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This thesis and any amended versions thereof are subject to a 30-day
embargo prior to submission to any public repositories.
This thesis is for circulation only to the members of the examination panel, as
the work presented here is under a non-disclosure agreement.
1
2
Metabolic Analysis of Solventogenic Clostridium saccharoperbutylacetonic
um N1-4 (HMT)
Elizabeth Saunders
Thesis submitted to the University of Surrey for the degree of Doctor of Philosophy
Department of Microbial Sciences Faculty of Health and Medical Sciences
University of Surrey, Guildford, UK
Copyright © 2017 Elizabeth Saunders
Declaration
This thesis and the work to which it refers are the results of my own efforts. Any ideas, data, images or text
resulting from the work of others (whether published or unpublished) are fully identified as such within the
work and attributed to their originator in the text, bibliography or in footnotes. This thesis has not been
submitted in whole or in part for any other academic degree or professional qualification. I agree that the
University has the right to submit my work to the plagiarism detection service TurnitinUK for originality checks.
Whether or not drafts have been so-assessed, the University reserves the right to require an electronic version
of the final document (as submitted) for assessment as above.
Elizabeth Saunders
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Dedication
For my parents
4
Abstract
The market for solvent production is predicted to reach $43.4 billion in 2018, with n-butanol
having over 20% market share value where n-Butanol is the chemical precursor of several
industrially important products, such as butyl-acetate, butyl-acrylate, glycol-ethers, and
plasticisers. Butanol is currently produced from crude oil, and therefore in light of dwindling
fossil fuel reserves, and more importantly, the need for green and clean production
processes, synthesis of bio-butanol from biomass using Clostridia represents a viable and
desirable alternative method.
This project focuses on the metabolic and physiologic characterisation of the acetone-
butanol-ethanol (ABE) producing species Clostridium saccharoperbutylacetonicum (Csb). A
minimal medium for Csb was defined based on literature data, modified by the addition of
glutamate to support growth. Interestingly, batch cultures using this medium showed that
Csb was able to grow and produce butanol under aerobic conditions, with titres of
approximately 74% of those observed under anaerobic conditions.
Steady state cultures in chemostats are essential to elucidate and characterise physiological
features of microorganisms. Steady state cultures of Csb were used to determine the effect
of acid production on solventogenesis, bacterial growth, and energy metabolism. Studies at
different pH in the range 5.5 to 6.5 showed no correlation with the onset of
solventogenesis. However, the pH and the growth rate seem to influence the productivity of
butanol. In those experiments, significant increases in the production rate of butanol were
observed when the dilution (growth) rate increased from 0.01 h-1 to 0.03 h-1 and the pH
decreased from 6.5 to 5.5. Growth is potentially linked to production rate due to an
increased demand for ATP and NADH recycling.
The use of genome scale metabolic models allows for the interpretation of metabolic and
physiological changes upon changes in the culture conditions. A metabolic model of Csb was
constructed based on the genome sequence of the microorganism and incorporating
biomass synthesis equations specific for Csb which were constructed based on the analysis
5
of the composition of the cells grown in the chemostat experiments, as opposed to current
models that use biomass composition from related species (e.g. B. subtilis).
The metabolic model was used to perform flux balance analysis to identify and interpret the
changes in the distribution of metabolic fluxes that would explain the metabolic changes
observed in Csb cultured under different conditions.
This work has demonstrated the basis for the presence of monophasic solventogenesis in C.
saccharoperbutylacetonicum and provided important tools (defined media, GSMN
equations) to improve industrial scale production of renewable sources of carbon-based
feedstocks and thus reducing reliance on crude oil.
6
Acknowledgements
Thank you, Dr. Claudio Avignone-Rossa for your guidance and mentorship throughout my
PhD. I’m incredibly grateful to have had the privilege of pursuing a PhD and I couldn’t have
done it without you.
Thank you to my co-supervisors, Dr. Preben Krabben (Green Biologics), Dr. Jose Jimenez and
Dr. Hazel Housden (Green Biologics) for their insight and mentorship.
Thank you, Dr Douglas Hodgson, for your mentoring with the bioreactors and friendship.
Thank you to Sonal Dahale for all of your help with the modelling and being so patient with
my endless questioning.
I am incredibly grateful to have had the privilege of a scholarship to fund my studies from
the BBSRC and Green Biologics.
To my Mum and Dad, I couldn’t have done it without you.
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Abbreviations
ABE fermentation – acetone, butanol, ethanol
ATP – Adenosine triphosphate
CGM - Clostridial growth medium
CHB – Choline Binding Proteins
Csb - C. saccharoperbutylacetonicum
DSMZ - Deutsche Sammlung von Mikroorganismen Zellkuluren
E. coli – Escherichia coli
ELSD – Evaporative light scattering detector
GDH - Glutamate dehydrogenase
GS-GOGAT - Glutamine synthetase- Glutamine oxoglutarate aminotransferase
FBA – Flux balance analysis
FVA – flux variability analysis
HSP – Heat shock protein
JGI – Joint Genome Institute
PABA – 4-aminobenzoic acid
PTS - Phosphotransferase System
PFOR – Pyruvate: Ferredoxin oxidoreductase
MES– 2-(N-morpholino) ethanosulfonic acid
MFA – Metabolic flux analysis
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RPM – Relative centrifugal force
RCM – Reinforced clostridial medium
RID – Refractive index detector
RnF – Ferredoxin: NAD+ oxidoreductase
RT - Retention time
Wt. – Wild type
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Contents
Chapter 1: Introduction..........................................................................................................18
1. 1: Biofuels and renewable chemicals..............................................................................19
1. 2: Industrial production of Acetone, Butanol, Ethanol (ABE) chemicals.........................21
1. 2. 1: Historical production...........................................................................................21
1. 3: Clostridia as chassis for biofuel production................................................................24
1. 4: Solvent production in Clostridium saccharoperbutylacetonicum................................27
1. 5: Genetics and metabolism of solventogenesis.............................................................28
1. 5. 1: Butanol Metabolism............................................................................................30
1. 5. 2: The influence of pH on the initiation of solventogenesis....................................33
1. 5. 3: Metabolic Modelling and analysis of metabolic capabilities...............................34
1. 5. 4: Efforts to model the initiation solventogenesis...................................................40
1. 5. 5: Butanol Toxicity...................................................................................................42
1. 6: Factors affecting metabolism and yields.....................................................................45
1. 6. 1: Growth of C. saccharoperbutylacetonicum in defined minimal media................45
1. 7: Theoretical and experimental approaches for studying solventogenesis...................46
1. 7. 1: Multifactorial statistical methods for high-throughput experiments..................47
1. 7. 2: Continuous Cultures in the Chemostat................................................................49
1. 8: Aims, objectives and impact.......................................................................................57
1. 8. 1: Aim:.....................................................................................................................57
1. 8. 2: Objectives............................................................................................................57
1. 8. 3: Impact................................................................................................................. 58
Chapter 2: Materials and Methods........................................................................................59
2. 1: Organism.....................................................................................................................60
2. 2: Culture media.............................................................................................................60
2. 3: Strains and Working cell bank.....................................................................................63
2. 4: Initial Media Screening...............................................................................................64
2. 5: Placket-Burman Design Experiment............................................................................64
2. 6: Analytical methods.....................................................................................................64
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2. 6. 1: Analysis of Solvents and Organic Acids................................................................64
2. 6. 2: Analysis of Sugars................................................................................................65
2. 6. 3: Cell Biomass concentration.................................................................................66
2. 6. 4: Determination of extracellular sucrose concentration by enzymatic assay........66
2. 6. 5: Determination of phosphate consumption.........................................................67
2. 6. 6: Determination of extracellular sucrose consumption by reflectometry..............67
2. 6. 7: Determination of extracellular ammonium concentration..................................68
2. 6. 8: Determination of extracellular phosphate concentration...................................68
2. 6. 9: Determination of extracellular L-glutamate concentration.................................69
2. 6. 10: Determination of the cellular macromolecular composition............................69
2. 7: Batch Culture..............................................................................................................72
2. 7. 1: Bioreactor............................................................................................................72
2. 8: Steady state cultures of C. saccharoperbutylacetonicum...........................................73
Chapter 3: Design of an optimised medium for metabolic analysis in C.
saccharoperbutylacetonicum.................................................................................................75
3. 1: Introduction................................................................................................................76
3. 2: Results and discussion................................................................................................79
3. 2. 1: Media Screening and Optimisation.....................................................................79
3. 2. 2: Yeast extract increases solvent production yields per biomass...........................82
3. 2. 3: Nutrient requirements for biomass production..................................................85
3. 2. 4: Organic nitrogen source (amino acids) is essential for growth............................89
3. 2. 5: The Effect of Glutamate Addition........................................................................90
3. 3: Solvent analysis shows drastic increase of butanol yields in defined media...............91
3. 4: Aerobic conditions do not inhibit solventogenesis in Clostridium Saccharoperbutylacetonicum.............................................................................................95
3. 5: Batch culture of C. saccharoperbutylacetonicum in Bioreactors................................97
3. 5. 1: Time profile of pH change in C. saccharoperbutylacetonicum culture................99
3. 6: Discussion.................................................................................................................101
Chapter 4: Analysis of solventogenesis in steady-state cultures..........................................109
4. 1: Chapter overview and objectives..............................................................................110
4. 2: Results and Discussion..............................................................................................112
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4. 2. 1: Maintenance Coefficients for C. Saccharoperbutylacetonicum in chemostat culture...........................................................................................................................119
4. 2. 2: Macromolecular composition and implication for physiology and metabolic modelling...................................................................................................................... 119
4. 2. 4: Carbon and nitrogen balances...........................................................................123
4. 2. 5: Specific production rates of solvents and organic acids in C. saccharoperbutylacetonicum........................................................................................131
4. 3: Chapter Summary.....................................................................................................135
Chapter 5: Reconstruction of the Genome Scale Metabolic Network of C.
saccharoperbutylacetonicum...............................................................................................141
5. 1: Chapter Introduction................................................................................................142
5. 2: Construction of the model and biomass equations..................................................145
5. 3: Constraining the GSMN............................................................................................152
5. 5 Validation of the GSMN using mutation data............................................................155
5. 3. 1: Alcohol dehydrogenase knockout mutant.........................................................156
5. 3. 2: Butanol- Ethanol knockouts..............................................................................157
5. 3. 3: Butanol Knockout via bdh..................................................................................159
5. 4: Conclusion................................................................................................................ 161
Chapter 6: Conclusions.........................................................................................................162
6. 1: Impact and recommendations for industrial applications........................................163
6. 2: Further Work............................................................................................................164
Chapter 7: References..........................................................................................................166
Appendix 1 Problem file Template.......................................................................................176
Appendix 12 Definition of the biomass equation (Chapter 5)..............................................191
Appendix 13 Placket-Burman calculations (Chapter 3)........................................................192
12
List of Figures
No. Title Page1 2D line structures (created in ChemDraw of n-butanol, isobutanol 2-butanol and tert-
butanol.21
1.1 Variation in the price of crude oil ($/barrel) from 1946-2016. Data from Trading Economics (Tradingeconomics.com, 7th April 2018).
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1.2 A phylogenetic tree of a sample of some of the diverse species within the genus exhibiting solventogenesis, pathogenesis and aerotolerance. Green circles denote butanol producers used in industrial biotechnology. Red squares denote risk group 2 species. Reproduced with permission from Gyulev et al., 2017.
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1.3 Comparison of the sol operons between three different species: C. acetobutylicum; C. saccharoperbutylacetonicum and C. beijerinckii. The percentage values represent the homology of nucleotides in each of the genes between the various species. Figure from (Kosaka et al., 2007) .
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1.4 Figure 1 1-4: A schematic representation of the butanol, acetone and ethanol production pathways (Becerra et al., , 2015; Dahlsten et al., 2014; Li et al., 2011; Matsuda et al., 2013; Millat, Janssen, Bahl, Fischer, & Wolkenhauer, 2011).
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1.5 An example of the cyclical nature of the change of elements between assemblies. 49
1.6 A photographed example of a bioreactor set up. B: A simplified schematic for the set-up of a bioreactor for chemostat culture.
55
3.1 Production of butanol, acetone and ethanol alongside the amount of sucrose consumed after incubation at 30oC for 72 hours in anaerobic conditions (n=3). Error bars indicate standard deviation of biological triplicates.
82
3.2 Different growth by optical density observed for various concentrations of yeast extract in the basic Biebl media after 24 hours anaerobic incubation (n=3) (error bars – standard deviation)
86
3.3 Of the 12 trials used in the Placket-Burman experiment (n=3; error bars represent standard deviation). Trial 12 is the control with no additional amino acids. Trials 8 and 9 contain the amino acids as detailed in the methods and were the worst and best performers respectively regarding cell density (by OD 600nm).
89
3.4 t-values for each amino acid tested plotted over the 24-hour period (n=3) (OD600nm). The purple line indicates the inhibitory effect of tyrosine over time. The pink line (lysine) appears to switch from negative to positive over time with the switch occurring between 13-15 h, approximately around the time of the phase transition from acidogenesis to solventogenesis is appears to happen.
91
3.5 The t-values calculated using multifactorial statistics for each amino acid analysed for the OD600 after 24 hours from the PBD experiment
92
3.6 Biomass (OD600) from batch culture in Biebl media supplemented with 10mM of the corresponding amino acids 24 h (n=3) (error bars – standard deviation) using the basic Biebl medium supplemented with 10mM of either: leucine, histidine, lysine or glutamate with no trials containing any yeast extract.
93
3.7 Biomass after 24hour incubation for Biebl recipes where a dose-response 94
13
experiment to glutamate was carried out (n=3) (error bars – standard deviation).
3.8 Results from Trials 1 to 12 of the Placket-Burman design experiment (n=3) for acetone, ethanol and butanol production. Figure B: actual concentration produced (g/l). Figure A: Proportion of solvents produced.
95
3.9 Butanol produced as a proportion of optical density for each of the 12 trials after 24 hours of incubation (n=2) (error bars – standard deviation) indicate the yield of butanol per biomass unit (OD). Throughout the experiment, ethanol showed trace levels.
97
3.10
The t-values taken from the results of the multifactorial Placket-Burman analysis taking into account the production of acetone and butanol after 24 hours; highlighting the change of effect of each of the amino acids over time which could elucidate how the different phases of acidogenesis verses solventogenesis are affected.
98
3.11
Composition of gases used to determine the optimal gas for use in the culture of C. saccharoperbutylacetonicum. Gas composition based on certificate of analysis from supplier. OFN -Oxygen Free Nitrogen. OFN(CO2) – Oxygen Free Nitrogen with carbon dioxide. AGM – Anaerionc Growth mixture. (BOC, a UK Industrial supplier of gasses, Guildford, UK).
99
3.12
Production of acetone, butanol and ethanol in glutamate-supplemented Biebl media when using different gas conditions after 16 h (see Table 3-3), 3 biological triplicates. A t-test was used to determine significance from the control, anaerobic growth mixture.
100
3.13
The pH evolution of the culture over time of biological triplicates 1.5l batch cultures with a pH control of ≥5.5pH. Error bars show standard deviation
102
3.14
The data points between 6 and 12 h (linear section of the data after being transformed) are shown here after being transformed using a Log10 function. The equation indicates that for these conditions the growth rate 0.044 h-1.
103
3.15
Accumulation of the organic acids and solvents as the pH was left uncontrolled in RCM. Error bars show standard deviation. n=3.
104
3.16
A schematic map of metabolism showing where the amino acids in the PBD experiment are synthesised
107
3.17
A simplified schematic show pyruvate about ABE fermentation and two amino 112
4 Detailed sampling protocol for one run of a chemostat. Full sample were taken at 96, 192 and 288 hours. The Additional readings for OD which were taken in the first replicate were not repeated in the second to reduce the risk of contamination.
118
4.1 Details of the consumption and production of substrates and products observed in the steady-state cultures. Carbon-moles calculated, where carbon is found in the molecules, by calculating the percentage weight of carbon in g/L in butanol and then converting into moles to make carbon moles. The same was applied to ammonium sulphate and phosphates for nitrogen and phosphate respectively.
120
4.2 Chemostat data for sucrose consumption over dilution rate indicating the 123
14
minimum requirements for growth (qSucrose) where the linear regression cross the y-axes (C in y=mx+c). With quotients of 3.058 (moles l -1 h-1)-1 for cell at pH 5.5 and 2.956 (moles l-1 h-1)-1 for cells at pH 6.5 when using defined media.
4.3 Schematic of ABE in C. acetobutylicum detailing the amount of carbon found in products (red) and the substrate (blue) (Papoutsakis, 2008)
132
5 Biochemical pathways of ABE fermentation and the location for knocked out genes discussed in this chapter. 160
List of TablesNumber
Title Page
1 Key details of other solventogenic clostridia (J. Lee et al., 2008; Milne et al., 2011; Senger & Papoutsakis, 2008).
41
2 Modifications to the Biebl media B1-5. 612.1 Details modifications made to the media described in "Plackett-Burman basal media”. 623 Composition of Biebl defined medium (Biebl, 1999). Glucose was used as the carbon source for
batch (28gl/L) and for continuous culture (60g).79
3.1 Details modifications made to the media described in "Plackett-Burman basal media”. 844 Yields of product per substrate consumed using the data in Figure 4. No units given as they are
cancelled out during the calculation122
4.2 Macromolecular composition of C saccharoperbutylacetonicum grown in chemostat cultures given as percentage. Data is presented as a percentage of biomass (calculated from DCW). **Carbohydrates are expressed in glucose equivalents. *Lipid content was calculated from a 1 000X concentrated overnight culture to meet the sensitivity range of the assay which the chemostat samples could not reach due to the low biomass concentrations observe.
125
4.3 Macromolecular composition of B. subtilis (Dauner & Sauer 2001) . 1264.3 Details the production of biomass, butanol, acetone, ethanol, acetic acid, butyric acid in carbon
moles. Along with the carbon-moles of carbon consumed in order to calculate the carbon balance for the steady-state culture of C. saccharoperbutylacetonicum Carbon-moles calculated, where carbon is found in the molecules, by calculating the percentage weight of carbon in g/L in butanol and then converting into moles to make carbon moles.
131
4.4 The theoretical yields of product if supplied with 11 glucose molecules as shown in Figure 4.1. This data does not reflect experimental as it does not consider regulation of the pathways, but this theoretical data can be used to give an indication of what pathways are active (or not active) when compared to experimental data. As the theoretical data shows what can be produced if there were no bias in the pathways, mediated by enzyme regulation.
133
4.5 Heat map depicting how closely the experimental data yields (carbon mole product: Carbon mmoles sucrose only) match that of the theoretical yields calculated in Table 4.5. Yields lower than theoretical move towards red. Yields higher than theoretical move towards green. Yellow, the midpoint, is set at the theoretical yield (bottoms of column).
134
4.6 Nitrogen balancing for biomass and nitrogen containing substrate. 1354.7 The specific production rates calculated for the products measured during the steady-state
investigation. ND = Not detected138
5.1 Macromolecular composition of C saccharoperbutylacetonicum grown in chemostat culture. **Carbohydrates are expressed in glucose equivalents. *Lipid content was calculated from a 1 000X concentrated overnight culture.
149
5.2 Chemical composition of DNA in C saccharoperbutylacetonicum from chemostat culture.
150
5.3 Protein composition curated from the genome and JGI database for Csb and using the compositions percentages shown in Table 5.1.
151
5.4 Chemical composition as percentage of biomass in C saccharoperbutylacetonicum from 152
15
chemostat culture.5.5 Details the constraints used in the problem files created for running the simulations in mmol h-1
DCW-1.156
5.6 Denotes the fluxes found during the simulations run with the constraints from Table 5.5. Results for the simulations run a 6.5, dilution rate 0.01, 0.2 and 0.03 h-1 were all the same and hence are represented in the Table below with a single entry for pH6.5. The same was found pH 5.5 and the data presented similarly.
156
5.7 Denotes the percentage change found in the production of products in Tryptone-Yeast media compared to wildtype C. saccharoperbutylacetonicum. Mutants and data supplied by Green Biologics. No change in production between wildtype and mutant is denoted at 0%. Due to the existing non-disclosure agreement, the raw data cannot be shown here.
158
5.8 The in-silico results for solvent and acid production at pH5.5 and 6.5 with and without the adhE knockout.
158
5.9 The in-silico results for solvent and acid production at pH5.5 and 6.5 with and without the crt/bcd knockout.
160
5.10 The in-silico results for solvent and acid production at pH5.5 and 6.5 with and without the crt/bcd adhE knockout.
161
5.11 The in-silico results for solvent an acid production at pH5.5 and 6.5 with and without the bdh knockout.
162
16
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Chapter 1: Introduction
18
1. 1: Biofuels and renewable chemicals
Considering the growing issue of finite fossil fuel stocks, and the development of expensive
and potentially environmentally detrimental techniques such as fracking, the search for
alternative, green and sustainable fuel sources has intensified (du Preez, 2016). Alternative
renewable energy sources like wind and solar, in addition to nuclear, are being researched.
However, there will still be a requirement for transportable liquid-based fuels such as
ethanol or butanol where practical and infrastructure limitations exist and delivering
electricity or gas is not feasible. Therefore, the market for butanol/ethanol based fuels and
blends with typical petroleum based products requires continued effort from academic and
industrial research (Savage et al., 2008) . These alcohol-based products have been produced
through various mechanisms. Traditionally these compounds can be synthesised from crude
oil however, with reserves reaching the limitations of economically competitive oil
extraction and climate change destroying communities and the environment, alternatives
are being sought after (Hoegh-Guldberg et al., 2008; Newman et al., 2009). Some
alternatives to crude oil for producing petro-chemicals are plant biomass and agricultural
waste (Lan & Liao 2013) . Agricultural waste along with the right bacterial catalyst can
produce a plethora of valuable compounds currently sourced from crude oil. First
generation biofuels primarily use carbon sources such as edible sugar, flours or vegetable
oil, for the synthesis of alcohol-based fuels. Ethical and societal concerns prompted the
replacement of those biofuels by second generation biofuels, produced from using
otherwise inedible carbon sources and so reducing the competition for agricultural
landmass if those are sourced from waste outlets (Savage et al., 2008). These carbon
19
sources can be converted to alcohol-based fuels via the utilisation of bacterial or algal
processes, for example.
Currently, a limitation of some of these processes is that they are not competitive with the
cost effectiveness of petroleum or shale gas sources. However, with decreases in oil
reserves, and the resulting increasingly expensive extraction processes, the potential exists
for a viable market for the production bio-alcohols as an fuel source alternative to crude oil
derivatives (Savage et al., 2008; Lan & Liao, 2013) .
Ethanol production has been one of the most intensely studied bio-alcohol processes, due
to its prehistoric role in beverage and food production. However, there are limitations in its
use, as it is corrosive in some storage environments, making stable and durable
infrastructure less economical. Butanol, however, is less corrosive in standard storage
conditions in addition to having a more similar energy density to gasoline (Al-Shorgani et al.,
2012; Savage et al., 2008) . Butanol production will be the focus of this work.
Butanol is present in four isomeric forms: n-butanol, isobutanol, 2-butanol and tert-butanol,
all of them clear colourless liquids (at room temperature). The primary alcohol is n-butanol,
with 2-butanol and tert-butanol the secondary and tertiary alcohols respectively (Figure 1.)
(Sarathy et al., 2012). The chiral nature of 2-butanol means that is appears as both
laevorotary and dextrorotary forms in racemic mixtures. In an industrial context, both 2-
butanol and tert- butanol are produced via chemical catalysis and can be used as solvents.
Both n-butanol and isobutanol can be produced biologically using Escherichia coli,
Saccharomyces species and Clostridium species.
20
Figure 1: 2D line structures of n-butanol, isobutanol 2-butanol and tert-butanol.
1. 2:Industrial production of Acetone, Butanol, Ethanol (ABE) chemicals
1. 2. 1: Historical production
Industrial solvent production by bacteria was first investigated during the First World War,
resulting in the development of the microbial production of acetone, which was used as a
solvent for making the explosive cordite (Gibbs, 1983). However, after the war, the interest
in this process disappeared, due to a decrease in demand for acetone and an increase in
knowledge of using crude oil to synthesise other organic products. It is believed that many
profitable strains were lost during this time, as they had not been preserved. It is recently,
within the last ten years, that the rise in crude oil prices due to increasing difficulty in
accessing reserves (De Almeida & Silva, 2009) plus pressures for more carbon neutral fuel
sources, has rekindled the worldwide interest in the microbial production of solvents
(Krabben, 2014).
Chaim (Charles) Weizmann started at Manchester University and was also part of a research
group at the Pasteur Institute focussed on rubber production from fermentation. Chaim
21
later left the group to continue his research at Manchester where he found that butanol was
an essential precursor for rubber production in microbial fermentations (Gabriel &
Crawford, 1930). Weizmann isolated several strains which were able to produce butanol in
starch based media (Jones & Woods, 1986). This research kick-started industrial scale
fermentation as we know it in the early 1900s with the Weizmann Organism (Clostridium
acetobutylicum) (Jones and Woods, 1986). With the support of the British government
(notably Winston Churchill and David Lloyd George) and British Gin distilleries, acetone
fermentation from maize was rolled out to meet acetone demand for cordite production
during World War 1, alleviating the Shell Crisis of 1915. Clostridium acetobutylicum became
the most widely studied organisms for acetone-butanol-ethanol fermentation on an
industrial scale.
1.2.1.1The need for biofuels.
Over the last 15 years there has been a significant increase in the cost of oil which has
resulted in bio-based fuel additives like bioethanol becoming a viable commercial option for
production (Demirbas, 2009). This also applies to biodiesel and biogas production used in
transport fuels and combined heating & power (Naik et al., 2010).
A classic example of biofuels produced by biorefining in the UK is the production of
bioethanol from food crops (sugar beet, sugar cane, grains) as a first generation biofuel
(Mohr & Raman, 2013).
