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THE APPLICATION OF PROTEOMICS TOOLS FOR CHARACTERIZATION OF
BIOPHARMACEUTICAL PROCESSES
A dissertation presented
By
Tyler Carlage
To
The Department of Chemistry and Chemical Biology
In partial fulfillment of requirements for the degree of Doctor of Philosophy
In the field of
Chemistry
Northeastern University
Boston, MA
September, 2011
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THE APPLICATION OF PROTEOMICS TOOLS FOR CHARACTERIZATION OF
BIOPHARMACEUTICAL PROCESSES
By
Tyler Carlage
ABSTRACT OF DISSERTATION
Submitted in partial fulfillment of the requirements
For the degree of Doctor of Philosophy in Chemistry
In the Graduate School of
Northeastern University, September 2011
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ABSTRACT
The production of biological drugs for the treatment of serious diseases is a complex
process utilizing a mammalian cell expression system combined with a complex set of
purifications steps to produce drugs of high quality and at optimized yields. In order to
maximize both quality and yield, the cell culture process must be developed ensuring maximal
cell growth and productivity characteristics, as well as scalability and robustness in a
manufacturing setting. The purification method must also be optimized for high yield of
product, while maintaining or enhancing the product quality profile of the drug and removing
cell culture related impurities to acceptably low levels. Limitations of the biopharmaceutical
process can have great impact on the cost of drug production, as well as on the safety and
efficacy of the drug. Understanding the biology of the cells used to produce biopharmaceuticals
can enable the development of better processes. This thesis describes the characterization of
biopharmaceutical processes using proteomics technology.
In chapter 1, the biopharmaceutical process is described, including the development of
cell culture processes, and methods used to enhance cell culture performance through various
genetic engineering strategies. Proteomics analysis can enable the identification of new cell
culture biomarkers related to cell growth and productivity. Tools used in the proteomics field
are outlined, as well as reported proteomics studies of mammalian cell cultures.
In chapter 2, a high-producing Chinese hamster ovarian cell culture which had been
transfected with the apoptosis inhibitor Bcl-XL gene was compared to a low-producing control.
Shotgun proteomics was used to compare the high and low-producing fed-batch cell cultures at
different growth timepoints. A total of 392 proteins were identified in this study, and 32 of these
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proteins were determined to be differentially expressed, including several proteins related to
protein metabolism such as eukaryotic translation initiation factor 3, and ribosome 40S. In
addition, several intermediate filament proteins such as vimentin and annexin, as well as histone
H1.2 and H2A, were downregulated in the high producer. A growth inhibitor, galectin-1, was
downregulated in the high-producer, which may be related to lower cell growth in the control.
The molecular chaperone BiP was upregulated significantly in the high-producer and may
indicate an unfolded protein response due to ER stress.
In chapter 3, an advanced proteomics method using two-dimensional liquid
chromatography and iTRAQ chemical labeling was used to probe the proteomic changes
occurring in CHO cells during exponential and stationary phases of cell culture. Using this
approach, 59 proteins were identified with significant dynamic trends. These proteins were
analyzed using pathway analysis tools, which identified a network of proteins associated with
cell growth and apoptosis. Molecular chaperones and isomerases, such as GRP78 and PDI, were
upregulated during stationary phase, and are associated with cellular response to endoplasmic
reticulum (ER) stress. Nucleic acid binding proteins including MCM2 and MCM5 were
downregulated during stationary phase, and are known cell growth markers. In addition, two
proteins with growth-regulating properties, transglutaminase-2 and clusterin, were identified.
These proteins are associated with tumor proliferation and apoptosis, and were observed to be
expressed at relatively high levels during stationary phase, which was confirmed by western
blotting.
Gene order in eukaryotes is not random, but rather genes related by function tend to be
clustered together and are regulated in similar patterns. It is thought that the co-regulation of
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nearby genes is related to chromatin remodeling and histone activity. In chapter 4, genes of
interest related to cell culture performance are mapped to mouse chromosomes, and analyzed for
evidence of clustering. Several clusters of known oncogenes are identified. Several other
clusters of genes of interest are identified from the list of differentially expressed proteins
described in chapter 3. This work provides some initial evidence of potential clustering of
growth-related genes in CHO, which can be expanded on with availability of the CHO genome
and gene expression data.
In the fifth chapter, the application of proteomics techniques to the analysis of secreted
host-cell proteins in process intermediate samples is described. Proteins present in the cell
culture media are identified, including glycolytic enzymes released from damaged cells and
several growth-regulating proteins secreted by the cells. In addition, the clearance of the host
cell proteins is studied by performing proteomics analysis on process intermediates from various
stages within the downstream purification process. Several relatively abundance proteins co-
purified throughout the process are identified, and physiochemical properties of these co-purified
proteins are analyzed.
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ACKNOWLEDGEMENTS
Writing this thesis is the culmination of years of trial and error, ups and downs, and
overcoming many obstacles both personal and professional. I could not have done it without the
help, guidance, and support of many people around me.
Firstly, I would like to thank my advisor Prof. William S. Hancock for giving me the
opportunity to be a part of his research group as a co-op student. Having students in this
program requires flexibility, patience, and understanding and you have shown all of these
qualities as my advisor. I have learned so much working for you. Also, thank you to Prof.
Marina Hincapie, your guidance has been greatly appreciated. Thank you to my fellow group
members Sam Tep, Agnes Rafalko, and Majlinda Kulloli for your help and friendship.
I have had the privilege to work with many great scientists at Biogen Idec to whom I owe
a great deal of thanks. Thank you to Damian Houde and Yelena Lyubarskaya for developing my
mass spectrometry skills which have served me well. Thank you to Li Zang, Yelena
Lyubarskaya, Rashmi Kshirsagar, Andy Weiskopf, Helena Madden, and Rohin Mhatre, whom
have all graciously allowed me the opportunity to pursue my PhD at Biogen Idec. I would like
to especially thank my manager Li Zang for your support and mentoring, which was critical for
my success. Also, thank you to Jason Wong, Vijay Janakiraman, and Matt Westoby for
providing relevant samples and information needed for this work.
Finally, I would like to thank my sister Calley, and my friends Ben, Peter, Kristian, Kurt,
Wes, Karl, and Adam for always supporting me. Nazira, thank you for being in my life, you
have made this all the more worthwhile. Thank you to my grandfather Fred for being an
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inspiring and supportive figure in my life. And finally, thank you to my mother Debra for your
neverending love and support and having faith in me, and for my father Dean who showed me
the value of perseverance and achieving your goals. I love you.
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TABLE OF CONTENTS
ABSTRACT 2
ACKNOWLEDGEMENTS 6
TABLE OF CONTENTS 8
LIST OF FIGURES 13
LIST OF TABLES 16
LIST OF ABBREVIATIONS 17
CHAPTER 1 22
OVERVIEW OF THE BIOPHARMACEUTICAL PROCESS AND THE APPLICATION
OF PROTEOMICS TO CELL CULTURE
1.1 The Biopharmaceutical Process 22
1.2 Targeted Engineering to Enhance Mammalian Cell Cultures 27
1.3 Proteomics Tools 31
1.3.1 Mass Spectrometry 32
1.3.1.1 Electrospray Ionization 33
1.3.1.2 Matrix Assisted Laser Desorption Ionization (MALDI) 35
1.3.1.3 Ion Trap Mass Spectrometer 36
1.3.1.4 Quadrupole Mass Spectrometer 39
1.3.1.5 Time-of-Flight Mass Spectrometer 40
1.3.1.6 Orbitrap Mass Spectrometer 42
1.3.1.7 Tandem Mass Analysis 43
1.3.2 Separation Methods 45
1.3.2.1 Two Dimensional Gel Electrophoresis (2DGE) 46
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1.3.2.2 Shotgun Proteomics 48
1.3.3 Quantitation 50
1.4 Proteomics Analysis of Mammalian Cell Cultures 53
1.5 Overview of Research 57
1.6 References 59
CHAPTER 2 71
PROTEOMICS COMPARISON OF LOW- AND HIGH-PRODUCING CHO CELL
CULTURES
2.1 Overview 71
2.2 Methods 73
2.2.1 CHO Cell Lines 73
2.2.2 Cell Culture Conditions 73
2.2.3 Cell Lysis 74
2.2.4 Trypsin Digestion 74
2.2.5 LC/MS Analysis 75
2.2.6 Protein Identification 75
2.2.7 Assessment of Relative Abundance of Peptides and Proteins 76
2.3 Results 77
2.3.1 Cell Growth and Specific Productivity 77
2.3.2 Extraction of Proteins from CHO Cells 78
2.3.3 Classification of Identified CHO Proteins 79
2.3.4 Identification of Proteomic Changes 80
2.3.5 Differential Expression between Control and High-Producer 81
2.4 Conclusion 87
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2.5 References 88
CHAPTER 3 91
ANALYSIS OF DYNAMIC CHANGES TO THE CHO PROTEOME DURING
EXPONENTIAL AND STATIONARY PHASES OF CELL CULTURE
3.1 Overview 91
3.2 Methods 93
3.2.1 Cell Culture 93
3.2.2 Cell Lysis 93
3.2.3 Protein Digestion and Labeling 93
3.2.4 HPLC Fractionation 94
3.2.5 LC/MS 95
3.2.6 Data Analysis 95
3.2.7 Pathway Analysis 97
3.2.8 Western Blotting 97
3.3 Results 98
3.3.1 Proteomic Analysis of Cell Lysates 99
3.3.2 Analysis of Dynamic Trends in Protein Expression 102
3.3.3 Identification of Growth-Regulating Proteins 108
3.3.4 Potential Implications on CHO Cell Culture 111
3.4 Conclusions 114
3.5 References 116
CHAPTER 4 121
CHROMOSOMAL MAPPING OF CHO GENES RELATED TO CELL GROWTH
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4.1 Overview 121
4.2 Methods 127
4.2.1 Chromosome Mapping 127
4.2.2. Pathway Analysis 128
4.3 Results 128
4.3.1 Identification of Cell Growth Gene Networks 128
4.3.2 Mapping of Genes of Interest on Mouse Chromosomes 131
4.3.3. Mapping of Differentially Expressed CHO Proteins 133
on Mouse Chromosomes
4.4. Conclusions 137
4.5 References 138
CHAPTER 5 143
PROTEOMICS CHARACTERIZATION OF HOST CELL PROTEINS PRESENT IN
VARIOUS STAGES OF A BIOPHARMACEUTICAL PROCESS
5.1 Overview 143
5.2 Methods 145
5.2.1 Purification of Cell Culture Harvest 145
5.2.2 Protein Digestion 146
5.2.3 HPLC Fractionation 146
5.2.4 LC/MS 147
5.2.5 Data Analysis 147
5.3 Results 148
5.3.1 Identification of Secreted Proteins in Process Intermediate Samples 148
5.3.2 Implications of Secreted CHO Proteins on Cell Culture 150
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5.3.3 Implication of Secreted CHO Proteins on Downstream Purification 159
5.4 Conclusion 162
5.5 References 164
CONCLUDING REMARKS 169
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LIST OF FIGURES
Figure 1.1: Cell line generation and development for cell culture processes. ............................. 24
Figure 1.2: The Biopharmaceutical Process ................................................................................ 26
Figure 1.3: Effect of Bcl-XL expression on CHO Productivity and Viability ............................ 29
Figure 1.4: Mammalian Proteome Complexity ........................................................................... 32
Figure 1.5: Schematic representation of electrospray ionization ................................................. 34
Figure 1.6: Schematic of the matrix assisted laser desorption ionization technique. .................. 36
Figure 1.7: A simplified schematic of a quadrupole ion trap mass analyzer ............................... 37
Figure 1.8: A simplified schematic of a Thermo Finnigan Ultra TSQ triple quadrupole mass
spectrometer .................................................................................................................................. 40
Figure 1.9: A schematic of an orthogonal acceleration time-of-flight mass spectrometer .......... 42
Figure 1.10: Schematic representation of a hybrid LTQ-Orbitrap mass spectrometer ................ 43
Figure 1.11: Fragment ions generated from tandem mass analysis. ............................................ 44
Figure 1.12: An example of a two-dimensional gel ..................................................................... 48
Figure 1.13: A schematic showing the MUDPIT workflow. ....................................................... 49
Figure 1.14: A typical 4-plex iTRAQ workflow ......................................................................... 51
Figure 1.15: An overview of SILAC ........................................................................................... 53
Figure 1.16: 2D-DIGE gel images of Cy2-labeled pools of Comparison A and B cell lysates . 56
Figure 2.1: Cellular Productivity Profiles .................................................................................... 78
Figure 2.2: Proteins Identified in CHO Samples ........................................................................ 80
Figure 2.3: Analysis of BSA-Spiked CHO Lysates ..................................................................... 81
Figure 2.4: Proteins Upregulated in the High-Producer .............................................................. 84
Figure 2.5: Proteins Downregulated in the High-Producer ......................................................... 85
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Figure 3.1: CHO Cell Growth and Viability................................................................................ 99
Figure 3.2: Protein Identification Summary .............................................................................. 101
Figure 3.3: Identification of Dynamic Proteomic Trends .......................................................... 103
Figure 3.4: Differentially expressed proteins were classified by protein class using PANTHER.
..................................................................................................................................................... 106
Figure 3.5: Abundance over Time for Proteins Involved in Relevant Pathways ...................... 107
Figure 3.6: Top Scoring Protein Network from Ingenuity Pathway Analysis........................... 109
Figure 3.7: Confirmation of Dynamic Trends in Transglutaminase-2 and Clusterin Expression
..................................................................................................................................................... 111
Figure 4.1: Mouse karyotype using the Giemsa (G-banding) technique ................................... 123
Figure 4.2: Karyotypic analysis of diploid Chinese hamster fibroblast chromosomes (LA-CHE)
and CHO-K1 chromosomes ........................................................................................................ 125
Figure 4.3: Karyotype of CHO-DG44 cells using Giemsa (G-banding) technique. .................. 126
Figure 4.5: The top network for mouse chromosome 11 ........................................................... 130
Figure 4.6: Summary of Growth Regulating Gene Clusters Identified on Mouse Chromosomes
..................................................................................................................................................... 132
Figure 4.7: Cluster of Genes of Interest on Mouse Chromosome 7 .......................................... 133
Figure 4.8: Gene Expression Activity of CHO Genes Mapped to Mouse Chromosomes......... 134
Figure 4.9: Chromosomal Mapping of Cell Growth Related Genes ......................................... 135
Figure 5.1: Downstream Process Overview .............................................................................. 149
Figure 5.2: Protein Identification Summary .............................................................................. 150
Figure 5.3: Cellular Compartment of Proteins Identified in HCCF .......................................... 151
Figure 5.4: Classification of Secreted Proteins from Cell Culture Harvest ............................... 152
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Figure 5.5: Sequence Alignment of Pyruvate Kinase M1 and M2 ............................................ 155
Figure 5.6: MS/MS Spectrum for Pyruvate Kinase M2 Peptide T48 (CCSGAIIVLTK).......... 156
Figure 5.7: Top Scoring Network of Extracellular Proteins in HCCF ...................................... 157
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LIST OF TABLES
Table 2.1: Comparison of Cell Lysis Techniques ........................................................................ 79
Table 2.2: Differentially Expressed Proteins ............................................................................... 83
Table 3.1: List of Proteins with Dynamic Trends in CHO Cell Culture .................................... 105
Table 4.1: Top Gene Networks Identified in Each Mouse Chromosome .................................. 128
Table 5.1: Top 5 Most Abundant Proteins in Cell Culture Harvest ........................................... 153
Table 5.2: List of Co-Purified Host Cell Proteins ...................................................................... 160
Table 5.3: Average Physiochemical Values for Co-Purified Proteins ....................................... 161
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LIST OF ABBREVIATIONS
2DGE
two dimensional gel electrophoresis
BCA
bicinchoninic acid
BHK
Baby hamster kidney
BiP
Immunoglobulin binding protein
Bp
base pair
BSA
bovine serum albumin
CHO
Chinese Hamster Ovary
CID
collision induced dissociation
Clu
clusterin
CYR61
cysteine-rich, angiogenic inducer, 61
DAVID
Database for Annotation, Visualization, and
Integrated Discovery
DC
direct current
DHFR
dihydrofolate dehydrogenase
DIGE
differential imaging gel electrophoresis
DNA
deoxyribonucleic acid
DTT
dithiothreitol
Eif3
eukaryotic transcription initiation factor 3
ELISA
enzyme-linked immunosorbent assay
ER
endoplasmic reticulum
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ESI
electrospray ionization
FABP4
Fatty acid binding protein 4
FDA
Food and Drug Administration
GAPDH
glyceraldehyde 3-phosphate dehydrogenase
GHT
glycine, hypoxyanthine, and thymidine
GnTIV
N-aceltylglucosaminyltransferase IV
GRAVY
Grand Relative Average Hydropathicity
GRP78
78 kDa glucose-regulated protein
HCCF
Harveseted cell culture fluid
HCl
Hydrochloric acid
HEK
Human embryonic kidney
HPLC
High performance liquid chromatography
IEF
isoelectric focusing
IgG
Immunoglobulin G
IPA
Ingenuity Pathway Analysis
IPG
immobilized pH gradient
ITRAQ
isobaric tag for relative and absolute quantitation
kDa
kilodalton
LC/MS
liquid chromatography mass spectrometry
m/z
mass to charge ratio
MALDI
matrix assisted laser desorption ionization
MCM2
minichromosome maintenance complex component
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2
mg
milligram
mL
milliliter
mm
millimeter
MOPS
3-(N-morpholino)propanesulfonic acid
M-PER
Mammalian Protein Extraction Reagent
MRM
multiple reaction monitoring
MS
mass spectrometry
Mtorc1
mammalian target of rapamycin complex 1
MUDPI
T
multidimensional protein identification technology
NS0
mouse myeloma null cell line
PAb
polyclonal antibody
PANTHER
Protein Analysis Through Evolutionary
Relationships
PBS
phosphate buffered saline
PBS-T
phosphate buffered saline with Tween
PDI
protein disulfide isomerase
PEP
phosphoenolpyruvate
pI
isoelectric point
PIBF
progesterone immunomodulatory binding factor 1
PIKK
phosphatidylinositol 3-kinase-related kinase
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PK
pyruvate kinase
PQD
pulsed Q dissociation
PSA
Prostate-specific antigen
PTM
post translational modification
r2
correlation coefficient
RACK1
Receptor for activated C kinase
RF
radio frequency
RP
reversed phase
RSD
relative standard deviation
SCX
strong cation exchange
SD
standard deviation
SDS-PAGE
sodium dodecyl sulfate polyacrylamide gel
electrophoresis
SILAC
stable isotopic labeling of amino acids in cell
culture
SIRNA
small interfering ribonucleic acid
ST3Gal-IV
Gal beta-1,3/4-GlcNAc alpha-2,3-sialyltransferase
ST6GalI
Gal beta-1,4-GlcNAc alpha-2,6-sialylatransferase I
TCTP
Translationally controlled tumor protein
TFA
trifluoroacetic acid
TG2
Transglutaminase-2
TOF
time-of-flight
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TP53
tumor protein 53
Tris
2-Amino-2-hydroxymethyl-propane-1,3-diol
UPR
unfolded protein response
UV
ultraviolet
VCD
viable cell density
VCP
valosin containing protein
µg
microgram
µL
microliter
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CHAPTER 1
OVERVIEW OF THE BIOPHARMACEUTICAL PROCESS AND THE APPLICATION OF
PROTEOMICS TO CELL CULTURE
1.1. The Biopharmaceutical Process
The commercialization of biopharmaceuticals developed to treat serious diseases has
undergone rapid growth over the past 30 years. These specialized drugs offer the potential to
target a disease more specifically than a conventional synthesized pharmaceutical, while
resulting in fewer side effects. Over 250 biological drugs have been approved worldwide since
the first approval of insulin in 1982 [1]. Biopharmaceuticals earned over $90 billion in revenue
in 2010. Despite the decreasing rate of FDA approvals over the past several years, market
research indicates that the revenue generated by biopharmaceuticals is expected to increase to
$167 billion by 2015 [2].
The process used to produce biopharmaceuticals (i.e. biopharmaceutical process or
bioprocess), is very complex. The upstream part of the process uses genetically modified cells to
express a recombinant protein drug product [3]. Mammalian cells such as Chinese hamster
ovary (CHO), baby hamster kidney (BHK), or mouse myeloma (NS0) are the most common
platform used for biopharmaceutical production, primarily because they produce proteins with
desired post-translational modifications. For example, glycoproteins expressed by CHO cells
exhibit glycosylation similar to human cells [4], which is critical for correct protein function and
pharmacokinetics [5, 6].
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The first stage of cell line development involves insertion of a target gene into the host
cell, which is typically accomplished using an engineered vector and utilizing a suitable selection
mechanism. For example, the commonly used CHO-DG44 cell line is a dihydrofolate
dehydrogenase (DHFR) deleted mutant which requires glycine, hypoxyanthine, and thymidine
(GHT) for growth [7]. The transfection of CHO-DG44 cells with a DHFR-containing vector
allows selection and gene amplification in GHT-minus media. Including the anti-folate drug
methotrexate in the medium provides further selective pressure on the cells, allowing for
selection of cells expressing DHFR, and in turn the target gene, at relatively high levels [8].
