Understanding and Engineering the Factors That Control Fatty Acid Biosynthesis in Escherichia Coli

UNDERSTANDING AND ENGINEERING THE FACTORS THAT CONTROL FATTY ACID BIOSYNTHESIS IN ESCHERICHIA COLI

DANIELLE GALLAGHER

IMPERIAL COLLEGE LONDON, DEPARTMENT OF LIFE SCIENCES

THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY OF THE UNIVERSITY OF LONDON, IMPERIAL COLLEGE LONDON 2018

I declare that this thesis is my own work and that it has not been submitted anywhere for any award. Where other sources of information have been used, they have been acknowledged.

1 Copyright Declaration

The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the licence terms of this work

2 ABSTRACT

This research identified and characterised several genetic and environmental factors that contribute to controlling the metabolic flux through fatty acid synthesis (FAS) in Escherichia coli, to enable high yield in production of fatty acids (FA) which can be further processed towards industrially relevant commodity products. Due to their essentiality to growth, high energy and carbon investment, E. coli have evolved several mechanisms that tightly coordinate FAS with phospholipid synthesis and the energy status of the cell. This makes the outcome of systematic engineering manipulations difficult to predict, but is an area of research that benefits greatly from computationally driven solutions that can be gained from modelling metabolism. In order to model metabolism effectively, parameters of the kinetic system must be obtained and enable subsequent associations with in vivo metabolism and physiology. To adequately make this association, mathematical formulas describing biochemical pathways must account for the control and regulation the system is subject to, so that metabolic engineers can design towards specific phenotypes and product yield.

This work investigated FAS regulation under a range of perturbations and how the system responds in vivo to these changes, to gain insight into the control and regulations that are in place during fluctuating conditions. Furthermore, the experiments of this study found adaptive evolution to FA to be a promising strategy in complementing a directed engineering approach in E. coli, in order to adapt to the burden that presents itself during FA overproduction. As a directed approach towards FA overproduction, three novel bypass routes to malonyl-CoA production were also investigated, and found to improve in vivo rates of malonyl-CoA. However, improving this rate-limiting step alone was not sufficient for to overcome the native regulatory and energetic limitations that are present in FAS. The bypass routes were therefore combined with specific process optimisations identified during this research, which was found to improve yield compared to non-optimised cultivations.

3 ACKNOWLEDGEMENTS

First and foremost, I would like to thank my supervisor Dr Patrik Jones, for giving me the opportunity to work in the MME lab, for his collaboration and the discussions that went into shaping this research and for the support it took to complete. Thanks to Paulina for the lab chats, Queenie for being a kind and funny bench mate, John and Ian for reading and giving valuable feedback on the thesis and being supportive in answering many questions over the years. My thanks also go to James Mansfield for his support and guidance during the work conducted at the bioreactor suite, and Mark Bennet for technical support of the LC-MS work.

I am thankful to my PRP panel Prof Anne Dell and Prof Mark Isalan for their invaluable feedback in the beginning and throughout the milestones of this PhD. To my student supervisions over the last three years for their invaluable discussions around this research. Special thanks to Dr. Danielle Belgrave for being a wonderful mentor and friend to me at Imperial, and for the inspiring lunch time retreats.

I am thankful for my family who have shaped me as a person, and given me my perspective on life. Páidín, Mícheál and Alex, thank you for the happy memories and the humour that kept me going here. I am so lucky and proud to have you as my brothers. My mother Mary, thank you for everything you’ve given and done for me over my whole life, for supporting, guiding and consoling me over the last three years - for being my friend and for motivating me. My father Danny, thank you for always encouraging my interests, for not letting me give up, and for giving me a healthy dose of ambition my whole life. I could never thank you enough for giving me the foundations to pursue this career.

To my other family, my framily in London and Ireland, thank you for the canal cans, Toy Shows, critical masses, emergency pints, birthdays, Christmas curries, soul- soothing brunches and stitch and bitch nights. Thanks for keeping it light and for simply being around over the years. Your friendships have meant the world to me.

Thanks to Lia my furry writing companion for the cuddles and scratches.

4 Finally, an infinite gratitude and love to Jimmer, mo chroí, mo solas ’s mo shaol. Thank you for brightening my life and bringing me so much joy. I could not have finished this PhD without you. The words of this thesis are dedicated to you, our life together and our future. Is tú mo chúis <3

5 TABLE OF CONTENTS

Abstract ...... 3

Acknowledgements ...... 4

Table of contents ...... 6

List of figures ...... 10

List of tables ...... 17

Abbreviations ...... 17

1 General Introduction ...... 20 1.1 Motivation and background ...... 20 1.2 Microbial commodity chemical production ...... 26 1.3 Renewable fatty acid synthesis ...... 29 1.4 Fatty acid biosynthesis and degradation pathways in E. coli ...... 31 1.5 The regulation of fatty acid synthesis in E. coli ...... 35 1.6 Phospholipid and cell membrane synthesis ...... 41 1.7 The contribution of central carbon metabolism toward fatty acid synthesis ...... 43 1.8 The use of predictive modelling to guide and inform fatty acid research ...... 46 1.9 Current practices and yields in engineering E. coli for fatty acid overproduction ..... 49 1.10 Objective of research ...... 53

2 Materials and Methods ...... 55 2.1 Media preparation and buffers ...... 55 2.2 Microbiological techniques ...... 56 Sterile technique ...... 56 Frozen glycerol stocks ...... 56 Strains ...... 56 Batch cultivation conditions ...... 57 Continuous cultivation in 1.5L bioreactor ...... 58 Continuous cultivation in turbidostats ...... 59 Preparation of competent cells ...... 60 Transformation of E. coli ...... 60

6 Quantification of cell culture density ...... 60 Growth assays ...... 61 2.3 Molecular biological techniques ...... 61 Plasmids ...... 61 Primers ...... 65 DNA purification and quantification ...... 68 Standard PCR ...... 69 Exponential megapriming PCR ...... 69 Splice overlap extension ...... 70 DNA assembly ...... 70 Digestion and ligation of DNA ...... 72 Chromosomal integrations ...... 72 2.4 Protein and metabolite analysis ...... 73 Protein Extraction ...... 73 Protein concentration quantifications ...... 73 Malonyl-CoA assays ...... 73 Acyl-CoA assays ...... 74 LC-MS selected reaction monitoring ...... 75 HPLC ...... 77 GC-MS ...... 77 ATP assays ...... 78

DiBAC4(3) assays ...... 78 Statistical analysis ...... 79

3 Improving engineered strain performance and optimising cultivation conditions for fatty acid synthesis (FAS) in E. coli ...... 80 3.1 Introduction ...... 80 Engineering E. coli for fatty acid overproduction ...... 80 Process optimisations ...... 82 Balancing process optimisations with physiological requirements ...... 84 3.2 Aims and Objectives ...... 86 3.3 Results and Discussion ...... 86

7 Evaluating thioesterase expression and genotype ...... 86 Optimising media for fatty acid overproduction ...... 96 Evaluating the impact of the cultivation processes on fatty acid production and growth ...... 98 Adaptive evolution of fatty acid overproducing strain ...... 101 3.4 Future directions ...... 109 Two phase cultivation ...... 109 Deciphering adaptive evolution mutations ...... 109 Exploiting the lipid sensing mechanism of SpoT ...... 110

4 An experimental study on the control of enzymes in the FAS biochemical pathway .... 112 4.1 Introduction ...... 112 The E. coli type II FAS enzymes ...... 112 4.2 Aims and Objectives ...... 119 4.3 Results and Discussion ...... 119 Quantifying the effects of overexpressing each FAS enzyme on protein profiles ...... 119 Individual FAS perturbations impact cell growth rates ...... 123 Relating growth rate to protein and phospholipid production ...... 123 Fatty acid quantifications ...... 127 fatty acid quantifications from low-copy perturbations reveal varying results compared to high-copy ...... 128 Membrane polarity quantifications ...... 131 ATP assays ...... 131 Ethanol quantifications ...... 134 Steady state cultivation of FA overproducing strain: FadR- ‘TesA in fadD host ...... 134 The burden of highly overexpressing individual enzymes on FAS ...... 139 Identifying phenotype from individual perturbations ...... 140 4.4 Future directions ...... 142

5 Evaluating enzymes that bypass the acetyl-CoA carboxylase step of FAS ...... 146 5.1 Introduction ...... 146 The application of ACC bypass routes to improve fatty acid yield ...... 148 Balancing a synthetic bypass and deciphering control distribution ...... 150

8 5.2 Aims and Objective ...... 152 5.3 Results and Discussion ...... 152 Selection and cloning of heterologous Acc bypass enzymes into expression vectors ... 152 Addition of FabD to Acc bypass expression vectors ...... 153 Complementation of an ACC deficient host ...... 153 Intracellular malonyl-CoA production ...... 154 Fatty acid quantifications ...... 158 Intracellular fatty acid acyl-CoA detection ...... 158 Enhanced malonyl-CoA production does not lead to an improvement in FA yield ...... 167 In vivo fatty acid methyl ester production ...... 172 Process optimisation in continuous cultivation ...... 173 Introducing a product that leaves the cell more readiliy than FA improves titre ...... 176 Process optimisation improves efficiency of Acc bypass towards FA production ...... 178 Design of further strategies during this study to optimise efficiency of the Acc bypass ...... 180 5.4 Future Directions ...... 181 Is malonyl-CoA reaching FabD? ...... 181 Regulating supply and demand to improve yield ...... 182 Introducing butanol producing pathways ...... 183 A chassis for FA overproduction ...... 184

6 General discussion ...... 186 6.1 Summary of Key Findings and Conclusions ...... 186

References...... 192

9 LIST OF FIGURES

Figure 1.1. Global greenhouse gas emissions under different scenarios and related global temperature increases compared to pre-industrial by 2100 (Climate Analytics, Ecofys and NewClimate Institute, 2016)

Figure 1.2. Overview of the biofuel generations 1st-4th. Process and outputs outlined.

Figure 1.3. Number of publications related to the different generations of biofuels (adapted from Dutta, et al., 2014)

Figure 1.4. Commodity chemical derivatives of fatty acids

Figure 1.5. Fatty acid biosynthesis and degradation pathways in E. coli. Protein abbreviations in blue, metabolites in black, ATP and reducing agents in red.

Figure 1.6. FadR, FabR and acyl-ACP regulation on FAS in E. coli. FadR activation and inhibition in blue, FabR inhibition in green, acyl-ACP feedback in red.

Figure 1.7. AcrA, Crp-cAMP and ppGpp regulation on FAS in E. coli. ArcA inhibition in blue, Crp-cAMP activation in green, ppGpp inhibition in red.

Figure 1.8. Phospholipid synthesis in E. coli. Protein abbreviations in blue, metabolites in black.

Figure 1.9. Acetyl-CoA biosynthesis and utilisation in E. coli. Protein abbreviations in blue, metabolites to and from acetyl-CoA in black.

Figure 2.1. Schematic of MODAL and BASIC DNA assembly methods. Parts for assembly (genes) are combined with linkers that also comprise prefix and suffix sequences that share homology to each gene. MODAL and BASIC vary by how these parts are generated (PCR or digestion ligation), and how final assembly is carried out (Gibson method or DNA-driven assembly)

Figure 3.1. Average and normalised (i-ii) growth curves and (iii-iv) fluorescent emissions from high-copy GFP expression, and dual low-copy ‘TesA plus high-copy GFP expressions. All in BW25113 host, IPTG concentrations varied

10 (0.01-0.1mM) at cell densities high to low (0.5- 0.25 OD600nm). Performed in 96-well, in duplicate; shaded regions represent SEM.

Figure 3.2. FAME quantifications normalized to OD from low-copy ‘TesA expressions in BW25113 genotype; cultivated at varying temperature, media type, and IPTG induction level. Performed in 250ml shake flasks in triplicate, error bars represent SEM.

Figure 3.3. FAME quantifications normalized to OD values from low-copy ‘TesA expression in genotypes BW25113, ∆fadL, ∆fadD and ∆fadE; cultivated at 30°C and 0.1mM IPTG induction level in minimal media. Performed in 250ml shake flasks in triplicate, error bars represent SEM.

Figure 3.4. FAME quantifications and OD values from integrated, high-copy and low-copy ‘TesA expression in ∆fadE genotypes; cultivated at 30°C in minimal media and 0.1mM IPTG induction, in 250ml shake flasks in triplicate, error bars represent SEM.

Figure 3.5. Average growth curves from low-copy ‘TesA expression in BW25113, ∆fadL, ∆fadD and ∆fadE genotypes; cultivated at 30°C, in minimal media, and 0.1mM IPTG induction level. Performed in 96-well in duplicate for each strain, shaded regions represent SEM.

Figure 3.6. FAME quantifications and OD values from low-copy ‘TesA expression in genotypes (i) BW25113, (ii) ∆fadL, (iii) ∆fadD, and (iv) ∆fadE cultivated at 30°C and 0.1mM IPTG induction level (except autoinduced cultures). Experiment performed in 250ml shake flasks, in triplicate, error bars represent SEM. Media optimisations were around a MOPS minimal media base, autoinduction, phosphate limitation (Plim) and pantothenic addition (panto) described in Table 2.1. * P <0.05 vs. MOPS control, ** P <0.01 vs. MOPS control ,one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

Figure 3.7. FAME quantifications and OD values from low-copy ‘TesA expressions in BW25113, ∆fadL, ∆fadD and ∆fadE genotypes continuously cultivated with 0.1mM IPTG at OD600nm values 0.4, 0.8 and 1 in minimal MOPS media. Performed as individual turbidostat experiments, error bars represent SEM of

11 technical replicates. * P <0.05 vs. other ODs in same genotype host, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

Figure 3.8. Average growth curves from C16 fatty acid ‘first-round’ adapted and non-adapted ∆relA strains subject to (i) C16 and C18 FA stress challenges and (ii) ppGpp plus FA stress challenges. Genotype and treatment type described in legends. Performed in 96-well, in duplicate, shaded regions represent SEM.

Figure 3.9. Average growth curves from C16 fatty acid evolved and control ∆relA strains: non-adapted ∆relA control (red; non evolved), ∆relA adapted 1 (dark blue dashed; first round exposure), ∆relA adapted 2 (light blue dashed; second round exposure), and evo∆relA (black; third round exposure) strains subject to varying stress challenges (i-v). Performed in 96-well, in duplicate, shaded regions represent SEM.

Figure 3.10. FAME quantifications normalized to OD values from low-copy ‘TesA expressions MOPS media cultivations of non-adapted control ∆relA (∆relA) and C16 fatty acid adapted ∆relA (evo∆relA). Cultures induced at 0.1mM and 1mM IPTG and performed in 250ml shake flasks, in triplicate, error bars represent SEM. * P <0.05 , ** P <0.01 vs. non adapted control, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

Figure 4.1. Acc multiplex overview: (i) Biotin carboxylation and carboxyl transferase reactions of acetyl-CoA carboxylase that catalyse formation of malonyl- CoA from acetyl-CoA by the Acc complex. (ii) Stoichiometry of the carboxyltransferase (heterotetramer), biotin carboxylase (homodimer) and bccp (homotetramer) subunit assembly into the Acc complex

Figure 4.2. Relative FAS protein quantifications from SRM LC-MS analysis in (i) BW25113 genotype host (ii) fadD genotype and (iii) BW25113 genotype plus ‘TesA dual expression. Each quantification represents the average variation in peptide abundance (green increase, pink decrease) in FAS protein overexpression strains relative to negative control strains (black bars). Performed in technical duplicates.

12 Figure 4.3. Growth curves of high-copy overexpression strains in (i) BW25113 genotype, and (ii) including co-expression with ‘TesA. Performed in 96-wells, in triplicate, shaded regions represent SEM.

Figure 4.4. Growth curves of high-copy overexpression strains in (i) fadD genotype, and (ii) including co-expression with ‘TesA. Performed 96-wells, in triplicate, shaded regions represent SEM.

Figure 4.5. FAME quantifications and OD values of high-copy overexpression strains in (i) BW25113 genotype (ii) BW25113 plus ‘TesA (iii), fadD genotype, and (iv) fadD plus ‘TesA. FAME quantifications represented as mg/ml in this figure as opposed to specific quantifications (per OD), due to the presence of very low cell densities from this experiment – dividing FAME per OD creates artificially high specific productivities and prevent accurate interpretation of results. Performed in 250ml shake flasks, in triplicate, error bars represent SEM.* P <0.05, **P <0.01 vs. wild -type control, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

Figure 4.6. FAME quantifications and OD values of low-copy overexpression strains in (i) BW25113 genotype plus ‘TesA and (ii) fadD plus ‘TesA. FAME quantifications represented as mg/ml in this figure as opposed to specific quantifications (per OD), due to the presence of very low cell densities from this experiment – dividing FAME per OD would create artificially high specific productivities and prevent accurate interpretation of results. Performed in 250ml shake flasks, in triplicate, error bars represent SEM. * P <0.05 vs. wild- type control, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

Figure 4.7. Membrane polarity quantifications using DiBAC3(4) on (i) low-copy and (ii) high-copy overexpression BW25113 strains plus ‘TesA. Scatter with lines are baseline polarization rates pre-treatment, scatter without lines are post CCCP stress induction. Performed in 96-well in duplicate.

Figure 4.8. Luciferase ATP assay on lysates of low-copy and high-copy overexpressions in BW25113 strains plus ‘TesA. Performed in 96-well luminescence plate in duplicate, error bars represent SEM

13 Figure 4. 9. Ethanol quantifications by HPLC of overexpressed strains in BW25113 induced with 0.25mM IPTG in minimal media. Cultivated in 250ml shake flasks aerobically, 100ml Wheaton flasks aerobically, +/- thioesterase. Performed in triplicate, error bars represent SEM.

Figure 4.10. Minimal media steady-state cultivation of high-copy FadR overexpression plus ‘TesA in fadD host, in continuous 0.1mM IPTG induction. Individual experiment in 1.5L bioreactor, measurements started after 3 volume changes of the reactor as standard protocol, error bars represent SEM of technical replicates.

Figure 4.11. Modes of regulation incurred by each genotype expression system used in overexpression studies. Inhibition by acyl-ACP in red, alleviation of acyl-ACP build-up in blue.

Figure 4.12. Mechanisms of growth rate regulation imposed by protein synthesis and phospholipid production. Inhibitions in red, activations in blue.

Figure 5.1. Acc bypass pathways proposed in red; with oxaloacetate as substrate catalyzed by malonyl-CoA carboxytransferase (Mcc), or malonate semialdehyde catlysed by malonyl-CoA reductase (Mcr), these enzymes are proposed to circumvent native ACC malonyl-CoA production as a rate limiting step.

Figure 5.2. FabD-bypass EMP assembly outline. Plasmid template generalized to represent Acc bypass gene expression vectors (Table 2.3).

Figure 5.3. Complementation studies on Acc bypass genes in thermosensitive mutant (LA1-6; purple) and parent (AB1623; grey) hosts (i) on solid media at varying temperatures, (ii) liquid media at 38˚C and (iii) liquid media at 30˚C. Experiments ii-iii performed in triplicate, error bars represent SEM.

Figure 5.4. Intracellular malonyl-CoA levels detected in Acc bypass strains by FapR hybrid promoter-regulator fluorescent sensor (i) in high-copy, (ii) low copy expression (iii) and the addition of FabD at high-copy. Performed in 96- well, in duplicate, fluorescence per OD plotted.

Figure 5.5. FAME quantifications normalized to OD values of high-copy overexpression strains in ∆fadE genotype plus ‘TesA in varying aeration

14 conditions. Performed in 250ml shake flasks in triplicate, error bars represent SEM. * P <0.05, * P <0.01, *** P <0.001 vs. wild-type control, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

Figure 5.6. FAME quantifications normalized to OD values of high-copy overexpression strains in ∆fadE genotype plus ‘TesA in varying temperature conditions. Performed in 250ml shake flasks in triplicate, error bars represent SEM. * P <0.05, * P <0.01, *** P <0.001 vs. wild-type control, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

Figure 5.7. FAME quantifications normalized to OD values of high-copy Acc bypass overexpression strains in ∆fadE genotype plus ‘TesA in varying carbon source cultivations. Performed in 250ml shake flasks in triplicate, error bars represent SEM. * P <0.05, * P <0.01, *** P <0.001 vs. wild-type control, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

Figure 5.8. FAME quantifications normalized to OD values of high-copy Acc bypass overexpression strains in BW25113 genotypic plus ‘TesA, induced with 1mM lactose at varying temperatures. Performed in 250ml shake flasks in triplicate, error bars represent SEM. * P <0.05, * P <0.01, *** P <0.001 vs. wild-type control, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

Figure 5.9. FAME quantifications normalized to OD values of high-copy Acc bypass overexpression strains in BW25113 genotypic plus ‘TesA, cultivated for 48hrs in varying media type. Performed in 250ml shake flasks in triplicate, error bars represent SEM. * P <0.05, * P <0.01, *** P <0.001 vs. wild-type control, one- way ANOVA with corrections for multiple comparisons (Tukey’s test).

Figure 5.10. FAME quantifications normalized to OD values of high-copy Acc bypass overexpression plus ‘TesA in varying genotype, supplemented with 5mM aspartic acid. Performed in 24-deep well culture plates in duplicate, error bars represent SEM. * P <0.05, * P <0.01, *** P <0.001 vs. wild-type control, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

15 culture plates in triplicate, error bars represent SEM. * P <0.05, * P <0.01, *** P <0.001 vs. wild-type control, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

Figure 5.12. FAME quantifications normalized to OD values of high- copy FabD + Acc bypass overexpression plus ‘TesA in BW25113, in varying media type. Performed in 250ml shake flasks in triplicate, error bars represent SEM. * P <0.05, * P <0.01, *** P <0.001 vs. wild-type control, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

Figure 5.13. Intracellular acyl-CoA levels detected in Acc bypass strains by FadR hybrid promoter-regulator fluorescent sensor (i-iii) in high-copy with varying IPTG concentrations, and (iv) from all concentrations at end-point measurements. Performed in 96-well in duplicate, fluorescence only plotted for IPTG titration, error bars represent SEM.

Figure 5.14. Intracellular acyl-CoA levels detected in Acc bypass strains by FadR hybrid promoter-regulator fluorescent sensor (i) with the addition of FabD with 0.1mM IPTG induction and (ii) with minimal media cultivation instead of standard rich media. Performed 96-well in duplicate, fluorescence per OD plotted.

Figure 5.15. FAME quantifications and OD values of high-copy Acc bypass overexpression strains in varying genotype plus ‘TesA-jhAMT low-copy expression in (i) LB and (ii) minimal media. Performed 24 deep-well in duplicate, error bars represent SEM.

Figure 5.16. FAME quantifications normalized to OD values of high-copy Acc bypass overexpression plus ‘TesA-jhAMT low copy expression in ∆fadD genotype cultivated in (1) MOPS (blue bars) and phosphate limited MOPS supplemented with pantothenic acid (orange bars). Experiment performed in turbidostats maintaining cell densities continuously at OD600 0.2 (blue bars) and 0.4 (orange bars) at 37C. Performed as individual turbidostat experiments, error bars represent SEM of technical replicates. * P <0.05 vs. control, one-way ANOVA with corrections for multiple comparisons (Holm Sidek’s test).

16 LIST OF TABLES

Table 1.1. Overview of genetic and process optimisations for improving FA production in E. coli

Table 2.1. Strains

Table 2.2. Solutions, media, buffers and gels

Table 2.3. Plasmids

Table 2.4. Primers

Table 2.5. Standard PCR reaction

Table 2.6. Peptides used for relative protein abundance quantification via LC-MS (SRM)

Table 4.1. Mullers classes of mutations (Prelich, et al., 2012)

Table 5.1. Specific activities of high-copy Acc bypass overexpression strains in varying genotype plus ‘TesA-jhAMT low -copy expression in (i) LB and (ii) minimal media

ABBREVIATIONS

ACP – acyl carrier protein

ATP – adenosine triphosphate

CO2 – carbon dioxide

COP – conference of the parties

CPPP – Carbonyl cyanide m-chlorophenyl hydrazine

CRP-cAMP - cAMP receptor protein cyclic AMP

17 DiBAC4(3) - Bis-(1,3-Dibutylbarbituric Acid)Trimethine Oxonol

DNA – deoxyribonucleic acid

FA – fatty acid

FAB – fatty acid biosynthesis

FAD – fatty acid degradation

FAME – fatty acid methyl ester

FAS – fatty acid synthesis

FFA – free fatty acid

GC-MS – gas chromatography mass spectrometry

GFP – green fluorescent protein

HPLC – high performance liquid chromatography

IPCC – international panel on climate change

IPTG - isopropyl β-D-1-thiogalactopyranoside

KanR – kanamycin resistance

MALDI-TOF – matrix assisted laser desorption ionization-time of flight mass spectrometry

MCA – metabolic control analysis

NADH – Nicotinamide adenine dinucleotide

NADPH – Nicotinamide adenine dinucleotide phosphate

OD – optical density

PCR – polymerase chain reaction ppGpp – guanosine tetraphosphate

RBS – ribosome binding site

18 RFP – red fluorescent protein

RNA – ribonucleic acid

SEM – standard error of the mean

LC-MS (SRM) – liquid chromatography mass spectrometry (selected reaction monitoring)

TCA – tricarboxylic acid

19 1 GENERAL INTRODUCTION

1.1 MOTIVATION AND BACKGROUND

As a society, we are currently facing an era of unsustainable fossil fuel consumption that continues to contribute to dangerous levels of greenhouse gas emissions (IPCC, 2007; IPCC, 2014). The need to secure renewable, carbon neutral and efficient energy supplies is of critical importance if modern civilisation is to continue. Diminishing worldwide fossil fuel reserves have created the pressing need for alternative energy research and production, and the urgency surrounding global climate change mitigation have stipulated that these alternative fuels must be limited in their carbon emissions, ideally carbon sequestered, if annual rises in temperatures are to remain below the 2C threshold set out by the in Paris Climate agreement in 2015 (United Nations Treaty Collection, 2015). Scientists endeavour to find renewable high-energy sources that fulfil these needs, while maintaining a compatibility with current engine technology (Renewable Energy Expert Group, 2005)(Rutz and Janssen, 2006). This search will no doubt be one of the defining quests of this generation, and one which has the potential of disrupting an industry that has defined our industrialisation, globalisation, and development as a society, and has controlled much of the economic market since it’s discovery (Armentano, 1981; Donwa, Mgbame and Julius, 2015).

Our dependence on the petrochemical industry continues to rise with growing populations and urban development since fossil fuels remain the dominant form of energy powering this global expansion, accounting for almost 80% of total energy reserves in 2035 (BP Report, 2016). The subject of energy security has implications on political, socio- economic and environmental aspects of human life. However, oil prices are subject to fluctuations according to supply and demand in the market which creates uncertainty in upstream and downstream investments in the petrochemical industry, as well as price volatility that disturb geo-political landscapes (Smith, 2017). It is therefore also in the interest of sustaining the global population

20 and easing political tension that alternative energy supplies are pursued to enable our independence on petrochemicals.

Over the years, research and development of the production of renewable energy from biological sources have been validated and in turn gained credibility. Alternative and clean energy approaches enable a sustainable industrial society for the survival of future generations, and promote a global awareness on the damage of climate change and environmental issues that will contribute to tackling greenhouse gas emissions (Stern, 2008). Based on assumptions made on future emissions, the Intergovernmental Panel on Climate Change have predicted that global temperatures will rise to between 1.5C and 4.8C relative to pre-industrial levels by 2100 (Figure 1.1) (IPCC, 2014). The difference in outcome will depend on whether GHG emissions remain low by adhering to guidelines or whether they will go unchecked, and as such they will contribute to the severity of disasters humanity face in terms of the impact on food and fresh water supplies, human health, coastal areas and ecosystems (Harris, Roach and Codur, 2017) .

Increasing climate change awareness and responsibility is reflected in the current political landscape, in meetings of word leaders such as during the Conference of the Parties (COP21) to enforce policy and regulations that will maintain low emission scenarios in order to sustain human societies and the planet. This political interest has incentivised and contributed to alternative fuel research gaining momentum over the years, which has led to sustainable energy development becoming a prominent theme across sectors and disciplines. However, these efforts are far from unanimous, with the announcement from the US in 2017 that they would cease participation in the 2015 Paris Agreement on climate change mitigation, instead focusing on an ‘America First Energy Plan’ which aims to cut oil prices, enable energy security and create energy jobs (Hai-bin et al., 2017). This comes with a proposal to cut US funding for clean energy innovation, which will also hamper efforts to achieve the goals of COP21. This move from the US is negative from the view of climate change mitigation, however it does create space for a new leader on this front. Already, indications are that China are cooperating with the EU to advance these policies without the US. Therefore, despite the setback, the commitment of world leaders with sustainable viewpoints are not detracted from and will continue to pursue viable solutions on mitigation.

21 The application and development of biofuels has become an increasingly attractive means of enabling energy security by reducing our dependency on petrochemicals. Biofuels are defined by their derivation from living material: the carbon that originates from plant matter or residues such as agricultural by-products, and urban waste. Following the global fuel crisis in the 1970s (Lifset, 2016), the need to diversify energy sources was globally acknowledged which gave rise to a push in biofuel research (Rutz and Janssen, 2006; Hannon et al., 2011; Benemann, 2014). Much focus went in to developing these alternative sources of commodity, with significant focus given to alcohols such as ethanol. A direct consequence of this was Brazil’s introduction of the National Alcohol Program (Proalcool) in 1975, which focused on the production of ethanol from sugarcane (Alle and Ortez, 1998). This initiative was highly successful and led to the development of Flex-Fuel technology in motor vehicles (allowing the driver a choice of gasoline or ethanol fuel), which gained the country oil-independence by becoming cost-competitive with petroleum based fuel (Hira, Guilherme and Oliveira, 2009) . However, the downsides to this process were quickly realised as the requirement of a large portion of arable land to grow feedstock crops needed for the bioethanol production process. To address these shortcomings and avoid contributing to a potential food crisis, the development of further generations of biofuel research followed.

Biofuels are categorised into generations, namely the first-, second- and third- generations, which have evolved sequentially with the aim of resolving the inefficiencies that were associated with each predecessor (Dutta, Daverey and Lin, 2014) (Figure 1.2). Biodiesel, ethanol and biogas are categorised as first-generation biofuels due to their synthesis from feedstocks such as sugar, starch or vegetable oil. However, as mentioned, these feedstocks are also human food products, extracted from crops such as sugar cane, corn, wheat and sugar beet using conventional technology. Their competition with arable land and for human food security arose considerable concern which then led to the development of second-, and third-, generation biofuels.

Second-generation biofuels use lignocellulose as a feedstock, which does not compete directly with food crops or agricultural land (Eisentraut, 2010). In their natural form, the carbohydrates from lignocellulose are inaccessible to fermenting organisms and requires the use of a pre-treatment to release their monomeric

22 sugars (Ragauskas et al., 2006). However, lignocellulose is the most abundant form of renewable carbon on Earth, making it an ideal feedstock for renewable energy production. The feedstocks from both first and second generation biofuels are

ultimately derived from CO2 fixation via photosynthesis, which is an advantage to the process due to the net cancellation of carbon emissions.

Third-generation biofuel production eliminates entirely the need for biomass as an intermediate, due to the use of photosynthetic organisms such as algae to directly

convert CO2 into biofuel or fuel precursor (Figure 1.2). The benefits of third- generation are that it does not compete with the agricultural production system and has the potential of harnessing solar energy and CO2 directly (Lee and Lavoie, 2018). However, operation costs are higher for this process, in combination with low lipid content, slow growth rates, and contamination problems that are associated with cultivating autotrophic microorganisms (Dragone et al., 2010). These are challenges that must be overcome through further research and development if a sustainable and consistent biofuel production process is to be reached on commercial scale.

As such, this has led to developing concepts around fourth-generation biofuel processes (Dutta, Daverey and Lin, 2014), which will involve the use of genetically engineered photosynthetic micro-organisms for higher yield and lipid accumulation, in combination with an integrated bio-refinery technology that also enables carbon

sequestration (Cuellar-bermudez et al., 2015). This CO2 capture will drive processes towards carbon negativity by creating artificial carbon sinks, making the process not only sustainable but creating a means of synthesising high energy density fuels through carbon sequestration and bioremediation processes (Gebreslassie, Waymire and You, 2013). These features make fourth-generation biofuels an exciting avenue to pursue in the quest for renewable sources of energy, however they too have their limitations. These include the need for large amounts of investment due their primary stage of research, along with the high cost of developing and operating an integrated bio-refinery and photo-bioreactor process. However, according to trends in research and publications related to the different generations of biofuel (Figure 1.3) it’s clear

23 Figure 1.1. Global greenhouse gas emissions under different scenarios and related global temperature increases compared to pre-industrial by 2100 (Climate Analytics, Ecofys and

NewClimate Institute, 2016)

24 Figure 1.2. Overview of the biofuel generations 1st-4th. Process and outputs outlined.

350

300 First generation 250 Second generation

200 Third generation

150 Fourth generation

100 Number of publications

0 1991-1995 1996-2000 2001-2005 2006-2010 2011-2012

Figure 1.3. Number of publications related to the different generations of biofuels

(adapted from Dutta, et al., 2014)

25 that third- and fourth- generation biofuels will receive more emphasis as socio- economic and political pressures increase, as will the need to drive fuel research that will sustain modern society and mitigate climate change issues. The motivation of this research is to contribute to the pursuit of viable alternative and sustainable fuel sources.

1.2 MICROBIAL COMMODITY CHEMICAL PRODUCTION

The use of microorganisms at an industrial level is deeply rooted in modern society and has impacted many industrial processes, some of the most industrially relevant microorganisms to date are associated with species of thermophilic bacteria, yeasts and fungi (Bajpai and Stahl, 2010).

Of industrially useful fungi, the genus Aspergillus contains hundreds of industrially relevant species, not least of all Aspergillus niger and the production of citric acid, which is widely used for pharmaceutical, cosmetic and food production (Kniemeyer, 2011). The better known fungus is arguably Pencillium chrysogenum, due to its role in antibiotic production and subsequent contribution to medicine, which in turn contributed to the increase in human life expectancy that started to rise in the 1940s (Bennett and Chungt, 2001; Adedeji, 2016). The mass production of penicillin transformed antibiotics as viable treatments during World War II, and is one of the most revolutionary medical discoveries of the 20th century.

Yeasts are also well known microbial catalysts that take the forefront in fermentation. They ferment sugars to alcohols, and have been applied to food processes which can be traced back to ancient times (McGovern, 2009). Saccharomyces cerevisiae is the most extensively studied eukaryote, and in addition to traditional industrial applications of yeast, it is also employed to produce useful biochemicals such as lactic acid (Sauer, 2013), succinic acid (Raab et al., 2010), and in the expression of heterologous protein for purification (Buckholz and Gleeson, 1991).

Numerous micro-organisms can produce a range of commercially relevant molecules by harnessing the natural catalytic machinery of their metabolisms. Species have also been identified that can directly produce a range of fuel molecules and precursors, some which have been mentioned already (Winter and Tang, 2012). Despite these discoveries, the natural rates of microbial fuel synthesis are typically

26 low, and result in yields that are unable to support industrial-scale production. To improve fuel production capabilities and economic viability, genetic engineering has been widely employed to engineer metabolism, to produce desired compounds at close to the maximum theoretical yield, while also maintaining high productivities and titre (Dai and Nielsen, 2015a).

Metabolic engineering is a powerful tool to improve microbial fuel production, either through engineering the biochemical pathways within the native microorganism to encourage high catalytic rates, or though transferring the biochemical fuel production pathway into a model organism for optimization. Recent publications have likened the catalytic optimisation of micro-organisms with a metaphor for ‘cell-factories’ (Kung, Runguphan and Keasling, 2012). Microbial conversion processes are paralleled to a production facility, in which the optimisation processes taking place are largely driven by metabolic engineering. The reality for engineering biological systems is unfortunately very far from the standardisation and predictive qualities of engineering or computing processes that are associated with factory productivity levels. Living cells have already been ‘programmed’ by evolution, which makes the consequences of inserting additional genetic components difficult to predict. Arriving at a level of characterisation that will allow for cells to be additionally ‘programmed’ with DNA reliably and predictably, the way computers execute code, will require the understanding of every component within at least one cell - a challenge that is not likely to be solved by any one research group alone. Genetic engineering therefore requires the contribution from the fields of mathematics, physics, theoretical biophysics, computational science and chemistry in addition to the biological sciences if it is to advance to a predictive, reliable and ‘factory’-like mode of engineering (Zadran and Levine, 2013; Guo, Sheng and Feng, 2017).