Regarding ethanol production, this can be done via chemical transesterification or
fermentation (Naik et al., 2010). The feedstock, corn or sugar for example, needs to be
ground down or steeped prior to fermentation. This can also involve an enzymatic step to
further make accessible the sugars contained within the grains. The fermentation then takes
22
place with the chosen organism and the resulting mash is then distilled to obtain the
ethanol. The expense of using an enzyme to release sugars from the biomass has fostered
interesting in using organisms that can produce ethanol from complex carbohydrate sources
(Naik et al., 2010).
1.2.1.2 Limiting Factors in Production
There are factors that limit the viability of biofuels produced this way, including cost-limiting
factors associated to the biorefinery process and the fluctuating price of oil. Furthermore,
social and political conflict can arise regarding balancing food crops for biofuels and
maintaining food security and food prices (Demirbas, 2009). In this respect second
generation biofuels could avert this conflict by using non-food crops and marginal land.
There is still need for sustainability assessments to aid the sustainable roll-out of this
technology (Mohr and Raman, 2013).
The volatility of the crude oil market of the last 100 years can be linked to various global
political events and can go some way to explain why investment in biofuel research faltered
after the Second World War (Figure 1.1). The data highlights the less volatile (relative to the
recent volatility in the last 20 years) period for crude oil prices in the post-second world war
period of just a few dollars per barrel. From the mid-1970’s, crude prices increase and
become much more volatile. It is both this volatility and high prices, coupled with political
motivations for energy security, that investment, both public and private, has seen
improvements in recent years.
23
Figure 1.1. Variation in the price of crude oil ($/barrel) from 1946-2016. Data from Trading Economics (Tradingeconomics.com, access date 7th April 2018).
1. 3: Clostridia as chassis for biofuel production
The genus Clostridium belong to the Clostridiaceae family, order Clostridiales, class
Clostridia, phylum Firmicutes. The members of the genus Clostridium are Gram positive
endospore formers. Their motility is mediated by the presence of pili covering the external
surface of the cell. Although Clostridia is considered an obligate anaerobic class, some
species, including C. saccharoperbutylacetonicum (Csb), are aerotolerant (Al-Shorgani, et al.,
2015). Clostridium is also known for its fermentative nature, which can potentially be self-
limiting depending on the products formed, as optimal growth rates are found around
neutral pH. This means that the production of compounds that are acidic could be
deleterious to survival as they lower the pH. This is observed in Csb (Schlegel & Zaborosch,
1993) .
There are four solventogenic Clostridium species that have primary use in industry:
Clostridium beijerinckii, Clostridium acetobutylicum, Clostridium saccharobutylicum and
24
Clostridium saccharoperbutylacetonicum. Their scientific and industrial interest has resulted
in the development and utilization of mutant selection via CRISPR for all four of the species.
Figure 1.2 highlights how these four species appear in two clades with Clostridium
beijerinckii and Csb clustering together; whilst Clostridium acetobutylicum and Clostridium
saccharobutylicum clustering nearer to the pathogenic strains Clostridium botulinum and
Clostridium tetani, suggesting that solventogenesis may be widespread throughout the
genus. However, further research is required to confirm this (Gyulev et al., 2017).
25
Figure 1.2: A phylogenetic tree of a sample of some of the diverse species within the genus exhibiting solventogenesis, pathogenesis and aerotolerance. Green circles denote butanol producers used in industrial biotechnology. Red squares denote risk group 2 species. Reproduced with permission from Gyulev et al., 2017.
26
The model strain for studying ABE production in Clsotridia is Clostridium acetobutylicum,
which has been the most widely studied in the literature (Lütke-Eversloh & Bahl, 2011).
Clostridium saccharoperbutylacetonicum (Csb) is a close relative and is the species used in
this work as it is considered a hyperproducer of butanol (Biebl, 1999), producing a higher
ratio of butanol to the other solvents (Herman et al., 2017) .
1. 4: Solvent production in Clostridium saccharoperbutylacetonicum
Csb was initially described in 1960 and was shown to produce a higher butanol/acetone
ratio (4:1 rather than 2:1) than other species of the genus Clostridium, such as C.
acetobutylicum (Biebl, 1999). Csb is a spore-forming species with larger cells than that of C.
acetobutylicum and containing a distinctly different genotype (Kalia et al., 2011).
Batch and continuous culture methods have been used to characterise the metabolism of
the bacterium regarding solvent production (Biebl, 1999) . In that work, different media
were used to highlight differences in butanol (and acetone & ethanol) production (Biebl,
1999) . The investigation was carried out in batch cultures and showed that when cultured
at different pH levels, butanol and acetone production in Csb started to decrease at a higher
pH than that of a C. acetobutylicum strain (Biebl, 1999) . Intermediates such as butyric acid
and acetic acid were constantly significantly lower throughout all the pH conditions tested
for the Csb strain compared to C. acetobutylicum. Continuous cultures mimicked the same
results regarding pH differences and intermediate production. The production of
acetone/butanol was about 20g/l lower than the Csb strain and lower than the DSM 795
strain (Biebl, 1999) . It was further demonstrated that at lower dilution rates, solvent
27
production was reduced in C. saccharoperbutylacetonicum. This is possibly due to a
reduction in the availability of the precursor acetone. It is substrate availability which
facilitates the action of CoA transferase as mediated by the reduction of metabolic
intermediates such as organic acids to solvents (Biebl, 1999) . Lower concentrations of
intermediates found in Csb could be attributed to a more efficient downstream metabolism.
Butyric acid and butanol are inhibitors of solvent production as they affect cell viability due
to their effect on pH and membrane stability (Baer et al., 1987). Although inhibition of
butanol production by butyric acid/butanol is comparable in both strains, the lower level of
intermediates produced in the Csb strain is promising for producing greater quantities of
solvent due to the reduced concentration of inhibitory intermediates (Biebl, 1999) .
1. 5: Genetics and metabolism of solventogenesis
In Csb, solventogenesis, the final step in ABE fermentation, is under control of the sol
operon, which contains the genes for solvent production, including: butyraldehyde
dehydrogenase (bld) ; CoA transferase subunits A and B (ctfA, ctfB) ; acetoacetate
decarboxylase (adc) ; and alcohol dehydrogenase (adh), as seen in Figure 1.3 (Fischer etal.,
1993). In Csb and C. beijerinckii the operon is located in the chromosome, unlike C.
acetobutylicum where it is located in a plasmid with the sol operon containing aad, ctfA and
ctfB. The loss of solventogenesis observed after repeated subcultures of C. acetobutlycim
has been associated with the genes being in a plasmid; thus Csb has potentially advantages
in industry as it is more likely to retain the capability for solventogenesis over long culturing
periods. However, the solventogenic genes contained within the sol operon vary between
solvent producing species. The saccharolytic species Clostridium beijerinckii and Csb have an
operon structure identical to the amylolytic C. acetobutylicum. However, an N1-4
28
degenerate strain containing the wild-type genotype of sol operon failed to induce butanol
production fully (Kosaka et al., 2007). Degenerate strains were created by recurrent
subculturing which eventually resulted in strains that did not produce butanol.
Transcriptomics confirmed that this was due to a significant reduction in the expression of
the sol operon (Fischer et al., 1993; Kosaka et al., 2007). This clearly indicates that other
factors such as regulatory proteins, RNA, and metabolites are essential for the induction of
solvent production and highlights the need to understand how different operons and
Induction mechanisms are connected to overcome such problems (Kosaka et al., 2007) .
In addition to having one of the largest genomes in the genus, Csb differs in that it has
choline in the teichoic acids of its cell walls. Furthermore, the presence of 66 choline binding
proteins (CHBs) in its genome which could be involved in physiological roles, has not been
observed in C. beijerinckii or C. acetobutylicum (Del Cerro et al., 2013). Some of these CHBs
have been found to be similar to those involved in pneumococcal cell wall lysis. These may
have played a role in Csb’s evolution as part of the soil microbiome where there may have
evolutionary lysogenic relationships (López et al., 1997).
1. 5. 1: Butanol Metabolism
Solvent and acid production (Figure 1- 4) occurs downstream of pyruvate formation.
Pyruvate is converted to acetyl-CoA, and this is the metabolic branching point towards the
synthesis of the different organic acids and solvents. Acetic acid is produced with the
release of 2 molecules of ATP: The acetic acid is used for acetone production (Maiti et al.,
2016). For acetone production, two molecules of acetyl-CoA condense to produce
29
acetoacetyl-CoA. Acetate acts as a CoA receiver, leading to the formation of acetoacetate,
from which a CO2 molecule is removed to produce acetone (Buehler & Mesbah, 2016).
Butanol and butyric acid synthesis also derive from acetoacetyl-CoA, which is reduced to β-
hydroxybutyryl-CoA. A water molecule is removed to form crotonyl-CoA, which is
subsequently reduced to butyryl-CoA. This is the branching point to produce butanol and
butyric acid. Acetate binds with CoA to form butyric acid, with the release of ATP. For
butanol, a two-step reduction converts butyryl-CoA first into butyraldehyde and then to
butanol. Solvent fermentation is an energy-producing mechanism in this anaerobic species
as it produces ATP and recycles NAD+ to be used in glycolysis. The production of butyric acid
and acetic acid generate ATP and the reduction reactions (two between acetoacetyl-CoA
and crotonyl-CoA and another two between butyryl-CoA and butanol) regenerate the NAD+
required in glycolysis, the primary energy-generating pathway in anaerobic metabolism
(Mazzoli 2012; Schlegel & Zaborosch 1993) .
However, the production of acetic and butyric acid generates an acidic environment. As it is
known that clostridia species prefer pH neutral growth media (pH 6-7) , a decrease in pH
damages the cells and inhibits growth, although this pH range (6-7) does not necessarily
support optimal conditions of solventogenesis (Mock et al., 2015).
Phosphotransferase system (PTS) is the mechanism by which glucose and fructose are taken
into the cell via active transport (Tchieu et al., 2001). There is a conserved metabolic
pathway between C. acetobutylicum and C. beijerinckii for utilisation of sucrose via PTS. The
genes for these are located on the scr operon. The sucrose operon first described by Reid et
al. (1999) showed that there are four genes responsible for the sucrose specific enzyme for
30
the PTS (ScrA), the transcription repressor (ScrR), a sucrose Hydrolase (ScrB and a
fructokinase (ScrK).
In C. beijerinckii, sucrose 6-phosphate is hydrolysed to glucose 6-phosphate and fructose.
The fructose is phosphorylated by fructokinase to fructose 6-phosphate. The two
phosphorylated sugars are converted by the glycolytic pathway, ultimately yielding
pyruvate.
31
Figure 1 1-4: A schematic representation of the butanol, acetone and ethanol production pathways (Becerra et al., , 2015; Dahlsten et al., 2014; Li et al., 2011; Matsuda et al., 2013; Millat, Janssen, Bahl, Fischer, & Wolkenhauer, 2011).
32
The enzyme pyruvate: ferredoxin oxidoreductase (PFOR) is a pyruvate synthase that
catalyses the reaction between pyruvate and acetyl-coA (Figure 1.4) and carbon dioxide in
many anaerobic and hydrogen producing organisms including Csb (Furdui & Ragsdale,
2000). Data shows that PFOR can catalyse the reaction between pyruvate and acetyl-coA in
either direction (Cho, et al., 2015). The RnF complex electrons are transferred from reduced
ferredoxin to NAD+ resulting in oxidised ferredoxin and NADH. This system is also coupled to
the transport of sodium ions across the cell membrane (Hess, Schuchmann, & Müller, 2013).
PFOR and the RnF complex generate large reducing capability, especially useful in anaerobic
bacteria which use oxidative phosphorylation to generate ATP, as these electron transfer
chains are an efficient way of producing ATP.
1. 5. 2: The influence of pH on the initiation of solventogenesis
The consensus is that C. acetobutylicum has a biphasic pattern for solventogenesis and
acidogenesis and the same is assumed for Csb although there is little in the literature
detailing compared to C. acetobutylicum. It is considered that acidogenesis is initiated at the
onset of a culture, potentially to generate ATP through the production of acetic acid and
butyric acid. Solventogenesis is thought to occur after a reduction in pH (possibly because of
the earlier acidogenic phase). Where the ctfA/B genes are activated it is possible to convert
butyric acid back into butyryl-coA, generating the capability to lower the pH that had
initiated solventogenesis (Grimmler et al., 2011; Grupe & Gottschalk, 1992; Sauer & Dürre,
1995).
The hypothesis that solventogenesis of butanol and acetone is a mechanism for reversing
the pH drop caused by the production of the organic acids, acetic acid and butyric acid was
33
analysed experimentally (Hüsemann & Papoutsakis 1988) . In batch cultures with controlled
pH, studies were done to elucidate the value of pH at which solventogenesis started.
Solventogenesis is considered to begin when the concentration of butanol reaches 1 mM
(Millat, Janssen, Thorn, et al. 2013) . Other work has gone on to explore the role of
dissociated butyric acid on the onset of solvent production in C. acetobutylicum. It was
found that solvent production does not occur at pH values of 6 and that the onset of
solventogenesis was strongly correlated with the exceeding 1.5 g/l of dissociated butyric
acid (Monot et al., 1984) .
The pH-induced metabolic shift from acid production (acetic acid, butyric acid, etc.) to
solvent production in C. acetobutylicum has been kinetically modelled and subsequently
experimentally confirmed (Millat et al., 2011). Interestingly, it was found that butanol
production commenced regardless of the pH of the culture. Experimental data showed that
the initiation of butanol production started at concentrations of butyric acid of around 6 –
12 mM.
1. 5. 3:Metabolic Modelling and analysis of metabolic capabilities
There are different approaches to creating networks. The networks can cover protein-
protein interactions, gene regulation and signalling networks. Networks can be constructed
using the genome and reactome data. The networks nodes and edges can be constructed
from the products and the reactants of the biochemical pathways and the enzymes that
catalyse them (O'Brien et al, 2015).
Alternatively, signalling cascades can be modelled. Signalling cascades in allow a cell to
respond their external environment. The more reactions in a signalling cascade the more
34
opportunity for other signalling pathways to influence one another. Creating a networks
allows us to test hypothesis against the whole systems of signalling pathways in a call and
how they interact (D. R. Hyduke & Palsson, 2010).
Without the need to define kinetic parameters, Constraint Based Modelling (CBM) allows for
Metabolic Flux Analysis (MFA). CBM therefore requires that organisms are at steady-state,
meaning that parameters such as growth rate and substrate uptake are kept constant. With
MFA we are then able to predict unknown fluxes using linear optimisation programming and
forms the basis of Flux Balance Analysis (FBA)(Cazzaniga et al., 2014; Orth, Thiele, & Palsson,
2010).
Metabolic models are used for in silico analysis of bacterial metabolism. The use of in silico
methods allows for the testing of hypothesis regarding the mechanic of metabolism in a way
that is more time efficient and economical than laboratory methods that require the
creation the media otherwise specified in the problem file of a metabolic model (Cazzaniga
et al., 2014). However, in vitro and in vivo data is still crucial to making accurate
presumptions about bacterial metabolism. Furthermore, a validated model can also offer a
quick method to predict the outcome of knocking out or over-expressing genes which may
be of interest before laboratory work commences (du Preez 2016; King et al. 2017) .
Along with this and the awareness of branch points (acetoacetyl-CoA, butyryl-CoA, etc.) and
their metabolic flux rates, experiments can be focused on the expression of key enzymes
and potential inhibitory products and pathways. Furthermore, the calculation of theoretical
maximum yields can be used to identify targets for the optimisation of product yields
(Stephanopoulos et al., 1998).
35
Metabolism can be represented using a GSMN. The construction of a GSMN require the
DNA sequence of an organism which is annotated to provide the reaction (enzyme) list for
the construction of the network. As an example, the metabolic model for Escherichia coli
showcases the amount of detail and power contained within a well annotated model that is
open to the larger scientific community for testing and improvement (Ruppin, et al., 2010).
Metabolic cellular processes within bacteria consist of a network of biochemical reactions
that can be mapped out using the genome sequence of the species under study. This makes
the analysis of the system specific to the microorganism in question (Fiest et al., 2009).
Such reconstruction requires curation and validation, involving a spectrum of experimentally
derived data sets. It is possible to group key cellular mechanics into 3 themes: metabolism,
transcription and regulation. This work is particularly concerned with constructing metabolic
networks.
Historically, microbial metabolic networks were pieced together using biochemical methods
to elucidate reactants and their enzymes. The encoding of enzymes catalyzing reactions
across species and genus is to an extent conserved. Therefore, modern genome sequencing
methods can reconstruct pathways across genomes without extensive and time-consuming
biochemical experiments being carried out for each species (Pepescu et al., 2005). That is, if
the products/reactants, coefficients, location and reversibility of each reaction/enzyme have
already been determined and stored in a repository. For instance, databases such as KEGG
and SEED contain extensive range of reactions across species. SEED offers an automated
tool to build initial reconstructions, making use of the available data. However, automation
of any kind often accumulates errors where characterizations were incorrect or become out
36
of date. Further, work is still ongoing to elucidate sequences for otherwise unsequenced
enzymes (Fiest et al., 2009).
The automatic reconstruction of metabolic networks consists of utilizing such databases to
initially piece together the enzymes with the reactions they catalyze, resulting in a list of
gene-to-protein-to reaction associations. There are also limiting parameters that are not
considered in this automatic type of reconstruction, such as elemental balancing, cofactor
availability and proton availability (Pharkya et al., 2004). Further, there may be reactions
that that are unique (unique within the current collective libraries of all characterizations
ever carried out) to an organism (Fiest et al., 2009).
Therefore, manual curation of automated models is needed to fill gaps, remove reactions
that may not actually be active, or complement the initial annotation to reflect the
requirements and ability of the species under consideration to utilize various media
components. Manual gap filling can be carried out by logical determination of what fills gaps
in an otherwise well characterized pathway or through direct experimentation in the species
(Fiest et al., 2009).
Once curation is "complete" (or at least at a level of completeness that can be determined
before running a simulation and finding errors), the reconstruction needs to be made into a
readable format that the chosen software can utilize. In general, these tools evaluate and
solve the system using a linear programming (LP or linear optimization) constrained by the
parameters defined from in vivo experiments (Schuetz et al., 2007).
In linear programming, the objective function is the value to be maximized (for biomass
production for example) based on the feasible solutions of the constraints. To have the
37
biomass as the objective function in LP, the composition of the biomass needs to be
determined, either experimentally or from literature data, in terms of lipids, protein, DNA,
RNA, and carbohydrates for example. Existing data and databases can then be used
(alongside genome data) to fill in the gaps to deliver an elemental composition of the
biomass. The use of chemostat experiments are particularly suited to providing this kind of
data due to their use of nutrient limited media and ability to maintain the culture at a
specified growth rate. Under these conditions, specific rates can be determined for product
(in this case, butanol) generation as well as biomass and for substrate consumption. Once
these requirements are known, the simulation will be able to determine whether the model
(consisting of the reactions and the available substrate) can support biomass generation.
Often, throughout this process additional gaps or other issues will highlight themselves and
this process for constructing the models is not as linear as described as there is occasional
need to circle back and add further curation (Fiest et al., 2009).
In addition to biomass as the objective function other coefficients can be calculated to
inform a GSMN. Non-growth associated maintenance and growth associated maintenance
(NGAM and GAM respectively) refer to the calculation of moles of ATP required to carry out
cellular activities, both those associated with growth and those which are not (Fiest et al.,
2009). Typical examples of reactions associated with growth can also be linked the cellular
material that is increased prior to cell duplication, namely: glycogen formation and DNA and
RNA replication; whereas maintenance not associated with growth may be linked to the
production of “house-keeping” metabolic functions, such as synthesis of motility proteins,
maintenance of ion gradients and cellular repair mechanisms (Goyal et al, 2017). Variation
in either of these can drastically affect estimations of growth rate in a species.
38
Once this refining is complete the model can be used as tool, to predict phenotypic changes
that occur when genes for pathways, or parts of pathways, are knocked out. This can be a
high throughput method to shortlist candidate genes to knockout to achieve a desired
change in phenotype (increased butanol production rates for example). Similarly, deletions
can be used to determine if there are alternative pathways available to make the same
product (Goyal et al, 2017). Additionally, such a model could be used for the elucidation of
new reaction pathways. For example, simulating growth in different substrates can lead to
the discovery of new transport and/or metabolism pathways.
A prime example of this can be found in the evolution of the library of knowledge available
for E. coli. Before genome sequences became publicly available, there was a steady flow of
data being published piecing together the pathways of cell wall, amino acid and nucleotide
synthesis using experimental data. With the advent of genome sequencing and the
development of associated metabolic, transcriptional and regulatory networks, a cascade of
workflows was unlocked that utilised and built upon previously discovered pathways.
Bringing together the data from network analysis, metabolic engineering, and phenotypic
profiles has resulted in E. coli being the most well characterised model organisms (Feist et
al., 2008).
Metabolic models have also been utilised in industry, for process involving Saccharomyces
and ethanol production. Models have been used to highlight several potential strategies to
alter redox metabolism, in order to redistribute the fluxes away from glycerol and towards
ethanol, leading ultimately to phenotypes with 40% decreased glycerol yield and 3%
increase in ethanol production. Here, the models provided a more targeted approach to
39
indicating a range of genes to target for deletion compared to that of the more time-
consuming repeated random mutagenesis cycles (Bro et al., 2006; Vemuri et al, 2007).
Another example of a model being used for improving industrial processes is the
development of E. coli amino acid overproducing strains. By deleting Threonine degradation
pathways in E. coli, a strain was developed that allowed increased production of L-
Threonine at 0.399 g per gram of glucose, up from 0.143g/g obtained with the initial strain
(Lee et al., (2007).
40
1. 5. 4:Efforts to model the initiation solventogenesis
There are metabolic models constructed for C. acetobutylicum and C. beijerinckii (Lee et al,
2008; Milne et al., 2011; Senger & Papoutsakis, 2008) but no model for Csb has been
constructed or annotated yet. The Clostridia models available could be used as a basis for a
model for Csb. More details about the construction of GSMN is given in the relevant
chapter.
41
Table 1 : Key details of models for solventogenic clostridia (J. Lee et al., 2008; Milne et al., 2011; Senger & Papoutsakis, 2008).
Species Model Reactions Metabolites
C. acetobutylicum Senger et al 2008 552 422
C. acetobutylicum Lee et al 2008 507 479
C. beijerinckii Milne et al 2011 938 881
Metabolic engineering can help elucidate mechanisms not only for greater final butanol
production concentrations but also but also increased solvent (butanol) tolerance, wider
utilisation of carbon sources, increased yields of butanol per carbon source and the
preferred production of butanol over acetone and ethanol.
The C. acetobutylicum (Lee et al., 2008) model predicted that 158 were essential for growth (and 38
partially essential). This has utility in initial screens to decide which genes to delete from
bacterial strains. In addition to strain development, the model can also be used for metabolic
anyalysis to understand the fundamental phsiology occuring in the cell (Lee et al., 2008). The model
correctly identified reduced production in butyric acid production and increased butanol production
for example, when buk (butyrate kinase) was deleted (Lee et al., 2008). The reduction in butyric acid
production and increase in butanol porudction was also validated experimentally.
The C. acetobutylicum model (Senger et al 2008) also correctly predicted the in vivo phenotype when
buk is deleted. Further, this model hypothesized that the TCA cycle, which is incomplete in C.
acetobutylicum, is completed through the reactions of the urea cycle and the reversal of the reaction
from isocitrate to 2-oxoglutarate (Isocitrate dehydrogenase EC 1.1.1.41). This is particularly
interesting as C. acetobutylicum can be grown in minimal medium without amino acids, thereby
42
suggesting how this organism is able to synthesise all the essential amino acids, and providing an
indication as to how this is achieved without a complete TCA cycle.
The C. beijerinckii model (Milne et al 2011) highlighted an metabolic flux. The model predicted that
70% of ethanol production appeared was derived from threonine and aspartate metabolism rather
than acetyl-coA (Figure 1.5); otherwise, the pathways for butanol, butyrate and acetate were those
expected from metabolic studies (Figure 1.5). This may suggest a reason for the favorably high titres
of butanol achieved in C. beijerinckii. The model also detailed a monophasic production of acids and
alcohols as opposed to the biphasic mechanism where alcohol production becomes favoured when
the pH is detrimentally lowered by organic acid production.
1. 5. 5: Butanol Toxicity
Butanol/solvent toxicity is a key factor when considering the culture of any solventogenic
bacterium. Butanol, acetone and ethanol are known to disrupt the phospholipid bilayer in
cell membranes causing an increase in fluidity (Sardessai & Bhosle, 2004). The toxic effects
of acetone and ethanol are not as potent as butanol: toxicity is observed at 40g/l for ethanol
and acetone compared to 7-13g/l for butanol in prokaryotes (Sardessai & Bhosle, 2004). The
mechanism of growth inhibition by butanol is thought to be associated with a deficient
uptake of glucose and other nutrients caused by the increased membrane fluidity (Baer et
al., 1987). High butanol concentrations can inhibit ATPase activity, therefore affecting the
intracellular energy levels as this may affect the phosphotransferase system for sugar
uptake. A possible solution to prevent toxicity is the generation of mutant strains that
exhibit a phenotype that is more resistant to butanol toxicity without compromising
production via natural selection. However, mutants are not always able to maintain
wildtype yields. At the industrial level, strategies to prevent butanol toxicity include process
43
optimisation where the solvents can be distilled away from the culture keeping the
concentrations of butanol low (Lee etal, 2008; Mann et al. 2012).
The stress response at a cellular level to high butanol concentrations is not entirely
elucidated. Research suggests that there is an increase in molecular chaperones such as
heat shock proteins (HSPs) in response to stress mediated by high butanol concentrations
(Mann et al., 2012) . HSPs are chaperones that can repair or refold partially denatured
proteins. Interestingly, these HSPs can also be detected during the normal acidogenesis-
solventogenesis shift and not controlled by external concentrations of butanol (Mann et al.,
2012) . The groESL operon controls the expression of two units of heat shock protein, groEL
and groES, and it has been found that their overexpression leads to an increase in butanol
tolerance (Tomas et al. , 2003). An additional mechanism could be that there are differences
in composition and the degree of saturation of fatty acids present in the membrane
protects the cell from the increased cell membrane fluidity caused by excess butanol (Mann
et al., 2012). It is worth noticing that the tolerance to low pH showed by acidophilic bacteria
such as Lactobacillus is believed be mediated by these fatty acids. The overexpression of
these genes may affect the phenotypic resistance to butanol and acidic environments in Csb
(Mann et al., 2012) .
