LESSONS FROM THE HAMSTER:
CHARACTERIZATION OF CHINESE HAMSTER OVARY (CHO) CELL LINES AND CRICETULUS GRISEUS TISSUES VIA PROTEOMICS AND GLYCOPROTEOMICS
by Kelley Marie Heffner
A dissertation submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy
Baltimore, Maryland
August, 2017
© 2017 Kelley Marie Heffner All Rights Reserved
Abstract
Chinese hamster ovary (CHO) cells were isolated in the late 1950’s and have
been the workhorse of biotherapeutics production for decades. While previous
efforts compared CHO cell lines by proteomics, research into the original Chinese
hamster (Cricetulus griseus) host has not been conducted. Thus, we sought to
understand proteomic differences across CHO-S and CHO DG44 cell lines in relation
to brain, heart, kidney, liver, lung, ovary, and spleen tissues. As glycosylation is
critical for recombinant protein quality, we additionally performed a
glycoproteomics and sialoproteomics analysis of wild-type and mutant CHO cell
lines that differ in glycosylation capacity.
First, wild-type CHO was compared with tunicamycin-treated CHO and
Lec9.4a cells, a mutant CHO cell line which shows 50% of wild-type glycosylation
levels. A total of 381 glycoproteins were identified, including heavily-glycosylated
membrane proteins and transporters. Proteins related to glycosylation
downregulated in Lec9.4a include alpha-(1,3)-fucosyltransferase and dolichyl- diphosphooligosaccharide-protein glycosyltransferase subunit 1. Next, wild-type
Pro-5 CHO was compared with Lec2 cells, which have a mutation in CMP-sialic acid transporter that reduces sialylation. A total of 272 sialylated proteins were identified. Downregulated sialoproteins, including dolichyl- diphosphooligosaccharide-protein glycosyltransferase subunit STT3A and beta-1,4- galactosyltransferase 3, detect glycosylation defects.
ii Next, a label-free quantitative proteomics analysis of CHO-S and CHO DG44
cell lines and liver and ovary tissue identified 11801 proteins, including 9359
proteins specifically in the cell lines, representing a 56% increase over previous
work. Additionally, 6663 proteins were identified across liver and ovary tissues
providing the first Chinese hamster tissue proteome. Overall, both gene ontology
and KEGG pathway analysis revealed enrichment of cell cycle activity in cells. In
contrast, upregulated molecular functions in tissue include glycosylation and lipid
transport.
Finally, we used labeled proteomics to compare CHO-S and CHO DG44 cell
lines with brain, heart, kidney, liver, lung, ovary, and spleen tissues to identify 8464
proteins. After protein expression and functional analyses, we combined the
proteomics with transcriptomic data obtained by RNAseq in order to correlate
mRNA and protein expression. Over 65% of genes show agreement between
transcriptome and proteome. The remaining genes were categorized as stable or
unstable and related to metabolic pathways. In conclusion, this large-scale
proteomics analysis delineates specific changes across cell lines and tissues, which
can help explain tissue function and the adaptations cells incur as biotherapeutics
production hosts.
Advisor: Dr. Michael J. Betenbaugh
Readers: Dr. Marc Donohue, Dr. Sharon Krag
iii Preface
Proteomics serves an important role in the elucidation of cell metabolism, with various applications for the biotechnology industry. Identification and quantification of thousands of cellular proteins, as well as their functional pathway interactions, can aid efforts to optimize genetic engineering and bioprocess development. In this work, we conducted label-free, labeled, and glyco- proteomics analyses of Chinese hamster ovary (CHO) cell lines and Cricetulus griseus tissues to expand knowledge of biotech’s top recombinant protein production host.
Chapter 1 provides an introduction to the field of proteomics and its increasing importance in the biotechnology industry. Applications of proteomics, such as media formulation, bioprocess development, and cell line engineering, are highlighted. A summary of developments for analysis, such as mass spectrometry and database development are also discussed.
Chapter 2 describes a glycoproteomics effort to identify the glycosylated proteins in wild-type CHO and mutant cell lines that lack the capability to glycosylate or sialylate proteins. An enrichment method coupled to identification by mass spectrometry was developed to identify proteins known to be glycosylated and sialylated. We used bioinformatics to relate changes in glycoprotein abundance to cell pathways in order to characterize metabolism of the cell lines.
In Chapter 3, label-free proteomics was used to characterize CHO cell lines at both exponential and stationary phases and Chinese hamster tissues. This represents the first liver and ovary proteomes developed for Cricetulus griseus. After
iv comparing the overall differences in protein abundance, both gene ontology and
KEGG pathway analyses are presented to show functional significance.
Chapter 4 describes a larger effort to map the Chinese hamster tissue
proteome. Along with CHO cell lines, we analyzed brain, heart, kidney, liver, lung,
ovary, and spleen tissue from the hamster using a labeled proteomics approach.
Overall protein intensity differences were compared between each permutation of
samples and we developed a high-abundance analysis for each tissue type. Next, the
outliers for each comparison were functionally analyzed using gene ontology and
ingenuity pathway analysis to identify up- and down-regulated pathways. Finally, a comparison between mRNA and protein expression was used from transcriptomics and proteomics combination.
Finally, Chapter 5 concludes the dissertation by drawing conclusions from the work and suggesting applications and next steps of research. We specifically summarize the proteins identified using label-free and labeled proteomics, as well as sialoproteomics and glycoproteomics. The sum total of Chinese hamster and CHO cell lines proteins is the largest to date and will be publicly available to aid further research by the CHO community.
v Acknowledgements
The completion of a doctoral program requires the support of many people
over the course of many years. Primarily, I wish to thank my advisor Dr. Michael J.
Betenbaugh for his guidance. At the start of the program, we had vague and general
ideas about how to improve our understanding of CHO cell metabolism. Over the
course of my work, we became interested in how to apply multiple ‘omics strategies
to identify novel insights about CHO. Dr. Betenbaugh has helped me to expand the
way I approach research by not limiting the possibilities. It is advantageous to try
addressing as many problems as possible and then the combination of work can
string together a story. I was also fortunate to use Dr. Betenbaugh’s extensive
relationship with the biotechnology industry to attend conferences and participate
in off-site industrial internships. Through the internships, I gained a real-world understanding of how research can be applied to improve bioprocess development.
Next, I am grateful for the opportunity to learn from a former Betenbaugh
Lab member who now heads research and development of biologics in Turkey, Dr.
Deniz Baycin Hizal. Deniz established the original CHO proteome during her time at
Johns Hopkins University and this laid the groundwork for my proteomics and
glycoproteomics analyses of CHO cell lines and hamster tissues. Her attention to
detail has instilled in me a desire to ask more questions and think analytically about
how to design and perform bioinformatics analyses.
In addition to Deniz, I also thank Dr. George Yerganian, Dr. Raghothama
Chaerkady, Dr. Robert O’Meally, Dr. Robert Cole, Dr. Amit Kumar, and Dr. Joseph
vi Priola for guidance related to ‘omics. Specifically, Dr. Yerganian shared invaluable
insight into the development of CHO cell lines from hamster tissue and provided the
female hamsters for tissue harvest. I thank Nadine Forbes-McBean and Dr. Cory
Brayton for guidance in tissue preparation. Raghothoma Chaerkady and Robert
O’Meally ran our mass spectrometry at the core facility of Dr. Cole.
Numerous people at Johns Hopkins University have also aided in my
progression as a graduate student. Members of the Betenbaugh Lab, such as Dr.
Andrew Chung, Dr. Yue Zhang, Dr. Bojiao Yin, Jimmy Kirsch, Chien-Ting Li, and
Qiong Wang, were helpful in sharing their experiences in the lab and providing
feedback about my work. I also had the pleasure of working with visiting, Master’s,
and undergraduate students.
Finally, my family has been an unwavering source of support along the
graduate school process. I look up to my parents Cheryl and Michael with the
greatest respect. They offer me guidance and set an example of what it means to
show unconditional love. I also am grateful for the support of my younger sister
Erica and have been thrilled to spend time close to her over the course of my
research. She is a strong-willed and independent woman that I hope to stay best
friends with for many years and she finally knows the difference between a
Bachelors, Masters, and Doctoral degree. In addition to my family, I value the
support of my friends as I have grown immensely during graduate school.
vii Table of Contents
Title Page i
Abstract ii
Preface iv
Acknowledgements vi
List of Tables ix
List of Figures ix
Chapter 1: Introduction to Proteomics 1
Chapter 2: Glycoproteomics Analysis of Chinese Hamster Ovary Cell Lines 30
Chapter 3: Proteomics Analysis of Chinese Hamster Cell Lines and Tissues 66
Chapter 4: Comparative Proteomics of Chinese Hamster Cell Lines and Tissues 106
Chapter 5: Conclusions 145
References 149
Curriculum Vitae 163
viii List of Tables
Table 1.1: Top Selling Biologics of 2012 24 Table 1.2: Summary of CHO Proteomics for Bioprocess Development 25
Table 2.1: Experimental Conditions 60 Table 3.1: Number of Peptides and Proteins 98
Table 4.1: TMT Doublet Information 137 Table 4.2: Top 10 Most Enriched Biological Processes for CHO-S and Tissues 138
Table 4.3: Top 10 Most Enriched Biological Processes for CHO DG44 and Tissues 139
List of Figures
Figure 1.1: Overview of Proteomics Experiment 28
Figure 1.2: Overview of Comparative Proteomics 29 Figure 2.1: Overview of Experimental Design 61
Figure 2.2: Distribution of Protein Intensity 62 Figure 2.3: Protein Intensity Heat Maps 63
Figure 2.4: Lec2 KEGG Pathway N-glycan Biosynthesis 64 Figure 2.5: Pathway Analysis Maps 65
Figure 3.1: Experimental Workflow 99
Figure 3.2: Proteomic Comparison of Samples 100 Figure 3.3: Protein Intensity Comparison of Samples 101
Figure 3.4: The Top 10 Enriched Biological Processes 102 Figure 3.5: K-Means Clustering of Molecular Functions 103
Figure 3.6: Cellular Components Enriched in Tissues 104 Figure 3.7: Pathway Map of Cell Cycle 105
ix Figure 4.1: Overview of Experimental Design and Workflow 140
Figure 4.2: Protein Intensity Analysis 141 Figure 4.3: Overall Distribution of Protein Intensity in Hamster Tissues 142
Figure 4.4: Highly Expressed Proteins 143 Figure 4.5: Comparison of Proteome and Transcriptome 144
x Chapter 1: Introduction to Proteomics
Abbreviations
CHO – Chinese hamster ovary, mAb – monoclonal antibody, MS – mass spectrometry,
DTT – dithiothreitol, CHAPS – 3-[(3-Cholamidopropyl)dimethylammonio]-1- propanesulfonate, SDS – sodium dodecyl sulfate, FASP – filter aided sample preparation, HCP – host cell protein, SILAC – stable isotope labeling with amino acids in culture, iTRAQ – isobaric tags for relative and absolute quantification, TMT
– tandem mass tags, MALDI TOF – matrix-assisted laser desorption/ionization time of flight, AEC – adenylate energy charge, VCP – valosin-containing protein
1.1 Summary
The genetic sequencing of Chinese hamster ovary (CHO) cells initiated the
systems biology era for biotechnology applications. In addition to genomics, now the
critical ‘omics data sets also include proteomics, transcriptomics, and metabolomics.
In recent years, the use of proteomics in cell lines for recombinant protein
production has increased significantly because proteomics is the study of all
proteins in an organism and their changes over time, and thus it is important for
applications in cell culture such as bioprocess development. Specifically,
identification of proteins that affect cell culture processes can aid efforts in cell line
engineering to improve growth or productivity, delay onset of apoptosis, or utilize
nutrients efficiently. Optimizations of sample preparations and database
development have improved the quantity and accuracy of identified CHO proteins.
1 The applications are widespread and suggest further applications of proteomics or
combined ‘omics experiments in the future.
1.2 CHO Hosts for Biotherapeutics Production
CHO cells are the desired recombinant protein production hosts. Their
growth rate in cell culture is scalable for high-density production of biotherapeutics.
As mammalian cells, CHO cells provide similarity in glycosylation pathways that allow for human biotherapeutic compatibility. In 2012, five out of the top five best- selling biologics were produced in CHO cells as shown in table 1.2 [1]. The top biotherapeutic is Humira, produced using the CHO expression system for the treatment of rheumatoid arthritis [1]. Characteristics such as human-compatibility
and manufacturing scalability help explain CHO cells’ widespread dominating use in
biotechnology applications.
Improved understanding of CHO cell physiology resulted from the recently
completed genome sequence [2] [3]. From this information, a variety of methods
have been used to quantify the genome, transcriptome, proteome, and metabolome.
These data sets offer new insights into cell physiology. Baycin-Hizal et al. completed
the proteome of the CHO cell line and included information on intracellular, secreted,
and glyco- proteins [4]. This study complemented the results from the CHO genome
[3] using a codon frequency analysis; the differences between CHO and human cells
may aid development of biocompatible therapeutics [4]. Additionally, this study
combined proteomic and transcriptomic data to analyze pathway changes; for
example, pathways such protein processing and apoptosis are enriched at the
protein level whereas steroid hormone and glycosphingolipid metabolism are
2 depleted at the protein level [4]. The complete CHO proteome offers new insights into bioprocess development by increasing knowledge of the most-widely used production host.
Proteins have diverse functions in the cell and are involved in growth, signaling, regulation, and metabolism. Their rapidly changing levels provide important information about subtle changes in the cell that may not be detected by differences in the transcriptome or genome. A variety of methods are used to generate large, complex data sets, which require processing and analysis to reveal useful information about cellular phenotype. Quantification of protein levels provides clearer understanding of cell physiology and can lead to development of improved cell culture for biotechnology applications.
Production of monoclonal antibodies (mAbs) for therapeutic use requires development of a scalable and consistent bioprocess, ensuring high yield and purity of the biotherapeutic. Proteomics can also be used during development in order to identify proteins that affect cell culture growth, apoptosis, recombinant protein productivity, and product quality. This information can suggest methods to improve the bioprocess through cell line engineering and rational media formulation, for example. Recently, combined ‘omics approaches were utilized. These approaches provide useful insight because direct 1:1 correlation between approaches rarely exists. The combination improves the reliability and accuracy of the results from either approach alone.
This chapter highlights how proteomics can be used for cell culture applications. Proteomics can provide identification and quantification of thousands
3 of cellular proteins and can be used to increase understanding of production hosts
such as CHO cells. In recent years, the number of published proteomic data sets has
expanded due to the availability of the CHO genome and new techniques have been
introduced to increase protein identification and accuracy.
The use of proteomics in cell culture applications is widespread. Increasing
application of proteomics for cell culture requires advanced methods, such as the
optimization of sample preparation, digestion, labeling and mass spectrometry (MS).
Proteomics is increasingly applied to cell culture in order to understand cell lines
and aid in cell line engineering or process development efforts to increase cell
growth, increase recombinant protein productivity, or maintain product quality, for
example. In the following sections, proteomics will be examined in great detail with
respect to the biotechnology industry.
1.3 Proteomics Method Optimization
Following the initial proteomics experiments in CHO cells, there have been widespread refinements in methods in order to improve the recovery of cell proteins and aid in their identification. The ability to identify increasing numbers of proteins with high accuracy is dependent on optimized sample preparation methods.
Proteomics methods include extraction, reduction, alkylation, digestion, and peptide
fractionation prior to quantification with MS. After cell culture extracts are obtained, the cells are lysed are extracted. Following the reduction, alkylation, digestion, and fractionation, the peptides can be individually identified. This requires use of software and databases to match the mass spectra to specific sequences and thus proteins. An overview of the proteomic workflow is shown in figure 1.1. In recent
4 years, there have been numerous developments to improve proteomic methods
including the optimization of sample preparation, digestion, optional labeling, and
MS.
1.3.1 Sample Preparation
Prior to advancements in data analysis, it was necessary to improve the
preparation of high-quality peptide samples by sample preparation using different
extraction and digestion techniques.
In order to improve protein recovery from CHO cells for two-dimensional
(2D) gel electrophoresis, the concentration of solubilizers such as urea, dithiothreitol (DTT), 3-((3-cholamidopropyl) dimethylammonio)-1- propanesulfonate (CHAPS), and sodium dodecyl sulfate (SDS) was optimized [5].
Maintaining solubility enables recovery of proteins with diverse physical and chemical properties. The optimum solubilizing factors for CHO cells were studied using a design of experiments approach; DTT, urea-DTT cross-interaction, and urea-
CHAPS cross-interaction were positive factors that improved protein recovery [5]. A final solution composition of 8M urea, at least 32.5mM DTT, and at least 2% CHAPS was selected for CHO cell lysates [5].
Besides the extraction efficiency, digestion efficiency is another criterion for increasing correct protein identifications. Two methods of in-gel and in-solution digestion methods can have separate advantages. In-gel digestion involves
solubilizing proteins with detergent and separating proteins by gel electrophoresis.
Separated proteins are then digested from the gel and quantified by MS. In-solution
digestion involves extracting proteins with strong reagents and digesting in the
5 solution. There are advantages and disadvantages to both methods. In-gel digestion
can protect against impurities but the protein recovery is poor in comparison to in-
solution digestion. On the other hand, in-solution digestion is efficient to implement but there is greater risk of impurities or incomplete solubilization. SDS is one of the
major detergents, which can be used for full extraction of the cell lysates including
insoluble membrane proteins; however, SDS has to be removed prior to MS analysis.
Filter-aided sample preparation (FASP) was developed to remove SDS prior to
digestion and MS [6]. This method improvement allows for more complete coverage of the proteome. The recent development of FASP was used for the CHO proteome prior to trypsin digestion in order to maximize the protein recovery [4].
Both in-gel and in-solution digestions continue to be used for biotechnology applications and there are various examples. In-gel digestion was used to identify important proteins for cell line engineering efforts [7] [8] [9]. Another application of in-gel digestion aided in quantifying differences between cell lines [10] [11]. In-gel digestion was used to identify phosphorylated proteins from CHO-K1 cell culture
[12] and was also used to elucidate the proteome of the CHO DG44 cell line [13].
In-solution digestion overcomes some of the limitations of in-gel digestion such as difficulties in separating proteins with low molecular weight, high molecular weight, or hydrophobic properties. Meleady used in-solution digestion to profile protein levels between cell lines with or without miR-7 overexpression [14]. In- solution digestion was also used to identify secreted proteins from the CHO-S and
CHO DG44 cell lines [15].
6 In other experiments, a combination of both in-gel and in-solution digestions was used [4]. Both in-gel and in-solution digestions were also used recently to analyze the CHO proteome [14]. Digestion methods vary but are important for preparing peptide samples for MS. An optional step that is now widely used for comparative proteomics is labeling of digested peptides.
Following digestion, fractionation can be used to increase protein identification. In one example, basic reversed phase liquid chromatography was used to separate samples into 96 fractions that were combined into 48 fractions for
MS analysis [4]. Such method refinements have increased the quantity of identified proteins.
1.3.2 Targeted Proteomics
In some applications, it is useful to target the identification of proteins related to a specific organelle or compare intracellular and extracellular compartments. Two recent examples include identification of the secretome and mitotic spindle, respectively.
The secretome consists of host cell proteins (HCPs) that must be removed prior to formulation of the final drug product. Identification of secreted proteins is limited by their low abundance. Design of experiments was used to optimize sample preparation methods to increase protein recovery for HCP identification [16].
Precipitation parameters such as the precipitant chemical, precipitant concentration, and incubation length, were evaluated for both gel-based and shotgun proteomics
[16]. The results were used to optimize a method for identification of HCPs, which differ in physical and chemical properties, as well as their physiological function;
7 this method used methanol precipitation for one hour to identify 178 HCPs, such as
clusterin, beta-actin, glyceraldehyde-3-phosphate dehydrogenase, and
immunoglobulin superfamily member 8 [16]. Optimization of sample preparation is critical for the secretome to maximize the recovery of low abundance proteins.
The mitotic spindle proteins were identified in CHO cells [17]. Cell division is
an important event to study, as it relates to increasing the growth of cell lines
expressing recombinant protein. Isolation of the spindle aids in identification of
factors affecting division and thus growth. After synchronizing all cells in metaphase,
over 1100 proteins were identified, of which 239 proteins were cell division factors
and 841 proteins were involved in early stages of division [17]. Of the proteome, 11
percent of proteins localized to the membrane, 7% were associated with
microtubules, and 3% were associated with actin [17]. Identification of cell division
factors can aid bioprocess development due to their importance in cell growth.
1.3.3 Protein Labeling
Labeling strategies are used in proteomics approaches to provide the relative
quantification in addition to the identification of proteins. Recent methods for
labeling proteins include stable isotope labeling of amino acids in culture (SILAC),
isobaric tags for relative and absolute quantification (iTRAQ), and tandem mass tags
(TMT). Various labeling methods as well as label-free methods are used for relative
quantification.
In SILAC, proteins are labeled when amino acids from the culture medium
are incorporated into the cell. Incorporation of amino acid labeling into proteins can
be detected by MS. SILAC experiments involve adapting cells to labeled media, cell
8 growth, and protein identification by MS and data analysis [18]. Both in-gel and in-
solution digestion can be used to extract proteins prior to MS. SILAC labeling
experiments provide information on protein dynamics because they quantitate the
incorporation of labeled amino acids. This method introduces experimental errors,
such as incomplete amino acid isotope incorporation and sample mixing errors [19].
To reduce error, label-swap replication experiments were used to average ratios of
individual replicates and validated with a triplet experiment [19]. The results
corrected for incomplete labeling and arginine to proline conversion, thus obtaining
consistent experimental ratios [19].
An alternative labeling method is iTRAQ, which is used to label proteins in a cell extract before proteins are run in the MS. In this method, Tag-specific reporter ions represent relative protein levels. The benefit of this method is that cells do not require adaptation to labeled medium because the labeling is performed on protein extracts. It is important to equalize masses of iTRAQ reagent added across samples because the quantification is relative.
A comparison of iTRAQ versus nonisobaric labeling (mTRAQ) revealed that iTRAQ labeling identifies over double the total number of proteins and approximately triple the phosphopeptides as compared to mTRAQ [20].
Additionally, kinase identification was significantly increased by using iTRAQ, thus aiding knowledge of protein-protein interactions involved in recombinant protein production [20]. All of these proteomics experiments can elucidate new information about CHO cells that leads to advancements in their use as biopharmaceutical production hosts.
9 1.3.4 MS
High-quality digested peptides are injected for MS. Components of the MS
include the source, which produces gas phase ions from the sample; mass analyzer,
which resolves the ions based on mass to charge ratio; and detector, which detects
ions that have been resolved by the analyzer. Both tandem MS and matrix-assisted
laser desorption/ionization time of flight (MALDI TOF MS) have been used to
identify and quantify proteins in cell lines with different growth or productivity
characteristics [7-9, 11, 12, 21].
Following MS, peaks are identified and attributed to specific proteins. Proper identification requires the use of search engines and databases. Examples of search engines include TagRecon and MyriMatch [4]. Examples of databases include NCBI
[7, 8, 12, 13], Swiss-Prot [9, 10, 22], OWL [9], and PANTHER [23]. These databases
increase confidence of successful protein identifications.
1.4 Proteomics for Bioprocess Development
Bioprocess development involves the steps necessary to produce high
quantity and quality of recombinant proteins such as mAbs. A major bottleneck in
the manufacture of biotherapeutics is efficient production. There is widespread
need for cell lines stably expressing recombinant protein at high yield and quality.
Recently, proteomics has been used to identify proteins that play an important role
in recombinant protein production for bioprocess optimization. Comparison of high
and low expressers can provide significant insights and understanding to the
recombinant protein production process, which aids developments for increasing
cell growth and optimizing the medium formulation. A labeling coupled comparative
10 proteomics strategy, which can be applied for comparing high and low expressers is
shown in figure 1.2.
Recently, comparative proteomics has been used in many studies to gain
insights for increasing the productivity of mAbs. Table 1.2 summarizes the recent
publications in this area.
1.4.1 Increase Cell Growth Rate and Viable Cell Density
A critical bioprocess development goal is to maximize growth rate in order to increase the produced drug quantity and decrease the cost of biotherapeutics. A combined approach using transcriptomics and proteomics was enacted to identify candidates for a high growth phenotype [24]. The proteomics results showed that
285 proteins were differentially expressed between cell lines with fast and slow growth rates, highlighting a correlation between protein synthesis and cell growth
[24]. Benefits of combining the ‘omics approaches include accounting for low abundance protein expression and identifying mRNA post-translational processing, thus reducing error [24]. Analysis showed that the most significantly enriched gene ontology terms were translational elongation, translation, generation of precursor metabolites and energy, oxidation-reduction, and aerobic respiration [24].
Combination of ‘omics strategies improves the confidence of either data set alone and results can help to generate cell lines or culture conditions that improve growth.
Comparative proteomics relates protein levels between cell lines. Commonly, cell lines with different growth and productivity characteristics are compared to identify differences in protein levels that correspond to these differences. A combined approach using transcriptomics and proteomics was used to compare cell
11 lines with fast and slow growth [21]. Valosin-containing protein (VCP) was shown to have a significant effect on cell growth and viability. This was confirmed by silencing
VCP expression, which resulted in decreased viable cell density and viability [21].
Proteomics was beneficial in the analysis of protein levels to reveal differences in
VCP between cell lines.
Proteomics was used to compare CHO cell lines with and without the cMyc gene, which had been previously shown to improve cell growth rate and concentration [10]. Over 100 proteins were differentially expressed between cultures. Culture performance improved as measured by the increase in nucleolin
(important for growth, preventing apoptosis, protein productivity, and energy
utilization) and decrease in regulation of adhesion proteins between cells and cell-
matrix [10]. Specifically, components of ATP synthetase were up-regulated which
may indicate a change in energy utilization to release more ATP in cMyc culture [10].
From the quantification of protein levels, it was possible to identify critical proteins
that may be further investigated through hypothesis-driven research.
One way to understand cell death is to elucidate what happens at the
transition of cell culture from exponential growth to stationary phase. Fast cell
growth initially occurs during the exponential phase; then cells transition into a
period of high protein production during the stationary phase. Comparative
proteomics, using iTRAQ labeling, identified 59 proteins with significantly different
protein expression levels between the exponential and stationary phases [25]. Some
of these proteins included binding immunoglobulin protein, protein disulfide
isomerase, DNA replication licensing factors MCM2 and MCM5, transglutaminase-2,
12 and clusterin [25]. These time points were compared in order to identify proteins associated with cell growth and apoptosis. After identifying and classifying differentially expressed proteins, it was found that growth and apoptotic proteins
are expressed highly during the stationary phase [25]. Results from this study aid in
understanding dynamic cell culture changes and can be used to identify protein
markers for prolonged exponential phase and improved process development.
