Journal of Biotechnology 203 (2015) 22–31

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Metabolic markers associated with high mannose glycan levels of therapeutic recombinant monoclonal antibodies Sohye Kang a,∗ , Zhongqi Zhang a , Jason Richardson a , Bhavana Shah a , Shivani Gupta b , Chung-Jr Huang b , Jinshu Qiu a , Nicole Le b , Henry Lin b,1 , Pavel V. Bondarenko a a b

Materials and Systems Analytics, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA Drug Substance Technologies, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA

a r t i c l e

i n f o

Article history: Received 16 October 2014 Received in revised form 28 February 2015 Accepted 5 March 2015 Available online 19 March 2015 Keywords: Metabolomics High mannose Ornithine Glycosylation Copper Chinese hamster ovary

a b s t r a c t High mannose (HM) glycan levels on secreted monoclonal antibodies can be influenced by external factors, including osmolality and copper deficiency, and by intrinsic factors determined by different cell lines. In order to identify the metabolic markers associated with HM glycan levels, metabolomics analysis was performed to assess the changes in the extracellular metabolites of recombinant cell lines at different time points during fed-batch production process. Ornithine was identified as the common metabolic marker influenced by both external and intrinsic factors when eight different medium conditions and eight different cell lines exhibiting different levels of HM were compared. A strong correlation was also observed between HM and mRNA expression levels of arginase 1, an enzyme that catalyzes the conversion of arginine to ornithine. The results from functional validation study showed that the supplementation of ornithine to the culture medium leads to an increased level of HM, while reduced concentration of spermine, a downstream product of ornithine metabolism, leads to a decreased level of HM. Additional metabolic markers correlating with HM glycan levels were identified from eight-cell line comparison analysis. A common feature shared by these identified markers is their previously described roles as contributors of cellular redox regulation. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Protein glycosylation, which entails the covalent attachment of sugar moieties to specific amino acids, is one of the most common but important posttranslational modifications as it affects multiple cellular functions including protein folding and quality control, molecular trafficking and sorting, and cell surface receptor interaction (Moremen et al., 2012). Protein glycosylation can also influence therapeutic efficacy of recombinant protein drugs. The influence of glycosylation on structural stability, bioactivity, pharmacokinetics, immunogenicity, solubility and in vivo clearance of therapeutic glycoproteins has previously been described; thus, monitoring and controlling glycosylation become critical for biopharmaceutical manufacturing (Butler, 2006; Hossler, 2012). High mannose (HM) glycan structures are characterized by unsubstituted terminal mannose sugars of five to nine residues

∗ Corresponding author. Tel.: +1 805 447 0873. E-mail address: [email protected] (S. Kang). 1 Present address: Process Development, Boehringer Ingelheim Fremont, Inc., 6701 Kaiser Drive, Fremont, CA 94555, USA. http://dx.doi.org/10.1016/j.jbiotec.2015.03.002 0168-1656/© 2015 Elsevier B.V. All rights reserved.

attached to the GlcNAc2 core. While the HM levels detected on the endogenous human IgG is typically very low (Flynn et al., 2010), the HM content of recombinant monoclonal antibodies (mAbs) can range from 1% to ≥20%. The N-linked HM glycans attached to the CH2 domain of therapeutic mAbs have shown to influence the pharmacokinetic properties due to faster serum clearance rate compared to other Fc-glycans (Goetze et al., 2011; Yu et al., 2012). Moreover, the HM glycoforms were associated with increased antibody-dependent cell-mediated cytotoxicity (ADCC) activity, implicating their influence on the antibody effector functions (Yu et al., 2012; Zhong et al., 2012). Therefore, the HM content of therapeutic mAbs can be considered an important quality attribute to monitor and control during the manufacturing process. External parameters that previously have been shown to influence the HM glycan levels on the recombinant proteins during cell culture process include osmolality, manganese, low glutamine or glucose concentrations, and process run duration (Chee Furng Wong et al., 2005; Pacis et al., 2011; Wu et al., 2013). Increasing the osmolality of culture media by altering NaCl concentrations induced the increase of HM glycan levels (Pacis et al., 2011; Wu et al., 2013), while supplementation with the osmoprotectant betaine resulted in the reduction of the HM content of the

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Table 1 Description of eight media conditions compared in cell line H. Condition

Base medium

CuSO4 ·5H2 O (base medium)

Feed medium

1 2 3 4 5 6 7 8

100% Medium powder 100% Medium powder + betaine 100% Medium powder without CuSO4 100% Medium powder without CuSO4 90% Medium powder 90% Medium powder + NaCl titrated 100% Medium powder 100% Medium powder

