The use of 'Omics technology to rationally improve industrial mammalian cell line performance

Biotechnology and Bioengineering

Volumn 113, Issue 1, 2016, Pages 26-38

  Lewis, Amanda M ^a   Abu Absi, Nicholas R ^a   Borys, Michael C ^a   Li, Zheng Jian ^a  

^a Bristol Myers Squibb Company   (United States)

Author keywords

Bioprocess; CHO; Metabolomics; Proteomics; Rational optimization; Transcriptomics

Indexed keywords

BIOINFORMATICS; BIOLOGY; CELL CULTURE; CELLS; CELLULAR MANUFACTURING; CULTIVATION; CYTOLOGY; INDUSTRIAL RESEARCH; MAMMALS; MANUFACTURE; PATIENT TREATMENT;

BIOPROCESSES; CHO; METABOLOMICS; PROTEOMICS; RATIONAL OPTIMIZATION; TRANSCRIPTOMICS;

MOLECULAR BIOLOGY;

BIOLOGICAL PRODUCT;

ANIMAL; BIOLOGY; BIOTECHNOLOGY; CELL CULTURE TECHNIQUE; CELL LINE; HUMAN; MAMMAL; METABOLISM; PROCEDURES; SYSTEMS BIOLOGY;

ANIMALS; BIOLOGICAL PRODUCTS; BIOTECHNOLOGY; CELL CULTURE TECHNIQUES; CELL LINE; COMPUTATIONAL BIOLOGY; HUMANS; MAMMALS; SYSTEMS BIOLOGY;

EID: 84947969484     PISSN: 00063592     EISSN: 10970290     Source Type: Journal    
DOI: 10.1002/bit.25673     Document Type: Review

References (75)

