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Response of Escherichia coli Processes

for the Production of Heterologous

Inclusion Bodies by

Oscillating Cultivation Conditions in a

Scale-Down Bioreactor

Ping Lu, Berlin 2016

Response of Escherichia coli Processes for the Production of

Heterologous Inclusion Bodies by Oscillating Cultivation

Conditions in a Scale-Down Bioreactor

vorgelegt von

M. Sc. Ping Lu

aus Henan (China)

von der Fakultät III – Prozesswissenschaften

der Technischen Universität Berlin

zur Erlangung des akademischen Grades

Doktor der Naturwissenschaften

- Dr. rer. nat. -

genehmigte Dissertation

Promotionsausschuss:

Vorsitzender: Prof. Dr. Roland Lauster

Gutachter: Prof. Dr. Peter Neubauer

Gutachter: Prof. Dr. Thomas Schweder

Tag der wissenschaftlichen Aussprache: 15.03.2016

Berlin 2016

D83

The present work was performed from October 2012 – March 2016 in the research

group of Prof. Dr. Peter Neubauer (Chair of Bioprocess Engineering) at the

Department of Biotechnology, Technische Universität Berlin.

I

Abstract

In the pharmaceutical, chemical and food industries, microorganisms are widely

cultivated under controlled conditions to produce recombinant proteins and organic

molecules. However, inhomogeneities unavoidably exist in large-scale cultivations.

The high concentration of feeding solution in industrial cultivations, combined with

limitation of mixing power, results in excess substrate and oxygen limitation at the

feeding zone. On the contrary, at other regions which are far away from the feeding

zone, cells are exposed to starvation where there is almost no substrate available. In

order to study the cellular responses to oscillations of substrate and oxygen

availability in Escherichia coli industrial large-scale fed-batch bioprocesses at the

laboratory scale, not only a two-compartment scale-down bioreactor (2CR) that

simulates the feeding zone characterized by a high substrate and low oxygen

availability, but also a three-compartment scale-down bioreactor (3CR) that mimics

additionally a zone of low substrate availability was used in this study. With the help

of the scale-down bioreactors, E. coli K-12 W3110 wild-type strain and two

recombinant strains expressing leucine-rich proteins were cultivated to investigate

the influence of heterogeneous condition on production and integration of the

non-canonical amino acids norvaline, norleucine and β-methylnorleucine into

recombinant proteins.

The results showed a diminished productivity of recombinant strains in the first few

hours after induction under oscillating conditions. The accumulation and

misincorporation of non-canonical amino acids, especially norleucine, into

recombinant proteins is affected by oscillating conditions. Though there are much

less methionine residues in both recombinant proteins, norleucine was observed

misincorporated into proteins with higher rate than norvaline and

β-methylnorleucine. The main carbon source for α-ketobutyrate, which is the

precursor of the considered non-canonical amino acids and isoleucine derived from

threonine under normal conditions, while it is directly derived from pyruvate under

oscillation conditions.

This study provides a solid basis for future cell engineering approaches to overcome

the challenges in view of the production quality.

Keywords scale-down, large-scale, non-canonical amino acids, norvaline, norleucine,

II

β-methylnorleucine, recombinant proteins, misincorporation, E. coli

III

Zusammenfassung

Die Kultivierung von Mikroorganismen unter kontrollierten Bedingungen zur

Produktion von rekombinanten Proteinen und anderen organischen Molekülen ist

weit verbreitet sowohl in der pharmazeutischen und chemischen als auch in der

Lebensmittelindustrie. Obwohl kontrolliert lassen sich Inhomogenitäten in

Kultivierungen unter Industriemaßstab nicht vermeiden. Die hohe Konzentration der

eingesetzten Feeding-Lösung in Verbindung mit limitierter Mischleistung führt zu

Substrat-Ü berschuss und Sauerstoff-Limitation im Bereich der Zufütterung.

Gleichzeitig erfahren Zellen in Bereichen entfernt der Zufütterungsstelle

Hungerbedingungen ohne verfügbares Substrat. Um die zellulären Reaktionen auf

diese Oszillationen hinsichtlich Substrat- und Sauerstoffverfügbarkeit in industriellen

Großmaßstabs-Bioprozessen mit Escherichia coli im Labormaßstab untersuchen zu

können, wurden ein Zwei-Kompartiment scale-down Bioreaktor (2CR), der einen

Fütterungsbereich mit hoher Substrat- und niedriger Sauerstoffverfügbarkeit

simuliert, sowie ein Drei-Kompartiment scale-down Bioreaktor (3CR), der zusätzlich

eine Zone mit geringer Substratverfügbarkeit darstellt, eingesetzt. In diesen

Scale-down-Systemen wurde der Einfluss von inhomogenen

Kultivierungsbedingungen auf die Produktion der nicht-kanonischen Aminosäuren

Norvalin, Norleucin und β-methylnorleucin in einem E. coli W3110 Wildtyp sowie

zwei rekombinanten Derivaten untersucht. Gleichzeitig wurde überprüft, ob und in

welcher Menge Fehleinbau dieser nicht-kanonischen Aminosäuren in die

produzierten Leucin-reichen rekombianten Proteine erfolgte.

Die Studien zeigten eine reduzierte Produktivität der rekombinanten Stämme

innerhalb einigen Stunden nach der Induktion unter oszillierenden Bedingungen.

Auch die Akkumulation und der Fehleinbau von nicht-kanonischen Aminosäuren in

die rekombinanten Proteine, speziell Norleucin, ist durch oszillierende

Kultivierungsbedingungen beeinflusst. Obwohl Methionin als Tauschpartner am

geringsten in beiden rekombinanten Proteinen vorkommt, zeigte Norleucine im

Vergleich zu Norvalin und β-methylnorleucin die höchste Fehleinbaurate. Als Quelle

für α-ketobutyrat, das Ausgangsmolekül für die genannten nicht-kanonischen

Aminosäuren und Isoleucin, dient Threonin unter normalen

Kultivierungsbedingungen, während es unter oszillerenden Bedingungen aus Pyruvat

abgeleitet wird.

IV

Diese Arbeit bildet eine gute Basis für zukünftige Stammverbesserungen, um

Probleme und Herausforderungen in Bezug auf Produktqualität zu überwinden.

Schlagworte: Scale-down, Industriemaßstab, nicht-kanonische Aminosäuren,

Norvalin, Norleucin, β-methylnorleucin, rekombinante Proteine, Fehleinbau, E. coli

V

Acknowledgements

Many people have made invaluable contributions, both directly and indirectly to the

successful completion of this work. I would like to take this opportunity to express

my gratitude to all of these people.

My deepest gratitude goes first and foremost to my supervisor Professor Peter

Neubauer for giving me the opportunity to work in his group. His inspired ideas and

enthusiastic attitude towards academic research deeply impressed and impacted me.

During last three and half years, he offered me generous encouragement, patient

guidance and instructive advice in the academic studies. I was deeply moved by his

kindness, gentleness during this period and full support for the application of

scholarships.

I would also like to express my great gratitude to Dr. Stefan Junne for his constructive

suggestions and inspiring advice during each discussion.

Especially, I feel grateful to Eva Brand for working together with me in the first year,

leading me to overall understand of this project, training me on the bioreactor

cultivation techniques and evaluating the data. Working with her and talking about

the things in life are really happy memories for me and I will never forget it. I would

also like to thank Christian Reitz for excellent teamwork on the second recombinant

strain cultivations. We performed so many bioreactor cultivations together. No

matter start very early in the morning or finish very late in the night, we gave each

other support and encourage and shared the exciting moment when succeeded in

each cultivation. Furthermore, I would like to extend my thank to Christoph Klaue for

his contributions in the interleuckin-2 cultivations, Sergej Trippel for teaching me

how GC-MS analysis really work and properly evaluating the BioScope data, Anika

Bockisch, Julia Glazyrina and Anna Maria for their kind help and discussion about

Flow Cytometry analysis.

I would like to acknowledge the funding of German Research Foundation (DFG) to

support this work through the project NE1360/2-1 “Metabolic responses to

bioreactor inhomogeneities: Understanding the flux to modified branched chain

VI

amino acids”. I would also like to express my graditude to Sanofi Chimie to support

part of the thesis by the collaboration project “Scale-up / scale-down of

bioprocesses”.

I would also like to gratefully acknowledge the China Scholarship Council (CSC) for

the main scholarship of initial three years and the Technische Universität Berlin for

the Promotions-Abschluss-Stipendium. In addition, I would like to thank the Berlin

International Graduate School of Natural Sciences and Engineering (BIG-NSE) of the

Cluster of Excellence “Unifying Concepts in Catalysis” (UniCat) for their support,

especially, many thanks to Dr. Jean-Philippe Lonjaret for his great effort on

organizations of all BIG-NSE activities. And I also would like to thank all the PhD

students from the BIG-NSE for the nice moments being together.

I would like to express my gratitude to all the colleagues at the Bioprocess

Engineering Laboratory. Irmgard Maue-Mohn, Brigitte Burckhardt and Thomas Högl

for their technical support, Sabine Lühr-Müller for help with bureaucracy, Dr. Xinrui

Zhou and Dr. Jian Li for being good friends and their endless encouragement and

useful advice in my difficult times, Emmanuel Anane for proof reading of the

dissertation by his perfect English, Dr. Mirja Krause for encouraging me and gave me

a “lucky pig” when she left our institute, Ongey Elvis Legala for nice talk about life

and study, Dr. Nicolas Cruz-Bournazou, Dr. Andreas Knepper, Florian Glauche, Andri

Hutari, Anja Lemoine, Erich Kielhorn, Basant El Kady, Heba Yehia, Funda Cansu Erterm,

Qin Fan and all other colleagues for creating a pleasant working environment.

I also would like to thank my Master’s supervisor Professor Shoutao Zhang for his

kind concern and support, all my lovely good friends for their love, care and

encouragement.

Most importantly, I greatly appreciate to my beloved family members, my parents

and husband for their love, continuous support and understanding. My heart swells

with gratitude to all the people who helped me.

VII

Contributions

Besides the author (Ping Lu), this dissertation work also involved contributions from

Eva Brand, Christian Reitz, Christoph Klaue and Sergej Trippel with detailed

description as below. And all the cultivations were supervised by Dr. Stefan Junne.

For E. coli W3110 wild-type stain cultivations, EB participated in the bioreactor

cultivations experiments, samples analysis and data evaluations.

For E. coli W3110_pCTUT7_His_IL2 cultivations, EB and CK participated in the

bioreactor cultivation experiments. CK performed samples analysis (except BioScope

samples) and participated in the relative data evaluations. And these data was also

used for the diploma thesis of CK. ST participated in the carbon labeling experiments.

For E. coli W3110M_pSW3 cultivations, CR participated in the bioreactor cultivations

experiments, performed the SDS-PAGE gel analysis, and carried out the samples

analysis laboratory work for carboxylic acids and relative data evaluation.

VIII

IX

Table of Contents

Abstract ................................................................................................................. I

Zusammenfassung ............................................................................................... III

Acknowledgements............................................................................................... V

Contributions ...................................................................................................... VII

Table of Contents ................................................................................................. IX

Abbreviations .................................................................................................... XIII

1. Introduction .................................................................................................. 1

1.1. Basic metabolism in E. coli cultivations ........................................................ 1

1.1.1. Glucose metabolism of E. coli ............................................................ 2

1.1.2. Overflow metabolism ......................................................................... 3

1.1.3. Mixed-acid fermentation and pyruvate metabolism in E. coli ........... 4

1.1.4. Biosynthesis of proteinogenic amino acids ........................................ 6

1.2. Overview of non-canonical amino acid synthesis and incorporation into

recombinant production in E. coli .......................................................................... 9

1.2.1. Biosynthesis of non-canonical amino acids ..................................... 10

1.2.2. Misincorporation mechanism of non-canonical amino acids into

heterogonous proteins ................................................................................. 14

1.2.3. Strategies to reduce misincorporation of non-canonical amino acids

into recombinant proteins ........................................................................... 16

1.3. E. coli fermentation processes ................................................................... 18

1.3.1. Cultivation medium .......................................................................... 18

1.3.2. Cultivation modes ............................................................................ 19

1.3.3. Inclusion body (IB) productions ....................................................... 20

1.4. Bioprocess Scale-up and Scale-down ......................................................... 21

1.4.1. Gradients in large-scale bioprocesses .............................................. 21

X

1.4.2. Simulating large-scale cultivations in Scale-down devices .............. 24

1.5. Metabolic flux analysis of biosynthesis pathway of non-canonical amino

acids in E. coli ....................................................................................................... 26

1.5.1. Basic idea of metabolic flux analysis ................................................ 26

1.5.2. Rapid sampling devices .................................................................... 27

1.6. Research motivation and objectives .......................................................... 30

2. Materials and Methods ................................................................................ 32

2.1. Bacterial strains .......................................................................................... 32

2.2. Cultivation media ....................................................................................... 32

2.2.1. LB medium ....................................................................................... 32

2.2.2. Mineral salts medium and EnBase Flo medium............................... 32

2.3. Bioreactor cultivation ................................................................................. 33

2.3.1. Preculture ......................................................................................... 35

2.3.2. On-line measurements .................................................................... 35

2.4. Sampling ..................................................................................................... 36

2.4.1. Cell growth determination ............................................................... 36

2.4.2. Supernatant sampling ...................................................................... 37

2.4.3. Methanol quenched sampling ......................................................... 37

2.4.4. Perchloric acid quenching ................................................................ 38

2.4.5. Protein sampling .............................................................................. 38

2.4.6. BioScope sampling ........................................................................... 38

2.5. Analysis of metabolites............................................................................... 39

2.5.1. Quantitative analysis of carboxylic acids by high performance liquid

chromatography (HPLC) ............................................................................... 39

2.5.2. Quantitative analysis of amino acids by high performance liquid

chromatography (HPLC) ............................................................................... 40

2.5.3. Quantitative analysis of amino acids by Gas Chromatography-Mass

Spectrometry (GC-MS) ................................................................................. 42

XI

2.5.4. Quantitative analysis of carboxylic acids by Gas

Chromatography-Mass Spectrometry (GC-MS) ........................................... 47

2.5.5. Quality of protein analysis ............................................................... 48

2.6. Flow cytometry analysis ............................................................................. 50

3. Results ......................................................................................................... 53

3.1. Behavior of E. coli W3110 in STR and 2CR cultivations .............................. 53

3.1.1. Cultivation characteristics ................................................................ 53

3.1.2. Carboxylic Acids ............................................................................... 57

3.1.3. Amino Acids ..................................................................................... 62

3.2. Behavior of the interleukin-2 producing strain E. coli

W3110_pCTUT7_His_IL2 in STR and 2CR cultivations ......................................... 65


3.2.2. Protein quantification ...................................................................... 69


3.2.4. Amino acids ...................................................................................... 73

3.2.5. Carbon Labeling Experiments .......................................................... 80

3.3. Behavior of the insulin producing strain E. coli W3110M_pSW3 in STR, 2CR

and 3CR cultivations ............................................................................................. 84


3.3.2. Protein quantification ...................................................................... 88


3.3.4. Amino acids ...................................................................................... 94

3.3.5. Flow cytometry analysis ................................................................. 101

4. Discussion .................................................................................................. 104

4.1. Accumulation of non-canonical amino acids during cultivations ............ 104

4.1.1. Effect of oscillations ....................................................................... 104

4.1.2. Effect of expression of leucine-rich proteins ................................. 105

4.1.3. Effect of addition of trace elements .............................................. 108

XII

4.2. The favored synthesis pathway of non-canonical amino acids under

different cultivations .......................................................................................... 109

4.3. Misincorporation of non-canonical amino acids into proteins ................ 110

4.4. Cell physiology .......................................................................................... 111

5. Conclusions and Outlook ............................................................................ 113

6. Theses ........................................................................................................ 114

7. References ................................................................................................. 115

8. Appendix .................................................................................................... 125

8.1. Behavior of E. coli W3110 in STR and 2CR cultivations ............................ 125

8.2. Behavior of E. coli W3110_pCTUT7_His_IL2 in STR and 2CR cultivations 132

8.3. Behavior of E. coli W3110M_pSW3 in STR, 2CR and 3CR cultivations ..... 141

Curriculum Vitae ................................................................................................ 146

XIII

Abbreviations

2CR Two-compartment reactor

3CR Three-compartment reactor

Acetyl-CoA Acetyl Coenzyme A

ACKA Acetate kinase

AHAS Acetohydroxy acid synthase

ATP Adenosine triphosphate

DCW Dry cell weight

DO Dissolved oxygen

DOT Dissolved oxygen tension

E4P Erythrose 4-phosphate

FADH2 Flavin adenine dinucleotide

FHL Formate hydrogen lyase

GTP Guanosine triphosphate

HCD High cell density

HPLC High performance liquid chromatography

IB Inclusion body

IMPS Isopropylmalate synthase

IPMD Isopropylmalate dehydrogenase

IPMI Isopropylmalate isomerase

IPTG Isopropylthiogalactoside

LB Luria Bertani

LDH Lactate dehydrogenase

MSM Mineral salts medium

NAD+ Nicotinamide adenine dinucleotide

NADPH Nicotinamide adenine dinucleotide phosphate

OD Optical density

XIV

PEP Phosphoenolpyruvate

PFL Pyruvate formate-lyase

PFR Plug flow reactor

pO2 Dissolved oxygen concentration

POXB Pyruvate oxidase

PTA Phosphotransacetylase

R5PP Ribose 5-phosphate

RID Refractive index detector

SDS-PAGE Sodium dodecyl sulfate-polyacrylamide gel

STR Stirrer tank reactor

TCA Tricarboxylic acid cycle

tRNA Transfer RNA

1. Introduction

1

1. Introduction

1.1. Basic metabolism in E. coli cultivations

Escherichia coli is a robust cell factory and very popular with scientists and industries

for recombinant protein productions (Zerbs et al. 2009), largely because of its simple

cultivation, well-understood primary metabolism, rapid growth rate, easiness to

handle and inexpensive investment, though E. coli as a heterologous host also has

significant drawbacks, such as lack of compatible post-translational enzymes,

incorrect folding of heterologous proteins, but many of the limitations can be

overcome by engineering technology and optimization.

The cellular metabolism of E. coli, like all the other organisms, is very complex,

containing reaction networks and regulatory mechanisms of response to conditions.

Therefore, this section will focus on the critical metabolism for cell growth and

expression of recombinant production in cultivation process. Understanding this is

all-important for efficient optimization of cultivation process. A reduced metabolic

network used in this thesis is shown in Figure 1.1.

1. Introduction

2

Figure 1.1: Schematic representation of simplified metabolism in E. coli.

1.1.1. Glucose metabolism of E. coli

As a facultative anaerobe E. coli can grow and generate energy not only under

aerobic conditions through respiration, but also via fermentation when oxygen is not

available. E. coli is capable of utilizing various organic compounds as carbon sources.

However, glucose is the preferred carbon source when available in the growth

medium (Deutscher 2008). Adaption to the preferred carbon source such as glucose

is regulated by carbon catabolite repression (CCR) where the synthesis of the

enzymes necessary for the transport and metabolism of less favorable carbon

sources are repressed (Görke and Stülke 2008) by the dominating carbon source. The

rapid utilization of glucose depends on the phosphoenolpyruvate (PEP):

carbohydrate phosphotransferase system (PTS), which catalyzes the uptake and

concomitant phosphorylation of several carbohydrates and plays a major role in

Gllucose-6-P

Glucose

Pyruvate

Acetyl-CoA

Formate pfl

Acetate

fhlH2 + CO2

Pentose-5-PHistedine

poxB

pdh

TCA

Lactate ldh

α -ketoisovalerate

Valine

Leucine

α -ketobutyrate Isoleucine

α -ketovalerate β-Methylnorleucine

Norvaline

Norleucine

3-P-Glycerate

PEP

Erythrose-4-P

ChorismateTyrsine

Phenylalanine

Tryptophan

Acka, pta

acs

Malic acid

Fumaric acid

Oxaloacetic acid

α-ketoglutarate

Succinic acid

Glycine

Serine

Cysteine

Alanine

Asparagine

Aspartate

Lysine

Threonine

Homoserine

Methionine

proline

Glutamate

Argnine

Glutamine

1. Introduction

3

bacterial CCR (Deutscher 2008; Deutscher et al. 2006). Glucose is a good substrate

and after uptake by cells it is catabolised mainly though glycolysis

(Embden–Meyerhof–Parnas pathway). Glycolysis decomposes each mole of glucose

into two moles of pyruvate, coupled with generation of two moles of adenosine

triphosphate (ATP) via substrate-level phosphorylation and two moles of NADH

(Madigan et al. 2014).

Pyruvate plays a key role in the main metabolite processes including the tricarboxylic

acid cycle (TCA), aerobic overflow metabolism into acetate in E. coli, mixed-acids

fermentation when oxygen is unavailable and amino acids syntheses. One molecule

of pyruvate in aerobic conditions via the TCA is oxidized into three molecules carbon

dioxide (CO2), along with formation of one molecule guanosine triphosphate (GTP),

four molecules of NADH+H+ and one molecule of flavin adenine dinucleotide (FADH2).

In electron transport chain, NADH+H+ and FADH2 with energy-rich electrons support

electrons to electron acceptor (O2). Finally, H2O and great yield of energy formed. In

addition, intermediates, such as oxaloacetate and α-ketoglutarate serve as

precursors of some amino acids (Madigan et al. 2014).

1.1.2. Overflow metabolism

The formation and accumulation of the undesired product acetate in aerobic

conditions using glucose as carbon source is a problem in high cell density

cultivations of E. coli. This has several disadvantages. Firstly, under substrate excess,

cells consume glucose with a higher rate and by the imbalance of the glycolysis and

TCA, they excrete acetate, and thus lose a significant portion of carbon source

(Farmer and Liao 1997; Kleman and Strohl 1994). Secondly, numerous reports have

shown that acetate inhibits cell growth in E. coli cultivations (Korz et al. 1995; Luli

and Strohl 1990; Yee and Blanch 1993). Thirdly, accumulation of acetate above ca. 1 g

L-1 also reduces the yield of the target recombinant protein synthesis (Contiero et al.

1. Introduction

4

2000; Jensen and Carlsen 1990; Sakamoto et al. 1994) and has a negative effect on

the stability of intracellular proteins (Stephanopoulos 1998), possibly by its influence

on the intracellular pH.

It is considered that overflow metabolites are produced under aerobic condition

when the carbon flux exceeds the capacity of TCA cycle and respiration. There are

two pathways to biosynthesis of acetate as by-product in E. coli under aerobic

conditions, either from Acetyl-Coenzyme A (Acetyl-CoA) via phosphotransacetylase

(pta)/acetate kinase (ackA) proceeding through the unstable intermediate acetyl

phosphate (acetyl-P), or directly from pyruvate by pyruvate oxidase (poxB) activity

(Figure 1.4) (Dittrich et al. 2005; Valgepea et al. 2010). The first pathway is active in

both anaerobic and aerobic conditions; cells metabolite Acetyl-CoA into acetate

using pta-ackA pathway and generating ATP (Kumari et al. 2000). The pta and ackA

genes which are encoded within one operon (Kakuda et al. 1994) are vital to the

balanced carbon flux during the exponential phase of cell growth (Chang et al. 1999).

While it was found that poxB is also crucial to aerobic growth efficiency in E. coli

(Abdel-Hamid et al. 2001) and more active during the late exponential and stationary

phase (Dittrich et al. 2005). Under aerobic fermentations the level of acetate

produced is complex, depending on the culture medium, actual glucose

concentration, the E. coli strains, and growth conditions (Wolfe 2005). So in high cell

density fed-batch E. coli cultivations the question of how to reduce the accumulation

of acetate on the bioprocess level and the genetic level has been widely studied over

the years (De Mey et al. 2007).

1.1.3. Mixed-acid fermentation and pyruvate metabolism in E. coli

Generally, E. coli is capable of making use of glucose in the culture medium under

both aerobic and anaerobic conditions. In the absence of oxygen as electron acceptor,

E. coli adapts to mixed-acid fermentation, which produces partly oxidised

1. Introduction

5

fermentation products acetate, lactate, formate, ethanol, succinate and format with

H2 and CO2 as the end products (Figure 1.2) (Clark 1989). All the mixed-acids

fermentation products except succinate come from pyruvate which is derived from

glucose oxidation though glycolysis pathway, whereas succinate is from

phosphoenolpyruvate (PEP) via oxaloacetate (Xu et al. 1999a).

Figure 1.2: Schematic representation of overflow metabolite (Manfredini et al.) and mixed-acids

fermentation (green) metabolism in E. coli.

In the absence of oxygen, lactate is produced by lactate dehydrogenase (LDH) from

pyruvate. At the same time, under anaerobic conditions, pyruvate is also cleaved

with the help of pyruvate formate-lyase (PFL) into acetyl-CoA which can be further

transformed into acetate though the PTA-ACKA pathway accompanying ATP

generation or ethanol with nicotinamide adenine dinucleotide (NAD+), and formate

which is further split into H2 and CO2 via formate hydrogen lyase (FHL) (Sawers and

Clark 2004; Xu et al. 1999a). It was found that a fully functional FHL complex needs

ADP

ATP

PEP

Glucose

Pyruvate

CoA, NAD

CO2, NADH2

Acetyl-CoA

Acetyl-CoA

Formate

PFL

PTA

CoASH PiAcetyl-P

ATP ADP

ACKAAcetate

PTA

CoASH PiAcetyl-P

ATP ADP

ACKAAcetate

ADH

NAD+,CoASH

NADHAcetaldehyde

NAD+ NADH

ADHEthanol

FHL complexH2 + CO2

OxaloacetateSuccinate

POXB

FADH

FAD+ PDH

TCA

Lactate NAD+

NADHLDH

1. Introduction

6

trace amounts of molybdenum, selenium, and nickel in the culture medium, due to

the three formate dehydrogenase (FDH) components of the FHL complex are

molybdo-seleno enzymes, and HycE component is NiFe hydrogenases in E. coli

(Pinsent 1954; Sawers 2005). Therefore, in anaerobic E. coli cultivations, these trace

elements are particularly added into cultivation medium (Yoshida et al. 2005). Result

on addition of trace elements proved that it can significantly reduce formate

accumulation (Soini et al. 2008b) and production of non-canonical amino acids

(Biermann et al. 2013).

1.1.4. Biosynthesis of proteinogenic amino acids

In living organisms, proteins play important roles and perform a vast majority of

functions, such as structural support, transport molecules, catalyzing biochemical

reactions. The monomers of proteins are amino acids. 20 of more than 300 AAs in

nature are directly encoded by the genetic code and serve as building blocks of

proteins. These amino acids which are known as proteinogenic AAs represent the

most essential components of living cells. E. coli is able to synthesise them using other

substrates in the cultivation medium, such as glycolysis and TCA cycle intermediates and

inorganic nitrogen source, to satisfy the demand for cell growth or produce recombinant

proteins (Madigan et al. 2014). On account of precursor molecules from which carbon

skeletons of amino acids are derived, they are divided into alanine family, serine family,

aromatic family, glutamate family, aspartate family and histidine (Figure 1.3).

