Mammalian Systems Biotechnology Reveals Global Cellular Adaptations in a Recombinant CHO Cell Line

Cell Systems

Volumn 4, Issue 5, 2017, Pages 530-542.e6

  Yusufi, Faraaz Noor Khan ^a   Lakshmanan, Meiyappan ^a   Ho, Ying Swan ^a   Loo, Bernard Liat Wen ^a   Ariyaratne, Pramila ^b   Yang, Yuansheng ^a   Ng, Say Kong ^a   Tan, Tessa Rui Min ^a   Yeo, Hock Chuan ^a,c   Lim, Hsueh Lee ^a   Ng, Sze Wai ^a   Hiu, Ai Ping ^a   Chow, Chung Ping ^a   Wan, Corrine ^a   Chen, Shuwen ^a   Teo, Gavin ^a   Song, Gao ^b   Chin, Ju Xin ^a   Ruan, Xiaoan ^b   Sung, Ken Wing Kin ^b   Hu, Wei Shou ^d   Yap, Miranda Gek Sim ^a,c   Bardor, Muriel ^a,e   Nagarajan, Niranjan ^b   Lee, Dong Yup ^a,c   more..

^a AGENCY FOR SCIENCE   (Singapore)

^b GENOME INSTITUTE OF SINGAPORE   (Singapore)

^c NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^d University of Minnesota   (United States)

^e UNIVERSITÉ DE ROUEN   (France)

Author keywords

Chinese hamster ovary; genome scale metabolic model; genomic rearrangements; glycosylation; lipid metabolism; monoclonal antibody producing cell line; systems biotechnology; tricistronic vector

Indexed keywords

RECOMBINANT ANTIBODY; RECOMBINANT PROTEIN; TRANSCRIPTOME;

ADAPTATION; ANIMAL CELL; ARTICLE; BIOTECHNOLOGY; CELL ENGINEERING; CELL METABOLISM; CHO CELL LINE; DNA DAMAGE; DNA REPAIR; ENERGY METABOLISM; FEMALE; GENE AMPLIFICATION; GENE DOSAGE; GENE EXPRESSION; GENE LOCUS; GENE REARRANGEMENT; GENE TARGETING; GENETIC LINKAGE; GENOME ANALYSIS; GENOMIC INSTABILITY; GENOMICS; GENOTYPE; GLYCOBIOLOGY; IMMUNOGLOBULIN PRODUCING CELL; INTERNAL RIBOSOME ENTRY SITE; LIPID METABOLISM; LIPIDOMICS; MAMMAL CELL; METABOLOMICS; NONHUMAN; PHENOTYPE; PRIORITY JOURNAL; PROTEIN ASSEMBLY; PROTEIN GLYCOSYLATION; PROTEIN SECRETION; PROTEIN TRANSPORT; TRANSCRIPTOMICS; TRANSGENE; WHOLE GENOME SEQUENCING; ANIMAL; BIOSYNTHESIS; CRICETULUS; GENETIC TRANSFECTION; GENETICS; GENOME; GLYCOSYLATION; MAMMAL; METABOLISM; PROCEDURES; SYSTEMS BIOLOGY;

ANIMALS; BIOTECHNOLOGY; CHO CELLS; CRICETULUS; GENE DOSAGE; GENOME; GLYCOMICS; GLYCOSYLATION; MAMMALS; METABOLOMICS; RECOMBINANT PROTEINS; SYSTEMS BIOLOGY; TRANSCRIPTOME; TRANSFECTION; TRANSGENES;

EID: 85019561406     PISSN: 24054712     EISSN: 24054720     Source Type: Journal    
DOI: 10.1016/j.cels.2017.04.009     Document Type: Article

References (72)

