Investigating Low Culture Temperature and Low Media Ph Of

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8-2019 Investigating Low Culture Temperature and Low Media pH of Two Chinese Hamster Ovary (CHO) Cell Lines with Next Generation RNA Sequencing (RNA-Seq) Technology Timothy James Lindquist Clemson University, [email protected]

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This Thesis is brought to you for free and open access by the Theses at TigerPrints. It has been accepted for inclusion in All Theses by an authorized administrator of TigerPrints. For more information, please contact [email protected]. INVESTIGATING LOW CULTURE TEMPERATURE AND LOW MEDIA PH OF TWO CHINESE HAMSTER OVARY (CHO) CELL LINES WITH NEXT GENERATION RNA SEQUENCING (RNA-SEQ) TECHNOLOGY

A Thesis Presented to the Graduate School of Clemson University

In Partial Fulfillment of the Requirements for the Degree Master of Science Bioengineering

by Timothy J. Lindquist August 2019

Accepted by: Dr. Sarah Harcum, Committee Chair Dr. Angela Alexander-Bryant Dr. Kimberly Paul ABSTRACT

Biologic drugs, or large molecule drugs, such as monoclonal antibodies, are proteins that are manufactured in living cells. Many biologics, such as Humira and

Enbrel, provide thousands of patients worldwide with lifesaving therapies. In order to produce these drugs, different expression systems, like Chinese hamster ovary (CHO) cells, are used. CHO cells are the most widely utilized mammalian cell line for recombinant protein production due to a long history of regulatory approval for producing safe biologic drugs and have the ability to produce therapeutic proteins at an industrial scale. CHO cells have many advantageous characteristics, like producing proteins with desired post-translational modifications. Many methods over the last 30 years have been used to improve productivity, including strain selection, media improvements, and physiologic studies. Yet despite significant improvements in yields and productivity, a major current drawback to using CHO cells is high production costs due to low product yields.

The recent increased availability of CHO genome information has provided fundamental knowledge needed to conduct studies to better understand CHO cell physiology. Transcriptomic studies allow for quantitative measures of gene expression changes when cells are grown in different culture conditions. In this study, the effects of culture temperature and pH for two CHO cell lines, a non-recombinant CHO K1-PF cell line and a recombinant rCHO DP-12 cell line was quantified using RNA-Seq to characterize the transcriptome. The culture temperatures compared were 37°C and 33°C, where 37°C represented the standard culture temperature. The 33°C culture temperature

ii represented a reduced culture temperature used to increase cell viability and sustain productivity in industry. The media pH levels examined were pH 6.95, a control level, and pH 6.70, a low pH level. At lower pH levels, lactate consumption is often observed to occur more readily.

The impact of pH on the transcriptome for both cell lines was determined to not be significant. The impact of temperature on the transcriptome, however, was determined to be significant and similar between the two cell lines. A total of 4184 genes were identified as temperature sensitive for the union of differential expressed genes for each cell line (FDR ≤ 0.05). Several genes related to glycosylation, apoptosis, and cell cycle were identified as temperature sensitive. The gene ontology analysis identified additional biological processes that could explain improved glycosylation at reduced temperatures.

Specifically, glycosylation genes, like Man1a1 and Man2a1, had higher expression for both CHO K1-PF and rCHO DP-12, and are linked to improving protein glycosylation at lower temperatures.

iii DEDICATION

This work is dedicated to my mother, Linda Lindquist, for her unwavering support throughout my entire academic journey thus far. Growing up and being exposed to her passionate work ethic and drive gave me a model to emulate as a student. Even when times were tough, she never ceased to amaze me in her ability to keep pushing forward, maintain focus, all while being the best mother I could ask for. I am forever grateful for all her advice, inspiration, and being my biggest supporter in all that I do.

iv ACKNOWLEDGMENTS

I would first like to thank my family for all of their support during my five years here at Clemson University. I would especially like to thank my parents, Rob and Linda, for teaching me that nothing worth having comes easy, and for their encouragement and support throughout my entire academic career thus far. I would like to give a big thanks to the person who made this all possible, Dr. Sarah Harcum, for all her guidance throughout my entire thesis project. I would also like to give a special thanks to Yogender

Gowtham for allowing me to use his RNA-Seq data in this work, and to my committee members Dr. Kim Paul and Dr. Alexander-Bryant for all their help. To my lab members, especially Tom Caldwell, Katie Elliott, Dan Odenwelder, Ben Synoground, Bradley

Skelton, and Stephanie Klaubert, for all their assistance, guidance, and most importantly all the laughs we shared in lab. I would also like to thank Dr. Rooksana Noorai for taking the time to help me with all my coding questions. I would finally like to thank Mallory

Killion for her unwavering support in all that I do, as well as Matt Wilson, Kenny Meyer, and Kyle Beerman for making my time here at Clemson one I will never forget.

v TABLE OF CONTENTS

Page

TITLE PAGE ...... i

ABSTRACT ...... ii

DEDICATION ...... iv

ACKNOWLEDGMENTS ...... v

LIST OF TABLES ...... viii

LIST OF FIGURES ...... ix

CHAPTER

I. INTRODUCTION ...... 1

1.1 Significance ...... 1 1.2 Prior work ...... 3 1.2 Organization ...... 4

II. LITERATURE REVIEW ...... 5

2.1 Biologics production ...... 5 2.2 Chinese Hamster Ovarian Cells ...... 5 2.3 RNA Sequencing ...... 8 2.4 Transcriptome Analyses ...... 9 2.5 Temperature and pH Analyses ...... 13

III. A TRANSCRIPTOMICS APPROACH TO INVESTIGATING LOW CULTURE TEMPERATURE & LOW MEDIA PH CONDITIONS IN CHO K1-PF & RCHO DP-12 CELL LINES ...... 17

3.1 Introduction ...... 17 3.2 Materials and Methods ...... 20 3.3 Differential Gene Expression and Pathway Analysis ...... 22

vi Table of Contents (Continued) Page

3.4 Results and Discussion ...... 24

IV. SUMMARY AND CONCLUSIONS ...... 73

APPENDICES ...... 75

A: Sample Descriptions ...... 75 B: MDS Plot ...... 76 C: Differentially Expressed Genes List ...... 77

REFERENCES ...... 92

vii LIST OF TABLES Table Page

3.1 Effect of culture temperature and initial culture pH on growth rates for CHO K1-PF and rCHO DP-12 cultures ...... 28

3.2 Differentially expressed genes for CHO K1-PF and rCHO DP-12 between the cultures with initial pH 6.70 vs. pH 6.95...... 32

3.3 Differentially expressed genes for CHO K1-PF and rCHO DP-12 due to temperature ...... 32

3.4 Cold shock-related genes that were identified as temperature-sensitive in at least one of the cell lines ...... 39

3.5 Heat shock-related genes that were identified as temperature-sensitive in at least one of the cell lines (FDR ≤ 0.05) ...... 41

3.6 Cell cycle-related genes that were identified as temperature-sensitive in at least one of the cell lines ...... 45

3.7 Glycosylation-related genes that were identified as temperature-sensitive in at least one of the cell lines ...... 53

3.8 Apoptosis-related genes that were identified as temperature sensitive in at least one of the cell lines...... 60

A-1 NCBI-BioProject Sequence Read Archive (SRA) accession numbers. The RNA-Seq data was submitted to the public database of the National Institutes of Health (NIH). Listed below are the cell lines, culture temperature, pH, and principle component coordinate values corresponding to Figure 3.3 ...... 75

C-1 Genes that were identified as temperature sensitive in at least one of the cell lines (FDR ≤ 0.05 and FD ≥ 1.5). The gene expression fold-differences are shown for the 33°C cultures relative to the 37°C cultures. Blanks indicate genes that did not meet these criteria...... 56

viii LIST OF FIGURES

Figure Page

3.1 Experimental design for CHO K1-PF and rCHO DP-12 cell lines. Four culture conditions were each conducted in duplicate for both cell lines ...... 25

3.2 Growth profiles for CHO K1-PF and rCHO DP-12 cultures...... 26

3.3 Glucose and lactate profiles for CHO K1-PF and rCHO DP-12 cultures ... 27

3.4 Principle component analysis (PCA) of all culture conditions for each cell line. PC1 represents 85% of the variance and PC2 represents 11% of the variance ...... 31

3.5 Venn diagram of temperature sensitive genes for CHO K1-PF and rCHO DP- 12 cultures. There were 3241 temperature sensitive genes for CHO K1-PF and 2936 temperature sensitive genes for rCHO DP-12 ...... 33

3.6 Distribution of temperature sensitive genes for CHO K1-PF ...... 34

3.7 Distribution of temperature sensitive genes for rCHO DP-12...... 35

3.8 Venn diagram of the temperature sensitive genes in CHO K1-PF and rCHO DP-12 cultures with at least 1.5-fold differences ...... 36

3.9 Cold shock-related gene expression profiles for CHO K1-PF and rCHO DP-12 cultured at 33°C and 37°C ...... 40

3.10 Cyclin gene expression profiles for CHO K1-PF and rCHO DP-12 cultured at 33°C and 37°C ...... 49

3.11 Cell division-related gene expression profiles for CHO K1-PF and rCHO DP- 12 cultured at 33°C and 37°C ...... 50

3.12 Sialidase gene expression profiles for CHO K1-PF and rCHO DP-12 cultured at 33°C and 37°C ...... 55

3.13 MA plot of all genes in CHO K1-PF (FDR ≤ 0.05) ...... 56

3.14 MA plot of all genes in rCHO DP-12 (FDR ≤ 0.05) ...... 57

ix List of Figures (Continued) Page

3.15 Pro-apoptotic gene expression profiles for CHO K1-PF and rCHO DP-12 cultured at 33°C and 37°C ...... 64

3.16 Anti-apoptotic gene expression profiles for CHO K1-PF and rCHO DP-12 cultured at 33°C and 37°C ...... 65

3.17 DNA replication-associated GO terms for the temperature sensitive genes for CHO K1-PF and rCHO DP-12 ...... 67

3.18 Metabolic process-associated GO terms for the temperature sensitive genes for CHO K1-PF and rCHO DP-12 ...... 68

3.19 Positive response to cold-associated GO terms for the temperature sensitive genes for CHO K1-PF and rCHO DP-12 ...... 69

3.20 Metabolic pathway-associated GO terms for the temperature sensitive genes for CHO K1-PF and rCHO DP-12 ...... 70

B-1 Multi-dimension scaling (MDS) plots representing gene expression profiles for all samples ...... 76

x CHAPTER 1

INTRODUCTION

1.1 SIGNIFICANCE

Biologics, like the anti-inflammatory drug Humira (Adalimumab) and the cancer drug Herceptin (Trastuzumab), are drug substances made from living organisms used in the prevention or treatment of diseases. Some of these living organisms include

Escherichia coli, yeast, and mammalian cell lines, such as Chinese hamster ovary (CHO) cells. CHO cells are used to produce over 70% of all recombinant therapeutics today by market value. In 1987, the FDA approved tissue plasminogen activator, the first CHO- derived recombinant protein, for use as a biotherapeutic drug. Since then, 374 individual biopharmaceutical products have gained a license in the United States and the European

Union (Walsh 2018). The use of CHO cells as a host cell line has exploded in the last few decades due to capacity of these cells for human-like post-translational modifications.

This has led researchers to focus on powerful gene amplification systems that help to produce higher concentrations of the desired products. Also, the demand for CHO- derived biotherapeutics has led to more than 100-fold yield improvements in the last few decades. Some of these yield improvements can be attributed to using serum-free media and optimizing feeding strategies (Kim, et al. 2012; Dean and Reddy 2013).

In this study, two CHO cell lines were used. The first was CHO K1-PF, an adapted version of the ancestral CHO K1 cell line. The other was a recombinant CHO cell line, rCHO DP-12. The two parameters studied in this work were culture temperature and pH. The culture temperatures were 37°C (control) and 33°C (mild hypothermic

1 condition). The two initial culture pH values were pH 6.95 (control) and pH 6.7 (low).

Hypothermic cultures are used with CHO cells to improve cell viability and reduce product degradation, and reduced culture pH conditions are used to promote lactate consumption. In order to assess the underlying causes observed when the culture changes are made, a transcriptomics approach was utilized, where RNA samples were extracted during the growth phases of paired cultures. The overall goal of this study is to advance the understanding of how CHO cells react to these different conditions, and to determine if CHO cell line engineering might be feasible to implement the positive effects of reduced temperature and pH without changing the culture conditions.

2 1.2 PRIOR WORK

The RNA-Seq data used in this paper was collected by Yogender K. Gowtham as part of his dissertation research (Gowtham 2016). This Master’s thesis is an extension of his dissertation work. The outcome of this analysis of the RNA-Seq data collected by Dr.

Gowtham will lead to a co-authored manuscript that will include authors Dr. Christopher

A. Saski and Dr. Sarah W. Harcum.

3 1.3 ORGANIZATION OF DOCUMENT

This Master’s thesis is divided into four chapters. Chapter One presents the explanation of parameters used and organization of the document. Chapter Two contains a brief literature review of biologics production, the use of Chinese hamster ovary cell lines, RNA sequencing, transcriptome analyses, and the bioprocessing parameters temperature and pH. Chapter Three presents the results of the transcriptome analyses, where the effects of reduced culture temperatures and low culture pH on two CHO cell line transcriptomes are explored. Finally, Chapter Four summarizes the work presented as well as discusses future directions. The Appendix contains three sections. Appendix A contains the sample descriptions. Appendix B contains a multidimensional scaling

(MDS) plot for all samples. Appendix C lists the genes that were identified as temperature sensitive in at least one of the cell lines.

4 CHAPTER 2

LITERATURE REVIEW

2.1 Biologics Production

Recombinant DNA technologies have revolutionized the way researchers approach medicine. At the center of the early breakthroughs of recombinant DNA was

Stanford University, where researchers slowly began to discover how genes from one organism can be isolated, cloned into vectors, and expressed in other, unrelated organisms. In 1976, Herbert Boyer founded Genentech, and after partnering with Eli

Lilly, helped produce the first FDA approved recombinant DNA product, Humulin, in

1982 (Kinch 2015). Since Humulin hit the market, the biologics market has increased exponentially, where global sales for biologics exceeded $200 billion in 2016. Due to this rapid growth, pharmaceutical companies are faced with many economic pressures

(Radhakrishnan et al. 2018). In order to tackle this problem head on, research and development into improving productivity of biologic drug production is vital in keeping up with the ever-increasing demand for these drugs.

2.2 Chinese Hamster Ovary cells

Chinese hamster ovary (CHO) cells are used for recombinant production because of this cell line’s ability to be grown in suspension in serum-free and chemically-defined culture medium, as well as in large-scale industrial-sized bioreactors with high production rates (Lolonde and Durocher 2017). Additionally, CHO cells have the capability to synthesize proteins that are comparable to those found in humans with

5 respect to biochemical properties and molecular structures. The productivity of mammalian cells cultivated in bioreactors has reached 10-15 g/L of monoclonal antibodies as a result of media optimization, selection methods, and process control (Zhu et al. 2012). Yet, these production levels are still low such that CHO cell protein production costs, like the monoclonal antibody drug Herceptin, are over $100,000 for a year’s worth of treatment (Kurian et al. 2007). Thus, there is still a need to further improve these processes.

A well-established manufacturing process for recombinant proteins in mammalian cells, like CHO, follows a conventional scheme. First, the recombinant gene with the necessary transcriptional regulatory elements is transferred to the cells, along with a second gene that confers to recipient cells a selective advantage. In the presence of the selection agent, only those cells that express the selector gene survive. Some common genes used for selection in CHO cells are dihydrofolate reductase (DHFR), an enzyme involved in nucleotide metabolism, and glutamine synthetase (GS). Other areas of host cell engineering include approaches to reduce lactate accumulation, manipulate cell growth, and decrease programmed cell death (Li 2010). Following selection, survivors are transferred as single cells to a second cultivation vessel, and the cultures are expanded to produce clonal populations. To generate stable cell lines with adequate productivity, hundreds to thousands of clones may be screened, where individual clones are evaluated for recombinant protein expression. The highest protein producers are then selected, where further work focuses to improve productivity by varying media formulations and process parameters (Wurm et al. 2004; Li et al. 2010).

