Dataset Information

Metabolic Flux Analysis of Catechin Biosynthesis Pathways Using Nanosensor.

ABSTRACT: (+)-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.

SUBMITTER: Kausar H

PROVIDER: S-EPMC7222200 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Metabolic Flux Analysis of Catechin Biosynthesis Pathways Using Nanosensor.

Kausar Habiba H Ambrin Ghazala G Okla Mohammad K MK Soufan Walid W Al-Ghamdi Abdullah A AA Ahmad Altaf A

Antioxidants (Basel, Switzerland) 20200331 4

(+)-Catechin is an important antioxidant of green tea (<i>Camelia sinensis</i> (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 ...[more]

PMID: 32244268

Similar Datasets

Project description:BackgroundThe importance and power of isotope-based metabolic flux analysis and its contribution to understanding the metabolic network is increasingly recognized. Its application is, however, still limited partly due to computational inefficiency. 13C metabolic flux analysis aims to compute in vivo metabolic fluxes in terms of metabolite balancing extended by carbon isotopomer balances and involves a nonlinear least-squares problem. To solve the problem more efficiently, improved numerical optimization techniques are necessary.ResultsFor flux computation, we developed a gradient-based hybrid optimization algorithm. Here, independent flux variables were compactified into [0, 1)-ranged variables using a single transformation rule. The compactified parameters could be discriminated between non-identifiable and identifiable variables after model linearization. The developed hybrid algorithm was applied to the central metabolism of Bacillus subtilis with only succinate and glutamate as carbon sources. This creates difficulties caused by symmetry of succinate leading to limited introduction of 13C labeling information into the system. The algorithm was found to be superior to its parent algorithms and to global optimization methods both in accuracy and speed. The hybrid optimization with tolerance adjustment quickly converged to the minimum with close to zero deviation and exactly re-estimated flux variables. In the metabolic network studied, some fluxes were found to be either non-identifiable or nonlinearly correlated. The non-identifiable fluxes could correctly be predicted a priori using the model identification method applied, whereas the nonlinear flux correlation was revealed only by identification runs using different starting values a posteriori.ConclusionThis fast, robust and accurate optimization method is useful for high-throughput metabolic flux analysis, a posteriori identification of possible parameter correlations, and also for Monte Carlo simulations to obtain statistical qualities for flux estimates. In this way, it contributes to future quantitative studies of central metabolic networks in the framework of systems biology.

Dataset Information

Metabolic Flux Analysis of Catechin Biosynthesis Pathways Using Nanosensor.

Publications

Metabolic Flux Analysis of Catechin Biosynthesis Pathways Using Nanosensor.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets