development of new cultivation strategies to …
TRANSCRIPT
DEVELOPMENT OF NEW
CULTIVATION STRATEGIES TO
ENHANCE HETEROLOGOUS
PROTEIN PRODUCTION IN PICHIA PASTORIS
A thesis submitted to University College London
for the degree of Doctor of Philosophy
By Baolong Wang
December 2018
The Advanced Centre for Biochemical Engineering
Department of Biochemical Engineering
University College London
London
WC1E 6BT
2
Declaration
I, Baolong Wang, confirm that the work presented in this thesis is my own. Where information
has been derived from other sources, I confirm that this has been indicated in the thesis.
Signed:……………………………………………………………………….
Date:…………………………………………………………………………..
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In loving memory of my grandfather
and to my parents and grandmother
4
Acknowledgement
First and foremost, I would like to thank my supervisor, Eli Keshavarz-Moore, for giving me
the opportunity to study under her supervision. Without her guidance, support and
encouragement, it would be impossible for me to move a step in my PhD study.
I also would like to thank my advisor, Darren Nesbeth, for his patience and constructive advices
in development of my project.
I would like to thank the people in the Research and Facilities Team, Dr. Michael Sulu, Dr.
Gareth Mannall and Dr. Brian O’Sulliva, for always being around to solve any problems of the
facilities and provide technical trainings.
I would like to thank Dr. Lourdes Velez Suberbie and Dr. Stefan Woodhouse for helping me
kick off the Pichia fermentation as well as for teaching me very detailed fermentation skills and
making me a qualified bioreactor operator.
I would like to thank all the postdoctoral research associates and PhD candidates in the
Vineyard office, Cheng, Steve, Shaleem, Ashikin, Haiyuan, Darryl, Neha, Folarin and Milena,
Vincent, Maximilian, Pedro, etc. for creating a funny and friendly environment to work in.
I would like to thank my good friends Chuanjie, Hongyu, Haoran, Yang, Yiling, Fuyao,
Zhongdi, Xinyu, Yi, Fang, Qingyu, etc. for accompanying me and driving away my loneliness
in the last four years.
Finally, I would like to thank my parents for giving me endless support and love since I was
born. It is not an easy path since the first day I went to school over twenty years ago. They have
always been behind me and given me everything they have. Without them, it would be
impossible for me to pursue a PhD degree.
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Abstract
Growth of the biopharmaceutical market and advances in host strain engineering have fuelled
the application of Pichia pastoris in recombinant protein production. Production of
recombinant protein in P. pastoris is commonly induced by continuous methanol feeding.
However, methanol induction challenges scale-up of cultivation due to high oxygen
requirement and substantial heat generation. Developing novel induction strategies to minimize
methanol consumption is desirable.
This thesis compared the standard methanol induction with a sorbitol/methanol mixed induction
strategy and developed the induction method at large scale by using oxygen transfer rates (OTR)
as a scale-down criterion. Influences of sorbitol/methanol mixed induction on product recovery
were also studied using scale-down methodology.
Compared to standard methanol induction, substituting 50% (C-mol/C-mol) methanol with
sorbitol improved cell viability from 92.8±0.3% to 97.7±0.1% but reduced product yield from
1.65±0.03 g•L-1 to 1.12±0.07 g•L-1. Oxygen uptake rate was reduced from 241.4±15.0 mmol•L-
1•h-1 to 145.5±4.8 mmol•L-1•h-1 by using the mixed induction. Proteomics study showed that
supernatant from mixed induction contained fewer host cell proteins (72 versus 96) and fewer
types of protease (1 versus 3).
OTR expected in the large scale bioreactors was used as scale-down criterion at one litre. By
measuring oxygen transfer coefficient (kLa) of the small bioreactor, a fermentation process with
OTR of 150 mmol•L-1•h-1 was defined. Standard methanol induction would cause oxygen
depletion and methanol accumulation in the medium. Residual methanol concentration
apparently influenced the cell growth and product expression. The biomass and product yield
reached 108.3 g•L-1 and 1.1 g•L-1 when the residual methanol concentration was below 5 g•L-
1, whereas they were reduced to 75.5 g•L-1 and 0.87 g•L-1 at a higher concentration (5~10 g•L-
1).
Decreasing methanol feeding rate avoided the oxygen depletion. However, the biomass and
product yield were reduced to 92.2 g•L-1 and 0.85 g•L-1. Partially replacing methanol with
sorbitol enhanced the biomass to 130 g•L-1 but the product yield was not enhanced.
An ultra scale-down approach enabled the prediction of cell culture dewatering in pilot and
industrial scale centrifuges. It was found that the cell culture from mixed induction had higher
centrifugal dewatering levels (84.5±3.3% versus 78.1±3.9%), which was likely to be attributed
to the decrease of cell diameter during induction.
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Impact statement
P. pastoris is becoming a popular host for production of heterologous proteins. Using methanol
as the inducer challenges P. pastoris cultivation especially at large scale. This thesis has
established a new induction strategy by using sorbitol as a co-substrate. The strategy has been
shown to improve product purity, process scalability and centrifugal dewatering in the
production of recombinant aprotinin.
In laboratorial research, findings of the project will provide guidance on how to select the
induction method of P. pastoris. The sorbitol/methanol mixed induction can be considered over
methanol induction when 1) the cell culture has low viability, 2) the products are easily
degraded by proteases, 3) a lot of host cell proteins are co-released with the product.
The key findings are also of benefit to the biopharmaceutical industry. Using the mixed
induction strategy will diminish the consumption of pure oxygen and thus reduce the capital
cost of manufacture. Cutting the usage of flammable methanol will make the manufacture safer
and easier to be managed. Since fewer host cell proteins are co-released, it is easier to reduce
the impurities to a suitable level for clinical use. In addition, cell culture from the mixed
induction has higher efficiency of centrifugal dewatering and thus product loss in product
recovery is minimized. Consequently, the manufacturing cost per unit of product will be
reduced.
Last but not least, scale-down methodology was used in product quantification, cell robustness
and centrifugal dewatering study in this project. It can be referred by other people either from
academic or industrial field. Using scale-down method will reduce the cost of materials, time,
capital and labours and improve efficiency of process development.
Overall, outputs of this project have real impact on different levels. The influence has been
expanded by showing the results at international conferences. Currently, two research
manuscripts are being prepared to be published in academic journals.
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Table of content
Declaration ................................................................................................................... 2
Acknowledgement ....................................................................................................... 4
Abstract ........................................................................................................................ 5
Impact statement ......................................................................................................... 6
Table of content ........................................................................................................... 7
List of figures ............................................................................................................. 11
List of tables ............................................................................................................... 14
Nomenclature ............................................................................................................. 15
Greek symbols ........................................................................................................... 17
Abbreviations and symbols ...................................................................................... 18
Chapter 1 Introduction ............................................................................................. 21
1.1 Trends in biopharmaceutical manufacturing ..................................................... 21
1.1.1 Overview of approved therapeutic proteins ............................................... 21
1.1.2 Recognised hosts for biopharmaceutical manufacturing ........................... 22
1.1.3 P. pastoris as an emerging host for biopharmaceutical ............................. 23
1.2 Introduction to P. pastoris expression system .................................................. 26
1.2.1 History of P. pastoris expression system ................................................... 26
1.2.2 Phenotype of P. pastoris strains ................................................................. 26
1.2.3 Promoters used in P. pastoris system ......................................................... 27
1.2.4 Cell engineering of P. pastoris strains ....................................................... 28
1.3 Fed-batch fermentation of P. pastoris ............................................................... 29
1.3.1 Cell culture medium of P. pastoris ............................................................ 29
1.3.2 Fed-batch fermentation of P. pastoris ........................................................ 29
1.3.3 Oxygen unlimited fed-batch fermentation ................................................. 31
1.3.4 Oxygen limited fed-batch fermentation ..................................................... 32
1.4 Mixed induction strategies of P. pastoris .......................................................... 33
8
1.4.1 Metabolism of methanol in P. pastoris ...................................................... 33
1.4.2 Metabolism of mixed carbons in P. pastoris .............................................. 34
1.4.3 Glycerol/methanol mixed induction ........................................................... 35
1.4.4 Sorbitol/methanol mixed induction ............................................................ 36
1.5 Impurities of host cell protein in P. pastoris processing ................................... 40
1.5.1 Control of host cell proteins in biopharmaceutical .................................... 40
1.5.2 Analytical methods of host cell proteins .................................................... 41
1.5.3 Host cell proteins in P. pastoris culture ..................................................... 42
1.5.4 Product degradation in P. pastoris culture ................................................. 42
1.6 Product recovery from P. pastoris culture ........................................................ 43
1.6.1 Typical process of product recovery .......................................................... 43
1.6.2 Types of centrifuge for product recovery ................................................... 44
1.6.3 Depth filtration as an alternative to centrifuge ........................................... 46
1.6.4 Product recovery using single microfiltration ............................................ 46
1.7 Thesis objectives ............................................................................................... 48
Chapter 2 Materials and methods ........................................................................... 50
2.1 Materials ............................................................................................................ 50
2.2 Culture medium of P. pastoris .......................................................................... 50
2.2.1 Buffered complex medium ......................................................................... 50
2.2.2 Basal salts medium ..................................................................................... 50
2.3 P. pastoris strain and working cell bank ........................................................... 50
2.4 Cell culture methods .......................................................................................... 51
2.4.1 Shake flask culture ..................................................................................... 51
2.4.2 Microplate culture ...................................................................................... 51
2.4.3 Infors bioreactor culture ............................................................................. 52
2.5 Prediction of dewatering and clarification ........................................................ 53
2.6 Characterization of oxygen transfer coefficient ................................................ 53
2.7 Cell breakage method ........................................................................................ 54
2.8 Cell robustness study ......................................................................................... 54
2.9 Analytical methods ............................................................................................ 54
2.9.1 Aprotinin measurement .............................................................................. 54
9
2.9.2 Cell density measurement .......................................................................... 56
2.9.3 Determination of methanol and sorbitol ..................................................... 56
2.9.4 DNA quantification .................................................................................... 56
2.9.5 Soluble protein quantification .................................................................... 56
2.9.6 NuPAGE Bis-Tris gel electrophoresis ....................................................... 57
2.9.7 Cell viability measurement ......................................................................... 57
2.9.8 Two-dimensional gel electrophoresis ......................................................... 57
2.9.9 Proteomics assay ........................................................................................ 59
2.9.10 Cell size measurement .............................................................................. 60
2.9.11 Rheology measurement ............................................................................ 60
2.9.12 AOX activity measurement ...................................................................... 60
Chapter 3 Impact of sorbitol/methanol mixed induction on cell growth and product
expression ................................................................................................................... 62
3.1 Introduction ....................................................................................................... 62
3.2 Theoretical considerations ................................................................................. 64
3.2.1 Specific growth rate ................................................................................... 64
3.2.2 Cell respiration ........................................................................................... 64
3.3 Results and discussion ....................................................................................... 65
3.3.1 Quantitative assay of aprotinin ................................................................... 65
3.3.2 Cell culture at small scale ........................................................................... 69
3.3.3 Cell culture at bioreactor scale ................................................................... 73
3.3.4 Impact of mixed induction on product yield and purity ............................. 79
3.4 Conclusion ......................................................................................................... 88
Chapter 4 Development of induction strategies in a bioreactor with limited oxygen
transfer rate ............................................................................................................... 89
4.1 Introduction ....................................................................................................... 89
4.2 Theoretical considerations ................................................................................. 91
4.2.1 Determining OTR of the bioreactor ........................................................... 91
4.2.2 Determining feeding rates of the induction solutions ................................ 92
4.3 Results and discussion ....................................................................................... 92
4.3.1 Measurement of oxygen transfer coefficient .............................................. 92
4.3.2 Induction in an oxygen limited condition .................................................. 95
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4.3.3 Induction in an oxygen unlimited condition ............................................ 101
4.3.4 Measurement of AOX enzyme activity .................................................... 108
4.4 Conclusion ....................................................................................................... 112
Chapter 5 Prediction of cell robustness and centrifugal dewatering using scale-down
methodology ............................................................................................................. 113
5.1 Introduction ..................................................................................................... 113
5.2 Theoretical considerations ............................................................................... 115
5.2.1 Mimic of shear in large scale centrifuges ................................................. 115
5.2.2 Scale down of large scale centrifuges ...................................................... 115
5.2.3 Calculation of clarification and dewatering ............................................. 116
5.3 Results and discussion ..................................................................................... 117
5.3.1 Determination of biomass and product .................................................... 117
5.3.2 Prediction of cell robustness to shear ....................................................... 120
5.3.3 Characterization of cell culture properties ............................................... 122
5.3.4 Prediction of centrifugal dewatering ........................................................ 126
5.4 Conclusion ....................................................................................................... 132
Chapter 6 Conclusions and future work ............................................................... 133
6.1 Conclusions ..................................................................................................... 133
6.2 Future work ..................................................................................................... 136
Chapter 7 References .............................................................................................. 139
Chapter 8 Appendix ................................................................................................ 158
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List of figures
Figure 1.1 (A) Number of therapeutic proteins approved by FDA from 2011 to 2016. (B)
Classes of therapeutic proteins approved by FDA from 2011 to 2016 (Lagassé et al. 2017).
..................................................................................................................................... 22
Figure 1.2 Diagram of carbon feeding (A) and cell growth (B) in P. pastoris fed-batch
fermentation... ............................................................................................................. 30
Figure 1.3 Metabolic pathways of methanol and sorbitol when P. pastoris was induced by pure
methanol or methanol/sorbitol mixture (Gao et al. 2012). .......................................... 34
Figure 1.4 Operating mode of tubular bowl (A), disc type (B) and decanter scroll (C) centrifuge.
Adapted from (Dévay 2013; Tarleton and Wakeman 2016). ...................................... 45
Figure 3.1 Effect of trypsin concentration on reaction rate of the enzymatic assay.. . 66
Figure 3.2 Inhibitory effect of BMMY and BSM mediums on the trypsin catalyzed reaction..
..................................................................................................................................... 67
Figure 3.3 Standard curve between inhibition rate and aprotinin concentration.. ...... 68
Figure 3.4 Study of cell growth on different sorbitol/methanol mixtures.. ................. 70
Figure 3.5 Growth and lysis of P. pastoris cells cultured on medium containing different
carbons.. ...................................................................................................................... 72
Figure 3.6 Comparison of methanol and sorbitol/methanol mixed induction strategies at
bioreactor scale.. .......................................................................................................... 74
Figure 3.7 Comparison of the OUR (A) and CER (B) of methanol and sorbitol/methanol (1:1,
C-mol/C-mol) mixture induced fermentations.. .......................................................... 76
Figure 3.8 Concentration of residual carbons during methanol and mixed induction..77
Figure 3.9 Comparison of the cell viabilities in methanol and sorbitol/methanol (1:1, C-mol/C-
mol) mixed induction strategies.. ................................................................................ 78
Figure 3.10 Comparison of product yields of methanol and sorbitol/methanol (1:1, C-mol/C-
mol) mixture induced fermentations.. ......................................................................... 80
Figure 3.11 Analysis of soluble proteins in supernatant using 2D protein gel.. ......... 83
Figure 3.12 Localization of the HCPs identified from methanol and sorbitol/methanol (1:1, C-
mol/C-mol) mixed induction strategies.. ..................................................................... 85
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Figure 3.13 Distribution of the molecular weights (A) and isoelectric points (B) of HCPs
identified from methanol and sorbitol/methanol (1:1, C-mol/C-mol) mixed induction strategies.
..................................................................................................................................... 87
Figure 4.1 kLa measurement using dynamic method.. ................................................ 94
Figure 4.2 Fermentation profile (A) and growth curve (B) of the OLFB fermentation..96
Figure 4.3 Residual methanol concentration in the OLFB fermentations. .................. 97
Figure 4.4 Change of the cell viability in the OLFB fermentations. ........................... 98
Figure 4.5 Product yields in the OLFB fermentations.. ............................................ 100
Figure 4.6 Fermentation profile (A) and growth curve (B) of P. pastoris in the oxygen unlimited
condition.. .................................................................................................................. 103
Figure 4.7 Residual methanol concentration in the oxygen unlimited condition.. .... 104
Figure 4.8 Cell viabilities in the oxygen unlimited condition.. ................................. 105
Figure 4.9 Product yields in the oxygen unlimited condition.. ................................. 107
Figure 4.10 Activity of AOX enzyme at different concentrations of total protein.. . 110
Figure 4.11 Comparing the activities of AOX enzyme from different induction strategies.. 111
Figure 5.1 Cell growth, viability and product expression of the cell culture used in dewatering
study.. ........................................................................................................................ 119
Figure 5.2 Predicting the cell robustness to shear stress in feeding zoon of the large scale
centrifuges.. ............................................................................................................... 121
Figure 5.3 Cumulative cell size distributions in methanol and sorbitol/methanol (1:1 C-mol/C-
mol) mixed induction.. .............................................................................................. 123
Figure 5.4 Comparing the viscosities of cell cultures in methanol and sorbitol/methanol (1:1 C-
mol/C-mol) mixed induction. .................................................................................... 125
Figure 5.5 Dewatering levels of the cell cultures induced by methanol and sorbitol/methanol
(1:1 C-mol/C-mol) mixture as predicted by the scale-down model of CSA-1 and BTPX305 disc
stack centrifuges.. ...................................................................................................... 128
Figure 5.6 Clarification levels of the cell cultures induced by methanol and sorbitol/methanol
(1:1 C-mol/C-mol) mixture as predicted by a scale down model of CSA-1 and BTPX305 disc
stack centrifuges.. ...................................................................................................... 129
13
Figure 5.7 Cumulative size distribution and dewatering of cell cultures in fermentations
performed in an OTR-limited bioreactor.. ................................................................ 131
Figure 8.1 Detection of glycerol, methanol and sorbitol using UltiMate 3000 HPLC with
Aminex HPX-87h column.. ....................................................................................... 158
Figure 8.2 Correlation between peak area and concentration of methanol (A) and sorbitol (B)..
................................................................................................................................... 159
Figure 8.3 Viscosities of methanol, 0.75 g•ml-1 sorbitol solution and sorbitol/methanol (1:1, C-
mol/C-mol) mixture at the shear rate of 800s-1. ........................................................ 160
Figure 8.4 The impact of biomass concentration and treating time on specific protein release.
................................................................................................................................... 160
Figure 8.5 Correlation between DCW and WCW of P. pastoris .............................. 161
14
List of tables
Table 1.1 Comparing the features CHO, P. pastoris and E. coli protein expression system.
..................................................................................................................................... 25
Table 1.2 Comparison of the P. pastoris phenotypes. ................................................ 27
Table 1.3 A summary of the literatures applying sorbitol as a co-substrate in P. pastoris
induction. ..................................................................................................................... 39
Table 2.1 Components of the reaction mixture in aprotinin quantification. ............... 55
Table 2.2 Voltage and time used in the IPG electro focusing. .................................... 59
Table 2.3 Components of the reaction mixtures in quantification of AOX enzyme. .. 61
Table 3.1 Number of host cell proteins, peptides, proteases and stress related proteins identified
from the methanol and sorbitol/methanol (1:1, C-mol/C-mol) mixed induction strategies. 85
Table 4.1 A summary of the induction strategies used in the bioreactor with an OTR of 150
mmol•L−1•h−1 ............................................................................................................... 90
Table 4.2 Feeding rates of different induction solutions ........................................... 102
Table 5.1 Dimensions of centrifuges used in the dewatering study. ......................... 126
Table 6.1 A summary of the fermentation and centrifugal dewatering investigated in this thesis.
................................................................................................................................... 135
Table 8.1 A summary of names, accession number, molecular weight, isoelectric point,
localization and function of the HCPs identified from the cell cultures induced by methanol or
sorbitol/methanol (1:1, C-mol/C-mol) mixture. ........................................................ 162
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Nomenclature
Symbol Description Units
ATM Standard atmosphere kPa
C Correlation factor -
DCW Dry cell weight g
DCWf Dry cell weight after filtration g
dwr DCWf by WCWf -
FO2 Volumetric fraction of oxygen -
FN2 Volumetric fraction of nitrogen -
FCO2 Volumetric fraction of carbon dioxide -
kLa Oxygen transfer coefficient h-1
m/v Mass by volume g•L-1
n Disc numbers of centrifuge -
N Rotation speed s-1
pH Power of hydrogen -
qp Specific productivity mg•gDCW-1•h-1
Q Liquid flow rate L•h-1
Qg Gas flow rate L•h-1
rmet Carbon fraction of methanol %
R Gas constant J•mol−1•K−1
r Radius of centrifuge rotor m
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Temp Temperature �
V Volume L
v/v Volume by volume -
WCW Wet cell weight g
WCWf Wet cell weight after filtration g
ΔH Enthalpy of combustion kJ•mol−1
17
Greek symbols
ε Energy dissipation rate W•Kg-1
θ half disc angle of centrifuge -
μ specific growth rate h-1
Σ settling area of centrifuge m2
ω angular velocity of centrifuge -
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Abbreviations and symbols
ABTS 2,2'-Azino-bis (3-ethylbenzthiazoline-6-sulfonic acid)
AOX Alcohol oxidase
ATP Adenosine triphosphate
ATPS Aqueous two-phase system
BAPNA Nα-Benzoyl-DL-Arginine-p-Nitroanilide
BCA Bicinchoninic acid
BMGY/BMMY Buffered glycerol/methanol complex medium
BPTI Bovine pancreatic trypsin inhibitor
BSA Bovine serum albumin
BSM Basal salts medium
CFD Computational fluid dynamics
CHO Chinese hamster ovary
CHAPS 3-[(3-Cholamidopropyl) dimethylammonio]-1-propanesulfonate hydrate
CNBr Cyanogen bromide
CQA Critical quality attribute
DCW Dry cell weight
DE-MF Dead end microfiltration
DO Dissolved oxygen
DTT Dithiothreitol
EBA Expanded bed absorption
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ELISA Enzyme-linked immunosorbent assay
EPX1 Extracellular protein X1
ER Endoplasmic reticulum
FDA Food and Drug Administration
HCHO Formaldehyde
HCl Hydrogen chloride
HCPs Host cell proteins
HPLC High-performance liquid chromatography
HRP Horseradish peroxidase
IPG Immobilized pH gradient
LC-MS/MS Liquid chromatography tandem-mass spectrometry
LDS Lithium dodecyl sulfate
MLFB Methanol limited fed-batch
MOPS 3-(N-morpholino) propanesulfonic acid)
MW Molecular weight
NaCl Sodium chloride
NAD+ Nicotinamide adenine dinucleotide
NADH Reduced NAD+
OD Optical density
OLFB Oxygen limited fed-batch
pAOX Promoter of alcohol oxidase
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pGAP Promoter of glyceraldehyde-3-phosphate dehydrogenase gene
pI Isoelectric point
PTMs Post–translational modifications
PTM1 Pichia trace metals
RCT Research Corporation Technologies
rHuEPO Recombinant human erythropoietin
rIFN-α Recombinant Interferon α
scFv Single chain antibody fragment
SCP Single cell protein
TCA Tricarboxylic acid
TE Tris-EDTA
TFF-MF Tangential flow microfiltration
USD Ultra scale-down
WCW Wet cell density
YNB Yeast nitrogen base
YPD Yeast extract peptone dextrose
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Chapter 1 Introduction
1.1 Trends in biopharmaceutical manufacturing
1.1.1 Overview of approved therapeutic proteins
The recombinant protein has become a major class of medicine since the idea of recombinant
DNA was proposed. In the early 1970s, scientists from Genentech successfully synthesized
recombinant human insulin in E. coli and demonstrated the feasibility of recombinant protein
production using recombinant DNA technology (Goldner, 1972). From then, nearly 380
recombinant therapeutic proteins have been developed and approved to treat various clinical
indications and another 1300 recombinant products were undergoing pre-clinical or clinical
studies by 2017 (Usmani et al., 2017). Therapeutic area of these products covers metabolic
disorders, haematological disorders and oncology, etc. As the incidence of cancer soared in
recent years, the number of therapeutic proteins with indications for oncology or hepatology is
expanding. Of the 62 therapeutic proteins approved by US. Food and Drug Administration
(FDA) from 2011 to 2017, 55% of these products are indicated for oncology or hepatology
(Lagassé et al., 2017).
Since recombinant human insulin was first produced, classes of the therapeutic proteins are also
expanding to benefit more therapeutic areas. In the 1980s, most of the approved recombinant
products, such as recombinant insulin, hormones, and interleukin, had relatively simple
structures and were recombinant version of native proteins from humans (Sanchez-Garcia et al.,
2016). With the advances in recombinant protein technology, recombinant proteins with
molecular weight (MW) over 100 kDa and complex post–translational modifications (PTMs)
are occupying a larger proportion of approved products in recent years (Sanchez-Garcia et al.,
2016). Meanwhile, new versions of recombinant protein with better efficacy or bi-functions are
developed using domain fusion technology (Levin et al., 2015, Strohl, 2015). Statistically, 48%
of the approved therapeutic proteins during 2011-2016 are monoclonal antibodies, whereas
coagulation factor, recombinant enzyme and fusion protein are the next three largest classes
(Fig 1.1).
22
Figure 1.1 (A) Number of therapeutic proteins approved by FDA from 2011 to 2016. (B)
Classes of therapeutic proteins approved by FDA from 2011 to 2016 (Lagassé et al. 2017).
As their classes and therapeutic areas expand, recombinant proteins are becoming the dominant
player in pharmaceutical market. Four of the top five best-selling drugs in 2017, which
contributed 48 billion dollars in total, were therapeutic proteins (Philippidis, 2017). According
to a market forecast, the biopharmaceutical market in which therapeutic proteins play a major
role will expand at an annual growth rate of 11% and reach 209 $ billion to 480 $ billion by
2020 (Ecker et al., 2015). Monoclonal antibodies have a larger market share than the other
classes of therapeutic proteins. It is expected over 70 monoclonal antibody drugs will be
approved by 2020 and the annual sales will reach 125 $ billion (Ecker et al., 2015). Growth of
the market is stressing the importance of effective expression systems for therapeutic protein
production.
1.1.2 Recognised hosts for biopharmaceutical manufacturing
Development of host systems has been accelerated by the growth of biopharmaceutical market
and the advances in cell physiology. Over 100 host systems have been approved for therapeutic
protein production by 2011 and the species cover microorganism, plant cells, insect cells,
mammalian cells and transgenetic animals (Rader, 2018). In all of the host systems, bacterial
Escherichia coli (E. coli) and a mammalian cell line, Chinese hamster ovary (CHO) cells, are
most prevalently used in industry nowadays (Thomas Purkarthofer, 2017).
Since it produced the first recombinant protein, E. coli has been recognized as a reliable and
cost-effective approach for therapeutic protein production (Johnson, 1983). E. coli has a
23
relatively small genome sequence and the sequence is readily edited by various molecular
biology tools (Sambrook et al., 1989). Thus, construction of the cell line is easily manipulated.
E. coli has a relative low requirement on nutrients and it can be easily cultured in minimal
medium with low cost. Scale-up of E. coli cultivation is straightforward and the fermentation
conducted over 200,000 litres has been reported (Alford, 2008). Nowadays, E. coli dominates
the production of ~30% recombinant products on the market and these products cover
recombinant insulin, interferons, hormone and antibody fragments (Overton, 2014). The major
drawback of E. coli system is that E. coli has limited ability of protein folding and lacks
endoplasmic reticulum and Golgi apparatus to perform PTMs. Overexpression of recombinant
proteins in E. coli often results in inclusion body and thus refolding is required in downstream.
Besides, E. coli lacks the capability to perform PTMs, such as glycosylation, which are critical
for the activity of some products. Eukaryotic expression systems, such as mammalian CHO
cells, are demanded in that situation.
In the current market, nearly 70% of the approved therapeutic proteins are produced by CHO
cells. CHO system is becoming very popular due to the following advantages: 1) ability to
perform human-like PTMs, 2) fewer endogenous secretory proteins, 3) robust cell growth in
chemical defined medium without serum, 4) relatively low susceptibility to human viruses, 5)
well-established history of the cell line. In 1987, FDA approved the first therapeutic protein
produced by CHO cells, recombinant human tissue plasminogen activator (Spellman et al.,
1989). Since then, CHO cells have been extensively used to produce protein drugs especially
monoclonal antibodies. As described in a review, over 50% of the products approved by FDA
after 2000 are produced by CHO cells (Lagassé et al., 2017). Kim JY and co-workers stated
that CHO cells would be more popular as a tool to produce therapeutic proteins in the near
future (Kim et al., 2012). However, the drawbacks of CHO cell system cannot be ignored. For
instance, cell line development of CHO cell, which usually takes over six months to obtain the
final clonal for production, is time-consuming (Lai et al., 2013). CHO cell cultivation requires
more complex medium than E. coli which increases the cost of goods in manufacture.
Meanwhile, it was reported that some products produced by CHO system had heterogeneous
product profiles (Yang and Butler, 2000). These drawbacks of CHO system call for other
eukaryotic hosts and Pichia pastoris (P. pastoris) is emerging as one of the most competitive
ones.
1.1.3 P. pastoris as an emerging host for biopharmaceutical
In the last few decades, P. pastoris has been well established as an effective host for
heterologous protein production, either in laboratory research or biopharmaceutical industry.
24
By 2005, over 500 heterologous proteins were successfully expressed in P. pastoris (Thor et
al., 2005). According to the statistic from Research Corporation Technologies (RCT), over 70
recombinant proteins produced by P. pastoris have been approved for human use or in late
stage of clinical trials (Ahmad et al., 2014).
As shown in Table.1, P. pastoris combines the advantages of CHO and E. coli systems. Compared to E. coli, P. pastoris has the eukaryotic protein processing machinery and is capable
to perform proper protein folding (Ciarkowska and Jakubowska, 2013). Moreover, P. pastoris
has the capability to secrete recombinant protein into supernatant. In comparison,
homogenization of whole cells is usually required in the processing of E. coli, which generates
numerous host cell proteins (Ciarkowska and Jakubowska, 2013). Besides, P. pastoris
advantages E. coli system by providing glycosylation which is required by some proteins to
maintain the correct structure and biological efficacy (Higgins, 2001).