1. Introduction

7

Figure 1.3: Overview of proteinogenic amino acid biosynthesis. The carbon skeletons of amino

acids are derived from glycolysis (green), citric acid cycle (TCA) (blue) and pentose phosphate

pathway (pink).

AA families derived from TCA cycle intermediates are the glutamate and aspartate

family. α-ketoglutarate is the precursor molecule for the biosynthesis of glutamate.

Further, ammonium salt in the mineral medium provides the nitrogen source for

glutamate and glutamine formation. Almost all the nitrogen assimilated from

ammonia enters the metabolism as glutamine amine group and glutamate amino

group which is then further distributed to the biosynthesis of other amino acids. The

glutamate and glutamine pathway play an important role in ammonia assimilation of

organisms (Nelson and Cox 2008; Reitzer and Magasanik 1996; Umbarger 1978).

Serine is derived from 3 –Phosphoglycerate which is an intermediate of glycolysis.

Glycine is synthesized from serine via serine hydroxymethyltransferase by removing a

carbon atom. Bacteria produce sulfide from environmental sulfate, then react with

the carbon skeleton from serine to form cysteine. Aspartate synthesis from TCA

1. Introduction

8

intermediate oxaloacetate via a glutamate-aspartate transaminase includes a

transamination of the amino group from glutamate, accompanied by α-ketoglutarate

as byproduct. Asparagine is produced via transamination reaction from aspartate.

Methionine, threonine and isoleucine are synthesized from aspartate via homoserine,

with lysine branching from this route (Greene 1996; Patte 1996; Umbarger 1978).

Alanine is directly derived from pyruvate via transamination. Alanine and aspartate

family combine together with branched-chain amino acids and synthesize valine,

leucine and isoleucine from pyruvate and α-ketobutyrate.

The biosynthesis of branched chain amino acids plays an important role in E. coli,

because it is closely connected with the overflow metabolism and the formation of

non-canonical amino acids. Pyruvate, which is directly linked to glycolysis, serves as

the common precursor for the synthesis pathway of branched chain amino acids. The

beginning of valine and leucine pathway is that pyruvate is catabolised into

α-acetolactate and α-ketobutyrate. Then α-acetolactate is metabolised to

α-ketoisovalerate, which is the precursor for either leucine synthesis via

α-isopropylmalate or valine (Umbarger 1996). Isoleucine biosynthesis pathway

occurs in parallel with valine pathway. α-ketobutyrate generated besides from

pyruvate, and also from threonine serves as precursor for isoleucine formation. The

first enzyme of isoleucine-valine synthetic pathway is acetohydroxy acid synthetase

(AHAS), which is encoded by the largest group of ilv family, containing AHAS I

(encoded by ilvBN) AHAS II (encoded by ilvGM) and AHAS III (encoded by ilvIH). The

function of AHAS is regulated by end-product feedback inhibition by one or more of

the branched chain amino acids. AHAS I and AHAS III can be inhibited by high

concentration of valine, while AHAS II is insensitive to valine inhibition (Andersen et

al. 2001). Since the E. coli K-12 strain has a frameshift mutation in the ilvG gene, that

is to say, AHAS II (encoded by ilvG) has no function in E. coli K-12 strain (Lawther et al.

1981). As a result, when excess valine accumulates in the cellular environment, it

1. Introduction

9

leads to the valine toxicity phenomenon, which is known as the inhibition of leucine

and isoleucine synthesis especially in E. coli K-12 strains, even in the case of leucine /

isoleucine starvation (Andersen et al. 2001).

1.2. Overview of non-canonical amino acid synthesis and incorporation

into recombinant production in E. coli

Apart from the 20 proteinogenic amino acids, there are also non-canonical amino

acids existing in E. coli or other gram-negative organisms, e.g. norvaline, norleucine

and β-methylnorleucine, which are formed as byproducts of the branched chain

amino acids pathway. As early as 1953, the first report about non-canonical amino

acids showed that norvaline was as part of antifungal peptide produced by Bacillus

subtilis (Nandi and Sen 1953). Later Kisumi and colleagues reported that norleucine

was synthesized as side product of isoleucine in mutants of Serratia marcescens

(Kisumi et al. 1976a; Kisumi et al. 1976b; Kisumi et al. 1977b). Besides, norleucine

(Kisumi et al. 1976a) and β-methylnorleucine formation (Sugiura et al. 1981a; Sugiura

et al. 1981b) was also found corresponding to norvaline pathway in Serratia

marcescens .

The formation of non-canonical amino acids is beginning to draw increasing attention

as it has been found that in various researches they can substitute proteinogenic

amino acids at different positions within recombinant proteins. Norvaline is able to

substitute leucine in proteins of E. coli (Apostol et al. 1997; Tang and Tirrell 2002),

Norleucine was shown to substitute methionine (Bogosian et al. 1989; Cirino et al.

2003; Kiick et al. 2001), and β-methylnorleucine can substitute isoleucine in bacterial

proteins (Muramatsu et al. 2003; Muramatsu et al. 2002). Lots of studies found that,

especially in leucine-rich recombinant proteins, non-canonical amino acids can be

misincorporated, e.g. norvaline into recombinant hemoglobin which includes 72

1. Introduction

10

leucine residues in 575 total amino acids residues (Apostol et al. 1997) norleucine

into interleukin2 containing 152 amino acid residues, of which 26 are leucine

residues (Lu et al. 1988; Tsai et al. 1988). Since pharmaceutically used proteins must

be of high product quality and purity, such incorporations are highly unwanted.

1.2.1. Biosynthesis of non-canonical amino acids

The exact synthesis pathway of non-canonical amino acids is not finally determined

yet. Figure 1.4 shows the biosynthetic pathway of branched chain amino acids and

proposed pathway of norvaline, norleucine and β-methylnorleucine based on former

literature surveys (Muramatsu et al. 2003; Sycheva et al. 2007; Umbarger 1996).

1. Introduction

11

Figure 1.4: Biosynthetic pathway of branched chain amino acids (blue) and proposed pathway of

non-canonical amino acids (red), adapted from (Muramatsu et al. 2003; Sycheva et al. 2007;

Umbarger 1996). AHAS: acetohydroxy acid synthesis; ilvA: threonine deaminase (TD); ilvC:

1. Introduction

12

ketoacid isomeroreductase; ilvD: dihydroxyacid dehydratase; ilvE: transaminase B; avtA:

transaminase C; tyrA: aromatic transaminase; tyrB: aromatic transaminase; leuA: isopropylmalate

synthase (IPMS); leuCD: isopropylmalate isomerase (IPMI); leuB: isopropylmalate dehydrogenase

(IPMD).

Kisumi and colleagues hypothesized that the synthesis of norvline is from

α-ketobutyrate and norleucine is from α-ketovalerate in Serratia marcescens, using

the leucine biosynthetic enzymes which are encoded by leuABCD operon, including

isopropylmalate synthase (IMPS, coded by leuA), isopropylmalate isomerase (IPMI,

coded by leuCD), and isopropylmalate dehydrogenase (IPMD, coded by leuB ), due to

their broad substrate specificity (Kisumi et al. 1976a). Furthermore, this was

confirmed by Sycheva and colleagues in E. coli (Sycheva et al. 2007). α-ketobutyrate

is catalised by chain elongation to α-ketovalerate, which is either directly catalysed to

norvaline or converted to α-ketocaproate serving as the precursor for the formation

of norleucine. Also it was proposed that β-methylnorleucine is synthesized also from

α-ketovalerate via α-keto-β-methylcaproate, via the isoleucine-valine biosynthetic

pathway enzymes that also have broad substrate specificity as well as leucine

biosynthetic enzymes (Muramatsu et al. 2003; Sugiura et al. 1981b).

It is well known that α-ketobutyrate serving as precursor of the branched chain

amino acids, is derived from the oxaloacetate precursor with aspartate and

homoserine as intermediates, and then threonine involved in deamination.

Additionally, it has been observed that blocking the synthetic pathway of

α-ketobutyrate from threonine by knocking out of the ilvA gene in E. coli was not

able to prevent the synthesis of norvaline and norleucine (Sycheva et al. 2007). Thus,

this indicated that there is an alternative way to form α-ketobutyrate, probably

directly from pyruvate.

1. Introduction

13

The conditions resulting in the biosynthesis of non-canonical amino acids are not

totally clear, but various studies discussed several factors. One factor is glucose

overflow in the cultivation process, which leads to pyruvate accumulation during

glucose overflow. Soini and colleagues showed that a combined high glucose

availability and oxygen limitation results in the accumulation of pyruvate and

consequently in an overflow towards α-ketobutyrate, and finally in an overflow to

norvaline synthesis. The high level of pyruvate was obtained by a high sudden

glucose influx after a shift from aerobic to anaerobic conditions. It was proposed that

a high pyruvate level is the prerequisite for the direct shunt to α-ketobutyrate, and a

high α-ketobutyrate level is a prerequisite for non-canonical amino acids formation

(Soini et al. 2008a). Because the enzyme IPMS has lower Km value for any other

substrates than α-ketoisovalerate (Kohlhaw et al. 1969; Umbarger 1996). But when

excess α-ketobutyrate presents, IPMS utilizes it instead of α-ketoisovalerate (Sycheva

et al. 2007). Additionally, Sycheva et al. deleted the ilvA gene in mutant E. coli, which

is responsible for the synthesis of α-ketobutyrate from threonine, but norvaline and

norleucine were still synthesised. This demonstrated that an alternative pathway for

the biosynthesis of α-ketobutyrate was functional, maybe directly from pyruvate,

rather than from threonine (Sycheva et al. 2007). Non-canonical amino acids are a

function and result of pyruvate overflow metabolism.

The other highly probable factor that leads to the formation of non-canonical amino

acids is the high expression of the leuABCD operon, which encodes the first enzyme

of isoleucine and non-canonical amino acid biosynthesis pathway and regulated by

the pool of free leucine in the cells. As shown in Figure 1.4 and explained above,

enzymes involved in branched chain amino acids and non-canonical amino acids

biosynthetic pathway are mainly encoded by the leuABCD operon and ilv operons.

Sycheva and coworkers showed that an increased expression of leuABCD operon

results in an elevated level of non-canonical amino acids (Sycheva et al. 2007).

1. Introduction

14

Moreover, it was found that leucine level regulates the expression of leuABCD operon

(Burns et al. 1966). Bogosian and co-workers postulated that high level expression of

leucine-rich proteins may promote the formation of norleucine in E. coli. This was

supported by Apostol and his colleagues that induction of leucine-rich protein

hemoglobin expression leading to leucine pool rapidly decrease and parallel with

pyruvate accumulation, resulting in an immediate norvaline accumulation, and with

a delay also in the accumulation of norleucine (Apostol et al. 1997). The synthesis of

leucine-rich heterologous proteins in E. coli demands for a large amount of leucine

during production, leading to an increased expression of the leuABCD operon by cell

adaptation to this higher demand of leucine. As a result, when leucine biosynthesis

enzymes are present at a sufficient high level, combined with high level of

α-ketobutyrate, the formation of non-canonical amino acids occurs.

Another factor that probably leads to the formation of non-canonical amino acids is

the activity of AHAS. Valine toxicity phenomenon exists in the E. coli K-12 W3110

strain, which has been described above. It was also proposed that AHAS II has the

highest efficiency in conversion of α-ketobutyrate (Barak et al. 1987). As a result,

except valine toxicity, inactivation of AHAS II and the low flux into the isoleucine

pathway can elevate the concentration of α-ketobutyrate, which in turn serves as the

precursor for non-canonical amino acids.

1.2.2. Misincorporation mechanism of non-canonical amino acids into

heterogonous proteins

Generally, under usual circumstances, non-canonical amino acids do not get

misincorporated into proteins, due to the well documented well-functioning of

protein synthesis in E. coli. The recognized mechanism of protein synthesis relies on

the acylation of transfer RNA (tRNA), binding aminoacyl transfer RNAs (aa-tRNAs) to

ribosome by the elongation factor Tu (EF-Tu) in bacteria, and the recognition

1. Introduction

15

verification process of messenger RNA (mRNA) codons with tRNA anticodons

(Cvetesic et al. 2013; Ling et al. 2009; Zaher and Green 2009).

The acylation of tRNA is a two-step reaction, catalyzed by aminoacyl tRNA

synthetases (aaRSs) including the conversion of single cognate amino acids with ATP

to aminoacyl adenylate (aa-AMP) and followed by transferring the appropriate amino

acid to the 3’-end of the cognate tRNA (Ibba and Soll 2000; Ling et al. 2009). Only

within the synthesis reaction, many aaRSs are not able to discriminate cognate

against non-cognate amino acids with great accuracy. Meanwhile, aaRSs also play an

important inherent role in editing and proof-reading activity which could hydrolyze

either misactivated non-cognate aa-AMP in pre-transfer editing activity or

misacylated aa-tRNA in post-transfer editing activity (Ling et al. 2009). So aaRSs most

stringently controls the protein synthesis (Ibba and Soll 2000).

Lots of reports showed that many aaRSs have lowered substrate specificity, because

some amino acids are so similar that aaRSs can hardly discriminate between them

with high specificity. For example, it was shown that leucyl-tRNA synthetase (LeuRS)

in E. coli is able to accept several leucine analogues, including norvaline (Martinis and

Fox 1997; Tang and Tirrell 2002). Moreover, Cvetesic and colleagues demonstrated

EF-Tu has similar affinities in binding Nva-tRNALeu and Leu-tRNALeu, leading leuRS has

incapability of hydrolytic editing against norvaline (Cvetesic et al. 2013). As a result,

norvaline is misincorporated into proteins instead of leucine (Apostol et al. 1997). A

similar principle was found as well for methionyl-tRNA synthetase (MetRS) (Bogosian

et al. 1989; Kiick et al. 2001) and isoleucyl-tRNA synthetase (IleRS) (Muramatsu et al.

2003). The premise of misincorporation is a high ratio of the substitute to natural

substrate. It was studied that the ratio of free norvaline to leucine pool plays a critical

role in misincorporation of norvaline for leucine, and results showed that the

percentage of substitutions in the recombinant protein is directly proportional to the

1. Introduction

16

ratio of norvaline to leucine available in the culture broth (Apostol et al. 1997). If

under certain growth conditions, which favor non-canonical amino acid syntheses

and accumulation, it will definitely cause misincorporation into recombinant proteins.

Soini and co-workers found that under oxygen limited cultivation conditions with

pyruvate accumulation, the amount of norvaline reaches millimolar concentrations,

which is already far above the level leading to a great threat to biosynthesis of

proteins in E. coli (Soini et al. 2008a).

1.2.3. Strategies to reduce misincorporation of non-canonical amino acids into

recombinant proteins

Along with the knowledge of misincorporation mechanism of non-canonical amino

acids into heterologous proteins, numerous scientists have tried to deal with such

situations with different methods and strategies to reduce the occurrence of

misincorpoation.

One strategy to reduce the misincorporation could be the optimization of the culture

medium. A common and reliably effective method is to interfere with the

incorporation of non-canonical amino acids into protein by supplying the culture

media with competitive canonical compounds of non-canonical amino acids. This

was supported by Bogosian and his colleagues using media supplementations with

exogenous methionine or 2-hydroxy-4-methylthiobutanoic acid that can convert into

methionine in E. coli to guarantee excess intracellular pool of methionine in the cells

to effectively compete with norleucine in charging of the methionyl tRNA, and as a

result, reduction in norleucine incorporation was observed (Bogosian et al. 1989). As

it is well known that the level of leucine is able to regulate the first enzyme of the

leucine biosynthesis pathway (Umbarger 1996), another medium supplementation

experiment with leucine performed by Tsai and his colleagues demonstrated an

exciting reduction of the norleucine incorporation in IL-2 from 19 to 2% in a minimal

1. Introduction

17

medium cultivation (Tsai et al. 1988). Most recently, another method was

demonstrated successfully with supplementation of trace elements molybdenum,

nickel and selenium, presenting significant justifications to prove that these trace

elements can effectively suppress norvaline and norleucine biosynthesis under

conditions of limited oxygen and excess glucose (Biermann et al. 2013). This is

because these trace elements play important roles in the fully functionality and

catalysis of formate hydrogen lyase (FHL) complex that is the most dedicated enzyme

in the pyruvate metabolism in E. coli under anaerobic conditions, which was

previously described in Section 1.1.3. So molybdenum, nickel and selenium lead to a

higher activity of the formate hydrogen lyase (FHL) complex to catalyse pyruvate and

release dihydrogen and carbon dioxide. Therefore pyruvate is not accumulated so

much after a downshift of oxygen under high glucose concentration and thus the

direct chain elongation from pyruvate to α-ketobutyrate, which is the precursor of

non-canonical amino acids, would be less.

The other strategy is using genetic tools to engineer E. coli strains to prevent the

misincorporation of non-canonical amino acids. One method is inactivating the genes

involved in their biosynthesis pathway. It was shown that deleting one or more genes

of the leu operon (namely leuA, leuB, leuC and leuD) or ilvA gene in E. coli, which

encodes the leucine biosynthetic enzymes, successfully eliminated the biosynthesis

of norleucine (Bogosian et al. 1989; Tsai et al. 1988). However, this approach has the

disadvantage of requiring the supplementation of leucine or other branched chain

amino acids in the culture medium, due to the bacterial strains inability to produce

its own. Another method is mutating genes involved in methionine biosynthesis and

regulation (metA, metK, and metJ) to obtain overexpression of methionine as an

alternative to continuous methionine feeding during cultivations to prevent

norleucine misincorporation and improve product purity and quality (Veeravalli et al.

2015).

1. Introduction

18

Another strategy to reduce or eliminate the misincorporation is using the cells and

DNA constructs to co-express or to enhance the expression of a protein like the

glutamate dehydrogenase, which is capable of degrading non-canonical amino acids

(Bogosian et al. 2013).

The strategy also could be choosing proper host expression strains of recombinant

proteins to reduce the misincorporation of non-canonical amino acids. Experimental

data gained by Ni and his colleagues showed that, a lower biosynthesis of norvaline

and norleucine, and misincorporation into vaccine candidate protein were measured

in the E. coli BL21(DE3) strain than E. coli K-12 strain, without a significant loss in

expression level (Ni et al. 2015).

1.3. E. coli fermentation processes

Whether in laboratory research or large-scale industrial processes, maximizing the

yield of productions is a major objective in cultivations. For this reason, attaining high

cell density is required to achieve high productivity (Fass et al. 1991; Riesenberg and

Guthke 1999; Seo and Chung 2011). In order to reach high cell density, various

strategies including medium compounds optimization, cultivation techniques

improvement, substrate feeding strategies together with molecular strain

development have been carried out (Choi et al. 2006; Shiloach and Fass 2005).

1.3.1. Cultivation medium

One of the vital factors to achieve high cell density and cultivation success is the

choice and optimization of medium used in cultivation process, which should contain

carbon source, nitrogen source, essential salts, minerals and certain growth factors

for the growth of E. coli (Shiloach and Fass 2005). Basically, media can be classified as

1. Introduction

19

chemically defined or undefined. An undefined or complex medium such as Luria

Bertani (LB) broth contains undefined compositions including yeast extract or protein

hydrolysates. Though complex medium has the advantages of lower price and more

robust cell growth, cultivation process may lead to unexpected productivity and

batch to batch variation with protein expression and quality because of the variations

of undefined materials (Zhang and Greasham 1999). In contrast, a defined medium,

such as mineral medium widely used in both laboratory and industrial scale, has

completely and precisely known constituents and can be easily controlled during

cultivation, showing the merit of improvement of upstream and downstream

processing, process control and scale-up (Lee 1996; Zhang and Greasham 1999).

1.3.2. Cultivation modes

A batch bioprocess is the simplest mode to cultivate microorganisms. Right at the

beginning of cultivation, it already has all the carbon and energy sources and other

nutrients, minerals and trace elements, which are needed for all the growth of the

cells. Though batch cultivation in shake flask is mainly popular in small scale

experiments (Panula-Perälä et al. 2008), there obviously exist disadvantages, for

instance, high substrate concentrations in the cultivation medium limit cell growth

and undesired inhibitory metabolites could be produced (Enfors et al. 2001; Xu et al.

1999a). Besides, when one of the substrate is depleted or one of the products

inhibits the growth, the culture comes to stationary phase (Jones and Kelly 1983).

Therefore, these constitute major challenges in bringing cells to a high density as

required in large-scale cultivations.

In order to obtain high cell density and increase recombinant protein volumetric

productivity, fed-batch is one of the successful approaches in bioreactor cultivations

and it has been applied widely for decades ago, e.g. see reviews by (Choi et al. 2006;

Gélinas 2014; Riesenberg and Guthke 1999; Shiloach and Fass 2005). The fed-batch

1. Introduction

20

strategy has many benefits: by-product inhibition can be avoided under glucose or

substrate limited fed-batch processes (Enfors et al. 2001; Xu et al. 1999a) and cell

growth rate could be also controlled by a controlled supply of the nutrient(s)

(Shiloach and Fass 2005). Usually, a glucose-limited fed-batch by supplying high

concentration of glucose is simple and most commonly used in cultivations. The

largest benefit in using this mode is the possibility to control the oxygen

consumption of E. coli which can keep cells in aerobic cultivation all the time

independent on the cell density, and the possibility to avoid overflow metabolism

(Shiloach and Fass 2005). This is why fed-batch is favored by both laboratory and

industry scale cultivations. Nowadays, lots of commercial production processes, such

as the production of baker’s yeast [for a recent review see (Gélinas 2014)], amino

acids (Konstantinov et al. 1991) and therapeutic proteins (Seo and Chung 2011) are

obtained through high cell density fed-batch processes in E. coli.

1.3.3. Inclusion body (IB) productions

The expression of recombinant proteins in bacterial hosts such as E. coli is a useful,

normal and essential tool to obtain productions for basic research containing of

structural proteomics, therapeutic applications and biotechnology industry. However,

numerous of products usually accumulate in insoluble aggregates which are

commonly called inclusion bodies (IBs) (Fahnert et al. 2004; Mayer and Buchner

2004). Most of them are caused by overexpression of heterogonous proteins.

Generally, the recombinant protein aggregates in IBs in an inactive form. Research on

HCD fed-batch fermentations at scale-down model with various IPTG inductions at

different time shows harmful effect on individual cell physiology, which causes the

final biomass yield significantly reduce (Hewitt et al. 2007). Until now, investigators

have put lots of efforts on methods optimization aiming to get soluble, active and

correctly folded form of proteins e.g. development on cultivation strategy,

production of new strains and vectors. However, the optimization process usually is

1. Introduction

21

unpredictable and time-lasting, mostly still resulting in low product yield (Fahnert et

al. 2004; Neubauer et al. 2006). Despite IB-based expression has above-mentioned

limitations, the most attractive advantage is high concentration of target proteins

and easy purification (Neubauer et al. 2006; Ventura and Villaverde 2006). Moreover,

it is an efficient and simple strategy of IB-based cultivation process, and many

current processes for recombinant proteins in E. coli are IB-based processes, see a

review (Neubauer et al. 2006).

1.4. Bioprocess Scale-up and Scale-down

1.4.1. Gradients in large-scale bioprocesses

In the pharmaceutical, chemical and food industries, microorganisms are widely

cultivated under controlled conditions to produce recombinant proteins and organic

molecules. However, inhomogeneities unavoidably exist in large-scale cultivations.

Industrial-scale bioreactors may have volumes from 10 to more than 500 m3.

Technical limitations in large-scale bioprocesses, such as power input or volumetric

oxygen transfer rate, cause the formation of gradients in substrate, dissolved oxygen

concentration (pO2), pH and other parameters (Enfors et al. 2001; Lara et al. 2006a).

Many studies have measured or predicted gradients in large-scale bioprocesses. Cells

move through different zones in a large-scale bioreactor and, consequently, are

exposed to the changing conditions. As a result, they show a different physiology

compared with cells grown in homogeneous cultures.

The most important parameter concerning gradients in large-scale bioreactors is the

pO2, This is because pO2 plays an important role in aerobic processes. For instance,

Manfredini and co-workers studied the axial oxygen distribution in a 112 m3

industrial stirred tank reactor with Streptomyces aureofacies cultivations using a

flexible vertically mounted dissolved oxygen and temperature probe, and measured

1. Introduction

22

that the dissolved oxygen concentration was up to 65% (with respect to air saturation)

near the sparger throughout the cultivation, while it was only 30% at the top part of

reactor (Manfredini et al. 1983). Similar phenomena were observed by Steel and

Maxon (Steel and Maxon 1966) and Oosterhuis (Oosterhuis 1984). Besides, there are

also many groups that studied and predicted the existence of dissolved oxygen

gradients in reactors through computational fluid dynamics (CFD) tools (Ochieng et al.

2009; Schmalzriedt et al. 2003).

With the popularity of fed-batch culture to gain high cell density in industrial

processes (Choi et al. 2006), gradients of substrate are observed with addition of

concentrated substrate into the reactor especially in larger bioreactor scales. For

instance, Lasson et al. investigated the gradients of glucose in time and space in a 30

m3 cultivation of Saccharomyces cerevisiae and showed the concentration of

substrate was at all times different at three sampling parts (bottom/middle/top) of

the reactor. In cultivations, the inhomogeneities of substrate are usually associated

with oxygen gradients as substrate (mostly glucose) consumption and respiration are

closely related with each other. Because in industry cultivations, the concentration of

the feeding solution is always very high (normally well above 500 g L-1), at the limited

mixing power in large-scale bioreactors, mostly substrate excess is coupled with

oxygen limitation at the feeding zone. This leads to alteration of intracellular

metabolic fluxes and synthesis of unexpected byproducts (Neubauer et al. 1995b; Xu

et al. 1999a). On the contrary, at other regions, which are far away from the feeding

zone where there is almost no substrate available, cells are exposed to starvation.

Thus, cells continuously move quickly between excess substrate and starvation zones,

whereby the time constants depend on the circulation and mixing time of the

distinctive process. With the mixing in the bioreactor, cells are hereby exposed to

substrate and oxygen oscillations (Enfors et al. 2001; Junne et al. 2011). Studies

focusing on substrate and pO2 gradients showed a reduced biomass yield and

1. Introduction

23

increased by-product formation of volatile fatty acids (Bylund et al. 1998).

Another important parameter is pH-value and its variable is often measured and

controlled throughout cultivations. In large-scale bioprocess, pH control is usually

based on the point measurements of local pH which usually at well-mixed part of the

bioreactor. By contract, the controlling agent (e.g. concentrated base or acid)

normally is added at poorly mixed top surface of the liquid (Singh et al. 1986). As a

result, the local pH values near the addition point may be different from the bulk pH,

leading to overfeeding of the pH controlling agent and pH gradients in the reactor.

One example is that Langheinrich and Nienow showed the pH gradients of high pH

value in the alkali addition zone in large-scale free suspension animal cell culture

(Langheinrich and Nienow 1999). Since microbial activity is affected by the medium

pH, small fluctuations in pH may therefore potentially cause oscillations of large

amplitudes in cell metabolism. One of the studies performed on oscillations of

ammonia to control the pH showed lower biomass yield and negative effect on cell

viability when cells were exposed to high pH (pH > 7.0) with a mean residence time

of more than 100 seconds (Onyeaka et al. 2003). Another study simulating the pH

fluctuations of B. subtilis cultivations in a scale-down bioreactor clearly showed that

spatial pH gradients led to the change of cellular metabolism and accumulation of

by-products (Amanullah et al. 2001).