A different pair of HSP genes, grpE and htpG, with groESL as control, was investigated in C.
acetobutylicum (Mann et al., 2012) . Strains overexpressing each of those genes exhibited
increased butanol tolerance compared to the wild type when challenged with 1 and 2% (v/v)
butanol. This data supports the case that the increased transcription of molecular
chaperones increases cell viability in high concentrations of butanol. This is supported by
44
earlier observation by Tomas et al., (2003) who also investigated the groESL and solvent
production relationship.
When investigating whether the increase in butanol tolerance conferred an increase in
butanol yield compared to the wild type phenotype, it was found that the groESL
overexpressing strain produced increased butanol yields up to 40-130% compared to the
wild type depending on the media used. However, this pattern is not followed for the grpE
and htpG over-expressing strains which showed butanol production at only 51 and 67% of
that produced in the wild-type, respectively. Further investigation found that the growth
rate of both these strains was slower than the wild type, potentially caused by the increased
ATP requirements due to the production and utilisation of molecular chaperones/ HSPs. This
is an important issue to consider when obtaining a butanol tolerating strain without
affecting yields (Mann et al. 2012) .
An issue that needs addressing in species producing acetone and ethanol in addition to
butanol is how to engineer the strain to only produce butanol. Acetone is corrosive to some
rubbers and plastics, so for both industrial and production purposes, it would be useful to
minimise its output (Lee et al., 2012) . There are three solventogenic genes relating to the
species C. acetobutylicum: These are adhE1 (alcohol/aldehyde dehydrogenase) , ctfA/B
(coenzyme A transferase) , and ADC (acetoacetate decarboxylase) (Lee et al., 2012) . It is
assumed that a reduction in pH triggers transcription of these genes and thus initiates
solventogenesis and that the activation of these genes is a mechanism to detoxify the cell
from carboxylic acids and protons. This is despite the evidence showing that solvent
production was as a result of high organic acids internally and not a low pH extracellularly in
45
the medium (Hüsemann and Papoutsakis, 1988) . Therefore, it should perhaps be
considered that butyric acid concentration and associated pH drop initiates solventogenesis.
1. 6:Factors affecting metabolism and yields
1. 6. 1: Growth of C. saccharoperbutylacetonicum in defined minimal media.
It is important to design a good medium for cell culture when carrying out physiological
characterisation. Physiological characteristics could be masked or highlighted by using a
medium that was lacking in a nutrient. Therefore, is it is crucial that in a defined medium
sufficient and adequate sources of carbon, nitrogen, sulphate and phosphates and trace
salts and minerals be present at the right concentrations (Egli, 2015). In the defined medium
developed in Chapter 3, sulphur is supplied as ammonium sulphate. C.
saccharoperbutylacetonicum N1-4 strain has been studied in various types of minimal
nutrient media where it was found that low concentrations of glucose inhibits solvent
production (Al-Shorgani et al., 2012).
In experiments designed to perform a quantitative stoichiometric analysis, it is a
requirement that the culture medium is chemically defined, as this allows for the calculation
of consumption for all the components of the medium, as well as the calculation of rates of
synthesis of metabolic products (Egli, 2015). For example, a defined medium with a sole
carbon source provides accurate calculation of the specific production rates for a given
solvent about carbon consumption. A defined medium cannot, for example, contain
undefined components such as tryptone or yeast extract, as the exact composition and
formulaic breakdown of these products are unknown and can vary between batches (Egli,
2015). A defined medium also allows for targeted nutrient limitation which is essential to
achieve the steady-state in chemostat cultures. Although defined, the medium must be
46
limited only in the nutrient targeted. Although defined media are essential for physiological
and metabolic studies of a microorganism, they are not necessarily ideal candidates for
industrial fermentations. For example, the supplementation of a single amino acid in a
defined medium is not typically economically viable at an industrial scale. Supplementing
instead with yeast extract would provide the required amino acids for a lower cost, in
addition to providing other (unidentified) compounds that may support biomass and/or
solvent production (Xue et al., 2017).
The process for creating a well-designed medium can be lengthy due to the vast range of
nutrient sources available. Fortunately, experiments can be specially designed to undergo
subsequent multifactorial statistical protocols which allow for additional pattern
identification not found via standard statistical methods (Bakonyi et al., 2011). These
methods are discussed in section 1.7, while the optimisation of a defined medium is
addressed in chapter 3.
Defined media are not suitable for use in industrial settings due to its costs, as any medium
used in industry needs to be designed to meet the elemental and biosynthetic requirements
of the cell, particularly to not only support growth but high yields of the target product.
However, a defined medium can work as a foundation to decide which complex ingredients
(yeast extract, molasses, cheese whey, etc.) are required to meet these demands (Hahn-
Hägerdal et al., 2005). However, it is worth noting that these complex ingredients may also
contain inhibitory compounds, and their effect could be elucidated during the development
of a defined medium.
1. 7: Theoretical and experimental approaches for studying solventogenesis
47
1. 7. 1:Multifactorial statistical methods for high-throughput experiments
Multifactorial statistical methods can be utilised to analyse data from different trials which
each had multiple variables. If it is necessary or desirable to examine the effects of a given
component in different combinations, the number of experiments needed to be carried out
increases exponentially. A Placket-Burman Design experiment is a type of multi-factorial
design experiment that allows the experimenter to trial the effect of many components (e.g.
different amino acids) on a given parameter (e.g., bacterial growth) in each trial without
conducting a fully factorial experiment (Plackett & Burman, 1946; Bakonyi et al., 2011) .
The Placket-Burman design reduces the workload by reducing the number of experiments to
be carried out. The design depicted below is worked out by considering L, the number of
levels by which a component in an assembly can occur. This is either the presence or
absence (or high and low concentrations) of a given component. N denotes the number of
trials to be carried out, which in this case is 12 (11 components + 1). Now, if 11 components
were going to be tested on two levels, this would require N = 211 =n 2048 trials in a fully
factorial experiment, a massively laborious experiment. Whereas the Placket-Burman fully
factorial design would only require N = 12 (n components + 1) (Plackett and Burman, 1946) .
48
+ + - + + + - - - + -- + + - + + + - - - ++ - + + - + + + - - -- + - + + - + + + - -- - + - + + - + + + -- - - + - + + - + + ++ - - - + - + + - + ++ + - - - + - + + - ++ + + - - - + - + + -- + + + - - - + - + ++ - + + + - - - + - +- - - - - - - - - - -
Figure 1-5 an example of the cyclical nature of the change of elements between assemblies.
49
1. 7. 2: Continuous Cultures in the Chemostat
Continuous culture refers to the steady rate of growth of a bacterium as controlled by the
dilution rate of fresh media into the bioreactor. This is carried out by continuously adding
fresh medium to an established bacterial culture whilst allowing enough media to elute to
maintain a constant working volume. In these chemostat pH, O2, CO2 and temperature are
also controlled. Note that if the dilution rate of the vessel exceeds the maximum growth
rate of the organism, the culture will wash out. The growth rate and thus the dilution rate
also rely on the media composition. A single component of the medium must be a limiting
substrate for the culture. The key rate limiting nutrients are nitrogen, phosphate and carbon
sources. Nitrogen, phosphate and carbon make up the bulk of biomass elementally and
therefore can limit biomass production in a culture. If one of these is limited, the growth
rate can be reduced from the maximum (Henson, 2003; Schlegel & Zaborosch, 1993).. Data
from continuous culture can increase reproducibility and accuracy in Proteomics and
transcriptomics. With transcriptomics the functions of genes in response to nutrient
limitations can be elucidated. Chemostat data have also been used to determine carbon flux
at various growth rates in relation to a target product synthesis (Hoskisson & Hobbs, 2005).
To carry out chemostat culture the maximum growth rate of the target species needs to be
calculated. This can be determined through data for time and optical density in batch
culture. Transforming the optical density data for the exponential phase (where the
maximum growth rate lies) with a log10 function the equation of the line can be determined
using a linear regression where m=µ (in y=mx+c).
50
Exponential growth can be extended to a limited extent in batch culture by adding fresh
media. The rate of further growth is equal to the rate at which the cell can utilise the fresh
substrate. This is also what takes place in continuous culture where there is also the
removal of culture at the same rate as fresh media is added. In batch culture, bacteria will
grow at their maximal growth rate until one or more nutrients are depleted, and
supplementing a culture at this stage will initiate a second exponential phase again, until the
same (or other) nutrient(s) becomes depleted. Therefore, growth rate can be controlled
between 0 and the maximum by limiting the rate of supply of a nutrient in the media.
However, as consumption of the nutrient occurs, it will need to be immediately replenished
resulting in the need of a continuous supply of fresh media in the bioreactor. This can be
shown as:
Equation 1 D (h-1) =F (ml h-1)/V (ml)
Where D is the dilution rate into the culture vessel. F is the flow rate and V is the volume of
the vessel. This relation shows that the dilution rate is equal to the flow rate of fresh media
per reactor volume.
Any changes in biomass over time can be calculated by the integral of the change of biomass
over time, dx/dt. Below, is the steady state equation for a simple chemostat:
Equation 2 dx/dt = µx- Dx
51
A further explanation of the derivation dx/dt = µx- Dx
The dilution rate in a bioreactor is defined by the ration of the flow (F) of fresh media
entering the vessel, and the volume of medium (V). In a continuous culture, the volume of
culture remains constant throughout the experiment. Therefore:
Equation 3 D = F/V
If there is perfect mixing, each cell in the bioreactor has an equal chance of being expelled
from the bioreactor as the flow of fresh media, or substrate, continues into the vessel. If all
the cells in the bioreactor stopped dividing and the flow of fresh media continued, this
would result in a wash out rate. Where with efficient mixing, there would be equal
probability of any given cell being leaving the vessel. This wash out rate could be
represented as:
Equation 4−dxdt
=Dx
With x as the concentration of the organism. Exponential growth has been previously
defined as (Herbert, Elsworth, & Telling, 1956):
Equation 51x
dxdt
=d ( loge x)
dt=μ=
loge2td
Where t=time, is the specific growth rate, and td is the doubling time of the organism,
with the latter two components (doubling time and specific growth rate) being considered
constant for a given species growing in a specific culture medium, and not possible to
manipulate in a species. Note that this is only true for exponential growth when all
substrates required for growth are present in excess and thus maximum growth rate is
being achieved.
52
Monod (1942) defined the relationship between the specific growth rate
and the concentration of the growth limiting nutrient. In that relationship, the specific
growth rate, µ, is proportional to the substrate concentration until the maximum growth
rate for the organism is reached. Adding fresh media to the culture, at a rate that exceeds
the maximum growth rate of the organism, a saturation of substrate would result in the
vessel. Where the organism is not able to fully consume the substrate at the rate of which it
is being fed, this saturation point is denoted by Ks. Equation 6 illustrates the critical dilution
rate:
Equation 6 µ = µmax * S/(Ks+S)
Where S is the substrate concentration, µmax is the maximum growth rate for the organism,
Ks is the saturation constant which is numerically equal to the concentration of substrate at
which µ=1/2µmax. From here it can be further derived that as exponential growth can be at
any rate between 0 and µmax. There is a previously defined relationship between utilisation
of substrate and the growth rate (Herbert, Elsworth, & Telling, 1956):
Equation 7 dx/dot = -Y ds/dot
Where growth rate is always a fraction of the rate of substrate utilisation, Y, or also known
as the yield constant/ Where Y is the yield constant, the amount of biomass that can be
obtained from 1 g substrate
Equation 8 Biomass (g/l) / Substrate (g/l) = Y (g/g)
53
If data for the constant µmax, KS (saturation constant) and Y (yield) are known, it is possible to
derive the mathematical equation which describes growth in continuous culture by using
the Equations 5 and 7.
In continuous culture the cells grow at the rate. At the same time, when the flow substrate
(media) is active, the expulsion of cells from the bioreactor is described by equation
2. Therefore, increase in biomass is equal to growth (Equation 5) minus the output
(Equation 4). Which can be described as:
Equation 9 dx/dt = µx - Dx
Where if u is greater than D, biomass concentration will increase and if D (dilution rate) is
greater than µ, the culture will eventually wash out of the vessel. This is because as fresh
media is added, the same amount of culture is evacuated from the vessel, with each
integration resulting in a more dilute biomass as the expulsion on culture is faster than the
organism’s ability to recover the biomass with the provided fresh nutrients. However, if
µ=D, which it does in chemostat experiments, dx/dt equal 0 and biomass production is
constant (Bull, 2010; Herbert, Elsworth, & Telling, 1956).
Going back to equation 2 above, change in biomass concentration is equal to the rate of
production (µ*x) minus the rate of removal (D*x). In a chemostat culture, the concentration
of biomass remains constant, and therefore dx/dt, the change in biomass concentration
over time, equals 0. Therefore, from the equation above:
Equation 10 µx = Dx
And
54
Equation 11 µ = D
Demonstrating that at the steady state, the growth rate µ is equal to the dilution rate
(Stanbury et al., 1995). The growth rate, a physiological parameter, can be controlled
externally by an operational parameter, the rate of supply of a nutrient into the culture. In
theory, the growth rate could be controlled between 0 and µmax.
To replicate the above in the lab, a large vessel can be used to maintain the culture at a set
volume by feeding in fresh media at the same rate at which the cultured media can flow
from the vessel. This bioreactor can also be designed to maintain pH concentration by using
a probe and two feeds for acid and alkali solutions (example of such a set up in Figure 1.6).
The control of growth by pH is called a pH-stat.
Figure 1.6: A: A photographed example of a bioreactor set up. B: A simplified schematic for the set-up of a bioreactor for chemostat culture.
55
Chemostat cultures are used for several types of physiological studies (Gresham & Hong,
2014). Of interest to this project is to determine the effects on bacterial growth and energy
metabolism of the stress caused by acid production in solventogenic clostridia (Millat, et al.,
2013; Millat, et al., 2013b). For instance, various physiological properties of C. cellulolyticum
are affected by pH within a fermentation system. These pH controlled systems allowed for
greater biomass production and a greater consumption of the cellulose feedstock (Desvaux
etal., 2001), as well as a higher production of acetate and ethanol. However, lactate and
pyruvate (upstream compounds of solvent production) were found at higher concentrations
in the non-controlled systems (Desvaux et al., 2001) . As acid production is a precursor
stage to solvent production, it could be of interest to optimise acid production (while
maintaining culture viability) to feed solvent production in the later stages to see if solvent
yields increase as a response.
Continuous cultures were used in C. acetobutylicum, to investigate a phosphate limited
media, the metabolic shift between acidogenesis to solventogenesis where the constant
product and substrate concentrations to minimise their effect on the transcriptome
compared to batch. It is particularly difficult to determine what is active during
solventogenesis in batch culture, as the stability of enzymes from acidogenesis can not be
ascertained (Grimmler et al., 2011). The phosphate limitation was used here to avoid to
limiting yeilds of product (butanol, ethanol acetone) which contain carbon backbones. The
chemostats descrived in Chapter 4 are carbon limited, this was chosen in order to
investigate whether there really is a solventogenic shift in Csb. To see when or if these
products with carbon backbones are produced, as a metabolic demand when under carbon
56
limitation. They found that that acidogenesis shifted over to acidognesis between pH5.8 and
pH4.5 (Grimmler et al., 2011).
Continuous culture in industry has the advantage in that it allows for the reactor to be used
for a longer time, minimising downtime and cost of decomissioning and recommisioning the
bioreactor; that is, if yeilds of product can be maintained through out the duration of the
culture. Continuous culture, couple with solvent stripping, can limit the inhibittory affect of
butanol on cell vaibility and product yeilds (S.H. Lee et al., 2016). With C. acetobutylicum
continuous cultures has advantages as it can draw out the solventogenic phase, continuous
culture with immobilised cells was shown to increase total solvent product from 15.9 g/l to
25.32h/L (Bankar, et al., 2012).
1. 8:Aims, objectives and impact
1. 8. 1:Aim:
The focus of this project is to explore the properties and kinetics of butanol production
using experimental metabolic and physiological data to determine rates and predict yields in
different environments. The effects of nutrients and metabolites on solvent yields are
investigated, and from this, a rational optimisation of butanol production can be designed
and translated to industrial production, using a genome-scale metabolic model to interpret
the results.
1. 8. 2: Objectives
• Develop a novel chemically defined minimal medium, suitable for metabolic studies
in C. saccharoperbutylacetonicum.
57
• Study the production of solvents under diverse culture conditions in batch and
continuous cultures.
• Understand and identify the physiological and metabolic limitations affecting solvent
metabolism by metabolic flux analysis, by:
A. Constructing a novel and validated GSMN for C. saccharoperbutylacetonicum N1-
4 (HMT) informed by the available genome sequence.
B. Validating the GSMN and the flux analysis model with experimental data from
continuous cultures, by quantification of exometabolome, macromolecular
composition and other factors.
C. Performing simulations for in silico optimisation of butanol production to
generate hypotheses for metabolic engineering of the strain.
Using the results of the simulations, identify genes highlighted by the model to knock
out or overexpress for optimisation of butanol production and compare with
experimental data.
1. 8. 3: Impact
The results of this work will generate impact in both industry and the wider scientific
community. The objectives presenting higher impact will be those resulting in novel tools of
application by others working in the same field, such as defined minimal media and a
validated GSMN. Also, this research will contribute to the development of a more
sustainable bio-economy based society by contributing to the pool of industrially relevant
research into the production of biosolvents
58
Chapter 2: Materials and Methods
59
2. 1: Organism
The organism used throughout is Clostridium saccharoperbutylacetonicum N1-4 (HMT)
(DSMZ 14923) sourced from the Deutsche Sammlung von Mikroorganismen
Zellkulturen(DSMZ).
2. 2: Culture media
Tryptone-Yeast Extract Medium ¼ strength (g/l unless otherwise stated): Tryptone, 2.5;
Yeast Extract, 2.5; (NH4) 2SO4 0.5; FeSO4, 0.025; Sucrose, 50. The medium was buffered at pH
6.5 with MES (0.1M).
Original Biebl medium: (g/l unless otherwise stated) : Sucrose, 50; 1 g/l KH2PO4; 0.5 K2HPO4;
(NH4) 2SO4, 4; MgSO4·7H2O, 0.2 ; CaCl2· 2H2O, 0.02; FeSO4, 5 mg/l; Trace element solution SL
7, 2 ml/l. Medium was adjusted to 6.5 with NaOH 1M(Biebl, 1999).
Biebl media B1 – B5: (g/l unless otherwise stated): Sucrose, 50; 1 g/l KH2PO4; 0.5 K2HPO4;
(NH4) 2SO4, 4; MgSO4·7H2O, 0.2; CaCl2· 2H2O, 0.02; FeSO4, 5 mg/l; 25µg/l Biotin; 4µg/l PABA;
Trace element solution SL 7, 2 ml/l. Bring to pH 6.5 with NaOH. See Table 2.1 for further
modifications
60
Table 2 Modifications to the Biebl media B1-5 for the experiment investigating why the original recipe for the media failed to support growth in Csb.
(g/l)
Tryptone Yeast extract
B1 2.5 2.5
B2 0 2.5
B3 2.5 0
B4 1 1
B5 0 1
Modified Biebl: The following modification were made to the Original Biebl solution: Yeast
extract, 0.01 g/l.; FeSO4, 25 mg/l.
Plackett-Burman basal media: (g/l unless otherwise stated): Yeast extract, 0.01; Sucrose,
50; KH2PO4, 1; K2HPO4, 0.5; (NH4) 2SO4, 4; MgSO4·7H2O, 0.2; CaCl2· 2H2O, 0.02; FeSO4, 25
mg/l; Trace element solution SL 7, 2 ml/l. Bring to pH 6.5 with NaOH. 43.5 ml of this media
was supplemented with 1.5 ml of the amino acid detailed in Table 2.1. Resulting in 1mM
concentration of each amino acid and a 27.5% dilution of the basal media.
61
Table 2.1 Details modifications made to the media described in "Plackett-Burman basal media”.
Trial
Glutamate Biebl: In g/l unless otherwise stated: Sucrose, 50; KH2PO4, 1; K2HPO4, 0.5; (NH4)
2SO4, 4; MgSO4·7H2O, 0.2; CaCl2· 2H2O, 0.02; FeSO4, 25 mg/l; 100mM Glutamate; 10mM
MES buffer. Trace element solution SL 7, 2 ml/l. Bring to pH 6.5 with NaOH. This medium
was selected for the chemostat experiments.
Trace element solution - SL7: In mg/l: MnCl2.4H20, 100; ZnCl2., 70; H3BO3, 60; NaMoO4.H20,
40; CoCl2.2H2O, 20; CuCl2.2H2O, 20; NiCl2.6H2O, 20; HCl, 25%, 1 ml. Dissolve the MnCl2.4H20.
in 1 ml HCL. Add remaining compounds and makeup to 1 litre.
62
Reinforced Clostridium Media (RCM) media (Oxoid): Typical Formula g/l: Yeast extract,
13.0; Peptone, 10.0; Glucose, 5.0; Soluble starch, 1.0; Sodium chloride, 5.0; Sodium acetate,
3.0; Cysteine hydrochloride, 0.5; Agar, 0.5; pH 6.8 ± 0.2.
Cooked Meat Media (Oxoid): Typical formula g/l: Heart muscle, 454; Peptone, 10.0; `Lab-
Lemco’ powder, 10.0; Sodium chloride, 5.0; Glucose, 2.0. pH 7.2 ± 0.2 @ 25°C.
Yeast Extract (Oxoid): Code, LP0021. A water soluble autolysate in powder form containing
nitrogen and water-soluble B vitamins. Total Nitrogen 10.0 – 12.5%.
Tryptone (Oxoid): Code, LP0042. Total nitrogen 12%.
2. 3: Strains and Working cell bank
A freeze-dried working cell bank of Clostridium saccharoperbutylacetonicum N1-4 (HMT)
(DSMZ, Braunschweig, Germany) was reactivated by inoculating 10 ml of Oxoid Cooked
Meat Media (1g in 10 ml H20) in anaerobic conditions and incubating at 30oC for 16 h. A
sterile flask containing 200 ml RCM was inoculated 10% (v/v) with the revived culture and
incubated for 16 h at 30oC in anaerobic conditions (gas: anaerobic growth mixture), reach an
OD600 approximately 2.0. An aliquot of 1 ml of culture was dispensed into cryotubes
containing 1 ml of sterile 40% (v/v) glycerol solution (final glycerol concentration 20%) and
stored at -80oC. Checks for contamination were carried out on three thawed cryotubes using
Gram staining and phase contrast microscopy. Subsequent revival was carried out using 1
vial per 9 ml of Reinforced Clostridium Media (RCM).
63
2. 4:Initial Media Screening
Tryptone-Yeast Extract Media ¼ strength medium and B1-5 (20 ml) were inoculated with
overnight cultures of frozen Csb stock incubated in Reinforced Clostridia Medium (RCM),
centrifuged for 10 minutes at 16.1 RPM and the pellet re-suspended in 10% glucose
solution. Cultures were incubated at 30oC for 72 hours.
2. 5: Placket-Burman Design Experiment
For each amino acid tested (see Table 2.2.1), a 40mM solution of filter-sterilized basal
media was prepared. Cultures were grown in serum bottles (120 ml) containing 43.5 ml of
basal media and 1.5 ml of the respective amino acid (Table 1-2). Overnight culture of N1-4
(HMT) grown in Biebl medium (0.5g/l yeast extract) was used to inoculate the bottles (10%).
The cultures were incubated at 30oC; sampling for OD600 every hour up until 16 hours and a
final reading was taken at 24 hours. Samples were collected and centrifuged at 16.1 RPM for
10 minutes and both the pellet and supernatant were kept at -20oC for further analysis. This
experiment was carried out in triplicate.
2. 6:Analytical methods
2. 6. 1:Analysis of Solvents and Organic Acids
The analysis of solvents and organic acids was performed by Gas chromatography. Culture
Samples were centrifuged at 16.1 RPM for 10 minutes and the supernatant stored at -20oC
until analysis. When ready for analysis, sample supernatants were thawed thoroughly and
spun again 5 minutes and 5x diluted with ultra-pure water. The protocol used a Flame
Ionisation Detector (FID) using a gas mix of H2 (50 ml/min flow rate) and air (350 ml/min
flow rate for air), with N2 as the carrier gas (42 ml/min flow rate). Injection volume was
64
0.1µL sample at 50% split ratio (half discarded to waste) into a column (Agilent 19091F-115E
HP-FFP, polyethylene glycol zx1TPA capillary, nominal dimensions: 50.0m X 320µm X
0.50µm) heated to 80oC then increased to 200oC at 10oC/min (The analysis was controlled by
the software Chemstation (Agilent)). All samples were run for 25 minutes. Standards were
made for acetone, butanol, ethanol, butyric acid and acetic acid at concentrations of 0.1,
0.5, 1, 5, 10, 15 and 20g/l for the calibration curve. The standards were prepared using HPLC
water and HPLC grade solvents and made up to volume in the required volumetric flasks. As
these are volatile compounds, the standards were prepared using a positive displacement
pipette.
Under the conditions of analysis, acetone elutes first (retention time (RT): 3.1min), followed
by ethanol (RT: 3.3min) and butanol (RT: 4.4min). The volatile fatty acid acetic acid elutes at
(RT: 12min) followed by butyric acid (RT: 23 min). The areas of the peaks for the standards
at the different concentrations are then used to construct the calibration curves to
determine the values for the sample data.
2. 6. 2:Analysis of Sugars
The concentration of sugars in the samples was measured by HPLC. Samples were thawed
and centrifuged and diluted 1 in 4 with ultrapure water. The column used was a Supelgogel
Pb 30cmX7.8mm (packing type: Sulphonated polystyrene divinylbenzene. Ionic form: Pb2+
Sigma-Aldrich). The column was heated to 65oC with an injection volume of 5µL. The flow
rate was 0.5 ml/min and each sample was run for 20 minutes. Standards of glucose, sucrose
and fructose were made at 1, 5, 10, 15 and 20g/l concentrations. The detector used was a
refractive index detector (RID).
65
2. 6. 3:Cell Biomass concentration
Dry Cell Weight. The method used was adapted from a standard method (Zhu et al., 1997).
Nitrocellulose membranes (GSWG047S6, Merck Millipore) of 47mm diameter and 0.22µm
pore size were placed in a 1000W microwave oven on medium (60%, 600W) power together
with a tightly screwed Duran bottle containing 500 ml of distilled water, and dried for 5
minutes, allowed to cool, dried a second time with fresh, cool water, and stored in a
desiccator until required. The dried membranes were weighed and moistened in a standard
Tween20 1 mM solution to soften the membrane and prevent it from cracking. Membranes
were then fixed onto a filter holder unit and were rinsed through with 10 ml of distilled
water using a vacuum pump. 10 ml of fresh, untreated culture sample is then passed
through and the filter unit is then rinsed out with 10 ml of distilled water to ensure all
soluble matter is washed off from the filtrate. The membranes are then dried using the
microwave method described above, allowed to cool down in a desiccator and re-weighed.
The difference in weight is calculated and the results given in g/l.
Optical Density
The optical density of the samples was used as a measure of cell concentration. 1 ml sample
(or appropriate dilutions) was dispensed into 1.5 ml cuvettes. The samples were then read
in a spectrophotometer with the wavelength set to 600nm. OD600 Readings were then taken
and recorded, ensuring that the OD was within the linear range.
2. 6. 4:Determination of extracellular sucrose concentration by enzymatic assay
66
Frozen samples were thawed and centrifuged at 16 000 RPM for 10 min prior to analysis
using the enzymatic assays for sucrose, D-fructose and D-glucose (Megazyme K-SUFRG
01/14) was used. The principle of the assay is the quantification of D-glucose after hydrolysis
of sucrose by fructosidase. In experiments using modified Biebl media the assay can be
considered specific to sucrose. The assay was carried out according to the manufacturers’
instructions (Megazyme).
2. 6. 5:Determination of phosphate consumption
The method determination of soluble phosphates was based on a standard method ( Chiba
& Sugahara, 1957) with some minor modifications. In boiling tubes 0.1 ml of sample or
standard was mixed with 0.1 ml of 11M sulphuric acid and incubated at 80oC for 60 minutes
in a water bath. After cooling to room temperature 0.5 ml of 0.5M hydrazine hydrate was
added and the mixture left to incubate for 60 minutes. For the assay 4.2 ml of water and 0.2
ml of ammonium molybdate solution (8.3g in 100 ml water) were added and the mixture
vortexed. Finally, 0.4 ml of amidol reagent (1g amidol in 100 ml 20% (w.v) sodium
metabisulphite) was added and the mixtures left to incubate at room temperature for 30
minutes. Absorbances were then read at 620nm.
2. 6. 6:Determination of extracellular sucrose consumption by reflectometry
Samples were thawed and spun down at 16 000 rpm for 10 minutes and the pellet discarded
prior to analysis. The Merck Reflectoquant® sucrose kit (0.25-2.50g/l) and reflectometer
(Merck 116970) was used for the determination of sucrose in real time. The procedure was
carried out according to manufacturer’s instructions, except that appropriate Gilson
pipettes were used in place of the syringe and measuring vestibule. Samples required
dilution of up to 25 times (for sterile media containing 50 g/l sucrose). Standards of 50, 25,
67
10 and 1 and 0.1 g/l of sucrose were measured on dilutions similar to those of the samples
to confirm the accuracy of the method. The principle of the method is the change from a
colourless tetrazolium salt to blue formazan that can be determined reflectometrically.
2. 6. 7: Determination of extracellular ammonium concentration
Samples were thawed and spun down at 16 000 RCF for ten minutes and the pellet
discarded prior to analysis. The Merck Reflectoquant® ammonium kit (15.5-140mg/l NH4+)
was used for the determination of ammonium in cell sample supernatants. The procedure
was carried out according to manufacturer’s instructions except that appropriate Gilson
pipettes were used in place of the syringe and measuring vestibule. Where remaining
sample volume were low, 500µL of sample and 1 drop of reagent was used instead of the
prescribed 5 ml sample and 10 drops of reagent. Standards of 4, 1, and 0.1 g/l of ammonium
sulphate were measured on dilutions similar to those of the samples to confirm the
accuracy of the method. The principle of the method is based on ammonium ions reacting
with Neßler’s reagent to form a yellow solution that can be determined reflectometrically.
2. 6. 8:Determination of extracellular phosphate concentration
Samples were thawed and spun down at 15 000 RCF for ten minutes and the pellet
discarded prior to analysis. The Merck Reflectoquant® phosphate kit (5-120mg/l PO43-) was
used for the determination of ammonium in cell sample supernatant. The procedure was
carried out per manufacturer’s instructions except appropriate Gilson pipettes were used in
place of the syringe and measuring vestibule. Where remaining sample volume were low
500µL of samples and 1 drop of reagent was used instead of the prescribed 5 ml sample and
10 drops of reagent. A 50mg/l standard of phosphate was created using di-potassium
phosphate. The principle of the method is based on phosphate reacting with molybdate
68
ions to form molybdophosphoric acid. This is then reduced to phosphomolybdenum blue
which can be determined reflectometrically.
2. 6. 9: Determination of extracellular L-glutamate concentration
Samples were thawed and spun down at 16 000 RCF for ten minutes and the pellet
discarded prior to analysis. Extracellular L-glutamate concentrations were determined using
the Megazyme L-glutamate kit. The sensitivity of the assay lies between 0.054-0.214 mg/l
glutamate. Samples and standards were prepared as described by the manufacturer's
methods and read using a microplate reader at 490nm wavelength. The principle of the
method is based on the oxidation of glutamate by NAD + of which the resulting NADH
reduces iodinitrotetrazolium into iodinitrotetrazolium – formazan which can be measured
spectrophotometrically at 490nm.
2. 6. 10: Determination of the cellular macromolecular composition
Samples stored at -20oC were thawed and spun down at 16 000 RCF for 10 minutes and the
pellets were then washed with PBS and sonicated for 30 seconds three times with 20 second
intervals. The cells were then suspended like for like in PBS unless stated otherwise.
Determination of total DNA. Samples of pellets were frozen in liquid nitrogen and stored at
-80°C until required. After thawing, the pellet was resuspended in 990 μl of 10% SDS lysis
buffer. After heating up to 37°C, 10 μl of 20 mg/ ml proteinase was added and the solution
incubated for 90 minutes at 37°C. After incubation, the contents were decanted in a 3.5 ml
tube and 1 ml of ice-cold ethanol was added and mixed by slowly pipetting. The tubes were
left to incubate at -20oC for 90 minutes. After incubating, the upper aqueous phase was
removed by pipette placed into a sterile centrifuge tube and 20 μl of RNAse (Thermo) was
69
added and the tubes left to incubate at room temperature for 30 minutes. Determination of
the total DNA was then carried out with a Nanodrop analyser.
Determination of total RNA. Pellets were frozen in liquid nitrogen and stored at -80°C until
required. For extraction the miRNeasy kit (Qiagen) was used. The pellets were resuspended
into 100 μl of lysis solution in 2 ml centrifuge tubes and incubated for 5 min at 37°C. Then
700 μl of Qiazol from the miRNeasy Kit was added to the lysate, mixed gently and incubated
for 3 min at room temperature. After mixing vigorously for 15 seconds, 160 μl of chloroform
was added to the tube followed by a 2-minute incubation at room temperature. The tubes
were then centrifuged at 15 000 RCF for 15 minutes at 4°C. After centrifuging, 400 μl of the
upper aqueous phase (contains the RNA) was collected and mixed well with 600 μl of 100 %
ethanol in a new 1.5 ml centrifuge tube.
After aliquoting 500 μl of this mixture into a spin column from the RNeasy kit, the tubes
were centrifuged at 15 000 RCF for 15 seconds at room temperature. This was repeated for
the remaining mixture in 1.5 ml centrifuge tube.
350 μl of RWT buffer was added to the column and centrifuged at 15 000 RCF for 15 seconds
at room temperature and the flow through discarded. A solution of 10 μl DNase I stock
solution and 70 μl RDD buffer from the RNase-free DNase set was prepared. This was then
applied to the membrane in the spin column and left to incubate for 15 minutes at room
temperature.
After aliquoting 350 μl of RWT buffer the column were centrifuged again at 15 000 RCF for
15 seconds at room temperature. Then 500 μl of RPE was added and the tubes centrifuged
at 15 000 RCF for 15 seconds at room temperature. This step was repeated a second time
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before transferring the column to a new 1.5 ml tube and left to air dry the ethanol. Then 50
μl of DEPC water was added to the spin column and incubated for 1 min at room
temperature. The RAN was the eluted by centrifugation at 15 000 RCF for 1 minute at room
temperature. Determination of the total RNA was then carried out the nanodrop analyser.
Determination of soluble carbohydrate.
Soluble carbohydrate was determined spectrophotometrically by reaction with the
anthrone reagent. The anthrone reagent consisted of 250 ml of concentrated sulphuric acid
added to 100 ml chilled distilled water with the addition of anthrone 0.5g. For the assay, 0.1
ml of samples/blank/standard was added to 5 ml of anthrone reagent in glass test tubes and
incubated in a boiling water bath for 10 minutes. The tubes were then cooled in a cold-
water bath for ten minutes before reading at 620nm spectrophotometrically. A standard
curve of 55.00, 27.5, 13.75, 6.85 and 0.00 mM using glucose was used.
Determination of total protein. The total protein kit Lowry (Petersons’ modification) from
Sigma Aldrich was used following procedure B (protein determination with precipitation) as
detailed in the manufacturers’ instructions. A set of 5 standards were created ranging from
50µg/l to 400µg/l. In centrifuge tubes 0.1 ml of deoxycholate solution was added to 1 ml of
sample or standard, mixed well and left to stand at room temperature for 10 minutes. In
the tubes 0.1 ml of trichloroacetic acid solution was added and mixed well. The tubes were
centrifuged for 10 minutes at 15 000 RCF to pellet the protein. The supernatant was
removed, and the pellets re-suspended in 0.1 ml of Lowry reagents. The contents were
transferred to test tubes and 1 ml of water used to washout the centrifuge tubes into the
test tubes. The solutions were then left to stand at room temperatures for 20 minutes
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before 0.5 ml of the Folin-Ciocalteu’s Phenol working reagent. After vigorous mixing the
mixtures were left to incubate at room temperature for 30 minutes. The samples and
standards absorbance were then analysed at 750nm.
Determination of total soluble lipids.
The concentration of lipids was measured using the vanillin method. Vanillin reagent was
prepared by dissolving 200mg vanillin in 5 ml ethanol and 4m ml deionised water,
completing to 100 ml with 85% phosphoric acid. For the assay, 100µL of
sample/standard/blank was added to 2.0 ml sulphuric acid (11M) and incubated in a boiling
water bath for 10 minutes. 200µL were removed and placed into a fresh tube, and 2.5 ml of
the previously prepared vanillin reagent were added and incubated for 25 minutes at 25oC.
The absorbance of the solution was measured at 530nm. A standard curve of 50, 25, 12.5,
6.25, 3.12, 1.62 mg/ ml lipid (17811-1AMP Sigma Aldritch) was used.
2. 7:Batch Culture
2. 7. 1:Bioreactor
A 2L Voyager/Xplora 1 bioreactor (Adaptive Biosystems, Luton, UK) containing 1.5L of Biebl
medium was used for batch cultures. The pH of the medium was adjusted to pH 6.5 with
40% KOH before filter sterilisation. The impeller speed was set to 50 rpm to ensure mixing,
and the head space was flushed with oxygen-free nitrogen prior to inoculation. The
bioreactor was inoculated with an overnight culture of the same medium (pH > 5, motile
cells with no evidence of spores), at a ratio of 20% (v/v). Throughout the culture, NaOH
(40%) was automatically added to control the pH at a set-point of 5.3. Samples were taken
hourly for dry cell weight analysis (5 ml), for optical density measurements (2 ml) and for
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sugar and solvent analysis (2 ml). The latter were centrifuged, and the supernatant kept
frozen until analysis.
2. 8: Steady state cultures of C. saccharoperbutylacetonicum
For the steady state cultures of Csb, a 2L Voyager/Xplora 1 bioreactor (Adaptive Biosystems,
Luton, UK) was equipped with a temperature probe and heating element, pH probe and
acid/base feeds, and a dissolved oxygen probe. An effluent run off maintained a culture
volume of 1.5 litres. The pH feeds used 1M HCl and 1M NaOH to maintain pH. Oxygen-free
nitrogen was sparged into the bioreactor from the bottom after passing through 0.22µm
filter using oxygen impermeable neoprene tubing. The inoculum for chemostat cultures
were grown to mid exponential phase in Biebl media containing 5g/l sucrose. Prior to
autoclaving the bioreactor was filled with pH 7 buffer to maintain the electrolytes in the pH
probe. The bioreactor was then sealed and sterilised by autoclaving at 121oC for 30 minutes.
After cooling, the buffer was removed through the sample collection tube using an
overpressure of OFN. The vessel was then filled with carbon limited Biebl media to 1.3L and
then left to warm to 30oC.
The bioreactor was then inoculated after dissolved oxygen was displaced from the medium
through sparging OFN, confirmed by a reading of dissolved oxygen of 0%. The culture was
left in batch until mid-exponential phase (defined as the point at which the culture reaches
OD600 = 0.9) and the media feed line was then turned on. The growth rate was controlled by
adjusting the flow rate of fresh media into the vessel to the value required for the chosen
dilution rate. Samples were collected after at least 5 volume changes or 7.5 litres of media
had been spent) to ensure the culture has reached the steady state. At least 20 ml were
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centrifuged for 10 min at 1600 RFC and both the pellet and supernatant stored at -20oC for
analysis for ABE, organic acids, macromolecular composition and nutrient consumption. For
analysis of metabolites such as ATP and NADP, up to 10 ml of sample was quickly decanted
into 1.8 ml cryotubes and frozen in methanol at -80oC. Samples for RNA were suspended in a
3 X dilution in RNAeasy. The OD and dry cell weight of the sample were also recorded at the
time.
The bioreactor was then allowed to fill to 1.5L before allowing another 5 volume changes to
occur before taking another sample. This was then completed one more time before then
changing either the pH or the dilution rate (Table 1.1)
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Chapter 3: Design of an optimised medium for metabolic analysis in C.
saccharoperbutylacetonicum
75
3. 1:Introduction
Designing a novel chemically defined minimal medium suitable for metabolic studies is
essential to carry out investigations into the mechanisms for optimising solvent, and butanol
production in C. saccharoperbutylacetonicum, Csb. A defined medium where all
constituents are known is essential for accurate calculations of yields and production rates,
as described in the Introduction. This chapter details the optimisation of a defined medium
improving from the standard Biebl medium (Biebl, 1999) and making it suitable for
metabolic analysis of Csb. This study also details the analysis of additional parameters for
cell culture, such as the optimisation of anaerobic conditions, and the determination of the
maximum growth rate, both necessary in subsequent studies of the steady-state culture of
the organism discussed elsewhere in this thesis.
Physiological and metabolic studies require the use of chemically defined media (Stanbury,
1994), as the presence of complex, undefined medium components may mask the utilisation
or excretion of metabolites, while causing interferences in chemical analyses (Egli, 2015).
Several media are available for Clostridium: among them, the medium described by Biebl
(Biebl, 1999) . However, in exploratory trials in this work using the Biebl medium, Csb failed
to show any growth nor produce solvents under anaerobic conditions.
The objectives for optimisation of any culture media is rendering it able to support the
growth of the target organism and, as in the case discussed here, the synthesis of valuable
products (in our case, solvents or acids). To this, the constituents of a given medium need to
be able to support the synthesis of the macromolecules component of the bacterial
biomass, namely: protein, lipids, DNA, RNA, etc (Pirt et al., 1975) . Approximately 95% of
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the elemental composition of a microbial cell are carbon, nitrogen, oxygen, hydrogen and
phosphate. However, it is not enough for media to solely comprise of the elemental
requirements, but these should be provided in an assimilable form. As such, complex media
contain components such as tryptone, which contains all the amino acids (free or as
oligopeptides) that can be assimilated and used for protein synthesis. Tryptone is a
comparatively cheap component, which allows for the reduction of the anabolic burden by
providing basic “building blocks” rather than synthesising amino acids de novo, for later use
in synthesising proteins (Egli, 2015). In a chemically defined medium, however, we are
unable to use complex nutrients such as Tryptone or yeast extract to supplement amino
acid production. In minimal synthetic media, nitrogen is provided by simple molecules, such
as ammonium sulphate, where the ammonium group can be utilised by the bacteria to
synthesise their amino acids. The same applies for carbon and phosphate sources.
To conclude, a reliable medium composition must include components essential for building
the biomass of a cell (C-, N-, P- sources and trace elements), and often this can be done by
substituting with undefined compound mixtures. Defined media, therefore, requires the use
of individually identifiable and quantifiable chemical compounds. Having a media defined in
such a way allows for the analysis of specific rates and yields calculated using elemental
molar fractions. In addition to accurately analysing production rates, this information also
allows for the realistic constraining of a Genome Scale Metabolic Network (GSMN) (See
chapter 5).
Previous attempts in industry (Green Biologics Pers. Comm.) had proven unsuccessful in
growing Csb in the defined medium used for C. acetobutylicum by Biebl, 1999. Investigating
why this medium did not work and how to overcome this issue was essential to later design
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the steady state experiments. Initial work in the project investigated whether parameters
unrelated to the original Biebl composition were preventing successful cultures of Csb.
These initial experiments included trying different head space gases (anaerobic growth
mixtures, oxygen free nitrogen, and air) and changes in the surface area of the culture
exposed to the gases; however, none of these led to improvements and Csb continued to
fail to grow on the original recipe (data not shown). Therefore, the experiments in this
chapter attempted to identify what components may be missing from the original Biebl
medium (Table 3.0).
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Table 3.0. Composition of Biebl defined medium (Biebl, 1999). Glucose was used as the carbon source for batch (28gl/L) and for continuous culture (60g).
Component Amount
KH2PO4 1.0 g
K2HPO4 0.5 g
(NH4) 2SO4 4.0 g
MgSO4·7H2O 0.2 g
CaCl2· 2H2O 0.02 g
FeSO4 5 mg
Trace element Solution: In mg/l: MnCl2.4H20, 100; ZnCl2, 70; H3BO3, 60; NaMoO4.H20, 40; CoCl2.2H2O, 20; CuCl2.2H2O, 20; NiCl2.6H2O, 20.
HCl 25%, 1 ml/l.
3ml
3. 2:Results and discussion
3. 2. 1:Media Screening and Optimisation.
In a set of initial experiments using the defined medium described in previous work with Csb
(Biebl 1999) , it was found that the strain was not able to grow under anaerobic conditions
using the media as described in the methods section (Original Biebl solution). The gases
used to maintain anaerobic conditions were nitrogen, anaerobic growth mixture (80%
nitrogen, 10% carbon dioxide, 10% hydrogen). Despite Biebl media containing all the
essential components described earlier (i.e. ammonium sulphate, glucose, phosphate and
79
various trace elements), it was no possible to successful culture Csb. Therefore, the research
focused on the optimisation of Biebl media to contain a fully defined chemical composition
(i.e. no potato “extract” as had been previously used).
As the original Biebl media does not include any sources of amino acids or protein, an
experiment adding tryptone and yeast extract to the recipe was carried out. Tryptone-yeast
extract media was used as the control media for this experiment to compare solvent
production. Tryptone-yeast extract media is an undefined media base used industrially for
the screening and physiological study of strains (P. Krabben and H. Housden, Pers. Comm.).
It contains both yeast extract and tryptone and has provided reproducible and measurable
yields during strain selection programmes. The results of this experiment showed Csb
growth in addition to butanol production. This supports the hypothesis that protein sources
are essential for the production of 1-butanol under anaerobic conditions (Wen & Shen,
2016). In that study, Escherichia coli was used to draw a comparison between producing
butanol in anaerobic and aerobic conditions. It was found that the removal of yeast extract
or tryptone was detrimental to butanol production. It is hypothesised that NADH
requirements for protein synthesis compete with those for butanol production. This
competition can be alleviated by supplementing with protein hydrolysates such as tryptone
and yeast extract. The idea that protein precursors support, or are essential for,
solventogenesis has also been studied comparing butanol production by C. beijerinckii, using
various sources of nitrogen/amino acids (Isar & Rangaswamy 2012). This also highlighted
how complex sources of nitrogen allowed for increased butanol production compared to
supplementing with inorganic sources of nitrogen such as ammonium sulphate or
ammonium phosphate (Isar & Rangaswamy 2012) . This can be explained by the fact that
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complex sources of nitrogen such as yeast extract, also contain additional components
which support both biomass and metabolite production. In fact, yeast extract is a rich
source of carbon sources and vitamins (Sigma Aldrich, 2018).
For physiological studies into solvent production, it is a priority to attempt to maximise
biomass production in a defined media so that there is an adequate concentration of cells to
carry out the relevant chemical and biochemical analyses required for metabolic analysis (in
particular, the steady state experiments in continuous cultures). It is then also essential that
the medium under study supports the production of solvents in enough concentration that
they can be measured, although it may not be a priority to maximise solvent production at
this stage.
1. 3. 1:Effect of supplementing Biebl’s medium with complex sources.
The initial experiments Biebl’s media was supplemented with either tryptone or yeast
extract. All media tested supported growth and production of butanol (Fig 3.1). Medium B3
shows the highest production of butanol at 7.27 g/l. However, this medium also showed the
lowest biomass production (approximately 55-59% of the highest producer), as measured by
optical density (OD600) , consistent with the low sucrose consumption observed (4.68 g/l)
compared to the other media presenting higher growth but lower butanol production,
which consumed between 12-13 g/l of sucrose over the 72h period. This suggests that
biomass and butanol production may not be directly correlated, i.e. there may not be a set
ratio of solvent production to biomass production. This result also suggests that yeast
extract inhibits butanol production, as B1 which contains 2.5 g/L of tryptone and yeast
extract. However, it is not possible to explain this observation. Tryptone consists of peptides
from the digestion of casein whereas yeast extract could be considered “richer” in nutrients
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as it consists of water-soluble components of yeast cells, such as amino acids, peptides, salts
and carbohydrates. Therefore, if yeast extract can provide more building blocks of
metabolism this may reduce demand for ATP and NAD recycling and thus the demand for
butanol. This result highlights an area of high potential application, where carbon fluxes
could be redirected towards the most valuable component, butanol, at the expense of
decreasing the amount of biomass produced. Medium B3 also showed that 96% of the total
solvent produced was butanol, an issue that may represent an advantage for the industrial
production. Not only does this indicate that flux of carbon has been diverted away from
acetone and ethanol production, but having higher titres of butanol production can allow
for economical product recovery processes. This can be investigated using the metabolic
model and Flux Balance Analysis (FBA), as discussed in future chapters.
Figure 3-1 Production of butanol, acetone and ethanol alongside the amount of sucrose consumed after incubation at 30oC for 72 hours in anaerobic conditions (n=3). Error bars indicate standard deviation of biological triplicates.
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3. 2. 2: Yeast extract increases solvent production yields per biomass.
To develop an optimised media for biomass production, media using minimal
concentrations of yeast extract were investigated. The methods to analyse biomass
composition require a minimum amount of biomass; therefore, yeast extract was utilised for
subsequent experiments as it was still essential to optimise biomass production at this
point. Media containing various concentrations of yeast extract were inoculated to
determine the minimal requirement of yeast extract for biomass production This is to
reduce the high nutrient content interfering with the low amino acid concentrations tested
In the Placket- Burman Design experiment. The high nutrient content of the yeast extract
could mask any effects of the amino acids used if its concentration is too high. Yeast extract
was selected for use in these studies as it leads to a higher biomass concentration despite
tryptone producing a greater proportion of butanol compared out of the three metabolites
(acetone, ethanol and butanol) perhaps due to the metabolic shift of the supply of
intermediates towards butanol production, diverting carbon fluxes away from acetone and
ethanol production (Figure 3.1). This is important for metabolic studies, as high biomass
levels ensure that all target metabolites are at measurable concentrations and within the
sensitivities of the analytical techniques. Amino acids were selected as the variable in the
Placket-Burman experiment as they have a profound effect on metabolism. Amino acids
were also a component that both tryptone and yeast extract would share in some form or
other (i.e. peptides in tryptone). It is worth noting that tryptone contains a higher
proportion of amino acids than yeast extract (as it consists of peptides) whereas yeast
extract supplies vitamins and other micronutrients. Amino acids are particularly suitable
candidates to supplement with as it is possible to measure their concentration to calculate
the consumption rates of the nitrogen and carbon sources contained within. Furthermore,
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amino acid anabolism and catabolism result in a large amount of different intermediates,
drastically influencing metabolism. Mixtures of amino acids such as CAS-amino acids were
another potential candidate; however, the large amount of different amino acids may cause
noise that may interfere or dampen effects that can be highlighted with the metabolic
model. Supplementing with a single amino acid allows for the isolation of specific anabolic
and catabolic routes within the metabolism. Furthermore, the selection and
supplementation of just one amino acid presents advantages for investigating butanol
metabolism as opposed to the complex metabolic scenario presented by the presence of a
wide range of amino acids.
Table 3-1 For the multifactorial Placket Burman design experiment, 11 trial media were constructed consisting of 1.25 g/L yeast extract in Biebl media and the amino acid supplementation in the Table. The “+” denotes were the corresponding amino acid was supplement; “-“denotes where 1.25 g/L yeast extract in Biebl media.
Trial
84
85
Figure 3-2 Different growth by optical density observed for various concentrations of yeast extract in the basic Biebl media after 24 hours anaerobic incubation (n=3) (error bars – standard deviation).
