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HIGHLIGHT www.rsc.org/molecularbiosystems | Molecular BioSystems A genome-scale, constraint-based approach to systems biology of human metabolism Monica L. Mo, Neema Jamshidi and Bernhard Ø. Palsson* DOI: 10.1039/b705597h The recent sequencing and annotation of the human genome enables a new era in biomedicine that will be based on an interdisciplinary, systemic approach to the elucidation and treatment of human disease. Reconstruction of genome-scale metabolic networks is an important part of this approach since networks represent the integration of diverse biological data such as genome annotations, high-throughput data, and legacy biochemical knowledge. This article will describe Homo sapiens Recon 1, a functionally tested, genome-scale reconstruction of human cellular metabolism, and its capabilities for facilitating the understanding of physiological and disease metabolic states. Introduction Metabolism plays a central role in determining the functional state within a cell, underscored by its involvement in integral cellular processes. It is an interconnected system of chemical transformations necessary to sustain various cellular functions and includes the degradation of molecules for energy production and the assembly of essential cellular constituents. The components in metabolism consist of thousands of metabolites, molecules that are either biosynthesized through anabolic processes or degraded in catabolic pathways, University of California, San Diego, 9500 Gilman Dr. 0412, La Jolla, CA 92093-0412, USA. E-mail: [email protected] Monica L. Mo and the enzymes that catalyze these biochemical conversions. Together, they form intricate interaction networks that govern cellular behavior which ultimately determines how the body functions. The complex nature of human metabolism is further evinced when considering diseases with complex genotype–phenotype relationships, such as obesity and Type 2 diabetes, which have emerged as prevalent epidemics in the U.S.1,2 and increasingly on a worldwide basis. Steps have been made towards the understanding of genotype–phenotype relationships in metabolic syndrome through the identification of genes resulting in metabolic dysfunction.1,3–5 While a genetic predisposition exists in acquiring these disorders, environmental factors Monica Mo received her BS degree in Bioengineering: Biotechnology at the University of California, San Diego in 2004 and is currently continuing as a PhD bioengineering student with the Systems Biology Research Group at UCSD. Her research focus is the integration and analysis of high-throughput datasets using genome-scale metabolic models. She is primarily interested in using in silico models to analyze and characterize physiological and disease states in human metabolism. 598 | Mol. BioSyst., 2007, 3, 598–603 are often a triggering influence on their development, which has led to more recent studies on how nutrient-gene interactions alter metabolic homeostasis.6,7 The development of metabolic disorders is essentially the consequence of an intricate interplay between genetic, environmental, and nutritional factors (Fig. 1). It has thus become clear that this is a multifaceted problem that requires a systemic approach to explore a system of causes. Advancements in high-throughput technologies have generated an abundance of information that enables the study of metabolism as a complex system. Concomitantly, there is a need for developing tools that enable systematic analysis of these large datasets to Neema Jamshidi Neema Jamshidi is an MD/PhD student at the University of California, San Diego. As a graduate student in Bernhard Palsson’s Systems Biology Research Group in the Department of Bioengineering at UCSD, he is involved in the construction and analysis of in silico models and the development of mathematical approaches to analyze these models. He is particularly interested in modeling mammalian cells in order to gain insight into mechanisms distinguishing physiological and pathophysiological states and for classifying these different functional states. This journal is ß The Royal Society of Chemistry 2007 Fig. 1 Metabolic phenotype as a consequence of the interactions between external (environmental and nutritional) and internal factors, such as genetic and proteomic complement. While both individual 1 and 2 are subjected to the same external factors, different metabolic phenotypes arise from the unique interactions between external factors and an individual’s internal factors. The result, in this figure example, is a healthy phenotype for individual 1 and a disease phenotype for individual 2. generate biologically meaningful interpretations. Systems biology emerges as an integrative discipline that seeks to organize, assess, and interpret heterogeneous data sources computationally, or in silico. The reconstruction of genomescale metabolic networks has facilitated these tasks by serving as a comprehensive resource database of biological components and a modeling platform to analyze network capabilities specific to a defined biological system. The procedures for reconstructing genome-scale networks and extending its capabilities to