SemOpenAlex |

SemOpenAlex

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2100045972> ?p ?o ?g. }

Showing items 1 to 100 of ±151 with 100 items per page.

W2100045972 endingPage "576" @default.
W2100045972 startingPage "576" @default.
W2100045972 abstract "HomeCirculation: Cardiovascular GeneticsVol. 4, No. 5Genomes, Proteomes, and the Central Dogma Free AccessResearch ArticlePDF/EPUBAboutView PDFSections ToolsAdd to favoritesDownload citationsTrack citationsPermissionsDownload Articles + Supplements ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toSupplementary MaterialsFree AccessResearch ArticlePDF/EPUBGenomes, Proteomes, and the Central Dogma Sarah Franklin, PhD and Thomas M. Vondriska, PhD Sarah FranklinSarah Franklin From the Departments of Anesthesiology (S.F., T.M.V.), Medicine (T.M.V.) and Physiology (T.M.V.), David Geffen School of Medicine, University of California, Los Angeles, CA. Search for more papers by this author and Thomas M. VondriskaThomas M. Vondriska From the Departments of Anesthesiology (S.F., T.M.V.), Medicine (T.M.V.) and Physiology (T.M.V.), David Geffen School of Medicine, University of California, Los Angeles, CA. Search for more papers by this author Originally published1 Oct 2011https://doi.org/10.1161/CIRCGENETICS.110.957795Circulation: Cardiovascular Genetics. 2011;4:576IntroductionArguably the greatest postmodern coup for reductionism in biology was the articulation of the central dogma.1 Not since “humors” were discarded from medical practice and logic and experiment instituted as the cornerstones of physiology (which they remain today) had such a revolutionary idea transformed biology and enabled scientific inquiry. Because of its simplicity, the central dogma has the tantalizing allure of deduction: If one accepts the premises (that DNA encodes mRNA, and mRNA, protein), it seems one cannot deny the conclusions (that genes are the blueprint for life). As a result, the central dogma has guided research into causes of disease and phenotype, as well as constituted the basis for the tools used in the laboratory to interrogate these causes for the past half century.The past decade, however, has witnessed a rapid accumulation of evidence that challenges the linear logic of the central dogma. Four previously unassailable beliefs about the genome—that it is static throughout the life of the organism; that it is invariant between cell type and individual2–4; that changes occurring in somatic cells cannot be inherited (also known as Lamarckian evolution5); and that necessary and sufficient information for cellular function is contained in the gene sequence—have all been called into question in the last few years. Revelations of similar scale have occurred in the transcriptome, with the discovery of the ubiquity (and variety) of mRNA splicing.6 So too with the proteome, which has undergone perhaps the most dramatic shift in understanding as a result of the aforementioned changes to the transcriptome and the genome, as well as by the explosion of technology development that has enabled both quantitative and qualitative analysis of large groups of proteins and their modifications in a single experiment. It is now clear that information flows multidirectionally between different tiers of biological information, of which genes, transcripts, and proteins constitute only the most obvious 3.The ostensible fourth step in the central dogma—how molecules “encode” cells—clearly lacks the crystalline formulas that relate DNA to protein. Although molecular details have been revealed for thousands of cellular events, no model exists that can explain how, for example, the modest erythrocyte is formed without error ≈2 million times per second in adult Homo sapiens. In contrast to a blueprint that can perfectly describe how to assemble a motorcycle or to build a city, we lack the knowledge to explain how a cell forms with correct processes operational, cellular structures formed, and signaling mechanisms in place. It is in attempting to extend the central dogma beyond proteins that one realizes the logic of biological systems and engineered ones are fundamentally different.7 Just as the central dogma did for the investigation of basic and medical biological problems, a new synthesis for how cells form and function will result in philosophical shifts in research, as well as technological breakthroughs to enable it.The purpose of the present work is to emphasize the contribution of proteomics and systems biology to extending the central dogma (Figure 1). Whenever possible, studies from the cardiovascular literature are used to highlight conceptual and technical breakthroughs. Excellent reviews exist on novel means of quantifying the proteome,8,9 methods to analyze posttranslational modifications,10,11 cardiovascular diagnosis,12–14 and organelle proteomics15–17; however, these areas are not the purview of the present work.Download figureDownload PowerPointFigure 1. Role of proteomics in systems biology. The advent of reproducible and high-accuracy genomic and proteomic instrumentation has enabled characterization