86
The results of the limitation experiment of yeast extract (Figure 3-2) indicates that
significant growth can still occur at 0.125g/l yeast extract. Therefore, for the Plackett-
Burman (PBD) experiment, that was the concentration of yeast extract deemed to be
adequate to understand the effect of amino acids on growth and solvent production. The
results also indicate that yeast extract is a limiting substrate within the range tested and so
is an interesting target to investigate further. For the PBD, this limitation is useful to have as
it will help to reduce further any masking effect of the yeast extract over the
supplementation of the amino acids that will be used in those experiments.
There is some work reporting the effects of inorganic and organic sources of nitrogen on
productivity. In Csb it was found that media with sources of organic nitrogen (amino acids,
yeast extract, CAS-amino acids) showed a 100-fold increase in hydrogen production and
glucose consumption compared to using inorganic sources of nitrogen (ammonium
sulphate, ammonium chloride, ammonium nitrate and urea). These studies were carried out
in an undefined tryptone-yeast extract medium able to support Csb growth without the
additional supplementation of the nitrogen sources tested (Ferchichi, et al., 2005).
Unfortunately, no data is provided on the biomass produced under the different conditions,
which would answer if glucose consumption went up due to their being more cells or due to
an increased efficiency in cellular uptake of glucose from the media.
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3. 2. 3:Nutrient requirements for biomass production
The PBD experiment was performed to evaluate the effect of amino acids on biomass
(measured by OD) and solvent production. The amino acids used in this experiment (Table
3.1) were chosen due to their metabolic significance regarding the TCA cycle and glycolysis;
as these pathways of central metabolism are essential for the supply of metabolic
intermediates leading to biomass production. The amino acids, as detailed in Table 3.1,
were supplemented into Biebl containing 0.125g/l of yeast extract. This concentration was
chosen as it still supported growth, which is required for the control trial containing no
amino acids. The lowest concentration of yeast extract to support growth is preferred over a
concentration that would support vigorous growth but may also dampen the signal of any
effects of the supplemented amino acids. The results of the experiment are shown in Figure
3-4. It was found that a medium containing histidine, serine, glutamate, lysine, isoleucine
and leucine, all at a concentration of 1mM (trial 9) supported the highest growth.
88
All the media supplemented with tyrosine showed lower biomass yields than those of the
control (trial 12,), suggestive of some degree of growth inhibition by that amino acid.
Figure 3-3 Growth curve of C. saccharoperbutylacetonicum over 24 hours in the 3 of the most significant of the 12 trials used in the Placket-Burman experiment (n=3; error bars represent standard deviation). Trial 12 is the control with no additional amino acids. Trials 8 and 9 contain the amino acids as detailed in the methods and were the worst and best performers respectively regarding cell density (by OD 600nm).
The data was analysed to determine whether there has been a measurable difference in
OD600 for that amino acid compared to the trial with no amino acids (trial 12). The
multifactorial analysis can be used to extrapolate the individual effect of each amino acid on
biomass production. Using a Student’s t-test analysis, a positive t- value indicates a positive
89
difference of an amino acid on growth, while a negative value would be obtained if the
effect was negative. Interestingly, there appears to be a significant change in t-value for
lysine between 12 and 16 hours after carrying out the multifactorial analysis (Figure 3.5).
The 12-16 h time point coincides with the observed decrease in pH to below 5, suggesting
that increased lysine uptake is required to either mediate or protect against the pH shift
related to the onset of solventogenesis as changes in pH can have significant impact on
metabolism and cellular integrity. This hypothesis could be tested using metabolic models,
helping to elucidate the role lysine plays in solventogenesis and providing strategies for its
application on an industrial scale (i. e using lysine rich substrates). Whilst the model does
not consider pH homeostasis and thus can’t elucidate if this was a direct response to
changes in pH, it could be used to investigate if it is a result of a secondary response, such as
a response to increased flux to butanol production (or away from acetate production)
triggered as a response to a change in pH. Analysis of the metabolic pathways involved
shows that precursor compounds to butanol are present downstream of lysine degradation.
There is a set of reversible reactions converting lysine to Acetoacetyl-CoA, proceeding
further to butanol biosynthesis or even entering the TCA cycle (Kanehisa et al., 2016).
Interestingly, genes involved in this pathway have not been reported in Csb, so the
metabolic analysis would help to elucidate this mechanism. Tyrosine appears to inhibit
biomass production (Figure 3.4).
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Figure 3-4: t-values for each amino acid tested plotted over the 24-hour period (n=3) (OD600nm). The purple line indicates the inhibitory effect of tyrosine over time. The pink line (lysine) appears to switch from negative to positive over time with the switch occurring between 13-15 h, approximately around the time of the phase transition from acidogenesis to solventogenesis is appears to happen.
Other amino acids that appeared to have a positive effect on growth include glutamate,
leucine, isoleucine and histidine (Figure 3.5). Inspection of the metabolic pathways shows
that Csb contains many of the genes required for glutamate catabolism. Glutamate is used
by several microbial species as both nitrogen and carbon sources, and it is a precursor in the
synthesis of purines and pyrimidine as well as in the synthesis of other amino acids
(Belitsky & Sonenshein, 1998; Stams & Hansen, 1984; Okabe et al., 1988) . The large
negative t-value observed for tyrosine (Figure 3-5) indicates that it is inhibitory to growth,
whereas amino acids such as lysine, glutamate and isoleucine appear to enhance growth. As
mentioned earlier, the t values here are a measure of the difference of the effect of each
amino acid compared to the control (containing no additional amino acids).
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Figure 3-5: The t-values calculated using multifactorial statistics for each amino acid analysed for the OD600 after 24 hours from the PBD experiment.
3. 2. 4:Organic nitrogen source (amino acids) is essential for growth
Further independent experiments leading from the PBD experiment included cultures in
Biebl medium supplemented with each of the amino acids in (Figure ). No significant
differences were found between the effect of different amino acids, and glutamate (10 mM)
was chosen to be added to the medium in replacement of yeast extract. The selection of
glutamate was based on operational reasons, as simple colorimetric methods are available
to quantify the amount consumed. Considering that the amino acids investigated enter
metabolism at different points it is unusual that they all support growth.
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Figure 3-6: Biomass (OD600) from batch culture in Biebl media supplemented with 10mM of the corresponding amino acids 24 h (n=3) (error bars – standard deviation) using the basic Biebl medium supplemented with 10mM of either: leucine, histidine, lysine or glutamate with no trials containing any yeast extract.
3. 2. 5: The Effect of Glutamate Addition
As the previous experiments with yeast extract indicated the possibility of nutrient
limitation, the effect of glutamate supplementation across a range of concentrations was
investigated. This information will ensure that the glutamate concentration in the final
defined medium composition is not limiting. This is an essential condition in the Carbon
limited medium to be used for chemostat investigation where the limiting substrate is
sucrose. The effect of the concentration of glutamate on growth was tested (Figure 3.7).
However, no significant differences were found between the various concentrations used
over 1mM, and no indication of “dose-response” type of behaviour was found. The data
shows an increase in OD from the previous experiment compared to those described (Figure
3.6). This effect is likely due to improved pre-culturing conditions by using an amended
minimal media containing glutamate. The observation that glutamate does not show
increased growth past 10mM could be attributed to other nutrients become limiting when
93
glutamate exceeds 10mM. This could be proven by checking the concentrations of all other
nutrients, but this exercise is beyond the scope of this work.
Figure 3-7 Biomass after 24hour incubation for Biebl recipes where a dose-response experiment to glutamate was carried out (n=3) (error bars – standard deviation).
3. 3: Solvent analysis shows drastic increase of butanol yields in defined
media
Analysis of the data from the Placket-Burman Experiment (Section 3.2.1) for solvent
production provides valuable insight not only to the effect of amino acid supplementation
on biomass production but also the effect of solvent formation.
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Figure 3-8 Results from Trials 1 to 12 of the Placket-Burman design experiment (n=3) for acetone, ethanol and butanol production. Figure B: actual concentration produced (g/l). Figure A: Proportion of solvents produced.
As seen in Figure 3-8, the control trial containing no amino acids (trial 12) produced the
lowest concentration of solvent, at around 50% of the values observed in trial 8, the lowest
solvent producing trial supplemented with an amino acid combination. This highlights the
significance of amino acid supplementation on solventogenesis. The medium that supported
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the highest growth, containing lysine, leucine, glutamate and histidine (Trial 9), also showed
the highest yield of butanol (0.71 g/l).
The results of specific solvent production (solvent produced in relation to cell concentration
measured as OD600) (Figure 3-9) show that specific butanol production ranged between 0.30
and 0.49 (butanol(g/l):Biomass (OD)for all the combinations of amino acids tested (trials 1-
11) , with the exception of the combination studied in trial 9. This suggests that the increase
in butanol production in the presence of lysine, leucine, glutamate, serine, isoleucine and
histidine (trial 9) was not solely dependent on the increase of biomass but also on the
presence of an amino acid (or a combination of amino acids) enhancing butanol production.
The metabolic pathways involved could be identified as those surrounding the TCA cycle, of
which its intermediates feed off into various amino acid anabolic pathways. Again, this can
be confirmed by metabolic modelling. The control (trial 12), containing no amino acids,
resulted in a lower OD compared to the other combinations, but butanol production did not
follow the same pattern. Figure 3-8 also details the ratio of butanol production to biomass
showing the enhanced performance of trial 9. These results suggest that the metabolism of
Csb can be redirected towards a higher production of butanol by supplementing the
medium with a combination of amino acids. The data further demonstrates that biomass
production is not necessarily coupled with solvent production. This matter can be further
investigated in the next chapter, where the results of chemostat cultures of Csb are
discussed.
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Figure 3-9: Butanol produced as a proportion of optical density for each of the 12 trials after 24 hours of incubation (n=2) (error bars – standard deviation) indicate the yield of butanol per biomass unit (OD). Throughout the experiment, ethanol showed trace levels.
The graphs in Figure 3-10 details the change of t values over time for the solvent production
observed in the presence of each amino acid. Tyrosine appears to inhibit acetone and
butanol production similarly to how it seemed to inhibit biomass, as described previously.
Furthermore, the general patterns of how each amino acid affects production seems to be
shared between acetone and butanol, but this is not replicated for ethanol.
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Figure 3-10 The t-values taken from the results of the multifactorial Placket-Burman analysis taking into account the production of acetone and butanol after 24 hours; highlighting the change of effect of each of the amino acids over time which could elucidate how the different phases of acidogenesis verses solventogenesis are affected.
3. 4:Aerobic conditions do not inhibit solventogenesis in Clostridium
saccharoperbutylacetonicum
Csb is an anaerobic species along with the closely related species, C. acetobutylicum.
However, there are some cases where Clostridia appear aerotolerant, although it is likely
that the presence of oxygen is detrimental to growth (Schlegel and Zaborosch, 1993) . For
example, when Clostridium glycolicum (an acetogen) is grown in aerobic conditions, acetate
production decreased and higher amounts of ethanol, hydrogen and lactate were produced.
Interestingly, it was found that some oxygen was consumed during exponential growth
(Küsel et al. 2001), prompting the speculation that enzymes like NADH oxidases or similar
might be used to reduce oxygen and hydrogen peroxide, as it has been reported in other
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aerotolerant/anaerobic species (Küsel et al. 2001) . As NADH is essential in ABE formation in
Clostridia, an experiment was designed using different gas compositions to elucidate the
effect that the presence of oxygen might have on ABE production. The culture that was
exposed to air was performed in a conical flask and sealed with a cotton wool bung, which
allows for moderate diffusion of air into the flask. The remaining trials were carried out in
serum bottles and sealed with rubber septa, to ensure anaerobic conditions. The optimised
Biebl media was used to culture Csb under the conditions indicated in Figure 3-11 and 3.12.
Figure 3-11 Composition of gases used to determine the optimal gas for use in the culture of C. saccharoperbutylacetonicum. Gas composition based on certificate of analysis from supplier. OFN -Oxygen Free Nitrogen. OFN(CO2) – Oxygen Free Nitrogen with carbon dioxide. AGM – Anaerionc Growth mixture. (BOC, a UK Industrial supplier of gasses, Guildford, UK).
99
Figure 3-12 Production of acetone, butanol and ethanol in glutamate-supplemented Biebl media when using different gas conditions after 16 h (see Table 3-3), 3 biological triplicates. A t-test was used to determine significance from the control, anaerobic growth mixture.
The results (Figure 3-12) show that although butanol production is significantly decreased
(p=0.05 compared to AGM) at atmospheric conditions, solventogenesis is not completely
inhibited, and is still at 74% of the levels observed under strict anaerobic conditions. The
same was also found for ethanol production (p=0.12) but not for acetone production.
Concentrations of oxygen higher than 21% (atmospheric) were not investigated due to
safety and laboratory restrictions. There were no significant differences in butanol or
acetone production between AGM and OFN, however, there was some significance for
ethanol (p=0.45).
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3. 5: Batch culture of C. saccharoperbutylacetonicum in Bioreactors
Batch cultures were carried out to investigate any effects of scaling up the culture volume
on the previously observed growth patterns. Operationally, larger volumes allow for larger
sample volumes which will be essential for the chemostat work where samples will feed into
a wide range of analytical procedures. Furthermore, it allows for the use of instrumentation
to monitor and regulate parameters to achieve the most metabolic static state possible. The
bioreactor allows for the control of pH and its continuous measurement throughout with
losing culture volume. Furthermore, it is possible to control gas flow, stirring, heating and
monitoring fluctuations in any of these.
The data presented in Figure 3-13 shows the increase of biomass over time during the batch
culture. An extended lag phase of approximately 7 h is observed, longer than the 4 h
typically seen in Biebl medium with 50g/l sucrose. Growth is observed between 7 and 24 h,
and a logarithmic plot (Figure 3-18) shows that the cells grew exponentially between 6 and
12 h, with a value of µ= 0.044 h-1 .
101
Figure 3-13 Biomass an pH evolution of the culture over time in 1.5l batch cultures with a pH control of ≥5.5pH. The pH control on the bioreactor was only set to add sodium hydroxide to keep a minimum pH of 5.5. The pH was only controlled for a lower limit to allow for solventogenesis to be initiated, as solventogenesis onset is associated with a pH drop Data points from biological triplicates Error bars show standard deviation.
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Figure 3-14: The data points between 6 and 12 h (linear section of the data after being transformed using a Log10 function). The equation indicates that for these conditions the growth rate 0.044 h-1.
The pH data (Figure 3-13) for the culture shows the expected trend for the growth of the
strain, due to the production of organic acids during the first stages of growth. The drop in
pH before 16 hours agrees with observations reported for a complex medium (Biebl, 1999).
3. 5. 1: Time profile of pH change in C. saccharoperbutylacetonicum culture
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Figure 3-15 Accumulation of the organic acids and solvents as the pH was left uncontrolled in RCM. Error bars show standard deviation. n=3.
The data in Figure 3-15 illustrates the onset of solventogenesis in Csb as observed in
experiments where the pH of the culture was not controlled. In Csb cultures, it is expected
that pH drops over time. These results show an increase in solvent/acid production with the
decrease of pH. The cultures were carried out in the nutrient rich RCM media supplemented
with 50g/l sucrose. RCM medium was used to determine the upper limits of solvent
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concentration that could be achieved in large-scale batch cultures. This experiment allowed
a comparison between the solvent production from a similar amount of sucrose when in a
rich medium to the defined Glutamate Biebl medium. The literature seems to be
inconclusive with how the onset of solventogenesis is triggered: low intracellular pH, low
extracellular pH, or high butyric acid concentration have all been suggested as metabolic or
physiological signals (Huang, et al., 1986; Monot et al., 1984; Terracciano & Kashket, 1986).
3. 6: Discussion
The results presented in this chapter demonstrate that the statistical approach taken
(Plackett-Burman design) resulted in the development of a minimal medium that can
support both growth and solvent production, a necessary condition for accurate metabolic
analysis. The characterisation of the cultures regarding the nutritional and environmental
requirements and the determination of growth rates are essential in the design of
subsequent steady state experiments. Furthermore, this work has provided a wide range of
questions that can be investigated using the metabolic model.
The experiments provided the previously unknown observation that amino acid
supplementation supports Csb growth. It is also possible that the amino acids are being
utilised as a carbon source particularly around the TCA cycle (Figure 3.16) to provide carbon
backbones to replenish oxoglutarate and oxaloacetate. For example, supplementing with
aspartate, theonine, lysine, alanine and valine may reduce the amount of flux from pyruvate
towards the anabolism of these amino acids, therefore allowing flux instead to divert to
parts of metabolism associated with growth associated maintenance. Similarly, serine can
be catabolised into pyruvate adding to the pool of pyruvate that can be diverted elsewhere.
105
That the supplementation of all these amino acids could be altering the flux through two key
compounds of central metabolism (glucose and pyruvate) which could explain the similarity
in their effects.
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Figure 3-16: A schematic map of metabolism showing where the amino acids in the PBD
experiment are synthesised (adopted in part from Millat et al, 2011)
107
The extensive negative affect of tyrosine on growth is unusual. Tyrosine feeds into
phenylpropanoid and isoquinaline metabolism. Isoquinaline is an aromatic compound and a
weak base (Sato et al., 2007), and it could be possible that it’s converted into a salt when
organic acids are produced. Although this phenomenon is typically seen with strong acids,
and not with these organic acids, it could be hypothesised that in the low pH environment
of Csb cultures, the reaction occurs at an extent sufficient to show a negative effect (LaVoie
et al., 1995).
Similarly, phenylpropanoids are also organic compounds, and derivatives of this group
present many properties such as UV protection and natural scents. However, they are also
known for their bioactive properties. It could be hypothesized however, that the phenol
group in tyrosine and/or phenylpropanoids has an antibacterial effect, perhaps sufficient to
inhibit growth (Ritter et al., 2004. Lucchini et al., 1990).
Furthermore, the observation that growth is enhanced by the addition of glutamate to Biebl
medium, which already contains ammonium sulphate, strongly suggests that both inorganic
and organic sources of nitrogen are essential for improved growth of Csb. Similar results
have been reported to produce Hydrogen by Csb, where the addition of inorganic sources of
nitrogen such as ammonium salts resulted in reductions of both hydrogen production and
glucose consumption. Interestingly, glucose consumption increased approximately 100-fold
in cultures supplemented with organic sources of nitrogen. Coupled with the increase in
hydrogen production, supported by Pyruvate ferredoxin oxidoreductase (PFOR) and
Ferredoxin NAD+ reductase (RnF) proteins, there might be an increased demand for protein
intermediates to support the high reducing power in Csb. However, nitrogen released from
amino acids is released as ammonia, which needs assimilation. The work compared defined
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inorganic nitrogen sources, such as ammonium sulphate, to undefined sources of organic
nitrogen, such as yeast extract, to find increased of production in hydrogen. A medium
containing CAS-amino acid as the semi-defined source of organic nitrogen produced better
yields of hydrogen compared to the ammonium salts. Complex sources such as yeast
extract performed better still in regard to hydrogen yields, which is to be expected as yeast
extract contains other components supportive of biomass and metabolite production
(Ferchichi et al., 2005).
Whilst generally, amino acid metabolism isn’t directly associated with redox balance, the
requirement of intermediate building blocks such as CoA, pyruvate and redox mediators
such as NADPH means that they are linked. With amino acid metabolism being central to
both growth associated and cellular maintenance mechanisms (Kramer, 1996), and
anaerobic organisms reliance on redox compounds such as ferredoxin; it is possible that
amino acid metabolism is more strongly linked with redox balance in anaerobic species
compared to aerobic species.
In defined media, growth of C. saccharobutylicum was directly proportional to the amount
of CAS-amino acids used; however, they could not be replaced entirely by supplementation
of glutamate, as is the case in Bacillus species (Amine, et al., 1990; Stutz, et al., 2007).
Growth with inorganic sources of nitrogen (ammonium acetate, ammonium chloride and
ammonium sulphate) as the sole nitrogen source was also severely inhibited. Steady-state
cultures showed that in a complete medium, GS GOGAT expression was low. GS GOGAT
expression was elevated in the media containing inorganic sources of nitrogen, ammonium
acetate, as the sole nitrogen source. However, in neither conditions was GDH detected
(Amine, et al., 1990; Stutz, et al., 2007).
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In C. acetobutylicum, Glutamine synthetase- Glutamine oxoglutarate aminotransferase (GS
GOGAT), an ATP dependent pathway, is the primary pathway for ammonia assimilation and
is the case for saccharolytic clostridia (Csb, C. beijerinckii). The NADPH-dependent glutamate
dehydrogenase (GDH) is required for nitrogen assimilation (Amine, et al., 1990; Stutz, et al.,
2007).
To elucidate the mechanisms for N assimilation and metabolism, it would be necessary to
carry out similar GS GOGAT inducing media and investigating whether GDH is also not
expressed. If the NADP-dependent GDH is expressed for ammonium assimilation, this may
explain the importance of using butanol to recycle NADP.
The ability for Csb to grow and produce butanol in aerobic conditions is of significant
relevance to industry when considering the cost maintaining required anaerobic conditions.
If yields of butanol can be maintained in aerobic conditions, then anaerobic conditions can
be relaxed in an industrial setting. This would require further investigation of the scaling up
of this process and its economics. The aerotolerant phenotype would mean that Csb could
be a viable candidate for co-culture with Clostridium straminisolvens, an aerotolerant
cellulose-degrading species. The cellulolytic activity of C. straminisolvens would release
consumable carbon sources for Csb from cellulosic material which is the keystone of third
generation bioproducts and biofuels (Kato et al. 2004) .
In addition to feeding into ABE formation, pyruvate is also upstream of the synthesis of
amino acids such as alanine, valine, and leucine. Acetate is produced when there is an
excess of acetyl coA. The data showed that acetone production is enhanced by valine and
inhibited by leucine. As an explanation of this observation, it can be hypothesised that the
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presence of valine in the medium drives the distribution of metabolic fluxes through to
leucine production. This reaction would need to utilise acetyl CoA, thereby potentially
resulting in acetyl CoA limitation which could, in turn, lead to reduced flux to acetate and a
consequent increase in acetone production. Leucine could be inhibitory to acetone
production because it reduces the amount of acetyl Co A utilised to produce lysine. This
could then lead to an excess of acetyl CoA, which could divert flux from acetone production
and into acetate production. Such hypothesis could be tested using the metabolic model
and thus elucidate how these reactions may influence butanol production. It needs to be
noted that if increased valine promotes flux towards acetone, it must first convert pyruvate
into Acetoacetyl CoA, which is a precursor to butanol and acetone. It was also shown that
Lysine inhibits acetone production but appears to enhance growth during solventogenesis. A
product of the degradation of lysine (and glutamate) is crotonyl coA which is a precursor to
butanol and could be competing with acetone production
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Figure 3-17 A simplified schematic show pyruvate about ABE fermentation and two amino acids of interest, valine and leucine.
To summarise, this work has produced a defined media and demonstrated considerable
aerotolerance which will be useful findings for both industry and for further work in
academia. Furthermore, the unusual phenomena of amino supplementation to support
growth has not been found elsewhere.
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Chapter 4: Chapter 4: Analysis of solventogenesis in steady-state
cultures
113
4. 1: Chapter overview and objectives
4. 2:Chapter overview and objectives
The advantages of using steady state cultures as investigative tools is that they permit
pausing the temporal changes that would otherwise happen in a batch culture, and where
individual parameters can be modified to control physiological and metabolic phenomena.
This feature is essential for the collection of data to be used in the analysis of stoichiometric
models such as GSMNs using the approach of flux balance analysis (FBA). In this work, a
steady state culture also allows the investigation of solventogenesis at the physiological
(cellular) level at different pH values, which cannot be carried out in batch due to the
continuum of different growth rates present throughout the time course of the culture.
Chemostats can control growth rate by the continuous supply of a nutrient limited medium
at a fixed flow rate, which does not occur in batch culture. In batch cultures, exponential
growth continues until the limiting substrate (e. g. sucrose) is exhausted. At this point the
addition of fresh media would allow for exponential phase to continue until the limiting
substrate is again depleted. This is essentially what happens in chemostat culture, by
feeding the medium at a fixed rate and removing spent medium to maintain a constant
working volume. Thus, the cells will grow at a growth rate controlled by the dilution rate of
the bioreactor (as discussed in section 1.7.2). As chemostats control growth rate, under
other constant parameters (pH, dissolved oxygen, temperature) we can obtain a detailed
characterisation of the cell metabolism and physiology at a given physiological state (growth
rate) (Stanbury et al., 1995; Gresham & Hong, 2014; Mao et al,. 2015) .
114
There are other uses for chemostat cultures in addition to informing metabolic models.
Chemostat culture also allows for the physiological and metabolic characterisation of the
strain being used, and have been used for the design and optimization of culture media,
among other applications (Hoskisson & Hobbs, 2005). Continuous and fed batch cultures
have also been used industrially for the large-scale production of valuable compounds. For
example, a patent for a two-stage continuous process for the production of butanol has
been filed (Green & Dominguez-Espinosa, 2015). However, the use of continuous culture is
limited in industrial settings. Chemostats also require media that are limited in a manner
that such rich wastes won’t be able to achieve without expensive pre-processing. This is at
least true for lower value commodity chemicals produced in bulk. However, the economics
change quickly with high value pharmaceuticals where mere grams can command a price of
thousands of pounds however, much lower volume is required (Budzianowski 2017); for
example, to think of the volume bioethanol required to fuel a fleet of haulage vehicles for a
week compared to the volume of flu vaccine to inoculate the population of the UK for a
year. An additional risk is contamination, the vessel is connected to both fresh and spent
media, and is an open system. There is also the risk of selective pressures in the chemostat
result in mutations or loss of plasmids in the strains resulting in an altered phenotype and
potentially being outside of GMP guidelines.
The usefulness of chemostat experiments for physiological characterisation of bacteria is
demonstrated throughout this work. Additionally, data from chemostat cultures allow for
the calculation of very important metabolic and physiological parameters, such as growth
associated and non-growth associated maintenance coefficients (Varma & Palsson, 1994).
These parameters determine the requirements for normal cellular function and normal
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cellular function with reproduction for organism being studied. There is a significant amount
of energy and flux directed towards biomass and cellular maintenance which are essential
areas to maintain and must be costed for before accurate fluxes towards secondary
metabolites.
