Quantitative prediction of genome-wide resource allocation in bacteria

Goelzer, Anne; Muntel, Jan; Chubukov, Victor; Jules, Matthieu; Prestel, Eric; Nölker, Rolf; Mariadassou, Mahendra; Aymerich, Stéphane; Hecker, Michael; Naveilhan, Philippe; Becher, Dörte; Fromion, Vincent

doi:10.1016/j.ymben.2015.10.003

Cited by 132 publications

(201 citation statements)

References 48 publications

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“…In this respect, cyanobacterial phototrophic growth is an ideal test case because the regular and periodic environmental changes allow us to formulate the global resource allocation problem in a welldefined way. As yet, similar efforts to investigate proteome allocation have primarily focused on heterotrophic organisms in time-independent environments (21,24,41). These recent analyses reveal several interesting differences with respect to our results.…”

Section: Discussioncontrasting

confidence: 53%

“…1. In contrast to conventional FBA, and following recent developments in constraint-based analysis (18,(23)(24)(25)(26), the capacity constraints of individual reactions are explicitly part of the model.…”

Section: Methodsmentioning

confidence: 99%

“…Our model significantly improves upon previous computational analyses of diurnal phototrophic growth (14,(16)(17)(18) and takes recent developments in constraintbased analysis into account (19)(20)(21)(22). Our approach is closely related to resource balance analysis (23,24) and dynamic enzymecost flux balance analysis (25), as well as integrated metabolism and gene expression (ME) models (21,26), but explicitly accounts for the properties of diurnal phototrophic growth.…”

mentioning

confidence: 92%

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Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth

Reimers

Knoop

Bockmayr

et al. 2017

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

100

129

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Cyanobacteria are an integral part of Earth's biogeochemical cycles and a promising resource for the synthesis of renewable bioproducts from atmospheric CO 2 . Growth and metabolism of cyanobacteria are inherently tied to the diurnal rhythm of light availability. As yet, however, insight into the stoichiometric and energetic constraints of cyanobacterial diurnal growth is limited. Here, we develop a computational framework to investigate the optimal allocation of cellular resources during diurnal phototrophic growth using a genome-scale metabolic reconstruction of the cyanobacterium Synechococcus elongatus PCC 7942. We formulate phototrophic growth as an autocatalytic process and solve the resulting time-dependent resource allocation problem using constraint-based analysis. Based on a narrow and well-defined set of parameters, our approach results in an ab initio prediction of growth properties over a full diurnal cycle. The computational model allows us to study the optimality of metabolite partitioning during diurnal growth. The cyclic pattern of glycogen accumulation, an emergent property of the model, has timing characteristics that are in qualitative agreement with experimental findings. The approach presented here provides insight into the time-dependent resource allocation problem of phototrophic diurnal growth and may serve as a general framework to assess the optimality of metabolic strategies that evolved in phototrophic organisms under diurnal conditions. constraint-based analysis | whole-cell models | bioenergetics | metabolism | circadian clock C yanobacterial photoautotrophic growth requires a highly coordinated distribution of cellular resources to different intracellular processes, including the de novo synthesis of proteins, ribosomes, lipids, and other cellular components. For unicellular organisms, the optimal allocation of limiting resources is a key determinant of evolutionary fitness. Owing to the importance of cellular resource allocation for understanding evolutionary trade-offs in bacterial metabolism, the cellular "protein economy" and its implications for bacterial growth laws have been studied extensively, albeit almost exclusively for heterotrophic organisms under stationary environmental conditions (1-7). For photoautotrophic organisms, including cyanobacteria, growthdependent resource allocation is further subject to diurnal lightdark (LD) cycles that partition cellular metabolism into distinct phases. Recent experimental results have demonstrated the relevance of time-specific synthesis for cellular survival and growth (8-10). Nonetheless, the implications and consequences of a diurnal environment for the cellular resource allocation problem are insufficiently understood, and computational approaches hitherto developed for heterotrophic growth are not straightforwardly applicable to diurnal phototrophic growth (11).Here, we propose a computational framework to quantitatively assess the optimality of diurnal resource allocation for phototrophic growth. We are primarily interested i...

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Section: Discussioncontrasting

confidence: 53%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 92%

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Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth

Reimers

Knoop

Bockmayr

et al. 2017

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

100

129

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“…Evolutionary theories suggest that protein expression levels maximize fitness (Dekel & Alon, 2005;Molenaar et al, 2009;. In particular, the growth rate-dependent regulations may contribute to cost minimization by providing reasonable solutions to the cost-benefit optimization of the cellular and metabolic processes that must be active under all growth conditions and consequently to fitness increase across growth conditions (Goelzer et al, 2015). We revealed that the variations in the free ribosome abundance can globally contribute to proper cellular (re)programming and have evidenced a unique, hard-coded, growth rate-dependent mode of translation regulation, which manages genome-wide gene expression in addition to specific post-transcriptional and translational regulations.…”

