Abstract
Development of efficient bioprocesses is essential for cost-effective manufacturing of recombinant therapeutic proteins. To achieve further process improvement and process rationalization comprehensive data analysis of both process data and phenotypic cell-level data is essential.
Here, we present a framework for advanced bioprocess data analysis consisting of multivariate data analysis (MVDA), metabolic flux analysis (MFA), and pathway analysis for mapping of large-scale gene expression data sets. This data analysis platform was applied in a process development project with an IgG-producing Chinese hamster ovary (CHO) cell line in which the maximal product titer could be increased from about 5 to 8 g/L.
Principal component analysis (PCA), k-means clustering, and partial least-squares (PLS) models were applied to analyze the macroscopic bioprocess data. MFA and gene expression analysis revealed intracellular information on the characteristics of high-performance cell cultivations. By MVDA, for example, correlations between several essential amino acids and the product concentration were observed. Also, a grouping into rather cell specific productivity-driven and process control-driven processes could be unraveled. By MFA, phenotypic characteristics in glycolysis, glutaminolysis, pentose phosphate pathway, citrate cycle, coupling of amino acid metabolism to citrate cycle, and in the energy yield could be identified. By gene expression analysis 247 deregulated metabolic genes were identified which are involved, inter alia, in amino acid metabolism, transport, and protein synthesis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Seth G, Hossler P, Yee JC et al (2006) Engineering cells for cell culture bioprocessing—physiological fundamentals. Adv Biochem Eng Biotechnol 101:119–164
Wlaschin KF, Hu WS (2006) Fedbatch culture and dynamic nutrient feeding. Adv Biochem Eng Biotechnol 101:43–74
Wurm FM (2004) Production of recombinant protein therapeutics in cultivated mammalian cells. Nat Biotechnol 22(11):1393–1398
Griffin TJ, Seth G, Xie H et al (2007) Advancing mammalian cell culture engineering using genome-scale technologies. Trends Biotechnol 25(9):401–408
O’Callaghan PM, James DC (2008) Systems biotechnology of mammalian cell factories. Brief Funct Genomic Proteomic 7(2):95–110
Europa AF, Gambhir A, Fu PC et al (2000) Multiple steady states with distinct cellular metabolism in continuous culture of mammalian cells. Biotechnol Bioeng 67(1):25–34
Gambhir A, Korke R, Lee J et al (2003) Analysis of cellular metabolism of hybridoma cells at distinct physiological states. J Biosci Bioeng 95(4):317–327
Clementschitsch F, Bayer K (2006) Improvement of bioprocess monitoring: development of novel concepts. Microb Cell Fact 5:19
Read EK, Park JT, Shah RB et al (2010) Process analytical technology (PAT) for biopharmaceutical products: Part I. concepts and applications. Biotechnol Bioeng 105(2):276–284
Read EK, Shah RB, Riley BS et al (2010) Process analytical technology (PAT) for biopharmaceutical products: Part II. Concepts and applications. Biotechnol Bioeng 105(2):285–295
Teixeira AP, Oliveira R, Alves PM et al (2009) Advances in on-line monitoring and control of mammalian cell cultures: supporting the PAT initiative. Biotechnol Adv 27(6):726–732
Gnoth S, Jenzsch M, Simutis R et al (2007) Process analytical technology (PAT): batch-to-batch reproducibility of fermentation processes by robust process operational design and control. J Biotechnol 132(2):180–186
Rathore AS (2009) Roadmap for implementation of quality by design (QbD) for biotechnology products. Trends Biotechnol 27(9):546–553