In a time-course experiment, protein samples collected throughout cell culture were analyzed by proteomics [26]. This experiment identified 40 differentially expressed proteins between exponential and stationary phases [26].
These results show that over time, apoptosis occurs due to the initiation of the unfolded protein response [26]. Thus, delaying apoptosis can increase protein production. This was validated in another study, where coexpression of the anti- apoptotis gene Bcl-xL with membrane proteins increased membrane protein expression in CHO cells [27].
1.4.2 Increase Recombinant Protein Productivity
An important goal in bioprocess development is to maximize the
recombinant protein productivity. In one experiment, proteomics was used to
identify proteins related to the improved protein productivity following
supplementation of butyrate and zinc sulfate to culture medium [9]. Increased
expression was observed for metabolic and chaperone proteins including GRP75,
enolase, and thioredoxin [9]. These proteins were identified as possible cell
engineering targets for improving recombinant protein productivity.
13 In another experiment, cell line comparison between high and low producing
cell culture was conducted. Overexpression of Bcl-xl reduced apoptosis and
increased growth. Proteomics analysis showed that over 30 proteins were
differentially expressed between high and low producing cultures [28]. In the high
producing cell line, eukaryotic translation initiation factor 3 and ribosome 40S were upregulated, whereas vimentin, annexin, and histone H1.2/H2A were downregulated [28]. Additionally, chaperone BiP was upregulated in the high producing cell line, from which it was concluded that the unfolded protein response
occurs as a consequence of endoplasmic reticulum stress [28].
Combined proteomics and transcriptomics were used to determine the effect
of low temperature and sodium butyrate on recombinant protein productivity [29].
This approach relied on the transcriptomic information to identify hundreds of
differentially expressed genes between treatments. From this data, proteomics was
used to further identify different protein levels. Butyrate treatment and low
temperature were shown to improve recombinant protein productivity by
improving cell secretory capacity [29]. Enriched pathways included Golgi processing, cytoskeleton binding, and GTPase mediated signal transduction [29].
In more recent years, the development of the CHO genome database has led to improvements in protein identifications. Different cell culture conditions were used for proteomics experiments in order to compare high and low producing cell lines [30]. From 180 differentially expressed proteins, there were 12 proteins that showed different expression levels over culture duration in bioreactors, including
ADP-ribosylation factor protein, V-type proton ATPase, colony stimulating factor 1,
14 and angiopoietin 4 [30]. The important proteins had functions related to growth, metabolism, organization, and protein synthesis [30], which can be used for process optimization by genetical or media supplement technologies.
Meleady also investigated differences in protein levels between high and low producing cultures [22]. There were 89 differentially expressed proteins between the high and low producing cultures [22]. Proteins were identified that contributed to the high producing phenotype [22]. This suggests that future applications overexpressing these proteins could improve cell line productivity. There were 12 proteins identified that differed in expression level in the same direction, including aldose reductase-related protein 2, annexin, eukaryotic translation initiation factor, glucose-6-phosphate 1-dehydrogenase, endoplasmin, and nuclear migration protein
[22]. Examples of proteins that were expressed at higher levels in the high- producing cell line include proteins involved in translation and folding [22] which are associated with recombinant protein production.
Overexpression of micro-RNAs may improve process development by regulating protein productivity. Proteomic comparison of a microRNA overexpressing cell line with a control cell line revealed differences in protein expression contributing to higher productivity in the miR-7 cell line [14].
Overexpression of miR-7 resulted in 93 downregulated proteins and 74 upregulated proteins [14]. Proteins with decreased levels were related to protein translation and
DNA/RNA processing and with increased levels were related to protein folding and secretion [14]. Upregulated proteins involved in protein folding and secretion may serve as cell line engineering targets for increasing recombinant protein
15 productivity [14]. The results show how miR-7 overexpression changes protein levels, which can aid in future cell line engineering to improve process performance.
In addition to the increased protein productivity, overexpression of microRNAs can affect other processes such as cell growth and apoptosis.
The genetic factors attributed to high productivity of a cell line were studied by maintaining the same process conditions [31]. Results from proteomics indicated positive correlation between productivity and the expression of dihydrofolate reductase, adaptor protein complex subunits, DNA repair proteins, and the ER translocation complex components [31]. Both transcriptomics and proteomics data was combined in order to improve understanding of the high production phenotype
[31]. Protein expression differences suggest important roles for these proteins in productivity. Such results can be used to generate high producing clones through cell line engineering and improved clone selection using biomarkers.
1.4.3 Optimize Media Formulation
The growth and productivity of a cell line is highly dependent on the media formulation. Media formulation is a key component of process development and methods to reduce development time are highly sought. The formulation of cell culture medium can have a significant effect on cell growth, viability, and protein productivity. Proteomics provides detailed information about cell protein levels that can aid in medium formulation. This drives hypothesis-based research to further optimize process development.
Recently, proteomics has been used to profile differences between medium formulations in order to correlate improved cell growth with protein levels.
16 Proteomics helps identify proteins for adaptation from serum-bearing to serum-free
media [7]. Results indicated that two molecular chaperones and four de novo
nucleotide synthesis related proteins were significantly increased in the serum-free
cell culture [7]. Subsequently, two of the chaperones identified (HSP60 and HSC70)
were overexpressed and this increased the cell growth rate up to 15% and
decreased adaptation time up to 33% [7]. Thus, quantification of protein levels aids
in cell line engineering strategies to improve cell growth and adaptation. It is
possible to identify candidate proteins through proteomics that play essential roles
in CHO cell culture.
The secretome consists of extracellular proteins processed through the
secretory pathway. These extracellular molecules are in low abundance, but
involved in diverse biological processes. In one approach, secreted proteins from
conditioned media samples were identified in order to develop a serum-free media
formulation [32]. Supplementation of identified growth factors, such as fibroblast
growth factor 8, growth regulated alpha protein, hepatocyte growth factor, and
macrophage colony stimulating factor 1, to the serum-free cloning media
formulation led to increased cell growth [32].
Analysis of the secretome can aid in the identification of proteins that have
important roles in cell growth and protein productivity. These proteins could be effective medium supplements for an optimized process. N-azido-galactosamine labeling was used to tag the mucin-type O-linked glycans of secreted proteins in order to enable their identification in cell-conditioned media [15]. This method helps to identify secreted proteins in low abundance and minimize background
17 proteins [15]. The secretome of CHO-S and CHO DG44 cell lines were compared and
it was found that 171 proteins were identified in both cell lines [15]. There were
also 96 proteins unique to CHO DG44 and 85 proteins unique to CHO-S [15].
Differences in protein levels were related to adhesion, cell growth, and proteases
[15]. Approximately 70 percent of the proteins identified were the same between
the CHO-S and CHO DG44 cell lines [15]. Important secreted proteins may be
investigated as medium supplements. Identification of important secreted proteins
aids in the development of media formulations to improve growth; it is also critical
that secreted proteins are identified and removed from the final biotherapeutic drug
product.
In another experiment, label-free comparative proteomics was used to identify differences between cells cultivated in serum-free medium formulations with or without hydrolysates [33]. The changes in protein expression upon addition of hydrolysates, containing blends of peptides, free amino acids, vitamins, and trace elements, helped to explain the increased recombinant protein productivity [33].
Proliferative proteins were upregulated whereas pro-apoptotic proteins were
downregulated in the culture containing hydrolysates [33].
Proteomics can thus be used to identify differences in protein expression
between media formulation and help to optimize formulations for high growth and
productivity. These studies highlight the applications of proteomics to understand
the roles of media supplements on cell growth and recombinant protein production
as well as to identify novel media supplements for the optimization of media
formulation.
18 1.4.4 Systems Biology Full characterization of the proteome significantly aids efforts to understand
cell physiology by analysis of metabolic pathways. The complete proteome was
recently elucidated for the CHO-K1 cell line [4]. Analysis of genomics, transcriptomics, and proteomics data at the systems biology level using Kyoto
Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that pathways for protein processing and apoptosis were enriched in CHO-K1, whereas
pathways for steroid hormone and glycosphingolipid metabolism were depleted [4].
The glycoproteomics study aided the identification of the major cell adhesion and
membrane proteins, which are difficult to identify with intracellular proteomics
approaches. Importantly, Baycin-Hizal et al. used both genomics and proteomics
information to generate the codon usage table for CHO for the first time. Codon
optimization is the main preferred method for the increased expression of drugs for
that reason CHO specific codon usage table will help biopharmaceutical companies
to increase the recombinant protein expression [4]. Proteomics was thus able to
elucidate new information about CHO protein levels that was not detectable by
genomics or transcriptomics.
In another experiment, various databases were used to improve the
confidence of identified CHO cell proteins [34]. By using multiple databases, it was possible to increase the number of identified proteins by over 40% [34]. Different
methods were used across locations and the results were compared to improve
confidence. Thus, proteomic data is reproducible and is improved by comparing
results to various CHO cell specific databases.
19 1.5 Database Development
In recent years, CHO-specific databases were developed to improve sequence
information for protein identification. Prior to this, CHO-specific sequences were not
publicly available requiring researchers to rely on cross species information.
Following the completed genome of the CHOK1 cell line, the draft Chinese hamster
genome was published [2, 35]. The published data sets from these genomes provide
unifying information about the genes and variations between CHO cell lines. Such
genomic data sets are now available online and have been compiled into databases
such as www.chogenome.org. Similar efforts have been made to establish proteomic
databases for CHO cells, including www.gofant.com/CHOproteomemainimage.html.
An evaluation of CHO databases increased protein identification by 282 proteins, a
40-50% increase in the total number of proteins identified [36]. CHO sequence
information was combined from the SwissProt database, CHOK1 draft genome [35],
and the Bielefeld-BOKU-CHO database [36]. Because more peptides matched, there is increased confidence in the results. The increased proteins identified were related to protein translation and energy metabolism, both important for bioprocess development [36]. The compilation of data sets from proteomics experiments around the world allows for data sharing and database generation. Multiple groups can use data to improve proteomics results.
1.6 Combined ‘Omics
The CHO genome was recently elucidated, providing high-level information
about cell function. Moving from the genome to transcriptome provides information
about the mRNA transcribed from DNA; subsequently the proteome provides
20 information about the proteins translated. The metabolome is the final step toward
understanding cell function. Small changes in metabolite levels can provide
information that would not have been previously possible with only a genomic,
transcriptomic, or proteomic approach.
Combined proteomics and transcriptomics was used to relate the CHO
proteome, secretome, and glycoproteome to mRNA data sets in order to provide
new information about amino acid utilization pathways, highly expressed proteins,
and depleted or enriched pathways [37]. The combined approach allowed for
relationship between mRNA and protein levels. Stable proteins were identified as
those with high protein intensity and low mRNA intensity, such as tubulin, myosin,
and binding proteins [4]. A combination of proteomic and transcriptomic data with
metabolomics could further increase understanding of CHO cell pathways relevant
for cell growth, recombinant protein production, and glycosylation allowing for new
engineering strategies.
A combination of proteomics and metabolomics was used to increase
understanding of CHO cells during prolonged cultivation [11]. The observation that
prolonged culture results in increased cell growth was related to an increase in
adenylate energy charge (AEC) and differential protein expression [11]. The increased AEC relates to a high energetic state of the cell. Additionally, 43 differentially expressed proteins were identified [11]. Some of the proteins, including phosphoglycerate mutase, phosphoglycerate kinase and pyruvate kinase isozymes, were associated with the improved growth rate [11]. Other differentially expressed endoplasmic reticulum stress proteins were related to cell robustness to
21 changing environmental conditions [11]. The combined approach was useful for
determining correlations between the different protein and metabolite levels and
the observed cell phenotype.
Recent approaches have used metabolomics in combination with other – omics approaches. Transcriptomics, metabolomics, and fluxomics were used to identify differences between a recombinant protein producing cell culture and the parental Hek-293 cell line [38]. This combined approach elucidated new information about cell line differences and indicated possible targets for metabolic engineering. The producer culture consumed less glucose and produced less lactate than the parental cell line. Nucleotides and nucleotide sugars were less abundant in the producer culture, as well as most glycolytic and TCA cycle intermediates [38].
With a combined ‘omics approach, Dietmair showed that the lower metabolite levels corresponded to downregulation of transcription and translation in the producer culture. The producer culture was shown to have an increased uptake of amino acids and increased expression of transporters needed for amino acid transport to growing proteins
1.7 Summary of Proteomics
Current developments in biotechnology rely on the wealth of information provided by the genome, transcriptome, proteome, and metabolome. Protein levels provide clear information about cell physiology, thus, proteomic methods are important for hypothesis-driven research and development. A variety of different digestion techniques and proteomics techniques have been developed for CHO proteomics. In addition to this, a variety of labeling techniques such as SILAC, iTRAQ
22 and TMT have been used to study differences between cell lines and analyze important pathways such as apoptosis, growth, and protein production. Not only does proteomics aid the identification of novel biomarkers for targeted mAb development, but it also aids in identifying protein-protein interactions during disease progression. In bioprocess development, applications of proteomics include identification of accumulating or depleting protein levels important in recombinant protein production. From this information, cell line engineering approaches or medium formulation developments help in the prevention of apoptosis, improvement of productivity, or utilization of nutrients. Thus, ‘omics approaches enable research that delves deeper into the workings of the cell.
In summary, proteomics is increasingly used in bioproduction to improve development of current biotherapeutics by increasing cell growth, minimizing cell death, and increasing recombinant protein production. This helps in the manufacture of biotherapeutics at large scale and with high product quality, reducing the development time. In recent years, sample preparation methods for proteomics have been optimized in order to obtain the maximum number of correct protein identifications. This has enabled more developed databases and identification of proteins localized to cell membranes, the cytoplasm, or other organelles. Future refinements in the preparation and analysis of proteomics samples will allow for in-depth information about critical proteins in biotechnology research and development.
23 Tables
Table 1.1: Top Selling Biologics of 2012
24 Table 1.2: Summary of CHO Proteomics for Bioprocess Development
Reference Purpose Method Conclusion
Identified increased levels of GRP78 To determine differences in Used in-gel digestion for and peroxiredoxin following proteins between CHO cell proteins. Used MALDI TOF treatment with sodium butyrate. Baik 2008 cultures with or without MS and MS/MS to identify Phosphopyruvate hydratase levels sodium butyrate proteins decreased with sodium butyrate supplementation treatment
Identified increased levels of HSP60 To determine differences in Used in-gel digestion for and HSC70 in serum-free media. protein levels during proteins. Used MALDI TOF Subsequent overexpression Baik 2011 adaptation to serum-free MS and MS/MS to identify resulted in improved cell medium proteins concentration during serum-free adaptation
Used SPEG to generate glycoprotein fractions and To identify the proteome, Identified important proteins in in-gel and in-solution Baycin-Hizal secretome, and CHOK1 cell line and combined digestion for proteins. 2012 glycoproteome of CHOK1 proteomic and transcriptomic data Used MS/MS to measure cell line for improved analysis proteome, secretome, and glycoproteome
Found differentially expressed proteins between To compare the proteomes cultures.Eukaryotic translation Proteins were digested Carlage 2009 of high and low producing initiation factor 3 and ribosome 40S and identified by LC-MS cell cultures over time were upregulated and vimentin, annexin, and histones were downregulated in the high producer
Identified proteins with changing To determine the changes levels over time from exponential to in the proteome over time Used iTRAQ labeling and Carlage 2012 stationary transition and related the for a CHO cell culture LC-MS to identify proteins differences to cell growth and overexpressing Bcl-Xl apoptosis
Compared miRNA, mRNA, and To determine important protein expression levels and proteins that elucidate how Used LC-MS to identify Clarke 2012 identified processes regulating cell miRNAs affect CHO cell proteins growth, such as ribosome synthesis, growth translation, and mRNA processing
To combine Identified valosin containing protein transcriptomics and Used in-gel digestion for as important regulator of cell Doolan 2010 proteomics to identify proteins. Used MALDI TOF growth. Overexpression resulted in important proteins that MS to identify proteins improved growth with no decrease relate to high growth rate in viability
Collected spent media Comparison of high and low samples for host cell expressing clones revealed 180 To identify proteins that proteins. Used in-gel differentially expressed proteins. Dorai 2013 affect high productivity in digestion for proteins. Identified proteins related to bioreactor cell cultures Used LC-MS to identify cytoskeletal organization, protein proteins synthesis, metabolism, and growth
25 Identified proteins with positive Used LC-MS/MS shotgun correlation to productivity, To identify differences proteomics to identify including DHFR, adaptor protein Kang 2013 between cell lines that protein expression complex subunits AP3D1 and affect antibody production differences between cell AP2B2, DNA repair protein DDB1, lines and ER translocation subunit SRPR
Found significant changes in protein To determine the effect of Used 2D gel expression in serum-free medium hydosylate electrophoresis combined supplemented with hydrosylates, Kim 2011 supplementation on with nano LC-ESI-QTOF such as upregulation of metabolic, antibody productivity in MS/MS to identify cytoskeletal organization, and serum-free cell cultures proteins growth regulated proteins
Found increase in nucleolin and decreased in regulation of proteins Used in-gel digestion for Kuystermans To determine the effect of related to matrix and cell adhesion. proteins. Used MS/MS to 2010 cMyc on proteome Also found increased ATP identify proteins synthetase and mitrochondrial protein levels
Improved protein identification by Used in-gel digestion for enrichment of medium and low To identify the proteome of proteins. Used MALDI TOF Lee 2010 abundance proteins. Most CHO DG44 cell line MS and MS/MS to identify identified proteins function in proteins energy metabolism
To identify growth factors Identified 290 secreted proteins that can be used as Used MS shotgun from CHO cell culture including 8 Lim 2013 supplements in serum-free proteomics to identify novel growth factors. Used growth cell cultures to improve proteins in spent medium factors as medium supplements to antibody production increase cell growth rate
To compare the proteomes Used in-gel digestion for Idetified 89 proteins with different Meleady of high and low producing proteins. Used LC-MS to expression between high and low 2011 cell cultures over time identify proteins producer cell lines
Identified 93 decreased proteins and 74 increased proteins resulting Used in-solution digestion from miR-7 overexpression. To determine the effect of Meleady for proteins. Used label- Decreased proteins related to miR-7 overexpression on 2012 free LC-MS to identify protein translation and DNA/RNA proteome proteins processing. Increased proteins related to protein folding and secretion
Used in-gel and in- solution digestion for To improve identification Improved protein identification by Meleady proteins. Used MALDI TOF of proteome by using 40-50% through mulptiple CHO- 2012 MS and electrospray ion multiple databases specific databases trap MS to identify proteins
Cell culture medium Identified differences between To develop a method for supplemented with CHO-S and CHO DG44 secreted Slade 2012 identifying secreted GalNAz and enriched by proteins. Found 70% similarity proteins copper catalyzed click between cell lines chemistry. Used iTRAQ labeling and MS to
26 identify proteins
To identify important Used in-gel digestion for Identified increased expression of Van Dyk proteins that affect protein proteins. Proteins GRP75, enolase, and thioredoxin in 2003 productivity in butyrate identified with MALDI-TOF response to media supplements and zinc treated culture MS
After prolonged cultivation, Used in-gel digestion for To identify the proteome of identified 40 proteins with different proteins. Electrospray Wei 2011 CHO cells during prolonged expression levels related to ionization tandem MS cultivation cytoskeletal proteins, chaperones, used to identify proteins and metabolic enzymes
27 Figures
Figure 1.1: Overview of Proteomics Experiment. After proteins are extracted from cell culture, sample preparation involves reduction, alkylation, filter aided sample preparation, and fractionation. Peptides are injected into the mass spectrometer and the resultant peaks are analyzed. Proteins are identified and quantified using CHO-specific databases and statistical pathway analysis.
28 Figure 1.2: Overview of Comparative Proteomics. Comparative proteomics can be used to identify differences in protein expression levels between culture conditions. Following sample preparation, peptides are labeled separately with unique tags and then mixed in equal amounts prior to mass spectrometer injection. Peptides are identified and quantified using CHO-specific databases.
29 Chapter 2: Glycoproteomics Analysis of Chinese Hamster Ovary
Cell Lines
Abbreviations
CHO – Chinese hamster ovary, ER – endoplasmic reticulum, CDG – congenital disorders of glycosylation, MS – mass spectrometry, iTRAQ – isobaric tags for relative and absolute quantification, SPE – solid phase extraction, FBS – fetal bovine serum, DMEM – Dulbecco’s modified essential medium, TCEP – tris (2-carboxyethyl) phosphine, SDS – sodium dodecyl sulfate, PAGE – polyacrylamide gel electrophoresis, SCX – strong cation exchange, LC – liquid chromatography, GO – gene ontology, KEGG – Kyoto encyclopedia of genes and genomes, IPA – Ingenuity pathway analysis, PCA – principal component analysis, ANOVA – analysis of variance,
MGAT2 – mannosyl (alpha-1,6-)-glycoprotein beta-1,2-N- acetylglucosaminyltransferase, B4GALT3 – beta-1,4-galactosyltransferase 3,
HSP90B1 – heat shock protein 90 beta family member 1, ADAM10 – a disintegrin and metalloproteinase domain 10, CTSF – cathepsin F, ICAM1 – intracellular adhesion molecule 1, MAN2B1 – mannosidase alpha class 2B member 1, BMP1 – bone morphogenetic protein 1, HEXA – hexosaminidase subunit alpha, ST3GAL1 –
ST3 beta-galactoside alpha-2,3-sialyltransferase 1, LGMN – legumain, 1ADGRG1 – adhesion G protein-coupled receptor G, HSPA5 – 78 kDa glucose-regulated protein,
HYOU1 – hypoxia up-regulated 1, GAA – glucosidase alpha, HSPA8 – heat shock cognate 71 kDa protein, GBA – glucosylceramidase, CSPG4 –chondroitin sulfate
30 proteoglycan 4, RPN1 – ribophorin 1, OST –oligylsaccharyltransferase, SILAC –
stable isotope labeling with amino acids in cell culture, TMT – tandem mass tags
2.1 Introduction
Biotherapeutics expressed in Chinese hamster ovary (CHO) cell lines
generate billions of dollars in revenue due to the similarities in glycosylation
patterns to humans and their applicability for therapeutic drug synthesis to treat
human diseases and conditions. An important characteristic that CHO cells share in
common with humans is that they are able to modify their glycoproteins by adding
sialic acid sugar groups. CHO contains different sialylation patterns, however, than
those in humans due to alpha-2,3-sialyltransferase activity in CHO cells which
differs from the alpha-2,6 sialic acid linkages found in humans [39]. Variations in
glycosylation patterns affect proteins’ biological recognition, including the ability to
remain in the human body for circulation and the stability of the protein’s structure.
These characteristics are crucial to the actions of protein drugs in terms of their
function, half-life, and immune response in the body. Therefore, investigating the
effects of different glycosylation patterns and mutations in CHO cells are essential to
their proteins’ potential therapeutic applicability.
N-Glycosylation is a process that begins in the endoplasmic reticulum (ER)
when the core unit of a sugar is transferred to a newly synthesized protein from a
lipid dolichol carrier. The dolichol molecule serves as a membrane anchor where the
sugar moiety (Glc3Man9GlcNAc2) is formed and then transferred to an asparagine
residue marked by the recognition sequences N-X-S or N-X-T in the nascent protein.
Dolichol is a product of the HmG-CoA, or mevalonate, pathway. The mevalonate
31 pathway produces dehydrodolichyl diphosphate, which loses both of its phosphates
to become polyprenol through a currently unknown enzyme. Polyprenol is alpha-
isoprenoid-saturated by polyprenol reductase and becomes dolichol. Finally,
dolichol kinase adds a phosphate to dolichol to make it into dolichyl phosphate, the
actual sugar carrier [40]. After the initial transfer of the oligosaccharide to the
protein, the sugar group undergoes a series of modifications in the Golgi before it
reaches its maturity. These modifications include the removal and addition of sugars
to the budding oligosaccharide by glycosyltransferases. Additionally, nucleotide
sugar transporters bring activated sugar substrates, like sialic acid, galactose,
GlcNAc, and fucose, to be added to the oligosaccharide.
Many CHO cell lines that have mutations pertaining to glycosylation have
been characterized [41]. Initially, the cells were used by the glycobiology
community to understand how loss-of-function mutations contribute to congenital disorders of glycosylation (CDG) in humans [41]. The high spontaneous rate of mutations in CHO was advantageous in order to study diverse effects of altered glycosylation [41]. Using molecular biology approaches, Stanley studied the specific genes responsible for glycosylation defects [41]. Next, mutagenesis was used to select for rare glycosylation mutants using plant lectins, which are specific to different sugar configurations [41]. Stanley subsequently yielded cell lines such as
Lec32, Lec2, Lec8, Lec13, and Lec9.4a. The creation of diverse cell lines aids both research of CDG and development of glycoengineered recombinant therapeutics.
CDG refers to the various glycosylation disorders in humans as a result of defects at different stages of N- and O-glycosylation pathways. For example,
32 SRD5A3-CDG (CDG-Iq) refers to the polyprenol reductase defect. Patients exhibit
symptoms such as ocular coloboma, optic nerve hypoplasia, visual loss, nystagmus,
hypotonia, developmental delay, and cerebellar ataxia [42] [43]. This CDG can be
studied using the Lec9.4a mutant. In contrast, SLC35A1-CDG (CDG-IIf) refers to a
defect in sialic acid transporter, which mimics the phenotype of the Lec2 mutant. A patient with this disorder experienced macrothrombocytopenia, neutropenia, and immunodeficiency prior to death as a toddler [44]. CDGs are the result of diverse
mutations across the glycosylation pathway.
One of the mutants, Lec9.4a, is known for having a mutation in the
polyprenol reductase enzyme that allows the conversion of polyprenol to dolichol.