of distinct types of gene, RNA, and protein networks. In many cases, quantitative data collected at fixed intervals after a stimulus (or, for example, during the development of disease) allow for dynamics of processes to be determined and for functional relationships in networks to be revealed. These technologies are fundamentally dependent on rigorously characterized models of disease (or human samples with extensive clinical data) to allow bioinformatic analyses to extract statistically significant relationships within and between large proteomic and genomic data sets. Mathematical modeling allows these observations to make predictions about behaviors underlying the system. The ultimate goal is to understand the relationships between different cellular networks (here the genome and proteome are represented; see also Figure 2) during health, such that we can engineer strategies to reprogram these network-network relationships therapeutically. PTM indicates posttranslational modification.Proteomics as a Tool in Systems BiologyWhether systems biology as a discipline differs other than in semantics from physiology is worthy of the briefest clarification. It is our contention that the principal difference is the greater abundance, and higher rate of accumulation, of data in the former. Although both concern themselves with the physical and chemical parts of a biological system and focus on revealing the oft-referenced mechanisms underlying phenotype, systems biology has been enabled by the development of “-omics” technologies over the past 15 years. The sheer magnitude of data accumulated in studies of the genome, transcriptome, metabolome, proteome, and so forth not only demanded a greater computational infrastructure for interpretation but simultaneously revealed new dimensionality within tiers of biological information (witness the genome-wide association studies and unsupervised clustering of gene and protein expression data from high-throughput studies). Physiologists long ago appreciated the utility of simple engineering principles to model biological systems—the giant squid axon studies of Hodgkin and Huxley, as well as Guyton's stunning mathematical (control theory) description of the brain and cardiovascular system come to mind—but the importance of mathematical biology has become pervasive in systems biology for interpretation (that is, bioinformatics) and hypothesis generation by modeling (wherein mathematical models become stand-alone entities that make predictions about future experiments). A hallmark of systems biology is the integration of large sets of data from measurements made on different tiers of the central dogma in the same experiment, to reveal emergent properties of the biological entity (Figure 2). Systems biology relies on the interplay between hypothesis- and discovery-driven research, and proteomics enables both these approaches.Download figureDownload PowerPointFigure 2. Evolution of the central dogma. As described in the text, a new synthesis of the central dogma is emerging in which different networks constitute each tier and information moves within and between the networks without strict directionality (see Table).Proteomics has traditionally concerned itself with cataloging proteins in different cells and tissues, with an increasing focus of late on organelles. Although this process has also been reviewed in greater depth elsewhere, it is important to note the conceptual advancement enabled by proteome maps. The first is the ability to navigate these maps, as proposed by Aebersold and colleagues,9 for which the current state of the art is directed mass spectrometry approaches (selected/multiple reaction monitoring experiments). This is now possible across the full dynamic range of expressed proteins in nonmammalian eukaryotes.18 Fortunately, generation of maps to guide this type of navigation is well under way for multiple organelles in the mammalian heart, most notably the mitochondria19–23 but also the nucleus20,24 and proteasome.25 In virtually all cases, these maps have been generated by semiquantitative (or in some cases, nonquantitative) tandem mass spectrometry, in which proteins extracted from purified organelles are separated intact and digested (usually by trypsin) to peptides, which are then separated by liquid chromatography and introduced into the mass spectrometer by electrospray. The tandem mass spectra that result from these experiments are then searched against a protein database to make identifications. These studies constitute the maps with which selected/multiple reaction monitoring experiments may now explore and quantify mammalian cardiac proteomes.Second, the conceptualization of proteomes as functional units of cellular phenotype has enabled researchers to analyze them as protagonists of cellular function, as well as readouts for the actions of other tiers. The measurement of proteomes as readouts of cardiac function is now widely used, and rigorous standards have been established for the evaluation of mass spectrometry data. Such a framework has been essential to