1. Ahn WS, Antoniewicz MR. 2013. Parallel labeling experiments with [1, 2-13C]glucose and [U-13C]glutamine provide new insights into CHO cell metabolism. Metab Eng 15:34-47.
2. Arakawa K, Tomita M. 2013. Merging multiple omics datasets in silico: Statistical analyses and data interpretation. Methods Mol Biol 985:459-470.
3. Aranibar N, Borys M, Mackin N, Ly V, Abu-Absi N, Abu-Absi S, Niemitz M, Schilling B, Li Z, Brock B, Russell R, Tymiak II, Reily A. 2011. NMR-based metabolomics of mammalian cell and tissue cultures. J Biomol NMR 49:195-206.
4. Baycin-Hizal D, Tabb DL, Chaerkady R, Chen L, Lewis NE, Nagarajan H, Sarkaria V, Kumar A, Wolozny D, Colao J, Jacobson E, Tian Y, O'Meally RN, Krag SS, Cole RN, Palsson BO, Zhang H, Betenbaugh M. 2012. Proteomic analysis of Chinese hamster ovary cells. J Proteome Res 11:5265-5276.
5. Becker J, Hackl M, Rupp O, Jakobi T, Schneider J, Szczepanowski R, Bekel T, Borth N, Goesmann A, Grillari J, Kaltschmidt C, Noll T, Puhler A, Tauch A, Brinkrolf K. 2011. Unraveling the Chinese hamster ovary cell line transcriptome by next-generation sequencing. J Biotechnol 156:227-235.
6. Bort JAH, Hackl M, Höflmayer H, Jadhav V, Harreither E, Kumar N, Ernst W, Grillari J, Borth N. 2012. Dynamic mRNA and miRNA profiling of CHO-K1 suspension cell cultures. Biotechnol J 7:500-515.
7. Bradley SA, Ouyang A, Purdie J, Smitka TA, Wang T, Kaerner A. 2010. Fermentanomics: Monitoring mammalian cell cultures with NMR spectroscopy. J Am Chem Soc 132:9531-9533.
8. Carlage T, Hincapie M, Zang L, Lyubarskaya Y, Madden H, Mhatre R, Hancock WS. 2009. Proteomic profiling of a high-producing Chinese hamster ovary cell culture. Anal Chem 81:7357-7362.
9. Casci T 2012. Bioinformatics: Next-generation omics. Nat Rev Genet 13:378-379.
10. Chong WP, Thng SH, Hiu AP, Lee DY, Chan EC, Ho YS. 2012. LC-MS-based metabolic characterization of high monoclonal antibody-producing Chinese hamster ovary cells. Biotechnol Bioeng 109:3103-3111.
11. Chrysanthopoulos PK, Goudar CT, Klapa MI. 2010. Metabolomics for high-resolution monitoring of the cellular physiological state in cell culture engineering. Metab Eng 12:212-222.
12. Clarke C, Doolan P, Barron N, Meleady P, O'Sullivan F, Gammell P, Melville M, Leonard M, Clynes M. 2011a. Large scale microarray profiling and coexpression network analysis of CHO cells identifies transcriptional modules associated with growth and productivity. J Biotechnol 155:350-359.
13. Clarke C, Doolan P, Barron N, Meleady P, O'Sullivan F, Gammell P, Melville M, Leonard M, Clynes M. 2011b. Predicting cell-specific productivity from CHO gene expression. J Biotechnol 151:159-165.
14. Clarke C, Doolan P, Barron N, Meleady P, Madden SF, DiNino D, Leonard M, Clynes M. 2012. CGCDB: A web-based resource for the investigation of gene coexpression in CHO cell culture. Biotechnol Bioeng 109:1368-1370.
15. Courtes FC, Lin J, Lim HL, Ng SW, Wong NS, Koh G, Vardy L, Yap MG, Loo B, Lee DY. 2013. Translatome analysis of CHO cells to identify key growth genes. J Biotechnol 167:215-224.
16. Datta P, Linhardt RJ, Sharfstein ST. 2013. An 'omics approach towards CHO cell engineering. Biotechnol Bioeng 110:1255-1271.
17. de Jong B, Siewers V, Nielsen J. 2012. Systems biology of yeast: Enabling technology for development of cell factories for production of advanced biofuels. Curr Opin Biotechnol 23:624-630.
18. Dietmair S, Hodson MP, Quek LE, Timmins NE, Chrysanthopoulos P, Jacob SS, Gray P, Nielsen LK. 2012a. Metabolite profiling of CHO cells with different growth characteristics. Biotechnol Bioeng 109:1404-1414.
19. Dietmair S, Nielsen LK, Timmins NE. 2012b. Mammalian cells as biopharmaceutical production hosts in the age of omics. Biotechnol J 7:75-89.
