Article pubs.acs.org/jpr
High-Antibody-Producing Chinese Hamster Ovary Cells Up-Regulate Intracellular Protein Transport and Glutathione Synthesis Camila A. Orellana,† Esteban Marcellin,*,† Benjamin L. Schulz,§ Amanda S. Nouwens,†,§ Peter P. Gray,† and Lars K. Nielsen† †
Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Roads (Bldg 75), Brisbane, QLD 4072, Australia § School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD 4072, Australia S Supporting Information *
ABSTRACT: Chinese hamster ovary (CHO) cells are the preferred production host for therapeutic monoclonal antibodies (mAb) due to their ability to perform post-translational modifications and their successful approval history. The completion of the genome sequence for CHO cells has reignited interest in using quantitative proteomics to identify markers of good production lines. Here we applied two different proteomic techniques, iTRAQ and SWATH, for the identification of expression differences between a high- and low-antibody-producing CHO cell lines derived from the same transfection. More than 2000 proteins were quantified with 70 of them classified as differentially expressed in both techniques. Two biological processes were identified as differentially regulated by both methods: up-regulation of glutathione biosynthesis and down-regulation of DNA replication. Metabolomic analysis confirmed that the high producing cell line displayed higher intracellular levels of glutathione. SWATH further identified up-regulation of actin filament processes and intracellular transport and down regulation of several growth-related processes. These processes may be important for conferring high mAb production and as such are promising candidates for targeted engineering of high-expression cell lines. KEYWORDS: iTRAQ, SWATH, quantitative proteomics, CHO cells, monoclonal antibodies, glutathione
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INTRODUCTION Monoclonal antibodies (mAb) therapy has revolutionized the treatment of a vast range of diseases, mostly in the areas of oncology and autoimmune/inflammatory disorders.1 With a world market exceeding 60 billion USD and six mAb-related products in the top-10 selling drugs, the industry continues to grow at a fast rate.2 Most mAbs are produced as humanized or human recombinant proteins in Chinese Hamster Ovary (CHO) cells, which are efficient at performing post-translational modifications and produce proteins with similar properties to the native human proteins.3 These cells are also easily adapted to grow in serum-free, high-density suspension cultures, thus avoiding the use of animal components and their associated problems of contamination, interference with downstream purification, and variations between different batches.4 Despite the high homology between mAbs, each production cell line is still developed through exhaustive screening, which is time-consuming and expensive because the factors controlling expression are largely unknown. Moreover, production lines generated from the same screen vary not only in productivity but also in fermentation performance. This results in expensive © XXXX American Chemical Society
end products; for example, the treatment for breast cancer with Herceptin (mAb) costs U.S. $60 000 per patient per year, and Soliris, which treats a rare blood disorder, costs more than U.S. $400 000 per patient per year.5,6 The increasing demand for high-quality recombinant therapeutics and the need to reduce the costs to the health care system have driven the development of host cells with enhanced production yield. Through bioprocess optimization yields have increased more than 100-fold over the past two decades, with 5−10 g/L of product common today. Most advances are the result of media optimization, novel feeding strategies, and optimized culture conditions.7 Other strategies have also contributed to these advances including improvement of expression vectors or host-cell enhancement via rational genetic engineering.8 Rational changes include modifications to cell cycle,9 central metabolism,10 apoptosis,11 and protein secretion12 to improve protein titer, but despite 25 years of intense research, success to date has been moderate compared Received: December 9, 2013
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DOI: 10.1021/pr501027c J. Proteome Res. XXXX, XXX, XXX−XXX
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Journal of Proteome Research with process optimization.7 Crucially, few if any of these “enhanced” cell lines have been used for industrial production. An alternative to guide the design of cell lines is the use of “omics” to identify key markers of good production cell lines. Transcriptomics, proteomics, and metabolomics have been applied to CHO cells to better understand the basis of high productivity.13,14 Studies have compared differences in cell line productivity15−18 and changes in culture conditions that increase productivity such as hyperosmolarity,19 lower temperature shift,20 and sodium butyrate treatment.21,22 As the ultimate step in cell function, proteomics is a powerful tool to identify key markers of good production lines. Most studies in CHO to date have suffered from the inherent limitation in coverage of 2D gel electrophoresis combined with mass spectrometry.23 The use of more advanced quantitative proteomics techniques such as isobaric tags for relative and absolute quantitation (iTRAQ)24 has been hampered by the need to use the mouse, rat, or human genome as a reference.18,25 The recent release of the CHO genome26 unleashes the full potential of MS-based quantitative CHO proteomics, including iTRAQ and more recent label-free quantitative proteomics, such as SWATH.27 In this study, we used SWATH and iTRAQ to study two CHO production cell lines derived from the same transfection but with different mAb productivities. Overexpression of enzymes involved in intracellular transport of proteins and in the synthesis of glutathione was observed. Overall, these data enabled identifying new gene targets for metabolic engineering.
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Protein Digestion
SWATH samples were prepared as follows: 200 μg of proteins was reduced using iTRAQ buffers, as per the iTRAQ manufacturer’s protocol. After the cysteine blocking step, samples were digested using filter-aided sample preparation with minor modifications.28 Briefly, 50 μg of proteins was diluted in 800 μL of 8 M urea/50 mM AMBIC and loaded into four Amicon Ultra-0.5 mL centrifugal filters with nominal cutoff of 30 00029 (Millipore). Proteins were washed twice with 200 μL of 8 M urea/50 mM AMBIC by centrifugation at 14 000g for 15 min, followed by two washes in 100 μL of 50 mM AMBIC. In-filter digestion was performed overnight at 37 °C with gentle rocking (50 rpm) using 5 μg of trypsin/Lys-C mix (Promega) in 30 μL of 50 mM AMBIC. Peptides were collected by centrifugation three times with 30 μL of 50 mM AMBIC. Samples were acidified to pH 95%, Supplementary Tables S1 and S2 in the Supporting Information) using 11 759 and 11 442 unique peptides for the two 4-plex experiments, respectively. Combined 2584 proteins were identified whereof 1906 were common between the two experiments. Of the common proteins, 115 proteins were differentially expressed (adjusted p value 95%, Supplementary Table S4 in the Supporting Information) with 4105 unique peptides. Only 1609 peptides were common between the IDA library and both iTRAQ experiments, while 89% (638 of 714) of the proteins were also found in iTRAQ. Of 398 proteins identified as differentially expressed in SWATH (Supplementary Table S5 in the Supporting Information), 70 were also classified as differentially expressed by iTRAQ (Figure 3B and Supplementary Table S6 in the Supporting Information). SWATH Predictions Are Supported by RNA-Seq Data
Despite a significantly lower coverage (714 versus 1906 proteins), SWATH identified far more significant proteins (398 versus 115) than iTRAQ. Thus, SWATH identifies 56% of the detected proteins as differentially expressed, whereas iTRAQ detects only 6% as differentially expressed. Considering that the two cell lines were derived from the same transfection pool, 56% seems like a high number for the differentially expressed proteins. Therefore, we used RNA-Seq data to validate our SWATH observations. Among the 637 proteins/ transcripts that were common between RNA-Seq and SWATH (Supplementary Table S7 in the Supporting Information), 63% were differentially transcribed. Furthermore, a known concentration of synthetic peptides (HRM standards from Biognosys) was spiked into the SWATH samples. Analysis of the peptide intensity showed no significant differences in expression, indicating that true negatives are not classified as differentially expressed by SWATH (Supplementary Figure S1 in the Supporting Information).
Differentially Expressed Proteins Revealed Up-Regulation of Secretion Pathways
Proteins identified as differentially expressed in both SWATH and iTRAQ analyses were clustered by biological process using Mus musculus homologues and subjected to gene set enrichment analysis. In iTRAQ, two clusters were found to be up-regulated (Benjamini-Hochberg adjusted p < 0.05), glutathione metabolic process and response to oxidative stress, while in SWATH, in E
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Journal of Proteome Research Table 1. Biological Processes Clusters of Differentially Expressed Proteins in iTRAQ and SWATH Benjamini− Hochberg q value
enrichment score
GSS, G6PDX, GSTT2, GCLM, GSTP2
1.24 × 10−3
3.5
G6PDX, TXNRD1, CAT, GCLM, PRDX1 55
4.24 × 10−2
2.4
MYL6, TLN1, ACTA 1, CAPZA2, CAPZA1, ACTN1, ARPC4, CSRP1, MYH9, PFN1, CFL1, RHOA, CNN2, CAP1 ACTR3, PFN1, ARPC2, CAPZA2, CFL1, CAPZA1, CAPG, RHOA, DSTN
5.32 × 10−6
6.1
2.02 × 10−5
3.5
MYL6, COPA, YWHAZ, KIF5B, CLTC, MYH9, CDC42, AP2A2, COPG2, YWHAG, YWHAH, COPB1, RAB1, EHD1, RAB10, SAR1A GSS, G6PDX, IDH1, GCLM, GSTP2
1.16 × 10−3
3.5
4.26 × 10−3
2.4
SEPT2, CAPZA2, TUBA3A, TUBB6, ARPC4, TUBG1 Down-Regulated
2.49 × 10−3
2.2
MCM2, MCM5, MCM6
6.96 × 10−2
1.7
Mus musculus homologue protein
biological process
Up-Regulated iTRAQ glutathione metabolic process response to oxidative stress SWATH actin filament-based process regulation of actin cytoskeleton organization intracellular transport glutathione metabolic process protein polymerization iTRAQ DNA replication initiation SWATH RNA splicing translation DNA replication initiation nucleocytoplasmic transport nitrogen compound biosynthetic process DNA metabolic process establishment of RNA localization protein catabolic process cellular amino acid biosynthetic process
SNRPA1, STRAP, EFTUD2, SYNCRIP, YBX1, SF3A3, HNRNPA3, NONO, SF3B1, DDX39, HNRNPK, TARDBP, PRPF8, PRMT5, SFPQ, DHX15, KHSRP, HNRNPC, PABPC1, SNRPF, SNRPE EIF2S3X, BF5, GARS, EPRS, BF5A, QARS, VARS, KARS, EIF4G1, WARS, BF3B, GSPT1, BF2S1, RPL9, BF2S2, BF4A1, KHSRP, LARS, RPL11, FARSA, RPS11, KPNA2 MCM7, MCM2, MCM3, MCM4, MCM5, MCM6
2.23 × 10−9
9.7
2.95 × 10−7
6.2
7.42 × 10−6
4.5
NUP155, CSE1L, IP04, NPM1, IP05, NOP58, NUTF2, KPNA4, KPNA3, MYBBP1A, KPNB1, KPNA2, TNP01 BCAT1, HSP90AA1, HPRT, CTPS, CAD, PRPSAP1, GMPS, PFAS, GART, MTHFD1, TYMS, GOT1, ADK, ADSL, EN0PH1, PSAT1, PAICS, PRPS2, IMPDH2 SSRP1, RAD23B, PHB, NASP, MCM2, MCM3, MCM4, MCM5, MCM6, RPA1, NONO, UBE2N, MCM7, SFPQ, PCNA, RUVBL1, PARP1, TRIP13 NPM1, G3BP2, KHSRP, BF5A, NUP107, NUP155
7.60 × 10−7
4.3
8.01 × 10−6
4.1
RAD23B, SKP1A, PSMA8, UBE2N, PSMB4, PSMC6, PSMD14, HSP90B1, PSMB7, PSMA6, PSMB1, OTUB1, PSMC3, UBE2K, PSMC2, PSMB2, CACYBP, PSMC1, UCHL5, CAND1 BCAT1, MTHFD1, GOT1, EN0PH1, PSAT1
−3
1.59 × 10
2.3
3.24 × 10−2
2.2
4.57 × 10−3
2.0
4.31 × 10−2
1.3
these vesicles where found to be significantly up-regulated, such as Sar1a, responsible for the assembly and disassembly of COPII vesicles;46 Copg2, Copa, and Copb1, subunits of the COPI vesicle; and Cltc and Ap2a2, involved in clathrin-coated vesicles. Other proteins were also classified as differentially regulated in both cell lines, such as Rab1, Rab1b, and Rab10 GTPases; Myh9 and Myl6 myosin subunits; and Ywhaz, Ywhag, and Ywhah signaling pathway regulators. Overexpression of Rab1b in HeLa cells, which regulates vesicle trafficking between the ER and Golgi,47 enlarges the Golgi and increases gene expression of vesicular transport-related genes.48 These proteins involved in intracellular transport represent potential targets for genetic engineering of high-producing cell lines. In iTRAQ, only DNA replication initiation was found to be down-regulated, while in SWATH 8 more clusters were found: RNA splicing, translation, nucleocytoplasmatic transport, nitrogen compound biosynthetic process, DNA metabolic process, RNA transport, and protein catabolic process (Table 1). The down-regulation of DNA replication, DNA metabolic process, and nitrogen compound biosynthesis such as nucleobases,
addition to glutathione metabolism, another four clusters were found: actin filament-based process, regulation of actin cytoskeleton organization, intracellular transport, and protein polymerization (Table 1). Glutathione is discussed in the next section. Actin is the major cytoskeletal protein of most cells, and the cellular microfilament network plays an important role in the structure and regulation of translation.40−42 While the specific role of the cytoskeleton in protein secretion is unclear, upregulation of this biological process in high-producing cell lines has been reported previously.20 Translation and secretion activity have been previously reported as bottlenecks for recombinant protein production.43,44 Here we found that proteins involved in intracellular transport were up-regulated. Three different transport vesicles exists in the cells: COPII-coated vesicles involved in protein transport from the rough endoplasmic reticulum (ER) to the Golgi apparatus, COPI-coated vesicles responsible for Golgi to ER retrograde transport, and clathrin-coated vesicles involved in endocytosis and transport from the trans-Golgi network to lysosomes.45 In high-producing cells, proteins that constitute F
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Figure 4. Suggested function of glutathione in protein metabolism. Glutathione (GSH) is synthesized in two steps, first by glutamate-cysteine ligase and then by glutathione synthetase (Gss). GSH can either be degraded by γ-glutamyl transferase (Ggt) and subsequently by a dipeptidase (Cndp2) or conjugated by glutathione S-transferase (Gst) to electrophilic centers on a wide variety of substrates to detoxify cellular environments. GSH is an important antioxidant, and the redox cycle starts by GSH oxidation (GSSG) by glutathione peroxidase (Gpx) to detoxify reactive oxygen species (ROS). GSSG is reduced again (GSH) by glutathione reductase (Gsr) using NADPH, provided mainly by the conversion of glucose 6-phosphate to 6-phosphogluconolactone by glucose 6-phospahte dehydrogenase (G6pdx). GSH is also capable of reducing cysteine mixed disulfides to form cysteine, serving as a reservoir of cysteine for protein synthesis. GSH may also enter the endoplasmic reticulum (ER) and reduce incorrect protein disulfide bonds by reducing protein disulfide-isomerase (Pdi), allowing for their correct folding. The exact mechanism remains elusive. Misfolded proteins can be denatured, can aggregate, or its overaccumulation can activate the unfolded protein response (UPR). Finally, actin microfilament network may play an important role in the structure and regulation of translation. Green, red, and gray checks represent up-regulated, downregulated, and non-detected proteins, respectively. Total intracellular GSH concentration for high producer (HP) and low producer (LP) cell lines with and without selection (MSX) are shown. *5 out of 7 detected Gst’s were up-regulated. (See the text for details.)