Stephanopoulos G, Locher G, Duff MJ et al (1997) Fermentation database mining by pattern recognition. Biotechnol Bioeng 53(5):443–452
Charaniya S, Hu WS, Karypis G (2008) Mining bioprocess data: opportunities and challenges. Trends Biotechnol 26(12):690–699
Kamimura RT, Bicciato S, Shimizu H et al (2000) Mining of biological data II: assessing data structure and class homogeneity by cluster analysis. Metab Eng 2(3):228–238
Karim MN, Hodge D, Simon L (2003) Data-based modeling and analysis of bioprocesses: some real experiences. Biotechnol Prog 19(5):1591–1605
Albert S, Kinley RD (2001) Multivariate statistical monitoring of batch processes: an industrial case study of fermentation supervision. Trends Biotechnol 19(2):53–62
Lennox B, Montague GA, Hiden HG et al (2001) Process monitoring of an industrial fed-batch fermentation. Biotechnol Bioeng 74(2):125–135
Kirdar AO, Conner JS, Baclaski J et al (2007) Application of multivariate analysis toward biotech processes: case study of a cell-culture unit operation. Biotechnol Prog 23(1):61–67
Kirdar AO, Green KD, Rathore AS (2008) Application of multivariate data analysis for identification and successful resolution of a root cause for a bioprocessing application. Biotechnol Prog 24(3):720–726
Mandenius CF, Brundin A (2008) Bioprocess optimization using design-of-experiments methodology. Biotechnol Prog 24(6):1191–1203
Gnoth S, Jenzsch M, Simutis R et al (2008) Control of cultivation processes for recombinant protein production: a review. Bioprocess Biosyst Eng 31(1):21–39
D’haeseleer P (2005) How does gene expression clustering work? Nat Biotechnol 23(12):1499–1501
Steuer R, Morgenthal K, Weckwerth W et al (2007) A gentle guide to the analysis of metabolomic data. Methods Mol Biol 358:105–126
Izenman AJ (2010) Modern multivariate statistical techniques: regression, classification, and manifold learning. Springer, New York
Shaw AD, Winson MK, Woodward AM et al (2000) Rapid analysis of high-dimensional bioprocesses using multivariate spectroscopies and advanced chemometrics. Adv Biochem Eng Biotechnol 66:83–113
Skibsted E, Lindemann C, Roca C et al (2001) On-line bioprocess monitoring with a multi-wavelength fluorescence sensor using multivariate calibration. J Biotechnol 88(1):47–57
Stark E, Hitzmann B, Schugerl K et al (2002) In situ-fluorescence-probes: a useful tool for non-invasive bioprocess monitoring. Adv Biochem Eng Biotechnol 74:21–38
Stephanopoulos G, Nielsen J (1998) Metabolic engineering: principles and methodologies. Academic, San Diego
Christensen B, Nielsen J (2000) Metabolic network analysis. A powerful tool in metabolic engineering. Adv Biochem Eng Biotechnol 66:209–231
Iwatani S, Yamada Y, Usuda Y (2008) Metabolic flux analysis in biotechnology processes. Biotechnol Lett 30(5):791–799
Nielsen J (2001) Metabolic engineering. Appl Microbiol Biotechnol 55(3):263–283
Koffas M, Stephanopoulos G (2005) Strain improvement by metabolic engineering: lysine production as a case study for systems biology. Curr Opin Biotechnol 16(3):361–366
de Graaf AA, Eggeling L, Sahm H (2001) Metabolic engineering for l-lysine production by Corynebacterium glutamicum. Adv Biochem Eng Biotechnol 73:9–29
Wiechert W (2002) Modeling and simulation: tools for metabolic engineering. J Biotechnol 94(1):37–63
Reed JL, Palsson BO (2003) Thirteen years of building constraint-based in silico models of Escherichia coli. J Bacteriol 185(9):2692–2699
Balcarcel RR, Clark LM (2003) Metabolic screening of mammalian cell cultures using well-plates. Biotechnol Prog 19(1):98–108