This chapter discusses the continuous culture experiments conducted in chemostats at pH
5.5 and pH 6.5 over three dilution rates (0.01, 0.02 and 0.03 h-1). The values were chosen to
elucidate physiological differences in Csb during solventogenesis and acidogenesis. The
study used the Carbon-limited modified Biebl media, with a concentration of 10g/l of
sucrose and supplemented with glutamate 100mM. The concentration of Glutamate was
higher than the value found in the experiments discussed in the previous chapter to
eliminate the possibility of having two limiting substrates (co-limitation).
The objective of these experiments was to determine the physiological in vitro constraints
for use in the GSMN and to inform the construction of the biomass equation to be used in
the construction of the metabolic model. This includes data such as consumption rate of
nutrients, the production rate of solvents and organic acids, and the macromolecular
composition of the Csb at pH 6.5 and 5.5 at the three dilution rates used. The data will then
be employed in the modelling described in the next chapter, to compose 6 different biomass
reactions specific for Csb. The determination of biomass composition has not been reported
before in constructing any model for solvent producing clostridia, so both this chapter and
the next will discuss the importance of biomass composition data in physiological
characterisation.
4. 3:Results and Discussion
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The work in this chapter involved continuous cultures at three dilution rates (0.01, 0.02 and
0.03 h-1) at pH 6.5, 5.5 and 4.5. However, no biomass could be maintained in the cultures at
pH 4.5, which demonstrates the physiological difference between the solventogenic Csb and
both C. acetobutylicum and C. beijerinckii, which have been shown to tolerate low pH
concentrations (George et al, 1983; Jiang et al., 2014) . Using pH 5.5 and 6.5 allowed for the
investigation of the differences in solvent and acid production at different pH values. This
also allowed for the investigation of whether there is a solventogenic switch in Csb.
The specific production rates shown in these Tables were calculated using Equation 4.1.
q=(Product produced (g/ l)Biomass(g /l) )μ (h¿¿−1)¿
Equation 4- 12 Formula for calculating specific production rates per biomass as used for Table 4.8.
Figure 4.0 illustrates how the chemostat replicates were carried out, in this case for those
ran at pH 6.5 at 0.03 h-1. After switching on the media pump, 3 volume changes were
allowed to pass before taking a sample, to ensure the system had reached the steady state.
After the first sample, the vessel was allowed to recover for an additional 3 volume changes
before taking another sample. Steady state was determined by constancy in the
concentration of biomass (OD600). After three samples were collected in this way the
bioreactor was sterilized, cleaned, disassembled, and set up again for the second replicate.
Two biological replicates were performed for each condition, and the results are the
average of three technical replicates for each dilution rate and pH investigated.
117
Figure 4 Detailed sampling protocol for one run of a chemostat. Full sample were taken at 96, 192 and 288 hours. The Additional readings for OD which were taken in the first replicate were not repeated in the second to reduce the risk of contamination.
The details of substrate consumption, production rates and yields (carbon-mole) can be
found in Figure 4.. Consumption rates for sucrose, glutamate and ammonium appear
constant throughout the steady state experiments, with variations only seen in phosphate
consumption. For pH 6.5, phosphate consumption peaks at 0.02 h-1, trebling the
consumption observed at 0.01 and 0.03 h-1. This increase at 0.02 h-1 does not correlate with
any changes in biomass or product formed.
Butanol production rates peak at 0.03 h-1 at both pH 6.5 and 5.5, with comparable values of
19.9 and 23.1 carbon-mmoles/l respectively. A similar pattern is also observed for butyric
acid. This suggests that early accumulation of organic acids is not required for increased
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butanol production and that solvent production does require to be triggered by low pH. This
would be contrary to what has previously been reported in the literature (Kosaka et al.,
2007; Biebl, 1999; Millat, Janssen, Bahl, et al., 2013a; Cho et al., 2017; Ezeji et al., 2013) .
Ethanol production rates are consistently elevated at pH 5.5 compared to pH 6.5, suggesting
that ethanol production is favoured during low pH. Ethanol production produces 2 NAD+ (Li
et al. 2011) , similarly to butanol synthesis. It could be hypothesised that this is to replenish
a deficit for NAD+ for use elsewhere in the metabolism.
Acetic acid production rates are higher at pH 6.5, which is what would be expected
according to the suggestion that organic acid production precedes solvent production by
occurring at a higher pH (Millat, Janssen, Bahl, et al. 2013b; Mazzoli 2012; Kosaka et al.
2007) ). However, acetone production rates were higher at pH 6.5 than 5.5, similarly to
butanol production at 0.03h-1.
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Figure 4.1: Details of the consumption and production of substrates and products observed in the steady-state cultures. Carbon-moles calculated, where carbon is found in the molecules, by calculating the percentage weight of carbon in g/L in butanol and then converting into moles to make carbon moles. The same was applied to ammonium sulphate and phosphates for nitrogen and phosphate respectively
120
121
122
pHDilution
rate
YIELDS (PRODUCT: SUBSTRATE (GLUTAMATE)) YIELDS (PRODUCT: SUBSTRATE (SUCROSE))
Butanol Acetone Ethanol Acetic acid
Butyric acid
Butanol Acetone Ethanol Acetic acid Butyric Acid
0.01 0.04 0.34 0 0.03 0.09 0.04 0.42 0 0.03 0.12
6.5 0.02 0.18 0.07 0.04 0.02 0 0.22 0.09 0.05 0.03 0
0.03 0.54 0.09 0.02 0.06 0.03 0.71 0.13 0.03 0.08 0.05
0.01 0.61 0 0.19 0 0.03 0.77 0 0.24 0 0.0
5.5 0.02 0.49 0.10 0.03 0.03 0 0.65 0.13 0.04 0.04 0
0.03 0.59 0.03 0.07 0.03 0.07 0.82 0.04 0.11 0.04 0.09
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Table 4.1: Yields of product per substrate consumed using the data in Figure 4. No units given as they are cancelled out during the calculation.
4. 3. 1: Maintenance Coefficients for C. Saccharoperbutylacetonicum in chemostat culture
The data for substrate consumption and the dilution rate used at steady state can be used
to determine the minimal energy requirements for cellular maintenance. This information is
very important in the construction of metabolic models.
Figure 4.2.: Chemostat data for sucrose consumption over dilution rate indicating the
minimum requirements for growth (qSucrose) where the linear regression cross the y-axis (C in
y=mx+c). With quotients of 3.058 (moles l-1 h-1)-1 for cell at pH 5.5 and 2.956 (moles l-1 h-1)-1
for cells at pH 6.5 when using defined media.
4. 3. 2:Macromolecular composition and implication for physiology and metabolic modelling.
Most often in the literature, the macromolecular composition of solventogenic Clostridia is
assumed to be the same as Bacillus subtilis (Dauner & Sauer, 2001), and this approach has
been used in numerous articles regarding the metabolic network of C. acetobutylicum (e.g.
Senger & Papoutsakis, 2008) . Other unrelated species, such as Streptomyces antibioticus
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and Streptomyces coelicolor for example, have been used to define the carbohydrate and
teichoic acid composition (Lee et al., 2008). However, contrary to the methods used in the
literature (Dauner & Sauer 2001; Feist et al. 2007), which used E. coli data for their
Clostridia models, in this work the macromolecular composition (protein, carbohydrate,
lipids, DNA and RNA) of Csb is determined in biomass samples collected from the chemostat
cultures previously described (Table 4.2).
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Table 4.2 : Macromolecular composition of C saccharoperbutylacetonicum grown in chemostat cultures given as percentage. Data is presented as a percentage of biomass (calculated from DCW). **Carbohydrates are expressed in glucose equivalents. *Lipid content was calculated from a 1 000X concentrated overnight culture to meet the sensitivity range of the assay which the chemostat samples could not reach due to the low biomass concentrations observe.
Product Dilution rate
PROTEIN (%)
CARBOHYDRATE** (%)
LIPIDS* (%)
DNA (%) RNA (%)
0.01 78 2.47 6.0 6.4 18.4
pH 6.5 0.02 88 3.33 6.0 9.6 25.9
0.03 72 2.62 6.0 7.3 27
0.01 54 2.90 6.0 8.0 17.7
pH 5.5 0.02 56 3.47 6.0 8.0 34.4
0.03 46 5.29 6.0 9.0 24.0
The data for the macromolecular composition of Csb provides some interesting results, and
will be compared to the data for B. subtilis to use a benchmark for comparison (Dauner &
Sauer 2001) (Table 4.3). The lipid fraction in Csb and B. subtilis are comparatively similar, as
is the protein fraction for pH. 5.5. However, the protein fraction for pH 6.5 is significantly
higher by 50%. As Csb is not considered an acidophile (i.e. species tolerant of pH as low as
2.0), it is unlikely to be a response to a pH concentration that is “too high”, if the increase in
protein is due to stress. It could be considered that higher pH concentrations (6.5) are
associated to the start of cultures and thus the lag phase. The expression of enzymes to
catabolise the sugars present in a new medium could account for the increased protein
content observed. Work on E. coli elucidated that protein composition increases
significantly throughout the lag phase (Azam et al., 1999) .
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It can be hypothesised that Csb senses the high pH and is synthesising additional enzymes
that catalyze the reactions involved in solventogenesis to create a lower pH environment by
utilising the organic acids previously produced.
The DNA and RNA composition of Csb are both higher than those found in B. subtilis.
However, the ratio between the two varies around 1:3 (DNA: RNA) which is similar to the
ratio found in B. subtilis.
Table 4.3: Macromolecular composition of B. subtilis (Dauner & Sauer 2001) .
Component Protein Lipids DNA RNA
% Biomass 52.84 7.6 2.6 6.55
The differences in composition at different pH values will be considered in the construction
of the model (described in the next chapter), by constructing two equations for biomass,
one using the average composition found at pH 6.5 and another for the composition at pH
5.5. This will reflect the metabolic changes caused by changes in pH.
4. 3. 3:
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4. 3. 4:Carbon and nitrogen balances
The medium used for the steady state cultures contained two sources of carbon: sucrose
and glutamate, the amino acid used for supplementing the medium, as described in
previous chapters. Considering the carbon content of each substrate, the concentrations of
sucrose (28mM) and glutamate (100mM) in the medium supplied 11.76 mM and 40.80 mM
of carbon, respectively. The products found in the culture supernatant include butanol,
ethanol, acetone, acetic acid and butyric acid. Due to the low growth rates and biomass
concentrations achieved in the chemostat cultures, and the low flow of N2 through the
culture (to ensure anaerobic conditions), it was not possible to quantify the amount of
carbon dioxide released by the growing culture. However, there are two possible ways to
calculate (or predict) the amount of carbon dioxide produced, which will be addressed later
in this section. The equation for calculating carbon balances is shown below (equation 4.2).
For the steady-state cultures used in this research, data was generated for all components
except carbon dioxide, which could not be detected. Thus the equation can be arranged to
solve for carbon dioxide (Pfromm et al. 2010) .
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Sucrose[ Substrate ] [ carbon ]+Glutamate [Substrate ][carbon ]→ Biomass [Product ] [carbon ]+Butanol [ Product ][carbon]+ Acetone [ Product ][carbon ]+ Ethanol [Product ] [carbon ]+ Acetic acid [ Product ][carbon ]+Butyric acid [ Product ] [carbon ]+CO2[ Product ][carbon]
Equation 4.2: Equation accounting for all carbon species contributing to the carbon balance. The moles of carbon is calculated using the concentration (moles) of each product found from the chemostat experiments.
Using the data for the macromolecular composition of Csb (Table 4. 3) it was possible to
calculate the elemental composition for each pH and dilution rate investigated. Using
experimental data for the macromolecular composition and further information from Feist
et al, 2007 , for defining glycogen, murein, LPS, Lipids and solute pool (based on E. coli), it
was possible to define the elemental composition of the biomass. Equations 4.3 – 4.7 detail
the elemental composition of biomass and can be used to calculate the carbon balance of
the system, detailed in Tables 4.2 – 4.6. This information will be used to constrain the
metabolic model presented in the next chapter. Constraining the model using rates found
experimentally should improve the relevance of predictions made using the model.
The equation 4.3 for biomass in steady state culture at pH 5.5 at dilution rate 0.01h-1 is
comparable to biomass equations found elsewhere, and is in agreement with the
regularities found for elemental biomass composition for a wide range of microbial species
(Minkevich & Eroshin 1973; Roels 1983), and in very good agreement with the composition
of C. celluloyticum , CH1.75O0.5N0.25 (Desvaux et al., 2001) Interestingly, the equations found
show that the content of oxygen is higher than those generally reported for
129
microorganisms. The lower proportion of hydrogen and higher fraction for oxygen, nitrogen
and phosphate in equation 4.7 can be attributed to experimental error.
As the rate of carbon dioxide produced was below the detection limits of the instruments,
the balancing can be used to predict the amount of CO2 produced. Assuming the only
compound missing is carbon dioxide, the carbon moles unaccounted for after biomass,
butanol, acetone, ethanol, and acetic and butyric acids can be assumed to be carbon
dioxide.
C H1.79O0.65N0.25 P0.07S0.005
Equation 4.3: Elemental composition of biomass from steady-state culture at pH 5.5, at a dilution
rate of 0.01 h-1
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C H 1.73O0.74N 0.27P0.09S0.004
Equation 4.4: Elemental composition of biomass from steady-state culture at pH 5.5, at a dilution rate of 0.02 h-1.
C H1.72O0.75N 0.26 P0.09S0.004
Equation 4.5: Elemental composition of biomass from steady-state culture at pH 5.5, at a dilution rate of 0.03 h-1.
C H 1.8O0.63N 0.24P0.06S0.005
Equation 4.6: Elemental composition of biomass from steady-state culture at pH 6.5, at a dilution rate of 0.01 h-1.
C H1.53O0.92N0.27 P0.16S0.002
Equation 4.7: Elemental composition of biomass from steady-state culture at pH 6.5, at a dilution rate of 0.02 h-1.
C H1.79O0.67N 0.26P0.07S0.005
Equation 4.8: Elemental composition of biomass from steady-state culture at pH 6.5, at a dilution rate of 0.01 h-1.
131
Carbon-moles produced Carbon-moles consumedpH Dilution
rate (h-1)Biomass Butanol Acetone Ethanol Acetic
AcidButyric
AcidTotal Sucrose Glutamate Total Difference error
6.5 0.01 0.014 0.014 0.135 0 0.01 0.037 0.210 0.32 0.396 0.716 0.5066.5 0.02 0.010 0.143 0.056 0.03 0.018 0 0.257 0.32 0.405 0.725 0.4686.5 0.03 0.008 0.666 0.121 0.03 0.079 0.046 0.950 0.31 0.414 0.724 -0.226 -0.238145.5 0.01 0.012 0.252 0 0.078 0 0.013 0.355 0.33 0.412 0.742 0.3875.5 0.02 0.020 0.423 0.086 0.026 0.027 0 0.582 0.32 0.426 0.746 0.1645.5 0.03 0.012 0.77 0.035 0.099 0.042 0.085 1.043 0.31 0.428 0.738 -0.305 -0.29259
132
Table 4.3. Production of biomass, butanol, acetone, ethanol, acetic acid, butyric acid at different dilution rates, expressed in carbon-moles, and carbon-moles of carbon consumed in order to calculate the carbon balance for the steady-state culture of C. saccharoperbutylacetonicum. Carbon-moles calculated by using the moles of product from the experiment and calculating the proportion of moles which are in carbon.
Figure 4.3 Schematic of ABE in C. acetobutylicum detailing the amount of carbon found in products (red) and the substrate (blue) (Papoutsakis, 2008).
The annotations in Figure 4.3 show that the production of butanol, ethanol, acetone, acetic
acid, butyric acid and carbon dioxide require 22 carbons, meaning that 11 whole glucose
molecules (providing 66 carbons) could be fed into the system to create the products listed
in Table 4.4. That is, 11 glucose molecules could create the amounts of butanol, butyric acid,
acetic acid and ethanol; and acetone and carbon dioxide described in the Table below. .
Therefore, a theoretical carbon balance can be calculated. Theoretical yields are not
replicated in experimental data as these calculations do not account for the regulation of
expression for the enzymes involved in the pathway, or the amount of substrate utilized to
133
produce biomass, maintenance, etc. However, these calculations can be used to suggest
whether experimental data is consistent and give indication to pathways that may be up or
down regulated under the circumstances under which the data were collected; this data is
visualised in Table 4.4. These calculation were based on using an integer value of glucose
molecules, whilst the schematic in Figure 4.3 only requires 22 molecules this would require
3.666 molecules of glucose.
Table 4.4 The theoretical yields of product if supplied with 11 glucose molecules as shown in Figure 4.1. This data does not reflect experimental as it does not consider regulation of the pathways, but this theoretical data can be used to give an indication of what pathways are active (or not active) when compared to experimental data. As the theoretical data shows what can be produced if there were stoichiometric conversion of substrate into products.
Number of molecules Number of carbons Yield
11 Glucose
(substrate)
66 NA
3 Butanol 12 12/66 0.18
3 Butyric acid 12 12/66 0.18
3 Acetone 9 9/66 0.14
6 Acetic acid 12 12/66 0.18
6 Ethanol 12 12/66 0.18
3 Carbon dioxide 9 9/66 0.14
134
Table 4.5 shows that the experimental data is in relatively good agreement to the
theoretical. However, there is a clear deviation from the theoretical stoichiometry in the
organism towards the production of butanol, acetone and ethanol with little build-up of
organic acids. The heat maps also highlight that there does not appear to be a clear
distinction between solvent production occurring at lower pH values compared to a higher
pH concentration. The growth rate appears to have a greater effect on butanol production,
with butanol production increasing at faster growth rates
Table 4.5 Heat map depicting how closely the experimental data yields (carbon mole product: Carbon mmoles sucrose only) match that of the theoretical yields calculated in Table 4.5. Yields lower than theoretical move towards red. Yields higher than theoretical move towards green. Yellow, the midpoint, is set at the theoretical yield (bottom of columns).
pH Dilution rate (h-1)
ButanolAceton
e EthanolAcetic Acid
Butyric Acid
6.5 0.010.0437
50.4218
75 0 0.031250.11562
5
6.5 0.020.4468
75 0.1750.0937
5 0.05625 0
6.5 0.032.1483
870.3903
230.0967
740.25483
870.14838
71
5.5 0.010.7636
36 00.2363
64 00.03939
39
5.5 0.021.3218
750.2687
50.0812
50.08437
5 0
5.5 0.032.4838
710.1129
030.3193
550.13548
390.27419
35Theoreti
cal 0.18 0.14 0.18 0.18 0.18
The advantage of having a monophasic metabolism regarding solvent production is that
there is no need for the production process to be adapted to having multiple stages to
obtain optimum yields. Although, not having been documented in the literature, a process
135
taking advantage of monophasic production has been patented by a UK butanol producing
company (Green et al.,2015). T Complex culture processes at large scale can be costly to
implement. Furthermore, the fact that organic acids do not accumulate in the medium
allows for downstream processing to maintain higher purities of the desired product, i.e.
butanol.
The samples from the steady-state experiments were analysed for glutamate consumption,
ammonium consumption and biomass production. This allows for the analysis of the
nitrogen balance, similar to the carbon balance performed previously. The balancing shows
that only a small proportion of nitrogen goes into biomass (Table 4.6). Further work of
interest would be to measure other nitrogen containing molecules that are secreted to
elucidate the fate of the nitrogen compounds consumed.
Table 4.6: Nitrogen balancing for biomass and nitrogen containing substrate.
Nitrogen Producedmmole
Nitrogen Consumedmmole
pH Dilution rate (h-1)
Biomass Ammonium
Glutamate
Total
6.5
0.01 5.34 7.72 79.18 86.90
6.5
0.02 3.50 7.78 81.06 88.84
6.5
0.03 3.32 8.13 82.80 90.92
5.5
0.01 2.94 7.55 82.34 89.89
5.5
0.02 7.89 7.55 85.24 92.79
5.5
0.03 4.71 7.84 85.61 93.44
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4. 3. 5: Specific production rates of solvents and organic acids in C. saccharoperbutylacetonicum
The specific production rates for butanol at pH 5.5 were high throughout all the dilution
rates tested, doubling from 0. 02 h-1 to 0.03 h-1. Interestingly, the specific production rate at
pH 6.5 at D = 0.03 h-1 was comparable to that at pH 5.5. This further demonstrates that low
pH is not essential for the onset of solventogenesis in Csb (Table 4.7).
The consensus amongst several authors (Janssen et al., 2010; Vasconcelos, et al., 1994)
(Millat et al., 2011; Mazzoli, 2012) is that solventogenesis and butanol production in C.
acetobutylicum is an evolved response to acid stress generated by the synthesis of the
organic acids, and the same has been assumed for Csb, but has not been investigated yet.
However, those studies were carried out in phosphate-limited cultures. In a glucose-limited
steady state culture, C. acetobutylicum also followed the biphasic pattern associated with a
drop in pH (Rao & Mutharasan, 1987). However, it has been shown that the presence of
organic acids does not initiate solventogenesis in C. acetobutylicum (Hüsemann &
Papoutsakis, 1988). This mechanism is also involved in the recycling of NAD needed during
glycolysis, crucial in anaerobic metabolism. Butyric acid is produced to generate ATP as an
alternative pathway for ATP production in anaerobic species. However, this work has
indicated that butanol production is high and comparable between pH 5.5 and 6.5 at a
dilution rate of 0.03 h-1, suggesting that butanol production is essential throughout the life
cycle of the microorganism. The observation that butyric acid and butanol specific
production rates both peak under the same circumstances (pH 6.5, D = 0.03h-1) may imply
that these pathways are of importance regarding energy supply and redox potential
respectively, and not only associated with the stress response to low pH. Furthermore, NAD
137
is also used in the TCA cycle, which is partially present in Csb (Mazzoli, 2012; Schlegel &
Zaborosch, 1993)
Specific production rates for ethanol were higher at pH 5.5 than at 6.5, which confirms that
ethanol production is regulated separately to that of butanol and acetone (Kosaka et al.
2007; Lee et al. 2012; Fischer et al. 1993) . The sol operon contains the genes for butanol
and acetone production bld, ctfA, ctfB and adc, as well as adh, the gene encoding for alcohol
dehydrogenase used to synthesise ethanol from acetaldehyde. However, adh is not on the
sol operon, upstream of the promoter. This difference was also highlighted in the previous
chapter, where the amino acids improving ethanol production are different to those amino
acids associated with an increase in both acetone and butanol production.
Similarly to butanol, specific production rates (Table 4.7) for acetone were higher at pH 6.5
than those at pH 5.5. Lower levels of acetic acid were also observed at pH 5.5. It can be
suggested that during growth in batch cultures, the production of butanol observed at lower
pH levels is a function of the physiological state, not pH. If converting butyrate to butyryl-
CoA is coupled to acetone production as shown in the ABE pathway (Figure 4.1) then a
proportional amount of acetone production would be observed when butanol production is.
However, this is not the case, thus suggesting that butanol is not produced from butyrate,
further supporting the conclusion that acid production is not a requirement for solvent
production.
It has been shown In C. acetobutylicum that the production of organic acids and butanol are
not mutually exclusive and can occur simultaneously (Kumar et al., 2013) . However, even
when acidogenesis and solventogenesis do occur simultaneously, butanol production
138
doubled at the lower pH investigated (Kumar et al., 2013) , which is contrary to what has
been found here. The observation that acidogenesis and solventogenesis can occur
concurrently, together with the fact that butanol production increases at higher pH levels,
weakens further the argument supporting the existence of an acidogenic/solventogenic
“switch” that responds solely to pH.
139
Table 4.7: The specific production rates calculated for the products measured during the steady-state investigation. ND = Not detected.
pH Dilution rate
SPECIFIC PRODUCTION RATES (MMOL G DW-1 H-1)
Butanol Acetone Ethanol Butyric acid Acetic acid
0.01 7.07 ±0.56 68.89±2.19 ND 14.78 ±1.72 11.94±1.47
6.5 0.02 81.00 ±10.39
33.29 ±4.18 20.74 ±2.19 ND 24.35±2.56
0.03 527 ±13.89 100.51 ±4.34
29.67 ±0.33 29.27±1.62 148±3.-8
0.01 195.27 ±5.68
ND 55.53 ±4.17 6.16 ±0.32 ND
5.5 0.02 131.61 ±17.65
27.85 ±3.78 10.12± 2.19 ND 20.24 ±2.55
0.03 394.14 18.71 ±3.52 63.14 ±24.6 35.35 ±6.84 50.98 ±5.39
140
4. 4:Chapter Summary
This work has shown that acid production does not precede solvent production in Csb but
rather showed that they both happen simultaneously. This is likely due to butanol and
butyrate production being essential for the recycling of ATP and NADP under the conditions
and growth rates investigated here. Furthermore, this suggests that there is no
solventogenic “switch” and that perhaps the cell does not respond to pH but accumulates
solvents over time and increases along with the growth rate of the cell.
Furthermore, the discovery of the monophasic production of solvents in Csb, allows for
economical large-scale production protocol that do not need to rely as heavily on fed-batch
or other processes. The lack of build-up of organic acids also allow for the efficient
downstream processing.
This work also suggests that an optimal pH for Csb is pH 6.5, and not 5.5, contrary to what
previous work has found for C. acetobutylicum (Bahl etal., 1982; Monot et al., 1984). This
optimal value for pH is however in line with the consensus that the optimal pH for
Clostridium as a genus is neutral. However, it is worth noting that the genus Clostridium is
particularly vast and metabolically diverse, so wide variability should be expected.
Ethanol follows a pattern of production dissimilar to butanol, and this is likely due to the
difference in regulation of each of these pathways because of adh not being on the Sol
operon.
The macromolecular composition of Csb is significantly different in several areas to that of
B. subtilis. Furthermore, it has been shown that there is an unprecedented increase in the
protein fraction at higher pH levels. This highlights the important of obtaining realistic data
141
for the species, as variations in the composition may cause large differences in the
metabolic analysis using stoichiometric models. For instance, significant changes in protein
content will likely have substantial knock-on effects on the calculation of metabolic fluxes.