Besides the gradients of parameters mentioned above, there are also other

inhomogeneities likely to appear in large-scale cultivations, such as temperature

gradients (Gorenflo et al. 2007). All above studies clearly prove that cells respond to

inhomogeneities that exist in large-scale bioprocesses. So an increasing number

researchers focus on finding ways to simulate large-scale bioprocess in laboratory

scale cultivations to study cellular responses to inhomogeneities with the aim to

better understand the metabolism of cells in large-scale industrial systems.

1. Introduction

24

1.4.2. Simulating large-scale cultivations in Scale-down devices

Scale-down approaches aim to provide a smaller scale experiment to simulate the

same inhomogeneites in the environment that exists at the larger industrial scale and

provide a cheaper and more efficient laboratorial tool to study the impact of

inhomogeneities in large-scale industrial cultivations. There are several approaches

to mimic large-scale conditions in scale-down models. Such simulators could be one-,

two- or more-compartment systems consisting of Stirrer tank reactors (STR),

modified STRs, or tubular reactors.

Examples for one-compartment scale-down system are single STRs that simulate the

fluctuations of dissolved oxygen with switching the air supply into the reactor on and

off (Namdev et al. 1993) or manipulating the compositions of inflowing gases

(nitrogen and oxygen) (Cortés et al. 2005). Simulations of substrate gradients can be

designed in one STR also with intermittent feeding of the glucose solution (Lin and

Neubauer 2000; Neubauer et al. 1995a) or coupled with a rapid sampling unit.

Considering where the disturbance taken place, the rapid perturbation technique can

be classified into substrate pulse into the bioreactor and external disturbance. For

instance, Schaefer et al. performed fast injection of a glucose solution into the

bioreactor and using an automated sampling device to investigate the dynamics of

intracellular metabolites in E. coli (Schaefer et al. 1999). External disturbance can be

achieved by mixing the culture from bioreactor with glucose solutions outside of the

bioreactor and connected with a rapid sampling unit to study cells response to

glucose gradients in large-scale bioreactors (De Mey et al. 2010; Lara et al. 2009).

Two comportment models, either with two coupled STRs (STR-STR) or one STR plus a

plug flow reactor (STR-PFR) are employed most commonly and many articles have

certified them as useful scale-down modules. Dissolved oxygen tension (DOT)

1. Introduction

25

oscillations were studied by Sandoval-Basurto et al. (Sandoval-Basurto et al. 2005)

and Lara et al. (Lara et al. 2006b) with two interconnected stirrer tank bioreactors

which each had different levels for the DOT. The STR-PFR device was originally set up

with an aerobic STR and a single PFR without static mixers inside. But recently lots of

researchers set up the PFR module containing static mixers in the by-pass with

efficient mixing especially the gas-liquid formulation. It was firstly used to study

substrate oscillation conditions in Saccharomyces cerevisiae (George et al. 1993) and

E. coli (Neubauer et al. 1995b). Remarkable research work has been carried out using

such STR-PFR device including simultaneous assessment of the heterogeneities of

glucose, dissolved oxygen, and pH concentrations (Onyeaka et al. 2003). Recently,

besides the sampling ports, the PFR module was updated by equipping it with five

dissolved oxygen and pH sensors as the most advanced scale-down model until now.

This advanced version consists of a normal STR and 4 meters high PFR equipped with

static mixers. Cells continuously go through the STR and PFR to be exposed to

oscillating conditions (Figure 1.5A) (Junne et al. 2011). This scale-down bioreactor

has been used to study substrate and dissolved oxygen concentration oscillations on

Bacillus subtilis in fed-batch cultivations. As a result, the authors found a reduced

glucose uptake, ethanol formation and changes in the amino acid biosynthesis (Junne

et al. 2011). Furthermore, Lemoine et al. investigated the response of

Corynebacterium glutamicum on substrate and oxygen supply oscillations not only in

such two-compartment reactor (2CR), but also on this basis newly developed a novel

three-compartment reactor (3CR) by adding an additional non-aerated PFR module

(Lemoine et al. 2015). In their set up the PFR1 simulated the feeding zone in

large-scale cultivations, and the PFR2 mimicked the starvation zone which is far away

from the feeding substrate, presenting substrate depletion (Figure 1.5B).

1. Introduction

26

Figure 1.5: Scheme of the advanced version of scale-down two-compartment reactor and the

novel three-compartment reactor.

1.5. Metabolic flux analysis of biosynthesis pathway of non-canonical

amino acids in E. coli

1.5.1. Basic idea of metabolic flux analysis

Metabolite flux analysis (MFA) has become one of the pivotal and valuable tools to

describe the insight of complex metabolic control mechanism in living cell, based on

metabolic fluxes’ detailed quantification in the central metabolism of a

microorganism, which is very useful in metabolic engineering (Zhao and Shimizu

2003). Fluxes are able to describe the rate of metabolite interconversion in the whole

cell. Metabolic fluxes are crucial to observe and to understand metabolic regulation

and to define targets for improving biotechnological processes (Schwender 2008).

Initially, MFA started with stoichiometric knowledge of metabolic fluxes in the early

1990s. One important assumption was that the regarded biological system is in a

stationary or quasi-stationary state with respect to no change of intracellular pool

size (Wiechert 2001). However, there exits apparently shortcomings and limits of

stoichiometric MFA. For example, it cannot be applied in parallel fluxes, certain

1. Introduction

27

metabolic cycles, bidirectional fluxes, and complex fluxes with energy resources

generated or re-assimilated (Wiechert 2001).

In consideration of above-mentioned drawbacks of stoichiometric MFA, 13C MFA

method emerged and can overcome these problems, hence it is becoming a much

more powerful tool for accurately quantifying the metabolic network. 13C MFA is

based on carbon labeling experiments (CLE) with cells exposed to 13C-labeled

substrate like glucose, following isotopomers of labeling patterns in certain

intracellular metabolites measured by either nuclear magnetic resonance (NMR) or

mass spectrometry (MS) (Shimizu 2013; Wiechert 2001; Wiechert et al. 2001).

Though Szyperski has shown NMR spectra as a useful approach in studying the

regulation of metabolite in E. coli (Szyperski 1995), it also has a shortcoming of

requirement for large amount samples, while gas chromatography mass

spectrometry (GC-MS) is more popular with less sample volume (Sauer 2006;

Wittmann 2007).

1.5.2. Rapid sampling devices

Kinetic modeling of the metabolism in in vivo conditions requires information of

enzyme levels, metabolic fluxes and concentrations of metabolites under different

conditions. These can be gained from perturbations of controlled steady-state

cultures.

Considering where the disturbance took place, the rapid perturbation technique can

be classified into substrate pulse into the bioreactor and external disturbance. For

example, this technique was initially applied inside the bioreactor to study the kinetic

metabolite responses of Saccharomyces cerevisiae in in vivo conditions, performed

by a fast sampling technique allowing sampling with a frequency of 5 s from a bench

scale bioreactor (Theobald et al. 1993). Another technique was also developed to

1. Introduction

28

connect a helical sampling tube device to the reactor to enable continuous sampling,

inactivation and extraction of the intracellular metabolites, studying the response to

a glucose pulse (Weuster-Botz 1997). Further, sampling methods were developed to

be taken from a continuous stirred tank reactor, by using an automated sampling

device with a sampling rate of 4.5 s-1 to investigate the dynamics of intracellular

metabolites in E. coli (Schaefer et al. 1999). These devices mentioned above have an

obvious shortcoming. When carrying out a perturbation experiment, the steady-state

condition in the reactor is disturbed and the culture can only be used again until

reaching a new steady-state condition. In order to avoid this drawback, there are also

some devices with disturbance outside the bioreactor without interference with the

steady-state condition. For instance, Buziol and colleagues introduced a sampling and

perturbation device based on stop-flow technique, performing with mixing the

continuous culture from bioreactor with glucose solutions outside of bioreactor by a

mixing chamber (Weuster-Botz 1997). Further it was improved with the name of

“BioScope” by equipping it with oxygen permeable silicon tubing, and allowing

longer perturbation times with residence times from 4 to 69 s (Visser et al. 2002).

This first generation of BioScope was further improved by Mashego et al. in yeast

cultivation and overcomes the drawbacks of first version, coupled with a silicon

membrane separating the gas and culture hemispherical channels and allowing O2

and CO2 diffusion (Mashego et al. 2006). Because the metabolites response of E. coli

is much faster, De Mey et al. redesigned the second generation of BioScope with

significantly decreased sampling intervals especially for E. coli (De Mey et al. 2010).

The BioScope device can be considered as a mini plug-flow reactor and is able to be

coupled with a bioreactor, allowing sampling intervals of sub-seconds to seconds and

studying cellular reaction within seconds to minutes (De Mey et al. 2010;

Taymaz-Nikerel et al. 2013).

1. Introduction

29

Figure 1.6: Schematic representation of the BioScope device. a - 2D figure of BioScope with

serpentine channels and valves; b – cross-section of the channel (De Mey et al. 2010).

The adapted BioScope system (Figure 1.6) is operated by feeding labeled substrate

(e.g. 13C glucose) into the broth, which is pumped out of the bioreactor, to realize

pulse experiments, with no perturbation of the steady-state condition inside the

bioreactor. Meanwhile, it is coupled with fast sampling from the several sampling

spots along the plug flow reactor part controlled by valves. There are two pumps

required in such equipment. One is used to regulate the speed of broth entering the

BioScope, and the other is applied to control the feeding velocity of labeled substrate

into the channel. Due to the serpentine-shaped hemispherical gas and liquid

channels separated by a thin silicon membrane allowing O2 permeability into the

broth and CO2 removal from the culture, it is possible to realize aerobic conditions

during pulse experiments by flushing air or O2 into the hemispherical gas channel of

the BioScope. Hence, the flow through is saturated with oxygen during all the time of

sampling (De Mey et al. 2010).

1. Introduction

30

1.6. Research motivation and objectives

The industrial microbial production strain E. coli K-12 W3110 is able to accumulate

non-canonical amino acids in the presence of excess glucose and anaerobiosis in the

broth during cultivations, which are always likely to occur in the feeding zone in

industrial large-scale bioprocess. Consequently the accumulated non-canonical

amino acids can be misincorporated into proteins, which is highly unfavorable for

proteins for pharmaceutical use. In addition, the level of the branched chain amino

acids leucine, isoleucine and valine, which changes with the expression rate of

leucine-rich recombinant protein, also affects the synthesis and misincorporation of

non-canonical amino acids, and this effect is not yet fully clear.

Therefore, this doctoral dissertation study performed with E. coli W3110 wild-type

and two different recombinant strains expressing leucine-rich proteins, aims to

investigate the cellular responses to the oscillations of substrate and oxygen

availability caused by insufficient mixing during industrial large-scale cultivations. The

oscillations were simulated in a 2CR and recently developed 3CR scale-down

bioreactors (described in 1.2.2), and especially focuses on the analysis of the

non-canonical amino acids norvaline, norleucine and β-methylnorleucine. Thus, the

principal objectives of this project were:

1) To better understand the synthesis pathway and conditions that favour the

formation of non-canonical amino acids norvaline, norleucine and

β-methylnorleucine.

2) To study the influence on incorporation of non-canonical amino acids into

recombinant proteins, since a strong expression of leucine-rich protein seems to

provoke accumulation and misincorporation of non-canonical amino acids into

the target protein.

3) To study the effect of recombinant, leucine-rich proteins expressions and cell

1. Introduction

31

physiology at oscillating large-scale conditions.

2. Materials and Methods

32


2.1. Bacterial strains

Bacterial strains used in this dissertation work were: E. coli W3110 wild-type stain

obtained from E. coli Genetic stock center, E. coli W3110_pCTUT7_His_IL2 and E. coli

W3110M_pSW3 obtained from laboratory strain collection.

E. coli strains were stored as cryo stocks, which was prepared as follows: Cells were

cultivated overnight at 37 °C on an LB agar plate. If necessary, appropriate antibiotics

were added to the medium depending on different strains. The next day, a colony

was isolated from this plate, and inoculated into 25 mL LB medium with the

appropriate antibiotics in 125 mL Ultra Yield FlasksTM (Thomson Instrument Company,

California, USA), and cultured overnight at 37 °C. Then 1 mL of the overnight

culture was added to 200 μL sterile 100% glycerol (Carl Roth GmbH + Co.KG,

Karlsruhe, Germany) and distributed into 60 μL to 1.5 mL Eppendorf tubes, then

frozen in liquid nitrogen and immediately stored at -80 °C.

2.2. Cultivation media

2.2.1. LB medium

LB medium contained 10 g L-1 tryptone, 5 g L-1 yeast extract (Carl Roth GmbH) and 10

g L-1 NaCl (VWR International GmbH, Darmstadt, Germany), followed by adjustment

to a pH of 7, then sterilized at 121 °C for 20 min, and stored at room temperature.

2.2.2. Mineral salts medium and EnBase Flo medium

The composition of the medium used for cultivations is described below. The macro

elements, trace elements, magnesium sulfate were autoclaved separately at 121°C

for 20 min. Before sterilization, the macro element solution was supposed to have a


33

pH=7.0. If not, it was adjusted to pH=7 with 2 M sodium hydroxide solution.

Thiamine hydrochloride and antibiotics were sterile-filtered through a 2 μm sterile

filter (Carl Roth GmbH), MgSO4 was not added to the media until just before the

inoculation into the reactor.

For E. coli W3110 wild-type cultivations, EnBase Flo medium (BioSilta, Oulu, Finland)

as described in previous reports (Krause et al. 2010) without complex additives was

used. Additionally, 0.1 ml L-1 Antifoam Sigma 204 (Sigma Aldrich) was added to the

medium to prevent foaming. 200 μl of polymer degrading catalyst (BioSilta, Oulu,

Finland) was added into the medium shortly before inoculation to catalyze releasing

glucose.

For E. coli W3110_pCTUT7_His_IL2 cultivations, the same EnBase Flo medium

described above with additional 2 g L-1 glucose and 34 μg L-1 Chloramphenicol was

used.

For E. coli W3110M_pSW3 cultivations, MSM medium was used, containing macro

elements [2 g L-1 Na2SO4, 2.468 g L-1 (NH4)2SO4, 0.5 g L-1 NH4Cl, 14.6 g L-1 K2HPO4, 3.6

g L-1 NaH2PO4, 1 g L-1 (NH4)2-H-citrate], 0.1 mL L-1 Antifoam Sigma 204), trace

elements [1 mg L-1 CaCl2 • 2H2O, 0.36 mg L-1 ZnSO4 • 7H2O, 0.2 mg L-1 MnSO4 • H2O,

40.2 mg L-1 Na2-EDTA, 33.4 mg L-1 FeCl3•6H2O, 0.32 mg L-1 CuSO4•5H2O, 0.36 mg L-1

CoCl2•6H2O], 2 mM MgSO4, 0.1 g L-1 Thiamine, 5 g L-1 glucose and 0.1 g L-1 Ampicillin.

2.3. Bioreactor cultivation

To investigate cell responses to the substrate oscillation conditions in industrial

large-scale cultivations, a scale-down two-compartment reactor (2CR, Figure 1.5A)

was used. It consists of a normal 15 L BioStat E stirred tank reactor (STR, Satorius


34

stedim Biotech GmbH, Göttingen, Germany) and a plug flow reactor (PFR) with a 1.2

L working volume. The PFR was equipped with four static mixer modules (Kenics

series KM, distributed by Lewa GmbH). In order to maintain the temperature in the

PFR module, it was insulated with a polymer foam jacket. Between each static mixer

module, spacers, equipped with three sample ports, are mounted. Each spacer

contains one manual sampling port and equipped with pH and DO sensors. Thus, five

sampling points along the PFR allows analysis of the kinetics of the cell responses,

coupled with online pH and DO monitoring. A pump (Lewa GmbH, Leonberg,

Germany) was connected between the STR and PFR, and the culture circulated

through the STR and PFR with a pump rate of 1.73 L min-1, corresponding to a

residence time of 68 s in the whole PFR module. The feed solution was added at the

entrance of the PFR and no extra air was introduced there. As a result, cells

repeatedly circulated between substrate excess / oxygen limitation zones for 68 s and

the aerated glucose-limited STR module. In order to avoid backflow at the feed pump,

a back-pressure valve was integrated between the feeding tube and PFR module.

Moreover, the three-compartment reactor (3CR, Figure 1.5B) was also used in this

study. It included one STR and two identical PFR modules, containing not only

feeding-loop, but also a starvation loop, which was connected no feed solution and

without aeration. The STR was sterilized at 121 °C for 20 min, and PFR was sterilized

with steam for 40 min.

The feed solution contained 400 g/L glucose, macro elements, trace elements and

thiamine of which the concentrations are the same as in the bioreactor. There is no

addition of IPTG in the feed solution. For stable growth of the cells, MgSO4 were

added into a bioreactor frequently after increasing of OD600 by 20.

This dissertation work was based on seven bioreactor cultivations, using three

different kinds of strains. All the cultivations were performed in a fed-batch mode.


35

For each strain, one homogeneous STR cultivation and one scale-down 2CR

cultivation with feeding-loop were done. In addition, for strain

W3110_pCTUT7_His_IL2, both of the STR and 2CR cultivation were connected with

Bioscope with an anaerobic sampling. At the entrance of the Bioscope, the culture

from the STR module was fed with 13C glucose and representing the substrate excess

conditions in the PFR. For W3110M_pSW3, a third scale-down 3CR bioreactor

cultivation was applied. Each of STR cultivation serves as a reference for respective

the scale-down cultivation in order to compare the impact of long-term oscillations

on the response to a sudden glucose pulse.

2.3.1. Preculture

Firstly, the cryo culture (50 μL) taken from a frozen stock stored at -80 °C, was

inoculated into LB medium in a sterile 125 mL Ultra Yield Flask, and incubated at

37 °C, 250 rpm. This was named as start-culture. The aim of this step was to grow

cells until exponential phase avoiding long lag phase or cell death. Because cells in

cryo culture had adapted to LB medium when frozen, if they would be directly

changed from a complex medium (LB) to a minimal medium (mineral salt medium),

cells maybe lack the ability to deal with this shock and adapt for a long time. When

the cells reached OD600 of appr. 1, they were used to inoculate mineral salt medium

in a sterile 2.5 L Ultra Yield Flask, which was called preculture. When the preculture is

ready, it was used to inoculate into the bioreactor. If necessary, the medium

mentioned above was added with appropriate antibiotics depending on different

strains.

2.3.2. On-line measurements

In the STR, a pt-100 temperature sensor, a DO sensor (Mettler-Toledo Deutschland

GmbH, Gießen, Germany) and a pH sensor (65/90 VT, Mettler-Toledo Deutschland

GmbH, Gießen, Germany) are installed to monitor the temperature, DO and pH,


36

respectively. At each spacer of the PFR, an optical DO sensor (Visiferm DO ARC 120;

Hamilton Inc., Bonaduz, Switzerland) and a pH sensor (Polilyte Plus ARC 120;

Hamilton Inc., Bonaduz, Switzerland) are used to measure the corresponding

parameters along the height of the PFR.

The oxygen sensors were calibrated to 0% with nitrogen-sparged water and to 100%

with air-sparged water, using the software (ARC sensor configurator) to set the

calibration values, while the pH sensors were calibrated with pH 7.0 and pH 4.0

standard buffers.

2.4. Sampling

Samples were collected in different ways for the analysis of optical density (OD), dry

cell weight (DCW), carboxylic acids, amino acids and protein. Generally, the

suspension sampling was taken from STR every one hour. The supernatant, methanol

and perchloric acid quenched sampling were taken from the STR every one hour, and

every two hours from the 5 sampling ports in the PFR.

2.4.1. Cell growth determination

To monitor cell growth in the bioreactors, about 10 mL of suspension sample was

taken from the STR sampling port, using a syringe. Firstly, 1 mL sample after a certain

dilution was used to measure OD600 values with a 1 cm path length cuvette at

different time points during the cultivation using a UV/Visible spectrophotometer

(Ultrospec 2100 pro, Amersham Biosciences, Germany). Secondly, for dry cell weight

measurement, 2 mL sample was centrifuged in a previously weighted and dry

Eppendorf tube at 4 °C, 15000 rpm for 10 min with a centrifuge CT15RE (VWR, by

Hitachi Koli Co., Ltd, Japan). The supernatant was discarded and the pellet was

washed with 1 mL 0.9% NaCl solution. After centrifugation under the same


37

conditions the pellet was collected and dried in an oven (WTC binder, Tuttlingen,

Germany) at 70 °C for 1 day, then transferred to a desiccator at room temperature for

1 day to cool down. Lastly, those Eppendorf tubes with dried samples were

reweighed and the difference with the empty tube represents the biomass in 2 mL

reactor volume. Both the OD and DCW were measured in triplicates at each sampling

point and an average of the three values was taken as the final OD or DCW. The

Offline pH measurement was done using a pH meter CG 842 series (Schott Geräte

GmbH, Hofheim, Germany).

2.4.2. Supernatant sampling

For analysis of the extracellular pool of amino acids, carboxylic acids and sugars, the

supernatant sample was pulled out by a 20 mL syringe through a 0.22 μm PVDF filter

directly from the STR bioreactor sampling port. The sample was quickly transferred

into a 1.5 mL Eppendorf tube and immediately frozen at -80°C.

2.4.3. Methanol quenched sampling

In order to immediately stop the cellular activity, quenching was applied by preparing

cooled -80 °C liquids in which the suspension was quenched. Methanol quenched

was selected for the amino acids analysis, because methanol does not destroy the

cell walls during the quenched procedure, which not only can keep the extra and

intra cellular amino acids separated, but also does not destroy any amino acids.

Therefore each 5 mL syringe was filled with 2 mL methanol (Carl Roth GmbH), and

closed with a Sarstedt adapter, then pre-cooled in -80 °C freezer at least for one day

before use. For sampling during cultivations, 3 mL suspension were pulled into each

of these syringes from the sampling port of STR or PFR, followed by immediate

mixing and freezing in -80 °C. These samples were stored until further analysis.


38

2.4.4. Perchloric acid quenching

Perchloric acid quenching was used for the analysis of carbonic acids, for HClO4 is

able to destroy the cells immediately and makes a later cell disruption unnecessary.

So 1-Butanol (Sigma-Aldrich Chemie GmbH) was added into HClO4 (Carl Roth GmbH)

to a final concentration of 0.5 g L-1. 1 mL of the mixture was pulled into each 5 mL

syringe and closed with a Sarstedt adapter, then pre-cooled in -80 °C freezer at least

for one day before use. For sampling during cultivations, 4 mL suspension was pulled

into each of these syringes from the sampling port of STR or PFR. After mixing, the

samples were immediately stored on ice on a horizontal shaker for 7.5 min and then

in the reverse direction for 7.5 min to ensure efficient cell lysis. Then sample was

transferred to a 50 mL falcon-tube, and 845 μL 5 M K2CO3 (Carl Roth GmbH) was

gently added, followed by centrifugation at 5200 rpm, -2 °C for 10 min with a

centrifuge Avanti J-26 XP (Beckman Coulter GmbH, Germany). Lastly, 2 mL

supernatant was pipetted to a new 2 mL Eppendorf tube and immediately stored at

-80 °C freezer for further analysis.

2.4.5. Protein sampling

For protein sampling, every hour 1 ml of suspension from STR sampling port was

pipetted into 1.5 ml Eppendorf tube from start of induction, then centrifuge at 4 °C,

15000 rpm for 10 min. The supernatant was discarded and the cell pellet was

immediately stored at -80 °C until protein analysis.

2.4.6. BioScope sampling

BioScope samples were also taken by methanol quenched method. Eppendorf tubes

were balanced before and after filling with 800 µL methanol. The tubes were put into

a vessel that was filled with ethanol (Carl Roth GmbH), and pre-cooled in -80 °C

freezer at least for 12 hours prior to the start of sampling. Each sampling port and


39

tube of BioScope should be washed with VE-water before sampling. Then connected

the N2-bottle and keep gas flow rate at 600 mL min-1 and connected the BioScope to

the bioreactor through a needle. The BioScope-Pump was started with 0.6 mL min-1

and the last sampling spot in the BioScope was opened until first drop came out.

Feed-Pump (Pharmacia, Pharmacia LKB Pump P-1) was started with 0.06 mL min-1

until equilibrium between the medium and the labeled substrate was reached, which

took for about three minutes. Then begin to do sampling as given in Table 2.1. When

the samples were done, the BioScope-Pump was changed to 1.73 ml min-1, and

sampling from spot #7 was done. When all the samples were finished, the vessel

with the samples in the Eppendorf tubes was stored at -80 °C until analysis.

Table 2.1 Sampling schedule and the residence time correspondingly in BioScope.

Eppi Nr. 1 2 3 4 5 6 7 8 9 10

Residence time 0 4.96 6.03 7.39 12.46 14.45 18.81 30.53 40.09 56.95

Port in BioScope Zero 1 3 5 7 5 6 7 8 9

1,73 ml/min +10% Feed x x x x x

0,6 ml/min +10% Feed x x x x x

2.5. Analysis of metabolites

2.5.1. Quantitative analysis of carboxylic acids by high performance liquid

chromatography (HPLC)

The analysis of carboxylic acids was done from supernatant samples (extracellular;

see 2.4.2) and from perchloric acid-quenched samples [total amount (cells plus

medium); see 2.4.4]. After the samples from -80 °C freezer thawed on ice, they were

centrifuged at 15000 x g; +4 °C for 10 min. 200 µL of centrifuged supernatant was

transferred into vial (FisherbrandTM) with 200 μL micro-inlets (FisherbrandTM) and

screwed with caps (Thermo Fisher Scientific GmbH, Schwerte, Germany) containing 8

mm silicone septum (VWR GmbH) inside. Here the gas phase above the liquid has to

be as small as possible to avoid evaporation of volatile compounds. Lastly, the


40

prepared vials with samples were directly placed into the sample port of the Agilent

1200 Series HPLC System (Agilent Technologies, Waldbronn, Germany) and analyzed.

The intracellular concentration of carboxylic acids was calculated from the total

amount of carboxylic acids (cells plus medium) minus extracellular concentration of

carboxylic acids.

Standard curves were obtained from analyzing D(+) glucose anhydrous (Carl Roth

GmbH), Formic acid Rotipuran®pur. > 99.8% (Carl Roth GmbH), Acetic acid (Merck

KgaA), Ethanol pur. > 99.8% (Carl Roth GmbH), Pyruvic acid > 98% (Carl Roth GmbH),

Lactic acid (Merck KgaA). Prepared samples were analyzed by refractive index

detector (HPLC-RID) using a HyperRez XP Carbohydrate H+ column, 300 × 7.7 mm,

particle size 8 μm (Thermo Fisher Scientific, GmbH, Schwerte, Germany). The eluents

used were HPLC grade water, purified using an RF ultrapure water system (Wilhelm

Werner GmbH, Leverkusen, Germany), and 5 mM H2SO4 (Carl Roth GmbH). The

isocratic pump rate was kept at 0.5 ml min-1 at 15 °C. The pressure limit was adjusted

to a maximum of 60 bars. The injection volume was set to 20 μL. The draw speed and

injection speed were both set to 200 μL min-1.