This mutation causes a decrease in glycosylation levels, as it is dependent on the
dolichol carrier in order to carry and deliver the sugar moiety. However, the cells
retain the capacity to glycosylate at about a 50% rate because the cell uses
precursor polyprenol molecules for saccharide unit formation to compensate for the
lack of dolichol [45]. Thus, the Lec9.4a cell line utilizes polyprenol as opposed to
dolichol, which affects the first step of N-glycosylation that assembles sugars onto a dolichol-phosphate carrier. In the mutant cells, the concentration of polyisoprenyl lipid phosphate is decreased which increases the amounts of monoglycosylated and phosphorylated lipids [45]. Overall, the ratio of active prenols is higher in wild-type cells than mutants [45]. Glycosylation is altered in Lec9.4a as a result of the inefficieny in which cells use polyprenol as opposed to dolichol. This inefficiency may result from the preference of flippases to use dolichol when translocating the growing polypeptide to the ER lumen [45]. In addition, the differential usage of
33 polyprenyl phosphate affects oligosaccharide lipid levels and thus decreases the
glycosylation levels [45]. Overall, the 50% reduction in Lec9.4a refers to a decreased glycosylation rate and lower total levels of glycosylated protein. A similar effect in blocking the transfer of the core sugars from dolichol can be achieved by treating
CHO-K1 cells, the original wild-type CHO cell line, with tunicamycin (TM) [46]. TM is an antibiotic that inhibits GlcNAc phosphotransferase, which catalyzes the addition of N-acetylglucosamine-1-phosphate to dolichol [47]. This inhibits N- glycosylation in the ER and provides an interesting basis of comparison with the effects of the Lec9 mutation. In addition, the comparison of wild-type and mutant cells yields insights in glycolsylation differences between dolichol and polyprenol as the lipid carrier.
The Lec2 mutant cell line possesses a deletion in the gene responsible for expressing the CMP sialic acid transporter, thus expressing a non-functional transporter that renders the cell unable to transport sialic acid across Golgi vesicles and limiting localization of sialic acid to the target proteins. This mutation severely decreases the cell’s ability to sialylate and to add N-linked glycosylation to its
proteins, yielding a 70-90% reduction in sialylation level [48] [49]. Comparison of
Lec2 with wild-type cells reveals differences between siaylated and asiaylated
proteins. In functional analyses, proteins in the Lec2 cell line exhibited altered
reactivity in enzymatic assays suggesting the protein structure is altered as a result
of the different glycoform [50]. Interestingly, the proteins did not have significantly
reduced in vivo half-life [50]. In contrast to Lec9.4a, the Lec2 cell line has a mutation
at the last step of glycosylation and is still expected to glycosylate proteins to the
34 same extent as wild-type cells. Pro-5 CHO is a parent cell line of the Lec2 line [51]. It is also a mutant of wild-type CHO cells that is deficient in proline synthesis.
Therefore, these cells cannot grow in the absence of proline in the culture medium.
They contain only 21 chromosomes, and a gene on the missing chromosome is thought to be responsible for the cell line’s inability to convert glutamic acid to glutamic- -semialdehyde, which blocks the synthesis of proline [52].
Acrossγ the cell lines, research efforts have focused on identifying the genes that cause the mutated phenotypes. One application of this led to CDG classification.
Similarly, glycomics approaches have been used to identify the glycan structures produced in different mutants. Specifically, Lec9.4a mutant cells show increased branching of beta-1,6 carbohydrates and overall reduced glycosylation of proteins
[53]. North used mass spectrometry (MS) to identify the N- and O-glycans of nine different Lec-based CHO mutants [54]. Surprisingly, there was higher glycan complexity than expected for the mutants, so North suggested that further analysis is required for cell line applications [54]. In other applications, the glycosylation mutants suggest targets for genetic engineering. As a result of identifying the point mutation for sialic acid transporter in Lec2 cells, Wong overexpressed the functional gene in CHO cells to increase recombinant protein sialylation [55]. This strategy achieved up to 20-fold increased gene expression and a 4-16% increase in sialylation levels [55].
Glycomics is not the only ‘omics method that can yield insights into cellular physiology. Proteomics aims to identify and quantify proteins within biological samples. In order to study the structure and glycosylation patterns of the proteins
35 produced by these cells lines, certain proteomic analytical techniques, such as isobaric tags for relative and absolute quantification (iTRAQ) and solid phase protein extraction (SPE), are necessary. iTRAQ is used to simultaneously analyze a variety of peptide samples and quantify proteins from different sources into a single experiment. Isotope coded covalent tags, 4-plex and 8-plex, label the peptides at their N-terminus and at lysine amino acid side chains [56]. The protein samples are extracted and digested with one or multiple enzymes to reduce them into their component peptides, which are then labeled with amine specific markers.
Afterward, the samples are pooled and fractionated for analysis in a mass spectrometer [57]. Through the use of tandem mass spectrometry (MS/MS) and these covalent tags, signature ions with a mass to charge ratio between 114 and 117 can be separated, observed, and quantified with greater sensitivity than other techniques, including Isotope Coded Affinity Tags and 2D gel electrophoresis [58]
[56]. The sensitivity of iTRAQ can be further increased through sub cellular fractionation [59]. Even with its many advantages, it is more susceptible to errors in precursor ion isolation.
SPE, a valuable tool for glycoprotein enrichment, can capture proteins with high fidelity when the protein is present in small amounts, which is very difficult with other methods. Desired proteins are dissolved in a fluid substance and then passed through a solid layer with high affinity for the solute. These are known as the liquid and solid phases, respectively. SPE quickly and efficiently separates the protein and undesired components, allowing for further analysis such as by MS [60].
Historically used in analyzing pesticides in soil samples [61], SPE has been used for
36 capturing glycoproteins on both a clinical [62] and laboratory setting [63]. In particular, laboratory applications of SPE can isolate N-linked glycosylated proteins using hydrazide beads. Because hydrazine forms hydrazone bonds with sugar- linked peptides and proteins, glycoproteins can be captured with high efficiency and then freed from the hydrazine with glycosidases for the specific proteins desired
[64]. Further digestion separates the sugar markers from proteins so that conventional MS can be used to derive further data.
A comparison between CHO and the glycosylation mutant cell lines, Lec9.4a and Lec2, can provide further insight into the initiation of glycosylation and to how the lack of a large part of the cell’s glycosylation processes impacts its ability to function. It can also shed light onto the importance of glycosylation to protein folding and availability within the cell. Currently, no clear picture exists into the specific differences between these cell lines at the proteomic level. The following study aims to establish the glycoproteome and sialylated proteome of CHO-K1 and
Pro-5 CHO as negative controls and compare the parental cell lines with Lec9.4a and
Lec2 cell lines, respectively. Specifically, glycosylated proteins will be compared
between CHO-K1 and Lec9.4a, whereas sialylated proteins will be compared between Pro-5 CHO and Lec2. These differences will shed light on differences
between the dolichol and polypropenol pathway, as well as increase our
understanding of pathway changes in CDG. Through this proteomic analysis, we
identify and quantify the differences that glycosylation and sialylation deficiencies
cause in global proteomic expression.
2.2 Materials and Methods
37 Briefly, the experiment section is divided into three parts: sample
preparation, MS peptide identification, and bioinformatics analysis.
2.2.1 Materials
The wild-type CHO-K1 (CCL-61) and wild-type Pro-5 CHO (CRL-1781) cell
lines were obtained from ATCC (Manassas, VA) and cultured by Deniz Baycin as
described in a previous experiment [4]. Two glycosylation mutant cell lines (Lec2
and Lec9.4a) were graciously provided by the lab of Dr. Pamela Stanley. Cell culture
supplies, including fetal bovine serum (FBS), L-Glutamine, non-essential amino acids,
DPBS, F-12K and Dulbecco’s modified essential medium (DMEM) media were obtained from Gibco (Grand Island, NY). Sequencing grade trypsin was purchased from Promega (Madison, WI). The BCA protein assay kit and Tris (2-carboxyethyl) phosphine (TCEP) were obtained from Thermo Scientific Pierce (Rockford, IL). The
sodium dodecyl sulfate – polyacrylamide gel electrophoresis (SDS-PAGE) gels4−12% were purchased from Invitrogen (Grand Island, NY). Affi-prep hydrazide resin beads and sodium periodate were purchased from Bio-Rad (Hercules, CA). PNGaseF was purchased from New England Biolabs (Ipswich, MA). The 1 mL C18 Sep-Pak cartridges were obtained from Millipore (Billerica, MA). All other chemicals used in this study were purchased from Sigma-Aldrich (St. Louis, MO).
2.2.2 Cell Culture and Preparation of Protein Lysate
Cell lines were cultured in the media specified by the manufacturer or cell line supplier. Wild-type CHO-K1 and the mutant Lec9.4a cell lines were cultured in
F-12K media supplemented with 10% FBS, 1% nonessential amino acids, and 2mM
L-glutamine. Wild-type Pro-5 CHO and mutant Lec2 cell lines were cultured using
38 DMEM, supplemented with 10% FBS, 1% non-essential amino acids, and 2mM L-
% confluence, they were lysed for
glycoproteomicglutamine. When analysis. cells reached 70−80
2.2.3 In-solution Digestion
The sample preparation prior to MS was performed by Baycin as decribed
previously [4]. Cells were lysed in 9M urea with protease inhibitor, pH 8.0, and
sonicated on ice three times for 20s using a probe sonicator. This step breaks open
the cells to expose the proteins. The protein concentration of the lysate was estimated by BCA assay. An equivalent amount of lysate from each cell line was first reduced with TCEP and subsequently alkylated with iodoacetamide before trypsin digestion. TCEP is used to reduce disulfide bonds. The free sulfhedryl groups on cysteine residues are then alkylated to prevent reformation of disulfide bonds.
2.2.4 Glycopeptide Capture with SPEG
Following tryptic digestion, which breaks the peptide bonds, the samples were desalted using SepPak C18 columns and oxidized with 10mM sodium periodate in the dark at room temperature for 1hr. Next, samples were mixed with
temperature50μL of hydrazide for the resins coupling containing reaction 50% for slurrythe glycoproteom and incubatede analysis. overnight For at the room sialylated proteome, the peptides were treated with sodium periodate on ice before incubating with hydrazide resins. The nonglycosylated peptides were subsequently
hloride andremoved water. by The washing N-glycosylated the immobilized peptides resins were with released 800μL from of 1.5M the immobilized sodium c
support by suspending the resins in 50μL of G7 reaction buffer, containing 50mM
39 platform.sodium phosphate Next, glycopeptides at pH 7.5, with released 2μL PNGaseFfrom the overnightsupport were at 37°C transferred on a shaking into a
solutionsglass vial andfrom the each resins sample were were washed added twice to C18 with SepPak 100μL columns. of water. Eluted The combined samples
were dried by Speedvac and labeled prior to MS.
2.2.5 iTRAQ Labeling
Each sample was labeled using iTRAQ reagents. A 4-plex was used to compare wild-type CHO, TM-treated CHO, and Lec9.4a CHO cell lines. Two 4-plex experiments were used to compare wild-type Pro-5 CHO and Lec2 CHO cell lines.
Each iTRAQ set was fractionated by strong cation exchange (SCX) into 8 fractions and each fraction was analyzed by liquid chromatography (LC) MS. Samples were submitted to the proteomics core at Johns Hopkins Medical School− run by Dr. Robert
Cole. LC-MS/MS analysis and data analysis were done as explained previously in the label-free CHO paper [4]. Briefly, the iTRAQ method labels the N-terminus and
lysine side chains. Each reagent has a reporter-, reactive-, and balance- group to
maintain the same mass. When the peptide is fragmented, the reporter breaks off
and produces a unique ion. The relative intensity of the reporter is proportional to
the abundance of each peptide in the sample.
2.2.6 Bioinformatics Analysis
Presumed protein identifications were made using Proteome Discoverer 1.4
software
(https://www.thermofisher.com/order/catalog/product/IQLAAEGABSFAKJMAUH).
The protein intensities were determined by fold change over the respective wild-
40 type CHO cell line (CHO-K1 for iTRAQ1 and Pro-5 CHO for iTRAQ2 and iTRAQ3).
Fold changes less than 0.7 and greater than 1.5 were used to compare differential
protein expression. For the functional analysis, protein accession numbers were
mapped to gene symbols using the biological database network (http://biodbnet-
abcc.ncicrf.gov) for gene ontology (GO) and Kyoto Encyclopedia of Genes and
Genomes (KEGG). For GO annotation, gene symbols were mapped to molecular
function, biological process, and cellular component using the GOCHO platform
(http://ebdrup.biosustain.dtu.dk/gocho). For KEGG annotation, database pathways
for Cricetulus grisueus were downloaded on December 31, 2015
(http://www.genome.jp/kegg). All programming for the hypergeometric test were
calculated in MATLAB version 2015vB
(http://www.mathworks.com/products/matlab) and RStudio
(https://www.rstudio.com). Enrichment and depletion p-values were calculated using the hygecdf and hygepdf functions in MATLAB. These values were used to generate heat maps and k-means clustering in Genesis software version 1.7.6 [65].
KEGG pathways of interest were plotted using the search and color feature on the
KEGG website (http://www.genome.jp/kegg/tool/map_pathway2.html). Finally,
Ingenuity Pathway Analysis (IPA) software
(http://www.ingenuity.com/products/ipa) was used to visualize upregulated and downregulated genes and pathways of interest.
2.3 Results and Discussion
In this study, wild-type and glycosylation-deficient CHO cell lines were compared by comparative glycoproteomics for the first time. Shown in Figure 2.1A
41 are the glycosylation steps affected for the mutant and treated cell lines. The overall workflow of sample preparation, MS, and bioinformatics is shown in Figure 2.1B.
Briefly, lysed cells were alkylated and digested followed by oxidation for glycopeptide enrichment. Following hydrazide bead coupling, the coupled glycopeptides were digested by PNGaseF and labeled using iTRAQ reagents. Three separate iTRAQ kits were used; the first set was used to compare Lec9.4a with wild- type and TM-treated CHO, the second set was used to compare Lec2 with wild-type
Pro-5 CHO, and the third set was used to compare Lec2 with wild-type Pro-5 CHO.
Each iTRAQ was fractionated and run on MS/MS, and the resulting spectra were
matched to the CHO database using Proteome Discoverer software. Bioinformatics
analysis was used to determine functional differences between cell lines.
2.3.1 Lec9.4a Glycoproteome Distribution A summary of glycopeptide and glycoprotein identifications is shown in
Table 2.1. First, wild-type CHO was compared with TM-treated CHO and Lec9.4a cells. A total of 973 unique peptides representing 381 different proteins were identified in this iTRAQ analysis and a stringent false discovery rate of 1%. Of these,
187 glycoproteins were identified containing at least 2 unique peptides. Shown in
Figure 2.2 is a histogram distribution of protein intensities for each sample. This highlights the spread of glycoprotein intensity across different samples. The bin
sizes represent the fold change of the sample over the wild-type CHO cell line and the frequency shows the number of identified glycoproteins with that specific fold change. The average fold change is lowest for the comparison between wild-type and TM-treated CHO cells (Figure 2.2B, purple), indicating that the majority of
42 glycoproteins identified in the TM-treated cells are detected at lower levels compared to the wild-type CHO cell line. This is in contrast to the histogram of
Lec9.4a, which shows the greatest spread (Figure 2.2A, green), with many
glycoproteins detected at both higher and lower levels relative to wild-type CHO.
The difference between Lec9.4a and CHO TM may be due to the transient treatment
of wild-type CHO with TM as opposed to cell line adaptation to dysfunctional
glycosylation in the Lec9.4a cells.
Principal component analysis (PCA), which is commonly used for various
proteomics applications, is used to compare cell lines in each iTRAQ experiment further [66]. Each axis represents a component, and the components are ordered in terms of decreasing variability within the data set [67]. In Figure 2.2C, all cell lines
are clustered independently and show variability in glycoprotein detection.
2.3.2 Lec2 Sialoproteome Distribution
Next, wild-type Pro-5 CHO was compared with Lec2 cells, which cannot fully
sialylate glycoproteins. More than 2100 peptides were elucidated representing at
least 500 unique proteins. Of these, a total of 272 unique sialylated proteins were
identified in the comparison (combination of proteins identified in both iTRAQ 2
and iTRAQ 3) containing at least 2 unique peptides and a stringent false discovery
rate of 1%. The histogram comparison in Figure 2.2 shows the average fold change
distribution. Lec2 shows a wide distribution in overall sialylated protein intensity
with many sialylated proteins identified at both lower and higher levels than Pro-5
CHO (Figure 2.2D, pink). The sharpest peak is at a fold change of 1 compared to Pro-
43 5 CHO, which indicates many proteins show similar expression between the cell lines. Identification of the up- and down-regulated glycoproteins is detailed further by functional analysis.
The PCA plot in Figure 2.2E shows wild-type Pro-5 CHO cluster separately from Lec2, a sialylation-mutant CHO cell line. The independent clustering show differences in the expression of sialylated proteins. Following the PCA, protein intensity differences were characterized functionally using KEGG and IPA.
2.3.3 Lec9.4a K-means Clustering One method for grouping proteins by expression levels between samples is
K-means clustering through Genesis software
(http://genome.tugraz.at/genesisclient/genesisclient_description.shtml). Similar to the PCA, this is an unsupervised method that groups proteins by levels rather than supervised tests such as analysis of variance [68] or t-tests [67]. Clustering is useful because data is organized into multiple groups with comparable expression patterns. As shown in Figure 3, heat maps were generated as a result of K-means clustering into groups of proteins with low levels in CHO but high levels in Lec9.4a and TM-treated CHO (Figure 2.3A) and high levels in CHO but low in Lec9.4a and
TM-treated CHO (Figure 2.3B). Clusters provide clues of proteins appropriate for further analysis.
Some of the glycoproteins at higher levels in Lec9.4a and TM-treated CHO shown in Figure 2.3A as compared to wild-type CHO include CD63 antigen, glyceraldehyde-3-phosphate dehydrogenase, vimentin, and ATP synthase. ATP-
44 synthase is critical for energy generation and when the a2 subunit is mutated, there
is a corresponding abnormal glycosylation of serum proteins that affects Golgi
trafficking [69]. On the other hand, proteins related to glycosylation are generally
downregulated in both Lec9.4a and TM-treated CHO as compared to wild-type CHO, shown in Figure 2.5B. For example, alpha-(1,3)-fucosyltransferase, dolichyl- diphosphooligosaccharide-protein glycosyltransferase subunit 1, uncharacterized glycosyltransferase AER61-like, and uncharacterized glycosyltransferase AGO61- like are all expressed very highly in wild-type CHO but not in the glycosylation- deficient cell lines. Interestingly, the majority of these proteins show more significant downregulation in TM-treated CHO as compared to Lec9.4a. The glycosylation-deficient Lec9.4a cell line has a mutation in polyprenol reductase the affects N-glycosylation, resulting in approximately 50% of expected glycosylation.
TM blocks glycosylation and also arrests the cell cycle at G1 [70]. Therefore, both of these modifications served to either downregulate or limit glycosylation on key glycosylating proteins. The correlation in expression profile indicates that the impact of TM treatment which inhibits glycosylation chemically can affect some of the same genes as the Lec9.4a mutation that minimizes glycosylation. The impact of both are clearly observed on a number of enzymes involved in the N-glycosylation pathway.
2.3.4 Lec2 K-means Clustering Figure 2.3 shows heat maps generated as a result of K-means clustering for the sialylated iTRAQs into groups of proteins with low expression in Pro-5 CHO but
45 high expression in Lec2 and high expression in Pro-5 CHO but low expression in
Lec2 (Figure 2.3D).
The Lec2 mutant cell line, with a deletion in the gene for sialic acid
transporter, can be used to study the effect of sialylation on glycoproteins. Sample
sialylated proteins that are higher in Lec2 cell line include membrane proteins and
transporters, such as cadherin-17-like, CD63 antigen-like, neural cell adhesion
molecule 1, CD97 antigen, and voltage-dependent calcium channel subunit alpha-
2/delta-1. In contrast, the sialylated proteins identified at high levels in Pro-5 CHO but low levels in Lec2 (Figure 3.3D) include legumain, clusterin-like,
transmembrane protein 2, dolichyl-diphosphooligosaccharide-protein
glycosyltransferase subunit STT3A, and beta-1,4-galactosyltransferase 3. This
highlights the sialylation enrichment method can identify sialylated proteins in
wild-type cells that are less sialylated in cells with defects in the sialylation pathway.
In the next section, bioinformatics methods such as GO, KEGG, and IPA were used to
identify the interaction between protein intensity levels and the relevance to
specific biological pathways.
2.3.5 Lec 9.4a KEGG analysis
Proteins from each iTRAQ were mapped to gene symbols for functional
analysis. The GO-CHO (http://ebdrup.biosustain.dtu.dk/gocho) and bioDBnet
(http://biodbnet-abcc.ncicrf.gov) databases were used to maximize the percentage
of Chinese hamster proteins annotated. The dual use of databases ensured that
increased annotations were made using those available for the updated 2014
46 Chinese hamster genome [71]. Each gene symbol was mapped to KEGG orthologs to
determine up- and down-regulated KEGG pathways. The hypergeometric
distribution is used to determine what terms are over- or under-represented in a
local sample list compared to the global population [72]. For iTRAQ 1, the global
population was chosen to be wild-type CHO. Therefore, enriched in Lec9.4a from
iTRAQ 1 means overrepresented in this sample compared to the wild-type CHO
control. The goal of this functional analysis was to determine how the glycosylation
mutant cell lines alter particular cellular processing pathways as a result of
glycosylation deficiencies.
In the comparison between Lec9.4a and CHO, 73 glycoproteins are detected
at significantly lower levels in Lec9.4a cells and 114 show higher levels in Lec9.4a
cells. The subsequent KEGG analysis revealed significant enrichment (p < 0.05) for
three pathways in Lec9.4a cells and significant depletion (p < 0.05) for six pathways
in Lec9.4a cells. This is an important reason why functional analyses are prevalent
in proteomics. Although the overall protein levels in Lec9.4a includes both up- and
down- regulated proteins (Figure 2.2A), the functional analysis that maps proteins
and genes within pathways shows more depletion than enrichment of pathways in
Lec9.4 cells.
2.3.6 Lec2 KEGG analysis
For the KEGG analysis in iTRAQ 2/3, the global population was represented
by the wild-type Pro-5 CHO. Thus, enriched in Lec2 from iTRAQ 2/3 refers to proteins that are overrepresented in the sample compared to the wild-type Pro-5
47 CHO control. In the comparison between Lec2 and Pro-5 CHO, 128 sialoproteins are significantly lower in Lec2 cells and 144 show higher levels in Lec2 cells. The KEGG analysis revealed significant enrichment (p < 0.05) for five pathways in Lec2 cells and significant depletion (p < 0.05) for seven pathways in Lec2 cells. Thus, similar to the comparison for Lec9.4a and CHO, even with overall protein levels upregulated,
the functional analysis shows that biological pathways are depleted in glycosylation-
and sialylation- deficient cell lines.
The top three pathways depleted in Lec2 cells as compared to wild-type Pro-
5 CHO cells are N-glycan biosynthesis (p = 0.00749), glycosaminoglycan biosynthesis (p = 0.00195), and glycosphingolipid biosynthesis (p = 0.00101). For the N-glycan biosynthesis pathways, genes that are significantly enriched in the wild-type Pro-5 CHO cells and concomitantly depleted in the Lec2 sialoproteome include MGAT2, which codes for mannosyl (alpha-1,6-)-glycoprotein beta-1,2-N- acetylglucosaminyltransferase, and B4GALT3, which codes for beta-1,4- galactosyltransferase 3. The depletion of these genes in the Lec2 sialoproteome significantly affects glycosylation as shown in Figure 2.4. In addition, the amino sugar and nucleotide sugar metabolism pathway is identified in the CHO sialoproteome but not identified in Lec2 sialoproteome. This confirms the Lec2 phenotype affects sialylation and prior glycosylation activities. Other cellular pathways, including mucin type O-glycan biosynthesis, RNA transport, and other types of O-glycan biosynthesis, are not significantly enriched or depleted in Lec2 as compared to wild-type Pro-5 CHO cells.
48 2.3.7 Lec9.4a IPA Pathway Analysis
Following KEGG, IPA software was used to further understand up- and down-
regulated cellular functions and pathways of interest. The results of this experiment
can suggest the effect of glycosylation deficiencies on cell metabolism and provide
novel targets for cell line engineering efforts to control the glycosylation of
recombinant protein therapeutics.
Figure 2.5A shows the pathway map for Lec9.4a, where green represents
genes mapped from proteins were downregulated from wild-type CHO and red
represents genes upregulated from wild-type CHO. The upregulated functions for
Lec9.4a include metabolism of protein, catabolism of protein, ER stress response, and glycosylation of protein. This is related to proteins such as heat shock protein
90 beta family member 1 (HSP90B1), a disintegrin and metalloproteinase domain
10 (ADAM10), and cathepsin F (CTSF). In contrast, the functions for protein and carbohydrate metabolism show downregulation. Example proteins include CD44 molecule, intercellular adhesion molecule 1 (ICAM1), and mannosidase alpha class
2B member 1 (MAN2B1). The Lec9.4a mutant is still capable of glycosylation at approximately 50% of wild-type levels. Interestingly, protein glycosylation glycoproteins are upregulated in Lec9.4a as a response to the dysfunctional pathway.
2.3.8 Lec2 IPA Pathway Analysis
Figure 2.5B shows the pathway map for Lec2, where green represents genes
mapped from proteins downregulated from wild-type Pro-5 CHO and red represents genes upregulated from wild-type Pro-5 CHO. Many genes related to protein
49 glycosylation are upregulated in Lec2, corresponding to proteins such as cathepsin
(CTSB, CTSD, CTSF), HSP90B1, SEL1L ERAD E3 ligase adaptor subunit (SEL1L), and bone morphogenetic protein 1 (BMP1). Other genes corresponding to functions in synthesis of carbohydrate, metabolism of carbohydrate, and glycosylation of protein were downregulated in Lec2 compared to Pro-5 CHO. Examples include ICAM1,
CD44, MAN2B1, hexosaminidase subunit alpha (HEXA), ST3GAL1, and STT3A. Lec2 cells show downregulation of other glycosylation-related enzymes, including ST3 beta-galactoside alpha-2,3-sialyltransferase 1 (ST3GAL1), and dolichyl-
diphosphooligosaccharide protein glycotransferase oligosaccharyl transferase
subunit STT3A (STT3A). The genes associated with low protein levels in Lec2 and
concomitantly high levels in Pro-5 CHO cells represent sialylated proteins.
Specifically, sialoproteins identified at high levels in Pro-5 CHO but low levels in Lec
2, M include legumain (LGMN), and adhesion G protein-coupled receptor G
(1ADGRG1).
There is close agreement between the KEGG and IPA analyses. Specifically, N- glyan biosynthesis was identified as depleted in Lec2 cells compared to wild-type
Pro-5 CHO (Figure 2.4). The IPA map for Lec2 also shows altered glycosylation functions, including lower levels of MGAT2. Whereas KEGG is useful to identify specific pathway enrichment and depletion using the hypergeometric distribution,
IPA is useful for comparing interrelated pathways through fold change analysis.
Overall, the glycosylation-deficient cell lines show corresponding changes in pathways especially related to glycosylation, protein synthesis and processing, and metabolism.