6 One important attribute of CHO cells is the capability of producing recombinant glycoproteins with human-like glycans. In this manner, the biologic drugs are more likely to be bioactive in human hosts and not cause immune reactions. The glycosylation profile of a biologic drug is considered a critical quality attribute as it effects how well it will function in a patient. (Zhang et al. 2016). Protein glycosylation is the addition of oligosaccharides onto a protein, mediated by many complex metabolic reactions and subdivided into N-linked and O-linked glycosylation. N-linked glycosylation is characterized by the attachment to an asparagine-residue in the protein, while O-linked glycosylation is the attachment to the oxygen atom of serine or threonine (Goh and NG

2017). In industrial processes, the large repertoire of glycans is not observed on recombinant glycoproteins expressed by CHO cells unlike if these same proteins were produced in plant cells or yeast. Additionally, control of these metabolic pathways has been tailored by cell line selection, cell culture media optimization, and altering bioprocess parameters (Hosslet et al. 2009). Therefore, controlling the glycan composition and structure of biologics for molecules such as immunoglobulin G proteins

(IgGs) appears to be a promising method to improve the efficacy of therapeutic antibodies that utilize antibody-dependent cellular cytotoxicity (ADCC) for biological activity (Li et al. 2010).

In 2011, the genomic sequence of the parental CHO-K1 cell line was determined, marking a major milestone for the cellular and metabolic engineering of CHO cell lines.

This genome sequence predicted that CHO-K1 cells had more than 24,000 genes (Xu et al., 2011). More recently, the CHO-K1 cell line genome is estimated to have 24,383

7 genes (Lewis et al. 2013, Xu et al. 2011). These improvements to the CHO genome have led to more site-specific cell line engineering to increase viable cell densities (VCDs) and specific productivity (qp) (Kim et al. 2012; Ritter et al. 2016; Tossolini et al. 2018;

Tamošaitis and Smales 2018).

2.3 RNA Sequencing

The transcriptome is the complete set of transcripts in a cell for a specific physiological condition, where the number of each transcript is also quantified.

Quantifying the transcriptome is essential for interpreting the functional elements of the genome (Wang et al. 2009). Formerly, the most powerful tools for transcriptome studies were DNA mircroarrays, RNA interference, and quantitative real-time PCR (q-RT-PCR)

(Datta et al. 2013). Another commonly used method, northern blots, were limited to measuring single transcripts, whereas DNA microarrays could analyze hundreds of genes at once (Kukurba & Montgomery 2016). The recent reductions in sequencing costs coupled to not needing a sequenced organism and the ability to attain more information from a single analysis has led researchers to switch from DNA microarray analysis to

RNA sequencing (RNA-Seq) (Chen et al. 2015; Melville et al. 2011; Doolan et al. 2013;

Reinhart et al. 2018).

RNA-Seq is a high-throughput next-generation sequencing (NGS) technology that essentially reads every mRNA transcript in a cell in a template independent manner. The mRNA in a cell is first transcribed into complementary DNA (cDNA) prior to reading, which improves sample stability. Every read of the cDNA is counted, and these counts

8 of the cDNA sequence are integer numbers. The cDNA reads are also mapped onto the genome to develop a full representation of the number of transcripts for each gene that were in the cell. (Rahmatallah et al. 2014, Kukurba & Montgomery 2016).

The typical RNA-Seq experiment involves several steps. First the RNA is isolated and converted to cDNA. The cDNA is prepared (fractionated) and sequenced using a next-generation sequencing platform (Kukurba & Montgomery 2016). Ensemble- platforms use fractionated cDNA resulting in short sequences from both ends (paired-end sequencing) and then are reassembled to form genes (Wang et al 2009). There are several important factors that affect the quality of the transcript data. For example, the greater number of transcripts that can be detected and quantified, the higher the quality of the data. The terms used to assess the quality of this data are error rates, sequencing depth, or number of reads per sample, and library size (Mortazavi et al. 2008 and Conesa et al.

2016). The Illumina sequencing platform used in this study uses an ensemble-based platform, which has been shown to give low sequencing error rates (Bentley et al. 2008).

2.4 Transcriptome Analyses

Once the sequencing is completed for an RNA-Seq sample, FASTQ files are generated for subsequent analyses. Pre-processing of short read sequences can be accomplished with packages such as CutAdapt (Martin 2001), ngsShoRT (Chen et al.

2014), and Trimmomatic (Bolger et al. 2014). The purpose of these packages is to identify and remove adapter sequences and perform quality filtering. Succeeding this step is alignment to a reference genome, where STAR and TopHat2 are the most commonly

9 used packages. STAR (Spliced Transcripts Alignment to a Reference) uses a novel strategy for spliced alignments, where it is advantageous for its high mapping speed and accuracy when dealing with large transcriptome datasets. TopHat2 is optimized for widely available long paired-end reads, which can cause an issue by spanning multiple splice sites rather than just one or two. For counting reads mapped to a reference genome, htseq-count function in HTSeq, the summarizeOverlap function in the

GenomicRanges Bioconductor package, and the tool featureCounts are the most frequently cited tools. featureCounts is used mainly for its processing speed and memory efficiency (Liao et al. 2014). HTSeq is one of the simplest quantification methods used to count how many aligned reads for each gene overlap its exons. When compared with other aligners, HTSeq frequently shows up at the top of the performance rankings

(Anders et al 2014; Fonseca et al. 2014).

There are several public domain tools to assist with analyzing transcriptome data.

These differential gene expression tools include edgeR, limma, and DESeq2. All of these software tools conduct statistical tests of raw RNA-Seq data and organize it. There are several review articles that have directly compared the analysis tools in real and hypothesized data to determine false positives and false negatives (Costa-Silva et al.

2017). One package, edgeR, moderates the dispersion estimate for each gene toward a common estimate across all genes, using a weighted conditional likelihood (Robinson et al. 2009). Limma, another common package, uses linear models to analyze entire experiments as an integrated whole rather than making comparisons between pairs of treatments. The limma software conducts this by assessing which genes are significantly

10 different, as well as collecting information about the expression level of each gene in a pairwise fashion (Ritchie 2015; McDermaid et al. 2018). DESeq2 analysis begins with constructing a count matrix K with rows indicating genes and columns indicating different samples used. For each gene, a generalized linear model (GLM) is fit, where read counts follow a negative binomial distribution. When comparing test and control samples, the GLM fit returns coefficients that indicate overall expression strength of genes and the log2 fold changes. DESeq2 is primarily used to control for false positives.

For both real data and hypothetical data sets, DESeq2 often achieved the highest sensitivity while also able to maintain accurate false discovery rates (FDR) (Love et al.

2014; Seyednasrollah et al. 2015).

Once differentially expressed genes have been identified, tools to find patterns in the transcriptome include GSEA algorithm (gene set enrichment analysis), GSA (gene-set analysis), and SAFE (spatial analysis of functional enrichment). While these different methods vary in statistics, all of these methods use the framework of statistical hypothesis testing by assigning p-values to a gene set (Maciejewski 2013). Key elements of GSA are the presence of known gene sets that are tested against, the type of hypothesis being tested and a method of controlling false positives. Known gene sets, or knowledge-based resources, include gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes

(KEGG), and Molecular Signatures Database (MSigDB) (Mathur et al. 2018). Usually, multiple sets of tools will be used during the transcriptome analysis processes, as different insight can be gained from each tool.

11 KEGG is a knowledge base for analysis of gene functions that links genomic information with higher order functional information. Because KEGG Orthology system represents conserved features of genes and maps, it is a widely used resource for transcriptome analysis (Kanehisa & Goto 2000; Kanehisa et al. 2019). Limitations of the

KEGG orthology system include not assigning “LOC” genes to a specific pathway. LOC genes are those that are not yet functionally characterized. Another knowledge-based resource, GO, is an evidence-based annotation tool used to explain the biological roles of individual genomic products, like genes, by classifying the genes with ontologies. The three major ontologies used are molecular function, biological process, and cellular components. A ‘GO annotation’ describes an association between an ontology, like biological processes, and a gene product. One advantage of GO is that for each annotation, it provides references to the evidence that support that association (Ashburner et al. 2000; The Gene Ontology Consortium 2015). While the GO database is beneficial in classifying genes based on a specific orthology, the reliability of the statistical significance tests of GO terms have been questioned (Mao et al. 2005). To discover significantly enriched GO terms, PANTHER database (Protein Analysis THRough

Evolutionary Relationships) can be used, which classifies genes from 104 organisms by structured representation of protein function. This includes the GO annotations and biological pathways. The PANTHER database is often used for gene set analysis because it not only assigns GO terms, but also provides associations of genes to biological pathways from the PANTHER Pathway resource (Mi et al. 2017).

12 2.5 Temperature and pH Analyses

Most mammalian cells, like CHO, are cultivated at 37°C to mimic near the natural organism temperature (Yoon, et al. 2003). While standard conditions are still commonly used, research has shown that reduced culture temperatures can produce a range of positive results. Typically, reducing culture temperatures from 37°C to temperature ranges between 28°C to 34°C for CHO have been shown to increase cell viability and productivity. Also, at reduced culture temperatures, decreased nutrient uptake has been observed, with optimal outcomes being dependent upon the cell line used and the desired product (Bedoya-López et al. 2016; Fox, et al. 2005; Kumar, et al. 2007; Sunley, et al.

2008; Bedoya-López, et al. 2016; Swiderek et al. 2007; Vergara et al. 2014). Moreover, when cells are cultivated at sub-physiological temperatures, global protein synthesis rates are decreased. While this seems counterintuitive, the recombinant protein expression has been observed to increase at these reduced temperatures due to less competition of recombinant mRNAs with endogenous mRNAs for the translational and protein folding machinery (Masterton and Smales 2014). While reducing culture temperature increases product yields, it can also have a significant impact on product quality, where glycosylation and protease degradation are often reported to be affected by temperature

(Yoon et al. 2003b; Woo et al. 2008; Sou et al. 2015).

Glycosylation is a critical product attribute of recombinant proteins because of its influence on stability, folding and intracellular trafficking. The degree of protein glycosylation is dependent upon amino acid composition, rate of synthesis, post- translational modification capability of hosts, and culture conditions (Gupta and Shukla

13 2018). The stabilization of physical properties of therapeutic proteins are imperative for the in-vivo efficacy of biologic drugs, which may be hindered by physicochemical instabilities of inappropriate glycosylation (Yamane-Phnuki & Satoh 2009). For example, the charge and solubility of recombinant proteins are influenced by the presence of charged monosaccharides like sialic acids. The negative charge of sialic acids has been shown to enhance the in-vivo stability of recombinant proteins (Runkel et al. 1998; Raju

2008; Costa 2014). Additionally, sialidases and proteases secreted by living cells or released into the culture upon cell death can play a significant role in the heterogeneity of the product. Therefore, decreasing culture temperatures is thought to protect product quality by decreasing protease and sialidase activity (Dorai and Ganguly 2014; Chuppa et al. 1996).

Cell cycle is another critical process characteristic that affects recombinant protein production. Specifically, genes involved in protein translation and ribosome biogenesis have higher expression in the G1 phase (Kumar et al. 2007; Moore et al. 1997;

Yoon et al. 2003; Kaufman et al. 1999; Fogolin et al. 2004). Reduction of culture temperature has also been shown to cause G1-phase cell cycle arrest and resulted in improved protein productivity through a prolonged stationary phase (Tossolini 2018;

Masteron and Smales 2014). Another process characteristic affecting protein production is apoptosis, a form of programmed and controlled cell death. Apoptosis lowers cell viability in CHO cell cultures and reduces overall productivity of upstream process development (Carlage et al. 2009). Understanding the factors that cause apoptosis as well as developing strategies to protect cells against it could lead to improved bioprocess

14 development (Grillo & Mantalaris 2019; Wong et al 2005). A decrease in culture temperatures showed the onset of apoptosis being delayed significantly, which was hypothesized to be a result of a reduction of cellular metabolism (Al-Fageeh & Smales

2006; Moore et al. 1997; Grilo and Mantalaris 2019). Additionally, several studies have observed that for CHO cells, reduced culture temperatures resulted in elevated cold shock protein expression, such as RNA binding motif protein 3 (Rbm3) and cold inducible

RNA binding protein (Cirbp) (Sonna et al. 2013; Jones and Inouye 1994; Al-Fageeh &

Smales 2006). A few studies also showed decreased heat shock protein expression in reduced culture conditions, and gene expression changes for CSPs and HSPs following a temperature reduction have been noted (Bettaieb & Averill-Bates 2005; Sonna et al.

2013; Lee et al. 2009).

Studies have shown that changes in pH can have a significant impact on cell growth rate, cell density, lactate production rate, and protein productivity and quality

(Radhakrishnan 2018; Zheng et al. 2018; Hossler 2009). Raising and lowering culture pH has been examined, where primarily only lowering the culture pH promotes positive outcomes in CHO productivity and in extending culture durations (Trummer et al. 2006;

Jiang et al 2018; Ivarsson et al 2014). Oguchi noted that combining reduced culture temperatures with lowering culture pH increased cell viability and increased the final antibody titer compared to control (Oguchi et al 2007). Other studies, however, have shown that decreasing culture pH could give rise to detrimental effects on cell production yields because it halts cell growth and can give rise to changes in cell metabolism (Varley and Birch 1999; Sauer et al. 2000). Overall, there appears to be a low pH range that

15 improves productivity by improving lactate utilization. However, culture pH levels that are too low can cause cell growth inhibition. Therefore, the best range of pH for CHO cells appears to be from 6.70 to 7.2.

16 CHAPTER 3

A TRANSCRIPTOMICS APPROACH TO INVESTIGATING LOW CULTURE

TEMPERATURE & LOW MEDIA PH CONDITIONS IN TWO CHO CELL

LINES

3.1 Introduction

The global biologics market was $221 billion in 2017, and there are currently over

1000 biologics under development. The widespread use of CHO cells in recombinant protein production, in comparison to other mammalian cells, is a result of an established record of product safety, product quality, and desirable process performance characteristics (Wuest et al. 2012; Farrell et al. 2014; Wurm 2004; Dean and Reddy 2013;

Tejwani et al. 2018). As of 2018, CHO cells produced 84% of the monoclonal antibody drugs of those produced in mammalian cells. CHO cells are also used for producing complex proteins such as cytokines and fusion proteins (Walsh 2018; Zhu 2012; Matasci et al. 2009).

To better understand CHO cellular mechanisms under certain physiological condition, a transcriptomics approach can be utilized. RNA sequencing (RNA-Seq) has become the primary method for transcriptome analysis due to lower costs, wider coverage than DNA microarrays, and greater dynamic range (Conesa et al. 2016; Wang et al. 2009; Fonseca et al. 2014; Mortazavi et al. 2008). For CHO cells, the availability of a reference genome has improved mapping of sequences with identities (Xu et al. 2001;

Datta et al. 2013; Tossolini et al. 2018; Monger et al. 2015; Sha et al. 2018).

17 For some time, both temperature and pH have been known to effect CHO cell growth, protein productivity, and critical product quality attributes (Hossler et al. 2009;

Vergara et al. 2014; Fox et al. 2005). Often, the culture temperature will be reduced mid- culture to reduce cell metabolism, arrest cell growth, and increase productivity (Yoon et al. 2003a, 2005; Bedoya-López 2016; Lolonde and Durocher 2017; Kaufmann et al.

1998; Kumar et al. 2007; Trummer et al. 2006). While higher cell viability has been shown to be a universal result of reduced culture temperatures, the effects of reducing the culture temperature on protein productivity has had variable results. In some studies it was observed that glycoprotein quality and productivity decreased for CHO cells

(Vergara et al. 2018; Yoon et al. 2003a), while other studies observed substantial increases in protein productivity (Bedoya-López et al. 2016; Al-Fageeh et al. 2006;

Kantardjieff et al. 2010; Kaufmann et al. 1998; Woo et al. 2008; Trummer et al. 2006).

Others, however, have demonstrated that the effect of reducing the culture temperature are cell line and protein specific (Yoon et al. 2003b; Yoon et al. 2004). Culture pH has also been observed to affect growth rates, protein productivity and product quality

(Radhakrishnan et al. 2018). One study showed that reducing the pH of a CHO cell culture to pH 6.85 increased cell viability (Zheng et al. 2018). Other studies have shown that lowering the culture pH can enhance specific productivity (Oguchi et al. 2006; Xie et al. 2016; Trummer et al. 2006). Therefore, there is a need to better understand the underlying transcriptional changes that occur in CHO cells at reduced culture temperatures and pH levels.

18 In this study, the impact of culture temperature and pH was examined using a transcriptome analysis. RNA-Seq was used to obtain the full transcriptome of two different cell lines cultured at two different temperatures and with two different initial pH levels. The two temperatures used were 37°C and 33°C and two initial pH levels were pH

6.95 and pH 6.70. 37°C and pH 6.95 represented the standard conditions, and 33°C and pH 6.70 represented the reduced temperature and low pH conditions, respectively. The two cells lines used were CHO K1-PF, a non-recombinant host cell line, and rCHO DP-

12, a recombinant cell line expressing anti-IL-8. The transcriptomes were analyzed for differential gene expression due to temperature and pH. Important bioprocess-related gene expression levels were examined, as well as cold and heat shock genes. Gene ontology methods were then used to identify gene expression patterns between the cell lines.