Milled into the medium powder Milled into the medium powder Supplemented separately Not supplemented Milled into the medium powder Milled into the medium powder Milled into the medium powder Milled into the medium powder

100% Medium powder 100% Medium powder 100% Medium powder 100% Medium powder 100% Medium powder 100% Medium powder 85% Medium powder 85% Medium powder + NaCl titrated

recombinant proteins (Wu et al., 2013). Although the impact of osmolality on the HM content has clearly been demonstrated, the specific mechanism(s) through which the modulation of HM levels is achieved has not been revealed. In addition to media and process-related factors, cell linespecific factors also influence the HM content of the recombinant glycoproteins. Lec1 mutant cells, for example, accumulate HM glycoforms while lacking in complex and hybrid N-glycans, primarily due to non-functional enzyme Golgi Nacetylglucosaminyltransferase I caused by point mutation in the Mgat1 gene (Puthalakath et al., 1996; Robertson et al., 1978). Expression analyses have demonstrated variability in the transcript and protein levels of the N-glycosylation pathway enzymes and nucleotide sugar transporters among different mAb expressing cell lines despite sharing the same host cell origin (unpublished data). However, N-glycosylation enzyme expression profiles alone do not explain the high basal levels of HM species detected in certain mAb expressing cell lines, since no substantial reduction in expression levels was detected in these cell lines compared to those exhibiting lower levels of HM glycans. These results suggest the existence of additional factors responsible for the HM glycan levels detected among different cell lines. In this study, we have utilized the in-house global non-targeted metabolomics platform to identify metabolic markers and pathways associated with HM glycan levels detected on secreted monoclonal antibodies (mAbs). By directly comparing eight different culture medium conditions and eight different recombinant cell lines which exhibit different levels of HM, the metabolites and pathways contributing toward the HM levels were identified and specific application strategies to modulate and control the HM glycan levels have been developed. 2. Materials and methods 2.1. Cell culture For eight media comparison experiment, cell line H was subjected to 3 L bioreactor fed-batch production process and exposed to eight different cell culture medium conditions described in Table 1. Cultures in each of the eight conditions were inoculated at the same seeding density, were fed on days 3, 5, 7, and 9, and were harvested on day 12. For eight-cell line evaluation, cultures were inoculated with the final volume of 50 mL in 250 mL shake flasks. All cell lines were cultured in the same incubator with the bolus feed delivered on days 3, 6, and 8, and harvested on day 11. For ornithine and spermine titration studies, cell line H was tested in 24-deep well plates with the indicated concentrations of lornithine hydrochloride or spermine tetrahydrochloride (100 mM stock solution). For all conditions, 3 mL working volume per well was used, and cultures were cultivated in the Kuhner incubator for a duration of 5 days. 2.2. Assays Small volumes of culture were taken on selected days during the fed-batch production process to assess viable cell density and

culture viability using the Cedex AS20 cell counter (Roche Innovatis, Bielefeld, Germany). For osmolality measurements, osmometer model 2020 (Advanced Instruments, Norwood, MA) was used. Centrifuged conditioned media samples were collected on the indicated days and were frozen in −80 ◦ C until ready for metabolomics and glycan analysis. Different N-glycan species, including high mannose, on secreted mAbs were analyzed by 2-AA hydrophilicinteraction liquid chromatography (HILIC) (Sellick et al., 2011), Endo H/CE-SDS method (Vuckovic, 2012) with few modifications, and/or peptide mapping analysis (Goetze et al., 2011; Milne et al., 2005).