of disulfide bonds in proteins, which can be a rate-limiting step in the synthesis of secreted proteins.51 Up-regulation of the glutathione pathway was identified by gene set enrichment in both the iTRAQ and SWATH data sets (Table 1). GSH synthesis occurs in two steps: (1) glutamatecysteine ligase (Gcl) condensates cysteine and glutamate and (2) a glycine is added by glutathione synthetase (Gss) forming GSH. Gcl consists of a heavy catalytic subunit (Gclc) and a light-modulating subunit (Gclm) and has been found to be the rate-limiting step in GSH synthesis.52 Homozygous deletion of Gclc but not of Gclm is lethal in mouse.53,54 Gclm reduces the Km of the enzyme for glutamate, thus increasing its affinity, and also increases the dissociation constant Ki for the Gclc inhibitor GSH, thus reducing negative feedback by GSH.55 GSH can be oxidized to GSSG by glutathione peroxidase (Gpx) to detoxify reactive oxygen species, and GSSG can be
nucleosides, nucleotides, and nucleic acids, in the high producer is probably linked to the slower growth rate (Figure 2A). Several down-regulated proteins from the clusters were associated with the late G1 and S phases of the cell cycle. High-Producing CHO Line Has Higher Glutathione Content
Glutathione (GSH) is an important antioxidant in most organisms and several properties, and functions are potentially advantageous for high mAb producers. First, GSH can act as storage for cysteine, which can then be redirected to protein production. GSH can also make cysteine available by reducing cystine and cysteine mixed disulfides. Second, GSH mediates correct protein disulfide bond formation in hyperoxidizing conditions by reducing incorrect disulfide bonds formation (Figure 4).49,50 Finally, oxidized glutathione (GSSG) in the endoplasmic reticulum might also be involved in the formation G
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reduced again by glutathione reductase (Gsr) using NADPH, completing the glutathione redox cycle.56 γ-Glutamyl transferase (Ggt) can degrade GSH by cutting the γ-glutamyl bond, followed by dipeptidase cleavage of the cysteine−glycine bond.56 GSH can also be used to detoxify the cell by its conjugation to xenobiotics catalyzed by glutathione S-transferase (Gst), comprised by seven subfamilies. Gst proteins are also involved in negative regulation of cellular signaling by sequestrating kinases that play a role in stress response, proliferation, and apoptosis.57 In this study, Gclm, the regulatory subunit of Gcl, and Gss were found to be up-regulated in the high-producing cell line. While Gpx and Ggt were not detected, mitochondrial Gsr was down-regulated in the high producer. Finally, Gsta3 (no mouse homologue), Gstp2 (Gstp1 for CHO), Gsto1, Gstt2, Gstm6, and Gstm7 were up-regulated, while Gstm1 and Gstm5 were down-regulated. Other authors have also reported that the glutathione pathway is affected when comparing high versus low CHO producer cell lines. Chong et al.16 postulated that CHO high producers experience oxidative stress due to two main reactive oxygen species sources: one derived from the ER stress during protein folding and the other from mitochondrial respiration. They found that high producers have a larger intracellular glutathione pool,16 while Doolan et al.58 and Nissom et al.18 observed that Mgst1 (microsomal glutathione S-transferase 1-like) gene and protein, respectively, were downregulated in high producers. Recent metabolomics studies have also proposed strategies to prevent glutathione depletion in CHO mAb-producing cells.16,59,60 To validate that the GSH content was higher in the high producing cell line, total GSH (GSH + GSSG) was measured. The high-producing cell line had 1.3 times more GSH than the low-producing cell line (Figure 4). Given that MSX can inhibit Gclc, GSH content in cell lines cultured without MSX selection was measured as well. Again, the high producer had a higher GSH content than the low producer, with a fold change of 1.5 (Figure 4). The actual increase in GSH production may be higher in the high producer given that many Gst proteins are highly up-regulated and Gst-mediated detoxification is an irreversible reaction. The proteomics, transcriptomics, and metabolomics data in this study, together with previous studies, suggest an important role of GSH in high productivity. To the best of our knowledge, the GSH content has not been deliberately increased in pursuit of improved mAb productivity. However, the GSH content in mammalian cells has been successfully increased by overexpressing Gclc and/or Gclm to study resistance against toxic compounds and protection from oxidative stress- and radiationinduced cell death.61−63 GSH content in COV434 cells increased between 1.3- and 2.4-fold following overexpression of Gclc and/or Gclm.61 In CHO cells specifically, a 2.2-fold change in GSH content was accomplished by overexpressing Gclc.63 Thus, deliberately increasing GSH in production lines should be possible. High GSH content is likely to be one of many factors required for high productivity. Increasing the GSH content alone is unlikely to convert a low to a high producer. Instead, validation will require a demonstration that parental cell lines with increased GSH content are more likely to produce good production clones than standard cell lines; that is, the frequency of high producer clones is increased in high GSH parental lines. We are currently generating high GSH parental lines for such a study.