19 3.2 Materials and Methods

3.2.1 Cell Lines, Culture Conditions, and RNA Extraction

CHO K1-PF (Sigma 00102307), a non-recombinant CHO cell line derived from CHO-

K1, and a recombinant CHO cell line rCHO DP-12, clone #1932 (ATCC® CRL-12445TM), which expresses a monoclonal antibody (mAb) against the chemokine Interleukin-8 were used. Both cell lines were grown in CD CHO medium (Life technologies, NY, USA).

2 The cells were cultured in 75 cm T-flasks in incubators with 5% CO2 maintained at either 33°C or 37°C. The CD CHO media had an initial pH of 6.95. To obtain pH 6.70,

1.2N HCl was added. The pH was measured with an UltraBasic benchtop pH meter UB-

10 (Denver Instruments). Cell viabilities and concentrations were measured using

TC20TM Automated Cell Counter (Bio-Rad). Duplicate cultures were conducted for each condition examined. For the RNA-Seq analysis, samples were harvested once the cells reached a concentration of greater than 2.0 x 105 cells / mL. Samples were added to

RNAprotect® cell reagent (Qiagen, Valecia, CA) immediately upon removal from T- flasks. Samples in RNAprotect® were stored at 4°C overnight or at -20°C for up to a month before processing the RNA.

3.2.2 RNA-Sequencing

The messenger RNA (mRNA) was purified using oligo-dT attached magnetic beads, and the cleaved RNA fragments were copied into first strand cDNA using reverse transcriptase. The second strand cDNA synthesis was performed using DNA Polymerase

I, and the cDNA fragments went through an end repair process with the addition of an

20 ‘A’ base and ligation of the adapters. The cDNA libraries were quality validated on an

Agilent 2100 Bioanalyzer System to confirm correct fragment distribution (~260 bp) and effective removal of adapter dimers. Sequence libraries were multiplexed, and sequence data was collected on 2 lanes of a HiSeq 2500 (Illumina, San Diego, CA) at the

Genomics division of David H. Murdock Research Institute (Kannapolis, NC) using a

2×101 bp, paired end read type.

3.2.3 Quality Control and Trimming Raw Data

Once sequencing was completed, the raw data files were output as FASTQ files, which contained the nucleotide sequences for each read along with base quality scores.

The FASTQC package (v0.10.1) conducted quality control checks including per base sequence content, per sequence GC content, and adapter content. (Andrews et al. 2010,

Monger et al. 2015). Then, the Trimmomatic Package (v0.38) was used for quality trimming, adapter removal, and read filtering (Bolger et al. 2014).

3.2.4 Mapping and Counting Reads

Following Trimmomatic, STAR(v2.7.0) was used to generate genome index files using the genome Fasta file as the input, where the Chinese hamster genome reference was used (RefSeq GCA_003668045.1). Next, reads were mapped to the genome using

STAR, which generated a BAM (Binary Alignment Map) output file (Dobin et al. 2015).

Samtools (v1.9) was used to convert the BAM files to SAM (Sequence Alignment Map) files as an input for HTSeq (Li et al. 2009). Finally, HTSeq (v0.11.0) was used to count

21 the number of reads mapped to each gene, called a feature in HTSeq. The output from

HTSeq was used for differential expression analysis using the package DESeq2 in R

(Anders et al. 2015).

3.3 Differential Gene Expression and Pathway Analysis

3.3.1 Statistical Analyses with DESeq2

DESeq2(v3.5.2) in R was used for differential gene expression analysis. After generating a gene count matrix for each condition, contrast arguments were constructed to isolate pair-wise arguments within the gene matrix, and DESeq2 used statistical techniques to moderate, or shrink, imprecise estimates toward zero. This avoids weakly expressed genes showing stronger differences than those with strongly expressed genes

(Love et al. 2014). Differential expression was defined by a Benjamini-Hochberg (BH) adjusted p-value of less than 0.05 (FDR ≤ 0.05) between at least a pair of conditions

(Chen et al. 2015; Benjamini and Hochberg 1995). The fold difference (FD) is defined relative to the control cultures at 37°C and pH 6.95. For example, a gene with higher expression in the 33°C and pH 6.70 cultures would have a positive fold difference, were as a gene with lower expression in the 33°C and pH 6.70 cultures would have a negative fold difference.

3.3.2 Gene Set Analysis

To match gene symbols and gene names with the unique gene identifiers, or

Entrez IDs, the Bioconductor package AnnotationDbi (v.146.0) was used. To import the

Chinese hamster annotation (CHOK1GS_HDv1) from the Ensembl database, the

22 Bioconductor package AnnotationHub (v2.10.1) was used. Once mapped, the resulting gene list included a list of gene symbols and gene names, as well as LOC genes that were both characterized and not functionally characterized. Using this list of genes, the gene ontology reference PANTHER (v14.1) was used to analyze the biological and molecular pathways (Mi et al. 2018).

23 3.4 Results and Discussion

To understand the transcriptomic differences of CHO cells when cultured at reduced temperatures (33°C) compared to the standard temperatures (37°C), as well as the effects of low pH of 6.70 compared to the control pH of 6.95, two different cells lines were utilized: CHO K1-PF and rCHO DP-12. A recombinant and nonrecombinant cell line were used to gain a better understanding of the impact of different culture temperatures as well as the additional burden associated with recombinant protein production. The cells were seeded at low cell concentrations into T flasks to avoid pH shift during culturing. Additionally, the low seeding concentrations allowed for relatively constant nutrient concentrations between the cultures. Figure 3.1 shows the experimental design for this study, where four culture conditions were each conducted in duplicate for both cell lines. The growth profiles are shown in Figure 3.2, where the sample times are represented by black circles. The metabolite profiles for the 33°C and 37°C cultures are shown in figure 3.3. The 37°C cultures were sampled at 48 hours, and the 33°C cultures were samples at 72 hours to have similar cell concentrations and metabolite levels to the

37°C cultures. Table 3.1 shows the growth rates for the four culture conditions by cell line. Both the 33°C and 37°C cultures had exponential growth for over 96 hours, and the samples for RNA-Seq analysis were taken during mid exponential phase. As expected, the growth rates for the 37°C cultures were higher than the 33°C cultures. Additionally, the growth rates for the rCHO DP-12 cultures were significantly higher compared to the

CHO K1-PF cultures (p ≤ 0.05). However, the growth rates were not different between cultures due to pH (p > 0.05).

Figure 3.1: Experimental design for CHO K1-PF and rCHO DP-12 cell lines. Four culture conditions were each conducted in duplicate for both cell lines.

Figure 3.2: Growth profiles for CHO K1-PF and rCHO DP-12 cultures. Cells were cultured at 37°C and 33°C as well as pH 6.95 and pH 6.70. The black circles indicate when RNA samples were taken, which were 48 hours for the 37°C cultures and 72 hours for the 33°C cultures. Error bars represent standard deviation.

Figure 3.3: Glucose and lactate profiles for CHO K1-PF and rCHO DP-12 cultures. Error bars represent standard deviation.

Table 3.1: Effect of culture temperature and initial culture pH on growth rates for CHO K1-PF and rCHO DP-12 cultures. The exponential growth rates with standard deviations are shown. pH was not a significant factor (p > 0.05), while cell line and temperature were significant factors (p ≤ 0.05). Temperature Cell Line Initial Culture pH 37°C 33°C

6.95 0.029 ± 0.001 0.010 ± 0.0002 CHO K1-PF 6.70 0.027 ± 0.002 0.009 ± 0.0004

6.95 0.034 ± 0.002 0.013 ± 0.002 rCHO DP-12 6.70 0.034 ± 0.00002 0.014 ± 0.001

28 3.5.1 Differential Gene Expression Analyses of both CHO cell lines

In order to determine if the culture temperature or pH had an effect on either cell line, RNA was collected in parallel cultures in the mid-exponential phase. Specifically,

RNA was collected from samples with at least 2 X 105 cells/mL and greater than 95% viability. Additionally, the glucose concentrations were above 5 g/L and lactate concentrations were below 1 g/L, demonstrating the cells were not lactate inhibited. A total of 26,125 genes with nonzero read counts were used for statistical analysis. A multidimensional scaling (MDS) analysis in edgeR and a principle component analysis

(PCA) in DESeq2 were used to identify major causes of variance of gene expression levels for different cell lines and culture conditions. The initial MDS plots identified that both CHO K1-PF 37°C, pH 6.70 samples contained outliers. Further analysis confirmed that these samples had been contaminated in the library preparation phase. Although the original harvest RNA was free of contamination, there was insufficient RNA to repeat the sequencing. The MDS plot with and without the CHO K1-PF 37°C, pH 6.70 samples are shown in Appendix B-1. Since DESeq2 was the tool used in the current analysis, the PCA plot of the remaining samples is shown in Figure 3.1, where PC1 represents 85% of the variance and PC2 represents 11% of the variance. The PCA plot also indicates that pH caused minimal variance between samples, since these samples were clustered very close to each other (Figure 3.2).

Since the objective of this work was to determine the effects of temperature and pH, and the PCA plots clearly show that cell line variance dominated, the statistical analysis needed to be conducted for each cell line. If the statistical analysis grouped the

29 cell lines together for temperature effects, the fact that each cell line had different baseline levels for a particular gene confounded the analysis. Yet, most genes changed in the same direction due to temperature for both cell lines. Table 3.2 shows the number of genes with differential expression due to pH by cell line. The number of genes passing the FDR due to pH are lower than the expected false positive number, which is in agreement with the PCA plot and considered a type I error. Table 3.3 shows the number of genes with differential expression due to culture temperature by cell line. The number of genes passing the FDR criteria is higher than the expected false positive values for both cell lines. Using the conventional FDR ≤ 0.05, there were 3241 temperature- sensitive genes in CHO K1-PF and 2936 temperature sensitive genes in rCHO DP-12

(Figure 3.4). Using the union of the FDR ≤ 0.05 and FD > 1.0 genes for both cell lines, there were 4184 genes identified as temperature sensitive. Of the 4184 temperature sensitive genes, 1993 genes were identified as significant in both cell lines (Figure 3.4).

For the 3241 temperature sensitive genes for CHO K1-PF, 471 genes had FDR ≤ 0.05 and FD ≥ 1.5 (Figure 3.5). For the 2936 temperature sensitive genes for rCHO DP-12,

185 genes had FDR ≤ 0.05 and FD ≥ 1.5 (Figure 3.6). The union of the FDR ≤ 0.05 and

FD ≥ 1.5 genes for both cell lines identified 553 genes (Figure 3.7). The 4184 temperature-sensitive genes for both cell lines were used to analyze the impact of temperature on specific bioprocess related processes such as cold and heat shock, cell cycle, glycosylation, and apoptosis related genes. Additionally, the 4184 temperature sensitive genes were used for gene ontology enrichment analysis to identify common control patterns in gene expression due to reduced culture temperature.

Figure 3.4: Principle component analysis (PCA) of all culture conditions for each cell line. PC1 represents 85% of the variance and PC2 represents 11% of the variance.

Table 3.2 Differentially expressed genes for CHO K1-PF and rCHO DP-12 between the cultures with initial pH 6.70 vs. pH 6.95. FDR represents FDR adjusted p-value cutoffs.

Number of Genes FDR Criteria CHO K1-PF rCHO DP-12 0.10 2152 195 0.05 1773 107 0.01 1237 43

Table 3.3 Differentially expressed genes for CHO K1-PF and rCHO DP-12 due to temperature. FD represents the fold-difference between the 33°C cultures relative to the 37°C cultures for each cell line. FDR represents FDR adjusted p-value cutoffs. The numbers in bold represent the genes used for the data analysis, unless specified otherwise.

Criteria Number of Genes FDR FD CHO K1-PF rCHO DP-12 FDR ≤ 0.1 FD > 1.0 3775 3444

FDR ≤ 0.1 FD ≥ 1.5 524 213

FDR ≤ 0.05 FD > 1.0 3241 2936 FDR ≤ 0.05 FD ≥ 1.5 471 185 FDR ≤ 0.01 FD > 1.0 2528 2279 FDR ≤ 0.01 FD ≥ 1.5 362 122

Figure 3.5: Venn diagram of temperature sensitive genes for CHO K1-PF and rCHO

DP-12 cultures. There were 3241 temperature sensitive genes for CHO K1-PF (dark gray) and 2936 temperature sensitive genes for rCHO DP-12 (white) (FDR ≤ 0.05 and

FD > 1.0). There were a total of 4184 temperature sensitive genes with at least a 1.0- fold difference in at least one cell line.

Figure 3.6: Distribution of temperature sensitive genes for CHO K1-PF. There were a total of 3241 temperature sensitive genes (FDR ≤ 0.05). Each group of genes is separated into both fold differences (purple) and expression levels (orange). The total temperature sensitive gene group is subdivided into genes with known and unknown functions. Higher expression levels (darker orange) indicate higher gene expression in colder culture temperatures (33°C) relative to the control (37°C). The fold differences are structured into genes with less than 1.5-fold (darkest purple), at least 1.5-fold, but less than 2-fold (middle purple) and at least 2-fold (light purple).

Figure 3.7: Distribution of temperature sensitive genes for rCHO DP-12. There were a total of 2936 temperature sensitive genes (FDR ≤ 0.05). Each group of genes are separated into both fold differences (purple) and expression levels (orange). The total temperature sensitive gene group is subdivided into genes with known and unknown functions. Higher expression levels (darker orange) indicate higher gene expression in colder culture temperatures (33°C) relative to the control (37°C). The fold differences are structured into genes with less than 1.5-fold (darkest purple), at least 1.5-fold, but less than 2-fold (middle purple) and at least 2-fold (light purple).

Figure 3.8: Venn diagram of the temperature sensitive genes in CHO K1-PF and rCHO DP-12 cultures with at least 1.5-fold differences. There were 471 differentially expressed genes for CHO K1-PF (dark gray) and 185 differentially expressed genes for rCHO DP-12 (white) (FD ≥ 1.5 and FDR ≤ 0.05). There were a total of 553 temperature sensitive genes with at least 1.5-fold difference.

36 3.5.2 Heat & Cold Shock Related Genes

Sub-physiological temperatures can induce the synthesis of several cold-shock proteins. In contrast, heat shock proteins were first observed under heat shock, but now these classes of proteins are known to be regulated under many stresses (Lleonart 2010;

Sõti et al. 2009). The 4184 temperature-sensitive genes were used to identify heat or cold shock-related genes by searching the gene descriptions. Three cold shock-related genes were observed to have greater than 1.0-fold difference (Table 3.4), whereas two well characterized cold shock genes, Rbm3 and Cirbp, had greater than 1.5-fold difference as shown in Figure 3.9. Forty heat shock related genes for CHO K1-PF and 43 heat shock related genes for rCHO DP-12 were identified with the temperature sensitive gene list

(Table 3.5). It was noted that most of the heat shock-related genes had lower expression in the colder culture temperature (33°C) compared to 37°C.

Often the change observed for genes due to temperature shifts from 37°C to 34°C or 28°C can range from 3.3 to 13.5-fold, whereas in this study, only 2.1 to 2.9-fold differences were observed (Bedoya-López et al. 2016). Cirbp and Rbm3 are well characterized cold shock proteins involved in binding mRNAs to help maintain mRNA stability in sub-physiological temperatures. More specifically, Rbm3 is one of the best- known proteins to facilitate translation during mild hypothermia (Masterton and Smales

2014; Gammell 2007). It was expected that Cirbp and Rbm3 genes would have higher expression in the 33°C cultures compared to 37°C cultures. Protein disulphide isomerase

(Pdi) has also been reported as up regulated at low culture temperatures, which is an enzyme in the endoplasmic reticulum that catalyzes the formation and breakage of

37 disulfide bonds with proteins during protein folding (Gammell et al. 2007; Kumar et al.

2007). Interestingly, three Pdi family members had higher expression in CHO K1-PF, while 2 Pdi family members had lower expression in rCHO DP-12 at lower culture temperatures.