Cells expressing the transfected genes will grow under the selective conditions. These
selected cells are then transferred as single cells to separate vessels, where they are grown to
produce clonal populations. These subclones are studied to determine cell growth and
productivity characteristics, and slowly scaled up to larger culture volumes as additional
parameters are monitored. After a final cell line displaying the best cell growth, productivity,
and product quality attributes is selected, optimization of the process begins. Process and media
optimization occurs in small scale systems including 96 well plates, small shake flasks, and
benchtop bioreactors (typically up to 5 L in total volume). The conditions used to grow the cells
can have a significant impact on the cell culture phenotype. The media components used to feed
the cells, dissolved oxygen levels, bioreactor temperature and pH all play key roles in supporting
high cell growth and productivity [9]. These parameters are typically optimized for each specific
cell line used, and vary between different cell culture processes.
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Figure 1.1: Cell line generation and development for cell culture processes.
Proteins of interest are marked protein o.i. Wavy lines indicate subcloning of individual cell
lines, to obtain optimal cell growth and productivity. Vials indicate frozen banks of cells. [9]
Upon harvesting the cells, the conditioned media containing the drug product is isolated
from the cellular material and moved into the downstream process where a series of filtration and
separation steps are used to purify the drug product from the cell culture matrix [10]. This part
of the process must focus on purifying away residual host cell materials, including proteins and
DNA, as well as maintaining a high yield of purified drug product. A simplified diagram of the
entire biopharmaceutical process is shown in Figure 1.2.
The biopharmaceutical process is optimized for both product yield, and product quality.
The yield of the process is critical, due to the relatively high cost of biopharmaceutical
production. The factors affecting yield from the upstream process are cell growth, cell viability,
and cell-specific protein production (i.e. specific productivity) [11]. High cell growth and
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productivity is selected during screening of cell lines, and is further driven by careful
optimization of cell culture conditions. In addition to cell growth and productivity, cell viability
is also important as it determines the duration of time the cell culture can maintain productivity.
Cell viability is limited by apoptosis, or programmed cell death, which can be triggered by
numerous factors including the accumulation of metabolic byproducts throughout the course of
the cell culture [12].
Product quality is also directly tied to the upstream process. Post-translational
modifications, as well as other aspects of product quality including protein aggregation and
enzymatic cleavage can all have an impact on protein function and must be carefully monitored
during cell culture development [13, 14]. Controlling product quality and process consistency
across different development stages and production scales is of high importance within the
industry [15].
The ability to produce high quality biopharmaceuticals using efficient and robust
processes is a critical component of success for a biopharmaceutical development. Corporations
with a wide portfolio of clinical and commercial programs must have the ability to rapidly
develop suitable processes for many biopharmaceuticals, while meeting program timelines.
Because of the importance of this aspect of biopharmaceutical development, an active area of
research is the identification and application of methods to enhance mammalian cell cultures
through various engineering efforts.
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Figure 1.2: The Biopharmaceutical Process
A simplified diagram of a typical large-scale biopharmaceutical process is shown. The cell
culture process starts with a small volume of cells called the seed, which is thawed and used to
inoculate a small bioreactor. The reactor size can be scaled up to 25,000 L, depending on the
manufacturing scale. Upon harvesting the cells, centrifugation and depth filtration is used to
separate cells from the media supernatant, which is then transferred to the purification process.
A series of chromatographic and filtration steps are used to purify the drug from the cell culture
matrix. Typically a combination of affinity and ion-exchange chromatography is used. The bulk
material generated from this process is then formulated and packaged to create the final drug
product.
Cell Culture
150L
Bioreactor
750L
Bioreactor
5,000L
Bioreactor
Depth
Filtration
Depth
Filtration
CollectionCollection
CentrifugeCentrifuge
Cell Culture
150L
Bioreactor
750L
Bioreactor
5,000L
Bioreactor
Depth
Filtration
Depth
Filtration
CollectionCollection
CentrifugeCentrifuge
Harvest
Collection
Tank
1,500L
Harvest
Collection
Tank
1,500L
Filter
Chromatography
Skid
Chromatography 1
Column
Eluate
Hold
Tank
Eluate
Hold
Tank
Chromatography
Skid
Column
Chromatography 2 Ultra Filtration
Diafiltration
Bulk
Fill
Purification
Harvest
Collection
Tank
1,500L
Harvest
Collection
Tank
1,500L
Filter
Chromatography
Skid
Chromatography 1
Column
Eluate
Hold
Tank
Eluate
Hold
Tank
Chromatography
Skid
Column
Chromatography 2 Ultra Filtration
Diafiltration
Ultra Filtration
Diafiltration
Bulk
Fill
Bulk
Fill
Purification
27
1.2. Targeted Engineering to Enhance Mammalian Cell Cultures
One method to enhance cell culture performance is through modification of relevant
biological pathways via cellular engineering. Certain pathways are known to be directly related
to important aspects of cell culture, including cell growth, productivity, and product quality.
Targeted overexpression of genes within these pathways has become a common method for
improvement of cell culture characteristics [16].
One example is the engineering of enhanced metabolism of carbon sources such as
glucose and glutamine, both of which are fed to cells in large quantities to drive cell growth.
Glutamine in particular is typically fed to cells in large concentrations because these cells cannot
produce them naturally due to low endogenous expression of glutamine synthase. The metabolic
conversion of glutamine and glucose into carbon dioxide and water produces lactate and
ammonia as byproducts. These compounds are known to negatively affect cell growth [17].
Overexpression of pyruvate carboxylase in CHO, BHK, and HEK-293 (human embryonic
kidney) cells was used to reduce glucose consumption and therefore reduced the buildup of toxic
ammonia and lactate in the cells [18-20]. In addition, overexpression of glutamine synthase in
NS0 cells was used to reduce glutamine consumption and ammonia levels in cell culture [21].
The effect of overexpression of the molecular chaperones immunoglobulin binding
protein (BiP) and protein disulfide isomerase (PDI) on mammalian cell cultures has also been
investigated. Both of these proteins reside in the endoplasmic reticulum (ER) and are involved
in folding of nascent polypeptides entering the ER after translation. It was thought that these
proteins may be part of a protein secretion “bottleneck” where high levels of expressed proteins
cannot be folded and processed efficiently to achieve optimal specific productivity. Therefore,
28
overexpression of these proteins may offer a way to overcome this bottleneck in productivity.
While BiP overexpression increased protein production in yeast cells [22], the opposite effect
was observed in CHO and NS0 cells [23]. Overexpression of PDI improved production of
monoclonal antibodies in CHO cells [23, 24]. However, it was observed that this approach gave
varied results between different cell lines, and does not offer a universal benefit in mammalian
cell culture performance.
Resistance to apoptosis is another area where targeted engineering has been applied to
improve cell cultures. The anti-apoptotic genes Bcl-2 and Bcl-XL have both been studied
extensively to assess their impact on cell growth, viability, and productivity. Various studies
have shown that overexpression of either Bcl-2 or Bcl-XL have resulted in extended viability of
cells in culture, as well as higher viable cell densities compared to control cell lines [25-28].
However, the impact of these genes on productivity is mixed. In one case, overexpression of
Bcl-XL resulted in up to 90% higher specific productivity of a recombinant antibody (see Figure
1.3) [29]. In another similar study, it was shown that overexpression of Bcl-XL did not improve
productivity in a CHO cell line expressing recombinant erythropoietin [30].
29
Figure 1.3: Effect of Bcl-XL expression on CHO Productivity and Viability
A: Comparison of titer of a monoclonal antibody expressed in a parent cell line 100AB-37 and
mixed populations co-expressing either bcl-xL (100AB-37/BclxL pool) or null vector (100AB-
37/null vector pool) .B: Comparison of percent viability of parent cell line 100AB-37 and mixed
30
populations expressing either bcl-xL (100AB-37/BclxL pool) or null vector (100AB-37/ null
vector pool). [29]
Efforts to modify protein glycosylation via overexpression of various glycosidases have
also been successfully used in mammalian cells. The product quality profile of the
biopharmaceutical is tied to the glycosylation on the protein, which can affect efficacy as well as
circulatory half-life. As an example, overexpression of GnTIV (N-
aceltylglucosaminyltransferase IV) modified the glycosylation such that higher numbers of tri-
and tetra-antenarry glycans were present on interferon-gamma [31]. This provides additional
potential sialylation sites on the molecule compared to the control material which contained
mostly bi-antennary glycan structures. Additional overexpression of sialtransferases ST3Gal-IV
and ST6Gal-I boosted overall sialylation to 80% compared to ~60% in the control [32].
The success of these efforts demonstrates the potential benefits of targeted engineering on
cell culture performance. However, it also underlines how the results of these efforts are
dependent on existing knowledge about relevant genes and biological pathways. While some
relevant pathways are well understood, others are not and therefore our knowledge of potential
targets is limited. In fact, our general understanding of the underlying biology of mammalian
cells used for biopharmaceutical production is lacking. Years of adaptation have changed the
cell biology to a significant degree from the original tissues which they originate from; for
example most CHO cell lines have lost their ability to properly control cell cycle [33]. These
changes make comparisons between different cell types and even different cell lines difficult.
Many of the genes playing key roles in cellular productivity, cell growth, and certain aspects of
product quality, are in fact not well understood. The study of mammalian cell cultures using
31
proteomics tools offers the potential of identifying novel proteins involved in cellular growth,
productivity, and product quality which could be used as potential targets for cellular
engineering. By directly comparing cell cultures with different phenotypes, i.e. low and high
producers, quantitative proteomics approaches could be used to identify differentially expressed
proteins which are related to that phenotype. The genetic manipulation of these potential
markers may result in positive enhancements to mammalian cell cultures, such as higher
productivity or better product quality.
1.3. Proteomics Tools
Proteomics analysis involves the large-scale characterization of the protein components
of a given biological sample [34]. Proteomics evolved as a complement to the field of genomics,
and is of high interest because of the close relationship between protein expression and
biological function.
There are many challenges associated with proteomics analysis. Mammalian proteomes
are extremely complex, even more so than genomes [35]. As a result of alternative splicing,
multiple isoforms of a protein may exist. In addition, many proteins contain post-translational
modifications, such as glycosylation, oxidation, deamidation, phosphorylation, etc. These
modifications increase sample complexity. Finally, compared to genomics, there is no direct
method for amplifying protein during analysis such as DNA polymerase chain reaction for
genomics studies. Therefore, proteomics method must be extremely sensitive to detect low-level
proteins present in biological samples.
32
Typically, proteomics analysis involves the use of separation techniques to isolate
proteins or peptides associated with the proteome, and mass spectrometric techniques to identify
the proteins of interest based on fragmentation data. Rapid advances in separations and mass
spectrometry technology over the past 10 years have accelerated the proteomics field and
provided many powerful tools to proteomics researchers.
Figure 1.4: Mammalian Proteome Complexity
The number of genes, mRNA transcripts, and proteins present in a human are shown.
1.3.1. Mass Spectrometry
A mass spectrometer is a mass-sensitive detector which detects charged analytes based on
their mass-to-charge (m/z) ratio. Analytes are first ionized, a process which transfers molecules
from solid or liquid phase into gas phase and imparts a positive or negative charge, allowing
them to be manipulated using electric or electromagnetic fields. The ions are then separated
33
based on their m/z ratio using a mass filter. Common mass filters include ion traps, quadrupoles,
time-of-flight chambers, and Orbitraps. All of these filters operate on the common principle that
an ion will travel through an electromagnetic field following a path dependent upon its m/z ratio.
Therefore, manipulation of that field enables the filtering of particular ions, such that only ions
of a specific m/z will pass to the detector [36].
1.3.1.1. Electrospray Ionization
The first stage of mass spectrometric analysis is ionization. One of the most popular and
powerful ionization methods for analysis of biomolecules is electrospray ionization. Pioneered
by John Fenn in the 1980’s, this process involves the application of high voltage to a liquid
sample flowing through a stainless steel needle [37]. The voltage produces charged molecules in
the sample, which form droplets as they exit the needle in a cone formation. The solvent
forming these droplets is evaporated in the electrospray source by application of high
temperature and heated gas, producing smaller and smaller droplets. Coulombic repulsion of the
ions within the droplet also facilitates desolvation, until single ions are present in the gas phase.
The amount of charge present on the ion depends on the chemical structure. For example, small
peptides may accept one or two positive charges during positive mode electrospray ionization.
On the other hand, a protein of 150 kDa would accept anywhere from 35 to 75 positive charges.
As a result, mass spectra of electrospray ionized compounds typically consists of many peaks
resulting from different charge states of the same analyte, complicating data analysis [38].
One of the advantages of electrospray is that it is easily interfaced with liquid
chromatography. A typical electrospray source works with a range of flow rates from 10 – 0.1
34
mL/min. Nanospray ionization is a scaled down electrospray format which works with flow
rates in the nL/min range. Peaks generated from nanoscale liquid chromatography typically have
higher analyte concentrations compared to microscale liquid chromatography, resulting in
greater sensitivity when coupled with nanospray-ESI mass spectrometry [39]. Compared to
standard ESI, nanospray typically uses a small diameter coated silica needle to which the high
voltage is applied, and uses little or no desolvation gas due to the smaller droplet sizes generated
during ionization. The major disadvantage of nanospray-ESI is less robustness due to the small
needle size, which can easily become clogged with particulates, destabilizing the spray.
Figure 1.5: Schematic representation of electrospray ionization [39].
35
1.3.1.2. Matrix Assisted Laser Desorption Ionization (MALDI)
Similarly to electrospray, MALDI is a “soft” ionization technique which is suitable for
protein and peptide analysis because it leaves the molecules relatively intact during MS analysis
[40]. This method uses pulsed light from a laser source to transfer analytes from a solid surface
to the gas phase. Ions generated during this process are transferred from the source into the mass
spectrometer under high vacuum. Samples are first mixed with an acidic matrix solution, and
deposited in small droplets onto a metal surface where the spot dries, forming a crystalline
structure on the surface of the plate [41]. A pulsed nitrogen laser, typically operating at 5 – 10
Hz, is directed at various regions of the sample spots on the plate. The energy from the laser
excites the matrix molecules, causing a transfer of charge to the analyte and desorption from the
plate surface.
MALDI typically generates singly charged ions for proteins and peptides, simplifying
spectra interpretation compared to electrospray. Other advantages include high sensitivity, high
tolerance of salts and impurities, and the ability to analyze an entire plate (up to 384 samples on
one plate) in a high-throughput manner using newer instruments with automated data acquisition
software. Disadvantages include matrix-related noise in the low mass region of the spectrum, as
well as low shot-to-shot reproducibility and short sample life span [42].
36
Figure 1.6: Schematic of the matrix assisted laser desorption ionization technique.
1.3.1.3. Ion Trap Mass Spectrometer
The ion trap mass spectrometer was originally developed by Wolfgang Paul, who won
the 1989 Nobel Prize in Physics for his invention [43]. The original ion trap design utilized three
electrodes, two endcaps and one ring electrode, to trap ions in a central region where they can be
ejected to a detector. A radio frequency voltage applied to the ring electrode traps the ions
around the ring, while a low pressure gas, typically helium, is used in the trap to collide with the
ions and lower their kinetic energies, thereby stabilizing their motion within the trapping region.
Further application of a DC voltage across the endcaps also provides a trapping effect, such that
the ions are trapped in the center of the three electrodes. Ramping of the RF and DC voltages
37
destabilizes the motion of the ions and causes them to eject from the trap to a detector in order of
increasing m/z ratio.
Figure 1.7: A simplified schematic of a quadrupole ion trap mass analyzer
Ions enter the trap from the ion source, and are trapped between the ring electrode and endcap
electrodes. Once ejected from the trap, the ions are detected by an electron multiplier detector.
[44]
Trapped ions may be subjected to tandem mass analysis by collision with gas molecules
under higher kinetic energy. In a data dependent analysis, precursor ions are detected in a single
ion trap scan, followed by selection of a particular precursor ion for fragmentation. The trap is
filled with the target precursor ion, and fragmented under high kinetic energy conditions.
Resulting fragment ions are then ejected to the detector. The ion trap mass spectrometer also has
the unique capability of multiple stage tandem mass analysis, or MSn. This allows multiple
38
stages of fragmentation on fragment ions to generate smaller and smaller fragments to assist in
identification of analytes. Tandem mass analysis is discussed further in section 1.3.1.7.
The ion trap suffers from the limitation of space-charging effects. Because of the defined
space occupied by the ion cloud within the mass spectrometer, the ions may destabilize each
other if they are too close in physical space, causing loss of signal. For this reason, ion trap mass
spectrometers use an automatic gain control to limit the number of ions allowed into the trap for
every scan event. This limits the overall sensitivity of an ion trap mass spectrometer.
The introduction of the linear ion trap reduced these limitations. The linear ion trap
utilizes parallel quadrupole rods to trap ions. Due to the design of the linear ion trap, more ions
can be stored in the trap without space-charging effects, thereby increasing the sensitivity of the
instrument up to ten times compared to the original 3-D ion trap design [45, 46]. The linear ion
trap also has faster scanning rates compared to the 3D ion trap.
In proteomics research, the linear ion trap is an instrument of choice because of its high
sensitivity and fast scan rates, which extends the proteome coverage of an analysis when
compared to other types of mass spectrometers. In addition, the linear ion trap is available in
hybrid configurations with other mass analyzers, such as the LTQ-Orbitrap and LTQ-FT which
combine the capabilities of the linear ion trap with the high mass resolving power and high mass
accuracy of the Orbitrap and Fourier transform mass analyzers.
39
1.3.1.4. Quadrupole Mass Spectrometer
A quadrupole mass spectrometer uses a quadrupole mass analyzer to filter ions based on
their m/z ratio. The quadrupole consists of four metal rods which are arranged in parallel. A
pair of radio frequency (RF) voltages is applied to two opposing pairs of rods. A DC voltage is
superimposed over the RF frequencies. As ions pass between the rods, ions of a specific m/z
ratio can be selected by adjusting the voltages applied to the rods. Other ions will not be able to
pass through the rods.
The quadrupole mass spectrometer is often configured as a triple quadrupole, with the
three quadrupoles arranged in series. In this configuration, the first and third quadrupoles (Q1
and Q3, respectively) act as mass filters, while the second quadrupole (Q2) acts as a collision cell
and is set at a higher pressure by introduction of a collision gas into the quadrupole chamber.
The triple quadrupole mass spectrometer can be used for selective monitoring reaction (SRM)
experiments, where Q1 selects a precursor ion, Q2 fragments the precursor, and Q3 selects a
specific fragment ion. This method is an extremely specific and sensitive method for detecting
analytes, and is commonly used for detecting drug metabolites from biological samples [47-49].
It has also been used for quantitation of peptide biomarkers in complex biological samples [50].
The quadrupole is typically used for quantitative applications because of its high
sensitivity and wide dynamic range, and the high specificity offered by MRM analysis.
However, due to a relatively low quadrupole scan speed the instrument is relatively slow when
performing MS scans over a wide mass window.
40
Figure 1.8: A simplified schematic of a Thermo Finnigan Ultra TSQ triple quadrupole mass
spectrometer is shown. Two shorter quadrupoles after the source focus the ion beam prior to
entering Q1. [51]
1.3.1.5. Time-of-Flight Mass Spectrometer
The time-of-flight mass spectrometer (TOF-MS) measures the m/z ratio of an ion based
on the time it takes to traverse a field-free drift chamber, called a time-of-flight chamber. The
amount of time it takes for an ion to move through the chamber to the detector is directly
proportional to its mass to charge ratio [52]. Heavier ions will take a longer time to reach the
detector, while lighter ions will take less time (assuming the same charge state). The ions are
accelerated into the TOF region by application of an accelerating voltage. In theory, all ions
should accelerate at the same time and energy into the TOF; however in reality the kinetic
energies of accelerated ions encompass a distribution due to imperfections in ion transmission.
To correct for differences in the distribution of kinetic energy of ions of the same m/z, use of a
reflectron has become standard in TOF MS. This device is installed at one end of the flight
chamber, and applies a constant electrostatic field to reflect the ion beam back towards the
41
detector. Ions with higher kinetic energies will penetrate the electrostatic field deeper, such that
lower kinetic energy ions will catch up to their higher energy counterparts (see Figure 1.9). The
use of reflectron technology in TOF-MS instruments enables high resolution and high mass
accuracy measurements [53].
The time-of-flight mass spectrometer is a pulsed ion mass analyzer. Ions must be
accelerated into the TOF chamber in discrete groups at the same time. For this reason, pulsed
ion sources such as MALDI are typically combined with TOF mass analyzers due to their
inherent compatibility. However, in order to make TOF compatible with continuous ion sources,
orthogonal extraction is used. In this case, ions are accelerated along an axis perpendicular to the
time of flight chamber, and the ion beam is focused and cooled using ion optics and collision
with a low-pressure gas. The focused beam is accelerated perpendicularly into the TOF where it
is further focused by a set of charged grids followed by acceleration from a charged pusher plate.