Section: Translation Efficiency Based On Michaelis-menten Kinetics: Tmentioning

confidence: 99%

Translation elicits a growth rate‐dependent, genome‐wide, differential protein production in Bacillus subtilis

Borkowski

Goelzer

Schaffer

et al. 2016

Molecular Systems Biology

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Complex regulatory programs control cell adaptation to environmental changes by setting condition‐specific proteomes. In balanced growth, bacterial protein abundances depend on the dilution rate, transcript abundances and transcript‐specific translation efficiencies. We revisited the current theory claiming the invariance of bacterial translation efficiency. By integrating genome‐wide transcriptome datasets and datasets from a library of synthetic gfp‐reporter fusions, we demonstrated that translation efficiencies in Bacillus subtilis decreased up to fourfold from slow to fast growth. The translation initiation regions elicited a growth rate‐dependent, differential production of proteins without regulators, hence revealing a unique, hard‐coded, growth rate‐dependent mode of regulation. We combined model‐based data analyses of transcript and protein abundances genome‐wide and revealed that this global regulation is extensively used in B. subtilis. We eventually developed a knowledge‐based, three‐step translation initiation model, experimentally challenged the model predictions and proposed that a growth rate‐dependent drop in free ribosome abundance accounted for the differential protein production.

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“…The approach we present here characterizes the quantitative relationship between enzymatic catalysis in vitro and in vivo and offers a highthroughput method for extracting enzyme kinetic constants from omics data. (1)(2)(3)(4)(5)(6). Many models of cellular metabolism include k cat values, the maximal turnover rates of enzymes, as key inputs to predict the behavior of metabolic pathways and networks (7)(8)(9).…”

mentioning

confidence: 99%

Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro k _cat measurements

Davidi

Noor

Liebermeister

et al. 2016

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

221

368

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Turnover numbers, also known as k cat values, are fundamental properties of enzymes. However, k cat data are scarce and measured in vitro, thus may not faithfully represent the in vivo situation. A basic question that awaits elucidation is: how representative are k cat values for the maximal catalytic rates of enzymes in vivo? Here, we harness omics data to calculate k vivo max , the observed maximal catalytic rate of an enzyme inside cells. Comparison with k cat values from Escherichia coli, yields a correlation of r 2 = 0.62 in log scale (p < 10 −10 ), with a root mean square difference of 0.54 (3.5-fold in linear scale), indicating that in vivo and in vitro maximal rates generally concur. By accounting for the degree of saturation of enzymes and the backward flux dictated by thermodynamics, we further refine the correspondence between k vivo max and k cat values. The approach we present here characterizes the quantitative relationship between enzymatic catalysis in vitro and in vivo and offers a highthroughput method for extracting enzyme kinetic constants from omics data. (1-6). Many models of cellular metabolism include k cat values, the maximal turnover rates of enzymes, as key inputs to predict the behavior of metabolic pathways and networks (7-9). However, most values have never been measured experimentally. Escherichia coli is the most intensely biochemically characterized organism, but k cat values are available for only about 10% of its ≈ 2, 000 enzyme-reaction pairs (Dataset S1). Indeed, k cat values are missing for several central metabolic enzymes. The scarcity of kinetic data limits the scope of models and necessitates generic parameter assignments that significantly reduce the predictive power of cellular models.Even if a larger collection of k cat values was made available, their current use poses a major difficulty: k cat values are measured through in vitro enzyme assays, representing the initial rate of the reaction, i.e., full substrates saturation and negligible levels of products. Such assays may underrepresent factors like cellular metabolite concentrations, thermodynamic constraints, posttranslational modifications, chaperones, cellular crowding, and activating and inhibiting molecules, which can substantially alter enzyme kinetics in vivo. These omissions call into question the relevance of k cat measurements in vivo (10-12). Furthermore, an effort to measure a large number of k cat values under in vivo-like conditions presents a daunting challenge, given how many unknown biochemical factors might be involved.Several studies grapple with missing k cat values by sampling from the distribution of k cat values measured in vitro or by using measurements of the same enzyme from related species (13-16). These approximations systematically ignore any errors resulting from the differences between in vitro and in vivo environments. Approximations of this sort may also introduce significant errors, as k cat values can deviate by orders of magnitude between isozymes in the same organism as well a...

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Quantitative prediction of genome-wide resource allocation in bacteria

Cited by 132 publications

References 48 publications

Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth

Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth

Translation elicits a growth rate‐dependent, genome‐wide, differential protein production in Bacillus subtilis

Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro k _cat measurements

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Quantitative prediction of genome-wide resource allocation in bacteria

Cited by 132 publications

References 48 publications

Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth

Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth

Translation elicits a growth rate‐dependent, genome‐wide, differential protein production in Bacillus subtilis

Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro k cat measurements

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Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro k _cat measurements