In contrast to CHO cell, P. pastoris has a relatively low nutrient requirement and cell culture is
easily implemented with cheap minimal medium. Doubling time of P. pastoris is much shorter
than that of CHO cell (2h versus 15h), thus P. pastoris exhibits higher growth rate in
fermentation and offers a faster speed platform for recombinant protein production (Maccani et
al., 2014). Meanwhile, P. pastoris is more robust to hydrodynamic shear and cell philosophy is
less influenced by culture condition, whereas shear stress and heterogeneity of environment
often impose great hurdles in scale-up of CHO cell fermentation (Senger and Karim, 2003).
However, single P. pastoris cell has lower productivity than CHO cell (Maccani et al., 2014),
and thus high density fermentation is required to achieve high titre. Glycosylation formed by P. pastoris is rich in mannose and genetic engineering of glycosylation pathway is required to
produce human like PTMs (Kalidas et al., 2001).
25
CHO P. pastoris E. coli References
Nutrient requirement High Low Low (Kunert and Reinhart, 2016)
Time of cell line
development 6-12 month ~two weeks 2-3 days
(Lai et al., 2013) (Maccani et al.,
2014)
Protein folding capability Yes Yes Very
limited (Thomas Purkarthofer, 2017)
Secretion capability 2-3 mg•g-1 DCW•h-1 <0.01 mg•g-1 DCW•h-1 None (Maccani et al., 2014)
Glycosylation pattern Human-like High mannose None (Kalidas et al., 2001)
Product yield 1-10 g•L-1 1-20 g•L-1 < 1 g•L-1 (Thomas Purkarthofer, 2017, Pybus et
al., 2014)
Cost of Goods High Low Low (Maccani et al., 2014)
Viral risk High Low Low (Thomas Purkarthofer, 2017)
Table 1.1 Comparing the features CHO, P. pastoris and E. coli protein expression system.
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1.2 Introduction to P. pastoris expression system
1.2.1 History of P. pastoris expression system
The cultivation of P. pastoris was initially studied by the Phillips Petroleum Company in the 1970s. The company attempted to use P. pastoris to produce single cell protein (SCP), a substitute for protein rich foods. The attempt was abandoned soon because the oil crisis increased the cost of methanol, a major substrate for P. pastoris cultivation. In the following years, Phillips Petroleum transformed P. pastoris into an efficient system for recombinant protein expression by collaborating with the Salk Institute
Biotechnology/Industrial Associates Inc. (SIBIA) (Macauley�Patrick et al., 2005).
Alcohol oxidase (AOX) gene was cloned from P. pastoris and its promoter was used to drive recombinant protein expression. Expression vectors, strains, and transformation protocols were also developed by SIBIA. Research Corporation Technologies (RCT) patented the P. pastoris expression system in 1993, while most of the commercial P. pastoris expression system kits are supplied by the Invitrogen Corporation (Carlsbad, CA, USA).
1.2.2 Phenotype of P. pastoris strains
In P. pastoris, AOX is the first enzyme in methanol metabolism pathway by which methanol is oxidised to formaldehyde (Harder and Veenhuis, 1989). Based on the genes encoding AOX enzyme, P. pastoris can be classified into three phenotypes, Mut+, Muts, and Mut- (Cregg et al., 1998). In the phenotype of Mut+, AOX enzyme is encoded by two genes, AOX1 and AOX2. AOX1 promoter is much stronger than that of AOX2. Therefore, nearly 85% of AOX enzyme is regulated by AOX1 gene while only 15% by AOX2 gene (Ellis et al., 1985). In phenotype of Muts, AOX1 gene is disrupted and all of the AOX enzyme is regulated by AOX2 gene. Therefore, methanol utilization is much slower in the phenotype of Muts than Mut+. In the phenotype of Mut-, both genes are deleted, and cells cannot use methanol as the carbon source.
Among the three phenotypes, Mut+ is most extensively used as a host of recombinant protein production. Mut+ cell has high cell growth rate and methanol consumption rate in a methanol sufficient condition, reaching 0.08 h-1 and 0.0682 g•gDCW−1•h−1 (Zhang et al., 2000), respectively. High oxygen demand is required in Mut+ P. pastoris fermentation especially at industrial scale since methanol is a high degree reductant (Schilling et al., 2001, Jenzsch et al., 2004). Muts is has slower methanol consumption
27
rate and lower oxygen demand which makes process control easier at large scale cultivation (Cos et al., 2005). Muts has slower growth rate compared to Mut+ (0.015 h-
1 vs 0.043 h-1) which may delay the production process. But its productivity on methanol was even higher (18.2 vs 9.3 mgproduct•gmethanol−1) (Pla et al., 2006). Scarce reports could be found where Mut- was used as the host of recombinant protein production.
Table 1.2 Comparison of the P. pastoris phenotypes.
1.2.3 Promoters used in P. pastoris system
The promoters used in P. pastoris expression system can be classified into two categories: inducible promoters and constitutive promoters, represented by the promoter of AOX1 (pAOX1) and promoter of glyceraldehyde-3-phosphate dehydrogenase gene (pGAP), respectively.
pAOX1 is the most extensively used promoter and is one of the major driving forces that make P. pastoris expression system very popular (Potvin et al., 2012). As mentioned above, AOX1 gene encodes the majority of AOX enzyme. Due to the low affinity between AOX enzyme with oxygen, pAOX1 activity is up-regulated to produce more AOX enzyme when P. pastoris is cultured on solo methanol (Cregg et al., 2000). Amount of AOX can reach up to 30% of total protein. Therefore, pAOX1 can effectively prompt transcription of foreign genes when P. pastoris is cultured with methanol. Besides, inducible pAOX1 provides tight control on protein expression. Transcription of foreign gene is repressed during cell growth on glycerol or glucose, and production is switched on by changing carbon to methanol. It is especially helpful for process control when products are toxic to cells (Liu et al., 2005).
Methanol induction limits the usage of pAOX1 especially at industrial scale (Liu et al., 2005). Methanol is a high degree reductant and the oxidation of one mole methanol consumes 0.8~1.1 mole oxygen (Cunha et al., 2004, Jahic et al., 2003). Therefore,
Phenotype Encoding gene Promoter Inducer
Mut+ AOX1 AOX2 pAOX1 Methanol
Muts AOX2 pAOX2 Methanol
Mut- NA - -
28
oxygen transfer and heat removal often constrain the feeding rate of methanol and cell density in large scale bioreactors (Potgieter et al., 2010b). Meanwhile, methanol is a flammable and hazardous liquid, and storing a large volume of methanol is a major concern to safety. Therefore, other inducible promoters, such as pAOX2, are also used as an alternative to diminish methanol consumption. Although the activity of pAOX2 is much weaker than that of pAOX1, successful recombinant protein expression controlled by pAOX2 has been reported (Mochizuki et al., 2001).
Using constitutive promoters, such as pGAP, can eliminate consumption of methanol. GAP is an enzyme in glycolysis and its promoter has been used to drive recombinant protein expression in P. pastoris without using methanol (Chen et al., 2007, Rupa et al., 2007). Protein expression controlled by pGAP was found to be affected by the carbon source. In a strain expressing recombinant β-lactamase, cells grew on glucose had higher product yield than that grew on glycerol (Waterham et al., 1997). Other constitutive promoters, such as promoter of translation elongation factor 1 (pTEF1), promotor of high affinity glucose transporter (pHGT1), were also reported (Prielhofer et al., 2013, Ahn et al., 2007).
1.2.4 Cell engineering of P. pastoris strains
Cell engineering, mainly glyco-engineering, has been used to improve recombinant protein production in P. pastoris. Glycosylation formed by wide type P. pastoris is rich in mannose. The glycan pattern has high immunogenicity to human and also reduces the in vivo activity of monoclonal antibodies (Bretthauer and Castellino, 1999). Therefore, genetic engineering has been used to reform the glycosylation pathway of P. pastoris to produce human-like glycoproteins. Manipulation of the genetic engineering mainly includes knocking out the enzymes introducing hypermannosylation, eliminating the enzymes for fungal glycosylation and introducing the enzymes for sialylation (Zha, 2012).
The monoclonal antibodies were successfully produced by the glyco-engineered P. pastoris strain with a product yield over 1 g•L-1 (Potgieter et al., 2009). The antibody produced by the glyco-engineered P. pastoris strain had more uniform glycan pattern compared to that produced by mammalian cells. Product yield of the antibody was enhanced to 1.6 g•L-1 by optimizing cultivation condition of the P. pastoris strain (Ye et al., 2011). In another study, specific productivity of antibody was found to be apparently affected by the specific growth rate of the strain (Potgieter et al., 2010b).
29
Product yield of the antibody could be further enhanced by studying its expression kinetics and maintain cell growth at the optimal rate for product formation.
1.3 Fed-batch fermentation of P. pastoris
1.3.1 Cell culture medium of P. pastoris
In most of the publications regarding P. pastoris cultivation, yeast extract peptone dextrose medium (YPD), buffered glycerol/methanol complex medium (BMGY/BMMY) or basal salts medium (BSM) are used. The YPD medium and BMGY/BMMY medium contain all the amino acids necessary for yeast growth and provide easily utilized nutrients for rapid cell growth (Hanko and Rohrer, 2004). However, complex components in both mediums, such as yeast extract and peptone, not only increase capital cost but cause batch to batch variations, which is not preferred by regulatory agencies (Van der Valk et al., 2010). Therefore, YPD and BMGY/BMMY mediums are mainly used in laboratory research, whereas BSM medium is more popular at industrial scale fermentation.
Formulation of the BSM medium was shown in the Section 2.2.2, Chapter 2. In the medium, the nitrogen source is provided by adding ammonium hydroxide during cultivation and carbon sources are from the feeding of glycerol, methanol or sorbitol. Trace elements are normally complemented to the medium before inoculation to improve cells growth and product synthesis (Hélène et al., 2001). BSM medium is cheaper and has more consistent quality compared to YPD and BMGY/BMMY medium. However, it was also reported that cells cultured on BSM medium had to synthesize all metabolic intermediates, which reduced cell growth rate (Matthews et al., 2018). The most critical amino acids for cell growth was found out by screening the individual amino acids in complex medium. By supplementing these critical amino acids in BSM medium, maximum growth rate, biomass and product yield were enhanced.
1.3.2 Fed-batch fermentation of P. pastoris
Currently, fed-batch fermentation is the most applicable technique of P. pastoris cultivation in bioreactors. It is defined in bioprocess that one or more nutrients are fed into bioreactor during fermentation, and biomass or product are not harvested until the end of fermentation (Yamanè and Shimizu, 1984). Fed-batch fermentation is preferred
30
over batch fermentation when cell growth is limited due to the lack of some nutrients. It is thought that fed-batch fermentation is easier to be manipulated and consumes less medium than continuous culture (Lim and Shin, 2013). Therefore, fed-batch fermentation is used in most reports about P. pastoris culture.
Figure 1.2 Diagram of carbon feeding (A) and cell growth (B) in P. pastoris fed-batch fermentation. The fed-batch protocol was recommended by Invitrogen (Invitrogen 2002a). Induction is indicated by the symbol of arrow where glycerol feeding is switched to methanol feeding.
Conventional fed-batch fermentation of P. pastoris contains three phases: a batch phase for cell growth on glycerol, a fed-batch phase for further cell growth on glycerol and a fed-batch phase for product synthesis using methanol. In batch phase, cells are cultured in medium with glycerol. A maximum specific growth rate (μmax) of 0.06 h-1 has been reported after cells adapted to the new environment (Cos et al., 2005). 40 g•L-1 glycerol is recommended to be used and further higher glycerol concentration may inhibit cell growth rate (Cos et al., 2006). After batch glycerol is consumed, fed-batch phase is started by feeding glycerol at a constant or decreasing rate. The specific growth rate in glycerol fed-batch phase is maintained to be lower than that in batch phase to avoid accumulation of toxic by-products. Finally, product synthesis is induced by feeding methanol. In the first few hours of induction, methanol feeding rate is increased stepwise to help cells adapt to methanol metabolism. Then it is maintained at constant until the end of fermentation. Feeding rate of methanol varied depending on strains in previous reports (Stratton et al., 1998, Tolner et al., 2006, Invitrogen, 2002a). Due to
31
the decreased amount of methanol available per biomass, specific growth rate declines over time during constant feeding of methanol. When methanol is fed at the rate recommended by Invitrogen, μmax is as high as 0.05 h-1 in the beginning and then decrease to < 0.01 h-1 (Invitrogen, 2002a). Exponential feeding can be used to maintain cell growth at a constant rate which is optimal product formation (Potgieter et al., 2010b, Schenk et al., 2008, Heyland et al., 2011). While exponential feeding improves product yield, it requires more sophisticated control of feeding in fermentation, and it consumes much time to find the best cell growth rate.
1.3.3 Oxygen unlimited fed-batch fermentation
Oxygen unlimited fed-batch fermentation (OULFB) or methanol limited fed-batch fermentation (MLFB) is a control strategy in which methanol feeding rate is constrained to maintain dissolved oxygen (DO) at an optimal level, normally 20% or 30% of saturation (Singh et al., 2008, Invitrogen, 2002a). OULFB is the most prevailing control strategy in P. pastoris fermentation and it has been scaled up to demonstrated and industrial scales (Liu et al., 2016).
Dissolved oxygen was found to affect the expression of foreign proteins (Cregg et al., 2000, Lee et al., 2003a). Therefore, it is critical to develop optimal methanol feeding strategies and proper DO control in OULFB. Control of DO is commonly achieved through coupling DO values with agitation speed and oxygen fraction in aeration (Goodrick et al., 2001, D'anjou and Daugulis, 2001). Dissolved oxygen in the medium is detected by a sensor and agitation speed gradually raises when dissolved oxygen dropped below the set point. Pure oxygen is mixed into aeration once dissolved oxygen cannot be maintained by the agitation. Besides, Lim HK and co-workers proposed a novel strategy to control dissolved oxygen by coupling it with both methanol feeding rate and oxygen fraction in aeration (Lim et al., 2003). Dissolved oxygen was set at 40%~45% and methanol feeding rate was decreased when DO dropped below the setting value through a feedback control, and vice versa. Compared to the traditional control strategy, the novel DO control strategy improved cell growth and increased volumetric yield of recombinant Guamerin by 40%.
OULFB is not always reliable although it has been successfully implemented in many studies (Lauer et al., 2005). For instance, methanol accumulation inhibits cell growth and viability, and as a result, DO will increase shapely in the circumstance. Rising DO will reduce oxygen supply and cause further methanol accumulation. Moreover,
32
complete exhaustion of methanol often happens in OULFB which is not desirable to maintain pAOX1 activity. It was reported that pAOX1 activity decreased to nearly zero at the end of OULFB fermentation.
1.3.4 Oxygen limited fed-batch fermentation
Although it is preferred to constrain methanol feeding and avoid oxygen limitation in P. pastoris induction, the fermentation with over-fed methanol and depleted oxygen, which is known as oxygen limited fed-batch (OLFB), were also successfully developed (Khatri and Hoffmann, 2006, Charoenrat et al., 2005, Barrigón et al., 2013). OLFB is a fermentation strategy where dissolved oxygen is depleted and methanol is accumulated in the medium.
OLFB was also reported to increase product yield. In one study, while biomass concentration was almost the same in OULFB and OLFB, volumetric product yield of recombinant β-glucosidase in OLFB was enhanced by 16% and percentage of product in total soluble proteins was improved by 64% (Charoenrat et al., 2005). Khatri NK and co-workers further investigated the impact of methanol concentration on product yield in OLFB fermentation (Khatri and Hoffmann, 2006). It was found that methanol concentration apparently affected the product yield of recombinant single chain antibody fragment (scFv). When methanol concentration increased from 0.3% to 3% (v/v), volumetric product yield of scFv increased from 60 mg•L-1 to 350 mg•L-1. However, it seems that methanol concentrations affected cell growth and product yield depending on cell strains. In another OLFB study, both cell growth and formation of
recombinant Rhizopus oryzae lipase were inhibited by 10 g�L-1 of methanol (Barrigón
et al., 2013). Compared to OULFB, both volumetric and specific productivities were enhanced in OLFB when methanol concentration was in the range of 3~10 g•L-1.
Methanol control strategy is critical in OLFB fermentation. Constant methanol concentration was achieved by a feedback control system (Khatri and Hoffmann, 2006). In the system, methanol sensor based on photoelectric plethysmograph was used to detect methanol concentration in the medium. Methanol addition was activated for a dosage time when methanol concentration dropped below the set point. When on-line monitoring of methanol is not available, methanol can be quantified using off-line methods, like high-performance liquid chromatography (HPLC), gas chromatography or enzymatic reaction-based method (Minning et al., 2001, Parpinello and Versari, 2000, Kučera and Sedláček, 2017).
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1.4 Mixed induction strategies of P. pastoris
1.4.1 Metabolism of methanol in P. pastoris
In the first step of methanol metabolism, methanol is oxidized to formaldehyde (HCHO) by AOX enzyme in peroxisome (Eq1.1). Half mole of methanol is consumed and no adenosine triphosphate (ATP) is produced in the initial oxidation. The further formaldehyde flux is divided into two pathways: formaldehyde dissimilatory (energizing) pathway and biomass synthesis pathway (Jahic et al., 2002, Gao et al., 2012). In the formaldehyde dissimilatory pathway, formaldehyde is oxidized to CO2 by NAD+ (nicotinamide adenine dinucleotide) and two mole NADH (reduced NAD+) is formed by oxidizing one mole HCHO (Eq1.2). NADH is further oxidized to NAD+ by O2 and ATP is also produced in the reaction (Eq1.3). In total, one mole formaldehyde consumes one mole O2 and produces two mole ATP in the formaldehyde dissimilatory pathway. In the biomass synthesis pathway, neither oxygen uptake nor ATP production is significant.
CH#OH + 0.5O) = HCHO + H)O Eq1.1
HCHO + 2NAD/ = 2NADH + CO) Eq1.2
O) + 2NADH + 2ADP + 2P1 = 2ATP + 2NAD/ + 2H)O Eq1.3
Flux distribution of formaldehyde varies in different culture conditions. In one study, 57% and 43% of carbon flux were divided into energizing and biomass synthesis pathway, respectively, when a P. pastoris strain producing recombinant Interferon α
(rIFN-α) was cultured at 30� (Gao et al., 2012). When temperature decreased to 20�,
flux distribution in energizing pathway decreased to 35% while distribution in biomass synthesis pathway increased to 65%. Volumetric product yield of IFN-α was significantly enhanced due to the increasing flux distribution in biomass synthesis pathway. Meanwhile, flux distribution also varied on specific growth rates of P.
pastoris cells (Jahic et al., 2002). At μ=0.033 h-1, about 33% of carbon flux was distributed to the biomass synthesis pathway, while it decreased to 20% when μ is maintained at 0.005 h-1. As a result, the oxygen uptake rate increased significantly at the slower growth rate. These studies indicate that flux distribution analysis is an effective approach to help determine culture conditions of P. pastoris.
34
1.4.2 Metabolism of mixed carbons in P. pastoris
Glycerol and sorbitol are utilized by a different pathway from methanol. In P. pastoris, glycerol or sorbitol is initially converted to glyceraldehyde 3-phosphate (GAP) in glycolysis. GAP is further divided into biomass synthesis and tricarboxylic acid (TCA) cycle (Niu et al., 2013). In the TCA cycle, one mole carbon is oxidized by two mole NAD+ and produces two mole NADH and 2/3mol ATP (Eq1.4). Afterwards, two mole NADH is oxidized by one mole O2 and produces two mole ATP (Eq1.5). Metabolism of one mole carbon by the TCA cycle consumes one mole oxygen and produces 2.66mol ATP.
C + 2NAD/ + 23ADP +23P1 = 2NADH + CO) +
23ATP Eq1.4
O) + 2NADH + 2ADP + 2P1 = 2ATP + 2NAD/ + 2H)O Eq1.5
Figure 1.3 Metabolic pathways of methanol and sorbitol when P. pastoris was induced by pure methanol or methanol/sorbitol mixture (Gao et al. 2012).
Adding sorbitol or glycerol as a co-substrate causes shift of methanol flux distribution. In one study, a P. pastoris strain producing recombinant β-galactosidase was cultured on sorbitol/methanol mixture with different ratios in a transient continuous culture (Niu et al., 2013). It was found that methanol flux in energizing pathway decreased after the
35
addition of sorbitol. Energy production was complemented by the increase of sorbitol flux in TCA cycle. Similar shift of methanol flux was observed when sorbitol/methanol mixed induction was used in the fed-batch fermentation (Gao et al., 2012). Methanol flux in energizing pathway reduced by half while flux in biomass synthesis enhanced by 63% compared to culture on sole methanol. A shift of methanol flux distribution was also observed in a strain producing recombinant human erythropoietin (rHuEPO), where the synthetic flux of rHuEPO was enhanced 4.4-fold after sorbitol addition (Çelik et al., 2010). The shift of methanol flux was likely to enhance product yield as reported by several studies (Wang et al., 2010, Çelik et al., 2009).
1.4.3 Glycerol/methanol mixed induction
As a high degree reductant, methanol has a high enthalpy of combustion (−727 kJ•C-mol−1). Considerable heat is generated when P. pastoris is grown on methanol. Glycerol has a lower enthalpy of combustion (−549.5 kJ•C-mol−1) than methanol and using glycerol as co-substrate reduces the heat generation in P. pastoris induction. Since heat removal is one of the major challenges at large scale, glycerol/methanol mixed induction has been developed to increase the scalability of cell culture (Jungo et al., 2007). The impact of mixed induction on cell growth, product expression and oxygen uptake were studied (Zhang et al., 2003, Berrios et al., 2017, Canales et al., 2015).
In the fermentation of a P. pastoris strain expressing recombinant CD40 ligand, glycerol/methanol (1:1, C-mol/C-mol) mixed induction increased cell growth and volumetric product yield by about one fold (McGrew et al., 1997). Product yield of recombinant β-glycoprotein I domain V was also significantly improved when glycerol was used as a co-substrate (Katakura et al., 1998). Transient continuous culture was used to determine the optimal ratio of glycerol and methanol (Jungo et al., 2007). It found that both volumetric and specific productivity kept stable when methanol fraction was kept between 60% and 100% (C-mol/C-mol), while the specific productivity declined when methanol fraction dropped below 60% (C-mol/C-mol). Methanol consumption was decreased by 40% without impairing the product yield.
The major drawback of glycerol/methanol mixed induction is that glycerol has an inhibitory effect on pAOX1. Glycerol addition may inhibit product expression during methanol induction. In one study, product expression was totally inhibited when glycerol feeding rate was higher than 6 mg•g WCW−1•h−1 (Hellwig et al., 2001). Although the product was expressed at a slower glycerol feeding, its volumetric product
36
yield decreased by half compared to solo methanol induction. Due to the inhibitory effect of glycerol, more attempts are seeking to use other non-inhibitory carbons such as sorbitol in mixed induction of P. pastoris.
1.4.4 Sorbitol/methanol mixed induction
Enthalpy of combustion of sorbitol is relatively lower than that of methanol (504.3 kJ•C-mol−1 versus −727 kJ•C-mol−1). Besides, sorbitol does not inhibit the activity of pAOX1 at the concentration as high as 50 g•L−1 (Çelik et al., 2009). Therefore, it has been widely used as a co-substrate of P. pastoris to reduce the drawbacks of methanol induction, such as high oxygen consumption and heat removal.
Strategies of sorbitol/methanol mixed induction can be categorized into batch-wise sorbitol addition and constant sorbitol feeding. Celik E and co-workers showed that sorbitol did not repressed pAOX1 activity with a concentration lower than 50g•L−1
(Çelik et al., 2009). Therefore, sorbitol/methanol mixed induction is easily implemented by batch-wise sorbitol addition. It was observed that adding sorbitol accelerated cell growth and enhanced volumetric product yield by 1.8 folds after 18 h induction compared to sole methanol induction. Constant sorbitol feeding was also used in several studies. In a strain expressing recombinant porcine interferon- α, it was shown that both sorbitol co-feeding and low temperature improved product yield (Gao et al., 2012). Compared to standard methanol induction, product yield was enhanced 10 to 200 folds by using sorbitol/methanol mixed induction. Transient continuous culture was used to determine the optimal ratio of methanol and sorbitol. In a continuous culture where methanol fraction was varied from 0 to 100% (C-mol/C-mol), it was found that comparable volumetric product yield was obtained by maintaining methanol fraction in the range of 45%~100% (C-mol/C-mol) (Niu et al., 2013). When the sorbitol/methanol (45:55, C-mol/C-mol) mixed induction was applied to a fed-batch fermentation, same amount of product was obtained while oxygen uptake was significantly reduced. Besides, it was also reported that sorbitol co-feeding reduced cell mortality from 23.1% to 8.8% in a strain expressing recombinant interferon-α compared to methanol induction (Niu et al., 2013). As a result, product degradation by co-released proteases was significantly reduced.
Compared to glycerol/methanol mixed induction, the major disadvantage of sorbitol/methanol mixed induction is that P. pastoris has a lower sorbitol uptake rate (Valero, 2013), which delays cell growth during induction. Besides, product yield is
37
influenced by sorbitol/methanol mixed induction depending on strains. The product yield was even reduced by the mixed induction in several reports (Woodhouse, 2016, Niu et al., 2013).
38
Mixed induction method Product Impact References
Exponential feeding Rhyzopus oryzae lipase Reduced product yield at 30� (Berrios et al., 2017)
Linear feeding and lower
temperature (20�) Interferon-α
1) Enhanced product yield by 1.3 folds
2) Enhanced product activity by 2.1 folds (Gao et al., 2015)
Automatically regulated
methanol and sorbitol feeding
Porcine circovirus cap
protein
1) DO stayed at stable
2) Product yield was increased by 64% (Ding et al., 2014)
Linear feeding Thermomyces lanuginosus lipase
Increased product yield by 1.4 folds (Fang et al., 2014)
Batch-wise sorbitol addition β-glucosidase Enhanced product yield by 1.4 folds (Batra et al., 2014)
Transient continuous cultures β-galactosidase 1) Comparable product yield
2) O2 uptake was reduced by 30% (Niu et al., 2013)
Linear feeding and lower
temperature (26�) β-mannanases Increased product yield by 1.5 folds (Zhu et al., 2011)
39
Linear constant feeding Interferon-α 1) Enhanced product yield by 1.85-fold
2) Decreased mortality from 23.1% to 8.8% (Wang et al., 2010)
Batch-wise sorbitol addition Growth hormone Maximum product yield at μ = 0.03 h−1 (Çalık et al., 2010)
Batch-wise sorbitol addition Erythropoietin
1) Accelerated cell growth
2) Enhanced product yield by 1.8 folds at
t=18 h
3) Reduced protease by 1.2 folds
4) Eliminated lactic acid accumulation
5) Decreased oxygen uptake by 2 folds
(Çelik et al., 2009)
Batch-wise sorbitol addition Rhizopus oryzae lipase Enhanced product yield by 1.7 folds (Ramón et al., 2007)
Table 1.3 A summary of the literatures applying sorbitol as a co-substrate in P. pastoris induction.
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1.5 Impurities of host cell protein in P. pastoris processing
1.5.1 Control of host cell proteins in biopharmaceutical
Once a recombinant product is produced by host cells, it requires to remove the impurities such
as host cell proteins (HCPs), DNA and lipids. HCPs, a complex mixture of proteins, are one of
major of these impurities that must be strictly controlled. These proteins are encoded by gene-
modified host cells and produced together with recombinant products during cell culture. They
are usually related to normal functions of cells such as growth, proliferation, and apoptosis
(Wang et al., 2015).
HCPs are introduced into the product through different ways during cell culture and product
harvest. Some HCPs are co-secreted into the medium with products in cell culture. Cell lysis
also happens especially in late stage of cell culture. As a result, intracellular proteins will be
released into the medium. When shear sensitive cells, such as mammalian cells, are used as an
expression host, stir agitation and bubble breakage also cause cell lysis and HCPs release (Walls
et al., 2017). During harvest using centrifuges, shear stress of fluid also damages cells and
introduces intracellular proteins into the culture medium (Tait et al., 2012). When the
recombinant product is expressed intracellularly, total break of host cells is required to harvest
products, which results in the release of endogenous proteins.
HCPs level is one of the critical quality attributes (CQA) that is required to be strictly controlled
during biopharmaceutical manufacturing. HCPs are usually removed from products based on
their different properties such as affinity to resins, protein charge, and hydrophobicity
(Bracewell et al., 2015). However, it was also observed that some HCPs and recombinant
products have similar properties or strong interaction with each other and therefore these HCPs
may co-eluted with the products during purification (Levy et al., 2014, Bracewell et al., 2015).
If HCPs are not thoroughly cleared, they may cause potential safety risk even at extremely low
levels in the final product by stimulating immune responses of patients (Doneanu et al., 2012,
Huang et al., 2009). Besides, some HCPs have proteolytic activity and they will degrade the
recombinant product and affect its stability (Wang et al., 2015). According to the regulations
of FDA and European Commission, HCPs level in final product must be controlled less than
100 parts per million (Liu et al., 2012). In order to minimize the risk, it is necessary to reduce
the release of HCPs in cell culture and monitor the clearance of HCPs in downstream.
41
1.5.2 Analytical methods of host cell proteins
Several methods have been developed to visualize, quantify and identify HCPs produced in
biological manufacture. This section will introduce the theory, advantages, and drawbacks of
the methods used in HCPs detection.
Protein electrophoresis is the most commonly used approach to separate and visualize HCPs.
HCPs are separated by a polyacrylamide gel based on their MW, PI or both. And HCP spots on
gel can be visualized by protein staining after the separation. Coomassie Blue, silver staining,
and Sypro Ruby staining are the most commonly used staining reagents (Olson et al., 2007).