The hybrid quadrupole-TOF-MS system is offered by several vendors, and combines the
strengths of the triple quadrupole system with the high resolution and mass accuracy of the TOF.
42
Figure 1.9: A schematic of an orthogonal acceleration time-of-flight mass spectrometer
Orthogonal acceleration time of flight mass spectrometer schematic:[54] 20 – ion source; 21 –
ion transport; 22 – flight tube; 23 – isolation valve; 24 – repeller plate; 25 – grids; 26 –
acceleration region; 27 – reflectron; 28 – detector.
1.3.1.6. Orbitrap Mass Spectrometer
The Orbitrap mass spectrometer is a novel mass analyzer which utilizes two electrodes,
an inner spindle and outer barrel electrode, to capture ions in an oscillating orbit [55]. Ions are
injected into the mass analyzer tangentially, and accumulate in the space between the two
electrodes due to the electrostatic repulsions with the electrodes and centrifugal balancing forces.
The harmonic oscillations of the orbiting ions are converted to m/z ratio using Fourier
transformation.
The main advantage of the Orbitrap is high resolution (up to 240,000), high mass
accuracy, and a wide dynamic range [56]. The instrument is available from Thermo Scientific as
a hybrid instrument coupled with a linear ion trap (LTQ-Orbitrap) and also as a standalone
43
benchtop mass analyzer (Exactive). The LTQ-Orbitrap is a popular instrument for bottom-up
proteomics work, and has been successfully used for identification of proteins from complex
mixtures [57-61] as well as in peptide quantitation [62]. The high mass accuracy of the Orbitrap
improves the confidence of peptide identification [63].
Figure 1.10: Schematic representation of a hybrid LTQ-Orbitrap mass spectrometer
(http://www.thermo.com).
1.3.1.7. Tandem Mass Analysis
Mass spectrometry is the technology enabling large-scale proteomic studies, because it is
the only instrument capable of sequencing large numbers of proteins quickly and easily [64].
This is accomplished by tandem MS analysis, where proteins or peptides are fragmented into
unique fragment ions. The resulting MS/MS spectra can be searched against a protein sequence
database to determine the identity of the protein which that peptide was derived from.
The most common fragmentation method is collision-induced dissociation (CID). In this
mode, ions are collided with neutral atoms (typically He, Ar, or N) inducing fragmentation along
44
labile bonds. Depending on instrument configuration, kinetic energy of the precursor ions may
also be increased by adjustment of the field strength within the collision chamber to facilitate
fragmentation. For a peptide, the common fragmentation is along the peptide backbone, creating
b and y type fragment ions. These fragment ions provide information that can be used to identify
peptides. Database search algorithms such as Sequest and Mascot are commonly used to search
tandem mass data against a protein sequence database for identification [65].
Figure 1.11: Fragment ions generated from tandem mass analysis.
Different possible fragment ions that can be generated from peptides are shown. Peptides
fragmented by collision-induced dissociation typically generate b and y type ions.
45
Another fragmentation method unique to ion trap mass spectrometers is called pulsed Q
dissociation (PQD) which is available on Thermo Scientific ion trap mass spectrometers. This
method first applies ramped up RF voltages (the transition of RF voltages is referred to as Q
values) within the ion trap to increase the kinetic energy of the trapped ions. The ions are held
for a set amount of time (usually 100 µs) before the Q value is adjusted to the starting point. The
pressure within the trap is then increased using collision gas as in typical CID, which causes
fragmentation of the trapped ions [66].
The use of PQD for tandem mass analysis in ion traps overcomes the limitation of using
CID, called the “1/3 cutoff rule”. When performing traditional CID using an ion trap instrument,
the mass range of the tandem mass spectrum is limited to 1/3 of the mass to charge ratio of the
precursor ion and above. Therefore, low m/z fragment ions are not detected. This is due to the
high Q values used within the trap to activate the ions during fragmentation, which causes
destabilization of low-mass ions.
When using PQD fragmentation, the Q value is lowered prior to fragmentation, which
allows more ions to be ejected to the detector, thus enabling detection of low m/z fragment ions.
However, the fragmentation efficiency of PQD is typically lower compared to CID because of
the lower kinetic energy of the precursor ions during fragmentation.
1.3.2. Separations Methods
Mass spectrometers have inherent limitations in performance. One such limitation
involves ion suppression, where analytes at low abundance or with weak ionization efficiency
46
will be detected with greatly compromised sensitivity when analyzed simultaneously with
analytes at higher abundance or with better ionization efficiency. Another limitation is
resolution. Depending upon the type of mass analyzer being used, mass spectrometers can have
up to 500,000 resolution, but more typically < 100,000 resolution for proteomics experiments.
In a complex proteomics sample, isobaric or near-isobaric peptides cannot be accurately
resolved, thereby interfering with protein identification. To mitigate these limitations, suitable
separation techniques are required prior to MS analysis to reduce sample complexity and
improve ionization for accurate identification of proteins.
1.3.2.1. Two Dimensional Gel Electrophoresis (2DGE)
Two types of electrophoresis are commonly used in proteomics analysis: isoelectric
focusing (IEF) and SDS-PAGE. Isoelectric focusing uses a pH gradient formed in the gel to
separate proteins based on their pI. SDS-PAGE separates proteins based on their molecular
weight as they migrate through a gel matrix of defined density.
Isoelectric focusing and SDS-PAGE can be combined into a two-dimensional gel
electrophoresis (2DGE) method where proteins are first separated by pI using an immobilized
pH gradient (IPG) strip, which is then loaded transversely across the top of an SDS-PAGE gel to
separate the proteins by molecular weight [67, 68]. Proteins are typically visualized by staining
the gel with non-specific UV-absorbing or fluorescent reagents. A popular method is Coomassie
Blue stain, which can detect down to ~ 40 ng of protein in a single band [69]. Other more
47
sensitive stains such as silver stain can detect below 1 ng of protein per band. Specific spots of
interest from the gel can be excised for identification using in-gel trypsin digestion followed by
mass spectrometric analysis [70].
Advantages of 2DGE include the ability to resolve thousands of proteins in a single gel,
and has been frequently applied in proteomics studies to analyze cell lysates. Smales et al. used
2DGE to analyze NS0 cell lysates, identifying over 2000 proteins in a single gel using Sypro
Ruby stain [71]. 2DGE also offers the ability to quantitate differences between samples using
difference gel electrophoresis (DIGE) where different fluorescent dyes are used to differentiate
proteins from different samples’ run in a single gel [72]. Disadvantages of these methods
include limited throughput and poor gel-to-gel reproducibility.
48
Figure 1.12: An example of a two-dimensional gel is shown, from the analysis of a CHO whole
cell lysate with identified proteins indicated [73]
1.3.2.2. Shotgun Proteomics
Another common proteomics approach is shogtun proteomics, which utilizes proteolytic
digestion of proteins into peptides, typically using trypsin, followed by LC/MS analysis for
identification of the proteins. Peptides are separated based on differences in hydrophobicity
using reversed-phase chromatography. For increased peak separation efficiency, this can be
49
coupled with other separation techniques. One common approach is MUDPIT
(multidimensional protein identification technology) which combined ion-exchange
chromatography and reversed-phase chromatography online for a two-dimensional separation of
peptides [74].
Figure 1.13: A schematic showing the MUDPIT workflow. Digested tryptic peptides are
separated using a special column consisting of strong cation exchange (SCX) and reversed-phase
solid phases. Peptides are eluted stepwise from the SCX to RP phase using increasing
concentrations of salt, followed by reversed-phase gradient to elute the peptides from the second
colunn into the MS. [75]
Advantages of the shotgun proteomics method are the ability to sequence hundreds or
even thousands of proteins in a single LC/MS run, making this the method of choice for large
numbers of proteomics samples. The major disadvantage of the method is the increased
complexity of the sample after trypsin digestion, which can make accurate protein identification
50
difficult due to ionization suppression of low-level peptides, poor MS/MS spectra quality, and
interference of near-isobaric peptides in MS/MS spectra.
1.3.3. Quantitation
Relative quantitation of proteins in shotgun proteomics can be achieved using label-free
methods, chemical labeling methods, or metabolic labeling methods. These methods enable
relative quantitation of proteins between different proteomic samples, allowing the determination
of differential expression.
Spectral counting is a label-free method based on the observation that the number of
MS/MS scans acquired for peptides associated with a particular protein is proportional to the
concentration of that protein in the sample [76]. As a result, the spectral count value for proteins
can be used as an estimate of relative abundance of the proteins, and compared between samples
to infer differences in relative abundance. The method is fairly accurate for more abundant
proteins, but accuracy is rather poor for low-abundance proteins.
The most common chemical labeling approach is the isobaric tagging reagent for relative
and absolute quantitation (iTRAQ). This method utilizes a chemical label which labels the N-
termini and lysine side-chains of peptides. Up to eight different samples can be labeled with a
different iTRAQ reagent. After labeling, the samples are mixed together, and analyzed by
LC/MS. Because the iTRAQ reagents are isobaric, peptides of the same sequence from the
different samples will be detected as a single ion during MS analysis. However, when the
labeled peptides are fragmented during tandem mass analysis, a unique reporter ion is generated
51
from each iTRAQ reagent. The relative intensity of these reporter ions can be used to infer
relative quantitation of the peptides [77, 78].
Figure 1.14: A typical 4-plex iTRAQ workflow
Samples are digested and labeled with different iTRAQ reagents. After labeling, the samples are
mixed together into a single tube. This mixed sample is subsequently analyzed by some form of
LC/MS analysis. During each peptide selected for tandem MS analysis will yield typical
fragment ions as well as reporter ions in the low m/z region of the mass spectrum. The relative
abundance of the peptide in the four samples can be determined from the intensity of these
reporter ions.
The SILAC approach (stable isotopic labeling by amino acids in cell culture) is a
metabolic labeling approach used in cases where proteomic samples can be generated from cell
culture. Two different cell cultures are fed with light and heavy media, the latter containing
52
stable isotopes of an amino acid such as arginine but otherwise identical to the light media. After
growing the cells in these media, the “heavy” amino acids are incorporated in the proteins
expressed by the cells. After preparing and analyzing protein samples from the light and heavy
cell cultures, a pair of ions will be detected for peptides corresponding to light and heavy media.
The ratio of these ions can be used to determine relative quantitation of proteins [79].
The SILAC method is a very strong quantitative method, because the stable isotopes are
incorporated into the proteins at a very early stage of the experiment. As a result, sample to
sample variability is minimized. One limitation of the method is the cost of stable isotope
labeled media. Because of the relatively high cost, these experiments are typically carried out in
small cell volumes, either in plates or small shake flasks. In addition, the common SILAC
method can multiplex two samples together, making analysis of larger numbers of samples more
time-consuming compared to the iTRAQ method.
53
Figure 1.15: An overview of SILAC
Cells are grown in “light” and “heavy” media. In this example, the heavy media contains a
stable isotope of arginine containing 6 13
C atoms. After passaging the cells three times, the cells
are harvested and proteins extracted. Subsequent shotgun proteomics analysis identifies two ions
for a given peptide, separated by a mass of 6 Da. The relative abundance of that peptide can be
determined from the intensity of the two ions in the MS spectrum.
1.4. Proteomics Analysis of Mammalian Cell Cultures
Proteomics has been utilized to study mammalian cell cultures, including the effect of
sodium butyrate treatment [80], low culture temperature [81], lactate metabolism [82], specific
productivity [41], and cell growth rates [83].
Addition of sodium butyrate to cell culture media has been shown to improve specific
productivity in mammalian cell cultures [84]. Proteomics and transcriptomics tools were used to
study the effect of butyrate treatment on CHO and hybridoma proteomes [80]. Whole cell
lysates generated from cells grown with and without sodium butyrate were analyzed by 2DGE
54
and stained with Sypro Orange. Gel spots were compared between gels and differences of 1.5 or
greater were considered significant. Over 600 spots were identified, and 43 spots were found to
be differentially expressed after sodium butyrate treatment. The differentially expressed proteins
were found to be related to protein folding, trafficking, redox control, and ER stress response.
The authors concluded that upregulation of proteins involved in folding and vesicle transport
may have a role in the increased productivity observed under butyrate treated conditions.
In another example, CHO cells with different expression levels of green fluorescent
protein (GFP) were compared using proteomics and transcriptomics tools [41]. Cell lysates
prepared at two different cell culture time points were digested with trypsin and labeled with
iTRAQ, followed by two-dimensional LC/MS analysis. A total of 864 proteins were identified.
Upregulated and downregulated proteins were identified at the two time points based on the
iTRAQ reporter ion ratios. Weak correlation between transcriptome and proteome results was
observed. Many differentially expressed proteins had protein biosynthesis functional attributes
including molecular chaperones. Other differentially expressed proteins included cytoskeletal
proteins, DNA binding proteins, and proteins involved in cellular metabolism.
Proteomics analysis using two-dimensional gel electrophoresis was used to investigate
cell cultures where metabolic shift was observed [82]. The metabolic shift was triggered by
reducing the concentration of glucose and glutamine in cell culture, resulting in altered cellular
metabolism and lower lactate production. Cell lysates were analyzed by 2DGE with silver
staining. Eight differentially expressed protein spots were identified. These spots were excised
and subjected to trypsin in-gel digestion followed by MALDI-TOF analysis. All of the spots
55
were identified, and included actin, GAG polyprotein, NADH-ubiquinone, and phosphoglycerate
mutase.
In another case, CHO cell lines with different growth rates were compared using 2DGE
and cDNA microarray tools [83]. During cloning experiments of four different CHO-K1 cell
lines expressing recombinant monoclonal antibodies, clones displaying different cell growth
properties were selected for further analysis. Cell lysates were analyzed by 2D DIGE. A total of
58 protein spots were identified as differentially expressed between different pairs of slow and
fast growing cells. After MALDI-TOF analysis of digested protein spots, several potential
growth related markers were identified, including cytoskeletal proteins, chaperones, and other
proteins involved in the secretory pathway such as valosin containing protein (VCP). Upon
functional validation by siRNA and overexpression studies, VCP was shown to have a direct
impact on CHO cell growth, boosting viable cell density by 20 - 30% across three different cell
lines.
56
Figure 1.16: 2D-DIGE gel images of Cy2-labeled pools of Comparison A and B cell lysates
Differentially expressed proteins that have been successfully identified by MALDI-TOF MS and
LC-MS/MS are represented on the gel using Decyder 6.5 software generated Master Number as
a reference. (i) 2D-DIGE gel image of Cy2-labeled pool of MAb-expressing clone V1-5 (Slow)
and MAb-expressing clone 9B10 (Fast) (Comparison A) cell lysates. (ii) 2D-DIGE gel image of
Cy2-labeled pool of PA DUKX 153.8 (Slow) and PA DUKX 378 (Fast) (Comparison B) cell
lysates. (iii) Zoomed in region of the 2D-DIGE gel image of Cy2-labeled pool of PA DUKX
153.8 (Slow) and PA DUKX 378 (Fast) (Comparison B) cell lysates demonstrating upregulated
expression of protein subsequently identified by MS as VCP. [83]
57
While these studies offer glimpses into the biological processes at work in different
mammalian cell cultures, the discovery of information that can directly lead to improved control
over process yield and product quality is arguably limited. The sheer complexity of a
mammalian cell lysate extends beyond the limits of what current proteomic analytical
technologies can detect, making identification and quantitation of proteins difficult. Issues of
biological variability and the dynamic aspects of cell culture pose additional obstacles to
comprehensive proteomic studies. The application of new proteomics tools and techniques to
this particular area of research is vital in the effort to improve our understanding of the biology
of mammalian cell cultures for bioprocessing.
1.5. Overview of Research
The work described in this thesis is the application of different analytical techniques and
tools aimed at identifying and quantifying proteomic changes within CHO cell cultures used for
bioprocessing. The goals of this work are 1) the establishment of a proteomic platform for
analysis of proteomic changes in CHO cell cultures and 2) the identification of proteins and
protein pathways which may be related to CHO cell growth and productivity as it relates to the
production of biopharmaceuticals. With many tools available for proteomics research, the
challenge is finding the right tools and the best way to apply them to obtain a comprehensive
analysis of the proteome.
58
In chapter 2, work is presented on the development of a “shotgun” proteomics method for
analysis of CHO cell culture samples, and the application of this method to comparison of low-
and high-producing CHO cell cultures. This study served as a proof-of-concept to show that
bottom-up proteomics approaches using enzymatic protein digestion and LC/MS analysis can be
applied to the analysis of CHO cell cultures and be used to identify relevant proteins. In addition
to identifying some proteins of interest in this study, it also highlighted some limitations of this
kind of method, in terms of identification and quantitation of proteomic differences in a complex
mammalian cell lysate.
In the chapter 3, the development of an improved proteomics method is described, which
combines isotopic chemical labeling with two-dimensional LC/MS analysis to increase the
dynamic range, as well as lower the limit of detection of the method. In addition, a novel data
analysis method was developed to identify dynamic changes in protein abundance over multiple
time-points of a cell culture. This method was applied to the identification of dynamic trends in
protein expression between the exponential and stationary growth phases of a CHO cell culture.
In the fourth chapter, CHO proteomics data is used to study correlations between gene
function and chromosomal location on the mouse chromosomes. Genes of interest are mapped
to the mouse genome, and evidence of co-expression of genes of similar function is shown. The
impact that this data may have on CHO cell biology and cell culture performance is discussed.
In the fifth chapter, the application of proteomics techniques to the analysis of secreted
host-cell proteins in process intermediate samples is described. The identification and
characterization of the secreted host cell proteins is examined, with discussion on the potential
impact of some of these proteins on cell culture performance. In addition, the clearance of the
59
host cell proteins is studied by performing proteomics analysis on process intermediates from
various stages within the downstream purification process. This study increases our
understanding of protein clearance during purification, can be used to support process
development, and provides additional host cell protein clearance data compared to current
industry standards.
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71
CHAPTER 2
PROTEOMICS COMPARISON OF LOW- AND HIGH-PRODUCING CHO CELL
CULTURES
This Chapter has been published in Analytical Chemistry:
Carlage T, Hincapie M, Zang L, Lyubarskaya Y, Madden H, Mhatre R, Hancock WS:
Proteomic profiling of a high-producing Chinese hamster ovary cell culture. Analytical
chemistry 2009, 81(17):7357-7362.
2.1 Overview
The aims of this study were to develop a proteomics method suitable for identification
and quantitation of differentially expressed proteins between different CHO cultures, and to
apply this method to compare the protein expression of a high-producing CHO cell culture to a
low-producing control over multiple time-points. This work served as a proof of concept to
show that LC/MS-based proteomics tools could successfully be used to identify relevant
differentially expressed proteins from CHO cell culture samples.
The method development consisted of three sections. First, a protein extraction method
was developed which utilized mass-spec compatible detergents and sonication to reproducibly
extract proteins from CHO cells. Secondly, a shotgun proteomics approach was developed for
the analysis of CHO cell lysates. While several previously reported proteomic studies of CHO
have relied on two-dimensional gel electrophoresis methods [1-3], shotgun proteomics offers the
advantages of higher throughput and the ability to be able to identify a large number of proteins
in a single LC/MS experiment [4]. The final part of the method development consisted of
72
evaluating a label-free quantitation strategy for the identification of differentially expressed
proteins. The method development data indicates that this method is suitable for identification of
differentially expressed proteins in CHO cell cultures.
This method was applied to the analysis of a low- and high-producing CHO cell culture.
Both cultures used the same CHO cell line expressing a recombinant fusion protein. However,
there were several differences between the two cell cultures which resulted in a marked
difference in overall yield. The high-producing cell culture used an optimized media profile to
enhance cell growth, and was also transfected with the anti-apoptotic gene Bcl-XL. This gene,
along with other Bcl family members such as Bcl-2, inhibits apoptosis by binding to pro-
apoptosis proteins in the mitochondrial membrane or cytoplasm, thereby disrupting the caspase
activation necessary for apoptosis to occur [5, 6]. The application of these inhibitors in CHO and
BHK cell cultures has been studied. In some cases, over-expression of Bcl-2 and Bcl-XL
prolonged cell viability; however these results varied between different cell lines and conditions
[7]. In another study, transfection of CHO with Bcl-XL was shown to increase overall
productivity by 80% through increased cell growth and specific productivity [8]. The benefits of
expressing apoptosis inhibitors in mammalian cell culture are not well understood; however, the
targeting of such proteins continues to be employed as one strategy for increasing cell growth
and productivity. For this reason, there was value in the analysis of proteomic changes
associated with the upregulation of this growth-regulating gene.
73
2.2 Methods
2.2.1 CHO Cell Lines
Both cell cultures studied were derived from the same Chinese hamster ovary DG44 host
cell line by stable transfection of a plasmid encoding genes for DHFR and a humanized
recombinant fusion protein. Fusion protein production was further amplified by cell line
selection in increasing concentrations of methotrexate. In addition, the high-producing cell line
was transfected with a plasmid encoding Bcl-XL and G418. Stable clones were selected in the
presence of neomycin and methotrexate.
