Transcriptome analysis of a CHO cell line expressing a recombinant therapeutic protein treated with inducers of protein expression

Accepted Manuscript Title: Transcriptome analysis of a CHO cell line expressing a recombinant therapeutic protein treated with inducers of protein expression Author: Dina Fomina-Yadlin Mirna Mujacic Kathy Maggiora Garrett Quesnell Ramsey Saleem Jeffrey T. McGrew PII: DOI: Reference:

S0168-1656(15)30104-8 http://dx.doi.org/doi:10.1016/j.jbiotec.2015.08.025 BIOTEC 7224

To appear in:

Journal of Biotechnology

Received date: Revised date: Accepted date:

9-6-2015 23-8-2015 26-8-2015

Please cite this article as: Fomina-Yadlin, Dina, Mujacic, Mirna, Maggiora, Kathy, Quesnell, Garrett, Saleem, Ramsey, McGrew, Jeffrey T., Transcriptome analysis of a CHO cell line expressing a recombinant therapeutic protein treated with inducers of protein expression.Journal of Biotechnology http://dx.doi.org/10.1016/j.jbiotec.2015.08.025 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Transcriptome analysis of a CHO cell line expressing a recombinant therapeutic protein treated with inducers of protein expression   

Dina Fomina-Yadlin, Mirna Mujacic, Kathy Maggiora, Garrett Quesnell, Ramsey Saleem and Jeffrey T. McGrew* [email protected]   

Drug Substance Development, Amgen Inc., Seattle, WA 98119   *

Corresponding author: Just Biotherapeutics, 454 N. 34th Street, Seattle, WA 98103 

Highlights  

RNA-Seq analysis was used to examine inducer effects on gene expression

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Inducer treatment increased RANK-Fc mRNA from 16% up to 45% of total cellular mRNA

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Specific productivity positively correlated to recombinant protein mRNA level

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Selectable marker and RANK-Fc were 2 out of 7 mRNAs positively correlated to qP

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Expression of many cell growth and housekeeping genes negatively correlated to qP

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Transcript level of the recombinant protein is a major qP determinant

 

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Abstract The search for specific productivity (qP) determinants in Chinese hamster ovary (CHO) cells has been the focus of the biopharmaceutical cell line engineering efforts aimed at creating “superproducer” cell lines. In this study, we evaluated the impact of small-molecule inducers and temperature shift on recombinant protein production, and used transcriptomic analysis to define gene-phenotype correlations for qP in our biological system. Next-generation RNA Sequencing (RNA-Seq) analysis revealed that each individual inducer (caffeine, hexamethylene bisacetamide (HMBA) and sodium butyrate (NaBu)) or a combination treatment had a distinct impact on the gene expression program of the RANK-Fc cell line. Temperature shift to 31°C impacted inducer action with respect to transcriptional changes and phenotypic cell line parameters. We showed that inducer treatment was able to increase expression level of the Fc-fusion mRNA and the selectable marker mRNA from 16% up to 45% of total mRNA in the cell. We further demonstrated that qP exhibited a strong positive linear correlation to transcript levels of both the RANK-Fc fusion protein and the dihydrofolate reductase (DHFR) selectable marker. In fact, these were 2 out of 7 transcripts with significant positive correlation to qP at both temperatures. Many more transcripts were anti-correlated to qP, and gene set enrichment analysis (GSEA) revealed that those were involved in cell cycle progression, transcription, mRNA processing, translation and protein folding. Therefore, we postulate that the transcript level of the recombinant protein is a major qP determinant in our biological system, while downregulation of routine activity within the cell is necessary to divert cellular resources towards recombinant protein production.  Abbreviations  CHO: Chinese hamster ovary; DHFR: Dihydrofolate reductase; GO: Gene Ontology; GS: Glutamine synthetase; GSEA: Gene set enrichment analysis; HC: Heavy chain; HMBA: Hexamethylene bisacetamide; IgG1: Human immunoglobulin G1;  

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IRES: Internal Ribosome Entry Site; LC: Light chain; mAb: Monoclonal antibody; NaBu: Sodium butyrate; PCA: Principal Component Analysis; qP: Specific productivity; RANK: Receptor Activator of NF-Kappa B; RNA-Seq: Next-generation RNA sequencing; RPKM: Reads Per Kilobase of exon model per Million mapped reads; VCD: Viable cell density. 

Keywords: Chinese hamster ovary; Specific productivity; Recombinant protein expression; Small-molecule inducers; Temperature shift; RNA-Seq 

 