2.5.2. Quantitative analysis of amino acids by high performance liquid

chromatography (HPLC)

The analysis of extracellular free amino acids by HPLC was carried out from

supernatant samples (see 2.4.2). After samples from -80 °C thawed on ice, they were

centrifuged at 15000 x g at 4 °C for 10 min. 100µL of supernatant was pipetted into

HPLC vial with inlet, added with 100µL of internal standard (225 µM α-aminobutyric

acid). Then the samples were placed into the sample ports of the Agilent 1290

Infinity HPLC System (Agilent Technologies, Waldbronn, Germany). Here the internal

standard was firstly prepared as 18 mM α-aminobutyric acid (Sigma Aldrich)


41

dissolved in 40 mM sodium dihydrogenphosphate-dihydrate (Buffer A). When

needed, it was diluted 1:80 to 225 µM with 1:10 Buffer A (40 mM NaH2PO4).

The methanol-quenched samples were used to analyze the concentration of total

free amino acids (cells plus medium). Because this analysis requires cell disruption,

samples were diluted with Buffer A (40 mM NaH2PO4) to final OD600 of 1 for

sonications. The final volume of diluted samples was 0.5 ml in 1.5 ml Eppendorf

tubes. The diluted samples were lysed under sonication by the ultrasonic processor

UP200S series (Hielscher Ultrasound Technology, Teltow, Germany). The amplitude of

the ultrasonic processor was set to 30%, and cells were then vortexed shortly and

sonication was performed in a mixture of water and ice for 5 cycles. One cycle lasted

for 30 seconds of sonication and 30 second of break to protect samples from

overheating. The lysed cells were then centrifuged at 15000 × g, 4 °C for 10 min. 250

µL of supernatant cell-free top phase was mixed with 250 µL internal standard in

HPLC vials. Prepared samples were placed into the sample ports of the Agilent 1290

Infinity HPLC System.

Prepared samples were analyzed by HPLC with a fluorescence detector (HPLC-FLD),

using a GEMINI® column (5 µ, 100 Å, 150 x 4.6 mm) with a Security Guard (Gemini

C18) pre-column (Phenomenex, Aschaffenburg, Germany). The applied method is

described in Table 2.2 below. The solvent compositions were Buffer A (40 mM

NaH2PO4), Buffer B (45% ACN: 45% methanol: 10% H2O). The column was heated at

40 °C. Injection volume was 10 μL, and injection speed was 600 μL min-1. Draw speed

was set to 200 μL min-1.

The concentration of intracellular free amino acid was calculated from the

concentration of total free amino acids (cells plus medium) minus extracellular free

amino acids.


42

Table 2.2 HPLC gradient operation for analysis of amino acids.

Time (min) Buffer A (%) Buffer B (%) Flow (mL min-1

)

0 100.0 0.0 1.000

40.5 59.5 40.5 1.000

41 39.0 61.0 1.000

43 39.0 61.0 1.000

44 18.0 82.0 1.000

44.5 0.0 100.0 1.000

59.5 0.0 100.0 1.000

61 100.0 0.0 1.000

64 100.0 0.0 1.000

2.5.3. Quantitative analysis of amino acids by Gas Chromatography-Mass

Spectrometry (GC-MS)

The free amino acids were analyzed by GC-MS from supernatant samples

(extracellular free amino acids; see 2.4.2) and methanol-quenched samples [total

free amino acids (cells plus medium); see 2.4.3]. The methanol-quenched samples

were firstly diluted to an OD600 of 5 for disruption with phosphoric buffer (4 mM

NaH2PO4) in 1.5 mL Eppendorf tubes to a final volume of 0.5 mL per tube. The

diluted samples underwent sonication as described before. After sonication, samples

were centrifuged at 15 000 x g, 4°C for 10 min. For supernatant samples, 150 µL

cell-free supernatant was transferred into a 1.5 mL Eppendorf tube after thawing on

ice, and centrifuged at 15 000 x g, 4°C for 10 min. 125 μL supernatant from each of

the centrifuged cell-free extracts as well as the sonication samples were added into

1.5 mL brown glass Fisherbrand™ vials with 9 mm neck (Thermo Fisher Scientific)

containing 125 μL of internal standard (225 µM α-aminobutyric acid) and 750 µL of

0.1 M HCl (VWR International GmbH). All opened vials containing samples were

stored in a speed vacuum pump (Bachofer GmbH, Reutlingen, Germany) for drying by

centrifugation at 30 °C for 3 hours.


43

The total amino acids of supernatant samples were analysed by acidic hydrolysis

followed by GC-MS measurement (extracellular total amino acids; see 2.4.2) as well

as methanol-quenched samples (total amino acids; see 2.4.3). The

methanol-quenched samples were firstly diluted to an OD600 of 1 for disruption with

phosphoric buffer (4 mM NaH2PO4) to a final volume of 0.5 mL per tube. The diluted

samples were dealt with sonication as described above and centrifuged at 15 000 x g,

4°C for 10 min. For supernatant samples, 150 µL was transferred into an 1.5 mL

Eppendorf tube, and centrifuged at 15 000 x g, 4°C for 10 min. 125 μL supernatant

from each of the centrifuged supernatant samples as well as the sonication samples

was added in 1.5 mL brown glass vials ø 9mm containing 125 μL of internal standard

(225 µM α-aminobutyric acid) and 750 µL of 6 M HCl. All vials containing samples

were put into a Block heater H250 (Carl Roth GmbH) and heated at 80 °C for 24 hours.

Then the vials were stored into a speed vacuum pump for drying by centrifugation at

30 °C for 3 hours.

After complete evaporation of liquid, 50 μL of acetonitrile (VWR International GmbH)

was added to each vial, followed by 50 μL

N-(tert-butyldimethylsilyl)-N-methyl-trifluoroacetamide (MTBSTFA) (Sigma-Aldrich

Chemie GmbH, Germany) and 5 μL 1-butanol (Fluka Chemie AG). Here, because

MTBSTFA is sensitive to oxygen, the opened MTBSTFA-vial was continuously flushed

with nitrogen via a pipe connection from nitrogen bottle to the headspace of the

MTBSTFA-vial, using a gas flow meter (Bailey-Fischer & Porter GmbH, Göttingen,

Germany) for adjustment of the gas flow rate at 2 L min-1. 1-butanol was previously

dewatered with molecular sieve 5 Å (Carl Roth GmbH). Then all the vials were closed

and transferred into a heating block for derivatization at 60 °C for 60 min. After

derivatization the liquid was transferred using pasteurized pipettes (Carl Roth GmbH)

into a 1.5 mL brown vial (ø 8mm), which contained a spring and a 50 μL micro inlet

(All from Thermo Fisher Scientific). Vials were then closed with black caps (ø 8mm)


44

with septum and placed at the injection platform of the GC-MS system (Agilent

Technologies Deutschland GmbH & Co. KG, Waldbronn, Germany) using

DB-5MS-column (5% Phenyl – 95% Methylpolysiloxan, 30m x 250µm x 0,25µm) in the

GC. The applied method for analysis of quantitative amino acids is summarized in

Table 2.3 and Table 2.4. The quadrupole settings for the quantitative analysis of

amino acids are shown in Table 2.5.

Table 2.3 General settings of the GC-MS for amino acids analysis.

Parameter Value

Heater 290 °C

Pressure 18.842 kPa

Total Flow 18 mL min-1

Septum Purge Flow 15 mL min-1

Split Ratio 2:1

Split Flow 2 mL min-1

Aux 2 150 °C for 0 min

MS Source 230 °C

Table 2.4 The settings used for column of the GC for amino acids analysis.

Parameter Value

Initial 150 °C

Pressure 91.924 kPa

Flow 1 mL min-1

Average Velocity 38.051 cm sec-1

Holdup time 1.314 min

Table 2.5 The Quadrupole settings for the quantitative analysis of amino acids. The bold labeled

fragments are basis-ion fragments.

Nr. Group name Fragments [m/z] Approx. retention time [min]

1 Ala

Gly

317 302 260 232 158

303 288 246 218 144

3.00 (Solvent Delay)

2 ABA 331 316 274 246 172 6.00

3 Val

Norval

345 330 288 260 186

345 330 288 260 186

6.80


45

4 Leu Ile Norl

Pro

359 344 302 274 200

343 328 286 258 184

7.90

5 Beta-Methylnorl 214 215 288 289 316

317 359 374

10.00

6 Met 377 362 320 292 218 14.69

7 Ser 447 432 390 362 288 15.20

8 Thr 461 446 404 376 303

302

16.15

9 Phe

Homoserin

393 378 336 308 234

461 446 404 376 302

17.90

10 Asp 475 460 418 390 316 19.88

11 Cystein 463 448 406 378 304 21.10

12 Glu

Asn

489 474 432 404 330

474 459 417 389 315

22.90

13 Lys Gln 488 473 431 403 329 25.40

14 Arg 499 484 442 414 340 27.40

15 His

Tyr

497 482 440 412 338

523 508 466 438 364

29.00

16 Tryptophan 273 347 375 417 432 30.93

The concentration of intracellular free amino acid was calculated from the

concentration of total free amino acids (cells plus medium) minus extracellular free

amino acids.

Samples for the analysis of isotope distribution in amino acids were obtained from

the BioScope experiments (see 2.4.6). They were based on the same preparation

procedure of analysis of free amino acids from methanol-quenched samples (see

above part of 2.5.3). The settings of the applied GC-MS method were also the same

with methods for the amino acids-analysis (see Table 2.3 and Table 2.4). The only

difference is the quadrupole settings which are shown in Table 2.6.

Table 2.6 The Quadrupole settings for the isotope distribution analysis of amino acids.

Nr. Group name Fragments [m/z] Approx.

retention

time [min]


46

1 Ala

Gly

144 145 158 159 160 218 219 232 233 234 246 247 248 260

261 262 263 288 289 290 302 303 304 305 317 318 319 320

3.00

2 ABA 172 246 274 316 331 6.00

3 Val

Norval

186 187 188 189 190 260 261 262 263 264 288 289 290 291

292 293 330 331 332 333 334 335 345 346 347 348 349 350

6.80

4 Leu Ile Norl

Pro

184 185 186 187 188 200 201 202 203 204 205 258 259 260

261 262 274 275 276 277 278 279 286 287 288 289 290 291

302 303 304 305 306 307 308 328 329 330 331 332 333 343

344 345 346 347 348 349 350 359 360 361 362 363 364 365

7.90

5 Beta-Methylnorl 214 215 216 217 218 219 220 288 289 290 291 292 293 294

316 317 318 319 320 321 322 323 324 359 360 361 362 363

364 365 366 374 375 376 377 378 379 380 381

10.00

6 Met 218 219 220 221 222 292 293 294 295 296 320 321 322 323

364 365 366 367 377 378 324 325 362 363 379 380 381 382

14.69

7 Ser 288 289 290 362 363 364 390 391 392 393 432 433 434 435

447 448 449 450

15.2

8 Thr 302 303 304 305 376 377 378 379 404 405 406 407 408 446

447 448 449 450 461 462 463 464 465

16.15

9 Phe

Homoserin

234 235 236 237 238 239 240 241 242 302 303 304 305 308

336 337 338 339 340 341 342 343 344 345 376 377 378 379

380 381 382 383 384 385 386 387 393 394 395 396 397 398

399 400 401 402 404 405 406 407 408 446 447 448 449 450

461 462 463 464

17.90

10 Asp 316 317 318 319 390 391 392 393 418 419 420 421 422 460

461 462 463 464 475 476 477 478 479

19.88

11 Cystein 304 305 306 378 379 380 406 407 408 409 448 449 450 451

463 464 465 466

21.10

12 Glu

Asn

315 316 317 318 330 331 332 333 334 389 390 391 392 404

405 406 407 408 417 418 419 420 421 432 433 434 435 436

437 459 460 461 462 463 474 475 476 477 478 479 489 490

491 492 493 494

22.90

13 Lys Gln 329 330 331 332 333 334 403 404 405 406 407 408 431 432

433 434 435 436 437 473 474 475 476 477 478 479 488 489

490 491 492 493 494

25.40

14 Arg 340 341 342 343 344 345 414 415 416 417 418 419 442 443

444 445 446 447 448 484 485 486 487 488489 490 499 500

501 502 503 504 505

27.40

15 His 338 339 340 341 342 343 412 413 414 415 416 417 440 441

442 443 444 445 446 482 483 484 485 486 487 488 497 498

499 500 501 502 503

29.00

16 Tyr 364 365 366 367 368 369 370 371 372 438 439 440 441 442 30.30


47

443 444 445 446 466 467 468 469 470 471 472 473 474 475

508 509 510 511 512 513 514 515 516 517 523 524 525 526

527 528 529 530 531 532

17 Tryptophan 273 274 275 276 277 278 279 280 281 282 283 347 348 349

350 351 352 353 354 355 356 357 375 376 377 378 379 380

381 382 383 384 385 386 417 419 420 421 422 423 424 425

426 427 428 432 433 434 435 436 437 438 439 440 441 442

443

30.93

2.5.4. Quantitative analysis of carboxylic acids by Gas Chromatography-Mass

Spectrometry (GC-MS)

Due to a sufficient separation of carboxylic acids with HPLC, only the distribution of

isotopes was investigated with GC-MS. Samples were obtained from BioScope

sampling (see 2.4.6), and firstly diluted to OD600=5 with 4 mM NaH2PO4 (pH=7.8) to a

final volume of 0.5 mL. Thereafter the diluted samples underwent sonication by the

same method mentioned above (see 2.5.3) and subsequent centrifugation at 15 000

x g, 4 °C for 10 min. 125 μL of the supernatant was carefully pipetted into a 1.5 mL

brown vial (ø 9mm) containing 125 μL of internal standard (225 µM α-aminobutyric

acid), 740 µL of 0.1 M HCl and 10 μL 56 mg mL-1 O-Ethylhydroxylamine hydrochloride

(Sigma Aldrich). The vials were closed with blue screw caps (ø 9mm) with septum

and heated at 40 °C for 90 min in the heating block. After oximation, the vials were

unscrewed and centrifuged in the vacuum centrifuge at 30 °C for 3 h. When the

samples were completely dry, 30 μL pyridine (Sigma Aldrich) was added to each vial,

as well as 70 μL N-(tert-butyldimethylsilyl)-N-methyl-trifluoroacetamide (MSTFA;

Fluka Chemie AG) and 5 μL 1-butanol. Here the MSTFA was flushed with nitrogen and

1-butanol was previously dewatered with molecular sieve. Then all the vials were

closed and transferred to a heating block for derivatization at 40 °C for 50 min. After

the derivatisation, the liquid was carefully transferred into a 1.5 mL brown vial (ø

8mm) containing a spring and a micro inlet inside. Then vials were closed and sent to

GC-MS (EI-quadrupole) for analysis. The applied method for the isotope distribution


48

analysis of carboxylic acids is summarized in Table 2.7 and Table 2.8.

Table 2.7 General settings of the GC-MS for the isotope distribution analysis of carboxylic acids

Parameter Value

Heater 290 °C

Pressure 60.732 kPa

Total Flow 26 mL min-1

Septum Purge Flow 15 mL min-1

Split Ratio 10:1

Split Flow 10 mL min-1

Aux 2 150 °C for 0 min

MS Source 230 °C

Table 2.8 The settings used for column of the GC for the isotope distribution analysis of carboxylic

acids.

Parameter Value

Initial 70 °C

Pressure 60.732 kPa

Flow 1 mL min-1

Average Velocity 36.796 cm sec-1

Holdup time 13.588 min

2.5.5. Quality of protein analysis

Protein extraction and purification was done using BugBuster Protein Extraction

reagent (Merck KGaA, Darmstadt, Germany). In order to compare bands directly in a

sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), 1 ml of

harvested frozen cell pellets were normalized to the same optical density with OD600

= 15. The samples were re-suspended in the corresponding volume of BugBuster

solution with 1 μL Benzonase nuclease (25 U) and 1 μL lysozyme (50 mg mL-1) in 1 mL

BugBuster solution. The amount of Bugbuster added per pellet was calculated

according to the following equation:


49

mount Bugbuster µL 600 culture volume µL

600

After the pellet was fully re-suspended by gently pipetting up and down, it was

incubated on a shaking platform for 15 min with a slow setting at room temperature,

followed by centrifugation at 16 000 x g, 4 °C for 20 min. Then 50 μL supernatant was

transferred into a fresh 1.5 mL Eppendorf tube as soluble protein fraction for

SDS-PAGE analysis. The pellet after discarding the rest of the supernatant was

re-suspended with the same amount of BugBuster solution as in the first step,

followed by washing with 1:10 diluted BugBuster solution three times with

centrifugation at 5 000 x g, 4 °C for 15 min. Lastly, the pellet was re-suspended in the

same amount of 1:10 diluted BugBuster solution like the beginning as the insoluble

fraction (inclusion bodies fractions). The soluble and insoluble proteins were

analyzed by SDS-PAGE, and amino acid content of proteins was analyzed by GC-MS

with inclusion bodies fractions.

a) SDS-PAGE analysis

In order to analyze the expression levels of recombinant proteins under different

cultivation conditions, 15% SDS-PAGE gels were used, which were prepared as

described in Table 2.9. Both soluble and insoluble fractions were prepared by 1:1

addition of with 2x SDS loading buffer (100 mM Tris-HCl pH 6.8, 20 % glycerol, 200

mM DTT, 4 % SDS, 0.2 % bromophenol blue). The samples were then incubated at

95 °C for 7 min for denature the proteins. After cooling to room temperature, 15 μL

of each sample and a protein molecular weight marker named Spectra Multicolor

Broad Range Protein Ladder (Thermo Scientific, Waltham, USA) were loaded on to 15%

SDS-PAGE gels for analysis. Electrophoresis was performed in running buffer (25 mM

Tris-HCl pH 8.3, 192 mM glycine, 500 mM urea, 0.1 % SDS, 1 mM EDTA) with

electrophoresis power supply – EPS 300 (Amersham Pharmacia Biotech Inc.) at 90 V


50

firstly until dye front passed the boundary of stacking gel, and then changed to 140V

until dye front reached the bottom of the gel. When finished, gels were washed

three times with ddH2O on a shaker for 5 min after heating in a 600W microwave for

30 s. Afterwards, the water was discarded, and the gels were stained in Coomassie

staining solution (80 mg L-1 Brilliant Blue G-250, 35 mM HCl) for 1 h on the shaker.

After staining, the gels were destained with ddH2O on the shaker until the protein

lines appear clearly. Here ddH2O was changed several times and paper towel was put

in the corner for fast and sufficiently distaining. Finally, gels were scanned and the gel

pictures were stored.

Table 2.9 The composition of the resolving and stacking gels for SDS-PAGE.

Component (per gel) 15﹪ Resolving gel (5 ml) [ml] 5﹪ Stacking gel (1 ml) [ml]

H2O 1.1 0.68

30﹪acrylamide 2.5 0.17

1.5M Tris-HCl (pH8.8) 1.3 /

1M Tris-HCl (pH6.8) / 0.13

10﹪SDS 0.05 0.01

10﹪APS 0.05 0.01

TEMED 0.002 0.002

b) Analysis of amino acids in proteins

The analysis of amino acid content, especially non-canonical amino acids, of proteins

was done though the analysis of inclusion bodies by GC-MS. 125 μL diluted inclusion

body samples were hydrolyzed in 1.5 mL brown glass vials ø 9mm containing 125 μL

of internal standard (225 µM α-aminobutyric acid) and 750 µL of 6 M HCl at 80 °C for

24 hours, then vials were prepared and analyzed with GC-MS as described in 2.5.3.

2.6. Flow cytometry analysis

Flow cytometry analysis was done using a MACSQuant® analyser (Miltenyi Biotec

GmbH, Bergisch Gladbach, Germany) and the method from the study of Marba et al.


51

(Marba et al. 2016). Calibration was performed automatically with MACSQuant®

Calibration Beads. Samples preparation was done as follows: 2 mL suspension taken

from the culture in the bioreactor was filtered by the vacuum filter with a vacuum

pump KNF L B (Neuberger, Freiburg, Germany) using a filter pore size of 0.2 μm

(Satorius Stedim, Göttingen, Germany), and washed with 2 mL of 0.9 % NaCl solution.

The cells stuck on the filter were re-suspended with 10 mL phosphate buffered saline

(PBS, pH=7.2) in a 15 mL falcon tube. Then the re-suspension was diluted and

adjusted the concentration to 106 particles mL-1 with PBS. The PBS used here was

filtered through a MCE filter with a pore size of 0.2 μm (Carl Roth GmbH) to avoid

undesired particles and background noises during the flow cytometry analysis. The

stock of PI (Sigma-Aldrich, Munich, Germany) was prepared as 1 mg mL-1, and BOX

(Sigma-Aldrich, Munich, Germany) as 5 mg mL-1. Syto13 was purchased as a

concentration of 1 mg mL-1. All the stock solutions were stored at -20 °C. The working

solutions of dyes were prepared freshly when needed with a concentration of 40 µg

mL-1 for PI, 25 µg mL-1 for BOX added with 4 mM ethylenediaminetetraacetic acid

(EDTA), and 8 µg mL-1 for Syto13.

Once the sample had the concentration of ca. 106 particles mL-1, the samples were

prepared for measurement in 1.5 mL Eppendorf tubes according to Table 2.10 with a

final volume of 200 µL and stained in the dark which was achieved either in the

closed drawer or cupboard. Meanwhile, both negative and positive controls were

also applied. The negative control for all dyes was considered to be the unstained

sample. This sample is used to set up the threshold and detector sensitivity settings.

The positive control was obtained by heating the cell suspension in a heating block

(Thermomixer comfort, Eppendorf, Hamburg, Germany) for 1 h, which resulted in

loss of the cell viability, following staining with the same methods as described above

of this section.


52

For measurements, the filters used were: a 488/10 bandpass filter for FSC/SSC,

525/50 bandpass filter for SYTO13 and BOX stained samples, and a 655-730 longpass

filter for PI stained samples. Every analysis was carried out in triplicate. Data

collection was performed using M CSQuantify™ software (Miltenyi Biotec GmbH,

Bergisch Gladbach, GER), and data analysis was done with FlowJo™ sofrware

(TreeStar Inc., Ashland, USA).

Table 2.10: Overview of the applied samples stained conditions for flow cytometric

measurements.

Dyes Final Concentration (µg mL-1

) Staining time (min) Temperature (°C)

PI 1 2 4

BOX 0.5 4 Room temperature

Syto13 0.6 2 4

3. Results

53

3. Results

3.1. Behavior of E. coli W3110 in STR and 2CR cultivations

In order to mimic the different zones in industrial scale, and to investigate the cellular

responses of E. coli to gradients of glucose and dissolved oxygen, E. coli W3110

wild-type strain was cultivated in a scale-down two-compartment reactor (2CR, see

Figure 1.5A) in lab scale. This 2CR system consisted of a normal stirred tank reactor

(STR, 15 L Bioreactor and working volume is 10 L) and a plug flow reactor (PFR,

working volume is 1.2 L), and feeding was performed at the inlet of PFR, so cells

continuously moved through the STR and PFR module, and were exposed to the

oscillating cultivation conditions with high concentration of glucose and oxygen

limitation for a short time (68 seconds) in the PFR. The cultivation in the STR without

PFR was also performed as the reference to the scale down 2CR cultivation, and the

feed solution was added to the top gas phase of the bioreactor (detail see 2.3).

3.1.1. Cultivation characteristics

The cultivation process is performed in two parts. Firstly, cells were cultivated

overnight in the EnBase Flo medium as starter culture for high cell density

cultivations, which would confer cells with consistent intracellular physiological

conditions, avoiding overflow metabolism in the initial cultivation phase (Glazyrina et

al. 2012). Enbase simulates a glucose-limited fed-batch cultivation with leaner

glucose feed rate by continuously releasing glucose from soluble starch-derived

polymer with the addition of a specified concentration of a polymer degrading

biocatalyst (Grimm et al. 2012; Panula-Perälä et al. 2008). During overnight

cultivation the cells grew until the glucose release rate became growth-limiting and

grew for about four hours of glucose limiting fed-batch. Secondly, the exponential

glucose feeding was applied with a targeted specific growth rate (μset) of 0.2 h-1.

During the whole cultivations, cells were maintained at 37 °C with the pH was

3. Results

54

controlled by the addition of 25 % ammonium hydroxide to control the pH at a set

point of pH 7. The DO was maintained above 30 % (Figure 3.1B).

At the end of the cultivations, the biomass of the 2CR cultivation reached 26 g L-1 at

6.75 h after feed start, while it was around 27 g L-1 at 7.42 h after feed start in the

reference STR cultivation (Figure 3.1A), which showed a similar profile. The feed

solution contained 400 g L-1 glucose, and was pumped into the inlet of PFR in 2CR

cultivation, while it was directly pumped into the bioreactor in the reference

cultivation. So in the results, in the PFR module of 2CR, cells were under oxygen

limitation from the first port of the PFR, which could be seen from Figure 3.1C.

3. Results

55

Figure 3.1: Shows the E. coli W3110 wild-type strain cultivation data of the STR cultivation

(reference) and the 2CR scale-down cultivation. Feed start point starts from 0 h. (A) Dry cell

weight. (B) Profile of online DO and pH data in the STR module. (C) Profile of online DO data in

the PFR module of 2CR scale-down cultivation.

In Figure 3.2A and B there is a sharp increase of qO2 and qCO2 values at about 2.83 h

after feed start in the reference cultivation, that is due to the removal of sharp bend

in exhaust gas pipe at this moment, which also led to the sharp DOT decrease at the

0 2 4 6 8

DO

[%

]

0

20

40

60

80

100

120

pH

[-]

0

2

4

6

8

10

12STR DO

2CR DO

STR pH

2CR pH

0 2 4 6 8

DO

[%

]

0

1

2

3

4

5

6Port 1

Port 2

Port 3

Port 4

Feed time [h]

-2 0 2 4 6 8

DC

W [

g L

-1]

0

5

10

15

20

25

30

STR DCW

2CR DCW

STR DCW fitted

2CR DCW fitted

Feed time [h]

A

B

C

3. Results

56

same time (Figure 3.1B). Under oscillating cultivation the specific oxygen

consumption rate (qO2) is continually higher than the control after feed start until the

end of cultivations, while the specific carbon dioxide formation rate (qCO2) of both

cultivations is similar. As a result, the relationship between these parameters, the

respiration quotient (RQ) was lower in 2CR scale-down cultivation than in the

reference cultivation. This demonstrates an increased oxygen demand of E. coli cells

under oscillating conditions, and indicates that cells have an increased respiratory

activity and a higher metabolite production in such cultivation.

