem to compromise efficiency, networks overcome this limitation through their “small-world” effects.12 As an illustration, consider an Escherichia coli using glycolysis to metabolize glucose to pyruvate to generate ATP, which suddenly finds itself exposed to a different substrate such as alanine (Figure 2A). If the E coli metabolism had a linear architecture in which alanine was far removed from glucose, then it could be energetically costly (and hence costly to survival) to interconvert a whole array of intermediate metabolites to synthesize glucose to produce ATP. In a highly interconnected network, however, enzymatic pathways exist to convert the alanine to a hub metabolite, which in turn is converted to the hub metabolite (pyruvate) in the glycolysis module, which is then metabolized to generate ATP. Thus, the energetic cost to the E coli is minimized as a result of the “short-circuiting” effect of the highly interconnected hub nodes in a small-world network. This is analogous to airport networks in which airplanes making many stops to fly the geographically shortest route between 2 small towns may take much longer than flying from the small town to a large hub city (eg, Chicago) and then backtracking to the final destination, even though the total distance traversed is greater. Another example is the “6 degrees of separation” in social networks.12Networks are inherently robust because there are multiple alternative pathways to get from one node to another; in a typical scale-free network, up to 80% of the links can be randomly destroyed before catastrophic network failure occurs.13 The redundancy of pathways also makes networks adaptable to changing environmental conditions, as illustrated by the E coli example above. Most critical for evolutionary adaptability, however, is the feature of “emergent properties,” which arise when the interactions between the nodes in a network are nonlinear. Unlike linear systems, in which the whole is equal to the sum of the parts, nonlinear interactions can create a remarkable new set of collective behaviors that make the whole much greater than the sum of the parts. The most fundamental emergent property in biology, the self-oscillation underlying the cell cycle, could not exist without nonlinear interactions between subcellular components in the biological network of the cell. Other examples of emergent behaviors include developmental morphogenesis (pattern formation), circadian rhythms, excitability, and cardiac pacemaking.From an evolutionary perspective, emergent behaviors provide a rich source of qualitatively new behaviors that can potentially confer a survival advantage to a biological system. Thus, it is not surprising that the logic of biological networks is typically nonlinear or that biological systems exhibit properties that may not be apparent until viewed as a whole. A key corollary is that pure reductionist approaches can provide us with a detailed parts list but not the whole view unless combined with integrative approaches.With this general background, we now describe examples of different approaches being used to model biological networks relevant to cardiovascular biology and disease.Networks Based on Prior KnowledgeThe data in the published literature can be mined through the use of known associations (such as those with diseases or defined pathways) or simply correlations across data sets (such as cocitation, phylogenetic profiling, coexpression, and sequence similarity), allowing the modeling of functional networks. Numerous vast databases derived from high-throughput technologies such as the Gene Expression Omnibus,14 the dbEST database at the National Center for Biotechnology Information, and GeneNetwork (www.genenetwork.org) are freely available. There are also systematic phenotyping projects such as the rat cardiovascular phenotyping project at the Medical College of Wisconsin.15 Such modeling can help reveal functions of genes about which little is known and can help identify genes underlying diseases.16 For example, Figure 1 summarizes published interactions in cardiovascular diseases derived from clinical and experimental databases.4,5 Our understanding of cardiovascular pathology is far from complete, so the network model is very imperfect, but the tremendous complexity of cardiovascular diseases is apparent. The goal of the studies described below is to create biological networks and to map them onto the disease network.Systems-based approaches frequently rely on technologies capable of broadly interrogating the components of a system such as DNA sequencing (genetic variation), expression arrays (transcript levels), tandem mass spectrometry (protein and metabolite levels), or Chip-chip and Chip-seq (DNA–transcription factor interactions). The result is generally a long list of components, perhaps with some differences between samples, but generally lacking any unifying biological theme. The challenge is to extract meaning from such lists. A number of approaches have been developed to determine whether such lists are enriched for known pathways.16For example, Ashley et al17 sought to identify networks involved in restenosis after a percutaneous coronary intervention. They examined 89 patients who underwent cardiac atherectomy for de novo atherosclerosis (n=55) or in-stent restenosis (n=34). Whole-genome gene expression profiling was then performed to examine the pathological samples, and the genes from the array were ordered in a rank list