20. Doolan P, Meleady P, Barron N, Henry M, Gallagher R, Gammell P, Melville M, Sinacore M, McCarthy K, Leonard M, Charlebois T, Clynes M. 2010. Microarray and proteomics expression profiling identifies several candidates, including the valosin-containing protein (VCP), involved in regulating high cellular growth rate in production CHO cell lines. Biotechnol Bioeng 106:42-56.
21. Doolan P, Clarke C, Kinsella P, Breen L, Meleady P, Leonard M, Zhang L, Clynes M, Aherne ST, Barron N. 2013. Transcriptomic analysis of clonal growth rate variation during CHO cell line development. J Biotechnol 166:105-113.
22. Dorai H, Liu S, Yao X, Wang Y, Tekindemir U, Lewis MJ, Wu SL, Hancock W. 2013. Proteomic analysis of bioreactor cultures of an antibody expressing CHOGS cell line that promotes high productivity. J Proteomics Bioinform 6:99-108.
23. Druz A, Chu C, Majors B, Santuary R, Betenbaugh M, Shiloach J. 2011. A novel microRNA mmu-miR-466h affects apoptosis regulation in mammalian cells. Biotechnol Bioeng 108:1651-1661.
24. Duarte TM, Carinhas N, Barreiro LC, Carrondo MJT, Alves PM, Teixeira AP. 2014. Metabolic responses of CHO cells to limitation of key amino acids. Biotechnol Bioeng 13:378-379.
25. Garcia-Alcalde F, Garcia-Lopez F, Dopazo J, Conesa A. 2011. Paintomics: A web based tool for the joint visualization of transcriptomics and metabolomics data. Bioinformatics 27:137-139.
26. Ghorbaniaghdam A, Chen J, Henry O, Jolicoeur M. 2014. Analyzing clonal variation of monoclonal antibody-producing CHO cell lines using an in silico metabolomic platform. PLoS ONE 9(8):e104725.
27. Hacker DL 2008. Recombinant protein production yields from mammalian cells: Past, present, and future. BioPharm Int.
28. Hammond S, Swanberg JC, Kaplarevic M, Lee KH. 2011. Genomic sequencing and analysis of a Chinese hamster ovary cell line using Illumina sequencing technology. BMC Genomics 12:67.
29. Hammond S, Kaplarevic M, Borth N, Betenbaugh MJ, Lee KH. 2012. Chinese hamster genome database: An online resource for the CHO community at www.CHOgenome.org. Biotechnol Bioeng 109:1353-1356.
30. Jayapal KP 2007. Recombinant protein therapeutics from CHO cells-20 years and counting. Chem Eng Progress 103:40-47.
31. Kang S, Ren D, Xiao G, Daris K, Buck L, Enyenihi AA, Zubarev R, Bondarenko PV, Deshpande R. 2014. Cell line profiling to improve monoclonal antibody production. Biotechnol Bioeng 111:748-760.
32. Kantardjieff A, Jacob NM, Yee JC, Epstein E, Kok YJ, Philp R, Betenbaugh M, Hu WS. 2010. Transcriptome and proteome analysis of Chinese hamster ovary cells under low temperature and butyrate treatment. J Biotechnol 145:143-159.
33. Kaufman RJ, Wasley LC, Spiliotes AJ, Gossels SD, Latt SA, Larsen GR, Kay RM. 1985. Coamplification and coexpression of human tissue-type plasminogen activator and murine dihydrofolate reductase sequences in Chinese hamster ovary cells. Mol Cell Biol 5:1750-1759.
34. Kildegaard HF, Baycin-Hizal D, Lewis NE, Betenbaugh MJ. 2013. The emerging CHO systems biology era: Harnessing the 'omics revolution for biotechnology. Curr Opin Biotechnol 24(6):1102-1107.
35. Kim J-S, Kim S-J, Park H-W, Youn J-P, An Y, Cho H, Hwang S. 2010. Array2KEGG: Web-based tool of KEGG pathway analysis for gene expression profile. BioChip J 4:134-140.
36. Kim JY, Kim YG, Lee GM. 2012. CHO cells in biotechnology for production of recombinant proteins: Current state and further potential. Appl Microbiol Biotechnol 93:917-930.
37. Kotera M, Hirakawa M, Tokimatsu T, Goto S, Kanehisa M. 2012. The KEGG databases and tools facilitating omics analysis: Latest developments involving human diseases and pharmaceuticals. Methods Mol Biol 802:19-39.
38. LaMarche BL, Crowell KL, Jaitly N, Petyuk VA, Shah AR, Polpitiya AD, Sandoval JD, Kiebel GR, Monroe ME, Callister SJ, Metz TO, Anderson GA, Smith RD. 2013. MultiAlign: A multiple LC-MS analysis tool for targeted omics analysis. BMC Bioinformatics 14:49.