0.05-0.1 μg protein per spot can be detected by coomassie blue while silver and Sypro Ruby
staining improve the detection limit to 1-5 ng/spot. Detecting HCPs using protein
electrophoresis is cost-effective and easily implemented. Spots of interest can be spliced from
gel and further analysed by mass spectrometry. The major drawback of this method is that low
abundant proteins are easily overlaid by overexpressed proteins such as the recombinant
product (Wang et al., 2015).
When it comes to quantitatively measure HCPs, enzyme-linked immunosorbent assay (ELISA)
is the standard method used in industry (Wang et al., 2015). In ELISA, anti-HCP antibodies are
firstly embedded on the bottom of microplate and afterwards, HCPs are added and bound. Anti-
HCP antibodies labelled by horseradish peroxidase (HRP) are added and free antibodies are
washed away. Substrate of HRP is finally added and causes a colour change. ELISA provides
a quantitative assay of HCPs with high throughout. However, the accuracy is highly impacted
by the HCPs used to generate anti-HCP antibodies (Wang et al., 2009). When new HCPs are
produced due to process change or fail, they will not be detected by ELISA assay. Besides, the
assay can only measure the total population of HCP, but some high immunogenic HCPs cannot
be separately determined.
Mass spectrometry (MS) based method makes identification of HCPs achievable (Doneanu et
al., 2012). HCPs are firstly separated and stained on protein gel, and each protein spot is spliced
and digested into peptides. Peptides are then separated and analysed by HPLC-MS. In addition,
proteomic method is also developed where peptides from digestion of HCPs mixture are
directly analysed by LC-MS/MS. HCPs can be identified by searching algorithm of peptides
against protein data bank. Although it requires expensive equipment, MS advantages protein
electrophoresis and ELISA by detecting low abundant and high immunogenic HCPs. HCPs
profile from cell culture to protein purification can also be tracked (Zhang et al., 2014).
42
1.5.3 Host cell proteins in P. pastoris culture
HCPs profile is closely related to the expression system and manner that product is produced
(Wang et al., 2009). Compared to CHO and E. coli systems, P. pastoris is featured by secreted
expression of products with low HCPs impurities. P. pastoris has ~7900 genes totally whereas
the gene number is ~30000 in CHO cells (Gibbs et al., 2004, Mattanovich et al., 2009). As a
result, the composition of HCPs in P. pastoris culture is much simpler than that in CHO cell
culture. Meanwhile, recombinant proteins can be secreted into supernatant by P. pastoris cells
and downstream processing does not cause further release of intracellular HCPs. However,
product from E. coli system often form inclusion body and homogenization of whole E. coli in
product recovery releases bulk of intracellular HCPs.
Generally, HCPs produced by P. pastoris can be separated into native secretome and released
intracellular proteins. P. pastoris only secretes a few native proteins into cell culture
(Mattanovich et al., 2009). In a P. pastoris secretome study, it was found that only 20 secreted
proteins were detectable when P. pastoris was grown on glucose. Major of these proteins had
theoretical pI values ranging from 4.0 to 6.2 and MW values less than 70 kDa. Despite the
simple secretome, high concentration of HCPs may be produced due to the high cell density.
As it was estimated, around one gram per litre of HCPs was secreted into the culture medium
in a fed-batch fermentation with 100g•L−1 final dry biomass (Heiss et al., 2013). A 65 kDa
protein, named extracellular protein X 1 (EPX1), was identified as the major secreted HCPs.
Secretion of EPX1 was affected by the fermentation condition, such as temperature, medium
osmolality and substrates. The authors also demonstrated the feasibility of enhancing product
purity by deleting the gene encoding EPX1.
Besides, intracellular proteins also release into the culture medium and contaminate the product
during fermentation especially in late stage of fermentation. It has been shown that methanol
induction caused cell lysis and intracellular protein release (Cregg et al., 2000). In a proteomic
study, 110 proteins were identified from the culture medium after a P. pastoris strain expressing
recombinant vaccine was induced by methanol for 24 h (Huang et al., 2011). While the majority
of the identified HCPs were cell wall related proteins, some intracellular proteins such as
alcohol oxidase were also detected. Besides, it was also found that growing cells at a lower
temperature (25) reduced concentration of co-released HCPs.
1.5.4 Product degradation in P. pastoris culture
Recombinant protein production by P. pastoris is challenged by proteolytic degradation
(Kobayashi et al., 2000, van den Hazel et al., 1996). While high density fermentation improves
43
expression of the desired product, it also increases the concentration of host cell proteases in
the culture medium. P. pastoris cells are stressed by starvation, toxic metabolites of methanol
and formation of products. Considerable proteases are released to culture medium due to high
cell concentration. Product degradation has been reported in several studies (Sinha et al., 2005,
Zhang et al., 2007). In the fermentation of a strain producing recombinant ovine interferon-τ,
proteases were undetectable during cell growth on glycerol but increased significantly after 48
h of methanol induction (Sinha et al., 2005). As a result, proteolytic degradation pattern of the
product was detected in the end of fermentation.
The protease can be divided into three catalogues: cytosolic proteases, vacuolar proteases and
proteases in secretory pathway (Jones, 1991, Zhang et al., 2007). Cytosolic proteases play a
pivotal in cell response to stress and are responsible for degradation of short life and detrimental
proteins (Hilt and Wolf, 1992, Zhang et al., 2007, Jones, 1991). Vacuolar proteases are mainly
involved in cellular proteolysis in yeast especially during cell starvation. Several different
vacuolar proteases have been identified including endoproteinases, carboxypeptidases,
aminopeptidases and dipeptidyl aminopeptidase B (Zhang et al., 2007). Vacuolar proteases
were often identified in culture medium and played a critical role in product degradation.
Finally, proteases in secretory pathway locate at Golgi complexes and plasmatic membrane. In
nature, they function in cutting signal peptide and maturation of protein precursors (Flores et
al., 1999).
Several strategies have been proposed to protect products from proteolytic degradation.
Protease-deficient strain was developed and it found to improve yield and product quality
(Cereghino and Cregg, 2000). Protease activity could be inhibited by running fermentation in
specific conditions. For instance, reducing the temperature from 30 to 23 was found to
improve cellular viability and prevent product degradation during induction (Jahic et al., 2003).
Besides, using sorbitol as a co-substrate reduced proteases release by decreasing cell mortality
during methanol induction (Wang et al., 2010). In addition, adding protease inhibitors in the
culture medium was also an effective way to protect the product (Shi et al., 2003).
1.6 Product recovery from P. pastoris culture
1.6.1 Typical process of product recovery
Product recovery from cell culture is the step after bioreactor production. In this step, cells, cell
debris and solid contaminant are removed and particle free liquid is generated for
chromatography steps (Sampath et al., 2014). A typical process of product recovery contains
44
primary recovery, secondary clarification and virus filtration (Yavorsky et al., 2003). Bulk of
cells, residual cell or cell debris and viruses are clarified from liquid in each step, respectively.
Primary recovery, the first step of clarification, refers to the process of removing bulk of whole
cells and cell debris. Due to the high cell density, harvesting products from P. pastoris culture
is quite challenging. Centrifugation, filtration and depth filtration are the most commonly used
options in primary recovery of P. pastoris culture (Sampath et al., 2014). Besides, expanded
bed absorption (EBA) and aqueous two-phase system (ATPS) are also developed as alternative
options (Dong et al., 2012, Charoenrat et al., 2006).
It is notable that primary recovery removes most of but not all particles. For instance, only
particles larger than the pore will be removed when filtration is applied in primary recovery. In
secondary clarification, liquid obtained from primary recovery is filtered to remove residual
cells, cell debris and any remaining particles that are not removed in primary recovery.
Secondary clarification provides a protection to the downstream by removing any particles that
may foul chromatography resin. One or two depth filtrations are used in secondary clarification.
Different options can be combined in clarification based on cell type, cell density and sample
volume. Maximizing product recovery and clarification is the major driver in selection of these
options. Meanwhile, time cost, process reproducibility and consistency should be considered.
Three clarification strategies, centrifugation followed by depth filtration, centrifugation
followed by filter-aid enhanced depth filtration, and microfiltration were compared in one study
(Wang et al., 2006). It was found all the three approaches achieved high product recovery and
clarification. Processing time was minimized by using centrifugation, but the capital cost was
high and process scale-up was challenging. Using microfiltration simplified the process but
significantly increased the filtration time. Therefore, it is necessary to consider each aspect of
the clarification options.
1.6.2 Types of centrifuge for product recovery
Centrifugation is a process that uses centrifugal force to separate solid and liquid based on their
different densities. P. pastoris cells are relatively large entities and present high sedimentation
velocities. Therefore, centrifuge is most commonly used to recovery P. pastoris culture in
industry. Compared to other approaches, centrifugation is feasible to handle with a large volume
of samples at high speed. The major drawback is that scaling up centrifugation from lab to
industrial scale is difficult to be predicted. Besides, installation and maintenance of centrifuges
are high-cost.
45
Figure 1.4 Operating mode of tubular bowl (A), disc type (B) and decanter scroll (C) centrifuge.
Adapted from (Dévay 2013; Tarleton and Wakeman 2016).
In industry, tubular bowl, disc stack and decanter scroll centrifuge are widely used (Fig 1.4).
Tubular bowl centrifuge has a rapidly rotating bowl that provides a sedimenting acceleration.
Feeding stream enters the bottom of the bowl and the solid is separated out by the centrifugal
force. Disc stack centrifuge is featured by a vertical stack of thin discs. Number of discs ranges
from 50 to 150 depending on dimension of the centrifuge. Feeding stream flows towards axis
of the bowl and fills the spaces between discs. Bowl rotation settles solid to underside of each
bowl, while clarified liquid stays in centre of the bowl and is discharged from top or bottom.
Decanter scroll centrifuge has a rotating bowl and conveyor scroll inside the bowl. Feeding
stream is introduced into the bowl through a pipe. Bowl rotation settles solids to wall of the
bowl and leaves clarified liquid in centre of the bowl. Conveyor scroll rotates in the same
direction at a slower speed and scroll the sediment to the end of the bowl.
Each type of centrifuge has advantages and drawbacks. Tubular bowl centrifuge has a higher
separation force than disc type or decanter scroll centrifuge. However, it has a much lower solid
capacity and solid can only be removed by stopping the centrifuge to flush solid out. Disc stack
46
centrifuge is able to generate liquid with higher clarification, but its installation is more
expensive. Decanter scroll centrifuge advantages over tubular bowl and disc stack centrifuges
by processing fluids with solid concentrations as high as 60%-80%. The major drawback is that
it can only provide an acceleration up to 4000 g.
1.6.3 Depth filtration as an alternative to centrifuge
Depth filtration is a process that particles are separated from liquid by retaining particles with
filter medium. Depth filters have an open-pore structure and structure inside filter is tortuous
and channel-like (Obrien et al., 2012). Pore size of depth filtration varies from 0.1 μm to over
10 μm to retain different cell types. When cell culture flow through a depth filter, cells are
retained in the channels and liquid flows throughout filters. In contrast, cells are only retained
on surface of the filter in microfiltration. Depth filters used in biopharmaceutical are normally
made of cellulose fiber. Each filter is a flat sheet and many filters are assembled together to
form a multistack column (Obrien et al., 2012).
Depth filtration can retain mass particles before being fouled (Shukla and Kandula, 2008), and
thus it can replace centrifuge as an option of primary recovery. In a case study, the centrifuge
was replaced by a disposable depth-filter system in clarification of CHO cell culture (Obrien et
al., 2012). Compared to centrifuge, depth-filter was easier to set up and had relatively low
capital cost. Besides, it eliminated the needs of equipment cleaning and cleaning validation.
Depth filtration was also found an effective option to clarify P. pastoris cell culture (Chandler
and Zydney, 2005). Filters with smaller size were found to provide higher clarification at low
capacity and vice versa.
Depth filtration can be scaled up by keeping a constant flux rate (flow per unit membrane area),
which is much easier than scale-up of centrifuge. Linear scaling of depth filtration from lab
scale to 10, 000 L has been reported (Deakin, 2012). Solid amount that can be retained in a
depth-filter is limited. Depth filtration is not suitable in primary harvest of high density cell
culture (Yavorsky et al., 2003). In that case, centrifugation followed by depth filtration or
microfiltration is more effective.
1.6.4 Product recovery using single microfiltration
Filtration, specifically microfiltration, is a process that cell culture flows through a specific
pore-sized membrane and cells are retained on surface of the membrane (Cheryan, 1998).
Compared to centrifugation, microfiltration provides higher efficiency of solid removal and
over 99.5% of solids can be removed in one step (Yavorsky et al., 2003). Therefore, one step
47
of microfiltration can be robust enough to provide clarified liquid for purification.
Microfiltration removes all partials upper the range of pore size through size exclusion, and
thus it is more resistant to variabilities in feeding stream (Yavorsky et al., 2003).
Dead end (DE-MF) and tangential flow microfiltration (TFF-MF) are two main types of
microfiltration. In DE-MF, feeding fluid is vertical to membrane surface and liquid is pushed
through the membrane (Igunnu and Chen, 2012). Solid tends to build upon membrane surface
of DE-MF and the increasing layer thickness will decline the flux. Thus, stopping filtration to
clean or change the membrane is generally required in DE-MF, which limits its application in
industry (Li and Li, 2015). TFF-MF is faster and more efficient than DE-MF and is more
preferable in industrial use. In TFF-MF, most of the liquid travels tangentially across the
membrane and keeps washing surface of the membrane, thus there is a lower tendency to build
up cake upon membrane surface (Igunnu and Chen, 2012). A single TFF-MF was found robust
to deliver high product recovery and clarification (Wang et al., 2006). It was also combined
with centrifugation to further avoid membrane fouling (Sampath et al., 2014).
TFF-DF was shown effective to harvest product from P. pastoris culture with over 30% (v/v)
cells (Wang et al., 2006, Polez et al., 2016). Comparable clarification was achieved by TFF-DF
and centrifugation followed by depth filtration.
48
1.7 Thesis objectives
Pichia pastoris (P. pastoris) is becoming a popular host for the manufacture of a wide range of
products including recombinant proteins, enzymes and vaccines. Whist high density P. pastoris
fermentation is currently in used, the bioprocessing challenges remain significant especially at
large scale. Methanol induction makes process scale-up difficult due to high oxygen
requirement and substantial heat generation. The productivity of the cell is also low and product
degradation caused by protease release is likely.
In a previous study, a sorbitol/methanol mixed induction strategy was established for a P.
pastoris strain producing recombinant aprotinin. Compared to standard methanol induction, the
mixed induction strategy was shown to efficiently induce product expression and reduce
oxygen consumption and heat generation. However, its impact on product quality attributes and
early downstream processing still remains unclear. Besides, sorbitol/methanol mixed induction
was only studied at small scale cell culture in most of the reports and its performance at large
scale has not been studied.
In this thesis, a sorbitol/methanol mixed induction strategy was investigated to assess its impact
on both upstream and early downstream processing of high density P. pastoris culture. In
addition, oxygen transfer rates (OTR) expected in large scale fermentation was used as the
scale-down criteria at 1 litre to compare the performances of methanol and mixed induction
strategies.
Chapter 1: To overview the current hosts of biopharmaceutical production, and to introduce P. pastoris expression host, fermentation strategies, host cell protein impurities, and product
recovery options.
Chapter 2: To introduce the materials, cell strain, equipment, sample analytical techniques and
experimental methodologies used in this research.
Chapter 3: A sorbitol/methanol mixed induction strategy was applied to P. pastoris
fermentation. A quantification assay of the product was established and the impact of
sorbitol/methanol mixed induction on cell growth, volumetric product yield, cell viability and
HCPs profile were studied.
Chapter 4: In this chapter, sorbitol/methanol mixed induction was applied in a bioreactor that
has comparable OTR with large scale ones. Oxygen transfer coefficient of one litre bioreactor
was characterized. Methanol and sorbitol/methanol mixed induction strategies were compared.
49
Chapter 5: To study the impact of sorbitol/methanol mixed induction on centrifugal dewatering.
Cell properties influencing centrifugal dewatering were studied and the dewatering efficiency
of cell cultures from methanol and sorbitol/methanol mixed induction strategies were compared
by using a scale-down model of disc stack centrifuge.
Chapter 6: To summarize the main conclusions and introduce possible future works.
50
Chapter 2 Materials and methods
2.1 Materials
Methanol and sorbitol were purchased from VWR International Ltd (Lutterworth, UK). All the
other materials were purchased from Sigma-Aldrich Cooperation (Dorset, UK) unless
otherwise specified.
2.2 Culture medium of P. pastoris
2.2.1 Buffered complex medium
Buffered complex medium was prepared by dissolving 20 g peptone, 10 g yeast extract, 13.4 g
yeast nitrogen base (YNB), 1.15 g potassium hydrogen and 5.9 g potassium dihydrogen
phosphate in one litre Milli-Q water. The medium was filtered using Millipore® Stericup™
filtration system (Merck Millipore, Watford, UK) and stored at 4 for further use. Before use,
the medium was supplemented with glycerol or methanol to obtain BMGY and BMMY
medium.
2.2.2 Basal salts medium
Basal salts medium (BSM) recipe developed by Invitrogen™ Thermo Fisher Scientific
(Carlsbad, CA, US) was used. One litre of the medium consisted of 26.7 ml 85% (v/v)
phosphoric acid, 0.93 g calcium sulfate, 18.2 g potassium sulfate, 14.9 g magnesium
sulfate•7H2O, 4.1 g potassium hydroxide, and 40.0 g glycerol. Before sterilization in an
autoclave, the pH was adjusted to 4.0 using 15% (m/v) ammonium hydroxide. Each litre of the
medium was supplemented with 4.35 ml Pichia trace metals (PTM1) solution before use.
PTM1 solution was prepared by dissolving 6.0 g cupric sulfate•5H2O, 0.08 g sodium iodide,
3.0 g manganese sulfate•H2O, 0.2 g sodium molybdate•2H2O, 0.02 g boric acid, 0.5 g cobalt
chloride, 20.0 g zinc chloride, 65.0 g ferrous sulfate•7H2O, 0.2 g biotin and 5.0 ml sulfuric acid
in one litre Milli-Q water. PTM1 solution was sterilized using Millipore® Stericup™ filtration
system and stored at 4 for further use.
2.3 P. pastoris strain and working cell bank
A recombinant strain of P. pastoris Mut+ strain is kindly provided by Fujifilm Diosynth
Biotechnologies (Billingham, UK). The strain has been stably transfected with a plasmid having
51
sequence of aprotinin and the recombinant protein is expressed extracellularly. Aprotinin, also
named as bovine pancreatic trypsin inhibitor (BPTI), consists of 58 amino acid residues and the
residues are arranged in a single chain that containing 3 disulfides. Aprotinin has a MW of 6512
Da and an isoelectric point of 10.5.
10 20 30 40 50
RPDFCLEPPY TGPCKARMIK YFYNIRSRSC EEFIYGGCEA KKNNFEAMED
CMRTCGGA
Seq.1 Amino acid sequence of aprotinin (Uniprot, N.A.).
To generate a working cell bank, frozen cells were thawed and cultured in one litre shake flask
with 150 ml BMGY. The flask was cultured at 30 and agitated at 250 RPM. Optical density
at 600 nm (OD600) of the cell culture was monitored by Ultraspec 500 pro spectrophotometer
(Amersham Bioscience Corp, Little Chalfont, UK) and when it reached 20, cell culture was put
into 15 ml Fisherbrand centrifuge tubes (Thermo Fisher Scientific, Cramlington, UK) and
centrifuged at 4000 RPM for 10 min using an Eppendorf 5810R benchtop centrifuge
(Eppendorf UK, Stevenage, UK). After the supernatant was discarded, cell pellet was re-
suspended using 50% (v/v) sterilized glycerol and aliquoted into 2 ml Eppendorf tubes for long
term storage at -70
2.4 Cell culture methods
2.4.1 Shake flask culture
Cells were grown on different carbons in shake flasks. 50 ml buffered complex medium was
added to 250 ml shake flask and was supplemented with 1% (v/v) glycerol, methanol, sorbitol
or methanol/sorbitol mixture (1:1 C-mol/C-mol). After flasks were inoculated to obtain starting
OD600 of 1.0, they were cultured at 30 and agitated at 250 RPM for 48 h. OD600 of cell culture
was measured every 12h by Ultraspec 500 pro spectrophotometer.
2.4.2 Microplate culture
Prior to cell culture in Corning 24 microplate (Corning Incorporated, Deeside, UK), cells were
initially cultured in one 250 ml shake flask to obtain enough cells. When OD600 of the culture
52
in shake flask reached 20, cells were harvested by centrifuging at 4000 RPM for 10 min using
an Eppendorf 5810R benchtop centrifuge. After the supernatant was discarded, cells were re-
suspended and diluted using buffered complex medium until OD600 reached 1.0. Each well was
pipetted with 2 ml of diluted cell culture and the plate was covered by a gas permeable sealing
film. The plates were cultured at 30 in an 80% (v/v) humidified shaker and agitation speeds
of 250 RPM or 400 RPM were used respectively. OD600 of the culture was measured by
Ultraspec 500 pro spectrophotometer.
2.4.3 Infors bioreactor culture
Fermentation was carried out in Multifors bioreactor (Infors UK Ltd., Reigate, UK) which has
four one-litre vessels. Before the pH probes were installed, they were calibrated using standard
calibration buffers, pH4 and pH7 (PCE Instruments, Southampton, UK), respectively. After the
calibration, each vessel was filled with 550 ml BSM medium and sterilized in an autoclave.
Vessels were put back on bioreactor’s platform and probes of pH and dissolved oxygen were
connected to the bioreactor for at least 6 h before use. When the probe reading stabilized, DO
was calibrated to 100% and pH was adjusted to 5.0 using 15% (m/v) ammonium hydroxide.
Finally, 2.5 ml sterilized PTM1 solution was added to each vessel before inoculation.
To prepare cells for inoculation, cells from the working cell bank were initially cultured in a
one litre flask containing 150 ml complex medium. After incubation in a shaker agitating at 200
RPM for 16 h, cells were sucked into 20 ml syringes and injected into the inoculation port of
the bioreactor to obtain an initial OD600 of 1.0. During the fermentation, pH was maintained at
5.0 using 15% (m/v) ammonium hydroxide. DO was kept at 30% by coupling with changing
agitation speed and oxygen proportion in the inlet. When DO dropped below 30%, the agitation
would increase from 300 RPM to a maximum of 1100 RPM. When the DO dropped below 30%
at the maximum agitation speed, oxygen proportion would increase from 21% to a maximum
of 80%.
Cells initially grew on glycerol in the medium after inoculation. When glycerol was consumed,
indicated by a DO spike, 50% (v/v) glycerol was fed at a rate of 18.0 ml•h-1•L-1 until the
culture’s OD600 reached ~300. After glycerol feeding was stopped, cells were starved for 1 hour
until the thorough consumption of residual glycerol. Induction was switched on after starvation
phase by using pure methanol or sorbitol/methanol mixture (Woodhouse, 2016). In order to
adapt cells to new carbons, methanol or sorbitol/methanol were fed at a rate of 3.6 ml•h-1•L-1
for one hour and the feeding rate was kept at 7.2 ml•h-1•L-1 for two hours afterward. Finally,
feeding rate was increased to 10.8 ml•h-1•L-1 and kept constant until the harvest. Cell culture
53
was harvested through sampling ports and stored at 4 for further use. After probes and gas
lines were disconnected with the bioreactor, remaining cell culture in vessels was killed by
autoclave.
Samples were taken regularly through sampling ports during fermentation. Samples were
pipetted into 1.5 ml Eppendorf tubes and centrifuged at 4000 RPM for 10 min in an Eppendorf
5424R benchtop centrifuge (Eppendorf UK, Stevenage, UK). The supernatant was collected
and stored at -20 for further analysis.
2.5 Prediction of dewatering and clarification
Cell culture was diluted to a volumetric cell fraction of 30% (v/v) using Milli-Q water. Then it
was added in 2 ml Eppendorf tubes or 15ml centrifuge tubes and spun by Eppendorf 5810R
(Eppendorf UK, Stevenage, UK) with the fixed rotor FA 45-30-11and Beckman Coulter Avanti
J-E Centrifuge (Beckman Coulter United Kingdom, High Wycombe, UK) with fixed rotor of
JA-21, respectively. Dewatering and clarification in CSA-1 or BTPX-305 disc stack centrifuges
were predicted by centrifugation in 2 ml and 15 ml tubes. Speed and dimension of centrifuges
were shown in Table 5.2. OD600 of supernatant was measured after centrifugation and
clarification was calculated using Eq 5.5. After supernatant was discarded thoroughly, tubes
with cell pellets were weighed before and after being dried at 100 for 24 h and dewatering
levels were calculated using Eq 5.6.
2.6 Characterization of oxygen transfer coefficient
Oxygen transfer coefficient (kLa) of the Multifors vessels were measured using the dynamic
method (Garcia-Ochoa and Gomez, 2009). DO probe was calibrated between 0% and 100%
using nitrogen and air flow. 700 ml BSM medium was added into each vessel and temperature
was set at 30. Nitrogen flow was initially introduced into the vessels until dissolved oxygen
dropped to 0%. Oxygen was then sparged into the vessels at the rate of 1 L•min-1 and DO
reading was recorded every 5 sec until it reached 100%. kLa values of bioreactor were calculated
at agitation speeds of 300, 500, 800, 1100 RPM, respectively.
54
2.7 Cell breakage method
For scale-down studies, Covaris E220 Focused-ultrasonicator (Covaris, Inc., Woburn, MA,
USA) was used to break yeast cells according to manufacturer’s instruction. Fresh cells were
obtained from vessels and centrifuged at 4000 RPM for 10 min using Eppendorf 5810R
benchtop centrifuge. The supernatant was discarded, and cell pellet was re-suspended and
washed twice with cold 200 mM phosphate buffer. Cells were diluted to the concentration of
20 g•WCW-1, and 1 ml cell culture was loaded in borosilicate glass vials and sonicated for 1200
s. Temperature, duty cycle and cycles per burst were set at 10, 20% and 1000, respectively
(Bláha et al., 2017).
2.8 Cell robustness study
Robustness of P. pastoris cells to mechanical stress was studied using an ultra scale-down (USD)
shear device according to manufacturer’s instruction (Boychyn et al., 2004, Hutchinson et al.,
2006). Briefly, fresh cells were harvested from vessels and diluted to volumetric cell fraction
of 30% (v/v) using Milli-Q water. 20 ml diluted sample was then injected into device’s chamber
using syringe. Once injection port was closed, the rotation was switched on and worked at
speeds of 167, 233, and 300 rps for 20 s. Samples were removed with a clean syringe after
rotation, and the device’s chamber was washed three times using chilled water. Soluble protein
concentration and cell viability in samples were immediately measured.
2.9 Analytical methods
2.9.1 Aprotinin measurement
Sigma-Aldrich provided quantification method of aprotinin in Enzymatic Assay of Aprotinin
(Sigma-Aldrich-a). Absorbance of the following reaction was measured consecutively and
aprotinin was determined by calculating inhibition rate of the reaction.
Nα − Benzoyl − DL − Arginine − p − Nitroanilide56789:;<⎯⎯⎯⎯> Eq 2.1
Nα − Benzoyl − DL − Arginine +p-Nitroaniline
55
200 mM triethanolamine buffer was obtained by preparing 13.6 mg•ml-1 solution of
triethanolamine using Milli-Q water. After pH was adjusted to 7.4 at room temperature with 1
M KOH, the buffer was sterilized using 0.22 μm filter and stored at room temperature. 0.1%
(w/v) Nα-Benzoyl-DL-Arginine-p-Nitroanilide (BAPNA) solution was obtained by preparing
1.0 mg•ml-1 BAPNA solution in Milli-Q water. The solution was prepared immediately before
use. Trypsin enzyme solution was obtained by dissolving enzyme into 1 mM hydrogen chloride
(HCl). Standard aprotinin solution was prepared immediately before use by dissolving aprotinin
in 0.9% (m/v) sodium chloride (NaCl) solution. The following reagents were pipetted into
Corning 96 wells plate with flat bottom (Corning Incorporated, Deeside, UK).
After
the
mixture
was
mixed
well in a
Thermomix, 100 μl 0.1% (w/v) BAPNA solution was added and mixed by pipette. Absorbance
at 405 nm was
measured consecutively every two minutes using a Tecan Microplate reader (Tecan Group Ltd.,
Männedorf, Switzerland).
Inhibition % was calculated by:
Inhibition% = 100 •∆405nm/minUninhibited − ∆405nm/min Inhibited∆405nm/minUninhibited − ∆405nm/minBlank Eq 2.2
Where ∆405 nm/min is the increasing rate of mixture’s absorbance at 405 nm.
Uninhibited Inhibited
Blank (μl) Control (μl) Tested sample (μl)
200 nm Triethanolamine 160 160 160
1 mM HCl 20 - -
Trypsin solution - 20 20
0.85% (v/v) NaCl 20 20 -
Inhibitor - - 20
Table 2.1 Components of the reaction mixture in aprotinin quantification.
56
2.9.2 Cell density measurement
OD600, wet cell density (WCW) and dry cell weight (DCW) were measured during fermentation.
Samples were diluted properly using Milli-Q water and loaded into 1 ml cuvette for absorbance
measurement by Ultraspec 500 pro spectrophotometer. For cell density measurement, weights
of blank 1.5ml Eppendorf tubes were measured before sample loading. 1ml samples were
pipetted into tubes and centrifuged at 4000 RPM for 10min using an Eppendorf 5424R benchtop
centrifuge. Tubes with wet cells were weighed again after supernatant was removed. Dry cell
weight was measured by weighing the remaining solids after wet cells were dried at 100 for
24 h.
