Multidimensional Optimality of Microbial Metabolism

Schuetz, Robert D.; Zamboni, Nicola; Zampieri, Mattia; Heinemann, Matthias; Sauer, Uwe

doi:10.1126/science.1216882

Cited by 388 publications

(439 citation statements)

References 37 publications

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“…We validate this prediction by measuring the intracellular pH in live cyanobacterial cells (Synechococcus elongatus PCC 7942). Thus, the CCM offers another example where the properties of complex systems central to bacterial growth are well-explained by the principle of energetic cost minimization (24)(25)(26), in this case explaining the remodeling of cytosolic pH and transport modalities to minimize the cost of Ci accumulation for the CCM.…”

mentioning

confidence: 99%

pH determines the energetic efficiency of the cyanobacterial CO ₂ concentrating mechanism

Mangan

Flamholz

Hood

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

180

181

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Many carbon-fixing bacteria rely on a CO 2 concentrating mechanism (CCM) to elevate the CO 2 concentration around the carboxylating enzyme ribulose bisphosphate carboxylase/oxygenase (RuBisCO). The CCM is postulated to simultaneously enhance the rate of carboxylation and minimize oxygenation, a competitive reaction with O 2 also catalyzed by RuBisCO. To achieve this effect, the CCM combines two features: active transport of inorganic carbon into the cell and colocalization of carbonic anhydrase and RuBisCO inside proteinaceous microcompartments called carboxysomes. Understanding the significance of the various CCM components requires reconciling biochemical intuition with a quantitative description of the system. To this end, we have developed a mathematical model of the CCM to analyze its energetic costs and the inherent intertwining of physiology and pH. We find that intracellular pH greatly affects the cost of inorganic carbon accumulation. At low pH the inorganic carbon pool contains more of the highly cellpermeable H 2 CO 3 , necessitating a substantial expenditure of energy on transport to maintain internal inorganic carbon levels. An intracellular pH ≈8 reduces leakage, making the CCM significantly more energetically efficient. This pH prediction coincides well with our measurement of intracellular pH in a model cyanobacterium. We also demonstrate that CO 2 retention in the carboxysome is necessary, whereas selective uptake of HCO 3 − into the carboxysome would not appreciably enhance energetic efficiency. Altogether, integration of pH produces a model that is quantitatively consistent with cyanobacterial physiology, emphasizing that pH cannot be neglected when describing biological systems interacting with inorganic carbon pools.carbon fixation | RuBisCO | cyanobacteria | inorganic carbon | systems biology C yanobacteria and many other autotrophs use a CO 2 concentrating mechanism (CCM) to increase the cellular pool of inorganic carbon and facilitate the Calvin-Benson-Bassham (CBB) cycle (1). Specifically, the CCM functions to supply CO 2 to ribulose bisphosphate carboxylase/oxygenase (RuBisCO), the primary carboxylating enzyme of the CBB cycle. High levels of CO 2 are essential to cyanobacterial metabolism because RuBisCO has relatively slow carboxylation kinetics and is promiscuous, catalyzing an off-pathway reaction with O 2 called oxygenation (2-4).RuBisCO oxygenation produces 2-phosphoglycolate (2PG), which is not part of the CBB cycle and must be recycled. Recycling 2PG through photorespiratory pathways is costly, consuming reduced carbon and energy resources (5, 6). The problem of RuBisCO's limited specificity is all the more pronounced because there is ≈ 20 times more O 2 than CO 2 in aqueous solutions equilibrated with present-day atmosphere (SI Appendix, Fig. S1). CCMs overcome these problems by concentrating CO 2 near RuBisCO, favorably increasing the ratio of CO 2 to O 2 . High concentrations of CO 2 maximize the rate of carboxylation and competitively inhibit oxygenation. Indeed, it is widel...

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mentioning

confidence: 99%

pH determines the energetic efficiency of the cyanobacterial CO ₂ concentrating mechanism

Mangan

Flamholz

Hood

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

180

181

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“…Szekely et al 26 calculate the Pareto front of biological homeostasis circuits in the space of employed parameters. Moreover, Schuetz et al 27 used a metabolic model to provide flux estimates 28 , and proposed that Escherichia coli has evolved towards optimal flux distributions in one condition while minimizing the changes required between conditions.…”