2.3. Non-targeted metabolomics analysis: LC–MS/MS A custom non-targeted LC–MS/MS metabolomics platform was developed based on two LC–MS methods, both using an Agilent HPLC system connected to a Thermo Scientific Orbitrap mass spectrometer with an electrospray ionization (ESI) interface (Richardson et al., 2015). An ion-pairing reversed phase column on an Agilent 1290 HPLC system with MS detection in a positive ion mode was used for the first method, with sample dilution of 40-fold into a solution containing 19 mM of HCl and the internal standards including 400 ␮g/mL of C13 labeled algal amino acid mixture and 1 ␮g/mL of C13 labeled dipeptide Phe-Phe. For the eight-cell line experiment, the internal standards also included 2 ␮g/mL of C13 labeled ornithine. 10 ␮L of each sample was injected into the LC–MS system, and the components were resolved on a Waters Acquity BEH C18 column (2.1 mm × 150 mm, 1.8 ␮M, 50 ◦ C) with a linear methanol gradient (0–30% in 12 min) at a flow rate of 0.4 mL/min, followed by column washing with 30–95% methanol. Each mobile phase contained 0.2% heptafluorobutyric acid (HFBA) as an ion-pairing agent. Metabolites were detected by MS in a positive mode, with high-resolution full scan at 60,000 resolution (at m/z 400), followed by two data-dependent collision-induced dissociation (CID) MS/MS scans in the linear trap. For the second method, a reversed phase chromatography on an Agilent 1260 HPLC system with MS detection in a negative ion mode was used, with samples diluted 40-fold into a solution containing the same concentration of C13 labeled internal standards and 5 mM ammonium fluoride. Twenty microliters of samples were injected into the LC-MS system, and the components were resolved on a Waters HSS T3 column (2.1 mm × 150 m, 60 ◦ C) with a methanol gradient at a flow rate of 0.25 mL/min. Mobile phase A was an aqueous solution containing 5 mM ammonium fluoride, and mobile phase B contained 90% methanol and 10% mobile phase A. Metabolites were eluted with a gradient from 0% to 10% B in 10 min, followed by 10–95% in additional 11 min. MS setup was similar as the first method except in a negative ion mode. LC-MS/MS data were analyzed on a custom program MassAnalyzer (Zhang, 2009), with additional functions for metabolomics data analysis (Richardson et al., 2015). Compounds were identified by comparing the experimental MS/MS with the reference spectra in the NIST11 MS/MS library (Scientific Instrument Services, Ringoes, NJ) and the reference spectra of compounds collected

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Fig. 1. Cell culture and HM profiles of Cell Line H exposed to eight different media conditions. (A) Viable cell density (VCD), (B) culture viability, (C) osmolality, (D) HM glycan levels (detected on secreted product).

in our own laboratory. The match between the experimental and the library reference MS/MS was measured by the similarity score (Zhang, 2004), with the threshold of 0.8 used as the criteria for a positive identification. A compound was quantified by the peak area under its selected ion chromatogram (SIC). To correct for runto-run variations, the ratio of peak area of each compound to the area of the internal standard was used to represent the relative abundance for quantitation purpose. When two or more internal standards were used in the analysis, a retention time weighted area of internal standards was used to calculate the peak area ratios. 2.4. Targeted LC–MS/MS metabolomics analysis Extracellular levels of ornithine, urea and several other metabolites were quantified by a targeted liquid chromatography-triple quadrupole mass spectrometry method with selected reaction monitoring (SRM). An Agilent LC system, consisting of 1100 series pumps, degassers, and autosampler, was coupled to a Thermo TSQ Quantum Access MAX triple stage quadrupole mass spectrometer. The ESI source of the mass spectrometer was operated in a positiveion mode. The chromatographic conditions were adopted from the non-targeted LC–MS/MS metabolomics analysis method described above. The media components were separated on an Atlantis T3 reversed phase C18 column (2.1 mm × 150 mm, 3 ␮m particle size) (Waters, Milford, MA) with the mobile phases containing 0.2% heptafluorobutyric acid in water and methanol. The internal standards used for quantitation were 13 C-labeled citrulline and ornithine, and 13 C,15 N uniform labeled arginine. The conditioned media samples were diluted in a 20 mM HCl solution, and the internal standards were added to both the samples and calibration standards. 2.5. QuantiGene analysis 50,000 viable cells were collected via centrifugation of cell suspensions. Cell pellets were snap-frozen and stored in −80 ◦ C until ready. A QuantiGene Plex 2.0 assay kit (Affymetrix, Santa Clara, CA) was used, according to the manufacturer’s protocol. A panel of probes corresponding to CHO-specific sequences was designed for

the multiplex system using the Luminex FLEXMAP 3D (Austin, TX). Signals generated were normalized using two different housekeeping genes, TBP and GusB, and the average values of the technical triplicates were calculated. Reproducibility was assessed by evaluating the coefficient of variation. 2.6. Statistical analyses Principal component analysis (PCA) was performed using Array Studio, version 5.2 from OmicSoft (Cary, NC). Heatmaps were generated from SVD clustering and by using robust center scale normalization method. The r values generated from correlation analysis represent Pearson product–moment correlation coefficient, and the R2 values were determined from linear regression analysis. 3. Results and discussion 3.1. High mannose glycan levels are modulated by osmolality and copper deficiency Cell line H is a recombinant CHO cell line engineered to express the monoclonal antibody. However, this cell line displays high basal levels of high mannose (HM) glycans on the secreted product. Since Man5, a predominant high mannose species, was previously shown to be affected by osmolality (Pacis et al., 2011), a couple of approaches were taken to lower the culture medium osmolality to affect HM levels. A reduction of osmolality in the base production medium was achieved by reducing the amount of medium powder addition by 10% (i.e., Condition #5 in Table 1), while a decrease in the osmolality of the feed medium was achieved by reducing the medium powder addition by 15% (Condition #7, Table 1). Neither of these altered medium conditions affected culture performance in terms of cell growth and culture viability (Fig. 1A andB, respectively). However, lower osmolality was detected in the spent media samples collected from the cultures exposed to low osmolality media (Fig. 1C). Cultures treated with reduced levels of base medium (i.e., Condition #5) exhibited lower levels of