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1. Introduction Although there are many recombinant proteins approved for therapeutic use, the cost of producing these molecules remains an impediment to their availability to patients. Considerable progress has been made by the biotechnology industry in improving expression vectors, cell line development methods, media, and process conditions to increase expression levels by 2 orders of magnitude in the last two decades (Butler and Meneses-Acosta, 2012; De Jesus and Wurm, 2011). Key areas of focus have been the methods for the isolation of high-expressing “super producer” clones using Chinese hamster ovary (CHO) cells (Browne and Al-Rubeai, 2007) followed by attempts to define the “hyper-productivity” profile of CHO cells using transcriptomic and proteomic approaches (Seth et al., 2007a; Vishwanathan et al., 2014a; Vishwanathan et al., 2014b). The methods for “super producer” cell line generation typically entail transfecting with appropriate expression vectors containing either dihyrofolate reductase (DHFR) or glutamine synthetase (GS) selectable markers, selecting for the expression of the selectable marker, and then screening clones for expression of the recombinant protein and suitability for manufacturing. Considerable clone-to-clone expression variability exists even when using the same cell line, expression vector, and molecule (Davies et al., 2013; Porter et al., 2010; Vishwanathan et al., 2014a). Some of the variation can be attributed to differences in copy number and integration site. However, because the molecular explanation for clone-to-clone variability is not always clear, systems biology approaches have been utilized to identify the molecular mechanisms of the variability and define desirable gene-phenotype relationships (Doolan et al., 2012; Kang et al., 2014; Nissom et al., 2006; Seth et al., 2007b; Vishwanathan et al., 2014a). These approaches have typically looked for genes whose expression correlates with high specific productivity (qP) or other desirable cell line and product characteristics. As a result, gene sets and pathways associated with high expression levels have been identified in several biological systems and experimental setups (Clarke et al., 2011b; Doolan et al., 2012; Kang et al., 2014; Kantardjieff et al., 2010; Nissom et al., 2006; Seth et al.,  

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2007b; Vishwanathan et al., 2014a; Yee et al., 2008). The underlying assumption of these strategies is that at least some of the genes identified as correlated with higher recombinant protein expression are also responsible for the observed increase in expression and, therefore, can be used as cell line engineering targets. However, in some cases the lists of productivity-associated genes are too extensive to interrogate easily with currently available methods (Clarke et al., 2011b; Doolan et al., 2012; Kantardjieff et al., 2010; Yee et al., 2008). In addition, these “hyper-productivity” associated gene sets vary across studies. For example, Vishwahathan et al. found that out of 451 genes reported in previous transcriptomic studies of cell line productivity, only 15 genes were differentially expressed between high and low producers in their comparison (Vishwanathan et al., 2014a). Thus, the molecular drivers associated with high levels of recombinant protein production remain elusive. Other strategies to increase recombinant protein expression have been applied following cell line generation. Many of these strategies have involved exposure of cells to either temperature shifts (Fox et al., 2004; Sunley and Butler, 2010; Yee et al., 2009), small molecules that increase protein expression, or both (Kantardjieff et al., 2010). For example, sodium butyrate (NaBu), hexamethylene bisacetamide (HMBA), caffeine, and other chemicals have been identified as expression “inducers” (Allen et al., 2008; Mimura et al., 2001; Takahashi et al., 2003; Van Ness et al., 2008; Yang et al., 2014). Butyrate is a histone deacetylase (HDAC) inhibitor, and HDAC inhibitors have been shown to increase heterologous mRNA levels, which could account for some or all of its effect on recombinant protein expression (Wulhfard et al., 2010). The effects of HMBA and caffeine on gene expression are less characterized and their mechanisms of action for increasing recombinant protein production in mammalian cells are unclear. In this study, we have treated a CHO cell line expressing a recombinant protein therapeutic with a temperature shift to 31°C, caffeine, HMBA, NaBu or a combination of these treatments and measured global changes in gene expression. The CHO cell line expressed RANK-Fc, a fusion protein antagonist of RANK-L containing the extracellular ligand-binding domain of the Receptor  

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Activator of NF-Kappa B (RANK) and the Fc portion of human immunoglobulin G1 (IgG1) (Sordillo and Pearse, 2003). Selected small-molecule inducers of protein expression affected qP of the RANK-Fc cell line to various degrees. The goal was to use this biological system for transcriptomic analysis to determine the influence of inducers on the cellular mechanisms that increase recombinant protein expression and define the “hyper-productivity” signature in this CHO cell line. Thus, we explored the global changes in gene expression following these treatments using next-generation RNA Sequencing (RNA-Seq) and then correlated gene expression to qP. We found that different inducers and combination treatments had divergent effects on global gene expression. However, increases in secreted recombinant protein were strongly associated with inducer effects on the RANK-Fc transcript level. Therefore, we propose a model where the increased Fc-fusion mRNA accumulation was the main predictor of qP, despite being achieved by various means, and the negatively qP-correlated genes were the result of crowding-out of other cellular mRNA. 2. Materials and methods 2.1. Cell culture and experimental treatments Serum-free CHO cell line expressing human RANK-Fc fusion protein was generated using a vector that yields 2 mRNA molecules by alternate polyadenylation (Fig. S1A) (McGrew, 2003). The longer transcript contains dihydrofolate reductase (DHFR), used as a selectable marker for the RANK-Fc cell line generation, linked to the RANK-Fc by an Internal Ribosome Entry Site (IRES) sequence. The expression of the DHFR protein is limited by reducing the transcript encoding DHFR via alternate polyadenylation, allowing for stringent selection of transfectants. RANK-Fc cells were cultured in a proprietary chemically-defined growth medium in vented shake-flasks at 36°C, 5% CO2, 70% relative humidity and shaken at 150-160 rpm in Kuhner incubators. Viable cell density (VCD) and viability were measured with a Vi-CELL automated cell counter (Beckman-Coulter, Inc., Brea, CA).