1. Alt, F.W., Kellems, R.E., Bertino, J.R., Schimke, R.T., Selective multiplication of dihydrofolate reductase genes in methotrexate-resistant variants of cultured murine cells. 1978. Biotechnology 24 (1992), 397–410.
2. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J., Basic local alignment search tool. J. Mol. Biol. 215 (1990), 403–410.
3. Baik, J.Y., Lee, M.S., An, S.R., Yoon, S.K., Joo, E.J., Kim, Y.H., Park, H.W., Lee, G.M., Initial transcriptome and proteome analyses of low culture temperature-induced expression in CHO cells producing erythropoietin. Biotechnol. Bioeng. 93 (2006), 361–371.
4. Barjesteh van Waalwijk van Doorn-Khosrovani, S., Spensberger, D., de Knegt, Y., Tang, M., Löwenberg, B., Delwel, R., Somatic heterozygous mutations in ETV6 (TEL) and frequent absence of ETV6 protein in acute myeloid leukemia. Oncogene 24 (2005), 4129–4137.
5. Becker, S.A., Palsson, B.O., Context-specific metabolic networks are consistent with experiments. PLoS Comput. Biol., 4, 2008, e1000082.
6. Bedoya-López, A., Estrada, K., Sanchez-Flores, A., Ramírez, O.T., Altamirano, C., Segovia, L., Miranda-Ríos, J., Trujillo-Roldán, M.A., Valdez-Cruz, N.A., Effect of temperature downshift on the transcriptomic responses of Chinese hamster ovary cells using recombinant human tissue plasminogen activator production culture. PLoS One, 11, 2016, e0151529.
7. Birzele, F., Schaub, J., Rust, W., Clemens, C., Baum, P., Kaufmann, H., Weith, A., Schulz, T.W., Hildebrandt, T., Into the unknown: expression profiling without genome sequence information in CHO by next generation sequencing. Nucleic Acids Res. 38 (2010), 3999–4010.
8. Bolger, A.M., Lohse, M., Usadel, B., Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30 (2014), 2114–2120.
9. Brinkrolf, K., Rupp, O., Laux, H., Kollin, F., Ernst, W., Linke, B., Kofler, R., Romand, S., Hesse, F., Budach, W.E., et al. Chinese hamster genome sequenced from sorted chromosomes. Nat. Biotechnol. 31 (2013), 694–695.
10. Busuttil, R.A., Rubio, M., Dollé, M.E.T., Campisi, J., Vijg, J., Oxygen accelerates the accumulation of mutations during the senescence and immortalization of murine cells in culture. Aging Cell 2 (2003), 287–294.
11. Carinhas, N., Oliveira, R., Alves, P.M., Carrondo, M.J.T., Teixeira, A.P., Systems biotechnology of animal cells: the road to prediction. Trends Biotechnol. 30 (2012), 377–385.
12. Cavill, R., Jennen, D., Kleinjans, J., Briedé, J.J., Transcriptomic and metabolomic data integration. Brief. Bioinform. 17 (2016), 891–901.
13. Charaniya, S., Karypis, G., Hu, W.-S., Mining transcriptome data for function-trait relationship of hyper productivity of recombinant antibody. Biotechnol. Bioeng. 102 (2009), 1654–1669.
14. Chen, Q., Ross, A.C., α-Galactosylceramide stimulates splenic lymphocyte proliferation in vitro and increases antibody production in vivo in late neonatal-age mice. Clin. Exp. Immunol. 179 (2015), 188–196.
15. Chong, W.P.K., Goh, L.T., Reddy, S.G., Yusufi, F.N.K., Lee, D.Y., Wong, N.S.C., Heng, C.K., Yap, M.G.S., Ho, Y.S., Metabolomics profiling of extracellular metabolites in recombinant Chinese Hamster Ovary fed-batch culture. Rapid Commun. Mass Spectrom. 23 (2009), 3763–3771.
16. Chong, W.P.K., Yusufi, F.N.K., Lee, D.-Y., Reddy, S.G., Wong, N.S.C., Heng, C.K., Yap, M.G.S., Ho, Y.S., Metabolomics-based identification of apoptosis-inducing metabolites in recombinant fed-batch CHO culture media. J. Biotechnol. 151 (2011), 218–224.
17. Chong, W.P.K., Thng, S.H., Hiu, A.P., Lee, D.-Y., Chan, E.C.Y., Ho, Y.S., LC-MS-based metabolic characterization of high monoclonal antibody-producing Chinese hamster ovary cells. Biotechnol. Bioeng. 109 (2012), 3103–3111.