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Fig. 2. Global metabolic profiles. (A) PCA of metabolomics data of four media conditions over culture duration, (B) PCA comparing eight media conditions, (C) Heatmap representing Component 1 (for comparison of Conditions #1 through #4). Each row of the heatmap represents different metabolite identified.

osmolality during the first three days of production, whereas the cultures treated with reduced feed medium (i.e., Condition #7) displayed reduced osmolality only after the first bolus feed on day 3 and maintained lower osmolality until the end of culture duration (Fig. 1C). The cultures exposed to these low osmolality conditions (i.e., Conditions #5 and #7) exhibited a slight reduction in the HM levels on days 9 and 10, similar to the cultures exposed to 24 mM betaine at the time of inoculation (i.e., Condition #2) (Fig. 1D). This observation reflects the osmoprotectant property of betaine (McNeil et al., 1999) and is consistent with the previous report describing its ability to reduce HM levels (Wu et al., 2013). The NaCl titrated conditions in the background of reduced levels of base or feed medium (i.e., Conditions #6 and #8, respectively) matched the osmolality and HM levels as those detected in 100% medium powder conditions (i.e., Condition #1 and #3). These results indicate that reduction in osmolality, rather than the reduction in the nutrient levels, is responsible for the observed decrease in the HM levels observed in Conditions #6 and #8 (Fig. 1C and D). Interestingly, a more substantial decrease in the HM levels was achieved by copper deficiency, as observed in Condition #4 (Fig. 1D). The effect of copper deficiency on lowering HM glycan levels was also detected in four other cell lines expressing different mAb sequences (data not shown). These results implicate copper’s role as a regulator of HM glycan levels. The effect of copper, however, is opposite that of manganese, which previously has been shown to reduce the Man5 glycoform level (Pacis et al., 2011). 3.2. The time component is the predominant factor associated with global metabolite level changes The eight media conditions described in the previous section serve as an appropriate experimental model to study the mechanistic details regulating the HM content of the secreted mAbs by

external factors including osmolality and copper deficiency, particularly since comparable growth and culture viability profiles were obtained for all conditions (Fig. 1A and B). The spent media samples that were collected on days 1, 3, and 6–10 were subjected to global, non-targeted metabolomics evaluation using an in-house developed metabolomics platform. A total of 138 unique extracellular metabolites were identified and quantified for samples collected from eight different bioreactor exposed to medium conditions described in Table 1. Unsupervised principal component analysis (PCA) has identified time as the predominant component (i.e., Component 1) associated with progressive changes in the metabolite levels (Fig. 2Aand B). The four conditions compared in Fig. 2A demonstrated that the impact of copper deficiency and betaine supplementation in the context of an overall global metabolic profile is rather negligible. The impact of feed medium reduction, however, became more pronounced starting on Day 8 for cultures exposed to reduced amount of feed medium (i.e., Condition #7, Fig. 2B). In contrast, only a small, transient divergence was detected in the condition exposed to reduced amount of base medium (i.e., Condition #5, Fig. 2B). Time-dependent progression of metabolite level changes is also clearly demonstrated by the heatmap reflecting Component 1 of the PCA generated from the four-condition comparison (Fig. 2C). The examples of metabolites contributing to PCA Component 1 include glycerophosphorylcholine and ␣-ketoglutarate which accumulate over time (Fig. 3A), and asparagine and pyroglutamate which display gradual reduction over time (Fig. 3B). Many of the amino acids and vitamins such as cysteine and folic acid were affected by the nutrient supplementation on feed days followed by the net nutrient consumption (Fig. 3C), contributing to the overall trend depicted by PCA Component 2. Metabolites such as methyladenosine and indolactate displayed time-dependent bell-curve trend, with the metabolic peak detected on day 6 or 7 (Fig. 3D).

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Fig. 3. Metabolites demonstrating time-dependent changes. (A) Examples of metabolites increasing over time, (B) examples of metabolites decreasing over time, (C) examples of metabolite affected by feed, (D) examples of metabolites accumulating in the first half of the culture duration followed by net consumption in the latter half of culture duration.