Figure 3.2: (A) Specific oxygen uptake rate, (B) Specific carbon dioxide production rate, and (C)

Feed time [h]

0 2 4 6 8

RQ

[-]

0.0

0.5

1.0

1.5

2.0

STR

2CR

0 2 4 6 8

qC

O2 [

mm

ol g

DC

W

-1 h

-1]

0

2

4

6

STR

2CR

0 2 4 6 8

qO

2 [

mm

ol g

DC

W

-1 h

-1]

0

2

4

6

STR

2CR

A

B

C

3. Results

57

Respiratory quotient in the STR module of the reference (black) and 2CR scale-down (red) E. coli

W3110 wild-type strain cultivations.

3.1.2. Carboxylic Acids

The properties of a culture and the behavior of microbes during a cultivation process

are reflected by their metabolic components at various time points, transcriptional

factors and physiological influences. In this study, the metabolic components of

carboxylic acids were studied. Therefore, samples taken from STR module from

cultivations were analyzed for TCA cycle and glycolytic intermediates at different time

points during cultivations. The carboxylic acids of interest here were analyzed by

HPLC chromatogram using the refractive index detector (RID) as describe in 2.5.1.

From the extracellular concentration of compounds of main carbon metabolism in

STR module of E. coli W3110 wild-type strain cultivations (Figure 3.3), which means

concentration and composition of carbonic metabolism in the supernatant, it could

be seen that during the 2CR scale-down cultivation, the concentration profile of

glucose polymer and glucose are almost the same as in the reference cultivation, and

both cultivations were glucose limited with a concentration of glucose lower than 0.3

g L-1. Pyruvate, as the starting point for most of the measured metabolites and of

special interest, was only slightly increased to 0.45 mM at 1.92 h after feed start

point in non-oscillation cultivation, then turned back below the limit for analysis in

two hours and kept depletion until the end. While it dramatically increased to 2.2

mM at 1.75 h after start of feeding in 2CR cultivation, then it decreased steeply to 0.1

mM in three hours and then remained quite constant until the end of cultivation.

Pyruvate decreased steeply from 1.75 h after feed start, because a great portion of

the main carbon flux was directed to carboxylic acids, like acetate, formate, malate

and fumarate. Among the intermediates of the tricarboxylic acid (TCA) cycle, the

concentrations of malate and fumarate were most affected by oscillating conditions.

3. Results

58

Compared to the non-oscillating control cultivation, both components showed an

increased level in the 2CR scale-down cultivation. No remarkable differences were

detected in the extracellular concentration of lactate, succinate and oxaloacetate

between the two cultivation types.

Figure 3.3: Extracellular concentration of compounds of the main carbon metabolism in STR

Glc

-2 0 2 4 6 80.0

0.3

0.6

0.9Glc polymer

-2 0 2 4 6 80

5

10

15

20

Feed time [h]

Concentr

ation [g L

-1]

Pyr

0

1

2

3

Oxa

-2 0 2 4 6 80.0

0.1

0.2

Mal

0.0

0.2

0.4

0.6

For

0

1

2

3Suc

0.0

0.1

0.2

0.3

Lac

-2 0 2 4 6 80.0

0.2

0.4

0.6

Concentr

ation [

mM

]

Ace

0.0

0.2

0.4 Fum

0.0

0.1

0.2

0.3

Feed time [h]

3. Results

59

module of E. coli W3110 wild-type strain cultivations, analyzed by HPLC. Data are shown from

around feed start (0h). White circles – STR reference cultivation; black triangles up – 2CR

scale-down cultivation.

Feeding solution with high concentration of glucose (400 g L-1) was fed at entrance of

PFR module in the 2CR scale-down system without the addition of extra oxygen. As a

result, in the PFR, cells with high local volumetric rates for consumption of glucose

and oxygen could easily cause oxygen limitation, or even oxygen depletion, which

was witnessed from Figure 3.1C. So in the plug flow loop (PFR), it is possible to study

the dynamics of metabolic and stress responses to a short period (68 s) of substrate

excess / oxygen limitation condition.

Figure 3.4 shows the extracellular concentration of compounds of the main carbon

metabolism along the PFR at different process times after feed start. With the

consumption of glucose over the residence time in the PFR, acetate and lactate

accumulated quickly under substrate excess and oxygen limitation conditions.

Especially at 3.75 h and 5.75 h after start of feeding the formation rate of both

components was even higher. Meanwhile, for formate and succinate, there was also

a slight increasing trend along the PFR. Acetate can be produced not only via glucose

overflow metabolism, but also via mixed acid fermentation, and is thus not an

exclusive product from oxygen starvation, but also from glucose excess (Xu et al.

1999a; Xu et al. 1999b). However, these two pathways use different enzymes for

acetate formation. Pyruvate is catalyzed by the aerobic pyruvate dehydrogenase to

produce acetyl-CoA, which is the precursor of acetate in aerobic glucose overflow

metabolism. Acetate is the main by-product. When E. coli is exposed to oxygen

limitation or anaerobic conditions, E. coli adapts to mixed-acid fermentation. So in

mixed-acid fermentation pyruvate is catalyzed by the anaerobic pyruvate

formate-lyase to produce formate. As a result, besides acetate, formate, succinate

3. Results

60

and lactate are formed. The rapid accumulation of lactate and acetate in the PFR also

obviously witnessed that enzymes inhibited by oxygen during aerobic growth

(formate-lyase and lactate dehydrogenase) were present in sufficient concentrations

to produce compounds of mixed acid fermentation within 1 minute when exposed to

oxygen limitation conditions inside the PFR (Enfors et al. 2001; Xu et al. 1999a).

Interestingly, though there was a significant accumulation of acetate and lactate

along the PFR (Figure 3.4), there was no higher accumulation of lactate and only a

little higher accumulation of acetate in the STR module of 2CR cultivation than the

reference cultivation (Figure 3.3). This shows re-assimilation when cells were

returned to the STR module, under aerobic and glucose limitation conditions. As it

was described in literature, the re-assimilation of mixed acid fermentation products

is usually observed at cultivations with gradients of substrate and dissolved oxygen

(Enfors et al. 2001; Käß et al. 2014). As a result, due to different re-assimilation rates

of the fatty acids, their accumulations during the process were affected.

For the last two intermediates of the TCA cycle, namely malate and fumarate, under

oscillating conditions, the extracellular concentration of malate fluctuated with an

increasing trend, resulting in a higher concentration than the control. The amount of

fumarate concentration in the extracellular environment continuously increased up

to 0.17 mM at the end of 2CR scale-down cultivation, while in the STR control the

value was around 0.05 mM (Figure 3.3).

For succinate no difference was seen in the supernatant samples of STR module in

two cultivations (Figure 3.3).

3. Results

61

Figure 3.4: Extracellular concentration of compounds of the main carbon metabolism in PFR

module (PFR with feed addition) of E. coli W3110 wild-type strain 2CR scale-down cultivation,

analyzed by HPLC. Data shown are the samples from time points 0.75 h (white triangles up), 1.75

h (gray triangles down), 3.75 h (dark gray squares) and 5.75 h (black diamonds) after feed start

over the residence time in the PFR.

Glc

Residence time [s]

0 30 40 50 60 70

Concentr

ation [

g L

-1]

0.0

0.2

0.4

0.6

0.8

STR

Pyr

0

2

4

Oxa

0 30 40 50 60 700.00

0.05

0.10

0.15

Mal

0.0

0.3

0.6

For

0

2

4 Suc

0.0

0.2

0.4

Lac

0 30 40 50 60 700

1

2

3

Concentr

ation [

mM

]

Residence time [s]

Ace

0.0

0.3

0.6

0.9 Fum

0.0

0.1

0.2

STRSTR

3. Results

62

3.1.3. Amino Acids

In order to provide reasonable information for cellular responses to oscillating

conditions of substrate and dissolved oxygen concentrations, the concentrations of

total free canonical amino acid plus three common non-canonical amino acids

(norvaline, norleucine and β-methylnorleucine) in the STR module were analyzed by

HPLC (Figure 3.5). With regards to leucine, valine and alanine which all belong to the

alanine family and use pyruvate as precursor molecule in their biosynthetic pathways,

in the STR module of 2CR sale-down cultivation, only alanine showed an increasing

trend up to 24.3 μmol gDCW-1 at 2.75 h after feed start, following with a drastic

decrease in one hour and then has a relatively constant course (Figure 3.5). No clear

difference could be seen between the two cultivations for the leucine and valine

concentration. For the decrease of alanine after 2.75 h, one possible explanation is

that the carbon flux flows to other directions. Soini et al. showed that the amino

acids of the alanine family accumulate during oxygen limitation which was performed

mainly with a downshift of oxygen supply during the cultivation, exposing cells to

oxygen depletion over the whole process (Soini et al. 2008a). In the PFR module of

the 2CR scale-down cultivation with feed addition at its inlet, cells were exposed to

substrate excess and oxygen-limited conditions. It was observed that alanine

accumulated rapidly within 68 s in the crude extract at 0.75 h, 1.75 h, 3.75 h and 5.75

h after feed start over the residence time in the PFR (Figure 3.6).

Isoleucine, which belongs to the aspartate family, substantially increased over the

time in scale-down cultivation, while it had a stable level in the control cultivation.

Thus, a huge difference between these two cultivations was observed with

significantly higher amounts under scale-down conditions. At the end of the

cultivation the concentration of isoleucine was about three times higher compared

to the control (Figure 3.5). Along the PFR, the formation of isoleucine marginally

increased at 3.75 h and 5.75 h after feed start (Figure 3.6).

3. Results

63

Regarding the non-canonical amino acids, it was observed that norvaline was

produced by the cells under heterogeneous and homogenous conditions, with the

amount substantially increasing over the time in both cultivations. However, under

oscillation conditions the formation rate of norvaline is higher than the control,

consequently the concentration of total free norvaline is about twice of that in the

reference approach, with the data 0.53 μmol gDCW-1 at 6.92 h after feed start in

control cultivation and 0.95 μmol gDCW-1 at 6.75 h after feed start under

heterogeneous conditions (Figure 3.5). This shows that the oscillations of substrate

and dissolved oxygen have a strong impact on the formation of norvaline. Soini et al.

reported norvaline accumulation under anaerobic conditions (Soini et al. 2008a). In

this study, the mount of norvaline per cell of crude extract data at 5.75 h after feed

start in PFR showed that norvaline was even able to massively synthesis within 68 s

under glucose excess and oxygen limited conditions (Figure 3.6).

β-methylnorleucine is not very well reported as a product of heterogeneities in

bioprocesses. In this work, β-methylnorleucine was also produced in both

cultivations with higher measured values for the scale-down cultivation over the time

after feed start. Figure 3.5 showed that under oscillating conditions, the amount of

β-methylnorleucine per cell was significantly elevated within two hours after feed

start, and fluctuated for the rest of the cultivation time.

In the scale-down approach, the accumulation of isoleucine and non-canonical

amino acids in the STR module and their fast formation within 68 s under oxygen

limitation conditions in the PFR could be an indication for an increased flux of carbon

over the enzymes of the leuABCD operon to α-ketobutyrate, which is the precursor of

isoleucine and non-canonical amino acids. Here the elongation of pyruvate takes

place as a side activity of the enzymes rather than the canonical pathway over

3. Results

64

threonine. Besides, the data also demonstrated that non-canonical amino acids are

synthesized as side product of isoleucine.

Figure 3.5: Concentration of total free alanine, leucine, valine, isoleucine, norvaline,

β-methylnorleucine in suspension samples (cells plus medium) over the time in STR module of E.

coli W3110 wild-type strain cultivations, analyzed by HPLC. The time point zero represents feed

start. White circles – STR reference cultivation; black triangles – 2CR scale-down cultivation. The

total concentrations reflect the intracellular concentrations, as the extracellular level was

neglectable for all shown amino acids.

Figure 3.6: Concentrations of total free alanine, isoleucine, norvaline from reactor suspension

samples (cells plus medium) in PFR module (PFR with feed addition) of E. coli W3110 wild-type

Ala

-2 0 2 4 6 80

10

20

30

Val

-2 0 2 4 6 80

50

100

150

200Nva

-2 0 2 4 6 80.0

0.5

1.0

1.5

Leu

-2 0 2 4 6 80

5

10

15

20Ile

-2 0 2 4 6 80

5

10

15

20

β - Mnl

-2 0 2 4 6 80

1

2

3

Co

ncentr

atio

n [μmolg

DC

W-1

]

Feed time [h]

Ala

0 30 40 50 60 700

20

40

60Nva

0 30 40 50 60 700

1

2

3

4

5Ile

0 30 40 50 60 700

20

40

60

80

STRSTRSTR

Co

ncentr

atio

n [μmolg

DC

W-1

]

Residence time [s]

3. Results

65

strain 2CR scale-down cultivation, analyzed by HPLC. Data shown are the samples from time

points 0.75 h (white triangles up), 1.75 h (gray triangles down), 3.75 h (dark gray squares) and

5.75 h (black diamonds) after feed start over the residence time in the PFR.

3.2. Behavior of the interleukin-2 producing strain E. coli

W3110_pCTUT7_His_IL2 in STR and 2CR cultivations

It has been found that in the scale-down cultivation E. coli W3110 wild-type strain is

able to accumulate non-canonical amino acids (norvaline and β-methylnorleucine)

under oscillation of glucose and oxygen concentrations conditions which is simulating

the gradients of substrate and dissolved oxygen in large-scale (see 3.1). It could be

proposed that in industrial large-scale cultivation non-canonical amino acids could be

synthesized if high substrate zones occur in these reactors. It is well known that when

non-canonical amino acids accumulated in the cell, they are falsely incorporated into

some recombinant proteins, e.g. norvaline was incorporated into human hemoglobin

(Apostol et al. 1997) and it was found that β-methylnorleucine was misincorporated

into recombinant hirudin (Muramatsu et al. 2003). Lots of studies found that,

especially in leucine-rich recombinant proteins, non-canonical amino acids can be

misincorporated, e.g. norvaline into recombinant haemoglobin which includes 72

leucine residues in 575 total amino acids residues (Apostol et al. 1997) norleucine

into interleukin2 containing 152 amino acid residues, of which 26 are leucine

residues (Lu et al. 1988; Tsai et al. 1988). But the effect of expression rate of

leucine-rich recombinant protein on the synthesis and misincorporation of

non-canonical amino acids into recombinant proteins is not yet fully clear. Such

misincorporations are highly unwanted and unfavored especially for pharmaceutical

proteins. Therefore in this work, a scale-down research was performed on cellular

responses to oscillation conditions using E. coli W3110_pCTUT7_His_IL2 strain which

expresses interleukin-2, a leucine-rich recombinant protein in inclusion bodies.

3. Results

66


The gradients of glucose and oxygen availability in industrial large scale were also

simulated in the same two-compartment reactor adding feed solution without

aeration at the inlet of PFR as described in Section 3.1. The cultivation process is also

performed with two parts. Firstly, cells were cultivated overnight only in STR module

in fed-batch-like EnBase Flo medium with 2 g L-1 glucose and 34 mg L-1

chloramphenicol as starter culture for high cell density cultivations until the glucose

release rate became limiting. For this phase the aeration of the culture was set to 0.5

vvm and the DOT was controlled above 30 % by regulating the stirrer speed. Secondly,

after overnight cultivation, the PFR module was connected and the mechanical

glucose feeding was applied in the inlet of PFR with a targeted specific growth rate

(μset) of 0.2 h-1. At 1 h after feed start, IPTG was injected into the STR module though

a 0.22 μm sterile filter to the final concentration of 1 mM for induction. During the

whole cultivations, in the STR module cells were kept at 37 °C and the pH was

controlled by addition of 25 % ammonium hydroxide to pH 7. The DO was

maintained above 30 % by manually changing the stirrer speed and the aeration rate.

Besides, the control cultivation was performed only in the STR without the PFR part,

and the feed solution was added to the top gas phase of the bioreactor.

Figure 3.7A shows that the biomass values from 2CR scale-down cultivation have no

distinct difference from the control until 3 hours after induction it became a bit lower.

At the same time of 3 h after induction the maximum of biomass concentration in

the 2CR scale-down cultivation was only 12 g L-1, while in control cultivation, it

reaches to about 13 g L-1, and at the end, the maximum biomass concentration in the

control was about 14 g L-1. This is because cells grow slower under oxygen limitation

conditions (Soini et al. 2008a). It is already well known that cells are in a stressful

environment when they are exposed to repeated oscillations of the substrate

3. Results

67

concentration and dissolved oxygen. The cells need to repeatedly try to adapt to such

stressful condition, which causes a loss of biomass (Enfors and Häggström 2000). The

oxygen limitation condition could be seen from Figure 3.7C, which shows that in the

PFR module of 2CR scale-down cultivation cells are under oxygen limitation from the

first port of the PFR immediately after feed start where with all the DOT values are

not more than 0.5 % throughout the whole cultivation.

Figure 3.7: Shows the E. coli W3110_pCTUT7_His_IL2 cultivations data of the STR cultivation

0 1 2 3 4 5 6

DO

[%

]

0

20

40

60

80

100

120

pH

[-]

0

2

4

6

8

10

12STR DO

2CR DO

STR pH

2CR pH

0 1 2 3 4 5

DO

[%

]

0

1

2

3

4Port 1

Port 2

Port 4

Port 5

Feed time [h]

-1 0 1 2 3 4 5 6

DC

W [

g L

-1]

4

6

8

10

12

14

16

STR DCW

2CR DCW

STR DCW fitted

2CR DCW fitted

Feed time [h]

A

B

C

3. Results

68

(reference) and the 2CR scale-down cultivation. Feed start point starts from 0 h. Dashed line –

induction time, 1h after feed start point. (A) Dry cell weight. (B) Profile of online DO and pH data

in the STR module. (C) Profile of online DO data in the PFR module of 2CR scale-down cultivation.

For the exhaust gas analysis, Figure 3.8C shows that the profile of the respiratory

quotient (RQ) in 2CR scale-down cultivation is almost the same as in the STR

cultivation. RQ was observed to be increasing after the mechanical glucose feeding in

both cultivations up to a value of about 1. After induction the level of RQ slightly

decreased, followed by relatively constant value until the end of both cultivations.

After the start of feed the specific gas consumption rates (qO2 and qCO2) increased to

a certain level, and increasing again after the induction time, and then staying

constant until the end of cultivations (Figure 3.8). It needs to be remarked, that there

were jumps of qO2 and qCO2 at 1 h and 2.3 h after feed start in control cultivation,

which are probably caused by the system. After induction, in both cultivations qO2

and qCO2 increased with a slightly decreased RQ, indicating an imbalance between

respiration and carbon dioxide production directly after induction. The increase of

qO2 and qCO2 after induction may be caused by the stress of induction of

interleukin-2.

3. Results

69


Respiratory quotient in the STR module of the reference (black) and 2CR scale-down (red) E. coli

W3110_pCTUT7_His_IL2 cultivations. Feed start point starts from 0 h. Dashed line – induction

time, 1h after feed start point.

3.2.2. Protein quantification

In order to quantify the recombinant protein when cells were exposed to different

conditions, the recombinant protein production was analyzed by SDS-PAGE with a 15 %

Feed time [h]

0 2 4 6

RQ

[-]

0.0

0.5

1.0

1.5

2.0

STR

2CR

0 2 4 6

qC

O2 [

mm

ol g

DC

W

-1 h

-1]

0

4

8

12

STR

2CR

0 2 4 6

qO

2 [

mm

ol g

DC

W

-1 h

-1]

0

4

8

12

STR

2CR

A

B

C

3. Results

70

SDS-gel. The first sample (0h) was taken just before the induction, following with

samples taken at 0.25 h, 0.5 h, 1 h, 2 h, 3 h, 4 h and 5.3 h after induction time. In

order to compare all the protein production among different samples at the same

level, the samples were normalized to the certain same OD as described previously

(Section 2.5.5). And protein samples were separated into soluble and insoluble

fraction using BugBuster Kit.

Figure 3.9: SDS-PAGE of the soluble and inclusion body protein fractions prepared by BugBuster

Kit for the reference and the 2CR scale-down cultivations of E. coli W3110_pCTUT7_His_IL2 from 0

h to 5.3 h after induction. The arrow indicates the expected position of interleukin-2. M= protein

molecular weight marker.

Figure 3.9 shows that the interleukin-2 was successfully expressed as inclusion

bodies with no interleukin-2 expressed at induction time. The concentration of

induced target protein is increasing over the cultivation time of both cultivations. But

it illustrated that the bands of interleukin-2 are a bit thinner in the first three hours

of 2CR scale-down cultivation than at the same time point of the control cultivation,

indicating less interleukin-2 expression and reduced productivity of strain E. coli

W3110_pCTUT7_His_IL2 under oscillating conditions of substrate and dissolved

oxygen compared with homogenous cultivation. However, the final amount of

interleukin-2 seems not to be significantly different between control and scale-down

cultivations.

Soluble inclusion body inclusion body solubleSTR 2CR

3. Results

71


Figure 3.10 shows a fast accumulation of lactate and acetate in 2CR scale-down

cultivation in comparison to the control. The concentration of lactate is always higher

in oscillating cultivation than the reference cultivation, even at the feed start point. In

the reference cultivation, the lactate concentration continuously increased to 0.35

mM at 5 h after feed start, while in 2CR scale-down approach, it significantly

increased to 0.72 mM at 4 h after feed start. For acetate, during the control

cultivation, it almost was not detected during the first 2 h after feed start, but from 3

h on, its concentration increased to reach a peak of 4.81 mM at 5 h after feed start.

While in 2CR scale-down cultivation, the concentration of acetate significantly

increased to 8.0 mM at 3 h after feed start (Figure 3.10). No remarkable differences

were detected in the extracellular concentration of malate, succinate and fumarate

between the two cultivation types (Figure 3.10). At 1 h after feed start in the PFR

module of 2CR scale-down cultivation, under oxygen limited condition with the

pyruvate accumulation, lactate and formate accumulated quickly over the residence

time along the PFR (Figure 3.11). It is apparent that the mixed-acid fermentation

enzyme system which is inhibited by oxygen under aerobic condition were present,

and could produce detectable mixed-acid fermentation products within 1 min even in

seconds when exposed to oxygen limitation in the PFR (Enfors et al. 2001). There was

no more accumulation of formate at 3 h after glucose feed start in the PFR module.

One possible explanation could be adaption of cells to the oscillating conditions of

substrate and dissolved oxygen. The mixed-acids products produced in the oxygen

limited zone (PFR module), and re-assimilated when exposed to oxygen sufficient /

substrate limited zone (STR module) could also be one reason which led to reduction

in growth rate (Figure 3.7) and the productivity of interleukin-2 decreased (Figure

3.9).

3. Results

72


module of E. coli W3110_pCTUT7_His_IL2 cultivations, analyzed by HPLC. Data are shown after

feed start (0h). Dashed line – induction time, 1 h after feed start point; white circles – STR

reference cultivation; black triangles up – 2CR scale-down cultivation.

Glc

Feed time [h]

-1 0 1 2 3 4 5 6

Conce

ntr

atio

n [

g L

-1]

0.0

0.3

0.6

0.9 Pyr

Feed time [h]

-1 0 1 2 3 4 5 60.00

0.15

0.30

Conce

ntr

atio

n [

mM

]

Feed time [h]

For

0.0

1.5

3.0

4.5

Lac

-1 0 1 2 3 4 5 60.0

0.4

0.8

1.2

Ace

0

4

8

12 Mal

0.0

0.4

0.8

1.2

Suc

-1 0 1 2 3 4 5 60.0

0.4

0.8

1.2

Feed time [h]

Fum

0.0

1.5

3.0

4.5C

once

ntr

atio

n [

mM

]C

once

ntr

atio

n [

mM

]

3. Results

73

Figure 3.11: Extracellular concentration of compounds of the main carbon metabolism in PFR

module (PFR with feed addition) of E. coli W3110_pCTUT7_His_IL2 2CR scale-down cultivation,

analyzed by HPLC. White triangles up – 1 h and grey triangles down – 3 h after glucose feed start.

3.2.4. Amino acids

The main objective of analyzing the amino acid concentration over the cultivation

time after glucose feed start is to focus on the response of substrate oscillation on

biosynthesis of leucine-rich protein interleukin-2, especially non-canonical amino

acids accumulation and their false incorporation into recombinant protein.

Co

nce

ntr

atio

n [

mM

]

Pyr

Residence time [s]

0 30 40 50 60 700.00

0.03

0.06

Residence time [s]

Mal

0.0

0.2

0.4

0.6

Suc

0 30 40 50 60 700.0

0.1

0.2

0.3

0.4

Fum

0.0

1.5

3.0

4.5C

once

ntr

atio

n [

mM

]C

once

ntr

atio

n [

mM

]

Glc

Residence time [s]

0 30 40 50 60 70

Co

nce

ntr

atio

n [

g L

-1]

0.0

0.4

0.8

1.2

Residence time [s]

For

0

1

2

3

4

Lac

0 30 40 50 60 700.0

0.4

0.8

1.2

Ace

0

4

8

12

STR STR

STRSTR

3. Results

74

With regards to canonical amino acids, the extracellular concentration of serine and

glycine increased during the 2CR scale-down cultivation compared with the control

(Figure 3.13). Alanine showed the most profound enhancement under oscillating

conditions, resulting in as much as 67.1 μM at 4 h after feed start, which is nearly

five-fold higher than control of 14.3 μM (Figure 3.13). Moreover, the total free amino

acid concentrations in the STR module of 2CR scale-down cultivation continuously

increased as well (Figure 3.12). These results show a strong redirection of carbon

fluxes to serine and alanine, while no clear difference between the two cultivations

for the leucine concentration could be seen (Figure 3.12). Isoleucine, which belongs

to the aspartate family, substantially increased over the time in the scale-down

cultivation, while it had an approximately constant level in the control cultivation.

Therefore a huge difference between these two cultivations is visible with

significantly higher amounts of isoleucine under scale-down conditions (Figure 3.12).

However, the concentrations of glutamine and aspartate involved in TCA cycle, were

lower under oscillating conditions compared to the reference STR cultivation (Figure

3.12). This may be caused by the insufficient supply of the carbon flux to the

precursors of these compounds though TCA cycle for their formation.

Along the PFR, leucine, valine and isoleucine show a slight increase in their

concentrations within 68 seconds in the crude extract in the 1 h and 3 h samples

after feed start over the residence time in the PFR (Figure 3.14).