50 2.4 Conclusions
Glycosylation and sialylation can play crucial roles in the activity and
circulatory lifetime of numerous biological glycoproteins. As the major producer of
biologics with numerous mutant cell lines available, CHO cells represent an
excellent model mammalian cell line in which to evaluate the impact of changes in
the glycosylation processing capabilities [45, 51]. Efforts to improve the
identification and understanding of glycoproteins and sialoproteins in cell culture
and its impact on protein expression and processing can help us to decipher which
biological processes are particularly sensitive to changes in these glycosylation
events. In this study, we analyzed glycoproteomic data to compare wild-type CHO
cell lines with Lec9.4a and Lec2 glycosylation mutant cell lines.
SPE enrichment can be used to identify various glycosylated proteins,
including membrane proteins, transporters, and secretory pathway enzymes. 381
different proteins were identified in the glycoproteome of iTRAQ1 comparing wild-
type CHO, TM-treated CHO, and Lec9.4a cells. Of the 187 glycoproteins, there were
36 glycoproteins not previously identified in the CHO-K1 proteome [4]. The
combination of ITRAQ2 and ITRAQ3 comparing wild-type Pro-5 CHO with Lec2 cells resulted in 272 sialylated proteins. When the data was compared with the previously published CHO-K1 proteome, there were 60 sialylated proteins not identified earlier [4]. The data sets were analyzed for overall differences in fold change and the gene symbol lists were used for functional analysis. Overall, there was no decrease in protein expression for either Lec9.4a or Lec2 as compared to the
51 wild-type CHO cells. This likely indicates the cellular adaptation and genetic drift of
the mutant cell lines in order to compensate for dysfunctional glycosylation.
Interestingly, the Lec9.4a cells show greater spread from the histogram than Lec2
(Figure 2.2) which may indicate that the mutation in dolichol transport at the very
beginning of glycosylation has a different effect than sialic acid transporter. Both cell
lines have at least some ability to glycosylate and sialylate proteins, respectively.
While both Lec9.4a and TM-treated CHO cells lack the ability to properly glycosylate, there are differences at the glycoproteomic level. Figure 2.2 shows the
average fold change of Lec9.4a is 2.0 (Figure 2.2A) whereas the average fold change
of TM-treated CHO is 0.7 (Figure 2.2B). This suggests that the changes manifested in
the Lec9.4a cells likely extend beyond an inhibition of glycosylation alone. TM
works through chemical inhibition of the transferase that synthesizes N-
acetylglucosaminyl(pyro)phosphoryldolichol from UDP-N-acetylglucosamine and
dolichol phosphate [73]. In contrast, although Lec9.4a cells lack the ability to fully glycosylate, there is not a similar decrease in overall protein intensity of the glycoproteins that are identified as with TM treatment. Thus, the mutant cell line has likely adapted to compensate for glycosylation deficiencies by upregulating and downregulating expression of glycosylation-related genes. In the sialoproteome comparison, the average fold change of Lec2 is 1.1 (Figure 2.2D) versus wild-type
Pro-5 CHO. Similar to Lec9.4a, Lec2 also shows upregulation, in this case of some sialoproteins. One important difference is between the protein expression level and the degree of glycosylation or sialylation, which suggests that transcriptomic data would improve the differentiation between transcription and post-transcriptional
52 modifications. In addition to histograms, Figure 2.2 shows the PCA analysis of
iTRAQ1(Figure 2.2C) and the combination of iTRAQ2/3 (Figure 2.2E). This clustering shows no overlap of samples and a distinct cluster for each sample. This indicates variance in samples, which show up- and down- regulation.
The combination of k-means clustering, KEGG, and IPA elucidate specific differences in glycoprotein and sialoprotein expression as well as affected cellular functions and pathways. In Lec9.4a, cell pathways such as protein metabolism, protein catabolism, carbohydrate metabolism, protein glycosylation, and ER stress response, were mapped with up- and down- regulated gene interactions (Figure
2.5A). One example of a gene upregulated and glycosylated in Lec9.4a is HSP90B1, a heat shock protein chaperone that assists in processing, folding, and secreting proteins. A reduction in glycosylation present in Lec9.4a is likely to challenge the ER processing capabilities of CHO cells including folding. Indeed, previous reports suggested that inhibition of glycosylation through TM treatment can trigger the ER stress and unfolded protein response that activates a number of chaperones and heat shock proteins [74]. Previous proteomics efforts have revealed that partial glycosylation of HSP90B1 occurs during TM treatment and the increased concentration of improperly glycosylated HSP90B1 serves to increase cell survival
[75]. Other ER stress response genes upregulated in the Lec9.4a cells include CTSD,
78 kDa glucose-regulated protein (HSPA5), MGAT2, hypoxia up-regulated 1
(HYOU1), and glucosidase alpha, acid (GAA). Similarly, GAA is highly glycosylated, with seven potential N-glycosylation sites [76]. When the majority of glycosylation sites were eliminated by site-directed mutagenesis, Hermans observed no effect on
53 structure or function [76]. However, Herman found one glycosylation site shows a
significant effect leading to synthesis of a mutant GAA precursor that accumulates in
the ER [76]. Indeed, GAA deficiency results in glycogen storage disease type II, also
known as Pompe disease [77]. Thus, the Lec9.4a cells appear to have incorporated a
number of adaptations that enable survival in a low glycosylation environment.
Interesting, the upregulation of ER stress in both Lec9.4a and TM-treated CHO
shows that TM treatment shows similar pathway effects and is a good model for
Lec9.4a.
In addition to the known changes to the dolichol lipid in Lec9.4a, pathway
analysis (Figure 2.5A) revealed altered protein metabolism, carbohydrate
metabolism, and protein catabolism. Specifically, protein catabolism showed
upregulated of genes including ADAM10, heat shock cognate 71 kDa protein
(HSPA8), BMP1, and glucosylceramidase beta (GBA). An example of protein
degradation enzyme is ADAM10, a transmembrane metalloprotease with four N-
glycosylation sites [78]. Escrevente studied glycosylation site mutants and showed that the non-processed precursor to ADAM10 accumulates in the ER as a result of the improper glycosylation [78]. Interestingly, defects in ADAM10 play a role in
Alzheimer’s disease pathogenesis in humans [79]. Upregulation of ADAM10, which
we observed in Lec9.4a cells, may be reveal an accumulation of improperly
glycosylated, less active, glycoprotein. Other upregulated genes in Lec9.4a cells
require activation and targeting for proper function. CTSF is synthesized inactively
and requires processing in order for the enzyme to function [80] [81]. Reduced
54 glycosylation will lower targeting and thus upregulation may be essential to
facilitate transport to the lysosome for proper function.
Many glycosylation-related genes are downregulated in Lec9.4a cells relative
to wild-type CHO including CD44, a highly glycosylated protein involved in cell to cell interactions and migration. CD44 glycosylation variants are related to tumorigenesis [82, 83]. In other research, treatment of human cell lines with TM to inhibit glycosylation resulted in decreased ability of CD44 to bind hyaluronan [84].
Bartolazzi showed that all five potential N-glycosylation sites are critical for CD44
binding [84]. Generation of CD44 glycosylation mutants revealed diverse effects
with respect to hyaluronan binding; terminal sialic acid inhibited binding, and the
first N-linked GlcNAc residue increased binding, Similarly, ICAM1 is a highly-
glycosylated protein and N-linked glycosylation is critical for the conformation and
immune function of the protein [85]. ICAM1 is observed lower in Lec9.4a cells, also
deficient in complete glycosylation and suggests the importance of glycosylation to
glycoprotein activity. The plasma membrane glycoprotein plays a role in human
glycosylation disorders, and ICAM1 was identified in congenital disorders of
glycosylation patient fibroblasts by He [86]. Thus, He suggested that ICAM1 can
represent a hypoglycosylation biomarker [86], and appears to be playing such a role
in our research as well.
In Lec2 cells, the pathway analysis showed changes in carbohydrate
synthesis, carbohydrate metabolism, protein glycosylation, and protein catabolism
(Figure 2.5B). The greatest upregulation in Lec2 is protein catabolism. BMP1 is one
55 gene upregulated in both Lec2 and Lec9.4a cells. In order for this extracellular
matrix protein to be activated, it must cleave the prodomain in the trans Golgi prior
to secretion [87]. Structural analyses have shown that BMP1 has six N-glycosylation sites, including one on the prodomain, which contain complex oligosaccharides with sialic acid residues [88]. Glycosylation and sialylation are essential modifications to
BMP1 because only properly-glycosylated BMP1 is secreted. Structural analyses have identified that five of six N-glycosylation sites contain sialic acid, including the prodomain [88]. Recombinant BMP1 lacking glycosylation sites were not secreted and trafficked to the proteasome for degradation [88]. The upregulation of protein catabolism may be important in recycling proteins without proper sialylation.
Another upregulated gene, SEL1L, is essential for degrading misfolded proteins [89].
SEL1L has been implicated in some Alzheimer patients as a polymorphism leading
to this phenotype [90]. The chondroitin sulfate proteoglycan 4 (CSPG4) also plays a
role in protein catabolism. During brain injury, CSPGs, including CSPG4 observed
here, are upregulated and glycosylation patterns were compared between the
recovering and recovered environment [91]. Kilcoyne showed that alpha-2,6-
sialylation activities were increased and inhibitory in the injured state [91].
Some genes related to glycosylation are upregulated in Lec2 cells, as well as
Lec9.4 cells, possibly suggesting cellular adaptation to sialic acid transporter
deletion in this phenotype and glycosylation defects in Lec9.4a. However, most
glycosylation genes in Lec2 are downregulated, including B3GNT2, MGAT2,
ST3GAL1, STT3A, and ribophorin 1 (RPN1). This differentiates the Lec2 and Lec9.4a
phenotype, which shows overall upregulation of glycosylation. STT3A is reduced in
56 Lec2; reduced expression may be one approach the cells can use to adapt to a
reduced sialylation potential. In essence, dispensing with glycosylation completely
is one approach for addressing sialylation deficiencies in Lec2. Also, ST3GAL1 is
lower in Lec2 as compared to wild-type Pro-5 CHO. ST3GAL1 most commonly adds sialic acid to O-glycans [92]. Specifically, ST3GAL1 has four N-glycan attachment sites with potential sialylation [93]. Reducing its activity would have been a reasonable adaptation since there was limited sialylation present due to deficiencies in the transporter. STT3A is involved in the oligylsaccharyltransferase (OST) transfer of oligosaccharides to asparagine residues with high specificity [94].
Hypoglycosylation of OST (with STT3A as a subunit) results in CDG type I, leading to mental retardation [94]. Mutations in STT3A cause a distinct CDG due to changes in glycosylation of the subunit [95]. Sialyltransferases thus are frequently used as targets for cell line engineering efforts in order to increase the sialic acid content of recombinant proteins. The downregulation of glycosylation also revealed depletion of MGAT2 in Lec2 cells. Studies of congenital disorders of glycosylation type IIa exhibit deficiencies in MGAT2, leading to deficiencies in N-glycan biosynthesis [96]
[97]. In mice with MGAT2 deletion, the N-glycans produced showed a hybrid bisected N-glycan structure with the bisecting GlcNAc residue exchanged for beta-
1,4-linked galactose or Lewisx [96]. Thus, glycosylation-deficient cells can produce
N-glycans, notably with changed structure and altered function. In general, there is a strong relationship between glycosylation and sialylation activities in cells. Lec2 cells may exhibit adaptations in their glycosylation machinery to adapt to under sialylation due to the transporter defect.
57 New advances in glycoproteomics will improve glycoprotein identification
and enable novel investigations into cell culture. In addition to iTRAQ used in this
experiment, other labeling methods such as stable isotope labeling (SILAC) and
tandem mass tags (TMT) can be used to compare glycoproteins between cell lines
and healthy or diseased tissue samples [98] [99]. Additionally, methods such as SPE
can be used to enrich for glycoproteins and identify more low-abundance proteins.
Recently, Yang et al. evaluated how glycoproteomic profiles change between
dyssynchronous heart failure and cardiac resynchronization therapy [100]. Relative
changes in the level of glycoproteins were determined by SPE enrichment, followed
by iTRAQ labeling [100]. Similarly, Tian used SPE to enrich glycoproteins from both
ovarian tumors and adjacent normal ovarian tissue [101]. Various tumor types were
labeled with iTRAQ reagents and relative protein abundance measured by MS [101].
Up- and down-regulated glycoproteins were identified and further verified by
Western blot; specifically, CEA5 and CEA6 showed significant upregulation in
ovarian mucinous carcinoma compared to healthy ovarian tissue [101]. These
examples highlight how glycoproteomics can be used to compare samples of clinical
relevance in addition to biotherapeutic development applications.
Our enrichment of glycoproteins allowed for the comparison between
Lec9.4a and CHO cells, and the enrichment of sialylated proteins enabled
comparison between Lec2 cells and Pro-5 CHO. The enrichment of glycoproteins in the wild-type CHO but not in the glycosylation-deficient Lec9.4a cells indicates
success of the glycoprotein enrichment protocol, whereas enrichment of
sialoproteins in wild-type Pro-5 CHO but not in Lec2 suggests successful
58 sialoprotein enrichment methods. In the iTRAQ1 comparison, we showed up- and down- regulation of glycoproteins involved in protein metabolism, protein catabolism, carbohydrate metabolism, protein glycosylation, and ER stress response.
In the iTRAQ 2/3 comparison, we identified differences in carbohydrate synthesis, carbohydrate metabolism, protein glycosylation, and protein catabolism. Higher expression of glycoproteins and sialoproteins suggests differences in genetic expression and post-translational modifications. For example, proteins with insufficient glycosylation or sialylation may accumulate intracellularly and require increased transcription to yield functional activity. In contrast, low expression of glycoproteins and sialoproteins in the deficient cell lines confirms that the mutation directly affects cell metabolism. Overall, results from the analysis of glycosylation mutant cell lines in this experiment validate the need for advanced glycoproteomic and sialoproteomic methods to yield new insight into cellular metabolism for CHO cell lines, as well as disease models. This serves to increase knowledge for physiological changes in response to defects in the cellular processing pathways.
59
Tables
Table 2.1: Experimental Conditions
Peptide Unique Unique iTRAQ Name Spectrum Peptides (#) Proteins (#) Matches (#)
1 CHO, CHO TM, Lec9.4a iTRAQ 973 381 14106
2 Pro-5, Lec2 iTRAQ 2188 500 22221
3 Pro-5, Lec2 iTRAQ 2723 613 18538
60 Figures
Figure 2.1: Overview of Experimental Design. A: Affected glycosylation steps for mutant and treated cell lines. Tunicamycin-treated and Lec9.4a mutant cells show deficient glycosylation. Lec2 mutant cells show deficient sialylation. B: Flow diagram of glycoproteomics sample preparation and bioinformatics analysis. Solid-phase extraction method was used which involves oxidation, hydrazide bead coupling to enrich specifically for glycopeptides, and PNGaseF digestion to cleave off N-glycans. Following labeling, samples are fractionated and subjected to mass spectrometry. Spectra are matched to CHO databases and bioinformatics is used to determine functional differences between samples. Abbreviations: TM – tunicamycin.
61 Figure 2.2: Distribution of Protein Intensity. A-C: Histograms of Lec9.4a compared to wild-type CHO and TM-treated CHO. Bin sizes are 0.2 and range from 0-6 fold change. Frequency indicates the number of proteins within each bin size. D: Principal component analysis of CHO, Lec9.4a, and TM-treated CHO showing the first two principal components on the axis. E-H: Histograms of Lec2 compared to wild-type Pro-5 CHO. I: Principal component analysis of Pro-5 CHO and Lec2, showing the first two principal components on the axis. Abbreviations: TM – tunicamycin, PC – principal component.
62 Figure 2.3: Protein Intensity Heat Maps. K means clustering was calculated for the cell line comparisons. A-D: glycoprotein groups with low levels in wild-type CHO but high in Lec9.4a and TM-treated CHO, glycoprotein groups with high levels in wild-type CHO but low in Lec9.4a and TM-treated CHO, glycoprotein groups with low levels in wild-type Pro-5 CHO but high in Lec2, and glycoprotein groups with high levels in wild-type Pro-5 CHO but low in Lec2. Protein intensity heat maps show distribution from low [43] to high (red). Abbreviations: TM – tunicamycin.
63 Figure 2.4: Lec2 KEGG Pathway N-glycan Biosynthesis. Pathway is significantly depleted in Lec2 cell line as compared to wild-type Pro-5 CHO. Specifically, MGAT2, MGAT4, and B4GALT3 are genes that are involved in pathway depletion (shown in pink).
64 Figure 2.5: Pathway Analysis Maps. A: Lec2 pathway map. IPA software (www.ingenuity.com/products/ipa) was used to evaluate biological functions that are up- and down- regulated in Lec2 as compared to wild-type Pro-5 CHO cells. Green represents predicted downregulated genes in Lec2 and red represents predicted upregulated genes in Lec2. Identified genes play a role in proliferation of cells, hydrolysis of protein fragment, metabolism of oligosaccharide, metabolism of carbohydrate, glycosylation of protein, biosynthesis of poly-N-acetyllactosamine, and synthesis of polysaccharide. B: Lec9.4a pathway map. IPA software (www.ingenuity.com/products/ipa) was used to evaluate biological functions that are up- and down- regulated in Lec9.4a as compared to wild-type CHO cells. Green represents predicted downregulated genes in Lec9.4a and red represents predicted upregulated genes in Lec9.4a. Identified genes play a role in apoptosis, binding of carbohydrate, metabolism of carbohydrate, glycosylation of protein, metabolism of protein, and endoplasmic reticulum stress response of cells.
65 Chapter 3: Proteomics Analysis of Chinese Hamster Cell Lines
and Tissues
Abbreviations
CHO – Chinese hamster ovary, mAb – monoclonal antibody, HEK – human
embryonic kidney, DHFR – dihydrofolate reductase, HCP – host cell protein, MS –
mass spectrometry, SDS – sodium dodecyl sulfate, EDTA –
ethylenediaminetetraacetic acid, PMSF – phenylmethylsulfonyl fluoride, BCA – bicinchoninic acid, TCEP – tris(2-carboxyethyl)phosphine, NSAF – normalized spectral abundance factor, GO – gene ontology, KEGG – Kyoto encyclopedia of genes and genomes, FDR – false discovery rate, iTRAQ – isobaric tags for relative and absolute quantification, PCA – principal component analysis, MCM –
minichromosome maintenance complex, BiP – binding immunoglobulin protein, PDi
– protein disulfide isomerase (PDi), GOLPH3 – golgi phosphoprotein 3, HPLC – high
performance liquid chromatography, IGF1 – insulin-like growth factor 1, ZP – zona
pellucida, ORC – origin recognition complex, Cdc25A – cell division cycle 25
homolog A, E2F – E2 transcription factor, Abl – Abelson murine leukemia viral
oncogene
3.1 Introduction
In 1957, Theodore Puck and colleagues established the Chinese hamster
ovary (CHO) cell line that is used today across the biotechnology industry to
generate therapeutic monoclonal antibodies (mAbs) and recombinant proteins that
generate billions of dollars in sales [102]. Biologics increasingly constitute a large
66 percentage of top drugs and play important roles in research and development
pipelines. Over the past three years there have been 11, 12, and 13 biological license
application approvals in 2014, 2015, and 2016 respectively [103-105]. While there
are many possible production hosts including human embryonic kidney [106],
mouse myeloma NS0, Saccharomyces cerevisiae, and Escherichia coli, CHO represents the most widely used biotechnology host for commercial proteins due to its wide regulatory acceptance, scalability and adaptability to manufacturing, and glycosylation capabilities among other characteristics [107]. CHO cell lines exhibit relatively high growth rates, are easy to genetically engineer, and exhibit post- translational modifications that are compatible with humans. Increasing our knowledge about CHO cellular capabilities as well as the original Chinese hamster can provide insights that biotechnologists can use to improve production of therapeutic proteins through cell line engineering and bioprocessing modifications.
Originally, ovarian tissues were isolated and used to establish a fibroblast- like cell type with nearly diploid chromosomes that became spontaneously immortal after months in culture [108]. Although commonly considered ovary cells,
CHO actually grew from the surrounding ovarian connective tissue [109]. Over time,
CHO use for industrial applications grew, as the cells were adapted to low-serum and eventually serum-free suspension culture. The first product made by CHO cells used the CHO-DXB11 cell line established at Columbia University to study dihydrofolate reductase (DHFR) activity [110]. Chasin and Urlaub established the
CHO DG44 cell line following deletion of both loci for DHFR on chromosome 2 [111].
The origin of CHO-S from the Puck CHO ancestor is less clear and may have resulted
67 from adaptations to grow the cells in suspension simultaneously in academia and
industry [2, 109]. As a result, CHO cell lines represent a ‘quasi-species’ due to the
high variability across parental and clonal cell lines [109]. Today, derivatives of the
original CHO cells exist across the biopharmaceutical industry and academia, having
undergone numerous genetic modifications and adaptations, yet they serve as the
dominant production platform cell line for biotherapeutics.
Given the wide application and variability of the CHO cells in biotechnology
as well as its origin from Chinese hamsters, burgeoning efforts are under way to
begin to characterize the production host from different sources and in comparison,
with the Chinese hamster host organism. Recently, a range of ‘omics efforts have
been initiated to characterize and differentiate CHO hosts from each other and the
Chinese hamster [37]. A draft genome of CHO-K1 was completed through a
collaborative effort in 2011 [3] followed by the Cricetulus griseus genome in 2013
[2] and several other cell lines. More recently, the CHO-DXB11 cell line was
sequenced in order to aid efforts at understanding the DHFR negative phenotype
and drift from other CHO cell lines [112]. In addition, genomic BAC libraries were constructed for CHO-K1 and CHO-DG44 cells to study the distributions and rearrangements of hamster chromosomes [113]. These initial efforts have paved the way for characterizing the diversity of the Chinese hamster against CHO parental and clonal cell lines at least at the genome level.
Equally important will be efforts to decipher the characteristics of these cell lines and the host organism at the RNA, protein, and metabolite level based on other
‘omics tools including transcriptomics, lipidomics, and proteomics among others.
68 Recently, diverse transcriptomics data sets for CHO cell lines under various culture
conditions were compiled into an online transcript database [71]. The approach
enabled the assembly of over 65000 CHO cell line transcripts [71]. In another study,
transcriptomics was used to compare CHO cell lines and hamster tissue by RNA-seq
[114]. Variability in gene expression was attributed to differences in glycolysis and
glycosylation thus suggesting the existence of metabolic engineering targets that
could be exploited [114]. Recently, a comparison of cell lines by lipidomics revealed
that phosphatidylethanolamine and phosphatidylcholine represent the major lipid
classes in CHO, SP2/0, and HEK cells [115]. HEK cells show increased lipid content,
suggesting that differences in cell lipids can contribute to cell secretory capacity
[115]. These previous studies highlight how increased ‘omics data sets for CHO can
be used for improved understanding of the production host.
Proteomics can now identify and quantify thousands of cellular proteins for
understanding cell physiology. These efforts, like those with transcriptomics and
lipidomics, can help assist biotechnologists in their efforts to characterize distinct
cell lines in developing stable host cell lines with desirable phenotypes. An initial
proteomic analysis of the CHO-K1 cell line by our group identified CHO host cell proteins and pathways that are enriched and depleted compared to the CHO genome and transcriptome [4]. This large-scale profiling identified approximately
6000 cellular proteins and, together with functional analysis, revealed enrichment
in genes related to protein processing and apoptosis at the proteomic level [4]. In
contrast, steroid hormone and glycosphingolipid metabolism were depleted [4].
Additionally, bioinformatics tools were developed to enable the identification of
69 CHO secreted proteins by detecting the presence of signal peptides, transmembrane
domains, and cellular localization features [116]. Over 1000 proteins were
determined to represent the CHO supernatant or superome, which enables further
research into high abundance host cell proteins (HCPs) for bioprocess development
applications [116]. These HCPs can accumulate in the production matrix and
represent a challenge to bioprocessing development, especially for biotherapeutic
purification steps. In another study, proteomics was used to identify host cell
proteins (HCPs) that accumulate over cell culture duration and can negatively affect
the process by interacting with recombinant protein [117]. Proteomics was also
used to evaluate changes in HCPs over 500 days in culture; results showed 92 HCPs
with significant changes in expression with numerous HCPs that are known as
difficult to purify [118]. While many cell lines and different bioprocess conditions
have been studied via transcriptomics and proteomics, comparative studies are still
in their infancy particularly in relation to the original hamster host.
As the relationship between derived cell lines and tissues of the original
Cricetulus griseus is not well-documented, our study was undertaken to characterize
the proteome of multiple CHO cell lines in the context of the Chinese hamster
organism from which these lines were derived. Liver and ovarian tissues were
chosen to represent biologically relevant tissues for this comparison. CHO cell lines
have been created from isolated ovarian connective tissue, and the liver has been
used for genome sequencing [2, 109]. Furthermore, one of the principal functions of the liver is to detoxify and metabolize chemicals; thus, the organ is highly
metabolically active. Specifically, the liver plays a role in bile production, cholesterol
70 production, protein synthesis, glucose storage and release, and substance clearance.
In contrast, the ovary serves the female reproductive system by producing eggs and reproductive hormones. The organ contains extensive loose and dense connective tissue that acts as a covering and bed for vasculature, respectively. As CHO was established, it was sequentially adapted from an adherent to suspension culture. It remains unclear what other physiological changes occurred during this process.
This research aims to begin to elucidate the different cellular characteristics at the proteomic level for representative CHO cell lines and hamster tissues. Consequently, two cell lines, CHO-S and CHO DG44, during both the exponential and stationary phases, were subjected to mass spectrometry proteomics analysis along with liver and ovary tissues. Peptide identification, protein quantification, and functional analysis were performed to characterize similarities and differences at the protein level between cell lines and tissues in order to explain physiological changes from tissue to cell line. While similar protein expression patterns were observed between
CHO-S and CHO DG44 in both growth phases, these expression characteristics were distinct from those observed in liver and ovary tissues. These differences likely reflect the diverse physiological adaptations that these specific cell lines have undergone as well as the complex cellular makeup of tissues or organs. In particular, differences were detected between cell lines during processes including the cell cycle, DNA replication, and membrane transport. Between exponential and stationary phases, both CHO-S and CHO DG44 also shift physiological activities from replication activities to metabolic processes. In addition, liver and ovary exhibit enhanced metabolism compared to cell lines as determined by an examination of
71 enriched pathways, including secretion, and in the case of ovary, extracellular
matrix and adhesion. Overall, the results indicate distinct variability across
physiological pathways of interest that are either upregulated or downregulated in
tissues as compared to cells. These differences highlight unique aspects of cellular
performance in organisms as compared to immortal cell lines in a tissue culture environment. Our findings will help us to better understand the changes that are manifested in adapting cells from tissue for different requirements, such as growth in suspension cultures and production of biotherapeutics for pharmaceutical applications.