18. Clarke, C., Doolan, P., Barron, N., Meleady, P., O'Sullivan, F., Gammell, P., Melville, M., Leonard, M., Clynes, M., Large scale microarray profiling and coexpression network analysis of CHO cells identifies transcriptional modules associated with growth and productivity. J. Biotechnol. 155 (2011), 350–359.
19. Datta, P., Linhardt, R.J., Sharfstein, S.T., An ‘omics approach towards CHO cell engineering. Biotechnol. Bioeng. 110 (2013), 1255–1271.
20. del Val, I.J., Kontoravdi, C., Nagy, J.M., Towards the implementation of quality by design to the production of therapeutic monoclonal antibodies with desired glycosylation patterns. Biotechnol. Prog. 26 (2010), 1505–1527.
21. Dunn, W.B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., Brown, M., Knowles, J.D., Halsall, A., Haselden, J.N., et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protoc. 6 (2011), 1060–1083.
22. Fahy, E., Subramaniam, S., Brown, H.A., Glass, C.K., Merrill, A.H., Murphy, R.C., Raetz, C.R.H., Russell, D.W., Seyama, Y., Shaw, W., et al. A comprehensive classification system for lipids. J. Lipid Res. 46 (2005), 839–861.
23. Farrell, A., McLoughlin, N., Milne, J.J., Marison, I.W., Bones, J., Application of multi-omics techniques for bioprocess design and optimization in Chinese hamster ovary cells. J. Proteome Res. 13 (2014), 3144–3159.
24. Federspiel, N.A., Beverley, S.M., Schilling, J.W., Schimke, R.T., Novel DNA rearrangements are associated with dihydrofolate reductase gene amplification. J. Biol. Chem. 259 (1984), 9127–9140.
25. Fomina-Yadlin, D., Mujacic, M., Maggiora, K., Quesnell, G., Saleem, R., McGrew, J.T., Transcriptome analysis of a CHO cell line expressing a recombinant therapeutic protein treated with inducers of protein expression. J. Biotechnol. 212 (2015), 106–115.
26. Gao, S., Bertrand, D., Chia, B.K.H., Nagarajan, N., OPERA-LG: efficient and exact scaffolding of large, repeat-rich eukaryotic genomes with performance guarantees. Genome Biol., 17, 2016, 102.
27. Gómez-Muñoz, A., Ceramide 1-phosphate/ceramide, a switch between life and death. Biochim. Biophys. Acta 1758 (2006), 2049–2056.
28. Hefzi, H., Ang, K.S., Hanscho, M., Bordbar, A., Ruckerbauer, D., Lakshmanan, M., Orellana, C.A., Baycin-Hizal, D., Huang, Y., Ley, D., et al. A consensus genome-scale reconstruction of Chinese hamster ovary cell metabolism. Cell Syst. 3 (2016), 434–443.e8.
29. Hillmer, A.M., Yao, F., Inaki, K., Lee, W.H., Ariyaratne, P.N., Teo, A.S.M., Woo, X.Y., Zhang, Z., Zhao, H., Ukil, L., et al. Comprehensive long-span paired-end-tag mapping reveals characteristic patterns of structural variations in epithelial cancer genomes. Genome Res. 21 (2011), 665–675.
30. Ho, S.C.L., Bardor, M., Feng, H., Mariati, Tong, Y.W., Song, Z., Yap, M.G.S., Yang, Y., IRES-mediated Tricistronic vectors for enhancing generation of high monoclonal antibody expressing CHO cell lines. J. Biotechnol. 157 (2012), 130–139.
31. Huang, D.W., Sherman, B.T., Tan, Q., Collins, J.R., Alvord, W.G., Roayaei, J., Stephens, R., Baseler, M.W., Lane, H.C., Lempicki, R.A., The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol., 8, 2007, R183.
32. Jayapal, K., Wlaschin, K., Hu, W., Yap, G., Recombinant protein therapeutics from CHO cells-20 years and counting. Chem. Eng. Prog. 103 (2007), 40–47.
33. Kaas, C., Kristensen, C., Betenbaugh, M.J., Andersen, M., Sequencing the CHO DXB11 genome reveals regional variations in genomic stability and haploidy. BMC Genomics, 16, 2015, 160.