A well-known story of genetic engineering for treatment commodity production is that of yeast as a production platform for artemisinic acid (Ro et al., 2006). Artemisinic acid is a precursor for the anti-malarial drug and commodity treatment, artemisinin, which was produced by engineering yeast strains to produce enzymes of the biochemical pathway from the plant Artemisia annua (Paddon and Keasling, 2014). This breakthrough for commodity chemical production was made by the company Amyris, a spin-out from academics at UC Berkeley (Grushkin, 2012), and changed the landscape for anti-malarial drug production. By significantly altering the metabolic

27 pathways of yeast to produce artemisinic acid, Amyris created an industrial process capable of supplementing the world supply of artemisinin from a second source that was independent of the uncertainties associated with botanical production. While it was argued that the original work in developing this commercial platform was inefficient due to requiring a decade of research and enormous amounts of funding, the work was then used to develop and contribute to further industrial chemicals, which the company could apply as a ‘plug-and-play’ platform to their next chemical of interest- farnesene (Jefferson, Lentzos and Marris, 2014)(Ubersax and Platt, 2010).

Farnesene, like artemisinic acid, is an isoprenoid, which is a class of chemicals that are ideal for advanced biofuels (Meadows et al., 2016). Due to their hydrocarbon chemical structure they are effective in acting as ‘drop-in’ replacements for gasoline, diesel, and jet fuel (Zhang and Keasling, 2012). Because yeast is used in the fermentation, this method of biofuel production still requires the use of sugar as feedstock. However, because of the higher energy density nature of farnesene, the cons of traditional first-generation fuels were outweighed by fulfilling combustion fuel requirements of the heavy transportation sector compared with ethanol, for example. The potential for this application to disrupt the petroleum industry was huge, and Amyris predicted 6 million gallons produced by their plants before 2012 to meet the demands (Grushkin, 2012). Unfortunately, the company could not meet their promises to investors on yield due to scaling-up issues and their simultaneously poor luck in competing with a drop in oil price in 2012 (La Monica, 2014), which ultimately lead to a reduction in share prices and eventual closing down of several production plants around the globe. The story of Amyris’ farnesene platform is a lesson on the difficulties of predicting the commercial prospects of novel and complex technologies such as those coming from genetic engineering microorganisms to meet the volatility, fluctuations and expectations of an economic market. It does not take away from the validity of the potential of the research there, which continues towards improving farensene production efficiency among other commodity productions (George, Alonso-gutierrez and Keasling, 2015).

Despite the unravelling of Amyris’ effort to produce farnesene at economic yields that would enable them to compete with the oil market, there is still hope for this field of research. Not least of all are the indications that oil companies themselves see the potential in biofuel research, with almost every independent oil company having

28 some investment or activity in the area (Dale, 2010). This comes from the fact that medium- and long-chain hydrocarbons are valid targets for microbial production from lignocellulosic feedstock. They have demonstrated potential in serving as a diesel replacement (Naik et al., 2010), a reduction in recovery costs from the fermentation process due to phase separation (Koutinas et al., 2014), and their ‘drop-in’ compatibility with existing vehicle engines.

Two major biochemical pathways exist in nature that produce highly reduced hydrocarbons of C8 or higher, which are elongated by a series of iterative cycles of carbon condensation reactions. One is the isoprenoid pathway, which yields highly valuable products in addition to farnesene, such as isoprene, geranylgerniol, and phytonene (George, Alonso-gutierrez and Keasling, 2015; Yoon et al., 2007; Tokuhiro et al., 2009). The second is fatty acid biosynthesis, from which a range of fuel products can be generated, such as alkanes, olefins, fatty alcohols, methyl ketones, and methyl and ethyl esters of fatty acids (FAs) (Handke, Lynch and Gill, 2011; Choi and Lee, 2013; Lennen and Pfleger, 2013; Janßen and Steinbüchel, 2014a). The application of microbial fatty acid biosynthesis to commodity chemical production is the focus of this thesis, and will be described in further detail in the following sections of this chapter.

1.3 RENEWABLE FATTY ACID SYNTHESIS

FAs and their derivatives play an essential role in several cellular processes, including cell signalling, transcriptional control, cell membrane synthesis and protein modification (Nunn, 1986). In addition to these activities in the cell, they represent valuable precursors to many industrially relevant commodity chemicals which have applications in the pharmaceutical, transport fuel and petrochemical sectors (Janßen and Steinbüchel, 2014b). Of particular relevance to the renewable fuel focus of this research are their application as precursors to high-energy density biofuels, examples of which are outlined in Figure 1.4 and described by Lennen and Pfleger (2013), Kalscheuer, Stölting and Steinbüchel (2006), and Janßen and Steinbüchel (2014).

To enable sustainable and renewable processes, the use of microbes as FA production hosts are well established and are typically categorised as second

29 generation biofuel production, as discussed in section 1.1. Common biotechnological organisms, such as Escherichia coli and Saccharomyces cerevisiae, are selected as production hosts due to the vast and robust range of genetic engineering tools available from decades of research (Duina, Miller and Keeney, 2014; Idalia and Bernardo, 2017), their fast growth rates, broad substrate preference and ability to grow anaerobically. These hosts have a successful track record for the producing many platform chemicals and biofuels (Chen et al., 2013) (Lu, Vora and Khosla, 2008), which has benefitted from the research that contributes to a deep understanding of their physiology, metabolism, and gene regulation over the years, making them ideal candidates for commodity chemical production at industrial levels. These hosts also have a native fatty acid biosynthesis pathway, which minimises the risk of heavy genetic engineering burden when designing overproduction platforms.

For third- and fourth-generation biofuel consideration, renewable fatty acid production requires an autotrophic host such as algae or cyanobacteria. However, these organisms lack the ideal qualities of industrial hosts (Dutta, Daverey and Lin, 2014). Namely that their lipid yield and growth rates are low compared to heterotrophic micro-organisms, and that cultivation processes costly and prone to contamination (Singh, Singh and Murphy, 2011). Therefore, for addressing and understanding factors related to the biosynthesis and production of fatty acid derived biofuels by microbes, E. coli is a valuable and robust research tool. If certain modifications and conditions to the E. coli biosynthesis pathway are found to obtain the maximum yield, there is the potential of applying and adapting them towards autotrophic systems. This could be by transferring the known traits to a photosynthetic host by genetic engineering, or by modifying the E. coli host further by introducing a carbon fixation pathway, such as that which was reported by Antonovsky, Gleizer and Milo (2017). In this way, the ideal industrial host traits of E. coli remain while adding the benefits of a renewable autotrophic system.

For the advantages outlined, E. coli was selected as the production organisms in this study. In addition to their simple and low cost nutrient requirements, they also lack compartments such as mitochondria or vacuoles found in eukaryotes, which makes secretion of the product less challenging. E. coli are also naturally capable of synthesising fatty acids as the precursors in cell membrane synthesis (O’Leary,

30 1962; Cronan, 1968; Magnuson et al., 1993), and have a well characterised and well-studied fatty acid synthesis (FAS) pathway (Ohlrogge and Jaworski, 1997)

FAS in E. coli is comprised of tightly regulated, anabolic and catabolic reactions (Magnuson, et al, 1993), and produces a range of chain lengths with varying degrees of saturation according to environmental or genetic stimuli (Xiao, et al., 2013). FAs also present a degree of toxicity to the E. coli during cultivation, due to their antimicrobial properties and depolarising effect on the cell membrane (Marounek, Skrivanova and Rada, 2003), which represents a significant challenge when engineering an overproducing host. Therefore, if FAS in E. coli is to be engineered into a feasible and efficient means of commodity chemical production, the fundamental challenges that present themselves in optimising this pathway while maintaining cell viability must be addressed. These challenges will be the focus of this study, which I aim to address by elucidating the factors which have been reported to contribute to the control of FAS flux, targeting them for engineering, and combining these directed strategies with process optimisations. Additionally, fundamental questions that have remained unanswered on the control of FAS flux in E. coli will be systematically addressed in the research of this thesis. The objectives of each research chapter are outlined fully in section 1.10.

1.4 FATTY ACID BIOSYNTHESIS AND DEGRADATION PATHWAYS IN E. COLI

In eukaryotes, bacteria and mammals, FAS is catalysed by a system of multi- enzyme protein complexes; the type of FAS system in place with each differs by the organisation of their catalytic domains (Wakil, Stoops and Joshi, 1983; Rock and Cronan, 1996). In E. coli, a type II catalytic FAS system is in place, where each reaction is catalysed by a discrete enzyme known as the fatty acid biosynthesis (FAB) enzymes. There are nine FAB enzymes, each carrying out distinct reactions which will be described briefly here but in more specific detail in chapter 4.

The biosynthetic pathway starts with an initiation reaction, followed by iterative cycles of condensation, reduction, dehydration and additional reduction reactions of carbon-carbon bonds (Cronan and Thomas, 2009) (Rock and Cronan, 1996) (Lu, Zhang and Rock, 2004) (Chan and Vogel, 2010) (Figure 1.5). The first commited step in FAS is the formation of malonyl-CoA via the carboxylation of acetyl-CoA,

31 Figure 1.4. Commodity chemical derivatives of fatty acids

32 Figure 1.5. Fatty acid biosynthesis and degradation pathways in E. coli. Protein abbreviations in blue, metabolites in black, ATP and reducing agents in red.

33 catalyzed by acetyl-CoA carboxylase (Acc). Malonyl-CoA is then converted to malonyl-ACP via malonyl-CoA:ACP tranacylase (FabD), and an inital condensation of malonyl-ACP with acetyl-CoA produces the first β-ketoacyl-ACP (acetoacetyl- ACP) by β-ketoacyl-ACP synthase III (FabH). Acetoacetyl-ACP enters the elongation cycle as a reduction to 3-hydroxybutyryl-ACP catalysed by 3-ketoacyl-ACP reductase (FabG), dehydration to 2-butenoyl-ACP by 3-hydroxyacyl- ACP dehydrase (FabZ) and further reduction to butyryl-ACP by Enoyl-ACP reductase (FabI). Butyryl- ACP enters the next turn of the cycle by a condensation with malonyl-ACP catalysed by 3-ketoacyl-ACP synthase I or II (FabB or FabF), which begins the cyclic process of two carbon additions, until long chain acyl-ACPs (16-18 carbons) are incorporated into phospholipid membrane by the acyltransferase enzymes (PlsB, PlsC, PlsX, PlsY). During the first step of FAS, the carboxylation of acetyl-CoA is driven by the

cleavage of ATP and the release of CO2 (Cronan and Waldrop, 2002) (Guchait et al., 1974). This subsequently results in the requirement of 7 molecules of ATP for the synthesis of a 16-carbon fatty acid; palmitic acid. 2 NADPH molecules are also reduced for every completion of the elongation cycle, resulting in a required 14 NADPH in total for the same molecule of palmitic acid. FAs therefore represent significant carbon and energy storages for the cell, and their degradation yields these same amounts of ATP and reducing equivalents during the degradation cycles.

E. coli can utilise FA as a carbon source during fatty acid degradation (FAD) (Clarkt, 1981) (Pech-Canul et al., 2011), which is initiated when extracellular free fatty acids (FFA) are transported into the cell by a transport process mediated by the outer- membrane transport protein FadL (Black et al., 1987). FAs are then activated by the inner-membrane-associated acyl-CoA synthetase (FadD) to an acyl-CoA ester (Figure 1.5), which starts a cycle of degradation reactions that are the catabolic versions of FAB, consisting of oxidation by acyl-CoA dehydrogenase (FadE), hydration and oxidation by FadB, and thiolation with CoA by thiolase (FadA). For every cycle completed, one molecule of acetyl-CoA is generated which can be used as the precursor to further metabolic processes, as well as the energy generated from oxidative steps. The enzymes of FAD can accept both short and long chain FAs as a substrate, though aerobically the requirement is long chain FAs of at least 12 carbon atoms to activate the pathway enzymes via transcription factor FadR (Clarkt,

34 1981). FAD operates under anaerobic conditions as a distinct pathway to aerobic FAD, due to E. coli utilising alternative enzymes which can catabolise FAs in the presence of an alternative electron acceptor (Campbell, Morgan-Kiss and Cronan, 2003). Anaerobic FAS is also under less regulatory influence compared to aerobic, due to the lack of transcriptional control on these enzymes by FadR (Campbell and Cronan, 2001). Due to the essentiality of these processes, regulation of both FAB and FAD is strict and on various levels (Magnuson, et al, 1993; Ohlrogge and Jaworski, 1997; Nunn, Kelly and Stumfall, 1977; Overath, Pauli and Schairer, 1969). As an area of research, it has been contributed to greatly over the years, notably from Cronan et al., however, many questions around several aspects of regulation remain. Therefore, if FAS is to be utilised for biofuel production much basic research is still needed to improve the yields of FFA and their related derivative commodity products.

1.5 THE REGULATION OF FATTY ACID SYNTHESIS IN E. COLI

Metabolic regulation is necessary to maintain balance in all organisms (Metallo and Vander Heiden, 2013)(Shimizu, 2013a), though it is particularly evident in microorganisms which need to adapt to physical and chemical fluctuations they are subjected to from to their environments (Roszak and Colwell, 1987). As such they have evolved regulatory mechanisms that enable survival during these fluctuations by acclimatising and adjusting their metabolism towards specific conditions.

Because FAs are energetically expensive compounds for the organisms to synthesise and essential to cell membrane synthesis, the evolution of multiple control and feedback regulations have enabled the maintenance of membrane homeostasis when environmental conditions fluctuate (Fujita, Matsuoka and Hirooka, 2007). Allosteric feedback of acyl-ACP, particularly the 16 carbon palmitoyl-ACP, reduces the activity of Acc, FabH and FabI by binding to the enzymes and blocking access for the relevant substrates to their active sites (Richard J Heath and Rock, 1996) (R. J. Heath and Rock, 1995a) (Davis and Cronan, 2001)(Janßen & Steinbüchel, 2014). This inhibitory regulation on the initiation and elongation steps ensures the termination of FAS cycles at specific carbon chain lengths for their

35 incorporation into phospholipids (Magnuson et al., 1993), while additionally limiting the investment of both carbon and energy for FAS reactions when acyl-ACP is in adequate supply.

Transcriptional control also plays a prominent role in regulating enzymes of both FAB and FAD (Janßen and Steinbüchel, 2014a). Acc is additionally subject to this type of regulation (Davis, et al., 2000), with all subunits of Acc tightly regulated due to their requirement of specific concentrations for the stable formation of the Acc multi-subunit complex (Broussard et al., 2014), which will be described in more detail in chapter 4. In rat liver adipose tissue, Acc is also allosterically activated by citrate (Martin and Vagelos, 1962), which acts as an indicator of the supply of acetyl-CoA substrate from the TCA cycle for the Acc reaction to proceed. Addition al acyl-ACP feedback inhibition is present at the last step of elongation, where FabI is further feedback inhibited by both palmitoyl-ACP and palmitoyl-CoA (Bergler et al., 1996) (R. J. Heath and Rock, 1995b), due to the ability of acyltransferases such as PlsB to incorporate both acyl-ACP and acyl-CoA to phospholipids (Y.-M. Zhang and Rock, 2008). This regulation is therefore also in place to avoid the accumulation of energy expensive acyl-ACP and acyl-CoA intermediates, while maintaining the supply of precursor for membrane synthesis.

The FA responsive transcription factor, FadR, coordinates the expression of both FAB and FAD genes according to levels of acyl-CoA (Henry and Cronan, 1992). FadR does this by covalently binding to either promoter sites upstream of or directly to coding regions of the FAB and FAD genes (My et al., 2015) (Campbell and Cronan, 2001). The characterization of the binding sites of FadR have revealed their upregulating effect by enabling RNA polymerase during the transcription of FAB genes, while also having a simultaneous inhibitory effect on FAD by blocking transcription when no acyl-CoA is present (Henry and Cronan, 1992). The coordination of both is in response to acyl-CoA concentration, which binds to FadR and antagonises further binding to DNA (Zhang and Rock, 2009). In the absence of acyl-CoA, FadR binds to DNA and activates or represses genes that are affiliated with both biosynthesis of degradation, respectively (Figure 1.6). This action is relieved when acyl-CoA (usually above 12 carbon in chain length) is available to bind to FadR and releases from the DNA binding sites which enables the regulation to be relaxed, resulting the deactivation and derepression of FAB and FAD gene

36 expression. This mechanism therefore functions as a switch for both FAB and FAD in coordination with FA levels that are available to the cell, which coordinates a balance between the anabolic and catabolic reactions.

Further transcriptional control is also in place to maintain the level of saturated and unsaturated FAs produced in response to external conditions such as temperature or stress (Marr and Ingraham, 1962). The fatty acid biosynthesis regulator FabR, directly maintains membrane lipid homeostasis by controlling unsaturated fatty acid synthesis (UFAS) (Zhang, Marrakchi and Rock, 2002). It has been established that FabR regulates the transcription of FAB genes which are responsible for UFAS, fabB and fabA, by responding to the ratio of unsaturated to saturated acyl-ACPs, and repressing gene expression when UFAs are in abundance (Lennen et al., 2011). FabR binds to and represses the transcription of these genes when it is in a complex with UFAs, while the binding is weakened when FabR is in a complex with saturated FAs, derepressing the transcription of fabB and fabA (Feng and Cronan, 2014). In this way UFAS is tuned precisely to balance the composition for incorporation into the membrane.

Environmental conditions such as oxygen availability also play a role in transcriptional regulation in E. coli globally (Levanon, San and Bennett, 2005), and in FA metabolism (Feng and Cronan, 2012). Anaerobically, the transcription of all genes of FAD are inhibited by the aerobic respiration control protein ArcA (Cho, Knight and Palsson, 2006), which is a global regulator required for optimal growth of E. coli during anaerobic conditions, in conjunction with an additional transcriptional regulator Fnr; the fumurate and nitrate reductase transcriptional activator (Unden and Schirawski, 1997). This regulation ensures that gene expression is mediated in response to redox conditions and that metabolism is switched accordingly, by restraining respiration in favour of fermentation when redox potential is decreased (Chubukov et al., 2014).

ArcA is activated in the absence of oxygen and directly binds to promoter regions of FAD regulon genes, resulting in their transcriptional repression (Cho, Knight and Palsson, 2006). ArcA incurs this repression in coordination with FadR (Feng and Cronan, 2012). When FAs are not present anaerobically, FadR is active and co- regulates FAD repression with ArcA (Figure 1.7). If FAs are present and no other

37 Figure 1.6. FadR, FabR and acyl-ACP regulation on FAS in E. coli. FadR activation and inhibition in blue, FabR inhibition in green, acyl-ACP feedback in red.

38 Figure 1.7. AcrA, Crp-cAMP and ppGpp regulation on FAS in E. coli. ArcA inhibition in blue,

Crp-cAMP activation in green, ppGpp inhibition in red.

39 carbon source is available, anaerobic FA utilization is only possible via the expression of FAD homologs YfcY , YfcX, YdiO and YdiD (Campbell, Morgan-Kiss and Cronan, 2003). These genes are not under anaerobic FadR control, nor inhibited transcriptionally by ArcA, and are effective in supporting anaerobic growth on FA as sole carbon and energy source, provided that a terminal respiratory electron acceptor such as nitrate is available. Catabolite repression is known to activate the transcription of this anaerobic pathway when no other carbon source is available (Campbell, Morgan-Kiss and Cronan, 2003) . This process ensures the ability of cells to survive in environments that are limited in oxygen levels and fluctuating carbon source, such as in the lumen of mammals.

Additional global regulators; such as the stringent response alarmone (p)ppGpp and cyclic-AMP regulator protein Crp, are also known to play critical roles in the regulation of FAS both directly and on a global regulatory level (Feng and Cronan, 2012). For example, the accumulation of (p)ppGpp concentrations during stress conditions influence open promoter stability and RNA polymerase activity (Magnusson, Farewell and Nyström, 2005) and as a result, certain genes are activated or inactivated. Elevated (p)ppGpp concentrations also activate the expression of genes for survival during starvation and virulence, thereby acting as an indicator of the stringent control response (Potrykus et al., 2011). During periods of amino acid or carbon source depletion, the stress response inhibits transcription from rRNA and ribosomal protein promoters but activates transcription for promoters of amino acid biosynthesis (Traxler et al., 2008). (p)ppGpp also represses transcription of genes of FAS and therefore leads to a cessation in phospholipid production and growth (Heath, Jackowski and Rock, 1994) (My et al., 2013). This regulation occurs via the FabH promoter, which drives expression of FabH, FabD and FabG. (p)ppGpp further represses transcription of subunit AccBC, as well as the activities of FadR and PlsB (Janßen & Steinbüchel, 2014). The degradation pathway, on the other hand, is positively regulated by CRP-cAMP under catabolite repression. This results in the upregulation of FadL, FadD, FadE, FadA, and FadB production and therefore catabolism of FA when carbon source is limited, serving as a response to carbon source availability to enable growth (Feng & Cronan, 2012).

40 1.6 PHOSPHOLIPID AND CELL MEMBRANE SYNTHESIS

Completed cycles of FAS result in acyl-ACPs of varying chain lengths and degree of saturation which is subject to temperature and enzymatic regulation (Magnuson et al., 1993). When hydrocarbon chains of acyl-ACP are elongated to 16 carbons, the acyltransferase enzymes start their incorporation into membrane phospholipids. During membrane synthesis, acyl-ACP are converted lysophosphatidic acid (LPA) via one of two routes (Cronan and Rock, 2013)(Y.-M. Zhang and Rock, 2008) (Figure 1.8). Firstly, acyl-ACP is directly condensed with glycerol-3-phosphate by PlsB, which can also use acyl-CoA as the acyl donor. Alternatively, acyl-ACP can also be activated with an inorganic phosphate group by PlsX to form acylphosphate, which then proceeds by PlsY to produce LPA from glycerol 3- phosphate. PlsC then transfers an acyl group to LPA. This produces phosphatidic acid, the key intermediate in membrane phospholipids.

FAS is therefore tightly coupled to phospholipid synthesis and growth, as it has been shown that during inhibition of phospholipid synthesis, pools of FAS intermediates accumulate and inhibit FAS (P Jiang and Cronan, 1994) (Nunn, Kelly and Stumfall, 1977). This is understandable due to the regulation of acyl-ACP feedback mechanisms on FAS as previously described. However, studies have also shown a certain level of FAS proceeds when phospholipid synthesis is inhibited (Jiang & Cronan, 1994a), suggesting that FA and phospholipid synthesis are not as tightly coupled as previously anticipated. The negative feedback inhibition of acyl-ACP on FAS is relieved when a thioesterase cleaves the thioester bond of the acyl chain, releasing FFA and ACP (Cho and Cronan, 1995). In this way, a sink reaction is created that pulls the flux through FAS by relieving product build-up and negative feedback. ‘TesA is a cytosolic E. coli thioestersase that exhibits substrate preferences for fatty acyl-ACPs that are 14 carbons in chain length, though is known to also produce FA ranging from 8-18 carbon chain lengths (Choi and Lee, 2013) (Steen et al., 2010).

Accumulation of FAs in the cell envelope is toxic as it can alter membrane integrity, incur a loss of cell viability and lead to possible defects in cell division (Lennen et al., 2011). The cell envelope of E. coli consists of an inner membrane, the periplasm, and an outer membrane (Silhavy, Kahne and Walker, 2010). FFAs can passively

41 Figure 1.8. Phospholipid synthesis in E. coli. Protein abbreviations in blue, metabolites in black.

42 diffuse across these layers into the environment via a flipping mechanism according to the hydrophobic interactions through the phospholipid layer and diffusion towards the low external concentration gradient (Lennen et al., 2011). It is thought that this rate of flipping and diffusion may be slower than that of intracellular production (Cronan, 2003), which would lead to a build-up and morphological changes that are detrimental to the cell. Passive diffusion is even less efficient under low nutrient conditions; therefore, FA transport proceeds via substrate specific and active membrane transporters (Van Den Berg, 2005) (Black and DiRusso, 2003). Transport proteins span the inner and outer membranes to facilitate FA export and relieve toxic build-up in the cell, they can also import exogenous FA when utilised as a carbon source for degradation. FadL is an outer membrane FA transporter which is known for its role in the uptake of exogenous FA for degradation (Van Den Berg, 2005) along with FadD, as previously described. While knowledge of the proteins responsible for export of FA remain incomplete, the role of the efflux pump AcrAB- TolC in transport has been established through deletion studies of these genes, which abolished FFA production (Lennen et al., 2013). TolC is an outer membrane channel for many efflux pumps in E. coli, and has been identified prior in having a role in alleviating toxicity (Koronakis et al., 2000) (Iyer et al., 2015). The AcrAB-TolC efflux pump spans all three membrane layers, of which TolC has been demonstrated as essential in the process of exporting FA from the cell (Lennen, et al., 2013). This transport system is activated in response to FA levels via the Rob regulon (Rosenberg et al., 2003), which is a transcriptional regulator that also shares sites of gene activations with FadR (F. Zhang, Ouellet, et al., 2012). Rob has also been demonstrated as important in maintaining cell viability in the presence of FFA (Rosenberg et al., 2003), along with phage shock proteins (Brissette et al., 1990)(Lennen et al., 2011).

1.7 THE CONTRIBUTION OF CENTRAL CARBON METABOLISM TOWARD FATTY ACID SYNTHESIS

Fatty acids are derived from acetyl-CoA as the exclusive carbon precursor and their biosynthesis requires energy in the form of ATP as well as reducing agent NADPH. Fatty acid synthesis in E. coli is therefore reliant on the metabolic processes that

43 generate these precursors and cofactors, such as those of central carbon metabolism (Magnuson et al., 1993). Acetyl-CoA is a key molecule in microbial central carbon metabolism as it is involved in a variety of cellular biochemical processes (Figure 1.9). Composed of a coenzyme A and acetyl group connected by a thioester bond, acetyl-CoA functions in transferring acetyl groups between molecules within E. coli metabolism that will generate biomass, energy or in the case of FAS, phospholipids. Acetyl-CoA can be reduced to ethanol by the actions of acetaldehyde dehydrogenase (A-ALD) and alcohol dehydrogenase (ADH), it can be produced from pyruvate aerobically via pyruvate dehydrogenase (PDH), or anaerobically via pyruvate formate lyase (PFL). It can also be generated from acetate directly by acetyl-CoA synthase (ACS), or by the actions of acetate kinase (ACK) and phosphate acetyltransferase (PTA) (Enjalbert et al., 2017). This central role of acetyl-CoA in metabolism also has many applications in generating chemicals that are of biotechnological interest (Leonard et al., 2007)(Xu et al., 2013). For this reason, much emphasis has been put on the engineering of acetyl-CoA pools to increase titres and various strategies have been reviewed previously (Lin et al., 2004)(Krivoruchko et al., 2015).

Due to the array of routes for acetyl-CoA consumption and production, engineering their pools of availability is a challenging target to understand in relation to the factors that control FAS flux, when considering the optimisation of acetyl-CoA supply for FAS, for example. As summarised in Figure 1.9, acetyl-CoA can be oxidized via enzymes of the TCA cycle to generate energy and building blocks for the biosynthesis of most amino acids; the regulation of the TCA cycle has therefore evolved towards meeting the needs of the cell that are required for biosynthesis and energy generation under varying conditions (Kern et al., 2007). For example, IclR is a transcription factor responsible for inducing the expression of the glyoxylate operon when acetate is accumulating in the exponential phase (Kotte, Zaugg and Heinemann, 2010; Renilla et al., 2012), to provide energy via the anaplerotic reactions of this modified cycle while conserving the 4 carbon intermediates of TCA. This is necessary when cells are grown solely on 2 carbon substrates such as acetate or when acetyl-CoA is generated from FAD, to maintain energy and cofactor levels which re-enter metabolism (Kornberg, 1965). The ATP and NADPH produced

44 Figure 1.9. Acetyl-CoA biosynthesis and utilisation in E. coli. Protein abbreviations in blue, metabolites to and from acetyl-CoA in black.

45 during the glyoxylate and TCA cycles enable the reactions of FAS to proceed, in this way acetate is linked to maintaining FAS reactions during exponential growth.

The pentose phosphate pathway (PPP) is an additional source of reducing equivalents NADPH, activities of the dehydrogenase enzymes of the pathway have been found to correlate with FA production under certain conditions while NADPH- producing malic enzyme has also been found to contribute to FAS (Tang, Lee and Ning, 2015; Zhang, Adams and Ratledge, 2018). Furthermore, engineering PPP to increase carbon flux has been reported to increase yields of products which are energy dependent in E. coli, such as succinic acid and beta carotene (Ng et al., 2015; Tan, Chen and Zhang, 2016). It is therefore through the affiliations discussed that FAS is tied directly to several aspects of central carbon metabolism in E. coli, and which need to be considered during engineering and process optimisation strategies.

1.8 THE USE OF PREDICTIVE MODELLING TO GUIDE AND INFORM FATTY ACID RESEARCH

Because FA production reactions are maintained by tightly coordinated regulations (Wakil, Stoops and Joshi, 1983), and the precursors and cofactors for FAs are shared with other essential metabolic pathways (Magnuson et al., 1993) the task of engineering E. coli for higher yield is complicated, unpredictable, and subject to many limitations. The field of FA research is fortunately well studied (Janßen and Steinbüchel, 2014b) and several engineering efforts have contributed towards improving productivity and FA yields already (Table 1.1). However, the routes to achieving further improvements is non-intuitive and in some cases inconsistent, with some identical engineering strategies giving varying results. Metabolic modelling has proven a useful tool in identifying target central carbon metabolism enzymes that will maximise flux through FAS, eliminate competing pathways such as the consumption of acetyl-CoA, as well as identify nodes which are ‘rigid’ and potentially the most influential on a biochemical system (Stephanopoulos & Vallino, 1991; Gaspar and Csermely, 2012).

Genome-scale metabolic models describe the biochemical reactions that take place in the cell, and can simulate the dynamics of the metabolic network state (Feist and

46 Palsson, 2011). Three types of metabolic models exist: structural, stoichiometric and kinetic (Bruggeman and Westerhoff, 2007). They are constructed from extensive biochemical and enzymatic information from databases such as KEGG, BioCyc and BiGG, built on whole-genome annotations that are made accessible through platforms such as the NCBI BioSystems Database, and can be used in conjunction with constraint-based techniques to characterize a metabolic system (Bordbar et al., 2014). Regulation in metabolic models give rise to networks that are complex in themselves, this information has been manually curated from publications in RegulonDB, which is the primary database of regulatory mechanisms in E. coli (Salgado et al., 2013).

Regulatory interactions provide key information on altered network capabilities (Covert and Palsson, 2002), and therefore their application to modelling is important if accuracy can be derived from the output of simulating pathways in a model. Kinetic modelling for example, describes a system’s dynamics and its relationship with regulatory mechanisms (Costa, Hartmann and Vinga, 2016; Stanford et al., 2013). For predictions to have a good agreement with experimental data, models of biochemical pathways with complicated regulatory mechanisms (such as FAS) must have as many functional properties of the system as possible (Peskov, Mogilevskaya, & Demin, 2012). Kinetic models help determine the conditions that will maximise a pathway’s productivity, and therefore are valuable tools for optimising FAS of E. coli.

However, given the large and densely connected nature of FAS regulatory networks, as we have begun to observe in earlier sections of this chapter, it is challenging to summarise regulatory interactions in a model for the influence they have on metabolism in its entirety (Chubukov, et al., 2014). Furthermore, within metabolic systems interactions such as shared substrate or higher level biochemical organization make pathways stable and therefore less inclined to externally modifying flux direct (Wright et al., 2006; Stephanopoulos & Vallino, 1991). It is therefore also important to identify stable points in a metabolic pathway, to find a means of overcoming their rigidity in strain improvement studies (He et al., 2014).

Tools that provide algorithmic frameworks in describing metabolic pathways simulate a set of conditions, and offer valuable points of consideration during the engineering

47 design process. For example, while some deletion mutants make intuitive sense for certain desired output or target, another combination of targets are not as obvious in their impact and this is where tools that simulate metabolic pathways are useful to the experimentalist. Frameworks of interest and which were considered during this study include: flux balance analysis (FBA) (Orth, Thiele and Palsson, 2010), regulatory flux-balance analysis (rFBA) (Covert and Palsson, 2002), integrative omics- metabolic analysis (IOMA) (Yizhak et al., 2010), metabolic control analysis (MCA)(Fell, 1992), cipher of evolutionary design (CiED) (Fowler, Gikandi and Koffas, 2009), OptForce (Ranganathan et al., 2012), and many of which have already been applied in generating computationally driven predictions for targets of genetic engineering to improve FAS in E. coli. For example, rFBA, the regulatory extension of FBA, considers the mass balance and reversibility of reactions to determine flux and was applied by Lin et al., (2013) to increase FA yield. This work demonstrated that knocking out 6 genes of the central carbon metabolism, as predicted by rFBA, increased FA yield in E. coli when combined with the traditional approaches.

Furthermore, Optforce (an optimization procedure based on the stoichiometry and conditions of a model) is used to identify all genetic manipulations which lead to the targeted overproductions by contrasting the metabolic flux patterns observed in an initial strain, with that of a strain overproducing the chemical at the target yield (Ranganathan, Suthers and Maranas, 2010). Several groups have applied this to identify intervention strategies for FAS improvement, which have ranged from targets that led to malonyl-CoA improvements (Xu et al., 2011), down-regulating TCA enzymes while overexpressing FAB enzymes (Ranganathan et al., 2012), and interventions that led to chain length specific FA production (Tee et al., 2014). This procedure was further developed into k-Optforce by Chowdhury, Zomorrodi and Maranas (2014), with the integration of enzyme kinetics with the FBA qualities of Optforce before, and was used to identify interventions which improved the production of L-serine in E. coli, as well as TAL in yeast. The interesting part of this application was the differences the information on regulation made to the flux predictions, by integrating kinetics into the algorithmic constraints. It demonstrated the influence adding this constraint had on network simulations, and is particularly relevant when making predictions for FAS flux. To date k-Optforce has not been applied to E. coli FAS.

48 When considering predictions made on metabolic fluxes, which are the rate of substrate conversion per cell, the flow of metabolite through biochemical networks are the target proxy and are argued to be the most informative and direct indication of the metabolic state of a cell (Nielsen, 2003) (Suthers et al., 2007). Since fluxes are tightly regulated through central carbon metabolism, accurate estimation under varying conditions aid in the prediction of metabolic outcome and the generation of valuable outputs. Metabolic control analysis (MCA) is a framework which considers the impact of changes in fluxes on the control of a system (R ao, Sauro and Arkin, 2004; Moreno-Sánchez et al., 2008). Developed independently by two groups; Henrik Kacser and Jim Burns in Edinburgh in 1973 (Kacser and Burns, 1995), and Reinhart Heinrich and Tom Rapaport in Berlin (Heinrich and Rapoport, 1974), the framework provides a rigorous quantitative foundation for defining control coefficients that measure the effects of a percentage change in pathway flux caused by a percentage change in the rate of a particular step of a system (Gunawardena, 2002). Known as flux control coefficients, these quantifications serve as a specialised sensitivity analysis and provide a platform for assessing the effectiveness of parameters in the systems which contribute to controlling flux. If one of the flux control coefficients is higher than all the others in a system, the indication is that the reaction in question is rate-limiting. However, if several reactions have equivalent levels of control while others lower, the indication is that no single rate-limiting step controls the biochemical pathway and that the control is distributed throughout. MCA is therefore a valuable tool for probing biological systems for quantitative measures of control, which can be utilised for fundamental understanding and directed approaches of optimising a system through engineering (Moreno-Sánchez et al., 2008). This framework has been extensively applied to interpret many biological systems (Wildermuth, 2000) though to date it has not been applied to FAS in E. coli previously.