2.2.2 Cell Culture Conditions
The fed-batch control culture was grown in a 2-L sparged B. Braun bioreactor (Sartorius,
Goettingen, Germany) for 13 days using a proprietary custom in-house serum-free medium
supplemented with protein hydrolysate. The high-producing culture was grown in a 200-L
custom-made stainless steel stirred tank bioreactor for 16 days using a modified version of the
media used for the control. The nutrient profile and complex media components are different
between the cell cultures, with the high producing culture having media optimized for higher cell
growth. pH was controlled at 7.15 using sodium carbonate for both cultures. Cell number and
viability were measured by trypan blue staining and using a Cedex automated cell counter
(Innovatis, Bielefeld, Germany). A volume equivalent to 3E7 cells was sampled from the
bioreactor at varying timepoints (day 0, 5, 10, 13 for the control culture, and 1, 5, 10, 16 for the
high producing culture). Samples were centrifuged at 500 g for 10 minutes, and the supernatants
were removed. Pellets were reconstituted in 5 mL PBS and centrifuged at 500 g for 10 minutes.
Supernatants were removed, and pellets stored at -70°C until further analysis.
74
2.2.3 Cell Lysis
Cell pellets were thawed at room temperature and reconstituted in a lysis buffer
consisting of 50 mM Tris, pH 7.5 and 0.1% Rapigest (Waters, Milford, MA). Samples were then
sonicated in a water bath for 3 cycles of 15 seconds each. Following sonication, samples were
centrifuged at 5,000 g for 10 minutes. Supernatants were transferred to clean tubes. The total
protein concentration of each cell lysate was measured by BCA (Pierce, Rockford, IL) according
to the manufacturer’s instructions. Samples were stored at -70°C prior to tryptic digestion.
2.2.4 Trypsin Digestion
Lysates were denatured and reduced by adding 20 µL of lysate (approx. 100 µg total
protein) to 45 µL of 8 M Guanidine, 50 mM Tris pH 7.5 and 1 µL of 500 mM DTT, for a final
concentration of 4 M Guanidine and 8 mM DTT. Samples were incubated at 60°C for 15
minutes. Five microliters of 300 mM iodoacetic acid was added to each sample, for a final
concentration of approx. 20 mM, and samples were incubated at room temperature in the dark
for 1 hour. Five microliters of 500 mM DTT was added to each sample to quench the remaining
iodoacetic acid. Each sample was applied to a Microspin SEC spin-column (BioRad, Hercules,
CA) that was pre-equilibrated in 50 mM ammonium bicarbonate, pH 8.0 for cleanup. After
centrifugation at 1,500 g for 4 minutes, the desalted samples were brought to a final volume of
200 µL in 50 mM ammonium bicarbonate, and 10 µL of trypsin was added. Samples were
incubated at 37°C for 18 hours. Five microliters of 1% TFA was added to each sample after
digestion to stop the reaction.
75
2.2.5 LC/MS Analysis
All samples were analyzed in triplicate using a Dionex Ultimate 3000 HPLC interfaced
with an LTQ linear ion trap mass spectrometer. The composition of solvent A was 0.1% (v/v)
formic acid in water, and solvent B was 0.1% (v/v) formic acid in acetonitrile. A volume of 1
µL of peptide digest for each sample was injected onto a CapTrap column (75pprox.. 2 µg on-
column) (Michrom Bioresources, Auburn, CA) using the Dionex autosampler. The trap column
was washed with 100% A at a flow rate of 20 µL/min for 10 minutes to desalt the sample. The
captured peptides were eluted from the CapTrap onto a 0.075 x 150 mm C18AQ column
(Michrom Bioresources, Auburn, CA) using an acetonitrile gradient at 300 nL/min. The gradient
was from 2% B to 35% B over 110 minutes, increased to 90% B in 20 minute, held at 90% B for
35 minutes, and back to 2% B in 5 minutes. The column was re-equilibrated for 60 minutes
before the next injection. The Dionex HPLC was controlled using Chromeleon v.6.80 and the
LTQ was controlled using Xcalibur 2.0.6 software (Thermo Fisher Scientific, Waltham, MA).
The electrospray conditions were as follows: spray voltage 1.70 kV, capillary voltage 48 V, tube
lens 70 V, capillary temperature 225 °C. Each MS scan was acquired in centroid mode from
300-2000 m/z, followed by 9 MS/MS scans of the 9 most intense peaks in data dependent mode.
Dynamic exclusion was enabled for a duration of 30 seconds and a repeat count of 1.
Normalized collision energy of 35%, isolation width of 2.0 m/z, and activation Q of 0.25 was
used for each MS/MS scan.
2.2.6 Protein Identification
Peptide sequences and proteins were identified by searching all MS2 spectra against
theoretical fragmentation spectra of a mouse protein database (Swiss-Prot, updated in September,
76
2007, 12,902 entries). For this, Sequest algorithm incorporated into the Bioworks software,
version 3.1, SR1.4 (Thermo Electron, San Jose, CA) was used. Search parameters included
carbamidomethylation of cysteines, ±1.4 Daltons and ±1.0 Dalton tolerance for precursor and
product ion masses, respectively. Only peptides resulting from tryptic cleavages with up to one
missed cleavage were searched. The Sequest results were filtered by correlation score (Xcorr)
values selected to obtain highly confident peptide and protein identifications: Xcorr 1.9, 2.2, 3.75
for singly, doubly and triply charged peptide ions, respectively, and all with dCn 0.1. Protein
identifications were then validated by using ProteinProphet software, accepting identifications
made with 95% confidence. Only proteins identified with two or more unique peptides were
considered.
2.2.7 Assessment of Relative Abundance of Peptides and Proteins
The spectral counting method was used for estimation of relative peptide and protein
abundance. This method uses the number of scans generated by the mass spectrometer for every
peptide identified from a specific protein as a semi-quantitative measurement of protein
abundance, and has been shown previously to be useful for comparing abundance between
different samples in LC/MS experiments [9].
77
2.3 Results
2.3.1 Cell Growth and Specific Productivity
The high-producer and control CHO cells are both DG44 clones, with the high-producer
being transfected with the Bcl-XL gene to inhibit apoptosis and enhance cellular productivity
(14). The media profile for the high-producer was also optimized in order to improve
metabolite availability and limit bottlenecks to cell growth. The viable cell density (VCD) of
both cell cultures was monitored on a daily basis using trypan blue staining and cell counting.
The control culture was harvested after 13 days, and reached a maximum density of 5.8E6
cells/mL on day 10 (see Figure 2.1). The cells maintained densities >5E6 cells/mL until day 13,
when it decreased to 3.6E6 cells/mL. This change also corresponds to a small decrease in cell
viability (data not shown). The high producer was harvested after 16 days, and it reached a
maximum density of 15.4E6 cells/mL on day 7 and maintained a similar cell density until day
16. The recombinant fusion protein titer was determined by measuring fusion protein
concentration in the secreted media at different days by affinity chromatography. Titer was
slightly higher in the high-producer compared to the control (data not shown).
78
Figure 2.1: Cellular Productivity Profiles
Viable cell density was measured by trypan blue staining using a CEDEX cell counter on various
days for both control and high-producer cell cultures.
2.3.2 Extraction of Proteins from CHO Cells
A robust and reproducible cell lysis method is imperative for proteomics. Many cell lysis
techniques use strong detergents to solubilize proteins. While having a high efficiency, these
methods are typically not compatible with mass spectrometric analysis. Hence, for solubilization
of proteins from CHO cells, a mass-spectrometry compatible detergent, Rapigest, was used [10,
11]. Cell pellets were reconstituted using a Tris buffer containing 0.1% Rapigest and subjected to
3 cycles of sonication to disrupt cell membranes. We evaluated the efficiency of this extraction
method and compared it to a commercially available Mammalian Protein Extraction Reagent (M-
PER®) kit (Pierce). Five replicates of the sample CHO cell culture sample were analyzed using
either method, and protein concentration was measured by the BCA method. The results are
presented in Table 2.1 and indicate that the two methods yield a similar amount of total protein
Cell Growth
0.00
5.00
10.00
15.00
20.00
0 5 10 15
Time (d)
Via
ble
Cell
Den
sit
y (
xE
6
Cell
s/m
L)
High Producer
Control
79
from CHO cells, while the sonication method was more reproducible between the 5 replicates.
Based on these results, the sonication method using Rapigest for protein solubilization was used
for proteomics analysis of the CHO lysates.
Table 2.1: Comparison of Cell Lysis Techniques
Cell pellets were lysed using Pierce Mammalian Protein Extraction Reagent ®, and a sonication
method using Rapigest®. The total protein concentration was measured for each lysate using the
Pierce BCA kit. RSD was calculated by dividing standard deviation by average concentration.
2.3.3 Classification of Identified CHO Proteins
Cell lysates were treated with trypsin to digest the proteins into peptide fragments. The
resulting fragments were analyzed by LC/MS/MS, and sequence information was generated by
searching against a Swissprot mouse database of protein sequences for identification. In this
study, 392 proteins were identified with conservative criteria for protein assignment as well as
the measurement of at least 2 unique peptides per protein (see the methods section).
To determine the cellular origin of the identified proteins, the DAVID bioinformatics tool was
used to categorize proteins based on cellular compartment from Gene Ontology
(http://david.abcc.ncifcrf.gov/). As shown in Figure 2.2, a similar number of total proteins were
Replicate Sonication M-PER
1 4.3 4.5
2 4.2 4.2
3 3.9 3.9
4 4.1 3.8
5 4.0 3.6
Average 4.1 4.0
Standard Deviation 0.2 0.4
RSD 3.9% 9.2%
Concentration of Cell
Lysate (mg/mL)
80
identified in both cell cultures. More nuclear and cytoskeletal proteins were identified in the
control cell culture, while more cytosolic and ribosomal proteins were identified in the high
producer. These results correlate with differential expression patterns that we identified with our
proteomic measurements and discussed in section 2.3.5.
Figure 2.2: Proteins Identified in CHO Samples
Identified proteins were categorized according to cellular compartment using DAVID
(http://david.abcc.ncifcrf.gov/). All proteins were identified by at least 2 unique peptides.
2.3.4 Identification of Proteomic Changes
A label free strategy was used to quantitate differences in protein expression between
CHO cell culture samples. This spectral counting approach uses the number of MSMS scans
assigned to peptides from a particular protein as an estimate of protein abundance [9]. Peptides
at higher abundance will trigger MSMS events more often than lower abundance peptides.
In order to examine how this method is suitable for complex CHO lysates, BSA was
spiked into a CHO lysate at various concentrations. The spiked lysate was analyzed by LC/MS
and recovery of BSA was measured by spectral counting. Figure 2.3 shows the correlation plot
0
50
100
# P
rote
ins
Control 352 105 67 44 47 34 24
High Producer 339 97 75 51 37 31 20
Total Nucleus Cytosol Ribosome Cytoskeleton MitochondrionEndoplasmic
Reticulum
81
between BSA spectral counts and actual protein concentration. The correlation was assessed by
linear regression, and gave an R2 value of 0.9990. This data indicates that spectral counting is
suitable for quantitating differences in protein expression between different CHO samples.
Figure 2.3: Analysis of BSA-Spiked CHO Lysates
A linear plot of spectral counts for BSA against amount of BSA spiked into a CHO cell lysate as
measured by shotgun proteomics.
2.3.5 Differential Expression between Control and High-Producer
Differentially expressed proteins were identified based on the ratio of spectral counts
between control and high-producer for each identified protein. The ratio of spectral counts was
used to calculate fold changes between control and high-producer at day 5, day 10, and the
endpoints (day 13 for control, day 16 for high-producer). In addition, we calculated the relative
standard deviation of the spectral count values measured with three replicates for both the control
and high producer cell lines. Proteins that showed a fold change of greater than 2.0 or less than -
2.0, and that had a relative standard deviation of less than or equal to 0.5 were identified as
R2 = 0.9990
0
20
40
60
80
100
120
140
160
180
0 50 100 150 200 250
Amount BSA (ug)
To
tal P
ep
tid
es
82
differentially expressed. A list of selected proteins with the greatest level of differential
expression is shown in Table 2.2.
A total of 32 differentially expressed proteins were identified. The major functionalities
of these proteins include protein metabolism, cytoskeletal structure, and cell cycle control. Both
BiP and the recombinant fusion protein showed the highest level of upregulation in the high-
producer, with fold changes over 2.0 at all three timepoints. Other proteins such as 40S
ribosome, eukaryotic translation initiation factor 3, and alanyl-tRNA-synthetase were
upregulated to a lesser degree across all 3 timepoints. Several proteins were consistently
downregulated at all 3 timepoints, such as histone H1.2, vimentin, and galectin-1. Other proteins
such as RACK1, alpha enolase, and calcylcin showed upregulation and certain timepoints, and
downregulation at others.
83
Table 2.2: Differentially Expressed Proteins
Differentially expressed proteins were identified by calculating ratio of spectral counts for each
protein at each timepoint. Proteins showing a two-fold change up or down, and which had a
relative standard deviation less than or equal to 0.5, were considered as differentially expressed.
Protein Metabolism 5 10 End
Alanyl tRNA synthetase 5.0 2.0 1.3
T-complex protein 1 subunit delta 1.9 2.9 1.0
T-complex protein 1 subunit eta 2.1 1.5 -1.1
Eukaryotic translation initiation factor 3 subunit 5 epsilon2.4 1.4 2.7
BiP 2.0 2.6 2.8
60S ribosomal Protein L30 1.5 4.8 2.4
40S ribosomal protein S6 2.1 1.6 1.5
40S ribosomal Protein S7 1.2 1.2 2.1
Transcription
Histone H1.2 -2.3 -3.0 -3.2
Histone H2A type 1-F -1.8 -4.0 -4.6
Nucleosome assembly protein 1-like 1 1.2 1.3 2.4
Heterogeneous nuclear ribonucleoprotein A2/B1 -1.1 -1.2 -2.1
Cytoskeleton
Annexin-A2 -1.4 -1.3 -2.2
Adenylyl cyclase-associated protein 1 2.1 1.6 1.0
Filamin-A -1.1 -1.1 -2.1
Myosin-9 -1.3 -2.3 -2.2
Myosin regulatory light chain 2-B -1.2 -2.4 -3.4
Vimentin -2.4 -6.2 -1.1
Cell Cycle Regulation
Receptor for activated C kinase 2.2 1.3 -1.4
Calcyclin 2.1 1.2 -2.3
GTP-binding nuclear protein Ran 2.0 1.6 -1.4
Cell Growth
Galectin-1 -1.3 -2.4 -2.8
Glycolysis
Alpha-enolase 2.3 1.9 -1.6
Glyceraldehyde-3-phosphate dehydrogenase 2.9 1.2 -2.7
Miscellaneous
Chloride intracellular channel protein 1 -1.2 -1.1 -2.0
Dihydrofolate reductase 3.1 1.6 2.0
Recombinant fusion protein 3.9 2.6 2.3
Osteoclast-stimulating factor 1 -1.2 -2.3 -1.3
Phosphoserine aminotransferase 2.5 1.5 1.3
Proteasome activator complex subunit 1 -2.1 -5.5 -1.2
Prostaglandin E synthase 3 -1.1 -2.0 -2.1
Thioredoxin 2.5 -1.1 -2.4
Fold Change
84
Figure 2.4: Proteins Upregulated in the High-Producer
Relative abundance of proteins was determined by spectral counts for (A) recombinant fusion
protein, (B) RACK1, (C) BiP, and (D) alpha-enolase. Error bars correspond to one standard
deviation (n=3).
Alpha-Enolase
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
0 2 4 6 8 10 12 14 16 18
Time (d)
Sp
ectr
al
Co
un
ts
RACK1
0.0
5.0
10.0
15.0
20.0
25.0
0 2 4 6 8 10 12 14 16 18
Time (d)
Sp
ectr
al
Co
un
ts
BiP
0.0
50.0
100.0
150.0
200.0
250.0
300.0
0 2 4 6 8 10 12 14 16 18
Time (d)
Sp
ectr
al
Co
un
ts
High-Producer
Control
Recombinant Fusion Protein
0.0
50.0
100.0
150.0
200.0
250.0
300.0
350.0
0 2 4 6 8 10 12 14 16 18
Time (d)
Sp
ectr
al
Co
un
ts
High-Producer
Control
High-Producer
Control
High-Producer
Control
85
Figure 2.5: Proteins Downregulated in the High-Producer
Relative abundance of proteins was determined by spectral counts for (A) annexin-A2, (B)
histone H2A, (C) galectin-1, and (D) vimentin. Error bars correspond to one standard deviation
(n=3).
The upregulation of proteins such as alanyl-tRNA synthetases, Eif3, and 40S ribosome
indicate that protein metabolism is increased in the high producer. These proteins play crucial
roles in the translation of proteins. The recombinant fusion protein was detected at 2-3 fold
higher levels in the high-producer compared to the control, indicating that the intracellular
concentration of this protein is reaching much higher levels. These results support the increased
productivity observed in the high-producing cell culture. Previous studies of Bcl-XL transfected
CHO cells have also shown an increase in specific productivity over non-transfected cells [8],
Histone H2A
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
20.0
0 2 4 6 8 10 12 14 16 18
Time (d)
Sp
ectr
al
Co
un
ts
Vimentin
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
0 2 4 6 8 10 12 14 16 18
Time (d)
Sp
ectr
al
Co
un
ts
Galectin-1
0.0
10.0
20.0
30.0
40.0
50.0
60.0
0 2 4 6 8 10 12 14 16 18
Time (d)
Sp
ectr
al
Co
un
ts
High-Producer
Control
High-Producer
Control
High-Producer
Control
Annexin-A2
0.0
5.0
10.0
15.0
20.0
25.0
30.0
0 2 4 6 8 10 12 14 16 18
Time (d)
Sp
ectr
al
Co
un
ts
High-Producer
Control
86
and our study which uses the insights generated by proteomic measurements suggests that the
increased levels of product is related to a greater level of protein biosynthesis under the
fermentation conditions used in this study.
The molecular chaperone BiP was significantly upregulated in the high-producer. This
is relevant since BiP is a key chaperone involved in protein folding in the endoplasmic reticulum
(ER). The upregulation of BiP may indicate ER stress in the high producer is due to high
intracellular concentrations of unfolded proteins, which can lead to an unfolded protein response
in the cell. When unfolded proteins accumulate to a certain level in the ER, chaperones such as
BiP are upregulated to clear the proteins from the ER for degradation [12]. The differential
expression of BiP may indicate a UPR event is occurring in the high-producer. The expression
profiles for BiP and the recombinant fusion protein are similar, which suggests that higher
intracellular concentrations of the product over time results in a proportional response by the cell
to express BiP.
Galectins are a class of carbohydrate-binding proteins that modulate various activities
within cells, such as differentiation, cell growth, apoptosis, and tumor progression [13].
Galectin-1 has specifically been shown to inhibit cell growth and activate apoptosis in T cells
and the expression of galectin-1 may be regulated by Bcl-XL [14]. Galectin-1 was
downregulated in the high-producer, indicating that it may be responsible for inhibiting cell
growth in the control and thus it is a candidate as a biomarker for successful cell engineering and
product yield.
Several proteins that are involved in cell cycle regulation were differentially expressed in
this study, including GTPase Ran, which has a role in the regulation of mitosis [15], and
87
RACK1, a kinase receptor. RACK1 has been shown to inhibit the tyrosine kinase Src, which can
lead to G0/1 cell cycle arrest [16]. Both of these proteins showed a similar expression profile
over time. They were upregulated in the high producer at day 5 and day 10, and were
downregulated at the endpoints. The differential expression of these proteins may indicate that
the cell cycle is controlled differently in the high-producer compared to the control. We also
observed the differential expression of several cytoskeletal proteins, such as vimentin, annexin,
myosin, and filamin. These intermediate filament proteins have several functions; including
maintaining cell shape, intracellular transport, and formation of mitotic spindles during cell
division. The differential expression of these proteins may also be related to control of the cell
cycle, and related to cellular productivity.
The downregulation of histones in the high-producer could also be evidence of
differential cell cycle control. Several histones, which are responsible for condensation of DNA
into chromatin structures, were downregulated in the high-producer. Lower expression of
histones results in greater accessibility of DNA for transcription. Similar results were observed
by Nissom et al., who observed downregulation of histone 1.2 in a high-producing CHO culture
[17].
2.4 Conclusion
The application of shotgun proteomics to CHO cell lysates has been shown to be a useful
tool for studying protein expression and identifying changes associated with cellular
productivity. In this study, we have successfully identified differentially expressed proteins
while comparing a low and high producing CHO cell culture. Several of these proteins are
88
related to cell growth and productivity, including the molecular chaperone BiP, and the growth-
regulating protein galectin-1. On limitation of this work is the limited number of proteins
identified. The method has a limited dynamic range, in part because of the simple one-
dimensional separation used which does not sufficiently separate the complex mixture of
peptides present in the trypsin-digested cell lysate. Additional separation modes would help to
increase the number of proteins identified and improve the results of proteomics studies of CHO
cell lysates.
