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Metabolic flux configuration determination using information entropy.

ABSTRACT: Constraint-based models use steady-state mass balances to define a solution space of flux configurations, which can be narrowed down by measuring as many fluxes as possible. Due to loops and redundant pathways, this process typically yields multiple alternative solutions. To address this ambiguity, flux sampling can estimate the probability distribution of each flux, or a flux configuration can be singled out by further minimizing the sum of fluxes according to the assumption that cellular metabolism favors states where enzyme-related costs are economized. However, flux sampling is susceptible to artifacts introduced by thermodynamically infeasible cycles and is it not clear if the economy of fluxes assumption (EFA) is universally valid. Here, we formulated a constraint-based approach, MaxEnt, based on the principle of maximum entropy, which in this context states that if more than one flux configuration is consistent with a set of experimentally measured fluxes, then the one with the minimum amount of unwarranted assumptions corresponds to the best estimation of the non-observed fluxes. We compared MaxEnt predictions to Escherichia coli and Saccharomyces cerevisiae publicly available flux data. We found that the mean square error (MSE) between experimental and predicted fluxes by MaxEnt and EFA-based methods are three orders of magnitude lower than the median of 1,350,000 MSE values obtained using flux sampling. However, only MaxEnt and flux sampling correctly predicted flux through E. coli's glyoxylate cycle, whereas EFA-based methods, in general, predict no flux cycles. We also tested MaxEnt predictions at increasing levels of overflow metabolism. We found that MaxEnt accuracy is not affected by overflow metabolism levels, whereas the EFA-based methods show a decreasing performance. These results suggest that MaxEnt is less sensitive than flux sampling to artifacts introduced by thermodynamically infeasible cycles and that its predictions are less susceptible to overfitting than EFA-based methods.

SUBMITTER: Rivas-Astroza M

PROVIDER: S-EPMC7717585 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Metabolic flux configuration determination using information entropy.

Rivas-Astroza Marcelo M Conejeros Raúl R

PloS one 20201204 12

Constraint-based models use steady-state mass balances to define a solution space of flux configurations, which can be narrowed down by measuring as many fluxes as possible. Due to loops and redundant pathways, this process typically yields multiple alternative solutions. To address this ambiguity, flux sampling can estimate the probability distribution of each flux, or a flux configuration can be singled out by further minimizing the sum of fluxes according to the assumption that cellular metab ...[more]

PMID: 33275628

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Project description:(+)-Catechin is an important antioxidant of green tea (Camelia sinensis (L.) O. Kuntze). Catechin is known for its positive role in anticancerous activity, extracellular matrix degradation, cell death regulation, diabetes, and other related disorders. As a result of enormous interest in and great demand for catechin, its biosynthesis using metabolic engineering has become the subject of concentrated research with the aim of enhancing (+)-catechin production. Metabolic flux is an essential concept in the practice of metabolic engineering as it helps in the identification of the regulatory element of a biosynthetic pathway. In the present study, an attempt was made to analyze the metabolic flux of the (+)-catechin biosynthesis pathway in order to decipher the regulatory element of this pathway. Firstly, a genetically encoded fluorescence resonance energy transfer (FRET)-based nanosensor (FLIP-Cat, fluorescence indicator protein for (+)-catechin) was developed for real-time monitoring of (+)-catechin flux. In vitro characterization of the purified protein of the nanosensor showed that the nanosensor was pH stable and (+)-catechin specific. Its calculated Kd was 139 µM. The nanosensor also performed real-time monitoring of (+)-catechin in bacterial cells. In the second step of this study, an entire (+)-catechin biosynthesis pathway was constructed and expressed in E. coli in two sets of plasmid constructs: pET26b-PT7-rbs-PAL-PT7-rbs-4CL-PT7-rbs-CHS-PT7-rbs-CHI and pET26b-T7-rbs-F3H-PT7-rbs- DFR-PT7-rbs-LCR. The E. coli harboring the FLIP-Cat was transformed with these plasmid constructs. The metabolic flux analysis of (+)-catechin was carried out using the FLIP-Cat. The FLIP-Cat successfully monitored the flux of catechin after adding tyrosine, 4-coumaric acid, 4-coumaroyl CoA, naringenin chalcone, naringenin, dihydroquercetin, and leucocyanidin, individually, with the bacterial cells expressing the nanosensor as well as the genes of the (+)-catechin biosynthesis pathway. Dihydroflavonol reductase (DFR) was identified as the main regulatory element of the (+)-catechin biosynthesis pathway. Information about this regulatory element of the (+)-catechin biosynthesis pathway can be used for manipulating the (+)-catechin biosynthesis pathway using a metabolic engineering approach to enhance production of (+)-catechin.

Dataset Information

Metabolic flux configuration determination using information entropy.

Publications

Metabolic flux configuration determination using information entropy.

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