The non-canonical amino acids concentrations were analyzed by HPLC, however this

method is not able to detect norleucine, so no data of total and extracellular free

norleucine concentrations are available (Figure 3.12, Figure 3.13). At the same time,

another analysis method with GC-MS is used to evaluate the misincorporation of

norleucine, norvaline and β-methylnorleucine into recombinant proteins (Figure

3.15). Figure 3.12 and Figure 3.13 show that, there are accumulation of norvaline

3. Results

75

and β-methylnorleucine in both cultivations, even in the control STR cultivation. But

the amount of norvaline per cell is not significantly increasing over the time in both

cultivations (Figure 3.12). Whereas norvaline accumulation occurred earlier in the

2CR scale-down cultivation with the cells exposed to oxygen limitations in the loop of

the PFR (Figure 3.13). Norvaline accumulation in the broth seems to occur during the

residence of the cells in oxygen limitation, due to Figure 3.14 showed that the mount

of free norvaline per cell of crude extract data at 1 h and 3 h after feed start has an

increasing trend under glucose excess and oxygen limited conditions. The

concentrations of total and extracellular free β-methylnorleucine all showed no

significant difference between the two cultivations, while except the data of total

free β-methylnorleucine at 4 h after feed start maybe an outlier (Figure 3.12 and

Figure 3.13). Even in the PFR module, the concentration of β-methylnorleucine is not

increasing, but has slightly decreasing trend over the residence in the PFR (Figure

3.14). One could assume that the non-canonical amino acid is bound by an aminoacyl

tRNA synthetase (aaRS) onto the tRNA and is therefore not measured anymore.

3. Results

76

Figure 3.12: Total free amino acid concentration of suspension samples (cells plus medium) in

the STR module of E. coli W3110_pCTUT7_His_IL2 cultivations from the glucose feed start (0 h),

analyzed by HPLC. Dashed line – induction time, 1 h after feed start point; white circles – STR


Ser

-1 0 1 2 3 4 5 60

5

10

15

20

25Gly

-1 0 1 2 3 4 5 60

10

20

30

40

50

60

70Ala

-1 0 1 2 3 4 5 60

10

20

30

40

50

60

70

Glu

-1 0 1 2 3 4 5 60

200

400

600

800

1000

1200

1400Gln

-1 0 1 2 3 4 5 60

2

4

6

8

10Asp

-1 0 1 2 3 4 5 60

20

40

60

80

100

120

140

160

Thr

-1 0 1 2 3 4 5 60

1

2

3

4

5

6

Val

-1 0 1 2 3 4 5 60

20

40

60

80

100

120

140

160

180

Leu

-1 0 1 2 3 4 5 60

2

4

6

8

10

12

14

16

18Ile

-1 0 1 2 3 4 5 60

5

10

15

20

25

30

Nva

-1 0 1 2 3 4 5 60

1

2

3

4

5 β - Mnl

-1 0 1 2 3 4 5 60

10

20

30

40

50

Co

ncentr

atio

n [μmolg

DC

W-1

]

Feed time [h]

3. Results

77

Figure 3.13: Extracellular free serine, glycine, alanine, norvaline and β-methylnorleucine

concentration of supernatant samples in STR module of E. coli W3110_pCTUT7_His_IL2

cultivations from the glucose feed start (0 h), analyzed by HPLC. Dashed line – induction time, 1 h

after feed start point; white circles – STR reference cultivation; black triangles up – 2CR


Figure 3.14: Total free branched chain amino acid concentration in the suspension (cells and

medium) samples over the residence time of the PFR module (PFR with feed addition) in E. coli

Ser

0

10

20

30

40Gly

0 1 2 3 4 5 60

20

40

60

80Ala

0 1 2 3 4 5 60

20

40

60

80

Nva

0 1 2 3 4 5 60

10

20

30

40 β - Mnl

0 1 2 3 4 5 60

10

20

30

40

Co

ncentr

atio

n [μM

]

Feed time [h]

Nva

0 30 40 50 60 700

2

4

6

8

Leu

0

5

10

15Ile

0

5

10

15

β -Mnl

0 30 40 50 60 700

2

4

6

8

Val

0 30 40 50 60 700

20

40

60

80

STR STR

STR

Co

ncentr

atio

n [μmolg

DC

W-1

]

Residence time [s]

3. Results

78

W3110_pCTUT7_His_IL2 2CR scale-down cultivation, analyzed by HPLC. White triangles up – 1 h

and grey triangles down – 3 h after glucose feed start.

Figure 3.15 shows the concentration of non-canonical amino acids and the natural

counterparts which they replace from the induction time (1 h after feed start) in

inclusion bodies of E. coli W3110_pCTUT7_His_IL2 cultivations over the cultivation

time. Regarding the composition of canonical amino acids in the inclusion bodies, a

continuously increasing trend of leucine, isoleucine and methionine over the time

period of protein expression was observed, indicating the increasing production of

interleukin-2. This was already clearly shown by the SDS-PAGE gel (Figure 3.9).

Looking at the non-canonical amino acids in control STR and 2CR scale-down

cultivation, a maximum amount of 0.13 μmol gDCW-1 and 0.24 μmol gDCW

-1 norvaline

were falsely incorporated into interleukin-2 proteins at 3 h after feed start,

respectively (Figure 3.15). For norleucine, the misincorporation reached a maximum

of 0.64 μmol gDCW-1 at 5 h after feed start in control and 1.2 μmol gDCW

-1 in the 2CR

scale-down cultivation at 4 h after feed start, while β-methylnorleucine was maximal

around 0.5 μmol gDCW-1 at the end of both cultivations (Figure 3.15). Though there

are much less methionine residues in IL2, norleucine was observed misincorporated

into proteins with higher rate than norvaline and β-methylnorleucine. The ratio of

non-canonical amino acid / natural substrate (for example norvaline / leucine) is an

important factor in non-canonical amino acid misincorporation into heterologous

protein (Apostol et al. 1997), because of lowered substrate specificity of the

aminoacyl-tRNA synthetase (aaRS) (Cvetesic et al. 2014; Kiick et al. 2001; Tang and

Tirrell 2002). In own results, the ratio of norvalin / leucine and β-methylnorleucine /

isoleucine in Figure 3.16 seemed to have a high correlation with the misincorporation

rate of non-canonical amino acids into proteins in Figure 3.15. A slight increased ratio

of norvalin / leucine led to slightly higher norvaline incorporation into proteins in 2CR

scale-down cultivation than the control cultivation.

3. Results

79

Though there are accumulation and incorporation into proteins of non-canonical

amino acids in both cultivations, it could be seen that in the 2CR scale-down

cultivation more non-canonical amino acid were synthesized and got

misincorporated into the target protein (Figure 3.15). Especially, norleucine has a

higher incorporation rate than norvaline and β-methylnorleucine, though there is

much less methionine residues in IL2. This may be explained by the oscillations of

substrate and dissolved oxygen in the 2CR scale-down cultivation, which was caused

by cells rapidly and repeatedly move between PFR (oxygen limitation and substrate

excess situation) and STR module (sufficient oxygen). Soini et al. showed that a

combination of oxygen limitation and pyruvate overflow result in norvaline

accumulation (Soini et al. 2008a).

Figure 3.15: A: The amino acid sequence of interleukin-2 protein and the possible incorporation

position by non-canonical amino acids. B: Shows the concentration of non-canonical amino acids

Nva

-1 0 1 2 3 4 5 60.0

0.5

1.0

1.5

Nle

-1 0 1 2 3 4 5 60.0

0.5

1.0

1.5

β - Mnl

-1 0 1 2 3 4 5 60.0

0.5

1.0

1.5

Leu

-1 0 1 2 3 4 5 60

300

600

900

1200

Met

-1 0 1 2 3 4 5 60

100

200

300

400

Ile

-1 0 1 2 3 4 5 60

100

200

300

400

IL2: 147 aa

Leucine residue 22 15.0 %

Methionine residue 5 3.4 %

Isoleucine residue 9 6.1 %

A B

1

21

41

61

81

101

121

MHHHHHHGGG GSASAPTSSS

GINNYKNPKL TRMLTFKFYM

SNINVIVLEL KGSETTFMCE

141 SIISTLT

YADETATIVE FLNRWITFCQ

PKKATELKHL QCLEEELKPL

EEVLNLAQSK NFHLRPRDLI

TKKTQLQLEH LLLDLQMILN

Co

ncentr

atio

n [μmolg

DC

W-1

]

Feed time [h]

3. Results

80

and the natural counterparts which they replace in inclusion bodies of E. coli

W3110_pCTUT7_His_IL2 cultivations from the induction time (1h), analyzed by GC-MS. Dashed

line – induction time, 1 h after feed start point ; White circles – STR reference cultivation; black

triangles up – 2CR scale-down cultivation.

Figure 3.16: The ratio of total free concentration of non-canonical amino acids / the natural

counterparts which they replace in the homogenized suspension (cells plus medium) samples

collected from the STR in cultivations with E. coli W3110_pCTUT7_His_IL2 from the glucose feed

start (0 h). Dashed line – induction time, 1 h after feed start point; white circles – STR reference

cultivation; black triangles up – 2CR scale-down cultivation.

3.2.5. Carbon Labeling Experiments

In order to study the changes in metabolic fluxes of E. coli w3110_pCTUT7_His_IL2

immediately exposed to substrate excess and combined oxygen limitation during

different cultivation conditions, carbon labeling experiments have been done for

Metabolic Flux analysis C13-labeled glucose using the rapid sampling unit Bioscope,

which was connected with the STR module of both 2CR scale-down and reference

cultivations at 2 h after feed start that also means 1 h after induction time.

Suspension samples from the STR were supplied with the C13-labeled glucose at the

entry point of the Bioscope and then pumped through its tube structure. Meanwhile,

the hemispherical gas channel of BioScope was flushed with nitrogen, representing

no aeration in the PFR of 2CR scale-down cultivation. Samples were taken by

Nva / Leu

-1 0 1 2 3 4 5 60.0

0.5

1.0

1.5

2.0

β - Mnl / Ile

-1 0 1 2 3 4 5 60.0

0.5

1.0

1.5

2.0

Ra

tio

[-]

Feed time [h]

3. Results

81

methanol quenched method (as described in 2.4.6) within 1 min representing the

residence time in the PFR.

The results are shown in Table 3.1 and Table 3.2, with numbers in cells of tables

giving information about values of the pool of fragments with 13C-isotopes and the

color intensity demonstrating the pool of labeled compounds by sight.

Table 3.1 shows the labeling of compounds in STR control cultivation with rapid

anaerobic sampling, indicating a steady increase of labeling in pyruvate, lactate and

malate. Alanine, aspartate and leucine labeling reached their maximal degree at the

end of the sampling time. The labeling profile of threonine shows fluctuations with

maximal at 5.0 s and 14.5 s. Glutamate reached a first peak at 5.0 s and maximal at

the end of sampling. Isoleucine shows highest labeling at 7.4 s. Norvaline, norleucine

and β-methylnorleucine have maximal labeling ratios at 6.0 s, 14.5 s and 12.5 s,

respectively. From the result, it could be seen that threonine reaches its maximal

portion of incorporation of labeled carbon before norvaline, norleucine,

β-methylnorleucine and isoleucine, so the main carbon source for α-ketobutyrate,

which is the precursor of non-canonical amino acids, derives from threonine through

TCA cycle under normal conditions. The labeling peak of aspartate can be explained

by the fork flux from aspartate to asparagine and homoserine, however these

compounds could not be detected in labeled fractions.

The labeling of compounds in 2CR scale-down cultivation with rapid anaerobic

sampling is shown in Table 3.2, which indicates a different behavior. The pyruvate

profile shows a small peak at 12.5 s and then fluctuations to the maximal at the end.

The maximal labeling value for lactate is shifted to an earlier time at 40.1 s. Under

the oscillating cultivation condition, fumarate and malate seems to have no

difference compared with STR cultivation. The maximal degree of labeling of

3. Results

82

glutamate and aspartate was detected at the end of sampling. Another amino acid

derived from the TCA cycle, threonine, shows fluctuations with its maximum at 18.8 s

and 40.1 s. The earlier labeling of non-canonical amino acids and isoleucine under

oscillation conditions is remarkable, with norleucine, β-methylnorleucineare and

isoleucine all labeled at 5.0 s, and norvaline labeled at 7.4 s. Obviously, the fast

13C-labeling in isoleucine and non-canonical amino acids, compared to a tardy

13C-labeling of the intermediates from TCA-cycle including threonine, reveals changes

in metabolic flux distribution within the branched chain amino acids biosynthesis

pathway. Therefore, the main carbon source for α-ketobutyrate, which is the

precursor of non-canonical amino acids and isoleucine, cannot be derived from

threonine, but are directly synthesized by chain elongation from pyruvate through

the much shorter direct pathway under oscillation conditions with the LeuABCD

complex. The highest labeling of valine and leucine also came earlier than in the STR

control experiment. The probable reason is the enhanced substrate availability under

oscillation conditions, which might change the distribution of the carbon flux from

pyruvate to valine and leucine.

3. Results

83

Table 3.1: Labeling of compounds in STR control cultivation of W3110_pCTUT7_His_IL2 with

anaerobic rapid samples taking, analyzed by GC-MS. Samples were taken at 2 h after glucose feed

start and 1 h after induction. Numbers in cells of tables gives information about values of the pool

of fragments with 13C-isotopes and the color intensity demonstrates the pool of labeled

compounds by sight.

Name Fragment Mass Isotope 0.0 5.0 6.0 7.4 12.5 14.5 18.8 30.5 40.1 56.9

Pyruvic acid 160 m3 0.0 1.9 2.2 2.6 3.5 4.1 5.2 6.9 13.0 16.0

Lactic acid 191 m2 0.3 1.9 2.2 2.1 1.9 2.2 2.2 3.0 3.7 4.1

Malate 233 m2 3.6 6.8 7.9 8.1 8.5 7.6 9.5 9.5 9.8 9.9

Alanine 158 m2 8.8 8.6 9.1 9.3 9.5 9.7 10.8 13.1 15.0 18.5

Glutamate 432 m3 3.9 4.3 4.1 4.1 4.1 4.2 4.2 4.2 4.2 4.4

Aspartate 418 m3 3.8 3.8 4.1 3.9 3.9 4.0 4.0 4.3 4.4 5.0

Threonine 404 m1 7.3 8.6 7.7 8.5 7.9 8.6 8.3 8.4 8.2 8.2

Isoleucine 200 m4 0.0 1.7 2.4 4.1 1.2 1.3 0.7 1.9 1.1 2.2

Norleucine 200 m1 16.1 14.5 15.5 15.2 15.9 17.4 15.1 15.8 17.0 14.9

Norvaline 288 m2 5.8 5.7 6.7 5.8 6.0 6.0 5.3 6.3 5.6 5.7

β-Methylnorleucine 214 m3 11.5 14.1 11.5 10.9 25.9 19.5 9.9 11.6 15.9 8.2

Valine 288 m1 10.6 9.2 9.9 9.2 9.3 9.2 9.6 9.2 8.4 8.9

Leucine 200 m1 15.0 15.4 15.4 15.0 15.2 15.4 16.1 15.9 17.1 17.6

Table 3.2: Labeling of compounds in 2CR scale-down cultivation of W3110_pCTUT7_His_IL2 with

anaerobic rapid samples taking, analyzed by GC-MS. Samples were taken at 2 h after glucose feed

start and 1 h after induction. Numbers in cells of tables gives information about values of the pool

of fragments with 13C-isotopes and the color intensity demonstrates the pool of labeled

compounds by sight.

Name Fragment Mass Isotope 0.0 5.0 6.0 7.4 12.5 14.5 18.8 30.5 40.1 56.9

Pyruvic acid 160 m3 3.9 2.7 0.9 1.2 4.0 1.8 3.6 3.6 6.2 7.9

Lactic acid 191 m2 1.7 1.8 2.1 1.9 1.8 1.9 2.1 2.0 2.6 2.5

Malate 233 m2 0.0 5.8 6.4 7.8 8.1 7.5 7.8 7.6 9.1 9.2

Alanine 158 m2 8.3 8.4 8.2 8.5 8.5 8.5 9.2 9.6 11.1 12.3

Glutamate 432 m3 4.0 4.1 4.1 4.1 4.2 3.9 4.1 4.1 4.2 4.2

Aspartate 418 m3 3.9 3.8 3.8 3.8 3.9 4.2 3.9 4.0 4.2 4.6

Threonine 404 m2 4.2 4.4 4.3 4.5 4.2 4.1 4.7 4.4 4.8 4.5

Isoleucine 200 m4 1.1 3.0 1.5 1.8 1.3 0.3 1.2 1.0 0.6 0.4

Norleucine 200 m1 15.4 25.8 17.8 15.1 13.3 14.9 16.2 15.5 15.9 16.0

Norvaline 260 m1 9.1 9.4 8.7 10.3 9.3 8.2 9.2 9.2 9.9 9.9

β-Methylnorleucine 214 m3 6.7 34.6 13.2 7.5 6.9 10.1 1.9 5.4 1.8 15.5

Valine 288 m3 0.7 0.6 0.3 0.6 0.6 0.9 0.6 0.6 0.6 0.7

Leucine 200 m3 1.1 1.7 2.0 1.4 1.5 1.6 1.3 1.8 1.4 1.4

3. Results

84

3.3. Behavior of the insulin producing strain E. coli W3110M_pSW3 in

STR, 2CR and 3CR cultivations

The 2CR scale-down cultivations with PFR with feed addition mimicking the feed

zone in industrial large-scale cultivations were performed with E. coli W3110

wild-type strain and E. coli W3110_pCTUT7_His_IL2 recombinant strain, indicated

that besides short fatty acids formation, non-canonical amino acids also accumulate

and are false incorporated into the recombinant target protein. Therefore it was

interesting to apply another recombinant strain with expression of a leucine-rich

protein and simultaneously simulating other zones in industrial large-scale

cultivations besides the feed zone. Due to the high affinity of E. coli to glucose,

corresponding to a low Ks value, one can assume glucose starvation zones exist in

large-scale bioreactors. Such zones could be either connected to high or low DOT.

Considering these, in this work, another recombinant leucine-rich protein was

expressed in E. coli. In the case of this insulin producing strain we also applied a

three-compartment system aside from the two-compartment reactor. This 3CR,

which was earlier described by Lemoine et al. consists of one normal STR and two

similar PFR modules with addition of feed solution and no aeration at the entrance of

PFR1 and no-feeding and no-aeration to the PFR2 (Lemoine et al. 2015). PFR1

simulates the feed zone while PFR2 mimics a starvation zone in large-scale

cultivations. In our system the residence time in both PFRs was 68 s. Here we applied

a higher feed rate of μset = 0.3 h-1 was applied, as this strain had a higher max and

such a higher feed rate would generate a higher flux of carbon to pyruvate and the

branched-chain amino acid pathway, also suggesting a higher production of the

non-canonical amino acids.


The scale-down cultivations were both performed in three phases. Firstly, cells were

3. Results

85

cultivated as a batch cultivation overnight only in STR module in MSM medium with

5 g L-1 glucose and 100 mg L-1 ampicillin until glucose depletion. For this phase the

aeration of the culture was set to 0.5 vvm and the stirrer speed was set at 400 rpm.

After overnight cultivation at around 14 hours, the PFR modules were connected and

the exponential glucose feeding by an external pump was applied to the inlet of PFR1

with a targeted specific growth rate (μset) of 0.3 h-1; meanwhile, the speed of the

stirrer was increased to 1100 rpm. 3 h after feed start, the inducer IPTG was added

into the STR module though a 0.22 μm sterile filter to a final concentration of 1 mM

(calculated for a volume of 10 mL 1 M IPTG into 10 L culture) for induction. And the

feeding strategy was changed into constant feeding. During the whole cultivations, in

the STR module cells were grown at 35 °C at a pH of 7.0, which was controlled by

addition of 25 % ammonium hydroxide. The control cultivation was performed only

in the STR without the PFR parts, and the feed solution was added to the top phase

of the bioreactor.

From Figure 3.17A, it can be seen that there are no differences in the biomass yield

between the three cultivations before induction. But after induction, the biomass

concentration of the 2CR scale-down cultivation was higher compared to the control

cultivation until the end, while the cell density was a bit lower in the 3CR scale-down

cultivation. As the cell density in 3CR scale-down cultivation decreased, the

cultivation was stopped at 2h after induction (Figure 3.17). Because of the steadily

increasing biomass, the concentration of dissolved oxygen decreased over the whole

cultivation time in PFR1 of the 2CR and 3CR scale-down cultivations (Figure 3.17B

and C). The DO at port 1 in PFR1 showed oxygen depletion at round 1.7 h after feed

start at a biomass concentration of 5.4 g L-1 in the 2CR scale-down cultivation (Figure

3.17B), while it was seen earlier in the 3CR scale-down cultivation (1.2 h after feed

start) at a biomass concentration of 4 g L-1 (Figure 3.17C). The reason was that the

additional PFR2 module led to overall lower liquid level in the STR module of 3CR

3. Results

86

scale-down cultivation, causing a lower oxygen transfer and a lower DO level with the

same stirrer set-up. Figure 3.17D shows the DOT profile measured in the PFR2

module of the 3CR scale-down cultivation. Here oxygen depletion occurred at around

2.8 h after feed start, and after this, the cells were exposed to a more stressful

environment in 3CR scale-down cultivation.

Figure 3.17: Shows the E. coli W3110M_pSW3 cultivations data of the STR cultivation (reference)

0 2 4 6 8

DO

[%

]

0

20

40

60

80

100Port 1

Port 2

Port 4

Port 5

Feed time [h]

0 2 4 6 8

DC

W [g L

-1]

0

5

10

15

20STR DCW

2CR DCW

3CR DCW

0 1 2 3 4

DO

[%

]

0

20

40

60

80

100PFR2_Port 1

PFR2_Port 5

0 1 2 3 4

DO

[%

]

0

20

40

60

80

100PFR1_Port 1

PFR1_Port 5

A

B

C

D

3. Results

87

and scale-down cultivations. Feed start point starts from 0 h. Dashed line – induction time, 3 h

after feed start. (A) Dry cell weight measured in the STR module of three cultivations (B) Profile

of online dissolved oxygen concentration data in the PFR1 module of 2CR scale-down cultivation.

(C) Profile of dissolved oxygen concentration measured in the PFR1 module of 3CR scale-down

cultivation. (D) Profile of dissolved oxygen concentration measured in the PFR2 module of 3CR

scale-down cultivation. As the cell density in 3CR scale-down cultivation decreased, the

cultivation was stopped at 2h after induction.

For the exhaust gas analysis, in the control STR cultivation, the rates for qO2 and qCO2

increased slightly within the first two hours after the exponential feed start but

afterwards showed a slight decrease (Figure 3.18) which most likely triggered by the

switch of the cultivation mode to constant feed at the time of induction.

In the 2CR scale-cultivation qO2 was higher than in the control cultivation after feed

start until the end of cultivations, while qCO2 was similar before induction, but

afterwards showed a slight continuous increase. This indicates an increased oxygen

demand and carbon dioxide formation of the cells under oscillating conditions.

Interestingly, this is even stronger in the 3CR cultivation. The increase of qO2 and

qCO2 in both scale-down cultivations was the same in relative terms, which is

obvious from the same and constant RQ in both cultivations (Figure 3.18). RQ in

these scale-down cultivations was lower than the reference. This demonstrated an

increased oxygen demand of cells under inhomogenous conditions, and indicating

that the cells have an increased respiration activity and a higher carbon dioxide

production in such cultivations.

3. Results

88


Respiratory quotient in the STR module of the reference and scale-down E. coli W3110M_pSW3

cultivations.

3.3.2. Protein quantification

The production of recombinant insulin in the different cultivation scenarios were

analyzed by SDS-PAGE in whole cell samples.

Feed time [h]

0 2 4 6 8

RQ

[-]

0

1

2

3

4STR

2CR

3CR

0 2 4 6 8

qC

O2 [

mm

ol g

DC

W

-1 h

-1]

0

5

10

15STR

2CR

3CR

0 2 4 6 8

qO

2 [

mm

ol g

DC

W

-1 h

-1]

0

5

10

15STR

2CR

3CR

A

B

C

3. Results

89

Figure 3.19: SDS-PAGE of the whole cells of the reference and scale-down cultivations from 0 h to

7 h after feed start. The arrow indicates the expected position of recombinant insulin. M= protein

molecular weight marker.

Figure 3.19 shows that the recombinant insulin can be not detected before induction

(0 and 3 h samples) and its concentration increases over the cultivation time in the

reference culture as well as in the 2CR scale-down cultivation. The product

accumulates mainly in the first two hours after induction in both cultivations.

However the accumulation of insulin is slightly lower in the 2CR cultivation, although

the final amount seems not to be significantly different. This indicates that in the

applied construct insulin expression is not significantly influenced by the oscillations.

Unfortunately the 3CR culture had to be stopped one hour after induction due to cell

lysis. However, the one hour sample still shows the same insulin concentration as the

other cultivations.


Though Figure 3.17A showed that the inhomogenous conditions have no impact on

the biomass yield before induction time for the three different cultivations, but it was

seen accumulation of glucose between the feed start and the induction event

increased to over 1 g L-1, indicating a slightly lower specific glucose uptake rate. So

the cultures were less limited for glucose under oscillating conditions. Because of the

STR 2CR

M 0 3 4 5 6 7 0 3 4 5 6 7 3 4

3CR

KDa26014010070

50

40

35

25

15

10

3. Results

90

increasing biomass yield, the volumetric glucose uptake rate increased, combined

with the change from exponential to constant feed at 3 h after feed start (induction

time), which caused the glucose concentration to decline and to be non-detectable

at 3 h after induction. It can be assumed that the 3CR shows a similar trend, although

the cultivation had to be stopped earlier.

Under oscillating conditions, extracellular accumulation of acetate and lactate, which

are typical for overflow metabolism and anaerobic conditions, were detected in the

STR module (Figure 3.20B). In the 2CR scale-down cultivation, the concentration of

acetate continuously increased to 0.6 mM at the end, achieving a 6 fold higher level

compared to the cultivation under homogeneous conditions with the value of 0.1

mM. The level of acetate had a further increased trend in the 3CR scale-down

cultivation. While no lactate was detected under non-oscillating conditions, a larger

amount of lactate was synthesized in the scale-down set-up s, with a profile

increasing until 0.5 mM at 5 h after feed start in 2CR scale-down cultivation and then

declining. Also here a stronger increase was detected in the 3CR scale-down

cultivation with a level of 1 mM at 4 h after feed start. No significant change was

detected for the extracellular concentrations of formate, fumarate and malate in the

STR module, indicating that oscillating conditions have no effect on the carbon fluxes

to these metabolites.

3. Results

91


module of E. coli W3110M_pSW3 cultivations, analyzed by HPLC. Data are shown after feed start

(0h). Dashed line – induction time, 3 h after feed start; white circles – STR; grey triangles up –

2CR scale-down cultivation; black squares – 3CR scale-down cultivation.

For the extracellular concentration of compounds of the main carbon metabolism

within the PFR module of scale-down cultivations, only the concentration of lactate

was slightly increased at 3 h, 5 h and 7 h after feed start over the residence time of

Mal

0.0

0.1

0.2

0.3

For

0.0

0.1

0.2

0.3

Lac

0 2 4 6 80.0

0.5

1.0

1.5

Conce

ntr

ation [

mM

]

Ace

0.0

0.5

1.0

1.5

Fum

0 2 4 6 80.0

0.1

0.2

0.3

Feed time [h]

A

B

Glc

0 2 4 6 80

1

2

3

4

Feed time [h]

Concentr

ation [

g L

-1]

3. Results

92

PFR1 in the 2CR scale-down cultivation (Figure 3.21), while in the PFR1 of the 3CR

scale-down cultivation acetate, lactate, formate and malate all accumulated along

the PFR module at 3 h after feed start (Figure 3.22) under the oxygen limitation in the

PFR (cf. Figure 3.17B, Figure 3.17C). However, due to the short cultivation time of 3CR

scale-down cultivation, the PFR2 was not really starvation yet at 3 h after feed start.