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ASSOCIATED CONTENT
S Supporting Information *
Table S1. iTRAQ experiment #1 protein summary. Table S2. iTRAQ experiment #2 protein summary. Table S3. iTRAQ relative protein expression profiles obtained with limma analysis. Table S4. SWATH protein summary. Table S5. SWATH relative protein expression profiles obtained with limma analysis. Table S6. Common differentially expressed proteins between iTRAQ and SWATH. Table S7. Log2-fold change for common proteins in iTRAQ, SWATH and RNASeq. Figure S1. Synthetic peptides abundance in SWATH samples. This material is available free of charge via the Internet at http://pubs.acs.org.
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AUTHOR INFORMATION
Corresponding Author
*Phone: +61 7 334 63158. Fax: +61 7 3346 3973. E-mail: e. [email protected]. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS We thank Robin Palfreyman, the Queensland node of Metabolomics Australia, the proteomics facilities at SCMB, and Sigma-Aldrich.
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ABBREVIATIONS Ap2a2, adaptor protein complex 2; CE, collision energy; CHO, Chinese hamster ovary cells; Cltc, clathrin heavy chain; Cndp2, cytosolic nonspecific dipeptidase; Copa, coatomer protein complex subunit alpha; Copb1, coatomer protein complex subunit beta 1; Copg2, coatomer protein complex subunit gamma 2; DIA, data-independent acquisition; DoF, degrees-offreedom; Gcl, glutamate-cysteine ligase; Gclc, catalytic subunit of glutamate-cysteine ligase; Gclm, regulatory subunit of glutamate-cysteine ligase; Ggt, γ-glutamyl transferase; Gpx, glutathione peroxidase; GSH, glutathione; Gss, glutathione synthetase; GSSG, glutathione disulfide; Gsr, glutathione reductase; Gst, glutathione S-transferase; IDA, informationdependent acquisition; Km, dissociation constant; Ki, inhibition constant; LC−MS/MS, liquid chromatography combined with tandem mass spectrometry; mAb, monoclonal antibody; MSX, L-methionine sulfoximine; Myh9, myosin heavy chain 9; Myl6, myosin light chain 6; NADPH, reduced nicotinamide; Rab1, Ras-related protein Rab-1A; Rab1b, ras-related protein Rab-1B; Rab10, ras-related protein Rab-10; RNA-Seq, RNA Sequencing; Sar1a, secretion associated Ras-related GTPase 1A; Ywhag, tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma; Ywhah, tyrosine 3-monooxygenase/ tryptophan 5-monooxygenase activation protein eta; Ywhaz, tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta
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gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 2012, 7 (3), 562−578. (40) Gross, S. R.; Kinzy, T. G. Improper organization of the actin cytoskeleton affects protein synthesis at initiation. Mol. Cell. Biol. 2007, 27 (5), 1974−1989. (41) Kim, S.; Coulombe, P. A. OPINION Emerging role for the cytoskeleton as an organizer and regulator of translation. Nat. Rev. Mol. Cell Biol. 2010, 11 (1), 75−81. (42) Stapulionis, R.; Kolli, S.; Deutscher, M. P. Efficient mammalian protein synthesis requires an intact F-actin system. J. Biol. Chem. 1997, 272 (40), 24980−24986. (43) Schroder, M. Engineering eukaryotic protein factories. Biotechnol. Lett. 2008, 30 (2), 187−196. (44) Dinnis, D. M.; James, D. C. Engineering mammalian cell factories for improved recombinant monoclonal antibody production: lessons from nature? Biotechnol. Bioeng. 2005, 91 (2), 180−189. (45) Cooper, G. M. The Cell: A Molecular Approach, 2nd ed.; Sinauer Associates: Sunderland, MA, 2000; p 689. (46) Kuge, O.; Dascher, C.; Orci, L.; Rowe, T.; Amherdt, M.; Plutner, H.; Ravazzola, M.; Tanigawa, G.; Rothman, J. E.; Balch, W. E. Sar1 promotes vesicle budding from the endoplasmic reticulum but not Golgi compartments. J. Cell Biol. 1994, 125 (1), 51−65. (47) Plutner, H.; Cox, A. D.; Pind, S.; Khosravi-Far, R.; Bourne, J. R.; Schwaninger, R.; Der, C. J.; Balch, W. E. Rab1b regulates vesicular transport between the endoplasmic reticulum and successive Golgi compartments. J. Cell Biol. 1991, 115 (1), 31−43. (48) Romero, N.; Dumur, C. I.; Martinez, H.; Garcia, I. A.; Monetta, P.; Slavin, I.; Sampieri, L.; Koritschoner, N.; Mironov, A. A.; De Matteis, M. A.; Alvarez, C. Rab1b overexpression modifies Golgi size and gene expression in HeLa cells and modulates the thyrotrophin response in thyroid cells in culture. Mol. Biol. Cell 2013, 24 (5), 617− 632. (49) Cuozzo, J. W.; Kaiser, C. A. Competition between glutathione and protein thiols for disulphide-bond formation. Nat. Cell Biol. 1999, 1 (3), 130−135. (50) Kojer, K.; Riemer, J. Balancing oxidative protein folding: The influences of reducing pathways on disulfide bond formation. Biochim. Biophys. Acta 2014, 1844 (8), 1383−1390. (51) Lappi, A. K.; Ruddock, L. W. Reexamination of the role of interplay between glutathione and protein disulfide isomerase. J. Mol. Biol. 2011, 409 (2), 238−249. (52) Meister, A. Glutathione, ascorbate, and cellular protection. Cancer Res. 1994, 54 (7 Suppl), 1969s−1975s. (53) Yang, Y.; Dieter, M. Z.; Chen, Y.; Shertzer, H. G.; Nebert, D. W.; Dalton, T. P. Initial characterization of the glutamate-cysteine ligase modifier subunit Gclm(−/−) knockout mouse. Novel model system for a severely compromised oxidative stress response. J. Biol. Chem. 2002, 277 (51), 49446−49452. (54) Dalton, T. P.; Dieter, M. Z.; Yang, Y.; Shertzer, H. G.; Nebert, D. W. Knockout of the mouse glutamate cysteine ligase catalytic subunit (Gclc) gene: embryonic lethal when homozygous, and proposed model for moderate glutathione deficiency when heterozygous. Biochem. Biophys. Res. Commun. 2000, 279 (2), 324−329. (55) Njalsson, R. Glutathione synthetase deficiency. Cell. Mol. Life Sci. 2005, 62 (17), 1938−1945. (56) Lu, S. C. Regulation of glutathione synthesis. Mol. Aspects Med. 2009, 30 (1−2), 42−59. (57) McIlwain, C. C.; Townsend, D. M.; Tew, K. D. Glutathione Stransferase polymorphisms: cancer incidence and therapy. Oncogene 2006, 25 (11), 1639−1648. (58) Doolan, P.; Melville, M.; Gammell, P.; Sinacore, M.; Meleady, P.; McCarthy, K.; Francullo, L.; Leonard, M.; Charlebois, T.; Clynes, M. Transcriptional profiling of gene expression changes in a PACEtransfected CHO DUKX cell line secreting high levels of rhBMP-2. Mol. Biotechnol. 2008, 39 (3), 187−199. (59) Chong, W. P.; Yusufi, F. N.; Lee, D. Y.; Reddy, S. G.; Wong, N. S.; Heng, C. K.; Yap, M. G.; Ho, Y. S. Metabolomics-based identification of apoptosis-inducing metabolites in recombinant fedbatch CHO culture media. J. Biotechnol. 2011, 151 (2), 218−224. J