None of the representative metabolites contributing to PCA Component 1 or Component 2 displayed a strong correlation to HM glycan levels or copper deficiency. Only six out of 138 unique metabolites showed copper sensitivity, while three metabolites were affected by betaine supplementation (Supplemental Fig. 1). Supplementary material related to this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.jbiotec.2015.03.002. 3.3. Ornithine correlates with high mannose glycan levels Out of 138 extracellular metabolites assessed, ornithine displayed the strongest correlation to HM levels (Table 2). Extracellular accumulation of ornithine was detected starting days 6–7, followed by continued increase as time progressed (Fig. 4A). While this trend was observed for all eight conditions compared, the extent of ornithine accumulation varied among different conditions. A strong correlation between HM and extracellular ornithine levels was observed for day 9 and 10 (Table 2, Fig. 4B). Similar to ornithine, 2-hydroxyglutarate also showed a similar

pattern of accumulation that was initiated between day 6 and 7, followed by more dramatic increase as the culture duration increased (Fig. 4C). However, no correlation was observed between 2-hydroxyglutarate and HM levels (Fig. 4D). These results indicate that the observed correlation between the extent of ornithine accumulation and HM levels is specific and unique, as no other metabolites demonstrated such patterns of correlation. Ornithine was again identified as an extracellular metabolite correlating with HM levels when a separate, independent experiment was performed comparing eight different cell lines exhibiting different levels of HM on secreted mAb products when exposed to the same media and process conditions in the shake flask experiments (Fig. 5A). Similar to the results obtained from eight media condition comparison (Table 2), a positive correlation between ornithine and HM levels was observed when eight different unique cell lines were compared (Fig. 5B, Table 3). The results were independent of media type tested, as correlation between ornithine and HM levels were consistently observed whether these eight cell lines were exposed to media #1 or media #2 (Table 3). The fact that ornithine has been identified from two completely independent

Table 2 Metabolites correlating with high mannose levels obtained from eight media condition comparison. Day 9 correlation Metabolites Positive correlation Ornithine Gamma-glutamylglutamine 1-Methyladenosine Pyridoxal Hydroxyproline 5-Methyluridine Negative correlation Glutamine Glutathione oxidized form 2-Hydroxyisocaproic acid Uridine Cytidine 5 -diphosphocholine Cytidine

Day 10 correlation Corr. coeff. 0.932 0.803 0.715 0.685 0.652 0.632 −0.714 −0.603 −0.601 −0.577 −0.571 −0.560

The listed r values reflect Pearson product-moment correlation coefficient

Metabolites Positive correlation Ornithine N.epsilon.-acetyl-l-lysine Kynurenic acid 1-Methyladenosine N(G) N(G )-dimethyl-l-arginine Gamma-glutamylphenylalanine Negative correlation 2-Hydroxyisocaproic acid l-Homoserine lactone N-Formyl-l-methionine Lactic acid Cystine Glutathione oxidized

Corr. coeff. 0.843 0.689 0.662 0.595 0.574 0.562 −0.709 −0.677 −0.668 −0.663 −0.647 −0.629

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Fig. 4. Extracellular ornithine levels correlating with high mannose glycan levels (cell line H). (A) A time course profile of relative extracellular levels of ornithine detected from eight media condition comparison described in Table 1. (B) Correlation between HM and Ornithine levels detected on day 9. (C) Time course profile of relative extracellular 2-hydroxyglutarate levels. (D) Correlation between HM vs. 2-hydroxyglutarate (2-HG) detected on day 9.

studies evaluating both the intrinsic (i.e., cell line-specific; Fig. 5, Table 3) as well as extrinsic (i.e., media-specific; Fig. 4, Table 3) parameters suggests ornithine as a promising metabolic marker associated with HM levels.

Ornithine is a non-protein coding amino acid involved in the urea cycle, polyamine synthesis and arginine metabolism (Casero and Marton, 2007; Morris, 2004) (for schematic overview, refer to Fig. 6A). Ornithine is also a precursor of glutamate and proline via ornithine-␦-aminotransferase (OAT) activities (Stranska et al., 2008). In humans, OAT deficiency results in gyrate atrophy of the choroid and retina, a disorder characterized by plasma ornithine accumulation and retinal degeneration (Takki and Simell, 1974). Ornithine decarboxylase (ODC) is an enzyme that catalyzes the conversion of ornithine to putrescine, and is considered the rate-limiting enzyme of the polyamine biosynthetic pathway (Pegg, 2006). ODC synthesis and stability, as well as polyamine transporter activity, have been shown to be affected by environmental osmotic conditions (Munro et al., 1975; Tohyama et al., 1991). Increased polyamine biosynthesis has been associated with increased resistance to osmotic stress in plants (Alcazar et al., 2006). In humans, deficiency of ornithine transcarbamylase (OTC),

Table 3 Metabolites correlating with high mannose levels obtained from eight-cell line comparison. Metabolites correlating to HM

Fig. 5. Correlation between ornithine and high mannose levels from eight-cell line comparison analysis. (A) High mannose glycan levels in eight cell lines exposed to Medium 1 or Medium 2. High mannose levels were determined either by peptide mapping analysis (Method 1) or by Endo H/CE-SDS (Method 2). (B) Correlation between HM and ornithine detected for eight-cell line comparison. A scatter plot generated from day 9 data (exposed to media 2) is depicted as a representative.