a nonkinetic, constraint-based model are wellestablished. Network analysis around a steady state has been shown to be informative, enabling hypothesis-driven biology and the assessment of metabolic capabilities in microorganisms.8 By applying physicochemical and biological constraints on the metabolic network to restrict feasible phenotypic behavior, various functional steady states can be Bernhard Ø. Palsson Bernhard Palsson is a Professor of Bioengineering and Adjunct Professor of Medicine at the University of California, San Diego. He earned a PhD from the University of Wisconsin in 1984. He held a faculty position at the University of Michigan from 1984 to 1995. He has been with UCSD since 1995, where his current research focuses on (1) the reconstruction of genome-scale biochemical reaction networks, (2) the development of mathematical analysis procedures for genome-scale models, and (3) the experimental verification of genome-scale models with emphasis on cellular metabolism. This journal is ß The Royal Society of Chemistry 2007 determined and characterized.25 With a more complex system in humans than in single-celled organisms, there is a greater need for the application of constraint-based models to understand the basis of normal and pathophysiological metabolism. The completion of the first genomescale human metabolic network, Homo sapiens Recon 1 is a key step to applying computational systems biology in a biomedical context.9 As a comprehensive, literature-based reconstruction of human metabolism, Recon 1 accounts for 1496 genes, 2004 proteins, 2,766 metabolites, and 3311 metabolic and transport reactions. This network reconstruction was converted into an in silico model of human metabolism and validated through the simulation of 288 known metabolic functions found in a variety of cell and tissue types. This highlight article aims to present: (i) a brief overview of genome-scale metabolic networks and examples of early success with a limited human metabolic network; (ii) the completion of H. sapiens Recon 1 and its preliminary network applications and assessments; (iii) further applications and potential capabilities of H. sapiens Recon 1. What makes genome-scale reconstructions BiGG Metabolic genes and enzymes have been studied for decades, resulting in an extensive knowledge base in the primary literature and texts, or bibliome, that describe and characterize their functions. The availability of annotated, sequenced genomes has made possible the construction of genome-scale reconstructions that describe these interactions in terms of their respective biochemical components and reactions. An organism-specific, biochemically, genetically, and genomically structured (BiGG) reconstruction can be built by incorporating different data types. This includes annotated genomic data, which defines its complement of metabolic enzymes, as well as bibliomic information, which describes detailed reaction mechanisms and interactions.8 The resulting reconstruction can then be represented as a mathematical in silico model to compute allowable metabolic states. Mol. BioSyst., 2007, 3, 598–603 | 599 In a general sense, systems analyses can be undertaken from two approaches, top-down and bottom-up. Each approach aims to address different types of questions and their appropriateness is often dictated by the type of available data. Top-down analysis approaches take a global view of a system from above, aiming to be comprehensive in scope while remaining generally amenable to statistical approaches for analysis. A drawback of top-down approaches is that they lack mechanistic details such that causal relationships often cannot be inferred and results may not always be self-consistent. Conversely, bottom-up approaches are built component by component and, in doing so, account for mechanisms of action in a selfconsistent manner. However, they may not be scalable and, practically speaking, they are comprehensive but not complete. The mechanistic basis when building these models has been helpful in providing explanations for why a network behaves in a particular fashion in a given environment through the assessment of their functional state, i.e., their phenotype. From reconstruction to in silico model Reconstructions of metabolic networks help fill the gap between top-down and bottom-up approaches since they are built in a bottom-up fashion and allow incorporation of top-down datasets. The term ‘‘reconstruction’’ describes an organized collection of genes, proteins, and biochemical reactions that operate within a particular biological system. A reconstruction can be converted into a model through the imposition of system constraints to rewrite the reconstruction content in terms of mathematical equations, thus enabling in silico calculations of an organism’s phenotype in terms of metabolic fluxes.8 Constraint-based methods have been used to interrogate metabolic networks of microorganisms and have enabled hypothesis-driven biology. Various studies include determining gene deletion lethality,13,15 predicting adaptive evolutionary endpoints under different environments,18–23 and identifying candidate genes for missing metabolic functions that were subsequently experimentally