A strong case has been made here for the importance of obtaining an accurate
macromolecular composition that is specific to the strain in question, as deviations from the
predicted norm could account for an interesting explanation for otherwise unexplained
metabolic phenomena. This importance is strongly demonstrated here, in particular by the
differences found between pH 5.5 and 6.6, and by the differences found between Csb and
the B. subtilis reference data. Therefore, further work should include a thorough break
down of the cellular composition so that models do not rely on any information from other
species.
Production of solvents and organic acids at different pH values.
Butanol production between pH 6.5 and pH 5.5 were not as starkly different as would have
been expected, considering what has been reported for C. acetobutylicum (Dabrock et al.
1992; Millat et al. 2013b; Haus et al. 2011) which all found a clear solventogenic phase and
acidogenic phase at pH 6.5 and 5.5, respectively. At pH 6.5, butanol production rates
steadily increased across the dilution rates used, from 0.14 to 1.43 and then to 6.66 carbon
mmol at 0.03h-1. At pH 5.5 however, the production of butanol remained relatively constant
(compared to pH 6.5) over the dilution rates investigated, producing increasing butanol at
rates of 2.53, 4.2 and 7.7 carbon mmol from dilution rates 0.01 through to 0.03 h-1,
respectively. Unexpectedly, at the highest growth rate, butanol production at pH 6.5 is
comparable to the production at pH 5.5. This suggests that although low pH must be
influential to the production of solvents in Csb, it is not a necessity. This may be due to the
142
solventogenic switch not being present, or not being triggered under the conditions of the
steady state cultures carried out here. It could be hypothesised that the production of both
butanol and butyric acid are required for the production and recycling of ATP, CoA (butyric
acid) and NAD (butanol), which are of importance in an anaerobic species whom rely heavily
on glycolysis for their energy metabolism and in this case, solvent synthesis.
Ethanol production overall was low. Ethanol production rates at pH 6.5 increased slightly as
the dilution rate increased, reaching 0.3 carbon mmol l-1 h-1 at the highest D. At pH 5.5
ethanol production was very high at 0.01h-1 with 0.78 carbon mmol h-1, which is an opposite
pattern compared to that observed for butanol, possibly as a result of the different
regulation mechanisms in the expression of these pathways (Kosaka et al. 2007) . The gene
adh, ethanol production is upstream of the promoter for the sol operon. Acetone
production was also affected by the pH: very low concentration was produced at pH 5.5,
while 1.35 carbon mmol were produced at the lowest dilution rates at pH6.5.
With regards to the acids, higher production of acetic acid and butyric acid was found in
general at pH 6.5 than 5.5, in line with the observation that organic acid production takes
place at higher pH, to be used as carbon sources for solventogenesis (Millat et al. 2011) .
This is further reinforced by the fact that the concentration of butyric acid was higher than
acetic acid, consistent with a higher production of butanol. This would be expected, as
butyric acid is a precursor of butanol, the solvent produced at highest rate in these
chemostats.
The results confirm that Csb is a butanol hyperproducer, even when grown at pH levels
higher than those reported to be optimal. Yet this work showed that significant butanol
143
production also occurs during acidogenesis, precluding the suggestion from the literature
that acidogenesis is not required for solventogenesis, at least regarding supplying
precursors in the form of organic acids (Haus et al., 2011; Millat, et al., 2013; Monot et al.,
1984; Xue, et al 2013).
Future work
The results from this chapter generate further questions to understand the mechanisms
involved in the synthesis of solvents by Csb. To investigate where consumed nitrogen diverts
other than biomass, it should be necessary to analyse samples for other secreted nitrogen
containing compounds.
Although GS-GOGAT is the primary path way for ammonium assimilation in C.
acetobutylicum, the case may not be the same for Csb, which may be expressing GDH due to
its efficiency for recycling NADP through butanol production. This would also confer an
advantage in anaerobic species as it does not require ATP as in GS-GOGAT. Transcriptome
analysis to investigate GHD and GS-GOGAT coupled with addition nitrogen limited
chemostat work may provide further insight.
Carbon-limitation was chosen for this series of chemostats to investigate how glucose is
utilised for acid and solvent production in limited conditions. The results led to the discovery
of monophasic production of solvent and acid. Therefore, it would be interesting to examine
how this pattern holds under phosphate-limitation and whether further work into C.
acetobutylicum and others could lead to a similar monophasic production, and whether
biphasic or monophasic production is an artefact caused by nutrient limitation.
144
There were some limitations to the work done in this chapter. Measurements of the
intracellular concentrations of ATP and NADH would have been useful to determine the
effect of the different pH concentrations on energy metabolism. However, the results of the
analysis performed with commercial kits for determination of ATP/ADP, NADP/NADPH and
NAD/NADH showed values below the detection limits. This was not due to low biomass
concentration as even after concentrating the sample the kits failed to detect their target
metabolites. It is unlikely that the metabolites degraded as the samples were snap frozen in
liquid nitrgoen and stored at -80oC (Liebert 2008) . A possible alternative in further work is
the use HPLC protocols to determine the concentations of these metabolites. Obtaining data
for ATP would for example allow for the calculation of growth and none-growth associated
mainenance energy which can be used to contrain a GSMN.
The tolerance of Csb to pH concentrations below 5.5 was lower than expected. Previous
work has demonstrated that it is possible to carry out steady state cultures with Csb at pH
between 5.5 and 4.0 (Millat et al. 2011; Biebl 1999) . This was not feasible in this work,
perhaps due to limitations of the defined media. It is possible that there are additional
energetic or nutritional demands to maintain a viable culture at such low pH. This was not
expected to happen as when the media was used in batch culture, production of butanol
could be observed, indicating that the cells were still viable. Further work would be necesery
to modify the medium to support growth at lower pH.
During this research, it was not possible to quantify the production rate of carbon dioxide
from the culture. This may provide some limitations on the determination of the carbon
balances between substrate and products.
145
Chapter 5: Reconstruction of the Genome Scale Metabolic Network of
C. saccharoperbutylacetonicum
146
5. 1: Chapter Introduction
One of the purposes of constructing a genome scale metabolic network (GSMN) is to be able
to test many hypotheses before carrying out any time- or resource- demanding
experiments. For example, using a GSMN to investigate in-silico the effects of deleting a
particular gene before performing experiments to gain an idea of the potential phenotype
of such a knock out. Additionally, GSMNs can also be used to test hypothesis to elucidate
mechanisms of why or how a product is synthesised under given conditions; in this case the
production of butanol (Cazzaniga et al., 2014). In this chapter, the potential uses and
applications of a GSMN for Csb are presented. An important part of the work focused on
developing biomass equations for Csb that could be utilised in a fully annotated GSMN. The
biomass equations are based on data from the chemostat experiments presented in the
previous chapter. Furthermore, as it has been shown earlier, the experimental evidence is
unclear and cannot explain why comparable titres of solvents are produced at both pH 5.5
and 6.5 or how these can be improved. A powerful tool to elucidate these limitations is
metabolic modelling (Chen & Henson, 2016), and the model developed here will be used to
explain these observations.
Genome scale metabolic modelling involves the coupling of genomes and biochemical
pathways to map out the metabolism of an organism. Available annotated genome
sequences show what genes are present to catalyse the thousands of reactions that can
take place in a cell. This information is then used to link these reactions together to create a
metabolic model of the cell in-silico. This results in a metabolic reaction network of the
147
organism, in which we can trace fluxes of substrate through the different metabolic
pathways applying approaches such as linear programming optimisation. The general idea is
that the consumption and production of metabolites (including biomass) can be expressed
as material balances. During growth, sources of carbon and energy (as well as a source of
nitrogen, oxygen, oligoelements and minerals) are utilized to synthesise biomass. By writing
the mass balances, and assuming that cells are growing at a steady-state, a set of linear
equations is constructed. The solution to this system is a distribution of fluxes through the
metabolic reactions that satisfies the steady-state condition, subject to the stoichiometric
and thermodynamic constraints imposed, maximizing the value of a chosen reaction (the
objective function), generally the synthesis of biomass. Linear programming optimisation is
only possible in steady-state conditions were the model is constrained according the
experimentally measured rates of nutrient uptake (Edwards et al.,2002; Gevorgyan et al.,
2010; Kell, 2006). However, for any GSMN to give reliable information it needs to be based
on physiological data and validated. For informing the model of physiological data, the
results from the chemostat cultures of Csb shown in the previous chapter are ideal.
Despite its power, this approach presents limitations, in that the impossibility of
incorporating of regulatory networks into the system prevents models giving the most
detailed picture of metabolism possible.
Metabolic models are available for other clostridial species as discussed is section 1.5.2.
However, as those models were specific for particular software, and was not possible to
reformat for the specific software to be used in this work (Surrey FBA), it was decided that it
would be more efficient to start with the Csb genomes using the C. beijerinckii and C.
acetobutylicum models as references and benchmarks.
148
In order to test the model some physiological data for four mutant strains (Table 5.7) were
supplied by Green Biologics1.
5. 2:Construction of the model and biomass equations
Metabolic and physiological data from the target species (Csb) are required to interpret the
information obtained from the genome sequence. To construct this model, the genome of
C. saccharoperbutylacetonicum N1-4 (HMT) was sourced from GenBank (CP004121)
(Poehlein et al. 2014) . Apart from the information related to primary metabolism,
important information about solventogenic pathways is required. The genome sequence
shows that Csb contains the Sol operon, the same as in the chromosome of C. beijerinckii.
However, it differs from C. acetobutylicum in that the gene adh for alcohol dehydrogenase is
not found on the sol operon. Instead, there is adhE, an alcohol/aldehyde dehydrogenase
(Poehlein et al., 2014). The genome sequence used for the construction of the biomass
reaction was also used for the construction of the first draft of the GSMN. The genome
sequence was submitted to the RAST (Rapid Annotation Subsystem Technology) server,
which allows for the automatic annotation of the genome (Aziz et al. 2008; Hyduke et al.
2011) . The model is converted to SBML format and is reformatted for use in Jymet. Jymet is
a graphical user interface for performing metabolic simulations and analysis in GSMN, using
approaches such as FBA and FVA (Gevorgyan et al., 2010).
Obtaining an accurate and reliable equation for biomass is essential for the construction of a
metabolic model, as they have a large effect on the solution of the model: Vast amounts of
metabolic energy are invested in the synthesis of biomass, therefore diverting metabolic
1 Green Biologics is a UK biotechnology company who use Clostridia to produce butanol at an industrial scale, They are the industrial partner in the BBSRC CASE studentship that supported this work.
149
fluxes away from the synthesis of other metabolic products. One of the most complete
macromolecular analysis for biomass is for E. coli (Feist et al, 2007). As it is one of the best
characterised species, it is possible to perform a very comprehensive analysis of the
synthesis of the different macromolecular components starting from their precursors to
create a robust biomass equation. In several cases, the equation for biomass synthesis in E.
coli has been used for other species, including some Clostridia. Furthermore, a metabolic
model for C. acetobutylicum uses B. subtilis data for the macromolecular composition (Lee
et al. 2008). The growth associated maintenance (GAM) for this model was originated from
the B. subtilis model.
Table 5.1 shows the macromolecular composition of Csb obtained in the experiments
discussed in the previous chapter. This information forms the basis for the construction of
the biomass equation for the metabolic model. The objective for this chapter is to create the
initial scaffold for a fully functional GSMN.
150
Table 5.1 : Macromolecular composition of C saccharoperbutylacetonicum grown in chemostat culture. **Carbohydrates are expressed in glucose equivalents. *Lipid content was calculated from a 1 000X concentrated overnight culture.
PH Dilution rate
Protein (%)
CARBOHYDRATE** (%)
Lipids*(%)
DNA(%)
RNA(%)
0.01 78 ±0.009 2.47 ±0.0003 6.0
±0.0486.4
±0.07118.4
±0.018
pH 6.5 0.02 88 ±0.001 3.33 ±0.01 6.0
±0.0489.6
±0.02925.9
±0.010
0.03 72 ±0.001 2.618 ±0.006 6.0
±0.0487.3
±0.00627±
0.0627
0.01 54 ±0.008 2.9 ±0.001 6.0
±0.0488.0
±0.01617.7
±0.009
pH 5.5 0.02 56 ±0.003 3.47 ±0.009 6.0
±0.0488.0 ±0.006
34.4 ±0.007
0.03 46 ±0.002 5.29 ±0.02 6.0
±0.0489.0
±0.00524.0
±0.062
The genome sequence along with the macromolecular composition allows for the
construction of a biomass equation. With data available from the JGI database it is possible
to incorporate information that takes into consideration nucleotide sequences and A:T and
G:C ratios to give an accurate depiction of the constituents of the DNA fraction of the
biomass (see Table 5.2 for example). From the data presented in Table 5.2, it is therefore
possible to breakdown the constituents of its DNA into its precursors, allowing for the
creation of a biomass equation with an elementally defined profile for DNA, based on the
genome sequence and the concentration of DNA calculated.
151
Table 5.2 : Chemical composition of DNA in C saccharoperbutylacetonicum from chemostat culture.
Overall weight as % of biomas
s
COMPOSITION (MOLAR
FRACTION)
METABOLITE Formula
0.083 0.356 Cysteine C9H12N3O14P3
0.083 0.144 Guanine C10H12N5O14P3
0.083 0.151 Uracil C9H11N2O15P3
0.083 0.348 Adenine C10H12N5O13P3
A similar approach as that described for DNA can be utilised for the protein fraction of the
biomass by using database data Joint Genome Institute (JGI) for amino acid composition in
Csb to achieve a “fully costed” profile for protein in the biomass equation. Equation 5.1 was
constructed using the macromolecular composition as detailed in Table 5.4. Similarly, using
the JGI databases, protein composition data and the Csb genome the protein composition
was curated.
152
Table 5.3 : Protein composition curated from the genome and JGI database for Csb and using the composition percentages shown in Table 5.1.
composition (molar fraction)
mmol/gDW (Calc.)
metabolite formula
0.058 0.269294 ala-L C3H7NO2
0.031 0.143529 arg-L C6H15N4O2
0.066 0.304481 asn-L C4H8N2O3
0.056 0.256790 asp-L C4H6NO4
0.012 0.055665 cys-L C3H7NO2S
0.025 0.115011 gln-L C5H10N2O3
0.074 0.341722 glu-L C5H8NO4
0.064 0.293282 gly C2H5NO2
0.014 0.063175 his-L C6H9N3O2
0.098 0.450838 ile-L C6H13NO2
0.089 0.409446 leu-L C6H13NO2
0.088 0.407839 lys-L C6H15N2O2
0.026 0.119526 met-L C5H11NO2S
0.044 0.203014 phe-L C9H11NO2
0.027 0.126543 pro-L C5H9NO2
0.064 0.294555 ser-L C3H7NO3
0.051 0.233272 thr-L C4H9NO3
0.008 0.037901 trp-L C11H12N2O2
0.042 0.195552 tyr-L C9H11NO3
0.062 0.287702 val-L C5H11NO2
153
Table 5.4 : Macromolecular composition as percentage of the biomass in C saccharoperbutylacetonicum from steady state cultures in chemostats.
Protein(%)
Carbohydrate**(%)
Lipids*(%)
DNA(%)
RNA(%)
52.00±5.29
2.31±1.57
6.00±0.048
8.33±0.57
27.10±8.57
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ala-L (0.0239) + arg-L (0.0251) + asn-L (0.0402) + asp-L (0.0339) + cys-L (0.00674) + gln-L
(0.0168) + glu-L (0.0499) + gly (0.0220) + his-L (0.00980) + ile-L (0.05913) + leu-L (0.0537) +
lys-L (0.0600) + met-L (0.0178) + phe-L (0.0335) + pro-L (0.0145) + ser-L (0.0309) + thr-L
(0.0277) + trp-L (0.00774) + tyr-L (0.0354) + val-L (0.0337) + datp (0.0468) + dctp (0.0179) +
dgtp (0.02050) + dttp (0.0449) + ctp (0.0459) + gtp (0.0828) + utp (0.125) + atp** (0.164) +
murein5px4p (0.0263) + kdo2lipid4 (0.0358) + pe160 (0.0276) + pe161 (0.0323) + k
(0.00692) + nh4 (0.000213) + mg2 (0.000189) + ca2 (0.000189) + fe2 (0.000397) + fe3
(0.000397) + cu2 (0.000200) + mn2 (0.000173) + mobd (0.000505) + cobalt2 (0.000186) +
zn2 (0.000201) + cl (0.000165) + so4 (0.0003792) + pi** (0.000378) + coa (0.000439) + nad
(0.00121) + nadp (0.000330) + fad (0.000174) + thf (9.90E-05) + mlthf (0.000101) + 5mthf
(0.000102) + 10fthf (0.000105) + thmpp (0.0000943) + q8h2 (0.000162) + pydx5p
(0.0000547) + pheme (0.000137) + enter (0.000149) + udcpdp (5.11E-05) + chor (5.00E-05) +
amet (8.92E-05) + ribflv (8.40E-05) + atp** (30.0) + h2o** (1.07) + adp (25.3) + h (0.060) +
pi** (5.74) + ppi** (0.0471) + ppi** (0.147) + h2o** (0.0830)
Equation 5.1 Reaction for biomass for C. saccharoperbutylacetonicum. Abbreviation listed in appendix 2.
5. 3:Constraining the GSMN
The model can be constrained using experimental data (Chapter 4) detailing the
consumption and production rates observed during the steady-state experiments.
Simulations can then be run using the model to determine if the predictions match what
would be expected from the metabolic characterisation carried out in the previous chapter.
The exchange of compounds between the cell and the medium for both uptake and
excretion are collected in a file called “problem”, a specific component of JyMet. The units
used for these are mmol of substrate uptake per hour per gram of dried cell weight (mmol h-
1 DCW-1). There were 6 problem files created, each representing the conditions found in the
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steady-state experiments (Table 5.5). Where data is labelled as “unconstrained”, this
denotes that the rates of uptake for sucrose, glutamate, ammonium and phosphate in the
problem files were set to a maximum of 10000 mmol h-1 DCW-1 effectively making the
uptake of substrate unrestricted. Finally, in the simulations Biomass was the (maximised)
objective function. A template of the problem file (presented in Appendix 1) can be edited
with the information in Table 5.5.
Initial simulations with the model presenting the full biomass equation resulted in no flux
towards biomass, suggesting that the model required adjustments. Components of the
biomass equation were eliminated sequentially to determine which pathways needed
debugging. This is a typical procedure in metabolic model construction. Once isolated, the
pathways for these components were then manually followed through and any missing
reactions were added. However, due to time constraints this process was limited to a sub-
set of all components of the equation. To highlight the extent of which the model required
manual annotation, equation 5.2 shows what was left of the biomass components in
Equation 5.1.
Equation 5.1 Reaction for biomass for C. saccharoperbutylacetonicum after removal of problem pathways
0.256 Aspartate + 0.133 CTP + 0.00473 Ca2_plus + 0.00315 Cu_plus + 0.341 GLU + 0.293 Gly + 55.2 H2O + 0.256 L-Asparagine + 0.0556 L-Cysteine + 0.341 L-Glutamine + 0.0631 L-
Histidine + 0.450 L-Isoleucine + 0.409 L-Leucine + 0.287 L-Valine + 0.407 Lysine + 0.119 Methionine + 0.00315 Mn2_plus + 0.294 Serine + 0.0939 TTP + 0.195 Tryptophan + 0.261
UTP + 0.00315 Zinc + 0.0961 dATP + 0.0389 dCTP + 0.00710 fe3 + 0.233 Threonine
Equation 5.1 highlights issues in significant pathways in amino acid and nucleotide
metabolism for example. Amino acid metabolic pathways needing debugging included the
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synthesis of arginine, phenylalanine, proline, and tyrosine. Additional pathways requiring
debugging included the synthesis of DGTP, DTTP, GTP, ATP and NAD.
Furthermore, key reactions in the pathways for ABE production were missing entirely. For example:
rxn1 NADH + Butyryl-CoA = Butanal + CoA + NAD
rxn2 Acetyl-CoA + Acetyl-CoA = Acetoacetyl-CoA + CoA
rxn3 Acetoacetate = Acetone + CO2
rxn4 Acetyl-CoA + NADH = Acetylaldehyde + NAD + H_plus
rxn5 Acetylaldehyde + NADH = Ethanol + NAD + H_plus
rxn6 Butanoylphosphate+ ADP = Butyrate + ATP
rxn9 Acetyl-CoA = Acetylphosphate
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Table 5.5 Constraints used in the problem files created for running the simulations in mmol h-1 DCW-1.
pH Dilution rate (h-1) Consumption (mmol h-1 DCW-1)
Sucrose Glutamate Ammonium Phosphate
6 0.01 0.10 0.30 0.01 0.39
6 0.02 0.18 0.53 0.05 0.65
6 0.03 0.18 0.58 0.02 0.73
5 0.01 0.09 0.27 0.01 0.3
5 0.02 0.32 1.01 0.07 1.26
5 0.03 0.28 0.93 0.06 1.11
Table 5.6 shows the fluxes calculated using the constraints in Table 5.5. Additional
constraints are shown in Appendix 1. It is clear that when constrained, the model does not
reflect ABE production in vivo. No flux is seen through acetone, ethanol or acetic acid.
However, butanol is produced consistently.
Table 5.6 Fluxes calculated during the simulations run with the constraints shown in Table 5.5. Results for the simulations run a 6.5, dilution rate 0.01, 0.2 and 0.03 h -1 were all the same and hence are represented in the Table below with a single entry for pH6.5. The same was found pH 5.5 and the data presented similarly.
Simulation Butanol
Acetone Ethanol Butyric
Acid Acetic Acid CO2 Biomass
pH 6.5 0.01 (h-1) 4171.19 1.22597
pH 6.5 0.02 (h-1) 5036.12
2128.29 1.22597
pH 6.5 0.03 (h-1) 5036.12
2128.29 1.22597
pH 5.5 0.01 (h-1) 5036.12
2128.29 1.22597
pH 5.5 0.02 (h-1) 4978.5 2455.78 848.762 1.22597
pH 5.5 0.03 (h-1) 433.44 0.147285 581.847 1884.4 1.22597
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5. 5 Validation of the GSMN using mutation data
In-silico knock outs can be performed in the model to analyse the metabolism of known Csb
mutant strains. There are four mutant strains that will be discussed in this chapter (Table
5.7) where the phenotypic data were supplied by Green Biologics2. The model was tested to
see if it could predict changes in phenotype when reactions are removed per the knockouts
made. The experimental data was compared to the in-silico knockouts to elucidate whether
the model can predict the phenotypes found experimentally.
However, there are some caveats when comparing the model to the data provided for the
knockout. The media used (tryptone-yeast extract) for culturing the mutants was undefined
and much more nutrient rich than the Biebl media used in Chapter 4 (see Section 1, Chapter
2). Furthermore, the mutants were grown in batch cultures and not in steady state.
2 Green Biologics is a UK biotechnology company who use Clostridia to produce butanol at an industrial scale, they are the industrial partner in the BBSRC CASE studentship that supported this work.
159
Table 5.7: denotes the percentage change found in the production of products in Tryptone-Yeast media compared to wildtype C. saccharoperbutylacetonicum. Mutants and data supplied by Green Biologics. No change in production between wildtype and mutant is denoted at 0%. Due to the existing non-disclosure agreement, the raw data cannot be shown here.
Genes
knocked
out
Acetone
Production
Acetic acid
Production
Butanol
Production
Butyric acid
Production
Ethanol
Production
adhE 0% 0% 0% 0% -50%
crt and
bcd-90%
No
production
No
production
No
production+1900%
crt, bcd
and adhENo production
No
production
No
production
No
production
No
production
bdh -60% +40% -60% 0% 60%
5. 3. 1: Alcohol dehydrogenase knockout mutant
Experimental results obtained in batch cultures of an adhE gene (alcohol dehydrogenase)
knock out mutant show that the mutant produced 50% less ethanol than the wildtype, but
the production of butanol remained unchanged between the mutant and the wildtype. The
mutant produced 200% more formic acid and 100% more lactic acid than the wildtype.
Glucose consumption for the mutant was the same as that of the wildtype throughout.
160
Table 5.8 shows the results of the simulations in Jymet. Ethanol production was not
suppressed after removing adhE (rxn5) from the model. The in-silico results for solvent
production do no match with the percentage change found in vitro.
Table 5.8: The in-silico results for solvent and acid production at pH5.5 and 6.5 with and without the adhE knockout.
pH Butanol Acetone Ethanol Butyric Acid
Acetic Acid CO2 Biomass
6.5 4724.75 0.431756 1320.72 1.22597
5.5 433.44 0.147285 1884.4 1.22597Wt pH
6.5 0.03 (h-1)
5036.12 2128.29 1.22597
Wt pH 5.5 0.03
(h-1)433.44 0.147285 581.847 1884.4 1.22597
5. 3. 2: Butanol- Ethanol knockouts
A double mutant strain with a deletion on the promoter for crt (crotonase) and bcd (butyryl-
CoA dehydrogenase), effectively knocking them both out. The mutant produced 90% less
acetone than the wildtype and produced no butanol, butyric acid or acetic acid. Compared
to the wild type, the mutant produced 1900% more ethanol and 100% more lactic acid.
Interestingly, glucose consumption was comparable to that of the wild type.
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The in-silico analysis showed that butanol and butyric acid were successfully knocked out by
removing reactions for crt (rxn02168) and bcd (rxn00868).
Table 5.9: The in-silico results for solvent and acid production at pH5.5 and 6.5 with and without the crt/bcd knockout.
pH Butanol
Acetone Ethanol Butyric
AcidAcetic Acid CO2 Biomass
6.5 4724.75 0.431756 1320.72 1.22597
5.5 4724.75 0.431756 1320.72 1.22597
Wt pH 6.5
D: 0.03 (h-1)
5036.12 2128.29 1.22597
Wt pH 5.5
D: 0.03 (h-1)433.44 0.147285 581.84
7 1884.4 1.22597
The strain with the crt/bcd promoter mutation was also subjected to a mutation in adhE,
resulting in a mutant carrying mutated versions of both crt, bcd and adhE, unable to
produce acetone, butyric acid, butanol, acetic acid or ethanol. The mutant produced 6 times
more lactic acid than the wildtype and twice as much formic acid. However, the mutant
grew very poorly (low OD), and glucose consumption was only 10% of the consumption
showed by the wild type.