Cystine 4-Hydroxybutanoic acid lactone Ornithine Niacinamide Glutathione disulfide Glutathione

Correlation coefficient (media #1)

Correlation coefficient (media #2)

0.907 0.916

0.877 0.826

0.803 0.881 −0.870 −0.838

0.709 0.676 −0.846 −0.760

Correlation coefficient (r) values represent an average of day 8 and 9 values. Only the common metabolites identified from both media #1 and media #2 conditions are listed in the table.

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Fig. 6. Expression of enzymes involved in the ornithine metabolism pathway. (A) A schematic overview of ornithine metabolism, ARG, arginase; AZ, antizyme; AZIN, antizyme inhibitor; GATM, glycine amidinotransferase; NOS, nitric oxide synthase; OAT, ornithine aminotransferase; ODC, ornithine decarboxylase; OTC, ornithine transcarbamoylase; SMS, spermine synthase; SRS, spermidine synthase, (B) Correlation between arginase 1 mRNA expression and HM levels detected on day 9 (eight-cell line comparison).

an enzyme which catalyzes the conversion of ornithine to citrulline, results in the accumulation of ammonia in the blood (Hopkins et al., 1969). Ornithine metabolism occurs in both cytosol and mitochondria, with OTC- and OAT-catalyzed metabolic steps occurring in mitochondria. Mitochondrial ornithine transporter 1 (ORNT1) is required for the import of ornithine into the mitochondria (Camacho et al., 1999). In humans, mutations in ORNT1 are responsible for hyperornithinemia–hyperammonemia–homocitrullinuria (HHH) syndrome, which is characterized by elevated plasma levels of ornithine and ammonia (Camacho et al., 1999). Interestingly, both osmolality and ammonia have previously been identified as exogenous inducers of HM level increase (Pacis et al., 2011; Wu et al., 2013), suggesting altered ornithine metabolism as a potential link between HM and its correlation with osmolality and ammonia. Although assessing both intracellular and extracellular metabolites would have provided more comprehensive profiles, we have limited our analysis to extracellular metabolites in this study. One of the advantages of evaluating extracellular metabolites over intracellular metabolites is the ease of sample collection and preparation for analysis. Investigation of intracellular metabolites requires more time-consuming, extra technical steps to preserve a high degree of precision (Sellick et al., 2011; Vuckovic, 2012). Several studies have reported utilization of extracellular metabolic profiling to identify key metabolites or markers associated with observed cellular phenotypes (Chong et al., 2009; Jain et al., 2012; James et al., 2015; Villas-Boas et al., 2006). In clinical applications, selective extracellular metabolites measured in the blood samples of humans are used as predictors or indicators of certain disease or metabolic states (Milne et al., 2005; Ridker et al., 2004; Wang et al., 2013). Ornithine is one of the metabolites that are frequently detected in high levels in the plasma samples of individuals with gyrate atrophy or other alterations in the genes regulating ornithine metabolism, as described previously (Camacho et al., 1999; Takki and Simell, 1974). While intracellular metabolomics analysis is warranted to confirm and complement the results from extracellular metabolite profiling, identification of ornithine as a metabolic marker from relatively simple analysis of spent media samples, without having to break open the cells, suggest the possibility of utilizing the identified extracellular markers as predictive indicators for screening purposes to improve speed and throughput. Evaluation of extracellular metabolites can also help to generate appropriate hypothesis and further develop experimental strategies to test and explore associated targets and pathways to gain