34. Kantardjieff, A., Jacob, N.M., Yee, J.C., Epstein, E., Kok, Y.J., Philp, R., Betenbaugh, M., Hu, W.S., Transcriptome and proteome analysis of Chinese hamster ovary cells under low temperature and butyrate treatment. J. Biotechnol. 145 (2010), 143–159.
35. Kildegaard, H.F., Baycin-Hizal, D., Lewis, N.E., Betenbaugh, M.J., The emerging CHO systems biology era: harnessing the ‘omics revolution for biotechnology. Curr. Opin. Biotechnol. 24 (2013), 1102–1107.
36. Kind, T., Liu, K.H., Lee do, Y., DeFelice, B., Meissen, J.K., Fiehn, O., LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat. Methods 10 (2013), 755–758.
37. Lakshmanan, M., Koh, G., Chung, B.K.S., Lee, D.-Y., Software applications for flux balance analysis. Brief. Bioinform. 15 (2014), 108–122.
38. Lee, T.S., Ho, Y.S., Yeo, H.C., Lin, J.P.Y., Lee, D.-Y., Precursor mass prediction by clustering ionization products in LC-MS-based metabolomics. Metabolomics 9 (2013), 1301–1310.
39. Lewis, N.E., Liu, X., Li, Y., Nagarajan, H., Yerganian, G., O'Brien, E., Bordbar, A., Roth, A.M., Rosenbloom, J., Bian, C., et al. Genomic landscapes of Chinese hamster ovary cell lines as revealed by the Cricetulus griseus draft genome. Nat. Biotechnol. 31 (2013), 759–767.
40. Li, H., Durbin, R., Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26 (2010), 589–595.
41. Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., 1000 Genome Project Data Processing Subgroup. The sequence alignment/map format and SAMtools. Bioinformatics 25 (2009), 2078–2079.
42. Li, R., Zhu, H., Ruan, J., Qian, W., Fang, X., Shi, Z., Li, Y., Li, S., Shan, G., Kristiansen, K., et al. De novo assembly of human genomes with massively parallel short read sequencing. Genome Res. 20 (2010), 265–272.
43. Liu, Y.-Y., Hill, R.A., Li, Y.-T., Ceramide glycosylation catalyzed by glucosylceramide synthase and cancer drug resistance. Adv. Cancer Res. 117 (2013), 59–89.
44. Nagarajan, N., Bertrand, D., Hillmer, A.M., Zang, Z.J., Yao, F., Jacques, P.-É., Teo, A.S.M., Cutcutache, I., Zhang, Z., Lee, W.H., et al. Whole-genome reconstruction and mutational signatures in gastric cancer. Genome Biol., 13, 2012, R115.
45. Ng, S.K., Tan, T.R.M., Wang, Y., Ng, D., Goh, L.T., Bardor, M., Wong, V.V.T., Lam, K.P., Production of functional soluble Dectin-1 Glycoprotein using an IRES-linked destabilized-dihydrofolate reductase expression vector. PLoS One, 7, 2012, e52785.
46. O'Brien, E.J., Monk, J.M., Palsson, B.O., Using genome-scale models to predict biological capabilities. Cell 161 (2015), 971–987.
47. Orellana, C.A., Marcellin, E., Schulz, B.L., Nouwens, A.S., Gray, P.P., Nielsen, L.K., High-antibody-producing Chinese hamster ovary cells up-regulate intracellular protein transport and glutathione synthesis. J. Proteome Res. 14 (2015), 609–618.
48. Park, J.Y., Takagi, Y., Yamatani, M., Honda, K., Asakawa, S., Shimizu, N., Omasa, T., Ohtake, H., Identification and analysis of specific chromosomal region adjacent to exogenous Dhfr-amplified region in Chinese hamster ovary cell genome. J. Biosci. Bioeng. 109 (2010), 504–511.
49. Patil, K.R., Nielsen, J., Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proc. Natl. Acad. Sci. USA 102 (2005), 2685–2689.
50. Pfaffl, M.W., A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res., 29, 2001, e45.
51. Rafi, S.K., El Gebaly, H., Qumsiyeh, M.B., ETV6/CBFA2 fusions in childhood B-cell precursor acute lymphoblastic leukemia with myeloid markers. Diagn. Mol. Pathol. 9 (2000), 184–189.
52. Ruan, X., Ruan, Y., Genome wide full-length transcript analysis using 5′ and 3′ paired-end-tag next generation sequencing (RNA-PET). Methods Mol. Biol. 809 (2012), 535–562.
