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68 Current Chemical Biology, 2008, 2, 68-82 Emerging Roles for Metabolic Engineering - Understanding Primitive and Complex Metabolic Models and Their Relevance to Healthy and Diseased Kidney Podocytes Mehmet M. Altintas1, Kutlu O. Ulgen2, Darryl Palmer-Toy3, Vivian E. Shih4, Dhinakar S. Kompala5 and Jochen Reiser*,1 1 Division of Nephrology and Program in Glomerular Disease, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02129-2020, USA 2 Depart
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  68  Current Chemical Biology , 2008,  2, 68-82 1872-3136/08 $55.00+.00 © 2008 Bentham Science Publishers Ltd.   Emerging Roles for Metabolic Engineering - Understanding Primitive and Complex Metabolic Models and Their Relevance to Healthy and Diseased Kidney Podocytes Mehmet M. Altintas 1 , Kutlu O. Ulgen 2 , Darryl Palmer-Toy 3 , Vivian E. Shih 4 , Dhinakar S. Kompala 5  and Jochen Reiser  *,1   1  Division of Nephrology and Program in Glomerular Disease, Department of Medicine, Massachusetts General  Hospital and Harvard Medical School, Boston, MA 02129-2020, USA 2  Department of Chemical Engineering, Bogazici University, Bebek 34342 Istanbul, Turkey 3  Regional Reference Laboratories, Southern California Permanente Medical Group, North Hollywood, California 91605, USA 4  Amino Acid Disorders Laboratory, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02129-2020, USA 5  Department of Chemical and Biological Engineering, University of Colorado, Boulder, Colorado 80309-0424, USA Abstract: The central metabolism of a cell can determine its short- and long-term structure and function. When a disease state arises, the metabolism (i.e., the transportation of nutrients into the cells, the overall substrate utilization and produc-tion, synthesis and accumulation of intracellular metabolites, etc.) is altered in a way that may permit organisms to survive under the changing physiologic constraints. Although the response of cells to injury was studied thoroughly using molecu-lar biology and structural morphology techniques, the knowledge regarding the metabolic signatures of the disease is lim-ited. However, recent advances in analytical methods and mathematical tools have led to new approaches to those ques-tions with the concept of computational biology which relies on the integration of experimentation, data processing and modeling. The attempt to formulate current knowledge in mathematical terms has led to the development of several mathematical modeling tools (i.e., metabolic flux analysis, metabolic control analysis, etc.) that helps us to understand an entire biological system from basic structure to dynamic interactions. This review provides an overview and summarizes the current status of applications of mathematical models for the quantification of fluxes. A specific example of kidney  podocyte cells illustrates how metabolic alterations, which occur during injury, can be used to aid in future therapeutic development. Keywords: Metabolic flux analysis, intracellular fluxes, podocyte metabolism, analysis of the health and disease. INTRODUCTION Biology in the past decades has been characterized by a qualitative and descriptive approach designed to investigate molecular behavior. Today, with computational technology (i.e., modeling and simulation of complex processes), the relationships between various parts of a biological system (e.g., gene and protein networks involved in cell signaling, metabolic pathways, organelles, cells, physiological systems, organisms, etc.) are potentially understandable and predic-tive to some extent. Together with the advances in biological science and technology, an enormous number of new data types and for-mats are emerging daily. The success of mathematical mod-els in biological and medicinal research requires an iterative interaction between experimentation, modeling and simula-tion, and theory. These mathematical representations of complex systems all have limitations, but they have the *Address correspondence to this author at the Nephrology Division, Massa-chusetts General Hospital, Harvard Medical School, 149, 13th Street, Room 8214, Boston, MA 02129-2020, USA; Tel: 617-726-9363; Fax: 617-726-5669; E-mail: jreiser@partners.org  potential to create a robust ‘computational biology’ by the collaborative work of biologists with mathematicians and researchers from other disciplines [1]. Important progress has been made towards understanding the complex systems in biological science from almost two decades of research in metabolic engineering. The overall goals of metabolic engineering are (i)  to model and predict the cellular metabolism in a quantitative manner and (ii)  to improve cellular properties by designing and implementing rational genetic modifications [2-4]. In this sense, metabolic engineering deals with the measurement of metabolic fluxes and their control together with the evaluation of the impact of genetic