2.5 References
1. Yee JC, de Leon Gatti M, Philp RJ, Yap M, Hu WS: Genomic and proteomic
exploration of CHO and hybridoma cells under sodium butyrate treatment.
Biotechnology and bioengineering 2008, 99(5):1186-1204.
2. Seow TK, Korke R, Liang RC, Ong SE, Ou K, Wong K, Hu WS, Chung MC: Proteomic
investigation of metabolic shift in mammalian cell culture. Biotechnology progress
2001, 17(6):1137-1144.
3. Baik JY, Lee MS, An SR, Yoon SK, Joo EJ, Kim YH, Park HW, Lee GM: Initial
transcriptome and proteome analyses of low culture temperature-induced
expression in CHO cells producing erythropoietin. Biotechnology and bioengineering
2006, 93(2):361-371.
4. Hancock WS, Wu SL, Shieh P: The challenges of developing a sound proteomics
strategy. Proteomics 2002, 2(4):352-359.
5. Hengartner MO: The biochemistry of apoptosis. Nature 2000, 407(6805):770-776.
89
6. Strasser A, O'Connor L, Dixit VM: Apoptosis signaling. Annu Rev Biochem 2000,
69:217-245.
7. Mastrangelo AJ, Hardwick JM, Zou S, Betenbaugh MJ: Part II. Overexpression of bcl-
2 family members enhances survival of mammalian cells in response to various
culture insults. Biotechnol Bioeng 2000, 67(5):555-564.
8. Chiang GG, Sisk WP: Bcl-x(L) mediates increased production of humanized
monoclonal antibodies in Chinese hamster ovary cells. Biotechnology and
bioengineering 2005, 91(7):779-792.
9. Liu H, Sadygov RG, Yates JR, 3rd: A model for random sampling and estimation of
relative protein abundance in shotgun proteomics. Anal Chem 2004, 76(14):4193-
4201.
10. Arnold RJ, Hrncirova P, Annaiah K, Novotny MV: Fast proteolytic digestion coupled
with organelle enrichment for proteomic analysis of rat liver. J Proteome Res 2004,
3(3):653-657.
11. Yu YQ, Gilar M, Lee PJ, Bouvier ES, Gebler JC: Enzyme-friendly, mass
spectrometry-compatible surfactant for in-solution enzymatic digestion of proteins.
Anal Chem 2003, 75(21):6023-6028.
12. Patil C, Walter P: Intracellular signaling from the endoplasmic reticulum to the
nucleus: the unfolded protein response in yeast and mammals. Curr Opin Cell Biol
2001, 13(3):349-355.
13. Yang RY, Liu FT: Galectins in cell growth and apoptosis. Cell Mol Life Sci 2003,
60(2):267-276.
90
14. Brandt B, Buchse T, Abou-Eladab EF, Tiedge M, Krause E, Jeschke U, Walzel H:
Galectin-1 induced activation of the apoptotic death-receptor pathway in human
Jurkat T lymphocytes. Histochem Cell Biol 2008, 129(5):599-609.
15. Rensen WM, Mangiacasale R, Ciciarello M, Lavia P: The GTPase Ran: regulation of
cell life and potential roles in cell transformation. Front Biosci 2008, 13:4097-4121.
16. Mamidipudi V, Zhang J, Lee KC, Cartwright CA: RACK1 regulates G1/S progression
by suppressing Src kinase activity. Mol Cell Biol 2004, 24(15):6788-6798.
17. Nissom PM, Sanny A, Kok YJ, Hiang YT, Chuah SH, Shing TK, Lee YY, Wong KT, Hu
WS, Sim MY et al: Transcriptome and proteome profiling to understanding the
biology of high productivity CHO cells. Mol Biotechnol 2006, 34(2):125-140.
91
CHAPTER 3
ANALYSIS OF DYNAMIC CHANGES TO THE CHO PROTEOME DURING
EXPONENTAL AND STATIONARY PHASES OF CELL CULTURE
3.1 Overview
This study describes the development of an improved proteomics methodology for analysis of
CHO cell lysates and the application of this method to the analysis of proteomic changes during
exponential and stationary phases of a CHO cell culture. Proteomics analysis of mammalian cell
cultures presents distinct analytical challenges due to the complexity and wide range of protein
concentrations in a typical sample, as well as the dynamic nature of cell culture experiments,
which run over the course of many days. During this time, various biological processes occur
which can affect the cell culture phenotype.
Many mammalian cell cultures demonstrate cell growth properties characterized by an
exponential growth phase where cell density increases rapidly, followed by a stationary phase
with little to no cell growth but relatively high specific productivity [1-3]. The factors
controlling this transition are not well understood; possible explanations include limitation of key
metabolites, accumulation of toxic waste products, or cellular response to ER stress [2, 4].
However, the transition from exponential to stationary phase has a significant impact on cell
culture performance since it is directly tied to cell growth, and in some cases can affect specific
productivity. Understanding the biological changes associated with the transition of mammalian
cells through different growth phases could increase our understanding of some of the underlying
mechanisms affecting cell growth and productivity. Also, attempts to identify cell culture
92
biomarkers should consider the dynamic changes that occur throughout cell culture, as biomarker
abundance could change over time.
In order to consider the dynamic aspects of cell culture during proteomics analysis, a method is
required that can identify quantitative changes in protein expression over multiple timepoints.
The method should also incorporate proper data analysis tools which enable the identification of
significant trends in protein expression over the different timepoints. This approach could
potentially identify proteins with trends in expression which correlates with dynamic changes in
cell culture. It could also be used to detect differential expression of proteins between different
cell culture conditions, by identifying differences in protein trends.
In this study, we applied a quantitative proteomics approach to monitor changes in the CHO
proteome through the course of a fed-batch cell culture expressing a monoclonal antibody. A
combination of multi-dimensional liquid chromatography, isobaric chemical tagging, and mass
spectrometry was used for the analysis of cell culture samples at different timepoints which
encompassed both exponential and stationary phases of the cell culture. This method was
combined with a novel data analysis approach designed to identify dynamic trends in protein
expression using linear regression calculations. Using this method, we identified proteins which
are differentially expressed over the course of cell culture, and may provide biological insight
into the transition of CHO cells from exponential to stationary phase.
93
3.2 Methods
3.2.1 Cell Culture
A CHO cell line genetically modified to express a recombinant antibody and the anti-apoptotic
gene Bcl-Xl was grown under fed-batch conditions in a 3-L sparged bioreactor for 16 days using
a proprietary custom in-house chemically defined medium. Cell number and viability were
measured using a Cedex (Innovatis, Bielefeld, Germany), an automated cell counter that uses
trypan blue staining. A volume equivalent to 1x107 viable cells was sampled from the bioreactor
on days 6, 9, 12, and 16. The cell samples were centrifuged at 1000 g for 2 minutes to collect the
pelleted cells. The pellets were reconstituted in 5 mL PBS and again centrifuged at 1000 g for 1
minute. The supernatants were removed, and the pellets were flash frozen in liquid nitrogen
followed by storage at -70°C until further analysis.
3.2.2 Cell Lysis
Cell lysates were prepared as described previously [5]. Cell pellets were thawed at room
temperature and reconstituted in a lysis buffer consisting of 50 mM Tris, pH 7.5 and 0.1%
Rapigest (Waters, Milford, MA). Samples were then sonicated in a water bath for 3 cycles of 15
seconds each. Following sonication, samples were centrifuged at 10,000 g for 10 minutes.
Supernatants were transferred to clean tubes. The total protein concentration of each cell lysate
was measured by BCA (Pierce, Rockford, IL) according to the manufacturer’s instructions.
Samples were stored at -70°C prior to tryptic digestion.
3.2.3 Protein Digestion and Labeling
For each sample, a volume equivalent to 50 µg of protein was dried by SpeedVac and
reconstituted in 25 µL of 8 M Urea, 3 µL of 125 mM tris(2-carboxyethyl)phosphine and 10 µL
94
of iTRAQ dissolution buffer. The samples were incubated at 37 °C for 60 minutes. After the
samples reach room temperature, 3.5 µL of 200 mM iodoacetamide was added to each, and
samples were incubated for 60 min at ambient temperature in the dark. A 5X volume of cold
acetone was added to each sample, and they were incubated at – 20 °C for 4 hrs. Samples were
then centrifuged at 10,000 g for 10 min. The supernatant was removed, and the pellet
reconstituted in 10 µL of 8 M Urea and 80 µL of iTRAQ dissolution buffer. Ten micrograms of
trypsin was added to each sample, which were incubated for 18 hrs at 37 °C. After digestion,
samples were evaporated by SpeedVac to a volume less than 30 µL. Each tube of 4-plex iTRAQ
reagent was reconstituted in 70 µL of ethanol and added to the digests. The day 6 samples were
labeled with iTRAQ-114, day 9 with iTRAQ-115, etc. The digests were incubated at ambient
temperature for 2 hrs. Following labeling, the samples were mixed together and evaporated to
dryness.
3.2.4 HPLC Fractionation
Labeled tryptic digests were fractionated using reversed-phase chromatography. After
reconstituting the labeled peptide digest in mobile phase A (20 mM Ammonium Formate pH
10.0), the digest was loaded onto a Waters XBridge C18 column (2.1 x 150 mm) heated to 45°C
at a flow rate of 300 µL/min. Mobile phase B was acetonitrile. The gradient conditions were
set to 2% B for 5 min, then 2% - 10% B over 5 min, followed by 10% - 40% B over 25 minutes.
Fractions were collected every 2 min from 13 to 45 min, for a total of 16 fractions per sample.
Each fraction was evaporated to dryness immediately after collection.
95
3.2.5 LC/MS
Reversed-phase fractions were evaporated to dryness and reconstituted in 100 µL of 0.1% formic
acid in water. Each fraction was analyzed in triplicate by LC/MS using 25 µL injection volumes.
An Agilent 1200 HPLC was connected to a Thermo Scientific Orbitrap Discovery mass
spectrometer. Separation was achieved using a Waters Acquity HSS T3 C18 column (1.0 x 100
mm) heated to 55°C. Mobile phase A was 0.1% formic acid in water, and mobile phase B 0.1%
formic acid in acetonitrile. Peptides were separated with a flow of 70 µL/min at a steady 2% B
for 5 min, then 2% - 35% B over 120 min, followed by a wash step at 90% B and re-equilibration
at 2% B for 20 min. The column was temperature controlled at 55°C. The mass spectrometer
was set up to scan MS followed by MS/MS on the top 4 precursor ions. In MS mode, a mass
range of 400 – 1500 m/z was scanned. For MS/MS scans, pulsed Q dissociation (PQD) was used
with a collision energy of 33 and an isolation width of 3.0. Each MS/MS event was comprised
of 2 microscans. Dynamic exclusion was enabled with a repeat count of 2, for a 15 sec window.
3.2.6 Data Analysis
The proteins were identified using Thermo Scientific Proteome Discoverer 1.1. The Sequest
algorithm was used to search MS/MS data against a mouse sequence database downloaded from
Uniprot on 12/07/2009. The dataset from each reactor was processed independently. The
proteins were identified with 10 ppm mass accuracy for precursor ions, and 0.6 Da for product
ions. The MS/MS data was searched with static modifications set to 4-plex iTRAQ at N-termini
and lysines, and Cys carbamidomethylation. Dynamic modifications were set to asparagine and
glutamine deamidation, methionine oxidation, and tyrosine iTRAQ labeling. The database was
96
searched in reverse to determine false discovery rates. Each peptide was identified with less than
a 5% false discovery rate.
The iTRAQ reporter ions were detected as the most confident centroid within a 0.3 Da window.
Ratios were calculated using iTRAQ-114 as the denominator and -115, -116, and -117 as
numerators. Only iTRAQ spectra with ion counts above 5.0 were used for quantitation, and only
unique peptides were used to calculate protein ratios. The ratios were normalized against the
global protein median for each dataset.
To identify proteins with significant trends, the slope of the linear regression was calculated
using relative intensity as the y-values and cell culture time as the x-values. Since the protein
intensity at day 6 was used as the denominator for all of the iTRAQ ratios, the relative intensity
at day 6 was set to 1.0 for all proteins, and the iTRAQ ratios were set as the relative intensity at
the three other timepoints. Proteins that were missing two or more iTRAQ ratios were ignored
from the calculations. The average and standard deviation of the distribution of the slopes were
calculated for each dataset, and the threshold for significant protein trends was set as +/- 1 SD
from the average for each distribution. The lists from the two datasets were compared and only
proteins present in both lists were considered significant trending proteins.
Proteins were classified by biological process and by protein class using PANTHER, an online
tool which uses annotations such as gene ontology to classify genes by category such as
biological process or molecular function [6].
97
3.2.7 Pathway Analysis
A dataset including a list of gene names and the average calculated slope for all differentially
expressed proteins was uploaded into Ingenuity Pathway Analysis (Ingenuity® Systems,
www.ingenuity.com). A Core analysis was used with both direct and indirect relationships
allowed, and specifying maximum of 70 proteins per network and 10 networks total. All data
sources were selected, and all cell and tissue types were selected. No expression value cutoff
was used. All network eligible molecules were overlaid onto a global molecular network
developed from information contained in Ingenuity’s Knowledge Base. Networks of network
eligible molecules were then algorithmically generated based on their connectivity.
3.2.8 Western Blotting
Cell lysate samples (10-20 µg) were separated under reducing conditions using a Novex 4-12%
Bis-Tris SDS-PAGE gel (Invitrogen, Carlsbad, CA) with MOPS running buffer according to the
manufacturer’s recommended protocol. The proteins were then transferred to a nitrocellulose
membrane using an iBlot (Invitrogen) set to 20 V for 7 min. The blot was blocked overnight
with milk blocking buffer (KPL, Gaithersburg, MD), and probed with primary antibody (mouse
monoclonal antiguinea pig transglutaminase-2 (Abcam, Cambridge, MA) or rabbit polyclonal
antimouse beta actin (Abcam) or mouse antirat clusterin (Abcam) at a concentration of 1,000 –
2,000 µg/mL. After washing 3X with PBS with 0.1% Tween-20 (PBS-T), the blot was probed
with appropriate secondary antibody (Abcam) at a concentration of 10,000 µg/mL. After a
second wash step with PBS-T, the blot was detected using the LumiGLO chemiluminescent kit
(KPL).
98
3.3 Results
A CHO cell culture expressing a recombinant antibody was grown under fed-batch conditions in
a 3 L bioreactor for 16 days in duplicate. During the exponential phase, the viable cell density
(VCD) increased from 2.5x106 viable cells/mL at day 3 to a maximum of approximately 3.2x10
7
viable cells/mL at day 12 (Figure 3.1a). The cell density remained nearly constant during the
stationary phase which occurred between day 12 and day 16, when the cells were harvested. The
cell viability stayed above 95% through day 14, and tapered over the last two days to 87% on
day 16 (Figure 3.1b). In order to study changes in protein expression over time, the reactors
were sampled at days 6, 9, 12, and 16 for proteomics analysis.
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Figure 3.1: CHO Cell Growth and Viability
Chinese hamster ovary cells were grown in a two duplicate 3 L bioreactor for 16 days.
Bioreactor A (Red) and B (Blue) were run under identical conditions. The viable cell density
(A) and percent viability (B) was measured each day by using a CEDEX.
3.3.1 Proteomics Analysis of Cell Lysates
The four time-point samples from each reactor were multiplexed using iTRAQ and analyzed by
two dimensional LC/MS. A total of 2836 unique proteins were identified, which resulted from
A
B
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over 7000 peptides detected in either bioreactor (Figure 3.2a). Using PANTHER, the
corresponding gene list was classified by biological process terms from Gene Ontology (Figure
3.2b). Major processes represented in the identified proteins included cell metabolism, transport,
cell communication, and cell development.
To account for sample variability and for more accurate measurement of the change in protein
expression, three different normalization techniques were incorporated into the workflow. First,
the reactors were sampled based on the viable cell density, and samples were frozen at a constant
cell density of 2x107 viable cells/mL. Secondly, after cell lysis the amount of sample used in
digestion and labeling was normalized based on the total protein concentration such that a
volume equivalent to 50 µg of total protein was used. Thirdly, the iTRAQ ratios were
normalized based on their global medians.
The iTRAQ chemical tagging method was well-suited for this application because of its ability to
multiplex samples from different bioreactor timepoints, which reduced the variability between
samples as well as the time and cost of the analysis compared to SILAC approach, which is
another common quantitative proteomics approach. The SILAC method utilizes metabolic
labeling of a cell culture with a light and heavy media containing isotopically labeled Arg, and
allows comparison of two cell cultures [7]. We chose not to use SILAC because our cell culture
experiments were performed at the 3 L reactor scale, which would require significant amounts of
labeled media to perform the experiment. We have also observed that spiking of isotopically
labeled Arg into a cell culture bioreactor after inoculation with standard media did not result in
significant incorporation of the heavy label in CHO proteins (data not shown). Therefore, we
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deemed the iTRAQ method more widely applicable for analysis of samples generated using
different production scales.
Figure 3.2: Protein Identification Summary
Samples generated from each bioreactor were analyzed and submitted separately for Sequest
search against the mouse database. A) A summary of the peptides and proteins identified in each
bioreactor. B) The list of genes identified by Sequest search was classified by biological process
using PANTHER.
BR#1 BR#2
# Unique Peptides 7399 7479
# Unique Proteins 2130 2026
# Total Proteins 2836
A
B
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3.3.2 Analysis of Dynamic Trends in Protein Expression
Proteins exhibiting significant changes in protein expression over time were identified by first
performing linear regression analysis on the plot of protein relative abundance (derived from
iTRAQ data) versus time. The slope of the regression line is an indicator of the trend of protein
abundance over time. An example is illustrated in Figure 3.3a for the protein GRP78. This
particular protein showed an increase in relative abundance over time, increasing from 1.0 on
day 6 to 3.2 on day 16. Although this is not a linear increase in abundance, plotting the linear
regression line of this plot provides useful information for determining the trend in abundance of
this protein. In this case, the slope is 0.24 indicating a positive trend. Similarly, a negative slope
would indicate a decreasing trend. The advantage of using such a method for identification of
trends is that the calculation takes into account the relative protein abundance at multiple
timepoints, and therefore is suitable for identifying real trends in protein abundance. By
performing this calculation for all proteins identified in each reactor, we obtained a distribution
of slopes (Figure 3.3b). The distribution indicates that the median slope for all proteins is 0.0,
thus most of the identified proteins are not significantly changing in abundance over time. A
threshold of +/- 1 standard deviation from the mean was used to identify proteins with significant
trends. This threshold was found to be suitable for identifying proteins with significant changes
in abundance, as most of the proteins found outside of these limits had at least 2-fold changes in
abundance from day 6 to day 16.
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Figure 3.3: Identification of Dynamic Proteomic Trends
The slope of the linear regression line was calculated for the plot of relative iTRAQ reporter ion
intensity versus time. A) An example of an increasing trend showed a positive slope. B) The
distribution was calculated from the slopes of all proteins identified in bioreactor A. The
thresholds are indicated in red, -0.055 and 0.085.
The determination of upregulated proteins over time using the slope of the linear regression line
has several advantages over pairwise comparison of iTRAQ intensities at specific timepoints. It
is useful for identification of positive or negative trends which take into account protein
A
B
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abundance at multiple timepoints, which may be more relevant to cell culture versus pairwise
comparisons between two timepoints. It allows visualization of the frequency of these protein
trends for an entire dataset, to help understand the data trends from a global perspective. Finally,
it is easily applied to large datasets and does not require the use of special software since the
calculations are easily applied using Microsoft Excel.
Using this approach, 59 proteins were identified with significant dynamic trends over the course
of the cell culture (Table 3.1). All listed proteins showed trends which were biologically
reproducible between the two reactors sampled. Thirteen of the proteins had negative trends, and
the other 44 had positive trends. The protein with the most positive trend was clusterin, with an
averaged slope of 0.36. Similarly, MCM2 had the sharpest decreasing trend with an averaged
slope of -0.11. To understand what functions were associated with these proteins, the list of
differentially expressed proteins was classified by PANTHER protein class (Figure 3.4). Major
functional groups represented by these proteins include chaperones, nucleic acid binding
proteins, isomerases, proteases, transporters, transferases, and oxidoreductases.