according to their differential expression between the classes. To construct an association network, the authors used text mining of Medline abstracts. Any 2 genes were considered an interaction verb if they appeared in the same sentence. In this way, certain highly connected genes, called nexus genes, were identified and proposed as candidates for involvement in restenosis.A related method is gene set enrichment analysis. The gene sets are defined on the basis of prior biological knowledge, primarily published information. More than 1000 such lists have now been compiled and are publicly available.18 Using a simple statistical test such as the Kolmogorov-Smirnov test or Fisher exact test, one can ask if the list is enriched for any such gene sets. An early example of the use of this approach is a study by Mootha et al,19 who analyzed data obtained from muscle biopsies of diabetics compared with healthy control subjects. The results did not reveal any single genes that differed significantly in expression, but they did show that genes involved in oxidative phosphorylation exhibited reduced expression in diabetics when taken as a pathway or a gene set. A similar approach was used to identify pathways contributing to differences in obesity in randomized populations of mice.9 Progeny from an intercross between 2 different inbred strains, C57BL/6J and DBA/2J, were studied for obesity-related traits and for global expression in liver. With the gene set enrichment analysis, 13 annotated metabolic pathways were found to be significantly enriched among genes with an expression that was associated with obesity (Figure 2). Interestingly, all of these pathways centered on the tricarboxylic acid cycle, and recent transgenic studies have validated some of these findings.11Networks Based on Physical InteractionsAn intuitive criterion for analyzing network structure is that of physical proximity. Components situated near one another are more likely to exhibit functional connections than are distant components. For example, many proteins mediate their biological functions through protein interactions, including aspects such as signaling, regulation of gene expression, immunity, and molecular machines. Such networks can be based on literature such as the Human Protein Reference Database20 or on unbiased experimental studies. A variety of high-throughput methods have been used to construct protein interaction networks, including global yeast 2 hybrid analysis, tandem affinity purification/mass spectrometry, and protein arrays. Various methods for the prediction of protein-protein interactions have also been developed (eg, from coevolution events). Such methods have been applied in most detail to yeast, but a variety of organisms, including flies, worms, and mammalian cells, have been examined.21–23 The first draft of the human interaction map (interactome) comprises >70 000 predicted physical interactions between 6231 proteins.24 In interaction networks, the individual proteins are nodes, and the interactions connecting 2 proteins are links.Another type of physical network can be constructed from functional interactions, eg, the modular compartmentalization of energy-generating systems in a cardiomyocyte (reviewed elsewhere25). These modules include glycolytic enzymes, glycogenolytic enzymes, and oxidative phosphorylation, which appear to be spatially distributed to optimize ATP delivery to specific ATPases (Figure 3). Glycolysis, which generates ATP through oxidation of glucose, preferentially serves energy channeling to the sarcolemma, where glucose is transported into the cell. Glycogenolysis appears to preferentially serve the sarcoplasmic reticulum to energize calcium cycling. Oxidative phosphorylation occurs in the mitochondria, channeling ATP to the myofilaments and throughout the cytoplasm. This is aided by the creatine kinase and adenylate kinase systems. Although the vast majority of energy is generated by oxidative phosphorylation, the glycolytic and glycogenolytic systems are low-capacity but high-specificity modules of the integrated metabolic network of a cardiomyocyte. There has been recent progress in identifying the protein components of mammalian organelles (eg, see the work by Foster et al26), and an important goal is to integrate proteomic networks with organelle networks. Download figureDownload PowerPointFigure 3. A spatially distributed, dynamic metabolic energy network in a cardiomyocyte. Energy modules include glycolytic enzymes (GE) associated with sarcolemma (SL) ATPases in red, glycogenolytic enzymes (GGE) associated with sarcoplasmic reticulum (SR) Ca ATPases (SERCA) in blue, and oxidative phosphorylation in mitochondria (Mito) associated with myofilament (MyoF) ATPases in green. Energy-distributing modules include creatine kinase (CK) in purple and adenylate kinase (AK) in orange. CP indicates creatine phosphate; Cr, creatine; KATP, ATP-sensitive K channel; and PRY, pyruvate.Networks Based on Experimental PerturbationsMost biological studies involve some kind of perturbation such as a chemical treatment or genetic alteration, followed by analysis of the resulting effects. Conclusions about the causal interactions can then be drawn. For example, the function of a gene can be defined by “knockdown” in tissue culture using treatment with siRNA or by “knockout” in mice using gene targeting. In such experiments, whatever changes are observed are clearly the result of the single perturbation. But such experiments