2.9.3 Determination of methanol and sorbitol
Methanol and sorbitol concentrations were determined using UltiMate 3000 HPLC (Thermo
Fisher Scientific, Cramlington, UK) with Aminex HPX-87 column (Bio-rad Laboratories In.,
Watford, UK). The column was balanced with 0.5% (v/v) trifluoroacetic acid at constant flow
rate of 0.6 ml•h-1 before use. After 20 μl samples were loaded through the column for 30 min,
the output was detected by Thermo Scientific™ RefractoMax 520 Refractive Index Detector
(Thermo Fisher Scientific, Cramlington, UK) at 55 (Parpinello and Versari, 2000).
2.9.4 DNA quantification
DNA concentration in the supernatant was quantified using Quant-iT™ PicoGreen dsDNA
Reagent (Thermo Fisher Scientific, Cramlington, UK). Samples were diluted properly using
TE buffer (10 mM Tris-HCl, 1 mM EDTA, pH 7.5). 100 μl diluted samples and 100 μl diluted
Quant-iT™ PicoGreen® Reagent were pipetted into a 96 wells plate. After being incubated at
room temperature for 5 min, fluorescence in the reaction mixture was measured by a microplate
reader (excitation ~480 nm, emission ~520 nm). Standard Lambda DNA provided in the kit
was used to build the standard curve.
2.9.5 Soluble protein quantification
Soluble protein concentration was measured using bicinchoninic acid (BCA) assay. Pierce BCA
Protein Assay Kit and standard bovine serum albumin (BSA) were purchased from Thermo
Fisher Scientific. 0.13 mg•ml-1, 0.25 mg•ml-1, 0.50 mg•ml-1 and 1.0 mg•ml-1 of BSA standard
solution were prepared, and samples were diluted using Milli-Q water. In a 96 wells plate with
flat bottom, each well was firstly pipetted with 20 μl BSA standard protein or sample and then
57
added with 200 μl BCA solution. After the plate was incubated at 37 for 10min, absorbance
was measured at 562 nm by a microplate reader.
2.9.6 NuPAGE Bis-Tris gel electrophoresis
Frozen sample was thawed at room temperature and centrifuged at 15000 RPM for 10 min to
remove precipitation. Supernatant was mixed with 4•Lithium Dodecyl Sulfate (LDS) sample
loading buffer (Thermo Fisher Scientific, Cramlington, UK) and heated at 100 for 10 min.
Samples were centrifuged at 15000 RPM for 10 min to remove precipitation. The 4–12%
gradient NuPage SDS Novex precast gel (Invitrogen, Paisley, UK) was set up in gel tank and
immersed into 3-(N-morpholino) propanesulfonic acid) (MOPS) buffer. 5 μl Mark12TM
Unstained Standard (Thermo Fisher Scientific, Cramlington, UK) or 10 μl supernatant was
loaded into each well of the gel. Electrophoresis was performed at a constant voltage of 200 V
for 35 min using Bio-rad Powerpac Basic (Bio-rad, Watford, UK). After the electrophoresis,
gel was immersed into Coomassie Brilliant Blue (Sigma-Aldrich, Poole, UK) and rotated
overnight for staining. Staining solution was discarded, and the gel was washed twice using
Milli-Q water. It was destained by a buffer containing 10% (v/v) acetic acid and 10% (v/v)
ethanol. Protein bands were visualized using Amersham Imager 600 after gel destaining (GE
Life Sciences, Little Chalfont, UK).
2.9.7 Cell viability measurement
Cellular viability was measured using BD Accuri™ C6 cytometer (BD Biosciences, Oxford,
UK). Fresh cell samples were diluted to a cell concentration with OD600 of 0.5 using 0.9% (w/v)
NaCl. Then 7 μl propidium iodide was added into 1ml sample and the sample was incubated in
dark for 10min. Percentage of cells stained by propidium iodide was measured by flow
cytometer. Threshold setting was set at 10000 to exclude cell debris from data acquisition.
Staining was measured through the FL3 (red channel) filter and 5•104 cells were analysed per
sample.
2.9.8 Two-dimensional gel electrophoresis
Cell culture medium was changed by an ultrafilter with a cut-off of 3000 Da. Soluble proteins
were precipitated using 13% (v/v) trichloroacetic acid (TCA) in cold acetone. Briefly, 0.1 ml
sample and 1 ml TCA solution were pipetted into a 1.5 ml Eppendorf tube and mixed well using
a thermomixer. Dithiothreitol (DTT) was added to a final concentration of 20 mM and the
mixture was kept at -20 overnight. The mixture was centrifuged at 15000 RPM for 10 min to
58
remove TCA solution and collect precipitation. In order to thoroughly remove residual TCA,
the precipitate was washed three times using cold acetone. After acetone was discarded by
centrifugation, the precipitate was dried at room temperature until residual acetone volatilized.
Protein pellet was re-dissolved using a rehydration buffer containing 8 M urea, 2% (v/v) 3-[(3-
Cholamidopropyl) dimethylammonio]-1-propanesulfonate hydrate (CHAPS), 0.002% (v/v)
bromophenol blue and 0.5% (v/v) ampholytes. Protein concentration was measured using BCA
method as described in the Section 2.9.5, Chapter 2. 200 μg protein was pipetted into a new
tube and added up to 150 μl using the rehydration buffer.
The first dimensional gel electrophoresis was done using the IPGphor Isoelectric Focusing
System (Stockholm, Sweden). Immobilized pH gradient (IPG) strip was put in the cassette and
protein sample was added in to immerse the gel. Finally, 300 μl sealing oil was added to cover
the IPG strip. The cassette was put on IPGphor and electro focusing was performed using the
voltage as shown in the table below.
59
Voltage Time
0 V 12 h
200 V 20 min
450 V 15 min
750 V 15 min
2000 V 120 min
Table 2.2 Voltage and time used in IPG electro focusing.
After the electro focusing, IPG strip was incubated in 5 ml 1•NuPAGE LDL buffer with 50 mM
DTT for 15 min. Strip was taken out from DTT solution and washed twice using Milli-Q water.
It was then incubated in 1•NuPAGE LDL buffer containing 125 mM iodoacetamide for 15 min
to remove residual DTT.
NuPAGE 4-12% Bis-Tris ZOOM Protein Gel was set up in gel tank and immersed in MOPS
buffer. IPG strip was loaded to the ZOOM gel and 500 μl 0.5% (m/v) agarose was added in the
well to seal the gap between IPG strip and ZOOM gel. After the agarose solidified, gel
electrophoresis was performed at 200 V for 40 min using Bio-rad Powerpac Basic. After the
electrophoresis, gel was stained by coomassie blue overnight. Gel was destained by washing in
a buffer with 10% (v/v) methanol and 7% (v/v) acetic acid for 30 min. Finally, protein dots
were visualized by Amersham Image 600.
2.9.9 Proteomics assay
Host cell proteins in the supernatant were identified using a method reported before (Kumar et
al., 2016). Supernatant containing 5 μg soluble proteins was loaded to a 20% SDS-PAGE gel
and electrophoresis was run until loading samples were concentrated. Gel slice that contained
all proteins were cut down and proteins in gel were digested into peptides by acid cyanogen
bromide (CNBr) in 70% (v/v) formic acid. After peptide mixture was suspended in 0.1% (v/v)
formic acid, it was analysed by electrospray liquid chromatography-mass spectrometry (LC-
MS/MS) (Surveyor, ThermoFinnigan, CA). Spectrum was processed using Proteome
Discoverer (Thermo Fisher Scientific Inc.) and searched against Uniprot database using Mascot
search algorithm (Matrix Science, London, UK). Protein identification was conducted using
Scaffold (Proteome Software Inc., Portland, OR, USA). Protein identification was considered
60
acceptable if threshold could be established over 95% probability and the protein contained at
least one identified unique peptide.
2.9.10 Cell size measurement
Cell size was measured by Malvern Mastersizer 3000 laser diffraction particle size analyser
(Malvern Panalytical Ltd, Malvern, UK) according to the manufacturer’s instruction. After
initialization and background measurement, samples were slowly dropped into the Hydro MV
liquid dispenser until light obscuration reached 15%. Each sample was measured 5 times to
obtain an average value for particle size. Water and P. pastoris cell culture were assumed to
have refractive indexes of 1.33 and 1.56, respectively.
2.9.11 Rheology measurement
Viscosity of sample was measured by Malvern Kinexus rheometer (Malvern Panalytical Ltd,
Malvern, UK) according to the user manual. After initialization of the rheometer and
temperature setting at 30, 1.2ml sample was loaded on the sample plate. Sample’s viscosity
in terms of Pa•s-1 between shear rates of 100 s-1 to 1000 s-1 was measured automatically.
2.9.12 AOX activity measurement
AOX activity was measured using a method recommended by Sigma-Aldrich in Enzymatic
Assay of Alcohol Oxidase (Sigma-Aldrich-b). Absorbance of the following reaction was
measured consecutively and AOX activity was determined from the reaction rate.
Methanol + ORSTU<⎯> Formaldehyde + HROR Eq 2.3
HROR + ABTSZT[<⎯> 2HRO + ABTS(oxidized) Eq 2.4
100nM phosphate buffer was obtained by preparing 13.6 mg•ml-1 solution of KH2PO4. Buffer
pH was calibrated to 7.5 with 1M KOH at room temperature. 2 mM 2,2’-Azino-bis (3-
ethylbenzthiazoline-6-sulfonic acid) (ABTS) solution was obtained by preparing 1mg•ml-1
solution of ABTS in phosphate buffer. 1.0% (v/v) methanol solution was obtained by preparing
1.0 mg•ml-1 HPLC grade methanol using Milli-Q water. 250 U•ml-1 peroxidase solution was
prepared immediately before use.
The following reagents were added into a 96 wells plate:
61
Reagent Test samples (μl) Blank (μl)
ABTS solution 280 280
Peroxidase solution 1 1
Phosphate buffer - 10
Methanol solution 10 10
Table 2.3 Components of the reaction mixtures in quantification of AOX enzyme.
After the reagents were mixed well, 10 μl test sample was added to each well except the blank
well. Absorbance at 405 nm was measured consecutively every two minutes. Three replicates
were used for each sample and blank. Activity was defined as:
Units/mg =(∆405nm/minTest − ∆405nm/minBlank) ∗ 301 ∗ df
36.8 ∗ 10 Eq 2.5
Where ∆405 nm/min is the increasing rate of mixture’s absorbance at 405 nm. Constant of 301
refers to the total reaction volume (in microlitres), df is the dilution factor, constant of 36.8
refers to extinction coefficient of ABTS at 405 nm, constant of 10 is volume (in microlitres) of
enzyme used.
62
Chapter 3 Impact of sorbitol/methanol mixed induction on cell
growth and product expression
3.1 Introduction
P. pastoris is becoming a popular host for the production of heterologous proteins. However,
P. pastoris cultivation faces challenges especially at industrial scale. As a methylotrophic yeast,
it uses methanol as the promoter inducer (Cereghino and Cregg, 2000). Methanol usage is
constrained by the high oxygen demand and need for heat removal in large scale bioreactors
(Hensing et al., 1995) which impose potential design restrictions. Besides, using methanol
imposes challenge to strict health and safety regulations. Thus, reducing methanol consumption
is potentially advantageous to process scale-up.
Partially replacing methanol with sorbitol has been shown to reduce drawbacks of pure
methanol induction and benefit P. pastoris cultivation (Çelik et al., 2009). Sorbitol has a lower
enthalpy of combustion and thus sorbitol/methanol mixed induction reduces the oxygen
consumption rate significantly. Besides, sorbitol/methanol mixed induction was reported to
reduce formation of toxic formaldehyde and enhance cellular viability (Wang et al., 2010).
Effect of sorbitol/methanol mixed induction on product yield is strain dependent. Celik and co-
workers reported that productivity of recombinant human erythropoietin was enhanced 1.8
times by using sorbitol as a co-substrate compared to linearly feeding methanol (Çelik et al.,
2009). Niu and co-workers found that product yield of β-galactosidase was comparable when
mole fraction of Cmethanol was maintained in the range of 45% ~100% (Niu et al., 2013). It was
also reported that using sorbitol/methanol (4:6, C-mol/C-mol) mixed induction reduced the
volumetric productivity of Rhyzopus oryzae lipase compared to methanol induction (Berrios et
al., 2017).
In a previous study, a sorbitol/methanol mixed induction strategy was developed for the
production of recombinant aprotinin, a competitive inhibitor of trypsin and related proteases
(Woodhouse, 2016). Aprotinin production using the sorbitol/methanol mixed induction was
successfully scaled up to pilot scale. It was found that the mixed induction strategy reduced the
heat generation and proteases co-released with the product. Despite the study, aprotinin yields
from methanol and mixed induction strategies have not been quantitatively compared, and the
best harvest time for the fermentation has not been determined. To better evaluate the
sorbitol/methanol mixed induction strategy, a quantitative assay of aprotinin was developed to
63
quantitively determine product yield in this study. Impact of mixed induction on cell growth,
cell viability, oxygen uptake and HCPs co-released with the product was studied.
The main objectives of this chapter were to
1) Developing the best ratio of methanol/sorbitol mixture for P. pastoris induction using
microplate scale culture.
2) Study the impact of sorbitol/methanol mixed induction on cell growth and viability at
bioreactor scale.
3) Compare the product yields of methanol and sorbitol/methanol mixed induction strategies.
4) Study the HCPs co-released with the product and predict the impact of mixed induction on
product purification.
64
3.2 Theoretical considerations
3.2.1 Specific growth rate
The specific growth rate (µ) is defined as the increasing rate of a cell population per unit of cell
concentration (Eq 3.1). Cell growth rate (μ) was calculated using Eq 3.2 (Clarke et al., 2011),
which is the slope of line between lnX and t. DCW2 and DCW1 refer to the dry cell weight at
sampling time of t2 and t1.
µ =lnXXt Eq 3.1
µ =ln[ijR − ln[ijk
tR − tk Eq 3.2
3.2.2 Cell respiration
The oxygen uptake rate (OUR) is one of the fundamental physiological characteristics of cell
culture. It refers to the rate of oxygen consumption by the cells. OUR can be determined by
measuring oxygen concentration in inlet and outlet gas phase. Correspondingly, carbon
evolution rate (CER), the rate of CO2 production by cells, can be determined by the mass
balance of CO2 in inlet and outlet gas. A gas analyser can be connected to the exhaust outlet of
bioreactor, and OUR and CER can be calculated using Eq 3.3 and Eq 3.4.
OUR =(pTR:; − pTRmno ∗ ppR:;/ppRmno) ∗ F ∗ ATM
RTVt Eq 3.3
CER =(piTRmno ∗ ppR:;/ppRmno − piTR:;) ∗ F ∗ ATM
RTVt Eq 3.4
Where PO2in and PO2out refer to volumetric fraction of oxygen in gas intake and outlet; PN2in and
PN2out refer to volumetric fraction of nitrogen in gas intake and outlet; PCO2in and PCO2out refer
to volumetric fraction of carbon dioxide in gas intake and outlet. F refers to the gas flow rate;
ATM refers to the standard atmosphere defined as 101 kPa; R refers to the gas constant with
value of 8.314 J•mol−1•K−1; V refers to the volume of cell culture; t refers to the duration of
measurement.
65
3.3 Results and discussion
3.3.1 Quantitative assay of aprotinin
A quantification method of aprotinin was developed by Sigma-Aldrich (Sigma-Aldrich-a). In a
reaction buffer containing Ca2+, BAPNA is degraded by trypsin and the degradation rate is
calculated by consecutively measuring OD405 of the reaction mixture. Trypsin activity is
inhibited after the addition of aprotinin. The inhibition rate is determined using the equation as
described in the Section 2.9.1, Chapter 2. In this method, the reaction mixture has a total volume
of 3.0 ml and OD405 of the reaction mixture is measured by spectrophotometer.
When this method is used to measure aprotinin in the BSM medium, it was noticed that the
reaction buffer precipitated after being mixed with the medium. The precipitation was formed
by the phosphate and sulphate ion in the BSM medium and the Ca2+ in the reaction buffer. The
precipitation could not be eliminated even the medium was diluted over ten times. In the
reaction, Ca2+ plays a role in stabilizing the structure of trypsin and in turn increasing its activity.
In order to develop an assay compatible to the BSM medium, Ca2+ was removed from the
formula of reaction buffer. Trypsin activity slightly decreased without the Ca2+ but precipitation
was avoided.
In the original method, samples can only be measured one by one using spectrophotometer. It
is quite challenging to consecutively measure optical density of several samples at the same
time. Therefore, the reaction volume was scaled down linearly with a factor of 10.0 to increase
throughput of the measurement. The reaction could be done in 96 well plates after the scale-
down and optical density of the reaction mixture could be measured by a microplate reader.
In the original method, 1000U of trypsin was recommended to use. Since removing Ca2+
significantly reduced the reaction rate, it is better to screen the trypsin concentrations and find
the optimal one for measurement. As shown in Fig 3.1, highest reaction rate was obtained when
3000 U of trypsin was used. OD405 of the mixture reached stationary phase after 14 min of
reaction. The reaction rates decreased significantly at concentrations of 2000 U, 1000 U and
500 U. At these concentrations, OD405 of the reaction mixture increased linearly within 20 min.
When this assay is used to measure aprotinin, 2000 U of trypsin was used because it resulted in
a comparable reaction rate with the original method developed by Sigma.
66
Figure 3.1 Effect of trypsin concentration on reaction rate of the enzymatic assay. Four
concentrations of trypsin, 500, 1000, 2000 and 3000 U were used in the reaction. Reaction
mixture’s absorbance at 405 nm was measured by microplate reader consecutively every two
minutes. Three replicates were used for each trypsin concentration and the data was shown by
mean�SD (n=3).
Effect of the culture medium on measuring accuracy was studied. A series of dilution of BMMY
and BSM mediums were added to the reaction mixture and inhibition rate was calculated. As
shown in Fig 3.2, the undiluted BMMY and BSM mediums inhibited the reaction by 40% and
23% respectively. The inhibitory effect was weakened by diluting the medium, and it was
negligible after BMMY and BSM mediums were diluted five and four times, respectively.
The reaction has an optimal pH of 7.8 (Sigma-Aldrich, n.d.-b) and the reaction rate was
apparently influenced by pH as noticed in a preliminary test. The BMMY and BSM mediums
have pH of 5.0 with phosphate buffer. Adding medium changed the of the reaction mixture and
thus inhibited the reaction. Aprotinin is measured by determining its inhibition rate to the
reaction. Thus, measuring aprotinin in the undiluted medium will be not accurate due to the
interference of medium. It is required to dilute the BMMY or BSM mediums for at least five
and four times in order to eliminate their interferences. The dilution is likely to make aprotinin
undetectable if the aprotinin concentration is very low which is common in early stage of
fermentation.
0 2 4 6 8 10 12 14 16 18 200
1
2
3
4
5
Reaction time min
Abs
orba
nce
at 4
05nm
500U 1000U
2000U 3000U
67
Figure 3.2 Inhibitory effect of BMMY and BSM mediums on the trypsin catalyzed reaction.
BMMY and BSM mediums were diluted up to five times and added to the reaction mixture.
Same volume of 0.85% (v/v) NaCl was added in the uninhibited reaction. Inhibition rates were
calculated as described in the Section 2.9.1, Chapter2. Three replicates were used for each point
and data was shown by mean�SD (n=3).
0 1 2 3 4 5 60
20
40
60
Dilution factor
Inhi
bitio
n %
BMMY mediumA
0 1 2 3 4 5 60
10
20
30
Dilution factor
Inhi
bitio
n %
BSM mediumB
68
Bovine aprotinin from Sigma-Aldrich was used to build a standard curve between the inhibition
rate and aprotinin concentration (Caprotinin). A series of standard arpotinin solutions with
concentration of 0.005, 0.010, 0.020, 0.040, 0.060, 0.080, 0.100 and 0.200 g•L-1 were prepared
and their inhibition rates on the reaction were determined.
As shown in Fig 3.3, aprotinin inhibited the reaction in a concentration dependent but not
linearly manner. The curve between aprotinin concentration and inhibition rate can be divided
into three parts: when the aprotinin concentration was below 0.06 g•L-1, it was related to the
inhibition rate with a function of Caprotinin=0.002•Inhibition%+0.0048, R2=0.99; when the
aprotinin concentration was in the range of 0.06~0.1 g•L-1, it was related to the inhibition rate
with a function of Caprotinin=0.0006•Inhibition%+0.045, R2=0.99; when aprotinin concentration
was over 0.1 g•L-1, the reaction was completely inhibited.
When the aprotinin concentration is unknow, it is necessary to dilute samples properly to
maintain the inhibition rate within the range of 0%~90%. It emphasizes the importance of
scaling down the assay to 96 well plate level. By using the plate, one sample can be diluted to
several concentrations and the inhibition rates can be measured in one plate. The suitable
inhibition rate for aprotinin determination can be picked.
Figure 3.3 Standard curve between inhibition rate and aprotinin concentration. Standard
aprotinin solutions with concentration of 0.005, 0.010, 0.020, 0.040, 0.060, 0.080, 0.100, 0.200
g•L-1 were prepared using bovine aprotinin. Inhibition rates of these aprotinin solutions were
calculated as described in the Section 2.9.1, Chapter 2. Three replicates were used for each
point and data was shown by mean�SD (n=3).
0 20 40 60 80 1000.00
0.05
0.10
0.15
0.20
0.25
Inhibition %
Sta
ndar
d ap
rotin
in g•L-1
69
3.3.2 Cell culture at small scale
In order to understand cell growth behaviour on sorbitol/methanol mixture and determine the
best sorbitol/methanol ratio for product expression, cells were grown on sorbitol/methanol
mixture with different ratios at microplate scale.
Firstly, cells were grown on a buffered complex medium containing 5•10-4 mol Cmethanol to
establish the feasibility of cell culture in microplate. The microplate had a flat bottom and a
working volume of 2 ml. In order to minimize evaporation of medium, the microplate was
cultured in a humidified shaker. Agitation slower than 250 RPM could not suspend cells
completely. The microplate was agitated at speeds of 250 RPM and 400 RPM by humidified
shaker, respectively. When the cells were cultured at 250 RPM, OD600 of the cell culture
reached a maximum value of ~30.0 after 12 hours of cultivation. The cells cultured at 400 RPM
grew much faster and OD600 reached the maximum after 6 h cultivation. Oxygen transfer rate
of the microplate system increases with agitation speed and thus the cells had higher growth
rate at 400 RPM.
The cells were grown on different ratios and concentrations of sorbitol/methanol mixture.
Agitation speeds of 250 RPM and 400 RPM were used and OD600 of the cell culture was
measured after 12 or 6 hours of cultivation, respectively. As shown in Fig 3.4B and 3.4C, when
P. pastoris was cultured in a medium containing 5•10-4 mol or 10•10-4 mol carbons, OD600 of
the cell culture was not affected by the ratio of Cmethanol:Csorbitol. When higher carbon
concentration such as 20•10-4 mol or 30•10-4 mol was used, cells grown on carbons with higher
sorbitol ratio had higher OD600 values. The cell growth was significantly inhibited by 30•10-4
mol methanol but was not affected by the concentration of sorbitol (5~30•10-4 mol).
The result was consistent with previous reports where high concentration of sorbitol did not
inhibit cell growth (Çelik et al., 2009), whilst over 0.4% (v/v) methanol significantly inhibited
cell growth (Trinh et al., 2003, Zhang et al., 2000). Real time monitoring of methanol
concentration is critical at large scale to avoid its over-accumulation (Ramon et al., 2004).
Using sorbitol as a co-substrate will minimize the challenge of methanol monitoring and makes
process control easier.
Aprotinin expression was measured using the enzymatic assay. However, no product was
detected by the assay which may be due to the short cultivation time and low biomass
concentration.
70
Figure 3.4 Study of cell growth on different sorbitol/methanol mixtures. P. pastoris cells were inoculated into medium to obtain an initial OD600
of 1.0. 2 ml medium was added into each well of microplate. (A) 5•10-4 mol CMethanol was added to each well and the plates were cultured at two
agitation speed, 250 RPM and 400 RPM, respectively. OD600 of cell culture was measured using spectrophotometer. (B) Cells were cultured on
medium with 5•10-4, 10•10-4, 20•10-4, 30•10-4 mol of carbon, respectively. The carbon contains different ratios of Cmethanol:Csorbitol ranging from
100:0 to 10:90. After the plate was agitated at 250 RPM for 12 hours, OD600 of cell culture was measured. (C) After the plate was agitated at 400
RPM for 6 hours, OD600 of cell culture was measured. Three replicates were used for each point and data was shown by mean�SD (n=3).
0 4 8 12 16 200
10
20
30
40
Time h
OD
600
nm
250RPM 400RPMA
1 2 3 4 5 6 7
24
28
32
36
!
!
! !
!!
!
! !
!
! !! !
!
!
! !
!!
!!
!
! ! !
!
!
Cmethanol:Csorbtol
OD
600
nm
5*10-4 mol C!10*10-4 mol C!
20*10-4 mol C!30*10-4 mol C!
M 85:15 70:30 55:45 40:60 25:75 10:90
B
1 2 3 4 5 6 7
24
28
32
36
!
!
!
!
!!
!
!
!!
! !! !!
!
! !!
!!! !
! !
!!
!
Cmethanol:Csorbtol
OD
600
nm
5*10-4 mol C!10*10-4 mol C!
20*10-4 mol C!30*10-4 mol C!
M 85:15 70:30 55:45 40:60 25:75 10:90
C
71
The optimal ratio of sorbitol/methanol mixture for product expression was not obtained
from the cell culture in microplate. Therefore, a mixed induction strategy developed in
a previous study was used (Woodhouse, 2016). The sorbitol/methanol (1:1, C-mol/C-
mol) mixed induction effectively induced product expression and the fermentation was
scaled up to 20 litre scale.
The cell growth on sorbitol/methanol (1:1, C-mol/C-mol) mixture was further evaluated
at shake flask scale. In 250 ml shake flasks, P. pastoris cells were cultured in the
buffered complex medium supplemented with 1% (v/v) glycerol, methanol, sorbitol or
sorbitol/methanol (1:1, C-mol/C-mol) mixture. As shown in Fig 3.5, cells grown on
glycerol had the highest growth rate and OD600 of the cell culture reached ~50 after 48
hours of cultivation. It was consistent with a previous report where cells grown on
glycerol had higher specific growth rate than that grown on methanol (Cos et al., 2005).
Cell growth rates on methanol, sorbitol and sorbitol/methanol (1:1, C-mol/C-mol)
mixture were comparable. Biomass concentrations at the harvesting time were also
similar, which was different from the study in microplate cell culture. This is possible
because the shaker was not humified in the shake flask culture and evaporation was
more significant.
DNA release to the culture medium is a key factor indicating cell viability (Newton et
al., 2016). After 48 hours of culture, released DNA in the supernatant was quantified
using a PicoGreen dsDNA assay kit. As shown in Fig 3.9A, a maximum amount of
DNA was released when the cells were grown on methanol, while the DNA release was
negligible when glycerol was used. Compared to cell culture on methanol, growing
cells on sorbitol/methanol (1:1, C-mol/C-mol) mixture apparently reduced the DNA
concentration in medium.
Soluble protein profile in the medium was analysed using protein gel. After 24 hours
of culture, protein profiles from different carbons were similar. Much more proteins
were released after 48 of hours. Protein profile from glycerol culture was much simpler
than the others, whilst protein profile from methanol culture was the most complex. It
indicates that using methanol as the solo carbon caused cell lysis in late stage of cell
culture and using sorbitol/methanol mixed carbons reduced cell lysis.
Aprotinin concentration in the medium was determined using the enzymatic assay as
established in the Section 3.3.1, Chapter 3. However, it was undetectable due to its low
concentration.
72
Figure 3.5 Growth and lysis of P. pastoris cells cultured on medium containing different carbons. P. pastoris cells were inoculated into medium
to obtain an initial OD600 of 1.0 ml and 75 ml medium was added into each shake flask. 1% (v/v) of glycerol, methanol, sorbitol or sorbitol/methanol
mixture (1:1, C-mol/C-mol) was added into each flask and flasks were agitated at 250RPM. (A) OD600 of cell culture was measured after 0, 12,
24, 36 and 48 hours of cultivation. One sample was analysed for each point. (B) Released DNA in medium was quantified using Picogreen DNA
assay after 48 hours of cultivation. Three replicates were used for each point and data was shown by mean�SD (n=3). (C) After 24 and 48 hours
of cultivation, soluble protein profile was analyzed using protein gel. 7.5 μl of sample was loaded into each well.
12 24 36 480
20
40
60
Time h
OD
600
nm
Glycerol MethanolMixture Sorbitol
A
1 2 3 4
0
25
50
75
100
ds D
NA
µg•ml-1
Glycerol Sorbitol Methanol Sor/Met
B
73
3.3.3 Cell culture at bioreactor scale
Methanol and sorbitol/methanol (1:1, C-mol/C-mol) mixed induction strategies were
compared at bioreactor scale. The bioreactor has one litre total volume. Fermentation
was done using the procedure recommended by Invitrogen (Invitrogen, 2002). P. pastoris has a high cell growth rate by culturing on glycerol. Therefore, cells were
initially grown on 4% (m/v) glycerol in the medium. After glycerol in medium was
consumed, 50% (m/v) glycerol was fed to further enhance biomass. Production was
induced by feeding methanol or sorbitol/methanol (1:1, C-mol/C-mol) mixture at a
constant rate of 10.8 ml•L-1•h-1 (270 mmol Carbon•L-1•h-1). The cell growth, respiration
and viability were studied.
As shown in Fig 3.6, the initial glycerol in medium was consumed after 18 hours of
culture and DCW reached 25.8 g•L-1 and 24.7 g•L-1, respectively. DCW was further
enhanced to 57.9 g•L-1 and 57.8 g•L-1 after 50% (m/v) glycerol feeding. It is known that
glycerol inhibits pAOX1 activity (Tschopp et al., 1987). Thus, cells were starved for
one hour after stopping glycerol feeding to thoroughly exhaust residual glycerol. In
early stage of methanol induction, an adaptation phase which lasted for about 4 hours
was observed where biomass did not increase. No adaptation was noticed in the mixed
induction and biomass increased immediately once sorbitol/methanol (1:1, C-mol/C-
mol) mixture was fed in. Biomass reached a stationary phase after 72 hours of
cultivation. At the harvest time, DCW reached 129.4 g•L-1 and 152.4 g•L-1 in methanol
and mixed induction, respectively.