mechanistic insights. In this study, the identification of ornithine has led to testing of other components of the urea cycle and the polyamine pathway to obtain mechanistic details. For example, when the mRNA levels were analyzed and compared in eight cell lines displaying different levels of HM, a strong correlation was observed between HM levels and the transcript levels of arginase 1, an enzyme which catalyzes the conversion of arginine to ornithine (illustrated in Fig. 6B). These results suggest the possibility that ornithine biosynthesis, in addition to ornithine utilization, could also contribute toward HM levels. In contrast, other ornithine metabolizing or transporting enzymes did not correlate to HM levels (Supplemental Fig. 2). Since only the transcript levels of these enzymes have been evaluated in this study, we cannot rule out the possibility of these enzymes involved in the ornithine metabolism at the post-transcriptional level. Supplementary material related to this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.jbiotec.2015.03.002. 3.4. High mannose levels can be altered by modulating the components of the ornithine metabolic pathway Identification of ornithine as a metabolic marker associated with HM levels (Figs. 4 and 5) implicated the possibility that HM glycan levels could be altered by modulating the components of ornithine metabolism. To increase the HM levels, ornithine supplementation was tested (Fig. 7A). A titration experiment with increasing amount of exogenously provided ornithine in the culture medium of cell line H resulted in a dose-dependent increase in HM levels without affecting titer (Fig. 7A). To lower the HM levels, decreasing amounts of spermine in the culture medium were tested to reduce the extent of ornithine accumulation (Fig. 7B). As a component of the polyamine pathway, spermine along with other polyamines such as spermidine and putrescine, has previously been shown to inhibit ODC activity through feedback regulation which induces the production of an antizyme that promotes degradation of ODC (Coffino, 2001; Pegg, 2006). Since ODC is the rate-limiting enzyme that converts ornithine into putrescine, we speculated that lowering the amount of spermine in the culture medium could reduce levels of ornithine accumulation and subsequently the HM levels. A titration experiment of different spermine concentration in culture medium showed that a reduction or depletion of spermine can indeed reduce HM glycan levels without significantly affecting titer

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Fig. 7. Modulation of HM levels by altering the components of ornithine metabolic pathway. (A) HM and Titer profiles obtained with indicated ornithine concentrations exogenously provided to cell line H at the time of inoculation. A control with a matching osmolality as detected in 2.5 g/L ornithine (grey-colored bar) was obtained by adjusting NaCl content in the medium, (B) HM and Titer profiles obtained with indicated spermine concentration. (C) Extracellular ornithine levels produced by cell line H when supplemented with indicated spermine concentrations.

(Fig. 7B). Targeted analysis for extracellular ornithine levels indicated that a decrease in the HM levels by dose-dependent reduction of spermine concentration was specifically achieved through a reduction of ornithine levels (Fig. 7C), suggesting the involvement of the polyamine pathway in the regulation of glycosylation. In contrast, urea levels were not affected by spermine concentration (data not shown). These results are in agreement with the regulatory system, in which spermine has a negative feedback effect on ODC and induces ornithine accumulation by inhibiting the conversion of ornithine into putrescine (Coffino, 2001; Pegg, 2006). Urea, however, is not part of this negative feedback loop and thus is not affected by spermine levels. Additional analyses have been performed in bioreactors and confirmed the involvement of ornithine/polyamine pathways in regulating HM levels (Barkhordarian et al., unpublished data). Details of these results will be discussed in a separate article. An additional strategy for lowering HM levels includes decreasing the amount of arginine in the culture medium. This speculation is based on the observation that arginase 1 expression levels correlate with HM levels (Fig. 6B and C). By reducing the amount of the substrate (i.e., arginine) for the enzyme arginase 1,

the accumulation of the product (i.e., ornithine) is expected to be lowered. It has previously been reported that reduction of ornithine accumulation in the plasma of the OAT-deficient mice could be achieved by feeding them an arginine-restricted diet (Wang et al., 2000). An alternative approach would include pharmacological inhibition of arginase 1 with specific inhibitors; however, considering the lack of specificity and off-target effects often associated with small molecule inhibitors, this approach could be challenging. Evaluation of some of these additional strategies has been conducted and will be discussed in a separate article. 3.5. The identified metabolites correlating with HM glycan levels have previously been identified as modulators of cellular redox In addition to ornithine, comparison of eight cell lines displaying different levels of HM species allowed identification of additional metabolic markers including cystine, 4-hydroxybutanoic acid lactone and niacinamide which correlated positively with HM, and glutathione (both oxidized and reduced forms) which displayed negative correlation to HM glycans (Table 3, Fig. 8). A common feature shared among these identified metabolites is their

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Fig. 8. Extracellular metabolites correlating to HM levels. The scatter plots represent the comparison between the levels of HM from eight cell lines and the indicated metabolites. The HM values plotted reflect day 9 values when cells were exposed to media 1. GBL, 4-hydroxybutanoic acid lactone; GSSG, glutathione disulfide.