3.2 Materials and Methods
Briefly, the experiment section is divided into three parts: sample
preparation, MS peptide identification, and bioinformatics analysis.
3.2.1 Tissue Isolation
Chinese hamsters were generously provided by the lab of Dr. George
Yerganian (Cytogen Research, Roxbury, MA). Euthanization was performed by CO2
and verified by puncture by veterinarians at Johns Hopkins Medical School.
Harvested tissues, including liver and ovary, were immediately frozen on dry ice
and stored at -80oC until analysis.
3.2.2 Cell Culture
CHO-S and CHO DG44 were the two suspension CHO cell lines we grew in
shake-flask batch culture to collect samples at both exponential and stationary
phases. CHO-S cells were cultured in CD-CHO medium supplemented with 8mM
glutamine (ThermoFisher Scientific, Waltham, MA). After achieving the growth
72 curves (data not shown), samples were collected on day 2 for exponential phase and
day 4 or 5 for stationary phase. CHO DG44 cells were culture in DG44 medium
supplemented with 2mM glutamine (ThermoFisher Scientific, Waltham, MA). Cells
were maintained at 37oC, 8% CO2, and 120RPM; viable cell density was determined
by hemocytometer and trypan blue. For sample collection, approximately 3 million
cells were spun down, washed with PBS on ice, frozen rapidly on dry ice, and stored
at -80oC until analysis.
3.2.3 Sample Preparation
Samples for proteomics were thawed on ice and suspended in 2% sodium dodecyl sulfate (SDS) supplemented with 0.1mM phenylmethane sulfonyl fluoride
(PMSF) and 1mM ethylenediaminetetraacetic acid (EDTA), pH 7-8. Samples were lysed by sonicating thrice for 60 seconds at 20% amplitude followed by 90 seconds pause in order to expose proteins. Protein concentration was measured by bicinchoninic acid (BCA) protein assay after briefly spinning to remove cell debris.
Three hundred micrograms of each sample was reduced in 10mM tris(2- carboxyethyl) phosphine (TCEP), pH 7-8, at 60oC for 1hr on a shaking platform.
After bringing the sample to room temperature, approximately 17mM iodacetamide
was added to alkylate the sample to final concentration for 30 minutes, thus
preventing the reformation of disulfide bonds. Next, samples were cleaned using
10kDa filters to reduce the SDS concentration as suggested by the filter aided
sample preparation (FASP) protocol [6]. The samples were digested by
trypsin/LysC (Promega V507A), overnight at 37oC on a shaking platform.
3.2.4 2D LC-MS
73 The MS was completed by the lab of Dr. Robert Cole at the Johns Hopkins
Medical School. Digested peptides were fractionated on basic reversed phase column (XBridge C18 Guard Column, 5µm, 2.1 x 10mm XBridge C18 Column, 5µm,
2.1 x 100mm). Fractions were concatenated into 48 samples prior to second dimension LC and mass spectrometry analysis. The use of fractionation with equal peptides in each was designed to mimic biological replicates for each sample.
Tandem mass spectrometry analysis of the peptides was carried out on the LTQ
Orbitrap Velos (www.thermoscientific.com) MS interfaced to Eksigent
(www.eksigent.com) nanoflow liquid chromatography system with the Agilent 1100 auto sampler. Peptides were enriched on a 2cm trap column (YMC gel ODS-A S-
10µm), fractionated on Magic C18 AQ, 5µm, 100Å (Michrom Bioresources), 75µm x
15cm column and electrosprayed through a 15µm emitter (PF3360-75-15-N-5, New
Objective). Reversed phase solvent gradient consisted of solvent A (0.1% formic acid) with increasing levels of solvent B (0.1% formic acid, 90% acetonitrile) over a period of 90 minutes. LTQ Orbitrap Velos parameters included 2.0kV spray voltage, full MS survey scan range of 350-1800m/z, data dependent HCD MS/MS analysis of top 10 precursors with minimum signal of 2000, isolation width of 1.9, 30s dynamic exclusion limit and normalized collision energy of 35. Precursor and fragment ions were analyzed at 60000 and 7500 resolutions, respectively. In summary, the peptides were ionized and dissociated to obtain fragment masses that are matched to the predicted masses for peptide sequences.
3.2.5 Bioinformatics
74 Peptide sequences were identified from isotopically resolved masses in MS
and MS/MS spectra extracted with and without deconvolution using Thermo
Scientific MS2 processor and Xtract software nodes. Data was searched against all
entries in Cricetulus griseus database for CHO cell lines. Oxidation on methionine, deamidation NQ and phosphoSTY were set as variable modifications, and carbamidomethyl on cysteine was set as fixed modifications in Mascot search node used in Proteome Discoverer (Version 1.4, http://portal.thermo-brims.com) workflow. Mass tolerances on precursor and fragment masses were 15 ppm and
0.03 Da, respectively.
Protein identifications were made using Proteome Discoverer software
(Thermo) with stringent high confidence cutoff (<1%FDR). The protein intensities were determined using NSAF, a method to generate reproducible counts and high linearity across biological and technical replicates [119]. It measures the number of spectra matched to a specific protein divided by its length over the sum of all proteins in the sample [119]. NSAF values were plotted for each sample comparison, and fold change values were used to identify significant differences in protein intensity. Protein accession numbers were mapped to gene symbols using the biological database network (http://biodbnet-abcc.ncicrf.gov) for functional analysis by GO and KEGG. For GO annotation, gene symbols were mapped to molecular function, biological process, and cellular component using the GOCHO platform (http://ebdrup.biosustain.dtu.dk/gocho). For KEGG annotation, latest
(Dec.2015) database pathways for Cricetulus grisueus were downloaded.
(http://www.genome.jp/kegg). All programming for the hypergeometric test were
75 calculated in MATLAB version 2015vB
(http://www.mathworks.com/products/matlab) and RStudio
(https://www.rstudio.com). Enrichment and depletion p-values were calculated using the hygecdf and hygepdf functions in MATLAB. These values were used to generate heat maps and k-means clustering in Genesis software version 1.7.6 [65]
KEGG pathways of interest were plotted using the search and color feature on the
KEGG website (http://www.genome.jp/kegg/tool/map_pathway2.html).
3.3 Results and Discussion
Our study was designed to identify differences in expression of key proteins between relevant tissues from the Chinese hamster and CHO cell lines for potential cell engineering applications. A proteomic evaluation and comparison of CHO cell lines and hamster tissues using proteomics will provide valuable insights into the similarities and differences between production cell lines and the host organisms.
Such a comparison is particularly relevant given the importance of CHO cell lines for production of numerous proteins of biotechnology research and clinical therapeutic interest. An overview of the workflow of the sample preparation and analysis is shown in Figure 3.1. Initially, cells and tissues were subjected to sonication, reduction, alkylation, and digestion, and then the peptides were fractionated in order to increase the number of identifications followed by LC/MS/MS profiling.
Previous studies by Baycin have demonstrated that increased fractionation improves number of protein identifications since more peptides can be identified for each protein [4].
76 In a recent comparison by Le, the 2012- and 2014- annotated genomes were used for ‘omics applications and it was observed that the newest annotation was preferred because it contained higher mapped rates for intra-genic regions [120].
Therefore, the recently annotated Chinese hamster genome [2] by Lewis was used in
this study to provide for the most accurate method for protein identification. The
protein accession numbers were converted to gene symbols for functional analysis.
For missing gene symbols, accession numbers were searched against mouse and
human databases using the biological database network bioDBnet [121]. The
website includes db2db which allows users to input protein accession numbers to
inter-convert between various databases and increase the percentage of protein
accession numbers and gene symbols matching.
Both GO and KEGG tools were used for functional analysis. In GO, gene
symbols are mapped to molecular functions, cellular components, and biological
processes in which the gene plays a role [122]. A related approach is pathway
analysis through a KEGG tool. In this method, gene symbols are mapped to all
involved KEGG pathways in order to identify significant pathway differences and the
presence or absence specific genes [123]. Both functional analyses use the
hypergeometric distribution in order to identify enriched and depleted p-values. A level of p < 0.05 was set for evaluating significance.
3.3.1 Protein Identifications
The numbers of spectra, peptides, and proteins from the different sources are shown in Table 3.1. With a FDR of 1% and at least 2 unique peptides per protein, at least 4400 proteins were identified in tissues, and over 5500 proteins were
77 identified in cell lines. Combining the unique proteins from each sample yielded a
proteome of 11801 proteins. As shown in Figure 3.2A, there were 2283 proteins
common to all samples while 5138, 1058, and 894 proteins were unique to cell lines,
ovary tissue, and liver tissue only, respectively. In addition, 801 proteins were
common between cell lines and liver tissue, whereas 1137 proteins were common
between cell lines and ovary tissue. Regarding CHO-S and CHO DG44 (Figure 3.2B),
5307 proteins were identified in both as well as 2176 proteins unique to CHO-S and
1876 proteins unique to CHO DG44. For the CHO cell lines alone, this represents a
huge improvement in protein identifications compared to the 2012 CHO-K1 proteome [4]. Between CHO-S and CHO DG44, there were over 9300 unique proteins identified compared to approximately 6000 CHO-K1 proteins, representing
a 56% increase in proteins elucidated in our previous study [4]. Also of note, this
experiment set the FDR at 1% whereas the CHO-K1 proteome used a slightly less
stringent 2% [4]. Therefore, the total number of proteins identified in this
experiment surpasses previous CHO proteomes and offers the first hamster tissue
proteomes for comparison. A comparison of 8-plex iTRAQ and label-free proteomics
revealed that label-free proteomics yields higher sequence coverage and is able to elucidate proteins that are differentially expressed [124]. Similarly, a comparison between iTRAQ and label-free proteomics suggests that label-free approaches can provide more values closer to the actual differentially expressed values as shown by
Western blotting [125]. Therefore, for this study, a label-free approach was used to maximize the proteome coverage and provide a thorough comparative analysis of specific proteins in different cells and tissues.
78 Proteins found only in cells often relate to processes, such as DNA replication
and transcription, which are most active in the growing cells. Likewise, some
proteins are tissue specific such as ovary-specific proteins including ovarian cancer
G-protein coupled receptor, oviduct-specific glycoprotein, polycystin-1, estradiol
17- -dehydrogenase 1, and zona pellucida sperm-binding protein. Many proteins
identifiedβ only in the ovary suggest roles in extracellular matrix maintenance and
reproductive development. In contrast, liver-specific proteins identified include
liver carboxylesterase 1, apolipoprotein M, bile salt sulfotransferase-like protein,
and glycolipid transfer protein domain-containing protein 2. These relate to liver-
specific functions, such as detoxification and secretion. Finally, cell-specific
identified proteins are related predominately to the cell cycle. Examples include cell
cycle checkpoint protein RAD17, DNA topoisomerase II-binding protein 1,
DNA/RNA-binding protein KIN17, and mitotic-spindle organizing protein 1. These
differences highlight the difference between actively-growing cells in culture and
differentiated tissues.
For each sample, the protein intensity was determined by dividing the
number of peptide spectrum matches by the number of amino acids to yield the
spectral abundance factor (SAF) [126]. The SAF was normalized for each sample to
yield the normalized SAF (NSAF) value. These values were used to generate a
principal component analysis (PCA) for shared proteins in the tissues as well as the
two cell lines in distinct growth phases, as shown in Figure 3.2C. As expected, the
different cell lines cluster together and are distinct from liver and ovary tissues.
Specifically, CHO-S exponential and CHO-S stationary are grouped together and CHO
79 DG44 exponential and CHO DG44 stationary are grouped together. Also, the cell lines cluster more closely to ovary than to liver. While PCA groupings can elucidate general similarities and differences, GO and KEGG can be used to identify functional differences as described later.
Next, a histogram of NSAF values compiled for all the proteins for each cell line and the two tissues is shown in Figure 3.2D. The histogram is used to represent the distribution of proteins from low to high abundance. In general, the total number of proteins identified is greatest in the cell lines, but the distribution of protein intensity is wider for the organs, which would be expected for complex tissues. Specifically, the most abundant proteins are observed in liver and ovary tissue, as shown by the increased levels at higher proteins intensities in Figure 3.2D.
Example proteins include actin, myosin, heat shock protein, fatty acid binding protein, and glyceraldehyde-3-phosphate dehydrogenase. This highlights that many metabolic activities are upregulated in tissues relative to cells. This finding is in general agreement with a proteomic comparison done between liver tissue and primary liver hepatocytes that showed overall downregulation of proteins in the cell line [127]. Furthermore, the peak for number of proteins shown in Figure 3.2D is larger in both cell lines during the exponential phase than the stationary phase. This may correspond to the high DNA replication activity during the growth phase and a requirement for higher numbers of cellular proteins during exponential growth.
3.3.2 Protein Intensity Comparison
The NSAF values were plotted for each sample combination including between exponential and stationary phases of CHO cell lines and between cell lines
80 and tissues. Linearity was measured using R2 to determine how close the points align to a regression fit. The R2 value ranges were determined to give a linearity estimate and are plotted from highest at 0.89 (Figure 3.3A) to lowest at 0.48 (Figure
3.3O). The highest degree of linearity is observed between CHO-S exponential and
CHO-S stationary (Figure 3.3A) and CHO DG44 exponential versus CHO DG44 stationary (Figure 3.3B). Interestingly, more linearity is observed within a cell line instead of between different cell lines at the exponential or stationary phase. This correlation suggests significant diversity has evolved between these cell lines that may affect protein expression and cellular characteristics. Indeed, the genomes of
CHO-K1, CHO-S, and CHO DG44 have diverged from Cricetulus griseus as indicated by the three million single nucleotide polymorphisms across various CHO cell lines
[2]. Likewise, the values between CHO cell lines and tissues vary from a high of 0.66
(between ovary and CHO-S stationary, Figure 3.3G) and a low of 0.48 (between liver and CHO-S stationary, Figure 3.3O).
CHO cells, which were derived from fibroblast connective tissue surrounding the ovary [109], show levels of protein expression that are more similar to the ovary than the liver proteome. For example, the R2 ranged from 0.61-0.66 for ovary versus cells (Figure 3.3G-J) versus R2 of 0.48-0.54 for the liver versus cells (Figure 3.3L-O), respectively. For the ovary comparison, there was a slightly higher linearity with the stationary phase data to suggest greater similarity of the CHO stationary phase to tissue characteristics (Figure 3.3G-H). The tissue to cell line comparison with the highest R2 value is CHO-S stationary versus ovary (Figure 3.3G). Proteins are colored in Figure 3.3 to show differential expression between samples, as measured
81 by a fold change of less than 0.5 or greater than 2 for each comparison. Example
proteins that are significantly higher in ovary include transthyretin, biglycan, fatty
acid binding protein, and actin. These proteins correlate well with results from the
human ovary-specific proteome, which identified 145 proteins with elevated
expression in ovary compared to other tissues [128]. The ovary mainly functions to
develop oocytes and produce reproductive hormones; it contains an extracellular
matrix of granulosa cells surrounding the follicle [128]. Many of the ovarian
upregulated proteins are related to extracellular matrix, which is not observed in
suspension CHO cells. This likely also highlights the loss of adhesion properties for
cells, especially those adapted to suspension growth, compared to tissues.
Liver versus both CHO lines in exponential and stationary phases (Figure
3.3L-O) show the greatest difference for the tissue to cell line comparisons at R2 from 0.48 to 0.54. Between the two cell lines, CHO DG44 has higher linearity than
CHO-S. Examples of proteins that are significantly higher in liver include sulfotransferase 1A1, liver fatty acid binding protein, cytochrome P450 2E1, and alcohol dehydrogenase, highlighting the tissue specificity of these highly expressed
proteins. Indeed, cytochrome P450 represents a family of enzymes with the highest
levels of expression in the human liver [128]. Many of the high-expressing liver
proteins correspond to its function as the largest gland in the human body; the liver
manages hormone and bile synthesis, glycogen storage, detoxification, and plasma
protein synthesis [128].
In general, the proteins expressed at significantly higher levels in cells over
tissues are related to DNA replication and delay of apoptosis as may be expected to
82 accommodate the cells’ rapid growth [129]. Furthermore, proteins involved in
apoptosis that play a role in extending growth are often upregulated in cells as
compared to tissues. One example is bcl-2, an anti-apoptotic gene that has been
commonly overexpressed in CHO cells to delay apoptosis and extend culture
duration [130-132]. In the current study, bcl-2-associated transcription factor 1
isoform X1 was identified in all cell samples, but not in either tissue. Other examples
of growth-related proteins that were identified in cells only include DNA helicase B,
DNA ligase 4, and DNA replication licensing factor MCM4. In the following sections,
we explore the functional significance of the differential protein expression between
cell lines and between cells and tissues in greater depth.
3.3.3 GO Functional Analysis
For functional analyses, each protein accession number was converted to a gene symbol using online tools. The GO-CHO
(http://ebdrup.biosustain.dtu.dk/gocho) and bioDBnet (http://biodbnet- abcc.ncicrf.gov) databases were used to maximize the percent annotated by mapping to the Chinese hamster genome and related species. This was required to account for the increased annotations made available for the 2014 Chinese hamster genome [71]. For GO, these gene symbols were matched to, biological processes, molecular function, and cellular component. Each GO category was analyzed to determine enrichment and depletion p-values. The top 10 most enriched terms
(lowest p-values) for biological processes are shown for all samples in Figure 3.4.
The slice size represents the numbers of genes identified within the sample that are
associated with the GO term. For all samples, the most enriched process is gene
83 expression. Also, the slices are ordered clockwise in decreasing order of p-value so that the 10th most enriched term for the different samples are mitotic cell cycle
(Figure 3.4A-D), translation elongation (Figure 3.4E), or mRNA splicing (Figure
3.4F).
In GO, biological processes represent a biological function involving the gene or gene product [122]. Interestingly, small molecule metabolic processes represent the second most enriched biological process for both cell lines during the stationary
phase (Figure 3.4B,D) and both tissues (Figure 3.4E-F), but are surprisingly absent
from the top 10 for both cell lines during the exponential phase (Figure 3.4A,C). This
process is enriched for the exponential growth samples, although not among the top
10 most enriched processes. Small molecule metabolic process encompasses
thousands of genes and gene products of low molecular weight [122]. This can
include activities such as fatty acid synthesis, amino acid metabolism, protein synthesis, and ATP conversion. Increased small molecule metabolic processes correlate with the increased metabolic activity of tissues such as the detoxification and metabolite production in the liver. The ovary also functions in production and secretion of hormones and thus shows higher metabolic activity than cell lines. For
CHO-S and CHO DG44 stationary, the small molecule metabolic processes also
include metabolite synthesis, conversion, and degradation. Some examples include
UDP-N-acetylglucosamine-dolichyl-phosphate N-
acetylglucosaminephosphotransferase, galactosylgalactosylxylosylprotein 3-beta-
glucoronosyltransferase 3, CTP synthase, and dipeptidase. Thus, glycosylation
enzymatic activities are upregulated in both cell lines at the stationary phase. This is
84 an important consideration for CHO engineering efforts to control glycosylation of
recombinant proteins, an important strategy in bioproduction [133, 134].
The second most enriched biological processes for CHO-S exponential phase
(Figure 3.4A) and CHO DG44 exponential phase (Figure 3.4C) are mRNA processing and mRNA splicing, respectively. This difference may represent increased requirements for mRNA synthesis and processing during growth. Interestingly, the dynamics of mRNA levels change during the cell cycle; peaks of mRNA degradation subsequently follow peaks of mRNA synthesis [135]. In CHO, a positive correlation exists among growth rate, specific productivity, and heavy chain mRNA [136]. As the cells transition to stationary phase, there is a concomitant shift in protein levels as cells shift to other metabolic activities. Previously, a comparative proteomics analysis was applied to understand what changes take place in protein expression between exponential and stationary phases of CHO cells [137]. This studied identified differential expression of proteins such as binding immunoglobulin protein (BiP), protein disulfide isomerase (PDi), and DNA replication licensing factors MCM2 and MCM5 during the exponential phase [137]. Similarly, these proteins were also upregulated in both cell lines in this study during exponential phase. In contrast, growth-regulating proteins such as transglutaminase-2 and clusterin, were upregulated during stationary phase concomitant with apoptosis induction [137]. Clusterin is a glycoprotein that plays a role in apoptosis through lipoprotein transport, complement-mediated cell lysis, and cell adhesion [138]. It is expressed at higher levels in both cell lines at stationary phase and in both tissues in the current study. Furthermore, previous proteomic comparisons between
85 stationary and exponential phases indicated the greatest differential expression was
related to growth regulation and apoptosis, suggesting that control of growth and
prevention of apoptosis are important priorities during stationary phase [137]
[139].
Often the differences in enriched proteins between cells and tissues relate to growth versus senescence. While the mitotic cell cycle term is present in cells
(Figure 3.4A-D), the tenth most enriched term is instead replaced by translational elongation in liver (Figure 3.4E) and mRNA splicing in ovary (Figure 3.4F). The mitotic process is active in rapidly growing cells and includes genome replication and separation of chromosomes into daughter cells. Thus, cells have increased replication activity, whereas liver tissues have increased activity related to addition of amino acid residues to growing polypeptide chains during protein synthesis. This increased processing activity is reasonable when one considers that one of the primary roles of the liver is protein synthesis. Outside of these differences, the majority of GO terms for biological processes are the same for all samples; these include gene expression, mRNA metabolic processing, RNA metabolic process, protein transport, translation, and RNA splicing (Figure 3.4). These processes encompass general processes in cells with thousands of genes and gene products that are essential for cellular activities [122]. In order to further characterize the
specific proteins that play a role in key molecular functions, further analysis was
completed with k-means clustering.
GO terms were clustered into categories using k-means in order to identify
the terms enriched in cells only or enriched in tissues only (p < 0.05). The resulting
86 heat maps are shown for different molecular functions in Figure 3.5. Among the
molecular functions enriched in cells (Figure 3.5A) are DNA replication origin
binding, DNA helicase activity, and RNA polymerase activity. MCM5 is one gene involved in the molecular functions for DNA helicase activity (Figure 3.5A) and DNA replication initiation that was identified in this study as well as in a proteomics comparison of CHO cells undergoing treatment with sodium butyrate [140].
Previous research found MCM5 was the most strongly downregulated protein as a result of sodium butyrate treatment, which results in a large reduction in growth rate accounts and significant increases in recombinant protein productivity [140].
MCM5 was also identified as a growth marker with significant differences in protein expression associated with the transition from exponential to stationary phase in
CHO cells [25]. Finally, recent efforts to differentiate high-producing CHO cell lines revealed significant downregulation of DNA replication initiation (MCM2, MCM5,
MCM6) in the high-producers corresponding to decreased cell growth but increased production [141]. Thus, efforts to knockdown genes involved in DNA replication may enable growth rate reductions that shift cell metabolism to an emphasis on protein production.
Among the molecular functions enriched in tissues (Figure 3.5B) are AMP binding, glucose-6-phosphate dehydrogenase activity, glycoprotein binding, lipid transporter activity, and mannose binding. These activities are expected to be important for protein processing and secretion. AMP binding, for example, has been studied in agricultural biotechnology applications for its relevance in synthesis and modification of lipids [142], and lipids comprise essential organelle membranes
87 involved in the secretory pathway. In addition, lipid transporter activity was
enriched only in tissues; this GO term groups genes responsible for transporting
lipids within and between cells. Example genes include glycolipid transfer protein
and apolipoprotein. In rat liver, glycolipid transfer protein was found to control
translocation of phospholipids across intracellular membranes [143]. In addition,
apolipoproteins are known to be synthesized in the liver, including apoB, apoA-1,
and apoE [144]. Their regulation is dictated post-translationally depending on the
lipid status with the cells. Overall changes in metabolism occur as immortal cell lines
are derived from tissue; similarly, we observe differences between CHO cell lines
and hamster tissues.
Adhesion is an important characteristic of tissues and generally comprises
various cell adhesion molecules and receptors, extracellular matrix proteins, and
peripheral membrane proteins. Figure 3.5B highlights tissue-enriched functions
such as cell adhesion molecule binding, glycoprotein binding, integrin binding,
laminin receptor activity, polysaccharide binding, and receptor binding that are not
enriched in cells. This emphasizes the adaption of cell lines to suspension culture
occurred with a loss of adhesion activity. This has been observed in a previous study
that adapted adherent Madin Darby canine kidney cells to suspension culture [145].
Using proteomics, the study found that many changes in protein expression related
to cytoskeletal architecture; specifically, myosin proteins were differentially
expressed and played a role in cellular detachment [145]. In our results, myosin
family proteins were expressed only in tissues, verifying the inhibition of myosin occurs as cells lose adhesion properties in suspension culture. Laminin is another
88 extracellular matrix component that is highly expressed in the outer layer of ovarian
tissue and plays a role in follicle development [146]. Overall, different laminin
family members were upregulated in ovary tissue in our results. Finally, integrins
play a role in cell attachment and show enriched activity in tissues compared to cells.
To illustrate our findings, Figure 3.6 highlights the cellular components
enriched in tissues, including secretory machinery, such as Golgi cisterna and trans-
Golgi network transport vesicles, and extracellular matrix proteins. For example,
Golgi phosphoprotein 3 (GOLPH3) plays an important role in cancer metabolism, as
its depletion results in increased apoptosis following DNA damage [147].
Subsequent overexpression of GOLPH3 can enhance cell survival as part of the DNA
damage response [147]. GOLPH3 also functions to control the localization of core 2
N-acetylglucosamine- -2,6-sialyltransferase 1 to transport vesicles [148]. This cantransferase affect the 1utilization and α of these enzymes for glycosylation activities. In another experiment, GOLPH3 knockdown suppressed cell migration and decreased N-glycan sialylation as measured by HPLC and LC/MS [149] -
2,6-sialyltransferase 1 was overexpressed in the knockdown cell line, cell migration. When α
and signaling were restored [149]. GOLPH3 isoform X2 was identified in both liver
and ovary tissue, but not in cells. Another gene enriched in tissues is insulin-like
growth factor 1 (IGF1), which is important for promotion of growth and prevention of apoptosis. In our results, IGF1 isoform X2 was identified in liver, which is the organ primarily responsible for IGF1 production. While IGF1 expression was confirmed in CHO-K1 cells through kinetic and growth curves [150], it also serves as
an important component of serum for dependent cell lines and can be important for
89 media development in serum-free conditions [151]. Finally, extracellular matrix proteins were enriched specifically in ovary tissue. This highlights that a difference between the structure of liver and ovary contributes to different organ functions.
