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Understanding the Control of Metabolism

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... A criterion for drug target selection in the intermediary metabolism networks can be based on the capacity that the target (enzyme or transporter) has to influence the flux of the metabolic pathway of interest, and the MCA fundamentals (Saavedra et al. 2019b;Fell 1997;Nelson and Cox 2017;Moreno-Sánchez et al. 2008a) provide a quantitative way to assess it. MCA was developed independently by two groups, Kacser and Burns in Edinburgh, Scotland (Kacser and Burns 1973), and Heinrich and Rapoport in Berlin, Germany (Heinrich and Rapoport 1974). ...
... MCA was developed independently by two groups, Kacser and Burns in Edinburgh, Scotland (Kacser and Burns 1973), and Heinrich and Rapoport in Berlin, Germany (Heinrich and Rapoport 1974). MCA is a theoretical framework that considers metabolic processes as a continuous flow in the steady state of matter and energy, in which each component of the metabolic pathway exerts some control over the pathway flux (Fell 1997;Nelson and Cox 2017). ...
... In contrast, Control is the capacity of an enzyme, transporter, or physiological process to affect the pathway flux under a defined metabolic steady state (Saavedra et al. 2019b;Fell 1997;Nelson and Cox 2017;Moreno-Sánchez et al. 2008a). To illustrate this, ATP phosphofructokinase (PFK-1) and pyruvate kinase (PyK) are highly regulated glycolytic enzymes able to drastically change the metabolic fluxes in different metabolic states to avoid futile cycles, e.g., during feed and starvation, which concur with changes of high and low blood glucose concentrations and thus with glycolysis and gluconeogenesis activation, respectively. ...
... Previous work in our labs. using flux control studies [20] showed that lipid assembly exhibited important control over TAG production in oilseed rape [21][22][23], confirming the results from previous biochemical experiments [24,25]. In fact, overexpression of DGAT1 (diacylglycerol acyltransferase) shifted control of lipid synthesis significantly [21] and increased oil yields [23]. ...
... A quantitative estimate of the degree of influence of PDAT on the rate of deposition of TAG in the seed was made in terms of its flux control coefficient, C J E , which represents the percentage change in flux J to TAG for a 1% change in the enzymic activity, E, of PDAT [20]. A more precise definition is: ...
... One possibility might be impacts on the metabolite pool sizes. It is known from MCA theory [20] and experiments that overexpression of enzymes can have larger impacts on metabolite concentrations than fluxes; an example is the overexpression of phosphofructokinase in potato tubers, which causes significant alterations to the concentrations of glycolytic intermediates, with reductions upstream and increases downstream, but has no measurable effect on glycolytic flux [60]. If extra PDAT has changed the relative proportions of the various substrates and products for LPAAT and DGAT in a way that reduces their net activities, then this could have a negative effect on the main route of TAG accumulation. ...
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The regulation of lipid metabolism in oil seeds is still not fully understood and increasing our knowledge in this regard is of great economic, as well as intellectual, importance. Oilseed rape (Brassica napus) is a major global oil crop where increases in triacylglycerol (TAG) accumulation have been achieved by overexpression of relevant biosynthetic enzymes. In this study, we expressed Arabidopsis phospholipid: diacylglycerol acyltransferase (PDAT1), one of the two major TAG-forming plant enzymes in B. napus DH12075 to evaluate its effect on lipid metabolism in developing seeds and to estimate its flux control coefficient. Despite several-fold increase in PDAT activity, seeds of three independently generated PDAT transgenic events showed a small but consistent decrease in seed oil content and had altered fatty acid composition of phosphoglycerides and TAG, towards less unsaturation. Mass spectrometry imaging of seed sections confirmed the shift in lipid compositions and indicated that PDAT overexpression altered the distinct heterogeneous distributions of phosphatidylcholine (PC) molecular species. Similar, but less pronounced, changes in TAG molecular species distributions were observed. Our data indicate that PDAT exerts a small, negative, flux control on TAG biosynthesis and could have under-appreciated effects in fine-tuning of B. napus seed lipid composition in a tissue-specific manner. This has important implications for efforts to increase oil accumulation in similar crops.
... Several frameworks have been developed in this context of uncertainty, including approximate rate laws, such as (log)-linear or power-law based on linear Taylor's approximation [11]. These strategies are valid in the proximity of a reference steady-state and usually are associated with Metabolic Control Analysis (MCA) [15][16][17][18][19] or the closely related Biochemical Systems Theory (BST) [20,21]. They provide the advantage of simplified formulations and are frequently used in different computational methodologies based on optimization [22][23][24][25] and sampling [17,[26][27][28]. ...