Therefore lactate showed a slight increasing trend over the residence time of PFR2

with over 1 g L-1 extracellular glucose (Figure 3.23). Interestingly, in the 2CR

scale-down cultivation, despite fast accumulation of lactate was detected at 5 h and

7 h after feed start along the PFR1 module (Figure 3.21), a continuously declining

concentration of lactate have been measured in the STR module after the swich to

constant feed (from 0.5 mM at 5 h to 0.2 mM at 7 h, Figure 3.20). This shows that

lactate is re-assimilated when cells are exposed to sufficient oxygen combined with

glucose limitation conditions in the STR module, as published before (Xu et al.

1999a).

Figure 3.21: Extracellular concentration of lactate in PFR1 module (PFR with feed addition) of E.

coli W3110M_pSW3 in 2CR scale-down cultivation, analyzed by HPLC. Data shown are the

samples from time points 0 h (white triangles up), 3 h (gray triangles down), 5 h (dark gray

squares) and 7 h (black diamonds) after feed start over the residence time in the PFR.

Lac

0 30 40 50 60 700.0

0.2

0.4

0.6

0.8

Concentr

ation [

mM

]

Residence time [s]

STR

3. Results

93

Figure 3.22: Extracellular concentration of compounds of the main carbon metabolism in PFR1

module (PFR with feed addition) of E. coli W3110M_pSW3 in 3CR scale-down cultivation,

analyzed by HPLC. Data shown are the samples from time points 1 h (white triangles up), 3 h

(gray triangles down) after feed start over the residence time in the PFR.

Mal

0.00

0.05

0.10

0.15

For

0.00

0.05

0.10

0.15

Lac

0 30 40 50 60 700.0

0.2

0.4

0.6

0.8

Conce

ntr

ation [

mM

]

Residence time [s]

Ace

0.0

0.2

0.4

0.6

0.8

Fum

0 30 40 50 60 700.00

0.05

0.10

0.15

Glc

Residence time [s]

0 30 40 50 60 70

Co

nce

ntr

atio

n [

g L

-1]

0.0

0.5

1.0

1.5

2.0

2.5

A

B

STR

STR

STR

3. Results

94

Figure 3.23: Extracellular concentration of compounds of the main carbon metabolism in the

PFR2 module (starvation loop) of E. coli W3110M_pSW3 in the 3CR scale-down cultivation,


(gray triangles down) after feed start over the residence time in the PFR.

3.3.4. Amino acids

The amino acid analysis in this chapter was performed by GC-MS (see Materials and

methods section 2.5.3).

Mal

0.00

0.05

0.10

0.15

For

0.00

0.05

0.10

0.15

Lac

0 30 40 50 60 700.0

0.2

0.4

0.6

0.8

Conce

ntr

ation [

mM

]

Residence time [s]

Ace

0.0

0.2

0.4

0.6

0.8

Fum

0 30 40 50 60 700.00

0.05

0.10

0.15

Glc

Residence time [s]

0 30 40 50 60 70

Co

nce

ntr

atio

n [

g L

-1]

0

1

2

A

B

STR

STR

STR

3. Results

95

Figure 3.24 shows the concentration of total free amino acids in the suspension (cells

plus medium) from samples collected from the STR module in reference and

scale-down cultivations. For canonical amino acids, concentrations of leucine, valine

and isoleucine were all lower compared to the reference cultivation after induction,

but there was no significant difference between 2CR and 3CR scale-down cultivations

(considering that the last sample from the 3CR reactor was collected 1 hour after

induction). No clear difference for the concentration of other free canonical amino

acids in the suspension could be observed among these three cultivations.

The amount of free non-canonical amino acids norvaline, norleucine and

β-methylnorleucine in the total broth remained low in the control cultivation before

induction. No significant increase for each of them was observed within the three

hours of feeding. However after induction all three increased, while the

accumulation was highest for β-methylnorleucine with the value as high as 1 μmol

gDCW-1, and lowest for norvaline with 0.3 μmol gDCW

-1 at 6 h after feed start (Figure

3.24). This picture changed in the 2CR cultivation. While β-methylnorleucine and

norvaline still remained low before induction, norleucine was already strongly

increased. After induction also the concentration of norleucine remained always

significantly higher compared to the control cultivation, but also the

β-methylnorleucine and norvaline were little bit higher compared to the control

fermentation (Figure 3.24). This indicates that the accumulation of

β-methylnorleucine is only minor influenced by oscillating conditions, while it can be

seen that norleucine is the most affected by such inhomogeneous conditions. The

total accumulation of all three non-canonical amino acids was highest in the 3CR

cultivation, which however unfortunately could not be continued until the end

(Figure 3.24). The results from the analysis of the cultivation broth (cells plus

medium) are also confirmed by the analysis of the extracellular free amino acid

3. Results

96

concentrations, which show the same trend (Figure 3.25).

These results allow the following conclusion, which however has to be still verified by

repetitions of the experiments. In our cultivation set-up the accumulation of the

non-canonical amino acids is mainly favored by the induction of the recombinant

product, which is a leucine-rich protein. However interestingly, we could not observe

a decrease of the leucine pool after induction in the well-mixed STR cultivation, but

even an increase. However, in the 2CR system the leucine pool was reduced by 50%

but this in turn did not result in a higher synthesis of either one of the non-canonical

amino acids. Therefore it seems that the changed metabolic fluxes after induction

cause the formation of these non-canonical amino acids and not the derepression of

the leu or ilv operons. On top to these metabolic changes oscillations as they occur in

the 2CR system seem to further increase this effect.

3. Results

97

Figure 3.24: Total free amino acid concentration in the suspension (cells plus medium) samples

collected from the STR module in cultivations with E. coli W3110M_pSW3 from the glucose feed

start (0 h), analyzed by GC-MS. Dashed line – induction time, 3 h after feed start point; white

circles – STR reference cultivation; grey triangles up – 2CR scale-down cultivation; black squares

– 3CR scale-down cultivation.

Ile

0 2 4 6 80

1

2

3Leu

0 2 4 6 80

1

2

3

Nva

0 2 4 6 80

1

2

3 β - Mnl

0 2 4 6 80

1

2

3Nle

0 2 4 6 80

1

2

3

Met

0 2 4 6 80

1

2

3

Ala

0 2 4 6 80

5

10

15Gly

0 2 4 6 80

1

2Val

0 2 4 6 80

1

2

Co

ncentr

atio

n [μmolg

DC

W-1

]

Feed time [h]

3. Results

98

Figure 3.25: Extracellular free amino acid concentration in the supernatant samples of STR

module of E. coli W3110M_pSW3 cultivations from the glucose feed start (0 h), analyzed by GC-MS.

Dashed line – induction time, 3 h after feed start; white circles – STR reference cultivation; grey

triangles up – 2CR scale-down cultivation; black squares – 3CR scale-down cultivation.

As described above, non-canonical amino acids can be falsely incorporated into the

recombinant (and cellular) proteins, where norvaline can substitute leucine,

β-methylnorleucine is able to substitute isoleucine and norleucine can replace

methionine. In order to analyse the quantity of recombinant protein, non-canonical

Ala

0 2 4 6 80

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200Gly

0 2 4 6 80

10

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30

40

Val

0 2 4 6 80

100

200

300Ile

0 2 4 6 80

50

100

150Leu

0 2 4 6 80

50

100

150

Nva

0 2 4 6 80

10

20

30 β - Mnl

0 2 4 6 80

10

20

30

Thr

0 2 4 6 80

20

40

60

Ser

0 2 4 6 80

5

10

15

20

Nle

0 2 4 6 80

10

20

30

Asp

0 2 4 6 80

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40

60Glu

0 2 4 6 80

500

1000

1500

2000

Co

ncentr

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n [μM

]

Feed time [h]

3. Results

99

amino acids and the natural counterparts which they replace, we performed a

quantitative amino acid analysis by GC-MS of the inclusion body fractions from 3 h

after feed start (start of induction) onwards. While the concentration profiles of

leucine, isoleucine and methionine in inclusion bodies were similar over the time in

three cultivations, maximal to 0.39 μmol gDCW-1 norleucine at 6 h after feed start was

falsely incorporated into recombinant proteins in 2CR scale-down cultivation, which

is about double as high as the amount in the well mixed STR control cultivation at the

same time, and further increased was detected in 3CR sale-down cultivation (Figure

3.26). For β-methylnorleucine, its concentration in inclusion bodies has similar

increasing trend in the 2CR scale-down cultivation as in the control, both with the

maximal amount of about 0.08 μmol gDCW-1 at 6 h after feed start, and only a slight

increased further in 3CR scale-down cultivation (Figure 3.26). In comparison to

β-methylnorleucine and norleucine, which were already misincorporated in the

control cultivation, no misincorporation of norvaline was found during the reference

cultivation. Surprisingly, even under scale-down cultivations norvaline was triggered

to incorporate into recombinant protein under the value of 0.02 μmol gDCW-1 (Figure

3.26), though leucine-rich recombinant protein contains much more leucine residues

than methionine and isoleucine. The misincorporation rate of these non-canonical

amino acids into proteins in Figure 3.26 seems parallel with the ratio of norvalin /

leucine, β-methylnorleucine / isoleucine and norleucine / methionine in Figure 3.27.

The ratio of norleucine / methionine was observed higher than norvalin / leucine and

β-methylnorleucine / isoleucine. Even in the STR control cultivation, the ratio of

norleucine / methionine was as high as 7. Moreover, this ratio was doubled in 2CR

scale-down cultivation, and further increased in 3CR cultivation (Figure 3.27). This led

to a similar increasing trend for the incorporation of norleucine into proteins (Figure

3.26). Obviously, all these data show that the misincorporation of norleucine was

largest affected by the substrate and dissolved oxygen oscillating conditions among

these three non-canonical amino acids, though there is only small amount of

3. Results

100

methionine residues in the recombinant protein.

Figure 3.26: Shows the concentration of non-canonical amino acids and the natural counterparts

which they replace in inclusion bodies of E. coli W3110M_pSW3 cultivations from the induction

time (3 h), analyzed by GC-MS. Dashed line – induction time, 3 h after feed start point ; white


– 3CR scale-down cultivation. Please remark that the last sample (2h after induction) of the

inclusion bodies in the 3CR scale-down cultivation was taken during the time when the culture

was lysing (see Figure 3.17).

Ile

0 2 4 6 80

20

40

60

80

100

Leu

0 2 4 6 80

50

100

150

200Nva

0 2 4 6 80.0

0.1

0.2

0.3

0.4

0.5

β - Mnl

0 2 4 6 80.0

0.1

0.2

0.3

0.4

0.5

Nle

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0.1

0.2

0.3

0.4

0.5Met

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20

40

60

80

100

Co

ncentr

atio

n [μmolg

DC

W-1

]

Feed time [h]

3. Results

101

Figure 3.27: The ratio of concentration of total free non-canonical amino acids / the natural

counterparts which they replace in the homogenized suspension (cells plus medium) samples

collected from the STR in cultivations with E. coli W3110M_pSW3 from the glucose feed start (0 h).

Dashed line – induction time, 3 h after feed start point; white circles – STR reference cultivation;

grey triangles up – 2CR scale-down cultivation; black squares – 3CR scale-down cultivation.

3.3.5. Flow cytometry analysis

In order to investigate the impact of oscillating conditions on cell physiology in

scale-down cultivations, flow cytometry studies were performed. In the three

cultivations, PI, BOX and Syto13 were applied to stain with cell samples. PI and BOX

staining was used to analyze the cellular viability. When cells lost their membrane

integrity (permeability), PI can enter the cells and has the ability to stain nucleic acids.

BOX cannot pass through a polarized membrane because of its anionic charge.

However when cells lose or partially lose their membrane potential, BOX can

passively diffuse through the membrane of structurally intact and stain with

positively charged proteins or unspecifically to the hydrophobic regions. It is

apparent from Figure 3.28 that the concentration of PI-staining cells is under 1 % and

BOX-staining cells is under 10 % over the whole cultivation time in all three

cultivations, and the profile of PI- and BOX-staining in the three cultivations are quite

similar, showing that the oscillating conditions have no influence on the cell viability.

Syto13 was applied for the purpose of staining the overall cell population or the

Nva / Leu

0 2 4 6 80.0

0.5

1.0

1.5

2.0

β - Mnl / Ile

0 2 4 6 80.0

0.5

1.0

1.5

2.0

Nle / Met

0 2 4 6 80

5

10

15

20

Ratio

[-]

Feed time [h]

3. Results

102

viable cells. Syto13 is a cell permeable dye, which is able to passively diffuse through

the membranes of E. coli cells and stain the nucleic acids DNA, and RNA as well with

lower affinity. Though Figure 3.28C indicates that there is no difference with the

Syto13 staining profile between the three cultivations, a different evolution of the

Syto13 stained cells can be seen from Figure 3.29. Over the feed time in the STR

reference cultivation, the culture was homogeneous except the value at 7 h after

feed start. In difference, two populations were clearly observed at 5 h after feed start

in the 2CR scale-down cultivation, and even earlier in the 3CR scale-down cultivation

at 3 h after feed start. This could be a suggestion of the weak cells which were going

to die, but this was not seen with PI- and BOX-stained cells. This could also be

doublets or some adaption of the cells under stress conditions. These are only initial

results, which have to be confirmed in the further.

Figure 3.28: Flow cytometric analysis (PI-, BOX- and Syto13 staining) of E. coli W3110M_pSW3

cultivations.

A B C

0

1

2

3

4

5

-3 -1.5 0 1.5 3 4.5 6 7.5

PI (

+)

%

Feed time [h]

PI

0

20

40

60

80

100

-3 -1.5 0 1.5 3 4.5 6 7.5

BO

X (

+) %

Feed time [h]

BOX

0

20

40

60

80

100

-3 -1.5 0 1.5 3 4.5 6 7.5

Syto

13

(+

) %

Feed time [h]

Syto13

STR

2CR

3CR

3. Results

103

Figure 3.29: Evolution of the Syto13 stained cells. Samples from the STR module of E. coli

W3110M_pSW3 STR reference, 2CR and 3CR scale-down cultivations. Time means after feed start.

The cross lines are the setting of gate using the negative control.

Figure 3.30: Density plots of the negative (A) and positive (B) control for Syto13 staining. Cells

obtained from the growth phase (1 h before feed start) from the STR, negative control: unstained

cells, positive control: cells heated at 80 for 1 h and stained with Syto13.

0 h 3.1 h 5 h 7 h

SSC

0 h 3 h 5 h 7 h

0 h 3 h

SSC

SSC

STR

2CR

3CR

Syto Syto Syto Syto

Syto Syto Syto Syto

Syto Syto

4. Discussion

104

4. Discussion

4.1. Accumulation of non-canonical amino acids during cultivations

4.1.1. Effect of oscillations

In large-scale fed-batch processes, the feeding with high concentrated glucose leads

to an immediate high glucose uptake and consumption by E. coli. Additionally, due to

insufficient mixing, oxygen limitation arises at the feed zone because of enhanced

respiration of the bacteria. As a result, this leads to gradients of substrate and

dissolved oxygen in large scale (Neubauer and Junne 2010). This problem has been

known for a long time. It is noticeable that, Junne et al. successfully used an

advanced STR-PFR scale-down bioreactor to study the response of Bacillus subtilis on

substrate and dissolved oxygen concentration oscillations in fed-batch cultivations

(Junne et al. 2011). Moreover, Lemoine et al. investigated the response of

Corynebacterium glutamicum on substrate and oxygen supply oscillations not only in

such two-compartment reactor (2CR), but also on this basis newly developed a novel

three-compartment reactor (3CR) by adding an additional non-aerated PFR module

(Lemoine et al. 2015). So, these 2CR and 3CR scale-down bioreactors were also used

in this study.

A study performed with the Bioscope indicated a fast response of E. coli to the high

glucose concentration is a high glycolytic flux, and as a result, pyruvate increased

immediately (De Mey et al. 2010). Under oscillating substrate and oxygen availability

conditions, Xu et al. indicated that the accumulation of pyruvate always occurred (Xu

et al. 1999a). In addition, it was suggested that pyruvate is the starting metabolite for

the formation of non-canonical amino acids norvaline and norleucine (Soini et al.

2008a; Sycheva et al. 2007). In the experiments, a wild-type E. coli K-12 strain was

cultivated aerobically without oxygen down-shift and showed no accumulation of

norvaline. After an oxygen down-shift, norvaline accumulation was detected. It is

4. Discussion

105

assumed, that due to the oxygen limitation the pyruvate concentration reaches a

limit and results in overflow to the formation of non-canonical amino acids (Soini et

al. 2008a). Our results also witnessed this. For E. coli W3110 wild-type strain, it was

observed that about maximal to 0.95 μmol gDcw-1 total free norvaline was

biosynthesized in 2CR scale-down cultivation, which is two-fold as in the well-mixed

control cultivation. The result about the accumulation of norvaline under

inhomogeneities was consistent with previous research. Additionally, under such

oscillating conditions, an elevated amount of β-methylnorleucine was also observed,

which is very interesting since seldom of earlier investigations reported.

4.1.2. Effect of expression of leucine-rich proteins

Two recombinant proteins were involved in this study. Interleukin-2 contains 22

leucine residues (15.0 %) and valine residues (2.7 %) out of 147 total amino acids.

Insulin contains similar leucine amount of residues to interleukin-2. In contrast,

typical E. coli proteins contain on average 8.4 % of leucine and 7.9 % valine

(Neidhardt and Umbarger 1996). This distinction classifies interleukin-2 and insulin as

leucine-rich proteins.

Researching the response of E. coli processes for the expression of leucine-rich

proteins to oscillating conditions in scale-down approaches is quite interesting. Since

as far as we know, this is the first time such investigation has been performed using

recombinant strains producing a target protein. Most attention of earlier researches

have been focused on different species of bacteria like the S. marcescens (Kisumi et

al. 1977a; Kisumi et al. 1977b), genetic knock-out studies in E. coli (Sycheva et al.

2007), expression of recombinant protein in E. coli in homogeneous conditions

(Apostol et al. 1997) and the use of wild-type E. coli strains to study oscillating

condition (Soini et al. 2008a; Soini et al. 2011).

4. Discussion

106

Bogosian et al. hypothesized that inducing a high level synthesis of a leucine-rich

protein was able to lead depression of the enzymes of the leucine biosynthetic

pathway, resulting in accumulation of non-canonical amino acids (Bogosian et al.

1989). Moreover, Sycheval et al. showed that leucine synthesis pathway enzymes

play a very important role in formation of non-canonical amino acids (Sycheva et al.

2007). LeuABCD operon, which encodes the first enzyme of isoleucine pathway, is

regulated by leucine (Burns et al. 1966). A depletion of leucine leads to an increased

expression of leuABCD. And an increased expression of leuABCD operon could cause

to an increased non-canonical amino acids level (Sycheva et al. 2007). However

interestingly, though in our cultivation set-up the accumulation of the non-canonical

amino acids is mainly favored by the induction of a leucine-rich protein, we could not

observe a decrease of the leucine pool after induction in the suspension of three

cultivations of W3110M_pSW3. It even increased in the W3110M_pSW3 well-mixed

STR cultivation and W3110_pCTUT7_His_IL2 2CR system. Furthermore, though in the

W3110_pCTUT7_His_IL2 STR control cultivation the leucine pool was reduced by 30%,

but this in turn did not result in a higher synthesis of either one of the non-canonical

amino acids. Our results are conflict with the observation by Apostol et al. In their

experiment, a leucine-rich protein human hemoglobin was expressed in E. coli, and it

was observed that, with the expression of hemoglobin, leucine concentration

decreased to a steady level, alongside pyruvate pool increased simultaneously,

followed by norvaline accumulation (Apostol et al. 1997). However our results have

to be still verified by further repetitions of the experiments.

Another important factor that has intimate relationship with the formation of

non-canonical amino acids is the activity of AHAS, which is encoded by the largest

group of ilv family. As it can be seen from the compositions of proteins, in typical E.

coli nearly the same amount of leucine and valine is required for cell biosynthesis,

which equals the close biosynthesis pathway. In contrary, the amount of leucine in

4. Discussion

107

interleukin-2 and insulin composition is much higher than the valine content. It

assumed that when expression such leucine-rich protein in E. coli K12 W3110, a

higher leuABCD operon expression initiates a stronger α-ketobutyrate synthesis. This

could lead to valine accumulation, due to a lower valine / leucine ratio in the

interleukin-2 and insulin than in the composition of host cell proteins. Since E. coli

K-12 W3110 strain has a frameshift mutation in ilvG gene, that is to say no function

of AHAS II (Lawther et al. 1981). As a result, when excess valine accumulates in the

cellular environment, it leads to the valine toxicity phenomenon, which is known as

inhibition of leucine and isoleucine product especially in E. coli K-12 strains, even in

the case of leucine / isoleucine starvation (Andersen et al. 2001). However

interestingly, we did not observe the accumulation of valine after induction in the

suspension of W3110_pCTUT7_His_IL2 well-mixed control cultivation and

W3110M_pSW3 scale-down cultivations, and it even decreased in the

W3110_pCTUT7_His_IL2 control cultivation. Meanwhile, though it was observed the

increasing valine pool in W3110_pCTUT7_His_IL2 2CR scale-down cultivation and

W3110M_pSW3 well-mixed reference cultivation, the leucine pool in both not

declined, even increased.

Therefore it seems that the changed metabolic fluxes after induction cause the

formation of these non-canonical amino acids and not the derepression of the leu or

ilv operons. On top to these metabolic changes oscillations as they occur in the

scale-down systems seem to further increase this effect. Moreover in this study, we

want to highlight the fact that the oscillating conditions have more effect on the

accumulation of norleucine than norvaline and β-methylnorleucine. There were

already high and noticeable amount of β-methylnorleucine even in the STR control

cultivation.

4. Discussion

108

4.1.3. Effect of addition of trace elements

In this study, E. coli W3110 and W3110_pCTUT7_His_IL2 strain were cultivated in

EnBase Flo medium with addition of molybdenum, nickel and selenium trace

elements. While the cultivations of W3110M_pSW3 were performed in MSM

medium without these three trace elements.

There were profound increased accumulations of non-canonical amino acids in

W3110M_pSW3 cultivations under oscillating conditions compared to the control

cultivation. In difference, for the scale-down cultivations of W3110_pCTUT7_His_IL2,

the concentrations of total free norvaline and β-methylnorleucine showed no

significant difference to the control cultivation. It could be thought, the addition of

molybdenum, nickel and selenium in the growth medium suppressed non-canonical

amino acids biosynthesis under conditions of limited oxygen and excess glucose, as

reported by Biermann et al. (Biermann et al. 2013). This is because these trace

elements play important roles in the fully functionality and catalysis of formate

hydrogen lyase (FHL) complex that is the most dedicated enzyme in the pyruvate

metabolism in E. coli under anaerobic conditions. Soini and his colleagues showed

that the supplementation of culture medium with these trace elements was able to

prevent formate accumulation in high cell density cultivations (Soini et al. 2008b). So

molybdenum, nickel and selenium lead to a higher activity of the formate hydrogen

lyase (FHL) complex to catalyse pyruvate and release dihydrogen and carbon dioxide.

As seen in the results, the extracellular concentration of formate in the 2CR

cultivation was not higher than the control cultivation. Moreover, there was

increased accumulation of free serine and alanine pool in the 2CR scale-down

cultivation compared to the reference cultivation, showing a strong redirection of

carbon fluxes to serine and alanine. Therefore pyruvate is not accumulated too more

under oscillating conditions than the control, and thus the direct chain elongation

from pyruvate to α-ketobutyrate, which is the precursor of non-canonical amino

4. Discussion

109

acids, would not too more than the control cultivation.

4.2. The favored synthesis pathway of non-canonical amino acids

under different cultivations

From the results of free amino acid concentration in the suspension of

W3110_pCTUT7_His_IL2 strain, it was observed that the amino acids from TCA cycle,

glutamate, glutamine, aspartate and threonine were lower in scale-down cultivations

than in the control, indicating redirection of carbon flux. However the concentrations

of isoleucine and misincorporation of non-caononical amino acids increased.

Generally α-ketobutyrate, as the precursor of isoleucine and non-cannonical amino

acids pathway, was considered originating from threonine pathway (Umbarger 1996).

This concept was contradicted with the accumulation of isoleucine and

non-canonical amino acids. Several earlier studies have presented alternative

isoleucine pathway from pyruvate for other organisms (Risso et al. 2008; Xu et al.

2004) or through knock-out strategies in E. coli (Sycheva et al. 2007). So this makes

sense it is therefore apparent that α-ketobutyrate was derived from accumulation of

pyruvate under oscillating conditions, and non-canonical amino acids were

synthesized as side product of the isoleucine pathway.

Moreover, the data of carbon labeling experiments using the rapid sampling unit –

Bioscope further proved this theory. Under normal conditions, threonine reaches its

maximal portion of incorporation of labeled carbon before norvlaline, norleucine,

β-methylnorleucine and isoleucine. The main carbon source for α-ketobutyrate,

which is the precursor of non-canonical amino acids, is derived from threonine

through the TCA cycle under normal conditions. While under oscillating conditions,

the fast 13C-labeling in isoleucine and non-canonical amino acids, compared to a

tardy 13C-labeling of the intermediates from TCA-cycle including threonine, reveals

4. Discussion

110

changes in metabolic flux distribution within the branched chain amino acids

biosynthesis pathway. Therefore, the main carbon source for α-ketobutyrate cannot

be derived from threonine, but is directly derived from pyruvate though a much

shorter pathway.

4.3. Misincorporation of non-canonical amino acids into proteins

Due to the lower demand of prokaryotic host cells, more and more different types of

recombinant proteins are produced in living E. coli cells in industrial productions. As a

result, additional stress is induced into cells when expressing heterogonous proteins.

In this study, a lower productivity of heterogeneous protein was observed in the first

few hours after induction in both recombinant stains expressing leucine-rich proteins

under oscillating conditions. This was caused by the redirection of carbon flow to

mixed-acid fermentation. An earlier research also showed that the production of

recombinant pre-proinsulin was reduced in recombinant E. coli cultivation under the

oscillating dissolved oxygen tension in a STR-STR two-compartment scale-down

system (Sandoval-Basurto et al. 2005). However, the impacts of oscillating conditions

on the foreign proteins productivity are different, depending on the strain and target

recombinant protein. For example, Bylund et al. reported that 10% increased amount

of the target recombinant protein was found under oscillating of high substrates and

low oxygen concentration conditions (Bylund et al. 2000).