The extensive ovary matrix helps to stabilize tissue structure and cell to cell communication. Collagen and laminin comprise the basal lamina, whereas integrins span the membrane to transmit signals within the cell. Collagens are highly abundant in ovary and their rearrangement is a major event in ovulation and follicle rupture [152]. In addition, zona pellucida proteins, such as ZP1 and ZP2 identified in our results, are unique to ovary tissue. The zona pellucida is a layer of glycoproteins that encases oocytes. Similar to the human zona pellucida, mass spectrometry analysis of the golden hamster demonstrates the expression of all four of the ZP glycoproteins (1-4) in a targeted glycoproteomics study [153], The ZP
proteins were identified in ovary tissue but not cells. Overall, these clustering
results demonstrate significant differences in the proteome between cell lines and
tissues, especially involving proteins that are components of the extracellular matrix
and secretory apparatus.
3.3.4 KEGG Functional Analysis
The GO functional analysis identifies molecular functions, biological processes, and cellular components that are enriched in samples at the proteomics level. Another functional analysis is KEGG, which also uses the hypergeometric distribution to identify enrichment and depletion values. In this case, the values represent pathways that are significantly upregulated or downregulated at the proteome level (p < 0.05). Analyses revealed between 117 and 125 enriched
90 pathways in CHO cells and about 140 enriched pathways in tissues. Depletion
analyses revealed between 32 and 46 depleted pathways in CHO cells as compared
to 30 depleted pathways in tissues to confirm more pathways are depleted in cells
as compared to tissues. These finding suggest that tissues are more metabolically
active as measured by an increase in the number of enriched pathways and a
decrease in the number of depleted pathways.
While tissues show more enriched pathways than cells, cells show slightly
more enrichment during the stationary phases versus exponential phases
Consequently, there are more pathways depleted during exponential phases than
stationary phases. One example is glycosphingolipid metabolism; this pathway is
depleted in CHO-S cells during the exponential phase (p = 0.038) but not at
stationary phase (p > 0.05). However, it was not found as a depleted pathway in
CHO DG44 cells. As another example, the MAPK signaling pathway is not enriched in
CHO DG44 cells at exponential phase (p > 0.05) but is enriched at stationary phase
(p = 0.045). While there are more enriched pathways in tissues than cells, some of
the most enriched pathways are similar between samples, including carbon metabolism, biosynthesis of amino acids, protein processing in the endoplasmic reticulum, and aminoacyl-tRNA biosynthesis. This suggests common metabolic
activities that play a significant role in cells and tissues.
Shown in Figure 3.7 is the KEGG cell cycle pathway with colors representing
genes that were identified at the proteome level. The cell cycle pathway is enriched
for CHO-S and CHO DG44 at both exponential and stationary phases (p < 0.05) but it
is not an enriched pathway for both liver and ovary tissues. Many cell cycle genes
91 are observed in all samples; however, some are unique to cells or tissues only. In
agreement with the GO analysis, many cell cycle functions are increased in CHO cell
lines but not in tissues (genes colored magenta in Figure 3.7). In our results, CDK4,6
is highlighted in blue (Figure 3.7) which means it was identified in both CHO cell
lines and tissues. One effort to shift cell lines from growth to production targeted
the cell cycle G1-checkpoint through inhibition of cyclin-dependent kinase 4/6
(CDK4,6) [154]. The inhibition precisely controlled cell growth while increasing
productivity to 110 pg/cell/day and decreasing the percentage of high-mannose
glycoforms [154]. Another class of DNA replication molecules included the MCM
complex, most of which were observed in both cells and tissues. The complex unwinds double stranded DNA, recruits DNA polymerase, and initiates DNA synthesis, thus important to the survival of both cells and tissues. Both cells and tissues show MCM2, MCM3, MCM5, MCM6, and MCM7 at the proteome level.
Interesting, MCM4 was identified in all the cells but not in either tissue.
Identification may be masked by the high abundance of other metabolic proteins in tissue that decrease the significance of DNA replication.
Another important class of cell cycle proteins includes cyclins, such as CycA,
CycB, and CycD, which were identified in the cell proteomes but not in liver or ovary
tissue. Specifically, CycA concentration peaks during the G2 phase, CycB
concentration peaks during the initiation of mitosis, and CycD maintains a high
concentration throughout the cell cycle. CycA is involved in both the S phase and the
G2 to M transition and may play a role in cancer progression [155]. This is in
agreement with the immortalized CHO cell lines. A study of DNA synthesis inhibition
92 showed that CHO cells could accumulate protein and continue to grow as CycB
levels accumulate [156]. This aberrant growth and protein accumulation are
determinants of cytotoxicity and cell death [156]. In addition to cyclins, there are
many proteins essential for regulating the cell cycle that are upregulated in cells
rather than tissues. These highlight key differences between immortalized CHO cell
lines and hamster tissues.
The origin recognition complex [157] represents another class of proteins identified in cell but not tissue proteomes. The first step in DNA replication initiation is the assembly of the ORC; the ORC subunits then undergo changes in affinity for chromatin as the cell cycle progresses. Orc1 and Orc2 concentrations are constant throughout, but Orc1 can selectively detach from chromatin during M and
S phases [158]. This could explain the identification of detached Orc1 in mitotic CHO cells. Other detached ORC subunits exist during M phase and the complete ORC loses its function. Unlike in yeast, the Orc1 and Orc2 subunits are not tightly bound to the
ORC in hamster [158]. In another study, Orc1, Orc2, and Orc4 were identified as metabolically stable proteins in CHO cells [159]. Orc6 also plays a role in cancer and its downregulation can lead to cell cycle arrest at G1 [160]. Thus, the ORC is important in actively growing cells. Interestingly, when comparing primary and transformed cell lines, it was observed that protein levels of several ORC subunits are upregulated in transformed cell lines [161]. This is in agreement with our results that show enrichment of cell cycle proteins in CHO cell lines but not in liver or ovary tissue.
93 Another cell cycle protein, p107, was identified in CHO cells only. p107
regulates the cell cycle through its phosphorylation at the S to M transition. This is
in agreement with a proteome comparison between primary liver cells and
immortalized tumor cells [127]. Growth-promoting activities were upregulated in the Hepa1-6 tumor cells, including increased expression of p107 [127]. Related to
p107, E2F transcription factor (E2F)4/5 was identified at the proteome level in cells
[162]only; it. Anti interacts-apoptotic with propertiesp107 to transduce of E2F4/5 TGFβ and receptor other E2F signals homologs upstream have ofresulted CDK in
in their application as a cell line engineering target for prolonging culture [163, 164].
This highlight how an ‘omics approach can identify worthwhile candidates for
controlling the cell cycle and cell death in culture.
There are few cell cycle genes identified at the proteome level in liver and
ovary tissues but not in cell lines. One important example is Cdc25A, which
regulates genomic stability by controlling cell cycle progression. Normal expression
controls the G1 to S transition; overexpression leads to tumor growth [165] [166].
Interestingly, Cdc25A has been targeted in CHO cell line engineering efforts. Lee
evaluated wild-type and overexpressing Cdc25A cell lines by transgene copy
number, recombinant protein productivity, and cell cycle progression, and found
that overexpression of Cdc25A increased the specific productivity up to 2.9-fold and
increased the G2 to M phase transition by 1.5-fold [167]. Our inability to identify
Cdc25A in any of the cell samples suggests that mRNA is not efficiently translated.
Another example of a protein identified in tissues is Abelson murine
leukemia viral oncogene [168], which has various functions related to cell growth,
94 differentiation, and migration. Interestingly, the attachment of fibroblasts to
extracellular matrix directs the nuclear export of Abl, which may explain why it is
identified only in the tissue proteomes [169]. Abl kinases also play a role in cancer
development, as increased expression is known to activate solid tumors, alter cell
polarity, and induce invasion [170]. Finally, stratifin (14-3- in the tissue proteomes. The protein is a downstream target3 ofσ) p53 was that identified enables only tumor cell proliferation. Its methylation status and expression can be modified in cancers of the liver, breast, stomach, and colon, for example [171]. In the ovary, the methylation of the stratifin gene reduces mRNA and protein expression. In turn, reduction or elimination of the stratifin methylation leads to changes in the expression levels in ovarian cancer tissue [172]. Thus, these genes can be intimately related with tissue cell cycle and cancer metabolism.
3.4 Conclusions
This study considerably expands on our current proteomics catalog for CHO cells by identifying proteins in two cell lines (CHO-S and CHO DG44) as well as hamster liver and ovary tissues. The total number of unique proteins with at least two unique peptides for CHO-S and CHO DG44 is 9359, representing a 56% improvement over previous work [4]. Additionally, we established the first tissue proteome with 6663 unique proteins. Combining the results yielded a CHO cell line and tissue proteome of 11801 proteins at a 1% false discovery rate (FDR) with at least two unique peptides per protein. Protein intensities were compared between cell lines in exponential and stationary phase and tissues using NSAF, and functional analysis was performed using GO and KEGG hypergeometric distributions.
95 Similarities and differences were observed at protein intensity level and in the analysis of genes and pathways.
Interestingly, for both CHO-S and CHO DG44 cell lines, greater linearity in protein expression was observed within cell lines even in the two different growth stages than across cell lines, suggesting large genetic differences across cell lines.
Also, the cell lines exhibited greater similarities in expression to the ovary tissues than to the liver.
The GO and KEGG analyses also demonstrated significant differences in cell function and pathways between cell lines and tissues. There is a shift in activities from replication and transcription events, such as RNA splicing, to small molecule metabolic processes when comparing cells in the exponential phase to cells in the stationary phases, and to tissues. Furthermore, cellular components such as Golgi cisternae and the trans Golgi network were enriched in both liver and ovary tissues, but not in cells, which may suggest an increased secretory capacity in tissues that could be harnessed for CHO cell line engineering approaches. Additionally, cellular components such as extracellular matrix and endomembrane system are enriched specifically in the ovary. For example, zona pellucida proteins 1 and 2 are membranous proteins upregulated specifically in the ovary. KEGG analyses indicated upregulation of the cell cycle pathway proteins in cells as compared to tissues. Thus, the functional analysis complements protein expression analyses to identify metabolic changes across samples.
In summary, our results elucidate differences between cell lines and the
Cricetulus griseus host from which these cell lines were originally derived. Overall,
96 this proteomics analysis provides new insights on a greater number of protein and the overall protein intensity differences, as well as functional physiological differences related to genes and pathways, between CHO cells at different growth stages and the tissues of the Chinese hamster host. This information will enable us both to better understand this important production host at the level of proteins and pathways and to harness this knowledge in order to make more effective bio- production platforms in the future.
97 Tables
Table 3.1: Number of Peptides and Proteins
Peptide Unique Unique Samples Type Spectrum Peptides (#) Proteins (#) Matches (#)
CHO DG44 Cell line day 2 58128 5950 461989 exponential
CHO DG44 stationary Cell line day 4 50194 5593 371379
CHO-S exponential Cell line day 2 53958 6089 501439
CHO-S stationary Cell line day 5 48205 5538 439336
Liver Flash frozen tissue 33902 4468 461007
Ovary Flash frozen tissue 32954 4968 342141
98 Figures
Figure 3.1: Experimental Workflow. The proteomics workflow is divided into three main phases: sample preparation, MS peptide identification, and bioinformatics analysis. During sample preparation, proteins were extracted from cells or tissues and subjected to reduction, alkylation, and filter-aided sample preparation (FASP). Next, proteins were digested into peptides that are fractionated prior to LC/MS/MS analysis. Data was searched using Proteome discoverer software configured with Mascot search engine to identify proteins and MATLAB, R, gene ontology, and KEGG pathway analysis tools were used to identify functional pathways involved in cell line engineering and bioprocessing.
99 Figure 3.2: Proteomic Comparison of Samples. A. Number of proteins identified in all or some of samples. ‘Cells’ represent a combination of all cell culture samples (CHO-S exponential, CHO-S stationary, DG44 exponential, and DG44 stationary). B. Number of proteins identified in each or both cell lines. ‘CHO-S’ represents the total number of unique proteins in CHO-S exponential and stationary samples. ‘CHO DG44’ represents the total number of unique proteins in CHO DG44 exponential and stationary samples. C. Principal component analysis of samples plotted on the first and second principal components. D. Histogram of protein intensities using normalized spectral abundance factor as bin values.
100 Figure 3.3: Protein Intensity Comparison of Samples. Protein intensity comparison of samples. Plots show NSAF values for all proteins identified in each comparison. Proteins highly expressed in the sample on vertical axis are colored yellow. Proteins highly expressed in the sample on horizontal axis are colored pink. A-O: CHO-S exponential vs. CHO-S stationary, CHO DG44 exponential vs. CHO DG44 stationary, CHO DG44 exponential vs. CHO-S exponential, CHO DG44 exponential vs. CHO-S stationary, CHO DG44 stationary vs. CHO-S exponential, CHO DG44 stationary vs. CHO-S stationary, CHO-S stationary vs. ovary, CHO DG44 stationary vs. ovary, CHO DG44 exponential vs. ovary, CHO-S exponential vs. ovary, liver vs. ovary, CHO DG44 stationary vs. liver, CHO DG44 exponential vs. liver, CHO-S exponential vs. liver, CHO-S stationary vs. liver.
101 Figure 3.4: The Top 10 Enriched Biological Processes. Gene ontology analysis was used to identify the biological processes with most significant enrichment (p- value < 0.05). The size of the slice represents the number of genes annotated to that process. The order starts from gene expression (most enriched) and rotates clockwise to the 10th most enriched process. A: CHO-S exponential, B: CHO-S stationary, C: DG44 exponential, D: DG44 stationary, E: liver, F: ovary.
102 Figure 3.5: K-Means Clustering of Molecular Functions. Following gene ontology analysis, the resulting p-values were used to determine molecular functions that were similar or different in CHO cells vs. tissues. The color scale ranges from low p- value in green (significant enrichment) to high p-value in red (not enriched). A: Selected molecular functions enriched in cells (p < 0.05) but not tissues. B: Selected molecular functions enriched in tissues (p < 0.05) but not cells.
103 Figure 3.6: Cellular Components Enriched in Tissues. The final result of gene ontology analysis was to identify cellular compartments enriched in tissues vs. CHO cells. The components are labeled and corresponding gene symbols are shown that correlated to the cellular component. Green: enrichment in both liver and ovary tissues, but not in CHO-S or CHO DG44 cells. Blue: enrichment in ovary tissue, but not in CHO-S or CHO DG44 cells or liver tissue.
104 Figure 3.7: Pathway Map of Cell Cycle. The Kyoto Encyclopedia of Genes and Genomes (KEGG) search and color tool was used to highlight genes that were identified at the proteome level for CHO cells and tissues. Blue represents genes identified at the proteome level in all samples, magenta represents genes identified at the proteome level in cells only (present in CHO-S exponential and CHO-S stationary and CHO DG44 exponential and CHO DG44 stationary), and orange represents genes identified at the proteome level in tissues only (present in liver and ovary but not in any CHO cells).
105 Chapter 4: Comparative Proteomics Analysis of Chinese
Hamster Cell Lines and Tissues
Abbreviations
CHO – Chinese hamster ovary, DHFR – dihydrofolate reductase, VCP – valosin
containing protein, MS – mass spectrometry, SDS – sodium dodecyl sulfate, EDTA – ethylenediaminetetraacetic acid, PMSF – phenylmethylsulfonyl fluoride, BCA – bicinchoninic acid, TCEP – tris(2-carboxyethyl)phosphine, BCA – bicinchoninic acid,
TCEP – tris (2-carboxyethyl) phosphine, TMT – tandem mass tags, LC – liquid chromatography, FPKM – fragments per kilobase of transcript per million mapped reads, FDR – false discovery rate, NSAF – normalized spectral abundance factor, GO
– gene ontology, KEGG – Kyoto encyclopedia of genes and genomes, IPA – Ingenuity pathway analysis, SILAC – stable isotope labeling with amino acids in cell culture, iTRAQ – isobaric tags for relative and absolute quantification, PCA – principal component analysis, BiP – binding immunoglobulin protein, PDi – protein disulfide isomerase, MCM – minichromosome maintenance, ROCK2 – Rho associated coiled- coil containing protein kinase 2, ARFGEF2 – ADP ribosylation factor guanine nucleotide exchange factor 2, ZEB1 – zinc finger E-box binding homeobox 1,
ANKFY1 – ankryin repeat and FYVE domain containing 1, AP2A2 – adaptor related protein complex 2 alpha 2 subunit, ROS – reactive oxygen species (ROS), SPARC – secreted protein acidic and rich in cysteine, Igbp1 – immunoglobulin CD79A binding protein 1, VASP – vasodilator-stimulated phosphoprotein, Rapgef1 – Rap guanine nucleotide exchange factor 1, Itgb1 – integrin beta-1, SIRT1 – sirtuin 1, CIRP – cold-
106 inducible RNA-binding protein, Lrp1 – low density lipoprotein receptor-related
protein, Thbs1 – thrombospondin, Mapk14 – mitogen-activated protein kinase 14,
Stim1 – stromal interaction molecule 1, Clcn3 – chloride voltage-gated channel 3,
ERAP1 – endoplasmic reticulum aminopeptidase 1
4.1 Introduction
The history of Cricetulus griseus dates back to the 1950s when Theodore T.
Puck established the CHO-K1 cell line [102]. Today, Chinese hamster ovary (CHO) cell lines generated from the Chinese hamster dominate the biopharmaceutical industry and their products generate billions of dollars in revenue annually [109].
The success of CHO cell lines for recombinant protein production can be attributed to their high growth rate, ease of genetic modification, and ability to post- translationally modify proteins via glycosylation and sialylation.
CHO is actually a misnomer as the cell line established by Puck grew out of the connective tissue surrounding the ovary. The cells became immortal spontaneously in culture and grew with nearly diploid chromosomes and fibroblast character [108]. Prior to their use in the biotechnology industry, the cells were frequently used in studies for induced and spontaneous mutation research because the chromosomes were easily observed [109]. After over ten months in culture, the cells morphed from a fibroblast type to epitheliod marking the origin of the CHO culture used today [109]. As early as the 1960s, CHO was adapted to serum free media and it was discovered the cells required proline supplementation in the culture medium [109]. Subsequently, CHO use became more widespread in industry.
107 The original CHO cell line was adapted and modified by various researchers
to create cell lines, such as CHO-DXB11, CHO DG44, and CHO-S, for example. The first CHO-generated product was made in the CHO-DXB11 cell line established at
Columbia University to study dihydrofolate reductase (DHFR) activity [173]. A related cell line is CHO DG44, which was generated in the same lab by fully deleting both loci for DHFR on chromosome 2 [111]. Finally, CHO-S is widely used but has a more complicated origin because it was simultaneously grown in both academia and industry as a suspension-based CHO cell line [109]. There are thus significant differences across CHO parental cell lines, as well as clonal- and process- dependent variations. This yields transcriptomes and proteomes with significant differences. It is widely recognized that variations in the CHO cell lines exhibit significant differences, so a single CHO-ome is not relevant for most applications. In addition to cell line differences, variations in the bioprocess including media formulation and bioreactor operation can alter the transcriptome and proteome.
Initial efforts to understand CHO include the sequencing of both the CHO and
Cricetulus griseus genomes. The draft CHO-K1 genome was established in 2011 [3] and the Chinese hamster genome followed two years later [2]. In addition, the CHO-
DXB11 genome was sequenced to understand the DHFR negative phenotype and the cell line drift [112]. Other efforts aimed to create bacterial artificial chromosome libraries for CHO-K1 and CHO-DG44 cell lines to visualize hamster chromosome rearrangements [113].
108 Finally, a combined approach using genomics and epigenetics was recently
used to study genetic responses as a result of cell line differences and bioprocess
perturbations, such as process conditions, media formulation, and cell specific
productivity [174]. Results indicated that genome sequence variation between the cell lines was high, resulting from single nucleotide polymorphisms, insertion/deletions, and structural variants [174]. Combined, genomic efforts play a
significant role in comparing CHO parental and clonal cell lines.
Similar efforts sought to understand mRNA and protein expression in CHO
using transcriptomics and proteomics, respectively. Through advances in sample
preparation and mass spectrometry (MS) technology, it is possible to identify and quantify thousands of cellular proteins. In addition, library preparation and annotation tools make RNAseq a sophisticated method for transcriptomic analysis.
Both methods aid biotherapeutics research and development because they can be used for target identification, monoclonal antibody discovery, and bioprocess optimization. To highlight one application, a proteomic comparison between pulmonary adenocarcinoma and surrounding tissue identified over 30 differentially expressed proteins, suggesting that cancer causes an upregulation of PKM2 and cofilin-1 [175]. Following the identification, a targeted RNA interference knockdown of PKM2 resulted in decreased cell growth and induction of apoptosis in a cancer cell line [175]. In other applications, proteomics is used to identify proteins and pathways that are significantly changed as a result of bioprocess differences. These studies seek to develop stable cell lines and bioprocess parameters that increase
recombinant protein yields and maintain consistent product quality. The initial
109 proteomic analysis was performed for the CHO-K1 cell line to identify pathways that
are enriched and depleted compared to the CHO genome and transcriptome [4].
Functional analysis revealed enrichment in protein processing and apoptosis
pathways at the proteomic level concomitant with depletion in steroid hormone and
glycosphingolipid metabolic pathways [4].
Finally, the combination of transcriptomic and proteomic data serves to
validate significantly changing pathways. To date, many cell lines and different
bioprocess conditions have been studied via transcriptomics and proteomics to
yield insights into protein production, cell growth, reduced apoptosis, favorable
glycosylation, and optimized medium formulations [176]. In a combined approach,
Clarke showed that 285 proteins were differentially expressed between cell lines
with fast and slow growth rates, and functional analysis identified translational
elongation, translation, generation of precursor metabolites and energy, oxidation-
reduction, and aerobic respiration as most significantly enriched gene ontology
terms [24]. Combination of proteomic and transcriptomic data enabled the
validation of low abundance protein information and identification of mRNA post-
translational processing [24]. Thus, results indicated a correlation between protein
synthesis and cell growth. In another approach, the combination of transcriptomics
and proteomics was used to compare cell lines with fast and slow growth rates [21].
Researchers identified valosin-containing protein (VCP) as a significant contributor
to the variation in cell growth and viability [21]. Following the ‘omics approach, VCP expression was knocked out which resulted in decreased viable cell density and
110 viability [21]. Thus, proteomics is important for identifying differential expression that can be confirmed or negated by mRNA expression using transcriptomics.
In this thesis, we combine both transcriptomics and proteomics to generate comparisons between CHO cell lines and the original Chinese hamster host.
Comparative proteomics data was established using TMT labeling and comparative transcriptomics data was established by RNAseq. Briefly, proteome samples were sonicated and subjected to reduction, alkylation, digestion, labeling, fractionation, and MS identification. After RNA isolation and library preparation, transcriptome sequencing data was generated. Different organs, including the brain, heart, kidney, liver, lung, ovary, and spleen, were used to generate the tissue proteome and identify characteristics of each that are highly abundant. In addition, the CHO-S and
CHO DG44 cell lines were compared to the tissues to suggest functions and pathways with significant differential expression. Finally, we combine the proteome data with the transcriptome to identify functions that confirm or negate each other.
Overall, tissues show upregulation in metabolic pathways such as protein processing, secretion, and amino acid metabolism. The results highlight tissue characteristics and genes of interest that may be used for future genetic engineering approaches of CHO host cell lines. In addition, depleted functions identified in CHO highlight areas of improvement that could increase cell capacity for biotherapeutics production using cell line engineering or optimization of media formulation, transfection method, bioreactor operation, and process scale-up.
4.2 Materials and Methods
111 For both proteomics and transcriptomics, the experiment involves sample
preparation, data collection, and bioinformatics analysis.
4.2.1 Tissue Isolation
Tissues from various organs, including brain, heart, kidney, liver, lung, ovary, and spleen, were harvested from female Chinese hamsters generously provided by the lab of Dr. George Yerganian (Cytogen Research, Roxbury, MA). Euthanization was performed by CO2 and verified by abdominal puncture. Upon harvest, each organ was divided into small pieces, rapidly frozen on dry ice, and subsequently stored at -80oC until analysis.
4.2.2 Batch Cell Culture
Two commercial suspension CHO cell lines, CHO-S and CHO DG44, were
grown in batch culture to collect samples during the mid-exponential phase. CHO-S
cells were cultured in CD-CHO medium supplemented with 8mM glutamine (both
from ThermoFisher Scientific, Waltham, MA). CHO DG44 cells were culture in DG44
medium supplemented with 2mM glutamine (both from ThermoFisher Scientific,
Waltham, MA). After preparing growth curves (data not shown), samples from both
cell lines were collected on day 2 which corresponded to mid-exponential phase.
Cells were incubated at 37oC with 8% CO2 shaking at 120RPM; viable cell density
was determined by hemocytometer and trypan blue. For sample collection,
approximately 3 million cells were spun down, washed with PBS on ice, frozen
rapidly on dry ice, and stored at -80oC until analysis.
112 4.2.3 Proteomics Sample Preparation
Samples for proteomics were thawed on ice and lysed in 2% sodium dodecyl sulfate (SDS) supplemented with 0.1mM phenylmethylsulfonyl fluoride (PMSF) and
1mM ethylenediaminetetraacetic acid (EDTA), pH 7-8. Lysates were sonicated thrice for 60 seconds at 20% amplitude followed by 90 seconds pause in order to expose proteins. Protein concentration was measured by bicinchoninic acid (BCA) protein assay. Three hundred micrograms of each sample were reduced in 10mM tris (2- carboxyethyl) phosphine (TCEP), pH 7-8, at 60oC for 1hr on a shaking platform.
After bringing the sample to room temperature, approximately 17mM iodacetamide was added to alkylate the sample to final concentration for 30 minutes in order to
prevent reformation of disulfide bonds. Next, samples were cleaned using 10kDa
filters to reduce the SDS concentration [6]. The samples were finally digested using trypsin/LysC enzyme mix (Promega V507A), overnight at 37oC on a shaking
platform.
4.2.4 Proteomics Labeling
In order to compare protein expression, peptide samples were labeled in
duplicate using two tandem mass tag (TMT)-10plex (ThermoFisher Scientific,
Waltham, MA). Each of the 10 reagents has the same nominal mass and chemical
structure so that for each sample, a unique reporter mass is used to relate protein
expression level. Triplicates were used for ovary tissue and CHO-S. In addition, CHO-
S included biological and technical replicates in order to compare samples across
each TMT. Following the digestion, TMT reagents were thawed and acetonitrile was
113 used to dissolve the reagents. One reagent tube was added to each sample and then
incubated at room temperature for 1hr. Hydroxylamine was subsequently added to
quench the reaction before the tubes were combined. One tube for each TMT was
submitted for MS analysis.