Heat shock proteins function as molecular chaperones, where these proteins recognize and bind to other proteins that are in non-native conformations due to protein- denaturing stresses, such as changes of temperature. Then, they either assist in folding the denatured proteins or degrade and remove them from the cell (Feder and Hodmann

2002). Of the few heat shock genes that had higher expression at 33°C compared to 37°C,

Hspd1 was the only HSP gene that met the FDR ≤ 0.05 and FD ≥ 1.5 criteria for both cell lines. However, the lower expression at 33°C is comparable to the other heat shock gene responses. Hspd1 is mainly located in the mitochondria and assists with folding mitochondrial proteins. Hspd1 also has a direct role in apoptosis, where Hspd/Hspe can promote caspase-3 activation (Samali et al 1999). While Hsbp1 only had a slight increase in expression of 1.3-fold difference and Hspb1 had a slight decrease of 1.3-fold difference in rCHO DP-12, both Hsbp1 and Hspb1 had higher expression at 33°C for

CHO K1-PF and met the FDR ≤ 0.05 and FD ≥ 1.5 criteria. However, these two genes had relatively low expression levels compared to other HSP genes with similar function, such that a major biological effect was unlikely. Most of the observed HSP genes were observed to have FD ~ 1.3 to 1.4, suggesting that this might be the expected fold difference mediated by the reduced temperature cultivation.

Table 3.4: Cold shock-related genes that were identified as temperature-sensitive in at least one of the cell lines (FDR ≤ 0.05). The gene expression fold-differences are shown for the 33°C cultures relative to the 37°C cultures. Fold differences in bold indicate genes that met FDR ≤ 0.05 and FD ≥ 1.5 criteria . Fold Difference CHO rCHO Gene Symbol Gene Name K1-PF DP-12 Cirbp cold inducible RNA binding protein 2.2 2.1 Csde1 cold shock domain containing E1 -1.5 -1.2 Pdia3 protein disulfide isomerase family A member 3 1.2 Pdia4 protein disulfide isomerase family A member 4 -1.3 Pdia5 protein disulfide isomerase family A member 5 2.2 -1.8 Pdia6 protein disulfide isomerase family A member 6 1.2 Pgk1 phosphoglycerate kinase 1 -1.4 -1.2 Rbm3 RNA binding motif protein 3 2.5 2.9

Figure 3.9: Cold shock-related gene expression profiles for CHO K1-PF and rCHO

DP-12 cultured at 33°C and 37°C. Error bars represent one standard deviation. An asterisk (*) indicates fold-differences were significant (FDR ≤ 0.05 and FD ≥ 1.5).

Table 3.5: Heat shock-related genes that were identified as temperature-sensitive in at least one of the cell lines (FDR ≤ 0.05). The gene expression fold-differences are shown for the 33°C cultures relative to the 37°C cultures. Fold differences in bold indicate genes that met FDR ≤ 0.05 and FD ≥ 1.5 criteria. Blanks indicate genes that did not meet these criteria. Fold Difference CHO rCHO Gene Symbol Gene Name K1-PF DP-12 Ahsa1 activator of HSP90 ATPase activity 1 -1.4 -1.4 Cct2 chaperonin containing TCP1 subunit 2 -1.3 -1.2 Cct3 chaperonin containing TCP1 subunit 3 -1.3 -1.3 Cct4 chaperonin containing TCP1 subunit 4 -1.4 -1.3 Cct5 chaperonin containing TCP1 subunit 5 -1.5 -1.3 Cct6a chaperonin containing TCP1 subunit 6A -1.5 -1.4 Cct7 chaperonin containing TCP1 subunit 7 -1.5 -1.2 Cct8 chaperonin containing TCP1 subunit 8 -2.1 Dnaja1 DnaJ heat shock protein family (Hsp40) member A1 -1.3 -1.4 Dnajb1 DnaJ heat shock protein family (Hsp40) member B1 -1.2 -1.3 Dnajb11 DnaJ heat shock protein family (Hsp40) member B11 1.2 Dnajb9 DnaJ heat shock protein family (Hsp40) member B9 1.4 Dnajc2 DnaJ heat shock protein family (Hsp40) member C2 -1.6 -1.3 Dnajc21 DnaJ heat shock protein family (Hsp40) member C21 -1.3 Dnajc22 DnaJ heat shock protein family (Hsp40) member C22 -1.3 Dnajc27 DnaJ heat shock protein family (Hsp40) member C27 -1.2 Dnajc30 DnaJ heat shock protein family (Hsp40) member C30 1.5 1.5 Dnajc6 DnaJ heat shock protein family (Hsp40) member C6 2.2 Dnajc9 DnaJ heat shock protein family (Hsp40) member C9 -1.6 -1.5 Hsbp1 heat shock factor binding protein 1 2.9 1.3 Hsp90aa1 heat shock protein 90 alpha family class A member 1 -1.4 -1.3 Hsp90b1 heat shock protein 90 beta family member 1 -1.2 Hspa4 heat shock protein family A (Hsp70) member 4 -1.3 -1.2 Hspa8 heat shock protein family A (Hsp70) member 8 -1.5 -1.5 Hspa9 heat shock protein family A (Hsp70) member 9 -1.3 -1.3 Hspb1 heat shock protein family B (small) member 1 2.2 -1.3 Hspb6 heat shock protein family B (small) member 6 1.3 -1.4 Hspb8 heat shock protein family B (small) member 8 -1.1 Hspd1 heat shock protein family D (Hsp60) member 1 -1.8 -1.7 Hsph1 heat shock protein family H (Hsp110) member 1 -1.7 -1.6 LOC100753142 10 kDa heat shock protein, mitochondrial -1.9 -2.0 LOC100754792 heat shock protein HSP 90-beta -1.3 -1.4

41 Fold Difference CHO rCHO Gene Symbol Gene Name K1-PF DP-12 LOC100755978 heat shock protein HSP 90-alpha pseudogene -1.4 -1.3 LOC100762784 60 kDa heat shock protein, mitochondrial pseudogene -1.9 -1.5 LOC100766036 10 kDa heat shock protein, mitochondrial -2.1 -1.5 LOC100766396 heat shock protein HSP 90-beta pseudogene -1.3 -1.3 LOC100774172 heat shock protein HSP 90-beta pseudogene -1.4 -1.3 Polr1a RNA polymerase I subunit A -1.4 -1.4 Polr1c RNA polymerase I and III subunit C -1.5 -1.3 Polr1d RNA polymerase I and III subunit D -1.2 -1.4 Polr2a RNA polymerase II subunit A -1.3 -1.3 Polr2c RNA polymerase II subunit C -1.1 Polr2f RNA polymerase II subunit F -1.2 Polr2h RNA polymerase II subunit H -1.3 Polr2k RNA polymerase II subunit K -1.2 Polr2l RNA polymerase II subunit L 1.3 Polr2m RNA polymerase II subunit M -1.4 -1.3 Polr3d RNA polymerase III subunit D -1.2 St13 ST13, Hsp70 interacting protein -1.2

3.5.3 Cell Cycle Related Genes

The cell cycle is composed of four phases: G0/G1, S, G2, and M, where cell cycle progression is regulated by complexes of cyclins and cyclin dependent kinases. Arrest of the cell cycle at the G1/G0 phase is a common strategy used to increase protein productivity in CHO cell cultures (Wolf et al 2019; Sunley 2008). In this study, the effects of reduced culture temperature on cell cycle-related genes was investigated for

CHO K1-PF and rCHO DP-12. The 4184 temperature sensitive genes were compared to the 312 cell cycle genes compiled by QIAGEN (https://www.qiagen.com/us/resources/).

It was observed that 116 cell cycle-related genes were temperature sensitive in at least one of the cell lines (Table 3.6) (FDR ≤ 0.05). Most of the 116 cell cycle genes have small fold differences and large FDR-values; however, 11 cell cycle genes have significant differences with FDR ≤ 0.05 and FD ≥ 1.5.

For cell cycle-related genes, several cyclin genes had lower expression in the

33°C cultures. Specifically, four of the five cyclin related genes, Ccna2, Ccnb1, Ccnb2, and Ccnf had lower expression levels in the 33°C cultures, even if not always significant for both cell lines (Figure 3.14). Cyclin A2 (Ccna2) accumulates at the G1/S phase transition, where cyclin-A associated kinase activity is required for entry into S phase and

M phase. For cells to exit mitosis, cyclin A and cyclins B (Ccnb1 and Ccnb2) must be degraded, and studies have shown that cyclin B/cdk1 kinases participate in the regulation of this destruction process. The lower expression levels of these cyclin genes agrees with the observed lower growth rates (Johnson and Walker 1999). Ccng1, however, had higher

43 expression in both cell lines. Ccng1, or cyclin G1, has been shown to have intrinsic growth inhibitory activity, which would explain the observed higher expression levels in colder culture conditions (Zhao et al. 2003). The cell division cycle genes that were observed to have significantly lower expression in the 33°C cultures for CHO K1-PF were Cdc25b, Ccdc6 and Cdca8. These genes, as well as Cdc7, Cdc23, and Cdc73, all had lower expression in both cell lines. It is very clear that these genes have a coordinated response due to temperature (Figure 3.15). Cdc25 phosphatases activate complexes of cyclins and cyclin-dependent kinases (cdks) which then regulate cell cycle transitions. Cdc25b, for example, regulates the G2-M phase of the cell cycle (Lee et al

2013; Ferguson et al 2005).

Aurora kinases, like Aurora kinase A (Aurka), are serine/threonine kinases required for the control of mitosis. Aurka was found to have lower expression in both cell lines. Since the function of Aurka is to coordinate centrosome maturation and chromosome separation, lower expression could indicate hindering of cell cycle progression in reduced culture conditions. Btg2, an anti-proliferation factor gene, had significant increased expression levels for both cell lines. The protein encoded by Btg2 is involved in the regulation of the G1/S transition of the cell cycle. This transition is a critical restriction point in the mammalian cell cycle, where cells respond actively to a decrease in culture temperatures by halting cell-cycle progression at this boundary prior to DNA replication (Wolf et al 2019; Kaufmann 1998).

Table 3.6: Cell cycle-related genes that were identified as temperature-sensitive in at least one of the cell lines (FDR ≤ 0.05). The gene expression fold-differences are shown for the 33°C cultures relative to the 37°C cultures. Fold differences in bold indicate genes that met FDR ≤ 0.05 and FD ≥ 1.5 criteria. Blanks indicate genes that did not meet these criteria.

Fold Differences Gene CHO rCHO Gene Name Symbol K1-PF DP-12 Anapc7 anaphase promoting complex subunit 7 -1.2 -1.2 Anp32e acidic nuclear phosphoprotein 32 family member E -1.5 -1.3 Aspm abnormal spindle microtubule assembly -2.2 -1.3 Atr ATR serine/threonine kinase -1.5 -1.5 Aurka aurora kinase A -2.3 -1.7 Aurkb aurora kinase B -2.6 -1.6 Bccip BRCA2 and CDKN1A interacting protein -1.4 -1.3 Brca2 BRCA2, DNA repair associated -1.8 Bub3 BUB3, mitotic checkpoint protein -1.2 -1.2 Calm1 calmodulin 1 -1.2 -1.1 Ccdc86 coiled-coil domain containing 86 1.2 Ccna2 cyclin A2 -2.6 -1.6 Ccnb1 cyclin B1 -2.6 -1.8 Ccnb2 cyclin B2 -2.1 -1.4 Ccnd2 cyclin D2 1.6 -1.2 Ccne1 cyclin E1 -1.3 Ccnf cyclin F -2.5 -1.5 Ccng1 cyclin G1 2.7 3.0 Ccng2 cyclin G2 3.5 Ccnh cyclin H -1.2 Ccni cyclin I 1.4 Ccnk cyclin K -1.3 Cct5 chaperonin containing TCP1 subunit 5 -1.5 -1.3 Cd320 CD320 molecule -1.5 Cdc6 cell division cycle 6 -2.3 -1.8 Cdc7 cell division cycle 7 -1.3 -1.3 Cdc23 cell division cycle 23 -1.2 -1.2 Cdc25a cell division cycle 25A -1.6 -1.4 Cdc25b cell division cycle 25B -2.6 -1.8 Cdc73 cell division cycle 73 -1.3 -1.2 Cdca5 cell division cycle associated 5 -1.6 -1.5 Cdca8 cell division cycle associated 8 -2.2 -1.5

45 Fold Differences Gene CHO rCHO Gene Name Symbol K1-PF DP-12 Cdh1 cadherin 1 1.7 Cdk2 cyclin dependent kinase 2 -1.5 -1.3 Cdk4 cyclin dependent kinase 4 -1.1 -1.2 Cdk5 cyclin dependent kinase 5 1.4 Cdk9 cyclin dependent kinase 9 -1.3 -1.1 Cdkn1b cyclin dependent kinase inhibitor 1B -1.3 Cdkn2a cyclin dependent kinase inhibitor 2A 1.2 Cdkn2c cyclin dependent kinase inhibitor 2C -2.6 Cenpe centromere protein E -2.1 Cenph centromere protein H -1.4 -1.4 Cfl1 cofilin 1 -1.2 -1.1 Chaf1b chromatin assembly factor 1 subunit B -2.8 Ckap2 cytoskeleton associated protein 2 -1.3 Cks1b CDC28 protein kinase regulatory subunit 1B -1.5 -1.5 Cks2 CDC28 protein kinase regulatory subunit 2 -2.3 -1.7 Cse1l chromosome segregation 1 like -2.8 Ctnnal1 catenin alpha like 1 -2.0 -1.2 Dbf4 DBF4 zinc finger -2.2 -1.5 Dbi diazepam binding inhibitor, acyl-CoA binding protein -1.2 -1.1 Dtymk deoxythymidylate kinase -1.7 -1.5 Ebp EBP, cholestenol delta-isomerase -1.8 -1.3 Eef1e1 eukaryotic translation elongation factor 1 epsilon 1 -1.4 -1.4 Fbl fibrillarin -1.6 -1.5 Fbxo5 F-box protein 5 -2.4 -1.6 Flnb filamin B 1.3 Gapdh glyceraldehyde-3-phosphate dehydrogenase -1.3 -1.2 Gins2 GINS complex subunit 2 -1.4 -1.3 Gmnn geminin, DNA replication inhibitor -1.9 -1.7 Gsto1 glutathione S-transferase omega 1 1.7 -1.5 Hmmr hyaluronan mediated motility receptor -2.0 -1.5 Hprt1 hypoxanthine phosphoribosyltransferase 1 1.3 Hspd1 heat shock protein family D (Hsp60) member 1 -1.8 -1.7 Kif20a kinesin family member 20A -2.1 -1.4 Kif23 kinesin family member 23 -2.1 -1.5 Kif4a kinesin family member 4A -2.2 -1.4 Kifc1 kinesin family member C1 -4.0 Mad1l1 mitotic arrest deficient 1 like 1 -1.5 -1.1 Mad2l1 mitotic arrest deficient 2 like 1 -2.0 -1.5 Mcm2 minichromosome maintenance complex component 2 -1.6 -1.3 Mdm2 MDM2 proto-oncogene 4.0

46 Fold Differences Gene CHO rCHO Gene Name Symbol K1-PF DP-12 Mnd1 meiotic nuclear divisions 1 -2.1 -1.4 Mrpl35 mitochondrial ribosomal protein L35 -1.2 Mrps28 mitochondrial ribosomal protein S28 -1.3 Mrto4 MRT4 homolog, ribosome maturation factor -1.7 -1.3 Myh10 myosin heavy chain 10 -1.3 -1.4 Ncapg non-SMC condensin I complex subunit G -1.9 -1.7 Ndc80 NDC80, kinetochore complex component -2.7 Nek2 NIMA related kinase 2 -2.1 -1.4 Pa2g4 proliferation-associated 2G4 -1.5 -1.4 Pfn1 profilin 1 -1.3 -1.3 Plk1 polo like kinase 1 -2.5 -1.3 Ppp2r1b protein phosphatase 2 scaffold subunit Abeta -1.5 -1.6 Ppp2r5d protein phosphatase 2 regulatory subunit B'delta -1.5 -1.2 Prdx6 peroxiredoxin 6 -1.3 -1.2 Prkca protein kinase C alpha -1.8 Psmb6 proteasome subunit beta 6 -1.1 Rad21 RAD21 cohesin complex component -1.5 -1.2 Rad51 RAD51 recombinase -1.8 -1.2 Rad52 RAD52 homolog, DNA repair protein -1.4 -1.4 Rad54l RAD54 like -2.0 -1.5 Rb1 RB transcriptional corepressor 1 1.2 Rbbp4 RB binding protein 4, chromatin remodeling factor -1.3 -1.8 Rock2 Rho associated coiled-coil containing protein kinase 2 -2.4 Rrm2 ribonucleotide reductase regulatory subunit M2 -1.4 -1.3 Scara3 scavenger receptor class A member 3 1.4 -1.2 Slc25a5 solute carrier family 25 member 5 -1.3 1.3 Smc1a structural maintenance of chromosomes 1A -1.7 Snrpa1 small nuclear ribonucleoprotein polypeptide A' -1.7 -1.3 Snrpd1 small nuclear ribonucleoprotein D1 polypeptide -1.4 -1.5 Snrpd3 small nuclear ribonucleoprotein D3 polypeptide -1.2 -1.2 Snrpg small nuclear ribonucleoprotein polypeptide G -1.3 Spag5 sperm associated antigen 5 -2.1 -1.2 Spc24 SPC24, NDC80 kinetochore complex component -1.7 -1.7 Stag1 stromal antigen 1 -1.7 Tipin TIMELESS interacting protein -1.5 -1.2 Tk1 thymidine kinase 1, soluble 1.6 -1.3 Top2a DNA topoisomerase II alpha -2.2 1.4 Tp53 tumor protein p53 -1.3 Trip13 thyroid hormone receptor interactor 13 -1.8 -1.5 Tuba1c tubulin, alpha 1C -1.3 -1.4

47 Fold Differences Gene CHO rCHO Gene Name Symbol K1-PF DP-12 Tyms thymidylate synthetase -1.3 Ube2c ubiquitin conjugating enzyme E2 C -3.0 -1.5 Ube2s ubiquitin conjugating enzyme E2 S -1.9 -1.8 Uck2 uridine-cytidine kinase 2 -1.5 -1.4 Vamp8 vesicle associated membrane protein 8 1.7 -1.5 Wee1 WEE1 G2 checkpoint kinase -1.7 Zw10 zw10 kinetochore protein -1.3 1.3 Zwint ZW10 interacting kinetochore protein -2.0 -1.4

Figure 3.10: Cyclin gene expression profiles for CHO K1-PF and rCHO DP-12 cultured at 33°C and 37°C. Error bars represent one standard deviation. An asterisk (*) indicates fold-differences were significant (FDR ≤ 0.05 and FD ≥ 1.5).