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High-Antibody-Producing Chinese Hamster Ovary Cells Up-Regulate Intracellular Protein Transport and Glutathione Synthesis Camila A. Orellana,† Esteban Marcellin,*,† Benjamin L. Schulz,§ Amanda S. Nouwens,†,§ Peter P. Gray,† and Lars K. Nielsen† †
Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Roads (Bldg 75), Brisbane, QLD 4072, Australia § School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD 4072, Australia S Supporting Information *
ABSTRACT: Chinese hamster ovary (CHO) cells are the preferred production host for therapeutic monoclonal antibodies (mAb) due to their ability to perform post-translational modifications and their successful approval history. The completion of the genome sequence for CHO cells has reignited interest in using quantitative proteomics to identify markers of good production lines. Here we applied two different proteomic techniques, iTRAQ and SWATH, for the identification of expression differences between a high- and low-antibody-producing CHO cell lines derived from the same transfection. More than 2000 proteins were quantified with 70 of them classified as differentially expressed in both techniques. Two biological processes were identified as differentially regulated by both methods: up-regulation of glutathione biosynthesis and down-regulation of DNA replication. Metabolomic analysis confirmed that the high producing cell line displayed higher intracellular levels of glutathione. SWATH further identified up-regulation of actin filament processes and intracellular transport and down regulation of several growth-related processes. These processes may be important for conferring high mAb production and as such are promising candidates for targeted engineering of high-expression cell lines. KEYWORDS: iTRAQ, SWATH, quantitative proteomics, CHO cells, monoclonal antibodies, glutathione
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INTRODUCTION Monoclonal antibodies (mAb) therapy has revolutionized the treatment of a vast range of diseases, mostly in the areas of oncology and autoimmune/inflammatory disorders.1 With a world market exceeding 60 billion USD and six mAb-related products in the top-10 selling drugs, the industry continues to grow at a fast rate.2 Most mAbs are produced as humanized or human recombinant proteins in Chinese Hamster Ovary (CHO) cells, which are efficient at performing post-translational modifications and produce proteins with similar properties to the native human proteins.3 These cells are also easily adapted to grow in serum-free, high-density suspension cultures, thus avoiding the use of animal components and their associated problems of contamination, interference with downstream purification, and variations between different batches.4 Despite the high homology between mAbs, each production cell line is still developed through exhaustive screening, which is time-consuming and expensive because the factors controlling expression are largely unknown. Moreover, production lines generated from the same screen vary not only in productivity but also in fermentation performance. This results in expensive © XXXX American Chemical Society
end products; for example, the treatment for breast cancer with Herceptin (mAb) costs U.S. $60 000 per patient per year, and Soliris, which treats a rare blood disorder, costs more than U.S. $400 000 per patient per year.5,6 The increasing demand for high-quality recombinant therapeutics and the need to reduce the costs to the health care system have driven the development of host cells with enhanced production yield. Through bioprocess optimization yields have increased more than 100-fold over the past two decades, with 5−10 g/L of product common today. Most advances are the result of media optimization, novel feeding strategies, and optimized culture conditions.7 Other strategies have also contributed to these advances including improvement of expression vectors or host-cell enhancement via rational genetic engineering.8 Rational changes include modifications to cell cycle,9 central metabolism,10 apoptosis,11 and protein secretion12 to improve protein titer, but despite 25 years of intense research, success to date has been moderate compared Received: December 9, 2013
A
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Journal of Proteome Research with process optimization.7 Crucially, few if any of these “enhanced” cell lines have been used for industrial production. An alternative to guide the design of cell lines is the use of “omics” to identify key markers of good production cell lines. Transcriptomics, proteomics, and metabolomics have been applied to CHO cells to better understand the basis of high productivity.13,14 Studies have compared differences in cell line productivity15−18 and changes in culture conditions that increase productivity such as hyperosmolarity,19 lower temperature shift,20 and sodium butyrate treatment.21,22 As the ultimate step in cell function, proteomics is a powerful tool to identify key markers of good production lines. Most studies in CHO to date have suffered from the inherent limitation in coverage of 2D gel electrophoresis combined with mass spectrometry.23 The use of more advanced quantitative proteomics techniques such as isobaric tags for relative and absolute quantitation (iTRAQ)24 has been hampered by the need to use the mouse, rat, or human genome as a reference.18,25 The recent release of the CHO genome26 unleashes the full potential of MS-based quantitative CHO proteomics, including iTRAQ and more recent label-free quantitative proteomics, such as SWATH.27 In this study, we used SWATH and iTRAQ to study two CHO production cell lines derived from the same transfection but with different mAb productivities. Overexpression of enzymes involved in intracellular transport of proteins and in the synthesis of glutathione was observed. Overall, these data enabled identifying new gene targets for metabolic engineering.