 

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For the inducer study, cells were seeded from day 4 cultures growing at 36°C into proprietary chemically-defined production medium at 10 x 106 viable cells/mL in vented 24 deep-well plates (3 mL volume per well) and were either left untreated or were treated with each individual inducer (1mM caffeine, 2mM HMBA or 0.5mM NaBu) or a combination of the three inducers at subconcentrations (0.5mM caffeine + 1mM HMBA + 0.25mM NaBu) in triplicate. Replicate plates were either maintained at 36°C or shifted to 31°C in Kuhner incubators and shaken at 220 rpm. Daily medium exchanges were performed using fresh media, and spent medium was used for daily titer analysis. Titer measurements were performed by affinity High Performance Liquid Chromatography (HPLC) using POROS A/20 Protein A column. Since the medium was exchanged daily, the qP (pg/cell/day) was calculated according to the formula: qP = daily titer/daily VCD. On day 3, 3 x 106 viable cells were collected per sample for gene expression analysis, snap-frozen and stored at -70°C for further processing. In addition, 10 x 106 viable cells were collected per sample for protein expression analysis, washed once with phosphate buffered saline (PBS), snap-frozen and stored at 70°C for further processing. 2.2. RNA-Seq sample preparation and analysis Total RNA was isolated from 3 x 106 viable cells per sample using the RNeasy Mini kit (Qiagen, Valencia, CA) according to the manufacturer's protocol, with optional on-column DNAse I digestion and 100μL elution volume. RNA concentration was measured on the Nanodrop 2000 (Thermo Scientific, Wilmington, DE), and RNA quality was assessed using the 2100 Bioanalyzer (Agilent, Santa Clara, CA) with the RNA 6000 Nano Kit (Agilent, Santa Clara, CA). All extracted RNA samples had RNA Integrity Number (RIN) > 9 and were used for RNA-Seq analysis. Illumina TruSeq RNA library preparations (according to the manufacturer's recommendations (Illumina, San Diego, CA)), sequencing reactions, and initial bioinformatics analysis were performed at GENEWIZ, Inc. (South Plainfield, NJ), as previously described (FominaYadlin et al., 2014). Following library preparation and validation, 30 samples were clustered on six  

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lanes of a flow cell, loaded on the Illumina HiSeq 2500 instrument (Illumina, San Diego, CA) according to the manufacturer’s instructions, and sequenced using a 1x50 Single Read (SR) configuration. Biological replicates were barcoded and run in different sequencing lanes to account for potential batch effects. Sequences for the RANK-Fc and the IRES-DHFR portions of the recombinant message were added to the Cricetulus griseus reference genome along with its mitochondrion (GCA_000223135.1 and NC_007936.1) prior to sequence alignment (Lewis et al., 2013). CLC Genomics Workbench 6.5.1 software (CLC Bio, Qiagen, Valencia, CA) was used to align the reads to the reference genome (2 mismatches maximum) and perform the gene hit count. Transcript abundance was expressed in Reads Per Kilobase of exon model per Million mapped reads (RPKM). The percent of reads that mapped to unique reference sequence ranged from 59.16% to 67.16%. The non-specifically mapped reads ranged from 4.34 to 6.73%, and the unmapped reads ranged from 28.4% to 33.73%. The unmapped reads can be due to a number of sources, but some of these may be due to the incomplete nature of the Cricetulus griseus reference sequence (Mortazavi et al., 2008) or differences between the CHO cell lines and the wild type hamster sequences (Lewis et al., 2013). Matrix of RPKM values was used for differential expression analysis of inducer effects at each temperature level using 1-way ANOVA in Array Studio (OmicSoft Corporation, Cary, NC). Significant changes in expression level were defined based on the combined False Discovery Rate (Benjamini and Hochberg, 1995) (FDR_BH) adjusted p-value (p<0.01) and fold-change (│FC│>1.5) cut-offs. Correlation of gene expression to qP was performed in Array Studio using Pearson correlation method with p-value<0.001. Gene set enrichment analysis (GSEA) on 1535 transcripts negatively correlated to qP was performed using the Molecular Signatures module in Array Studio. 2.3. Protein synthesis rate analysis Protein synthesis was measured using Click-iT® Plus OPP Protein Synthesis Assay (Molecular Probes, Grand Island, NY). This assay utilizes O-propargyl-puromycin (OPP), which is efficiently incorporated into newly translated proteins. Proteins that incorporate OPP are then fluorescently  

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labeled with photostable Alexa Fluor®, and measured with a Guava EasyCyte (Millipore, Billerica, MA). For the validation of the assay, cells seeded in the proprietary chemically-defined production medium were incubated with Click- iT® OPP reagent and increasing doses of protein synthesis inhibitor cycloheximide (Sigma-Aldrich, St. Louis, MO) at 36oC for 30 minutes in vented 24 deepwell plates. To measure the impact of temperature on protein synthesis, cells were seeded at 10 x 106 viable cells/mL into proprietary chemically-defined production medium in vented 24 deep-well plates and were either placed at 36oC or 31oC for overnight incubation. The next day, Click- iT® OPP reagent was spiked into the cultures that were held at the two temperatures for an additional 30minute incubation. As controls, a condition with a high cycloheximide dose and a condition with no Click-iT® OPP reagent were also set up. Cells were fixed with 70% ethanol prior to execution of Click-iT® OPP detection steps. Samples were analyzed using Guava EasyCyte’s ExpressPro application. The background fluorescence from samples that received no Click-iT® OPP reagent was subtracted from all samples. Background-adjusted fluorescence values were normalized to the highest fluorescence value in the set. 2.4. Proteomic sample preparation and mass spectrometry analysis Samples were lysed by probe tip sonication in a lysis buffer of 50 mM Ammonium Bicarbonate, 0.1% Rapigest and protease inhibitor (Sigma-Aldrich, St. Louis, MO). Following lysis the samples were centrifuged and the resulting supernatants reduced with 5 mM Dithiothreitol (DTT) at 55°C for 30 min then alkylated with 15 mM Iodoacetamide at room temperature for 15 min. The reaction was quenched by the addition of 2 mM DTT. Protein concentrations were measured using a Biorad protein assay. Samples were digested with Trypsin at 1:20 dilution for 1 hour at 37°C and then acidified with 0.1% Trifluoroacetic acid. After centrifugation the supernatant was desalted using a mixed cation exchange column (Waters Oasis MCX 1cc) as per the manufacturer’s instructions. Eluates were dried down and resuspended in 0.1% Formic acid.