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Table 3.1: List of Proteins with Dynamic Trends in CHO Cell Culture
Differentially expressed proteins were identified with a slope greater than 1 standard deviation
from the mean, or less than 1 standard deviation from the mean. Proteins where similar trends
Gene Name 6 9 12 16 Slope
Cell Metabolism ALDH2 aldehyde dehydrogenase 2 family (mitochondrial) 1.00 0.81 1.23 2.11 0.12
GPD2 glycerol-3-phosphate dehydrogenase 2 (mitochondrial) 1.00 0.67 1.71 3.00 0.22
MDH2 malate dehydrogenase 2, NAD (mitochondrial) 1.00 0.78 1.09 2.07 0.11
NDUFA5 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 5, 13kDa 1.00 2.18 1.60 4.59 0.32
Chaperones / Protein Folding CANX calnexin 1.00 0.79 1.08 2.07 0.11
CALR calreticulin 1.00 0.96 1.37 2.55 0.16
DNAJB11 DnaJ (Hsp40) homolog, subfamily B, member 11 1.00 1.08 1.69 2.84 0.19
HSPE1 heat shock 10kDa protein 1 (chaperonin 10) 1.00 0.68 1.11 1.97 0.11
HSPA5 heat shock 70kDa protein 5 (glucose-regulated protein, 78kDa) 1.00 0.95 1.47 3.45 0.25
HSPA9 heat shock 70kDa protein 9 (mortalin) 1.00 0.82 1.22 1.92 0.10
HSP90B1 heat shock protein 90kDa beta (Grp94), member 1 1.00 0.94 1.34 2.63 0.17
HYOU1 hypoxia up-regulated 1 1.00 0.86 1.35 2.25 0.13
P4HB prolyl 4-hydroxylase, beta polypeptide 1.00 0.90 1.23 1.95 0.10
PDIA3 protein disulfide isomerase family A, member 3 1.00 0.95 1.27 2.28 0.13
PDIA4 protein disulfide isomerase family A, member 4 1.00 0.96 1.16 2.36 0.14
PDIA6 protein disulfide isomerase family A, member 6 1.00 1.21 1.63 3.02 0.20
TXNDC5 thioredoxin domain containing 5 (endoplasmic reticulum) 1.00 1.10 1.28 1.97 0.10
Nucleic Acid Binding CHMP2B chromatin modifying protein 2B 1.00 0.83 0.43 0.39 -0.08
EIF2B4 eukaryotic translation initiation factor 2B, subunit 4 delta, 67kDa 1.00 0.68 0.58 0.26 -0.07
HMGB2 high-mobility group box 2 1.00 0.55 0.62 0.15 -0.08
HIST1H1D histone cluster 1, H1d 1.00 0.50 0.72 0.23 -0.06
MCM2 minichromosome maintenance complex component 2 1.00 0.59 0.23 0.00 -0.11
MCM5 minichromosome maintenance complex component 5 1.00 0.72 0.57 0.36 -0.06
MCM6 minichromosome maintenance complex component 6 1.00 0.63 0.59 0.25 -0.07
SF3B1 splicing factor 3b, subunit 1, 155kDa 1.00 0.77 0.41 0.12 -0.08
Kinase MAST1 microtubule associated serine/threonine kinase 1 1.00 0.80 1.32 2.68 0.17
PRKCSH protein kinase C substrate 80K-H 1.00 1.05 1.20 2.28 0.13
PRKAG2 protein kinase, AMP-activated, gamma 2 non-catalytic subunit 1.00 0.94 1.30 2.08 0.11
Lipid Metabolism HMGCS1 3-hydroxy-3-methylglutaryl-CoA synthase 1 (soluble) 1.00 0.76 0.36 0.16 -0.09
ACAA2 acetyl-CoA acyltransferase 2 1.00 0.73 1.42 2.20 0.13
PLIN2 adipose differentiation related protein 1.00 2.13 2.12 2.57 0.14
CYB5R3 cytochrome b5 reductase 3 1.00 1.45 1.10 2.96 0.18
PNPLA8 patatin-like phospholipase domain containing 8 1.00 2.19 1.57 3.87 0.25
Oxidoreductase MT1F metallothionein 1F 1.00 0.79 1.39 2.38 0.15
PRDX3 peroxiredoxin 3 1.00 0.95 1.38 2.41 0.15
PRDX5 peroxiredoxin 5 1.00 1.15 1.48 2.27 0.13
SOD2 superoxide dismutase 2, mitochondrial 1.00 0.86 1.35 2.33 0.14
Protease CTSD cathepsin D 1.00 1.10 1.19 2.12 0.11
CLPP ClpP caseinolytic peptidase, ATP-dependent, proteolytic subunit homolog 1.00 1.07 1.26 2.38 0.14
KLK11 kallikrein-related peptidase 11 1.00 1.56 0.65 2.40 0.11
USP10 ubiquitin specific peptidase 10 1.00 1.81 0.87 0.25 -0.08
Transport ABCB5 ATP-binding cassette, sub-family B (MDR/TAP), member 5 1.00 1.56 1.71 2.82 0.17
CAPRIN1 cytoplasmic activation/proliferation related protein 1 1.00 0.92 0.85 0.21 -0.08
ATP5B ATP synthase, H+ transporting, mitochondrial F1 complex, beta polypeptide 1.00 0.76 0.92 1.93 0.10
ATP5G1 ATP synthase lipid-binding protein 1.00 0.44 1.07 3.14 0.22
Other CAV3 caveolin 3 1.00 1.07 1.79 2.81 0.19
SSR1 signal sequence receptor, alpha 1.00 0.88 0.61 0.17 -0.07
MANF mesencephalic astrocyte-derived neurotrophic factor 1.00 1.12 1.60 2.23 0.13
ADSL adenylosuccinate lyase 1.00 1.33 2.11 2.70 0.18
ARGLU1 arginine and glutamate rich 1 1.00 1.27 0.74 0.41 -0.07
CLU clusterin 1.00 1.27 2.23 4.56 0.36
ETHE1 ethylmalonic encephalopathy 1 1.00 0.84 1.11 2.01 0.10
GOT2 glutamic-oxaloacetic transaminase 2, mitochondrial 1.00 0.78 1.24 1.99 0.11
1500003O03Rik novel protein RP23-22A15.1 1.00 1.00 1.67 2.31 0.14
TGM2 transglutaminase 2 1.00 1.96 2.88 3.20 0.22
WDR65 WD repeat domain 65 1.00 1.35 1.74 2.44 0.14
BCL2L1 BCL2-like 1 (Bcl-XL) 1.00 0.67 1.79 3.08 0.21
N/A monoclonal antibody light chain 1.00 1.28 1.71 2.32 0.13
N/A monoclonal antibody heavy chain 1.00 1.17 1.93 2.34 0.14
Relative Abundance
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were observed in both bioreactors were considered significant. The average relative abundance
and slope values from both bioreactor datasets are shown.
Figure 3.4: Differentially expressed proteins were classified by protein class using PANTHER.
Different trends were observed within the protein classes. For example, many ER proteins were
upregulated in the stationary phase. These include the molecular chaperones GRP78, calnexin,
GRP94, and hypoxia upregulated protein 1, as well as several protein disulfide isomerases.
Another ER-residing protein, armet, was also present at higher levels in the stationary phase.
Armet does not have reported chaperone activity, but is associated with inhibition of ER-
mediated stress [8]. In contrast, several proteins associated with nucleic acid binding such as
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
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minichromosome maintenance complexes 2, 5, and 6 were downregulated in the stationary
phase. Cytoplasmic activator/proliferation-associated protein-1 (caprin1) was also
downregulated. To further illustrate some of the trends in protein abundance, the relative
abundance plots for GRP78, armet, MCM2, and caprin1 are shown in Figure 3.5.
Figure 3.5: Abundance over Time for Proteins Involved in Relevant Pathways
The relative intracellular concentration of A) GRP78 B) armet C) MCM5 D) caprin1 is shown
for both bioreactor experiments (denoted by red and blue traces). The protein abundance was
determined from the iTRAQ ratios using day 6 as the denominator and days 9, 12, and 16 as
numerators.
GRP78
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3.3.3 Identification of Growth-Regulating Proteins
The resulting list of differentially expressed proteins was analyzed using Ingenuity Pathway
Analysis to identify common pathways and networks among the proteins. The highest scoring
network identified in the pathway analysis had a score of 50, and included 29 of the proteins
from the significant trend group (Figure 3.6). This network is associated with hematological
function and development, hematopoesis, and cell death.
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Figure 3.6: Top Scoring Protein Network from Ingenuity Pathway Analysis
Genes correlating to differentially expressed proteins were analyzed for functional networks
using Ingenuity. The top network shown gave a score of 50, and is associated with
hematological function and development, hematopoesis, and cell death. Gene names indicated in
green are downregulated in stationary phase, and red indicates upregulated. Solid lines indicate
protein-protein interactions, and dotted lines indicate relationships based on gene expression.
110
Two proteins included in the top-scoring network, clusterin and transglutaminase-2, were of
particular interest because both have cell growth regulating properties [9, 10]. In order to
confirm the dynamic trends observed for these two proteins, western blotting was performed on
the four timepoints from both bioreactors for both proteins. The results confirmed the changes in
abundance observed using quantitative proteomics (Figure 3.7). Transglutaminase-2 was found
to increase an average of 2.8-fold over 10 days by western blot based on spot volume analysis,
which corresponds to a 3.2-fold change observed in the proteomics analysis. Clusterin was
detected as several bands, which has been reported previously due to the presence of a secreted
form which is heavily glycosylated, as well as a nuclear non-glycosylated form [11, 12]. The
major band observed in this blot migrates at approximately 28 kDa, which corresponds to the
nuclear form of clusterin. This clusterin band was found with an average 2.0 fold increase over
10 days, compared to a 4.5 fold increase observed by proteomics.
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6A 9A 12A 16A 6B 9B 12B 16B
Figure 3.7: Confirmation of Dynamic Trends in Transglutaminase-2 and Clusterin Expression
The relative abundance of transglutaminase-2 and clusterin as measured using iTRAQ reporter
ion ratios and B) western blotting results for transglutaminase-2 and clusterin. Cell lysates
corresponding to days 6, 9, 12, and 16 from both bioreactors were analyzed in parallel.
3.3.4 Potential Implications on CHO Cell Culture
The observation of upregulation of molecular chaperones and isomerases involved in protein
folding is likely due to cellular response to ER stress. The increased expression of the molecular
chaperones BiP, calnexin, and GRP94, are associated with the unfolded protein response (UPR)
[13-15]. Another upregulated protein, armet, is reportedly associated with inhibition of ER-
mediated stress [8]. The unfolded protein response is triggered by high levels of unfolded
Transglutaminase-2
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proteins in the ER lumen, which can activate the UPR through three different receptors: Ire1,
ATF6, and PERK. It results in the enhanced transcription of UPR genes including molecular
chaperones, isomerases, and proteases [16]. The resulting increase in protein folding and
degradation capability enables the cell to increase its ability to process proteins entering the ER.
Another upregulated group of proteins observed in this study are oxidoreductases, which are
associated with having antioxidant function. The increased expression of antioxidants may be
related to the unfolded protein response, since an oxidizing environment in the ER lumen is
critical for protein folding [17, 18]. The upregulation of UPR-related proteins also correlates
with increased intracellular abundance of the monoclonal antibody during stationary phase.
Similarly, higher extracellular abundance of the IgG was also observed, which is indicated by the
higher specific productivity during stationary phase. Previous proteomic studies of CHO and
NS0 cells have also reported evidence of the unfolded protein response in mammalian cell
cultures [19, 20]. This process may play a role in the observed cell growth characteristics since
the UPR has been reported to trigger cell cycle arrest [4], however this has not been confirmed
for our particular cell culture process.
Pathway analysis of the list of differentially expressed proteins revealed a network consisting of
29 proteins which had functional associations with hematological function and development,
hematopoesis, and cell death. Although the cells studied have no direct relation to blood cells,
these hematological functions were associated with the results because the filtering parameters
used for identification of networks were intentionally relaxed. This was intentional, since
Ingenuity does not contain filters specific for CHO cells. The results from Ingenuity indicate
that the protein network is associated with development and growth of blood cells. These
113
associations may also translate to cellular growth and development for CHO cells. Several
proteins in the network are directly associated with regulation of cell growth. One such protein
is Bcl-XL, which is associated with inhibition of apoptosis in mammalian cells [21]. The CHO
cells used in these experiments had been engineered to overexpress Bcl-XL as a strategy to
regulate cell growth through inhibition of apoptosis in cell culture [22]. Our results indicate that
Bcl-XL is expressed at higher levels during stationary phase compared to exponential growth
phase in this cell culture.
Several proteins associated with cellular proliferation such as MCM2, MCM5, MCM6, and
caprin1 were downregulated in the stationary phase. High levels of MCM proteins have been
previously identified as cell proliferation markers, and are involved in control of DNA synthesis
[23]. Caprin1 has also been reported to be involved in cell proliferation [24, 25]. The dynamic
trends observed for these proteins correlates with the relatively static cell growth observed
during stationary phase, and indicate that these known growth-related marker proteins are
indicators of cell growth in this particular CHO culture. Downregulation of MCM3 and MCM5
was also reported to be associated with high productivity in sodium butyrate treated CHO cells
[26].
Other differentially expressed proteins associated with regulation of cell growth are
transglutaminase-2 and clusterin. Transglutminase-2 (TG2) is an 82-kDa membrane protein that
catalyzes crosslinking of lysine and glutamine residues [27, 28]. It has been reported to be
overexpressed in certain tumors and to have anti-apoptotic activity [10, 29]. Clusterin is a
protein present in nuclear and secreted forms in mammalian cells that has both pro- and anti-
apoptotic properties and is also associated with tumor progression [9, 11, 30, 31]. The role of
114
clusterin is difficult to decipher due to the multiple forms present in mammalian cells. It is
expressed as a secreted protein which is heavily glycosylated, as well as a nuclear non-
glycosylated form [32]. The band that we observe by western blot seems to correspond to a
partially-processed variant of clusterin which lacks glycosylation. A band of 28-30 kDa was
previously observed by O’Sullivan et al, by western blotting of nuclear clusterin under reducing
conditions [33]. The nuclear form of clusterin has been associated with signaling of apoptosis in
cancer cells [11].
Our data indicates that several growth-regulating proteins Bcl-XL, transglutaminase-2 and
clusterin are expressed at higher levels in the stationary phase of the cell culture, while other
marker proteins associated with cell growth including MCM2 and caprin-1 are downregulated in
stationary phase. One possible explanation for these observations is that several of these proteins
are involved in control of cellular growth during the cellular response to ER stress. Bcl-XL has
been previously reported as having an active anti-apoptotic role during cellular response to ER
stress by inhibiting the translocation of BIM [34]. The differential expression of clusterin,
transglutaminse-2, minichromosome maintenance complex proteins and caprin-1 may also be
tied into this cellular response, as playing roles in cellular adaptation to ER stress and the
concomitant regulation of cellular growth. However, the roles that these proteins play directly
on cell growth and productivity will require additional study to validate the observations.
3.4 Conclusions
The main goal of this study was to establish a suitable approach for identifying trends in protein
expression over the course of a mammalian cell culture. The transition of mammalian cells from
115
exponential to stationary phase during cell culture is an important attribute of cell culture
performance, as it involves the transition of cells from a high growth phase into a high
productivity phase with little to no cell growth. This study utilized a proteomics strategy
designed to identify dynamic protein trends in a CHO mammalian cell culture over a period of
time encompassing both exponential and stationary phases. Using a quantitative proteomics
approach, we identified differentially expressed proteins with increasing or decreasing trends in
protein expression. The results obtained here provide us a baseline of proteomic changes related
to the transition of cell growth from exponential to stationary phase in one CHO cell culture
process, and these results can be validated in future studies of other CHO processes to determine
how biologically reproducible they are. The differential expression of some of these proteins
may be related to the changes in cell growth observed throughout the CHO cell culture. The
differential expression of translgutminase-2 and clusterin is of particular interest due to their role
in the regulation of cell growth. The nature of the relationships between these proteins and cell
culture performance will be the subject of future studies. In addition to identifying differential
protein expression over the course of a cell culture, the proteomics strategy described here can be
applied to identify proteins with different trends in expression between different cell culture
conditions, which may be useful for identifying protein markers associated with productivity and
product quality.
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28. Griffin M, Casadio R, Bergamini CM: Transglutaminases: nature's biological glues.
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analyses of nuclear clusterin, a cell death protein. The Journal of biological chemistry
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31. Flanagan L, Whyte L, Chatterjee N, Tenniswood M: Effects of clusterin over-
expression on metastatic progression and therapy in breast cancer. BMC cancer
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CHAPTER 4
CHROMOSOMAL MAPPING OF CHO GENES RELATED TO CELL GROWTH
4.1 Overview
Until recently, gene order in eukaryotes was assumed to be random. However, statistical
analysis of the expression patterns of various genomes resulted in the observation that in many
cases genes with similar expression patterns are clustered together [1, 2]. Evidence was first
provided for gene clustering by Cho et al, who showed that 25% of co-expressed genes involved
in cell division in yeast were clustered by chromosomal location [3]. Subsequently, many
studies have also found strong evidence of gene clustering, including gene clustering in A.
thaliana [4, 5], clustering of muscle-specific genes in C. elegans [6], and tissue-specific gene
clustering in humans [7].
Gene clustering has major implications on genomic evolution, aging, and cellular
development. Tandem pairs of co-expressed genes are thought to be attributable to regulation by
a shared promoter [8, 9]. However, this explanation alone does not explain the broader scale of
gene clustering observed in many eukaryotes. Chromatin structure is also thought to play a key
role in regulating gene expression. In support of this, histone deacetylases in yeast were shown
to play a role in regulating expression of gene clusters, including genes involved in
gluconeogenesis and ribosomal genes by modification of chromatin structure [10]. Modification
of histones likely plays a role in regulation of the co-expression of gene clusters.
Evidence of clustering of genes involved in cell cycle arrest has been reported. Zhang et
al showed clustering of human fibroblast genes associated with replicative senescence [11]. This
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form of cell cycle arrest occurs in somatic cells which have undergone many cycles of cell
division. Using cDNA microarray analysis and statistical analysis of gene expression, they
showed that 150 of the 376 genes upregulated during senescence were clustered when using 1
Mbp as the maximum cluster size.
The oncoprotein c-MYC and its target gene MT-MC1 regulate expression of various
genes implicated in tumor proliferation [12]. In order to understand the genes targeted by MT-
MC1 (and in turn c-MYC), transcriptional profiling was used to identify differentially expressed
genes in myeloid cancer cells overexpressing MT-MC1 [13]. Interestingly, 34% of the target
genes were clustered on six chromosomal loci, which indicate some functional organization of
the complex set of tumor-controlling genes targeted by these oncoproteins.
Strong evidence of gene clustering in eukaryotes, including genes implicated in cell
growth, creates an argument for analysis of gene clustering in CHO cells. This is an emerging
area of interest in biomarker discovery, and could increase our understanding of the genetic
pathways involved in cell growth and productivity. However, the lack of a complete publicly
available CHO genome creates a significant obstacle.
In order to analyze gene co-expression, the chromosomal structure of the organism must
be well understood. Most mammalian cells are diploid, which means that they contain two sets
of autosomal chromosomes. Humans have 22 autosomal chromosomes and two sex
chromosomes, resulting in a total of 46 chromosomes in each cell. Similarly, the common
mouse Mus musculus is also diploid and has a total of 40 chromosomes per cell (see Figure 4.1).
In contrast, the Chinese hamster was an attractive model for early genetics research due to its
relatively low chromosome number of 22. Theodore T. Puck created the first cultured CHO cell
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line in 1957 by extracting Chinese hamster ovary cells and growing them in a cultured
monolayer for genetic studies [14]. Researchers also found these cells to be easily adapted to
suspension cultures. Over the next several decades, CHO cells became a popular model to study
toxicity, genetics, and as a gene expression platform due to its high growth rate and production
yield [15-17].
Figure 4.1: Mouse karyotype using the Giemsa (G-banding) technique. [18]
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As mentioned, Chinese hamsters only have 22 total chromosomes, a very low number for
a mammal [19]. A study of the chromatin structure of CHO cells in 1969 showed that they are
aneuploid, indicating that the chromsomes had lost their diploid properties, thus changing their
genetic composition significantly compared to the ovarian tissue cells from which they were
derived. Karyotypic analysis of CHO-K1 cells indicate that this difference is due to
translocation and deletion of certain chromatin structures from the native CHO chromosomes
(see Figure 4.2) [20].
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Figure 4.2: Karyotypic analysis of diploid Chinese hamster fibroblast chromosomes (LA-CHE)
and CHO-K1 chromosomes. Altered chromosomes are marked “Z”. [20]
A more recent and thorough genetic characterization of CHO-DG44 chromosomes
identified 20 unique chromosomes, with only 7 being normal [21]. Four Z group chromosomes
originally identified in the CHO-K1 study, 7 derivative chromosomes with known origin, and 2
marker chromosomes of unidentified origin were described (see Figure 4.3). Further analysis of
different CHO-DG44 recombinant cell lines illustrated marked differences in chromosome
composition between the different lines, including aneuploidy, deletions, and complex
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rearrangements. Apparently, the CHO genome is not very stable; however this does not seem to
affect the stability of recombinant protein expression.
Figure 4.3: Karyotype of CHO-DG44 cells using Giemsa (G-banding) technique. Normal
Chinese hamster chromosomes are shown in the top row. [21]
Both mouse and CHO share significant gene sequence homology [22]. Despite the
difference in chromosome number and composition, there likely exists some conservation in
gene localization between the two species. In this chapter the activity and function of genes
mapped to the mouse chromosomes are studied. Known genes of interest relevant to cell culture,
including genes identified from the proteomics work in Chapter 3, oncogenes related to tumor
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proliferation, and reported target genes for cellular engineering of enhanced mammalian cell
cultures are analyzed for evidence of clustering on mouse chromosomes. Using specific
thresholds, clusters of functionally similar genes are identified. The results described here
establish initial information regarding potential CHO gene clustering, which can be further
expanded upon in the future using gene expression analysis tools to positively identify gene
clustering in CHO.