49 1.9 CURRENT PRACTICES AND YIELDS IN ENGINEERING E. COLI FOR FATTY ACID OVERPRODUCTION

Given the known points of regulation and the characterised activity of the enzymatic steps in FAS (Rock and Jackowski, 2002), engineering the pathway has focused on harnessing this information to guide strategies of improving flux towards the maximum achievable yield. These have ranged over the years from genetic to process optimisations, adaptive cultivations and cell-free assays, and have resulted in varying improvements in yield (Table 1.1). Common genetic strategies have included 1) introducing a thioesterase which disrupts FAS elongation cycles at acyl- ACP and simultaneously relieves the negative feedback inhibition of acyl-ACP accumulation (P. Jiang and Cronan, 1994), 2) optimising enzyme levels that upregulate FAS or overcomes the appointed rate-limiting steps in the pathway (Davis, Solbiati and Cronan, 2000) and 3), knocking out steps of the degradation pathway so that the FA produced do not get recycled and degraded back into FA metabolism (Lu, Vora and Khosla, 2008). This combinatorial engineering strategy of overproducing FA has been referred to as a ‘push, pull and block’ routine due to the influence each optimisation has on carbon flux (He et al., 2014), resulting in the accumulation of FA in the media. Several more recent publications have also outlined process optimisations that have contributed to improving FA yield, when done in combination with traditional engineering practices (Liu et al., 2012) (Youngquist, Rose and Pfleger, 2013) (Jawed et al., 2016) ( Ledesma-Amaro et al., 2016). These have ranged from adjustments made to cultivation media such as phosphate limitation, to a two-stage cultivation temperature setting, both of which have yielded improvements compared to the relative controls.

While some of these strategies have contributed to achieving close to maximum theoretical yields (Zhang et al., 2012; Xu et al., 2014; Xiao et al., 2016), there are also inconsistencies across the field. These include irreproducibility where similar or identical manipulations give varying yields, contradicting approaches reporting improvements, and lack of consistent use of the ideal genotype host or thioesterase for expression (all outlined in Table 1.1). Contributing information to understanding control that will further optimise engineering and process strategies for FA production in E. coli is therefore an ongoing area of research, and that which is the remit of this thesis in the chapters to follow.

50 Table 1.1. Overview of genetic and process optimisations for improving FA production in E. coli

Modification Genotype Total FA yield Theoretical Cultivation Reference Notes yield media Acc + 'TesA BL21(DE3) 6-fold increase NA LB Davis (2000) FA analysed by TLC compared to 'TesA Acc+ 'TesA + acyl-ACP ΔfadD 2.5g/L 12.80% M9 Lu (2008) Identified FA accumulation inside cell thioesterase (Cinnamonum camphorum) Acc + acly-ACP thioesterase ΔfadD 0.8g/L 42% LB 0.4% glycerol Lennen (2010) Alkane production (Umbellularia californica) 'TesA ΔfadE 1.2g/L 18% M9 Steen (2010) FAEE and fatty alcohol Acc+ 'TesA + acyl-ACP ΔfadD N/A N/A LB Liu (2010) Identified FAS sensitivity to malonly-CoA, thioesterase (C. camphorum) NADPH, ACP in cell-free acyl-ACP thioesterase (Jatropha ΔfadD 2g/L 29% LB Zhang (2011) Plant thioesterases curcas) optimisted ratio of all FAS cell free 1.5 fold improved N/A N/A Yu (2011) in vitro FAS reconstitution enzymes compared to 'TesA only 51 acyl-ACP thioesterase (U. ΔfadD 0.7g/L 40% LB 0.4% glycerol Lennen (2011) Membrane stresses recorded californica) Acc + FabD + acyl-ACP MG1655 0.1g/gDCW N/A LB Jeon (2011) AccD not included in expression thioesterase (Streptococcus pyogenes) Acc (Acinetobacter BL21(DE3) 0.2g/gDCW 1.89% M9 Meng (2011) No 'TesA calcoaceticus) + MaeB acyl-coA thioesterase FadM + fadR atoC(c) 7g/L (52.4% Minimal 3% Dellomonaco Reversal beta oxidation FadB + FadA crp* ΔarcA calculated by glucose (2011) ΔadhE Δpta Xu) 70% ΔfrdA ΔyqhD calculated by ΔfucO ΔfadD Lennen acyl-ACP thioesterase BL21(DE3) 3.6g/L N/A M9 Zheng (2012) New cytosolic thioesterase (Acinetobacter baylyi thioesterase) FadR + 'TesA ΔfadE 5.2g/L 73% Zhang (2012) 3 days cultivation (He (2013) suggests comparible) 'TesA ΔfadE 3.8g/L 28% (56% Minimal Zhang (2012) FadR regulated 'TesA expression Lennen) FabZ + 'TesA ΔfadD 1.7g/L 39% MOPS 1% glucose Ranganathan Computationally guided modifications (2012) acyl-ACP thioesterase (U. ΔfadD ΔfadE 0.22g/L 15% MOPS 0.4% Youngquist (2013) 400% increase in maintenace energy californica) ΔfadAB glucose phosphate than WT limited acyl-ACP thioesterase (Ricinus ΔfadD 2.1g/L 41% LB 1.5% glucose Zhang (2012) FabD (Streptomyces avermitilis) gave no communis) improvement acyl-ACP thioesterase (R. ΔfadD Δack 2.1g/L 41% LB 1.5% glucose Li (2012) FFA only improved in ΔfadD, Δack Δpta communis) Δpta ΔpoxB ΔpoxB improved at later times TesA ΔfadL 4.8g/L 12% M9 Liu (2012) Two-stage cultivation temperature, ampicillin, on-line extraction method FadR + 'TesA ΔfadE 3g/L 48% M9 He (2013) Recorded higher energy requirement in engineered strain by 13C labelled metabolomics Acc + FabD + acyl-ACP ΔfadD 8.6 g/L 22% Minimal Xu (2013) Modular expression of genes associated thiesterase (Cocos nucifera) + with acetylCoA production and activation, FabA+ FabH+ FabG+ FabI +Pgk and FAS + GapA + AceE + AceF + LpdA Acc + 'TesA △cyoA△adhE 0.2g/gDCW N/A M9 Lin (2013) rFBA guided modifications △nuoA△ndh △pta△dld Thioesterase Fatb1 (Cuphea ΔfadD Δpta 0.26g/L N/A M9 0.5% glucose Torella (2013) Mutated FabF (*) and dynamic 52 palustris) + FabF* + FabBDeg ΔlacY degradation of FabB for octanoate production Acc + 'TesA + FabA + FabD+ BL21(DE3) 3.9g/L 56% Minimal Xu (2014) Dynamic expression of ACC and FAS FabG+ FabI according to malonyl-CoA Acc + 'TesA BL21(DE3) 2g/L N/A M9 MOPS Liu (2015) Malonyl-CoA negative feedback regulated Acc FadR + 'TesA + PopQC for high ΔfadE 21.5g/L 43% Minimal Xiao (2016) Non-genetic cell-to-cell variation FA selection selection method: produced highest titre and production rate repored for FA to date acyl-ACP/acyl-CoA thioesterase MG1655 17.5g/L 28% MOPS phosphate Jawed (2016) Butyric acid specific and highest yield TesBT (Bacteroides limited recorded thetaiotaomicron) FadL + FabZ + acyl-ACP ΔompF 2.3g/L N/A MOPS Tan (2017) Positive relationship between FadL thioesterase (R. communis) abundance, membrane integrity and FA yield PDC (Enterococcus faecalis) cell free 6-fold increase N/A N/A Liu (2017) In vitro GLY - FAS reconstitution compared to unmodified glycolysis 1.10 OBJECTIVE OF RESEARCH

As described during this introduction, the challenges in engineering FAS to enhance FA yield in E. coli are several-fold. They can be summarised as a combination of circumventing the native regulation that controls FAS and membrane homeostasis, overcoming the toxic effects imposed by FA, and in predicting the success in achieving high FA yield from combinatorial engineering approaches. The objective of this thesis is therefore to provide strategies that will address or contribute to solving these challenges through a combination of both fundamental and direct research queries, and which comprise the following chapters:

Chapter 3; An optimisation of cultivation conditions that improve strain performance towards FA production. An assessment was made on the cultivation processes and host genotype for FA production, according to those which are published in the literature, and evaluated systematically for their contribution towards FA yield and productivity. Adaptive evolution was also applied for evaluation of E. coli FA tolerance and productivity. The outcomes of this chapter were further utilised to guide subsequent experiments of this thesis, due to the optimal impact on FA productivity as discussed in section 3.4.

Chapter 4; A characterisation of the genetic and metabolic factors that regulate and control flux through FAS in E. coli. This was achieved by manipulating the levels of all FAS enzymes individually and from a variety of experimental conditions, so that in vivo data could be gathered on the FAS biochemical pathway. This acquisition of in vivo data aimed to contribute to the construction of a predictive and quantitative FAS model, so that the outcomes of systematic engineering on FAS as a regulated process can be predicted more accurately. The results of this chapter are outlined in section 4.3, and will be made available for systems biology computational modellers, with the aim of improving the availability of FAS data for model fitting and validation, for predictive modelling.

53 Chapter 5; Identify and apply alternative routes to FAS that circumvent the native energetic and regulatory limitations on the pathway, so that yield can be improved. Enzymes that bypass the native route to malonyl-CoA synthesis were introduced to E. coli and evaluated for their impact on FA yield. This final chapter also applied the conclusions gained from all previous chapters in the thesis, so that a synergistic effect on increasing FA production in E. coli can be gained from the work on optimising the process, the fundamental insights on engineering specific FAS targets, and from quantitative measure of control on FAS. The outcome of this approach is discussed in section 5.3.

54 2 MATERIALS AND METHODS

2.1 MEDIA PREPARATION AND BUFFERS

All media and solutions prepared for this project are listed in Table 2.1 below. Chemicals were obtained from Sigma-Aldrich (UK), Fisher Scientific (UK) or Merck Chemicals (UK). Media was sterilised by autoclaving for 15min at 120˚C. Once prepared and cooled, appropriate selective antibiotics (100µg ml-1 ampicillan, 50µg ml-1 kanamycin, 20µg ml-1 streptomycin, 50µg ml-1 chloramphenicol) were added. For agar plates, when molten agar media had cooled to ~50˚C, the appropriate antibiotics were added as before. Isopropyl β-D-1- thiogalactopyranoside (IPTG) was added for induction of gene expression under the lac promoter. Buffers and heat labile solutions were filter sterilised using 0.2 μm Millipore syringe filters. The pH of media was adjusted as required using HCl or KOH, accordingly.

Table 2.1. Solutions, media, buffers and gels

Preparation Components Agarose gel 0.8% agarose; 1x TAE buffer 50x TAE buffer 2M Tris-HCl (pH 7.6); 5.7% glacial acetic acid; 500mM NaEDTA SDS-PAGE 12% acrylamide, 0.474M Tris-CL pH8.8, 0.1% SDS, 0.04% N, N, N', N'- tetramethylethylenediamine (TEMED) 0.1% ammonium persulphate (APS), overlaid with stacking gel; 5% acrylamide, 0.126M Tris-CL pH6.9, 0.1%l SDS, 0.1% TEMED, 0.1% APS 5x SDS sample 125mM TrisHCl pH 6.8, 4% SDS, 20% glycerol, 0.1% bromophenol buffer blue, 10% β-mercaptoethanol Tris-Glycine 25mM Tris, 190mM glycine, 0.1% SDS, pH8.3 running buffer Coomassie Blue Solution II- 2% Coomassie 1.25% stock; 25% isopropanol; 10% glacial solutions I, II & III acetic acid. Solution II- 0.25% Coomassie 1.25% stock; 10% isopropanol; 10% glaciel acetic acid. Solution III- 0.25% Coomassie 1.25%; 10% glacial acetic acid. De-stain 10% glacial acetic acid; 10% glycerol 0.5% SOC 2% Bacto-tryptone, 0.5% yeast extract, 10mM NaCl, 2.5mM KCl, 10mM MgCl2, 10mM MgSO4, 20mM glucose Luria-Bertani 0.5% yeast extract; 1% tryptone; 1% NaCl medium M9 salts 10X 6% NaH2PO4; 3% Na2HPO4; 0.5% NaCl; 1% NH4Cl2 1X

55 M9 minimal 1X M9 salts; 1mM MgSO4; 100uM CaCl2; 1uM FeSO4 2% glucose media MOPS salts 10X 83.72 g/L MOPS, 7.17 g/L tricine, 28 mg/L FeSO4·7H2O, 29.2 g/L NaCl, 5.1 g/L NH4Cl, 1.1 g/L MgCl2, 0.48 g/L K2SO; 0.18 g/L (NH4)6Mo7O24, 1.24 g/L H3BO3, 0.12 g/L CuSO4, 0.8 g/L MnCl2, 0.14 g/L ZnSO4 MOPS media 1X MOPS salts; 1.32mM K2HPO4; 2% glucose

MOPS media 1X MOPS salts; 0.44mM K2HPO4; 2% glucose phosphate limited RF1 buffer, RF2 100mM RbCl; 50mM MnCl2; 30mM KAc; 10mM CaCl2; 15% glycerol, buffer 10mM MOPS; 10mM RbCl; 75mM CaCl2; 15% glycerol 5x Isothermal 25% PEG-800, 500mM Tris-HCL pH7.5, 50mM MgCl2, 50mM DTT, reaction buffer 1mM each dNTP, 5mM NAD, 0.8U T5 exonuclease, 160U Taq DNA ligase, 0.1U µl-1 Phusion DNA polymerase

2.2 MICROBIOLOGICAL TECHNIQUES

STERILE TECHNIQUE

The handling of any microorganisms for cultivation purposes was carried out safely and by proper application of the sterile technique, using either a flame Bunsen burner or laminar flow hood. All media, instruments, containers were autoclaved as before and sterile lab gloves used to avoid contamination.

FROZEN GLYCEROL STOCKS

Frozen stocks of strains in this study were prepared by mixing 1ml fresh overnight culture with 500μl sterile 80% w/v glycerol in Nalgene cryogenic vials (Thermo Scientific, UK), and rapidly frozen by transfer to -80 °C freezer.

STRAINS

E. coli strains used in this study are outlined in Table 2.2.

56 Table 2.2. Strains

Strain Use Genotype description Source DH5α Cloning 80lacZΔM15 Δ(lacZYA-argF) U169 Invitrogen (Thermo recA1 endA1 hsdR17 (rK–, mK+) Scientific, UK) phoA supE44 λ– thi-1 gyrA96 relA1 BW25113 Expression Δ(araD-araB)567, Keio collection lacZ4787(del)::rrnB-3, LAM-, rph-1, DE(rhaD-rhaB)568, hsdR514 – – BL21 Expression ompT gal dcm lon hsdSB(rB mB ) Invitrogen (Thermo + S [malB ]K-12(λ ) Scientific, UK) AB1623 LA1-6 parent thi-1, gltA5, ara-14, lacYl, galK2, CGSC collection strain and xyl-5, mtl-i, tfr-5, tsx- 57, str-20 control LA1-6 Thermosensi thi-1, gltA5, ara-14, lacYl, galK2, CGSC collection tive host xyl-5, mtl-i, tfr-5, tsx- 57, str-20, accD6 (ts) ∆fadL Expression Δ(araD- Keio collection in fadL araB)567, ΔlacZ4787(::rrnB-3), λ- deletion host , ΔfadL752::kan, rph-1, Δ(rhaD- rhaB)568, hsdR514 ∆fadD Expression Δ(araD- Keio collection in fadD araB)567, ΔlacZ4787(::rrnB-3), λ- deletion host , ΔfadD730::kan, rph-1, Δ(rhaD- rhaB)568, hsdR514 ∆fadE Expression Δ( araD-araB)567, Keio collection in fadE lacZ4787(del)::rrnB-3, LAM-, rph-1, deletion host Δ(rhaD-rhaB)568, hsdR514,ΔfadE739::kan ∆relA Expression Δ(araD- Keio collection in relA araB)567, ΔlacZ4787(::rrnB-3), λ- deletion host , ΔrelA782::kan, rph-1, Δ(rhaD- rhaB)568, hsdR514

BATCH CULTIVATION CONDITIONS

E.coli strains were grown in LB medium at 37˚C for routine cultivations. Temperature was lowered to 30˚C when grown for protein expression or if temperature sensitive (Ts) strains were used. When minimal M9 or MOPS media was used, cells were cultivated in a two-step fermentation protocol, whereby fresh colonies inoculated in LB with appropriate antibiotic were grown overnight at 37˚C. 500 µL of overnight culture was then used to inoculate 5ml M9 plus antibiotic for 6-8 hrs, to adjust the cells to the change in nutrient conditions. Depending on the density

57 of this pre-culture, 1-4% of this inoculum was transferred to 30ml M9 or MOPS (containing 20g/L glucose) in 250ml Erlenmeyer flasks for the experimental cultivation, and induced for protein expression when OD600nm reached 0.4-0.6. Temperatures and aeration were kept constant throughout incubation, using an Innova44 shaking incubator (New Brunswick Scientific) set to 160rpm, and cultivations were grown for 20-22hrs. When cultivating anaerobically, the headspace of Wheaten serum bottles was sparged for ~1min with nitrogen gas, capped with a butyl septa and secured with aluminum seals and incubated at temperatures and shaking as before. IPTG was used in ranges of 0.1-1mM, anhydrotetracycline (ATc) was used at a final concentration of 2µM. 96-well cultivations were made in clear flat bottomed sterile plates (734-0954, VWR International Ltd., UK), at 200µL total cultivation volume. As before, overnight cultures were used to inoculate the wells. Inoculum was transferred at certain densities using a multichannel pipette. Optical densities were recorded as absorbance at 600nm, in a Tecan Infinite M200 Pro multimode reader.

CONTINUOUS CULTIVATION IN 1.5L BIOREACTOR

Continuous cultivation experiments were performed at the bioreactor suite under the supervision and technical guidance of Mr. James Mansfield (Bioreactor suite technician, Department of Life Sciences, Imperial College London, UK). The bioreactor utilised for this study was a 1.5 L Applikon (Worcestershire, UK) reactor vessel fitted with an Applikon (Worcestershire, UK) control unit, with Bio Console ADI 1025 and Bio Controller ADI 1010. Bioreactor gas composition was analysed using a Tandem LC gas analyser (Magellan Instruments Ltd, Limpenhoe, UK). Media and waste feeds were controlled by Masterflex console drive model 7518-00 (Cole Palmer, Hanwell, UK). Prior to sterilisation, the pH probe was calibrated using buffers at pH 4.0 and pH 7.0. M9 medium delivered to the vessel was automatically adjusted to pH 7.0 by addition of 4.5 M KOH and maintained by the control unit. Temperature was maintained at 30 °C by the passing of heated water through an external bioreactor jacket by the control unit. Compressed air passed through a 0.45 µM PFTE filter was delivered to the reaction vessel via an internal aeration bar; gas

58 flow was controlled by rotamer valves on the Applikon control unit. Culture agitation was maintained by a top drive agitator fitted with two six-bladed Rushton impellers at 400 rpm. Overnight inoculum was introduced through a syringe and kept in batch conditions until cells reached early log phase growth, at which point 0.1mM IPTG

was added at an OD600nm of around 0.4 and steady state was monitored on Applikon BioXpert software build 3.1. Medium flow rates for continuous culture were calculated by timing the movement of the meniscus of the medium through a 5 ml glass pipette before medium was diverted to the reactor vessel. Three bioreactor working volumes were passed through the reactor under constant conditions before the culture was considered as reaching steady state. Samples were then taken by extracting with a syringe into 50mL Falcon tubes for density measurements and fatty acid methyl ester (FAME) analyses.

CONTINUOUS CULTIVATION IN TURBIDOSTATS

Complete construction and assembly instructions, design files (including 3D CAD and circuit boards), and a full bill of materials can be found on the Klavins lab website (http:// klavinslab.org), where this open source equipment was purchased. The turbidostat apparatus was assembled as outlined on the website. The closed system of culture vessels attached to media bottles by tubes were autoclaved for sterilization, foiling over the filter ends and clamping shut the tubes leaving media bottles; batch cultures were grown from colonies in 50mL Falcon tubes in a shaking incubator as before and used to inoculate the Kimble 45500-30 KIMAX 30ml glass tube vessels; LB or M9 minimal media was used; temperatures were kept constant at 30°C or 37°C in a temperature controlled growth chamber; agitation was provided by a metal stirrer inside the culture vessels which was rotated by the motorised magnets within the turbidostat apparatus; aeration was provided by an aquarium Hidom 3W HD-603 airpump and passed through 0.45 µM PFTE filters to both the culture vessels and media bottles. To start the software python 2.7, pySerial and the FTDI VCP serial drivers were installed on a HP Windows laptop, to run the syphon

clamping hardware and record OD600nm readings in the command window. As outlined in the documentation, the command-line python script is run as 'python

59 main.py' from the python directory. OD was kept constant automatically with the software and hardware; by measuring and recording the turbidity of the culture through the laser diode and light-to-frequency converter, the script was set to release the syphon clamp when a certain OD had passed, releasing a small amount of liquid media to dilute to culture and enable growth at a constant OD.

PREPARATION OF COMPETENT CELLS

Chemically competent E. coli cells were prepared from an overnight cell culture, using a rubidium chloride method as described by the Hanahan method (Green and Sambrook, 2018). Cells suspended in RF2 buffer were stored in 100µl aliquots at -80˚C. Electrocompetent cells were prepared using 10% glycerol washes as described by Wu et al.( 2010), and stored in 100µl aliquots at -80˚C.

TRANSFORMATION OF E. COLI

Chemically and electrocompetent cells were thawed on ice for 10mins. 1-5µl (1pg-100ng) of purified plasmid DNA was then added. For heat shock, cells were kept on ice for 15mins, followed by 42˚C incubation for 45secs. Incubation followed on ice for an additional 5mins. For electroporation, cells plus plasmid DNA were added to a 1mm gapped cuvette and electroporated at 1800 volts, resistance of 200Ω and capacitance of 25μF. Both methods of transformation were recovered by adding 500 µl of LB media to the cells for 45min recovery incubation at the permissive temperature (30˚C/37˚C). 100-200 µl of recovery cell culture was then spread on LB agar plates, containing the appropriate antibiotic, in sterile conditions and incubated at the permissive temperature overnight.

60 QUANTIFICATION OF CELL CULTURE DENSITY

Cell culture dilutions were added either to a 1ml cuvette or a 96-well plate, and OD measurements were made of 600nm absorbance using a Tecan Infinite 200 PRO multimode plate reader. A multiplier was implemented to 96-well measurements for an OD equivalent of 1cm path length.

GROWTH ASSAYS

OD measurements were taken every 15 mins for 20hrs, with temperature and shaking kept constant in 96-well plates at 30/37 ˚C, with orbital shaking using Tecan infinite Pro200 instrumentation settings. Growth rates were calculated from the exponential growth phase, which was identified by plotting on a logarithmic scale (Maier, 2009).

2.3 MOLECULAR BIOLOGICAL TECHNIQUES

PLASMIDS

Plasmids assembled or sourced in this study are referenced and outlined in Table 2.3., assembly techniques described in DNA assembly section.

Table 2.3. Plasmids Plasmid name Description Source AccA tesA- accA ‘tesA expression vector This study ecbPACYC-k AccA-ecb-k accA expression vector This study AccA-ecbPACYC-k accA expression vector This study AccA-pJET accA ligated to pJET storage vector This study AccABCD tesA- accABCD 'tesA expression vector This study ecbPACYC-k AccABCD-ecb-k accABCD expression vector This study AccABCD- accABCD expression vector This study ecbPACYC-k AccB tesA- accB 'tesA expression vector This study ecbPACYC-k AccB-ecb-k accB expression vector This study AccB-ecbPACYC-k accB expression vector This study AccB-pJET accB ligated to pJET storage vector This study

61 AccC tesA- accC 'tesA expression vector This study ecbPACYC-k AccC-ecb-k accC expression vector This study AccC-ecbPACYC-k accC expression vector This study AccC-pJET accC ligated to pJET storage vector This study AccD tesA- accD 'tesA expression vector This study ecbPACYC-k AccD-ecb-k accD expression vector This study AccD-ecbPACYC-k accD expression vector This study AccD-pJET accD ligated to pJET storage vector This study ACP-pJET acpP ligated to pJET storage vector This study ACPs-pJET acpS ligated to pJET storage vector This study AMCC tesA- Mcc 'tesA expression vector This study ecbPACYC-k AMCR1 tesA- Mcr1 'tesA expression vector This study ecbPACYC-k AMCR1-his-kan aspC-panD-gabT-Mcr1 expression vector his tag This study added AMCR2 tesA- Mcr2 'tesA expression vector This study ecbPACYC-k AMCR2-his-kan aspC-panD-gabT-Mcr2 expression vector his tag This study added ECB-GFP-Kan Expression vector for BASIC assembly containing This study parts: methylated placeholder GFP, Lac promoter, BioBricks termintor B0015; pUC derived pMB1 origin of replication, kanamycin resistance. Parts were sourced from the Baldwin lab (ICL) as pJET vectors. ECB-GFP-Kan* Expression vector for *MODAL assembly; features as This study ECB-GFP-Kan ECB-K-MCC Mcc expression vector This study ECB-K-MCR1 Mcr1 expression vector This study ECB-K-MCR2 Mcr2 expression vector This study ECB-K-native A- aspC-panD-gabT-Mcr1 expression vector This study MCR1 ECB-K-native A- aspC-panD-gabT-Mcr2 expression vector This study MCR2 ECB-Kan-empty empty expression vector for negative control This study FabA tesA - fabA 'tesA expression vector This study ecbPACYC-k FabA-ecb-k fabA expression vector This study FabA-ecbPACYC-k fabA expression vector This study FabB-ecb-k fabB expression vector This study FabD tesA- fabD 'tesA expression vector This study ecbPACYC-k FabD-ecb-k fabD expression vector This study FabD-ecbPACYC-k fabD expression vector This study FabD-pJET fabD ligated to pJET storage vector This study FabDGH-ecb-k fabDGH expression vector This study

62 FabF tesA - accA 'tesA expression vector This study ecbPACYC-k FabF-ecbK fabF expression vector This study FabF-ecbPACYC-k fabF expression vector This study FabG-pJET fabG ligated to pJET storage vector This study FabH tesA - fabH 'tesA expression vector This study ecbPACYC-k FabH-ecb-k fabH expression vector This study FabH-ecbPACYC-k fabH expression vector This study FabH-pJET fabH ligated to pJET storage vector This study FabI tesA- fabI 'tesA expression vector This study ecbPACYC-k FabI-ecb-k fabI expression vector This study FabI-ecbPACYC-k fabI expression vector This study FabZ tesA- fabZ 'tesA expression vector This study ecbPACYC-k FabZ-ecb-k fabZ expression vector This study FabZ-ecbPACYC-k fabZ expression vector This study FadR tesA- fadR 'tesA expression vector This study ecbPACYC-k FadR-ecb-k fadR expression vector This study FadR-ecbPACYC-k fadR expression vector This study FadR-pJET fadR ligated to pJET storage vector This study jhAMT-tesA-CAM- jhAMT 'tesA expression vector This study pACYC MCC-ecbPACYC-k Mcc expression vector This study MCC-fabD-ecbK Mcr1 fabD expression vector This study MCC-his-kan Mcc expression vector his tag added This study MCR1-ecbPACYC- Mcr1 expression vector This study k MCR1-fabD-ecbK Mcr1 fabD expression vector This study MCR2-ecbPACYC- Mcr2 expression vector This study k MCR2-fabD-ecbK Mcr1 fabD expression vector This study neg tesA- negative control 'tesA expression vector This study ecbPACYC-k neg-ecbPACYC-k empty control expression vector This study pACYC-GFP-CAM Expression vector for BASIC assembly containing This study parts; methylated placeholder GFP, Lac promoter, BioBricks termintor B0015; p15a origin of replication, chloramphenicol resistance. Parts were sourced from the Baldwin lab (ICL) as pJET vectors pACYC-pck-CAM pck expression vector This study pCP20 FLP recombinase expression Datsenko and Wanner (2000) pdCas9-bacteria aTc-inducible expression of a catalytically inactive Addgene bacterial Cas9 (S. pyogenes) for bacterial gene knockdown

63 pET-eGFP pETDuet carrying eGFP Xu et al., (2014) pET-eGFP-fapR pETDuet carrying fapO operator and eGFP and fapR Xu et al., (2014) pET-fapO-eGFP pETDuet carrying fapO operator and eGFP Xu et al., (2014) pET-fapO-eGFP- pETDuet carrying eGFP and fapR Xu et al., fapR (2014) pgRNA-bacteria Expression of customizable guide RNA (gRNA) for Addgene bacterial gene knockdown pIDT-MCC1 Mcc subunits ligated to pJET storage vector This study pIDT-MCC2 Mcc subunits ligated to pJET storage vector This study pJET- pACYC ori p15a origin of replication ligated to pJET storage This study (p15a) vector pJet-aspC aspC ligated to pJET storage vector This study Escherichia pJet-gabT gabT ligated to pJET storage vector This study Escherichia pJet-MCR1 Mcr1 ligated to pJET storage vector This study Sulfulobus pJet-MCR2 Mcr2 ligated to pJET storage vector This study Metallosphaera pJet-panD panD ligated to pJET storage vector This study Escherichia pJet-pck pck ligated to pJET storage vector This study Escherichia pJet-ppc ppc ligated to pJET storage vector This study Escherichia pKD13 Kanamycin resistance cassette flanked by P1 and P2 Datsenko and FRT sites Wanner (2000) pKD46 Red recombinase expression Datsenko and Wanner (2000) pLR RFP tesA CAM pLR RFP 'tesA expression vector (acyl-CoA sensor) This study PlsB tesA- plsB 'tesA expression vector This study ecbPACYC-k PlsB-ecb-k plsB expression vector This study PlsB-ecbPACYC-k plsB expression vector This study PlsB-pJET plsB ligated to pJET storage vector This study RFP pJET RFP ligated to pJET storage vector This study tesA-CAMpACYC ‘tesA expression vector This study tesA-ecb-k ‘tesA expression vector This study tesA-pJET ‘tesA ligated to pJET storage vector This study

64 PRIMERS

All primers utilised in this study are listed in Table 2.4 and were supplied by Integrated DNA Technologies.

Table 2.4. Primers

Primer Sequence (5'-3') Target 5' phosphorylated atgagtctgaatttccttgat AccA for integration F AccA 5' phosphorylated atgagctggattgaacgaatta AccD for integration F AccD AccA F HOM tcattggttcggagcaggtgg AccA genomic check check AccA F pJET tctggtgggtctctgtccgaaggagatataccatgagt AccA with prefix overhang ctgaatttccttgat AccA R HOM ggcgataaaaaagggccaccgaagt AccA genomic check check AccA R pJET cgataggtctcccgagccttacgcgtaaccgtagctc AccA with suffix overhang a AccB F pJET tctggtgggtctctgtccgaaggagatataccatggat AccB with prefix overhang attcgtaagattaaaaa AccB R pJET cgataggtctcccgagccttactcgatgacgaccag AccB with suffix overhang cg AccC F pJET tctggtgggtctctgtccgaaggagatataccatgctg AccC with prefix overhang gataaaattgttattgc AccC R pJET cgataggtctcccgagccttatttttcctgaagaccga AccC with suffix overhang AccD F pJET tctggtgggtctctgtccgaaggagatataccatgag AccD with prefix overhang ctggattgaacgaattaa AccD int check F ccccaaagataaaactggcatc Genomic check for integration AccD int check R cggtgagaatagcaaaagggca Genomic check for integration AccD R pJET cgataggtctcccgagcctcaggcctcaggttcctga AccD with suffix overhang t ACP pJET F tctggtgggtctctgtccgaaggagatataccatgag ACP with prefix overhang cactatcgaagaacgc ACP pJET R cgataggtctcccgagccttacgcctggtggccgttg ACP with suffix overhang at AcpS pJET F tctggtgggtctctgtccgaaggagatataccatggc AcpS with prefix overhang aatattaggtttaggc AcpS pJET R cgataggtctcccgagccttaactttcaataattaccg AcpS with suffix overhang AspC prefix F tctggtgggtctctgtccgaaggagatataccatgtttg aspC with prefix overhang agaacattaccgccg AspC suffix R cgataggtctcccgagccttacagcactgccacaat aspC with suffix overhang cgct CAM-pACYC F ggagacctatcggtaataacagtccaatctggtgtaa CAM-pACYC backbone for MODAL MODAL cttcggaatcttgataccgggaagccctgg assembly CAM-pACYC R taatagtgtttccacgaagtgcgagttcttacccgagc CAM-pACYC backbone for MODAL MODAL gagccacaacttatatcgtatggggctg assembly CAM-pLR R gtaccagattgtcaaatctggtcgtaccagatcgagc RFP under pLR promoter for EMP ccaatagacataagc assembly with 'TesA

65 EMP Mcc R ttaaccgattttgatcag EMP FabD to expression contructs EMP Mcr1 R ttatttttcgatgtaacc EMP FabD to expression contructs EMP Mcr2 R ttaacgtttgtcgatgta EMP FabD to expression contructs EMP pACYC F 5’ gcgctagcggagtgtatactggctt p15a 'TesA EMP assembly phosphoylated EMP pACYC aacgccagcaacgggacaggtaata p15a 'TesA EMP assembly insert R EMP pACYC plus atcgagcctcaaaggatcttcacaacttatatcgtatg p15a 'TesA EMP assembly hom R gggctg EMP tesA F 5’ tcacacaggaaagtactagat p15a 'TesA EMP assembly phosphorylated EMP tesA insert ggacgattccgaagttacaccagat p15a 'TesA EMP assembly R M2 EMP tesA insert R ggacagagacccaccagataatagt p15a 'TesA EMP assembly M1 EMP tesA plus actgagcctttcgttttatttgatgcctggggacttatga p15a 'TesA EMP assembly hom R more gtcatgatttactaaaggc EMP2 mcc R aacgagccttaaccgattttgatcag EMP FabD to expression contructs EMP2 mcr1 R aacgagccttatttttcgatgtaacc EMP FabD to expression contructs EMP2 mcr2 R aacgagccttaacgtttgtcgatgta EMP FabD to expression contructs F KanR-AccA tcattggttcggagcaggtggaactggagtttgactaa Kanamycin AccA cassette HOM tacaggaatacttgtaggctggagctgcttc F KanR-AccD ccccaaagataaaactggcatcgaaccaggttcag Kanamycin AccD cassette HOM acagaaaggtccctatgtaggctggagctgcttc F pACYC plus tctggtgggtctctgtccgaaggagatataccgcgct P15a ori with prefix overhang prefix agcggagtgtatac FabA pJET F ctggtgggtctctgtccgaaggagatataccatggta FabA with prefix overhang gataaacgcgaatc FabA pJET R cgataggtctcccgagcctcagaaggcagacgtatc FabA with suffix overhang ct FabB pJET F tctggtgggtctctgtccgaaggagatataccatgaa FabBwith prefix overhang acgtgcagtgattac FabB pJET R cgataggtctcccgagccttaatctttcagcttgcgca FabB with suffix overhang FabD EMP R tagttcaataaataccct EMP FabD to expression contructs FabD F gaaggagatataccATGACGCAATTTGCA EMP FabD to expression contructs 5'phosphorylated T FabD F pJET tctggtgggtctctgtccgaaggagatataccatgac FabD with prefix overhang gcaatttgcatttgtgtt FabD MP R tgatgcctggcgataggtctcccgagccttaaagctc EMP FabD to expression contructs gagcgcc FabD R pJET cgataggtctcccgagccttaaagctcgagcgccgc FabD with suffix overhang tg FabF pJET F tctggtgggtctctgtccgaaggagatataccgtgtct FabF with prefix overhang aagcgtcgtgtagt FabF pJET R cgataggtctcccgagccttagatctttttaaagatca FabF with suffix overhang FabG F pJET tctggtgggtctctgtccgaaggagatataccatgaat FabG with prefix overhang tttgaaggaaaaatcgc FabG R pJET cgataggtctcccgagcctcagaccatgtacatccc FabG with suffix overhang gc FabH F pJET tctggtgggtctctgtccgaaggagatataccatgtat FabH with prefix overhang acgaagattattggtac FabH R pJET cgataggtctcccgagccctagaaacgaaccagcg FabH with suffix overhang cgg FabI pJET F tctggtgggtctctgtccgaaggagatataccatgggt FabI with prefix overhang tttctttccggtaa