4.2.5 2D Liquid Chromatography (LC)-MS
Samples were submitted to the proteomics core run by Dr. Robery Cole.
Briefly, digested peptides were fractionated on basic reversed phase column
(XBridge C18 Guard Column, 5µm, 2.1 x 10mm XBridge C18 Column, 5µm, 2.1 x
100mm). Fractions were concatenated into final 24 prior to second dimension LC
and MS analysis. MS/MS analysis of the peptides were carried out on the LTQ-
Orbitrap Velos (www.thermoscientific.com) MS interfaced to Eksigent
(www.eksigent.com) nanoflow LC system with the Agilent 1100 auto sampler.
Peptides were enriched on a 2cm trap column (YMC gel ODS-A S-10µm), fractionated on Magic C18 AQ, 5µm, 100Å (Michrom Bioresources), 75µm x 15cm column and electrosprayed through a 15µm emitter (PF3360-75-15-N-5, New
Objective). Reversed-phase solvent gradient consisted of solvent A (0.1% formic acid) with increasing levels of solvent B (0.1% formic acid, 90% acetonitrile) over a period of 90 minutes. LTQ Orbitrap Velos parameters included 2.0kV spray voltage, full MS survey scan range of 350-1800 m/z, data dependent MS/MS analysis of top
10 precursors with minimum signal of 2000, isolation width of 1.9, 30s dynamic exclusion limit and normalized collision energy of 35. Precursor and the fragment ions were analyzed at 60000 and 7500 resolutions, respectively.
114 4.2.6 Transcriptomics
Tissues and cells were also analyzed by Clarke using RNAseq to prepare
transcriptomics data, similar to previously published analyses [177]. Transcriptome
data, provided as fragments per kilobase of transcript per million mapped reads
(FPKM) values was provided to compare RNA expression across replicated samples.
In this method, RNA is isolated from the cells and tissues using commercial kits
(Qiagen, Germantown, MD). Isolation by 3’ polyadenylated tails yields only coding
mRNA, which is reversed transcribed into cDNA. Fragmentation and size selection
are used to select for high-quality sequences. Challenges with transcriptome
analysis include the assembly of raw sequence reads into the complete
transcriptome. The de novo assembly by Dr. Colin Clarke was provided for our study.
4.2.7 Bioinformatics
Peptide sequences were identified from isotopically resolved masses in MS
and MS/MS spectra extracted with and without deconvolution using Thermo
Scientific MS2 processor and Xtract software nodes. Data was searched against all
entries in Cricetulus griseus database for CHO cell lines. Oxidation on methionine, deamidation NQ and phosphoSTY were set as variable modifications, and carbamidomethyl on cysteine was set as fixed modifications in Mascot search node used in Proteome Discoverer (Version 1.4, http://portal.thermo-brims.com) workflow. Mass tolerances on precursor and fragment masses were 15 ppm and
0.03Da, respectively.
115 Protein identifications were made using Proteome Discoverer software
(Thermo) with stringent high confidence cutoff (<1% false discovery rate, FDR) and
provided as an excel file by the lab of Dr. Robert Cole. The protein intensities were
determined using normalized spectral abundance factor (NSAF), a method to
generate reproducible counts and high linearity across biological and technical
replicates [119]. It measures the number of spectra matched to a specific protein
divided by its length over the sum of all proteins in the sample [119]. NSAF values
were plotted for each sample comparison, and fold change values were used to
identify significant differences in protein intensity. Protein accession numbers were
mapped to gene symbols using the biological database network (http://biodbnet-
abcc.ncicrf.gov) for functional analysis by gene ontology (GO) and Kyoto
Encyclopedia of Genes and Genomes (KEGG). For GO annotation, gene symbols were
mapped to molecular function, biological process, and cellular component using the
GOCHO platform (http://ebdrup.biosustain.dtu.dk/gocho). For KEGG annotation,
latest (Dec.2015) database pathways for Cricetulus grisueus were downloaded.
(http://www.genome.jp/kegg). All programming for the hypergeometric test were
calculated in MATLAB version 2015vB
(http://www.mathworks.com/products/matlab) and RStudio
(https://www.rstudio.com). Enrichment and depletion p-values were calculated using the hygecdf and hygepdf functions in MATLAB. These values were used to generate heat maps and k-means clustering in Genesis software version 1.7.6 [65].
Pathways of interest were plotted using Ingenuity pathway analysis (IPA) software
(http://www.ingenuity.com/).
116 4.3 Results and Discussion
Our work compares protein and mRNA expression of various CHO cell lines and hamster tissues, creating the first global map of the Cricetulus griseus proteome and transcriptome. The ‘omics analysis aims to improve our understanding of CHO as the dominant biopharmaceutical production host and identify significant differences between cells and tissues. As proteomics is the main focus of this paper, an overview of the workflow is shown in Figure 4.1. Following sonication, reduction, alkylation, and digestion, peptides were labeled by TMT and combined into two
TMT-10plex to determine relative abundance. Each TMT was fractionated prior to
LC/MS/MS in order to increase the number of identifications.
Following MS identification, the protein accession numbers were determined using the 2014-annotated CHO genome. In a recent comparison between the 2012- and 2014-annotated CHO genomes, it was determined that the 2014 version showed higher mapped rates for intra-genic regions, making it the preferred reference for future applications [120]. Protein accession numbers were converted to gene symbols for functional analysis. For missing gene symbols, the accession numbers were searched against mouse and human databases using the online database, bioDBnet [121]. In this experiment, GO and IPA were used for functional analysis of the differentially expressed proteins. The purpose of GO is to convert gene symbols to molecular function, cellular component, and biological process in order to evaluate the relationship between the molecular activities of gene products, location of activity, and pathways comprising the activity of multiple gene products,
117 respectively [122]. In another functional analysis, each gene symbol is mapped to
the IPA pathways it is involved in, thus suggesting enrichment and depletion
between different comparisons [123]. In both GO and IPA, the enrichment and
depletion are quantified by p-values that are calculated via the hypergeometric distribution. In this case, p < 0.05 was set for evaluating significance.
4.3.1 Protein Identification and Total Protein Intensity Differences
In order to reduce costs and multiplex samples, we chose a labeled approach for comparative proteomics. TMT was not the only option, as isobaric tags for relative and absolute quantification (iTRAQ) and stable isotope labeling with amino acids in cell culture (SILAC) are alternative labeling methods [178] [179]. The main difference between iTRAQ and TMT is the number of samples that are multiplexed.
With TMT, it is possible to analyze 2, 6, or 10 samples in a set thus providing greater flexibility for sample design [180]. For this experiment, TMT was selected in order to ensure replicates for each of the conditions. Labeled approaches show some disadvantages as compared to label-free, including lower sequence coverage and fewer differentially expressed proteins [124]. In addition, comparison between iTRAQ and label-free proteomics suggests that label-free approaches are more accurate [125]. In conclusion, labeled proteomics has both advantages and disadvantages necessitating thorough analysis in order to confidently identify points of interest.
For each of the TMT samples, the number of unique proteins, unique peptides, and spectra is shown in Table 4.1. Over 8000 proteins were identified in
118 each TMT with at least 2 unique peptides per protein; this corresponded to a total of
8464 unique proteins with a false discovery rate (FDR) of 1% for protein
identification. Protein intensity fold change ratios were initially evaluated through
the principal component analysis (PCA) as shown in Figure 4.2 [66]. The cell
samples cluster together and are distinct from all tissues as shown by PCA. For the
tissues, spleen, liver, and heart cluster together while lung and kidney cluster together. Ovary and brain are clustered separately from the other organs involved in the circulatory, digestive, and respiratory systems, which is consistent with the plots and outliers identified. Next, replicates for each tissue were plotted. Fitting a curve to the scatter plot and calculating the R2 value assessed the linearity of the
replicates. Shown in Figure 2B to 2E are the plots for the tissues with the highest R2
value for each cluster, specifically brain, ovary, lung from the lung/kidney cluster,
and heart from the heart/kidney/spleen cluster.
To compare between samples, protein fold change ratios were averaged for
biological replicates and plotted in Figure 4.3. Blue and yellow represent points of
differential expression (outliers) between the samples compared. More consistency
is observed between CHO-S and CHO DG44 rather than between cells and tissues.
The highest degree of consistency (as measured by the fewest number of outlier
points) is observed between CHO-S and CHO DG44 (Figure 4.3A). There are 247
proteins with significantly higher expression in CHO-S and 236 proteins with significantly higher expression in CHO DG44. For the cell to tissue comparison, the highest consistency is for CHO-S to ovary (Figure 4.3E) or CHO DG44 to ovary
(Figure 4.3I). This agrees with the fact that CHO cells were derived from ovary tissue,
119 even though cells grew out of a mixture of ovary and surrounding connective tissue.
Between CHO-S and ovary, there are 1587 proteins with significantly higher
expression in CHO-S and 832 proteins with significantly higher expression in ovary.
Similarly, between CHO DG44 and ovary, there are 2216 proteins with significantly
higher expression in CHO DG44 and 962 proteins with significantly higher
expression in ovary. After ovary, CHO cells show the greatest consistency to heart
(Figure 4.3C and 4.3G). Between CHO-S and heart, there are 603 proteins with significantly higher expression in CHO-S and 1073 proteins with significantly higher expression in heart. Similarly, between CHO DG44 and heart, there are 864 proteins with significantly higher expression in CHO DG44 and 1557 proteins with significantly higher expression in heart. Unlike the other organs, heart is predominately comprised of muscle cells so this metabolic function may account for significant differences in protein intensity [181]. Over 50% of the cells in this organ are cardiac fibroblasts. In addition, the heart has endothelial, smooth muscle, and pacemaker cells. This high degree of specialization is likely important for differences between CHO and heart. Next, both CHO-S and CHO DG44 have low consistency for the comparison against lung tissue (Figure 4.3D and Figure 4.3H, respectively), containing a total of 2130 and 2536 proteins with differential expression, respectively. Finally, the total number of outliers, as measured by the number of blue and yellow points in Figure 4.3, is highest between cells and brain (Figure 4.3B
and Figure 4.3F). There are 2373 and 2618 differentially expressed proteins in CHO-
S and CHO DG44, respectively, as compared to brain. Brain and ovary show distinct
separation in terms of embryonic development from the other organs. Brain
120 originates from the ectoderm whereas ovary develops from the mesoderm [182]. In
terms of tissue to tissue comparisons, brain shows the most outliers compared to
other tissues; it is the only organ derived from the ectoderm and thus is highly
specialized for its function [182]. Ovary is also highly specialized for its role in
reproduction, containing extensive extracellular matrix and epithelioid cells [183].
The remaining organs derive from mesoderm and endoderm, including kidney, liver,
heart, and lung.
Similar to other efforts at whole organism proteomes, we evaluated some of
the most highly expressed proteins in each tissue [128, 184, 185]. Hierarchical
clustering of protein expression is shown in Figure 4.4. Pink represents areas of high
expression, for which we observed clusters in each sample. These groups represent
highly expressed proteins with tissue specificity. For each sample, the top 200
proteins, corresponding to approximately 3% of the total proteins, were identified
in order to highlight tissue specificity. Example proteins significantly high in ovary
include disintegrin and metalloproteinase domain-containing protein,
methyltransferase, serine/threonine-protein phosphatase, and sodium/glucose costransporter 2. This agrees with results from the human ovary-specific proteome,
which identified 145 genes with elevated expression in ovary compared to other
tissues [128]. Interestingly, the metalloproteinases were observed in the mouse
proteome, but only within lung and placental organs [185]. The ovary controls
oocyte development and reproductive hormone production; it contains extracellular
matrix of granulosa cells surrounding the follicle [128]. Similarly, the placenta is
part of the reproductive system and develops in order to provide oxygen and
121 nutrients to a fetus growing in the uterus. CHO cells, which were established from
fibroblast connective tissue surrounding the ovary [109], show protein expression levels that are more similar to the ovary than other tissues.
Next, we identified highly expressed proteins in lung tissue (Figure 4.4). The human proteome identified 193 genes elevated in the lung and saw the greatest similarity to protein expression in the testis [128]. The lung is responsible for breathing, controlling gas exchange. Some proteins highly expressed in the hamster lung include branched chain amino acid aminotransferase, glycine N-
methyltransferase, integrin alpha-6, and vesicle-associated membrane protein.
These proteins likely account for the increased metabolic and secretory functions of
the lung. The heart is a strong muscle that must contract nonstop and is
predominately comprised of cardiomyocytes. 201 genes were shown to have
elevated expression in the human proteome [128], including retinal dehydrogenase
which was also significantly upregulated in our hamster heart proteome. In addition,
we observed high expression for proteins including actin-related protein,
glutathione S-transferase, protein O-glycosyltransferase, and Ras GTPase- activvating protein. Finally, brain tissue clustered separately in the PCA analysis and shows distinct embryonic development from other tissues. Highly expressed brain proteins include amyloid beta A4 protein, calcium and integrin binding protein, neuron navigator 1, and serine/threonine protein kinase. As expected, many of the highly expressed proteins in brain relate to signaling and transport. In general, the proteins expressed at significantly higher levels in cells over tissues include proteins related to DNA replication and transcription, as expected to the cells’ rapid growth.
122 Among the most highly expressed proteins in either CHO-S or CHO DG44 are 60S ribosomal protein, apoptosis inhibitor 5, DNA-directed RNA polymerase II, and eukaryotic translation initiation factor. Thus, analysis of the most abundant proteins
for each tissue or cell line highlights each sample’s function.
4.3.2 IPA Pathway Analysis
Next, the proteins were annotated with gene symbols in order to perform
pathway analysis. Fold change values of less than 0.5 or greater than 2.0 were used
for each comparison to determine downregulation and upregulation of pathways in
IPA software. As shown in Figure 4.4, protein intensity hierarchical clustering can be
used to identify proteins with significantly higher expression in a specific tissue.
Protein functions identified in IPA that are enriched in brain include
internalization of protein, organization of Golgi apparatus, and translation of
proteins. The Golgi likely plays a role in cholesterol metabolism; almost 25% of the human body’s unesterified cholesterol is present in brain [186]. The input of cholesterol into the central nervous system comes almost entirely from in situ synthesis, involving the endomembrane system [186]. Additionally, glycosphingolipids are abundant in the nervous system, and are synthesized in the endoplasmic reticulum and completed in the Golgi apparatus [187]. Protein functions enriched in lung include metabolism of amino acids and trafficking of vesicles. Lung shows high secretory capacity and thus trafficking of the secretory apparatus will be important. In recent research, the secretome of lung cancer cell lines was profiled to identify differential expression of secreted proteins [188].
123 Protein functions enriched in heart include metabolism of alpha-amino acid, sorting of protein, and translation. Amino acid metabolism was studied in rat heart; as amino acid levels rose up to 5x plasma levels, there was a 40% increase in whole heart protein and myosin synthesis [189]. A prior analysis of human heart mitochondria similarly showed upregulation of metabolic proteins, including those involved in signaling, protein synthesis, ion transport, and lipid metabolism [190].
Protein functions enriched in ovary include development of extracellular matrix, docking of vesicles, and exocytosis. The matrix is important for all metabolic activities, including growth, migration, and differentiation [183]. When the matrix is remodeled, it can lead to ovarian cancer [191]. The extensive extracellular matrix is in agreement with previous research in our lab quantifying the hamster ovary tissue proteome.
4.3.3 GO Functional Analysis
For GO functional analysis, gene symbols were annotated to molecular function, biological process, and cellular component. Each GO category was analyzed to determine enrichment and depletion p-values for the cell-to-tissue or tissue-to- tissue comparisons. Tables 4.2 and 4.3 list the top 10 most enriched biological processes for CHO-S (Table 4.2) and CHO DG44 (Table 4.3) to tissue comparisons.
Enrichment is determined by hypergeometric distribution, with a p-value of < 0.5 used for significance.
Biological processes represent a biological function involving the gene or gene product. This is used to complement the pathway analysis in IPA. A process is
124 accomplished using at least one molecular function assembly [122]. In both CHO-S
and CHO DG44, the most common processes enriched involve DNA, mRNA, and RNA.
Some of the most common enriched processes in CHO-S include RNA metabolic process, mRNA processing, and transcription from RNA polymerase II promoter.
Overall, differences between cells and tissues relate to growth and senescence.
Growth activities are high in cells, whereas signaling and transport processes are significantly enriched in tissues. A previous proteomics comparison of CHO cells undergoing treatment with sodium butyrate identified minichromosome maintenance deficient 5 (MCM5) as a differentially expressed gene involved in DNA replication [140]. Of the MCM family members we identified between CHO-S and
CHO DG44, MCM4, MCM6, and MCM3 were all identified at nearly equal levels (fold change near 1.0). In addition, the levels in both cell lines were higher than in tissues.
In comparison, signaling and transport are significantly enriched across different hamster organs. For example, enriched brain biological processes include axon guidance, ion transport, metabolic process, and synaptic transmission. Eight of the ten top processes are equivalent in the comparison against CHO-S or CHO DG44.
The brain comprises the central nervous system that regulates signaling throughout the organism. The GO term for axon guidance describes the chemotaxis process that directs axon migration to a specific target in response to attractive and repulsive cues [122]. It involves genes such as homeobox protein, neural cell adhesion molecule, plexin, laminin, and ephrin receptor.
125 Next, the identification of enriched biological processes in heart shows
functions related to circulation and metabolism. Complement activation and protein heterotrimerization are significantly enriched in heart tissue compared to either
CHO-S or CHO DG44. Other processes enriched in heart include regulation of blood coagulation, positive regulation of vasoconstriction, phospholipid efflux, and sodium-independent organic ion transport. For example, complement activation involves mannose-binding lectin and complement components, C3, C4, CD46, and
CD59. Additionally, the activities for vasoconstriction, phospholipid efflux, and sodium-independent organic ion transport are enriched in heart. These results are indicative of heart specialization, such as the relationship between complement activity and heart failure [192].
Similar to the pancreas, thymus, and spleen, the lung is a highly secretory organ. It performs metabolic functions similar to other tissues, but has selectivity that differentiates the lung from liver [193]. Biological processes we observed that are enriched in lung as compared to either CHO cell line include G-protein coupled receptor signaling pathway, vesicle-mediated transport, and intracellular signal transduction. Indeed, vesicle transport is an important component of secretory pathway machinery. Genes related to this GO term are transferrin receptor protein,
Golgi SNAP receptor, growth arrest-specific protein, and vesicle-fusing ATPase.
Additional lung-enriched processes include transport and signaling. Unraveling the complexities of lung secretions may yield new insight into the control of CHO secretory pathway engineering.
126 Finally, enriched biological processes in ovary highlight differences between
the cells and tissue from which CHO cells were derived. Overall, there is high
agreement between CHO-S and CHO DG44 related to ovary. Ovary biological
processes enriched as compared to both cell lines include protein transport,
transport, transmembrane transport, and vesicle-mediated transport.
Transmembrane transport is associated with genes such as sodium/hydrogen
exchanger, transferrin receptor protein, peroxisomal targeting signal 2 receptor,
and growth arrest-specific protein. Interestingly, the ovary tissue has high transport
capacity for proteins and other transmembrane materials that may highlight
reasons for the dominance of CHO cells in recombinant protein production.
Specifically, vesicle production rate was quantified in the ovary and the turnover
indicated high vesicle recycling across the endomembrane system [194]. However,
the same processes are not enriched in the cell line, highlighting possibilities for
metabolic improvements.
4.3.4 Transcriptome Analysis
Transcriptomics was used to complement the proteomics study and compare mRNA expression across CHO cell and tissue samples. RNAseq fold change ratios were used in combination with fold change ratios from TMT labeling in order to compare and contrast the data sets. The results of the transcriptome to proteome comparison are shown in Figure 4.5A for cell lines to tissue comparisons. For each
of the cell line to tissue comparisons studied, over 65% of gene symbols are in
agreement, indicating that mRNA and protein expression are either both low or both
127 high. The minority of gene symbols in red represent situations where mRNA and
protein expression differ, with one of the fold changes greater than 1 and the other
fold change less than 1. These situations represent stable (high protein expression
and low mRNA expression) and unstable (high mRNA expression and low protein
expression) mRNA.
For the conditions tested, the greatest agreement is for the cell line to cell
line comparison (Figure 4.5A). A total of 1427 gene symbols express the same fold change whereas 349 proteins have conflicting fold changes between mRNA and
protein, representing over 80% correlation. This shows that mRNA and protein
expression correlate much more for cells than complex tissues. Across the tissues,
the greatest agreement is between brain mRNA and protein versus CHO-S.
Specifically, in brain there are 1741 gene symbols with fold change in agreement and 539 gene symbols that differ in fold change between mRNA and protein. Lung and ovary both show approximately 65-70% correlation between the mRNA and protein fold changes in relation to CHO-S. The greatest disagreement is between heart mRNA and protein versus CHO-S. For the heart, there are 1399 gene symbols in agreement and 637 in disagreement between mRNA and protein.
4.4 Conclusions
The results from the Chinese hamster proteome provide new insight into the global protein expression across tissues. Comparative proteomics by TMT labeling was used to identify differential expression between CHO cell lines and hamster tissues. A total of 8464 unique proteins with a false discovery rate of 1% and
128 containing at least 2 unique peptides per protein were identified by combining the
TMT datasets. This represents the most extensive Chinese hamster proteome to-
date. Initially, we compared protein expression by comparing the fold change for
each sample on the basis of the CHO-S technical replicates (Figure 4.3). Overall,
there is high similarity between CHO-S and CHO DG44. When incorporating tissue
comparisons, there is greater consistency between CHO-S or CHO DG44 cell lines and ovary tissue and lower consistency between CHO-S or CHO DG44 cell lines and other tissues. Next, the highly expressed proteins in each tissue were further characterized to identify tissue-specific functions.
The top 200 proteins were identified in each tissue cluster. Due to the high reproducibility of the replicates, we specifically chose to analyze brain, ovary, heart, and lung in greatest detail. Interestingly, the metalloproteinases (ADAM7, 10, 15) were highly enriched in ovary (Figure 4.4). ADAMs were observed in the mouse proteome, but only within placental organs and lung [185]. The ovary controls oocyte development and reproductive hormone production; it contains extracellular matrix of granulosa cells surrounding the follicle [128]. Similarly, the placenta is part of the reproductive system and develops in order to provide oxygen and nutrients to a fetus growing in the uterus. CHO cells, which were established from fibroblast connective tissue surrounding the ovary [109], show protein expression levels that are more similar to the ovary than other tissues. Next, the lung highly- expressed proteins may participate in the critical metabolic and secretory functions.
The human proteome identified 183 genes highly expressed in the lung including similar membrane and secretory proteins [128]. Specifically, branched chain
129 aminotransferase shows high expression in our results and in the human lung as
compared to the tissues we evaluated. In the heart, 201 genes were shown to have
elevated expression in the human proteome [128], including retinol dehydrogenase
(RDH1) which was also significantly upregulated in our hamster heart proteome.
The knock-down of RDH1 led to abnormal neural crest cell migration and an
abnormal heart loop in mutant embryos, showing that it plays an important role in
heart tissue [195]. Finally, many of the highly expressed proteins in brain relate to signaling and transport similar to the 1437 genes that were identified at high levels in the human brain proteome [128], such as amyloid beta A4 protein and neuron
navigator 1 (NAV1). NAV1 is specialized specifically to the nervous system [196].
Following the protein intensity analysis, we further investigated protein
functions by IPA and GO. As shown in Figure 4.4, tissues show increased metabolic
and secretory activity as compared to cells and there are tissue-specific functions
identified as clusters in each grouping. Some of the differences between CHO cells
and ovary tissue may be attributed partly to the mixture of ovary cell types. In an in-
depth proteome analysis of ovarian cell lines and tissues, it was possible to
differentiate the proteomes between epithelial, clear cell, and mesenchymal tissues
[68]. We identified various highly active pathways in ovary such as exocytosis and
extracellular matrix development. There are similarities of protein functions across
tissues with related functions, notably the spleen, liver, and heart which cluster
together in the PCA from Figure 4.2. The liver and spleen share many proteins with
near equivalent protein expression levels, including vacuolar protein sorting-
associated protein 4B, proteasome maturation protein, and alpha-mannosidase
130 (fold change is equal to 1). Between the spleen and heart, proteins with near
equivalent expression include spartin, adenylate kinase isoenzyme 4, and receptor- type tyrosine-protein phosphatase alpha (fold change is equal to 1). Finally, between the liver and heart, proteins with a fold change equal to 1 include apolipoprotein A-1 binding protein, plexin, and transforming growth factor beta receptor type 3. Overall, these organs are critically important and share an extensive blood supply. Heart, liver, and spleen were used to compare oxidative stress in rats following DNA damage due to their systemic relationship [197].
Next, the GO analysis is used to highlight specific differences in functions that dominate cell and tissue metabolism (Table 4.2 and Table 4.3). Cells show enrichment of transcription machinery whereas tissues show increased transport and signaling as measured by significant p-values. Specifically, there are 82 and 109 enriched biological processes in CHO-S and CHO DG44, respectively, and 194-229 enriched biological processes in brain in the comparison with the two CHO cell lines.
In the cell to lung comparison, there are 126-137 enriched biological processes in the cell lines and 240-253 enriched biological processes in lung. Finally, In the cell to ovary comparison, there are 89-113 enriched biological processes in CHO-S and
CHO-DG44 and 110-160 enriched biological processes in ovary. Overall, there are more biological processes that are enriched in tissues as compared to either cell line with more enriched processes in the non-ovary tissues. The next section further investigates the role of genes, mRNA, aby comparing the protein expression fold changes to mRNA expression fold changes between CHO cells and hamster tissues.
131 For the transcriptome analysis, we identified stable and unstable functions
for each of the samples as indicated in Figure 4.5B. In tissues, stable and unstable
functions are often tissue- and organ-specific, relative to those in other tissues. For
example, in brain stable functions include protein synthesis, vesicle formation,
neuron branching, and neuron development whereas unstable functions include death, interphase, cell attachment, and neuron differentiation. Identification of brain-specific functions such as neuron branching and development need high protein levels. Interestingly, neuronal differentiation requires proteins with relatively rapid turnovers [198, 199]. Another interesting stable function was
vesicle-related. In the 2D mouse brain proteome, approximately 25% of proteins
were membrane-associated and involved transport, a function associated with
vesicle transport [200]. For vesicle formation, we identified genes including Rho
associated coiled-coil containing protein kinase 2 (ROCK2), ADP ribosylation factor
guanine nucleotide exchange factor 2 (ARFGEF2), zinc finger E-box binding
homeobox 1 (ZEB1), SRC proto-oncogene, clathrin heavy chain, ankyrin repeat and
FYVE domain containing 1 (ANKFY1), and adaptor related protein complex 2 alpha
2 subunit (AP2A2). Many of these genes, including AP2A2, were also identified in a
model of Huntington’s disease in the human brain [201]. ARFGEF2 is critical for the
brain; mutations causes movement disorder and neuronal migration because vesicle
trafficking, growth, and neurotransmitter receptor function are affected [202].