AOX enzyme oxidises methanol to formaldehyde in peroxisome. AOX enzyme
synthesis took several hours after methanol was fed instead of glycerol (Cámara et al.,
2017). In contrast, sorbitol is utilized by the same pathway as glycerol and thus no
adaptation phase was observed when the mixed induction was used. It has been reported
that sorbitol co-substrate complemented energy production and increased methanol
fraction in biosynthesis pathway (Çelik et al., 2010). Therefore, biomass in mixed
induction was higher than that in methanol induction at the harvest time.
Specific growth rate (μ) was calculated using Equation 3.1 (Clarke et al., 2011) and
shown in Fig 3.5C. μ reached maximum (0.18 h-1) in the end of glycerol feeding and
dropped to below 0.05 h-1 after 72 hours of fermentation. As the accumulation of
biomass, carbons available per biomass became very limited in the end of fermentation
and thus the cell growth nearly stopped.
74
Figure 3.6 Comparison of methanol and sorbitol/methanol mixed induction strategies at bioreactor scale. Cells were grown on glycerol and then
induced by methanol (A) or sorbitol/methanol (1:1, C-mol/C-mol) mixture (B). OD600, WCW and DCW of cell culture were measured at each
sampling time. In measurement of WCW and DCW, three replicates were used for each point and data was shown by mean�SD (n=3). (C) Specific
growth rate (μ) was calculated using Eq 3.1 based on data of DCW.
0 24 48 72 96 1200
200
400
600
800
1000
0
40
80
120
160
200
Time h
Abs
orba
nce
at 6
00nm
WC
W g
•L-1
OD600nm WCW DCW
Induction
DC
W g•L
-1
A
0 24 48 72 96 1200
200
400
600
800
1000
0
40
80
120
160
200
Time h
Abs
orba
nce
at 6
00nm
WC
W g
•L-1
OD600nm WCW DCW
Induction
DC
W g•L
-1
B
0 24 48 72 960.00
0.05
0.10
0.15
0.20
Time (h)
Spe
cific
gro
wth
rate
(h-1
)
Methnaol MixtureC
75
During the fermentation, oxygen fraction in gas-in was recorded by control system of
the bioreactor. Gas exhaustion of the fermentation was measured by a gas analyser.
Oxygen uptake rate (OUR) and CO2 evolution rate (CER) were calculated using Eq 3.3
and Eq 3.4.
As shown in Fig 3.7. OUR of two fermentations were similar prior to induction. An
OUR spike was observed in methanol induction in the first 24 hours of induction while
the spike in the mixed induction was not apparent. In both of the induction methods,
the OUR maintained stable after 24 hours of induction. The average OUR were 256.42
mmol•L−1•h−1 and 150.2 mmol•L−1•h−1, respectively.
An CER spike was also noticed in the CER profile of methanol induction (Fig 3.7B).
The CER dropped to about 78 mmol•L−1•h−1 after the spike and slightly increased to
100 mmol•L−1•h−1 during the induction. Compared to the methanol induction, the
sorbitol/methanol (1:1, C-mol/C-mol) mixed induction also had a lower average CER
value (79 mmol•L−1•h−1 versus 96 mmol•L−1•h−1).
P. pastoris was unable to metabolize methanol in the first few hours of induction and
thus methanol was accumulated in the medium. After cells adapted themselves to
methanol, large amount of oxygen was required to oxidize the accumulated methanol
and caused the OUR and CER spikes. Methanol accumulation was reduced by using
mixed induction and thus the spikes was not apparent. The difference of OUR spike in
two induction strategies was consistent to another study (Woodhouse, 2016).
The CER increased slightly in both of the inductions. It is because more carbons were
used for cell maintenance as the increase of biomass concentration. Compared to the
methanol induction, the OUR was reduced by 41.4% in the mixed induction. Sorbitol
has a relative lower enthalpy of combustion than methanol (-504.3 kJ•C-mol−1 versus
−727 kJ•C-mol−1) and thus the mixed induction reduced oxygen uptake rate (Niu et al.,
2013, Çelik et al., 2009). Large scale bioreactors have average OTR of 150-250
mmol•L−1•h−1 and it is quite challenging to maintain cell growth with an OUR over 250
mmol•L−1•h−1 (Carly et al., 2016). Using sorbitol/methanol mixed induction will make
scale-up of P. pastoris fermentation easier.
From the aspect of energy metabolism, sorbitol is a better carbon source than methanol
because 1mol CSorbitol produced more ATPs than 1mol CMethanol (2.6 mol versus 2.0 mol)
(Niu et al., 2013). As a result, less carbon was used for energy metabolism and less CO2
was produced in mixed induction.
76
Figure 3.7 Comparison of OUR (A) and CER (B) of the fermentations from methanol
and sorbitol/methanol (1:1, C-mol/C-mol) mixed induction strategies. The OUR and
CER were calculated using Eq 3.2 and Eq 3.3.
0 24 48 72 960
200
400
600
800
1000
Time h
OU
R mmol•L-1•h-1
Methanol MixtureA
Induction
0 24 48 72 960
50
100
150
200
Time h
CE
R m
mol
•L-1
•L-1
Methanol Mixture
Induction
B
77
Carbon utilization in methanol and sorbitol/methanol (1:1, C-mol/C-mol) mixed
induction was studied (Fig 3.8). Sorbitol and methanol concentrations in the medium
was determined after 18, 42, 66 and 90 hours of induction. Methanol concentration was
nearly undetectable in the fermentation induced by either methanol or
sorbitol/methanol (1:1, C-mol/C-mol) mixture. Around 2.0 g•L-1 of residual sorbitol
was detected during mixed induction.
Sorbitol uptake rate is much lower than that of methanol in P. pastoris. In a bioreactor
with maximum kLa values of 800 h-1, maximum methanol and sorbitol uptake rates
were 0.077 g•g DCW−1•h−1 and 0.044 g•g DCW−1•h−1, respectively (Carly et al., 2016).
Slower uptake rate resulted in the slight accumulation of sorbitol. However, its effect
on process control is negligible since sorbitol does not inhibit production even at a
concentration over 50 g•L−1 (Çelik et al., 2009).
Figure 3.8 Concentration of residual carbons during methanol and mixed induction.
Samples were taken after 18, 42, 66 and 90 hours of induction and concentrations of
methanol and sorbitol were determined using HPLC. One sample was analysed for each
point.
0 24 48 72 960.0
0.5
1.0
1.5
2.0
2.5
Time of induction h
Res
idua
l car
bon g•L-1
Methanol Mixture (Residual methanol)Mixture (Residual sorbitol)
78
As found in shake flask study, the cells grown on methanol had more lysis than that on
glycerol or sorbitol (Fig 3.5). Here viabilities of the cell culture induced by methanol
and sorbitol/methanol (1:1, C-mol/C-mol) mixture were compared using flow
cytometry. Propidium iodide, a red-fluorescent nuclear staining, was used to stain cells.
The staining is impenetrable to live cells but is capable to enter nucleus and bind DNA
when cell membrane is not intact.
Fresh cells were incubated with the propidium iodide and analysed by flow cytometry.
As shown in Fig 3.9, the cell viabilities were over 98% before induction. After 18 hours
of induction, the viability dropped to 95.8% in methanol induction which was much
lower than that in mixed induction. After 90 h induction, the cell viabilities in methanol
and mixed induction were 92.5% and 97.6%, respectively.
The finding was consistent with a previous report where using sorbitol as a co-substrate
enhanced cell viability of a P. pastoris strain expressing recombinant interferon-α
(Wang et al., 2010). The enhancement of cell viability will minimize HCPs and
proteases co-released with the product during induction.
Figure 3.9 Comparison of the cell viabilities in methanol and sorbitol/methanol (1:1,
C-mol/C-mol) mixed induction strategies. Samples were taken after cells were induced
by methanol or sorbitol/methanol (1:1, C-mol/C-mol) mixture for 18, 42, 66, 90 and
114 hours. After cells were spun down by centrifuge and stained by propidium iodide
in 0.9 (m/v) NaCl solution, cell viabilities were measured by flow cytometry. One
sample was analysed for each point.
0 24 48 72 9690
92
94
96
98
100
Induction time h
Cel
lula
r via
bilit
y %
Methanol Mixture
79
3.3.4 Impact of mixed induction on product yield and purity
After getting an understanding of how the sorbitol/methanol mixed induction affected
cell growth and viability, its impact on the product yield and purity was further studied.
Aprotinin in the supernatant was quantified using the method described in the Section
2.9.1, Chapter 2. HCPs co-released with the product were studied using 2D gel and LC-
MS/MS.
In a previous study, protein gel analysis showed that cells produced less aprotinin when
mixed induction was used (Woodhouse, 2016). Here aprotinin expression was studied
by protein gel and enzymatic assay. Samples were analysed using protein gel after they
were induced for 24, 48, 72 and 96 hours. In both methanol and mixed induction,
aprotinin bands were visible after 24 hours of induction and product amount kept
accumulating until harvest. Meanwhile, only a few HCP bands were visible on the gel
which indicated the high product purity. The major HCP band has a molecular weight
of ~65 kDa and it also accumulated over time during induction.
It was reported that the major contaminating HCP in P. pastoris culture was an
extracellular protein X1 (EPX1) with a molecular weight of 65 kDa (Heiss et al., 2013).
Expression of EPX1 was affected by temperature, osmolality and substrates. It suggests
that changing induction condition affects product purification by affecting the secretion,
especially when the product has similar properties as EPX1.
In order to quantitatively compare product yields of two induction strategies and
determine the best harvest time of fermentation, product levels in two induction
strategies were determined using the enzymatic assay. Cells were induced for 96 h and
samples were taken after 18, 36, 42, 60, 66, 84 and 90 hours of induction, respectively.
As shown in Fig 3.10B, the product titre of methanol induction increased from 0.09
g•L-1 to 1.69 g•L-1 from 18 h to 90 h, while it raised from 0.20 g•L-1 to 1.05 g•L-1 in
mixed induction. Product yields of two induction strategies were comparable from 18
h to 42 h. However, it nearly stopped increasing after 60 h in mixed induction, while it
raised from 1.24 g•L-1 to 1.69 g•L-1 in methanol induction. Complete depletion of
methanol is not preferable during P. pastoris induction because it impairs pAOX1
activity (Vogl et al., 2018). As the accumulation of biomass, methanol available per
biomass became limited in mixed induction and thus product synthesis nearly stopped.
Since product yield nearly stopped increasing after 72 hours of induction, it is a good
time to harvest products (Heiss et al., 2013).
80
Figure 3.10 Comparison of product yields of methanol and sorbitol/methanol (1:1, C-
mol/C-mol) mixture induced fermentations. (A) Samples were taken after cells were
induced for 24, 48, 72 and 96 hours. Soluble proteins in supernatant were analyzed
using protein gel. 5 μl sample was loaded into each well. Aprotinin was shown by the
symbol of arrow. (B) Samples were taken after cells were induced for 18, 36, 42, 60,
66, 84 and 90 hours. Aprotinin in medium was quantified using the enzymatic assay.
Three replicates were used for each point and data was shown by mean�SD (n=3).
0 24 48 72 960.0
0.5
1.0
1.5
2.0
Induction time h
Apr
otin
in g
•L-1
Methanol MixtureB
81
While high density fermentation enhances product expression, it also secretes a large
amount of host cell proteins (HCPs). Besides, intracellular proteins are release into the
medium and especially in the late stage of fermentation. As shown in the cell viability
study (Fig 3.9), nearly 8.0% of P. pastoris cells died at the harvest time in methanol
induction. The cell mortality is not very high compared to that in CHO cell fermentation.
However, biomass of the dead cells was high after the high density was considered. In
terms of DCW, 10.5 g of cells were dead after 96 hours of methanol induction, while it
dropped to 3.0 g•L-1 when sorbitol/methanol mixed induction was used. Therefore, cell
cultures from the two induction strategies was likely to have different profiles of HCPs.
The profiles of HCPs were firstly studied by 2D protein gel to show a visible
distribution of all proteins.
Prior to the 2D gel assay, soluble proteins are required to be extracted from the culture
medium. The extraction is usually performed by mixing the culture medium with
solvents that reduce solubility of proteins. Cold acetone, trichloroacetic acid (TCA) and
ammonium persulfate solution are popularly used solvents. However, the protein
extraction from P. pastoris medium was not very successful which was probably
because of the high salt concentration. The ammonium persulfate solution failed to
extract any protein from the medium. And less than 10% (m/m) of proteins were
precipitated when acetone or 13% (v/v) TCA in water was used. Besides, it was
observed that acetone was prone to precipitate proteins with large molecular weights
while 13% (v/v) TCA in water extracted more proteins with small molecular weights,
as it was analysed by protein gels. In order to enhance the extraction, the culture
medium was changed by ultrafiltration with a cut-off of 3000 Da. A new solvent was
made by preparing 13% (v/v) TCA in acetone. The extraction rate was enhanced to over
40% (v/v) using the new protocol. During the extraction, 20mM dithiothreitol (DTT)
was added to prevent any proteolytic degradation. Finally, the precipitation was re-
dissolved by urea solution.
Isoelectric points (pI) of most nature proteins are in the range of 4-7 (Ciborowski and
Silberring, 2016) which are much lower than that of aprotinin (pI: 10.5). Therefore,
IPG strips with the pH range of 3-11 were used as the separating gel for first dimension.
Protein amount up to the loading capacity was used on each gel to visualize as many
spots as possible. In the second dimension, the proteins were separated by NuPAGE 4-
12% Bis-Tris Protein Gel based on their different molecular weights. Finally, the
protein gels were stained by coomassie blue.
82
As shown in Fig 3.11, about eight spots were visible on the gel before induction. These
proteins had MW and pI within the range of 40~200 kDa and 3.0~6.0. The gel was
quite clear outside this area. This finding was consistent to a previous report where P. pastoris grown on glucose only secreted a few proteins into the culture (Ciborowski
and Silberring, 2016).
After 24 hours of methanol induction, over 20 protein spots were visible on the gel. pI
of these proteins located within the range of 3.0~6.0. The spot of aprotinin was also
clear visible on the right bottom corner of the gel. On the contrast, protein spots were
fewer on the gel analysis of sorbitol/methanol mixed induction. After 72 hours of
methanol induction, intensity of aprotinin spot increased to a very strong level. Besides,
several new spots were detected within the pI range of 5.0~7.0.
In gel analysis of both induction strategies, the intensity of aprotinin spot was much
stronger than these of HCPs. Besides, the MW and pI of aprotinin are apparently
different from most of the HCPs. It indicates that the secreted product has a high purity
and most of the co-released HCPs are likely to be easily removed in chromatographic
steps.
Compared to the 2D gel analysis of CHO cell culture (Tait et al., 2012), the HCP profile
of P. pastoris was much simpler. One possible reason is that CHO cells are much easier
to lose cell integrity due to the lack of cell wall. As a result, a larger population of HCPs
can be released into the medium. In addition, the product had a high abundance in the
protein mixture as it was shown on the gel. The HCPs loaded on the gel had relatively
lower amount and thus many spots were not visible. Columns with equal affinity to
most proteins have been developed to remove the high-abundance proteins (Bellei et
al., 2011). In the future work, these columns can be applied to remove the aprotinin,
which is likely to increase the number of visible spots on 2D gel.
83
Figure 3.11 Analysis of soluble proteins in supernatant using 2D protein gel. Samples were taken before induction and after cells were induced by
methanol or sorbitol/methanol (1:1, C-mol/C-mol) mixture for 24 and 72 hours. Proteins were recovered from the medium and re-suspended by
rehydration buffer. Buffer containing 200 μg protein was loaded on each gel. Protein spot of aprotinin was indicated by the symbol of arrow.
84
Tandem mass spectrometry, also known as LC-MS/MS analysis, was used to identify
HCPs in the culture medium. The analysis mainly has two approached: top–down and
bottom–up analyses (Feist and Hummon, 2015). The top-down approach analyses the
whole proteins and the bottom-up approach analyses the digested proteins. In both of
the approaches, proteins or peptides are separated by reversed-phase chromatography
based on polarities before they are ionized by an ion source. Mass-to-charge ratios of
the proteins or peptides are measured, and their species can be identified by comparing
with a standard database. The top-down method is used to identify a purified protein,
whilst the bottom-up approach can analyse a protein mixture. Identification of HCPs in
P. pastoris culture using the bottom-up approach has been reported (Huang et al., 2011).
In this report, the protein mixtures were digested to peptides using trypsin before being
analysed by LC-MS/MS. However, trypsin would be inhibited by the product in this
study. Thus, the protein mixtures from P. pastoris culture were digested by acid
cyanogen bromide (CNBr) in 70% (v/v) formic acid.
As shown in Table 3.1, 96 proteins and 393 peptides were identified from the cell
culture of methanol induction. The numbers decreased to 72 and 262 in the culture of
mixed induction. Besides, more types of protease were identified from the culture of
methanol induction (3 verses 1). Name, MW, pI, localization and function of the
identified proteins were obtained from the database of Uniport and listed in Appendix
Table 8.1.
Fig 3.12 shows the number of identified HCPs located at different cell substructures.
Proteins were identified from the secretion, cell wall, cell membrane and intracellular
organelles. Expect the unknow proteins, proteins from the cytoplasm had the largest
number, while proteins from secretion, cell wall and membrane are quite fewer.
Compared to the mixed induction, the cell culture from methanol induction contained
more intracellular proteins from cytoplasm and nucleus, which indicated that more cells
lost integrity in the methanol induction.
The result was comparable to a previous study where 75 proteins were identified from
a P. pastoris culture producing a protein named Sm14-C64V (Huang et al., 2011). As
reported by Huang and co-authors, both intracellular and extracellular proteins were
identified, and proteins from cytoplasm had the largest number. Compared to CHO cell
culture, the P. pastoris has much simpler HCP profiles. The number of HCPs in CHO
culture was nearly 500 (Tait et al., 2012). It shows the advantage of P. pastoris as a
production host.
85
Protein identified
Peptide identified Protease
Methanol 96 393 3
Mixture 72 262 1
Table 3.1 The number of host cell proteins, peptides, proteases and stress related
proteins identified from the methanol and sorbitol/methanol (1:1, C-mol/C-mol) mixed
induction strategies.
Figure 3.12 Localization of the HCPs identified from methanol and sorbitol/methanol
(1:1, C-mol/C-mol) mixed induction strategies. Localization of these protein was
searched from the database of Uniprot.com.
Secre
ted
Cell W
all
Membra
ne
Cytoplas
m
Nucleus
Unknow
0
20
40
60
Numbers
Methanol Mixture
86
In order to study the impact of mixed induction on purification, distributions of
molecular weight (MW) and isoelectric point (pI) of these proteins were compared (Fig
3.13).
57 proteins were identified in both of the cultures from methanol and mixed induction.
39 proteins were only identified from the culture of methanol induction, while the
number dropped to 15 in the mixed induction. Within the MW range of 0~24 kDa, 8
proteins were only identified from the methanol induction, while only one was from
the mixed induction. Within the MW range of 24~80 kDa, 26 and 12 unique proteins
were identified from the two cultures, respectively. These two ranges can be defined as
‘sweet ranges’ where products from the mixed induction are easier to be purified.
Within the pI range of 6.0~14, the number of unique proteins in two cultures were 21
and 4, respectively. The range can be defined as a ‘sweet range’ for the ion exchange
chromatography.
This study indicated that using sorbitol/methanol mixed induction is an efficient tool to
simplify purification of products with specific MWs and pIs. For instance,
commercialized products including aprotinin (MW/pI, 6.5 kDa, 10.5), Glucagon-like
peptide-1 (MW/pI, 3.3 kDa, 9.3), Interferon gamma (MW/pI, 18.0 kDa/8.72),
Interferon beta (MW/pI, 22.0 kDa/ 9.69) and Keratinocyte growth factor (MW/pI, 22.5
kDa/9.29) locates within the ‘sweet range’ of MW and pI (0~24 kDa, 6.0~14). Some
antibody fragments, (MW/pI, 50 kDa and ~8.5) are within the ‘sweet range’ of MW
and pI (24~80 kDa, 6.0~14). The HCP profiles from different cell strains are likely to
be different. In the future, it will be interesting to compare the HCPs of methanol and
mixed induction using other strains and verify if the ‘sweet range’ will change.
87
Figure 3.13 Distribution of the molecular weights (A) and isoelectric points (B) of
HCPs identified from methanol and sorbitol/methanol (1:1, C-mol/C-mol) mixed
induction strategies.
88
3.4 Conclusion
In this chapter, an enzymatic assay of aprotinin that was compatible with culture
mediums of P. pastoris was developed. The undiluted medium interfered accuracy of
the enzymatic assay, but the interference could be minimized by diluting the medium
sufficiently. Cell culture at microplate scale showed that sorbitol did not inhibit cell
growth even at high concentrations, while methanol did. At shake flask scale, cell lysis
was reduced by growing cells on sorbitol rather than methanol. Aprotinin produced at
the microplate and shake flask scales were undetectable which was likely due to the
short cultivation time and low biomass.
Standard methanol induction and sorbitol/methanol mixed induction were compared
using parallel bioreactor. At bioreactor scale, sorbitol/methanol mixed induction
improved cell growth and cell viability. The mixed induction also benefited the
fermentation by reducing oxygen uptake rate. Since it is quite challenging to provide
sufficient oxygen at large scale, scaling-up P. pastoris fermentation with mixed
induction will be easier.
The mixed induction decreased product yield compared to standard methanol induction.
But fewer host cell proteins were co-released with the product into medium when the
mixed induction was used. Therefore, the mixed induction is likely to simplify the
product purification. Besides, fewer proteases were identified in the culture from the
mixed induction and thus product degradation will be minimized by using mixed
induction when the products are sensitive to proteolytic degradation.
89
Chapter 4 Development of induction strategies in a bioreactor with limited oxygen transfer rate
4.1 Introduction
It was shown that standard methanol induction achieved higher product yield compared
to the sorbitol/methanol mixed induction. However, supplying sufficient oxygen for
standard methanol induction is challenging in large-scale bioreactors.
Methanol is a high degree reductant with high heat of combustion. It consumes 0.8~1.1
mole O2 to utilize one mole methanol by P. pastoris (Jahic et al. 2003).
Correspondingly, the bioreactor requires an OTR value over 230~290 mmol•L-1•h-1 for
standard methanol induction where it is fed at the rate of 10.8 ml•L-1•h-1 (Invitrogen
2002). However, the large-scale bioreactors only have an average OTR of 150~200
mmol•L-1•h-1 (Benz 2011). Using standard methanol induction in the large-scale
bioreactors will cause oxygen depletion and methanol accumulation.
In order to maintain the DO at 20%~30% and avoid methanol over-accumulation, the
methanol feeding rate has to be declined in the OTR-limited bioreactors. Consequently,
the cell growth will be decreased and product yield may be compromised compared to
the standard methanol induction (Maccani et al., 2014). Sorbitol has a relatively lower
enthalpy of combustion and its metabolism by P. pastoris consumes less oxygen.
Therefore, more carbons can be fed by using sorbitol/methanol mixed induction in the
OTR-limited bioreactor. It is likely to enhance the cell growth and product yield
compared to slower methanol induction.
In this paper, several induction strategies (Table 4.1) were compared in a benchtop
bioreactor that has a similar oxygen transfer rate (OTR) as large-scale ones.
1) kLa values of the vessels were measured using dynamic method and a fermentation
process with OTR of 150 mmol•L-1•h-1 was defined.
2) Induction in an oxygen limited condition was studied, and the impact of residual
methanol concentration on cell growth, viability and product yield was studied.
3) Induction in an oxygen unlimited condition was studied, and methanol and
sorbitol/methanol mixed inductions were compared.
4) AOX enzyme activities of the different induction strategies were compared.
90
Abbr. of induction strategies
shown in figures Inducer feeding rate ml•h−1•L−1
Desired DO%
Pure methanol induction 100% Methanol 6.7 30%
Sorbitol/methanol (60% Cmethanol) mixed induction 60% Methanol 7.7 30%
Sorbitol/methanol (50% Cmethanol) mixed induction 50% Methanol 8.0 30%
Sorbitol/methanol (40% Cmethanol) mixed induction 40% Methanol 8.3 30%
Induction with residual methanol of 0~5 g•L-1 Met0-5 Variable 0%
Induction with residual methanol of 5~10 g•L-1 Met5-10 Variable 0%
Table 4.1 A summary of the induction strategies used in the bioreactor with an OTR of 150 mmol•L−1•h−1
91
4.2 Theoretical considerations
4.2.1 Determining OTR of the bioreactor
Determining kLa of the bioreactor is required to define a fermentation process with a
limited OTR. Methods to measure the kLa include dynamic method, OTR measurement
and sulphite method, etc (Schlüter and Deckwer, 1992). Dynamic method is commonly
used when the bioreactor has no biological oxygen uptake. This method was used to
measure the kLa of one litre bioreactor in this study. In the measurement, dissolved
oxygen in the medium is eliminated to zero by sparging nitrogen into the vessels. Then
air is ventilated at a constant rate and dynamic change of the dissolved oxygen is
measured. The kLa values were determined from the slope of ln f(CL) and time graph
as described in Eq 4.1 (Garcia-Ochoa and Gomez, 2009).
k"a = −1tln(1 −
C,C∗) Eq 4.1
Where C* is the saturated concentration of dissolved oxygen in % when air is aerated;
Ct is the concentration of oxygen concentration in % at time t.
The kLa is influenced by conditions such as temperature, air flowrate and agitation. The
same temperature and air flowrate are usually used for kLa measurement and cell culture.
When the desired OTR cannot be reached with the maximum agitation, enriching the
air inlet with pure oxygen is required. The oxygen fraction in air inlet can be calculated
using Eq 4.2 and Eq 4.3. Both air and pure oxygen flows are connected to the bioreactor
and the desired oxygen fraction can be achieved automatically by control system of the
bioreactor.
C∗∗ =OTRk"a
− C" Eq 4.2
p34 = p34(567)C∗∗C34
Eq 4.3
Where OTR is defined as 150 mmol•L-1•h-1; C** is the theoretical concentration of
dissolved oxygen in mmol•L-1 when air/O2 gas mixture is aerated; CL is the
concentration of dissolved oxygen in mmol•L-1 in medium; pO2 is the maximum
fraction of oxygen in % in gas inlet; pO2(air) is the oxygen fraction in % in air; CO2 is the
saturated concentration of dissolved oxygen in mmol•L-1 at 30�.
92
4.2.2 Determining feeding rates of the induction solutions
From average OUR of a fermentation where pure methanol was fed at a rate of 10.8
ml•L-1•h-1 (270 mmol Carbon•L-1•h-1), it is estimated that utilizing per millilitre
methanol consumed 22.2 mmol O2 by P. pastoris. Based on enthalpies of combustion
of methanol and sorbitol (-504.3 kJ•C-mol−1 versus −727 kJ•C-mol−1), it is derived that
15.4 mmol O2 is consumed by each millilitre 0.75 g•L-1 sorbitol solution. Pure methanol
and sorbitol/methanol mixtures with 60%, 50% and 40% (C-mol/C-mol) of methanol
were prepared and used as induction solutions. Their feeding rates were determined
using Eq 4.4
F =1000 • OTR
22.2r>?, + 15.4rCD7 Eq 4.4
Where F is the feeding rate of induction solution; OTR is defined as 150 mmol•L-1•h-1;
rMet and rSor represent fractions of Cmethanol and Csorbitol in the induction solution.
4.3 Results and discussion
4.3.1 Measurement of oxygen transfer coefficient
In order to define a fermentation process with an OTR of 150 mmol•L-1•h-1, kLa of the
Infors bioreactor was measured using the dynamic method. Dynamic method is ideal
to study the change of kLa under different operation conditions (Garcia-Ochoa and
Gomez, 1998, Garcia-Ochoa and Gomez, 2009). In this method, liquid is deoxygenated
and oxygenated by nitrogen and air flows, respectively. DO profile is recorded in
oxygenation phase, and the kLa value is determined from the slope of ln(1-Ct/C*) vs
time graph.
The bioreactor has four one-litre vessels. Each vessel has two impellers with the
diameter of 0.038 m. The same DO probe was used for the kLa measurement of four
vessels. Response time of the DO probe was determined by putting the probe in a beaker
containing deoxygenated water (DO = 0%) and transferring it rapidly to the vessel that
was filled with oxygenated water (DO=100%). The response time is defined as the time
period that the reading reached ~80%. The probe was determined to have a response
time of 2.6 sec.
93
Each vessel was filled with 700 ml BSM medium before the kLa measurement.
Temperature and air flow were set at 30� and 1.4 vvm. Fig 4.1A shows the change of
DO profile with time during kLa measurement. From 5 sec to 45 sec, the DO dropped
linearly from 105.6% to 13.7%. It nearly dropped to 0% after 50 s of deoxygenation.
Once air flow was ventilated, DO rapidly increased to over 80% in 25 sec and then
slowly increased to 100% afterward.
kLa values were measured at agitation speeds of 300, 500, 800 and 1100 RPM,
respectively. As shown in Fig.4.1B, the kLa values of four vessels were similar at the
same agitation speed. The kLa enhanced from 50.3±7.5 h-1 to 391.3±25.3 h-1 when
agitation speed increased from 300 RPM to 1100 RPM. A linear equation with variables
of kLa and agitation speed was obtained: kLa=(0.42±0.08)N-(49.48±20.43), R2=0.94.
The kLa obtained in this study was comparable to that in previous reports (Garcia-
Ochoa and Gomez, 2004).