Figure 3.11: Cell division-related gene expression profiles for CHO K1-PF and rCHO DP-12 cultured at 33°C and 37°C. Error bars represent one standard deviation. An asterisk (*) indicates fold-differences were significant (FDR ≤ 0.05 and FD ≥ 1.5).

50 3.5.4 Glycosylation Related Genes

Glycosylation is vital for the efficacy of therapeutic glycoproteins. As production yields continue to increase to meet growing therapy demands, the impact of the cell culture process on glycosylation patterns is critical (Fung Wong et al. 2004). Factors that influence the degree of glycosylation include culture media, culture pH, nutrient levels, waste product levels, and culture temperature. Reducing the culture temperature has been observed to improve glycosylation profiles and product quality of recombinant proteins

(Wong et al 2009; Gupta and Shukla 2018; Yamane-Phnuki & Satoh 2009). In this study, the effects of culturing cells at 33°C vs 37°C on glycosylation-related gene expression was examined for two CHO cell lines: CHO K1-PF and rCHO DP-12. The 4184 temperature sensitive genes were compared to the 298 glycosylation-related genes obtained from Xu et al. (2011). It was observed that 63 glycosylation-related genes were temperature sensitive in at least one of the cell lines (Table 3.7) (FDR ≤ 0.05). Most of the identified 63 genes had small fold-differences due to temperature and large FDR- values; however, 13 glycosylation genes had significant differences with FDR ≤ 0.05 and

FD ≥ 1.5.

Neu1 and Neu2 are two of four sialidases responsible for the removal of the sialic acid content of glycoproteins. Sialic acids are a critical quality attribute that affects in- vivo activity, circulation longevity, and ultimately the efficacy of glycoprotein therapeutics (Xu 2011; Yin et a. 2017). In the 33°C cultures, it was observed that Neu2 had significantly lower expression in both cell lines, while Neu1 only had significantly higher expression in CHO K1-PF. Figure 3.12 shows that both Neu1 and Neu2 had

51 similar responses to colder culture temperatures between CHO K1-PF and rCHO DP-12 cell lines. Neu1 also had much higher basal expression levels than Neu2 and Neu3 even though it was observed to be expressed in both cell lines, while Neu4 expression was not observed in either cell line (Figure 3.12). Neu1 functions to remove sialic acid on N- glycan residues and could have higher activity in lower culture temperatures (Tejwani et al. 2018).

Solute carrier family members function as nucleotide sugar transporters, which translocate nucleotide substrates for glycosylation from the cytosol, where they are mainly synthesized, into the lumen of the golgi apparatus (Berninsone and Hirschberg

1998). Figures 3.13 and 3.14 show MA plots of 4 solute carrier family members, which transforms data onto M (log ratio) and A (mean count averages) scale, for both cell lines.

It was observed that Slc35a1, Slc35b1, and Slc35d1 had higher expression for both cell lines, while Slc35e1 had lower expression at the lower culture temperatures. Slc35d1 had significant differences in expression with FDR ≤ 0.05 and FD ≥ 1.5 for both cell lines. It was also noted that there were no major changes of sialyltransferase enzyme levels, such as galactosyltransferases.

52 Table 3.7: Glycosylation-related genes that were identified as temperature-sensitive in at least one of the cell lines (FDR ≤ 0.05). The gene expression fold-differences are shown for the 33°C cultures relative to the 37°C cultures. Fold differences in bold indicate genes that met FDR ≤ 0.05 and FD ≥ 1.5 criteria. Blanks indicate genes that did not meet these criteria.

Fold Differences Gene CHO rCHO Gene Name Symbol K1-PF DP-12 Aga aspartylglucosaminidase -1.1 Alg11 ALG11, alpha-1,2-mannosyltransferase -1.2 Arsa arylsulfatase A 2.4 B3galt6 beta-1,3-galactosyltransferase 6 1.4 1.1 B3gat3 beta-1,3-glucuronyltransferase 3 1.3 B4galt2 beta-1,4-galactosyltransferase 2 -1.3 B4galt3 beta-1,4-galactosyltransferase 3 -1.5 B4galt4 beta-1,4-galactosyltransferase 4 1.3 B4galt5 beta-1,4-galactosyltransferase 5 1.5 C1galt1c1 C1GALT1 specific chaperone 1 1.4 1.3 Chst14 carbohydrate sulfotransferase 14 1.4 1.2 Chst2 carbohydrate sulfotransferase 2 1.4 Chsy1 chondroitin sulfate synthase 1 -1.4 -1.3 Ctbs chitobiase 1.8 1.3 Ctsa cathepsin A 1.6 1.1 Dpm3 dolichyl-phosphate mannosyltransferase subunit 3 1.7 Ext1 exostosin glycosyltransferase 1 1.2 Fuca1 alpha-L-fucosidase 1 1.7 Fut11 fucosyltransferase 11 -1.4 -1.5 Galnt7 polypeptide N-acetylgalactosaminyltransferase 7 1.8 Gba glucosylceramidase beta 2.1 1.5 Gm2a GM2 ganglioside activator 1.6 1.2 Gmppb GDP-mannose pyrophosphorylase B 1.6 1.3 Gnb1 G protein subunit beta 1 -1.1 glucosamine (UDP-N-acetyl)-2-epimerase/N- Gne 1.3 1.3 acetylmannosamine kinase Gns glucosamine (N-acetyl)-6-sulfatase 1.5 1.4 Hpse heparanase 1.4 1.5 Hs6st1 heparan sulfate 6-O-sulfotransferase 1 -1.3 Hs6st3 heparan sulfate 6-O-sulfotransferase 3 4.2 Hyal1 hyaluronidase 1 1.9 Idua iduronidase, alpha-L- 2.1 Lipa lipase A, lysosomal acid type 1.6 1.3 Man1a1 mannosidase alpha class 1A member 1 3.1

53 Fold Differences Gene CHO rCHO Gene Name Symbol K1-PF DP-12 Man1a2 mannosidase alpha class 1A member 2 -1.2 Man1c1 mannosidase alpha class 1C member 1 -1.4 Man2a1 mannosidase alpha class 2A member 1 1.5 1.2 Man2b1 mannosidase alpha class 2B member 1 1.6 1.5 Naga alpha-N-acetylgalactosaminidase 1.7 1.6 Naglu N-acetyl-alpha-glucosaminidase 2.3 Nans N-acetylneuraminate synthase -1.2 Neu1 neuraminidase 1 2.4 1.7 Neu2 neuraminidase 2 -3.2 -3.7 Neu3 neuraminidase 3 1.8 Pgm1 phosphoglucomutase 1 1.2 1.1 Pomt2 protein O-mannosyltransferase 2 1.4 Rpn1 ribophorin I -1.2 Slc35a1 solute carrier family 35 member A1 1.4 1.2 Slc35a2 solute carrier family 35 member A2 1.8 1.3 Slc35a3 solute carrier family 35 member A3 -1.2 Slc35b4 solute carrier family 35 member B4 1.3 Slc35c1 solute carrier family 35 member C1 -1.3 Slc35d1 solute carrier family 35 member D1 1.9 2.0 Slc35e1 solute carrier family 35 member E1 -1.3 -1.2 Slc35e3 solute carrier family 35 member E3 1.3 St3gal2 ST3 beta-galactoside alpha-2,3-sialyltransferase 2 1.4 1.2 St3gal4 ST3 beta-galactoside alpha-2,3-sialyltransferase 4 -1.3 ST8 alpha-N-acetyl-neuraminide alpha-2,8- St8sia6 3.3 sialyltransferase 6 Sulf2 sulfatase 2 70.9 8.6 Tsta3 tissue specific transplantation antigen P35B 2.0 1.5 Ugcg UDP-glucose ceramide glucosyltransferase 2.1 1.5 Ugp2 UDP-glucose pyrophosphorylase 2 1.3 Ust uronyl 2-sulfotransferase -1.4 -1.2 Xylt2 xylosyltransferase 2 1.4 1.4

Figure 3.12: Sialidase gene expression profiles for CHO K1-PF and rCHO DP-12 cultured at 33°C and 37°C. Error bars represent one standard deviation. An asterisk (*) indicates fold-differences were significant due to reduced temperature (FDR ≤ 0.05 and FD ≥ 1.5).

Figure 3.13: MA plot of all genes in CHO K1-PF (FDR ≤ 0.05). Fold differences are positive for genes with higher expression at 33°C relative to 37°C. Data in red indicates temperature sensitive genes with FD > 1.0. The four circled genes are glycosylation related genes.

Figure 3.14: MA plot of all genes in rCHO DP-12 (FDR ≤ 0.05). Fold differences are positive for genes with higher expression at 33°C relative to 37°C. Data in red indicates temperature sensitive genes with FD > 1.0. The four circles genes are glycosylation related genes.

57 3.5.5 Apoptosis Related Genes

In bioprocessing, cell death is a major barrier to maintain high cell densities and increase protein yields. One approach to delay cell death is through anti-apoptosis engineering. Apoptosis-resistant CHO cell lines have been developed by overexpression of anti-apoptotic proteins or lowering the expression of apoptotic proteins (Wong et al.

2005, Kim et al. 2012). To screen genes for potential cell engineering selection, transcriptomic studies are fundamental to correlate apoptosis to different culture conditions. In this study, the effects of the reduced culture temperature on apoptosis- related genes was investigated for CHO K1-PF and rCHO DP-12. The 4184 temperature- sensitive genes were compared to the 532 apoptosis-related gene list compiled by

QIAGEN (https://www.qiagen.com/us/resources/). It was observed that 126 apoptosis- related genes were temperature sensitive in at least one of the cell lines (Table 3.8) (FDR

≤ 0.05). Most of the 126 genes had small fold differences and large FDR-values; however, 14 apoptosis-related genes had significant differences with FDR ≤ 0.05 and FD

≥ 1.5.

Early events in apoptosis had no significant gene expression changes.

Bid, Bax, and Bak1 expression levels, which are important pro-apoptotic genes in the apoptosis cascade and upstream of cytochrome c release from the mitochondria, are shown in Figure 3.15. Bax, a gene that encodes a Bcl-2 family protein, had significantly higher gene expression in both cell lines at 33°C. While Bax is found in the cytosol in healthy cells, it is translocated to the mitochondria upon induction of apoptosis, where it is directly involved in mitochondria dysfunction and cytochrome c release (Lim et al

58 2006, Arden and Betenbaugh 2004). Bid had higher expression in lower culture temperatures, while Bak1 had slightly lower expression in both cell lines. Caspase family genes are known to be involved in apoptotic cell death. Casp3 was observed to have significantly higher gene expression at 33°C compared to 37°C for rCHO DP-12, while

Casp6 was observed to have significantly higher expression for CHO K1-PF (Figure

3.15). Caspases 3 and 6 are downstream effector classes of caspases, where activation of these caspases leads to the final execution of cell death (Mohan et al 2008; Arden and

Betenbaugh 2004). Casp8, which functions to drive cell-extrinsic apoptosis when activated, had decreased expression at 33°C, but this decrease was not significant

(Mandal et al. 2014). Additionally, Casp1, Casp4, Casp7, and Casp10 expression was not observed in either cell line (Figure 3.15).

Anti-apoptotic genes Mcl1, Bcl2, Akt1, and Akt2 are shown in Figure 3.16. Mcl1 had lower expression in CHO K1-PF but had higher expression in rCHO DP-12. Similar to proapoptotic genes, there was not a coordinated response in anti-apoptotic gene expression due to temperature. Overall, the concept of suppressed apoptosis levels at reduced temperatures was not observed in this study for either cell line. Specifically, no observations of higher anti-apoptotic gene expression in either cell line contradicts reports of other CHO cell transcriptome studies assessing the impacts of reducing culture temperatures (Bedoya-López et al. 2016; Tossolini et al. 2018)

Table 3.8: Apoptosis-related genes that were identified as temperature sensitive in at least one of the cell lines (FDR ≤ 0.05). The gene expression fold-differences are shown for the 33°C cultures relative to the 37°C cultures. Fold differences in bold indicate genes that met FDR ≤ 0.05 and FD > 1.5 criteria. Blanks indicate genes that did not meet these criteria.

Fold Differences Gene CHO rCHO Gene Name Symbol K1-FP DP-12 Aatf apoptosis antagonizing transcription factor -1.3 -1.2 Adam17 ADAM metallopeptidase domain 17 1.3 Aifm1 apoptosis inducing factor mitochondria associated 1 -1.2 Akt1 AKT serine/threonine kinase 1 -1.3 Akt2 AKT serine/threonine kinase 2 1.3 Angptl4 angiopoietin like 4 3.3 Apex1 apurinic/apyrimidinic endodeoxyribonuclease 1 -1.2 -1.3 Arid4a AT-rich interaction domain 4A -1.4 -1.3 Aven apoptosis and caspase activation inhibitor -1.2 -1.2 Bag4 BCL2 associated athanogene 4 -1.4 -1.2 Bak1 BCL2 antagonist/killer 1 -1.3 Bard1 BRCA1 associated RING domain 1 -3.0 -1.6 Bax BCL2 associated X, apoptosis regulator 2.2 2.1 Bcl10 B cell CLL/lymphoma 10 -1.2 -1.3 Bcl2l1 BCL2 like 1 1.4 Bid BH3 interacting domain death agonist 1.3 1.2 Bnip1 BCL2 interacting protein 1 -1.7 Capn1 calpain 1 1.2 1.5 Capn11 calpain 11 4.3 Capn6 calpain 6 3.9 Card6 caspase recruitment domain family member 6 1.7 1.7 Casp2 caspase 2 -1.4 Casp3 caspase 3, apoptosis-related cysteine peptidase 1.8 2.1 Casp6 caspase 6 2.4 1.8 Casp8 caspase 8 -1.4 -1.2 Cav1 caveolin 1 1.2 1.2 Cav2 caveolin 2 -1.2 Cbl Cbl proto-oncogene -1.2 Cd28 CD28 molecule -1.7 -4.0 Cdc25a cell division cycle 25A -1.6 -1.4 Cdkn2a cyclin dependent kinase inhibitor 2A 1.2 Cse1l chromosome segregation 1 like -2.8 Ctnnal1 catenin alpha like 1 -2.0 -1.2 Ctsd cathepsin D 1.8 1.2

