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Thermodynamic and Probabilistic Metabolic Control Analysis of Riboflavin (Vitamin B2) Biosynthesis in Bacteria

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Abstract

In this study, we applied a coupled in silico thermodynamic and probabilistic metabolic control analysis methodology to investigate the control mechanisms of the commercially relevant riboflavin biosynthetic pathway in bacteria. Under the investigated steady-state conditions, we found that several enzyme reactions of the pathway operate far from thermodynamic equilibrium (transformed Gibbs energies of reaction below about −17 kJ mol−1). Using the obtained thermodynamic information and applying enzyme elasticity sampling, we calculated the distributions of the scaled concentration control coefficients (CCCs) and scaled flux control coefficients (FCCs). From the statistical analysis of the calculated distributions, we inferred that the control over the riboflavin producing flux is shared among several enzyme activities and mostly resides in the initial reactions of the pathway. More precisely, the guanosine triphosphate (GTP) cyclohydrolase II activity, and therefore the bifunctional RibA protein of Bacillus subtilis because it catalyzes this activity, appears to mainly control the riboflavin producing flux (mean FCCs = 0.45 and 0.55, respectively). The GTP cyclohydrolase II activity and RibA also exert a high positive control over the riboflavin concentration (mean CCCs = 2.43 and 2.91, respectively). This prediction is consistent with previous findings for microbial riboflavin overproducing strains.

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Abbreviations

i :

Enzyme reaction number (i = 1 ,  …  , 9)

Δ r G ' :

Vector of transformed Gibbs energies of reaction

Δr G ' :

Transformed Gibbs energy of reaction (general symbol)

Δr G ' i :

Transformed Gibbs energy of reaction for reaction i

Δ r G '0 :

Vector of standard transformed Gibbs energies of reaction

Err(Δ r G '0):

Vector of possible error values of the standard transformed Gibbs energies of reaction

Err(Δ r G '0) MAX :

Vector of maximal error values of the standard transformed Gibbs energies of reaction

R :

Universal gas constant

T :

Temperature

N T :

Transposed stoichiometric matrix in the thermodynamic analysis

c :

Vector of metabolite concentrations

c min :

Vector of minimum metabolite concentrations

c max :

Vector of maximum metabolite concentrations

ρ :

Vector of disequilibrium ratios

ρ i :

Disequilibrium ratio of reaction i

v :

Vector of steady-state fluxes

V :

Diagonal matrix of steady-state fluxes

v net :

Vector of steady-state net fluxes

v − , i :

Backward steady-state flux of reaction i

v + , i :

Forward steady-state flux of reaction i

v net , i :

Steady-state net flux of reaction i

M :

Vector of steady-state metabolite concentrations in the metabolic control analysis

[X]:

Steady-state concentration of metabolite X

e :

Vector of enzyme concentrations

\( {\mathbf{C}}_{\mathbf{e}}^{\mathbf{v}} \) :

Matrix of scaled flux control coefficients

\( {C}_{e_i}^{v_{net,7}} \), \( {C}_{e_Y}^{v_{net,7}} \) :

Scaled flux control coefficient of reaction i or of a specific enzyme Y with respect to the riboflavin synthase flux v net , 7

\( {\mathbf{C}}_{\mathbf{e}}^{\mathbf{M}} \) :

Matrix of scaled concentration control coefficients

\( {C}_{e_i}^{\left[X\right]} \), \( {C}_{e_Y}^{\left[X\right]} \) :

Scaled concentration control coefficient of reaction i or of a specific enzyme Y with respect to metabolite concentration [X]

N MCA :

Stoichiometric matrix of the metabolic control analysis

\( {\mathbf{E}}_{\mathbf{M}}^{\mathbf{v}} \) :

Matrix of scaled elasticities: local sensitivities of v to changes in M

\( {\boldsymbol{\Pi}}_{\mathbf{e}}^{\mathbf{v}} \) :

Matrix of scaled elasticities: local sensitivities of v to changes in e

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Acknowledgments

This work was financed by the German Federal Ministry of Education and Research (BMBF) in the context of the NANOKAT graduate program (ref. no. 0316052A). This study was inspired by interdisciplinary and open-minded discussions within the graduate program.

Conflict of Interest

The authors declare that they have no competing interests.

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Authors and Affiliations

  1. Institute for Chemical Process Engineering, Mannheim University of Applied Sciences, Paul-Wittsack-Straße 10, 68163, Mannheim, Germany

    Markus Birkenmeier & Thorsten Röder

  2. Institute for Technical Microbiology, Mannheim University of Applied Sciences, Paul-Wittsack-Straße 10, 68163, Mannheim, Germany

    Matthias Mack

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  1. Markus Birkenmeier
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Birkenmeier, M., Mack, M. & Röder, T. Thermodynamic and Probabilistic Metabolic Control Analysis of Riboflavin (Vitamin B2) Biosynthesis in Bacteria. Appl Biochem Biotechnol 177, 732–752 (2015). https://doi.org/10.1007/s12010-015-1776-y

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