allow proteomic investigations to yield biologically and clinically relevant insights. In the realm of signaling, expression profiling proteomics have been used in the heart to study targets of type 5 adenylyl cyclase26 and the role of the mitochondria27,28 or matrix metalloproteinases29 in cardiac ischemia and protection, as well as to study direct kinase targets important for cardiac phenotype,30,31 to name only a few examples in what is now a rapidly growing body of literature.Where proteomes as readouts have the greatest potential to enhance our understanding of cardiac function is when simultaneous measurements are made on other tiers of biological information or when proteomic data are integrated with rigorous gain- and loss-of-function approaches. Both are discussed in greater detail below; the former is still quite a nascent field in the heart, although some progress has been made with coupled transcriptomic and proteomic analysis of cardiac protection.32 In one example of the latter approach, Ago and colleagues33 used targeted proteomics to identify novel regulatory mechanisms that would have been virtually impossible to discover with traditional laboratory biochemistry approaches. The investigators used mass spectrometry to identify specific oxidized residues on HDAC4 that alter its nuclear-cytoplasmic partitioning in the setting of hypertrophy and revealed that thioredoxin counteracts this process by reducing these residues. In this study, the use of proteomics to identify novel modified residues facilitated the use of reductionist approaches (genetic and pharmacological) to test causality, which highlights the powerful interplay of discovery-based and hypothesis-driven approaches.The challenge is to shift the focus from proteomes as readouts to measuring them as the active agents of biological function. To use proteomics to reveal basic mechanisms of disease, one is, in essence, trying to develop a molecular ECG, an emergent property of the system that will allow us to diagnose the health of the cell. All of the proteins that influence the ECG are not known; however, we need not have a perfect understanding of all the factors that contribute to the network of molecules as long as we can quantify its existence and demonstrate its linkage to phenotype in a reproducible manner. The objective of a complete static wiring diagram for a biological system is incomplete, because biological systems defy the linear logic of those engineered by humans.Network Biology: Insights From ProteomicsTypes of Biological NetworksThere has been an explosion in the last decade in our understanding of the role of networks in all aspects of biology. Several excellent reviews on the properties of networks and how they relate to cell biology exist34–36; herein, we will only highlight what we see as a few critical considerations for network logic in the interpretation of proteomic and genomic data and highlight how these data in turn are affecting the study of networks.Although active debate continues about the most accurate mathematical representation of biological networks (whether, for instance, their structures are scale-free or exponential, and the importance of local versus global features), there is almost universal agreement that network structure is nonrandom and plays a fundamental role in how information is processed and decisions are made in the cell. How nodes (or individual features) are linked (also known as edges) to each other reveals core features about the network, including how a signal is transmitted, whether it is amplified or dampened, the relative importance of individual nodes versus that of groups of nodes, and the dynamic processes that the network can perform. Topology, or how the network is organized, determines the local and global structural features, which, when dynamics are incorporated, include the emergence of feedback and feedforward motifs (both excitatory and inhibitory), as well as threshold behavior and biological memory.37–39 Indeed, most “-omics” studies to date have used only static data representations to produce networks. The limitation of this approach is that cellular behavior results from dynamic properties of the network that cannot be appreciated from the static topology.A fundamental defining feature of biological networks is whether they are physical or functional. In a physical network, the nodes are connected to each other directly or indirectly in a physical manner, an obvious example being a protein interaction network, in which the proteins are the nodes and the links exist between proteins that bind to each other. Information flowing through such a network is also physically defined (and physically relayed in a direct manner). In the same example, this could include one protein posttranslationally modifying another or otherwise directly altering its activity. Physical networks can also exist between different types of biological molecules, for instance, the network defined by the genes whose promoters are bound by a transcription factor. Again the network is physical so long as the given protein directly binds to an individual gene; information flows through this network in the form of alterations in gene expression (although the actual process of altering gene expression likely does not arise from the solitary act of a protein binding a DNA sequence, and thus, this example is instructive of how a physical network then becomes a functional one). Functional networks, or influence networks, are those in which nodes are connected by virtue of their ability solely to influence each other, without implication (or at least without direct evidence) of physical interaction between the nodes. Simply put, a link in an influence network means that the 2 nodes affect each other in some manner and/or participate in some shared process in the cell. Moving from the previous gene expression example, the protein transcription factor is unlikely to be sufficient to express a gene: It must bind to DNA and/or histones and recruit other factors, notably RNA polymerase II, to initiate transcription. Some of these nodes interact directly, whereas others clearly are connected only through influence. The most extensively studied influence networks are gene coexpression networks, in which genes (nodes) are connected if their expression behavior is similar before or after an intervention. Clearly, there could be scores of intermediate physical links, not directly measured in a microarray or RNA sequencing study, that influence the connection between 2 nodes in such a network. In the cardiac realm, several proteomic and genomic investigations have revealed both physical31,40 and functional41,42 networks relevant for heart function (to cite just a few examples).Network-Network InteractionsLike proteins and genes, molecular networks are interconnected and interdependent. Information flows between networks, and one must integrate distinct data sets to represent a complete picture of how the cell functions. Several recent studies are illustrative of this point.Lage and colleagues43 used multiple forms of data, including protein interaction studies and phenotype-linked mutational studies on individual genes, to construct networks that control specific processes in different anatomic locations of the heart. The result was a systems-level view of the networks underlying spatial (in terms of within the organ) and temporal development of the prenatal myocardium. The multidimensionality of the data analysis allowed the investigators to extract features of these networks, such as the relationship between protein module complexity and protein/transcript abundance, which would escape appreciation if intuition alone were used to evaluate the data. Furthermore, the networks examined in this study included physical networks based on published protein interactions and functional networks based on shared involvement in different stages or features of embryonic heart formation.An intuitive example of obligatory symbiosis between tiers of biological molecules is the eukaryotic genome, defined herein as the DNA molecules and the chromatin structural proteins that constitutively bind to them. The packaging of DNA in the nucleus can be understood on the basis of the following structural hierarchy: A segment (≈146 nucleotides in length) of the double helical DNA molecule wraps around a protein complex that contains 2 copies each of 4 core histones (H2A, H2B, H3, and H4), which constitutes a nucleosome; this octomeric, DNA-bound protein complex in turn forms higher-ordered structures of less well-defined architecture through interactions with linker histones (like H1) and other chromatin structural proteins (such as high-mobility group proteins). These chromatin domains determine the overall shape and presentation of each individual chromosome. Although the organization of these DNA-protein complexes—the chromosomes—during cell division is well established, and extensive work has been done to characterize changes in nucleosome positioning, there exists no dogma articulating how chromatin structure regulates gene expression. To address this issue, an adaptation of conventional 3C (chromosomal conformation capture), a technique that allows determination of physical proximity of specific genetic loci in 3 dimensions, was used to map the physical arrangement of entire genomes in intact interphase nuclei. This approach was applied separately to human44 and yeast45 genomes to reveal that the 3-dimensional structure of the genome resembles a fractal globule, a structure that has several desirable features, such as nonoverlapping segments and modular architecture, both of which would facilitate differential accessibility by transcription factors.46 Although elegant, this model is incomplete in that it cannot account for differential modification of this genome structure in different cell types and within the same cell under varied transcriptional states. Furthermore, little is known about how these interesting observations from noncardiac systems will be examined in the setting of the heart, with its inherent challenge of heterogeneity in terms of cell type. Extensive