66 FabI pJET R cgataggtctcccgagccttatttcagttcgagttcgt FabI with suffix overhang FabZ pJET F tctggtgggtctctgtccgaaggagatataccttgact FabZ with prefix overhang actaacactcatac FabZ pJET R cgataggtctcccgagcctcaggcctcccggctacg FabZ with suffix overhang ag fadD int check F ggtaattatcaagctggtat Genomic check for integration fadD int check R catcgtccgtggtaatcatttg Genomic check for integration fadE int check F ttaaataattagcggataaagaa Genomic check for integration fadE int check R gtgtaccggataccgccaaaag Genomic check for integration FadR F pJET tctggtgggtctctgtccgaaggagatataccatggtc FadR with prefix overhang attaaggcgcaaagccc FadR R pJET cgataggtctcccgagccttatcgcccctgaatggct FadR with suffix overhang a gabT prefix F tctggtgggtctctgtccgaaggagatataccatgaa gabT with prefix overhang cagcaataaagagtta gabT suffix R cgataggtctcccgagccctactgcttcgcctcatcaa gabT with suffix overhang a IC fadE tesA int F aaaaacagcaacaatgtgagctttgttgtaattatatt SOE for Lac promoter - 'TesA - KanR gtaaacatattgttatgagtcatgatttacta IC fadE tesA int R aaacggagcctttcggctccgttattcatttacgcggct SOE for Lac promoter - 'TesA - KanR tcaactttccgattccggggatccgtcgacc jhDM_AMT 5' ggctgaaggagatatacc EMP jhAMT to 'TesA expression contructs phosphorylated F jhDM_AMT EMP gtccatctagtactttcctgtgtgaagccttagttgatac EMP jhAMT to 'TesA expression contructs R cttta jhDM_AMT pLac aattgttatccgctca EMP jhAMT to 'TesA expression contructs R KanR F ttagaagaactcgtcaagaag Kanamycin resistance cassette KanR R gattgaacaagatggattgc Kanamycin resistance cassette MCC1 R agtgcggaccttcagctctg Mcc subunit from Probionbacterium MCC2 F ctctttcaacatcccgct Mcc subunit from Probionbacterium MCR1 F atgatcctgatgcgtcgtacc Mcr from Sulfulobus MCR1 R tcgatgtaacctttttcaac Mcr from Sulfulobus MCR2 F atgcgtcgtaccctgaaagct Mcr from Metallosphaera MCR2 R cctttttcaaccagcagttc Mcr from Metallosphaera OmpF KO F gacggcagtggcaggtgtcataaaaaaaaccatga Kanamycin resistance cassette targeting gggtaataaataatgtgtaggctggagctgcttcg OmpF on genome OmpF KO R ttttcggcatttaacaaagaggtgtgctattagaactgg Kanamycin resistance cassette targeting taaacgataccattccggggatccgtcgacc OmpF on genome panD prefix F tctggtgggtctctgtccgaaggagatataccatgatt panD with prefix overhang cgcacgatgctgca panD suffix R cgataggtctcccgagcctcaagcaacctgtaccgg panD with suffix overhang aa pck F prefix tctggtgggtctctgtccgaaggagatataccatgcg pck with prefix overhang cgttaacaatggtttga pck R suffix cgataggtctcccgagccttacagtttcggaccagcc pck with suffix overhang PlsB F pJET tctggtgggtctctgtccgaaggagatataccatgtcc PlsB with prefix overhang ggctggccacgaatttact PlsB R pJET cgataggtctcccgagccttacccttcgccctgcgtcg PlsB with suffix overhang R AccA plus OH aagtataggaacttcagagcgctttttacgcgtaacc AccA for integration gtagctca

67 R AccD plus OH aagtataggaacttcagagcgcttttcaggcctcagg AccD for integration ttcctgat R KanR-AccA ggcgataaaaaagggccaccgaagtgaccctttttc Kanamycin AccA cassette HOM agaacttttgcgaaattccggggatccgtcgacc R KanR-AccD cggtgagaatagcaaaagggcagagccagtggcc Kanamycin AccD cassette HOM ctgcccttatcagttaattccggggatccgtcgacc R pACYC plus cgataggtctcccgagccacaacttatatcgtatggg P15a ori with suffix overhang suffix gc R pKD13 tgaagctcacgctgccgcaa EMP reverse primer for Acc integrations RFP pacyc R ctggatactgacttttcacaccgattataaacgcaga RFP under pLR promoter for EMP aaggccca assembly with 'TesA RFP pLR F gaccagatgatactgagcacatcagcaggacgcac RFP under pLR promoter for EMP tgaccaaagaggagaaatactagatggt assembly with 'TesA RFP pLR F 5' gaccagatgatactgagcac RFP under pLR promoter for EMP phos assembly with 'TesA Term R ccagtgtgactctagtagag Terminator in expression constructs TesA F 1 modal ttacttacgacactccgagacagtcagagggtatttat TesA for MODAL assembly tgaactatcacacaggaaagtactagatggacacgt tattgattctggg TesA MP1 modal gcacttcgtggaaacactattatctggtgggtctcttca TesA for MODAL assembly cacaggaaagtactagatggacacgttattgattctg gg TesA MSR modal aagttacaccagattggactgttattaccgataggtct TesA for MODAL assembly ccttatgagtcatgatttactaaa TesA pJET F tctggtgggtctctgtcctcacacaggaaagtactag TesA with prefix overhang TesA pJET R cgataggtctcccgagccttatgagtcatgattta TesA with suffix overhang TesA R 2 modal aggtaataagaactacacgactggatactgacttttc TesA for MODAL assembly acaccgatttatgagtcatgatttactaaa

DNA PURIFICATION AND QUANTIFICATION

DNA was extracted from 0.8% agarose gel using a Gel Extraction kit (Qiagen), soluble DNA was purified using a Clean and Concentrate purification kit (Qiagen) and plasmid DNA was purified from cell cultures using a Mini Prep kit (Qiagen). AMPure magnetic beads (Beckman Coulter) were used for BASIC DNA purifications, using a 96 well magnetic plate. All methods were carried out according to the manufacturer’s instructions. Estimations of DNA fragment size and quantities were made by electrophoresis of samples through a 0.8% agarose gel, and measurement of migration and intensity relative to a 1kb GeneRuler (Fermentas). Gels were run at 100V in 1X TAE buffer for 20-30mins, and visualised under a UV system (GelDoc-It, UVP, Cambridge, UK). DNA concentration was determined using a nanodrop for ng/µl quantification (Tecan Infinite M200). Anticipated fragment sizes were deciphered using SnapGene or AplasmidEditor (ApE) software.

68 STANDARD PCR

PCR reactions were carried out on ~1ng of DNA, using Phusion or Q5 Polymerase (NEB), according to the manufacturer’s instructions. For routine colony screening, DreamTaq polymerase was used on 1 µl of boiled colony DNA in ddH2O. The standard programme used for PCR cycles is described in Table 2.5, annealing temperature varied according to the lowest Tm of the primers used for amplification of DNA fragments, extension time varied depending on polymerase used.

Table 2.5. Standard PCR reaction CYCLE TEMPERATURE (˚C) TIME (S) NO. CYCLES INITIAL DENATURATION 98 60 1 DENATURATION 98 15 30 ANNEALING Tm-1 30 30 EXTENSION 72 30s-60s/kb 30 FINAL EXTENSION 72 420 1 HOLD 16

EXPONENTIAL MEGAPRIMING PCR

Exponential megapriming PCR cloning was applied for inserting single fragments to vector backbones, as described in (Ulrich, Andersen and Schwartz, 2012). Briefly, first step of EMP cloning is to amplify the insert sequence have a 3’ overhang complimentary to the target plasmid. This fragment is then used in a second PCR as a megaprimer, together with a short reverse primer, to exponentially amplify the target plasmid. For the synthesis of the megaprimer, the forward primer is 5’ phosphorylated to facilitate ligation, the reverse primer includes a 20-25bp homology to the target plasmid. During insertion of the megaprimer, a second reverse primer is used that is a reverse complimentary to the region 5’ of the insertion site on the target plasmid, as well as the reuse of the first forward primer (5’ phosphorylated) in order to ensure high product yield.

69 PCR is carried out as described, using 10ng megaprimer and 28 cycles. PCR product is DpnI treated, gel purified, ligated with 200U T4 DNA ligase in 10ul reaction for 1h at room temperature, and transformed into DH5α competent cells.

SPLICE OVERLAP EXTENSION

Primers were designed to amplify a target sequence that included at least 12bp upstream/downstream of the join between adjacent DNA fragments (Figure 2.1). Overhangs were added to the 5’ of each primer, resulting in a 24bp homology between two PCR products which serves as the point of fusion through overlap extension PCR. 5ng per fragment to be fused is included in standard PCR reaction, to proceed for 30 cycles.

DNA ASSEMBLY

E. coli DNA fragments were PCR amplified and assembled into vector products, using the Biopart Assembly Standard for Idempotent Cloning (BASIC, Storch et al., 2014), Modular Overlap-Directed Assembly with Linkers (MODAL, Casini et al., 2014), EMP (Ulrich, Andersen and Schwartz, 2012) or by traditional restriction digestion and ligation. Both BASIC and MODAL rely on computationally derived orthogonal linkers, and integrated prefix and suffix sequences, a schematic description of both is depicted in Figure 2.1. BASIC utilizes a dual digestion and ligation step, followed by purification and sequential DNA driven assembly of parts via 21 overlapping bases that are present in the linkers. While MODAL involves the one-step isothermal assembly method as described by Gibson et al., (2009), whereby primers used to amplify DNA products yield 25-30bp overlap with which the fragment are to be assembled with. Up to 100ng per DNA fragment was added to a 15µl aliquot of the 5x isothermal reaction buffer on ice, following incubation at 50˚C for 60mins. Incubated mixtures of all assembly types were transformed directly to DH5α cells. Colonies were PCR screened using primers to anneal upstream and

70 Figure 2.1. Schematic of MODAL and BASIC DNA assembly methods. Parts for assembly

(genes) are combined with linkers that also comprise prefix and suffix sequences that share homology to each gene. MODAL and BASIC vary by how these parts are generated (PCR or digestion ligation), and how final assembly is carried out (Gibson method or DNA-driven assembly)

71 downstream of the inserted gene(s), followed by sequence confirmation of the purified plasmid DNA (Source BioScience, Nottingham, UK).

DIGESTION AND LIGATION OF DNA

Restriction enzymes (Fermentas) were used to digest approximately 500ng DNA, in a standard reaction volume of 20 µl, containing 10U enzyme, appropriate buffer and DNA to be digested. Double digests were done using 10x buffers appropriate, according to the manufacturer’s guidelines. Samples were incubated at 37 for 1hr, heat inactivated at appropriate temperatures for 20mins and purified. Alternatively, digests were inactivated via gel extraction. Ligations were carried out on linear DNA fragments with compatible ‘sticky-ends’, in a 3:1 molar ratio of insert to vector. T4 DNA ligase (Fermentas) was used, in a total reaction of 10 µl, which contained 5U ligase, appropriate buffer and DNA fragments to be ligated. Samples were incubated at room temperature (~22˚C) for 30mins, and up to 5µl of the ligation were used to transform 50µl competent cells.

CHROMOSOMAL INTEGRATIONS

Integrations to the chromosome were achieved following the Red recombinase protocol as described in (Datsenko and Wanner, 2000). Briefly, E. coli strains harbouring pKD46 were grown at 30°C in 100 ml LB broth and supplemented with

0.2% L- arabinose when OD600nm reached 0.4. Cells were then prepared to be electro-competent, as before. For transformation, ~1ug linear DNA was added to a 50µl aliquot of cells before transferring to a 1ml gapped electroporation cuvette (Yorkshire Biosciences, York, UK), and applying an exponential pulse (25µF, 1800V and 200Ώ) using the Xcell gene pulser (Bio-Rad, Hemel Hempstead, UK). Immediately after pulsing, 1ml of SOC broth was added to the cuvette and the contents transferred to an eppendorf for recovery at 37°C with shaking (250 rpm) for 2 hours, followed by plating onto selective LB agar plates. Chromosomal integrations

72 of ‘tesA and Acc bypass genes were evaluated in Chapter 3 and Chapter 5, respectively.

2.4 PROTEIN AND METABOLITE ANALYSIS

PROTEIN EXTRACTION

Protein extractions from 10-100ml overnight cell cultures were harvested by centrifugation at 3000xg and 4˚C for 15mins. Pellets were resuspended in appropriate assay buffer or media (depending on what lysates were used for), and disrupted via probe sonication using a Soniprep 150 at 5 amplitude microns, alternating 10s on 10s off for 5min. Cell lysates were then centrifuged at 4000rpm at 4˚C for 20mins and supernatant containing soluble cell extract was stored at - 80˚C until use.

PROTEIN CONCENTRATION QUANTIFICATIONS

Standard protein quantification assays were carried out according to the DC protein assay (Bio-Rad) micro-plate protocol. Standard dilutions of bovine serum albumin (BSA) were prepared, ranging 0- 1.5mg/ml, so that a standard curve could be produced when absorbance at 750nm was measured. Protein concentrations of lysates were determined from the standard curve.

MALONYL-COA ASSAYS

These assays were performed using sensor constructs received from the Koffas lab and outlined in Xu et al. (2014). Briefly, fluorescence signal intensity was used as a proxy for intracellular malonly-CoA content due to the use of the FapR sensing

73 molecule in the sensor construct and of promoter binding sites upstream of GFP. BL21 cells containing the sensor construct were grown overnight in LB at 37 °C and 250 rpm agitation. Following this, 10 mL of fresh LB was inoculated with 8% (v/v) overnight culture in 50 mL Falcon tubes and grown at 37 °C, 250 rpm, for approximately 1 h (OD of 0.2 in 96 well plate). Subsequently, 200 μL of cell culture was transferred to a 96-well clear plate (Bio-Greiner, flat clear bottom) using a multichannel pipette. Different amounts of IPTG were added to the cell culture to induce the expression of GFP, together with 10uM of cerulenin inhibitor for malonyl- CoA accumulation. Cells were left to grow at 37 °C with orbital shaking in Tecan Infinite 200 PRO plate reader. Optical density and GFP signal were simultaneously detected every 5-10mins at 600nm, and the excitation and emission wavelengths for GFP were set at 485 ± 20 and 528 ± 20 nm, respectively. Fluorescence was measured in arbitrary units (a. u.) and normalised to cell density so that final measurements are expressed as fluorescence a. u. / OD600nm.

ACYL-COA ASSAYS

Intracellular acyl-CoA assays were performed using a sensor design based on FadR responsive binding sites on the promoter region pLR, as published by Zhang, Carothers and Keasling (2012), that drives the expression of red fluorescence protein in the host. The procedure then monitors fluorescence signal intensity as a proxy for intracellular acyl-CoA concentrations. As in the previous malonyl-CoA sensor experiment, cells containing the acyl-CoA sensor construct were grown overnight in LB at 37 °C and 250 rpm agitation. Following this, 10 mL of fresh LB was inoculated with 8% (v/v) overnight culture in 50 mL Falcon tubes and grown at 37 °C, 250 rpm, for approximately 1 h (OD of 0.2 in 96 well plate). Subsequently, 200 μL of cell culture was transferred to a 96-well clear plate (Bio-Greiner, flat clear bottom) using a multichannel pipette. Different amounts of IPTG were added to the cell culture to induce the expression of RFP. Cells were grown at 37 °C with orbital shaking in Tecan Infinite 200 PRO plate reader. Optical density and RFP signal were simultaneously detected every 5-10 min at 600 nm, and the excitation and emission wavelengths for GFP were set at 535 ± 20 and 620 ± 20 nm, respectively.

74 Fluorescence was measured in arbitrary units (a.u.) and normalised to cell density so that final measurements are expressed as fluorescence a. u. / OD600nm.

LC-MS SELECTED REACTION MONITORING

Analytical measurements of specific peptides of E. coli FAS were performed by Mr. Mark Bennett (Mass Spectrometry Service Manager; Department of Life Sciences, Imperial College London, UK). Pre-processing of measurement data was also done by Mr. Mark Bennett; sample preparation and subsequent data evaluation were carried out by the author of this thesis. Samples were prepared as described in Schumacher et al., (2014). Prior to sonication, cell pellets were suspended in 1ml media containing 7M urea 1mM TCEP (tris(2- carboxyethyl)phosphine). Sonication was as described previously, and supernatant recovered at 4˚C for 45mins at 15,000g. 40µl of urea soluble protein was digested with 4µg trypsin (sequencing grade from Promega Ltd.) in 50mM NH4HCO3 & 1mM TCEP at 37˚C overnight. Digestions were stopped with 1mM formic acid. Trypsin digested peptides were analysed using LC-MS in triple quadrupole mode, on an ABSIEX 6500QTrap MS coupled to Eksigent nano 400 LC system. Transition peaks were assessed using Skyline software, where 3–5 transitions per peptide were selected for analyis. In the final method, on average 3 peptides per protein were used for identification and quantification of the corresponding protein. Signature peptides for each protein are listed in Table 2.6. Protein quantifications were performed on relative peak intensities of the analysed peptides, and normalized against crude protein quantifications estimated using BSA standard. Each normalised transition area relative to protein quantification were assessed for distribution against their ratios of all the transitions for that peptide. Once transition ratios were evaluated, the average of a representative peptide transition, taken from technical replicates, was incorporated into relative quantifications in Chapter 4.

75 Table 2.6. Peptides used for relative protein abundance quantification via LC-MS (SRM)

Protein Peptide sequence Retention time (mins) AccA AIVGGIAR 9 AccA IDSLTAVSR 10 AccA LIDSIIPEPLGGAHR 21 AccB AFIEVGQK 11.5 AccB SPMVGTFYR 14 AccC TNVDLQIR 12 AccC IAAGQPLSIK 12 AccC SGFIFIGPK 20 AccD LASILAK 11 AccD SEFLIEK 13 AccD ALIGFAGPR 16 ACP IIGEQLGVK 12.7 FabA ALGVGEVK 8 FabA GELFGAK 8.8 FabA FTGQVLPTAK 12 FabB ELAAIR 7.6 FabB VGLIAGSGGGSPR 9.8 FabB AVGPYVVTK 10 FabD LAVELAK 11 FabD QLYNPVQWTK 17.5 FabF TIFGEAASR 10 FabF FAGLVK 9.9 FabF ASTPLGVGGFGAAR 16 FabG AIAETLAAR 9.6 FabG IALVTGASR 10.5 FabG AGILAQVPAGR 14 FabH IIGTGSYLPEQVR 17 FabH YALVVGSDVLAR 20.4 FabI ASLEANVR 6.5 FabI VNAISAGPIR 11 FabI EGAELAFTYQNDK 15.1 FabZ VLDFEEGR 12 FabZ LEPGELYYFAGIDEAR 26 FadR SLALGFYHK 14 FadR NLPGDLAIQGR 15 FadR LLLDAGAR 16 PlsB YVFIHGGPR 9.8

76 PlsB AELFLR 14 PlsB TLQLLAAGAR 15 TesA GFQPQQTEQTLR 10.8 TesA YNEAFSAIYPK 17 TesA WVLVELGGNDGLR 24.5

HPLC

For analysis of metabolite products, 1-2ml samples from cell cultures (performed according to batch cultivation conditions as described) were centrifuged at 10,000rpm for 10min, supernatants were placed into glass screw top vials (Agilent) and measured in an Agilent HPLC system using an Aminex HPX-87H column. Sample peaks were then analysed and quantified against their appropriate standard curves.

GC-MS

Fatty acid methyl esters (FAME) were prepared following protocols as described by Politz, Lennen and Pfleger (2013). Briefly, 2.5ml cell culture was transferred to glass tubes, adding appropriate internal standard (25-50µg C20:0 fatty acid) to all samples. In a fume hood, 100µl acetic acid was added to each culture to acidify. 5ml 1:1 chloroform:methanol was added and vortexed for 1-2mins. To separate the organic chloroform layer, the tubes were centrifuged for 10mins after which the bottom chloroform layer was transferred to a new glass tube via glass Pasteur pipette. Chloroform extracts were dried under nitrogen stream until fully evaporated, at which point 500µl HCL-methanol was added to methylate fatty acids overnight at 50 ˚C. Following this, the reaction was quenched at room temperature with 5ml 100mg/ml NaHCO3. 500µl hexane was added, the mixture vortexed and centrifuged for 10mins. Upper hexane phases were transferred to 2ml glass GC vials (Agilent). An additional 500µl hexane was added to mixtures as before, vortexed & centrifuged. The second hexane extract added to the corresponding GC vial.

77 ATP ASSAYS

Intracellular ATP was measured using a bioluminescence kit supplied by Sigma (Roche diagnostics, UK) following the manufacturer’s instructions. Briefly, cells were 5 8 grown to an OD600nm of ~ 1.0 and diluted to concentration of 10 -10 cells/ml. Cell suspension was transferred to 9 volumes of boiling 100mM Tris, 4mM EDTA, pH 7.75, and incubated at 100 ˚C for 2 min. ATP standards were prepared insterilised dH20, in concentrations ranging from 10-5 - 10-10 M. 50 µl of sample/standard was added to wells of clear Greiner 96-well plates, followed by the addition of 50 µl luciferase reagent. Luminescence was measured using a CLARIOstar plate reader from individual wells, subtracting the blank from raw data and calculating ATP concentrations from a plot of the standard curve data.

DIBAC4(3) ASSAYS

Protocol adapted from Clementi et al. (2014); 2mL cells were grown to log phase in LB media and induced with 0.1mM IPTG for ~2 hours, at which point they were harvested and washed twice in the same volume 1X PBS. Cells were diluted 1:10

with 1X PBS plus 1mM EDTA, so that final cell concentration was 0.8-1 OD600nm. The

bis-oxonol dye, DiBAC4(3), was then added to cells at a final concentration of 500µM. 200µl of dyed cells were transferred to individual wells for measuring basal level of depolarization. Carbonyl cyanide m-chlorophenyl hydrazone (CCCP) was added to the remaining dyed cells at a concentration of 100µM, before transferring 200µl of these treated cells for depolarization measurements. Fluorescence was monitored at 410 nm excitation and 516nm emission wavelengths, and increased fluorescent rates indicating an accumulation of anionic dye in depolarized cells, was measured in arbitrary units (a. u.). All experiments were performed in triplicate.

78 STATISTICAL ANALYSIS

To determine significance of difference between experimental groups, data was analysed using GraphPad Prism v6. One-way analysis of variance (ANOVA) was applied and corrections for multiple comparisons were made using Tukey’s post-hoc test. Significance was accepted at P<0.05.

79 3 IMPROVING ENGINEERED STRAIN PERFORMANCE AND

OPTIMISING CULTIVATION CONDITIONS FOR FATTY ACID

SYNTHESIS (FAS) IN E. COLI

3.1 INTRODUCTION

ENGINEERING E. COLI FOR FATTY ACID OVERPRODUCTION

To make FA production in E. coli a process which is optimised for industrial application, several features of the cultivation conditions and engineering design must be characterised and validated for the scale-up requirements of commercial applications (Jackson, 1985). Because of the high energy and carbon investment associated with generating acyl-ACPs, they are reserved and regulated solely for the synthesis of membrane phospholipid and lipid A (Magnuson et al., 1993), and as such very little ‘free’ fatty acid (FFA) is produced by wild-type E. coli. However, in cases where FFAs are produced, this is facilitated by the periplasmic thioesterase enzymes native to E. coli (TesA and TesB) which have a catalytic preference for hydrolysing the acyl-CoA thioester as opposed to acyl-ACP thioester (Spencer et al., 1978). It has therefore been proposed that these enzymes function in affiliation with FadD of the degradation cycle (Lennen & Pfleger, 2012), which is linked with FadL in transport mechanisms across the cell membrane (Black and DiRusso, 2003).

A key aspect in engineering FAS in E. coli, as mentioned in section 1.9, is expressing an acyl-ACP specific thioesterase which will terminate the elongation cycles at a specific acyl chain length according to its substrate specificity, prevent acyl-ACP negative feedback regulation and release FFA as its product (Figure 1.6). Leaderless and cytosolic thioesterase (‘TesA) expression is commonly applied to strain modifications that are focused on improving FA production in E. coli, and has resulted in yields and productivities that vary according to the genetic or process

80 engineering strategy applied (Table 1.1). The expression of cytosolic acyl-ACP thioesterase enzymes may also be heterologous, with varying ranges of chain length specificities and activities (Zheng et al., 2012; Youngquist et al., 2013; Zhang et al., 2011; Li et al., 2012). However, when overexpressing any thioesterase careful consideration must be given to factors which influence expression strengths, to avoid an imbalance between the incorporation of acyl-ACP to phospholipids and the toxic build- up of FFA (Shin, Kim and Lee, 2016; Desbois and Smith, 2015). This control can be introduced in the form of promoter or RBS strength, plasmid copy number or external inducer concentration (Jones, Toparlak and Koffas, 2015), and promotes a trade-off between optimal growth rates in the microbial population with the production of commodity chemical in cultivation. The challenge of balancing thioesterase expression is essential for FA overproduction, as it aims to avoid outcompeting essential processes such as phospholipid synthesis in E. coli, while finding the optimal expression level for maximised FA yield.

The E. colil host genotypes for FA overproduction are also interchanged routinely in the literature, varying in deletions in the degradative pathway genes; fadD, fadE or fadL. These knock-outs are in place to enhance engineering efforts towards FA overproduction, allowing for the accumulation of FFA in the media for harvesting due to removal of degradation enzymes (Voelker and Davies, 1994). Although it has been highlighted before that when cultivated in glucose FAD genes are under catabolite repression and therefore inactive (Pauli, Ehring and Overath, 1974), these deletions are now common practice in E. coli FAS engineering.

Additionally, engineering strategies in E. coli which aim to increase carbon flux of FAS have included the overexpression of transcription factor FadR, and of the rate- limiting enzyme Acc. Both versions of these strain modifications have been reported to enhance FA yield compared to negative controls (Davis et al., 2000; Lu et al., 2008; Lennen et al., 2010). Furthermore, modifying levels of FAB enzymes in combination with a thioesterase have led to improvements in FA production rates (Yu, et al., 2011; Zhang, et al., 2012). The deletion of genes responsible for acetate and ethanol formation have also been applied in successfully improving malonyl-CoA titres for increasing FA yield, due to increasing the availability of acetyl-CoA for Acc (Zha et al., 2009).

81 In addition to metabolic engineering strategies, there is also the potential to regulate and manipulate control of FA productions rates and composition through various cultivation approaches (Ledesma-Amaro et al., 2016). Because FAS is regulated and responsive to changes in environmental conditions such as temperature, growth rate, aeration level, and substrate availability (Marr and Ingraham, 1962; Li and Cronan, 1993; Levanon, San and Bennett, 2005; Gerosa et al., 2015); both genetic and process engineering strategies should be combined to balance the metabolic and physiological needs of the cell with FAS and FA overproduction.

FA overproduction from E. coli is an area of research that has received much contribution over the years (Table 1.1), however limitations and variations to achieving highest optimal yields of FFAs in engineered E. coli are still present. The highest reported yield reached 21.5g/L when FadR, ‘TesA were overexpressed in a ∆fadE genotype, in combination with a screening method that selects for high- performing non-genetic variants in fed batch (Xiao et al., 2016). There are fundamental questions that remain unchallenged in optimising the processes around FA production in E. coli, that have gone undeveloped or are inconsistent in published data. The limitations in addressing these challenges can be drawn from the shortcomings attributed to understanding the outcome of manipulating regulatory mechanisms in FAS, some of which remain unknown (Parsons and Rock, 2014; Fujita, Matsuoka and Hirooka, 2007; Magnuson et al., 1993), and without a parameterised kinetic model to guide efforts and predict the effects of combinatorial genetic perturbations this will remain a huge challenge (Khodayari and Maranas, 2016; Jahan et al., 2016). This is highlighted further by the fluctuations and relatively low percentage of the theoretically obtainable yield of FFA production in E. coli, compared to the production of other commodities such as alcohols, drug precursors, flavonoids or aromatic molecules using microbial catalysts (Antoni, Zverlov and Schwarz, 2007; Du and Shao, 2012). This suggests the need to develop an optimised genetic background and cultivation system for FAS, which is the objective of this chapter.

82 PROCESS OPTIMISATIONS

Cultivation processes such as growth phase (Ihssen and Egli, 2004), oxygen availability (Weusthuis et al., 2011), temperature (Liu et al., 2012), preventing the build-up of toxic intermediates, and nutrient availability play a huge role in the metabolic performance of an organism and are inherent to any microbiological commodity and FAS process. These environmental states, which can be modified during cultivation, are significant in regulating FAS and can be harnessed to have positive impact in productivity and yield (Youngquist et al., 2012; Jawed et al., 2016). The inhibitory effect of FFAs in culture medium establishes the requirement for fed- batch or continuous fermentation processes for optimal cultivation for FAS, or the use of solvent overlay for extraction (Rosenberg et al., 2003; Lennen and Pfleger, 2013; Sherkhanov, Korman and Bowie, 2014). It is well known that continuous cultivation techniques have advantages over batch cultivation for chemical production (Humbird and Fei, 2016; Mukherjee, Das and Sen, 2006; Heerden and Nicol, 2013), these advantages relevant to E. coli for FA production include; the ability to optimise aeration for growth rate, maintain stable pH during production, and maintain populations at specific growth rates.

Solvent overlay is widely used during cultivation to the prevention of toxic build-up of chemicals in the media, and has been applied to FA production in E. coli with varying success (Steen et al., 2010; Lennen and Pfleger, 2013; Sherkhanov, Korman and Bowie, 2014; Haushalter et al., 2015) . The problem with solvent overlay use is the cost of application to industrial style cultivation vessels, and the overall sustainability of the process. A semi-continuous process has been suggested by Lennen, (2012), where the alkane layer containing the extracted FFA is passed over a catalyst bed for derivatisation and recycled back for reuse. While the application has not been tested to date, it proposes the minimisation of large-scale cultivation costs, enabling a more sustainable and economic process of FA production via E. coli.

Additionally, since membrane fluidity alters in response to cellular influences such as stress and toxicity, the condition of the phospholipid bilayer plays a role on the rate of phospholipid synthesis and composition, to compensate for any instability incurred (Nicolaou, Gaida and Papoutsakis, 2010). An example of this membrane adaptation is the change the ratio of saturated to unsaturated fatty acids present in response to

83 temperature (Fulco, 1974). An increase in temperature causes an increase in membrane fluidity initially, which is counteracted by the synthesis of lipids containing more saturated fatty acids. Similarly, when E coli are subjected to lower temperature, they incorporate more unsaturated fatty acids into phospholipids, of which FabF activity is the key control measure (Garwin, Klages and Cronan, 1980; Zhang and Rock, 2008). This establishes a kink in the lipid bilayer is such that fluidity remains at lower temperatures and supports a level of diffusion across the membrane; the exchange of nutrients and metabolites can therefore still proceed. Furthermore, Ingram, (1977), demonstrated that E. coli can alter the composition of fatty acids in lipid production, when grown with alcohol. It has therefore been established that membrane functions are protected by increasing saturated FA production for restoring order in the membrane, whereas unsaturated FA minimize the effects of solvents on bilayer structure by the isomerisation of cis into trans FA which increases rigidity (Eberlein et al., 2018). The degree of unsaturation is therefore an important factor not only physiologically but in many industrial applications which require specific chemical configurations (Biermann et al., 2011). Modifying FA composition of E. coli and understanding the factors responsible for this is therefore critical in gaining a well-rounded understanding of control in FAS flux, and in promoting its practical application in industry.

BALANCING PROCESS OPTIMISATIONS WITH PHYSIOLOGICAL REQUIREMENTS

At the minimal level, regulation is in place to optimise metabolism in response to fluctuating environments and in the context of this thesis, it can be categorised as processes which affect FAS on both a local and global scale. Local regulation takes place in the form of the allosteric feedback, translational and transcriptional control and is mentioned in Chapter 1, and therefore will not be described in detail here. Global regulation is in place when many modifications are coordinated in response to stimuli, acted on by global regulators (Chubukov et al., 2014; Shimizu, 2013). Global regulators, such as the nucleotide based secondary messengers (p)ppGpp and cyclic-AMP, are pleiotropic regulators of key molecules and are known to contribute to fatty acid regulation (Janßen and Steinbüchel, 2014b). (p)ppGpp negatively

84 regulates the FabH promoter, Acc subunits AccBC, transcription factor FadR and acyltransferase PlsB during stringent control, and therefore inhibits FAS (Heath, Jackowski and Rock, 1994; My et al., 2013). While CRP-cAMP positively regulates FAD genes under catabolite repression, so that expression of FadL, FadD and FadH, and fatty acid catabolism, are upregulated when no other carbon sources are available (Feng & Cronan, 2012). Both global regulators serve as a response to amino acid and carbon source availability, to constrain or enhance points in FAS as relevant. ppGpp is synthesized by RelA in E. coli, and can be further hydrolysed or synthesized by SpoT (Magnusson, Farewell and Nyström, 2005). The accumulation of ppGpp and the incurring effects on cellular processes is known as the stringent response (Stent and Brenner, 1961).

For FAS to be optimal, malonyl-CoA and NADPH levels must be maintained so that the rate of biosynthesis is coordinated with the rate of phospholipid production and cell growth (Xu et al., 2013; Jiang and Cronan, 1994). Carbon flux through central metabolism affects both malonyl-CoA and NADPH availability, but on a wider scale oxygen availability is also critical (Feng and Cronan, 2012). Lipid production correlates with cell division and, anaerobically, E. coli grow at a slower than aerobically due to less ATP availability from fermentative pathways. A lower rate of FAS anaerobically may be partly explained by the fact that malonly-CoA levels are dependent on Acc expression, which is subject to autoregulatory control under growth rate (Li & Cronan, 1993). Since the transcription of subunits AccB & AccC positively correlate with growth rate, a lack of oxygen means less ACC expressed, lowering the rate of malonyl-CoA formation. Further, growth rate is associated with the ability of PlsB to incorporate long-chain acyl-ACPS into phospholipids, thereby alleviating any inhibition of ACC. Both ACC and PlsB affect malonyl- CoA formation and are transcriptionally inhibited by ppGpp (Janßen & Steinbüchel, 2014), which links to growth and oxygen availability. NADPH generation in E. coli is largely derived from the PP pathway or from NADP+ dependent isocitrate dehydrogenase in the TCA cycle. Further, E. coli possess global regulators FNR and ArcA that activate the expression of genes based on redox state and oxygen availability (Levanon, San, & Bennett, 2005). Pfl is anaerobically activated by ArcA, which redirects flux from TCA to ethanol and acetate production, as well as blocking TCA at 2- oxoglutarate (Levanon et al., 2005). Therefore, during anaerobic conditions a

85 combination of flux re-routing, lower Acc expression, less NADPH generation from TCA cycle and lower growth rate contribute to an overall lower flux through FAS.

3.2 AIMS AND OBJECTIVES

The challenge in engineering FAS to enhance FA yield is largely in circumventing the native regulation that controls membrane homeostasis and lipid synthesis, and that which is further embedded in the response of the cell to its environment, growth phase and genotype. The objective of this chapter was to identify and characterise the key processes and genetic manipulations that enhance FAS in E. coli, so that further optimisations can be applied to enhance production for commodity chemical applications. This study has taken guidance from published data in outlining inconsistent experimental parameters for further development. It has taken a combinatorial ‘top-down, bottom-up’ approach to experimental design, where targeted manipulations were tested in varying conditions, as well as applications in adapted evolution using continuous cultivation to arrive at a strain optimised for FA tolerance and production. Overall the objective was to gain an understanding of the key processes and genetic manipulations required for optimal FA yield in E. coli.