verified 24. To 600 | Mol. BioSyst., 2007, 3, 598–603 date, the best developed genome-scale metabolic reconstructions have been those for microorganisms, including E. coli, S. cerevisiae, H. pylori, S. aureus, and H. influenzae.10–17 These in silico models have played an important role in the study of metabolism through systems biology, which has been illustrated through their diverse uses in biological analysis and discovery in recent years. Preliminary work at the organellescale: the human cardiomyocyte mitochondria Prior to the establishment of Recon 1, a metabolic model of the cardiomyocyte mitochondria was reconstructed and used successfully in the systemic analyses of various biological properties.26 The network was constructed predominantly using proteomic and biochemical data and consisted of 189 elementally and charge-balanced reactions. Three functional capabilities were assessed (ATP production, heme synthesis, and phospholipid synthesis), wherein flux variability analysis was used to characterize inherent network flexibility when each metabolic function was carried out.26 The study indicated that a large portion of mitochondrial network reactions was accessible for optimal heme and phospholipid biosyntheses while it was severely restrictive for ATP production. A study of diabetic, ischemic, and dietetic metabolic states was performed in which random sampling of the steadystate solution space was used to calculate candidate flux distributions based on imposed constraints derived from experimental data.27 Subsequent simulations demonstrated how reaction flux distributions, reaction correlations, and network flexibility under these conditions deviated from the normal physiologic condition. Interestingly, the flux through pyruvate dehydrogenase (PDH) was found to be severely limited under a diabetic state in comparison to the normal condition. This was in agreement with published observations that PDH activity is often restricted in diabetics,28,29 thus suggesting that PDH inhibition in vivo may be due in part to stoichiometric restrictions rather than entirely to regulatory actions.27 One approach in constraint-based models to simplify metabolic networks includes the calculation of correlated reaction sets (co-sets), sets of reactions that are always active or inactive together due to mass conservation constraints. This means that a flux through one reaction implies a flux through other reactions in the co-set (conversely if there is a reaction without any net flux in one reaction, all of the other reactions in the co-set do not carry any flux). These cosets can be calculated by running a series of optimizations as in flux coupling30 or by sampling the flux solution space and calculating the perfectly correlated sets of reactions.27 Mapping of calculated mitochondrial co-sets to diseases described in the Online Mendelian Inheritance in Man (OMIM) database (http://www. ncbi.nlm.nih.gov/) enabled the classification and correlation of causal SNPs in a systemic and mechanistic fashion.31 Most of the co-sets involved contiguous sets of reactions, but those involving noncontiguous groups of reactions could potentially provide novel insights. One such example involved a reaction co-set found in the urea cycle, in which deficiencies due to different reactions presented similar phenotypes for three out of the four reactions in the co-set. The results from this study indicated that network-wide analysis of reaction co-sets can be used to elucidate the genotypephenotype relationship. H. sapiens Recon 1: a genomescale human metabolic reconstruction Building the Recon 1 network The overall procedure for the reconstruction has previously been described in detail 9 and will be discussed in brief while highlighting the major differences from previous metabolic network reconstructions. The initial component list based on the genome annotation of Build 3532 provided the basic framework for the whole reconstruction and was followed by a manual curation phase which involved perusing through 1500 citations in the bibliome in addition to the use of available internet resources and databases. These manual curation steps (outlined in Fig. 2) are unchanged in comparison to the standardized approach and accounts for the most time-consuming process. This journal is ß The Royal Society of Chemistry 2007 are neither one-to-one nor completely standardized; however, it can highlight areas in which little experimental evidence is available. The dominant pathways in the areas with consistently low confidence were intracellular transport reactions. This relatively neglected area of metabolism can have significant impact on the types of predictions made by the model. For example, the difference between active and passive transport can have quantitative and qualitative implications in energetic predictions. An interesting observation across many of the pathways was that the knowledge landscape was ‘uneven’, that is, there were significant differences among all the pathways and within pathways as well. The dips, or so-called valleys, in the knowledge landscape direct areas in which further experiments can help make new contributions to the scientific infrastructure. Identifying