60 Fold Differences Gene CHO rCHO Gene Name Symbol K1-FP DP-12 Dapk1 death associated protein kinase 1 2.8 1.8 Ddx41 DEAD-box helicase 41 -1.2 Dffa DNA fragmentation factor subunit alpha 1.5 1.7 Diablo diablo IAP-binding mitochondrial protein -1.3 -1.2 Dnase1 deoxyribonuclease 1 41.6 Dnase1l3 deoxyribonuclease 1 like 3 2.1 Dnase2 deoxyribonuclease 2, lysosomal 1.6 1.3 Dusp6 dual specificity phosphatase 6 -1.6 Dusp7 dual specificity phosphatase 7 -1.8 -1.8 E2f1 E2F transcription factor 1 -1.4 Ei24 EI24, autophagy associated transmembrane protein 1.6 1.3 Eif4g2 eukaryotic translation initiation factor 4 gamma 2 -1.5 -1.2 Emp1 epithelial membrane protein 1 -1.5 Emp2 epithelial membrane protein 2 1.6 1.4 Emp3 epithelial membrane protein 3 -1.3 Enc1 ectodermal-neural cortex 1 2.5 1.5 ERCC excision repair 3, TFIIH core complex helicase Ercc3 -1.1 subunit F2r coagulation factor II thrombin receptor 1.9 1.4 Fadd Fas associated via death domain -1.5 -1.3 Faf1 Fas associated factor 1 -1.5 -1.1 Fem1b fem-1 homolog B -1.5 -1.2 Fosl2 FOS like 2, AP-1 transcription factor subunit 1.3 Foxk2 forkhead box K2 -1.2 Gpx1 glutathione peroxidase 1 1.7 Gpx4 glutathione peroxidase 4 1.3 1.2 Gsk3b glycogen synthase kinase 3 beta 2.7 Gsto1 glutathione S-transferase omega 1 1.7 -1.5 Gstp1 glutathione S-transferase, pi 1 -1.2 Hsp90b1 heat shock protein 90 beta family member 1 -1.2 Htra2 HtrA serine peptidase 2 -1.4 Ier3 immediate early response 3 -1.6 Igfbp4 insulin like growth factor binding protein 4 1.5 Ikbkg inhibitor of nuclear factor kappa B kinase subunit gamma 1.2 1.2 Il10 interleukin 10 -3.7 Junb JunB proto-oncogene, AP-1 transcription factor subunit -1.8 Lgals1 galectin 1 1.4 1.1 Litaf lipopolysaccharide induced TNF factor 1.3 Mageh1 MAGE family member H1 1.2 Map2k1 mitogen-activated protein kinase kinase 1 -1.4

61 Fold Differences Gene CHO rCHO Gene Name Symbol K1-FP DP-12 Mapk1 mitogen-activated protein kinase 1 -1.3 Mcl1 MCL1, BCL2 family apoptosis regulator 1.2 Mdm2 MDM2 proto-oncogene 4.0 Mmd monocyte to macrophage differentiation associated 2.2 Mrps30 mitochondrial ribosomal protein S30 -1.2 Ndufa13 NADH:ubiquinone oxidoreductase subunit A13 1.2 Nfkbia NFKB inhibitor alpha 1.2 -1.5 Nfkbie NFKB inhibitor epsilon 1.5 Nod2 nucleotide binding oligomerization domain containing 2 2.3 1.4 Nrg2 neuregulin 2 -2.8 -4.0 Nupr1 nuclear protein 1, transcriptional regulator 2.4 1.8 Pak1 p21 (RAC1) activated kinase 1 3.0 Parp1 poly(ADP-ribose) polymerase 1 -1.6 -1.4 Pawr pro-apoptotic WT1 regulator -1.6 -1.4 Pdcd4 programmed cell death 4 8.4 Pdcd5 programmed cell death 5 -1.3 -1.2 Phlda2 pleckstrin homology like domain family A member 2 34.2 phosphatidylinositol-4-phosphate 3-kinase catalytic Pik3c2g -2.8 subunit type 2 gamma Pml promyelocytic leukemia 1.4 Ppm1f protein phosphatase, Mg2+/Mn2+ dependent 1F 1.1 Ppp2r1b protein phosphatase 2 scaffold subunit Abeta -1.5 -1.2 Prkca protein kinase C alpha -1.8 Pten phosphatase and tensin homolog 2.9 Ptgs2 prostaglandin-endoperoxide synthase 2 -1.6 Rad21 RAD21 cohesin complex component -1.5 -1.2 Rad23b RAD23 homolog B, nucleotide excision repair protein -1.3 -1.2 Raf1 Raf-1 proto-oncogene, serine/threonine kinase -1.2 Rb1 RB transcriptional corepressor 1 1.2 Rbbp4 RB binding protein 4, chromatin remodeling factor -1.3 -1.3 Rbl1 RB transcriptional corepressor like 1 -1.4 -1.2 Rbl2 RB transcriptional corepressor like 2 1.6 1.4 Rhob ras homolog family member B 1.4 Ripk2 receptor interacting serine/threonine kinase 2 -2.0 Scarb1 scavenger receptor class B member 1 -1.1 Serpinb2 serpin family B member 2 -8.5 Sod1 superoxide dismutase 1 -1.2 -1.2 Tdgf1 teratocarcinoma-derived growth factor 1 2.3 1.7 Tgfb1 transforming growth factor beta 1 -1.3 -1.7 Tlr2 toll like receptor 2 2.4

62 Fold Differences Gene CHO rCHO Gene Name Symbol K1-FP DP-12 Tnfrsf12a TNF receptor superfamily member 12A -1.5 -1.7 Tnfrsf1b TNF receptor superfamily member 1B -1.2 1.2 Tnfsf4 TNF superfamily member 4 4.8 Tnfsf9 TNF superfamily member 9 1.6 -1.5 Tp53 tumor protein p53 -1.3 Traf4 TNF receptor associated factor 4 1.3 -1.2 Traip TRAF interacting protein -2.2 -1.6 Txnl1 thioredoxin like 1 -1.2 Vdac1 voltage dependent anion channel 1 -1.3 -1.1 Wdr3 WD repeat domain 3 -1.2 -1.3 tyrosine 3-monooxygenase/tryptophan 5-monooxygenase Ywhab -1.1 activation protein beta tyrosine 3-monooxygenase/tryptophan 5-monooxygenase Ywhah -1.3 -1.3 activation protein eta tyrosine 3-monooxygenase/tryptophan 5-monooxygenase Ywhaq -1.2 -1.1 activation protein theta tyrosine 3-monooxygenase/tryptophan 5-monooxygenase Ywhaz -1.3 -1.1 activation protein zeta

63 Figure 3.15: Pro-apoptotic gene expression profiles for CHO K1-PF and rCHO DP-12 cultured at 33°C and 37°C. Error bars represent one standard deviation. An asterisk (*) indicated fold-differences were significant (FDR ≤ 0.05 and FD ≥ 1.5).

Figure 3.16: Anti-apoptotic gene expression profiles for CHO K1-PF and rCHO DP-12 cultured at 33°C and 37°C. Error bars represent one standard deviation

(FDR ≤ 0.05).

65 3.5.6 Gene Ontology

To further understand the types of biological processes affected by culture temperature, a gene ontology (GO) enrichment analysis was conducted using the

PANTHER database (Muruganujan et al. 2019). GO analysis was performed using the identified 4184 temperature-sensitive genes for CHO K1-PF and rCHO DP-12. 2939 genes had unique GO terms (FDR ≤ 0.05). A PANTHER statistical enrichment test was used on the gene set, where biological processes with GO terms were used for further analysis (FDR ≤ 0.05). Specific GO categories were then identified, such as Cell Cycle

DNA Replication (GO:0044786), Tricarboxylic Acid (GO:0006099) and Glycolytic

Process (GO:0006096), where genes in each category were investigated to assess if any coordinated response in gene expression were found for both cell lines.

Figure 3.17: DNA replication-associated GO terms for the temperature sensitive genes for CHO K1-PF and rCHO DP-12 (FDR ≤ 0.05).

Figure 3.18: Metabolic process-associated GO terms for the temperature sensitive genes for CHO K1-PF and rCHO DP-12 (FDR ≤ 0.05).

68 Figure 3.19: Positive response to cold-associated GO terms for the temperature sensitive genes for CHO K1-PF and rCHO DP-12 (FDR ≤ 0.05).

Figure 3.20: Metabolic pathway-associated GO terms for the temperature sensitive genes for CHO K1-PF and rCHO DP-12 (FDR ≤ 0.05).

70 Cell Cycle DNA Replication and DNA Replication Initiation GO terms are found in

Figure 3.17. The 11 genes in Cell Cycle DNA Replication all had lower expression at reduced culture temperatures for both cell lines. Cdc45, for instance, is required for both initiation and elongation of DNA replication, and lower expression could indicate a reduced cell growth rate at 33°C (Gambus et al. 2006). Additionally, genes in the GO category DNA Replication Initiation had lower expression for a majority of both CHO

K1-PF and rCHO DP-12 genes at 33°C. Specifically, 14 of the 21 genes in this category were enriched, where 10 of the 14 genes had over 1.5-fold difference in either or both cell lines. The initiation of DNA synthesis in mammalian cells, like CHO, is a multistep process. The first step involves the binding of origin recognition complexes (Orc) to replication origins. Orc1, Orc5, and Orc6 had decreased expression levels in both cell lines, indicating decreased DNA synthesis. Next in this process is the recruitment of

Cdc6 and multiple Mcm proteins (minichromosome maintence proteins) to form a complex. Cdc6, along with a majority of Mcm genes like Mcm3, Mcm4, and Mcm6 had lower expression in both cell lines. Since this complex needs to become activated to initiate DNA synthesis, lower expression of these genes could indicate a hindering of

DNA synthesis at lower culture temperatures and result in a slower cell growth rate (Tye

1999). Other GO terms included Carbohydrate Metabolic Process and Regulation of

Glycoprotein Metabolic Process (Figure 3.18). For the Carbohydrate Metabolic Process

GO category, Man2a1, Man2b1, Man2b2 and Man2c1 were observed to have higher expression levels, which are mannosidases involved in N-glycan chain extension.

Specifically, Man2a1 is an a-Mannosidase that cleaves mannoses, which is a required

71 trimming reaction before the formation of N-glycan residue during the glycosylation process (Ha et al. 2015). The Response to Cold GO category had genes with mostly higher expression for both cell lines, where the well characterized cold-shock genes

Rbm3 and Cirbp are shown (Figure 3.19). Unlike cell cycle-related genes, genes involved in central metabolic pathways did not have any type of coordinated response to reduce culture temperatures for CHO K1-PF or rCHO DP-12. GO categories for metabolic pathways, Glycolytic Process and the Tricarboxylic Acid Cycle, were assessed (Figure

3.20). While the Tricarboxylic acid (TCA) cycle had 14 enriched GO terms, only Pdha1 had a higher than 1.3-fold difference for both cell lines. For the Glycolytic Process Go category, 6 of the 7 genes had lower expression in at least one cell line. However, Pkm was the only gene to have a greater than 1.5-fold difference which was only observed in

CHO K1-PF.

72 CHAPTER 4

SUMMARY AND CONCLUSIONS

CHO cells are the most widely used mammalian cell line for producing complex biologics due to their history of safety, high specific productivity, and ability to be adapted to serum-free suspension systems, especially at an industrial scale. While more than 100-fold yield improvement for biologics produced in CHO cells has resulted in the last few decades, increasing demands for biotherapeutics persists. Process parameters, such as temperature and pH, have a significant effect on CHO cell performance.

Lowering the culture temperature, for example, has been shown to improve recombinant protein production depending on the cell line used. Lowering the culture pH has also been implemented to increase product titers. In this study, two CHO cell lines, a non- recombinant CHO K1-PF and recombinant rCHO DP-12, were used to assess the impact of reduced culture temperature and reduced pH. Due to the minimal influence of pH in the range investigated, only temperature effects were analyzed for differential expression analysis. A total of 4184 temperature-sensitive genes were identified and used to assess the impact temperature had on cold and heat shock, cell cycle, glycosylation and apoptosis gene expression levels. Glycosylation-related genes impacted by temperature included solute carrier family members Slc35a1 and Slc35d1 and the sialidases Neu1 and

Neu2. While improved enzymatic control for glycosylation of biologic drugs has been noted in past studies at reduced culture temperatures, investigating gene expression changes can give insight into why these changes occur. Cell cycle related genes, like

73 cyclins and cell division-related genes, were also found to be temperature sensitive with mostly lower expression levels observed. Slow growth rates at lower temperatures is most likely the result of these genes having lower expression. Apoptosis did not have any type of coordinated response, which differs from reports of decreased apoptotic expression levels of CHO cells at reduced culture temperature. In the future, using apoptotic assays at the time of mRNA extraction, like immunofluorescence or flow cytometry, could provide a link between gene expression and overall apoptosis levels for each cell line grown in different conditions. Additionally, if apoptotic related genes have a more coordinated response later in the cell culture, linking a change in gene expression to improve anti-apoptosis, like silencing Bid or Bax genes, could be investigated via RNA interference in future cell line engineering studies. A GO analysis was used to identify other major gene expression patterns, where genes in subcategories like Cell Cycle DNA

Replication (GO:0044786) had lower expression for both cell lines, while genes in GO categories like Tricarboxylic Acid Cycle (GO:0006099) and Glycolytic Process

(0006096) did not show any trends. GO analysis could be used further to assess other pathways and investigate specific gene expression trends within different GO categories.

Additionally, future work could involve investigating other types of ‘omics studies, such as proteomics and metabolomics, to attain a better understanding of how reduced culture temperatures and pH impacts the cellular and biological processes of CHO cells.

74 APPENDIX A

Sample Descriptions

Table A-1: NCBI-BioProject Sequence Read Archive (SRA) accession numbers. The RNA-Seq data was submitted to the public database of the National Institutes of Health (NIH). Listed below are the cell lines, culture temperature, pH, and principle component coordinate values corresponding to Figure 3.3. The data can be accessed in the NCBI BioProject database (https://www.ncbi.nlm.nih.gov/bioproject).

BioProject # PRJNA378769 Culture Conditions Coordinate Values Sequence Read Cell Line Temperature (°C) pH PC1 PC2 Archive Accession # SAMN06560482 rCHO DP-12 clone 1934 37 6.95 -13.17 -3.98

SAMN06560483 rCHO DP-12 clone 1934 37 6.95 -13.32 -3.99 SAMN06560484 rCHO DP-12 clone 1934 33 6.95 -11.77 5.01 SAMN06560485 rCHO DP-12 clone 1934 33 6.95 -11.89 4.48 SAMN06560486 rCHO DP-12 clone 1934 37 6.7 -13.12 -4.08 SAMN06560487 rCHO DP-12 clone 1934 37 6.7 -13.22 -4.07 SAMN06560488 rCHO DP-12 clone 1934 33 6.7 -11.45 4.94 SAMN06560489 rCHO DP-12 clone 1934 33 6.7 -11.44 5.01 SAMN06560490 CHO K1-PF 37 6.95 14.12 -9.11 SAMN06560491 CHO K1-PF 37 6.95 13.81 -9.46

SAMN06560492 CHO K1-PF 33 6.95 17.58 3.43

SAMN06560493 CHO K1-PF 33 6.95 18.11 3.37 SAMN06560494 CHO K1-PF 33 6.7 17.8 4.24 SAMN06560495 CHO K1-PF 33 6.7 17.96 4.21

75 APPENDIX B

MDS PLOT

Figure B-1: Multi-dimension scaling (MDS) plots representing gene expression profiles for all samples.