work has been conducted, including in the heart, to show how various classes of proteins modulate chromatin accessibility and thereby gene expression (including but not limited to histone deacetylases/histone acetyltransferases, high-mobility group proteins, lysine and arginine methyltransferases/demethylases, kinases/phosphatases, and chromatin remodeling proteins47,48). Importantly, recent reviews have highlighted critical considerations for analyzing these types of networks (ie, those involving DNA and protein), including how to capture discrete molecular details of genetic circuits, and considerations for distinguishing specificity and affinity in studying regulation.49 However, these studies have yet to reveal an invariant relationship between structure and function. There is no code that can account for how the hierarchical structure of DNA and proteins establishes the complex genomic regulatory programs that exist in distinct differentiated cells, as there is to explain, for example, how DNA encodes RNA and RNA, protein.Other work has recently advanced our view of the landscape of genomic regulation by networks of proteins in the eukaryotic nucleus. One recent study demonstrated the power of combining proteomic and genomic approaches to reveal insights into both physical and influence networks on a large scale. Using a modified histone-derived peptide as bait, potential chromatin modifiers were identified by subsequent mass spectrometry analysis.50 The genome-wide localization of select members of this group was then mapped by chromatin immunoprecipitation followed by DNA sequencing. The core insight from this study was at the level of 2 types of networks and their integration in the setting of the activation or inhibition of gene expression, as dictated by the respective histone posttranslational modifications. An innovative approach to use protein influence networks to define genomic influence networks also comes from the realm of chromatin regulation.51 A sizable group (53) of chromatin-binding proteins with known genome occupancy profiles but with no preconceived relationship to each other were selected. The occupancy profiles were used to discriminate 5 domains of chromatin based on principal component analyses (in brief, regions where the proteins had similar occupancy across the genome were defined as functional domains). As a result, all loci were assigned to 1 of these 5 exclusive chromatin types, thus constituting a previously unrecognized influence network. Importantly, these genomic features do not reveal themselves on the basis of a single DNA sequence feature, posttranslational modification, or bound protein; it is only through the combination of proteins bound to a given region that the features of this genome-proteome interaction emerge. A third study undertook a massive analysis that combined published data and RNA interference screens to identify a list of target proteins that were subsequently engineered with green fluorescent protein or tandem affinity tags and used for microscopy-based localization or affinity purification and mass spectrometry analysis, respectively.52 This is a noteworthy example of unbiased, discovery-based analysis of a system. In addition, the combination of approaches gave physical information on protein interactions and anatomic reference within the context of an intact cell. The result was a phenotype and function-linked catalog of chromosomal segregation proteins that regulate mitosis in mammalian cells.Literature on protein interaction networks is extensive, and this remains an area of active technology development in proteomics. An important and potentially obfuscated consideration for these studies is that virtually all protein interaction data are recovered in a binary manner, in which knowledge on levels of interactions, if present, is lost, along with insights into whether interactions are persistent or fleeting. This issue was addressed in a novel way by a recent report that focused on structural features of the individual proteins,53 such as disordered regions, protein interaction domains, and states of activation. High-throughput heterologous systems such as yeast 2-hybrid assays and tandem affinity purification tagging in Escherichia coli are not amenable to fine-tuning experimental conditions to capture distinct types of protein interactions, and thus, much of this information needs to be recovered by repeat experimentation in different systems (eg, reciprocal purification) or by an alternative method such as in situ colocalization.As a final example for this section, consider a study in which numerous types of experiments were performed to first generate and then explore hippocampal neuronal networks.54 In this study, the nodes were primarily proteins, and the edges included direct interactions (as in protein complexes), signal transduction (indirect or imperfectly defined interactions), ligand-receptor binding, enzymatic activity, and common use of cofactors. This analysis revealed signaling motifs that conferred distinct behavior and could account for many of