4.2 Methods
4.2.1 Chromosome Mapping
The complete Mus musculus genome was downloaded from the Global Proteome
Machine (http://www.thegpm.org). A list of genes of interest was generated from some of the
proteins identified in Chapter 3. This list was referenced to the mouse genome using MS Excel
to determine the chromosomal locations of those genes. Potential gene clusters were identified
by filtering the genes by +/- 150000 bp from the starting position of each gene of interest.
4.2.2 Pathway Analysis
Twenty one lists of genes corresponding to the 21 unique chromosomes of the mouse
genome were uploaded into Ingenuity Pathway Analysis (Ingenuity® Systems,
www.ingenuity.com). A Core analysis was used with both direct and indirect relationships
allowed, and specifying maximum of 35 proteins per network and 10 networks total. All data
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sources were selected, and all cell and tissue types were selected. No expression value cutoff
was used. All network eligible molecules were overlaid onto a global molecular network
developed from information contained in Ingenuity’s Knowledge Base. Networks of network
eligible molecules were then algorithmically generated based on their connectivity.
4.3 Results
4.3.1 Identification of Cell Growth Gene Networks
To get an initial assessment of the correlation between genetic function and chromosome
location, the set of genes corresponding to each mouse chromosome was analyzed using
Ingenuity Pathway Analysis (http://www.ingenuity.com) to determine the top networks
associated with each individual chromosome. Chromosomes which had top-scoring networks
associated with cell growth and proliferation, cell death, or protein synthesis are shown in Figure
4.4. Eleven chromosomes were associated with cell growth and proliferation, 11 were associated
with cell death, and only chromosomes 2 and 6 were associated with protein synthesis. An
example of a top-scoring network from chromosome 11 is shown in Figure 4.5.
Table 4.1: Top Gene Networks Identified in Each Mouse Chromosome
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The top 5 scoring networks were determined for each mouse chromosome gene set using
Ingenuity Pathway Analysis (http://www.ingenuity.com). The chromosomes with networks
associated with cell growth and proliferation, cell death, or protein synthesis are shown.
130
Figure 4.5: The top network for mouse chromosome 11 is shown, based on direct and indirect
relationships. The network is associated with cell cycle, cellular growth and proliferation, and
hematological system development and function. Only genes shown in gray are present on
chromosome 11.
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4.3.2 Mapping of Genes of Interest on Mouse Chromosomes
To determine the degree of clustering of genes known to be involved in regulation of cell
growth, a list of genes of interest was generated and mapped to the mouse genome. This gene
list was comprised of two components: a list of known oncogenes generated by the Cancer
Genome project at the Wellcome Trust Sanger Institute, and a list of known gene targets for
cellular engineering. The oncogene list was downloaded directly from the Wellcome Trust
Sanger Institute, and contained 458 genes (http://www.sanger.ac.uk/genetics/CGP/Census/). In
addition, a list of genes used as cell engineering targets was generated based on previously
reported studies [23]. This list represents all genes known to have a direct impact on cell culture
performance upon modification of their expression levels, and includes 36 genes involved in
cellular metabolism, cell cycle control, protein secretion, and apoptosis. The combined gene list
was mapped to the mouse genome, which was downloaded from the Global Proteome Machine
(http://www.thegpm.org).
Gene clusters were identified based on chromosome location. Genes with starting codons
within 400,000 bp of each other were classified as significant clusters. A summary of the gene
clustering analysis is shown in Figure 4.6.
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Figure 4.6: Summary of Growth Regulating Gene Clusters Identified on Mouse Chromosomes
The number of clusters identified on each chromosome is shown in blue. The number of genes
mapped to clusters is shown in red.
Evidence of gene clustering was identified on nearly every mouse chromosome. Most
clusters consisted of only two genes; however clusters of three genes were identified on
chromosomes 6, 7, 10, and 11. Two relevant clusters identified on chromosome 7 are shown in
Figure 4.7. The cluster of ERCC2, CBLC, and BCL3 are within 530 kbp of a second cluster of
CD79A and CIC. These are all known oncogenes, and are involved in tumor proliferation. For
example, CBLC is a protein that has been shown to interact with EGFR, a cell surface receptor
which is involved in regulation of cell growth and DNA synthesis [24]. Another gene in this
cluster, Bcl3, is a proto-oncogene candidate which regulates transcription of genes associated
with tumor proliferation by interaction with NF-kappa-B [25].
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Figure 4.7: Cluster of Genes of Interest on Mouse Chromosome 7.
Descriptions of top scoring cell compartment are shown in purple ovals, biological process in
blue diamonds, and molecular functions are shown in orange boxes for each gene. Each
description is followed by the total number of entries for each ontology present in GO. The red
box shows the top 5 interactants for each gene, and the red circles show the total number of
interacting proteins listed by Genecards (http://www.genecards.org).
4.3.3 Mapping of Differentially Expressed CHO Proteins on Mouse Chromosomes
The relative activity of each gene set corresponding to a mouse chromosome was
determined by mapping the CHO proteins identified from the study described in chapter 3 to
their respective gene location in the mouse genome. This approach assumes that the most active
genes are most highly represented in the list of identified proteins from the proteomics
experiment. The number of genes mapped for each mouse chromosome is shown in Figure 4.8.
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Figure 4.8: Gene Expression Activity of CHO Genes Mapped to Mouse Chromosomes
The proteins identified in the proteomics study described in chapter 3 were mapped to the mouse
genome. The (A) number of genes corresponding to proteins identified for each mouse
chromosome and (B) number of identified genes divided by the total number of genes for each
chromosome is shown.
The results indicate that chromosomes 7, 2, and 11 were the most represented in terms of
the number of gene products identified in the proteomics study. These chromosomes were also
A
B
135
associated with cell growth and proliferation based on the networking data shown in Figure 4.4.
Chromosomes 15, 18, and 9 had the highest percentage of identified genes per chromosome,
with close to 10% of all chromosomal genes identified. This differences observed between the
two charts is due to the wide range of chromosomal gene content, as chromosomes 7, 2, and 11
contain the highest number of genes of all of the mouse chromosomes.
The data was probed for evidence of co-expression by mapping differentially expressed
genes identified in the proteomics characterization of CHO cells in exponential and stationary
growth phases (see Table 3.1) and analyzing the data for evidence of clustering. In this study,
none of the differentially expressed proteins were found to be co-localized on the mouse
chromosomes. However, some evidence of clustering with other genes of interest was identified.
A diagram illustrating some evidence of clustering between these genes is shown in Figure 4.9.
Figure 4.9: Chromosomal Mapping of Cell Growth Related Genes
Proteins of interest identified in Chapter 3, shown in red, were mapped to the mouse genome.
Genes located within 300 kbp are shown within the boxes. Genes found to have similar
functions as the mapped genes are indicated in black italics.
Both Tti1 and Rprd1b were clustered near Tgm2 on chromosome 2. Tti1 has been shown
to interact with mammalian target of rapamycin (mTOR), a member of the
Tti1Rprd1bTgm2
D630003M21Rik 1700060C20Rik
Bpi
VprbpManf
Rbm15b
Esco2Ccdc25
1110020C17Rik Scara3
CluGulo
Adam2 Ephx2
2 9 14
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136hosphatidylinositol 3-kinase-related kinase (PIKK) family which regulates cell growth [26].
Immunoprecipitation and size-exclusion chromatography was used to demonstrate stabilization
of both mTORC1 and mTORC2 complexes via Tti1 binding [27]. Therefore, this protein seems
to play a key role in regulating mTOR activities in mammalian cells.
The gene Rprd1b, also known as C20orf77, was found by Jung et al. to be highly
expressed in lung cancer cells [28]. Suppression subtractive hybridization was used to analyze
gene expression in lung cancer cell lines and a list of genes including C20orf77 were consistently
detected at high levels of expression in all lung cancer cell lines. The gene may play a role in
carcinogenesis. Due to the proximity and functional similarities in Tgm2, Tti1, and Rprd1b,
these genes are candidates for expression analysis to determine if any co-expression exists
between the three, which could indicate some shared functional impact of these genes on cell
growth in CHO cells.
Vprbp encodes the gene product DCAF1, a receptor for ubiquitin ligase [29]. Transy et
al. demonstrated cell cycle arrest upon DCAF1 interaction with Vpr [30]. Vpr hijacks DDB1
ubiquitin ligase complex through interaction with DCAF1, causing cell cycle arrest in G2 phase.
Vbrpb is located near Manf on chromosome 9. Manf is upregulated as a response to ER stress
conditions, and likely plays a role in protein folding [31]. The close proximity of these two
genes on chromosome 6 may indicate co-expression as a component of the unfolded protein
response, since cell cycle arrest has been reported as being a common trait of mammalian cells
under ER stress [32].
Both Scara3 and Clu are co-located on chromosome 14. Scara3 codes the protein CSR1,
which is downregulated in prostate cancer cell lines, and is implicated as a tumor-suppressor
137
[33]. While the mechanism of tumor suppression is not well understood, Zhu et al. showed that
CSR1 binds to CPSF3, a protein involved in conversion of heteronuclear RNA to mRNA [34].
Upon binding to CSR1, CPSF3 translocates from the nucleus to the cytoplasm, inhibiting
polyadenylation of RNA and resulting in cell death. Downregulation of CSR1 inhibits cell death
in prostate cancer cell lines. Both clusterin and CSR1 play a role in regulation of cell death, and
the co-location of these genes on chromosome 14 makes them candidates for expression analysis
in future experiments.
4.4 Conclusions
Analysis of gene co-expression is an emerging area of interest in biomarker discovery.
Identification of related sets of genes that share regulation of expression can help to elucidate
biological pathways. The analysis of the location of genes of interest on mouse chromosomes
described here gives an initial assessment of possible clustering of CHO genes related to cell
growth which may comprise part of the biological machinery driving cell growth in CHO, and
consequently may play a significant role in cell culture process performance. From the limited
data available, three potential gene clusters were identified near the genes coding for clusterin,
Manf, and transglutaminase-2, which were identified as proteins of interest in Chapter 3.
Without the full CHO genome available with chromosomal locations, it is difficult to
make an accurate assessment of CHO gene expression. The apparent genetic instability of CHO
makes the study of gene location an interesting area for future research. This study lays the
groundwork which can be expanded to use gene expression tools and a complete CHO genome
138
to identify gene expression patterns relevant to CHO cell culture performance and identify
further potential growth and productivity related biomarkers.
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143
CHAPTER 5
PROTEOMICS CHARACTERIZATION OF HOST CELL PROTEINS PRESENT IN
VARIOUS STAGES OF A BIOPHARMACEUTICAL PROCESS
5.1 Overview
The secreted proteins generated by mammalian host cells during cell culture have
significant implications on the entire biopharmaceutical process. For one, the interaction of
mammalian cells is mediated by soluble elements released by cells into the extracellular
environment. Major classes of secreted proteins include cytokines, proteases, protein hormones,
growth factors, chemokines, or other extracellular matrix proteins. In addition to proteins which
are released from the cells by the classical secretory pathway, other extracellular proteins can be
shed from the cell membrane, or released through other pathways such as exosomes [1].
Proteomics has been used to study secreted proteins released from cancer cells, with the
hopes of discovering biomarkers that could be used for early diagnosis or elucidating disease
pathways, and which would only require analysis of biological fluids rather than tissue biopsies
[2]. Kawanishi et al. compared the secretomes of poorly invasive RT112 bladder carcinoma
cells to highly invasive T24 cells using shotgun proteomics and cDNA microarray analysis [3].
The T24 cancer cells were found to overexpress several proteins including the chemokine
CXCL1, which is associated with tumor progression. Urine levels of CXCL1 also correlated
with disease invasiveness.
As another example, shotgun proteomics analysis of conditioned media from three
prostate cancer cell lines identified several potential biomarkers [4]. These included follistatin,
144
pentraxin 3, and spondin 2. Testing of serum levels of these proteins by ELISA showed that
levels of these proteins were increased in prostate cancer patients compared to healthy controls,
and also correlated with levels of prostate specific antigen (PSA), the currently established tumor
marker for prostate cancer.
The growth-regulating protein clusterin described previously in Chapter 3, is expressed as
both a nuclear and secreted form, with the latter having anti-apoptotic function [5]. It is likely
that proteins such as secreted clusterin may be expressed by CHO cells and are present in the
conditioned media, playing roles in cell growth and apoptosis. Proteomics profiling of the CHO
secretome, by analysis of the harvested cell culture fluid (HCCF), would enable the identification
of these proteins, and help to understand the impact they may have on cell culture performance.
In addition to the impact of secreted proteins on cell culture, these proteins also play a
role during the downstream purification process. The presence of secreted host cell proteins in
the final drug product may cause immunogenic responses in patients if not cleared to an
acceptably low level. For this reason, it is important to demonstrate clearance of host cell
proteins using a suitable analytical method at each step of the downstream process. The gold
standard method for host cell protein analysis is an immunoassay platform such as an ELISA,
which utilizes a polyclonal antibody (pAb) targeting various secreted host cell proteins released
from the host cells [6]. Known limitations of these methods include potential binding
interference from the sample matrix, as well as the risk of the generic pAb lacking specificity to
certain secreted proteins. The latter becomes more likely as the same antibody is typically used
in different host cell protein assays for different cell culture processes, which may not
necessarily secrete the same proteins. For these reasons, a supplementary method utilizing mass
145
spectrometry would be beneficial towards identifying and quantifying secreted host cell proteins
from various intermediate steps of a biopharmaceutical process.
The chapter describes the application of proteomics methodology to the analysis of
secreted proteins present in the extracellular matrix of a CHO cell culture, as well as the
subsequent intermediate steps of downstream purification. The proteomics dataset was analyzed
to determine which proteins may have an impact on cell culture performance. Several growth-
related proteins were identified. In addition, analysis of physiochemical properties and protein
pathways and networks help understand the relevance of the proteins present in the extracellular
matrix and how they were cleared during downstream purification. The results described here
increase our understanding of both the upstream cell culture and downstream purification
processes, and further demonstrates proteomics as an important analytical tool for the
characterization of the biopharmaceutical process.
5.2 Methods
5.2.1 Purification of Cell Culture Harvest
A CHO cell culture expressing a recombinant monoclonal antibody was harvested from a 400 L
bioreactor and subjected to centrifugation and filtration to isolate the supernatant from the
cellular material. The resulting harvested cell culture fluid (HCCF) was purified by sequential
separation steps including affinity, ion exchange, and phenyl chromatography. After each step,
the pertinent chromatographic fraction was collected on-line into a vessel and aliquoted to an
appropriate tube. The titer of the HCCF sample was measured by protein A chromatography
146
with UV detection. The concentrations of other samples were measured by A280. The protein
concentration was measured by A280. The samples were stored at -70 C until analysis.
5.2.2 Protein Digestion
For each sample, a volume equivalent to 200 µg of protein was dried by SpeedVac and
reconstituted in 10 µL of water, 5 µL of 1 M Tris pH 8.0 buffer, 75 µL of 8 M Guanidine HCl,
and 10 µL of 100 mM DTT. The samples were incubated at 60 °C for 30 minutes. After the
samples reach room temperature, 10 µL of 250 mM iodoacetamide was added to each, and
samples were incubated for 60 min at ambient temperature in the dark. The alkylation reaction
was quenched by adding 5 µL of 1 M DTT to each tube and mixing. A 5X volume of cold
acetone was added to each sample, and they were incubated at – 20 °C for 4 hrs. Samples were
then centrifuged at 10,000 g for 10 min. The supernatant was removed, and the pellet
reconstituted in 20 µL of 8 M Guandine HCl and 180 µL of 50 mM Tris pH 8.0. Ten
micrograms of trypsin was added to each sample, which were incubated for 18 hrs at 37 °C.
5.2.3 HPLC Fractionation
Tryptic digests were fractionated using reversed-phase chromatography. The digest was loaded
onto a Waters XBridge C18 column (2.1 x 150 mm) heated to 45°C at a flow rate of 300 µL/min.
Mobile phase B was acetonitrile. The gradient conditions were set to 2% B for 5 min, then 2% -
10% B over 5 min, followed by 10% - 40% B over 25 minutes. Fractions were collected every 2
min from 13 to 45 min, for a total of 16 fractions per sample. Each fraction was evaporated to
dryness immediately after collection.
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5.2.4 LC/MS
Reversed-phase fractions were evaporated to dryness and reconstituted in 100 µL of 0.1% formic
acid in water. Each fraction was analyzed in duplicate by LC/MS using 40 µL injection
volumes. An Agilent 1200 HPLC was connected to a Thermo Scientific Orbitrap Discovery
mass spectrometer. Separation was achieved using a Waters Acquity HSS T3 C18 column (1.0 x
100 mm) heated to 55°C. Mobile phase A was 0.1% formic acid in water, and mobile phase B
0.1% formic acid in acetonitrile. Peptides were separated with a flow of 70 µL/min at a steady
2% B for 5 min, then 2% - 35% B over 120 min, followed by a wash step at 90% B and re-
equilibration at 2% B for 20 min. The column was temperature controlled at 55°C. The mass
spectrometer was set up to scan MS followed by MS/MS on the top 8 precursor ions. In MS
mode, a mass range of 400 – 1500 m/z was scanned. For MS/MS scans, collisionally induced
dissociation (CID) was used with a collision energy of 35 and an isolation width of 3.0. Each
MS/MS event was comprised of 2 microscans. Dynamic exclusion was enabled with a repeat
count of 2, for a 15 sec window.
5.2.5 Data Analysis
The proteins were identified using Thermo Scientific Proteome Discoverer 1.1. The Sequest
algorithm was used to search MS/MS data against a mouse sequence database downloaded from
Uniprot on 12/07/2009. The dataset from each reactor was processed independently. The
proteins were identified with 10 ppm mass accuracy for precursor ions, and 0.6 Da for product
ions. The MS/MS data was searched with static modifications set to Cys carbamidomethylation.
Dynamic modifications were set to asparagine and glutamine deamidation, and methionine
148
oxidation. The database was searched in reverse to determine false discovery rates. Each
peptide was identified with less than a 5% false discovery rate.
Proteins were classified by biological process and by protein class using PANTHER, an online
tool which uses annotations such as gene ontology to classify genes by category such as
biological process or molecular function [7].
5.3 Results
5.3.1 Identification of Secreted Proteins in Process Intermediate Samples
Samples were obtained from various stages of an IgG process, starting with the harvested
cell culture fluid (HCCF), which is conditioned cell culture media subjected to centrifugation to
remove solid cellular material, followed by filtration. After this step, the material is subjected to
a series of chromatography steps which include Protein A affinity chromatography, anion
exchange chromatography, cation exchange chromatography, and phenyl chromatography. The
anion exchange and phenyl columns were operated in flow-through mode where the IgG was not
captured by the column, and therefore the flow-through fractions contained the IgG and not the
eluate fractions.
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Figure 5.1: Downstream Process Overview
The process utilized four stages of chromatography followed by ultrafiltration / diafiltration. The
column eluates from ProA and cation exchange chromatography steps were collected, as well as
flow-through fractions of the anion-exchange and phenyl steps as these columns were operated
in flow-through mode for purification (no binding of IgG).
Samples from each intermediate step were collected and analyzed using a shotgun
proteomics approach described in section 5.2. Each sample was subjected to trypsin digestion,
reversed-phase HPLC fractionation, and LC/MS analysis using an Orbitrap Discovery mass
spectrometer. Each fraction was analyzed in duplicate. A total of 2671 peptides were identified
in the HCCF sample, which corresponded to 323 proteins. In the subsequent process
intermediate samples, approximately 80 proteins were identified in each sample, from several
hundred peptide IDs.
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Figure 5.2: Protein Identification Summary
In-process samples were analyzed using a two-dimensional LC/MS method. Peptides were
identified by Sequest search against the mouse sequence database (Uniprot Dec 2010). Each
peptide was identified with <5% false discovery rate.
5.3.2 Implications of Secreted CHO Proteins on Cell Culture
As mentioned in section 5.1, secreted proteins can play important roles in cellular processes
associated with disease progression. In this study, the analysis of secreted CHO proteins can
potentially identify proteins which may be involved in various important processes including cell
signaling, cell adhesion, and regulation of cell growth. However, the proteins identified in the
extracellular matrix of the harvested cell culture are not all secreted proteins. Some of the
proteins are intracellular proteins, which have leaked into the extracellular matrix. Proteins can
leak out of cell membranes due to the loss of integrity of the cellular membrane. This will
HCCF ProA AEX CIEX Phenyl
# Proteins ID'd 323 80 82 84 82
# Peptides 2671 473 345 430 503
0
500
1000
1500
2000
2500
3000
151
happen as a result of apoptosis, which causes the cells to break up into smaller vesicles. Another
source of cell leakage is during the centrifugation process, when the cells are subjected to
mechanical shearing forces which can disrupt cell membranes. In order to identify the presence
of extracellular and intracellular proteins in the cell culture harvest, PANTHER
(http://www.pantherdb.org) was used to categorize proteins based on cellular compartment as
shown in Figure 5.3. Fifty-nine of the proteins were classified as extracellular, while the rest
were either membrane or intracellular proteins.