The impeller of Infor bioreactor has a maximum agitation speed of 1100 RPM. When
the air flow is ventilated at 1.4 vvm, the OTR is calculated to be 95.45 mmol•L-1•h-1.
In order to obtain an OTR of 150 mmol•L-1•h-1, it is calculated that the maximum
oxygen fraction in aeration should be set at 33% (v/v). During the fermentation, air
flow was maintained at the constant rate of 1.4 vvm. The agitation initially started from
300 RPM and gradually increased to 1100 RPM by correlating with DO. When the
desired DO could not be maintained with the maximum agitation, pure oxygen was
merged into the air flow until the oxygen fraction reached 33% (v/v).
94
Figure 4.1 kLa measurement using dynamic method. (A) shows the representative
profile of DO in the kLa measurement. The profile is divided into deoxygenation and
oxygenation phases where N2 and air are ventilated, respectively. The vessel was filled
with 700 ml BSM medium. Temperature, aeration and agitation were set at 30�, 1.4
vvm and 300 RPM, respectively. (B) The kLa values of four vessels as a function of
agitation speed. Temperature and aeration were set at 30� and 1.4 vvm, and kLa was
measured at agitation speeds of 300, 500, 800 and 1100 RPM.
0 20 40 60 80 100 1200
20
40
60
80
100
120
Time sec
DO
% N2 O2
A
0 300 600 900 12000
100
200
300
400
500
Agitation RPM
k La
h-1
Bio-1 Bio-2
Bio-3 Bio-4
B
95
4.3.2 Induction in an oxygen limited condition
When the OTR of bioreactor is constrained under 150 mmol•L-1•h-1, standard methanol
feeding will cause oxygen depletion and methanol accumulation. Although maintaining
DO at 20%~30% is desirable in most studies, induction in an oxygen limited condition
where DO dropped to nearly 0% (OLFB fermentation) was also reported. It even had
higher product yield and purity compared to oxygen unlimited fermentation
(Charoenrat et al., 2005, Khatri and Hoffmann, 2006). Besides, it was found that
residual methanol concentration affected both cell growth and product yield and the
effect depended on cell strains (Khatri and Hoffmann, 2006, Barrigón et al., 2013).
In this study, the OLFB fermentation was performed in the bioreactor with an OTR of
150 mmol•L-1•h-1. In the Chapter 3, it has been shown methanol over accumulation
inhibited the cell growth (Fig 3.). Therefore, concentration of residual methanol was
controlled by a feedback control system, and the OLFB fermentations with residual
methanol concentrations of 0~5 g•L-1 and 5~10 g•L-1 were studied, respectively.
Fig 4.2A shows the fermentation profile of OLFB fermentation with residual methanol
concentration of 0~5 g•L-1. The temperature and pH were maintained at 30� and 5.0
during the fermentation. The DO decreased from 100% to 30% after cells were
inoculated for several hours. The agitation gradually increased to 1100 RPM to
maintain the DO at 30%. Once glycerol feeding was started, the DO sharply decreased
from 30% to nearly 0% and maintained at nearly 0% during the glycerol feeding and
induction stages. Two spikes of agitation speed were observed which indicated the
consumption of glycerol in batch and fed-batch phases. Similar fermentation profile
was also observed in the OLFB fermentation with residual methanol concentration of
5~10 g•L-1.
Fig 4.2B shows the growth curves of OLFB fermentation with residual methanol
concentration of 0~5 g•L-1 and 5~10 g•L-1. The biomass concentration reached 49.4
g•L-1 and 56.2 g•L-1, respectively, before induction. When the residual methanol
concentration was maintained within 0~5 g•L-1, the DCW kept increasing in the whole
induction and reached 108.2 g•L-1 after 96 hours of induction. Whilst the DCW nearly
stopped increasing after 48 hours of induction and a final DCW of 79.1 g•L-1 was
obtained when the residual methanol concentration was maintained within 5~10 g•L-1.
96
Figure 4.2 Fermentation profile (A) and growth curve (B) of the OLFB fermentation.
Change of temperature, pH, agitation speed and DO were shown in the fermentation
profile. Growth curves of P. pastoris in the OLFB fermentation with residual methanol
concentration of 0~5 g•L-1 and 5~10 g•L-1 were shown. Three replicates were used for
each point and data was shown by mean±SD (n=3).
0 24 48 72 96 1200
30
60
90
120
150
Time h
DC
W g•L-1
Met0-5 Met5-10
Inducton
B
97
It is critical to control the residual methanol concentration in the OLFB fermentation.
The residual methanol can be measured by on-line and off-line tools. The online tool
such as methanol sensor based on photoelectric plethysmograph was reported
previously (Khatri and Hoffmann, 2006). The offline tools include HPLC, gas
chromatography and enzymatic reaction-based method (Minning et al., 2001,
Parpinello and Versari, 2000, Kučera and Sedláček, 2017).
Since on-line methanol sensor was not available in this study, an off-line HPLC based
method was used. Samples were taken intervally, and residual methanol was measured
by HPLC. Methanol feeding rate was turned up and down based on the residual
methanol concentration. As shown in Fig 4.3, the residual methanol concentration was
successfully controlled within the aimed ranges, 0~5 g•L-1 and 5~10 g•L-1. However, it
failed to maintain the residual methanol at a constant concentration. In a previous report,
residual methanol was maintained at constant level by using a feedback control system
with a commercial on-line methanol analyzer (MC168) (Ramon et al., 2004). The
residual methanol concentration was successfully maintained at 4.0 g•L-1 for over 60
hours. It was shown that the residual methanol concentration significantly affected cell
growth in the OLFB fermentation. Thus, using the on-line methanol analyser can be
considered in the future work.
Figure 4.3 Residual methanol concentration in the OLFB fermentations. Samples were
taken after 16, 24, 40, 48, 64, 72, 88 and 96 hours of induction and the methanol
concentration was determined using HPLC. One sample was analysed for each point.
0 24 48 72 960
3
6
9
12
Induction time h
Res
idua
l met
hano
l g•L-1
Met0-5 Met5-10
98
Cell viabilities in the OLFB fermentations were studied. As shown in Fig 4.4, the cell
viabilities decreased to ~95.0% after 24 h of induction in both of the OLFB
fermentations. At the harvest time, viabilities decreased to 94.3% and 92.8%,
respectively, in the OLFB fermentation with the residual methanol concentration of
0~5 g•L-1 and 5~10 g•L-1.
At the harvest time, the cell viability in the OLFB fermentation with residual methanol
concentration of 5~10 g•L-1 was comparable to the viability of standard methanol
induction, as described in the Section 3.3.3, Chapter 3. The proteomic study in the
Section 3.3.4, Chapter 3 showed that loss of the viability caused release of the
intracellular HCPs and proteases. Therefore, the product purity is likely to be one of
the critical attributes of the OLFB fermentation.
The viability at harvest time was higher when the residual methanol concentration of
0~5 g•L-1 was used, which was consistent to a previous study. In a cell line expressing
recombinant scFv, less than 2.0% cells died when the residual methanol was maintained
below 3g•L-1, while the mortality increased to 4.0% when the residual methanol was
over 3g•L-1 (Khatri and Hoffmann, 2006). Therefore, it is necessary to control the
residual methanol concentration in OLFB fermentation which influences cell growth
and viability.
Figure 4.4 Change of the cell viability in the OLFB fermentations. The residual
methanol concentrations were maintained within 0~5 g•L-1 and 5~10 g•L-1, respectively.
Samples were taken after 16, 24, 40, 48, 64, 72, 88 and 96 hours of induction and cell
viability was measured by flow cytometry. One sample was analysed for each point.
0 24 48 72 9690
92
94
96
98
100
Time of induction h
Cel
lula
r via
bilit
y %
Met5-10Met0-5
99
Product yields in the OLFB fermentations with residual methanol concentration of 0~5
g•L-1 and 5~10 g•L-1 were studied using protein gel and enzymatic assay. As shown in
Fig 4.5, the product bands were clearly visible after 24 h of induction in both of the
OLFB fermentations. The intensity of product bands kept increasing from 24 h to 96 h
induction. At the same time point, the intensity in the OLFB fermentation with residual
methanol concentration of 0~5 g•L-1 was higher than that with residual methanol of
5~10 g•L-1. Only a few HCP bands were visible on the gel which indicated the high
product purity. The major HCP band has a molecular weight of ~65 kDa, which is likely
to be the extracellular protein X1 (EPX1). Intensities of the EPX1 band were
comparable in both of the OLFB fermentations.
Result of the enzymatic assay was in agreement with the protein gel assay. The
aprotinin concentration kept increasing from 0.59 g•L-1 to 1.10 g•L-1 in OLFB
fermentation with the residual methanol concentration of 0~5 g•L-1 and increasing from
0.62 g•L-1 to 0.87 g•L-1 in the OLFB fermentation with the residual methanol
concentration of 5~10 g•L-1. The product yield was enhanced by 26% by maintaining
the residual methanol concentration below 5 g•L-1. Therefore, it is necessary to
optimize the residual methanol in OLFB fermentations to obtain a high product yield.
In future work, the impact of residual methanol concentration on product yield can be
better studied by maintaining methanol concentration at constant levels by using a
methanol sensor.
The impact of residual methanol concentration on product yield seems to vary on strains.
In one study, 3.0% (m/v) of methanol was the optimal concentration for the production
of recombinant scFV (Khatri and Hoffmann, 2006). However, 1.0% (m/v) of residual
methanol was observed to inhibit the expression of recombinant Rhizopus oryzae lipase
in another study (Barrigón et al., 2013).
Compared to the standard methanol induction as described in the Section 3.3.3, Chapter
3, the product yields in OLFB fermentations were reduced by 35% and 47%,
respectively. It is likely be attributed to the lower biomass concentration obtained in
the OLFB fermentations. The biomass concentrations at harvest time were reduced by
17% and 38%, respectively, compared to the standard methanol induction. However,
the high methanol feeding rate used in standard induction cannot be maintained in the
OTR-limited bioreactor. In that situation, the product yield in OLFB fermentation with
the residual methanol concentration of 5 g•L-1 looks acceptable.
100
Figure 4.5 Product yields in the OLFB fermentations. The residual methanol
concentrations were maintained within 0~5 g•L-1 and 5~10 g•L-1, respectively. (A)
Samples were taken after cells were induced for 24, 48, 72 and 96 hours. Soluble
proteins in the supernatant were analyzed using protein gel. 5 μl sample was loaded
into each well. (B) Samples were taken after cells were induced for 16, 24, 40, 48, 64,
72, 88 and 96 hours. Aprotinin in the supernatant was quantified using the enzymatic
assay. Three replicates were used for each point and data was shown by mean�SD
(n=3).
0 24 48 72 960.0
0.4
0.8
1.2
Time h
Apr
otin
in g
•L-1
Met0-5 Met5-10B
101
4.3.3 Induction in an oxygen unlimited condition
In the small scale fermentation, DO can be maintained at an optimal value, normally
20% or 30% of saturation (Singh et al., 2008, Invitrogen, 2002) by supplementing
sufficient pure oxygen into the gas inlet. However, it is difficult to be achieved at large
scale. In the bioreactor with a limited OTR, using the standard methanol induction will
decrease the DO to ~0%. Therefore, reducing the methanol feeding rate is required.
However, it may compromise the cell growth and product yield.
Based on the measured OUR of standard methanol induction (Fig 3.7), it is estimated
that consuming per millilitre methanol will require 22.2 mmol O2 by P. pastoris. In the
bioreactor with an OTR of 150 mmol•L-1•h-1, a maximum methanol feeding rate of 6.7
ml•L-1•h-1 can be used to maintain the DO at 30%. Compared to the standard methanol
induction, the feeding rate is declined by 38%.
Sorbitol has a lower enthalpy of combustion than methanol. Based on enthalpies of
combustion of methanol and sorbitol (-504.3 kJ•C-mol−1 versus −727 kJ•C-mol−1), it is
calculated that 15.4 mmol O2 is consumed by each millilitre 0.75 g•L-1 sorbitol solution.
Theoretically, partially replacing methanol with sorbitol is able to enhance the carbon
feeding rate while maintain the DO at 30%. As calculated by Eq 3.4, the rate can be
enhanced to 7.7 ml•L-1•h-1 by using sorbitol/methanol (60% Cmethanol) mixture. The
higher carbon feeding rate is likely to enhance the cell growth and product yield
compared to slower methanol induction. Besides, increasing sorbitol fraction in the
sorbitol/methanol mixture will further enhance the carbon feeding rate. The rate can be
enhanced to 8.0 ml•L-1•h-1 and 8.3 ml•L-1•h-1, respectively, by using sorbitol/methanol
(50% Cmethanol) and sorbitol/methanol (40% Cmethanol) mixtures. However, reducing the
methanol fraction it is likely to reduce the promoter activity, which will decrease the
product yield.
In the bioreactor with an OTR of 150 mmol•L-1•h-1, sorbitol/methanol mixtures with
100%, 60%, 50% and 40% (Cmethanol) of methanol were used for P. pastoris induction.
The DO is maintained at 30% by using the feeding rates shown in Table 4.2. Cell
growth, viability, product yield of these induction strategies was compared. Besides,
induction with the DO of 30% was compared with the OLFB fermentations, as
described in the Section 4.3.1, Chapter 4.
102
Induction solution
Feeding rate
ml•L-1•h-1
100% Methanol 6.7
60% Methanol 7.7
50% Methanol 8.0
40% Methanol 8.3
Table 4.2 Feeding rates of different induction solutions
Fig 4.6A shows the representative fermentation profile of P. pastoris in the OTR
limited bioreactor. The cell culture was initially grown on glycerol and then induced by
pure methanol. Temperature and pH were maintained at 30� and 5.0 in the
fermentation. The DO decreased dramatically in the first few hours of fermentation.
Once it dropped to ~30%, the agitation speed increased gradually to maintain the DO
level. In the first 30 hours, two DO spikes were observed which indicated the
exhaustion of batch and fed glycerol. The agitation speed also decreased when the DO
spikes occurred. Once pure methanol was fed, the agitation immediately increased to
the maximum speed and worked at that speed in the whole induction. The DO was
successfully maintained at 30% when 6.7 ml•L-1•h-1 of pure methanol was fed. Similar
profiles were observed when sorbitol/methanol mixtures with 60%, 50% and 40%
(Cmethanol) of methanol were used.
Fig 4.6B shows the growth curve of P. pastoris in the OTR limited bioreactor. The
biomasses reached 42.9±3.0 g•L-1 after cell growth on batch and fed glycerol, and
different vessels had comparable values. When pure methanol was used, the DCW kept
increasing during the whole induction and reached 90.2 g•L-1 after 96 hours. The DCW
was enhanced to 114.6 g•L-1, which was 27% higher compared to methanol induction,
by using sorbitol/methanol mixture with 60% (Cmethanol) of methanol. It was further
enhanced to ~130.0 g•L-1 when the methanol fraction was declined to 50% and 40%
(Cmethanol).
Compared to the standard methanol induction described in the Section 3.3.2, Chapter
3, the DCW decreased by 30% when 6.7 ml•L-1•h-1 of pure methanol was used, whilst
comparable biomass was obtained by using sorbitol/methanol mixtures with 50% or 40%
(Cmethanol) of methanol. Compared to the OLFB fermentation with residual methanol
103
concentration of 0~5 g•L-1, methanol induction with DO of 30% reduced the DCW by
16%, whilst the sorbitol/methanol (50%, Cmethanol) mixed induction enhanced it by 20%.
In the OTR-limited bioreactors, sorbitol/methanol mixed induction advantages over
methanol induction in terms of enhancing the biomass and maintaining the DO.
Figure 4.6 Fermentation profile (A) and growth curve (B) of P. pastoris in the oxygen
unlimited condition. After cell growth on glycerol, cells were induced by feeding
methanol at the rate of 6.7 ml•L-1•h-1. Growth curves of P. pastoris induced by limited
feeding of methanol or sorbitol/methanol mixtures. Three replicates were used for each
point and data was shown by mean�SD (n=3).
0 24 48 72 96 1200
30
60
90
120
150
Time h
DC
W g•L-1
100%Methanol 60%Methanol
50%Methanol 40%Methanol
B
104
During the feeding of methanol and sorbitol/methanol mixtures, samples were taken
after 16, 24, 40, 48, 64, 72, 88, and 96 hours of induction. After the supernatant was
collected by centrifugation, residual methanol in the medium was measured using
HPLC. As shown in Fig 4.7, the residual methanol concentration was less than 1g•h-1
in both of the methanol and sorbitol/methanol mixed induction strategies. It was even
below the detecting limitation at some time points.
Differently from the OLFB fermentation (Fig 4.3), the methanol was nearly exhausted
when the DO was maintained at 30%. The residual methanol profile was more
comparable to that of standard methanol induction where the DO was also set at 30%
(Fig 3.8). As it is shown in the study of OLFB fermentation, the residual methanol
concentration could not be maintained stable without using online methanol sensor.
Although the HPLC based method is an alternative, it could only keep the residual
methanol within a specific range. There is the probability that the residual methanol
exceeds the desired range at some time points, which may cause failure of fermentation.
In that situation, it will make the process control much easier by maintaining the DO at
30% rather than at 0%. On the other aspect, nearly exhaustion of methanol may
compromise the promoter induction and decrease the product yield.
Figure 4.7 Residual methanol concentration in the oxygen unlimited condition. Pure
methanol and sorbitol/methanol mixtures with 60%, 50% and 40% (Cmethanol) of methanol were
used for induction. Samples were taken after 16, 24, 40, 48, 64, 72, 88 and 96 hours of
induction and residual methanol in the supernatant was measured using HPLC. One
sample was analysed for each point.
0 24 48 72 960
3
6
9
12
Induction time h
Res
idua
l met
hano
l g•L-1
100%Methanol 60%Methanol
50%Methanol 40%Methanol
105
Cell viabilities in the methanol and sorbitol/methanol mixed induction strategies were
studied. As shown in Fig 4.8, the cell viabilities changed in a similar manner among
different induction strategies. The cell viabilities were over 98% in four vessels before
induction. They kept decreasing slowly with the feeding of induction solutions. After
96 hours of induction, an average viability of 97.5±0.6% was obtained. Although the
viability of sorbitol/methanol (60%, Cmethanol) mixed induction was lower, it has no
significant difference compared with viabilities of the other induction strategies.
Compared to the OLFB fermentations, induction with DO of 30% resulted in higher
cell viabilities at the harvest time (93.5±0.8 versus 97.5±0.6%, p=0.004). The
proteomic study in the Section 3.3.4, Chapter 3 showed that loss of the viability caused
release of the intracellular HCPs and proteases. Therefore, induction with DO of 30%
is likely to result in higher product purity comparing to the OLFB fermentations, which
can be validated in the future study.
Compared to standard methanol induction at the rate of 10.8 ml•L-1•h-1 as described in
the Section 3.2.3, Chapter 3, reducing methanol feeding rate to 6.7 ml•L-1•h-1 (167
mmol Carbon•L-1•h-1) significantly improved the cell viability. It is likely to be because
less toxic by-product was formed when the slower methanol feeding is used.
Figure 4.8 Cell viabilities in the oxygen unlimited condition. Samples were taken after
16, 24, 40, 48, 64, 72, 88 and 96 hours of induction by methanol or sorbitol/methanol
mixtures. Cell viabilities were measured by flow cytometry. One sample was analysed
for each point.
0 24 48 72 9690
92
94
96
98
100
Induction time h
Cel
lula
r via
bilit
y %
100%Methanol 60%Methanol
50%Methanol 40%Methanol
106
Although sorbitol/methanol mixed induction enhanced cell growth in the OTR-limited
bioreactor, it would be not beneficial without increasing the product yield. The product
yields in the methanol and sorbitol/methanol mixtures induced fermentations were
studied using protein gel and enzymatic assay. As shown in Fig 4.9, the intensities of
product band in methanol and sorbitol/methanol (60%, Cmethanol) mixed induction
strategies were comparable to each other. When sorbitol/methanol mixtures with 50%
and 40% (Cmethanol) of methanol were used, the intensities were reduced. Only a few
bands of HCPs were visible in all the induction strategies. The major HCP band has a
molecular weight of ~65 kDa, which is likely to be the extracellular protein X1 (EPX1).
The product yields were quantitatively determined using the enzymatic assay. The
aprotinin concentration increased in a similar rate in the four inductions. It increased
from 0.29 g•L-1 to 0.85 g•L-1 when pure methanol induction was used and increased
from 0.29 g•L-1 to 0.79 g•L-1 when sorbitol/methanol (40%, Cmethanol) mixture was used.
After 96 hours of induction, the aprotinin concentration reached 0.85, 0.86, 0.78 and
0.79 g•L-1, respectively.
As described above, sorbitol/methanol (40%, Cmethanol) mixed induction enhanced the
biomass by 27% compared to the pure methanol induction. However, it reduced the
product yield by 8% (p=0.001). The specific productivity was much lower in the mixed
induction (0.062 mg•g DCW-1•h-1 versus 0.096 mg•g DCW-1•h-1). It is likely that the
sorbitol/methanol mixed induction compromised the induction of promoter. Since high
cell density increases challenges in product recovery (Salte et al., 2006, Lopes and
Keshavarz�Moore, 2012), induction using pure methanol is preferred rather than using
sorbitol/methanol mixtures. Impact of sorbitol/methanol mixed induction on product
synthesis depends on cell strains. It was reported that the specific productivity of β-
galactosidase was even slightly enhanced when sorbitol/methanol mixed induction was
used (Niu et al., 2013). If that cell line is cultured in an OTR-limited bioreactor, using
sorbitol/methanol mixed induction may enhance both biomass and product yield.
Compared to the OLFB fermentation with residual methanol of 0~5 g•L-1, the methanol
induction with DO of 30% reduced the product yield by 18%, while the
sorbitol/methanol mixed inductions reduced the yield by 26%. However, the protein
gel looks much clearer when the DO was maintained at 30%, which indicated a higher
product purity. Besides, the enhancement of viability may reduce the proteases released
into the medium.
107
Figure 4.9 Product yields in the oxygen unlimited condition. (A) Samples were taken
after 96 hours of induction. Soluble proteins in the supernatant were analyzed using
protein gel. 10 μl sample was loaded into each well. Aprotinin was shown by the
symbol of arrow. (B) Samples were taken after 16, 24, 40, 48, 64, 72, 88 and 96 hours
of induction. Aprotinin in the medium was quantified using the enzymatic assay. Three
replicates were used for each point and data was shown by mean�SD (n=3).
16 24 40 48 64 72 960.0
0.2
0.4
0.6
0.8
1.0
Induction time h
Apr
otin
in g
•L-1
40%Methanol50%Methanol
60%Methanol100%MethanolB
108
4.3.4 Measurement of AOX enzyme activity
In the last section, it was shown that partially replacing methanol with sorbitol reduced
the specific productivity of P. pastoris cell. It is likely to due to the lower activity of
promoter in the mixed induction compared to methanol induction. The activity of AOX
enzyme was used to show the induction level of pAOX1 under different induction
strategies. The activities in different induction strategies were compared.
AOX enzyme locates in peroxisome of P. pastoris cells (Cámara et al., 2017). In order
to extract the enzyme, the condition of cell breakage is optimized. Breakage of P. pastoris cell is challenging because of the mechanically rigid cell wall. High pressure
homogenizer is widely used where cells are disrupted by high velocity, cavitation, fluid
shear and decompression. This method needs a large volume of sample and is not
compatible for small scale study. In this study, adaptive focused acoustics (AFA),
which break cells through focused bursts of ultrasonic acoustic energy, was used as an
alternative option. The AFA has been shown to be an efficient approach to break P. pastoris cells (Woodhouse, 2016, Bláha et al., 2017). An AFA device, Covaris E210,
only requires one millilitre sample each time.
In this study, cell culture was taken from the bioreactor and the medium was removed
by centrifugation. The P. pastoris cells were washed three times by cold 200 mM
phosphate buffer. The cells were re-suspended using the buffer after washing.
Operating parameters of Covaris E210 such as duty factor, intensity, cycles per burst
were set on the maximum values to obtain the best efficiency of cell breakage.
The impact of biomass concentration and treating time on efficiency of cell breakage
was studied. The cells were diluted to concentrations of 5, 10, 20, 30, and 40 g WCW
•L-1 using the buffer and were treated with the ultrasonic acoustic for 300, 600, 1200,
1800 sec, respectively. After the treatment, the cells were centrifuged at 12000RM for
10min and the supernatant was collected. Total protein in the supernatant was
quantified using BCA method and specific protein release (mg protein • g WCW-1) was
calculated in each condition.
No protein was detected by the BCA in samples without treatment of ultrasonic acoustic.
A maximum specific protein release of 41.5 mg protein • g WCW-1 was obtained when
40.0 g WCW •L-1 was treated for 1800 sec (Fig 8.4, Appendix). When 20.0 g WCW
•L-1 was treated for 1200 sec, the specific protein release reached 37.5 mg protein • g
109
WCW-1 which was comparable to the maximum value. Therefore, 20.0 g WCW •L-1
and 1200 s were selected as the cell break condition to save time sample and time.
The DCW and WCW of P. pastoris cells were correlated by DCW= 0.289WCW-4.99,
R2=0.99 (Fig 8.5, Appendix). The concentration of 20.0 g WCW•L-1 used in this study
was consistent with 4.06 g DCW•L-1. In a previous study, it was estimated that 48.3%
of contents in dry P. pastoris cells were proteins (Ihl and Tagle, 1974). Theoretically,
the maximum protein release is about 98 mg in each gram of wet cell. And the
efficiency of cell breakage by Covaris was about 38.5%. The efficiency was considered
to be acceptable since our aim was not to release all the intracellular proteins but to
release some of the AOX enzyme.
An enzymatic assay of AOX enzyme has been developed by Sigma-Aldrich (Sigma-
Aldrich-b). In the assay, the AOX enzyme catalyses the oxidation of methanol and
formed hydrogen peroxide (H2O2). H2O2 is reduced by ABTX and the oxidized ABTX
is determined by measuring OD405 of the reaction mixture. A total volume of 3.01 ml
is used in the assay and the samples can only be measured one by one using
spectrophotometer. In this study, the reaction volume was scaled down linearly with a
factor of 10. After the scale-down, the reaction could be done in 96 well plates and the
reaction rate could be measured by microplate reader.
The activity of AOX enzyme was determined from the reaction rate as described in the
Section 2.9.12, Chapter 2. However, the activity could not be determined because the
reaction reached stationary phase very fast. Therefore, diluting the samples to an
appropriate concentration is necessary prior to the assay.
In order to find the optimal concentration, the samples were diluted to different
concentrations of total protein. The reaction rates using the diluted samples were
measured. As shown in Fig 4.10, the reaction rate increased with the concentration of
total protein. When 0.48 mg•ml-1 and 0.24 mg•ml-1 of total proteins were used, OD405
of the reaction mixture reached stationary phase after 5 min and 7 min of reaction,
whilst the reaction rates were too slow if the concentration of total protein was lower
than 0.06 mg•ml-1. Therefore, samples would be diluted to a total protein concentration
of 0.12 mg•mL-1 prior to the activity assay in the future. The total activity assay of AOX
enzyme was obtained after multiplying the dilution factor of sample.
110
Figure 4.10 Activity of AOX enzyme at different concentrations of total protein. 20
g•L-1 of wet cells were treated for 1200 s. The released proteins were diluted to different
concentrations varying from 0 mg•mL-1 to 0.48 mg•mL-1. The diluted samples were
added into the reaction mixture and absorbance at 405 nm was measured consecutively
by microplate reader every two minutes. Three replicates were used for each
measurement and data was shown by mean�SD (n=3).
Activities of AOX enzyme from different induction strategies used in the bioreactor
with an OTR of an OTR of 150 mmol•L-1•h-1 were compared. Fig 4.11A shows the
AOX enzyme activities in oxygen unlimited fermentation. It was observed that the
activities of AOX enzyme decreased over time during the induction. Compared to the
pure methanol induction, sorbitol/methanol mixed induction resulted in lower activities
of AOX enzyme. It is likely to be the reason that sorbitol/methanol mixed induction
only enhanced the cell growth but not product yield.
In the OLFB fermentations, activities of AOX enzyme also decreased over time. The
enzyme activity was also affected by the residual methanol concentration. After 48
hours of induction, the enzyme activity was much higher when the residual methanol
concentration was kept in the range of 0~5.0 g•L-1.
After 48 hours of induction, the activity of AOX enzyme was much higher in the OLFB
fermentations compared to the induction in oxygen unlimited condition. It is probably
due to the sufficient methanol in the medium in the OLFB fermentation.
0 1 2 3 4 5 6 7 8 90.0
0.5
1.0
1.5
2.0
2.5
Time min
Abs
orba
nce
at 4
05nm Blank
0.0035mg•ml-1
0.007mg•ml-1
0.015mg•ml-1
0.030mg•ml-1
0.060mg•ml-1
0.12mg•ml-1
0.24mg•ml-1
1 2 3 4 5 6 7 8
0.48mg•ml-1
111
Figure 4.11 Comparing the activities of AOX enzyme from different induction
strategies. Samples were taken after 16, 24, 40, 48, 64, 72, 88 and 96 hours of induction.
20.0 g•L-1 of wet cells were sonicated for 1200 sec to release the AOX enzyme. The
released proteins were quantified by BCA assay and AOX enzyme activity was
measured using the method described in the Section 2.9.12, Chapter 2. The activities
of AOX enzyme from oxygen unlimited fermentations and OLFB fermentations were
shown in A and B, respectively. One sample was analysed for each point.