Bonarius HP, Hatzimanikatis V, Meesters KP et al (1996) Metabolic flux analysis of hybridoma cells in different culture media using mass balances. Biotechnol Bioeng 50(3):299–318
Bonarius HP, Houtman JH, Schmid G et al (2000) Metabolic-flux analysis of hybridoma cells under oxidative and reductive stress using mass balances. Cytotechnology 32(2):97–107
Follstad BD, Balcarcel RR, Stephanopoulos G et al (1999) Metabolic flux analysis of hybridoma continuous culture steady state multiplicity. Biotechnol Bioeng 63(6):675–683
Dalm MC, Lamers PP, Cuijten SM et al (2007) Effect of feed and bleed rate on hybridoma cells in an acoustic perfusion bioreactor: metabolic analysis. Biotechnol Prog 23(3):560–569
Bonarius HP, Ozemre A, Timmerarends B et al (2001) Metabolic-flux analysis of continuously cultured hybridoma cells using (13)CO(2) mass spectrometry in combination with (13)C-lactate nuclear magnetic resonance spectroscopy and metabolite balancing. Biotechnol Bioeng 74(6):528–538
Altamirano C, Illanes A, Casablancas A et al (2001) Analysis of CHO cells metabolic redistribution in a glutamate-based defined medium in continuous culture. Biotechnol Prog 17(6):1032–1041
Altamirano C, Illanes A, Becerra S et al (2006) Considerations on the lactate consumption by CHO cells in the presence of galactose. J Biotechnol 125(4):547–556
Goudar C, Biener R, Zhang C et al (2006) Towards industrial application of quasi real-time metabolic flux analysis for mammalian cell culture. Adv Biochem Eng Biotechnol 101:99–118
Niklas J, Schneider K, Heinzle E (2010) Metabolic flux analysis in eukaryotes. Curr Opin Biotechnol 21:63–69
Quek LE, Dietmair S, Kromer JO et al (2010) Metabolic flux analysis in mammalian cell culture. Metab Eng 12(2):161–171
Boghigian BA, Seth G, Kiss R et al (2010) Metabolic flux analysis and pharmaceutical production. Metab Eng 12(2):81–95
Goudar C, Biener R, Boisart C et al (2010) Metabolic flux analysis of CHO cells in perfusion culture by metabolite balancing and 2D [13C, 1H] COSY NMR spectroscopy. Metab Eng 12(2):138–149
Birzele F, Schaub J, Rust W et al (2010) Into the unknown: expression profiling without genome sequence information in CHO by next generation sequencing. Nucleic Acids Res 38(12):3999–4010
Jacob NM, Kantardjieff A, Yusufi FN et al (2009) Reaching the depth of the Chinese hamster ovary cell transcriptome. Biotechnol Bioeng 105(5):1002–1009
Kantardjieff A, Nissom PM, Chuah SH et al (2009) Developing genomic platforms for Chinese hamster ovary cells. Biotechnol Adv 27(6):1028–1035
Korke R, Gatti ML, Lau AL et al (2004) Large scale gene expression profiling of metabolic shift of mammalian cells in culture. J Biotechnol 107(1):1–17
Schaub J, Clemens C, Schorn P et al (2010) CHO gene expression profiling in biopharmaceutical process analysis and design. Biotechnol Bioeng 105(2):431–438
Wong VV, Nissom PM, Sim SL et al (2006) Zinc as an insulin replacement in hybridoma cultures. Biotechnol Bioeng 93(3):553–563
Spens E, Haggstrom L (2009) Proliferation of NS0 cells in protein-free medium: the role of cell-derived proteins, known growth factors and cellular receptors. J Biotechnol 141(3–4):123–129
Trummer E, Ernst W, Hesse F et al (2008) Transcriptional profiling of phenotypically different Epo-Fc expressing CHO clones by cross-species microarray analysis. Biotechnol J 3(7):924–937
Clark KJ, Griffiths J, Bailey KM et al (2005) Gene-expression profiles for five key glycosylation genes for galactose-fed CHO cells expressing recombinant IL-4/13 cytokine trap. Biotechnol Bioeng 90(5):568–577
Wong DC, Wong KT, Lee YY et al (2006) Transcriptional profiling of apoptotic pathways in batch and fed-batch CHO cell cultures. Biotechnol Bioeng 94(2):373–382