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Metabolic efficiency underpins performance trade-offs in growth of Arabidopsis thaliana

et al. 2014

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Growth often involves a trade-off between the performance of contending tasks; metabolic plasticity can play an important role. Here we grow 97 Arabidopsis thaliana accessions in three conditions with a differing supply of carbon and nitrogen and identify a trade-off between two tasks required for rosette growth: increasing the physical size and increasing the protein concentration. We employ the Pareto performance frontier concept to rank accessions based on their multitask performance; only a few accessions achieve a good trade-off under all three growth conditions. We determine metabolic efficiency in each accession and condition by using metabolite levels and activities of enzymes involved in growth and protein synthesis. We demonstrate that accessions with high metabolic efficiency lie closer to the performance frontier and show increased metabolic plasticity. We illustrate how public domain data can be used to search for additional contending tasks, which may underlie the sub-optimality in some accessions. L iving organisms perform many different and sometimes contending tasks, leading to trade-offs in which optimal performance of one task comes at the cost of a sub-optimal performance of another task. Trade-offs are influenced by the environment and their resolution depends on metabolic resources and plasticity 1,2 . One important environmental variable affecting plant growth is resource availability 3-6 . We first ask whether there is a trade-off between two tasks during vegetative growth of Arabidopsis thaliana: the maximization of physical size and the maximization of protein concentration. We then apply two methods to rank accessions: the first based solely on the performance of tasks, and the second based on the efficiency with which metabolic resources are deployed to perform tasks. In addition, we investigate whether the trade-off and the ranking of accessions are affected by the availability of carbon (C) and nitrogen (N) and the way in which it shapes the accession-specific metabolic profiles.Plants use light energy to transform CO 2 and inorganic nutrients into a plethora of metabolic precursors that are used to drive growth. As plants are sessile, resource acquisition is constrained by their physical size. In land plants, the majority of a mature cell is occupied by the vacuole, allowing the generation of a much larger physical size per unit protein than is usually obtained by microbes or animals 7 . For simplification, we do not consider the impact of shape and phenology on the relation between physical size and resource acquisition. On the other hand, proteins are required to catalyse the transformation of resources into biomass. In particular, the majority of the protein in a plant leaf is involved in photosynthesis [8][9][10] . We hypothesize that there is a trade-off between physical size and protein concentration. While production of leaves with a higher protein concentration will allow higher rates of photosynthesis and metabolism per unit biomass, it also increases the costs of growth a...

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“…49 Previous studies showed that objective functions compete against each other. 27,47,50 This means that one objective function can only be improved if another is worsened. Optimal solutions for competing objectives are called Pareto optimal.…”

Section: Response Of Saccharomyces Cerevisiae To a Glucose Pulsementioning

confidence: 99%

MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis

et al. 2015

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. (2015). MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis. Molecular BioSystems, 11(1), 137-145. DOI: 10.1039/c4mb00510d General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Understanding cellular adaptation to environmental changes is one of the major challenges in systems biology. To understand how cellular systems react towards perturbations of their steady state, the metabolic dynamics have to be described. Dynamic properties can be studied with kinetic models but development of such models is hampered by limited in vivo information, especially kinetic parameters.Therefore, there is a need for mathematical frameworks that use a minimal amount of kinetic information. One of these frameworks is dynamic flux balance analysis (DFBA), a method based on the assumption that cellular metabolism has evolved towards optimal changes to perturbations. However, DFBA has some limitations. It is less suitable for larger systems because of the high number of parameters to estimate and the computational complexity. In this paper, we propose MetDFBA, a modification of DFBA, that incorporates measured time series of both intracellular and extracellular metabolite concentrations, in order to reduce both the number of parameters to estimate and the computational complexity. MetDFBA can be used to estimate dynamic flux profiles and, in addition, test hypotheses about metabolic regulation. In a first case study, we demonstrate the validity of our method by comparing our results to flux estimations based on dynamic 13 C MFA measurements, which we considered as experimental reference. For these estimations time-resolved metabolomics data from a feast-famine experiment with Penicillium chrysogenum was used. In a second case study, we used time-resolved metabolomics data from glucose pulse experiments during aerobic growth of Saccharomyces cerevisiae to test various metabolic objectives.

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Multidimensional Optimality of Microbial Metabolism

Cited by 388 publications

References 37 publications

pH determines the energetic efficiency of the cyanobacterial CO ₂ concentrating mechanism

pH determines the energetic efficiency of the cyanobacterial CO ₂ concentrating mechanism

Metabolic efficiency underpins performance trade-offs in growth of Arabidopsis thaliana

MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis

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Multidimensional Optimality of Microbial Metabolism

Cited by 388 publications

References 37 publications

pH determines the energetic efficiency of the cyanobacterial CO 2 concentrating mechanism

pH determines the energetic efficiency of the cyanobacterial CO 2 concentrating mechanism

Metabolic efficiency underpins performance trade-offs in growth of Arabidopsis thaliana

MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis

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pH determines the energetic efficiency of the cyanobacterial CO ₂ concentrating mechanism

pH determines the energetic efficiency of the cyanobacterial CO ₂ concentrating mechanism