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Protein Digestion
SWATH samples were prepared as follows: 200 μg of proteins was reduced using iTRAQ buffers, as per the iTRAQ manufacturer’s protocol. After the cysteine blocking step, samples were digested using filter-aided sample preparation with minor modifications.28 Briefly, 50 μg of proteins was diluted in 800 μL of 8 M urea/50 mM AMBIC and loaded into four Amicon Ultra-0.5 mL centrifugal filters with nominal cutoff of 30 00029 (Millipore). Proteins were washed twice with 200 μL of 8 M urea/50 mM AMBIC by centrifugation at 14 000g for 15 min, followed by two washes in 100 μL of 50 mM AMBIC. In-filter digestion was performed overnight at 37 °C with gentle rocking (50 rpm) using 5 μg of trypsin/Lys-C mix (Promega) in 30 μL of 50 mM AMBIC. Peptides were collected by centrifugation three times with 30 μL of 50 mM AMBIC. Samples were acidified to pH 95%, Supplementary Tables S1 and S2 in the Supporting Information) using 11 759 and 11 442 unique peptides for the two 4-plex experiments, respectively. Combined 2584 proteins were identified whereof 1906 were common between the two experiments. Of the common proteins, 115 proteins were differentially expressed (adjusted p value 95%, Supplementary Table S4 in the Supporting Information) with 4105 unique peptides. Only 1609 peptides were common between the IDA library and both iTRAQ experiments, while 89% (638 of 714) of the proteins were also found in iTRAQ. Of 398 proteins identified as differentially expressed in SWATH (Supplementary Table S5 in the Supporting Information), 70 were also classified as differentially expressed by iTRAQ (Figure 3B and Supplementary Table S6 in the Supporting Information). SWATH Predictions Are Supported by RNA-Seq Data
Despite a significantly lower coverage (714 versus 1906 proteins), SWATH identified far more significant proteins (398 versus 115) than iTRAQ. Thus, SWATH identifies 56% of the detected proteins as differentially expressed, whereas iTRAQ detects only 6% as differentially expressed. Considering that the two cell lines were derived from the same transfection pool, 56% seems like a high number for the differentially expressed proteins. Therefore, we used RNA-Seq data to validate our SWATH observations. Among the 637 proteins/ transcripts that were common between RNA-Seq and SWATH (Supplementary Table S7 in the Supporting Information), 63% were differentially transcribed. Furthermore, a known concentration of synthetic peptides (HRM standards from Biognosys) was spiked into the SWATH samples. Analysis of the peptide intensity showed no significant differences in expression, indicating that true negatives are not classified as differentially expressed by SWATH (Supplementary Figure S1 in the Supporting Information).
Differentially Expressed Proteins Revealed Up-Regulation of Secretion Pathways
Proteins identified as differentially expressed in both SWATH and iTRAQ analyses were clustered by biological process using Mus musculus homologues and subjected to gene set enrichment analysis. In iTRAQ, two clusters were found to be up-regulated (Benjamini-Hochberg adjusted p < 0.05), glutathione metabolic process and response to oxidative stress, while in SWATH, in E
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Journal of Proteome Research Table 1. Biological Processes Clusters of Differentially Expressed Proteins in iTRAQ and SWATH Benjamini− Hochberg q value
enrichment score
GSS, G6PDX, GSTT2, GCLM, GSTP2
1.24 × 10−3
3.5
G6PDX, TXNRD1, CAT, GCLM, PRDX1 55
4.24 × 10−2
2.4
MYL6, TLN1, ACTA 1, CAPZA2, CAPZA1, ACTN1, ARPC4, CSRP1, MYH9, PFN1, CFL1, RHOA, CNN2, CAP1 ACTR3, PFN1, ARPC2, CAPZA2, CFL1, CAPZA1, CAPG, RHOA, DSTN
5.32 × 10−6
6.1
2.02 × 10−5
3.5
MYL6, COPA, YWHAZ, KIF5B, CLTC, MYH9, CDC42, AP2A2, COPG2, YWHAG, YWHAH, COPB1, RAB1, EHD1, RAB10, SAR1A GSS, G6PDX, IDH1, GCLM, GSTP2
1.16 × 10−3
3.5
4.26 × 10−3
2.4
SEPT2, CAPZA2, TUBA3A, TUBB6, ARPC4, TUBG1 Down-Regulated
2.49 × 10−3
2.2
MCM2, MCM5, MCM6
6.96 × 10−2
1.7
Mus musculus homologue protein
biological process
Up-Regulated iTRAQ glutathione metabolic process response to oxidative stress SWATH actin filament-based process regulation of actin cytoskeleton organization intracellular transport glutathione metabolic process protein polymerization iTRAQ DNA replication initiation SWATH RNA splicing translation DNA replication initiation nucleocytoplasmic transport nitrogen compound biosynthetic process DNA metabolic process establishment of RNA localization protein catabolic process cellular amino acid biosynthetic process
SNRPA1, STRAP, EFTUD2, SYNCRIP, YBX1, SF3A3, HNRNPA3, NONO, SF3B1, DDX39, HNRNPK, TARDBP, PRPF8, PRMT5, SFPQ, DHX15, KHSRP, HNRNPC, PABPC1, SNRPF, SNRPE EIF2S3X, BF5, GARS, EPRS, BF5A, QARS, VARS, KARS, EIF4G1, WARS, BF3B, GSPT1, BF2S1, RPL9, BF2S2, BF4A1, KHSRP, LARS, RPL11, FARSA, RPS11, KPNA2 MCM7, MCM2, MCM3, MCM4, MCM5, MCM6