Wong DC, Wong KT, Nissom PM et al (2006) Targeting early apoptotic genes in batch and fed-batch CHO cell cultures. Biotechnol Bioeng 95(3):350–361
Shen D, Kiehl TR, Khattak SF et al (2010) Transcriptomic responses to sodium chloride-induced osmotic stress: a study of industrial fed-batch CHO cell cultures. Biotechnol Prog 26(4):1104–1115
Wu MH, Dimopoulos G, Mantalaris A et al (2004) The effect of hyperosmotic pressure on antibody production and gene expression in the GS-NS0 cell line. Biotechnol Appl Biochem 40(Pt 1):41–46
Yee JC, Gerdtzen ZP, Hu WS (2009) Comparative transcriptome analysis to unveil genes affecting recombinant protein productivity in mammalian cells. Biotechnol Bioeng 102(1):246–263
Al-Fageeh MB, Marchant RJ, Carden MJ et al (2006) The cold-shock response in cultured mammalian cells: harnessing the response for the improvement of recombinant protein production. Biotechnol Bioeng 93(5):829–835
De Leon GM, Wlaschin KF, Nissom PM et al (2007) Comparative transcriptional analysis of mouse hybridoma and recombinant Chinese hamster ovary cells undergoing butyrate treatment. J Biosci Bioeng 103(1):82–91
Wang M, Senger RS, Paredes C et al (2009) Microarray-based gene expression analysis as a process characterization tool to establish comparability of complex biological products: scale-up of a whole-cell immunotherapy product. Biotechnol Bioeng 104(4):796–808
Stansfield SH, Allen EE, Dinnis DM et al (2007) Dynamic analysis of GS-NS0 cells producing a recombinant monoclonal antibody during fed-batch culture. Biotechnol Bioeng 97(2):410–424
Pascoe DE, Arnott D, Papoutsakis ET et al (2007) Proteome analysis of antibody-producing CHO cell lines with different metabolic profiles. Biotechnol Bioeng 98(2):391–410
Seow TK, Korke R, Liang RC et al (2001) Proteomic investigation of metabolic shift in mammalian cell culture. Biotechnol Prog 17(6):1137–1144
Kumar N, Gammell P, Meleady P et al (2008) Differential protein expression following low temperature culture of suspension CHO-K1 cells. BMC Biotechnol 8:42
Smales CM, Dinnis DM, Stansfield SH et al (2004) Comparative proteomic analysis of GS-NS0 murine myeloma cell lines with varying recombinant monoclonal antibody production rate. Biotechnol Bioeng 88(4):474–488
Alete DE, Racher AJ, Birch JR et al (2005) Proteomic analysis of enriched microsomal fractions from GS-NS0 murine myeloma cells with varying secreted recombinant monoclonal antibody productivities. Proteomics 5(18):4689–4704
Carlage T, Hincapie M, Zang L et al (2009) Proteomic profiling of a high-producing Chinese hamster ovary cell culture. Anal Chem 81(17):7357–7362
Baik JY, Lee GM (2010) A DIGE approach for the assessment of differential expression of the CHO proteome under sodium butyrate addition: effect of Bcl-x(L) overexpression. Biotechnol Bioeng 105(2):358–367
Jin M, Szapiel N, Zhang J et al (2010) Profiling of host cell proteins by two-dimensional difference gel electrophoresis (2D-DIGE): implications for downstream process development. Biotechnol Bioeng 105(2):306–316
van der Werf MJ, Overkamp KM, Muilwijk B et al (2007) Microbial metabolomics: toward a platform with full metabolome coverage. Anal Biochem 370(1):17–25
Chong WP, Goh LT, Reddy SG et al (2009) Metabolomics profiling of extracellular metabolites in recombinant Chinese Hamster Ovary fed-batch culture. Rapid Commun Mass Spectrom 23(23):3763–3771
Oldiges M, Lutz S, Pflug S et al (2007) Metabolomics: current state and evolving methodologies and tools. Appl Microbiol Biotechnol 76(3):495–511