the higher-level phenotypic properties of neurons that individual proteins, or simple “pathways,” could not. Furthermore, the use of graph theory to investigate the role of local and global connectivity in the flow of information through networks allowed the large data sets to yield meaningful insights into specific cellular processes. Similar integrated models have been developed and explored for the cardiac myocyte,55–57 which, like the neuron, offers itself to representation based on electric engineering principles.How Do Biological Networks Form?We would like to briefly address the following paradox with regard to the formation of cellular networks: Unlike any other networks (including human interactions, the Internet, transportation, power grids, computers, predator-prey interactions, and so forth), cellular networks appear to inherently require the absence of flexibility in their formation. This statement requires some clarification. On the one hand, cellular networks are infinitely flexible after they are formed (that is, in a normally functioning cell); this is the observation we are all well aware of and that is commonly written off as the large degree of redundancy in cellular networks. However, inflexibility appears to be a requirement during the formation of the network. We must refute the hypothesis that formation of networks during the birth of a cell is a deterministic event if we wish to claim that variability exists in the formation of these networks. Although the response to a given stimulus in an existent network may be enacted by more than 1 mechanism (illustrating the so-called redundancy), the network allowing for this occurrence must, if we reason the response was nonrandom and reproducible, harbor properties that specifically produce this behavior. The presence of such properties suggests emergent control in the formation of the network in addition to emergent control of its function once formed. How can this emergence be measured rather than just observed? An enigma in biology is how a cell with (at least) thousands of proteins can reproducibly form and behave in the same manner without central control. This argument means 1 of 2 explanations must be true: Either there is an as yet unknown process that governs invariant formation of networks, or biological networks are not invariant (eg, the precise structure and/or means of formation of a protein interaction network in 2 cells can differ). Clearly, we must return to the laboratory to resolve this issue. An experiment to resolve this issue would be one in which a given network (eg, gene expression or protein expression) was mapped multiple times in distinct sets of the same cells without pooling the data between the individual technical replicates. If sufficient resolution was achieved (which is now possible because of next-generation nucleotide sequencing58 and high-mass-accuracy mass spectrometers9 and other techniques59), algorithms could be devised to distinguish noise from true variation in the system. A simulation of how much noise should result from the known limits of the instrumentation when compared with the data resultant from said experiment would reveal whether there is detectable variation in the architecture of the networks in multiple copies of the same cell. If sufficient variation existed between distinct samples of the same network, this observation would refute the hypothesis that architecture of molecular networks is invariant.Specific Challenges for Cardiac ProteomicsAs with any cell or organ, the challenges with proteomic and genomic dissection mirror the unique physiology of the system. Four prescient challenges for cardiovascular proteomics are the contribution of distinct cell types, distinguishing between primary and secondary effects, determining what aspects of endogenous remodeling are beneficial versus harmful, and the availability of healthy human samples.The heart's reason for existence is to pump blood; hence, the cells of primary interest in this objective are the cardiac myocytes. Experimental harnessing of cardiac-specific genetic manipulation has enabled extensive analysis of in vivo roles for individual proteins in cardiac myocytes.60,61 The result has been an astounding advancement in our understanding of cardiac cellular networks62,63 from the standpoint of adding or removing individual nodes. What we currently lack is the ability to manipulate the roles of individual proteins in the many other cell types that constitutively populate the heart (such as vascular smooth muscle, endothelial cells, and fibroblasts64) along with tho" @default.
W2100045972 created "2016-06-24" @default.
W2100045972 creator A5010655614 @default.
W2100045972 creator A5088650648 @default.
W2100045972 date "2011-10-01" @default.
W2100045972 modified "2023-09-27" @default.
W2100045972 title "Genomes, Proteomes, and the Central Dogma" @default.
W2100045972 cites W1524149821 @default.
W2100045972 cites W1599207601 @default.
W2100045972 cites W1629621594 @default.
W2100045972 cites W1644749979 @default.
W2100045972 cites W1698053015 @default.