 

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Samples were analyzed by nanospray LC-MS2 mass spectrometry on a Thermo-Fisher Orbitrap Elite coupled in-line to a Waters Acquity UPLC. Trypsin digest cleavage products were loaded onto a precolumn (5µ Symmetry C18 180 µm x 20 mm nanoAcquity) and separated by reversed phase chromatography on a 1.7µ BEH 130 C18, 75 µm x 200 mm nanoAcquity column in 0.1% Formic acid with a gradient of Acetonitrile over 170 minutes. The mass spectrometer was run in datadependent acquisition mode, selecting the top 15 ions, with dynamic exclusion. Flow rates were held constant at 0.250 μl/min. MS2 spectral files were processed with Proteome Discoverer, using Sequest as the search engine (Eng et al., 1994). Searches were done against the RANK-Fc and DHFR sequences, the NCBI Chinese Hamster Ovary reference proteome (GenBank Assembly ID GCA_000223135.1 consisting of 24240 entries) and trypsin and keratin sequence data sets, as well a reverse decoy database. Searches also included the static modification of cysteine carbamidomethylation (57.0215 Da) and a variable modification of methionine oxidation (15.995 Da). Semi-tryptic fragments with up to 2 missed cleavages were allowed. The precursor mass tolerance for the Sequest search was 3 Da and the fragment mass tolerance was 0.6 Da. Sequest search results were validated using Percolator in Proteome Discoverer (Brosch et al., 2009). Protein quantification was done using a normalized spectral index as described (Griffin et al., 2010). Normalized spectral index combines three MS abundance features: peptide count, spectral count and fragment-ion (tandem MS or MS/MS) intensity. As described by Griffin et al. spectral index largely eliminates variances between replicate MS measurements, permitting quantitative reproducibility and highly significant quantification of thousands of proteins detected in replicate MS measurements of the same and distinct samples. For each protein, only high-confidence peptides were considered. The sum of the intensities for all the high-confidence peptides was used to compute the spectral index for each protein. The spectral index for each protein was then averaged and a standard deviation calculated for those proteins that were

 

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identified in each of the three biological replicates for a given condition. The experimental sample was then compared to the relevant control sample and only those proteins identified in both the experimental and control conditions were considered. Those proteins that show a 1.5 fold change, as well as a 2 standard deviation change over the control were considered to be differentially regulated. 2.5. Western blot analysis 25 µg of each sample used for proteomic analysis were run on E-Page 48 gels (Invitrogen, Grand Island, NY) and transferred to PVDF membranes using an iBlot (Invitrogen, Grand Island, NY). Two replicate blots were generated, one was probed with 1:500 mouse anti-Human IgG-Fc antibody (ab113636) (Abcam, Cambridge, MA), and the other with 1:2,000 mouse anti-β-actin antibody (A1978) (Sigma-Aldrich, St. Louis, MO). Following incubation with 1:5,000 anti-mouse IRDye 800CW secondary antibody (LI-COR, Lincoln, NE), blots were scanned on Odyssey Infrared Imaging System (LI-COR, Lincoln, NE) and quantified using ImageJ software. Each anti-Fc band was normalized to the corresponding β-actin signal. 3. Results Three inducers (caffeine, hexamethylene bisacetamide (HMBA) and sodium butyrate (NaBu)) and two temperatures (36°C and 31°C) were chosen for this study based on previously published findings describing the effects of inducers and temperature on qP of the RANK-Fc cell line (Van Ness et al., 2008). Highest concentrations that had minimal impact on cellular viability following 3day treatment were selected for each inducer (1mM caffeine, 2mM HMBA and 0.5mM NaBu). In addition, a combination of the three inducers at sub-concentrations (0.5mM caffeine + 1mM HMBA + 0.25mM NaBu) was also examined. Individual inducers were able to suppress culture growth and enhance qP to various degrees (Fig. 1). The cultures were inoculated at a high cell density (10 x 106 viable cells/mL) that limited the growth of even the control 36°C cultures to less than one cell doubling over the 3 day culture period. Temperature shift to 31°C suppressed growth of the RANK-

 