39. Levy NE, Valente KN, Choe LH, Lee KH, Lenhoff AM. 2014. Identification and characterization of host cell protein product-associated impurities in monoclonal antibody bioprocessing. Biotechnol Bioeng 111:904-912.
40. Lewis NE, Liu X, Li Y, Nagarajan H, Yerganian G, O'Brien E, Bordbar A, Roth AM, Rosenbloom J, Bian C, Xie M, Chen W, Li N, Baycin-Hizal D, Latif H, Forster J, Betenbaugh MJ, Famili I, Xu X, Wang J, Palsson BO. 2013. Genomic landscapes of Chinese hamster ovary cell lines as revealed by the Cricetulus griseus draft genome. Nat Biotech 31:759-765.
41. Lim UM, Yap MGS, Lim YP, Goh L-T, Ng SK. 2013. Identification of autocrine growth factors secreted by CHO cells for applications in single-cell cloning media. J Proteome Res 12:3496-3510.
42. Lohse M, Bolger AM, Nagel A, Fernie AR, Lunn JE, Stitt M, Usadel B. 2012. RobiNA: A user-friendly, integrated software solution for RNA-Seq-based transcriptomics. Nucleic Acids Res 40:W622-W627.
43. Luo J, Vijayasankaran N, Autsen J, Santuray R, Hudson T, Amanullah A, Li F. 2012. Comparative metabolite analysis to understand lactate metabolism shift in Chinese hamster ovary cell culture process. Biotechnol Bioeng 109:146-156.
44. Ma N, Ellet J, Okediadi C, Hermes P, McCormick E, Casnocha S. 2009. A single nutrient feed supports both chemically defined NS0 and CHO fed-batch processes: Improved productivity and lactate metabolism. Biotechnol Prog 25:1353-1363.
45. Meleady P, Hoffrogge R, Henry M, Rupp O, Bort JH, Clarke C, Brinkrolf K, Kelly S, Müller B, Doolan P, Hackl M, Beckmann TF, Noll T, Grillari J, Barron N, Pühler A, Clynes M, Borth N. 2012. Utilization and evaluation of CHO-specific sequence databases for mass spectrometry based proteomics. Biotechnol Bioeng 109:1386-1394.
46. Mohmad-Saberi SE, Hashim YZ, Mel M, Amid A, Ahmad-Raus R, Packeer-Mohamed V. 2013. Metabolomics profiling of extracellular metabolites in CHO-K1 cells cultured in different types of growth media. Cytotechnology 65:577-586.
47. Mueller D, Heinzle E. 2013. Stable isotope-assisted metabolomics to detect metabolic flux changes in mammalian cell cultures. Curr Opin Biotechnol 24:54-59.
48. Nicolae A, Wahrheit J, Bahnemann J, Zeng AP, Heinzle E. 2014. Non-stationary 13C metabolic flux analysis of Chinese hamster ovary cells in batch culture using extracellular labeling highlights metabolic reversibility and compartmentation. BMC Systems Biol 8:50. doi:10.1186/1752-0509-8-50
49. North SJ, Huang HH, Sundaram S, Jang-Lee J, Etienne AT, Trollope A, Chalabi S, Dell A, Stanley P, Haslam SM. 2010. Glycomics profiling of Chinese hamster ovary cell glycosylation mutants reveals N-glycans of a novel size and complexity. J Biol Chem 285:5759-5775.
50. Pascoe DE, Arnott D, Papoutsakis ET, Miller WM, Andersen DC. 2007. Proteome analysis of antibody-producing CHO cell lines with different metabolic profiles. Biotechnol Bioeng 98:391-410.
51. Patti GJ, Yanes O, Siuzdak G. 2012. Innovation: Metabolomics: The apogee of the omics trilogy. Nat Rev Mol Cell Biol 13:263-269.
52. Pilobello KT, Slawek DE, Mahal LK. 2007. A ratiometric lectin microarray approach to analysis of the dynamic mammalian glycome. Proc Natl Acad Sci USA 104:11534-11539.
53. Rupp O, Becker J, Brinkrolf K, Timmermann C, Borth N, Pühler A, Noll T, Goesmann A. 2014. Construction of a public CHO cell line transcript database using versatile bioinformatics analysis pipelines. PLoS ONE 9:e85568.
54. Salvioli A, Bonfante P. 2013. Systems biology and "omics" tools: A cooperation for next-generation mycorrhizal studies. Plant Sci 203-204:107-114.
55. Schaub J, Clemens C, Schorn P, Hildebrandt T, Rust W, Mennerich D, Kaufmann H, Schulz TW. 2010. CHO gene expression profiling in biopharmaceutical process analysis and design. Biotechnol Bioeng 105:431-438.