alone are not very useful for constructing gene networks because the components being analyzed have only 2 states (ie, wild type or knockout). Thus, to determine how the various components interact with each other, a series of single perturbations is required. For example, one could analyze a series of mouse knockouts or transgenics affecting overlapping pathways. The resulting changes in transcript levels or protein levels or activities could then be mathematically modeled as a network. Another kind of experiment involves the use of multiple perturbations. For example, natural populations exhibit functional variations (such as in gene expression) in hundreds or thousands of genes. Thus, one could examine the status of components in different individuals in the population and construct networks based on correlations between the components. In contrast to single perturbations, such studies have clear advantages for the construction of biological networks. Below, we discuss 2 examples of each.Inflammation and macrophage activation are clearly implicated in atherogenesis, and a recent study by Ramsey and coworkers27 explored the macrophage transcriptional network mediated by Toll-like receptors (TLR) using a series of single gene perturbations. TLR recognize a variety of pathogen-associated molecules and certain endogenous ligands through adaptor molecules such as TRIF and Myd88 and then parallel crosstalking signaling pathways. In the case of macrophage TLR4, when stimulated with lipopolysaccharide, these activated pathways initiate a program leading to the differential expression of >1000 genes, including hundreds of transcription factors.28 Although these differentially expressed genes are known, the network of these interactions has proved difficult to address with traditional biochemistry. The authors combined 2 types of data to explain the network. First, they performed computational scanning of promoter sequences of clusters of coexpressed genes for known TF binding sites. Second, they used expression dynamics, modeling time course expression data to best fit the expression of a TF with potential target genes. The whole-genome expression array analyses were performed on primary bone marrow macrophages from 5 strains of mice (1 wild-type and 4 targeted strains) treated with 6 different TLR agonists at multiple time points between 0 and 48 hours. In all, 95 different combinations of strains, stimuli, and elapsed times were measured. The set of differentially expressed genes was clustered; promoter sequences of each gene were scanned for TF binding sites; and the temporal patterns of expression of TF and targeted genes were compared to identify potential causal influences. When integrated, the results provided a broad picture of the dynamic transcriptional program of the TLR network. The general approach seems applicable to other mammalian systems for which time course data are available.Skogsberg and colleagues29 used a series of single gene perturbations to create a network of genes mediating the development of advanced atherosclerotic lesions in mice. Briefly, they followed the development of atherosclerotic lesions in low-density lipoprotein receptor–null mice (a model of familial hypercholesterolemia) and observed a gradual initial growth phase, followed by an accelerated phase of rapid foam cell development and finally a plateau phase. Using a genetic switch that blocked secretion of lipoproteins to rapidly lower cholesterol, they observed that the switch blocked the development of advanced lesions if performed before the expansion phase and that this block was associated with altered expression of 37 genes, some of which had previously been associated with foam cell formation (CD36, PPARA). They then used siRNA knockdown of a subset of these genes to construct a network of cholesterol-responsive atherosclerosis target genes. For this, the authors examined expression patterns and cholesterol ester in a macrophage cell line (THP-1) after treatment with acetylated low-density lipoprotein in the presence or absence of siRNA to one of the candidate genes. Computational modeling of the expression data30 revealed a directed network of 8 genes involved in foam cell formation (Figure 4). Download figureDownload PowerPointFigure 4. A regulatory network for foam cell formation based on in vivo expression analyses in mice followed by perturbation in vitro. A, The above genes were found to be differentially regulated in the response to cholesterol lowering in atherosclerotic lesions in mice. They were then subjected to in vitro siRNA “knockdown” studies in a macrophage cell line. The predicted interactions (edges) are indicated, along with the percent change in cholesterol accumulation after cholesterol loading. Blue indicates an inhibition of cholesterol loading; red, an increase; and black, no change. B, Representative siRNA-treated cells are shown after 48 hours of incubation with acetylated low-density lipoprotein and staining for neutral lipids with Oil Red O. AGPAT3 indicates 1-acylglycerol-3-phosphate O-acyltransferase 3; AGL, amylo-1,6-glucosidase, 4-α-glucanotransferase; PVRL2, poliovirus receptor-related 2; GYPC, glycophorin C; HMGB3, high-mobility group box 3; HSDL2, hydroxysteroid dehydrogenase-like 2; CD36, CD36 molecule; PPARA, peroxisome proliferator-activated receptor-α. Reproduced from Skogsberg et al29 under the Creative Commons Attribution License. Copyright © 2008 Skogsberg et al.One of the