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Fc cell line independent of inducer addition (Fig. 1A). All experimental conditions maintained high viability following 3-day compound treatment (Fig. 1B). Product titer was minimally affected by each individual inducer or the combination within a given temperature (Fig. 1C). However, temperature shift to 31°C resulted in at least a 2-fold reduction in titer and lower qP compared to the 36°C treatments. Within each temperature, inducers were able to enhance qP (compared to the untreated control cultures), but their effects were more pronounced at 36°C (Fig. 1D). Following 3-day compound treatment, all samples were used for genome-wide expression study of inducer effects using RNA-Seq. Exploration of this high-dimensional dataset using principal component analysis (PCA) revealed that the first component of variability, principal component 1 (PC1), with 27.96% of the variance within the dataset, clustered samples by temperature (Fig. 2A). The second component of variability, principal component 2 (PC2), accounted for 13.41% of the total variance and clustered samples by inducer treatment. However, performing PCA for each temperature level individually, i.e. only treatments at 36°C (Fig. 2B) or 31°C (Fig. 2C), demonstrated that all inducers had different effects on gene expression in the RANK-Fc cell line. Caffeine treatment was the most similar to untreated control, HMBA and NaBu have divergent effects, and the combination treatment of the three inducers appeared to have an additive effect (Fig. 2B and C). Closer examination of the transcriptional changes following treatment showed that each inducer had a different effect on gene expression within the cell, and those effects also differed between the two temperatures (Fig. S2; see supplementary tables S1-S4 for lists of differentially expressed genes). These differences were reflected in the numbers and identity of significantly changed transcripts in response to inducer treatment, determined via differential expression analysis using one-way ANOVA within each temperature level (Fig. S2A and B). Caffeine treatment, which had the smallest effect on qP, had the smallest effect on gene expression, and the combination treatment had the largest impact on both gene expression and qP (Fig. 1D, Fig. S2A and B). HMBA and NaBu had

 

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much larger gene expression effects than caffeine, and many more genes were down-regulated than up-regulated by HMBA, NaBu and the combination at 36°C (Fig. S2A) and 31°C (Fig. S2B). However, there was little overlap between the up-regulated (Fig. S2C) and partial overlap between the down-regulated (Fig. S2D) transcripts following HMBA and NaBu treatments at either 36°C or 31°C. Our experimental setup allowed us to measure the transcript levels of the RANK-Fc fusion protein and the DHFR selectable marker and to quantify percent of recombinant mRNA in the cell. The expression construct used to generate the RANK-Fc cell line yields 2 mRNA molecules by alternate polyadenylation, one containing only the RANK-Fc portion, and the other—RANK-FcIRES-DHFR (Fig. S1A). For the RNA-Seq analysis, sequences for the RANK-Fc portion and the IRES-DHFR portion of the construct were added to the Cricetulus griseus reference genome sequence prior to transcript mapping and quantification. This allowed us to measure both the message amount for the recombinant protein of interest (RANK-Fc) and the selectable marker (DHFR), expressed as RPKM values. Good correlation was observed between the RPKM values for the RANK-Fc and for the IRES-DHFR within each temperature (Fig. S1B and C). Since, unique gene reads for the RANK-Fc portion came from both recombinant transcripts and the ones for the IRES-DHFR only from the longer one (RANK-Fc-IRES-DHFR), higher RPKM values were obtained for the RANK-Fc than for the IRES-DHFR (Fig. S1B and C). Percent recombinant protein message was calculated by dividing the sum of unique gene reads that map to the RANK-Fc and the IRES-DHFR sequences by the total message reads. Inducers had variable effects on cell size (Fig. S1D) and the total cellular RNA content (Fig. 3A) without a clear correlation between them; HMBA treatment at 36°C had the largest effect on RNA content (a 29% decrease) (Fig. 3A). However, percent recombinant protein message was increased by all inducers at each temperature, from 16% in control condition at 36°C up to 45% of total mRNA by the three-inducer combination at 31°C (Fig. 3B). Effect on the RANK-Fc message level was even larger at 31°C than 36°C, but this effect did not  

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translate to a larger effect on qP. We observed a very good linear correlation between the RANK-Fc message level and qP, both for the 36°C (Fig. 3C) and the 31°C (Fig. 3D) samples, but with different slopes for each temperature. The observed discrepancy between the percent of recombinant protein message at 31°C and 36°C with respect to qP led us to examine potential causes of qP reduction following temperature shift. First, we evaluated whether temperature shift affected protein synthesis rate in our biological system. Protein synthesis rates were measured using Click-iT® Plus OPP Protein Synthesis Assay as described in materials and methods, using cycloheximide treatment as a positive control (Figure S3A). Cells expressing RANK-Fc were labeled for 30 minutes at either 31oC or 36oC, and newly translated proteins were measured by relative green fluorescence. Protein synthesis is 33% lower at 31oC compared with 36 oC (Figure S3B). This reduction in global protein synthesis can account for most of the reduction in qP. Second, we assessed recombinant protein level inside the cell by Western blot analysis. Inducers did not have a major effect on the total soluble cellular protein levels, but some were able to increase the RANK-Fc recombinant protein level (Fig. S4A and B). The intracellular RANK-Fc protein amount did not correlate to qP as well as the RANK-Fc transcript amount, but there was a positive correlation at each temperature level (Fig. S4C and D). Samples treated with sodium butyrate were also analyzed by quantitative label-free mass spectrometry which measures relative protein levels between equivalent amounts of protein samples. Using the spectral index as a proxy for protein mass, treatment with sodium butyrate at 36°C resulted in differential regulation of 14% of the proteome over the untreated control. Of the differentially regulated portion of the proteome, 71% was upregulated (10% of the total mass) and 29% was downregulated (4% of the total mass) (Fig S5A). Similar results were obtained at 31°C, where 15% of the proteome was differentially regulated compared to the untreated control, with 67% of the differentially regulated portion of the proteome upregulated (10% of the total mass) and 33 % -downregulated (5% of the total mass). Despite the observation that a greater proportion of the total  