2.23 × 10−9
9.7
2.95 × 10−7
6.2
7.42 × 10−6
4.5
NUP155, CSE1L, IP04, NPM1, IP05, NOP58, NUTF2, KPNA4, KPNA3, MYBBP1A, KPNB1, KPNA2, TNP01 BCAT1, HSP90AA1, HPRT, CTPS, CAD, PRPSAP1, GMPS, PFAS, GART, MTHFD1, TYMS, GOT1, ADK, ADSL, EN0PH1, PSAT1, PAICS, PRPS2, IMPDH2 SSRP1, RAD23B, PHB, NASP, MCM2, MCM3, MCM4, MCM5, MCM6, RPA1, NONO, UBE2N, MCM7, SFPQ, PCNA, RUVBL1, PARP1, TRIP13 NPM1, G3BP2, KHSRP, BF5A, NUP107, NUP155
7.60 × 10−7
4.3
8.01 × 10−6
4.1
RAD23B, SKP1A, PSMA8, UBE2N, PSMB4, PSMC6, PSMD14, HSP90B1, PSMB7, PSMA6, PSMB1, OTUB1, PSMC3, UBE2K, PSMC2, PSMB2, CACYBP, PSMC1, UCHL5, CAND1 BCAT1, MTHFD1, GOT1, EN0PH1, PSAT1
−3
1.59 × 10
2.3
3.24 × 10−2
2.2
4.57 × 10−3
2.0
4.31 × 10−2
1.3
these vesicles where found to be significantly up-regulated, such as Sar1a, responsible for the assembly and disassembly of COPII vesicles;46 Copg2, Copa, and Copb1, subunits of the COPI vesicle; and Cltc and Ap2a2, involved in clathrin-coated vesicles. Other proteins were also classified as differentially regulated in both cell lines, such as Rab1, Rab1b, and Rab10 GTPases; Myh9 and Myl6 myosin subunits; and Ywhaz, Ywhag, and Ywhah signaling pathway regulators. Overexpression of Rab1b in HeLa cells, which regulates vesicle trafficking between the ER and Golgi,47 enlarges the Golgi and increases gene expression of vesicular transport-related genes.48 These proteins involved in intracellular transport represent potential targets for genetic engineering of high-producing cell lines. In iTRAQ, only DNA replication initiation was found to be down-regulated, while in SWATH 8 more clusters were found: RNA splicing, translation, nucleocytoplasmatic transport, nitrogen compound biosynthetic process, DNA metabolic process, RNA transport, and protein catabolic process (Table 1). The down-regulation of DNA replication, DNA metabolic process, and nitrogen compound biosynthesis such as nucleobases,
addition to glutathione metabolism, another four clusters were found: actin filament-based process, regulation of actin cytoskeleton organization, intracellular transport, and protein polymerization (Table 1). Glutathione is discussed in the next section. Actin is the major cytoskeletal protein of most cells, and the cellular microfilament network plays an important role in the structure and regulation of translation.40−42 While the specific role of the cytoskeleton in protein secretion is unclear, upregulation of this biological process in high-producing cell lines has been reported previously.20 Translation and secretion activity have been previously reported as bottlenecks for recombinant protein production.43,44 Here we found that proteins involved in intracellular transport were up-regulated. Three different transport vesicles exists in the cells: COPII-coated vesicles involved in protein transport from the rough endoplasmic reticulum (ER) to the Golgi apparatus, COPI-coated vesicles responsible for Golgi to ER retrograde transport, and clathrin-coated vesicles involved in endocytosis and transport from the trans-Golgi network to lysosomes.45 In high-producing cells, proteins that constitute F
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Figure 4. Suggested function of glutathione in protein metabolism. Glutathione (GSH) is synthesized in two steps, first by glutamate-cysteine ligase and then by glutathione synthetase (Gss). GSH can either be degraded by γ-glutamyl transferase (Ggt) and subsequently by a dipeptidase (Cndp2) or conjugated by glutathione S-transferase (Gst) to electrophilic centers on a wide variety of substrates to detoxify cellular environments. GSH is an important antioxidant, and the redox cycle starts by GSH oxidation (GSSG) by glutathione peroxidase (Gpx) to detoxify reactive oxygen species (ROS). GSSG is reduced again (GSH) by glutathione reductase (Gsr) using NADPH, provided mainly by the conversion of glucose 6-phosphate to 6-phosphogluconolactone by glucose 6-phospahte dehydrogenase (G6pdx). GSH is also capable of reducing cysteine mixed disulfides to form cysteine, serving as a reservoir of cysteine for protein synthesis. GSH may also enter the endoplasmic reticulum (ER) and reduce incorrect protein disulfide bonds by reducing protein disulfide-isomerase (Pdi), allowing for their correct folding. The exact mechanism remains elusive. Misfolded proteins can be denatured, can aggregate, or its overaccumulation can activate the unfolded protein response (UPR). Finally, actin microfilament network may play an important role in the structure and regulation of translation. Green, red, and gray checks represent up-regulated, downregulated, and non-detected proteins, respectively. Total intracellular GSH concentration for high producer (HP) and low producer (LP) cell lines with and without selection (MSX) are shown. *5 out of 7 detected Gst’s were up-regulated. (See the text for details.)