potential alternative drug targets Fig. 2 Overview of Recon 1 reconstruction process from the Build 35 genome annotation. An initial list of components was compiled from Build 35 and the network was manually curated. The manual curation steps are among the most time consuming aspects of the reconstruction, however they also contribute significantly towards making a biologically accurate, functional model. The iterative debugging process was carried out for the 288 metabolic functional tests at each iteration. The reconstruction effort was carried out in a parallelized manner divided among six researchers based on metabolic pathways. Like the other metabolic reconstructions, the reconstruction and model building process involved iterative steps; however, these debugging steps were more involved in building Recon 1 due to the fact that multiple researchers were reconstructing different parts of the network at the same time and a ‘‘global’’ cell was being reconstructed, not a single cell. The latter point was addressed by determining 288 bibliome-defined, metabolic objectives that exist in the human body, which include basic physiological functions such as ATP production and ketogenesis.9 In addition, an internal QC/QA process was maintained in which changes and additions were tracked and each metabolic function was re-simulated at every iteration, thus ensuring that newer model iterations did not lose functionality. These efforts resulted in a BiGG structured reconstruction of global human metabolism (http://bigg.ucsd.edu). Characterizing the knowledge landscape The manual curation procedure combined with the testing of functional capabilities resulted in the definition of the knowledge landscape for human metabolism. Almost all of the reactions were assigned ‘confidence’ scores which provided an indication of the strength of evidence for making the gene annotation and the associated reactions. Experimental evidence, such as demonstrated enzymatic activity, had the highest confidence scores, whereas reactions that were added due to modeling evidence (i.e. a simulation could not be carried out without the inclusion of a particular reaction) received the lowest. Once the reconstruction was complete, it was possible to look over the whole set of reactions and classify the pathways in terms of confidence scores. These classifications are obviously subjective due to the semi-standardized pathway classifications. Pathway definitions, for example, This journal is ß The Royal Society of Chemistry 2007 Recon 1 was analyzed under aerobic glucose conditions using flux coupling30 to identify over 250 coupled reaction sets. One of the largest involved the cholesterol biosynthesis pathway, which passes multiple intracellular compartments (cytosol, endoplasmic reticulum, and peroxisome). 3-Hydroxy-3-methylglutaryl-Coenzyme A (HMG-CoA) reductase, the metabolic target of the anti-lipidemic class of statin drugs, was in this coupled reaction set. As a variation on the study with the cardiac mitochondria which suggested that causal SNP associated diseases in one co-set have similar phenotypes,31 one can make the case for proposing alternative drug targets for reactions in the same co-set. With the HMG-CoA co-set, other members of the set are thus identified as potential alternative drug targets for treating hyperlipidemia. As demonstrated for the human mitochondria, deficiencies in enzymes belonging to the same functionally coupled reaction set may have similar phenotypes.31 Another example with potential disease applications involved a coupled reaction set associated with glutathione metabolism in which multiple enzymes were causally associated with hemolytic anemia (when deficiencies of the enzymes were observed Mol. BioSyst., 2007, 3, 598–603 | 601 in patients). These examples demonstrate that in silico simulations can provide an analytical approach to study the causes and consequences of disease, leading to potential insight into new drug treatment targets. Mapping and analyzing expression data H. sapiens Recon 1 includes information about biological relationships between gene transcripts, their corresponding protein products, and the reactions these proteins catalyze. As such, gene expression profiles from the skeletal muscle of patients before and after bariatric surgery were mapped to the network.33 This particular dataset was valuable because gene expression profiling was carried out on the same patient before and after surgery, so each patient could serve as their own control. Rather than identifying a handful of genes that were statistically determined as up-regulated or down-regulated, all metabolic genes from each expression profile were mapped onto Recon 1 to observe overall expression changes. Changes among the three patients were observed to be consistent with one another, wherein the overall patterns observed indicated an increased reliance on anaerobic pathways and decreased expression in enzymes involved in oxidative phosphorylation. These patterns were consistent with expression profiles in primates subjected to longterm caloric restriction34 thus suggesting the muscle tissue in patients experience caloric restriction one year following surgery. Future