3.3 RESULTS AND DISCUSSION

EVALUATING THIOESTERASE EXPRESSION AND GENOTYPE

Firstly, the effect thioesterase expression has on cell fitness and on the performance of additional protein expression was evaluated, where a dual plasmid expression system was used to monitor GFP expression in combination with ‘TesA in Figure 3.1. The pupose of this experiment was to assess fluorescent measurements from GFP expression as a proxy for the efficiency of dual protein expression with ‘TesA, and to take into consideration the optimal experiment parameters for these conditions, such as IPTG induction level, cell density during induction and protein expression when FAs are produced. A low copy thioesterase vector and a high copy GFP expression

86 vector were co-transformed in the BW25113 host (denoted GFP plus ‘TesA in legend) and monitored for growth and fluorescence in Figure 3.1. The experiment was done in minimal media at 30°C and in the BW25113 host only, with the corresponding negative control comprised of GFP high-copy expression only (denoted GFP in legend). These experiments illustrate the effect inducing ‘TesA expression at varying IPTG levels (0.01-0.1mM), and alternate starting ODs, has on GFP fluorescence and growth. In Figure 3.1 (i), ‘TesA coexpression was found impact growth rate positively when induced at lower concentrations 0.01mM and 0.05mM IPTG, while high induction of both proteins 0.1mM lowered growth rate relative to all other conditions. Figure 3.1 (ii) illustrates that induction at higher cell density alleviates the negative impact of 0.1mM ‘TesA and GFP expression observed previously, which demonstrates the cell growth fitness maintained in most cases when FAs are produced in the culture and informs parameter conditions for further experiments. Figure 3.1 (iii-iv) then illustrate fluorescence readings from the same conditions, and demonstrated a decrease in GFP fluorescence when combined with ‘TesA, induced at both low and high cell densities. This decrease in fluorescence is correlated to inducer strength at the lower cell density, compared to higher cell density where, except for the highest IPTG concentration, the effect on fluorescence does not vary as much between inducer strengths. These results reveal that higher cell densities are more stable for dual expression of GFP and ‘TesA during FA production, and that lower induction levels are more effective for both growth and fluorescence in all cases.

87 i) ii)

high cell density

iii) iv)

Figure 3.1. Average and normalised (i-ii) growth curves and (iii-iv) fluorescent emissions

from high-copy GFP expression, and dual low-copy ‘TesA plus high-copy GFP expressions.

All in BW25113 host, IPTG concentrations varied (0.01-0.1mM) at cell densities high to low

(0.5- 0.25 OD600nm). Performed in 96-well, in duplicate; shaded regions represent SEM.

88 Further analyses of cultivation conditions progressed by monitoring the effect inducer strength had on FA production in the BW25113 host, when temperature and media type were altered between rich (LB) and minimal (MOPS) (Figure 3.2). This was to gather information on what the optimal conditions for FA production, of which the fatty acid methyl ester (FAME) quantifications illustrate that specific productivity is highest for ‘TesA in BW25113, when in MOPS minimal media cultivated at 30°C, and when 0.1mM or 0.5mM concentrations of IPTG is used to induce protein expression. These parameters were therefore chosen as the conditions for further experiments on FAS evaluation in subsequent results chapters.

89 0.14 30°C 37°C 0.12

0.1 LB

0.08 MOPS

0.06

0.04 mg/ml FAME per OD

0.02

0 0.1 0.05 0.01 0.1 0.05 0.01 mM IPTG

Figure 3.2. FAME quantifications normalized to OD from low-copy ‘TesA expressions in

BW25113 genotype; cultivated at varying temperature, media type, and IPTG induction level. Performed in 250ml shake flasks in triplicate, error bars represent SEM.

90 The study continued with an evaluation of the genotype host, where all FAD deletion genotype variables reported to have been used in the literature were compared for performance towards cell density and FA production in Figure 3.3. This data suggests that ∆fadE is the better FA producer when induced at 0.1mM at 30°C, in MOPS minimal media.

Further investigation continued on questioning the effect of ’TesA copy number towards FA content (Figure 3.4). This experiment included an analysis of leaderless and IPTG-inducible ‘TesA which was integrated into the ∆fadE locus of the chromosome, compared to a low-copy expression plasmid from previous observations, as well as a high copy expression plasmid. These results show that induction does not lead to any increase of FA content in the ‘TesA integrated strains compared to multi-copy vectors, most likely due to the inefficiency or a lack of effect one chromosomal copy has towards FA production. High-copy ‘TesA induced at 0.1mM IPTG performed better than uninduced, low-copy induced ‘TesA vectors demonstrate highest FA yield (albeit with high variability). This experiment further highlights the potential of low-copy ‘TesA expression in ∆fadE for FA overproduction.

A final analysis of thioesterase performance was performed assessing the effect of expressing low-copy ‘TesA at 0.1mM IPTG in minimal media and 30°C on growth rates, in the various genotypes previously examined (Figure 3.5). These data show that cell growth is best in BW25113 and ∆fadD when inducing ‘TesA for FA production, which was taken into consideration in further experiment design, following the reasoning that efficiently growing cells can translate to higher specific productivities during continuous cultivation studies later.

To summarise, this preliminary study of the first chapter of experiments examined the effect of dual plasmid expression systems on FA production (Figure 3.1), induction level on productivity and fitness (Figure 3.2 and 3.5), and copy number on FA yield (Figure 3.4) as a means of interpreting ‘TesA expression which is balanced with growth rate. Obtaining a balanced ‘TesA expression with the rate of phospholipid synthesis is important when optimising for FA production, due to the shared use of acyl-ACP substrate between ‘TesA and the acyltransferase PlsB, along with the essential maintenance of phospholipid synthesis rates in E. coli. An

91 0.35

0.3

0.25

0.2

0.15 mg/ml FAME per OD 0.1

0.05

0 BW25113 ΔFadL ΔFadD ΔFadE

Figure 3.3. FAME quantifications normalized to OD values from low-copy ‘TesA expression

in genotypes BW25113, ∆fadL, ∆fadD and ∆fadE; cultivated at 30°C and 0.1mM IPTG induction level in minimal media. Performed in 250ml shake flasks in triplicate, error bars represent SEM.

92 0.16 1.8

0.14 1.6 FAME mg/ml

0.12 1.4 OD 600nm 1.2 0.1 1 0.08 0.8

0.06 OD600nm FAME mg/ml 0.6 0.04 0.4 0.02 0.2 0 0 uninduced induced uninduced induced uninduced induced 'TesA integra`on 'TesA high-copy 'TesA low-copy

Figure 3.4. FAME quantifications and OD values from integrated, high-copy and low-copy

‘TesA expression in ∆fadE genotypes; cultivated at 30°C in minimal media and 0.1mM IPTG induction, in 250ml shake flasks in triplicate, error bars represent SEM.

93 Figure 3.5. Average growth curves from low-copy ‘TesA expression in BW25113, ∆fadL,

∆fadD and ∆fadE genotypes; cultivated at 30°C, in minimal media, and 0.1mM IPTG induction level. Performed in 96-well in duplicate for each strain, shaded regions represent

SEM.

94 imbalance was noted in Figure 3.1 (i) when cells were highly induced for ‘TesA + GFP production before reaching an optimal cell density, both growth and productivity were negatively affected compared to all other conditions (Figure 3.1 (iii)). This effect was absent when cells were induced at a higher cell density, potentially due to a stabilised phospholipid rate in cells further into the exponential phase of cell growth.

Similarly, Figure 3.2 gives further insight into how specific productivity is altered according to induction level and temperature across two media type variations. We see that at 30°C in minimal media, when growth is comparably slower than at 37°C in rich media, FAME yield per OD is higher at induction concentrations 0.1mM and 0.05mM IPTG. This is likely due to the known stabilisation of protein expression and folding at lower temperatures (Baneyx, 1999). Since induction at 0.01mM IPTG concentration does not give similar high yields at 30°C in minimal media as in rich, it can be said that this media composition does not support high rates of FA production when induced at a low IPTG concentration - possibly due to PlsB outcompeting the levels of ‘TesA for acyl-ACP substrate, therefore this expression level is not appropriate for FA overproduction. When cultivated at 37°C in rich media, however, specific productivity increased with decreasing IPTG concentration (Figure 3.2). This could be due to higher growth rates enabling faster protein expression turnaround, therefore requiring smaller inducing levels to obtain an increase in FA compared to OD, as seen from this experiment. This experiment further highlighted parameters required for optimal FA yield during ‘TesA expression as 30°C cultivation temperature, minimal media, and 0.05 - 0.1mM IPTG concentrations. These parameters were applied to further experiments for evaluation such as Figure 3.4, where the importance of balancing ‘TesA expression for productivity was outlined again. This experiment demonstrated that one chromosomal copy is insufficient for FA production, high-copy expression improves yield, while low-copy expression gives an optimal (though fluctuating) FA production compared to cell density.

The effect of ‘TesA expression on growth measured among all genotypes (Figure 3.5), was observed as most efficient in ∆fadD host, followed by BW25113. The negative effects on growth in ∆fadL as expression host could be linked to inefficient export of FA across the membrane causing cell rupture. While the effect from ∆fadE is not as immediately apparent, it is assumed that a build-up of acyl-CoA is inhibiting normal cell growth by the action of FadR, which is responding to acyl-CoA levels and

95 in response deactivating transcription of FAS genes. The common feature of both higher-yielding strains is that they retain the FadL protein, which may be a key factor in optimising ‘TesA expression with growth for FA production and will be taken into consideration during further interpretations of this thesis.

OPTIMISING MEDIA FOR FATTY ACID OVERPRODUCTION

Fatty acid quantifications are reported in the literature to have been measured from a range of media types and nutrient concentrations (Table 1.1), without any aparent consensus to which of these conditions are optimal for FA overproduction when combined with protein expression. Therefore, this section of results evaluates the effects of a range of media adjustments on FA production in combination with ‘TesA expression and genotype. Pantothenic acid supplementation promotes an increase in CoA availability which could promote flux through FAS, since acetyl-CoA generation is subject to the availability of CoA as an intermediate, and is therefore a prerequisite to FAS (Vadali, Bennett and San, 2004). Phosphate limitation has also been reported as a viable method of initiating FA overproduction in the cell, due to the effect this limitation has on NADPH availability (Shimizu, 2013b; Youngquist, Rose and Pfleger, 2013), as well as an autoinduction media composition, which promotes an optimal expression level balanced with metabolic requirements (Studier, 2005). As such, the effects of various media adjustments on FA production yields were examined in Figure 3.6. As before, this was carried out in all genotypes harbouring low-copy ‘TesA expression (Figure 3.6 (i-iv)), and it was found that where pantothenic acid supplementation improved overall FA yield, OD was also improved. This lead to a lower specific productivity, overall, in pantothenic acid supplemented strains compared to non-supplemented controls, and as such was deemed not an ideal system for FA overproduction during this study. Similarly, for phosphate limitation; no improvement in specific productivity was observed in any host strain. However, when phosphate limitation was combined with pantothenic acid supplementation, notable improvements were made in all genotypes except ∆fadE (Figure 3.6 (iv)), which was a similar outcome to pantothenic acid only supplementation. Further analysis on the use of autoinduction media reveal no

96 0.12 1 0.1 BW25113 0.8 0.08 0.6 0.06 0.4 0.04 OD 600nm 0.02 * 0.2 mg/ml FAME per OD FAME per mg/ml 0 0 FAME MOPS MOPS MOPS Plim MOPS panto MOPS panto MOPS panto OD i) autoinduction autoinduction Plim

0.2 0.8 ΔfadL 0.15 ** 0.6 0.1 0.4

0.05 0.2 OD600nm 0 0 mg/ml FAME per OD FAME per mg/ml MOPS MOPS MOPS Plim MOPS panto MOPS panto MOPS panto FAME autoinduction autoinduction Plim OD ii)

0.15 1.5 ΔfadD 0.1 * 1 0.05 0.5 OD600nm

0 0 mg/ml FAME per OD FAME per mg/ml MOPS MOPS MOPS Plim MOPS panto MOPS panto MOPS panto FAME autoinduction autoinduction Plim OD iii)

0.3 ΔfadE 1 0.25 0.8 0.2 0.6 0.15 * * 0.4 0.1 OD600nm 0.05 ** ** ** 0.2 mg/ml FAME per OD per FAME mg/ml 0 0 FAME MOPS MOPS MOPS Plim MOPS panto MOPS panto MOPS panto OD iv) autoinduction autoinduction Plim

Figure 3.6. FAME quantifications and OD values from low-copy ‘TesA expression in

genotypes (i) BW25113, (ii) ∆fadL, (iii) ∆fadD, and (iv) ∆fadE cultivated at 30°C and 0.1mM

IPTG induction level (except autoinduced cultures). Experiment performed in 250ml shake

flasks, in triplicate, error bars represent SEM. Media optimisations were around a MOPS

minimal media base, autoinduction, phosphate limitation (Plim) and pantothenic addition

(panto) described in Table 2.1. * P <0.05, ** P <0.01 vs. MOPS control, one-way ANOVA

with corrections for multiple comparisons (Tukey’s test).

97 improvement, bar an accumulation of FA in ∆fadD (Figure 3.6 (iii)). Statistical evaluation of these data denotes that MOPS minimal media which is phosphate limited (compared to nitrogen and carbon ratio in the media) combined with pantothenic acid supplementation is supportive of systems which are engineered towards FA overproduction in E. coli. These media optimisations significantly improve specific productivity in ∆fadL and ∆fadD host genotypes only (Figure 3.6 (ii-iii)). Because the productivity is increased in a way that is comparable to the relative productivities in MOPS media only, and in the interest of cost effectiveness, this media adjustment will only be applied to systems where a directed overproduction approach is applied in the final chapter of this thesis.

EVALUATING THE IMPACT OF THE CULTIVATION PROCESSES ON FATTY ACID PRODUCTION AND GROWTH

Up to this point in experiments, batch cultivation processes had been exclusively applied for evaluating FA production strains. However as outlined earlier, batch cultivation leads to problems when producing a product that is both toxic to the cell and alters pH, such as FAs. Therefore, continuous cultivation processes using turbidostats were applied to strains expressing ‘TesA in various host genotypes, as before. These experiments were conducted in minimal MOPS media (Figure 3.7), where strains were continuously induced at 0.1mM IPTG and kept diluted at varying cell densities, as outlined. Figure 3.7 illustrate the final yields of FA in relation to final OD, denoted as specific productivities, and are shown to be significantly high in all cases at the lower OD of 0.4 compared to higher OD concentrations of 0.8 and 1.0 (Figure 3.7). Furthermore, all deletion strains have higher FAME content at lower cell density (OD 0.4) compared to wild-type BW25113, which was expected, as the degradation cycle of BW25113 is intact and metabolising FAs as they are produced. Interestingly, this yield at OD 0.4 also decreases sequentially in relation to the position of the FAD gene that has been deleted from the degradation cycle (Figure 1.5). This suggests that a deletion of the first step of the degradation cycle (∆fadL) is the most efficient at accumulating FAs, when continuously cultivating at OD600nm 0.4, followed by a deletion of the second step (∆fadD), then finally the third (∆fadE).

98 0.4 OD600nm

0.8 OD600nm

0.07 1 OD600nm ** 0.06 * -*- - -- - 0.05 -- -

0.04 ***

0.03

mg/ml FAME per OD 0.02

0.01

0 BW24113 ΔfadL ΔfadD ΔfadE

Figure 3.7. FAME quantifications and OD values from low-copy ‘TesA expressions in

BW25113, ∆fadL, ∆fadD and ∆fadE genotypes continuously cultivated with 0.1mM IPTG at

OD600nm values 0.4, 0.8 and 1 in minimal MOPS media. Performed as individual turbidostat

experiments, error bars represent SEM of technical replicates. *** P <0.001, **P <0.01,

*P <0.05 vs. other ODs in same genotype host, one-way ANOVA with corrections for

multiple comparisons (Tukey’s test).

99 This effect is lost at higher OD600nm concentrations, and was considered during the design of further experiments.

The premise for experiments conducted so far were to understand the conditions and an efficient genotype for FA production in E. coli. The evaluation of genotypes with ‘TesA expression in Figures 3.3, 3.5, 3.6 and 3.7 were conducted to understand which of the reported host strains were most advantageous for FA production and why. In the literature and as outlined in the introduction of this thesis, there does not appear to be a consensus on the choice of host strain selection for FA overproduction. Studies on the ∆fadL host strain, for example, have contrasting theories on the role of this gene in relation to a host optimised for FA production. Liu et al., (2012) report that deletion of fadL makes the membrane weaker and therefore improves FA export from the cell, while Tan et al., (2017) state that overexpressing fadL increased membrane integrity, FA tolerance and FA titer. Published data also fluctuate between the use of ∆fadD and ∆fadE, therefore these experiments aimed to clarify which hosts are advantageous during ‘TesA expression.

As described earlier in this chapter, Figure 3.5 highlights BW25113 and ∆fadD as enabling high cell density and growth for reasons that may attribute to each retaining FadL. When looking at specific productivities under the same conditions however, we see that ∆fadE outperforms both BW25113 and ∆fadD for yield. These experiments were not comparable in their oxygen transfer rates however, since growth rates were measured in 96-well plates and FAME analyses were taken from sampling 250mL shake flasks, which may have contributed to improvements seen later in ∆fadE FAME analysis. As anticipated, however, all FAD deletion strains had higher FA yield than wild-type BW25113.

Figure 3.6 then examined genotype performance over a range of optimisations made to the media, and found that a combination of phosphate limitation and pantothenic acid supplementation had a synergistic effect on improving FA yields in all strains, which is hypothesised to be due to their effect on improving energy availability from the pentose phosphate pathway when combined with acetyl-CoA and free CoA availability. These experiments were performed in batch, however Figure 3.7 expanded the analysis into continuous cultivation using turbidostats. These data outlined specific productivities over ranges of cell densities, and found that an OD of

100 0.4 was optimal for continuous FA production in minimal medial with 0.1mM induction. As the lowest of the ODs examined, it is interesting that keeping cells continuously growing at this phase was optimal for FA production. This could be due to the growth rate regulation of Acc, in which the auto-regulation of AccAD transcripts is alleviated in the presence of optimal acetyl-CoA levels during exponential phase (Meades et al., 2010). It is hypothesised that that when cells are kept at higher OD and stationary phase in minimal media, acetyl-CoA levels are lowered to inhibit AccAD translation, and therefore keep an overall bottleneck over FAS activity. Figure 3.7 also highlights the effect FAD deletions have on yield when OD is kept at 0.4, and reveals a correlation of the stage within FAD cycle with increasing yield, i.e., ∆fadL which is the first step of FAD had the highest specific productivity, followed by the second step ∆fadD, finally ∆fadE. These contrast the results from batch (Figure 3.3), and highlight the dependencies cultivation and genotype parameters have on their outcome. This data combined, suggests that the selection of host is subject to specific experimental conditions applied and therefore could explain the discrepancies reported in literature to date. This chapter gave fundamental insight into the cultivation practices which are optimal for FA overproduction around the experimental procedures that were in place for this study. These were in terms of ‘TesA expression level, media, temperature and aeration level, and the optimisations were applied to further experiment design to later chapters of this thesis.

ADAPTIVE EVOLUTION OF FATTY ACID OVERPRODUCING STRAIN

To increase FA production through an acquired tolerance or adaptive evolution, the ∆relA genotype strain was evolved in a turbidostat through sequential rounds of increasing FA concentration in the media, by introducing palmitic acid at increasing concentrations of 10mM per round, to rich media over long term cultivation periods. ∆relA E. coli strain was chosen for this study due to its lack of control over phospholipid synthesis via the stringent response, which is characterised by its inability to synthesise alarmone (p)ppGpp, thus preventing inhibition of PlsB and FAS flux (Heath, Jackowski and Rock, 1994).

101 After the first round of adaptive evolution in 10mM palmitic acid, the evolved strain displayed increased ability to tolerate and grow in C16 and C18 FAs compared to their non-adapted controls when cultivated in batch (Figure 3.8 (i)), and interestingly, the evolved strain had increased resistance to ppGpp + C16 FA treatment compared to their non-adapted controls (Figure 3.8 (ii)). The dual effect of ppGpp + C16 FA tolerance in the adapted strain was promising as a FA overproducing strain, and therefore was taken for further rounds of adaptive FA treatment. The final concentration of palmitic acid that adapted strains were evolved to was 30mM in rich media. This final evolved strain, gained thorugh three rounds of adapted evolution in palmitic acid and named evo∆relA, was evaluated for resistance to stress challenges again, and compared to the negative control as well as the first evolved ∆relA; ∆relA adapted 1, and the second evolved ∆relA; ∆relA adapted 2 (Figure 3.9). The data show that all strains have normal growth in LB (Figure 3.9 (i)), that C16 and C18 treatments induce a prolonged lag phase in evo∆relA compared to apparent toxicity in all other strains (Figure 3.9 (ii-iii)), that ppGpp treatment promotes growth in evo∆relA (Figure 3.9 (iv)) and that strains evo∆relA and ∆relA adapted 2 are resistant to a combination of C16 + ppGpp (Figure 3.9 (v)). These results indicate that adaptive evolution was successful in conferring resistance to FA toxicity as well as introducing further resistance to the stringent response alarmone ppGpp.

To evaluate the evo∆relA improvements in FA production, both evo∆relA and control ∆relA were transformed with low-copy ‘TesA expression constructs, for examination of whether the recorded increase in tolerance to FA translates to any increase in FA production. Figure 3.10 illustrates that specific productivity of FA production has been improved significantly in evo∆relA when ‘TesA is induced with 0.1mM IPTG. This demonstrates the potential of using adaptive evolution to increase FA production in E. coli, particularly when applied in combination with continuous cultivation techniques and the appropriate degradation deletion strain, as outlined during this study.

This study has highlighted the potential application of adaptive evolution of increasing FA tolerance in a chassis strain for enabling high FA production and yield. Strains that have evolved tolerance to FAs previously have demonstrated higher production capabilities that were conferred by the alterations made on their membrane composition, in terms of polarisation and integrity (Royce et al., 2015;

102 i)

ii)

Figure 3.8. Average growth curves from C16 fatty acid ‘first-round’ adapted and non-

adapted ∆relA strains subject to (i) C16 and C18 FA stress challenges and (ii) ppGpp plus

FA stress challenges. Genotype and treatment type described in legends. Performed in 96- well, in duplicate, shaded regions represent SEM.

103 Control C16 treatment

i) ii)

C18 treatment ppGpp treatment

iii) iv)

Figure 3.9. Average growth curves from C16 fatty acid evolved and control ∆relA strains:

non-adapted ∆relA control (red; non evolved), ∆relA adapted 1 (dark blue dashed; first

round exposure), ∆relA adapted 2 (light blue dashed; second round exposure), and

evo∆relA (black; third round exposure) strains subject to varying stress challenges (i-v).

Performed in 96-well, in duplicate, shaded regions represent SEM.

104 *** *

0.3

0.1mM IPTG 0.25 * 1mM IPTG 0.2

0.15

0.1 mg/ml FAM per OD 1

0.05

0 ΔrelA evoΔrelA

Figure 3.10. FAME quantifications normalized to OD values from low-copy ‘TesA expressions MOPS media cultivations of non-adapted control ∆relA (∆relA) and C16 fatty acid adapted ∆relA (evo∆relA). Cultures induced at 0.1mM and 1mM IPTG and performed in 250ml shake flasks, in triplicate, error bars represent SEM. * P <0.05 , *** P <0.001 vs. non adapted control, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

105 Tan et al., 2016). This research highlights the importance of membrane composition during tolerance and production of FAs, which has been reinforced in publications around the significance of FadL in membrane integrity and FA production (Tan et al., 2017). The adaptive evolution experiments of this thesis included the use of a relA strain which is deficient in ppGpp synthesis, and therefore unable to inhibit FAS flux during amino acid depletion. SpoT is also involved in ppGpp metabolism, but has more of a hydrolyase role, while RelA is a synthetase of the alarmone. ppGpp levels are known to accumulate in E. coli cells growing in chemostats (Gresham and Hong, 2015), and further lineages adapted to continuous phosphate limitation have been attributed to having spoT mutations that aim to redirect cellular resources away from stress responses that are redundant in a constant selection environment (Wang et al., 2010).

Transcriptome studies have found evidence for lowering FAS gene expression during the stringent response, therefore deleting relA was to disarm that effect during the directed evolution procedures. OmpF was also considered a target for deletion due to its reported benefit of improving membrane integrity and FA production (Tan et al., 2017), and while attempts were made to construct a double knock out of both relA and ompF during this study, they were unsuccessful. The reasons for not selecting a FAD deletion for evolving experiments was to limit the contributions, in combination with the FA selective pressure, that these mutations have on aspects of FAS and regulation in terms of the varying reports on the role of FadL on membrane integrity as mentioned, the role of FA transport with FadD (Black and DiRusso, 2003) and the accumulation of acyl-CoA levels by knocking out fadE. Once a productive strain was achieved from adaptive evolution, these deletions could be introduced along with other modifications for FA productions.

The adaptive experiments have shown to yield mutant strains which are responsive to subsequent stress challenges in Figure 3.9 (ii-v) at varying degrees, according to the round of adaptation they were subjected to in the turbidostat. Because the turbidostats are selecting for cells that maintain a certain growth rates when subject to a selection such as FA tolerance, the populations maintained are either high performing non-genetic variants (Xiao et al., 2016) or they have undergone sufficient mutations that enable their growth under FA tolerance. The latter is most likely the case, since FA adaptation has led to high performing mutants due to changes in the

106 membrane (Royce et al., 2015; Tan et al., 2016; Rowlett et al., 2017). If we assume that membrane alterations have led to the responses of evo∆relA during subsequent C16 treatments in Figure 3.9 (ii), where a prolonged lag phase is evident, it is valid to hypothesise that this could be due to FAs lessened ability to cross the cell membrane. An increase in membrane composition integrity such as those brought about by FadL upregulations reported in Tan et al, (2017), enable the recapture of LCFA for maintenance of membrane biosynthesis and repair and could also be in effect here. The lack of permeability is a reasonable cause of the outcome in Figure 3.9 (iii), where evo∆relA does not leave lag phase, compared to earlier adaptations and control undergoing an apparent cell death from the declining growth curve. Figure 3.9 (iv) shows a puzzling positive response of evo∆relA towards ppGpp (induced by serine hydroxamate) treatment compared to all other strains. Here, evo∆relA has overcome the negative impact ppGpp has on growth, a resistance which appears to present itself also in the adaptation prior to this one, ‘∆relA adapted 2’, while other strains are in decline or death phase during this treatment. This suggests that mutations have arisen in evo∆relA which started to present themselves in ‘∆relA adapted 2’, deeming them robust towards ppGpp treatment. This robustness could arise from several possibilities. As with the C16 FA treatments in Figure 3.9 (ii), this response could be due to changes in the membrane, saturation level or otherwise, enabling the strain to be less permeable to toxic products in the media. The effect is still negative compared to the control growth curve (Figure 3.9 (i), reaching less than two-fold final OD, which indicates that ppGpp has had an effect, though not as prominent. This leads to the suggestion of further modifications associated with ppGpp metabolism.

From investigating the literature, one possibility around this modification could involve SpoT, which is known to switch between its prevalent ppGpp hydrolase activity and synthase ability (Murray and Bremer, 1996). This switching has been shown to correlate with ACP interaction (Seyfzadeh, Keener and Nomura, 1993; Battesti and Bouveret, 2006), thus serving as a means of sensing the lipid metabolic state of E. coli. Specific mutations on the TGS domain on the N terminus (named based on three protein families containing it: threonyl-tRNA synthetase, Obg family of GTPases and SpoT (Wolf et al., 1999)) have been shown to prevent the binding of ACP with SpoT, resulting in a locked ppGpp synthesis activity, while this same

107 mutation with an additional TGS domain included enabled the binding of ACP with SpoT which resulted in a locked ppGpp degradation activity of SpoT (Battesti and Bouveret, 2006). It has also been proposed that the signal sensed by SpoT to synthesise or degrade ppGpp during FAS inhibition may be incurred in the same way by FA derivatives such as acyl-CoA (Seyfzadeh, Keener and Nomura.,1993). Therefore, both ACP and CoA are involved in SpoT regulation and metabolic sensing, where ACP is involved for FA syntheses and CoA is involved in FA degradation. It could be said that SpoT mutations have contributed to the observations in Figure 3.9 (iv-v), if continuous exposure to acyl-coA has locked it in a ppGpp degradative mechanism. This could explain the effect of C16 + ppGpp treatment in Figure 3.9 (v), which has bridged the phenotypic response between evo∆relA and ‘∆relA adapted 2’. It appears that the presence of C16 fatty acid has enabled ‘∆relA adapted 2’ to grow in ppGpp, which could be from inducing SpoT activity to ppGpp degradative. The fact that this does not happen in earlier ∆relA is from their lack of tolerance to C16 fatty acid as mentioned earlier, though why exactly the combinatorial treatment improves growth for both evo∆relA and ‘∆relA adapted 2’ in Figure 3.9 (iv) compared to 3.9 (ii) remains unclear. It could be the effect of ppGpp is stabilising the growth in FA, while simultaneously enabling SpoT activity to degrade ppGpp and limit the effect of stringent control on FAS.

Further interrogation of the mutations involved in the evolved phenotypes is required if a better understanding is to be gained of mechanisms behind these adapted traits. What is clear from Figure 3.10 is the potential adaptive evolution has on evolving FA tolerance to improve productivity, with the improvement present at 0.1mM IPTG ‘TesA inductions, though not at 1mM. Presumably, the higher induction has reached a limitation on the improvement of the adapted strain evo∆relA, though interestingly it is more productive in the non-adapted ∆relA strain at this induction level. This indicates that high protein synthesis for FA production in the evolved strain is not efficient, which could be from mutations on SpoT deregulating the normal stringent control response to amino acid depletion. Titrating IPTG would reveal more about the expression limitations of this experiments, and sequencing genomic SpoT would answer questions around its putative mutation and role in these experiments.

108 3.4 FUTURE DIRECTIONS

TWO PHASE CULTIVATION

These experiments have demonstrated that the optimal conditions for FA production are conditional to the parameters of the FAS system involved, such as ‘TesA expression level and genotype. However, it is widely accepted the optimal scenario for microbial production processes is that a cell reaches a stage where it is not dividing constantly but instead dedicating its metabolic processes to chemical production (Chen et al., 2015), such as FA. Metabolically active but non-growing cells have been suggested in the context of optimising FA production already (Youngquist, Rose and Pfleger, 2013; Jawed et al., 2016) and this strategy has been used in E. coli to increase the production of shikimic acid (Johansson et al., 2005) and recombinant proteins under phosphate limitation (Huber et al., 2011). Phosphate limitation has also proven useful in limiting biomass in favour of FA production during this study (Figure 3.6 (iii)). Building on the need to optimise a trade-off between growth and production, this study could be advanced by applying a two-stage temperature control cultivation techniques, as outlined by Liu et al., (2012) for FA production. This optimisation to the process involved standard cultivation conditions at 34°C induced from 8 h to 26 h, and then shifting the temperature to 30°C from 26 h to 72 h. Because lower induction temperatures of 25-30°C can increase the activity of recombinant enzymes at the expense of final yield, this approach successfully balanced enzyme expression, cell growth and product formation by having the temperature in favour of cell growth for the first part of induction, shifting to lower temperature which favours enzyme expression when the cells are at a higher cell density to support the activity. This strategy was effective in increasing extracellular FA production while maintaining cell growth, and would be relevant for the continuation of this work.

109 DECIPHERING ADAPTIVE EVOLUTION MUTATIONS

While the adaptive evolution experiments have yielded interesting results that have contributed to a strain with higher FA tolerance and production capabilities, the questions around the mutations that have led to this phenotype remain unanswered. On a broader scale of research, knowledge about the mutations that infer FA tolerance are also relevant for the development of drug targets that tackle antimicrobial resistance, since these agents usually target membrane properties (Delcour, 2010; Epand et al., 2016).

A high throughput reporter system to elucidate the points of mutation which have led to evolved traits would therefore be beneficial to the fundamental questions around FAS, to directed approaches around strain optimisation and to research on antimicrobial resistance. This could also be extended into a comparative analysis on whether such mutations have any sequence alignment to the genomes of certain species (within genus Yarrowia and Pseudomonas) which already have high lipid and FA tolerating capabilities (Meng et al., 2011; Tai and Stephanopoulos, 2013; Thevenieau and Nicaud, 2013; Levering, Broddrick and Zengler, 2015). Strategies for deciphering these adaptive mutations include the use of an array-based discovery of adaptive mutations (ADAM) developed by Goodarzi, Hottes and Tavazoie, (2009), which searches the entire bacterial genome for mutations that contribute to a phenotypic variation between the evolved and parent strains. This process involves the infection of the evolved strain with a P1 phage infected parent strain, which also contains a KanR transposon library. When selected on kanamycin, the library of embedded markers in the DNA of the parent strain is transferred to the evolved, at sites which replace and correct the mutations of the evolved strain with transposon insertions. The frequency of these events is measured via transposon footprinting, which is a hybridization genetic footprinting technique (Hare et al., 2015). The genetic regions that have lower transposon insertions under selective sampling, relative to nonselective sampling, then indicate the position of functional mutations. Once these mutation targets have been identified, single base sequence variations can be detected by methods involving MALDI-TOF MS (Honisch, Raghunathan, Cantor, Palsson and Boom, 2004).

110 EXPLOITING THE LIPID SENSING MECHANISM OF SPOT

From this chapter and the literature published, we have learned of the strong regulatory link between the stringent response signal and lipid metabolism in E. coli (Seyfzadeh, Keener and Nomura, 1993; Magnusson, Farewell and Nyström, 2005; Battesti and Bouveret, 2006; Potrykus and Cashel, 2008; Traxler et al., 2008; Potrykus et al., 2011; Yao et al., 2012; My et al., 2013). These activities are from enzymes RelA and SpoT, and manifest as the synthesise or hydrolysis of ppGpp in response to signals such as amino acid depletion, nutritional stress, the energetic state of the cell membrane (Potrykus et al., 2011; Ross et al., 2013; Merlie and Pizer, 1973; Rowlett et al., 2017), and variations in FA metabolic status of the cell (Yao et al., 2012). SpoT has been found to interact with ACP, a central cofactor of fatty acid and phospholipid synthesis, which induces a conformational change in SpoT that switches its activities to ppGpp synthesis when FA availability is low(Battesti and Bouveret, 2006) . Perhaps the TGS domain of SpoT, which recruits derivatives of ACP, could be utilised in a genetic circuit which aims to increase FA productivity by sensing when it is low, therefore increasing effort that go into optimising genetic and cultivation processes. This circuit could implement TGS induced conformational changes in SpoT to hydrolase activity when bound to ACP, to drive promoter activity. This SpoT responsive promoter would then drive the expression of genes that will enhance FAS when needed, and switch off when FA levels have returned to a productive level so that burden is minimised. There are however many factors to address for this strategy to become a practical, not least of all a promoter responsive to the conformational changes of SpoT, but also the feasibility in using intracellular ACP levels to sense FAS and drive expression accordingly. In doing so a quorum-sensing mechanism to engineer FA production could be applied.

111 4 AN EXPERIMENTAL STUDY ON THE CONTROL OF ENZYMES IN THE FAS BIOCHEMICAL PATHWAY

4.1 INTRODUCTION

The enzymes that perform FAS in E. coli are subject to many forms of regulation, whether this is transcriptional, translational, allosteric feedback mediated, competitive substrate inhibition, stress related or growth coupled, it is imperative to understand the implications each of these regulations has on FAS when viewed as a complete set of biochemical pathways. Overexpression and deletion studies have been done on most of these enzymes to measure the impact they have on fatty acid yield (Janßen and Steinbüchel, 2014b); however, these have either been conducted as individual studies on specific points in the pathway, such as the ‘rate-limiting’ steps, or have been a characterisation of the enzymes in vitro (Heath and Rock, 1995b; Heath and Rock, 1996; Yu et al., 2011; Liu et al., 2017). Since engineering FAS aims to maximise the achievable FA yield which involves specifically modifying multiple steps in the pathway for an optimised flux, it is crucial to capture the response of the pathway towards individual perturbations in vivo, in order to fully understand the regulation that governs each of these reactions. In this way, we gain information on the effect of changing the availability of these enzymes on the metabolic pathway, and on how a combination of modifications contributes to product yield. Each enzyme has a characterised activity and defined role in FAS which will be outlined in this chapter. Their significance in terms of contribution towards cell phenotype, their essentiality, effect on specific productivity of FAS, and role in reaction network will also be described in this chapter.