of disulfide bonds in proteins, which can be a rate-limiting step in the synthesis of secreted proteins.51 Up-regulation of the glutathione pathway was identified by gene set enrichment in both the iTRAQ and SWATH data sets (Table 1). GSH synthesis occurs in two steps: (1) glutamatecysteine ligase (Gcl) condensates cysteine and glutamate and (2) a glycine is added by glutathione synthetase (Gss) forming GSH. Gcl consists of a heavy catalytic subunit (Gclc) and a light-modulating subunit (Gclm) and has been found to be the rate-limiting step in GSH synthesis.52 Homozygous deletion of Gclc but not of Gclm is lethal in mouse.53,54 Gclm reduces the Km of the enzyme for glutamate, thus increasing its affinity, and also increases the dissociation constant Ki for the Gclc inhibitor GSH, thus reducing negative feedback by GSH.55 GSH can be oxidized to GSSG by glutathione peroxidase (Gpx) to detoxify reactive oxygen species, and GSSG can be
nucleosides, nucleotides, and nucleic acids, in the high producer is probably linked to the slower growth rate (Figure 2A). Several down-regulated proteins from the clusters were associated with the late G1 and S phases of the cell cycle. High-Producing CHO Line Has Higher Glutathione Content
Glutathione (GSH) is an important antioxidant in most organisms and several properties, and functions are potentially advantageous for high mAb producers. First, GSH can act as storage for cysteine, which can then be redirected to protein production. GSH can also make cysteine available by reducing cystine and cysteine mixed disulfides. Second, GSH mediates correct protein disulfide bond formation in hyperoxidizing conditions by reducing incorrect disulfide bonds formation (Figure 4).49,50 Finally, oxidized glutathione (GSSG) in the endoplasmic reticulum might also be involved in the formation G
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reduced again by glutathione reductase (Gsr) using NADPH, completing the glutathione redox cycle.56 γ-Glutamyl transferase (Ggt) can degrade GSH by cutting the γ-glutamyl bond, followed by dipeptidase cleavage of the cysteine−glycine bond.56 GSH can also be used to detoxify the cell by its conjugation to xenobiotics catalyzed by glutathione S-transferase (Gst), comprised by seven subfamilies. Gst proteins are also involved in negative regulation of cellular signaling by sequestrating kinases that play a role in stress response, proliferation, and apoptosis.57 In this study, Gclm, the regulatory subunit of Gcl, and Gss were found to be up-regulated in the high-producing cell line. While Gpx and Ggt were not detected, mitochondrial Gsr was down-regulated in the high producer. Finally, Gsta3 (no mouse homologue), Gstp2 (Gstp1 for CHO), Gsto1, Gstt2, Gstm6, and Gstm7 were up-regulated, while Gstm1 and Gstm5 were down-regulated. Other authors have also reported that the glutathione pathway is affected when comparing high versus low CHO producer cell lines. Chong et al.16 postulated that CHO high producers experience oxidative stress due to two main reactive oxygen species sources: one derived from the ER stress during protein folding and the other from mitochondrial respiration. They found that high producers have a larger intracellular glutathione pool,16 while Doolan et al.58 and Nissom et al.18 observed that Mgst1 (microsomal glutathione S-transferase 1-like) gene and protein, respectively, were downregulated in high producers. Recent metabolomics studies have also proposed strategies to prevent glutathione depletion in CHO mAb-producing cells.16,59,60 To validate that the GSH content was higher in the high producing cell line, total GSH (GSH + GSSG) was measured. The high-producing cell line had 1.3 times more GSH than the low-producing cell line (Figure 4). Given that MSX can inhibit Gclc, GSH content in cell lines cultured without MSX selection was measured as well. Again, the high producer had a higher GSH content than the low producer, with a fold change of 1.5 (Figure 4). The actual increase in GSH production may be higher in the high producer given that many Gst proteins are highly up-regulated and Gst-mediated detoxification is an irreversible reaction. The proteomics, transcriptomics, and metabolomics data in this study, together with previous studies, suggest an important role of GSH in high productivity. To the best of our knowledge, the GSH content has not been deliberately increased in pursuit of improved mAb productivity. However, the GSH content in mammalian cells has been successfully increased by overexpressing Gclc and/or Gclm to study resistance against toxic compounds and protection from oxidative stress- and radiationinduced cell death.61−63 GSH content in COV434 cells increased between 1.3- and 2.4-fold following overexpression of Gclc and/or Gclm.61 In CHO cells specifically, a 2.2-fold change in GSH content was accomplished by overexpressing Gclc.63 Thus, deliberately increasing GSH in production lines should be possible. High GSH content is likely to be one of many factors required for high productivity. Increasing the GSH content alone is unlikely to convert a low to a high producer. Instead, validation will require a demonstration that parental cell lines with increased GSH content are more likely to produce good production clones than standard cell lines; that is, the frequency of high producer clones is increased in high GSH parental lines. We are currently generating high GSH parental lines for such a study.
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ASSOCIATED CONTENT
S Supporting Information *
Table S1. iTRAQ experiment #1 protein summary. Table S2. iTRAQ experiment #2 protein summary. Table S3. iTRAQ relative protein expression profiles obtained with limma analysis. Table S4. SWATH protein summary. Table S5. SWATH relative protein expression profiles obtained with limma analysis. Table S6. Common differentially expressed proteins between iTRAQ and SWATH. Table S7. Log2-fold change for common proteins in iTRAQ, SWATH and RNASeq. Figure S1. Synthetic peptides abundance in SWATH samples. This material is available free of charge via the Internet at http://pubs.acs.org.
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AUTHOR INFORMATION
Corresponding Author
*Phone: +61 7 334 63158. Fax: +61 7 3346 3973. E-mail: e. [email protected]. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS We thank Robin Palfreyman, the Queensland node of Metabolomics Australia, the proteomics facilities at SCMB, and Sigma-Aldrich.
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ABBREVIATIONS Ap2a2, adaptor protein complex 2; CE, collision energy; CHO, Chinese hamster ovary cells; Cltc, clathrin heavy chain; Cndp2, cytosolic nonspecific dipeptidase; Copa, coatomer protein complex subunit alpha; Copb1, coatomer protein complex subunit beta 1; Copg2, coatomer protein complex subunit gamma 2; DIA, data-independent acquisition; DoF, degrees-offreedom; Gcl, glutamate-cysteine ligase; Gclc, catalytic subunit of glutamate-cysteine ligase; Gclm, regulatory subunit of glutamate-cysteine ligase; Ggt, γ-glutamyl transferase; Gpx, glutathione peroxidase; GSH, glutathione; Gss, glutathione synthetase; GSSG, glutathione disulfide; Gsr, glutathione reductase; Gst, glutathione S-transferase; IDA, informationdependent acquisition; Km, dissociation constant; Ki, inhibition constant; LC−MS/MS, liquid chromatography combined with tandem mass spectrometry; mAb, monoclonal antibody; MSX, L-methionine sulfoximine; Myh9, myosin heavy chain 9; Myl6, myosin light chain 6; NADPH, reduced nicotinamide; Rab1, Ras-related protein Rab-1A; Rab1b, ras-related protein Rab-1B; Rab10, ras-related protein Rab-10; RNA-Seq, RNA Sequencing; Sar1a, secretion associated Ras-related GTPase 1A; Ywhag, tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma; Ywhah, tyrosine 3-monooxygenase/ tryptophan 5-monooxygenase activation protein eta; Ywhaz, tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta
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