76 APPENDIX C: Differentially Expressed Genes List

Table C-1: Genes that were identified as temperature sensitive in at least one of the cell lines (FDR ≤ 0.05 and FD ≥ 1.5). The gene expression fold-differences are shown for the 33°C cultures relative to the 37°C cultures. Blanks indicate genes that did not meet these criteria. Fold Difference CHO rCHO Gene Symbol Gene Description K1-PF DP-12 Abcb1 ATP binding cassette subfamily B member 1 -2.0 ADAM metallopeptidase with thrombospondin Adamts14 4.8 type 1 motif 14 Adgrv1 adhesion G protein-coupled receptor V1 9.9 Adm adrenomedullin 2.2 Agt angiotensinogen -15.9 Ahnak2 AHNAK nucleoprotein 2 2.0 Angptl4 angiopoietin like 4 3.3 Aprt adenine phosphoribosyltransferase 1.9 Areg amphiregulin -2.2 Arhgap31 Rho GTPase activating protein 31 -2.0 ArfGAP with SH3 domain, ankyrin repeat and PH Asap3 3.1 4.6 domain 3 Asb13 ankyrin repeat and SOCS box containing 13 2.1 Aspm abnormal spindle microtubule assembly -2.2 Aurka aurora kinase A -2.3 -1.7 Azin1 antizyme inhibitor 1 -1.8 Bard1 BRCA1 associated RING domain 1 -3.0 Bax BCL2 associated X, apoptosis regulator 2.2 2.1 Baz1b bromodomain adjacent to zinc finger domain 1B -1.8 branched chain keto acid dehydrogenase E1, alpha Bckdha 2.1 polypeptide Bet1 golgi vesicular membrane trafficking protein Bet1l 2.0 like Bora bora, aurora kinase A activator -1.9 Btg2 BTG anti-proliferation factor 2 20.6 12.5 C1r complement C1r 1.7 C1rl complement C1r subcomponent like 4.9 3.5 Cacna1i calcium voltage-gated channel subunit alpha1 I 2.0 Calhm2 calcium homeostasis modulator family member 2 3.3 2.1 Calhm5 calcium homeostasis modulator family member 5 2.9 Capn12 calpain 12 24.5 Casp3 caspase 3, apoptosis-related cysteine peptidase 2.1

77 Fold Difference CHO rCHO Gene Symbol Gene Description K1-PF DP-12 Casp6 caspase 6 2.4 Casq1 calsequestrin 1 4.9 Ccdc122 coiled-coil domain containing 122 7.8 5.1 Ccdc24 coiled-coil domain containing 24 -4.7 Ccn2 cellular communication network factor 2 -5.3 Ccna2 cyclin A2 -2.6 Ccnb1 cyclin B1 -2.6 -1.8 Ccnb2 cyclin B2 -2.1 Ccnf cyclin F -2.5 Ccng1 cyclin G1 2.7 3.0 Cd28 CD28 molecule -4.0 Cd59 CD59 molecule (CD59 blood group) 2.8 Cd81 CD81 molecule 2.3 1.8 Cdc25b cell division cycle 25B -2.6 Cdc42ep2 CDC42 effector protein 2 2.9 Cdc6 cell division cycle 6 -2.3 Cdca8 cell division cycle associated 8 -2.2 Cdr2 cerebellar degeneration related protein 2 -2.8 Cdsn corneodesmosin 3.3 2.1 Cdt1 chromatin licensing and DNA replication factor 1 -2.3 Cenpa centromere protein A -2.4 -1.7 Cir1 corepressor interacting with RBPJ, 1 2.7 Cirbp cold inducible RNA binding protein 2.2 2.1 Ckap2l cytoskeleton associated protein 2 like -1.9 Cks2 CDC28 protein kinase regulatory subunit 2 -2.3 Col3a1 collagen type III alpha 1 chain 2.3 Cpn1 carboxypeptidase N subunit 1 3.0 Crybg1 crystallin beta-gamma domain containing 1 -2.8 Cspg4 chondroitin sulfate proteoglycan 4 -2.5 Ctnnal1 catenin alpha like 1 -2.0 Ctsd cathepsin D 1.8 CUNH3orf70 chromosome unknown C3orf70 homolog 2.4 Cxcl12 C-X-C motif chemokine ligand 12 2.6 Cxcl3 chemokine (C-X-C motif) ligand 3 5.2 Dapk1 death associated protein kinase 1 2.8 Dbf4 DBF4 zinc finger -2.2 Dchs1 dachsous cadherin-related 1 12.7 Dclk1 doublecortin like kinase 1 2.4 Dcps decapping enzyme, scavenger -1.9 Ddit4 DNA damage inducible transcript 4 8.4 2.1

78 Fold Difference CHO rCHO Gene Symbol Gene Description K1-PF DP-12 Ddx20 DEAD-box helicase 20 -2.2 Des desmin 8.1 Dhcr24 24-dehydrocholesterol reductase -1.8 Dhfr dihydrofolate reductase -2.0 Dipk1c divergent protein kinase domain 1C 3.0 Dkc1 dyskerin pseudouridine synthase 1 -2.0 Dlgap5 DLG associated protein 5 -2.3 Dnase1l1 deoxyribonuclease 1 like 1 2.4 Dock6 dedicator of cytokinesis 6 4.0 Donson downstream neighbor of SON -1.9 Dpyd dihydropyrimidine dehydrogenase 3.2 Drc7 dynein regulatory complex subunit 7 14.9 Duoxa1 dual oxidase maturation factor 1 26.0 Dusp1 dual specificity phosphatase 1 3.2 2.7 Dusp4 dual specificity phosphatase 4 -2.2 Dzip1 DAZ interacting zinc finger protein 1 7.4 Ebp EBP, cholestenol delta-isomerase -1.8 Edn1 endothelin 1 5.6 Efcab8 EF-hand calcium binding domain 8 3.7 Egr1 early growth response 1 -2.0 Enc1 ectodermal-neural cortex 1 2.5 Epas1 endothelial PAS domain protein 1 2.6 Ercc6l ERCC excision repair 6 like, spindle assembly -2.5 checkpoint helicase Ereg epiregulin -2.7 Esco2 establishment of sister chromatid cohesion N- -2.2 acetyltransferase 2 Etnk1 ethanolamine kinase 1 1.9 Exd1 exonuclease 3'-5' domain containing 1 2.9 Exoc4 exocyst complex component 4 3.0 2.5 Ezr ezrin 1.8 F2r coagulation factor II thrombin receptor 1.9 Fabp4 fatty acid binding protein 4 2.7 Fads6 fatty acid desaturase 6 211.3 Fam163a family with sequence similarity 163 member A 16.3 Fam189a2 family with sequence similarity 189 member A2 3.0 Fam43a family with sequence similarity 43 member A 4.7 3.1 Fam83d family with sequence similarity 83 member D -2.1 Fbxo32 F-box protein 32 2.1 Fbxo5 F-box protein 5 -2.4

79 Fold Difference CHO rCHO Gene Symbol Gene Description K1-PF DP-12 Fbxw7 F-box and WD repeat domain containing 7 43.8 Fech ferrochelatase 2.1 Fmnl1 formin like 1 6.1 Fndc10 fibronectin type III domain containing 10 4.2 2.7 Fstl1 follistatin like 1 1.7 Fzd5 frizzled class receptor 5 -2.3 Gabarapl1 GABA type A receptor associated protein like 1 2.0 Gabrr3 gamma-aminobutyric acid type A receptor rho3 6.4 subunit Gask1b golgi associated kinase 1B 3.8 Gba glucosylceramidase beta 2.1 Gfer growth factor, augmenter of liver regeneration 2.0 Gins1 GINS complex subunit 1 -2.0 Glud1 glutamate dehydrogenase 1 1.8 1.7 Gmnn geminin, DNA replication inhibitor -1.9 Gng11 G protein subunit gamma 11 2.2 Got1 glutamic-oxaloacetic transaminase 1 -1.7 Gpr152 G protein-coupled receptor 152 3.6 Gpr153 G protein-coupled receptor 153 9.8 Gpr158 G protein-coupled receptor 158 10.1 Gprasp2 G protein-coupled receptor associated sorting 3.8 protein 2 Gpsm3 G protein signaling modulator 3 2.2 Grina glutamate ionotropic receptor NMDA type subunit 2.4 associated protein 1 Grn granulin precursor 2.5 Gsdme gasdermin E 3.4 Hap1 huntingtin associated protein 1 81.6 77.3 Hapln4 hyaluronan and proteoglycan link protein 4 2.2 Hdgf heparin binding growth factor -2.9 Hfe homeostatic iron regulator 2.2 Higd2a HIG1 hypoxia inducible domain family member 2.0 2A Hmgb1 high mobility group box 1 -2.0 Hmmr hyaluronan mediated motility receptor -2.0 Hnrnpd heterogeneous nuclear ribonucleoprotein D -1.8 Hs6st3 heparan sulfate 6-O-sulfotransferase 3 4.2 Hsbp1 heat shock factor binding protein 1 2.9 Hsd11b1 hydroxysteroid 11-beta dehydrogenase 1 6.4 Hsd3b7 hydroxy-delta-5-steroid dehydrogenase, 3 beta- and 2.7 steroid delta-isomerase 7

80 Fold Difference CHO rCHO Gene Symbol Gene Description K1-PF DP-12 Hspb1 heat shock protein family B (small) member 1 2.2 Hspd1 heat shock protein family D (Hsp60) member 1 -1.8 -1.7 Htr1b 5-hydroxytryptamine receptor 1B -2.6 Icam1 intercellular adhesion molecule 1 1.9 Ifi35 interferon induced protein 35 2.9 Ifitm3 interferon induced transmembrane protein 3 2.0 Ifrd2 interferon related developmental regulator 2 -2.3 Il33 interleukin 33 -5.6 Inka2 inka box actin regulator 2 6.3 8.7 Insig1 insulin induced gene 1 -2.1 Ints4 integrator complex subunit 4 2.0 Isg15 ISG15 ubiquitin-like modifier 2.1 Itga9 integrin subunit alpha 9 115.2 Itih5 inter-alpha-trypsin inhibitor heavy chain family 1.9 member 5 Itm2b integral membrane protein 2B 1.9 1.8 Itm2c integral membrane protein 2C 2.2 Kcp kielin cysteine rich BMP regulator 8.8 Kctd11 potassium channel tetramerization domain 3.4 containing 11 Kctd17 potassium channel tetramerization domain 2.3 containing 17 Kctd17 potassium channel tetramerization domain 2.1 containing 17 Kif20a kinesin family member 20A -2.1 Kif23 kinesin family member 23 -2.1 Kif2c kinesin family member 2C -2.1 Kif4a kinesin family member 4A -2.2 Kifc1 kinesin family member C1 -4.0 Klf4 Kruppel like factor 4 2.0 2.4 Knstrn kinetochore localized astrin (SPAG5) binding -2.1 protein Kpna2 karyopherin subunit alpha 2 -2.1 Kpnb1 karyopherin subunit beta 1 -1.8 Lama3 laminin subunit alpha 3 3.9 Ldb3 LIM domain binding 3 23.1 Lgals3bp galectin 3 binding protein 4.5 3.6 Lgi4 leucine rich repeat LGI family member 4 4.2 3.9 Lif LIF, interleukin 6 family cytokine 5.0 Lmnb1 lamin B1 -2.4 Lpl lipoprotein lipase 1.8

81 Fold Difference CHO rCHO Gene Symbol Gene Description K1-PF DP-12 Lrrc59 leucine rich repeat containing 59 -1.8 Lss lanosterol synthase -2.1 Lxn latexin 2.1 2.6 Mad2l1 mitotic arrest deficient 2 like 1 -2.0 Malrd1 MAM and LDL receptor class A domain 5.9 containing 1 Man2b2 mannosidase alpha class 2B member 2 2.0 Maoa monoamine oxidase A 2.4 2.2 Map1lc3b microtubule associated protein 1 light chain 3 beta 2.3 Mcm3 minichromosome maintenance complex component -1.9 3 Mcm7 minichromosome maintenance complex component -1.8 7 Med4 mediator complex subunit 4 2.3 Mei4 meiotic double-stranded break formation protein 4 26.2 Mettl16 methyltransferase like 16 -1.9 Mettl7a methyltransferase like 7A 3.3 2.2 Mis18a MIS18 kinetochore protein A -2.6 Mmd monocyte to macrophage differentiation associated 2.2 Mmp12 matrix metallopeptidase 12 -2.2 Mmp24 matrix metallopeptidase 24 -3.7 Mms22l MMS22 like, DNA repair protein -2.0 Moxd1 monooxygenase DBH like 1 17.3 6.9 Mthfd2 methylenetetrahydrofolate dehydrogenase (NADP+ -1.8 dependent) 2, methenyltetrahydrofolate cyclohydrolase Naglu N-acetyl-alpha-glucosaminidase 2.3 Nampt nicotinamide phosphoribosyltransferase -1.9 Nasp nuclear autoantigenic sperm protein -2.5 -2.2 Ncl nucleolin -1.9 Ndufc2 NADH:ubiquinone oxidoreductase subunit C2 2.2 Neil3 nei like DNA glycosylase 3 2.5 Nek11 NIMA related kinase 11 7.7 Nek2 NIMA related kinase 2 -2.1 Neu1 neuraminidase 1 2.4 Neu2 neuraminidase 2 -3.2 -3.7 Neurl1b neuralized E3 ubiquitin protein ligase 1B -2.6 Neurl3 neuralized E3 ubiquitin protein ligase 3 -2.1 Nnmt nicotinamide N-methyltransferase 2.7 Notch3 notch 3 2.8 Nppb natriuretic peptide B 2.8

82 Fold Difference CHO rCHO Gene Symbol Gene Description K1-PF DP-12 Npr2 natriuretic peptide receptor 2 2.5 Nr1d1 nuclear receptor subfamily 1 group D member 1 2.0 Nrg2 neuregulin 2 -4.0 Nsdhl NAD(P) dependent steroid dehydrogenase-like -2.0 Nup37 nucleoporin 37 -1.9 Nup85 nucleoporin 85 -1.9 Nupr1 nuclear protein 1, transcriptional regulator 2.4 Oip5 Opa interacting protein 5 -2.2 Opn4 opsin 4 8.5 Orc1 origin recognition complex subunit 1 -2.1 Orc6 origin recognition complex subunit 6 -1.9 Osr1 odd-skipped related transciption factor 1 3.1 Ostf1 osteoclast stimulating factor 1 1.7 P2rx4 purinergic receptor P2X 4 2.3 P2ry1 purinergic receptor P2Y1 2.3 Pabpc1 poly(A) binding protein cytoplasmic 1 -1.7 Pak1 p21 (RAC1) activated kinase 1 3.0 Parp14 poly(ADP-ribose) polymerase family member 14 2.5 Parpbp PARP1 binding protein -2.6 Paxip1 PAX interacting protein 1 -1.9 Paxx PAXX, non-homologous end joining factor 2.1 Pcif1 PDX1 C-terminal inhibiting factor 1 3.1 4.4 Pclaf PCNA clamp associated factor -2.3 Pcolce procollagen C-endopeptidase enhancer 2.7 Pdgfc platelet derived growth factor C 1.9 Pdia5 protein disulfide isomerase family A member 5 2.2 1.8 Peg10 paternally expressed 10 -2.8 Pgap2 post-GPI attachment to proteins 2 2.4 Phf19 PHD finger protein 19 -2.2 Phlda3 pleckstrin homology like domain family A member 2.4 2.3 3 Pink1 PTEN induced kinase 1 4.0 2.0 Pkp1 plakophilin 1 4.7 Pla2g7 phospholipase A2 group VII 2.2 Plat plasminogen activator, tissue type 2.3 2.7 Plin3 perilipin 3 2.2 Plk1 polo like kinase 1 -2.5 Plk2 polo like kinase 2 4.8 Plpp3 phospholipid phosphatase 3 1.9 Plpp6 phospholipid phosphatase 6 2.0 1.9

83 Fold Difference CHO rCHO Gene Symbol Gene Description K1-PF DP-12 Pm20d2 peptidase M20 domain containing 2 2.5 Pole DNA polymerase epsilon, catalytic subunit -1.8 Ppid peptidylprolyl isomerase D -5.5 -3.6 Pqlc3 PQ loop repeat containing 3 1.9 Prom2 prominin 2 16.6 Psat1 phosphoserine aminotransferase 1 -2.0 Ptger4 prostaglandin E receptor 4 2.0 Ptgr1 prostaglandin reductase 1 -1.8 Ptma prothymosin alpha -1.9 Qsox1 quiescin sulfhydryl oxidase 1 2.1 Rab27b RAB27B, member RAS oncogene family 8.4 5.2 Rabgap1 RAB GTPase activating protein 1 3.9 2.5 Rad54l RAD54 like -2.0 Ranbp1 RAN binding protein 1 -1.8 Rarres2 retinoic acid receptor responder 2 2.5 Rasl11a RAS like family 11 member A 6.2 2.1 Rbm3 RNA binding motif protein 3 2.5 2.9 Rdh14 retinol dehydrogenase 14 1.8 Retsat retinol saturase 3.9 2.9 Rgs16 regulator of G protein signaling 16 -2.4 Rnf145 ring finger protein 145 2.1 Rnf169 ring finger protein 169 2.8 1.9 Rnf20 ring finger protein 20 5.6 3.0 Rprm reprimo, TP53 dependent G2 arrest mediator 1.9 homolog Rpusd3 RNA pseudouridine synthase D3 2.1 Rsf1 remodeling and spacing factor 1 2.1 Rtl9 retrotransposon Gag like 9 4.0 Rtn4rl1 reticulon 4 receptor like 1 2.1 Rxfp4 relaxin family peptide/INSL5 receptor 4 3.4 S100a11 S100 calcium binding protein A11 1.9 Sarm1 sterile alpha and TIR motif containing 1 7.1 5.2 Scly selenocysteine lyase -2.7 Sel1l3 SEL1L family member 3 2.1 Selenom selenoprotein M 2.4 Selenop selenoprotein P 1.8 Sema3c semaphorin 3C 2.3 Sept11 septin 11 -1.8 Serinc4 serine incorporator 4 2.3 Serpine1 serpin family E member 1 1.8 2.4