112 THE E. COLI TYPE II FAS ENZYMES

As mentioned in section 1.4, E. coli FAS takes place as a type-II synthase which is predominant in prokaryotes as well as plastids of plants (Rock and Cronan, 1996), and is organised in such a way that the enzymes that carry out FAS are not comprised of one single gene or operon, which is in contrast to type-I FA synthase (Wakil, Stoops and Joshi, 1983). In E. coli, FAS is performed by a complex of freely dissociable subunits, in a cyclic manner (Figure 1.5), each of which is subject to regulation which will be described in detail during this introduction.

Acetyl-CoA carboxylase: Acc

Acetyl-CoA carboxylase (Acc) is the first committed step towards FAS in E. coli, catalysing the conversion of acetyl-CoA to malonyl-CoA that takes place in the form of two half-reactions (Broussard et al., 2013; Davis, Solbiati and Cronan, 2000) (Figure 4.1 (i)). The Acc enzyme is the largest of the FAS pathway, made of four subunits that comprise a multiplex, consisting of a homodimer (AccC), a homotetramer (AccB) which forms the biotin carboxylase- biotin carboxyl carrier protein (BC-BCCP), and a heterotetramer (AccA & AccD) which forms the carboxyl transferase (CT) (Figure 4.1 (ii)). In the first of the half-reactions BC-BCCP carboxylates biotin at the expense of ATP, while in the second half-reaction carboxyltransferase transfers the carboxyl group to acetyl-coA, forming malonyl- CoA.

The genes encoding these subunits are all essential genes to E. coli due to their significance in FAS and lipid synthesis (Gerdes et al., 2003), and for each to be synthesised in the stoichiometry needed for stable multiplex formation requires precise regulation (Li et al., 1992; Li and Cronan, 1993)(Cronan and Waldrop, 2002). The disruption of this regulation can lead to variety of mutant phenotypes as well as differences in specific activity of the enzyme (Davis, Solbiati and Cronan, 2000). The rates of transcription of all four Acc genes are known to be regulated with respect to growth rate (Li and Cronan, 1993). An accumulation of AccB negatively regulates the transcription of accBC operon by binding to its operon, though this process can be overcome by the action of transcription factor FadR which also binds upstream of this promoter to drive expression (My et al., 2015), which suggests a correlation between acyl-CoA requirement and Acc transcription. Autoregulation is also in place for the carboxyltransferase protein of Acc, mediated by the inhibitory action of a zinc

113 i).

ii).

Figure 4.1. Acc multiplex overview: (i) Biotin carboxylation and carboxyl transferase reactions of acetyl-CoA carboxylase that catalyse formation of malonyl-CoA from acetyl-CoA by the Acc complex. (ii) Stoichiometry of the carboxyltransferase (heterotetramer), biotin carboxylase (homodimer) and bccp (homotetramer) subunit assembly into the Acc complex

114 finger on the AccD subunit; this form of translation regulation has been shown to act as a ‘dimmer switch’ in order to control levels of protein production and activity according to levels of acetyl-CoA concentrations during different growth phases (Meades et al., 2010). These regulations are in place to avoid the unnecessary investment in energy since the reaction is driven by ATP cleavage, which are further evident by the mechanisms of allosteric feedback inhibition on Acc (Davis and Cronan, 2001). This ensures that when sufficient acyl-ACP are present in the cell to support phospholipid synthesis, negative feedback on Acc slows down or completely stops the reaction to avoid a surplus of malonyl-coA at the expense of ATP when it is not required.

Malonyl-CoA:ACP transacylase: FabD

The second step of FAS is the transfer of the malonyl moiety of malonyl-CoA to holo- ACP, producing malonyl-ACP catalysed by malonyl-CoA:ACP transacylase (FabD). Malonyl-ACP provides 2-carbon extender units to the cycles of FA elongation, contributing towards FAS and phospholipid synthesis significantly. Deletion studies have also shown this enzyme to be essential (Gerdes et al. , 2003), as well as a relevant candidate in improving titres (Zhang, Agrawal and San, 2012). FabD is also highly specific, requiring both malonyl-ACP and active holo-ACP to be present at the optimal amounts required to facilitate direct contact between the donor and acceptor ACP and allow the reaction to occur (Joshi and Wakil, 1971).

Transcription of fabD occurs as part of the fab-cluster (Rawlings and Cronan, 1992), and its regulation by the transcription factor FadR as described in section 1.5. Further regulation occurs in its activity, acetyl-CoA has been found to competitively inhibit the enzyme (Joshi and Wakil, 1971); therefore unless enough malonyl-CoA is present to occupy the active sites a surplus of acetyl-CoA may represent a potential bottleneck in the system. Furthermore, the same study found that when free CoA is in excess the binding of holo-ACP is also competitively inhibited, while when these molecules are in equal ratios the synthesis of malonyl-CoA is favoured over the production of malonyl-ACP. Therefore, an optimal balance of holo-ACP to CoA as well as malonyl-CoA to acetyl-coA are required to maximise productivity of this particular step in FAS.

115 3-ketoacyl-ACP synthase I, II and III: FabB, FabF and FabH

Production of FAS intermediate 3-ketoacyl-ACP is catalysed by 3-ketoacyl-ACP synthases I, II and III (FabB, FabF and FabH), and comprises the initiation step of chain elongation in FAS (Magnuson et al., 1993) . In the first instance, condensation of malonyl-ACP with acetyl-coA is catalysed by FabH to form acetoacetyl-ACP, which then enters the elongation cycle to produce the first C4 acyl-ACP(Lai and Cronan, 2003). Subsequent elongation cycles are then performed by FabF or FabB condensing malonyl-ACP with acyl-ACP, the temperature dependent activity of either enzyme controls the saturation level of intermediate acyl-ACP esters produced for membrane homeostasis (Garwin, Klages and Cronan, 1980; Zhang and Rock, 2008). Overproduction of FabF has been found to be toxic to E. coli by dysregulating FAS flux leading to a cessation of phospholipid synthesis (Subrahmanyam and Cronan Jr., 1998). From this study, it has been hypothesized that during initiation of the elongation cycles; FabD interacts with FabH, FabF and eventually with FabB. Therefore, increasing FabF levels leads to a monopoly on theses complexes formed leaving no FabD available to interact with FabH or FabB, hindering their activity and blocking subsequent initiation reactions necessary for FAS. The balance of protein levels at this stage of the pathway are therefore critical in regulating carbon flux and phospholipid production.

3-ketoacyl-ACP reductase: FabG

Once the FAS cycle is initiated, the following step is the reduction of 3-ketoacyl-ACP to 3-hydroxyacyl-ACP at the expense of cofactor NADPH, catalysed by FabG. FabG belongs to the short-chain dehydrogenase/reductase enzyme family (SDR), it is an essential gene and has been shown to display activity that is conditional on NADPH availability ( Lai and Cronan, 2004). NADH specific homologs of FabG have been studied for their potential in increasing anaerobic FA yield in E. coli (Javidpour et al., 2014), which led to improved FA titers as well as providing preliminary evidence that further downstream processing could be enhanced by substituting the native FabG and cultivating anaerobically. FabG is also active on longer chain 3-ketoacyl-CoA of the degradation cycle which has enables its potential in polyhroxyalkanoate biosynthesis(Park, Park and Lee, 2002). FabG is under stringent and transcriptional regulation as discussed in section 1.5.

116 3-hydroxyacyl-ACP dehydrase: FabA and FabZ

The dehydration of 3-hydroxyacyl-ACP is catalysed by enzymes FabA and FabZ, which are both capable of forming trans-2-enoyl-ACP (Heath and Rock, 1996). FabA is specifically involved in introducing a cis-double bond to the growing acyl chain and therefore essential to synthesis of unsaturated fatty acids (UFA), though this activity is dependent on the simultaneous and optimal presence of FabB to enhance the proportion of UFA (Xiao, Yu and Khosla, 2013). This is due to the reversibility of the reaction being favoured in either direction depending on the substrate concentrations, thus, if cis-3-dienoyl-ACP Is not further reduced it re-enters the cycle for saturated FAS. FabZ has been found to specifically catalyse the dehydration of 3-hydroxymyristoyl-ACP as well as all 3-hydroxyacyl-ACP with shorter chain length (Mohan et al., 1994). FabA is under both stringent and transcriptional regulation (FadR and FabR) due to the role of UFA and temperature regulation of membrane composition, while FabZ is under stringent regulation only (Kanjee, Ogata and Houry, 2012).

Enoyl-ACP reductase: FabI

The last step of FAS in E. coli is catalysed by the enoyl-ACP reductase FabI, which reduces 2-enoyl-ACP to acyl-ACP by reducing equivalents NADPH or NADH (R. J. Heath and Rock, 1995b; Bergler et al., 1996). FabI is known to play a determinant role in completing the cycles of FAS, acting as a ‘pull’ on the flux through the pathway (Heath and Rock, 1995b) and as such, E. coli has evolved to regulate its activity according to allosteric feedback regulation by acyl-ACP (Richard J Heath and Rock, 1995). As with Acc, FabD and FabH and illustrated in Figure 1.6, this regulation is in place to prevent unnecessary energy expenditure when there is sufficient acyl-ACP in supply for phospholipid and membrane synthesis.

Cytosolic thioesterase I: ‘TesA

Free fatty acids (FFA) are produced in E. coli by the expression of a thioesterase which cleaves the thioester bond of an acyl-ACP, creating a metabolic sink for the FAS pathway by relieving the inhibition from acyl-ACP build up (Davis, Solbiati and Cronan, 2000; Zhang et al., 2012). E. coli contains two periplasmic enzymes that cleave thioester bonds of acyl-CoA to give fatty acid and free CoA, both have been shown to have less activity on acyl-ACP than acyl-CoA in vitro which has led to some the suggestion that these thioesterases have acyltransferase activity in vivo

117 (Spencer et al., 1978). When expressed as its leaderless form (due to a deletion in a 5’ peptide region and known as ‘TesA), the periplasmic thioesterase I (TesA) becomes cytosolic and gains access to acyl-ACP substrate, though still retains native activity for acyl-CoA thioester bonds (Cho and Cronan, 1995). Cytosolic thioesterases from different organisms can also be used to disrupt the elongation cycles and produce FFA with considerable effect on yield, chain length and saturation level (Choi and Lee, 2013; Yuan, Voelker and Hawkins, 1995).

Glycerol-3-phosphate acyltransferase: PlsB

PlsB is an acyltransferase involved in the condensation of glycerol-3-phosphate with acyl-ACP, producing acyl-glycerol-3-phosphate or, lysophophatidic acid, that is then used to synthesise the components of membrane lipids (Y.-M. Zhang and Rock, 2008) (Figure1.8). In E. coli, PlsB is active with both acyl-ACP and acyl-CoA with similar affinity (Zhang and Rock, 2008), which means that external FA can be incorporated into the membrane when the -oxidation enzyme FadD is present. PlsB is also subject to regulation by the stringent response whereby elevated (p)ppGpp levels reduce the activity of the enzyme by posttranslational inhibition (Heath, Jackowski and Rock, 1994). PlsB is also strongly inhibited by inactive apo-ACP and to a lesser degree by holo-ACP (Keating, Carey and Cronan, 1995; Rock, Goelzg and Grant, 1981).

Acyl carrier protein: ACP

Acyl carrier protein (ACP) is a small, highly conserved acidic protein that is essential to many metabolic processes, not least of all FAS (Polacco and Cronan, 1981; Rock and Cronan, 1996; Cronan and Thomas, 2009; Misra, Surolia and Surolia, 2009; Liu, Vora and Khosla, 2010). It is one of the most abundant proteins in E. coli (Lu et al., 2007), though best known for its role in carrying acyl intermediates during FAS and phospholipid synthesis. It is expressed in its inactive apo- form, and converted to active holo-ACP by ACP synthase which transfers the 4’-phosphopantetheinine arm from CoA to ACP. In its unmodified form, apo-ACP has been shown to inhibit lipid synthesis and therefore growth, as mentioned. We have also learned the significance of this protein in relation to facilitating the malonyl transferase or FabD reaction (Joshi and Wakil, 1971. Additional to this is the reported ability of ACP in E. coli to exhibit malonyl transferase and self malonylation activities, by their reported

118 ability to complement FabD deficient mutant strains (Misra, Surolia and Surolia, 2009). These points highlight the physiological significance this protein has on FAS regulation and function.

4.2 AIMS AND OBJECTIVES

This study is a systematic investigation of productivity in response to altering FAS enzyme levels, where individual genetic perturbations were made in the biochemical pathway to measure the response in vivo. In doing so, the aim is to interrogate the mechanisms of regulation in FAS that have been reported in the literature, and compare that with in vivo responses obtained from experiments of this study. The responses measured were in terms of cell growth, protein content, membrane depolarisation, ATP content, fatty acid yield and fatty acid specific productivity. Overall, the aim is to further our fundamental knowledge on the effect changing individual enzymes has on FAS and its regulation, and to ultimately contribute this data to developing computational models that aid strain engineering design.

4.3 RESULTS AND DISCUSSION

QUANTIFYING THE EFFECTS OF OVEREXPRESSING EACH FAS ENZYME ON PROTEIN PROFILES

Each enzyme of FAS overexpressed under the control of an inducible lac promoter, was evaluated for their effect of protein redistribution. These experiments show that while the target overexpression protein is elevated in most cases, some have no effect or are affecting the FAS protein profile elsewhere (Figure 4.2 (i-iii)), compared to the relative control profile. Expression genotype controls have a specific profile of protein abundance when relative to one other, while also having specific abundances of FAS proteins for every target overexpression. In most cases, each overexpression has a deregulating effect on the FAS proteomic profile, BW25113 expression host has a more downregulating effect when individual perturbations are made (Figure 4.2 (i)). The same overexpressions have an upregulating effect on other FAS proteins when expression host is changed to ∆fadD (Figure 4.2 (ii)), while the

119 Figure 4.2. Relative FAS protein quantifications from SRM LC-MS analysis in (i) BW25113 genotype host (ii) ΔfadD genotype and (iii) BW25113 genotype plus ‘TesA dual expression.

Each quantification represents the average variation in peptide abundance (green increase, pink decrease) in FAS protein overexpression strains relative to negative control strains

(black bars). Performed in technical duplicates.

120 BW25113 host with additional ‘TesA expression (Figure 4.2 (iii)) has a more robust protein profile when subsequent perturbations are made, compared to the negative control. The introduction of thioesterase ‘TesA does however alter the ACP abundance compared to previous host strains, and represents the typical protein profiles for the strains when FA are produced in the cell.

These results contribute an understanding to the balance of control in FAS protein distribution, when individual nodes, proteins, are perturbed. In the field of strain engineering, it is well known that there must be a balance of gene expression when the goal is to have an optimally producing strain without impairing cell viability (Pickens, Tang and Chooi, 2014), there have been several reports relating FA yield or productivity to quantitative FAS protein profiles or stoichiometry (Tao et al., 2016; Liu et al., 2017). What is most relevant from interpretations of this chapter is the ability to potentially identify rate-limiting steps in which increasing the expression of certain genes triggers a response that is observed as increased FAS productivity. It is known that loss-of function or deletion studies are insufficient to identify such rate- limitations, or even fully deduce gene functions (Prelich, 2012), therefore this overexpression approach was taken to contribute any unknown information to the field.

The impact of changing individual FAS protein levels during the overexpression studies of this chapter, has demonstrated an effect of inducing further upregulation on the natively expressed FAS proteomic profile, such as FadR upregulating AccA (Figure 4.2 (i-ii)), and ‘TesA on ACP (Figure 4.2(iii)). This is further evident when comparing the profiles of varying genetic backgrounds, i.e. genotypes BW25113 compared to ∆fadD having marked differences in their respective control or ‘wild-type’ protein distributions (Figure 4.2(i-ii)). The instances of overexpressed protein having off-target effects on native proteins is a common feature of pleiotropism, epistasis, or of the stress response in E. coli (Lenski, 1988; Wang et al., 2010; Østman, Hintze and Adami, 2012; Dragosits and Mattanovich, 2013). Mutations can affect more than one trait (pleiotropy), or interact with other mutations (epistasis) and are crucial components of maintaining the fitness landscape of an organism during evolutionary adaptation. When probing these mechanisms they can provide functional links and information on complementary interactions to uncharacterised processes (Prelich, 2012). What is relevant to this study are the states of dysregulation each

121 perturbation has on the FAS protein profile abundances that are subject to pleiotropic interactions of the host genotype. Figure 4.2 (i) has the most downregulating effect from each overexpressed perturbation, which can be explained by the known negative feedback interactions from acyl-ACP accumulation, as well as disrupting Acc subunit stoichiometries and KAS enzyme interactions. Figure 4.2 (ii) however, moves to a system which no longer produced acyl-CoA due to a FadD deletion, therefore FadR no longer responds to acyl-CoA concentrations and is in a permanent state of upregulating FAS enzymes FabD, FabB, FabH, FabZ, FabG and the Acc subunits (My et al., 2015). This is especially observed in the FadR overexpressing strain in Figure 4.2 (ii), where all proteins bar FabG, PlsB and FabB are upregulated, and is hypothesized to account for the overall upregulating effect on further perturbations from this experiment. When observing a further change in genotype, Figure 4.2 (iii) demonstrates an overall relatively stabilized system to perturbations compared to the previous genotypes observed. This is thought to be due to the known pleiotropic effects overexpressing ‘TesA has on relieving acyl-ACP feedback inhibition and on subsequently increasing acyl-CoA availability for FadR deactivation. Therefore, increasing individual enzymes in a system which is already in a proteomic distribution that enables FA overproduction has less pleiotropic impact, which is the case for all perturbations of this study bar FadR and AccA overexpressions. FadR and AccA still retain upregulating effects elsewhere in the system, which suggests they have been introduced at a concentration that can still influence the regulation of a FA overproducing strain. This is shown further in later FAME data (Figure 4.7 (ii)), where perturbations have little effect on yield with the exception of the Acc subunits, FadR, FabH and PlsB.

Of further relevance to this study is the consideration of the known effect that lipophilic compounds such as FAs have on protein abundance and activity (Rosenberg et al., 2003). If we consider the alterations in protein profile as a response to changes in FA composition in Figure 4.2 (iii), which have been recorded in response to solvent, ROS and FA treatments (McDougald et al., 2002; Rowlett et al., 2017; Ingram, 1977); the question is whether observable phenotypes are a symptom of overexpressing FAS enzymes or the effect of FA content of the cell. Both are relevant to deciphering the results of this chapter and will be considered for interpretation.

122 INDIVIDUAL FAS PERTURBATIONS IMPACT CELL GROWTH RATES

To complement the cultivation conditions during protein and FAME quantifications, growth rates were measured in strains grown in minimal media and induced with 0.1mM IPTG, in BW25113 genotype background (Figure 4.3), ∆fadD genotype background (Figure 4.4) and repeated in the presence of ‘TesA (Figure 4.3 (ii), 4.4 (ii)). These results show the effect of increasing individual FAS enzyme level has on growth rate compared to their respective negative controls, where Figure 4.3 (i) highlights several strains with lower growth rates than the negative control. Interestingly Figure 4.3 (ii) illustrates the addition of ‘TesA expression greatly improves all growth rates in comparison. In contrast, Figure 4.4 (ii) illustrate a larger frequency of strains growing below that of a negative control reference while Figure 4.4 (i) has lower overall growth rates compared to Figure 4.3 (i) and similar to which, a select few that displaying no growth whatsoever. Growth profiles were taken from measurements in 96-well plates cultivations, and as such the differences in oxygen transfer rate compared to those of batch cultivations, when comparing to protein and fatty acid quantifications is considered when deciphering results in the discussion of this chapter.

RELATING GROWTH RATE TO PROTEIN AND PHOSPHOLIPID PRODUCTION

Growth rate in bacteria is a highly responsive indicator of fitness and cell physiology (Marr, 1991; Russell and Cook, 1995; Scott et al., 2010). Similarly, protein production is a central physiological process which is inherently linked with cell division and cell size. When proteins are expressed optimally, the growth rate of that cell matches the increasing ribosomal protein-fraction, which in turn depends on the rate at which the cell can efficiently make new proteins (Bosdriesz et al., 2015). When overexpressing certain proteins, however, the total protein pool shifts at the expense of others, including the ribosomal protein-fraction and thus impacts growth. Furthermore, when down-regulating metabolic proteins, ribosomes may be less

123 i)

ii)

Figure 4.3. Growth curves of high-copy overexpression strains in (i) BW25113 genotype,

and (ii) including co-expression with ‘TesA. Performed in 96-wells, in triplicate, shaded regions represent SEM.

124 i)

ii)

Figure 4.4. Growth curves of high-copy overexpression strains in (i) ΔfadD genotype, and

(ii) including co-expression with ‘TesA. Performed 96-wells, in triplicate, shaded regions represent SEM.

125 efficient due to low amino acid availability which translates to an imbalance in growth. It has been reported that the synthesis of non-functional protein at low levels have small but significant effects on growth rate (Andrews and Hegeman, 1976), therefore even slight perturbation in protein expression may have consequences for cell fitness and growth.

The rate at which E. coli cells grow and divide is also governed by the global regulatory processes that respond to changes in environmental stimuli, a necessity that bacteria have evolved to survive in fluctuating environments (Erickson et al., 2017). One such process is the stringent response, which occurs during amino acid or carbon depletion and serves as a mechanism for survival during starvation by directly inhibiting the transcription of FAS genes via elevated levels of the alarmone ppGpp (Merlie and Pizer, 1973; Heath, Jackowski and Rock, 1994; My et al., 2013). This negative regulation of FAS therefore has a direct impact on growth due to the cessation in phospholipid production.

Further to this is catabolite repression, which is a central regulatory strategy employed by all free-living bacteria and mediates the preference at which different carbon sources are metabolised, as well as the coordination of this uptake with the conversion to biomass (Stulke and Hillen, 1999). The transcriptional activator for this process (Crp-cAMP) positively and/or negatively influences over 180 genes in E. coli (Grainger et al., 2005), including the positive regulation of fatty acid catabolic genes in response to carbon source availability (Feng and Cronan, 2012). This enables the efficient breakdown of FA available to produce energy and precursors that then enable cell growth.

Cell growth can also be considered a function of the rate at which carbon and metabolic precursors are converted to biomass, which in turn is controlled by the rate phospholipids are produced for cell membrane incorporation. Phospholipid production depends on the FAS pathway for acyl-ACP substrate, therefore limitations on the flux of FAS inhibits cell growth and division. This system of inter- regulation that has been described can be summarised in Figure 4.12, and has been applied in the interpretation of growth rate data from Figure 4.3 and 4.4 to derive hypotheses from observations.

126 Beginning with the overexpression experiments in BW25113 (Figure 4.3), we see the growth of the wild-type control as a model representation of acyl-ACP inhibition acting to naturally suppress FAS flux. Deviations from the wild-type are due to overexpressing a single protein, but the effect on FAS flux eventually reaches the natural point of inhibition from acyl-ACP accumulation. When ‘TesA co-expression is added to this experiment we see an overall stabilisation in growth, which could be due to the relief of acyl-ACP inhibition (Figure 4.11), but potentially also to the known effects ‘TesA has on elevating native Acc expression levels (Ohlrogge et al., 1995). Once the expressions are moved into a ∆fadD host, the cell is unable to synthesise acyl-CoA and therefore FadR cannot monitor and regulate intracellular FAS levels accordingly (Bushman, 1992). Figure 4.4 therefore demonstrates the combinatorial effect acyl-ACP inhibition when FadR is unable to sense high levels of acyl-CoA, potentially in permanent states of upregulating FAS genes. As before, the negative control of this experiment is a wild-type representation of the system, and deviations from this are from the effect of overexpressing individual FAS proteins. Lethal effects from FabA and AccA overproduction were observed in Figure 4.4 (i) as before, which is relieved when ‘TesA is introduced to the system in Figure 4.4 (ii). This is noted at the relief of acyl-ACP inhibition as before, but further to this is the higher growth potentials that have been reached compared to the same system in BW25113. This could be due to higher FAS flux from FadR acting in combination with native Acc upregulation from ‘TesA, providing more acyl-ACP for phospholipid production and growth. It’s also noted that in this system, FFA are unable to re-enter FA degradation cycles due to deletion of FadD, therefore any decline in stationary phase could represent a level of toxicity from extracellular FFA.

FATTY ACID QUANTIFICATIONS

Fatty acid content was measured in the form of fatty acid methyl ester (FAME) quantifications on whole cell culture samples of high-copy overexpressed strains in both BW25113 and ∆fadD genotype, with and without additional ‘TesA expression (Figure 4.5). Figures 4.5 (i) and (iii) illustrate the effect high-copy overexpression of individual FAS enzymes has on FA yield, while (ii) and (iv) demonstrates the effect of

127 introducing ‘TesA to the same perturbations, where we see an increase in FAME as expected. Figure 4.5 (ii) also highlights the stabilizing effect of ‘TesA dual expression on FA yield and cell density, especially in the case of Acc subunits, while in Figure 4.5 (iv), ‘TesA does not have this same influence, however, specific activities (FA yield per cell) are improved compared to Figure 4.5 (iii).

The effect FA content has on cell density is also noted, where Figure 4.5 (i) and (ii), highlight an increase in FA content at the expense of OD which is lower when ‘TesA is induced in BW25113. While in Figure 4.5 (iii) and (iv) except for strains that did not grow (AccA, FabA Figure 4.5 (iii), and PlsB in (iv)), the majority of strains expressing ‘TesA and therefore producing FA have a similar OD compared to that of non ‘TesA expression in ∆fadD. This indicates a correlation to genotype with FA impact on cell density. Translating this data to specific productivity by comparing FA yield per cell, we see how cell density correlates to FA production in ’TesA induced strains and that none of the overexpression strains have significantly increased FA yield compared to negative controls in high copy expression systems, which is supported by statistical evaluation.

FATTY ACID QUANTIFICATIONS FROM LOW-COPY PERTURBATIONS REVEAL VARYING RESULTS COMPARED TO HIGH-COPY

To investigate whether copy number was having detrimental effects on expression hosts and interfering with the interpretation of results, each FAS enzyme was introduced on low copy expression vectors as an alternative to the high copy system. As before, FAME quantifications were made in both expression hosts and compared to respective OD (Figure 4.6). With the exception of FabA in BW25113 and FadR in ∆fadD, no strain significantly overtakes the negative control strain in each experiment in low copy expression systems. OD is higher in BW25113 than ∆fadD, which is counter to what was observed in Figure 4.5. This indicates that low copy expression is better for cell growth in BW25113, while in ∆fadD the advantage of specific productivity seen in Figure 4.5 (iv) is lost at low copy (Figure 4.6 (ii)).

128 0.1 2.5 0.08 2 0.06 1.5 0.04 1 OD600nm

mg/ml FAME 0.02 0.5 FAME (i) 0 0 OD

0.2 1.5

0.15 1 0.1 * 0.5 0.05 *** *** OD600nm mg/ml FAME FAME 0 0 (ii) OD

0.06 2.5 0.05 2 0.04 1.5 0.03 1 0.02 * OD600nm mg/ml FAME 0.01 ** 0.5 0 0 FAME (iii) OD

0.2 2.5 0.15 2 1.5 0.1 *** *** *** *** 1 0.05 *** *** *** OD600nm mg/ml FAME *** *** *** 0.5 0 0 (iv) FAME OD

Figure 4.5. FAME quantifications and OD values of high-copy overexpression strains in (i) BW25113

genotype (ii) BW25113 plus ‘TesA (iii), ΔfadD genotype, and (iv) ΔfadD plus ‘TesA. FAME

quantifications represented as mg/ml in this figure as opposed to specific quantifications (per OD),

due to the presence of very low cell densities from this experiment – dividing FAME per OD would

create artificially high specific productivities and prevent accurate interpretation of results.

Performed in 250ml shake flasks, in triplicate, error bars represent SEM. * P <0.05, **P <0.01,

***P <0.001 vs. wild-type control, one-way ANOVA with corrections for multiple comparisons

(Tukey’s test).

129 0.2 1.26 0.18 * 1.24 FAME 0.16 1.22 OD 0.14 1.2 0.12 1.18 0.1 1.16 0.08 1.14 OD 600nm mg/ml FAME 0.06 1.12 0.04 1.1 0.02 1.08 (i) 0 1.06

0.12 0.96 0.95 FAME 0.1 OD 0.94 0.08 0.93 0.06 * 0.92 0.91 OD 600nm mg/ml FAME 0.04 * 0.9 0.02 0.89 (ii) 0 0.88

Figure 4.6. FAME quantifications and OD values of low-copy overexpression strains in (i)

BW25113 genotype plus ‘TesA and (ii) ΔfadD plus ‘TesA. FAME quantifications represented

as mg/ml in this figure as opposed to specific quantifications (per OD), due to the presence

of very low cell densities from this experiment – dividing FAME per OD would create

artificially high specific productivities and prevent accurate interpretation of results.

Performed in 250ml shake flasks, in triplicate, error bars represent SEM. * P <0.05 vs. wild-

type control, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

130 MEMBRANE POLARITY QUANTIFICATIONS

DiBAC3(4) assays, which quantify membrane depolarization rate, were performed on high- and low-copy overexpression strains to evaluate the impact these perturbations had on membrane potential. Subsequent to inducing stress from CCCP on each strain, membrane depolarisation was monitored over time in kinetic intervals (Figure 4.7). We see the notable difference in low copy strain response to treatment compared to high copy, where the response between treated and untreated cells is indistinguishable. This indicates that the stress from high copy expression exerts an effect on the membrane that is comparable to the stress associated with CCCP treatment, which may explain the observations made in FAME, growth and protein profiles thus far.

ATP ASSAYS

Intracellular ATP levels were monitored using Roche bioluminescence kit, as described in methods section 2.4. Since electron transport and ATP synthesis are coupled to the proton motive force, and results from Figure 4.7 indicate a depolarised cell membrane when high copy level perturbations are induced, these experiments aimed to establish a quantitative measure of any loss in ATP synthesis due to an impaired proton gradient. Furthermore, FFAs have previously been shown to reduce or prevent ATP synthesis (Desbois and Smith, 2015) therefore adding to conditions where FAS is impaired. These results show a low level of ATP in high copy expression strains relative to the negative control (Figure 4.8), while low copy strains produce similar amounts of ATP compared negative control, but lower overall compared to their high copy expression counterparts. This indicates that FFA may be causing an ATP limitation in both high and low copy expression strains compared to their controls, with high copy further impaired by membrane depolarisation as seen from experiments on membrane potential.

131 7000

6000

5000

4000

3000 Fluorescence a. u.

2000

1000 0 1000 2000 3000 4000 5000 6000 7000 Time (s) PlsB FadR AccABCD AccA AccB AccC (i) AccD FabI FabZ FabA FabH Neg PlsB FadR AccABCD AccA AccB AccC AccD FabI FabZ FabA FabH Neg

8000

7000

6000

5000

4000 Fluorescence a. u.

3000

2000 0 1000 2000 3000 4000 5000 6000 7000 Time (s)

PlsB FadR AccABCD AccA AccB AccC AccD FabI FabH FabA FabZ Neg PlsB FadR AccABCD AccA AccB AccC (ii) AccD FabI FabH FabA FabZ Neg

Figure 4.7. Membrane polarity quantifications using DiBAC3(4) on (i) low-copy and (ii)

high-copy overexpression BW25113 strains plus ‘TesA. Scatter with lines are baseline

polarization rates pre-treatment, scatter without lines are post CCCP stress induction.

Performed in 96-well in duplicate.

132 0.014

0.012

0.01

0.008

0.006 Low copy High copy 0.004 uM ATP concentra`on

0.002

Figure 4.8. Luciferase ATP assay on lysates of low-copy and high-copy overexpressions in

BW25113 strains plus ‘TesA. Performed in 96-well luminescence plate in duplicate, error bars represent SEM

133 ETHANOL QUANTIFICATIONS

As a proxy for acetyl-coA and FAS flux, ethanol production was quantified via HPLC for all strains subject to FAS perturbation, as indicated, in cultivations grown both aerobically and anaerobically. Figure 4.9 illustrates a higher ethanol production among the overexpression strains in combinations with ’TesA expression, both aerobically and anaerobically. Negative control strains harboring ‘TesA produce similar levels of ethanol both aerobically and anaerobically, however the effect of overexpressing any of the FAS enzymes along with ‘TesA greatly amplifies ethanol production anaerobically. Without ‘TesA dual expression, ethanol levels are only present anaerobically and at a lower level than ‘TesA expression. This suggests a ‘pull’ or sink on the flux of acetyl-CoA from FAS in ‘TesA dual expressions, which has led to an increased acetyl-CoA availability for ethanol production.

STEADY STATE CULTIVATION OF FA OVERPRODUCING STRAIN: FADR- ‘TESA IN ∆ FADD HOST

FA overproducing strain harbouring both a high-copy FadR overexpression plasmid plus a low-copy ‘TesA plasmid in ∆fadD genotype host, was cultivated continuously in a 1.5 L bioreactor, as described in section 2.2, for ~200 hours (Figure 4.10). This experiment was done to asses any improvement in specific productivity from continuous cultivation. Figure 4.10 shows an increase in FA production at the point of induction, correlating to a decrease in cell density, brought on by the increase in pH which has induced toxic effects on cell membrane and growth. While cell density stabilizes nearing the end of this experiment, FA is still increasing. At the end-point of this experiment, specific productivity (FA produced per cell) was at a higher rate than the same strain cultivated in batch during this study (Figure 4.5 (iv))), and verifies the improvement to be gained by continuous cultivation systems for FA production.

134 70 Anaerobic 60 Aerobic 50

30 mM Ethanol

Figure 4. 9. Ethanol quantifications by HPLC of overexpressed strains in BW25113 induced with 0.25mM IPTG in minimal media. Cultivated in 250ml shake flasks aerobically, 100ml

Wheaton flasks aerobically, +/- thioesterase. Performed in triplicate, error bars represent

SEM.

135 Figure 4.10. Minimal media steady-state cultivation of high-copy FadR overexpression plus

‘TesA in ΔfadD host, in continuous 0.1mM IPTG induction. Individual experiment in 1.5L bioreactor, measurements started after 3 volume changes of the reactor as standard protocol, error bars represent SEM of technical replicates.

136 Figure 4.11. Modes of regulation incurred by each genotype expression system used in overexpression studies. Inhibition by acyl-ACP in red, alleviation of acyl-ACP build-up in blue.

137 Figure 4.12. Mechanisms of growth rate regulation imposed by protein synthesis and phospholipid production. Inhibitions in red, activations in blue.

138 THE BURDEN OF HIGHLY OVEREXPRESSING INDIVIDUAL ENZYMES ON FAS

The results measured so far must also be interpreted in the context of the burden a cell undergoes when engineered for protein overexpression. Protein production in living cells can be thought of as a physiological economy that is tightly coordinated with external conditions and internal demands. This regulation is in place due to the cost that is associated with protein production, which can manifest itself as a cellular burden when a protein is produced whose function is not compatible with current needs (Wu et al., 2016; Kaleta et al., 2013; Kafri et al., 2016). The cost of protein production comes in many forms, namely in the consumption of energy and nutrients for building blocks, as well as the occupation of cellular machinery such as ribosomes, polymerases and chaperones. All instances can impose limiting factors on cell growth, division and cell size, and highlights the connection between protein production and cell adaptation (Kotte, Zaugg and Heinemann, 2010).

However, in instances such as the case of the experiments of this chapter, where the overexpression of a single FAS protein is forced and a cell is unable to gradually adapt its needs, the impact is observed as deviations in cell growth from their respective negative controls (Figure 4.3 (i) and 4.4 (ii)). When a detrimental reduction in growth is observed, this presents itself as the metabolic burden which has been imposed on the cell due to the cost of producing a protein which does not meet the specific needs of the cell at that time. Taking this consideration with the metabolic role of the FAS enzymes that impose burden, it is clear that elevating certain enzymes such as the Acc subunits and PlsB have a deregulating effect on the coordination of flux through FAS. These results can be deciphered when taking into further consideration the native regulation that is incurred, as described in Figure 4.11, along with the potential for substrate competition for acyl-ACP as it exists in the case of PlsB and ‘TesA leading to cessation in growth.