modifications on cellular physiology [5-8]. Based on this definition, metabolic engineering utilizes various mathematical modeling tools (i.e., metabolic flux analysis, metabolic control analysis, etc.) to identify fluxes through critical metabolic pathways in the cell or tissue of interest. Then, these quantitative methods allow precise con-trol of the metabolic network throughout the pathway of in-terest and optimization of the metabolic flow to target me-tabolites or products. Besides providing a convenient frame-work for the integration of fluxes with the intracellular vari-   Emerging Roles for Metabolic Engineering Current Chemical Biology , 2008,  Vol. 2, No. 1  69   ables (metabolites, enzyme activities, proteins, etc.), meta- bolic engineering has a novel contribution with its capacity to analyze more global networks of several enzymes and reactions. In this review, we describe the use of metabolic engineer-ing techniques to analyze the physiology of normal and dis-ease states in different model systems. We focus on the  pathway engineering approach towards novel therapies for  patients with injuries and/or chronic diseases, with an em- phasis on disease processes occurring in podocytes, cells essential to maintain kidney filtration. MODELING METABOLIC NETWORKS Mathematics and computation play critical roles in un-derstanding the physiological behavior of cells and system-atic integration of this information into a predictive model that can be used for controlling the fate of the organism. In this context, the essence of metabolic engineering is the ca- pacity to engineer pathways that regulates the overall me-tabolism on the basis of a set of stoichiometric and/or kinetic rules. In the following sections, mathematical tools of the metabolic engineering will be discussed briefly before re-viewing its applications to bacterial, yeast and mammalian systems. Construction of the Metabolic Networks A metabolic pathway is defined as the series of feasible and observable biochemical reactions connecting a specified set of input and output metabolites [7]. A metabolic pathway may be linear, cyclic, branched, tiered, directly reversible, or indirectly reversible. Various metabolic pathways within a cell (glycolysis, tricarboxylic acid cycle, pentose phosphate    pathway, gluconeogenesis, glyoxylate shunt, oxidative phos- phorylation, etc.) form the cell’s metabolic network that is generally a complex and nonlinear system of cellular (me-tabolites, nucleic acids, etc.) and extracellular constituents (substrates, protons, etc.) and reactions. The metabolic net-works including central carbon metabolism, regulation mechanisms, energetic and transport reactions have a key role in sustaining cellular functions by coordinating the ac-tivity of different metabolic pathways [9-11]. Since the metabolic pathways and fluxes are at the core of metabolic modeling, mapping biochemical networks in the cell or organ of interest is the priority for the application of metabolic engineering. Those networks are currently or-ganized into databases such as KEGG [12] and MetaCyc [13]. These databases are based on information from ex- perimental data and store valuable information about hun-dreds of pathways and cellular processes. In addition, the complete metabolic network may not be fully described, i.e.,  pathway(s) within the network may consist of alternative reaction(s) that produce the same set of metabolites from the same set of precursor metabolites and cofactors [9]. There-fore, the network map is refined in an iterative fashion for the most accurate reflection of the existing biochemical knowledge (Fig. 1 ). Measurement of Fluxes Metabolic flux is the rate of material that flows along a metabolic pathway or even through a single reaction con-necting two or more metabolites. Measurement of metabolic fluxes is an important quantitative approach in metabolic engineering due to its capacity to characterize the flux distri- butions, reaction mechanisms and associated parameters. It is possible to determine the intracellular fluxes by us-ing isotope tracer methods. In isotopomer analysis, the bio-logical system (of the microorganism) is fed with a specifi-cally labeled substrate (usually 13 C or 15  N) during the sta-tionary growth phase and the labeling patterns of the com- pounds in the central C or N metabolism (e.g., isotopic en-richment in the intracellular metabolite pools) are measured after the labeling isotopic balances are reached. This tech-nique is either based on nuclear magnetic resonance or mass spectrometry [14-20]. The resulting data, together with the metabolite balancing, provide a large amount of additional information regarding pathway identification (i.e., identifica-tion of active pathways, analysis of cyclic, parallel and split  pathways, etc.), flux distribution and subcellular compart-mentation. Analysis of Fluxes by Mathematical Tools Intracellular fluxes can also be determined from the measured extracellular