Moreover, besides the quantity, the quality of the proteins was analyzed. The results

showed that the oscillating conditions in scale-down cultivations increased

misincorporation of non-canonical amino acids into recombinant proteins. The

misincorporation of norvaline into interleukin-2 and β-methylnorleucine into insulin

was slightly increased under oscillating conditions. Furthermore, the

misincorporation of norleucine into both proteins was significantly enhanced in

4. Discussion

111

scale-down cultivations. The misincorporation of non-canonical amino acids occurs

over mis-aminoacylation of tRNAs, due to a lowered substrate specificity of the

aminoacyl-tRNA synthetase. Several articles have been published and reported that

this misincorporation of non-canonical amino acids was triggered by the ratio of

accumulation non-canonical amino acids to the corresponding canonical amino acid

(Apostol et al. 1997; Barker and Bruton 1979; Muramatsu et al. 2003). This is verified

in our results. The misincorporation of non-canonical amino acids into proteins

appeared to be paralleled closely by its ratio to corresponding canonical amino acid.

The higher of this ratio, the more non-canonical amino acids incorporate into

proteins. The most remarkable is the misincorporation of norleucine into insulin was

mostly affected by the oscillating conditions, though there are much lower

methionine residues than leucine in insulin. This can also be explained by the ratio of

norleucine / methionine. Because the accumulation of norleucine which is the most

elevated by the heterogeneous conditions, combined with lower amount of free

methionine in the cells with reducing carbon flux to TCA cycle, lead to higher ratio of

norleucine / methionine in scale-down cultivations. Eventually, this caused higher

misincorporation of norleucine into the recombinant proteins. Compared with the

reference cultivation, the misincorporation raised in 2CR cultivation with the ratio of

norleucine / methionine = 15, and further enhanced in 3CR scale-down cultivation

with further elevated norleucine / methionine ratio.

4.4. Cell physiology

In the W3110M_pSW3 cultivations, flow cytometry analysis was applied to

investigate the impact of oscillating conditions on cell physiology in scale-down

cultivations. Though lower quality of insulin with higher misincorporation rate of

non-canonical amino acids into proteins were observed under environmental

oscillating conditions, the W3110M_pSW3 strain showed robust under gradients of

4. Discussion

112

substrate and dissolved oxygen availability. Results of flow cytometry analysis

showed that the oscillating conditions have no significant influence on the cell

viability and cytoplasmic membrane polarity. This is quite surprising and interesting.

It might be expected that PI- and BOX-stained cells should be higher under stress

environment. But this phenomenon is not unique. Our results agreed with earlier

studies. Onyeaka et al. showed lower BOX- and PI-stained E. coli cells under

oscillatory conditions than small-scale well-mixed cultivation, indicating cell viability

not reduced by such oscillations (Onyeaka et al. 2003). Similar result also was

reported by Hewitt et al. (Hewitt et al. 2000). Another flow cytometry analysis

studies on cellular response to inhomogeneities also demonstrated that oscillatory

conditions had no impact on viability of C. glutamicum, even elevated cell

polarisability in 3CR scale-down cultivation (Lemoine et al. 2015). The reason is

thought to be one of a number of interlinked regulatory stress response pathways

was induced when E. coli exposed to unfavorable growth conditions. This is reflected

as the response that the amount of certain intracellular signaling molecules (ppGpp

and cAMP) and the induction of alternative sigma factors increase quickly in E. coli

(Andersson et al. 1996; Teich et al. 1999). As a result, the cell physiological activity

decreased, what is called dormant cells to survive under stressful conditions.

5. Conclusions and Outlook

113

5. Conclusions and Outlook

A higher degree of accumulation and misincorporation of non-canonical amino acids

into recombinant proteins was observed under the oscillating scale-down bioreactors

compared to the homogeneous control cultivation. This suggests that the oscillating

conditions as they typically occur in large-scale bioreactors may be critical for the

production quality. This study provides a solid basis for cell engineering approaches

to overcome such misincorporation challenges of product quality in the future and

allows to estimate, which degree of heterogeneity becomes critical for the process.

Furthermore it is necessary to more closely investigate the mechanisms, which

trigger the formation of these non-canonical amino acids, regarding their formation

even under homogenous conditions in the wild-type strain.

The scale-down models used in this study are able to simulate the feed zone, bulk

zone and starvation zones in industrial large-scale cultivation. Moreover, there exists

several and complex inhomogeneous conditions, the setup of simulators in lab scale

can be optimized in future in order to mimic even closer conditions of the industrial

scale process.

6. Theses

114

6. Theses

1. Oscillating conditions not always lead to a reduction in growth of E. coli.

2. Inhomogeneous cultivation conditions lead to a diminished productivity of

recombinant strain in the first few hours after induction, but the final expression

amount seems not significantly different.

3. It seems that the changed metabolic fluxes after induction cause the formation of

the non-canonical amino acids and not the derepression of the leu or ilv operons.

On top to these metabolic changes oscillations as they occur in the scale-down

systems seem to further increase this effect.

4. The accumulation and misincorporation of non-canonical amino acids, especially

norleucine, into recombinant proteins is affected by oscillating conditions.

5. The addition of molybdenum, nickel and selenium in the growth medium

suppressed non-canonical amino acids biosynthesis under conditions of limited

oxygen and excess glucose.

6. Norleucine was observed misincorporated into proteins with higher rate than

norvaline and β-methylnorleucine.

7. The misincorporation of non-canonical amino acids into proteins appeared to

have high correlation with its ratio to corresponding canonical amino acid.

8. The main carbon source for α-ketobutyrate, which is the precursor of the

considered non-canonical amino acids and isoleucine derived from threonine

under normal conditions, while it is directly derived from pyruvate under

oscillation conditions.

9. Oscillating conditions have no significant influence on the cell viability and

cytoplasmic membrane polarity.

7. References

115

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8. Appendix

125

8. Appendix

8.1. Behavior of E. coli W3110 in STR and 2CR cultivations

Figure 8.1: Total concentration of compounds of the main carbon metabolism in the suspension

(cell plus medium) samples from the STR module of E. coli W3110 wild-type strain cultivations,

analyzed by HPLC. Data are shown from around feed start (0h). White circles – STR reference

cultivation; black triangles up – 2CR scale-down cultivation.

Pyr

0.000

0.005

0.010

0.015

Oxa

-2 0 2 4 6 80.00

0.01

0.02

Mal

0.00

0.02

0.04

For

0.0

0.1

0.2

0.3Suc

0.00

0.01

0.02

0.03

Lac

-2 0 2 4 6 80.00

0.05

0.10

0.15

Concentr

ation [

mm

ol g

DC

W

-1]

Ace

0.00

0.01

0.02

0.03Fum

0.00

0.03

0.06

0.09

Feed time [h]

8. Appendix

126

Figure 8.2: Intracellular concentration of compounds of the main carbon metabolism in STR

module of E. coli W3110 wild-type strain cultivations, calculated from the total concentration

(cells plus medium) minus extracellular concentration of each compound. Data are shown from

around feed start (0h). White circles – STR reference cultivation; black triangles up – 2CR


Pyr

0.000

0.005

0.010

0.015

Oxa

-2 0 2 4 6 80.000

0.005

0.010

0.015

Mal

0.000

0.003

0.006

For

0.00

0.05

0.10

0.15 Suc

0.000

0.005

0.010

Lac

-2 0 2 4 6 80.00

0.03

0.06

0.09

Con

cen

tration

[m

mol g

DC

W

-1]

Ace

0.00

0.02

0.04 Fum

0.00

0.03

0.06

0.09

Feed time [h]

8. Appendix

127


(cell plus medium) samples from the PFR module (PFR with feed addition) of E. coli W3110

wild-type strain 2CR scale-down cultivation, analyzed by HPLC. Data shown are the samples from

time points 0.75 h (white triangles up), 1.75 h (gray triangles down), 3.75 h (dark gray squares)

and 5.75 h (black diamonds) after feed start over the residence time in the PFR.

Pyr

0.00

0.05

0.10

0.15

Oxa

0 30 40 50 60 700.00

0.01

0.02

0.03

Mal

0.00

0.02

0.04

For

0.0

0.1

0.2

0.3Suc

0.00

0.01

0.02

0.03

0.04

Lac

0 30 40 50 60 700.00

0.05

0.10

0.15

Concentr

ation [

mm

ol gD

CW

-1]

Residence time [s]

Ace

0.00

0.03

0.06 Fum

0.00

0.03

0.06

0.09

STRSTR

8. Appendix

128

Figure 8.4: Intracellular concentration of compounds of the main carbon metabolism in PFR

module (PFR with feed addition) of E. coli W3110 wild-type strain 2CR scale-down cultivation,

calculated from the total concentration (cells plus medium) minus extracellular concentration of

each compound. Data shown are the samples from time points 0.75 h (white triangles up), 1.75 h

(gray triangles down), 3.75 h (dark gray squares) and 5.75 h (black diamonds) after feed start

over the residence time in the PFR.

Pyr

0.00

0.03

0.06

0.09

Oxa

0 30 40 50 60 700.00

0.01

0.02

Mal

0.000

0.002

0.004

0.006

For

0.0

0.1

0.2 Suc

0.000

0.005

0.010

0.015

Lac

0 30 40 50 60 700.00

0.02

0.04

Concentr

ation [

mm

ol gD

CW

-1]

Residence time [s]

Ace

0.000

0.005

0.010 Fum

0.00

0.03

0.06

0.09

STRSTR

8. Appendix

129

Figure 8.5: Total free amino acid concentration of the suspension samples (cells plus medium)

over the time in STR module of E. coli W3110 wild-type strain cultivations, analyzed by HPLC. The

time point zero represents feed start. White circles – STR reference cultivation; black triangles –

2CR scale-down cultivation.

Asp

-2 0 2 4 6 80

10

20

30Glu

-2 0 2 4 6 80

100

200

300

400Gln

-2 0 2 4 6 80

5

10

15

Ser

-2 0 2 4 6 80

10

20

30Gly

-2 0 2 4 6 80

100

200

300Ala

-2 0 2 4 6 80

10

20

30

Thr

-2 0 2 4 6 80

20

40

60

Val

-2 0 2 4 6 80

50

100

150

200Nva

-2 0 2 4 6 80.0

0.5

1.0

1.5

Leu

-2 0 2 4 6 80

5

10

15

20Ile

-2 0 2 4 6 80

5

10

15

20

β - Mnl

-2 0 2 4 6 80

1

2

3

Co

ncentr

atio

n [μmolg

DC

W-1

]

Feed time [h]

8. Appendix

130

Figure 8.6: Concentration of intracellular free amino acids over the time in STR module of E. coli

W3110 wild-type strain cultivations, calculated from the concentration of total free amino acids

(cells plus medium) minus extracellular free amino acids. The time point zero represents feed

start. White circles – STR reference cultivation; black triangles – 2CR scale-down cultivation.

Asp

-2 -1 0 1 2 3 4 5 6 7 80

10

20

30Glu

-2 0 2 4 6 80

100

200

300Gln

-2 0 2 4 6 80

5

10

Ser

-2 0 2 4 6 80

10

20

30Gly

-2 0 2 4 6 80

100

200

300Ala

-2 0 2 4 6 80

20

40

Thr

-2 0 2 4 6 80

20

40

60

Nva

-2 0 2 4 6 80.0

0.5

1.0

1.5

Leu

-2 0 2 4 6 80

5

10

15Ile

-2 0 2 4 6 80

10

20

β - Mnl

-2 0 2 4 6 80

1

2

3

Co

ncentr

atio

n [μmolg

DC

W-1

]

Feed time [h]

8. Appendix

131

Figure 8.7: Concentration of extracellular free amino acids over the time in STR module of E. coli

W3110 wild-type strain cultivations, analyzed by HPLC. The time point zero represents feed start.

White circles – STR reference cultivation; black triangles – 2CR scale-down cultivation.

Asp

-2 -1 0 1 2 3 4 5 6 7 80

10

20

30Glu

-2 0 2 4 6 80

50

100

150

200Gln

-2 0 2 4 6 80

20

40

60

Ser

-2 0 2 4 6 80

2

4

6Gly

-2 0 2 4 6 80

10

20

30

40Ala

-2 0 2 4 6 80

5

10

15

20

Thr

-2 0 2 4 6 80.0

0.1

0.2

0.3

0.4Leu

-2 0 2 4 6 80

50

100Ile

-2 0 2 4 6 80

20

40

60

β - Mnl

-2 0 2 4 6 80.0

0.5

1.0

1.5

Co

ncentr

atio

n [μM

]

Feed time [h]

8. Appendix

132

Figure 8.8: Total free amino acids concentration from the suspension samples (cells plus medium)

in PFR module (PFR with feed addition) of E. coli W3110 wild-type strain 2CR scale-down

cultivation, analyzed by HPLC. Data shown are the samples from time points 0.75 h (white

triangles up), 1.75 h (gray triangles down), 3.75 h (dark gray squares) and 5.75 h (black

diamonds) after feed start over the residence time in the PFR.

8.2. Behavior of E. coli W3110_pCTUT7_His_IL2 in STR and 2CR

cultivations

Asp

0

20

40

60

80Glu

0

200

400

600Gln

0

5

10

15

Ser

0

20

40

60Gly

0

200

400

600

Val

0 30 40 50 60 700

50

100

150

Ala

0

20

40

60

Thr

0

50

100

150

Nva

0 30 40 50 60 700

1

2

3

4

5

Leu

0

20

40

60Ile

0

20

40

60

80

β -Mnl

0 30 40 50 60 700

3

6

9

STRSTRSTR

Co

ncentr

atio

n [μmolg

DC

W-1

]

Residence time [s]

8. Appendix

133

Figure 8.9: Intracellular concentration of compounds of the main carbon metabolism in STR

module of E. coli W3110_pCTUT7_His_IL2 cultivations, calculated from the total concentration

(cells plus medium) minus extracellular concentration of each compound. Data are shown after

feed start (0h). Dashed line – induction time, 1 h after feed start point; white circles – STR


Pyr

0.0000

0.0025

0.0050

0.0075 Mal

0.000

0.004

0.008

0.012

For

Concentr

ation [

mm

ol gD

CW

-1]

0.000

0.025

0.050

0.075 Suc

-1 0 1 2 3 4 5 60.000

0.001

0.002

0.003

Lac

-1 0 1 2 3 4 5 60.00

0.02

0.04

0.06 Feed time [h]

Ace

0.0

0.2

0.4

0.6Fum

0.0

0.1

0.2

0.3

Feed time [h]

8. Appendix

134


samples (cells plus medium) from STR module of E. coli W3110_pCTUT7_His_IL2 cultivations,

analyzed by HPLC. Data are shown after feed start (0h). Dashed line – induction time, 1 h after

feed start point; white circles – STR reference cultivation; black triangles up – 2CR scale-down

cultivation.

Pyr

0.000

0.005

0.010Mal

0.00

0.03

0.06

For

0.0

0.1

0.2

0.3Suc

-1 0 1 2 3 4 5 60.00

0.03

0.06

Lac

-1 0 1 2 3 4 5 60.00

0.05

0.10

Concentr

ation [m

mol g

DC

W

-1]

Feed time [h]

Ace

0.0

0.4

0.8

1.2Fum

0.0

0.2

0.4

Feed time [h]

8. Appendix

135

Figure 8.11: Intracellular concentration of compounds of the main carbon metabolism in PFR

module (PFR with feed addition) of E. coli W3110_pCTUT7_His_IL2 2CR scale-down cultivation,

calculated from the total concentration (cells plus medium) minus extracellular concentration of

each compound. White triangles up – 1 h and grey triangles down – 3 h after glucose feed start.

Pyr

0.000

0.004

0.008

For

0.00

0.02

0.04

Lac

0 30 40 50 60 700.00

0.03

0.06

Conce

ntr

ation [

mm

ol gD

CW

-1]

Residence time [s]

Ace

0.0

0.1

0.2

0.3

Mal

0.000

0.003

0.006

Suc

0 30 40 50 60 700.00

0.01

0.02

0.03

Fum

0.00

0.02

0.04

Residence time [s]

STR

STR

8. Appendix

136


samples (cells plus medium) from the PFR module (PFR with feed addition) of E. coli



Pyr

0.000

0.005

0.010

For

0.00

0.07

0.14

0.21

Lac

0 30 40 50 60 700.00

0.06

0.12

0.18

Conce

ntr

atio

n [

mm

ol gD

CW

-1]

Residence time [s]

Ace

0.0

0.3

0.6

0.9

1.2

Mal

0.00

0.02

0.04

Suc

0 30 40 50 60 700.000

0.015

0.030

0.045

Fum

0.0

0.1

0.2

0.3

Residence time [s]

STR

STR

8. Appendix

137

Figure 8.13: Intracellular concentration of free amino acid in STR module of E. coli

W3110_pCTUT7_His_IL2 cultivations from the glucose feed start (0 h), calculated from the

concentration of total free amino acids (cells plus medium) minus extracellular free amino acids.

Dashed line – induction time, 1 h after feed start point; white circles – STR reference cultivation;

black triangles up – 2CR scale-down cultivation.

Asp

-1 0 1 2 3 4 5 60

50

100

150

Glu

-1 0 1 2 3 4 5 60

500

1000

1500

2000Gln

-1 0 1 2 3 4 5 60

2

4

6

8

Ser

-1 0 1 2 3 4 5 60

5

10

15

20

25

30Gly

-1 0 1 2 3 4 5 60

10

20

30

40

50

60Ala

-1 0 1 2 3 4 5 60

20

40

60

80

Thr

-1 0 1 2 3 4 5 60

1

2

3

4

5

6

Nva

-1 0 1 2 3 4 5 60

2

4

6

Leu

-1 0 1 2 3 4 5 60

2

4

6

8

10

12Ile

-1 0 1 2 3 4 5 60

10

20

30

β - Mnl

-1 0 1 2 3 4 5 60

20

40

60

Co

ncentr

atio

n [μmolg

DC

W-1

]

Feed time [h]

8. Appendix

138

Figure 8.14: Extracellular free amino acid concentration over the time in the STR module of E. coli

W3110_pCTUT7_His_IL2 cultivations from the glucose feed start (0 h), analyzed by HPLC. Dashed

line – induction time, 1 h after feed start point; white circles – STR reference cultivation; black

triangles up – 2CR scale-down cultivation.

Asp

0

1000

2000

3000

4000Glu

0

1500

3000

4500 Gln

0 1 2 3 4 5 60

10

20

30

40

50

Ser

0

15

30

Gly

0 1 2 3 4 5 60

10

20

30

40

50

60

70Ala

0 1 2 3 4 5 60

20

40

60

80

Thr

0 1 2 3 4 5 60.0

0.5

1.0

1.5

2.0

2.5

3.0

Nva

0 1 2 3 4 5 60

5

10

15

20

25

30

35

Leu

0 1 2 3 4 5 60

20

40

60

80

100Ile

0 1 2 3 4 5 60

10

20

30

40

50

60

β - Mnl

0 1 2 3 4 5 60

2

4

6

8

10

12

14

16

Co

ncentr

atio

n [μM

]

Feed time [h]

8. Appendix

139

Figure 8.15: Additional total free amino acid concentration in the suspension samples (cells plus

medium) over the residence time of the PFR module (PFR with feed addition) in E. coli



Asp

0 30 40 50 60 700

20

40

60

80Glu

0

100

200

300

400

500

Ser

0

5

10

15

20

Gln

0 30 40 50 60 700

1

2

3

4

5

6

7

Gly

0

5

10

15

20

25

30

35Ala

0

10

20

30

40

50

Thr

0 30 40 50 60 700

1

2

3

4

5

STR

STR STR

Co

ncentr

atio

n [μmolg

DC

W-1

]

Residence time [s]

8. Appendix

140

Figure 8.16: Intracellular free amino acid concentration over the residence time of the PFR

module (PFR with feed addition) in E. coli W3110_pCTUT7_His_IL2 2CR scale-down cultivation,

calculated from the concentration of total free amino acids (cells plus medium) minus

extracellular free amino acids. White triangles up – 1 h and grey triangles down – 3 h after

glucose feed start.

Asp

0

20

40

60

80Glu

0

100

200

300

400

500Gln

0

1

2

3

4

5

6

Ser

0

5

10

15

20

25Gly

0

5

10

15

20

25

30

35

Val

0 30 40 50 60 700

10

20

30

40

50

60

70

Ala

0

20

40

60

Thr

0

1

2

3

4

5

Nva

0 30 40 50 60 700

2

4

6

Leu

0

2

4

6

8

10

12

14 Ile

0

2

4

6

8

β -Mnl

0 30 40 50 60 700.0

0.5

1.0

1.5

2.0

STRSTRSTR

Co

ncentr

atio

n [μmolg

DC

W-1

]

Residence time [s]

8. Appendix

141

Figure 8.17: Extracellular free amino acid concentration over the residence time of the PFR

module (PFR with feed addition) in E. coli W3110_pCTUT7_His_IL2 2CR scale-down cultivation,

analyzed by HPLC. White triangles up – 1 h and grey triangles down – 3 h after glucose feed start.

8.3. Behavior of E. coli W3110M_pSW3 in STR, 2CR and 3CR

cultivations

Asp

0

100

200

300

400

500Glu

0

500

1000

1500

2000

2500Gln

0

10

20

30

Ser

0

5

10

15

20

25

30Gly

0

20

40

60

80

Val

0 30 40 50 60 700

100

200

300

400

Ala

0

20

40

60

80

100

Thr

0

1

2

3

4

Nva

0 30 40 50 60 700

10

20

30

40

Leu

0

20

40

60

80Ile

0

20

40

60

80

β -Mnl

0 30 40 50 60 700

10

20

30

40

STRSTRSTR

Co

ncentr

atio

n [μM

]

Residence time [s]

8. Appendix

142

Figure 8.18: Extracellular concentration of compounds of the main carbon metabolism in PFR1

module (PFR with feed addition) of E. coli W3110M_pSW3 in 2CR scale-down cultivation,


(gray triangles down), 5 h (dark gray squares) and 7 h (black diamonds) after feed start over the


Mal

0.00

0.05

0.10

0.15

For

0.00

0.05

0.10

0.15

Lac

0 30 40 50 60 700.0

0.2

0.4

0.6

0.8

Conce

ntr

ation [

mM

]

Residence time [s]

Ace

0.0

0.2

0.4

0.6

0.8

Fum

0 30 40 50 60 700.00

0.05

0.10

0.15

Glc

Residence time [s]

0 30 40 50 60 70

Concentr

ation [

g L

-1]

0.0

0.5

1.0

1.5

2.0

2.5

A

B

STR

STR

STR

8. Appendix

143

Figure 8.19: Complete concentration of amino acids pool after hydrolysis in the suspension (cells

plus medium) samples of STR module of E. coli W3110M_pSW3 cultivations from the glucose feed

start (0 h), analyzed by GC-MS. Dashed line – induction time, 3 h after feed start point; white


– 3CR scale-down cultivation.

Ala

0 2 4 6 80

200

400

600Gly

0 2 4 6 80

100

200

300

Val

0 2 4 6 80

100

200Ile

0 2 4 6 80

100

200Leu

0 2 4 6 80

200

400

600

800

Nva

0 2 4 6 80.0

0.5

1.0

1.5 β - Mnl

0 2 4 6 80

2

4

Thr

0 2 4 6 80

50

100

Ser

0 2 4 6 80

20

40

60

80

Nle

0 2 4 6 80

2

4

Asp

0 2 4 6 80

100

200

300Glu

0 2 4 6 80

200

400

Co

ncentr

atio

n [μmolg

DC

W-1

]

Feed time [h]

8. Appendix

144

Figure 8.20: Total free amino acid concentration in the suspension samples (cells plus medium)

of STR module of E. coli W3110M_pSW3 cultivations from the glucose feed start (0 h), analyzed by

GC-MS. Dashed line – induction time, 3 h after feed start point; white circles – STR reference

cultivation; grey triangles up – 2CR scale-down cultivation; black squares – 3CR scale-down

Ala

0 2 4 6 80

5

10

15Gly

0 2 4 6 80

1

2

Val

0 2 4 6 80

1

2

Thr

0 2 4 6 80

1

2

3

4

Ser

0 2 4 6 80.0

0.5

1.0

Asp

0 2 4 6 80

5

10

15

Glu

0 2 4 6 80

50

100

150Gln

0 2 4 6 80.0

0.5

1.0

1.5

Lys

0 2 4 6 80.0

0.1

0.2

0.3

0.4

0.5

Ile

0 2 4 6 80

1

2

3Leu

0 2 4 6 80

1

2

3

Nva

0 2 4 6 80

1

2

3 β - Mnl

0 2 4 6 80

1

2

3Nle

0 2 4 6 80

1

2

3

Met

0 2 4 6 80

1

2

3

Co

ncentr

atio

n [μmolg

DC

W-1

]

Feed time [h]

Co

ncentr

atio

n [μmolg

DC

W-1

]

Feed time [h]

8. Appendix

145

cultivation.

Figure 8.21: Total free amino acid concentration of the suspension samples (cells plus medium)

in PFR1 module (PFR with feed addition) of E. coli W3110M_pSW3 in 2CR scale-down cultivation,

analyzed by GC-MS. Data shown are the samples from time points 0 h (white triangles up), 3 h

(grey triangles down), 5 h (dark gray squares) and 7 h (black diamonds) after feed start over the


Ala

0

10

20

30Gly

0.0

0.5

1.0

1.5

2.0

Val

0

2

4

6Ile

0

2

4

6Leu

0

1

2

3

Nva

0 30 40 50 60 700

1

2Nle

0 30 40 50 60 700

1

2 β - Mnl

0 30 40 50 60 700

1

2

Thr

0

1

2

3

Ser

0.0

0.2

0.4

0.6

0.8

1.0

Asp

0

2

4

6Glu

0

50

100

150

STRSTRSTR

Co

ncentr

atio

n [μmolg

DC

W-1

]

Residence time [s]

Curriculum Vitae

146

Curriculum Vitae

Ping Lu (Female)

Place of Birth: Henan, China

Education:

10/2012 – 03/2016 Technische Universität Berlin (Berlin, Germany)

Ph.D student, research in Bioprocess Engineering

09/2009 - 07/ 2012 Zhengzhou University (Henan, China)

Master of science in Biochemistry and Molecular Biology

09/2005 - 06/2009 Zhengzhou University (Henan, China)

Bachelor of science in Biotechnology

Publications (10/2012 – 03/2016):

Li J, Jaitzig J, Lu P, Sussmuth R, Neubauer P. 2015. Scale-up bioprocess development

for production of the antibiotic valinomycin in Escherichia coli based on consistent

fed-batch cultivations. Microbial Cell Factories 14(1):83.

Poster presentation (10/2012 – 03/2016):

Ping Lu, Eva Brand, Christoph Klaue, Sergej Trippel, Christian Reitz, Stefan Junne,

Peter Neubauer. “Cellular responses in large-scale fed-batch bioprocess: Effects of

substrate oscillation on the synthesis of interleukin2 in Escherichia coli”.

Biotechnology day, 16 July, 2015, Berlin, Germany.

Christian Reitz, Ping Lu, Franziska Vera Ebert, Peter Neubauer. “Simulating large-scale

conditions in a scale-down bioreactor: impacts on cell physiology and product quality

of recombinant Escherichia coli”.

Documents

Response of Escherichia coli Processes for the Production ... · Response of Escherichia coli Processes for the Production of Heterologous Inclusion Bodies by ... chemical and food