first examples of the use of multiple, common genetic perturbations to construct functional networks for cardiovascular traits is described by Nadeau et al.31 These investigators examined a variety of cardiovascular traits, as well as exercise endurance and body weight, in a panel of genetically randomized mice (ie, a series of inbred strains differing in their genetic backgrounds as a result of mendelian segregation). Using echocardiographic and treadmill assays, the authors measured functions such as cardiac output, end-systolic dimensions, septal wall thickness, and heartbeats per minute. None of these traits represented adverse pathology but instead constituted genetically controlled differences in the normal range of variation. In addition, none of the traits showed mendelian inheritance but rather exhibited continuous variation that was consistent with multigenic control. A network was then constructed in which the traits (nodes) were assigned edges based on significant correlations (Figure 5). The assumption in such networks is that traits are correlated as a result of shared genetic determinants or causal interactions. The resulting network correctly identified known functional relationships based on physiological studies, and some interactions were confirmed through the use of single-gene mutant mice or treatment with pharmacological agents. Thus, this proof-of-principle study demonstrated that such networks are a powerful approach for characterizing functional relationships in complex biological systems. Download figureDownload PowerPointFigure 5. A network of cardiovascular traits based on trait correlations in genetically randomize mice. Solid lines indicate positive relationships between traits; broken lines, inverse relationships. The body weight and exercise nodes did not exhibit significant cosegregation with any other traits. Adapted from Nadeau et al31 with permission from Cold Spring Harbor Laboratory Press. Copyright © 2003 Cold Spring Harbor Laboratory Press.A similar strategy, using multiple genetic perturbations, was used to model an inflammatory network associated with atherosclerosis.32 Oxidized lipids are thought to promote atherosclerosis by stimulating endothelial cells to produce inflammatory cytokines such as interleukin-8, but the pathways involved are poorly understood. To examine this, transcript levels in the presence and absence of oxidized lipids were quantified in cultured endothelial cells derived from a series of random individuals (heart transplant donors). Altogether, >1000 genes were found to be significantly influenced by the oxidized lipids. In addition, between endothelial cells from different donors, there were striking differences in the responses of individual genes. This result was due to the fact that, in natural populations, there are many polymorphisms that perturb gene expression. These multiple common genetic variations were then used to group the genes according to the similarity of expression across individuals and thus create a “coexpression” network (Figure 6). In such networks, the nodes are genes and the edges represent correlations in the transcript levels between pairs of genes. A tutorial on the analysis of coexpression networks can be found at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/. Altogether, 15 modules of highly connected genes were identified, and they were significantly enriched in genes for known pathways, including modules corresponding to 2 different arms of the unfolded protein response. In addition to identifying key pathways involved in the inflammatory response, the network was also useful for predicting regulatory mechanisms and identifying gene functions. For example, interleukin-8 and certain other cytokines thought to be involved in atherosclerosis were observed to occur in the XBP1 arm of the unfolded protein response, suggesting that these cytokines were regulated in part by the unfolded protein response. This prediction was validated through the use of siRNA and overexpression. The authors also were able to predict the functions of certain genes on the basis of their presence in modules. For example, a gene of unknown function (MGC4504) was a hub in the ATF 4 arm of the unfolded protein response, suggesting that it was an unfolded protein response gene regulated by ATF 4. This was confirmed, and subsequent studies have indicated that the function of the gene is related to apoptosis.33Download figureDownload PowerPointFigure 6. Coexpression analysis of the inflammatory responses of human endothelial cells based on common genetic variations in the population. Small pieces of human aortic arteries (obtained during the course of heart transplant surgery) were used to obtain pure, early-passage cultures of human endothelial cells. The endothelial cells from different donors were observed to exhibit significant differences in response to oxidized lipids. A, Production of interleukin-8 (IL8) by the cultures after treatment with biologically active oxidized phospholipids (solid symbols) vs control levels (open symbols). A total of 12 endothelial cell cultures isolated from individuals exhibiting various responses to oxidized lipids were subjected to microarray analy" @default.
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W2103237492 date "2010-01-05" @default.
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W2103237492 title "Cardiovascular Networks" @default.
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