Title: Metabolic Modeling of Streptomyces Lividans, a Cell Factory for Heterologous Protein Production (Metabolische modellering van Streptomyces lividans, een celfabriek voor heterologe eiwitproductie)
Other Titles: Metabolic Modeling of Streptomyces Lividans, a Cell Factory for Heterologous Protein Production
Authors: D'Huys, Pieter-Jan
Issue Date: 9-Dec-2013
Abstract: Streptomycetes are worldwide used for commercial production of antibiotics and industrial enzymes. Nowadays, several genera of Grampositive bacteria are being tested as cell factories for the production of heterologous proteins owing to their ability to efficiently secrete proteins in the culture medium. Among them Streptomyces lividans is the cell factory of choice for the secretory production of heterologous proteins. To acquire a commercially attractive secretion yield of heterologous protein, the availability of suitable nitrogen sources in the medium is often essential. A complex mixture of amino acids, e.g., casamino acids, is usually added to the medium for this purpose. Besides acting as building blocks for biomass production, these amino acids are presumed to play an important role in the biosynthesis of heterologous protein. This dissertation focuses on the qualitative and quantitive characterization of the growth and metabolism of S. lividans TK24 as a cell factory for heterologous protein production. Based on a central set of experimental data, amino acids metabolization is investigated and a framework for the analysis of intracellular fluxes in a complex medium is set up. Experiments are performed with the wild-type and heterologous protein producing S. lividans under controlled bioreactor conditions and in the presence of casamino acids. Mouse Tumor Necrosis Factor alpha is taken as a model heterologous protein. First, the metabolic footprint, also called exometabolome (all extracellular metabolites), is thoroughly analyzed for wild-type and recombinant strain grown in a defined medium supplemented with casamino acids. The metabolite concentration profiles and calculated specific conversion rates reveal amino acid uptake preferences, by-products formation, and the impact and relevance of amino acids on biomass growth and heterologous protein production. In view of optimization of heterologous protein production levels in S. lividans , a deeper understanding of the effects of C- and N-substrates on the (intracellular) metabolism is necessary. Analysis of the intracellular metabolic fluxes via metabolic network modeling techniques is the tool of choice to further unravel this metabolism. To this end, a general metabolic modeling framework is developed to determine metabolic fluxes in S . lividans TK24 grown on a nutrient-rich medium. The plethora of substrates taken up motivates the use of a genome-scale metabolic network model, which makes it also possible to thoroughly study the whole-cell effects (metabolic impact) of heterologous protein production on the host cellÂ’s metabolism in a further stadium. A genome-scale metabolic network model for wild-type S. lividans TK24 is constructed first. Next, the obtained experimental data are confronted with this genome-scale model using a combination of constraint-based stoichiometric metabolic network modeling techniques. Genome-scale hierarchical flux balance analysis and randomized sampling of the solution space are combined to extract maximum information from the exometabolome profiles. In this dissertation, new insights into the metabolic background of S. lividans as a cell factory forheterologous protein production are presented. The results are based ona systems approach and industrial relevant bioreactor conditions. Besides the exhaustive metabolic footprinting returning qualitative information, a systematic and integrative approach is presented to address the hurdles encountered in genomescale flux analysis by using a combination ofhierarchical flux balance analysis and randomized sampling of the flux solution space. This general approach can easily be adopted in future studies of S. lividans , e.g., as a host for heterologous protein production.
Table of Contents: Abstract v
Korte inhoud vii
List of abbreviations ix
List of symbols xiii
Contents xvii

1 General introduction 1
1.1 Objective 3
1.2 Chapter by chapter overview 5

2 The genus Streptomyces 9
2.1 Ecological niche 10
2.2 Morphology and developmental model 10
2.3 Genome 14
2.4 Cellular metabolism 15
2.4.1 Growth 15
2.4.2 Central carbon metabolism 16
2.4.3 Nitrogen metabolism 21
2.4.4 Control of the primary metabolism 22
2.4.5 Secondary metabolism 23
2.5 Heterologous protein production 24
2.5.1 Gram negative versus Gram positive expression systems 24
2.5.2 Protein secretion pathways in Streptomyces spp. 25
2.5.3 Heterologous protein secretion in Streptomyces lividans 28