metabolite fluxes (e.g., the fluxes of substrates into the cells and the fluxes of metabolites that are secreted out of the cells) using mathematical tools such as  biochemical systems theory [21-25], metabolic control analysis [26-29], metabolic flux analysis [30-35] and cyber-netic modeling [36-42]. With the exception of metabolic flux analysis (MFA), these   approaches require information on the kinetics of the individual   reactions although the level of de-tail in the functional form of kinetics may be rather low. The computational study of metabolic systems is a sub- ject with a long history [43]. Mathematical methods for sen-sitivity analysis of metabolic regulation began in the late 1960’s [44-46] and led to the biochemical systems theory (BST). All processes are represented as products of power-law functions (either S-systems or the alternative variant of generalized mass action systems, GMA) in BST to investi-gate metabolic and gene-regulatory systems. A major advan-tage of the BST approach is that it does not require a priori structural information on the underlying pathway and models are designed based solely on the identity of the reactants and their reactional and regulatory interconnections. The metabolic control analysis (MCA) shares the same fundamental definition with BST. Both methods use the same experimental information to study the responses of metabolic systems to changes in their parameters that can be exerted by genetic manipulations, enzymatic titrations or enzymatic inhibitors. MCA (also known as metabolic control theory) assumes that there is a definite amount of flux con-trol that emerges as a parameter of critical importance in the identification of feasible enzymatic modifications having maximal impact on the network flux. In metabolic systems, this control is normally spread quantitatively among several enzymes in the system. Since its introduction three decades ago, MCA has received much theoretical and experimental attention since it allows us to understand how metabolic fluxes are controlled by certain enzyme activities and me-tabolite concentrations [47]. Assuming that cellular metabolism is at a steady state, MFA (also referred to as flux balance analysis, FBA) allows us to calculate the unknown internal fluxes over each reac-  70 Current Chemical Biology , 2008,  Vol. 2, No. 1 Altintas et al. tion using data on external fluxes, metabolic stoichiometry and mass balances around intracellular metabolites. Besides quantification of pathway fluxes, MFA is useful for the iden-tification of the rigidity (or flexibility) of key metabolic  branch points, the determination of non-measured extracellu-lar fluxes, the calculation of maximum theoretical yields, the identification of alternative pathways and the observation of the function of metabolic pathways in vivo  [6, 48, 49]. Al-though MFA has the ability to estimate the unmeasured in-ternal fluxes, it does not incorporate any kind of regulation mechanisms. On the other hand, MFA can incorporate addi-tional information when it is available and is a powerful technique when used in conjunction with novel physiological experiments. Another powerful methodology for describing the com- plex phenomena observed in biological cells is the cyber-netic modeling framework which was developed by Ram-krishna and coworkers [50-52]. The cybernetic approach is  based on the idea of proposing an optimal mechanism such that the cell regulates the synthesis and activity of the key enzymes to promote perceived local or global cellular objec-tive. The application of cybernetic framework to metabolic engineering requires limited kinetic information combined with cybernetic variables that modify the rates of enzyme  production and activation to modify the metabolic reaction rates. The definition of the cybernetic variables varies de- pending on the nature of the metabolic pathway being exam-ined [40]. In the presence of detailed kinetic information about the specific cellular processes (e.g. enzyme catalyzed reactions,  protein-protein interactions, or protein-DNA binding), it is  possible to combine kinetics with the known stoichiometry of the model to interpret and predict cellular behavior [25, 53, 54]. In particular, a kinetic model is constructed by (i)  defining all reactions with their stoichiometry, (ii)  specifying the kinetics of each reaction, and (iii)  setting up the proper mass balances for each metabolite. One major drawback of kinetic modeling is that a realistic kinetic modeling of meta- bolic   networks needs mechanistically-correct rate equations that require detailed knowledge of all physiological effectors influencing   the activity of the catalyzing enzyme. Moreover, our biochemical knowledge is limited to the enzymatic prop-erties in vitro  which are very different from the conditions inside the microorganism, in vivo . Therefore, kinetic model-ing is not suitable for developing metabolic engineering strategies in the absence of