... Sensitivity coefficients are implicitly associated with kinetic models and most frequently explicitly derived from them. MCA has been defined using formulations that are slightly different [15,16,18,19,28], although equivalent, to those used in BST [21,29], and where the dependencies of sensitivity coefficients at the systemic and molecular levels are explicitly described. At the systemic level, sensitivity coefficients can formally be divided into control and response coefficients. ...
... The control coefficients are redistributed during the adaptation from one state to the other. Taking a known flux control coefficient, assuming that variations in enzyme activity are small enough for this control coefficient to be constant, approximate predictions can be made about the change in the flux value by applying the following expression [16,21,52]: ...
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Metabolic adaptations to complex perturbations, like the response to pharmacological treatments in multifactorial diseases such as cancer, can be described through measurements of part of the fluxes and concentrations at the systemic level and individual transporter and enzyme activities at the molecular level. In the framework of Metabolic Control Analysis (MCA), ensembles of linear constraints can be built integrating these measurements at both systemic and molecular levels, which are expressed as relative differences or changes produced in the metabolic adaptation. Here, combining MCA with Linear Programming, an efficient computational strategy is developed to infer additional non-measured changes at the molecular level that are required to satisfy these constraints. An application of this strategy is illustrated by using a set of fluxes, concentrations, and differentially expressed genes that characterize the response to cyclin-dependent kinases 4 and 6 inhibition in colon cancer cells. Decreases and increases in transporter and enzyme individual activities required to reprogram the measured changes in fluxes and concentrations are compared with down-regulated and up-regulated metabolic genes to unveil those that are key molecular drivers of the metabolic response.
... The model confirms that over 90% of Trp in whole blood is channeled toward Kyn through the activity of IDO, compared to tryptophan-2,3-dioxygenase (TDO) in the liver model. Kyn itself is the substrate for different reactions, and flux control analysis (Fell, 1992;Fell, 2007) reveals that KMO is the most important control point for Kyn utilization. Extension of the blood model during the log phase of the infection shows for both host species that the transport of both Trp and Kyn through the cell membrane is lowered in comparison to the baseline (Figure 4). ...
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Malaria has a complex pathology with varying manifestations and symptoms, effects on host tissues, and different degrees of severity and ultimate outcome, depending on the causative Plasmodium pathogen and host species. Previously, we compared the peripheral blood transcriptomes of two macaque species ( Macaca mulatta and Macaca fascicularis ) in response to acute primary infection by Plasmodium knowlesi . Although these two species are very closely related, the infection in M. mulatta is fatal, unless aggressively treated, whereas M. fascicularis develops a chronic, but tolerable infection in the blood. As a reason for this stark difference, our analysis suggests delayed pathogen detection in M. mulatta followed by extended inflammation that eventually overwhelms this monkey’s immune response. By contrast, the natural host M. fascicularis detects the pathogen earlier and controls the inflammation. Additionally, M. fascicularis limits cell proliferation pathways during the log phase of infection, presumably in an attempt to control inflammation. Subsequent cell proliferation suggests a cell-mediated adaptive immune response. Here, we focus on molecular mechanisms underlying the key differences in the host and parasite responses and their coordination. SICAvar Type 1 surface antigens are highly correlated with pattern recognition receptor signaling and important inflammatory genes for both hosts. Analysis of pathogen detection pathways reveals a similar signaling mechanism, but with important differences in the glutamate G-protein coupled receptor (GPCR) signaling pathway. Furthermore, differences in inflammasome assembly processes suggests an important role of S100 proteins in balancing inflammation and cell proliferation. Both differences point to the importance of Ca ²⁺ homeostasis in inflammation. Additionally, the kynurenine-to-tryptophan ratio, a known inflammatory biomarker, emphasizes higher inflammation in M. mulatta during log phase. Transcriptomics-aided metabolic modeling provides a functional method for evaluating these changes and understanding downstream changes in NAD metabolism and aryl hydrocarbon receptor (AhR) signaling, with enhanced NAD metabolism in M. fascicularis and stronger AhR signaling in M. mulatta . AhR signaling controls important immune genes like IL6, IFNγ and IDO1. However, direct changes due to AhR signaling could not be established due to complicated regulatory feedback mechanisms associated with the AhR repressor (AhRR). A complete understanding of the exact dynamics of the immune response is difficult to achieve. Nonetheless, our comparative analysis provides clear suggestions of processes that underlie an effective immune response. Thus, our study identifies multiple points of intervention that are apparently responsible for a balanced and effective immune response and thereby paves the way toward future immune strategies for treating malaria.