56. Schaub J, Clemens C, Kaufmann H, Schulz TW. 2012. Advancing biopharmaceutical process development by system-level data analysis and integration of omics data. Adv Biochem Eng Biotechnol 127:133-163.
57. Sellick CA, Croxford AS, Maqsood AR, Stephens G, Westerhoff HV, Goodacre R, Dickson AJ. 2011. Metabolite profiling of recombinant CHO cells: Designing tailored feeding regimes that enhance recombinant antibody production. Biotechnol Bioeng 108:3025-3031.
58. Selvarasu S, Karimi IA, Ghim GH, Lee DY. 2010. Genome-scale modeling and in silico analysis of mouse cell metabolic network. Mol Biosyst 6:152-161.
59. Selvarasu S, Ho YS, Chong WP, Wong NS, Yusufi FN, Lee YY, Yap MG, Lee DY. 2012. Combined in silico modeling and metabolomics analysis to characterize fed-batch CHO cell culture. Biotechnol Bioeng 109:1415-1429.
60. Slade PG, Hajivandi M, Bartel CM, Gorfien SF. 2012. Identifying the CHO secretome using mucin-type O-linked glycosylation and click-chemistry. J Proteome Res 11:6175-6186.
61. Strotbek M, Florin L, Koenitzer J, Tolstrup A, Kaufmann H, Hausser A, Olayioye MA. 2013. Stable microRNA expression enhances therapeutic antibody productivity of Chinese hamster ovary cells. Metab Eng 20:157-166.
62. Taverner T, Karpievitch YV, Polpitiya AD, Brown JN, Dabney AR, Anderson GA, Smith RD. 2012. DanteR: An extensible R-based tool for quantitative analysis of -omics data. Bioinformatics 28:2404-2406.
63. Templeton N, Dean J, Reddy P, Young JD. 2013. Peak antibody production is associated with increased oxidative metabolism in an industrially relevant fed-batch CHO cell culture. Biotechnol Bioeng 110:2013-2024.
64. Tep S, Hincapie M, Hancock WS. 2012. The characterization and quantitation of glycomic changes in CHO cells during a bioreactor campaign. Biotechnol Bioeng 109:3007-3017.
65. Valente KN, Lenhoff AM, Lee KH. 2014a. Expression of difficult-to-remove host cell protein impurities during extended Chinese hamster ovary cell culture and their impact on continuous bioprocessing. Biotechnol Bioeng 112(6):1232-1242.
66. Valente KN, Schaefer AK, Kempton HR, Lenhoff AM, Lee KH. 2014b. Recovery of Chinese hamster ovary host cell proteins for proteomic analysis. Biotechnol J 9:87-99.
67. Vucic EA, Thu KL, Robison K, Rybaczyk LA, Chari R, Alvarez CE, Lam WL. 2012. Translating cancer 'omics' to improved outcomes. Genome Res 22:188-195.
68. Wuest DM, Harcum SW, Lee KH. 2012. Genomics in mammalian cell culture bioprocessing. Biotechnol Adv 30:629-638.
69. Xia J, Fjell CD, Mayer ML, Pena OM, Wishart DS, Hancock RE. 2013. INMEX-a web-based tool for integrative meta-analysis of expression data. Nucleic Acids Res 41:W63-W70.
70. Xu X, Nagarajan H, Lewis NE, Pan S, Cai Z, Liu X, Chen W, Xie M, Wang W, Hammond S, Andersen MR, Neff N, Passarelli B, Koh W, Fan HC, Wang J, Gui Y, Lee KH, Betenbaugh MJ, Quake SR, Famili I, Palsson BO. 2011. The genomic sequence of the Chinese hamster ovary (CHO)-K1 cell line. Nat Biotechnol 29:735-741.
71. Young JD 2013. Metabolic flux rewiring in mammalian cell cultures. Curr Opin Biotechnol 24:1108-1115.
72. Yuk IH, Zhang JD, Ebeling M, Berrera M, Gomez N, Werz S, Meiringer C, Shao Z, Swanberg JC, Lee KH, Luo J, Szperalski B. 2014. Effects of copper on CHO cells: Insights from gene expression analyses. Biotechnol Prog 30:429-442.
73. Zamboni N, Kummel A, Heinemann M. 2008. AnNET: A tool for network-embedded thermodynamic analysis of quantitative metabolome data. BMC Bioinformatics 9:199.
74. Zang L, Frenkel R, Simeone J, Lanan M, Byers M, Lyubarskaya Y. 2011. Metabolomics profiling of cell culture media leading to the identification of riboflavin photosensitized degradation of tryptophan causing slow growth in cell culture. Anal Chem 83:5422-5430.
75. Zhu J, Sova P, Xu Q, Dombek KM, Xu EY, Vu H, Tu Z, Brem RB, Bumgarner RE, Schadt EE. 2012. Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation. PLoS Biol 10:e1001301.