well-characterized kinetic pa-rameters [55, 56]. On the other hand, once kinetically judged and experimentally validated, these models replace time-consuming and expensive   experiments for accurate predic-tion of the fluxes and form a valuable basis for viewing the whole pathway, which adds to our understanding of the sys-tem of interest [57]. Beyond their intrinsic differences, all these modeling approaches provide a mathematical basis for the representa-tion of the existing relationship between metabolic network components (metabolites, enzymes, etc.) and the cellular systemic behavior exhibited by the network of interest. As metabolite levels are governed by changes in fluxes and en-zyme activities, understanding the changes on a metabolic level will then help in understanding the regulatory mecha-nisms that lead to these changes in fluxes [58]. The meta- bolic model can thus form a solid biochemical basis on com-  Fig. (1).  The necessary steps to construct a metabolic model.   Emerging Roles for Metabolic Engineering Current Chemical Biology , 2008,  Vol. 2, No. 1  71    puting the implications of the functions of the cell, such as signaling and regulatory networks. Despite the enormous potential and success of these ap- proaches, the components of a system and their interactions in the cellular network need to be studied further in order to have a system-wide picture and system-level understanding of biology [59, 60]. It is important as many properties of life arise at the systems level only. More recently, systems biol-ogy has emerged as an integrative approach that investigates  pathways and networks by combining data about genes, pro-teins, enzymes and metabolites to generate a comprehensive  picture of the system (e.g., tissue, organ or organism) as a whole [61-71]. Because of the biological complexity, system biology enables a wide spectrum of mathematical modeling tech-niques such as the ones discussed above and bioinformatics to visualize large networks of cellular components and gen-erate hypotheses about the cell behavior. These hypotheses can then be tested experimentally in systems biology frame-work by integrating the detailed information about the entire genome of an organism (genomics), small molecules that cells assimilate or synthesize (metabolomics), cellular pro-teins and their physical interactions (proteomics), the tempo-ral and spatial distribution of all gene transcripts (transcrip-tomics) and the data from other accompanying high-throughput experiments. METABOLIC MODELING OF HEALTHY AND DIS-EASED PODOCYTES Podocytes, also known as the visceral glomerular epithe-lial cells, are terminally differentiated cells with a complex cellular architecture contributing to many functions of the normal kidney glomerulus [72]. They possess a highly  branched array of actin-rich foot processes (FP) that are es-sential to glomerular filtration on the kidney. The FPs of neighboring podocytes regularly interdigitate, leaving be-tween filtration slits that are bridged by an extracellular structure, known as the slit diaphragm (SD). The FPs are anchored on the outer aspect of the glomerular basement membrane (GBM) (Fig. 2 ). Therefore, podocytes define the final barrier to protein loss, which explains why podocyte injury is typically associated with marked urinary protein loss (proteinuria) which is a risk factor for morbidity and mortality in these patients. Characteristically, podocyte FPs are rearranged upon injury leading to loss of FP interdigita-tions and forming a flattened sheet of cytoplasm above the GBM (referred to as FP effacement). If FP effacement is reversed, coordinated kidney filtration is re-established. However, in many cases, FP effacement progresses into more severe and irreversible podocyte injury resulting in  progressive kidney failure. The metabolic signatures that are involved to maintain normal and diseases podocyte struc-tures need to be defined to better understand and treat prote-inuric kidney disease. Podocytes are terminally differentiated cells and their metabolism is complicated and its understanding is just be-ginning. These complexities render the material balances for  podocyte cell cultivation a difficult problem to solve. Never-theless, detailed material balance analysis in podocyte cell culture systems would be desirable to provide insights to-ward a better understanding of podocyte cell metabolism. One means for characterizing the intracellular metabolism of cultured podocytes under normal and disease conditions is the identification of the flux distributions by MFA as it Fig. (2).  Podocytes are highly specialized cells within the glomerulus that are essential for ultrafiltration. They form foot processes (FP) that are highly dynamic cellular extentions. FPs, that rest on glomerular basement membrane (GBM), are interconnected by the slit diaphragms (SD) to form the final component of the kidney permeability barrier.
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