3 Metabolic network modeling of microbial cell factories 31
3.1 Model structure and complexity 32
3.2 Unstructured unsegregated macroscopic bioprocess models 35
3.3 Constraint-based stoichiometric metabolic network models 38
3.3.1 Stoichiometric matrix 38
3.3.2 Pseudo steady-state assumption 38
3.3.3 Constrained flux space 40
3.3.4 Analysis of the metabolic fluxes 42
3.4 Dynamic metabolic network models 49

4 Metabolic footprinting of wild-type and recombinant Streptomyces lividans TK24 batch fermentations 51
4.1 Introduction 52
4.2 Materials and methods 53
4.2.1 Bacterial strains 53
4.2.2 Design of the fermentation medium 54
4.2.3 Inoculum preparation 54
4.2.4 Bioreactor experiments 54
4.2.5 Off-line measurements 55
4.2.6 Data alignment and phase detection 57
4.2.7 Calculation of yield and exchange fluxes 61
Methods for unstationary process conditions 62
Calculation based on pseudo steady-state assumption 66
4.3 Results and discussion 69
4.3.1 Composition of BactoTM casamino acids 69
4.3.2 Data alignment and growth characterization 71
4.3.3 Estimation of yield coefficients and exchange fluxes 72
4.3.4 Exometabolome profiles 81
4.3.5 Utilization of glutamate and aspartate for biomass growth 95
4.3.6 Overflow metabolism during growth on NMMP supplemented with Casamino acids 96
4.3.7 Impact of heterologous protein production on the metabolic footprint 98
4.3.8 m-TNF-alpha secretion in relation to biomass formation 99
4.4 Conclusions 100

5 Reconstruction of a genome-scale metabolic network model for Streptomyces lividans TK24 103
5.1 Introduction 104
5.2 The genome-scale metabolic network model for Streptomyces coelicolor 105
5.3 Construction of the genome-scale metabolic network for Streptomyces lividans TK24 107
5.4 Analysis of model growth capability on measured substrates 110
5.5 Conclusions 115

6 Genome-scale metabolic flux analysis of Streptomyces lividans growing on a complex medium 117
6.1 Introduction 118
6.2 Materials and methods 119
6.2.1 Experimental data and data processing 119
6.2.2 Core principles of the constraint-based metabolic modeling approach 120
6.2.3 Flux balance analysis 121
6.2.4 Flux variability analysis 124
6.2.5 Hierarchical flux balance analysis 124
6.2.6 Details on implementation of secondary objective functions 125
6.2.7 Random sampling of the solution space 129
6.2.8 Combining hierarchical FBA and uniform random sampling 129
6.3 Results and Discussion 130
6.3.1 Exometabolome versus optimal biomass growth principle 130
6.3.2 Characterization of the optimal growth genomescale metabolic steady-state solution space 136
6.3.3 Selection of a unique intracellular flux vector 142
6.3.4 Study of the intracellular flux distribution 145
6.3.5 Role of amino acids in the medium 150
6.4 Conclusions 153

7 General conclusions and future research 155
7.1 General conclusions 155
7.2 Open problems and suggestions for future research 159
7.2.1 Validation of flux predictions with 13C-labeling metabolic flux analysis 159
7.2.2 Further constraining the genome-scale FBA solution space 160
7.2.3 Extension of the flux analysis to heterologous protein production 160
7.2.4 Process operation towards higher TNF-alpha protein yields 161
7.2.5 Studying S. lividans as a cell factory for other heterologous proteins 162
7.2.6 Dynamic flux balance analysis 163
7.2.7 Building in heterogeneity 163

Bibliography 165
Appendix A Yields and exchange rates 187
Appendix B Metabolic model & FBA solutions 199
Curriculum vitae 201
List of publications 203
ISBN: 978-94-6018-769-8
Publication status: published
KU Leuven publication type: TH
Appears in Collections:Bio- & Chemical Systems Technology, Reactor Engineering and Safety Section
Departement Industriële Wetenschappen en Technologie - UC Limburg
Laboratory of Molecular Bacteriology (Rega Institute)

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