0 24 48 72 960
400
800
1200
1600
Induction time h
AO
X a
ctiv
ityun
its•m
g-1 to
tal p
rote
in
100% Methanol 60% Methanol
50% Methanol 40% Methanol
A
0 24 48 72 960
400
800
1200
1600
Induction time h
AO
X a
ctiv
ityun
its•m
g-1 to
tal p
rote
in
Met5-10B Met0-5
112
4.4 Conclusion
Methylotrophic P. Pastoris has been developed into an efficient host for heterologous
protein production. However, supplying sufficient oxygen in standard methanol
induction is quite challenging in most large-scale bioreactors. In this paper, several
induction strategies were compared in a benchtop bioreactor having similar oxygen
transfer rate (OTR) as large-scale.
kLa of the bioreactor was characterized using dynamic method to define a fermentation
process with an OTR of 150 mmol•L-1•h-1. The kLa was found to correlate with the
agitation speed and was consistent in different vessels. In the OTR-limited bioreactor,
the standard methanol induction caused oxygen depletion and methanol accumulation.
It was observed that the residual methanol concentration significantly affected cell
growth and product expression. Therefore, strict control of the residual methanol is
required to avoid that the methanol accumulates to an inhibitive level.
Induction using slower methanol feeding avoided the oxygen depletion and eliminated
the need for control of residual methanol. But the biomass and product yield were
reduced by 15.0% and 19.0%, respectively, compared to the OLFB fermentation. The
carbon feeding rate could be enhanced by using sorbitol/methanol mixed induction.
Compared to pure methanol induction, the sorbitol/methanol mixed induction only
enhanced cell growth but not product yield, consequently, resulted in a lower specific
productivity. Since high cell density challenges the product recovery, pure methanol
induction seems to be preferable than sorbitol/methanol mixed induction in that
condition.
Activities of AOX enzyme from different induction strategies were compared.
Compared to the pure methanol induction, sorbitol/methanol mixed induction reduced
the enzyme activities. It may explain why the mixed induction strategies only enhanced
the cell growth but not product yield. The OLFB fermentations had higher activities of
AOX enzyme than oxygen unlimited fermentations in late stage of the induction.
This study helps to determine the best induction strategies of P. pastoris in OTR-limited
bioreactors. The OLFB fermentation with residual methanol concentration of 0~5.0
g•L-1 is the best in terms of obtaining the highest product yield. However, process
control of the OLFB fermentation is challenging. The pure methanol induction in an
oxygen unlimited condition is preferable when the online methanol sensor is not
available.
113
Chapter 5 Prediction of cell robustness and centrifugal
dewatering using scale-down methodology
5.1 Introduction
In the first chapters, it has been established that sorbitol/methanol mixed induction
benefited upstream by reducing oxygen uptake, enhancing cell viability and improving
product purity. However, the impact of mixed induction on early downstream
processing still remains unclear.
Centrifugation is commonly used to separate product from the high density P. pastoris
culture in industry. However, the cells may be damaged by shear stress at the feeding
zone of large scale centrifuge (Boychyn et al., 2000). In the Chapter 3, it was shown
that around 8.0% cells lost viability in the end of methanol induction (Fig 3.9). Shear
stress in the centrifuge may cause further lysis of these dead cells. Cell lysis will release
cell debris, host DNA and proteins into the medium, which will reduce clarification and
product purity. It is necessary to study the impact of shear stress on the robustness of
P. pastoris cells.
Efficiency of centrifuge can be evaluated by clarification of cell culture. Clarification
refers to the efficiency of separating solid from liquid. Getting high clarification is
desirable in the centrifugation because any remaining cell solid or debris in the
supernatant will foul chromatographic resins in further downstream (Hutchinson et al.,
2006). Clarification efficiency is influenced by centrifugal conditions and properties of
the feeding liquid. Disc stack centrifuges is able to achieve higher clarification
efficiency than tubular bowl and scroll continuous centrifuges. Clarification is also
affected by operating condition of centrifuges such as bowl speed and liquid feeding
rate. Meanwhile, cell culture properties like cell size, shape, density and liquid viscosity
also affect clarification.
Dewatering of cell culture is another consideration to evaluate centrifugation.
Dewatering refers to the efficiency to purge liquid away cell solids by centrifugal force
(Lopes, 2013). High dewatering level is desired in P. pastoris centrifugation since any
liquid remaining in the sediment results in product loss. Dewatering at large scale is
affected by the centrifugation speed and liquid flow rate (Lee et al., 2003b). Besides, it
is influenced by cell culture properties such as cell morphology and liquid rheology
(Salte, 2006).
114
In a previous study, an ultra scale-down methodology was established to predict the
dewatering in a pilot and industrial scale disc stack centrifugation (Lopes and
Keshavarz�Moore, 2012). It was shown that dewatering was affected by the choice of
P. pastoris strains (Lopes et al., 2012). Whether induction strategies influence the
centrifugal dewatering has not been studied.
The objective of this chapter was to
1) predict the cell robustness to shear stress in large scale centrifuges using an ultra
scale-down device.
2) characterize the properties of cell culture that may influence dewatering in
centrifugation.
2) predict the dewatering of cell culture using an ultra scale-down model of pilot and
industrial scale centrifuges.
3) study the impact of induction strategies on dewatering of cell culture.
115
5.2 Theoretical considerations
5.2.1 Mimic of shear in large scale centrifuges
An ultra scale-down shear device was developed to produce energy dissipation rates (ε)
that were equivalent to those generated in the feeding zone of large scale centrifuges
(Boychyn et al., 2004). The device contains a stainless disc (diameter 40 mm, thickness
0.14 mm) in a cylindrical chamber (diameter 50 mm). The chamber has a capacity of
20 ml to hold cell culture. Energy dissipation rate (ε) and rotation speed (N) of the disc
are correlated by Eq 5.1, as being characterized by the computational fluid dynamics
(CFD) (Boychyn et al., 2001). By using this device, the shear stress generated in a large
scale centrifuge can be mimicked by rotating the disc at a specific speed. By using the
device, the impact of shear stress on cell integrity can be studied with a small volume
of cell culture.
ε=1.7•10-3•N3.71 Eq 5.1
5.2.2 Scale down of large scale centrifuges
Sigma (Σ) theory is commonly used to scale down large scale centrifuges (Ambler,
1959, Boychyn et al., 2000). Sigma (Σ) concept of equivalent settling area, which was
developed by Ambler, refers to the surface area required to achieve the same
centrifugation results as the gravitational force. It is used to predict the performance of
large scale centrifuges using benchtop ones and to compare centrifuges with different
sizes, designs and operating modes (Boychyn et al., 2004). By using Eq 5.2, flow rates
at scale can be mimicked by lengths of centrifugation time at benchtop scale.
QCΣ
=V"5N
t"5NC"5NΣ"5N Eq 5.2
where Q is the liquid flow rate at large scale, Σ and Σlab are settling area of large and
laboratory scale centrifuges, Vlab is the sample volume used in laboratory scale, tLab is
the settling time of sample at laboratory scale, C and CLab are correlation factors for
deviation of non-ideal liquid in large and laboratory scale centrifuge. For a laboratory
scale benchtop centrifuge, Σlab can be calculated by Eq 5.3 (Maybury et al., 1998).
116
Σ"5N =V"5NO4(3 − 2x − 2y)
6gln(2R4R4+RT
) Eq 5.3
where ω is the angular velocity of centrifuge, R2 and R1 are outer and inner radius of
centrifuge rotor, x and y are fractional time of acceleration and deceleration in
centrifugation, g is the gravitational acceleration.
For a disc stack centrifuge, ΣDs can be calculated by Eq 5.4 (Boychyn, Yim et al. 2004).
ΣUV =2πXO4(R4
Y − RTY)
3gtanθ Eq 5.4
where n is the disc numbers, θ is the half disc angle.
5.2.3 Calculation of clarification and dewatering
Centrifugation speed and residence time are critical factors in predicting clarification
using scale-down approach, whilst dewatering is also affected by solid heights (Chu
and Lee, 2001). In the scale-down model, it is necessary to maintain the same relative
centrifugal force (RCF) as large scale. Liquid flow rate determines the residence time
of solids at large scale. In order to mimic flow rates, samples can be centrifuged for
different time periods at small scale. Solid height affects the dewatering by determining
the pressure applied to the solid. Thus, a cell concentration that would give the same
solid height as large scale should be used in scale-down model.
As shown in Eq 5.5, clarification efficiency is calculated by measuring optical densities
of the feeding stream and the supernatant after centrifugation.
%Clarification =
ODa??b − ODVcd?7e5,5e,ODa??b − OD7?a
• 100 Eq 5.5
where ODfeed is the optical density of fermentation broth before centrifuge, ODsupernatant
is the optical density of supernatant after centrifuge and ODref is the optical density of
supernatant that is equivalent to 100% clarification.
117
Dewatering level can be calculated by Eq 5.6 (Salte et al., 2006).
%D = 100 −100(WCW− DCW/di7)
WCW Eq 5.6
dw7 =DCWa
WCWa Eq 5.7
where WCW is the weight of wet cell cake and DCW is the weight of dry cells. dwr is
the ratio of dry cell weight to wet cell weight after maximum removal of water in
extracellular space using filtration. DCWf is the weight of dry cells and WCWf is the
weight of wet cells after filtration.
5.3 Results and discussion
5.3.1 Determination of biomass and product
Cell culture for the dewatering study was prepared using the fermentation process as
described in the Chapter 3. Briefly, the cell culture was growth on glycerol firstly and
then induced by pure methanol or sorbitol/methanol (1:1, C-mol/C-mol) mixture at the
feeding rate of 10.8 ml•h-1•L-1 (Invitrogen, 2002). The cell cultures were induced 72
hours before harvest, and duplicate fermentations were performed for each induction
method using the parallel bioreactor.
Fig 5.1 shows the cell growth curve, cell viability and product expression during the
fermentation. After initial cell growth on 40 g•L-1 of batch glycerol, the DCW reached
20.1�1.5 g•L-1 in four vessels. The DCW was further enhanced to 59.7�5.6 g•L-1 by
feeding 50% (v/v) glycerol for six hours. After the growth on glycerol, cells were
induced by pure methanol or sorbitol/methanol (1:1, C-mol/C-mol) mixture. The DCW
nearly reached the maximum after 48 hours of induction in both of the strategies. At
the harvest time, the DCW reached 137.1�5.6 g•L-1 and 149.7�4.8 g•L-1 in methanol
and sorbitol/methanol (1:1, C-mol/C-mol) mixed induction, respectively. The harvested
biomass concentrations were comparable to the result described in the Section 3.2.3,
Chapter 3.
118
After the harvest, the cell cultures were added to 15 ml falcon tubes and were
centrifuged at 4000RPM for 10min. Volumetric fraction of cell solids in the tubes were
determined. It was observed that the volumetric fractions of cell solids in methanol and
sorbitol/methanol (1:1, C-mol/C-mol) mixed induction strategies were 40.8% and
47.2%, respectively.
Cell viabilities were determined by staining the cells with propidium iodide and
measuring fraction of stained cells using flow cytometry. Prior to induction, the cell
viabilities were as high as ~98% in all the vessels. The viabilities dropped to ~92%
after 72 hours of methanol induction, which was much lower than the cell viabilities
(~97%) in sorbitol/methanol mixed induction.
Samples were taken after 24, 48, and 72 hours of induction and product expression was
determined using protein gel. In both of the induction strategies, the product bands
(~6.5 kDa) were clear visible after 24 hours of induction. The intensities of product
band kept increasing from until the harvest time. Compared to the methanol induction,
the intensity of product band in sorbitol/methanol mixed induction was lower at the
same time point. It was consistent to the finding in the Section 3.3.3, Chapter 3 where
sorbitol/methanol mixed induction decreased the product yield. The major HCP had a
molecular weight of 66 kDa. Besides, several other HCP bands were also visible, but
their amount were much lower.
119
Figure 5.1 Cell growth, viability and product expression of the cell culture used in dewatering study. The cell cultures were firstly grown on
glycerol and then induced by methanol or sorbitol/methanol (1:1 C-mol/C-mol) mixture. Duplicate experiments were run for each induction method.
(A) shows the growth curve of cell cultures. Three replicates of DCW were measured at each sampling time and data was shown by mean ± SD
(n=3). (B) shows the change of cell viabilities during fermentation. Samples were taken before and after 24, 48 and 72 hours of induction. One
sample was analysed for each point. (C) shows the profile of soluble proteins after 24, 48 and 72 hours of induction by pure methanol or
sorbitol/methanol mixture.
120
5.3.2 Prediction of cell robustness to shear
A rotating shear device was has been used to mimic the shear stress generated in large
scale centrifuges (Boychyn et al., 2004). The device contains a stainless disc and
rotation of the disc could mimic different energy dissipation rate (ε) generated by the
large scale centrifuges (Boychyn et al., 2001). Industrial scale centrifuges had a
maximum ε value of 1.3•106 W•kg-1 in the feeding zone, as predicted by the
computational fluid dynamics (Boychyn et al., 2004). Here the disc was rotated at
speeds of 167, 233, and 300 rps to generate ε values of 0.2, 0.75 and 1.3•106 W•kg-1,
respectively.
Prior to shear treatment, the cell cultures were diluted to a solid fraction of 20% (v/v)
using Milli-Q water because disc stack centrifuge can only process a fluid with solid
concentration up to 20% (v/v). Afterwards, the cell cultures were treated with the shear
for 20 sec to mimic the residence time of cells in the feeding zone of large scale
centrifuges. Protein release and cell viability were measured to determine the change
of cell integrity before and after shear treatment.
The supernatant was collected by a centrifuging the cell cultures at 4000RPM for 10min.
Concentration of soluble proteins in the supernatant was quantified using BCA assay.
As shown in Fig 5.2A, total protein concentrations in the cell cultures from methanol
and sorbitol/methanol (1:1, C-mol/C-mol) mixed induction strategies were comparable
before the shear treatment (1.46 ± 0.065 mg•ml-1 versus 1.52 ± 0.035 mg•ml-1, p=0.445).
Treating the cells with the shear did not cause an increase of the protein concentration.
The cell viabilities were determined using flow cytometry. Consistent to Fig 5.1B, the
cell cultures induced by methanol had higher mortalities than that induced by
sorbitol/methanol (1:1 C-mol/C-mol) mixture. After being treated with the shear of
1.3•106 W•kg-1, the mortality increased from 9.3 ± 1.2% to 11.5 ± 0.5% (p=0.236).
Change of the cell mortality after the shear treatment was negligible in the cultures
induced by sorbitol/methanol mixture.
Most of the dead cells stayed intact due to the protection of cell wall, and thus the shear
treatment caused little damage to the cell integrity. Compared to shear sensitive
mammalian cells, both live and dead P. pastoris cells are more resistant to the shear
stress generated in industrial centrifuges, which makes P. pastoris a robust host for
protein production at large scale.
121
Figure 5.2 Predicting the cell robustness to shear stress in feeding zoon of the large
scale centrifuges. After 72 hours of induction, the cell cultures were harvested and
diluted to a cell solid concentration of 20% (v/v). Cells were then exposed to a series
of energy dissipation rates (0.2•106, 0.75•106, 1.3•106 W•kg-1) for 20 sec by a shear
device. Cell cultures were centrifuged at 4000 RPM for 10 min after the shear treatment.
(A) Soluble proteins in the supernatant were quantified using BCA assay. Data in the
graph are presented as mean ± SD (n = 3). (B) Cells were stained by propidium iodide
and percentage of dead cells were measured by flow cytometry. One sample was
analysed for each point.
122
5.3.3 Characterization of cell culture properties
Cell culture properties, such as cell shape, size distribution, viscosity and density of
culture were shown to influence sedimentation and package of cells and thus affect
dewatering efficiency in centrifuges (Salte, 2006). Characterization of the cell culture
properties will help to predict the centrifugal dewatering of cell cultures. P. pastoris
cells are round as observed by an optical microscope. The densities of cell culture from
methanol and mixed induction methods were ignored. The cell size distribution and
culture viscosity were compared in this study.
Fig 5.3A shows the cell size distribution measured at different time points of
fermentation. No peak of cell debris was detected after 72 hours of induction by either
methanol or sorbitol/methanol mixture, which indicated that most cells penetrating to
propidium iodide still stayed intact. The cell size distribution in two induction strategies
changed in different manners. The cell diameter stayed stable during methanol
induction, whereas the cell diameter shifted to smaller values over time in
sorbitol/methanol mixed (1:1, C-mol/C-mol) induction. Dv50 value of the cell size
decreased from 3.85 ± 0.3 μm to 3.14 ± 0.2 μm (p=0.002) after 72 h of mixed induction,
while the change of D50 value in methanol induction was not so significant (3.80 ± 0.6
versus 3.74 ± 0.3 μm, p= 0.465). The Dv50 values of cell cultures from methanol and
sorbitol/methanol mixed induction had significant difference at the harvest time (p=
0.003).
It was reported that P. pastoris cultured on methanol had larger cell diameter than that
grown on glucose (Rebnegger et al., 2016). However, no report was found where the
cell diameters grown on methanol and sorbitol were compared. It is likely that the
carbon influences cell philosophy and thus changes the cell diameter. Similarly,
diameter of CHO cells were observed to change during the cell culture (Kiehl et al.,
2011).
A previous study showed that centrifugal dewatering of P. pastoris was significantly
influenced by the cell diameter (Lopes et al., 2012). As being predicted by a scale-down
model of large scale centrifuge, cells with diameter of 3~4 μm had much higher
dewatering levels than that with diameter of 4~6 μm. As a result, the smaller cells had
a higher product recovery in centrifugation. Thus, it becomes interesting to study
whether the sorbitol/methanol mixed induction affects centrifugal dewatering of the
cell culture.
123
Figure 5.3 Cumulative cell size distributions in methanol and sorbitol/methanol (1:1 C-
mol/C-mol) mixed induction. (A) Samples were taken before and after 24, 48 and 72
hours of induction. Cell size distribution was measured using Mastersizer 3000. (B)
shows the cumulative cell size distributions at the harvest time.
124
Viscosities of the cell cultures induced by methanol and sorbitol/methanol mixture were
measured by rheometer. Cell cultures were diluted to a solid fraction of 30% (v/v)
before measurement. During the measurement, shear rate of the disc gradually
increased from 100 sec-1 to1000 sec-1. It was observed that the viscosities decreased
slowly with the increasing of shear rate (Fig 5.4A), which was a typical behaviour of
non-Newtonian fluid. At the rate of 800 sec-1, cell culture from sorbitol/methanol mixed
induction had higher viscosities than that from methanol induction (0.0014 ± 0.00006
versus 0.0020 ± 0.00013, p=0.04). The viscosities of P. pastoris culture are quite low
which will make cell sedimentation easier in centrifugation (Körner, 2013).
Viscosities of the cell cultures were measured at different time points of fermentation
and shown at the shear rate of 800 sec-1 (Fig 5.4B). It was observed that the viscosities
increased with the biomass. The viscosities were below 0.001 in the batch phase and
increased to nearly 0.003 after 24 hours of induction. At the harvesting time, viscosities
of cell cultures from methanol and sorbitol/methanol mixed induction were similar
(0.0027 ± 0.00015 versus 0.0027 ± 0.00017, p=0.98).
The cell cultures used in dewatering study had quite low viscosities, and thus the impact
of viscosities on dewatering was ignored.
125
Figure 5.4 Comparing the viscosities of cell cultures in methanol and sorbitol/methanol
(1:1 C-mol/C-mol) mixed induction. (A) After 72 hours of induction, the cell cultures
were diluted to a cell concentration of 30% (v/v) using Milli-Q water. Shear rate of
rheometer’s disc linearly increased from 100 sec-1 to1000 sec-1. And the viscosities
were recorded. (B) The cell cultures were taken before and after 24, 48 and 72 hours of
induction. The viscosities within shear rates of 100 sec-1 to1000 sec-1 were measured
and the viscosities at shear rate of 800 sec-1 was shown. One sample was analysed for
each point.
126
5.3.4 Prediction of centrifugal dewatering
Here dewatering levels of cell cultures were evaluated using a scale-down model of
pilot scale CSA-1 centrifuge and industrial scale BTPX305 centrifuge (Lopes. 2013).
Centrifugation speed is critical to predict dewatering. In the scale-down model, it is
necessary to maintain the same relative centrifugal force (RCF) as large scale. Here
speeds of 9700 RPM and 7488 RPM were used in the benchtop centrifuges to mimic
the bowl speeds of CSA-1 and BTPX305 centrifuges. Liquid flow rates of large
centrifuges were mimicked by the ratio of sample volume to centrifugation time at
small scale. Dewatering levels at flow rates of 8 L•h-1~110 L•h-1 and 200 L•h-1~1600
L•h-1 were predicted in this study. Solid height affects dewatering by determining extra-
pressure applied to the solid at the bottom (Chu and Lee, 2001). In order to obtain the
average solid heights at scale, cell cultures were spun in 2.2 ml Eppendorf tube and 15
ml centrifuge tube by Eppendorf 5810R and Beckman Coulter Avanti J-E Centrifuge,
respectively. Dimensions of the centrifuges were shown in Table 5.1 and Σ values were
calculated using Eq 5.3 and Eq 5.4. By using Eq 5.2, flow rates of disc stack centrifuges
were transferred to different lengths of centrifuge time in benchtop centrifuges.
Centrifuge Dimensions N (r·sec-1) C Σ (m2)
Eppendorf 5810R
R1 (0.075 m) R2 (0.1 m)
149 1.0 0.66~0.77
Beckman Coulter
Avanti J-E
R1 (0.073 m) R2 (0.102 m)
92 1.0 1.12~1.82
CSA-1
R1 (0.026 m) R2 (0.055 m)
n (45)
θ (38.5�)
162 0.4 1444
BTPX-305
R1 (0.036 m) R2 (0.085 m)
n (82)
θ (40�)
125 0.4 7127
Table 5.1 Dimensions of centrifuges used in the dewatering study.
127
Fig 5.5 shows the dewatering levels of cell cultures induced by methanol or
sorbitol/methanol mixture. Within the studied flow rates, dewatering levels were over
70% in both cell cultures. Dewatering levels at different flow rates were also
comparable. Compared to the cell culture induced by methanol, the cell culture from
mixed induction had higher dewatering levels in both centrifuges. Difference between
the dewatering levels were significant at some but not all the predicted flow rates. It is
likely to because of the variations. Average dewatering levels were improved from
77.3±4.6% to 83.0±3.8% (p<0.01) in CSA centrifuge and from 78.5±3.6% to 83.1±1.9%
(p<0.01) in BTPX305 centrifuge by using the mixed induction. In addition, clarification
was nearly 100% in both centrifuges (Fig 5.6).
Larger particles are more difficult to be packed in centrifugation and more liquid
accumulates in interstitial space (Wu et al., 1997). In a previous study, it was shown
that the centrifugal dewatering of P. pastoris was affected by cell size (Lopes et al.,
2012). The dewatering reached ~80% in disc stack centrifuges when cells with an
average size of 3~4 μm were used, while it was less than 60% for cells with an average
size of 4~6 μm. The difference of dewatering levels of methanol and sorbitol/methanol
mixture induced cultures is likely to be attributed to the different cell sizes.
P values calculated using student’s t-test indicates significant statistical differences
between the average dewatering levels in both centrifuges. This leads to a prediction of
a loss of 41.3±5.3 g product from a 1000 litre culture induced by methanol, whereas a
loss of 17.1±2.1 g if mixed induction is used. This indicates that changing induction
method is an effective way to minimize product loss in centrifugal separation. This
becomes a valuable process optimization tool specially when high value products are
manufactured.
128
Figure 5.5 Dewatering levels of the cell cultures induced by methanol and
sorbitol/methanol (1:1 C-mol/C-mol) mixture as predicted by the scale-down model of
CSA-1 and BTPX305 disc stack centrifuges. Data in the graph are presented as mean
± SD (n = 3). The significance of dewatering between methanol and mixed induction
strategies were shown by * (p<0.05) and ** (p<0.01).
8 16 32 64 12860
70
80
90
100
Flow rate L·h-1
Dew
ater
ing
%
Methanol-1
Methanol-2
Mixture-1
Mixture-2
A
***
*
256 512 1024 204860
70
80
90
100
Flow rate L·h-1
Dew
ater
ing
%
Methanol-1
Methanol-2
Mixture-1
Mixture-2
B
** *** *
129
Figure 5.6 Clarification levels of the cell cultures induced by methanol and
sorbitol/methanol (1:1 C-mol/C-mol) mixture as predicted by a scale down model of
CSA-1 and BTPX305 disc stack centrifuges. Data in the graph are presented as mean
± SD (n = 3).
8 16 32 64 12898
99
100
101
102
Flow rate L·h-1
Cla
rific
atio
n %
Methanol-1Methanol-2
Mixture-1Mixture-2
A
128 256 512 1024 204898
99
100
101
102
Flow rate L·h-1
Cla
rific
atio
n %
Methanol-1
Methanol-2
Mixture-1
Mixture-2
B
130
Centrifugal dewatering of the cell cultures from an OTR-limited bioreactor was also
studied using the ultra scale-down method. As described in the Chapter 4, the cell
cultures were induced by methanol or sorbitol/methanol mixtures with 40%~60%
(Cmethanol) of methanol in a bioreactor with OTR of 150 mmol•L-1•h-1. The cell cultures
were harvested after 96 hours of induction. Cell size distribution and centrifugal
dewatering of the cultures were studied.
As shown in Fig 5.7, the cell culture from methanol induction had smaller cell diameter
than that from sorbitol/methanol mixed induction methods. The D50 values of cell
cultures from methanol and sorbitol/methanol mixed induction were 3.23 μm and 3.74
± 0.7 μm (p=0.011), respectively.
Consequently, the cell cultures from sorbitol/methanol mixed induction had higher
dewatering levels than that from methanol induction, as predicted by the scale-down
model of CSA-1 disc stack centrifuge. Difference between the dewatering levels were
significant at all the predicted flow rates. The average dewatering of cell cultures from
three mixed induction methods reached 84.2±1.6%, 81.8±1.1% and 82.3±1.3%,
respectively, while the average dewatering level was 79.1±1.0% in cell culture from
methanol induction. The result further indicated that sorbitol/methanol mixed induction
enhanced centrifugal dewatering of cell culture by influencing cell diameter.
131
Figure 5.7 Cumulative size distribution and dewatering of cell cultures in fermentations
performed in an OTR-limited bioreactor. The cell cultures were induced by methanol
or sorbitol/methanol mixtures containing 40%~60% (Cmethanol) of methanol in a
bioreactor with OTR of 150 mmol•L-1•h-1. Samples were taken after 96 hours of
induction and cumulative size distributions of the cell cultures were measured using
Mastersizer 3000. (B) Dewatering of the cell cultures was predicted by a scale-down
model of CSA-1 centrifuge. Data in the graph are presented as mean ± SD (n = 3). The
significance of dewatering between methanol and mixed induction strategies were
shown by ** (p<0.01) and *** (p<0.001).
4 8 16 32 64 12870
80
90
Flow rate L·h-1
Dew
ater
ing
%
100%Methanol 60%Methanol
50%Methanol 40%Methanol
B
***** **
***
132
5.4 Conclusion
Disc stack centrifuge is widely used to harvest product from high density P. pastoris
culture. Efficiency of the centrifugation is influenced by the cell culture properties.
Besides, shear stress of the large scale centrifuges is likely to damage the cells. In this
chapter, the cell cultures induced by methanol and sorbitol/methanol mixture were
compared in terms of dewatering levels and robustness to shear stress using ultra scale-
down method.
Cell mortality was much higher in the standard methanol induction. Despite the
mortality, the cell cultures were robust to shear stress in feeding zone of the disc stack
centrifuges, as predicted by an ultra scale-down device. The shear treatment caused
litter damage to the cell integrity due to the presence of cell wall. It makes P. pastoris
a good host for the production of recombinant proteins compared to shear sensitive
mammalian cells.
Cell culture properties affecting centrifugal dewatering were characterized. It was
observed that the cell diameter became smaller and smaller over time in the mixed
induction, while the diameter in methanol induction did not change. In an OTR-limited
bioreactor, cell size from the mixed induction was also smaller than that from methanol
induction. Besides, the cell cultures had low viscosities which was beneficial for the
cell sedimentation in centrifuges.
Due to the difference of cell diameter, the cell culture from mixed induction had higher
dewatering levels as predicted by an ultra scale-down model of pilot and industrial
centrifuges. Consequently, less product got lost in centrifugation of the methanol
induced cell culture. This finding establishes the sorbitol/methanol mixed induction as
an effective way to minimize product loss in centrifugation. This becomes a valuable
process optimization tool specially when high value products are manufactured.
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Chapter 6 Conclusions and future work
6.1 Conclusions
In this thesis, the impact of sorbitol/methanol mixed induction on P. pastoris
fermentation was studied in terms of cell growth, viability, product yield, host cell
protein profile and centrifugal dewatering efficiency. Besides, methanol and
sorbitol/methanol mixed induction strategies were compared in a bioreactor with
comparable OTR as large scale.
An enzymatic assay of the product that was compatible with the culture medium was
established. The cell culture in microplate showed that sorbitol did not inhibit cell
growth even at high concentrations while methanol did. The study in shake flasks
showed that using sorbitol as a substrate reduced cell lysis during cell culture.
Standard methanol induction and sorbitol/methanol (1:1, C-mol/C-mol) mixed
induction were compared using a parallel bioreactor. It was observed that the
sorbitol/methanol mixed induction enhanced cell growth, improved cell viability and
reduced oxygen uptake.
Using sorbitol/methanol (1:1, C-mol/C-mol) mixed induction resulted in a lower
product yield compared to the standard methanol induction. However, fewer HCPs
were co-released into the medium with the product. It is likely to make the product
purification easier. Fewer types of protease were identified from the mixed induction,
and thus product degradation may be reduced when the products are sensitive to
proteolytic degradation.
Oxygen transfer coefficient (kLa) of the parallel bioreactor was measured using
dynamic method. The kLa was observed to linearly correlated with the agitation speed.
By using the kLa, a fermentation process with an OTR of 150mmol•L-1•h-1 was defined
and was used to study the cell culture at large scale.
Induction strategies in an OTR-limited bioreactor was developed. Using standard
methanol induction caused oxygen limitation and methanol accumulation in the
bioreactor. In the oxygen limited fermentation, biomass concentration and product
yield were significantly influenced by the concentration of residual methanol. Thus,
strict control of residual methanol concentration is required to avoid that it rises to an
inhibitive level.