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mass of protein was up regulated, the number of unique proteins that were downregulated is greater than those upregulated at both temperatures (Fig S5B). At 31°C, 57% of the identified proteins are down regulated, while at 36°C, 58% of the identified proteins are down regulated. This apparent discrepancy can be explained since intracellular RANK-Fc accounted for 7% of the total protein mass at 31°C and 5% of the mass at 36°C in the sodium butyrate treated samples, and represents the bulk of the differentially regulated protein at both temperatures (Fig S5A). Intracellular RANK-Fc protein was upregulated 1.7-fold and 1.6-fold over the control at 31°C and 36°C, respectively, consistent with what was observed by both transcriptional and Western blot analyses. We further used generated global gene expression data to evaluate gene-phenotype correlations for qP in our biological system. Examination of transcripts significantly correlated to qP within each temperature level revealed that fewer transcripts (an order of magnitude) are positively correlated to qP than negatively correlated (Fig. 4 A and B). Furthermore, there was little overlap between different temperatures for the positively (Fig. 4A) and the negatively (Fig. 4B) qP -correlated transcripts. Out of 7 positively-correlated transcripts (Table S5) at both 36°C and 31°C, two were messages for the recombinant protein (RANK-Fc) and the selectable marker (IRES-DHFR). The other five transcripts are associated with solute transport, actin cytoskeleton regulation, a calcium/integrin binding protein and a Prolyl 4-hydroxylase subunit. The 1535 negatively correlated transcripts at both 36°C and 31°C (Table S6) were enriched for the Gene Ontology (GO) terms describing cell cycle, transcription, splicing, translation, protein folding and intracellular transport (Table 1). Previous studies showed that external RNA standards (ERCC RNAs) can be used to allow gene expression normalization to cell number when RNA yield differs among cells responding to different treatments or among different cell lines (Loven et al., 2012). We showed that this normalization strategy can impact the interpretation of qP studies since many factors that influence qP also influence

 

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cell size and total RNA yield (Fomina-Yadlin et al., 2014). However, when we applied this normalization strategy to the RNA-Seq data in this study, we found that the absolute numbers of significantly changed RNAs were typically lower mainly due to higher levels of noise this normalization strategy introduced to the data, as has been documented previously (Risso et al., 2014). For HMBA treated cells a 36oC and the combination treatment at 31oC, the total number of downregulated genes increased as would be expected since the total RNA levels decreased for these treatments. The ratio of up-regulated to down-regulated transcripts was also influenced by this normalization strategy. However, the interpretation of the results was not significantly different, and there was a strong correlation between qP and the RANK-Fc transcript amount determined from the ERCC-normalized RNA-Seq data (data not shown). 4. Discussion Analysis of differential gene expression following inducer treatment revealed that inducers of recombinant protein production acted by different cellular mechanisms and, furthermore, that the mechanisms varied between the two temperature levels evaluated in this study. Divergent effects of compound treatment in different cell lines have been previously observed. For example, in the study of NaBu effects in CHO and mouse hybridoma (MAK) cells, out of more than 1000 transcripts differentially-expressed in treated and untreated cells, only 11 genes were significantly changed in the same direction in both CHO and MAK cell lines (Yee et al., 2008). However, different gene expression effects of the same compound in the same cell line between two different temperature levels have not been previously described. Despite the divergent inducer effects on global gene expression, the observed effect on the RANK-Fc transcript level appeared to account for the increased recombinant protein production. Specifically, there was a strong positive linear correlation between the mRNA level of the recombinant protein and the measured qP of the RANK-Fc cell line. Our results are consistent with previous studies showing a strong correlation of qP and mRNA levels for CHO cell lines expressing a  

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recombinant antibody and treated with sodium butyrate (Jeon and Lee, 2007; Lee et al., 2009). Fann et al. also showed a correlation between mRNA levels and activated protein C secretion in cell lines treated with sodium butyrate (Fann et al., 1999). In contrast to the observed linear dependence of qP on the message amount of recombinant protein expression in a clonal cell line treated with inducers, the case of monoclonal antibody (mAb) expression in independent clonal cell lines is likely more complicated and may be influenced by the expression vector and methods of cell line development. The heavy chain (HC) to light chain (LC) ratios of both the transcript and the polypeptide amounts play important roles in mAb production, and stable mAb-expressing CHO cell lines are generally characterized by LC excess. (Ho et al., 2013; Lee et al., 2009; McLeod et al., 2011; Schlatter et al., 2005; Vishwanathan et al., 2014a). In one study, only a weak positive relationship was observed between antibody transcript amounts and qP (Vishwanathan et al., 2014a), whereas, other studies showed stronger correlations between mAb mRNA and qP levels (Jiang et al., 2006; Lee et al., 2009). Consistent with mRNA limiting recombinant protein expression, empirical modeling of CHO cell lines previously revealed that HC transcription rate, mRNA stability, and translation rate were the major qP determinants (McLeod et al., 2011). Surprisingly, the increase in the percent recombinant transcript following inducer treatment was even larger at 31°C than 36°C. However, this effect did not result in a larger effect on qP at 31°C, suggesting a roadblock in the later processing steps, i.e. translation, folding and secretion, at 31°C. Indeed, translation rates were found to be 33% lower at 31 oC compared with 36 oC. The difference in translation can account for at least some of the reduction in qP at the lower temperature. These data are consistent with a previous study of hypothermia effects on protein synthesis in CHO cells where decreasing temperature below 37°C lowered both translation initiation and elongation rates (Oleinick, 1979). These results are in contrast to previous studies where lowering temperature increased qP (Nam et al., 2008; Sou et al., 2014; Wulhfard et al., 2008). This may reflect the unique features of the RANK 