The in-silico results show that ethanol, butanol and butyric acid were successfully knocked
out. However, it was clear that in-vitro, these reactions were essential as the growth was so
poor. This suggests that the model is not accurate in recognising essential reactions for
growth in Csb , possibly due to the incomplete curation of the biomass equation.
162
Table 5.10: The in-silico results for solvent and acid production at pH5.5 and 6.5 with and without the crt/bcd adhE knockout.
pH Butanol
Acetone Ethanol Butyric
AcidAcetic Acid CO2 Biomass
6.5 4274.53 1319.66 1.22597
5.5 4274.53 1319.66 1.22597
Wt pH 6.5
D: 0.03 (h-1)
5036.12 2128.29 1.22597
Wt pH 5.5
D: 0.03 (h-1)433.44 0.147285 581.847 1884.4 1.22597
5. 3. 3:Butanol Knockout via bdh
A strain with bdh (hydroxybutyryl-CoA dehydrogenase) knocked out produced only 30% of
the amount of acetone, ethanol and butanol compared to the wild type. However, it
produced 40% more acetic acid and 30% more lactic acid, and consumed 20% less glucose
than the wild type.
The in-silico simulation for the bdh knock out was similar to the simulation run for the
crt/bcd, which can be explained by the locations of the knockouts (see Figure 5.1). The
simulation showed that there was no butanol, acetone, ethanol or acetic acid produced.
163
Table 5.11: The in-silico results for solvent an acid production at pH5.5 and 6.5 with and without the bdh knockout.
pH Butanol Acetone Ethanol Butyric
AcidAcetic Acid CO2 Biomass
6.5 9928.16 74.74 1.22597
5.5 9928.16 74.74 1.22597
Wt pH 6.5
D: 0.03 (h-1)
5036.12 2128.29 1.22597
Wt pH 5.5
D: 0.03 (h-1)433.44 0.147285 581.847 1884.4 1.22597
164
Figure 5.1 Biochemical pathways of ABE fermentation and the location for knocked out genes discussed in this chapter. Schematic produced from a variety of sources including Beible, 1999; Haus et al., 2011; Millat, et al., 2013; Monot et al., 1984.
165
5. 4:Conclusion
In this chapter, it is demonstrated that extensive further manual curation is required,
particularly with the biomass equation and constraints on nutrient uptake. However, the
results of the simulations showed some agreement with the experimental data, strongly
suggesting that a fully annotated metabolic model could provide potential predictions for
solvent production and criteria for identifying genes related to solvent and acid metabolism
that could be knocked out to improve yields and productivities. The work here represents an
incremental advance in understanding clostridium metabolism and should have great
impact in the industrial production of solvents if further work is carried out.
The introduction of more accurate and specific equations for biomass constructed would
have an impact in the improvement of other models for Clostridium species. Those
equations could be introduced in metabolic models already available, such as C.
acetobutylicum and C. beijerinckii (which use biomass equations from other genera). It
should therefore be possible to carry out comparative in-silico metabolism studies.
The limitations of the modelling approach in terms of regulation of metabolic pathways are
evident, especially the impossibility of capturing mono- or bi-phasic solventogenesis in
clostridial strains. It is necessary to explore the possibility of developing models involving
regulatory networks and network analysis. Further work to determine a specific value for
growth associated maintenance energy for Csb would also improve on this and other
models that use B. subtilis data.
166
Chapter 6: Conclusions
The objectives of this thesis were the development of a defined medium for the culture of
Csb, the study of solventogenesis in batch and continuous culture, and the construction of a
GSMN for Csb.
To summarise, the main important findings here include the development of a relatively
simple defined media that supports growth and solventogenesis in batch and continuous
cultures, essential for metabolic studies. Equally important, the finding that there is no pH-
induced solventogenic switch in Csb in batch or continuous culture is of extreme importance
from the metabolic and physiological point of view, but also for applications in the
production of solvents. Further, the increased butanol production with growth rate in
chemostat culture has not been reported before. Finally, growth and butanol production
were seen to occur in aerobic conditions, obtaining 74% of the butanol obtained under
anaerobic conditions, making Csb starkly different to C. acetobutylicum and potentially
generating large industrial advantage if high yields are still achievable when less stringent
operations needed to maintain anaerobic conditions in a large-scale bioreactor.
The development of a fully defined medium supporting growth of Csb has made it possible
to carry out a detailed physiological characterisation using steady-state experiments.
Interestingly, the need for glutamate suggests that there is an essentiality for C.
saccharoperbutylacetonicum to be provided by a combination of both inorganic and organic
sources of nitrogen, confirming previous observations (Ferchichi et al. 2005) .
167
The aerotolerant phenotype shows that Csb is a viable candidate for co-culture with
aerotolerant species such as Clostridium straminisolvens, able to breakdown cellulosic
material, generating suitable substrates for C. saccharoperbutylacetonicum (Kato et al.
2004).
The steady-state experiments have shown that acid production does not precede solvent
production in Csb but rather that they both happen concurrently. These central reactions
downstream of glycolysis are therefore extremely important in an anaerobic species.
Furthermore, this suggests that the solventogenic “switch” might be triggered by other
phenomena.
The macromolecular composition of Csb is significantly different in several areas to that of
B. subtilis, the species most commonly used as a surrogate biomass composition to
Clostridium, highlighting the importance for accurate physiological characterisation of
strains to include biomass composition.
6. 1:Impact and recommendations for industrial applications
Based on the results obtained in this work, a recommendation would be to monitor the
nutritional content (protein, amino acids) of crops used for feedstock throughout different
harvests over the year and correlate this to variations in butanol yields. Csb was shown to
present varying titres of solvent during the Plackett-Burman experiment, probably affected
by the composition of the medium.
The model has proven itself to be of potential promise to industry in terms of predicting the
phenotype after knocking out various genes encoding for metabolic reactions. A limitation
of this model is that it was constrained using steady-state data on defined media. Accuracy
168
of predictions could be improved by culturing mutants on the same defined media that was
described here.
The production of a new GSMN for Csb is a valuable tool for industry, as clostridia are the
only species used for production of butanol. The model can be easily edited to include or
exclude, as relevant, those reactions that have been modified via genetic manipulation of
the industrial strain in order to make quick in silico predictions of changes in media (e.g
seasonal variation in crop nutrients, as mentioned above), culture conditions, or other
purposes without incurring the cost of completing the experiment in the lab.
6. 2:Further Work
It will be very important to build on the work in Chapter 3 and the discovery of the single
supplementation of a range of amino acids supporting growth of Csb in defined media. For
instance, using carbon-13 labelled glutamate (or other amino acids) to confirm they might
be being utilised as a carbon source. Further, transcriptome analysis would be useful to
investigate the effect of glutamate, glutamine and ammonium sulphate (or other inorganic
sources of nitrogen) on GDH and GS-GOGAT expression.
The production of an optimised defined minimal media described in this thesis is an
essential tool for the study of metabolic pathways. Furthermore, having such a media allows
for further validation of any GNMN as the media lends the opportunity to test the effects of
additional nutrients or combination of nutrients (e.g. Amino acids). Additionally, such media
can lead to the calculation of specific yields of product produced to nutrients consumed.
169
A case has been made here for the importance of obtaining and using macromolecular
composition that is specific to the strain in question, as deviations from the predicted could
account for an interesting explanation for otherwise unexplained metabolic phenomena.
Therefore, further work should include a thorough break down of the cellular composition
so that models do not rely on information from other species. However, clearly more work
is required for the model in terms of manual curation of the reactions. Furthermore,
obtaining ATP concentrations from chemostat experiments would improve the model by
providing the basis for calculating growth and non-growth associated maintenance energy
specific to the strain and culture conditions. Also, measurement of metabolite pools
(including ATP and NADH) would have been incredibly useful to determine the energetics
demanded by different pH concentrations used.
Further work could also be done to calculate the cost of sparging large scale bioreactors to
ensure anaerobic conditions and evaluate whether it is economical to continue doing so if
sufficiently anaerobic conditions are achieved that still support solvent production. This
would remove the need to sparge the bioreactor with nitrogen, thus resulting in an
economic impact.
170
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Appendix 1 Problem file Template
A copy of the problem file referred to in chapter 5 section 3
Biomass_Synth 0 10000 #Biomass = Biomass_extEX_(R)-3-Hydroxybutanoate_ext 0 10000
#(R)-3-Hydroxybutanoate_ext = (R)-3-Hydroxybutanoate_e
EX_1-Butanol_ext -10000 0 #1-Butanol_ext = 1-Butanol_eEX_2-keto-3-deoxygluconate_ext 0 0
#2-keto-3-deoxygluconate_ext = 2-keto-3-deoxygluconate_e
EX_ACET_ext -10000 0 #ACET_ext = ACET_eEX_Acetone_ext 0 10000 #Acetone_ext = Acetone_eEX_Ala-Gln_ext 0 0.5 #Ala-Gln_ext = Ala-Gln_eEX_Ala-His_ext 0 0.5 #Ala-His_ext = Ala-His_eEX_ala-L-asp-L_ext 0 0.5 #ala-L-asp-L_ext = ala-L-asp-L_eEX_Ala-Leu_ext 0 0.5 #Ala-Leu_ext = Ala-Leu_eEX_ala-L-glu-L_ext 0 0.5 #ala-L-glu-L_ext = ala-L-glu-L_eEX_ala-L-Thr-L_ext 0 0.5 #ala-L-Thr-L_ext = ala-L-Thr-L_eEX_arabinose_ext 0 0.5 #arabinose_ext = arabinose_eEX_Arginine_ext 0 0.5 #Arginine_ext = Arginine_eEX_BET_ext -10000 10000 #BET_ext = BET_eEX_BIOT_ext -10000 10000 #BIOT_ext = BIOT_eEX_butanesulfonate_ext -10000 10000 #butanesulfonate_ext = butanesulfonate_eEX_Butyrate_ext -10000 0 #Butyrate_ext = Butyrate_eEX_Ca2_plus_ext -10000 10000 #Ca2_plus_ext = Ca2_plus_eEX_Cbl_ext -10000 10000 #Cbl_ext = Cbl_eEX_Cd2_plus_ext -10000 10000 #Cd2_plus_ext = Cd2_plus_eEX_CELB_ext 0 0 #CELB_ext = CELB_e
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EX_Choline_ext 0 0 #Choline_ext = Choline_eEX_Choline-Sulfate_ext 0 0 #Choline-Sulfate_ext = Choline-Sulfate_eEX_CO2_ext -10000 10000 #CO2_ext = CO2_eEX_Co2_plus_ext -10000 10000 #Co2_plus_ext = Co2_plus_eEX_Cu_plus_ext -10000 10000 #Cu_plus_ext = Cu_plus_eEX_Cys-Gly_ext 0 0.5 #Cys-Gly_ext = Cys-Gly_eEX_D-Ala_ext 0 0.5 #D-Ala_ext = D-Ala_eEX_D-Galactonate_ext 0 0.5 #D-Galactonate_ext = D-Galactonate_eEX_D-Lactate_ext -10000 0.5 #D-Lactate_ext = D-Lactate_eEX_D-Methionine_ext 0 0.5 #D-Methionine_ext = D-Methionine_eEX_D-Serine_ext 0 0.5 #D-Serine_ext = D-Serine_eEX_Dulcose_ext 0 0 #Dulcose_ext = Dulcose_eEX_ethanesulfonate_ext 0 0 #ethanesulfonate_ext = ethanesulfonate_eEX_Ethanol_ext -10000 0 #Ethanol_ext = Ethanol_eEX_Fe2_plus_ext -10000 10000 #Fe2_plus_ext = Fe2_plus_eEX_fe3_ext -10000 10000 #fe3_ext = fe3_eEX_FORM_ext -10000 0 #FORM_ext = FORM_eEX_GALC_ext 0 0 #GALC_ext = GALC_eEX_GlcNAc_ext 0 0 #GlcNAc_ext = GlcNAc_eEX_GLU_ext 0 0.5 #GLU_ext = GLU_eEX_Glucose_ext 0 0 #Glucose_ext = Glucose_eEX_GLUM_ext -5000 0.58123 #GLUM_ext = GLUM_eEX_Gly_ext 0 0.5 #Gly_ext = Gly_eEX_gly-asn-L_ext 0 0.5 #gly-asn-L_ext = gly-asn-L_eEX_gly-asp-L_ext 0 0.5 #gly-asp-L_ext = gly-asp-L_eEX_GLYC_ext 0 0.5 #GLYC_ext = GLYC_eEX_GLYC-3-P_ext 0 0.5 #GLYC-3-P_ext = GLYC-3-P_eEX_Gly-Cys_ext 0 0.5 #Gly-Cys_ext = Gly-Cys_eEX_Gly-Gln_ext 0 0.5 #Gly-Gln_ext = Gly-Gln_eEX_gly-glu-L_ext 0 0.5 #gly-glu-L_ext = gly-glu-L_eEX_Gly-Leu_ext 0 0.5 #Gly-Leu_ext = Gly-Leu_e
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EX_Gly-Met_ext 0 0.5 #Gly-Met_ext = Gly-Met_eEX_Gly-Phe_ext 0 0.5 #Gly-Phe_ext = Gly-Phe_eEX_gly-pro-L_ext 0 0.5 #gly-pro-L_ext = gly-pro-L_eEX_Gly-Tyr_ext 0 0.5 #Gly-Tyr_ext = Gly-Tyr_eEX_H_plus_ext -10000 10000 #H_plus_ext = H_plus_eEX_H2O_ext -10000 10000 #H2O_ext = H2O_eEX_H2S2O3_ext -10000 10000 #H2S2O3_ext = H2S2O3_eEX_HCl_ext -10000 10000 #HCl_ext = HCl_eEX_Heme_ext -10000 10000 #Heme_ext = Heme_eEX_hexanesulfonate_ext -10000 10000 #hexanesulfonate_ext = hexanesulfonate_eEX_Hg2_plus_ext -10000 10000 #Hg2_plus_ext = Hg2_plus_eEX_HYXN_ext -5000 4999 #HYXN_ext = HYXN_eEX_Isethionate_ext -10000 10000 #Isethionate_ext = Isethionate_eEX_K_plus_ext -10000 10000 #K_plus_ext = K_plus_eEX_LACT_ext 0 0 #LACT_ext = LACT_eEX_L-alanylglycine_ext 0 0 #L-alanylglycine_ext = L-alanylglycine_eEX_LCTT_ext 0 0 #LCTT_ext = LCTT_eEX_L-Cysteate_ext 0 0.5 #L-Cysteate_ext = L-Cysteate_eEX_L-Cysteine_ext 0 0.5 #L-Cysteine_ext = L-Cysteine_eEX_Levulose_ext 0 0 #Levulose_ext = Levulose_eEX_L-Glutamine_ext 0 0.5 #L-Glutamine_ext = L-Glutamine_eEX_L-Isoleucine_ext 0 0.5 #L-Isoleucine_ext = L-Isoleucine_eEX_L-Leucine_ext 0 0.5 #L-Leucine_ext = L-Leucine_eEX_L-methionine-R-oxide_ext 0 0.5
#L-methionine-R-oxide_ext = L-methionine-R-oxide_e
EX_L-Methionine-S-oxide_ext 0 0.5
#L-Methionine-S-oxide_ext = L-Methionine-S-oxide_e
EX_L-Proline_ext 0 0.5 #L-Proline_ext = L-Proline_eEX_L-Valine_ext 0 0.5 #L-Valine_ext = L-Valine_eEX_Lysine_ext 0 0.5 #Lysine_ext = Lysine_eEX_MALA_ext 0 0 #MALA_ext = MALA_e
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EX_Maltohexaose_ext 0 0 #Maltohexaose_ext = Maltohexaose_eEX_Maltose_ext 0 0 #Maltose_ext = Maltose_eEX_Maltotriose_ext 0 0 #Maltotriose_ext = Maltotriose_eEX_MANN_ext 0 0 #MANN_ext = MANN_eEX_Melibiose_ext 0 0 #Melibiose_ext = Melibiose_eEX_Menaquinone7_ext 0 10000 #Menaquinone7_ext = Menaquinone7_eEX_methanesulfonate_ext 0 10000 #methanesulfonate_ext = methanesulfonate_eEX_Methionine_ext 0 10000 #Methionine_ext = Methionine_eEX_met-L-ala-L_ext 0 10000 #met-L-ala-L_ext = met-L-ala-L_eEX_Mg_ext -10000 10000 #Mg_ext = Mg_eEX_Mn2_plus_ext -10000 10000 #Mn2_plus_ext = Mn2_plus_eEX_MNTL_ext -10000 10000 #MNTL_ext = MNTL_eEX_Molybdate_ext -10000 10000 #Molybdate_ext = Molybdate_eEX_MOPS_ext -10000 10000 #MOPS_ext = MOPS_eEX_Na_plus_ext -10000 10000 #Na_plus_ext = Na_plus_e
EX_NH3_ext -100000.01726
9 #NH3_ext = NH3_eEX_Ni2_plus_ext -10000 10000 #Ni2_plus_ext = Ni2_plus_eEX_Nitrate_ext -10000 10000 #Nitrate_ext = Nitrate_eEX_Nitrite_ext -10000 10000 #Nitrite_ext = Nitrite_eEX_O2_ext 0 0 #O2_ext = O2_eEX_Ornithine_ext 0 0 #Ornithine_ext = Ornithine_eEX_Pb_ext -10000 10000 #Pb_ext = Pb_eEX_Phenylpropanoate_ext 0 10000 #Phenylpropanoate_ext = Phenylpropanoate_eEX_Phosphate_ext -10000 0.726 #Phosphate_ext = Phosphate_eEX_PPi_ext -10000 10000 #PPi_ext = PPi_eEX_PUTR_ext -10000 10000 #PUTR_ext = PUTR_eEX_RIBF_ext -10000 10000 #RIBF_ext = RIBF_eEX_Salicin_ext -10000 10000 #Salicin_ext = Salicin_eEX_Serine_ext 0 0.5 #Serine_ext = Serine_eEX_SLF_ext -10000 10000 #SLF_ext = SLF_e
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EX_Sorbitol_ext 0 0 #Sorbitol_ext = Sorbitol_eEX_Spermidine_ext -10000 0 #Spermidine_ext = Spermidine_eEX_Stearate_ext 0 10000 #Stearate_ext = Stearate_e
EX_SUCR_ext 00.18336
9 #SUCR_ext = SUCR_eEX_Sulfoacetate_ext -10000 10000 #Sulfoacetate_ext = Sulfoacetate_eEX_Taurine_ext -10000 10000 #Taurine_ext = Taurine_eEX_THI_ext -10000 10000 #THI_ext = THI_eEX_TRHL_ext -10000 10000 #TRHL_ext = TRHL_eEX_Tryptophan_ext 0 0.5 #Tryptophan_ext = Tryptophan_eEX_Uracil_ext 0 10000 #Uracil_ext = Uracil_eEX_Urea_ext -10000 10000 #Urea_ext = Urea_eEX_Ursin_ext -10000 10000 #Ursin_ext = Ursin_eEX_Vitamin-B12_ext -10000 10000 #Vitamin-B12_ext = Vitamin-B12_eEX_Vitamin-B12r_ext -10000 10000 #Vitamin-B12r_ext = Vitamin-B12r_eEX_Xylose_ext 0 0 #Xylose_ext = Xylose_eEX_Zinc_ext -10000 10000 #Zinc_ext = Zinc_e
Appendix 2 Definition of the biomass equation (Chapter 5)
10-Formyltetrahydrofolate, (10fthf); 2-Octaprenyl-6-hydroxyphenol, (2ohph); L-Alanine, (alaL); Adenosyl methionine, (amet); L-Arginine, (argL); L-Asparginine, (asnL); L-Aspartate, (aspL); Adenosine triphosphate, (atp); Calcium, (ca2); Chlorine, (cl); CoA, (coa); Cobalt2+, (cobalt2); Cytidine triphosphate, (ctp); Copper, (cu2); L-Cysteine, (cysL); Deoxyadenosine triphosphate, (datp); Deoxycytedine triphosphate, (dctp); Deoxyguanosine triphosphate, (dgtp); Deoxythymidine triphosphate, (dttp); Flavin adenine dinucleotide, (fad); Iron2+,
185
(fe2); Iron3+, (fe3); L-Glutamine, (glnL); L-Glutamate, (gluL); Glycine, (gly); Guanosine triphosphate, (gtp); Water, (h2o); L-Histidine, (hisL); L-Isolecine, (ileL); Potassium, (k); KDO(2)-lipid IV(A), (kdo2lipid4); L-leucine, (leuL); L-Lysine, (lysL); Methionine, (metL); Magnesium2+, (mg2); Methylfolate, (mlthf); Mangenese2+, (mn2); mobd disacharide linked murein units, pentapeptide crosslinked tetrapeptide (A2pm->D-ala) (middle of chain), (murein5px4p); Nicotinadmide adenine dincucleotide, (nad); Nicotinadmide adenine dincucleotide phosphate, (nadp); Amonium, (nh4); Phosphatidylethanolamine (dihexadecanoyl, n-C16:0), (pe160); Phosphatidylethanolamine (dihexadecanoyl, n-C16:1), (pe161); L-Phenylalanine, (pheL); Protoheme, (pheme); L-Proline, (proL); Pyridoxal 5'-phosphate, (pydx5p); Riboflavin, (ribflv); L-Serine, (serL); Siroheme, (sheme); Sulphate, (so4); 5,6,7,8-Tetrahydrofolate, (thf); Thiamine diphosphate, (thmpp); L-Threonine, (thrL); L-Tryptophan, (trpL); L-Tyrosine, (tyrL); Undecaprenyl diphosphate, (udcpdp); Uracil triphosphate, (utp); L-Valine, (valL); Zinc2+, (zn2); Adenosine diphosphate, (adp); Hydrogen, (h); Phosphate, (pi); Diphosphate, (ppi).
Appendix 3 Placket-Burman calculations (Chapter 3)
Table 13. 1 lists the flasks and their OD after 24 h in addition to denoting which amino acids were present
Sample OD600 HIS SERLYS ALA ASP TYR ILEU VAL LEU GLU ARG
Flask 1 0.557 1 1 1 0 1 1 1 0 0 0 1 0
Flask 2 0.603 2 0 1 1 0 1 1 1 0 0 0 1
Flask 3 0.589 3 1 0 1 1 0 1 1 1 0 0 0
Flask 4 0.711 4 0 1 0 1 1 0 1 1 1 0 0
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Flask 5 0.647 5 0 0 1 0 1 1 0 1 1 1 0
Flask 6 0.579 6 0 0 0 1 0 1 1 0 1 1 1
Flask 7 0.796 7 1 0 0 0 1 0 1 1 0 1 1
Flask 8 0.549 8 1 1 0 0 0 1 0 1 1 0 1
Flask 9 0.92 9 1 1 1 0 0 0 1 0 1 1 0
Flask 10 0.75910 0 1 1 1 0 0 0 1 0 1 1
Flask 11 0.74611 1 0 1 1 1 0 0 0 1 0 1
Flask 12 0.64812 0 0 0 0 0 0 0 0 0 0 0
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Table 13.2 Lists the OD found for each trial where the particular amino acid was present and adds them together.
Sums of High values
HIS SER LYS ALA ASP TYR ILEU VAL LEU GLU ARG
0.557 0.557 0 0.5570.55
7 0.557 0 0 0 0.557 0
0 0.603 0.603 00.60
3 0.6030.60
3 0 0 0 0.603
0.589 0 0.589 0.589 0 0.5890.58
9 0.589 0 0 0
0 0.711 0 0.7110.71
1 00.71
1 0.7110.71
1 0 0
0 0 0.647 00.64
7 0.647 0 0.6470.64
7 0.647 0
0 0 0 0.579 0 0.5790.57
9 00.57
9 0.579 0.579
0.796 0 0 00.79
6 00.79
6 0.796 0 0.796 0.796
0.549 0.549 0 0 0 0.549 0 0.5490.54
9 0 0.549
0.92 0.92 0.92 0 0 0 0.92 0 0.92 0.92 0
0 0.759 0.759 0.759 0 0 0 0.759 0 0.759 0.759
0.746 0 0.746 0.7460.74
6 0 0 00.74
6 0 0.746
0 0 0 0 0 0 0 0 0 0 0
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4.157 4.099 4.264 3.941 4.06 3.5244.19
8 4.0514.15
2 4.258 4.032
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Table 13.3 The High means from the previous Table (13.2) by dividing the total OD by the number of trails which contained that amino acid (6).
The Low means are calculated by subtracting total OD from Table 13.3 from the sum of all ODs listed in Table 13.1. The E values are calculated
by subtracting the Low means from the High means. The t-value is then calculated by diving E by the “dummy” constant calculated for this
experiment. The dummy constant is the square root of the sum of the values squared for the following: The sum OD values in Table 13.2 with
the Low means described earlier.
Means HIS SER LYS ALA ASP TYR ILEU VAL LEU GLU ARG
High
0.6928333
0.683166
7
0.710666
70.6568333
0.676666
70.5873333
0.699666
70.6751667
0.692
0.709666
7 0.672
Low
0.6578333
0.6675 0.64
0.6938333
0.674
0.7633333
0.651
0.6755
0.658666
70.64
10.6786667
E0.035
0.015666
7
0.070666
7-
0.037
0.002666
7-
0.176
0.048666
7
-0.0003333
0.033333
3
0.068666
7
-0.0066667
t(x)
0.1256664
0.056250
7
0.253726
5
-0.1328474
0.009574
6
-0.6319226
0.174736
2
-0.0011968
0.119682
3
0.246545
6
-0.0239365
Table 13.4 list the t-values from Table 13.3 in order of value.
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Ranking
T-values
LYS0.25372
65
GLU0.24654
56
ILEU0.17473
62
HIS0.12566
64
LEU0.11968
23
SER0.05625
07
ASP0.00957
46
VAL
-0.00119
68
ARG
-0.02393
65
ALA
-0.13284
74
TYR
-0.63192
26
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