Figure 5.3: Cellular Compartment of Proteins Identified in HCCF
The list of proteins identified in the harvested cell culture fluid (HCCF) was uploaded to
PANTHER (http://www.pantherdb.org) and analyzed by cellular compartment term in Gene
Ontology. The proteins classified as extracellular were counted, and any other proteins not
included in that category were counted as intracellular.
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This result indicates that many intracellular proteins are leaked into the extracellular matrix
during the cell culture process. These intracellular proteins are likely high-abundance proteins
from the cytosol of the cell, or membrane proteins cleaved from the cell surface. The proteins
identified in the HCCF sample were categorized by protein class using PANTHER
(www.pantherdb.org). The two major classes or proteins were transferase (145 proteins) and
oxidoreductases (148 proteins). In addition, 80 proteases were identified in the extracellular
matrix.
Figure 5.4: Classification of Secreted Proteins from Cell Culture Harvest
The list of proteins identified in HCCF were uploaded to PANTHER (www.pantherdb.org) and
classified by protein class.
153
The presence of proteases is important from a biopharmaceutical process perspective since the
presence of residual protease activity can cause clipping of the IgG product [8]. Similarly,
presence of oxidoreductases could disrupt the structure of the IgG by reduction of the interchain
disulfide bonds holding the light chains and heavy chains together. The data in figure 5.4
indicates that significant numbers of these proteins are present in the extracellular matrix.
The top 5 most abundant proteins identified in HCCF based on spectral counts were the
chaperone HSPA8, pyruvate kinase isozyme M1/M2, glyceraldehyde-3-phosphate
dehydrogenase (GAPDH), alpha-enolase, and fatty acid binding protein (FABP4). Information
related to these proteins in shown in Table 5.1.
Table 5.1: Top 5 Most Abundant Proteins in Cell Culture Harvest
The top 5 native CHO proteins identified in HCCF as indicated by number of spectral counts are
shown.
Heat shock cognate 71 kDa is a molecular chaperone involved in protein folding [9]. It
also functions to disassemble clathrin-coated vesicles.
Fatty acid binding protein (FABP4) plays a role in lipid transport across cell membranes
[10]. As described in Chapter 3, there is strong evidence of an unfolded protein response in
CHO cells used in mammalian cell culture. One result of the UPR is increased lipid metabolism,
which is necessary to increase the physical size of the ER in order to achieve higher protein
Gene Description # AAs MW [kDa] calc. pI ΣCoverage Σ# Peptides Function
Hspa8 Heat shock cognate 71 kDa protein 646 70.8 5.52 60.84 183 Protein folding and transport
Pkm2 Pyruvate kinase isozymes M1/M2 531 57.8 7.47 32.02 167 Glycolysis, cell death and tumor proliferation
Gapdh Glyceraldehyde-3-phosphate dehydrogenase 333 35.8 8.25 38.44 148 Carbohydrate metabolism, early secretory pathway
Eno1 Alpha-enolase 434 47.1 6.80 33.64 147 Glycolysis, cell growth control, stimulates IgG production
Fabp4 Fatty acid-binding protein 132 14.6 8.40 38.64 97 Fatty acid uptake, transport, and metabolism
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folding capacity within the cells [11]. Fatty acid binding protein may play a role in this process,
as it appears to be expressed at a high level in the CHO cells.
Three glycolytic enzymes were in the “top 5” list. Alpha-enolase is a glycolytic enzyme
comprising the ninth step of glycolysis, and catalyzes the conversion of 2-phosphoglycerate to
phosphoenolpyruvate (PEP). Glyceraldehydes-3-phosphate dehydrogenase (GAPDH) is an
enzyme involved in the sixth step of glycolysis, which catalyzes the conversion of
glyceraldehydes-3-phosphate to D-glycerate 1,3-bisphophate. In addition, GAPDH has been
shown to initiate apoptosis and have transcription-regulating activity [12, 13].
Pyruvate kinase (PK) is a glycolytic enzyme involved in the conversion of
phophoenolpyruvate (PEP) into pyruvate as tenth step of glycolysis. Pyruvate is then converted
into lactate, where it is excreted or enters the citric acid cycle. In normal tissues the M1 isoform
of PK is observed, while it has been reported that in rapidly dividing cells and especially in
cancer cells, the M2 isoform of pyruvate kinase is the prevalent form [14]. The upregulation of
pyruvate kinase M2 leads to increased rate of glycolysis and increased lactate production [15].
This phenomenon, widely observed in cancer cells, has been named the Warburg effect when
first observed 75 years ago by Otto Warburg [16]. The upregulation of PKM2 was thought to
explain the onset of the Warburg effect in highly proliferating cancer cells. However, a recent
study by Bluemlein et al. used quantitative mass spectrometry to show that PKM1 and PKM2
levels are tissue specific, and do not significantly change between normal and cancerous tissues
[17]. The link between PKM2 and cancer metabolism remains unclear.
The M2 isoform of PK is generated by alternative splicing, resulting in a different
primary structure compared to PKM1 [18]. The BLAST alignment is shown in Figure 5.5.
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Figure 5.5: Sequence Alignment of Pyruvate Kinase M1 and M2
The sequences of both M1 and M2 isoforms of pyruvate kinase (Uniprot accession P52480) were
compared using BLAST sequence alignment.
The difference in primary structure results in the generation of several unique tryptic
peptides which distinguish the two isoforms of PK. Data analysis identified peptides unique to
isoform M2 in the HCCF sample, while peptides unique to isoform M1 were not detected. An
MS/MS spectrum for the peptide T48 which corresponds to residues 423-433 of the M2 isozyme
is shown in Figure 5.6.
PK M2 PK M1
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Figure 5.6: MS/MS Spectrum for Pyruvate Kinase M2 Peptide T48 (CCSGAIIVLTK)
The tandem MS spectrum of pyruvate kinase M2 tryptic peptide T48 is shown. The matching b-
type ions are shown in red, and y-type ions shown in blue.
The presence of high levels of glycolytic enzymes in the HCCF indicate that these
proteins are expressed at high levels to help drive cellular metabolism. In particular, the
presence of PKM2 indicates that these cells may be operating under metabolic conditions similar
to tumor cells. In addition, expression of PKM2 can also produce high levels of lactate, which
can trigger apoptosis in cell culture [19].
The top 5 proteins shown in Table 5.1 are intracellular proteins that have leaked out of
cells with compromised cell membrane integrity. To better understand the function of the
secreted proteins identified in the HCCF, the list of 59 extracellular proteins as identified by
PANTHER was subjected to pathway analysis using IPA (http://www.ingenuity.com). The top
network identified is shown in Figure 5.7.
157
Figure 5.7: Top Scoring Network of Extracellular Proteins in HCCF
Genes correlating to extracellular proteins identified in the HCCF were analyzed for functional
networks using Ingenuity. Gene names indicated in gray are identified in the HCCF, while white
are genes not identified in the sample.
This network has a score of 24, included 15 of the extracellular proteins identified in
HCCF, and is associated with cancer, cell death and proliferation, and cellular movement. Most
of the relationships indicated in this network are indirect through other proteins not identified in
this study, including leptin (LEP), and tumor protein p53 (TP53). This network provides strong
158
evidence that the CHO cells are secreting many proteins that are associated with cell growth and
apoptosis.
Four proteins identified in this network were of particular interest due to their reported
cell-growth regulating properties. Clusterin, as reported in chapter 3, is a protein that is present
in both secreted and nuclear forms. In that study, western blotting analysis of clusterin indicated
that the nuclear form of clusterin is upregulated during the transition of CHO cells into stationary
phase. Here, we see evidence of a secreted anti-apoptotic form of clusterin present in the
extracellular matrix.
Cysteine rich, angiogenic inducer 61 (CYR61) is a secreted, heparin-binding protein.
CYR61 is an immediate-early gene which is activated by growth factor stimulation. It is
expressed transiently throughout the cell cycle through G1, causing the protein to accumulate in
rapidly growing cells despite a short half-life [20]. One of the most important functions of
CYR61 is the promotion of cell adhesion. In cell culture, CYR61 immobilized to a solid surface
causes cell adhesion through integrins and heparan sulfate proteoglycans (HSPGs) [21]. CYR61
also has roles in cell growth. Babic et al. showed that expression of CYR61 in adenocarcinoma
cell lines promoted angiogenesis and tumor proliferation [22]. In addition, studies inhibiting
expression of CYR61 by siRNA in ovarian cancer cells resulted in decreased proliferation and
increased apoptosis [23].
The Fas receptor (Fas) is a death receptor on the surface of cells that triggers caspase-
dependent apoptosis [24]. It is also known as CD95, Apo-1, and tumor necrosis factor receptor
superfamily, member 6 (TNFRSf6). Upon binding with FAS ligand (FasL), the death-inducing
159
signaling complex is formed, which leads to activation of caspase-8. The protein is observed in a
secreted form, which may be due to cleavage from the cell membrane.
Translationally controlled tumor protein (TCTP) is widely expressed in many tissues and
is present both intra- and extracellular environments. It has been implicated in important cellular
processes, including cell growth, cell cycle progression, and malignant transformation and in the
protection of cells against various stress conditions and apoptosis [25, 26]. It binds several
growth-regulating proteins. Liu et al. showed that TCTP interacts with the anti-apoptotic protein
Mcl-1, modulating the function of the protein by protecting it from degradation [27].
Overexpression of TCTP in lung carcinoma cells was shown to reverse p53 mediated apoptosis,
by destabilization of p53 upon TCTP binding [28].
Screening of growth and apoptosis related proteins in the extracellular matrix could
provide valuable information about cell culture performance. The relative abundance of these
proteins in the extracellular matrix may correlate with cell growth, or may be early indicators of
apoptosis in cell culture. In addition, these proteins may be valuable makers for targeted
engineering to manipulate cell growth and apoptosis.
5.3.3 Implications of Secreted CHO Proteins on Downstream Purification
Proteomics analysis of CHO host cell proteins from intermediate steps of the purification process
was used to assess host cell protein clearance. As shown in Figure 5.1, four modes of
chromatography were used to purify an IgG from HCCF. The proteins identified in the
intermediate samples are shown in Table 5.2. These co-purified proteins are defined as proteins
160
that were observed in any of the four intermediate samples. Many of the proteins identified in
later intermediate process samples were contaminants such as keratin. These proteins were
excluded from the list of co-purified proteins shown in Table 5.2. In order to understand the
physiochemical properties of the co-purified proteins, distributions of pI, molecular weight, and
Grand Relative Average Hydropathicity (GRAVY) were determined based on the spectral counts
measured for each protein.
Table 5.2: List of Co-Purified Host Cell Proteins
The proteins shown in this list are observed in at least one of the intermediate samples. Spectral
counts for each protein are shown from each of the four intermediate samples. pI and MW
values were obtained from Sequest, while GRAVY values were obtained from the Gravy
Calculator (http://www.gravy-calculator.de/).
Description
pI MW GRAVY Affinity Anion Cation Phenyl
Plexin D1 6.8 193 -0.19 11 11 4 17
Progesterone immunomodulatory binding factor 1 9.1 25 -0.89 11 2 10 11
Kinesin family member 24 7.2 135 -0.68 6 3 12 6
Thrombospondin 3 (Fragment) 8.0 11 0.22 5 15 7 6
Potassium voltage-gated channel, subfamily H member 8 4.6 10 -0.22 3 2 2 6
GRIP and coiled-coil domain containing 2 6.5 48 -0.85 7 3 6 5
Myotubularin related protein 11 9.4 30 -0.38 4 3 7 4
Ras-related protein Rab-33A (Small GTP-binding protein S10) 7.9 27 -0.22 7 3 6 4
IQ motif containing GTPase activating protein 3 8.7 84 -0.35 2 2 4 4
Fat 1 cadherin (Fragment) 5.0 506 -0.28 21 7 8 3
BAI1-associated protein 2 8.8 32 -0.62 2 3 4 3
NKR-P1E 6.3 20 -0.27 0 2 3 3
BTB/POZ domain-containing adapter for CUL3-mediated RhoA degradation protein 3 6.3 36 -0.52 1 0 0 2
Lymphocyte transmembrane adapter 1 5.0 45 -0.61 0 0 0 2
LIM and calponin homology domains 1 5.5 109 -0.92 0 2 8 2
ArfGAP with SH3 domain, ankyrin repeat and PH domain 2 5.6 62 -0.48 2 2 2 2
Cholinergic receptor, nicotinic, alpha polypeptide 7, isoform CRA_b 8.5 17 -0.14 2 2 2 2
Zinc finger RNA binding protein 9.1 114 -0.59 2 1 2 2
KRAB-zinc finger protein 73 (Fragment) 8.5 14 -0.18 1 0 1 2
Phosphatidylinositol-4-phosphate 3-kinase C2 domain-containing subunit alpha 8.0 191 -0.30 0 0 0 1
U3 small nucleolar RNA-associated protein 14 homolog B 8.9 86 -0.84 0 0 0 1
Exocyst complex component 4 7.0 65 -0.28 0 0 0 1
Uncharacterized protein KIAA1737 9.4 46 -0.49 0 0 0 1
Opsin 3 9.7 20 0.17 0 0 4 1
Novel protein (9030409G11Rik) (Fragment) 7.1 79 -0.72 2 0 3 1
Ran-binding protein 6 (RanBP6) 5.0 125 -0.08 0 0 3 1
MAPK-interacting and spindle-stabilizing protein 9.5 28 -0.71 3 1 3 1
MKIAA1405 protein (Fragment) 9.3 33 -0.96 0 1 3 1
Oral-facial-digital syndrome 1 protein homolog 5.8 117 -0.89 1 3 2 1
Solute carrier family 13 (Sodium-dependent dicarboxylate transporter), member 3 8.0 61 0.47 5 0 1 1
Tumor necrosis factor receptor superfamily member 6 8.0 37 -0.77 4 2 1 1
Aurora kinase A 9.4 45 -0.66 0 1 1 1
Spectral CountsPhysiochemical Properties
161
The top two proteins present in the final phenyl eluate step are plexin D1 and progesterone
immunomodulatory binding factor 1. The other co-purified proteins are detected at lower levels
compared to these proteins based on their spectral count values. Plexin D1 is a secreted
signaling protein associated with axonal growth and development, and has been linked to tumor
invasiveness [29]. Progesterone immunomodulatory binding factor 1 is synthesized after binding
of progesterone to binding receptors during pregnancy. It has multiple functions including
immunosuppression by regulation of cytokine synthesis, NK activity, and arachidonic acid
metabolism, all of which play a role in maintenance of pregnancy [30]. Full-length PIBF is
predominantly present in the nucleus, however a shorter spliced variant is secreted outside of the
cell [31]. It has been shown that PIBF expression is also expressed by tumor cells [32]. These
two proteins represent the most highly abundant in this particular biopharmaceutical process.
Their association with cancer further strengthens the links between CHO cell cultures and cancer
cells described in previous chapters.
To assess the physiochemical properties of the proteins co-purified in each intermediate step, the
weighted average isoelectric point, molecular weight and GRAVY values were calculated for
each sample.
Table 5.3: Average Physiochemical Values for Co-Purified Proteins
The average pI, molecular weight, and GRAVY values for the proteins identified in each
intermediate sample were determined by calculating weighted averages based on the number of
spectral counts for each identified protein.
Affinity Anion Cation Phenyl
pI 7.18 7.27 7.52 7.41
MW 157.16 111.73 96.18 87.74
GRAVY -0.40 -0.32 -0.48 -0.42
162
These results indicate changes in average pI, molecular weight, and GRAVY values of co-
purified host cell proteins as they go through several purification stages. A trend is observed in
decreasing molecular weight of co-purified proteins throughput the purification process. The
average molecular weight of the affinity chromatography eluate is 157 kDa, which decreases to
an average value of 88 kDa in the phenyl eluate. This indicates that higher molecular weight
proteins are cleared more efficiently during purification compared to lower molecular weight
proteins.
A small increase in pI of co-purified proteins across the purification steps is observed, increasing
from 7.27 in the affinity chromatography eluate to 7.41 in the phenyl eluate. This may be
indicative of the selectivity of ion exchange chromatography, which will tend to co-purify
proteins of similar pI as the target analyte. In this case, the IgG has a theoretical pI of 8.61,
which would explain the selectivity of higher pI proteins during purification.
The GRAVY values of co-purified proteins do not significantly change throughput purification.
This indicates that relative hydrophobicity may not play a significant role in protein clearance in
this particular process.
5.2 Conclusion
The analysis of secreted proteins generated from CHO cells during the cell culture process in the
conditioned media as well as in intermediate processing steps provides information critical to the
performance of the biopharmaceutical process.
163
These results indicate that many intracellular proteins involved in glycolysis are released into the
conditioned media during the process. These include several glycolytic enzymes, including
PKM2 which has been associated with the Warburg effect observed in tumor cells. These
enzymes play a key role in primary metabolism which drives CHO cell growth.
In addition, several extracellular proteins with growth-regulating properties were identified.
These include Fas, a critical receptor for caspase-dependent apoptosis, CYR61, a secreted
signaling protein associated with tumor proliferation, and TCTP, a tumor-related protein with
anti-apoptotic properties. These proteins are of interest due to their potential use as diagnostic
markers of cell culture, as well as cellular engineering targets to enhance CHO cell growth
properties.
Analysis of co-purified proteins in various intermediate processing steps of purification indicates
changes in the composition of proteins in both pI and molecular weight. This particular
purification process seems to clear high molecular weight proteins and low pI proteins most
effectively. The most abundant co-purified proteins identified in this study were plexin D1 and
progesterone immunomodulatory binding factor 1 (PIBF). These proteins are candidates for
targeted quantitation using either immunoassays or MRM-type mass spectrometry analysis to
monitor the clearance of these proteins quantitatively during different purification steps. This
approach could serve as a supplement to the host cell protein information provided by ELISA-
type assays, and could be useful in cases where it is difficult to quantitate host cell protein levels
using an immunoassay due to matrix interferences or lack of specificity.
164
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CONCLUDING REMARKS
The future growth of the biopharmaceutical industry is dependent upon robust processes
generating drugs at high yields and of high quality. In order to push the envelope of what a
biopharmaceutical process can deliver, “omics” techniques will play a critical role in
understanding the cell biology and identifying biomarkers related to productivity and product
quality. As described in chapters 2 and 3, proteomics technology can identify differentially
expressed proteins associated with cell growth and productivity from CHO cell cultures. From
these studies, several general observations can be made about CHO cell cultures. In both cases,
proteins were identified which are associated with cellular response to ER stress, also referred to
as the unfolded protein response. This biological process plays a key role in mammalian cell
cultures, as the cells are engineered to express target genes at high levels and the cells respond to
high level of intracellular non-processed proteins. Modification of the pathways related to the
UPR is one area of interest from a cellular engineering point of view, as this process can directly
affect cellular productivity.
Another observation from chapter 3 is the differential expression of cancer-related proteins
throughout cell culture. These proteins, such as clusterin and transglutaminase-2, likely play
roles in the regulation of cell growth, and along with other published studies indicate similarity
between tumor cells and mammalian cells used in cell culture. These similarities may be due to
the adaptation and selection of the cells towards high cell growth. The processes driving tumor
proliferation may be another possible area to target for engineering of faster growing cell
cultures.
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The chromosomal structure of CHO cells is another area where further research may unlock
some clues to the biology driving cell culture performance. As described in chapter 4, the CHO
chromosomes are commonly subject to various genetic mutations, including deletion and
insertion of large segments of DNA, resulting in different chromosomal structures even between
different CHO subclones. Any significant genetic mutations affecting cell growth are likely
selected out early on during process development. However, the effect of these mutations on cell
culture performance is seemingly unexplored. As described in chapter 4, clusters of co-
expressed genes involved in regulation of cell growth potentially exist in CHO, which makes the
understanding of chromosomal structure even more important.
Proteomics can also play a role in understanding secreted proteins in cell culture media, and how
those proteins are cleared during the purification process. More stringent requirements by
regulatory agencies to demonstrate protein clearance has resulted in the need for more specific
and sensitive detection methods. At this stage, proteomics can play a supporting role by
identifying the relevant proteins present in intermediate steps of purification. Mass spectrometry
may also play a role in quantitating these proteins in different samples, using sensitive methods
such as MRM. The applicability of such approaches for monitoring of host cell protein clearance
compared to the standard ELISA type methods will need to be evaluated in the future.
The constant advancement and maturation of proteomics technology will continue to enable the
discovery of insights into the biological processes driving the biopharmaceutical process. This
information, combined with information from other “omics” areas including genomics and
metabolomics, will facilitate the engineering of better biopharmaceutical processes in the future.
Recombinant proteins will be produced in larger quantities, and with greater control over the
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product quality profile. In the end, this will potentially lead to lower costs, greater efficacy, and
greater safety of the drugs for patients and will represent a major push forward for the
biopharmaceutical industry.