previously described functional role as mediators or regulators of cellular redox. For example, cystine, which is a dimeric oxidized form of cysteine, is an effective metabolic precursor for the synthesis of glutathione, a potent antioxidant responsible for maintaining cellular redox balance (Cotgreave et al., 1991; Lu, 2009). While both cystine and cysteine have been demonstrated to be effective metabolic precursors for glutathione biosynthesis, cystine was speculated to be the likely precursor in vivo (Cotgreave et al., 1991). An opposite trend observed between cystine and glutathione in regard to the direction of correlation with HM suggests that glutathione biosynthesis is one of the functional bottlenecks in cell lines displaying high levels of HM. These results and the previously reported observation describing an inverse correlation between cystine uptake and levels of glutathione production (Cotgreave and Schuppe-Koistinen, 1994) implicate the importance of not only providing sufficient supply of cystine but also efficient transport of cystine into cells to generate optimal levels of glutathione biosynthesis. Interestingly, it has been reported that the high affinity cystine uptake system can be inhibited by dibasic amino acids such as ornithine, arginine, and lysine (Foreman et al., 1980). 4-Hydroxybutanoic acid lactone, also known as gammahydroxybutyric acid lactone (GBL), is another metabolite that demonstrated a positive correlation with HM levels (Table 3, Fig. 8). GBL is a lactone derivative of gamma-hydroxybutyric acid (GHB), which when administered to an animal model, has shown to induce oxidative stress in the cerebral cortex (Sauer et al., 2007). Redox imbalance has been implicated in a human patient with GHB accumulation caused by a genetic defect, with substantially lower levels of glutathione (Niemi et al., 2014). In addition to GBL, niacinamide, also known as nicotinamide, was identified to correlate with HM levels (Table 3). Niacinamide is a metabolic precursor of NAD, an important redox cofactor facilitating numerous intracellular redox reactions (Bogan and Brenner, 2008). Increased levels of extracellular niacinamide detected in cell lines with higher HM levels could be the result of inefficient uptake or utilization of niacinamide for the biosynthesis of NAD. Similar to glutathione biosynthesis, NAD biosynthesis could potentially play a significant role in maintaining the HM levels low by providing sufficient amount of redox cofactors. In a recent study, ornithine was also implicated as a contributor of cellular redox state (Zanatta et al., 2013). A direct impact of ornithine on reduction of glutathione concentration has been observed in the rat cerebellum. Moreover, ornithine was shown to significantly increase levels of lipid peroxidation byproducts, further implicating its influence on disturbance of redox homeostasis and impairment of antioxidant defenses. Free radical scavengers such as glutathione and melatonin were able to prevent ornithineinduced lipid oxidative damage, suggesting that the pro-oxidant effect elicited by ornithine involves generation of hydroxyl and

other radicals. Activity of arginase, an enzyme which catalyzes the conversion of arginine to ornithine, has also been reported to be regulated through redox modulation of intracellular glutathione (Iyamu, 2010), and increased arginase expression has been shown to induce superoxide production by nitric oxide synthase and cause oxidative stress in the vasculature of women with preeclamsia (Sankaralingam et al., 2010). These findings in the literature suggest that a common trait shared among different metabolic markers identified in the present study involves their potential role in regulating cellular redox homeostasis. It is yet to be determined whether the cell lines exhibiting higher levels of HM are also subjected to more severe oxidative stress compared to the cell lines with lower HM levels. While the effect of a cellular redox status on glycosylation pattern is speculated based on the metabolic profiles obtained in the present study, additional experiments are required to reveal the specific details and mechanisms associated with the observed correlation. 4. Conclusion Metabolomics analysis of recombinant CHO cells can provide insight into cellular metabolism in the context of various parameters associated culture performance, including cell growth, productivity and lactate metabolism (Chong et al., 2009; Jain et al., 2012; Ridker et al., 2004; Wang et al., 2013). In this study, extracellular metabolites associated with high mannose (HM) glycan levels of therapeutic monoclonal antibodies were identified by applying the in-house developed, non-targeted metabolomics platform. The discovery of ornithine as a marker allowed the development of application strategies to modulate HM levels by altering the components of the polyamine pathways or urea cycle. A putative functional link between HM glycan levels and cellular redox state could potentially provide additional avenues for development of application strategies to efficiently modulate HM levels. Acknowledgements The authors wish to thank Janice Chen, Tuong-Vi Don, Dazy Johnson, Mee Ko and Chao-Hsiang “Richard” Wu from ATO High Throughput Lab for their support with glycan and titer analysis. Special acknowledgement goes to Hedieh Barkhordarian for performing additional validation studies in bioreactors to confirm proposed strategies on altering HM levels by modulating various components of ornithine and polyamine pathways. We are also grateful to Joseph Phillips and Rohini Deshpande for their management support. Special thank goes to Linda Narhi for carefully reviewing the manuscript and providing helpful suggestions. Research funding was provided by Amgen, Inc.

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Metabolic markers associated with high mannose glycan levels of therapeutic recombinant monoclonal antibodies.

High mannose (HM) glycan levels on secreted monoclonal antibodies can be influenced by external factors, including osmolality and copper deficiency, a...
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