30,475 research outputs found

    The protein cost of metabolic fluxes: prediction from enzymatic rate laws and cost minimization

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    Bacterial growth depends crucially on metabolic fluxes, which are limited by the cell's capacity to maintain metabolic enzymes. The necessary enzyme amount per unit flux is a major determinant of metabolic strategies both in evolution and bioengineering. It depends on enzyme parameters (such as kcat and KM constants), but also on metabolite concentrations. Moreover, similar amounts of different enzymes might incur different costs for the cell, depending on enzyme-specific properties such as protein size and half-life. Here, we developed enzyme cost minimization (ECM), a scalable method for computing enzyme amounts that support a given metabolic flux at a minimal protein cost. The complex interplay of enzyme and metabolite concentrations, e.g. through thermodynamic driving forces and enzyme saturation, would make it hard to solve this optimization problem directly. By treating enzyme cost as a function of metabolite levels, we formulated ECM as a numerically tractable, convex optimization problem. Its tiered approach allows for building models at different levels of detail, depending on the amount of available data. Validating our method with measured metabolite and protein levels in E. coli central metabolism, we found typical prediction fold errors of 3.8 and 2.7, respectively, for the two kinds of data. ECM can be used to predict enzyme levels and protein cost in natural and engineered pathways, establishes a direct connection between protein cost and thermodynamics, and provides a physically plausible and computationally tractable way to include enzyme kinetics into constraint-based metabolic models, where kinetics have usually been ignored or oversimplified

    The kynurenine pathway and the brain: challenges, controversies and promises

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    Research on the neurobiology of the kynurenine pathway has suffered years of relative obscurity because tryptophan degradation, and its involvement in both physiology and major brain diseases, was viewed almost exclusively through the lens of the well-established metabolite serotonin. With increasing recognition that kynurenine and its metabolites can affect and even control a variety of classic neurotransmitter systems directly and indirectly, interest is expanding rapidly. Moreover, kynurenine pathway metabolism itself is modulated in conditions such as infection and stress, which are known to induce major changes in well-being and behaviour, so that kynurenines may be instrumental in the etiology of psychiatric and neurological disorders. It is therefore likely that the near future will not only witness the discovery of additional physiological and pathological roles for brain kynurenines, but also ever-increasing interest in drug development based on these roles. In particular, targeting the kynurenine pathway with new specific agents may make it possible to prevent disease by appropriate pharmacological or genetic manipulations. The following overview focuses on areas of kynurenine research which are either controversial, of major potential therapeutic interest, or just beginning to receive the degree of attention which will clarify their relevance to neurobiology and medicine. It also highlights technical issues so that investigators entering the field, and new research initiatives, are not misdirected by inappropriate experimental approaches or incorrect interpretations at this time of skyrocketing interest in the subject matter

    Anthocyanin absorption and metabolism by human intestinal Caco-2 cells: a review

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    Anthocyanins from different plant sources have been shown to possess health beneficial effects against a number of chronic diseases. To obtain any influence in a specific tissue or organ, these bioactive compounds must be bioavailable, i.e., effectively absorbed from the gut into the circulation and transferred to the appropriate location within the body while still maintaining their bioactivity. One of the key factors affecting the bioavailability of anthocyanins is their transport through the gut epithelium. The Caco-2 cell line, a human intestinal epithelial cell model derived from a colon carcinoma, has been proven to be a good alternative to animal studies for predicting intestinal absorption of anthocyanins. Studies investigating anthocyanin absorption by Caco-2 cells report very low absorption of these compounds. However, the bioavailability of anthocyanins may be underestimated since the metabolites formed in the course of digestion could be responsible for the health benefits associated with anthocyanins. In this review, we critically discuss recent findings reported on the anthocyanin absorption and metabolism by human intestinal Caco-2 cells

    Long-term trends of changes in pine and oak foliar nitrogen metabolism in response to chronic nitrogen amendments at Harvard Forest, MA