W2100045972 cites W1964430604 @default.
W2100045972 cites W1966083128 @default.
W2100045972 cites W1967163877 @default.
W2100045972 cites W1979003242 @default.
W2100045972 cites W1980023815 @default.
W2100045972 cites W1980092520 @default.
W2100045972 cites W1980825481 @default.
W2100045972 cites W1981509058 @default.
W2100045972 cites W1989690629 @default.
W2100045972 cites W1990786456 @default.
W2100045972 cites W1992329706 @default.
W2100045972 cites W1996868531 @default.
W2100045972 cites W2002264839 @default.
W2100045972 cites W2006312138 @default.
W2100045972 cites W2014937237 @default.
W2100045972 cites W2015094506 @default.
W2100045972 cites W2020018374 @default.
W2100045972 cites W2020298426 @default.
W2100045972 cites W2020594690 @default.
W2100045972 cites W2020867212 @default.
W2100045972 cites W2031393760 @default.
W2100045972 cites W2041063545 @default.
W2100045972 cites W2043492633 @default.
W2100045972 cites W2045234988 @default.
W2100045972 cites W2049445541 @default.
W2100045972 cites W2055214702 @default.
W2100045972 cites W2057099146 @default.
W2100045972 cites W2060789589 @default.
W2100045972 cites W2062533676 @default.
W2100045972 cites W2063040470 @default.
W2100045972 cites W2067305633 @default.
W2100045972 cites W2071367521 @default.
W2100045972 cites W2072616787 @default.
W2100045972 cites W2077307563 @default.
W2100045972 cites W2081931663 @default.
W2100045972 cites W2082516237 @default.
W2100045972 cites W2084108486 @default.
W2100045972 cites W2085630110 @default.
W2100045972 cites W2085641766 @default.
W2100045972 cites W2087221674 @default.
W2100045972 cites W2087706354 @default.
W2100045972 cites W2090037139 @default.
W2100045972 cites W2091303550 @default.
W2100045972 cites W2093050872 @default.
W2100045972 cites W2095884782 @default.
W2100045972 cites W2096083625 @default.
W2100045972 cites W2096847688 @default.
W2100045972 cites W2103237492 @default.
W2100045972 cites W2103579660 @default.
W2100045972 cites W2105058587 @default.
W2100045972 cites W2111154621 @default.
W2100045972 cites W2116126626 @default.
W2100045972 cites W2118105142 @default.
W2100045972 cites W2123880844 @default.
W2100045972 cites W2129998113 @default.
W2100045972 cites W2133963004 @default.
W2100045972 cites W2135498889 @default.
W2100045972 cites W2136969659 @default.
W2100045972 cites W2136977237 @default.
W2100045972 cites W2138248682 @default.
W2100045972 cites W2140738271 @default.
W2100045972 cites W2144328273 @default.
W2100045972 cites W2145619687 @default.
W2100045972 cites W2148828613 @default.
W2100045972 cites W2155804510 @default.
W2100045972 cites W2156355974 @default.
W2100045972 cites W2158272128 @default.
W2100045972 cites W2158534900 @default.
W2100045972 cites W2159965404 @default.
W2100045972 cites W2160294830 @default.
W2100045972 cites W2162103775 @default.
W2100045972 cites W2162142896 @default.
W2100045972 cites W2163270745 @default.
W2100045972 cites W2163328048 @default.
W2100045972 cites W2163480486 @default.
W2100045972 cites W2164727176 @default.
W2100045972 cites W2168142506 @default.
W2100045972 cites W2171584616 @default.
W2100045972 cites W2171777347 @default.
W2100045972 cites W2172136041 @default.
W2100045972 doi "https://doi.org/10.1161/circgenetics.110.957795" @default.
W2100045972 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3200573" @default.
W2100045972 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/22010165" @default.
W2100045972 hasPublicationYear "2011" @default.
W2100045972 type Work @default.
W2100045972 sameAs 2100045972 @default.