... Sensitivity analysis is used to understand how sensitive a model's output is to variations in parameter values or initial conditions, and is perhaps best known in systems biology via metabolic control analysis for metabolic systems (Fell, 1996;Heinrich and Schuster, 1996;Ingalls and Sauro, 2003;Kell and Westerhoff, 1986). It is particularly useful for complex biological networks that involve a large number of variables and parameters. ...
Conference Paper
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Taking an IκB-NF-κB signal pathway system as an example, both local and global sensitivity analysis have been performed on the dynamic model to analyze its oscillatory characteristics. While the time-dependent local analysis investigates the impacts of each parameter on the behavior of the oscillations, global sensitivity analysis based on the modified Morris method reveals nonlinear and interactive effects over a wide parameter range. These two methods are not equivalent for the analysis of a nonlinear oscillatory system, although they do provide similar information under certain conditions. For the IκB-NF-κB signal pathway, the study shows that its oscillation patterns are closely related to sensitive parameters. Observations also suggest that, in addition to the sensitive parameters discovered in the previous local studies, several other parameters may also reasonably be expected to contribute significantly to the system output.
... This is due to the fact that pathways are often controlled by a single enzyme typically catalysing the final reaction and thus the metabolic pathway flux (and the concentration of the final product of the pathway) [186]. It is, thus, physiologically more common to change a metabolic flux and the production of the final metabolite in the pathway than varying the intermediary concentrations [187]. Nevertheless, in this case, the most promising results were obtained from the overexpression of upstream enzymes such as MCAT [107]. ...
Article
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Microalgae have a great potential for the production of healthy food and feed supplements. Their ability to convert carbon into high-value compounds and to be cultured in large scale without interfering with crop cultivation makes these photosynthetic microorganisms promising for the sustainable production of lipids. In particular, microalgae represent an alternative source of polyunsaturated fatty acids (PUFAs), whose consumption is related to various health benefits for humans and animals. In recent years, several strategies to improve PUFAs’ production in microalgae have been investigated. Such strategies include selecting the best performing species and strains and the optimization of culturing conditions, with special emphasis on the different cultivation systems and the effect of different abiotic factors on PUFAs’ accumulation in microalgae. Moreover, developments and results obtained through the most modern genetic and metabolic engineering techniques are described, focusing on the strategies that lead to an increased lipid production or an altered PUFAs’ profile. Additionally, we provide an overview of biotechnological applications of PUFAs derived from microalgae as safe and sustainable organisms, such as aquafeed and food ingredients, and of the main techniques (and their related issues) for PUFAs’ extraction and purification from microalgal biomass.
Article
The systems view on life and its emergence from complex chemistry has remarkably increased the scientific attention on metabolism in the last two decades. However, during this time there has not been much theoretical discussion on what constitutes a metabolism and what role it actually played in biogenesis. A critical and updated review on the topic is here offered, including some references to classical models from last century, but focusing more on current and future research. Metabolism is considered as intrinsically related to the living but not necessarily equivalent to it. More precisely, the idea of “minimal metabolism”, in contrast to previous, top-down conceptions, is formulated as a heuristic construct, halfway between chemistry and biology. Thus, rather than providing a complete or final characterization of metabolism, our aim is to encourage further investigations on it, particularly in the context of life's origin, for which some concrete methodological suggestions are provided.
Chapter
Metabolic control analysis is a theoretical, systems biology framework for understanding how changes in the activity of one or more enzymes affect metabolic rates and concentrations. This chapter begins with a brief account of some of its terms and equations relevant to characterizing the responses of metabolic networks to modulation of enzymes, with emphasis on the difficulties involved in greatly increasing the flux to a desired metabolic product. The concepts developed are then used to examine the outcome of different types of intervention used in metabolic engineering, illustrated by experimental and computational examples.
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The scientific world has the clear perception that an interdisciplinary approach is urgently needed for attacking the many yet unsolved problems of today science. This attitude has several roots, including the search of biologists for quantitative foundations, and that of physicists for better approaches to the mesoscopic systems. This feeling is highly exacerbated in the field of complexity studies. The Meeting ‘Complexity in the Living’ attracted scientists from very diverse fields. A most profitable area of interaction resulted to be the description of methods, conceived as “stratagems” used in the different fields to cope with complex and intermingled problems. These good-faith “stratagems” are probably the only strong commonality amongst so diverse scientists, and thus are a basis for an interdisciplinary science. On this ground, the Meeting was characterized by intense discussion and exchange, whose trace is reported in these proceedings. Key words: Computational biology, Ecology, Systems, Statistics, Interdisciplinary studies.
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