Thesis
Metabolic flux analysis of Streptomyces fradiae C373-10
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2004
- Thesis identifier
- T11037
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- In order to obtain high yields of antibiotics, the flow of carbon through the primary metabolic pathways (i.e., glycolysis, the TCA cycle, pentose phosphate pathway, etc) must be radically redirected from the pathways that normally support balanced growth, towards pathways that support antibiotic synthesis. Such metabolic flux alterations directly oppose the enzyme level control mechanisms that are responsible for maintaining flux distributions optimal for growth. This enzyme resistance is referred to, as metabolic or network rigidity, which must be removed in order to attain improvements in product yield. Although modifications of the primary metabolism can be achieved through molecular biology, the choice of enzymes to be amplified or attenuated to mitigate network rigidity remains uncertain, and yield enhancements via metabolic modifications are largely pursued by trial and error. Hence, there is a clear need to develop a robust technique to identify limitations in the primary metabolism. To this end, Metabolic Flux Analysis (MFA) has been used to map the flow of carbon through primary metabolic pathways of Streptomyces fradiae C373-10 during batch cultures grown on a number of different carbon sources. MFA has been researched synergistically by a number of researchers, who have employed vastly different mathematical styles. Metabolic flux analysis (MFA) is a theoretical methodology used to determine fluxes through metabolic pathways, in terms of specific rates of reactions through a stoichiometric model of cellular reactions, using mass balances for the intracellular metabolites. Two approaches have generally been used; (1) the monomeric composition (amino acids, lipids, carbohydrates, RNA & DNA) and extracellular measurements of the cell are used to build a simple arithmetic model of cellular metabolism, (2) differential equations are used to model metabolism from extracellular metabolites only. In essence they are trying to achieve the same objectives. The alternative approach of Harry Holms (1986) has been adopted by this laboratory in the past. It offers a logical place to start, building compositional tables that need to be constructed to undertake any form of flux analysis. The main aim of this project was to prepare such a theoretical material balance of Streptomyces fradiae fermentations in batch culture. It would be of interest to see which of the approaches could be best applied to a Streptomyces fermentations, in the same way as MFA has been applied, to well defined bacteria such as E. coli (Holms, 1986, 1991, 1996, 1997, 2001; Aristidou et al., 1998; Varma et al., 1993a, 1993b, Varma & Palsson, 1994a, 1994b, 1995; Van Gulik & Heijnen, 1995; Pramanik & Keasling, 1997; Yang, 1999; Yang et al., 1999a, b) & Corynebacterium glutamicum (Vallino & Stephanopoulos, 1993, 1994a, b). It was therefore necessary to develop an adequate defined medium, to acquire all of the data for S. fradiae biomass required to calculate these fluxes. Additional information that was needed to achieve this objective is listed below. (1) A number of different medium compositions were tested for their suitability, optimised, and stepped up to bench top fermentation. To undertake a flux analysis, the main requirements are simple nitrogen and carbon sources that produce, reasonable antibiotic yields (Chapter 4). (2) Determine the macromolecular composition of S. fradiae C373-10 & S. coelicolor 1147 during exponential growth phase. (3) Investigate methods for the fractionation of biomass into its macromolecular and monomeric contents. Previous workers have shown considerable analytical error and reproducibility of standard assay techniques to collect bacterial compositional data. The intension was to reduce the inconsistencies in calculating compositional data by applying a number of analytical protocols and reconciling the data (see Chapter 8, discussion). (4) Determine the elemental composition of S. fradiae C373-10. Although this was not required for calculation of the fluxes, it would give an overall view of the composition of the biomass. (5) Investigate the differences between the elemental, monomeric, and macromolecular content of the biomass; inaccuracies of 20 % or more are commonly accepted in the literature. Since the molecular composition should directly define the elemental composition further investigation is needed. One theory is that metabolites such as shunt metabolites or cell wall material are not adequately accounted for. (6) The monomeric composition of S. coelicolor 1147, S. fradiae C373-10, and E. coli ML308 will be converted to compositional tables as Holms (1986)[see Chapter 6 and Appendix B]. Where appropriate the monomeric composition will be used to determine the (monomeric) composition of S. fradiae & S. coelicolor. In addition macromolecular data will be used where monomeric data analysis was not feasible, i.e., monomer content for DNA may be obtained from the macromolecular content; for example, approximately 70 % of Streptomyces DNA is comprised of guanine and cytosine bases (Pridham & Tresner, 1974). The DNA content may be expressed in terms of its bases. However, not all monomer amounts can be calculated from the macromolecular composition. For example, for the monomeric content of amino acids; high pressure liquid chromatography (HPLC) was undertaken. (7) Investigate the amino acid composition of S. fradiae C373-10 & S. coelicolor 1147; it will be of interest to see, how the amino acid contents of these streptomycetes differ due to the consequences of codon bias. (8) Collect the following information throughout the fermentations. specific rates of substrate uptake, specific growth rate, specific oxygen uptake and specific carbon dioxide evolution. (9) Identify and quantify the excretion rates of organic acids of S. fradiae C373-10 & S. coelicolor 1147 under different growth conditions. (10) Identify and quantify secondary metabolites excreted by S. fradiae C373-10 through out the fermentation, to allow for the determination of fluxes to these metabolites. (11) Determine the throughputs and fluxes through the central metabolic pathways of S. fradiae C373-10 & S. coelicolor 1147 to biomass. The throughputs would be calculated from the monomeric compositional data using the Holms (1986) approach. Assumptions were made, that central metabolic pathways were similar to E. coli, when there was no literature available to prove otherwise. (12) Compare the fluxes through the central metabolic pathways of S. fradiae C373-10 & S. coelicolor 1147 to biomass and to antibiotic production. Although the magnitude of fluxes in batch culture will be significantly different even between similar cultures. It should be possible to compare the ratio of flux to biosynthesis, to identify alterations in fluxes with the view of highlighting possible sites of regulation. (13) To investigate and develop on existing matrix algebra flux based techniques to the analysis of the fluxes through the central metabolic pathways of S. fradiae C373-10 & S. coelicolor 1147. The ultimate goal being to compare the strategies for flux analysis and undertake a further investigation in sensitivity analysis. With the main emphasis on defining how differences in compositional data and isoenzymes may affect the overall partitioning of flux. The above research has been undertaken; to investigate whether observations on specific rates of substrate uptake, the fate of individual medium components, specific growth rate, antibiotic production, shunt metabolites, oxygen uptake and carbon dioxide evolution could identify the enzymes or metabolic pathways most responsible for the overall reaction rate. This could result in the identification of areas concerned with regulation of these fluxes. Identification of such areas by flux determination would provide a foundation upon which further physiological and genetic studies could be based, thus contributing to a further understanding of the switch from primary to secondary metabolism in Streptomyces.
- Advisor / supervisor
- Hunter, Iain S.
- Resource Type
- DOI
- EThOS ID
- uk.bl.ethos.405328
- Funder
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