Bradley SA, Ouyang A, Purdie J et al (2010) Fermentanomics: monitoring mammalian cell cultures with NMR spectroscopy. J Am Chem Soc 132(28):9531–9533
Ma N, Ellet J, Okediadi C et al (2009) A single nutrient feed supports both chemically defined NS0 and CHO fed-batch processes: improved productivity and lactate metabolism. Biotechnol Prog 25(5):1353–1363
Dietmair S, Timmins NE, Gray PP et al (2010) Towards quantitative metabolomics of mammalian cells: development of a metabolite extraction protocol. Anal Biochem 404(2):155–164
Sellick CA, Hansen R, Maqsood AR et al (2009) Effective quenching processes for physiologically valid metabolite profiling of suspension cultured Mammalian cells. Anal Chem 81(1):174–183
Baik JY, Lee MS, An SR et al (2006) Initial transcriptome and proteome analyses of low culture temperature-induced expression in CHO cells producing erythropoietin. Biotechnol Bioeng 93(2):361–371
Kantardjieff A, Jacob NM, Yee JC et al (2010) Transcriptome and proteome analysis of Chinese hamster ovary cells under low temperature and butyrate treatment. J Biotechnol 145(2):143–159
Doolan P, Meleady P, Barron N et al (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(1):42–56
Nissom PM, Sanny A, Kok YJ et al (2006) Transcriptome and proteome profiling to understanding the biology of high productivity CHO cells. Mol Biotechnol 34(2):125–140
Jolliffe IT (1986) Principal component analysis. Springer, New York
Zhang J, Martin EB, Morris AJ (1997) Process monitoring using non-linear statistical techniques. Chem Eng J 67(3):181–189
Schaub J, Mauch K, Reuss M (2008) Metabolic flux analysis in Escherichia coli by integrating isotopic dynamic and isotopic stationary 13C labeling data. Biotechnol Bioeng 99(5):1170–1185
Pruitt KD, Tatusova T, Maglott DR (2007) NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res 35(Database issue):D61–D65
van der Heijden RT, Heijnen JJ, Hellinga C et al (1994) Linear constraint relations in biochemical reaction systems: I. Classification of the calculability and the balanceability of conversion rates. Biotechnol Bioeng 43(1):3–10
van der Heijden RT, Romein B, Heijnen J et al (1994) Linear constrain relations in biochemical reaction systems III. Sequential application of data reconciliation for sensitive detection of systematic errors. Biotechnol Bioeng 44(7):781–791
Haggstrom L, Ljunggren J, Ohman L (1996) Metabolic engineering of animal cells. Ann N Y Acad Sci 782:40–52
Wlaschin KF, Hu WS (2007) Engineering cell metabolism for high-density cell culture via manipulation of sugar transport. J Biotechnol 131(2):168–176
Schneider M, Marison IW, von Stockar U (1996) The importance of ammonia in mammalian cell culture. J Biotechnol 46(3):161–185
Stouthamer AH (1973) A theoretical study on the amount of ATP required for synthesis of microbial cell material. Antonie Van Leeuwenhoek 39(3):545–565
Xie L, Wang DI (1996) Energy metabolism and ATP balance in animal cell cultivation using a stoichiometrically based reaction network. Biotechnol Bioeng 52(5):591–601
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Schaub, J., Clemens, C., Kaufmann, H., Schulz, T.W. (2011). Advancing Biopharmaceutical Process Development by System-Level Data Analysis and Integration of Omics Data. In: Hu, W., Zeng, AP. (eds) Genomics and Systems Biology of Mammalian Cell Culture. Advances in Biochemical Engineering Biotechnology, vol 127. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10_2010_98
Download citation
DOI: https://doi.org/10.1007/10_2010_98
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28349-9
Online ISBN: 978-3-642-28350-5
eBook Packages: Chemistry and Materials ScienceChemistry and Material Science (R0)