Further implications of perturbing expression levels of FAS enzymes are their impact on membrane integrity, since FAS is tied to membrane synthesis and homeostasis (Zhang & Rock, 2008), alterations made to FAS inevitably affect membrane composition which has implications for impairing the protective barrier function of the membrane. Since experiments on membrane potential illustrate a marked increase

139 of membrane depolarisation in cells highly overexpressing enzymes compared to low (Figure 4.7), it is reasonable to hypothesise that this translates to cells with a higher membrane permeability, susceptibility to ion leakage, low protein motive force and impaired ATP synthesis (Sikkema, Jan and Poolman, 1995; Hellingwerf and Konings, no date). Further effects of an impaired cell membrane include an inability to control intracellular pH, impaired enzyme activities, and lowered solute uptake (Harold, 1970). It is only through taking on the additional information of burden and toxicity, that the phenotypes observed so far for the overexpressed mutant strains can be attributed to model-building data.

IDENTIFYING PHENOTYPE FROM INDIVIDUAL PERTURBATIONS

Analysing the characteristics of microbial mutant strains for phenotype interpretation is a task that has gathered much effort over the years (Harper et al., 2011; Lewis, Nagarajan and Palsson, 2012; Gonçalves et al., 2013; Goodwin et al., 2014; Caglar et al., 2016), with the development of high-throughput screening as a powerful tool for identifying causal mutations that relate to microbial functions (Honisch, Raghunathan, Cantor, Palsson, et al., 2004; Smith et al., 2008). This chapter took an alternative approach toward phenotype interpretation, by generating single gene perturbations in various genetic backgrounds and followed with an investigation of phenotype by profiling protein content, growth and fatty acid characteristics (Figure 4.2-4.6). The purpose of this direction was to interrogate the complex regulatory network of FAS by making individual changes so that potential instances of pleiotropism, rate-limitation or dysregulation could be highlighted for interpretation, to be followed with a relay of the data towards model fitting and parameterisation. Furthermore, the task of identifying phenotype from overexpression studies has a rich history of insight, with the gain of mechanistic detail into how mutant phenotypes occur. An example is the categorisation of phenotypes into classes such as hypermorphic, hypomorphic, antimorphic or neomorphic according to Muller’s classic criteria (Table 4.1) (Muller, 1932; Prelich, 2012), which will be considered when deciphering phenotypes from this study.

140 Table 4.1. Mullers classes of mutations (Prelich, et al., 2012)

Classification Muller's terminology Hypermorph A mutation that causes a gain of a wild-type function, such as hyperactivity or unregulated activity toward a normal target. Antimorph A mutant allele that antagonizes its coexpressed wild-type gene product, resulting in reduction of total activity. Neomorph A mutation that causes a gain of an abnormal function, such as an enzyme targeting a new substrate, a DNA-binding protein obtaining altered binding specificity, or a protein localizing to an abnormal location. Hypomorph Partial loss-of-function mutation that results in reduced activity

Although the implications of high-copy expression on burden have been observed from Figure 4.7, it is still apparent that a select few mutants have more significant impacts on phenotype in terms of fatty acid production that others. For example, overexpressing FadR in BW25113 genotype has the most upregulating or hypomorphic influence, while overexpression of subunits AccA, AccB, and AccC significantly decrease production and growth (Figure 4.3 (i) and 4.5 (i)). The negative effect of Acc subunit expression is most likely caused by the inhibition from disrupting the stoichiometric complex of AccABCD, as well as the growth rate autoregulatory mechanisms that take place with expression and translation of the subunits, as mentioned in section 4.1 of this chapter. It is known that overexpression of a single subunit can lead to multiple contacts of subassemblies, which result in a reduced amount of intact functional protein complex (Prelich, 2012). These effects are eradicated almost completely when the co-expression of ‘TesA Is included, which can be attributed to the known effects cytosolic thioesterase expression has on the native upregulation of Acc subunits as discussed previously (Ohlrogge et al., 1995). When PlsB and ‘TesA are co-expressed in FadD, however, antimorphic effects are observed whereby a reduction in total activity is present in the form of growth and FAS in Figure 4.5 (iv), which is most likely due to substrate competition for acyl-ACP that is highlighted by the fact acyl-CoA is no longer available from FadD. Besides this instance, the expression of ‘TesA in both genotypic backgrounds

141 appear to stabilise growth rates (Figure 4.3 and 4.4 (ii)), which provides additional evidence of an advantageous balance in native gene expression levels, despite the potential toxic effects excreting fatty acid has on cell viability. The co-expression of ‘TesA on fatty acid productions also demonstrate an inability for most overexpression mutants to outperform the negative control strains in terms of FAME yield (Figure 4.5 (ii) and (iv)), which further highlights the advantage ‘TesA has on native upregulation, and suggests the need for increasing ‘TesA expression to provide more of a ‘sink’ action of the flux in order to observe the effects of single protein overexpression on phenotype. The data suggests that the effects of the overexpressed mutants are lost to the native upregulating effect on protein content, which we see in Figure 4.2 (iii). These ratios of relative protein abundances from negative control strains compared to overexpressed mutants however, may give an indication into the FAS proteins that require specific modulation in their expression levels, if the goal is to outperform the negative controls in terms of fatty acid production.

It is clear from decades of research into metabolic pathway engineering and recombinant protein production that there are multiple factors to consider when the goal is to optimise the flux of multi-step biochemical processes such as FAS, not least of all the combination of target proteins and expression levels that are optimal for the desired output (Oyarzún et al., 2009; Holtz and Keasling, 2010; Zhang, Carothers and Keasling, 2012; Xu et al., 2014; Liu et al., 2016). While it is understood that single perturbations may not be sufficient to lead to any significant observable phenotype in FAS production, and which may have been the case in several instances here, the underlying goal was to report on ways in which the overexpressions from this study could dismantle aspects of FAS regulation. This was achieved in part by combining the analysis of several datasets from protein level, growth rate, burden and fatty acid yield. The incorporation of the data into novel modelling approaches of FAS is being considered for continuation of this research.

4.4 FUTURE DIRECTIONS

The outcome of this chapter has been an accumulation of in vivo datasets that have been collected form overexpression studies of individual enzymes of the FAS

142 pathway. The control response of FAS has been captured under a range of altered regulatory conditions and perturbations as highlighted (Figure 4.11), and this has been compared to what is known of FAS regulation in E. coli. While this reinforces our understanding of existing information on FAS regulation and control, the more difficult tasks of identifying unknown mechanisms of control through unravelling complex links between the physiological and molecular interactions of the pathway requires the assistance of computational modelling techniques. A kinetic model would greatly benefit the understanding of fluctuations of these complex mechanisms, as well as assisting in further engineering design strategies. A system of protein interactions that translates in mathematical terms to ordinary differential equations of what is known in the network kinetically, can formulate predictions and aid experimental validation (Chakrabarti et al., 2013; Stanford et al., 2013; Miskovic et al., 2015) . This was an initial objective to this work, however, a hurdle to the kinetic model building process is a requirement for precise kinetic measurements for parameterisation. While there are kinetic rates available from the Braunschweig enzyme database (BRENDA) for a vast majority of the FAS enzymes, these values were obtained from in vitro measurements under varying experimental conditions, therefore the combination of all available kinetic rates into one model would be a huge generalisation, and inevitably lead to inaccuracies. This experimental approach of gathering in vivo measurements, could instead be interfaced with a ‘top-down’ modelling strategy such as one that has been published by Klumpp, Zhang and Hwa, (2009), which utilises the known qualitative regulatory processes along with some flux constraints, to derive a quantitative model of growth that is not dependent on kinetic parameters. This approach uses certain global resource allocation considerations such as carbon influx, protein synthesis and proteome allocation, along with regulatory functions of a protein in their model. It would be interesting to apply this to a FAS network context, since the molecular control exerted by messengers cAMP and ppGpp in the model structure is also relevant to FAS regulation, as described in sections 1.5 and 3.1. The ability to train the model using the growth rate datasets obtained from this study, correlating to phospholipid synthesis and therefore obtaining a predictive FAS network, is an interesting challenge to pursue and one which could be inititated with the use of this data and collaboration with modellers. It could also be improved upon by accounting for the metabolic burden that has been observed both from ATP measurements and from

143 the overexpressions studies, which would add a further layer of information on the energetic capacity for FAS engineering. This could also account for many of the inconsistencies so far recorded in accomplishing the maximum theoretical achievable yield, since the FAS pathway has a high ATP requirement.

Solving the problem of uncertainties in structure and parameter values is paramount to improving quantitative predictive modelling (Segrè et al., 2003; Lubitz et al., 2010; Carta, Chaves and Gouzé, 2012; Cotten and Reed, 2013; Tohsato et al., 2013; Gábor and Banga, 2015). This has been addressed by the community through the development and use of an approach called ensemble modelling (Tran, Rizk and Liao, 2008; Contador et al., 2009; Dean et al., 2010; Tan and Liao, 2012), which is applied by simultaneously using various models to arrive at a collective description (an ensemble), the average of which is then used to guide predictions. In this way, high levels of uncertainty in single mathematical representations can be avoided, by instead integrating a range of model structures and parameter values to generate predictions. Ensemble modelling is used in fields such as weather forecasts and financial market predictions (Gneiting and Raftery, 2005; Lai et al., 2006), as well as in strain design for metabolic engineering (Tan and Liao, 2012). Furthermore, the application of machine learning algorithms to the in vivo datasets generated from this study, to construct an ensemble model for predictive modelling is a likely direction for this research. The method of using ensemble methods in machine learning for biological applications is well established and has already been applied to model construction (Winkler et al., 2015), which provides direction for furthering this work. Self-organising maps (SOM) is another computational pattern recognition technique, which organizes input data according to a given similarity criteria (Goodwin et al., 2014). It is an unsupervised method that results in a two-dimensional map, where similar data profiles are compared and can be visualised against one another. SOM analysis has been implemented into a metabolomics framework previously for disease characterization (Zarkogianni et al., 2013), as well as the application to analyse and interpret lipid data (Kumpula et al., 2010). This direction would be interesting for visual interpretation of the overexpressed mutants generated from this study, as the analysis has been challenging to decipher systematically and via relative comparison to controls.

144 It is clear that there are many options to further this research computationally, the ideal outcome being the gain of a predictive FAS model for strain engineering purposes. Several options have been discussed for ways the phenotypic and physiological data generated from this study could be used for dynamic model building to gain a predictive tool. In strain engineering, the ability to predict the effects of varying titres of gene expression on phenotype and on improving production capabilities is invaluable though non-trivial to develop, as we have learned. Often a starting point, however, in the development of kinetic models are assessing parameters such as enzyme level, turnover rates and kinetic constants and inhibitions, for their sensitivity or impact on overall flux rates. An elegant mathematical theory has been developed which demonstrates this, called metabolic control analysis (MCA) (Kacser and Burns, 1995; Heinrich and Rapoport, 1974), and which is relevant to the continuation of this work.

145 5 EVALUATING ENZYMES THAT BYPASS THE ACETYL-COA CARBOXYLASE STEP OF FAS

5.1 INTRODUCTION

Metabolism, carried out by enzymes of the cell, is a process which utilises substrates to transform energy and intermediates into end products in certain arrangements and quantities according to the constraints of the metabolic system. Regulation and control in metabolism, as we have learned, are adaptive mechanisms which are necessary to maintain the conditions that can support life, as they enable a flexible system which can adjusts to change. Due to their abilities to sense metabolic states, highly regulated points in metabolism also tend to become the point in a series of reactions which operate the ‘slowest’ or at a rate-limiting pace (Metallo and Vander Heiden, 2013). Under certain conditions a cell may require an adjustment to a metabolic route according to the needs of the cell at that time. These alternative routes, shunts, or bypasses, can be taken to redirect metabolism to save resources or when substrate availability is limited. A classic example of this is the glyoxylate shunt in E. coli (Kornberg, 1965), whereby steps which lose CO2 during TCA are bypassed by two key enzymes; isocitrate lyase and malate dehydrogenase A, during growth on acetate. This adjustment enables the provision of energy and cell components when acetate is the sole carbon source available, as it conserves the four-carbon TCA intermediates by bypassing them and taking up two acetyl-CoA molecules per turn. The glyoxylate shunt is repressed during growth on glucose and induced by growth on acetate by the repressor protein IclR (Wolfe, 2005; Gui et al., 1996).

Acetate also plays a key role during fermentation in yeast, where it is produced by the pyruvate dehydrogenase bypass for acetyl-CoA synthesis in the cytosol (Remize and Andrieu, 2000). This alternative route of acetyl-CoA production enables a supply that may be solely utilized for lipid production as opposed to entering the TCA cycle during pyruvate dehydrogenase production, that takes place in the mitochondrion.

146 A further addition to natural existing bypass capabilities in metabolism is that of gluconeogenesis, which occurs as a fundamental reversal of glycolysis except for two steps in E. coli (Otto, 1984; Russell, 2007). The steps that are unique to gluconeogenesis in E. coli are catalyzed by irreversible fructose 1,6-bisphosphatase and PEP synthetase reactions, which bypass steps catalyzed by 6- phosphofructokinase and pyruvate kinase, respectively. These steps in gluconeogenesis are anaplerotic, and replenish pools of metabolite intermediates as they are required by the cell, therefore coordinated regulation occurs between glycolysis and gluconeogenesis to meet the energy requirements of the organism under varying conditions (Sauer and Eikmanns, 2005).

In nature, enzymes can share substrates, have broad specificities, or comprise putative reactions (determined by their shared homology with enzymes of conserved reactions). These features have given rise to the application of ‘flexibility’ to direct engineering approaches in modifying metabolism toward a specific objective- replacing native reactions with alternative routes. Genetic engineering has enabled the practice of introducing heterologous enzymes into host organisms to catalyse novel reactions that can outperform the activity of the enzyme in native conditions (Liu et al., 2013; Zhang et al., 2017). These have involved the introduction of enzymes that are (i) not subject to regulatory limitations, (ii) perform with higher catalytic rates or a wider substrate range, and (iii) operate with less energetic cost than the reactions they aim to replace. This tailoring of metabolism is especially relevant in industrial biotechnology applications concerning the optimization of biochemical pathways in organisms that enable efficient commodity chemical production, as outlined in section 1.2.

Many bypass routes have been introduced in organisms to achieve this, including the mevalonate-dependent pathway in E. coli for the production of terpenoids such as artemisnic acid (Martin et al., 2003), and the application of phosphoketolase (PKT) which presents a carbon-efficient biocatalysis strategy, through bypassing most of central metabolism in E. coli to generate activated acetyl moieties directly from phosphosugars (Henard, Freed and Guarnieri, 2015). Bypasses to photorespiration, with the objective of reducing losses in the cholorplast, have also been suggested with varying benefits reported (Kebeish et al., 2007). Alternative routes to photorespiration have been suggested for years as a means of improving

147 photosynthesis (Peterhansel, Blume and Offermann, 2017), a resource all or most living things are dependent on but that which is currently at most risk due to climate change. Strategies of engineering autotrophic systems are currently in their infancy, while significant advances in the development of genetic tools have recently been achieved with microalgal model systems (Ng et al., 2017), they must be improved further if processes of photosynthesis are to be engineered or bypassed for meaningful benefit, including to biofuel applications.

THE APPLICATION OF ACC BYPASS ROUTES TO IMPROVE FATTY ACID YIELD

In order to circumvent the energetic and regulatory limitations that face FAS, a novel and alternative pathway to malonyl-CoA production was proposed during this study to by-pass the traditional ACC route in E. coli (Figure 5.1). Acc is a highly regulated and complexly formed enzyme (Cronan, 2001), as outlined in previous sections. This regulation and complexity places it in the ‘rate-limiting’ category of biosynthetic steps, often referred to as a gatekeeper to FAS and is known to impose significant control on the rate of these reactions (Janßen & Steinbüchel 2014; Davis et al. 2000). Acc imposes further limitations on the system through feedback regulation of acyl-ACP, autoregulation of subunit transcription, and the energetic requirement of ATP for every catalytic conversion of acetyl-CoA to malonyl-CoA (Figure 1.6, see also section 4.1). Furthermore, since E. coli maintain low levels of malonyl-CoA for FAS, insufficient supplies of malonyl-CoA limit the yield of FFA production (Fowler, Gikandi and Koffas, 2009; Zha et al., 2009; Feher et al., 2015). If these bottlenecks could be circumvented, there is the potential to improve FA yield by enhancing the supply of malonyl-CoA without the restraints of the native reaction.

Three routes of ATP saving pathways to malonyl-CoA synthesis are proposed in this chapter, involving non-native enzymes malonyl-CoA reductase (Mcr) & malonyl-CoA carboxytransferase (Mcc) (Figure 5.1). The Mcr pathway has two candidate enzymes for the conversion of malonate semi-aldehyde to malonyl-CoA. The Mcc pathway involves an enzyme which couples two carboxylation reactions:

oxaloacetate + acetyl-CoA  pyruvate + malonyl-CoA

148 Figure 5.1. Acc bypass pathways proposed in red; with oxaloacetate as substrate catalyzed by malonyl-CoA carboxytransferase (Mcc), or malonate semialdehyde catlysed by malonyl-

CoA reductase (Mcr), these enzymes are proposed to circumvent native ACC malonyl-CoA production as a rate limiting step.

149 These enzymes are not subject to the native regulatory limitations that are imposed on FAS in E. coli, and therefore may provide higher catalytic rates to contribute an improvement in flux.

Optimising the availability of precursors oxaloacetate and malonate semialdehyde for the heterologous bypass enzymes are also relevant points to research, and are more likely to enhance further activity towards malonyl-CoA production compared to optimising the native and central metabolite precursor, acetyl-CoA. Further modifications in tailoring the flux rate through the FAS pathway by dynamic expression, so that substrate levels match enzyme activity and feedback inhibitions are limited for example, could then be applied.

BALANCING A SYNTHETIC BYPASS AND DECIPHERING CONTROL DISTRIBUTION

When aiming to circumvent reactions for the improvement in product yield, one challenge to consider during the engineering process is the response the cell has towards modulations of intracellular metabolite levels, particularly if these biochemical reactions are as highly regulated as FAS. Adjusting levels of protein or metabolite can have knock-on or detrimental effects that may impede the efficiency of the biochemical pathway in question, by causing unintentional bottlenecks or inhibitory mechanisms in the system. Overcoming these challenges in genetic engineering has led to adopting an iterative ‘design-build-test’ scheme into metabolic engineering research practices (Dai and Nielsen, 2015b; Guo, Sheng and Feng, 2017; Miskovic et al., 2017; Wintle et al., 2017), which stipulate the challenges that present themselves in predicting the outcome of novel genetic manipulations in chassis host strains. Through this iterative process, novel reactions can be characterised for extrapolation into predictive modelling techniques for further testing.

Balancing the response of an organism to an introduced genetic modulation can be achieved in various ways; translationally through RBS strength, posttranslational modification, codon optimisation, the addition of affinity tags for purification, localisation or degradation; and transcriptionally through promoter strength and

150 inducibility, plasmid copy number and transcription terminators (Chen et al., 1994; Nicole et al., 2011; Yang et al., 2013; Mahalik, Sharma and Mukherjee, 2014; Parret, Besir and Meijers, 2016). Genetic engineering also employs the use of well characterised transcription regulators to modulate the expression of genetic material in response to certain intracellular stimuli (Babu and Teichmann, 2018). This enables a system which can adapt to the perturbation made on metabolite fluctuations, which advances the efficiency of overexpressing certain enzymes for metabolic use (Chen et al., 2017). By optimising a system which can balance expression of heterologous pathways under the specified conditions, chemical output can reach its maximum potential.

When introducing heterologous components (as with this study, through the use of extremophilic enzymes to bypass traditional malonyl-CoA biosynthesis in E. coli), it is important to consider the impact expression will have in terms of toxicity, productivity, biochemical intermediates and in metabolic burden. Many of the techniques discussed here can be applied to optimise these points of regulation, though more fundamental is the consideration of enzyme activity when expressed in the host. Due to differences in protein folding from altered temperature, pH and codon usage in alternative hosts, varying degrees of limitations are imposed on heterologous enzyme activity (Vieille and Zeikus, 2001). Once activity is established, the consideration is then on the impact that introduced bypass enzymes have on metabolic control distribution. If the effect of introducing alternative routes is sufficient to overtake the native route, subsequent downstream reactions must also be adjusted to gain the benefit from bypass routes, which is in keeping with the theory around MCA. In this way, this chapter aims to combine the findings of previous chapters from process optimisations, in engineering individual enzymes in FAS and in determining the potential for further ‘rate-limiting’ points (or altered control distribution) in Acc bypass strains, so that an improvement in FA yield can be gained.

151 5.2 AIMS AND OBJECTIVE

This chapter of the thesis aims to circumvent the reported regulatory and energetic limitation of Acc in FAS, by introducing three bypass pathway enzymes; Mcr1, Mcr2 and Mcc, herein known as the Acc bypass pathways. The experiments that follow aim to report on their efficiency on rerouting the traditional malonyl-CoA synthesis pathway, and further contribute to observations and evidence gathered from previous chapters; on whether Acc is the main limiting factor on FAS flux and the conditions required for an increase FA production. The impact of introducing Acc bypass pathways on malonyl-CoA and FA production levels are evaluated experimentally, as is their effect on deregulating the Acc step of FAS.

If Acc bypass routes are shown to counter the rate-limiting effect of Acc, the secondary aim is then to apply what has been observed from the process optimisation study for FA overproduction, so that the chapters from this thesis can contribute synergistically to an improvement in E. coli FA production. Overall, the objective of this Chapter is to contribute to attaining the maximum theoretically achievable yield of FAS in E. coli, from meeting both the physiological and genetic requirements necessary to do so.

5.3 RESULTS AND DISCUSSION

SELECTION AND CLONING OF HETEROLOGOUS ACC BYPASS ENZYMES INTO EXPRESSION VECTORS

Candidate enzymes for the Acc bypass pathways were selected by searching the literature and relevant databases for malonyl-CoA producing reactions and their efficiencies. The enzymes chosen to bypass Acc for this study were malonyl-CoA reductase from Sulfulobus tokadaii (Mcr1), malonyl-CoA reductase from Metallosphera sedula (Mcr2), and malonyl-CoA carboxylase (Mcc) from Propionbacterium frieundrechii. The enzymes selected were then codon optimised for E. coli (Grote et al., 2005) and synthesised via IDT (UK) to have flanking prefix

152 and suffix regions that are compatible with the BASIC assembly protocol. An RBS (pETDuet) was also included upstream of the start codon. The constructs were obtained by modular assembly using BASIC, as before. Once assembled into high and low copy expression constructs, transformants were confirmed via colony PCR, mini-prepped and sequenced for confirmation. These verified constructs were used in conjunction with malonyl-CoA sensor, low copy ‘TesA, pLR-RFP-‘TesA and ‘TesA- jhAMT expression constructs throughout this study (Table 2.2).

ADDITION OF FABD TO ACC BYPASS EXPRESSION VECTORS

To alleviate a potential bottleneck introduced by the Acc bypass enzymes, the addition of FabD on Acc bypass expression vectors was incorporated into this study and achieved via EMP (Figure 5.2). Primers were designed to amplify the FabD gene with 3’ homology to the terminator region of the Acc bypass constructs; this megaprimer was then used to exponentially amplify the Acc bypass constructs using a reverse primer annealing upstream to the region of FabD insertion. These assemblies were confirmed via colony PCR and sequence verified.

COMPLEMENTATION OF AN ACC DEFICIENT HOST

To assess the viability and activity of these extremophilic enzymes towards essential malonyl-CoA production, the Acc bypass enzymes were expressed in an Acc defective mutant E. coli host, LA1-6 (Harder et al., 1972), for Acc complementation studies. This strain, derived from parent AB1623, has a thermo-sensitive mutation on subunit AccD and therefore is unable to transfer the carboxyl group from acetyl-CoA to malonyl-CoA at temperatures above 30˚C (Harder et al., 1972). Therefore, at non- permissive temperatures of 31˚C and above, LA1- 6 strain cannot synthesise the acyl-ACP species required for incorporation into the phospholipids necessary for biomass and growth and have a conditional lethality.

To harness this Acc selection LA1-6 was transformed with Acc bypass constructs, transformants were spotted on selective plates in duplicate at 30˚C and 37˚C (Figure

153 5.3 (i)). These transformants did not display temperature complementation due to the presence of the Acc bypass genes on solid agar, as shown. However, cultivation in liquid media revealed complementation of all Acc bypass enzymes in the selective host, as improvements in doubling times of LA1-6 when all constructs are present at 38˚C in Figure 5.3 (ii), compared to the control empty vector. Figure 5.3 (iii) illustrates the same growth experiment at 30˚C and highlights the metabolic burden associated with Acc bypass expression, from the prolonged doubling times observed of all selective hosts when compared to the negative control.

INTRACELLULAR MALONYL-COA PRODUCTION

Intracellular malonyl-CoA levels were monitored using a hybrid promoter-regulator fluorescent sensor, as developed by Xu et al., (2014). When used in parallel with the antibiotic cerulenin to promote malonyl-CoA accumulation (Omura, 1976), fluorescence levels were detected in by-pass strains Mcr1, Mcr2 and Mcc, compared to those of the negative control (Figure 5.4). In Figure 5.4 (i), an improvement in malonyl-CoA levels are observed for Mcr1 and Mcc compared to the negative control and Mcr2. When examining the same by-pass enzymes under the low copy expression in Figure 5.4 (ii), malonyl-CoA fluxes are improved in Mcr1 and Mcr2, compared to negative control and Mcc. These observations indicate that malnoyl- CoA improvements are conditional to copy-number for Mcr2 and Mcc, but not Mcr1.

When examining the addition of FabD expression with Acc by-pass enzymes in terms of malonyl-CoA levels in Figure 5.4 (iii), intracellular pools are depleted compared to previous observations and controls. This is expected as FabD catalyses malonyl-CoA to malony-ACP. The question then follows as whether this observation translates to an increase in FA flux or if there is subsequent rate-limitation on the pathway. Interestingly, Acc overexpression in Figure 5.4 (iii) shows a delay in malonyl-CoA accumulation, and Mcc outperforms all strains for malonyl-CoA accumulation (over two-fold improvement from negative control), followed by Mcr1. The FabD control outperforms both Mcr1-FabD, Mcr2-Fabd, and the negative control. This could be due to alleviating a potential FabD bottleneck from the native system

154 Figure 5.2. FabD-bypass EMP assembly outline. Plasmid template generalized to represent

Acc bypass gene expression vectors (Table 2.3).

155 Figure 5.3. Complementation studies on Acc bypass genes in thermosensitive mutant

(LA1-6; purple) and parent (AB1623; grey) hosts (i) on solid media at varying temperatures, (ii) liquid media at 38˚C and (iii) liquid media at 30˚C. Experiments ii-iii performed in triplicate, error bars represent SEM.

156 Figure 5.4. Intracellular malonyl-CoA levels detected in Acc bypass strains by FapR hybrid promoter-regulator fluorescent sensor (i) in high-copy, (ii) low copy expression (iii) and the addition of FabD at high-copy. Performed in 96-well, in duplicate, fluorescence per OD plotted.

157 FATTY ACID QUANTIFICATIONS

FAs produced from Acc by-pass strains were quantified via FAME analysis using GC-MS, testing a variety of cultivation conditions in batch to find optimal processes for overproduction. Unless stated otherwise, the cultivation conditions were in 30mL MOPS minimal media, induced with 0.1mM IPTG at 30°C for 22 hours. Firstly, levels of aeration were evaluated in Figure 5.5, where it was found that neither aerobic nor anaerobic conditions improved FA production in strains expressing Mcr1, Mcr2 or Mcc compared to negative control. Following this several conditions were further examined, including altering temperature and carbon sources (Figure 5.6 and 5.7), inducer type (Figure 5.8), cultivation length and rich media substitution (Figure 5.9), aspartic acid supplementation for oxaloacetate supply (Figure 5.10) and dodecane overlay (Figure 5.11). In all cases shown, no significant or reproducible improvements in FAME quantifications were observed for strains expressing by-pass enzymes compared to negative controls, including when FabD was added to Acc bypass constructs for expression (Figure 5.12).

These results show that malonyl-CoA improvements from Acc by-pass expressions (Figure 5.4) do not translate to improvements in FA yield under the cultivation conditions examined. These results are similar to what has been reported by Davis et al., (2000), where a 100-fold increase in malonyl-CoA levels corresponded to only 6- fold improvement in FA yield, indicating further FAS steps becoming rate-limiting to the flux. The next line of enquiry was to ask whether effects on FAS flux could be observed intracellularly, which was subsequently investigated using the acyl-CoA responsive sensor constructs (Zhang, Carothers and Keasling, 2012).

INTRACELLULAR FATTY ACID ACYL-COA DETECTION

Intracellular acyl-CoA levels were monitored using a promoter that also comprised FadR binding sites, as characterized by Zhang et al., (2012). This system drives expression of RFP when FadR is unbound to the promoter in the presence of high levels of acyl-CoA, therefore acting as a proxy for intracellular acyl-CoA levels, and

158 * ** *** ** ** ** **

Figure 5.5. FAME quantifications normalized to OD values of high-copy overexpression strains in ∆fadE genotype plus ‘TesA in varying aeration conditions. Performed in 250ml shake flasks in triplicate, error bars represent SEM. * P <0.05, * P <0.01, *** P <0.001 vs. wild-type control, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

159 * *** * **

Figure 5.6. FAME quantifications normalized to OD values of high-copy overexpression strains in ∆fadE genotype plus ‘TesA in varying temperature conditions. Performed in

250ml shake flasks in triplicate, error bars represent SEM. * P <0.05, * P <0.01, *** P <0.001 vs. wild-type control, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

160 800

700

600

500 glycerol 400 ** glucose 300 *** ***

ug total FMAE for OD 1200

100

0 MCR1 MCR2 MCC FadR Control

Figure 5.7. FAME quantifications normalized to OD values of high-copy Acc bypass overexpression strains in ∆fadE genotype plus ‘TesA in varying carbon source cultivations.

Performed in 250ml shake flasks in triplicate, error bars represent SEM. ** P <0.01,

*** P <0.001 vs. wild-type control, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

161 140

120 100 * ** 80 * * ** 30°C 60 37°C

40 ug total FAME for OD1

0 MCR1 MCR2 MCC Control

SEM. * P <0.05, ** P <0.01 vs. wild-type control, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

162 0.045

0.04

0.035 0.03 * 0.025 MOPS 0.02 LB 0.015 ** mg/ml FAME per OD 1 0.01

0.005

0 MCR1 MCR2 MCC Control ACC FADR

Figure 5.9. FAME quantifications normalized to OD values of high-copy Acc bypass

overexpression strains in BW25113 genotypic plus ‘TesA, cultivated for 48hrs in varying media type. Performed in 250ml shake flasks in triplicate, error bars represent SEM.

* P <0.05, ** P <0.01 vs. wild-type control, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

163 0.25

0.2

0.15 * BW25113 0.1 ΔfadL mg/ml FAME per OD 1 0.05 *** ***

0 MCR1 MCR2 MCC Control ACC

Figure 5.10. FAME quantifications normalized to OD values of high-copy Acc bypass overexpression plus ‘TesA in varying genotype, supplemented with 5mM aspartic acid.

Performed in 24-deep well culture plates in duplicate, error bars represent SEM. * P <0.05,

*** P <0.001 vs. wild-type control, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

164 0.07

0.06

0.05

0.04 MOPS 0.03 ** LB

mg/ml FAME per OD 1 0.02

0.01 ***

0 MCR1 MCR2 MCC Control ACC FADR

Figure 5.11. FAME quantifications normalized to OD values of high-copy Acc bypass overexpression strains in BW25113 genotypic plus ‘TesA, cultivated for 48hrs in varying media type with dodecane overlay. Performed 24-deep well culture plates in triplicate, error bars represent SEM. ** P <0.01, *** P <0.001 vs. wild-type control, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

165 0.07

0.06

0.05

0.04 LB 0.03 MOPS 0.02 * * mg/ml FMAE per OD 1 0.01

0 MCR1 + MCR2 + MCC + ACC FabD Control FabD FabD FabD

Figure 5.12. FAME quantifications normalized to OD values of high-copy FabD + Acc bypass overexpression plus ‘TesA in BW25113, in varying media type. Performed in 250ml shake flasks in triplicate, error bars represent SEM. * P <0.05 vs. wild-type control, one-way

ANOVA with corrections for multiple comparisons (Tukey’s test).

166 FA flux when ∆fadE is used as the expression host. Higher acyl-CoA intracellular concentrations were observed following low induction (0.1mM IPTG) of Mcc and Mcr1 compared to negative control (Figure 5.13 (i) and (iv)). This effect was lost when inducing at higher levels of IPTG (0.5mM and 1mM, Figure 5.13 (ii) - (iv)). Mcr2 appeared unresponsive to this assay, similarly as it did in high-copy evaluation of malonyl-CoA levels Figure 5.4 (i) compared with the control, which indicates a negative effect of high-copy expression on this gene. Repeating this assay with FabD addition reveals a responsive Mcr2, high performing FabD control and lowest performing Mcc (Figure 5.14 (i)). In minimal media, this experiment illustrates all overexpressing strains are unable to maintain a rate of acyl-CoA flux which is comparable to the empty vector pLR control (Figure 5.14 (ii)), which is likely due to metabolic burden of producing multiple proteins (Acc bypass + FabD + ‘TesA) as opposed to only ‘TesA in pLR control.

ENHANCED MALONYL-COA PRODUCTION DOES NOT LEAD TO AN IMPROVEMENT IN FA YIELD

The functionality of Acc bypass enzymes have been demonstrated in Acc complementation experiments (Figure 5.3), which led to their evaluation on impacting FAS in vivo. Malonyl-CoA assays demonstrated an increase in accumulation inferred by the Acc bypass enzymes Mcr1 and Mcc (Figure 5.4 (i)), which did not as readily translate to an increase in acyl-CoA for these strains (Figure 5.14). This data suggests the presence of further limiting points in the system which are interfering with improving FAS flux from the application of the Acc bypass enzymes, and raise further questions about the flexibility of engineering E. coli to improve FAS flux. It is possible that enzymes further downstream of the pathway are simply too rigid to observe improvements from simply increasing malonyl-CoA levels. This, however, is an opposing statement to data that has been reported in the literature on one of the Acc bypass enzymes, Mcc, (Soo and Kuk, 2017), which was published after this study commenced. The study reports a 120% improvement in FA titre compared to an Acc overexpression control. However, these reported improvements in FAS flux were as a result of combining other optimisations such as

167 Figure 5.13. Intracellular acyl-CoA levels detected in Acc bypass strains by FadR hybrid promoter-regulator fluorescent sensor (i-iii) in high-copy with varying IPTG concentrations, and (iv) from all concentrations at end-point measurements. Performed in

96-well in duplicate, fluorescence only plotted for IPTG titration, error bars represent SEM.

168 Figure 5.14. Intracellular acyl-CoA levels detected in Acc bypass strains by FadR hybrid

promoter-regulator fluorescent sensor (i) with the addition of FabD with 0.1mM IPTG

induction and (ii) with minimal media cultivation instead of standard rich media.

Performed 96-well in duplicate, fluorescence per OD plotted.

169 increasing Ppc and MaeB, as well as addition of aspartate as an oxaloacetate precursor to gain an improvement in flux. Furthermore, the negative control used here is Acc overexpression, which, as we have seen from chapter 4, requires specific expression of each subunit to meet the stoichiometric requirements for stable formation of this multiplex enzyme, and avoid subjecting the system to impaired efficiency of FAS. Taking into account the results observed from chapter 4; overexpressing Acc subunits rarely outperforms the wild-type negative controls due to potential deregulation of the stoichiometric complex, it could therefore be the case that the reported improvements from Soo and Kuk, (2017), are artificially higher compared to inefficiencies in their negative control. A true negative control, and one that applies to this study also, would ideally be the expression of inactive versions of the Acc bypass enzymes. This would give a more accurate representation of the ability of the enzymes to bypass Acc, while accounting for the cost or protein production, and eliminating inaccuracies or unpredictable effects from overexpressing a highly-regulated enzyme such as Acc.