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Fc protein or our experimental model. For example, temperature shift to 31°C may be too stressful for the translation, folding and secretion machinery in this particular cell line, and a smaller temperature shift may result in growth suppression combined with qP enhancement. These previous studies have observed that shifting cultures to lower temperatures reduced growth while increasing qP allowing cultures to maintain a high productivity state for a longer period of time. The growth inhibitory effects of the inducers described in this study could also be utilized in perfusion or fedbatch cultures to simultaneously slow growth and increase qP. Previous studies with these inducers showed they could increase titer and qP over 7-14 day cultures, indicating the potential of these inducers to be used in commercially relevant processes (Van Ness et al, 2008). Furthermore, the observation that the effects of the inducers are additive suggests that use of optimized combinations of these inducers might provide ways of controlling growth and productivity while maintaining high viability (Fig. 1). Each inducer described in this study has a distinct effect on gene expression suggesting they have different mechanisms of action. HMBA and butyrate induced a wide variety of transcription factors that could potentially influence expression from the CMV enhancer promoter (Tables S2 and S3). Butyrate has been well documented to act as a histone deacetylase inhibitor, and has a profound influence on global gene expression (Davie, 2003). In CHO cells, butyrate has been shown to increase expression from the CMV promoter and our results are consistent with previous studies (Kim et al., 1999; Paterson et al., 1994). HMBA has been shown to reactivate HIV viral production in chronically infected cell lines by increasing expression from the HIV promoter, and this induction requires the presence of AP-1 transcription binding sites (Vlach and Pitha, 1993). The CMV promoter/enhancer used in this study contains AP-1 binding sites suggesting a similar mechanism for induction of RANK-Fc. Another mechanism that HMBA may increase transcript levels is by promoting transcription elongation. HMBA acts through the PI3K/Akt pathway to disrupt the inhibitor HEXIM1/2 binding to positive transcription elongation factor b (P-TEFb) and release P 

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TEFb from its transcriptionally inactive complex with HEXIM1 and 7SK small nuclear RNA (snRNA). Upon release from the inhibitory HEXIM1/2 protein, P-TEFb then can promote transcription elongation of many genes. Both HEXIM1 and HEXIM2 are induced by HMBA in CHO cells (Table S2), consistent with other cell lines where this impact on transcription elongation has been observed (Contreras et al., 2007). Caffeine has complex effects with multiple targets within different cell types (Fredholm et al., 1999). At the millimolar concentrations used in this study, caffeine acts as an inhibitor of phosphodiesterase potentially leading to increase in cAMP levels (Fredholm et al., 1999), and previous studies with phophodiesterase inhibitors have been shown to induce expression from the CMV promoter/enhancer which contains cAMP response elements (Paterson et al., 1994; Stamminger et al., 1990). The transcriptomic data suggests another potential mechanism by which caffeine could enhance transcription from the CMV enhancer/promoter. Expression of Eid3, a protein that inhibits CPB/P300 (Bavner et al., 2005), was reduced in cells treated with caffeine (Table S1), suggesting that reducing expression of this inhibitor of transcription could potentially enhance transcription of CMV promoter/enhancer. The additive effects of the HMBA, butyrate, and caffeine on mRNA levels and their varied effects on gene expression are consistent with these compounds having different mechanisms of action with regard to enhancing qP. Examination of the qP determinants in our biological system revealed that substantially more genes negatively correlated than positively correlated to qP of the RANK-Fc cell line. In addition, there was limited overlap in significantly qP-correlated transcripts between the two temperature levels examined. A previous study examining correlations of gene expression to qP, generated using CHO production microarray dataset composed of 295 samples from cell lines expressing a wide range of recombinant protein modalities and from various production scales and formats (Clarke et al., 2011a), also showed more genes negatively correlated to qP (Clarke et al., 2012). DHFR was one of the few positively-correlated genes (Clarke et al., 2012), likely because many production cell lines used for the study were generated using DHFR as a selectable marker (Clarke et al., 2011a). We  