applications of Recon 1 Just as it was not possible to predict the future applications of genome-scale in silico E. coli models seven years ago when they first appeared,13 it is very difficult to try to predict the future applications of Recon 1. However, the three general areas where we anticipate Recon 1 to have an impact are (1) the development of context specific networks and models, (2) the functional characterization of such models, and (3) the application of models to predictive biology. Fig. 3 shows how the illustrative examples carried out with Recon 1 can employ data integration, data mapping, and analysis, none of which 602 | Mol. BioSyst., 2007, 3, 598–603 Fig. 3 Three BiGG applications of Recon 1, defining the knowledge landscape, providing a context for content, and using simulations to identify alternative drug targets, and how they map to overall applications of the model in contributing to context specific models, the functional characterization of models, and predictive biology. These three areas result from overlapping between data integration, data mapping, and analysis, three important aspects towards developing systems level models. The center triangle describes the three initial applications of Recon 1. For example, functional characterization of the model was possible due to data mapping and analysis, which were applied in practice in Recon 1 as constraint-based analyses and topological analysis which enabled to the calculation of co-sets and the analysis of the obesity study. could be carried out without the BiGG reconstruction. The future uses of Recon 1 will be tightly linked to the ability to incorporate disparate data types. Experimental advancements in high-throughput data generations have resulted in situations in which data is being generated faster than it can be extensively analyzed. The current reconstruction can incorporate, to varying levels of detail, transcriptomic, proteomic, fluxomic, and metabolomic data (Fig. 4). For example, the first draft of the human metabolome has recently been described35 and analyzing this data in the context of Recon 1 may help further define the knowledge landscape and suggest experiments to carry out. Expression profiles can be integrated into the network as a means to refine the global reconstruction for subsets of genes or proteins and corresponding network reactions unique to the data content. This can also be extended to the initial development of genome-scale reconstructions for other complex mammalian systems through the mapping of homologous genes to network reactions using the H. sapiens Recon 1 framework. In addition to data integration and mapping, Recon 1 will play an important role in enabling simulations and calculations. Various constraint-based analysis methods25,36 can be applied to Recon 1 to assess network capabilities as a function of applied biological constraints and disease perturbations. This can lead to novel network-level interpretations of physiological states and the identification of potential new drug targets. Studies with the cardiomyocyte mitochondrial network have proven to be useful in interpreting metabolic capabilities of a smaller system, and the increased scope and content of Recon 1 provides a foundation to drive more comprehensive analyses of human metabolism. The development of genome-scale networks have enabled in silico analyses of biological systems to assess and interpret systemic aspects of cellular function. Network reconstructions of microorganisms have already successfully enabled the interrogation of metabolism at a This journal is ß The Royal Society of Chemistry 2007 Fig. 4 An example of a Gene-Protein-Reaction (GPR) association in Recon 1 and the associated high-throughput data sets that can be mapped to the reconstruction. GPR associations are the logical relationships between biological components described in the reconstruction. The multi-level component structure organized in Recon 1 enables the incorporation and analysis of various ‘-omic’ data types in the context of the network. systems level and many of these continue to be a widely used resource in the scientific research community. Recon 1 is such an analogous, high-quality resource for human metabolism that can readily incorporate newly identified biological components and function as a modeling platform to assess novel network capabilities as a consequence of imposed constraints. This multitude of preliminary and future applications highlights the fact that Recon 1 can provide a wellstructured foundation to drive complex in silico analyses for various disease and health aspects in human metabolism. The completion of H. sapiens Recon 1 is a critical step towards the advancement of human systems biology and to demonstrate its potential capabilities in predictive and personalized medicine. Acknowledgements We thank the other contributing authors to this work: Natalie Duarte Hurlen, Scott Becker, Ines Thiele, Thuy Vo, and Rohith Srivas. The described work was supported by Training Grants from the National Institutes of Health. References 1 D. Muoio and C. B. Newgard, Annu. Rev. Biochem., 2006. 2 C. Stein and G. A. 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