The fact that these results could not be replicated by this study when aspartic acid was used as a precursor (Figure 5.10) does highlight the significant challenge that attribute engineering FAS in E. coli; that the system of achieving high FA yield is extremely conditional and sensitive to changes in experimental parameters, which presents a huge challenge in reproducibility and has been apparent during the course of this study. This is further evident in the literature, where several instances of identical genetic manipulations were made in different studies and have resulted in varying FA yield (Table 1.1), and highlights further the need for a kinetic model to aid in predicting and translating the uncertainties that face engineering a system such as FAS. From this study however, it is valuable to understand exactly what mechanisms are behind preventing the high malonyl-CoA levels from Acc bypass strains translating to any improvement in FA yield, and what factors in this experimental system are causing the robustness or rigidity downstream to malonyl- CoA.

Several explanations are available for interpretation around known and putative limitations of FabD and further FAS enzymes. Firstly, they likely stem from the fact that acetyl-CoA is not being utilised by Acc for malonyl-CoA production in Acc bypass strains, therefore causing known competitive inhibition in FabD as discussed

170 in chapter 4, and in accordance with its known ping-pong bi-bi kinetic mechanism (Joshi, 1972). While FabD is specific for the malonyl group and does not transfer the acetyl group from actyl-CoA, it is competitively inhbited by acetyl-CoA. Furthermore, the transacylase reaction is subject to further competitive inhibition; where CoA is a competitive inhibitor of ACP, and malonyl-ACP is a competitive inhibitor of malonyl- CoA. Therefore, dysregulating these intracellular levels in any way have knock on inhibitory effects, which could be a result of not balancing the Acc bypass enzymes with the metabolic state of the cell. Since E. coli have evidently evolved these tightly controlled mechanisms to balance FAS to the requirements of the cell to growth, transcriptional, translational, allosteric and stress regulations (section 1.5), it is likely that the uncontrolled overproduction of malonyl-CoA has inferred one or several of these inhibitions, which is why we do not see an elevated FA production rate.

An expansion on the discussion as to why elevated malonyl-CoA levels from the Acc bypass systems have not given anticipated FA productivity, is in relation to a theory involving co-localisation. It is known that the type II fatty acid synthase enzymes in E. coli are distinct in their localisation and activities compared to the type I systems, which are multi-enzyme complexes containing all the catalytic units of FAS as distinct domains covalently linked into one or two polypeptides (Wakil, Stoops and Joshi, 1983; Rock and Cronan, 1996) . While type II operates as discrete protein activities, and reaction intermediates are transported through the cytosol as thioesters of the ACP, it could be that transportation of intermediates is still dependent on the proximity of the FAS enzymes, and that E. coli have evolved scaffolding mechanisms to maximize the efficiency of these energy and carbon expensive series of cyclic reactions. Several FAS enzymes are in close proximity on the genome and are known as the fab gene cluster (Rawlings and Cronan, 1992), whereas Acc subunits map themselves separately and at distant sites to this cluster, which may contribute the need for the Acc multiplex to proximate close to corresponding reactions for efficiency. This theory therefore suggests that the introduction of heterologous enzymes for FAS contributions, such as the Acc bypass enzymes, cannot improve FAS because they do not have native co-localisation capabilities, making intermediates less efficient in reaching their corresponding reaction in the system. Following this line of reason, the accumulation of malonyl- CoA brought on by the Acc bypass enzymes suggests that the metabolite is less

171 capable of reaching subsequent enzymes of FAS, than when they are subject to the potential compartmentalization as hypothesized here. This line of enquiry is purely speculative, however, and would require specific experiments if they were to be justified further, which will be outlined in the future directions section of this chapter to follow.

IN VIVO FATTY ACID METHYL ESTER PRODUCTION

The juvenile hormone acid O-methyltransferase (jhAMT) gene sequence was introduced to constructs expressing ‘TesA via EMP, for in vivo FAME production (Sherkhanov et al., 2016), and subsequently added as a separate plasmid transformation to all strains harbouring Acc by-pass constructs for evaluation. In FAME evaluations of the Acc by-pass expressions in varying genotypes Figure 5.15, specific productivities are improved upon compared to those from experiments without the addition of jhAMT (Table 5.1). This is particularly relevant in minimal media cultivations, where Acc bypass strains are improved in ∆fadE genotypes compared to the negative control (Figure 5.15 (ii)). ∆fadD also show some improvement in yield from Mcr2 expression for this experiment, as well improvement in specific productivity compared to ∆fadE (Table 5.1).

Table 5.1. Specific activities (calculated as mg FAME per OD) of high-copy Acc bypass overexpression strains in varying genotype plus ‘TesA-jhAMT low- copy expression in (i) LB and (ii) minimal media (Figure 5.15). Cases where Acc bypass strains outperform negative control are highlighted in bold. * P <0.05 vs. wild-type control, one-way ANOVA with corrections for multiple comparisons (Tukey’s test).

(i) BW25113 ∆fadD Mcr1 0.025 0.057 Mcr2 0.026 0.052 Mcc 0.022 0.035 Control 0.022 0.07

172 (ii) BW25113 ∆fadD ∆fadE Mcr1 0.04 0.037 0.026 Mcr2 0.025 0.056 0.05* Mcc 0.041 0.049 0.038* Control 0.047 0.058 0.032

PROCESS OPTIMISATION IN CONTINUOUS CULTIVATION

Experiments in chapter 3 have shown an increase in specific productivity when cultivated in continuous systems (Chapter 3; Figure 3.7, Chapter 4; Figure 4.10) and an increase in Acc bypass productivity when jhAMT is included in the expression system (Figure 5.15). Therefore, both approaches were combined in Figure 5.16 to improve the yield compared to negative controls, by improving secretion of the product from the cell, accounting for toxicities from FA accumulation in batch, and accounted for the hypothesised energy and precursor limitations in place during cultivation for high FA yield. As before, all Acc bypass strains were expressed in ∆fadD genotype and cultivated MOPS media with 0.1mM IPTG at 37°C for at least 3 volume changes once the steady state was reached and stabilised, and repeated in phosphate limited MOPS supplemented with pantothenic acid. Figure 5.16 highlights the effect continuous cultivation had on Acc bypass expression in combination with ‘TesA-jhAMT, in terms on FAME content. For the first time, an increase in FAME quantifications from Acc by-pass expression compared to the negative control was observed. In MOPS cultivation at steady state, Mcr1 and Mcr2 outperform the control, while in MOPS ‘Plim + panto’ all strains perform better than the control.

These results correspond to the earlier process optimisation data from Chapter 3 in the modified cultivation conditions (Figure 3.6). They illustrate the problems encountered with optimising flux were a combination of energetic and precursor limitation, as well as a lack of an efficient removal of FA build-up when engineering for overproduction with the Acc by-pass systems.

173 (i) 0.07 1.2

0.06 1

0.05 0.8 0.04 0.6 0.03 OD 600nm FAME mg/ml 0.4 0.02

0.01 0.2

0 0 MCR1 MCR2 MCC Control

Figure 5.15. FAME quantifications and OD values of high-copy Acc bypass overexpression

strains in varying genotype plus ‘TesA-jhAMT low-copy expression in (i) LB and (ii) minimal media. Performed 24 deep-well in duplicate, error bars represent SEM.

174 0.16 0.14 * 0.12 MOPS 0.1 MOPS Plim + panto 0.08

0.06 *

mg/ml FAME per OD 1 0.04

0.02

0 MCR1 MCR2 MCC Control

Figure 5.16. FAME quantifications normalized to OD values of high-copy Acc bypass

overexpression plus ‘TesA-jhAMT low copy expression in ∆fadD genotype cultivated in (1)

MOPS (blue bars) and phosphate limited MOPS supplemented with pantothenic acid

(orange bars). Experiment performed in turbidostats maintaining cell densities continuously at OD600 0.2 (blue bars) and 0.4 (orange bars) at 37°C. Performed as individual turbidostat experiments, error bars represent SEM of technical replicates. * P <0.05 vs. control, one- way ANOVA with corrections for multiple comparisons (Tukey’s test).

175 INTRODUCING A PRODUCT THAT LEAVES THE CELL MORE READILIY THAN FA IMPROVES TITRE

Fatty acids are composed of a single aliphatic chain connected to a carboxyl group. They gain polarity due to their carboxylic acid functional group, and therefore are hydrophilic, whereas their hydrocarbon chain attributes and determines their non- polar hydrophobic properties. This means that their solubility in water is determined by the chain length of their hydrocarbon tails, i.e., longer chain lengths of 14-22 carbons mean that the hydrophobicity of these molecules minimises their solubility in water. Medium chain lengths of 6-12 carbons long have better solubility, while short chain fatty acids containing 4 carbons are highly soluble and are also referred to as volatile fatty acids. FAs also have critical aggregate concentrations of 0.1−100 mM, depending exponentially on their chain length (Mansy and Szostak, 2008). Because of the tendencies of FAs to form membranes spontaneously given the correct pH and aggregate concentration, FAs have also been subject to studies on developing a model for the origin of life (Schrum, Zhu and Szostak, 2010; Exterkate et al., 2017). They display higher permeability than phospholipids, and allow for the passive diffusion of charged small molecules such as nucleotides across the membrane (Hentrich and Szostak, 2014), making them attractive subjects for research on primitive life prior to the evolution of membrane transporters.

Expression of the leaderless thioesterase ‘TesA, that has a substrate specificity of 12-18 carbon chain length acyl-ACP (and acyl-CoA) substrate, results in the production of FAs that have varying solubility from both medium and long chain length FA production. This could lead to diffusion problems from the cell, where the ability of FAs to transport themselves from intracellular environments to the polar extracellular environments of media cultivations is limited by their hydrophobic hydrocarbon tails. While it has been demonstrated that FAs tend to form ‘clumps’ in the media during overproduction from a thioesterase (Ledesma-Amaro et al., 2016), it is also plausible that when they are produced they are either forming aggregates or spontaneous membranes intracellularly therefore impeding transport from the cell. It has also been reported that FA may accumulate in the periplasm (Lennen et al., 2011), an accumulation within the cell may lead to cell lysis if they are disruptive to normal function, i.e., their accumulation takes up cellular space or they bind around

176 intracellular components blocking their function. To resolve these issues, the expression of a broad-spectrum methyltransferase from Drosophilia was applied to this study for the in vivo methylation of FAs to FAMEs (Sherkhanov et al., 2016), in an attempt to enable the production of a FA derived compound that more readily leaves the cells. The methyltransferase used here is a Juvenile Hormone Acid O- Methyltransferase (DmJHAMT), and methylates the carboxyl group of FA, in an S- adenosyl-L-methionine (SAM)- dependent manner, to FAME. The approach published was applied in conjunction with the incorporation of S-adenosylmethionine synthetase gene Mat1A from rat liver into the genome of E. coli, which successfully improved the titre of FAME by 35-fold compared to previous studies reporting the use of a bacterial fatty acid methyltransferase (Sherkhanov et al., 2016). The goal of applying DmJHAMT expression in conjunction with ‘TesA in this case was to rule out the potential limitations of FA aggregate formation, either intracellularly or extracellularly. Overall, this was also to prevent the antimicrobial effects of FA while simultaneously increasing the solubility of a FA derived product such as FAME, in order to gain a measure of the rate of FAS that is not limited by secretion and solubility issues, and whether relieving these limitations contribute to improvements of the Acc bypass enzymes in terms of FA yield.

As demonstrated in (Figures 5.15), the expression of jhAMT has enhanced the specific productivity of FAS in batch cultivations, in both rich and minimal media. For the first time FA ‘droplets’ were visible in the culture media of fadD expression hosts, which was a promising sign of improving yield that had not been observed previously. This could be a combination of increasing the mobility of FAME from the cell and solubility to the media, the lessening of toxic antimicrobial effects that were previously incurred by FA or, further, by the action of methyltransferase enhancing the ‘sink’ effect of ‘TesA on FAS flux. However the improvement compared to the negative control is not substantial, and comparable to previous experiments without the addition of a methyltransferase. As with all experiments observed previously, improvements in the Acc bypass strains compared to the wild-type controls were minimal (Mcr2 in Figure 5.15 (ii)), if at all. While this suggests that the addition of jhAMT to the process does not solve the previous existing problems underlying the Acc bypass strains, it does display improvements to FAS productivity from the higher yields achieved in Figure 5.16. This strategy has also provided direction to the Acc

177 bypass research, into the relevant applications of FA derived products, since FAMEs are biodiesel molecules and E. coli is an attractive renewable platform hosts to produce fuel and commodities.

PROCESS OPTIMISATION IMPROVES EFFICIENCY OF ACC BYPASS TOWARDS FA PRODUCTION

This study has taken a comprehensive approach in finding and testing the right cultivation conditions that could translate the observed increased intracellular malonyl-CoA levels into an improvement in FA yield compared to negative controls, by testing a wide range of variables in optimising the cultivation conditions, as well as applying continuous cultivation to the process parameters from chapter 3 (Figures 3.6). The outcomes of this approach have consistently led to a lack in improvement incurred by the Acc bypass, compared to the negative control of ‘TesA and ‘empty vector’ expression, apart from the final experiment where continuous cultivation was employed (Figure 5.16). While this effectively highlights the importance of cultivation conditions to Acc bypass improvement, it also demonstrates the apparent limitations that exist elsewhere in the FAS system, such as downstream enzymes, energy and precursor availability, that must be improved for the bypass is to be effective. It also reinforces the sensitivity of FAS as a system to improvement in yield, and its conditionality to the improvements that have been published to date (Table 1.1).

To expand further on the results from cultivations at steady state, it is relevant to question whether the improvements observed from Figure 5.16 are due to maintaining optimal levels of pH, aeration and induction level, or whether they are a result of the gradual adaptation of cells to increased FA levels, as illustrated in Chapter 3 during adaptive evolution. It is possible that these results have benefitted from both conditions as a symptom of the continuous cultivation of a population that produces and secretes a non-native chemical to the media. Non-genetic variation is common and widespread in microbial cultivations, and are attributed to naturally inherent factors such as uneven cell division, variations in gene copy numbers, stochastic gene expression, variable mRNA stabilities and protein activities (Harms, Bley and Mu, 2010; Li, Xie and Hirschfeld, 2011). This variation in an isogenic

178 population gives rise to sub-populations of both low and high producing strains, which can then become competitive if a growth advantage is present. In the case of FAME in the media during turbidostat cultivations of Figure 5.16, if high-producing strains secrete and evolve continuously to more product than their low producing counterparts, they quickly acquire a growth advantage and out compete the low producers. Combined these features of microbial populations with the results observed in Chapter 3 when relA was adaptation to palmitic acid led to an advantage towards FA production when ‘TesA was expressed, it is reasonable to assume that some adaptive evolution is at play during these continuous cultivations also.

More relevant to FAS regulation at continuous cultivation, is the effect growth rate has on the metabolic state of the cell and how this contributes to the improvement of the Acc bypass enzymes on FA yield. Since the cells are kept exponentially growing in turbidostats, they enter a pseudo steady-state of exponential growth. Their metabolisms must adjust to meet the demands of continuous exponential growth, generating the biomass and energetic precursors necessary to maintain cell division. It has previously been established that the TCA cycle functions at reduced capacity during rapid growth such as exponential phase, and that the TCA cycle and glyoxylate shunt are upregulated upon entry to stationary phase in E. coli (Rolfe et al., 2012). It is also known that the supply of oxaloacetate is required for biosynthesis in E. coli, and is maintained from phosphoenolpyruvate either via Ppc or the glyoxylate shunt (Noronha et al., 2000; De Mey et al., 2007). During exponential growth on glucose, Ppc is the main route for oxaloacetate (Sauer and Eikmanns, 2005). The maintenance of the precursor for Acc bypass reactions during exponential growth are potential reasoning for improvement of both Mcr1 and Mcr2 in MOPS cultivation in Figure 5.16, whereas the lack of improvement in Mcc is presumably tied with acetyl-CoA availability, since this enzyme couples two carboxylation reactions; oxaloacetate + acetyl-CoA -> pyruvate + malonyl-CoA. The limiting level of acetyl-CoA could also explain the fact that both Mcc and control (native Acc) are at similar FA yields. Once cells were moved in to phosphate limited media supplemented with pantothenic acid, growth rates and production were slowed due to less phosphate available for essential processes and the physiological changes induced by limitation (Bogelen et al., 1996), however, FA productivities

179 compared to the negative control improved slightly (Figure 5.16). These advantages are not as apparent as those obtained from results in Chapter 3, where the same media optimisations gave the highest yield in batch cultivations (Figure 3.6). It must be the case that this optimisation is more efficient when cells are in stationary phase during batch cultivation, when TCA is upregulated to maintain growth and therefore contributes to the NADPH supply, in addition to the improvement in energy from phosphate limitation and CoA availability from pantothenic acid. This final experiment (Figure 5.16) could therefore be improved further from a fed-batch cultivation process instead of continuous, to incur the native regulations from stationary phase growth as a starting population, while maintaining some of the benefits of steady-state cultivation to FA production in terms of media replenishment.

DESIGN OF FURTHER STRATEGIES DURING THIS STUDY TO OPTIMISE EFFICIENCY OF THE ACC BYPASS

Further attempts to improve the efficiency of the Acc bypass enzymes were designed and attempted during this study. One strategy involved the simultaneous knocking out of the genomic copy of an Acc subunit, AccD, while introducing, or ‘knocking-in’, a copy of the Acc bypass genes at the same genomic loci. This process was to enable an extension on the earlier complementation studies on the use of a selective and temperature defective Acc mutant strain, however, due to the essentiality of Acc subunits (Gerdes et al., 2003), even with the implementation of previously complemented Acc bypass constructs, successful knock outs were not possible during this project.

Another optimisation included the design of a dynamic balancing of FabD expression in Acc bypass constructs by malonyl-CoA levels, using the FapR responsive promoter (fapO) as the previous malonyl-CoA sensor assays (Xu, et al., 2014). FapR was added to FabD-Acc bypass constructs via EMP, though subsequent fapO promoter addition proved difficult due to sequence similarity in the overhangs which impeded efficiency. Time constraints meant unfortunately this experiment was not completed, though this strategy has much potential in achieving a balanced

180 metabolic pathway which senses and responds to the intricate dynamics of FAS regulation and control.

5.4 FUTURE DIRECTIONS

IS MALONYL-COA REACHING FABD?

Perhaps one of the more fundamental hypothesis generated from this chapter is the question around co-localisation of the FAS enzymes in E. coli, due to the established discreteness of the enzymes of type II FA synthase compared to type I. Though evidence is required for this theory to progress there is a substantial justification gained to do so from the results generated from this work; namely that the heightened malonyl-CoA levels produced from a heterologous Acc bypass do not get incorporated to FAs to the same extent by enzymes of FAS in E. coli. Initially, this could be investigated using heavy isotope labelled intermediate, ideally something that traces oxaloacetate uptake by the Acc bypass enzymes to malonlyl-CoA, and the eventual incorporation into FA for comparison with the negative control, which will have a distinct labelling pattern depending on the route taken to FAS. If this experiment were to demonstrate no differences between the distribution of label isotope found in FAs obtained from both Acc bypass and wild-type control, then it would confirm the lack of incorporation of malonyl-CoA produced by the bypass into FAS. It would indicate the need for using chaperones or scaffolding into the engineering design of Acc bypass expression, to ensure these enzymes are in closer proximity to enzymes of FAS (Dueber et al., 2009; Horn and Sticht, 2015). If, however, labelled intermediate is found to incorporate as expected, then this would support the need for regulating or balancing the expression of the pathway towards the needs of the cell’s metabolism. It is clear that either scenario would provide useful information for further development, and would help in deciding which route to take for further work into optimising this strategy for improving FA yield. Therefore, this experiment is the first recommendation for further work in developing a successful Acc bypass pathway.

181 REGULATING SUPPLY AND DEMAND TO IMPROVE YIELD

Following either outcome of the labelling experiment, it is relevant to consider introducing regulation to the Acc bypass enzymes. As previously highlighted in this chapter and earlier, there are many ways that introducing a non-native route for metabolite production can deregulate biochemical processes in an organism, particularly in the case of bypassing a component of the FAS pathway which is subject heavily to regulation. The design of metabolite driven expression of enzymes to balance the flux a biochemical pathway, introduces a form of control which can be implemented in metabolic engineering for overcoming the native regulatory mechanisms, or burden, on the host. The design of this strategy began during the end this study, as the incorporation of a promoter for malonlyl-CoA driven expression of FabD (Xu et al., 2014).

The method proposes a way to counteract the potential competitive inhibition of acetyl-CoA on FabD, by increasing the supply of FabD according to malonyl-CoA levels. This would ensure that FabD is elevated at levels that are appropriate for balancing the flux of FAS with increased malonyl-CoA, i.e. malonlyl-CoA accumulation would level off after a certain point due to the activity of FabD accounting for its increase in supply. As we have seen from the addition of unbalanced FabD expression in Acc bypass strains in Figure 5.4 that malonyl-CoA levels have depleted as anticipated, however this did not lead to increasing FAS flux in Figure 5.12 which could be due to competitive inhibition, the burden associated with unbalanced expression, or from further limitations on the system (such as NADPH or CoA availability).

The importance of increasing subsequent FabH levels with FabD, has been highlighted in earlier chapter 4 of this thesis. It’s relevance to this chapter can also be applied - since FabD interacts with all KAS enzymes, leading to possible bottlenecks by blocking the earlier initiation step of FabH (Subrahmanyam and Cronan Jr., 1998). To this effect, an optimised design for overexpression would then include FabH as an operon with FabD under a promoter which is malonyl-CoA

182 expression dependent, and incorporate varying RBS strengths to ensure that more FabD is present compared to FabH.

Furthermore, we have also learned of the significance in balancing ‘TesA expression in relation to acyl-ACP levels from chapter 3 and the effect of uncoupling FAS with phospholipid synthesis (Voelker and Davies, 1994; Zhang et al., 2011), which outlines the importance in avoiding uncoupling to the extent that growth is inhibited due to acyl-ACP substrate competition. A design strategy around this to this could therefore be acyl-CoA driven expression of ‘TesA (Zhang et al., 2012), since acyl-CoA levels act as a proxy for FAS rate or a healthy FA flux and indicate the potential to elevate FA flux further without interfering with phospholipid synthesis. However, a potential limitation to this is the futile cycling to occur with ‘TesA, acyl-CoA, FA and back again, therefore counteracting the effectiveness of a genetic circuit. The principle of coupling ‘TesA expression to a FAS fitness indicator is still valid, and could expand into other determinants of this such as redox potential (Qiao et al., 2017), since every cycle of FAS requires NADPH and could be improved by balancing activities towards their availability.

INTRODUCING BUTANOL PRODUCING PATHWAYS

We have noted the improvement in specific productivity of FAS from introducing an enzyme that catalyses the methylation of FA to FAME, which it is hypothesised in doing so by improving the secretion of a FA derived product, alleviating toxic accumulation within cells and as a result the measure on FAS flux improves. This strategy will further develop into the introduction of a butanol production pathway to Acc bypass strains, by introducing a four-carbon specific thioesterase from Bacteroides fragilis, a carboxylic acid reductase from Mycobacterium marinum and native aldehyde reductase as reported by Akhtar, Turner and Jones, (2013) for alternative fuel production. Because butanol represents a substitute for petroleum- derived fuel products and due to improved technical specifications for conventional combustion engines compare to ethanol it is an attractive commodity candidate to research (Tornatore et al., 2011). Much work has already reported improvements in butanol tolerance in E. coli (Bui et al., 2015), while also being a simpler molecule to

183 measure analytically, requiring only HPLC, compared to derivatisation and GC-MS analysis of FAME. Both features are marked advantages to the continuation of this research, and will be resumed by members of the Jones group.

A CHASSIS FOR FA OVERPRODUCTION

It is clear there is much to be gained from evolving FA tolerance in a strain for industrial production purposes, as it imposes specific modifications that enable the organism to be resistant to toxicity, such as membrane alterations as discussed in Chapter 3, and therefore enhances that strains capabilities to overproduce an otherwise toxic product such as FAs. This process is therefore extremely relevant to optimising the Acc bypass strains, as it effectively skips the requirement to incorporate and test a vast range of the proposed pathway balancing during the engineering process, as well as avoiding a trial and error process of searching and applying the right cultivation conditions for high fatty acid yield. In this way, Orgel’s second rule of evolutionary biology is true - “Evolution is cleverer than you are”; and perhaps a more effective way in taking direct approaches towards commodity chemical production such as the application of bypass pathways to enhance metabolism towards candidate molecules, is to harness the existing and elegant design processes of evolution and natural selection.

The potential in expanding the metabolic engineering toolbox with adaptive or directed evolution is already widely established as an application (Abatemarco, Hill and Alper, 2013), though perhaps not as readily applied or integrated into the ‘design-build-test’ iteration of the engineering process as it could be for universal benefit to be gained in the community. This could mostly be explained by the uncontrolled nature of adaptive evolutionary methods, and the potential loss of insight or information after several rounds of mutation incurs. However, selective or controlled measures could be incorporated to the process to complement the needs of the research carried out. For example, Xiao et al., (2016) utilised the inherent non- genetic cell-to-cell variation that exists in microbial cultivation, to establish a selection method of in vivo population quality control (PopQC) to continuously select for high- performing, non-genetic variants. This method produced the highest titre and

184 production rate reported for FA to date and is an extremely promising avenue for future work.

Furthermore, the adaptive evolution strategy could be enhanced by the application of MAGE (multiplex automated genome engineering) (Wang et al., 2009), whereby multiple genomic locations could be simultaneously modified to significantly alter the phenotype towards desired traits, such as high FA productivity and tolerance. MAGE is mediated by lambda red bacteriophage, and directs the introduced oligos to lagging strands of the replication form during DNA replication (Pines et al., 2015). These oligos could be those of defined sequences to produce well-defined modifications, or with degenerate sequences which aim to produce high-diversity modifications tailored for exploring the vast sequence space. FAS relevant alterations in the membrane have been previously identified and could be applied in this context initially (Royce et al., 2015; Tan et al., 2016; Rowlett et al., 2017), in combination with the PopQC method to isolate mutated populations conferring a high FA yield.

185 6 GENERAL DISCUSSION

6.1 SUMMARY OF KEY FINDINGS AND CONCLUSIONS

This study was a combination of fundamental queries on methods of cultivation that optimize the processes for FAS in E. coli, a systems interrogation of the entire FAS pathway by making perturbations and measuring their impact during experiments, and a final effort to combine this knowledge along with published data by introducing alternative routes to FAS to directly improve FA yield.

Of the first subject that contributed to this research, several aspects on process optimisation were found to influence the yield of FA in E. coli in Chapter 3. These included copy-number of ‘TesA expression, the expression host genotype, specific adjustments to minimal media and, most notably, adaptive evolution. Low copy expression of ‘TesA was found to have higher yield compared to chromosomal integrations and high-copy number (Figure 3.4), which is also in agreement with observations made by Lennen et al., (2010) and was applied to further experiments during this research. Subsequent evaluations on genotype were less apparent than copy-number, as they were found to be influenced by other aspects of the cultivation process design. For example, in batch cultivations ∆fadE gave the highest FA yield in Figure 3.3 and 3.6, but transferring to continuous growth in turbidostats at a certain OD, ∆fadL and ∆fadD outperformed ∆fadE (Figure 3. 7). This suggests that toxic accumulation of FA in batch cultivation limited these hosts compared to ∆fadE previously, as FA re-enter the cell are get utilised by the acyl-ACP synthetase, Aas, which functions in ligating FFA to ACP - incurring a level of futile cycling but also preventing harmful accumulation. While at continuous cultivations the media is constantly replenished therefore FA accumulation does not burden the cells, increasing FA yield in genotypes that do not allow FA to re-renter the cell or degradation cycle. Media composition also affected the performance of strains towards FA production in Figure 3.6, particularly when pantothenic acid was supplemented to the media, and in addition to this with phosphate limitation. This study is the first to analyse in comparison these conditions in terms of FA productions in E. coli, and highlight the importance of balancing the energetic

186 requirements of a biochemical pathway during the engineering design process. What was gained was an understanding on imbalances on catabolic and anabolic reactions which lead to implications such as energy spillage in E. coli, and must be accounted for in the process optimisation design.

The experiments on adaptive evolution also informed this study on the impact continuous cultivation in FA has on yield, due to increased mechanisms of FA tolerance which are acquired during the adaptive evolution process. Figure 3.10 illustrated the improvements in evolved ∆relA compared to non-evolved when inducing low-copy expression at 0.1mM IPTG. Further insight on the role of ppGpp during adaptive evolution and higher FA tolerance was provided by the stress challenges experiments in relation to the growth curves of Figure 3.9, where it was found that the evolved strain acquired tolerance to ppGpp as well as C16 FA treatments. These experiments are a promising contribution in using adaptive evolution techniques for FA tolerance, through acquiring changes in the membrane composition as reported by Royce et al., (2015), Tan et al., (2016), and Rowlett et al., (2017), as well as highlighting potential adaptive changes to the stringent response that could be applied in engineering E. coli towards high FA production capacities.

Chapter 4 then followed with a study on individual and combinatorial genetic perturbations made on the FAS biochemical pathway, and was conducted through capturing several responses in terms of productivity such as cell growth, FA yield, relative protein abundance profiles, ATP content and ethanol production. This acquisition of data was to contribute to predictive modelling approaches as well as to uncover fundamental knowledge on the mechanisms of control and regulation of FAS in E. coli, so that further engineering processes can be informed by them. The key findings from this chapter found that several perturbations had more negative impact on growth than others, particularly in cases where stoichiometries were important in regulation, such as the Acc subunits and KAS enzymes which require certain levels of expression for optimal flux, as demonstrated in Figure 4.3 (i) and 4.4 (i). Some perturbations also had a positive impact on growth such as the overexpression of all subunits AccABCD (Figure 4.3 (i) & Figure 4.4 (ii)) which is due to their measured influence on increased flux through FAS (Figure 4.5 (ii)). When comparing these with protein abundances in Figure 4.2 (i), the negative growth

187 effects from individual subunits are further understood by their detrimental effect on abundances on many other essential proteins in relative to the negative control. Whereas in Figure 4.2 (iii) not much variation in growth rate is present among mutant strains, the comparison with protein profiles for this expression system (BW25112 plus ‘TesA) highlights the adjustments in profile distributions for each perturbation that result in similar growth rates and FA yield (Figure 4.5 (ii)). These results reveal the responsive regulatory mechanisms that are in place to maintain a certain level of growth and FA production when ‘TesA is co-expressed with any other perturbation, for example in the case of AccA which was detrimental to growth otherwise. Figure 4.2 (iii) illustrates how most proteins are upregulated in response to AccA and ‘TesA upregulation, and gives insight into how the proteome is rearranged in response to ‘TesA overexpression. Comparative protein data from Figure 4.2 had not been acquired previously, and is expected to guide the building of a predictive modelling technique for FAS such as those outlined in section 1.8. This data is useful in further engineering of FAS for high yield as it quantifies what levels of FAS protein distribution lead to certain yields. This information can be used to optimized expression levels to better yields in future, perhaps more robustly in a cell free system where protein titers can be easily controlled such as the experiments by Liu et al., (2017). This chapter also outlined the fundamental impact engineering strains towards FA had on membrane polarity, ATP content and ethanol production, which are also informative about the metabolic state of the cell to guide strategies toward whole cell modelling approaches. The study revealed important insights into the higher levels of membrane depolarisation when perturbations were made under high-copy expression levels compared to the same low-copy perturbations, on the influence of ATP limitations on FA yield according to individual perturbations, and on the aerobic production of ethanol when strains co-expressed ‘TesA. Furthermore, this chapter outlines the effect cultivating FA overproducing stain FadR plus ‘TesA in ∆fadD at steady state had on FA productivity (Figure 4.10) compared to the same strain in batch cultivation (Figure 4.6 (ii)). The higher specific activity in Figure 4.10 demonstrates the advantages of continuous cultivation has on FA productivity, which reinforces the known advantages of these processes in terms of continuously maintaining pH, aeration and preventing toxic levels of FA accumulation in the media. The information gained from these experiments informed experimental

188 design of the directed FA overproduction approach in the final chapter of experiments, Chapter 5.

Chapter 5 examined the effects of introducing an alternative route to malonyl-CoA synthesis, which bypassed the native Acc enzymatic step with enzymes that utilised oxaloacetate as the precursor. The hypotheses tested were that FAS could proceed at rates that are not limited by allosteric feedback, transcriptional or translational control, or by ATP availability when using Acc bypass pathways. This reasoning was followed because the heterologous enzymes are not under the same native regulatory and energetic limitations of Acc, which has been categorised as the ‘rate- limiting’ reaction of FAS for this reason. The key findings of this chapter were several; first that the introduction of the Acc bypass pathways were successful in increasing the level of malonyl-CoA synthesis compared to wild-type negative control, as anticipated. Unexpectedly, however, was that this improvement did not directly translate to an increase in FAS flux under the same conditions tested. This suggested that other elements of FAS had become rate-limiting when malonyl-CoA was increased, or that the increase in malonyl-CoA was not enough to give an increase in FAS. A similar finding was observed by Davis, et. al., (2010), whereby malonyl-CoA concentrations were increased 100-fold only to increase FA yield by 6- fold. It is therefore likely that FAS as a biochemical system distributes its control on flux not only at the Acc ‘pacemaker’ stage, but at further downstream reactions catalysed by FabD and FabH. This hypothesis is further supported by the theory behind control distribution in MCA; which is a framework for probing biological systems for quantitative measures of control but does not focus on rate-limitation, it is to be utilised for fundamental understanding and directed approaches of optimising a system through engineering. Further finding from the bypass studies were around testing the hypothesis that FAs produced were unable to efficiently leave the cell, meaning that the FA measurements taken did not accurately depict the rate of FAS. This was examined by introducing a new a methyl transferase enzyme that utilises FA and produces FAME (Figures 5.15 – 5.16). The experiments found that, while the introduction of this enzyme improved yield in batch cultivation in some instances compared to the negative control, such as Mcr2 in ∆fadD and ∆fadE genotypes (Figure 5.15 (ii)), most conditions remained unimproved in FA yield compared to the negative control. This demonstrated that the experimental system

189 was still conditional, and not optimal for consistent or reproducible measurements of high FA producing strains. This was reinforced when experiments were moved into continuous cultivations in Figure 5.16, at which point both Mcr1 and Mcr2 improved in specific activity compared to negative control in both conditions, with greater improvements in MOPS cultivations compared to modified phosphate limited and pantothenic acid addition. These results highlight the importance of cultivating at steady state compared to batch, when the target compound to overproduce is FA or FAME derivative, which was also observed in the process optimisation section of this thesis. Overall this section highlights the significance of process optimisations, which must coincide with genetic alterations when the objective is to enhance flux toward a certain product. This is especially true of the objective product of this work, FA, since FAS is dependent on precursors that are also required during central carbon metabolism, phospholipid maintenance and energy production. Unless process optimisations are such that pH is maintained, oxygen and carbon availability is adequate, and energetic requirements are satisfied, efforts to guide metabolism through genetic engineering alone will not meet the capacity for which it has been designed for. This was extensively observed in the batch cultivations of this section (Figures 5.5 - 5.12), during which no consistent data was acquired on the conditions that could capture the otherwise functional Acc bypass pathways toward increased FA yield, compared to the negative control.

In conclusion, this thesis has highlighted key features of process optimisations towards E. coli FA overproduction which lacked a consensus in the literature (Chapter 3); highlighting key process optimisations and evolution strategies that contributed to an increase on FAS and FA yield. It subsequently acquired results from a fundamental systems-wide study on the effect of enzyme perturbation in FAS (Chapter 4); which quantified the effect and burden of fundamental perturbations on FAS and FA yield to aid in predictive model parameterisation in future studies. Finally, this study demonstrated the utilization of novel routes to malonyl-CoA synthesis that bypass an essential enzyme in E. coli - for the purpose of increasing FA yield thro ugh circumventing native regulatory and energy limitations (Chapter 5). Overall, engineering FAS in E. coli requires careful consideration of the process optimization design in conjunction with any genetic manipulations, adaptive evolution is a strong tool in engineering towards productions that have toxic effects on the cell,

190 cell burden and imbalances coincide with engineering a pathway that is subject to regulation and control, and directed engineering strategies still require a balancing with metabolism – it is not enough to target a putative rate-limiting step only as control is redistributed, subsequent steps must be engineered to respond to the upregulations. This work reports on new information through the application of both fundamental and directed queries to the experimental approach, which contributes to the subject of engineering FAS in E. coli while also highlighting further questions to examine, so that this area of research can progress.

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