Additionally, ZEB1 is a biomarker for glioblastoma that plays a role in invasion,
chemoresistance, and tumorigenesis; it shows high expression in the brain [168].
132 The unstable brain functions represent transient activities, where high mRNA levels
are important such as interphase and neuronal differentiation.
For heart tissue, the stable functions include protein hydrolysis, connective
tissue shape change, cell homeostasis, and vesicle formation whereas unstable
functions include RNA transcription, production of reactive oxygen species (ROS),
and cell spreading. Genes associated with connective tissue morphology
upregulated in heart are secreted protein acidic and rich in cysteine (SPARC),
Immunoglobulin (CD79A) Binding Protein 1 (Igbp1), vasodilator-stimulated
phosphoprotein (Vasp), Rap Guanine Nucleotide Exchange Factor 1 (Rapgef1),
catenin CTNND1, and Integrin beta-1 (Itgb1). SPARC is an extracellular matrix component with high expression in the heart of mice [203]. In SPARC-null mice, the response to myocardial infarction involves increased cardiac rupture, dysfunction, and death as a result of collagen disorganization [204]. Catenins also play an important role in the heart, as the cadherin-catenin interaction compartmentalize cardiac cells during early vertebrate development [205]. Finally, Vasp shows localization to heart tissue. VASP and mammalian enabled (Mena) are associated with blood vessels and the intercalated discs of cardiac myocytes, and they co- localize with connexin-43 [206]. Overall, important activities for the heart show agreement between transcriptomic and proteomic data. Vesicle formation shows high protein expression in both brain and heart tissue with low expression in cells indicating that tissues likely have increased capacity for protein secretion. Unstable heart functions include RNA transcription and reactive oxygen species (ROS) production. Generation of ROS correlates with cardiac injury [207]. These oxidative
133 stress markers are precursors to heart failure [157]. RNA transcription results in
the generation of mRNA thus increasing the pool of mRNA relative to protein. These
activities are expected to show high mRNA expression.
In ovary tissue, stable functions include protein hydrolysis, protein
metabolism, microtubule dynamics, and cell viability whereas unstable functions
include nucleotide metabolism, ATP synthesis, cell homeostasis, and glycolysis.
Protein metabolism is upregulated in ovary and includes genes such as CAPN2,
POLDIP3, Snx1, ITCH, sirtuin 1 (SIRT1), CASP1, CRBN, Cold-inducible RNA-binding protein (CIRP), and Naglu. SIRT1 is overexpressed in ovarian cancer and inactivates p53 [208]. As an NAD+-dependent histone deacetylase, SIRT1 can affect glucose homeostasis and insulin sensitivity. The protein was observed by immunohistochemistry in human ovarian tissues and human luteinized granulosa cells [209]. Expression changes as a result of calorie restriction [209]. CIRP, identified at high levels in the ovary, has been a candidate for study in CHO cells.
CIRP was downregulated in order to evaluate the effect on cell growth and erythropoietin [155] production in CHO cells at low culture temperature [210].
Results showed that reduced CIRP expression did not recover the growth arrest
[210]. However, in another experiment, CIRP was overexpressed at standard culture temperature and improved titer by 25-40% in adherent and suspension cell lines
[211]. In this case, identification of proteins high in the ovary correlated with genetic engineering targets. Overall, the upregulation of protein metabolism in ovary could be important for recombinant protein production, one of the chief applications in CHO. The unstable functions represent activities that are
134 continuously active and highly metabolic such as glycolysis and ATP synthesis.
Because of the rapid turnover, it is expected that mRNA expression should be high.
Finally, the stable functions in lung tissue include death, shape change, and cytoskeletal rearrangement whereas unstable functions include cell membrane formation, endocytosis, molecular transport, and RNA expression. In differentiated lung tissue, the shape and cytoskeleton are maintained by structural proteins such as actin, dynactin, and alpha-actinin. Actin reorganization was correlated with cytoskeleton organization during surfactant secretion [212]. Intersectin (ITSN)-1 and ITSN-2 also show high protein levels in lung. In models of lung cancer progression, ITSN-1s contributes to proliferation, migration, and metastasis [213].
In contrast, endocytosis and molecular transport show high mRNA levels but low protein levels. Example genes include Vav2, low density lipoprotein receptor- related protein (LRP1), thrombospondin (THBS1), Cltc, mitogen-activated protein kinase 14 (Mapk14), stromal interaction molecule 1 (Stim1), KRAS, chloride voltage- gated channel 3 (Clcn3), and ERAP1. High expression of LRP1 is associated with high secretory capacity. When LRP1 expression is low, there is reduced migration, survival, proliferation, and differentiation [214]. Thbs1 is an extracellular matrix glycoprotein that shows inverse correlation with metastatic potential. At high expression levels, there is less metastatic potential [215]. Mapk14 is a kinase that is upregulated in lung cancer and affects inflammation. Signaling pathways triggered by cytokines upregulate Mapk14 expression and trigger the inflammatory response
[216]. Mutations in chloride voltage-gated channels, including Clcn3, affect secretion and cell volume; in the lung specifically, mutation leads to cystic fibrosis [217].
135 Another channel (Clc2) is expressed on the epithelium of respiratory cells and regulates fluid production and lung morphogenesis [218]. Finally, KRAS is a well- studied oncogene in lung cancer [219]. The lung in this experiment represents an organ with high secretory capacity that may help to elucidate secretion inefficiencies in CHO cells. The stable functions represent activities that are established within tissues. Molecular transport and RNA expression are continuously-occurring activities with high mRNA expression detected.
Our experiment generates the most-extensive tissue map of the Chinese hamster proteome, including over 8000 total proteins. Because of the hamster’s relevance to the biotechnology industry, we further compare the tissue proteome to
CHO cell lines in order to identify functional areas of differential expression.
Specifically, we observed enrichment of many metabolic pathways in all tissues that were not enriched in cells, such as protein processing, vesicle transport, and carbohydrate metabolism. Correlation between transcriptomic and proteomic data was in agreement for the most part, with at least 65% agreement for all samples.
Differences between mRNA and protein abundance reveal stable and unstable expression levels that further increase our knowledge about CHO gene and protein expression. In conclusion, this experiment expands on CHO knowledge by establishing a catalogue to differentiate between cells and tissues, thus creating the most extensive Chinese hamster proteome to-date.
136 Tables
Table 4.1: TMT Doublet Information
Peptide Unique Unique Experiment Spectrum Proteins (#) Peptides (#) Matches (#)
TMT 1 9,152 74,334 665,867
TMT 2 8,507 73,313 685,479
11,454 total
proteins
137 Table 4.2: Top 10 Most Enriched Biological Processes for CHO-S and Tissues
138 Table 4.3: Top 10 Most Enriched Biological Processes for CHO DG44 and Tissues
139 Figures
Figure 4.1: Overview of Experimental Design and Workflow. A: Proteome experiment compares hamster tissues (including brain, heart, lung, kidney, spleen, liver, and ovary) and CHO cell lines (including CHO-S and CHO DG44). B: Each TMT 10-plex contains a mix of tissues and cell line samples. Sample preparation involves protein extraction from tissues and cells, followed by reduction, alkylation, FASP, and digestion. Next, peptides are labeled and combined into two TMT 10-plex experiments for fractionation and mass spectrometry identification. In the bioinformatics analysis, peptides are matched to proteins and gene symbols for functional analysis in order to understand cell and tissue metabolism. Abbreviations: TMT (tandem mass tags), FASP (filter aided sample preparation), RPLC (strong cation exchange).
140 Figure 4.2: Protein Intensity Analysis. A: principal component analysis of CHO cells and hamster tissues. Clustering of overall protein intensity fold changes is calculated using the first and third principal components to show variation. B-E: normalized protein intensity is plotted for replicates of each sample corresponding to a different cluster from principal component analysis. B: heart, C: brain, D: lung, E: ovary.
TMT1 TMT2
141 Figure 4.3: Overall Distribution of Protein Intensity in Hamster Tissues. Protein intensity plotted as normalized fold change ratio. Outliers represented in yellow for proteins highly expressed for y-axis. Outliers represented in blue for proteins highly expressed for x-axis.
142 Figure 4.4: Highly Expressed Proteins. Center: heat map of highly expressed proteins. The top 200 proteins are plotted for each sample. Coloring is shown from low [43] to high (pink) abundance. Distinct clusters are shown for each sample. Clockwise from top: clusters of protein functions specific to tissues. Proteins were mapped to gene symbols for functional analysis. The protein functions exhibiting high expression for each tissue are shown relative to other tissues for brain, lung, heart, and ovary.
143 Figure 4.5: Comparison of Proteome and Transcriptome. A. Fold change ratio comparison between transcriptome and proteome. Green represents fold change ratio is in agreement between transcriptome and proteome, whereas red represents fold change ratio is not in agreement between transcriptome and proteome. B. Evaluation of stable and unstable protein expression for brain, heart, ovary, and lung all relative to CHO-S (clockwise from left). Stable represents proteome fold change >1 and transcriptome fold change <1. Unstable represents proteome fold change < 1 and transcriptome fold change >1. Functions were identified using IPA.
144 Chapter 5: Conclusions
CHO cells represent the dominant host for therapeutic recombinant protein
production. However, few large-scale datasets have been generated to characterize this host organism and derived CHO cell lines at the proteomics level. In this thesis,
numerous proteomics experiments using both label-free and labeled approaches
were used to characterize the Chinese hamster and CHO cell lines. The results
represent the largest CHO proteomics datasets to date and expand our knowledge of
the capabilities of CHO as a production host.
In the glycoproteomics comparison of CHO cell lines and glycosylation
mutants, a labeled proteomics experiment was designed to identify glycosylated and
siaylated proteins. We combined glycoprotein and sialylated protein enrichment
with iTRAQ peptide labeling to compare samples from cell lines differing in
glycosylation capabilities. Specifically, wild-type CHO was compared with
tunicamycin-treated CHO and Lec9.4a cells, which show approximately 50% of wild-
type glycosylation levels. A total of 381 unique glycoproteins were identified in the
comparison, with 187 glycoproteins identified with at least 2 unique peptides (194
glycoproteins with 1 unique peptide) and a stringent false discovery rate of 1%.
Results from the glycoproteomic analysis identified heavily-glycosylated proteins, such as membrane proteins and transporters. Upregulated glycoproteins in Lec9.4a cells include CD63 antigen, glyceraldehyde-3-phosphate dephydrogenase, vimentin, and ATP synthase. Proteins related to glycosylation are downregulated in Lec9.4a, such as alpha-(1,3)-fucosyltransferase, dolichyl-diphosphooligosaccharide-protein
145 glycosyltransferase subunit 1, uncharacterized glycosyltransferase AER61-like, and
uncharacterized glycosyltransferase AGO61-like. Next, wild-type Pro-5 CHO was compared with Lec2 cells, which have a mutation in CMP-sialic acid transporter causing significant reduction in sialylation. A total of 272 unique sialylated proteins were identified across 2 experiments, with at least 2 unique peptides and a stringent false discovery rate of 1%. Upregulated sialylated proteins include cadherin-17-like,
CD63 antigen-like, neural cell adhesion molecule 1, and CD97 antigen. The
downregulated sialoproteins, such as legumain, clusterin-like, transmembrane
protein 2, dolichyl-diphosphooligosaccharide-protein glycosyltransferase subunit
STT3A, and beta-1,4-galactosyltransferase 3, detect defects in glycosylation. Thus,
results from the glycoproteomic and sialoproteomic analysis of glycosylation
mutant cell lines demonstrate differences in cell metabolism.
Next, an extensive label-free quantitative proteomics analysis of two cell lines (CHO-S and CHO DG44) and two Chinese hamster tissues (liver and ovary) was used to identify a total of 11801 unique proteins containing at least two unique peptides. 9359 unique proteins were identified specifically in the cell lines, representing a 56% increase over previous work. Additionally, 6663 unique proteins were identified across liver and ovary tissues providing the first Chinese hamster tissue proteome. Protein expression was more conserved within cell lines during both growth phases than across cell lines, suggesting large genetic differences across cell lines. Overall, both gene ontology and KEGG pathway analysis revealed enrichment of cell cycle activity in cells. In contrast, upregulated molecular functions in tissue include glycosylation and lipid transporter activity. Furthermore,
146 cellular components including Golgi apparatus are upregulated in both tissues. In
conclusion, this large-scale proteomics analysis enables us to delineate specific
changes between tissues and cells derived from these tissues, which can help
explain specific tissue function and the adaptations cells incur for applications in
biopharmaceutical productions.
Finally, TMT labeling was used to compare protein expression across CHO
cell lines and eight hamster tissues including the brain, heart, kidney, liver, lung,
ovary, and spleen organs. In this experiment, we sought to compare various tissues
from the Chinese hamster (Cricetulus griseus) from which CHO cells were derived in order to improve our knowledge of engineering the CHO host for biotherapeutics production. Over 8500 proteins were identified in each of two TMT experiments; this corresponded to a total of 11454 unique proteins with a false discovery rate of
1%. Protein intensity plots were generated between tissues and cell lines to identify proteins with significant differential expression. In addition, the top 200 (3%) of proteins highly expressed by each sample were determined to compare cell and tissue specificity. Finally, we combined the proteomics with transcriptomic data using RNAseq in order to correlate mRNA and protein expression for each of the samples. Over 65% of genes show agreement between transcriptome and proteome fold changes.
Detailed comparisons of hamster tissues and CHO cell lines further improve our knowledge of Chinese hamster physiology. One caveat of proteomics is that the protein expression and functional differences need future work in order to be validated and proven. We envision that our predictions will suggest future
147 directions of research in the CHO community. In this work, we have greatly
expanded knowledge of the CHO host by thorough label-free and labeled proteomics
approaches. Specifically, we identified both glycosylated and sialylated proteins in
CHO cell lines, created the largest CHO proteome to-date, and established the first
Chinese hamster tissue proteome. We hope this work can be used across the CHO community to design new experiments by harnessing our knowledge of the Chinese hamster.
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162 Curriculum Vitae Kelley Marie Heffner
23115 Robin Song Drive • Clarksburg, MD 20871 • 301-518-2042 • [email protected]
EDUCATION Johns Hopkins University, Baltimore, MD, September 2012–August 2017 PhD in Chemical and Biomolecular Engineering University of Maryland, College Park, MD, August 2008–May 2012 Bachelor of Science in Bioengineering Summa Cum Laude (4.0 GPA), Honors College Citation, Gemstone Research Program Citation EXPERIENCE Michael J. Betenbaugh Lab, Baltimore, MD, September 2012–June 2017 Graduate Researcher . Analyzed growth of Chinese hamster ovary (CHO) cell lines and harvested hamster organs. . Established CHO cell line and first hamster tissue proteomes with over 10,000 unique proteins. . Compared statistical differences at transcriptomic and proteomic scale for CHO cell lines and hamster tissues. . Applied bioinformatics to identify enriched and depleted pathways in glycosylation- deficient CHO cell lines.
MedImmune/AstraZeneca, Gaithersburg, MD, July 2009–September 2016 Antibody Discovery and Protein Engineering Intern (June 2016–September 2016) . Designed proteomics workflow to increase throughput and robustness using automated liquid handling steps. . Established proteomics plasma depletion method to remove high-abundance proteins and compare expression between healthy and disease samples for clinical biomarker identification. Antibody Discovery and Protein Engineering Intern (June 2013–September 2013) . Developed immunoprecipitation-comparative proteomics method to identify secretory pathway proteins. . Evaluated phosphoproteomics, glycoproteomics, membrane enrichment, and iTRAQ comparative proteomics to analyze cell and tissue protein expression. Process Cell Culture Co-op, (March 2011–August 2012) . Established alternating tangential flow perfusion process to improve protein productivity and quality. . Developed high-throughput phenotypic microarray to investigate effect of media components on cell growth. . Dipeptide supplementation was implemented during tech transfer to improve antibody productivity and reduce lot-to-lot variability at manufacturing scale. Process Cell Culture Intern (June 2010–August 2010) . Increased recombinant glycoprotein sialylation using DOE to alter process parameters and media formulation. Process Cell Culture Intern (July 2009–August 2009) . Optimized pilot facility Pod-filtration for bioreactor harvests by reducing rinse water and chase buffer volumes.
Adam Hsieh Lab, College Park, MD, December 2010–May 2012 Undergraduate Researcher
163 . Investigated the proliferation of nucleus pulposus cell populations on plastic and collagen thin films. . Researched the effect of fluid shear stress on intervertebral disc cells to understand cellular mechanobiology.
Gemstone Research Program, College Park, MD, August 2008–May 2012 Web Liaison, Team Member of ‘Ligament Elasticity post-Graft Surgery (LEGS)’ . Part of 14-member team that sectioned, stained, and imaged cadaveric ACL grafts to correlate collagen organization and tension loss in order to advocate for ACL graft repair material. . Managed the team website by designing and uploading relevant project information.
SKILLS
Mammalian cell culture, CHO suspension culture, adherent culture, media formulation, bioreactors, data analysis, Microsoft Office, MatLab, R, proteomics, IPA, JMP, HPLC, MS, lab automation, DOE, process development, tissue culture, perfusion culture, AssayMap robot, Pod filtration, technical writing, literature review, time management
LEADERSHIP
Fleet Feet Sports, Gaithersburg, MD, June 2016–present Marathon Run Pacing Instructor . Schedule track and long runs and coach runners of various paces to complete marathons.
Cellular and Molecular Biotechnology Course, Baltimore, MD, Fall 2013, Fall 2014 Teaching Assistant . Led grading of homework, projects, and exams. . Taught weekly 3-hour course and managed office hours for students.
Graduate Student Liaison Committee, Baltimore, MD, August 2012–present Student Volunteer . Establish link between graduate students and elementary schools through STEM activities. . Led organization of departmental Toys for Tots drive and collected holiday donations.
Women in Engineering Program, College Park, MD, August 2009–May 2011 Peer Mentor . Advised freshmen engineering females on academics, campus life, and career development.
Engineers Without Borders, College Park, MD, August 2008–May 2011 Executive Board Secretary, Ethiopia Team Member . Traveled to Ethiopia to develop sustainable engineering technology for youth center. . Elected to Executive Board and maintained written notes of meetings.
AWARDS
. Walter L. Robb, Whiting School of Engineering Graduate Fellowship, 2012–2017 . National Science Foundation Graduate Research Fellowship, 2012–2017 . Fischell Department of Bioengineering Outstanding Senior Award, 2012 . Howard Hughes Medical Institute Undergraduate Research Fellowship, 2011 . Howard Hughes Medical Institute Gemstone Research Fellowship, 2010, 2009 . Fischell Department of Bioengineering Outstanding Junior Award, 2011 . Society of Women Engineers Life Technologies Scholarship, 2011 . Armed Forces Communications and Electronics Association Scholarship, 2011, 2008 . Suez Energy Generation Scholarship, 2011, 2010 . Baltimore Post American Society of Military Engineers Scholarship, 2010 . A. James Clark School of Engineering Knust Memorial Scholarship, 2009
164 . Association for Women in Science Scholarship, 2009 . Armed Forces Communications and Electronics Association Scholarship, 2009, 2008 . Clarksburg Parent-Teacher-Student Association Scholarship, 2008 . Clarksburg Athletic Boosters Scholarship, 2008 . Maryland Distinguished Scholarship, 2008–2012 . University of Maryland Banneker Key Scholar, 2008–2012
PRESENTATIONS 1) K. Heffner, D. Baycin Hizal, G. Yerganian, M. Betenbaugh, “Lessons from the hamster: A combined ‘omics approach to analyze CHO cell lines and Cricetulus griseus tissues”, ACS National Meeting, San Francisco CA, oral presentation April 2017. 2) K. Heffner, K. Lekstrom, D. Baycin Hizal, R. Chaerkady, R. Woods, M. Bowen, “HiT ME UP: High throughput method evaluation using proteomics”, MedImmune Intern Scientific Poster Session, Gaithersburg MD, August 2016. 3) K. Heffner, D. Baycin Hizal, M. Betenbaugh, H. Zhang, S. Krag, “Applying iTRAQ proteomics and systems biology approach to understand the glycosylated and sialylated pathways of CHO mutants”, American Society for Mass Spectrometry, San Antonio TX, June 2016. 4) K. Heffner, D. Baycin Hizal, M. Bowen, G. Yerganian, M. Betenbaugh, “Lessons from the hamster: Cricetulus griseus tissue and CHO cell line proteomes inform bioprocessing”, Cell Culture Engineering, La Quinta CA, May 2016. 5) K. Heffner, J. Lee, R. Venkat, M. Betenbaugh, “High throughput cell culture media component screening”, Cell Culture Engineering, Quebec City Canada, May 2014. 6) K. Heffner, D. Baycin Hizal, M. Bowen, “You belong with me: Development of an immunoprecipitation coupled mass spectrometry-based proteomics approach to identify interacting proteins involved in monoclonal and bispecific antibody production”, MedImmune Intern Scientific Poster Session, Gaithersburg MD, August 2013. 2nd Place Award 7) K. Heffner, A. Hsieh, “Effect of fluid shear stress on nucleus pulposus cell mechanotransduction”, Bioengineering Departmental Honors Thesis Presentation, College Park MD, May 2012. 8) A. Chandramani, M. Costales, B. Garbus, J. Hartstein, K. Heffner, R. Jain, K. Klein, A. McDonnell, P. Patel, V. Stefanelli, J. Wang, J. Weinberg, T. Zhang, A. Hsieh, H. Kim, C. Bennett, “Effect of tensile strength loss on collagen organization in ACL grafts post-reconstruction surgery”, Biomedical Engineering Society Annual Meeting, Hartford CT, October 2011. 9) K. Heffner, J. Lee, “High-throughput cell culture media component screening”, MedImmune Intern Scientific Poster Session, Gaithersburg MD, August 2011. 1st Place Award 10) J. Lee, K. Heffner, C. Barton, B. Holman, “Controlling sialylation of recombinant glycoprotein in cell culture process”, European Society for Animal Cell Technology Meeting, Vienna Austria, May 2011. 11) K. Heffner, J. Lee, B. Holman, J. Reier, “High-throughput methods for cell culture media investigation”, University of Maryland Undergraduate Research Day, College Park MD, April 2011. 12) K. Heffner, J. Lee, B. Holman, R. Heckathorn, “Improving recombinant blood factor II process development and sialylation”, MedImmune Intern Presentation Day, Gaithersburg MD, August 2010. 13) K. Heffner, A. Groves, “Optimization of pod harvest”, MedImmune Intern Presentation Day, Gaithersburg MD, August 2009. 3rd Place Award
PUBLICATIONS 1) K. Heffner, D. Baycin Hizal, M. Bowen, M. Betenbaugh, “Lessons from the hamster: Cricetulus grisues tissue and CHO cell line proteomes inform biotherapeutics research and development”, submission in progress. 2) K. Heffner, D. Baycin Hizal, M. Betenbaugh, “GlycoCHO: characterization of Chinese hamster ovary (CHO) glycosylation-deficient cell lines using a glycoproteomics approach”, submission in progress. 3) K. Heffner, D. Baycin Hizal, Q. Zhang, M. Betenbaugh, “Glycoengineering of mammalian expression systems on a cellular level”. In: E. Rapp, U. Reichl, Advances in Biochemical Engineering, Springer, submitted.
165 4) J. Lee, J. Reier, K. Heffner, C. Barton, D. Spencer, A. Schmelzer, R. Venkat, “Production and characterization of active recombinant human factor II with consistent sialic acid”, 2017, Biotechnology and Bioengineering, online DOI: 10.1002/bit.26317. 5) Y. Zhang, D. Baycin Hizal, A. Kumar, J. Priola, M. Bahri, K. Heffner, M. Wang, X. Han, M. Bowen, M. Betenbaugh, “High-throughput lipidomic and transcriptomic analysis to compare SP2/0, CHO, and HEK-293 mammalian cell lines”, 2016, Analytical Chemistry, 89(3), p. 1477-1485. 6) S. Bennum, D. Baycin Hizal, K. Heffner, O. Can, H. Zhang, M. Betenbaugh, “Systems glycobiology: integrating glycogenomics, glycoproteomics, glycomics, and other ‘omics data sets to characterize cellular glycosylation processes”, 2016, Journal of Molecular Biology, 428(16), p. 3337-3352. 7) A. Kumar, D. Baycin Hizal, D. Wolozny, L. Pedersen, N. Lewis, K. Heffner, R. Chaerkady, R. Cole, J. Shiloach, H. Zhang, M. Bowen, M. Betenbaugh, “Elucidation of the CHO super-ome (CHO-SO) by proteoinformatics”, 2015, Journal of Proteome Research, 14(11), p. 4687-4703. 8) K. Heffner, C. Schroeder Kaas, A. Kumar, D. Baycin Hizal, M. Betenbaugh, “Proteomics in cell culture: from genomics to combined ‘omics for cell line engineering and bioprocess development”. In: M. Al-Rubeai, Animal Cell Culture, Dublin, Ireland, Springer, 2015, p. 591-614. 9) A. Kumar, K. Heffner, J. Shiloach, M. Betenbaugh, D. Baycin Hizal, “Harnessing Chinese hamster ovary cell proteomics for biopharmaceutical processing”, 2014, Pharmaceutical Bioprocessing, 2(5), p. 421-435. 10) S. Bennun, K. Heffner, E. Blake, A. Chung, M. Betenbaugh, “Control of biotherapeutics glycosylation”. In: H. Hauser, R. Wagner, Animal Cell Biotechnology, Berlin, Germany, De Gruyter, 2014, p. 247-279. 11) K. Heffner, D. Baycin Hizal, A. Kumar, J. Shiloach, J. Zhu, M. Bowen, M. Betenbaugh, “Exploiting the proteomics revolution in biotechnology: from disease and antibody targets to optimizing bioprocess development”, 2014, Current Opinion in Biotechnology, 30, p. 80-86. 12) V. Jadhav, M. Hackl, A. Druz, S. Shridhar, C. Chung, K. Heffner, D. Kreil, M. Betenbaugh, J. Shiloach, N. Barron, J. Grillari, N. Borth, “CHO microRNA engineering is growing up: Recent successes and future challenges”, 2013, Biotechnology Advances, 31(8), p. 1501-1513.
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