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    We evaluated the long-term (1995–2008) trends in foliar and sapwood metabolism, soil solution chemistry and tree mortality rates in response to chronic nitrogen (N) additions to pine and hardwood stands at the Harvard Forest Long Term Ecological Research (LTER) site. Common stress-related metabolites like polyamines (PAs), free amino acids (AAs) and inorganic elements were analyzed for control, low N (LN, 50 kg NH4NO3 ha−1 year−1) and high N (HN, 150 kg NH4NO3 ha−1 year−1) treatments. In the pine stands, partitioning of excess N into foliar PAs and AAs increased with both N treatments until 2002. By 2005, several of these effects on N metabolites disappeared for HN, and by 2008 they were mostly observed for LN plot. A significant decline in foliar Ca and P was observed mostly with HN for a few years until 2005. However, sapwood data actually showed an increase in Ca, Mg and Mn and no change in PAs in the HN plot for 2008, while AAs data revealed trends that were generally similar to foliage for 2008. Concomitant with these changes, mortality data revealed a large number of dead trees in HN pine plots by 2002; the mortality rate started to decline by 2005. Oak trees in the hardwood plot did not exhibit any major changes in PAs, AAs, nutrients and mortality rate with LN treatment, indicating that oak trees were able to tolerate the yearly doses of 50 kg NH4NO3 ha−1 year−1. However, HN trees suffered from physiological and nutritional stress along with increased mortality in 2008. In this case also, foliar data were supported by the sapwood data. Overall, both low and high N applications resulted in greater physiological stress to the pine trees than the oaks. In general, the time course of changes in metabolic data are in agreement with the published reports on changes in soil chemistry and microbial community structure, rates of soil carbon sequestration and production of woody biomass for this chronic N study. This correspondence of selected metabolites with other measures of forest functions suggests that the metabolite analyses are useful for long-term monitoring of the health of forest trees

    Physiology-based IVIVE predictions of tramadol from in vitro metabolism data

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    To predict the tramadol in vivo pharmacokinetics in adults by using in vitro metabolism data and an in vitro-in vivo extrapolation (IVIVE)-linked physiologically-based pharmacokinetic (PBPK) modeling and simulation approach (SimcypA (R)). Tramadol metabolism data was gathered using metabolite formation in human liver microsomes (HLM) and recombinant enzyme systems (rCYP). Hepatic intrinsic clearance (CLint(H)) was (i) estimated from HLM corrected for specific CYP450 contributions from a chemical inhibition assay (model 1); (ii) obtained in rCYP and corrected for specific CYP450 contributions by study-specific intersystem extrapolation factor (ISEF) values (model 2); and (iii) scaled back from in vivo observed clearance values (model 3). The model-predicted clearances of these three models were evaluated against observed clearance values in terms of relative difference of their geometric means, the fold difference of their coefficients of variation, and relative CYP2D6 contribution. Model 1 underpredicted, while model 2 overpredicted the total tramadol clearance by -27 and +22%, respectively. The CYP2D6 contribution was underestimated in both models 1 and 2. Also, the variability on the clearance of those models was slightly underpredicted. Additionally, blood-to-plasma ratio and hepatic uptake factor were identified as most influential factors in the prediction of the hepatic clearance using a sensitivity analysis. IVIVE-PBPK proved to be a useful tool in combining tramadol's low turnover in vitro metabolism data with system-specific physiological information to come up with reliable PK predictions in adults

    The solution space of metabolic networks: producibility, robustness and fluctuations

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    Flux analysis is a class of constraint-based approaches to the study of biochemical reaction networks: they are based on determining the reaction flux configurations compatible with given stoichiometric and thermodynamic constraints. One of its main areas of application is the study of cellular metabolic networks. We briefly and selectively review the main approaches to this problem and then, building on recent work, we provide a characterization of the productive capabilities of the metabolic network of the bacterium E.coli in a specified growth medium in terms of the producible biochemical species. While a robust and physiologically meaningful production profile clearly emerges (including biomass components, biomass products, waste etc.), the underlying constraints still allow for significant fluctuations even in key metabolites like ATP and, as a consequence, apparently lay the ground for very different growth scenarios.Comment: 10 pages, prepared for the Proceedings of the International Workshop on Statistical-Mechanical Informatics, March 7-10, 2010, Kyoto, Japa

    Predicting drug metabolism: experiment and/or computation?

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    Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.This is the accepted manuscript of a paper published in Nature Reviews Drug Discovery (Kirchmair J, Göller AH, Lang D, Kunze J, Testa B, Wilson ID, Glen RC, Schneider G, Nature Reviews Drug Discovery, 2015, 14, 387–404, doi:10.1038/nrd4581). The final version is available at http://dx.doi.org/10.1038/nrd458
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