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found that 2 out of 7 transcripts that positively correlated to qP at both temperature levels in our biological system were messages for the recombinant fusion protein of interest and the selectable marker used for cell line generation. This strong correlation between recombinant protein transcript level and qP suggests that transcript levels are limiting qP for both 31oC and 36oC cultures, and the difference in qP between the two temperatures is a result of slower translation, and possibly folding and secretion, rates at lower temperatures. The strong correlation between recombinant protein transcript level and qP suggests a simple explanation for the effect of the inducers, occurring at the level of the recombinant mRNA. The other five transcripts correlated with qP have diverse functions that do not appear to regulate transcription, suggesting these correlations are incidental rather than directly contributing to increasing qP. The negatively correlated transcripts encode proteins involved in cell cycle, and metabolic RNA and protein processes. These results suggest the need to down regulate cell cycle progression and many of the routine activities within the cell to have enough resources to devote to increased recombinant protein production. We observed the amount of mRNA encoding RANK-Fc increased from 16% up to 45% when treated with inducers which would necessarily require reducing a fraction of transcripts encoding other proteins. Consistent with this interpretation is the proteomic analysis of butyrate treatment where the bulk of the up-regulated protein is RANK-Fc, and of the remaining proteins, more were down-regulated than up-regulated. The inverse relationship between culture growth and productivity has been previously observed (Du et al., 2014; Fomina-Yadlin et al., 2014; Sunley and Butler, 2010; Vishwanathan et al., 2014a), and the 1535 transcripts identified to have significant negative correlation to qP present a mechanistic explanation for this phenomenon in our biological system.

 

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Acknowledgements We thank Scott Freeman for antibody titer analysis and Rajnita Charan, Sumana Dey and Louiza Dudin for media preparation. We thank Carole Heath for critical reading of the manuscript. All authors are current or former employees of Amgen Inc.

 

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Figure Captions  Figure 1. Effects of inducers and temperature on phenotypic parameters of the RANK-Fc cell line. (A) Viable cell density, (B) percent viability, (C) titer and (D) specific productivity are shown for each experimental condition following 3-day treatment. Data represent the mean ± standard deviation of three biological replicates; ‘*’ p-value <0.05, ‘**’ p-value <0.01 and ‘***’ p-value <0.001 represent statistically significant differences between control at 36°C and all other conditions. Figure 2. Principal component analysis of the RNA-seq dataset. (A) 2-component PCA for the entire dataset, as well as for (B) only treatments at 36°C and (C) only treatments at 31°C are shown. Percentages represent percent variance captured by each principal component in each analysis. Experimental conditions are colored by inducer and shaped by temperature. Figure 3. Effects of inducers and temperature on the message encoding the recombinant protein. (A) Total RNA per cell is shown for each inducer and temperature. Data represent the mean ± standard deviation of three biological replicates; ‘*’ p-value <0.05, ‘**’ p-value <0.01 and ‘***’ pvalue <0.001 represent statistically significant differences between control at 36°C and all other conditions. (B) Percent of total mRNA that accounts for the recombinant protein message is shown with a bar graph. Specific productivity for each condition is shown on the secondary y-axis (scatter plot). Data represent the mean ± standard deviation of three biological replicates. Correlations between the RANK-Fc transcript level (RPKM) and specific productivity are shown for the (C) 36°C and the (D) 31°C samples. Experimental conditions are colored by inducer and shaped by temperature. Pearson’s correlation coefficient (r) is displayed. Figure 4. Numbers of transcripts significantly correlated to specific productivity for each temperature. Venn diagrams of the transcripts that are (A) positively and (B) negatively correlated to specific productivity (Pearson correlation, p-value<0.001) are shown for 36°C and 31°C.

 

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Table  Table 1. Top 20 Gene Ontology (GO) Biological Process terms from the Gene Set Enrichment Analysis (GSEA) of 1535 transcripts negatively correlated to specific productivity at both temperatures. Gene sets are ranked by the FDR_BH adjusted p-value of enrichment.   GO term

Name

GO:0043283 Macromolecule metabolic process GO:0003723 RNA binding GO:0016070 RNA metabolic process GO:0044267 Cellular protein metabolic process GO:0044260 Cellular macromolecule metabolic process GO:0006396 RNA processing GO:0006139 Nucleobase-containing compound metabolic process GO:0019538 Protein metabolic process GO:0000278 Mitotic cell cycle GO:0008380 RNA splicing GO:0009056 Catabolic process GO:0016071 mRNA metabolic process GO:0044248 Cellular catabolic process GO:0007067 Mitotic nuclear division GO:0051649 Establishment of localization in cell GO:0007049 Cell cycle GO:0000087 Mitotic M phase GO:0006457 Protein folding GO:0046907 Intracellular transport GO:0051641 Cellular localization      

 

 

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Genes in geneset (#)

Genes in raw pquery (#) value

FDR_BH adjusted p-value

1638 292 793 1138 1151

193 59 111 138 139

7.21E-19 4.09E-18 5.45E-16 5.69E-15 6.16E-15

7.85E-17 4.15E-16 4.73E-14 4.21E-13 4.51E-13

177 1196

41 140

6.06E-14 6.54E-14

3.90E-12 4.14E-12

1248 155 97 226 92 217 82 353 306 85 64 282 370

144 32 24 39 21 37 21 50 46 21 17 42 50

8.92E-14 8.89E-10 2.44E-09 3.65E-09 1.03E-08 1.21E-08 1.32E-08 1.61E-08 2.02E-08 2.74E-08 3.36E-08 5.68E-08 8.25E-08

5.50E-12 2.86E-08 7.29E-08 1.07E-07 2.77E-07 3.20E-07 3.47E-07 4.19E-07 5.19E-07 6.77E-07 8.12E-07 1.30E-06 1.84E-06

Figure 1 

   

 

 

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Figure 2 

   

 

 

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Figure 3 

   

 

 

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Figure 4 

 

 

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