53. Schaub, J., Clemens, C., Schorn, P., Hildebrandt, T., Rust, W., Mennerich, D., Kaufmann, H., Schulz, T.W., CHO gene expression profiling in biopharmaceutical process analysis and design. Biotechnol. Bioeng. 105 (2010), 431–438.
54. Schellenberger, J., Palsson, B.Ø., Use of randomized sampling for analysis of metabolic networks. J. Biol. Chem. 284 (2009), 5457–5461.
55. Schellenberger, J., Que, R., Fleming, R.M.T., Thiele, I., Orth, J.D., Feist, A.M., Zielinski, D.C., Bordbar, A., Lewis, N.E., Rahmanian, S., et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat. Protoc. 6 (2011), 1290–1307.
56. Sellick, C.A., Croxford, A.S., Maqsood, A.R., Stephens, G., Westerhoff, H.V., Goodacre, R., Dickson, A.J., Metabolite profiling of recombinant CHO cells: designing tailored feeding regimes that enhance recombinant antibody production. Biotechnol. Bioeng. 108 (2011), 3025–3031.
57. Selvarasu, S., Ho, Y.S., Chong, W.P.K., Wong, N.S.C., Yusufi, F.N.K., Lee, Y.Y., Yap, M.G.S., Lee, D.-Y., Combined in silico modeling and metabolomics analysis to characterize fed-batch CHO cell culture. Biotechnol. Bioeng. 109 (2012), 1415–1429.
58. Sha, S., Agarabi, C., Brorson, K., Lee, D.-Y., Yoon, S., N-glycosylation design and control of therapeutic monoclonal antibodies. Trends Biotechnol. 34 (2016), 835–846.
59. Smith, C.A., Want, E.J., O'Maille, G., Abagyan, R., Siuzdak, G., XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78 (2006), 779–787.
60. Storey, J.D., A direct approach to false discovery rates. J. R. Stat. Soc. Series B Stat. Methodol. 64 (2002), 479–498.
61. Tautenhahn, R., Böttcher, C., Neumann, S., Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics, 9, 2008, 504.
62. Trapnell, C., Williams, B.A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M.J., Salzberg, S.L., Wold, B.J., Pachter, L., Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28 (2010), 511–515.
63. Trapnell, C., Roberts, A., Goff, L., Pertea, G., Kim, D., Kelley, D.R., Pimentel, H., Salzberg, S.L., Rinn, J.L., Pachter, L., Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7 (2012), 562–578.
64. Varki, A., Cummings, R.D., Esko, J.D., Freeze, H.H., Stanley, P., Marth, J.D., Bertozzi, C.R., Hart, G.W., Etzler, M.E., Symbol nomenclature for glycan representation. Proteomics 9 (2009), 5398–5399.
65. Vishwanathan, N., Yongky, A., Johnson, K.C., Fu, H.Y., Jacob, N.M., Le, H., Yusufi, F.N.K., Lee, D.Y., Hu, W.S., Global insights into the Chinese hamster and CHO cell transcriptomes. Biotechnol. Bioeng. 112 (2015), 965–976.
66. Walsh, G., Biopharmaceutical benchmarks 2014. Nat. Biotechnol. 32 (2014), 992–1000.
67. Wilkens, C.A., Gerdtzen, Z.P., Comparative metabolic analysis of CHO cell clones obtained through cell engineering, for IgG productivity, growth and cell longevity. PLoS One, 10, 2015, e0119053.
68. Wurm, F.M., Production of recombinant protein therapeutics in cultivated mammalian cells. Nat. Biotechnol. 22 (2004), 1393–1398.
69. Xu, X., Nagarajan, H., Lewis, N.E., Pan, S., Cai, Z., Liu, X., Chen, W., Xie, M., Wang, W., Hammond, S., et al. The genomic sequence of the Chinese hamster ovary (CHO)-K1 cell line. Nat. Biotechnol. 29 (2011), 735–741.
70. Ye, K., Schulz, M.H., Long, Q., Apweiler, R., Ning, Z., Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 25 (2009), 2865–2871.
71. Yee, J.C., Gerdtzen, Z.P., Hu, W.-S., Comparative transcriptome analysis to unveil genes affecting recombinant protein productivity in mammalian cells. Biotechnol. Bioeng. 102 (2009), 246–263.
72. Yeo, H.C., Chung, B.K.-S., Chong, W., Chin, J.X., Ang, K.S., Lakshmanan, M., Ho, Y.S., Lee, D.-Y., A genetic algorithm-based approach for pre-processing metabolomics and lipidomics LC–MS data. Metabolomics, 12, 2016, 5.