134
Induction in an oxygen unlimited condition eliminated the need to strictly control the
residual methanol. Due to the slower methanol feeding rate, the biomass and product
yield were significantly reduced compared to the oxygen limited fermentation. Partially
replacing methanol with sorbitol enhanced the carbon feeding in oxygen unlimited
fermentation. Consequently, the biomass concentration was significantly improved.
However, the higher biomass did not result in higher product yields compared to
methanol induction. Since high cell density challenges product recovery, methanol
induction is preferable to sorbitol/methanol mixed induction in the oxygen unlimited
fermentation.
Activity of AOX enzyme in different induction strategies were compared. In the oxygen
unlimited fermentation, sorbitol/methanol mixed induction resulted in lower activities
of AOX enzyme compared to methanol induction. It is likely to be the reason that
sorbitol/methanol mixed induction only enhanced the cell growth but not product yield.
The activity of AOX enzyme was much higher in the oxygen limited fermentations
compared to that in oxygen unlimited condition. It is probably due to the sufficient
methanol in the medium in the OLFB fermentations.
Finally, the impact of sorbitol/methanol mixed induction on centrifugal dewatering was
studied. An ultra scale-down shear device was used to predict the cell robustness to
shear stress in large scale centrifuges. It was found that the cells induced by methanol
or sorbitol/methanol mixture were robust to the shear stress. While treating cell culture
from methanol induction resulted in a slight decline of cell viability, it did not cause
the release of intracellular proteins.
Cell culture properties influencing dewatering levels was studied. It was observed that
the cell diameter became smaller and smaller over time in sorbitol/methanol mixed
induction. As a result, cell cultures from sorbitol/methanol mixed induction had higher
dewatering levels, as predicted by an ultra scale-down model of pilot and industrial
scale disc stack centrifuges. This indicates that changing induction method is an
effective way to minimize product loss in centrifugal separation.
135
Induction strategy Biomass
g•L-1
Cell viability
%
Average OUR
mmol•L-1•h-1
Product yield
g•L-1 Identified
HCP Dewatering in CSA
centrifuge %
Standard methanol induction 129.4 92.5 256.4 1.69 96 77.3%
Sorbitol/methanol (1:1, C-mol/C-mol) mixed induction 152.4 97.6 150.2 1.05 72 83.0%
Fermentation in an OTR-
limited bioreactor
OLFB (Met0-5) 108.2 94.3 152.2 1.04 - -
OLFB (Met5-10) 79.1 92.8 155.7 0.87 - -
100% Methanol 90.2 97.9 130.6 0.86 - 79.1%
Mixed induction (60% Methanol) 114.6 96.6 135.0 0.86 - 84.2%
Mixed induction (50% Methanol) 130.0 98.1 127.1 0.79 - 81.8%
Mixed induction (40% Methanol) 130.2 98.0 129.2 0.78 - 82.3%
Table 6.1 A summary of the fermentation and centrifugal dewatering investigated in this thesis.
136
6.2 Future work
In this project, sorbitol/methanol mixed induction strategy was developed to offset the
drawbacks of pure methanol induction. Future works will extend the findings described
in this thesis.
In the production of recombinant aprotinin, sorbitol/methanol mixed induction strategy
benefited the cell culture by reducing oxygen uptake and enhancing cell viability.
Nevertheless, its product yield was much lower compared to using standard methanol
induction, which made the production less efficient. In the OTR-limited bioreactor,
methanol induction advantages over sorbitol/methanol mixed induction by producing
comparable yield with fewer cells. On the other hand, the impact of sorbitol/methanol
mixed induction on product yield varies on cell strains. As it was summarized in Table
1.3, sorbitol/methanol mixed induction significantly improved the product yield of
recombinant Interferon-α, porcine Circovirus cap protein, Thermomyces lanuginosus
lipase, β-Glucosidase, β-Mannanases, Erythropoietin and Rhizopus oryzae lipase.
Therefore, the advantages of sorbitol/methanol mixed induction are likely to be shown
better by applying it to other strains in the future. In addition, sorbitol’s price is over
ten times higher than that of methanol. A mixing tank is also required to prepare the
sorbitol solution. The mixed induction strategy is likely to increase cost of goods of the
cell culture especially at large scale. Although it diminishes the usage of expensive pure
oxygen, its impact on the whole capital cost needs to be well calculated in the future.
It was shown that sorbitol/methanol mixed induction reduced the number of HCPs and
types of proteases identified from the cell culture. In a specific range of molecular
weight (MW) and isoelectric point (PI), fewer HCPs were identified from the cell
culture induced by sorbitol/methanol mixture. When the products have MW and PI
within the range, using sorbitol/methanol mixed induction is likely to make the
purification much easier. In the future work, it will be interesting to apply the mixed
induction strategy to production of recombinant Interferon-γ, Interferon-β and
Keratinocyte growth factor, etc., and investigate whether the mixed induction is an
efficient tool to simplify the purification. Although proteolytic degradation of aprotinin
was not observed in this study, it was observed in expression of other products such as
recombinant interferon-τ (Sinha et al., 2005). In the future, sorbitol/methanol mixed
induction can be applied to the production of protease-sensitive proteins and investigate
whether the mixed induction will protect the products from degradation.
137
Residual methanol concentration has a significant impact on cell growth and product
expression in oxygen limited fermentation. It was observed that residual methanol
higher than 5 g•L-1 reduced both biomass and product yield in the OTR-limited
bioreactor. However, the concentration was not maintained at a constant level by using
a HPLC-based feedback control. In the future, commercial methanol sensor can be
applied to control the residual methanol better. An optimal methanol concentration can
be found to get the maximum product yield.
Sorbitol/methanol mixed induction was observed to enhance centrifugal dewatering of
the cell culture, as predicted by a scale down model of disc stack centrifuge. In the
future, the finding can be further verified by using other cell strains and studying the
dewatering in a pilot or large scale centrifuge. Product loss in methanol and
sorbitol/methanol mixture induced cell cultures can be calculated. Besides, it is
interesting to study whether shear stress in the centrifuges influences the product quality.
The soluble protein profile can be analysed using protein gel or proteomics before and
after the shear treatment. It was reported that shear stress caused aggregation and
conformation change of some proteins (Di Stasio and De Cristofaro, 2010, Nesta et al.,
2017). In the future, more complex product, such as monoclonal antibody, can be used
and whether the shear stress in centrifugation induces protein aggregation can be
studied. The finding will guide the selection of product recovery options for these
proteins.
Currently, high density fermentation is popularly used in P. pastoris culture. It results
in high oxygen uptake in upstream and challenges product harvest in downstream.
Alternatively, continuous fermentation can be used where product is produced with a
relatively lower cell density. In continuous fermentation, a smaller scale of bioreactor
and centrifuge can be used, which will cut the cost of facility installation in industry.
In the future, a small scale continuous culture can be developed at small scale and its
productivity, efficiency of product recovery and cost of goods can be compared with
the traditional fed-batch fermentation. In addition, sorbitol/methanol mixed induction
enhanced the cell viability in this study. That is beneficial to a continuous culture in
which the induction is performed for a longer period. It will be interesting to apply the
mixed induction to the continuous culture and study its impact on the cell viability and
product quality.
It was reported that adding sorbitol as a co-substrate causes shift of methanol flux
distribution. It was found that the methanol flux decreased in energizing pathway while
138
increased in biomass synthesis pathway. The shift of methanol flux was likely to
influence product yield as reported by several studies (Wang et al., 2010, Çelik et al.,
2009). In the future, the intracellular metabolic fluxes in sorbitol/methanol mixed
induction can quantitatively studied by labelling methanol with C13. Change of
metabolic flux under different sorbitol/methanol ratios and feeding rates can be studied.
From the deeper insights into the metabolic fluxes, potential metabolic bottlenecks for
protein production can be revealed, which will further guide the optimization of carbon
feeding strategy.
139
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Chapter 8 Appendix
Figure 8.1 Detection of glycerol, methanol and sorbitol using UltiMate 3000 HPLC
with Aminex HPX-87h column. 0.5% (v/v) trifluoroacetic acid at flow rate of 0.6 ml•h-
1 was used as the mobile phase. Standard solutions with 1.0 g•L-1 of glycerol, methanol
and sorbitol were prepared, respectively. 20 μl solution was loaded through the column
for 30min, and absorbance was detected by the RefractoMax 520 Refractive Index
Detector.
159
Figure 8.2 Correlation between peak area and concentration of methanol (A) and
sorbitol (B). 1%, 5%, 10%, 20% and 30% (v/v) of methanol and sorbitol solutions
(0.75g•L-1) were prepared and assayed using the method described in the Section 2.9.3,
Chapter 3.
160
Figure 8.3 Viscosities of methanol, 0.75 g•ml-1 sorbitol solution and sorbitol/methanol
(1:1, C-mol/C-mol) mixture at the shear rate of 800s-1.
Figure 8.4 The impact of biomass concentration and treating time on specific protein
release.
0 400 800 12000.000
0.005
0.010
0.015
0.020
Shear rate s-1
Vis
cosi
ty P
a s
Methanol Sorbitol Mixture
161
Figure 8.5 Correlation between DCW and WCW of P. pastoris
162
Table 8.1 A summary of names, accession number, molecular weight, isoelectric point, localization and function of the HCPs identified from the cell cultures induced by methanol or sorbitol/methanol (1:1, C-mol/C-mol) mixture.
Methanol Name No. MW PI Localization Function
1 Vacuolar proteinase B (YscB) C4QYT0 52 5.51 Secreted protease
2 Vacuolar proteinase B (YscB) C4QWH2 59 5.93 Vacuole protease, cellular response to starvationfamily
3 Vacuolar aspartyl protease (Proteinase A)
C4R6G8 44 4.64 Vacuole protease, cellular response to starvation
4 Uncharacterized protein C4R862 42 7.19 Cytosol Nucleus Peroxisome
aspartate biosynthetic process
5 Uncharacterized protein C4R8H7 64 3.96 Extracellular region or secreted
cellulase activity
6 Uncharacterized protein C4R743 34 5.19 N/A N/A
7 Uncharacterized protein C4R6P1 26 4.75 N/A calcium ion binding
8 Uncharacterized protein C4R3Q7 61 4.18 N/A aspartic-type endopeptidase activity
163
9 Uncharacterized protein C4R6F1 13 8.60 N/A N/A
10 Uncharacterized protein C4R5N2 95 6.45 Membrane N/A
11 Uncharacterized protein C4R4B2 24 6.06 N/A N/A
12 Uncharacterized protein C4R489 32 4.44 N/A N/A
13 Uncharacterized protein C4R3H3 32 5.26 Extracellular region or secreted
N/A
14 Uncharacterized protein C4R3C4 63 4.26 N/A N/A
15 Uncharacterized protein C4R3B1 41 3.84 N/A N/A
16 Uncharacterized protein C4R3A0 63 3.90 N/A N/A
17 Uncharacterized protein C4R2D7 91 4.02 N/A N/A
18 Uncharacterized protein C4R2B9 45 4.15 Cell Wall N/A
19 Uncharacterized protein C4R2M0 27 6.83 N/A N/A
20 Uncharacterized protein C4R1Q1 31 4.17 Cell Wall structural constituent of cell wall
21 Uncharacterized protein C4R0W4 57 5.94 N/A oxidoreductase activity
164
22 Uncharacterized protein C4QZQ5 93 6.28 N/A GTPase activity
23 Uncharacterized protein C4QZN3 29 4.80 N/A protein domain specific binding
24 Uncharacterized protein C4R0Z8 24 6.09 N/A N/A
25 Uncharacterized protein C4QY91 10 4.83 N/A atty-acyl-CoA binding
26 Uncharacterized protein C4QXM2 31 4.25 Extracellular region or secreted
N/A
27 Uncharacterized protein C4QUZ5 21 5.73 N/A catalytic activity
28 Uncharacterized protein C4QWA6 26 5.69 N/A catalytic activity
29 Uncharacterized protein C4QW56 46 7.05 N/A N/A
30 Uncharacterized protein C4QW08 45 5.40 Membrane N/A
31 Uncharacterized protein C4R9F6 45 5.08 N/A N/A
32 Transketolase, similar to Tkl2p C4R5Q0 79 6.03 N/A catalytic activity
33 Transketolase, similar to Tkl2p C4R5P8 79 6.37 N/A catalytic activity
34 Thioredoxin reductase C4R2E7 34 5.23 cytoplasm Catalytic activity
165
35 Thiol-specific peroxiredoxin C4R0V9 19 8.63 N/A oxidoreductase activity
36 Suppressor protein STM1 C4QY12 30 9.73 N/A N/A
37 Superoxide dismutase [Cu-Zn] C4R8X7 16 5.92 N/A Catalytic activity
38 Superoxide dismutase C4QXC7 25 7.89 Mitochondrion Catalytic activity
39 Subunit of the RAVE complex (Rav1p, Rav2p, Skp1p)
C4R0E0 158 6.47 N/A N/A
40 Sortilin C4R564 167 4.97 Golgi apparatus hydrolase activity
41 S-adenosylmethionine synthase C4R5U7 42 6.06 Cytosol Nucleus Catalytic activity
42 S-(hydroxymethyl)glutathione dehydrogenase
C4R6A5 41 6.08 Cytosol Nucleus Catalytic activity
43 Putative chitin transglycosidase C4R894 50 4.31 Cell Wall hydrolase activity
44 Protein of the SUN family C4R2Z5 45 4.37 N/A Glycoside Hydrolase Family
45 Plasma membrane localized protein that protects membranes from desiccation
C4R8G2 12 4.81 Cytosol Endosome Plasma Membrane
cell adhesion cellular response to stress
166
46 Phosphoglycerate mutase C4R5P4 28 6.03 Cytosol Mitochondrion Catalytic activity
47 Phosphatidylglycerol/phosphatidylinositol transfer protein
C4QZC2 19 4.49 introcellular intracellular sterol transport
48 Peptide hydrolase C4QWD7 46 5.14 N/A metal ion binding
49 Ornithine carbamoyltransferase C4R533 38 6.46 Mitochondrion amino acid binding
50 O-glycosylated protein required for cell wall stability
C4R7G9 34 4.50 Cell Wall structural constituent of cell wall
51 Nucleoside diphosphate kinase C4R300 17 6.13 Cytosol Nucleus Catalytic activity
52 Mucin family member C4QVR8 89 3.82 Membrane N/A
53 Mitochondrial protein involved in maintenance of the mitochondrial genome
C4QXV0 16 8.91 N/A N/A
54 Mitochondrial porin (Voltage-dependent anion channel)
C4R1Z2 30 9.02 Mitochondrion voltage-gated anion channel activity
55 Mitochondrial outer membrane and cell wall localized SUN family member
C4R6P9 36 4.90 N/A Glycoside Hydrolase Family
167
56 Mitochondrial matrix ATPase C4R4C3 70 5.41 Mitochondrion ATPase activity
57 Major exo-1,3-beta-glucanase of the cell wall
C4R0Q7 48 4.53 N/A Hydrolase activity
58 Lysophospholipase C4R703 70 4.11 N/A Catalytic activity
59 Lectin-like protein with similarity to Flo1p
C4QYW7 51 4.36 N/A N/A
60 Histone H2B C4R0M7 14 10.1 Nucleus DNA bindingprotein heterodimerization activity
61 Histone H2A C4R0M8 14 10.3 Nucleus DNA bindingprotein heterodimerization activity
62 Glycerol kinase C4R8X4 68 5.25 Mitochondrion glycerol kinase activity
63 Glyceraldehyde-3-phosphate
dehydrogenase C4R0P1 36 6.24 N/A Catalytic activity
64 Fusion protein C4R0U2 15 9.87 ribosome structural constituent of ribosome
65 Fructose-1,6-bisphosphatase C4R5T8 38 6.12 Cytosol Nucleus cellular response to glucose starvation
168
66 Fructose 1,6-bisphosphate aldolase C4QWS2 39 6.08 N/A fructose-bisphosphate aldolase activity
67 Fructose 1,6-bisphosphate aldolase C4QW09 40 6.02 N/A fructose-bisphosphate aldolase activity
68 Formate dehydrogenase C4R606 40 6.61 cytoplasm Catalytic activity
69 Enolase I C4R3H8 47 5.44 Cytosol magnesium ion binding
70 Endo-beta-1,3-glucanase C4QYF3 34 4.07 N/A hydrolase activity
71 Elongation factor 1-alpha C4QZB0 50 9.12 cytoplasm GTPase activity
72 Dihydrolipoyl dehydrogenase C4R312 52 6.30 N/A dihydrolipoyl dehydrogenase activity
73 Daughter cell-specific secreted protein with similarity to glucanases
C4QW71 110 4.65 N/A glucan endo-1,3-beta-glucanase activity
74 Cytosolic and mitochondrial glutathione oxidoreductase
C4R686 50 8.13 Cytosol Mitochondrion electron carrier activity
75 Cytoplasmic ATPase that is a ribosome-associated molecular chaperone
C4R5E4 67 5.09 cytoplasm ATPase activity
169
76 Cytochrome c, isoform 1 C4R6L9 12 9.61 Mitochondrion electron carrier activity
77 Cytochrome c oxidase assembly protein/Cu2+ chaperone
C4R5F9 7 k 6.13 Mitochondrion copper chaperone activity
78 Chitin deacetylase C4QW42 35 5.14 N/A hydrolase activity
79 Cell wall protein with similarity to glucanases
C4QZH9 49 4.44 N/A Glycoside Hydrolase Family
80 Cell wall protein with similarity to glucanases
C4QVL7 36 4.96 N/A hydrolase activity
81 Cell wall protein that contains a putative GPI-attachment site
C4R2Q4 43 3.87 N/A N/A
82 Catalase C4R2S1 58 6.56 N/A Catalytic activity
83 Carboxypeptidase C4R546 61 4.88 N/A serine-type carboxypeptidase
activity
84 Branched-chain-amino-acid aminotransferase
C4R7A4 45 5.81 N/A Catalytic activity
170
85 Aspartic protease C4R458 52 4.19 N/A aspartic-type endopeptidase activity
86 Aspartate aminotransferase C4QWE4 48 6.68 Mitochondrion aminotransferase activity
87 Amine oxidase C4R098 90 4.40 N/A copper ion bindingprimary amine
oxidase activity
88 Alanine: glyoxylate aminotransferase (AGT)
C4R7U0 45 7.77 N/A transaminase activity
89 Acid trehalase required for utilization of extracellular trehalose
C4R7L0 114 4.79 N/A catalytic activity
90 ATPase involved in protein import into the ER
C4QZS3 74 4.78 N/A ATP binding
91 ATPase involved in protein folding and
the response to stress C4R3X8 71 5.09 N/A ATP binding
92 ATPase involved in protein folding and nuclear localization signal directed nuclear transport
C4R887 70 4.89 Cell Wall Cytosol Nucleus ATPase activity
171
93 6,7-dimethyl-8-ribityllumazine synthase C4R6B4 18 6.30 MitochondrionOther locations
Catalytic activity
94 1,3-beta-glucanosyltransferase C4QVL5 58 4.00 Plasma Membrane transferase activity
95 1,3-beta-glucanosyltransferase C4QVL4 57 4.02 Plasma Membrane transferase activity
96 1,3-beta-glucanosyltransferase C4R9F4 54 4.12 Plasma Membrane transferase activity
Mixture Name No MW PI Localization Function
1 Vacuolar aspartyl protease (Proteinase A) C4R6G8 44 4.64 Vacuole protease, cellular response to starvation
2 Uncharacterized protein C4R862 42 7.19 Cytosol Nucleus Peroxisome aspartate biosynthetic process
3 Uncharacterized protein C4R7K4 50 9.43 Membrane N/A
4 Uncharacterized protein C4R8H7 64 3.96 Extracellular region or secreted cellulase activity
5 Uncharacterized protein C4R885 36 5.57 N/A N/A
6 Uncharacterized protein C4R3Q7 61 4.18 N/A aspartic-type endopeptidase activity
172
7 Uncharacterized protein C4R6F1 13 8.60 N/A N/A
8 Uncharacterized protein C4R4B2 24 6.06 N/A N/A
9 Uncharacterized protein C4R489 32 4.44 N/A N/A
10 Uncharacterized protein C4R3H3 32 5.26 Extracellular region or secreted N/A
11 Uncharacterized protein C4R3C4 63 4.26 N/A N/A
12 Uncharacterized protein C4R3B1 41 3.84 N/A N/A
13 Uncharacterized protein C4R3B0 72 4.12 N/A N/A
14 Uncharacterized protein C4R3A0 63 3.90 N/A N/A
15 Uncharacterized protein C4R2D7 91 4.02 N/A N/A
16 Uncharacterized protein C4R2B9 45 4.15 Cell Wall N/A
17 Uncharacterized protein C4R2M0 27 6.83 N/A N/A
18 Uncharacterized protein C4R1Q1 31 4.17 Cell Wall structural constituent of cell wall
19 Uncharacterized protein C4QZQ5 93 6.28 N/A GTPase activity
173
20 Uncharacterized protein C4R0Z8 24 6.09 N/A N/A
21 Uncharacterized protein C4QZD0 29 5.67 N/A N/A
22 Uncharacterized protein C4QY91 10 4.83 N/A atty-acyl-CoA binding
23 Uncharacterized protein C4QXM2 31 4.25 Extracellular region or secreted N/A
24 Uncharacterized protein C4R9F6 45 5.08 N/A N/A
25 Translation elongation factor EF-1 gamma
C4R6E8 24 6.41 N/A translation elongation factor activity
26 Transaldolase C4R245 36 5.07 Cytosol Nucleus Catalytic activity
27 Superoxide dismutase [Cu-Zn] C4R8X7 16 5.92 N/A Catalytic activity
28 Superoxide dismutase C4QXC7 25 7.89 Mitochondrion Catalytic activity
29 Putative chitin transglycosidase C4R894 50 4.31 Cell Wall hydrolase activity
30 Protein of the SUN family C4R2Z5 45 4.37 N/A Glycoside Hydrolase Family
31 Plasma membrane ATPase C4QVS9 98 4.96 Membrane Catalytic activity
174
32 Phosphatidylglycerol/phosphatidylinositol transfer protein
C4QZC2 19 4.49 introcellular intracellular sterol transport
33 Peroxisomal 2,4-dienoyl-CoA reductase C4R8U1 31 5.60 N/A oxidoreductase activity
34 Peptide hydrolase C4QWD7 46 5.14 N/A metal ion binding
35 O-glycosylated protein required for cell wall stability
C4R7G9 34 4.50 Cell Wall structural constituent of cell wall
36 Nucleoside diphosphate kinase C4R300 17 6.13 Cytosol Nucleus Catalytic activity
37 Nuclear protein required for transcription of MXR1
C4R6V3 47 6.12 N/A translation elongation factor activity
38 Mucin family member C4QVR8 89 3.82 Membrane N/A
39 Mitochondrial protein involved in maintenance of the mitochondrial genome
C4QXV0 16 8.91 N/A N/A
40 Mitochondrial porin (Voltage-dependent anion channel)
C4R1Z2 30 9.02 Mitochondrion voltage-gated anion channel activity
41 Mitochondrial outer membrane and cell wall localized SUN family member
C4R6P9 36 4.90 N/A Glycoside Hydrolase Family
175
42 Mitochondrial alcohol dehydrogenase isozyme III
C4R0S8 37 5.84 N/A Oxidoreductase activity
43 Major exo-1,3-beta-glucanase of the cell wall
C4R0Q7 48 4.53 N/A Hydrolase activity
44 Lysophospholipase C4R703 70 4.11 N/A Catalytic activity
45 Lectin-like protein with similarity to Flo1p
C4QYW7 51 4.36 N/A N/A
46 Integral membrane protein localized to mitochondria (Untagged protein) and eisosomes
C4R441 32 5.95 Plasma Membrane N/A
47 Histone H4 C4R2J6 11 11.3 Nucleus DNA bindingprotein heterodimerization activity
48 Glycerol kinase C4R8X4 68 5.25 Mitochondrion glycerol kinase activity
49 Glyceraldehyde-3-phosphate dehydrogenase
C4R0P1 36 6.24 N/A Catalytic activity
50 Fusion protein C4R0U2 15 9.87 ribosome structural constituent of ribosome
176
51 Fructose 1,6-bisphosphate aldolase C4QW09 40 6.02 N/A fructose-bisphosphate aldolase activity
52 Formate dehydrogenase C4R606 40 6.61 cytoplasm Catalytic activity
53 Endo-beta-1,3-glucanase C4QYF3 34 4.07 N/A hydrolase activity
54 Dihydroxyacetone kinase C4R5Q6 65 5.36 N/A ATP binding glycerone kinase activity
55 Daughter cell-specific secreted protein with similarity to glucanases
C4QW71 110 4.65 N/A glucan endo-1,3-beta-glucanase activity
56 Cytochrome c, isoform 1 C4R6L9 12 9.61 Mitochondrion electron carrier activity
57 Chitin deacetylase C4QW42 35 5.14 N/A hydrolase activity
58 Cell wall protein with similarity to glucanases
C4QZH9 49 4.44 N/A Glycoside Hydrolase Family
59 Cell wall protein with similarity to glucanases
C4QVL7 36 4.96 N/A hydrolase activity
60 Cell wall protein that contains a putative GPI-attachment site
C4R2Q4 43 3.87 N/A N/A
177
61 Catalase C4R2S1 58 6.56 N/A Catalytic activity
62 Aspartic protease C4R8B8 63 4.45 N/A aspartic-type endopeptidase activity
63 Aspartic protease C4R458 52 4.19 N/A aspartic-type endopeptidase activity
64 Amine oxidase C4R098 90 4.40 N/A copper ion bindingprimary amine oxidase activity
65 Alanine:glyoxylate aminotransferase (AGT)
C4R7U0 45 7.77 N/A transaminase activity
66 Acid trehalase required for utilization of extracellular trehalose
C4R7L0 114 4.79 N/A catalytic activity
67 ATPase involved in protein import into
the ER C4QZS3 74 4.78 N/A ATP binding
68 ATPase involved in protein folding and the response to stress
C4R3X8 71 5.09 N/A ATP binding
69 ATP synthase subunit beta C4R2N5 54 5.15 proton-transporting ATP synthase complex
Catalytic activity,
70 1,3-beta-glucanosyltransferase C4QVL5 58 4.00 Plasma Membrane transferase activity
178
71 1,3-beta-glucanosyltransferase C4QVL4 57 4.02 Plasma Membrane transferase activity
72 1,3-beta-glucanosyltransferase C4R9F4 54 4.12 Plasma Membrane transferase activity
179
Python Code for mining protein properties from Uniport.com
from bs4 import BeautifulSoup
import requests
from xlrd import open_workbook,cellname
import xlwt
import xlutils
from xlutils.copy import copy
from lxml import html
n=116
existing_file="D:/Users/lanselibai/Downloads/WebCrawler/Baolong.xls"
new_file="D:/Users/lanselibai/Downloads/WebCrawler/Baolong_new.xls"
code_all=[None] * n #create empty list to store code, pI, location and function
pI_all=[None] * n
location_all=[None] * n
function_all=[None] * n
book = open_workbook('D:/Users/lanselibai/Downloads/WebCrawler/Baolong.xlsx')
sheet = book.sheet_by_index(0)
#store the code list
for row_index in range(n):
code_all[row_index]=sheet.cell(row_index+1,3).value
180
#store the pI list
for row_index in range(n):
print("pI "+str(row_index))
url='https://web.expasy.org/cgi-bin/compute_pi/pi_tool1?'+code_all[row_index]+'@noft@average'
wb_data=requests.get(url)
soup=BeautifulSoup(wb_data.text,'lxml')
pI=soup.select(Potgieter et al.sib_body > p:nth-of-type(2)')
string_pI = str(pI)
pI_all[row_index]=string_pI[27:31]
#print(pI_all)
#store the location list
for row_index in range(n):
print("location " + str(row_index))
url='http://www.uniprot.org/uniprot/'+code_all[row_index]+'#subcellular_location'
root = html.parse(url)
location_1=root.xpath('//*[@id="table-go_annotation"]/div/ul/li[1]/h6/text()')
location_2 = root.xpath('//*[@id="table-go_annotation"]/div/ul/li[2]/h6/text()')
location_3 = root.xpath('//*[@id="table-go_annotation"]/div/ul/li[3]/h6/text()')
location_4 = root.xpath('//*[@id="table-go_annotation"]/div/ul/li[4]/h6/text()')
location_5 = root.xpath('//*[@id="table-go_annotation"]/div/ul/li[5]/h6/text()')
location_together=location_1+location_2+location_3+location_4+location_5
#print(location_together)
181
location_all[row_index] = location_together
#print(location_all)
#store the function list
for row_index in range(n):
print("function " + str(row_index))
url = 'http://www.uniprot.org/uniprot/' + code_all[row_index] + '#function'
root = html.parse(url)
f1 = root.xpath('//*[@id="function"]/h4[1]/span/text()')
f2 = root.xpath('//*[@id="function"]/ul/li/a/text()')
f3 = root.xpath('//*[@id="function"]/h4[2]/span/text()')
f4 = root.xpath('//*[@id="function"]/h4[3]/span/text()')
f5 = root.xpath('//*[@id="function"]/h4[4]/span/text()')
f6=root.xpath('//*[@id="section_x-ref_family"]/text()')
f_together = f1 + f2 +f3+f4+f5+f6
function_all[row_index] = f_together
#The following is to write the data in "pI_all", "location_all", "function_all" into existing excel by copying&write.
rb = open_workbook(existing_file,formatting_info=True)
rs = rb.sheet_by_index(0)
wb = copy(rb)
ws = wb.get_sheet(0)
for row_index in range(n):
182
ws.write(row_index + 1, 5, pI_all[row_index])
ws.write(row_index + 1, 6, location_all[row_index])
ws.write(row_index + 1, 7, function_all[row_index])
wb.save(new_file)