84 Fold Difference CHO rCHO Gene Symbol Gene Description K1-PF DP-12 Sgca sarcoglycan alpha 7.3 Sh3rf3 SH3 domain containing ring finger 3 3.2 Shmt1 serine hydroxymethyltransferase 1 -2.0 Ska3 spindle and kinetochore associated complex -2.6 subunit 3 Slc16a1 solute carrier family 16 member 1 -2.0 Slc25a23 solute carrier family 25 member 23 2.7 Slc35d1 solute carrier family 35 member D1 1.9 2.0 Slc44a1 solute carrier family 44 member 1 3.2 3.0 Slc46a1 solute carrier family 46 member 1 3.1 2.5 Slc48a1 solute carrier family 48 member 1 2.1 Slc9a9 solute carrier family 9 member A9 1.9 2.0 Slpi secretory leukocyte peptidase inhibitor 4.6 2.6 Smc4 structural maintenance of chromosomes 4 -2.8 Smg5 SMG5, nonsense mediated mRNA decay factor -1.8 Smim4 small integral membrane protein 4 3.9 Sorl1 sortilin related receptor 1 -2.8 Spag5 sperm associated antigen 5 -2.1 -1.7 Spdef SAM pointed domain containing ETS transcription 5.0 factor Spn sialophorin 4.0 Sqstm1 sequestosome 1 1.8 Sspn sarcospan 2.4 2.8 Ssu72 SSU72 homolog, RNA polymerase II CTD 2.2 phosphatase Stap2 signal transducing adaptor family member 2 3.3 Stk24 serine/threonine kinase 24 -7.1 Sulf2 sulfatase 2 70.9 Sumf1 sulfatase modifying factor 1 1.9 Susd6 sushi domain containing 6 2.6 2.1 Syngap1 synaptic Ras GTPase activating protein 1 2.1 Synpo2 synaptopodin 2 3.4 Taf15 TATA-box binding protein associated factor 15 -1.9 Tap2 transporter 2, ATP binding cassette subfamily B 2.5 member Tax1bp3 Tax1 binding protein 3 3.0 2.6 Tbc1d8 TBC1 domain family member 8 5.6 2.9 Tdgf1 teratocarcinoma-derived growth factor 1 2.3 Tenm3 teneurin transmembrane protein 3 8.0 18.3 Tes testin LIM domain protein 1.9 Tgfbr3 transforming growth factor beta receptor 3 1.9

85 Fold Difference CHO rCHO Gene Symbol Gene Description K1-PF DP-12 Thbd thrombomodulin 2.4 Thra thyroid hormone receptor alpha 2.4 Ticrr TOPBP1 interacting checkpoint and replication -2.3 regulator Timeless timeless circadian regulator -1.8 Timp2 TIMP metallopeptidase inhibitor 2 1.9 1.8 Tlr2 toll like receptor 2 2.4 Tmbim1 transmembrane BAX inhibitor motif containing 1 2.8 1.9 Tmc4 transmembrane channel like 4 2.0 Tmed4 transmembrane p24 trafficking protein 4 1.8 Tmeff2 transmembrane protein with EGF like and two 2.9 6.2 follistatin like domains 2 Tmem123 transmembrane protein 123 3.4 1.9 Tmem150a transmembrane protein 150A 3.3 2.1 Tmem151b transmembrane protein 151B 2.3 Tmem203 transmembrane protein 203 2.4 Tmem38a transmembrane protein 38A 3.0 Tmem43 transmembrane protein 43 2.0 2.0 Tmem50b transmembrane protein 50B 2.6 Tmem63b transmembrane protein 63B 4.5 3.7 Tmem86a transmembrane protein 86A 4.2 Tmem8a transmembrane protein 8A 2.1 Tmem97 transmembrane protein 97 -2.1 Tmpo thymopoietin -2.6 Tnfsf4 TNF superfamily member 4 4.8 Top2a DNA topoisomerase II alpha -2.2 Tor4a torsin family 4 member A 2.9 2.3 Tp53inp1 tumor protein p53 inducible nuclear protein 1 8.1 9.8 Tpp1 tripeptidyl peptidase 1 2.2 Tprg1 tumor protein p63 regulated 1 5.6 Trabd2b TraB domain containing 2B 2.3 Traip TRAF interacting protein -2.2 Trappc1 trafficking protein particle complex 1 2.1 Trim54 tripartite motif containing 54 6.3 2.4 Trip13 thyroid hormone receptor interactor 13 -1.8 Trpm6 transient receptor potential cation channel 4.2 subfamily M member 6 Tsta3 tissue specific transplantation antigen P35B 2.0 Tulp3 tubby like protein 3 2.1 Txnip thioredoxin interacting protein 2.0 2.2 Ube2c ubiquitin conjugating enzyme E2 C -3.0 -1.8

86 Fold Difference CHO rCHO Gene Symbol Gene Description K1-PF DP-12 Ube2s ubiquitin conjugating enzyme E2 S -1.9 Ugcg UDP-glucose ceramide glucosyltransferase 2.1 Uggt2 UDP-glucose glycoprotein glucosyltransferase 2 2.1 2.0 Usp1 ubiquitin specific peptidase 1 -2.2 Usp35 ubiquitin specific peptidase 35 3.4 Vangl2 VANGL planar cell polarity protein 2 2.3 Vnn1 vanin 1 3.5 Vwa2 von Willebrand factor A domain containing 2 14.2 Wfikkn1 WAP, follistatin/kazal, immunoglobulin, kunitz 3.5 2.1 and netrin domain containing 1 Wfs1 wolframin ER transmembrane glycoprotein 2.2 Wipi1 WD repeat domain, phosphoinositide interacting 1 1.9 2.0 Wnk4 WNK lysine deficient protein kinase 4 2.2 Wwox WW domain containing oxidoreductase 2.0 Xpnpep2 X-prolyl aminopeptidase 2 4.1 Xrcc2 X-ray repair cross complementing 2 -2.0 Ypel1 yippee like 1 4.4 Ypel2 yippee like 2 2.7 Zadh2 zinc binding alcohol dehydrogenase domain 1.9 2.0 containing 2 Zc3h6 zinc finger CCCH-type containing 6 2.3 Zmat3 zinc finger matrin-type 3 5.5 3.3 Znf365 zinc finger protein 365 2.7 Zwint ZW10 interacting kinetochore protein -2.0 LOC103160274 3-beta-hydroxysteroid sulfotransferase-like -18.8 LOC103159983 60S ribosomal protein L17 3.0 LOC100771285 60S ribosomal protein L36 pseudogene -6.7 LOC100760000 acetyl-CoA acetyltransferase, cytosolic -2.2 LOC100753241 acyl-CoA desaturase 4 4.7 LOC100769250 acylcarnitine hydrolase 62.7 27.9 LOC100763954 amiloride-sensitive amine oxidase [copper- -3.4 containing] LOC113830710 angiotensin II receptor type 2 8.3 LOC100767344 annexin A7 pseudogene 6.9 LOC100773073 annexin A8 17.9 65.2 LOC100770874 ATP-binding cassette sub-family A member 3-like 44.2 31.4 LOC103158905 ATP-dependent RNA helicase DHX8 1.7 LOC113834873 BTB/POZ domain-containing protein KCTD14- 3.1 like LOC100763833 C-C motif chemokine 2 2.8 1.7 LOC100763261 C-C motif chemokine 7 5.7 2.4

87 Fold Difference CHO rCHO Gene Symbol Gene Description K1-PF DP-12 LOC103160461 C-type lectin domain family 2 member A 3.0 4.0 LOC100761692 C-type lectin domain family 2 member D11 2.9 LOC113833874 calcium/calmodulin-dependent protein kinase II 2.6 inhibitor 1 LOC113833727 centrosomal protein of 55 kDa -2.1 LOC100755608 chromosome unknown open reading frame, human 1.9 C4orf3 LOC100772424 chromosome unknown open reading frame, human 1.9 C8orf48 LOC100760794 class II histocompatibility antigen, M alpha chain 3.3 LOC100761082 class II histocompatibility antigen, M beta 1 chain 5.4 LOC107979148 clathrin light chain A pseudogene -3.0 LOC100756343 coiled-coil domain-containing protein 171 -3.8 LOC100751341 complement factor H-related protein 2-like 14.8 LOC100760951 cornifin alpha 3.9 2.1 LOC100772471 cytochrome c oxidase assembly protein COX14 2.1 LOC100768211 cytochrome P450 4A14 6.3 LOC113838100 desmoglein-2-like -4.9 LOC113833115 ephrin A4, transcript variant X1 2.5 LOC103164600 gasdermin-A-like 4.1 LOC100757489 glutamyl aminopeptidase 7.3 LOC100753467 glutathione S-transferase alpha-3 3.9 LOC100767530 glutathione S-transferase Mu 3 5.0 3.1 LOC100753288 glycogen phosphorylase, liver form 4.0 LOC113833391 growth/differentiation factor 15-like 107.3 LOC113832455 H-2 class I histocompatibility antigen, Q10 alpha 3.7 chain-like LOC113833176 helix-loop-helix protein 2 3.2 LOC100758202 heterogeneous nuclear ribonucleoprotein C-like -2.0 LOC100763852 histone H2A.Z -2.2 -1.7 LOC100761268 histone H2B type 1 11.0 LOC113835792 interferon alpha-inducible protein 27-like protein 3.2 2B LOC107977182 interferon-induced very large GTPase 1-like -2.7 LOC113833125 late cornified envelope protein 1C-like 3.8 LOC100689011 MHC class I antigen Hm1-C3 2.3 LOC100773718 MLV-related proviral Env polyprotein-like 3.4 2.1 LOC113836370 MMP24 opposite strand 2.0 LOC100773141 MOB-like protein phocein -2.1 -1.8 LOC100772072 myomegalin 2.4 LOC100756978 nucleophosmin pseudogene -1.9

88 Fold Difference CHO rCHO Gene Symbol Gene Description K1-PF DP-12 LOC100762223 nucleosome assembly protein 1-like 1 pseudogene -1.8 LOC100755641 peptidyl-prolyl cis-trans isomerase D-like -5.6 -3.6 LOC100772948 poly(A)-specific ribonuclease PARN-like domain- 5.9 containing protein 1 pseudogene LOC113831948 PRAME family member 8-like -2.9 LOC113832707 protein phosphatase 1 regulatory subunit 3E 2.2 LOC113834099 protein SET-like -3.4 LOC113836540 prothymosin alpha-like -2.0 LOC113833705 protocadherin beta-14-like, transcript variant X1 6.1 LOC100771727 protocadherin beta-3 2.5 LOC100772593 protocadherin beta-5-like 3.6 LOC100754141 protocadherin gamma-B1 2.6 LOC113833573 putative fidgetin-like protein 2 2.7 LOC113838544 putative protein ZNF720, transcript variant X1 10.2 LOC100754646 putative small proline-rich protein 2J 2.0 3.8 LOC100768367 retinal dehydrogenase 1 3.1 LOC113836685 serine/arginine-rich splicing factor 2-like -1.8 LOC100754565 stromelysin-1-like -2.9 LOC113837993 translation initiation factor IF-2-like -8.7 LOC100755423 UDP-glucuronosyltransferase 1-6 2.1 LOC100769218 zinc finger protein 420 2.6 2.2 LOC100761126 zinc finger protein 431-like 2.2 LOC100761886 zinc finger protein 883 -2.4 LOC113830743 uncharacterized LOC113830743 -2.0 LOC100751512 uncharacterized LOC100751512 -2.1 LOC100754175 uncharacterized LOC100754175 2.0 LOC100754872 uncharacterized LOC100754872 4.3 LOC100764856 uncharacterized LOC100764856 12.1 4.5 LOC100773771 uncharacterized LOC100773771 15.7 LOC103158756 uncharacterized LOC103158756 3.8 LOC103159189 uncharacterized LOC103159189 19.2 LOC103159228 uncharacterized LOC103159228 30.8 LOC103159336 uncharacterized LOC103159336 3.3 LOC103159353 uncharacterized LOC103159353 5.9 LOC103159433 uncharacterized LOC103159433 7.6 LOC103159534 uncharacterized LOC103159534 -15.8 LOC103159589 uncharacterized LOC103159589 2.4 LOC103159835 uncharacterized LOC103159835 5.4 LOC103160142 uncharacterized LOC103160142 2.7 LOC103160523 uncharacterized LOC103160523 3.1

89 Fold Difference CHO rCHO Gene Symbol Gene Description K1-PF DP-12 LOC103160860 uncharacterized LOC103160860 5.1 LOC103161056 uncharacterized LOC103161056 9.3 LOC103161127 uncharacterized LOC103161127 3.1 LOC103161423 uncharacterized LOC103161423 4.0 LOC103161512 uncharacterized LOC103161512 6.0 2.6 LOC103161665 uncharacterized LOC103161665 16.9 24.6 LOC103162230 uncharacterized LOC103162230 -4.1 LOC103162302 uncharacterized LOC103162302 -2.6 LOC103162356 uncharacterized LOC103162356 8.1 LOC103162751 uncharacterized LOC103162751 2.7 LOC103163253 uncharacterized LOC103163253 2.0 LOC103163716 uncharacterized LOC103163716 5.8 LOC103163843 uncharacterized LOC103163843 2.8 LOC103163910 uncharacterized LOC103163910 3.6 LOC107978267 uncharacterized LOC107978267 6.9 LOC107978279 uncharacterized LOC107978279 3.6 2.7 LOC107978535 uncharacterized LOC107978535 2.4 LOC107978541 uncharacterized LOC107978541 2.4 LOC107978675 uncharacterized LOC107978675 5.4 6.5 LOC107978970 uncharacterized LOC107978970 2.3 LOC107979404 uncharacterized LOC107979404 2.4 LOC107979406 uncharacterized LOC107979406 -5.9 LOC107979474 uncharacterized LOC107979474 4.3 LOC107979531 uncharacterized LOC107979531 5.5 7.7 LOC107979615 uncharacterized LOC107979615 -6.7 LOC107979868 uncharacterized LOC107979868 3.8 4.3 LOC107979955 uncharacterized LOC107979955 -4.0 LOC107980313 uncharacterized LOC107980313 6.5 LOC107980379 uncharacterized LOC107980379 2.6 LOC113830912 uncharacterized LOC113830912 3.1 LOC113830989 uncharacterized LOC113830989 12.8 LOC113831598 uncharacterized LOC113831598 2.3 LOC113832576 uncharacterized LOC113832576 -3.3 LOC113832687 uncharacterized LOC113832687 3.6 LOC113832972 uncharacterized LOC113832972 5.6 LOC113833126 uncharacterized LOC113833126 4.4 2.7 LOC113833189 uncharacterized LOC113833189 2.9 LOC113833204 uncharacterized LOC113833204 8.2 LOC113833272 uncharacterized LOC113833272 2.7 LOC113833275 uncharacterized LOC113833275 41.0

90 Fold Difference CHO rCHO Gene Symbol Gene Description K1-PF DP-12 LOC113833460 uncharacterized LOC113833460 2.2 LOC113833557 uncharacterized LOC113833557 6.2 LOC113833858 uncharacterized LOC113833858 2.2 LOC113833948 uncharacterized LOC113833948 2.3 LOC113834127 uncharacterized LOC113834127 -7.3 LOC113834172 uncharacterized LOC113834172 4.9 LOC113834875 uncharacterized LOC113834875 4.0 LOC113834877 uncharacterized LOC113834877 6.7 LOC113834998 uncharacterized LOC113834998 3.1 2.7 LOC113835046 uncharacterized LOC113835046 3.5 LOC113835047 uncharacterized LOC113835047 5.5 LOC113835137 uncharacterized LOC113835137 3.3 LOC113835176 uncharacterized LOC113835176 8.6 LOC113835325 uncharacterized LOC113835325 13.3 LOC113835610 uncharacterized LOC113835610 4.0 LOC113835612 uncharacterized LOC113835612 5.4 LOC113835715 uncharacterized LOC113835715 -7.8 LOC113835897 uncharacterized LOC113835897 3.9 2.3 LOC113835917 uncharacterized LOC113835917 -2.0 LOC113835922 uncharacterized LOC113835922 4.0 3.5 LOC113836233 uncharacterized LOC113836233 -3.5 LOC113836268 uncharacterized LOC113836268 2.1 LOC113836333 uncharacterized LOC113836333 -2.1 LOC113836714 uncharacterized LOC113836714 3.2 LOC113837117 uncharacterized LOC113837117 19.1 2.2 LOC113837467 uncharacterized LOC113837467 3.9 LOC113837481 uncharacterized LOC113837481 2.8 LOC113837489 uncharacterized LOC113837489 4.8 LOC113837508 uncharacterized LOC113837508 117.0 LOC113837582 uncharacterized LOC113837582 6.9 2.8 LOC113837758 uncharacterized LOC113837758 -3.5

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