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A metabolic network-based approach for developing feeding strategies for CHO cells to increase monoclonal antibody production

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Abstract

Chinese hamster ovary (CHO) cells are the main workhorse in the biopharmaceutical industry for the production of recombinant proteins, such as monoclonal antibodies. To date, a variety of metabolic engineering approaches have been used to improve the productivity of CHO cells. While genetic manipulations are potentially laborious in mammalian cells, rational design of CHO cell culture medium or efficient fed-batch strategies are more popular approaches for bioprocess optimization. In this study, a genome-scale metabolic network model of CHO cells was used to design feeding strategies for CHO cells to improve monoclonal antibody (mAb) production. A number of metabolites, including threonine and arachidonate, were suggested by the model to be added into cell culture medium. The designed composition has been experimentally validated, and then optimized, using design of experiment methods. About a two-fold increase in the total mAb expression has been observed using this strategy. Our approach can be used in similar bioprocess optimization problems, to suggest new ways of increasing production in different cell factories.

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Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information files. The metabolic model of CHO cells is publicly available in the supporting information of the original article (Hefzi et al. Cell Systems, 2016), which has been cited in our article. The cell line which has been used in our study is available in Radin Biotech Company of Iran.

Abbreviations

CHO:

Chinese hamster ovary

mAbs:

Monoclonal antibodies

GEMs:

Genome-scale metabolic network models

GPR:

Gene–protein–reaction

FVSEOF:

Flux variability scanning based on enforced objective flux

DoE:

Design of experiment

PB:

Plackett–Burman

RSM:

Response surface methodology

CCD:

Central composite design

References

  1. Walsh G (2014) Biopharmaceutical benchmarks 2014. Nat Biotechnol 32(10):992

    Article  CAS  PubMed  Google Scholar 

  2. Weiner GJ (2015) Building better monoclonal antibody-based therapeutics. Nat Rev Cancer 15(6):361–370

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Lim Y, Wong NS, Lee YY, Ku SC, Wong DC, Yap MG (2010) Engineering mammalian cells in bioprocessing–current achievements and future perspectives. Biotechnol Appl Biochem 55(4):175–189

    Article  CAS  PubMed  Google Scholar 

  4. Kim JY, Kim Y-G, Lee GM (2012) CHO cells in biotechnology for production of recombinant proteins: current state and further potential. Appl Microbiol Biotechnol 93(3):917–930

    Article  CAS  PubMed  Google Scholar 

  5. Richelle A, Lewis NE (2017) Improvements in protein production in mammalian cells from targeted metabolic engineering. Current Opinion in Systems Biology 6:1–6

    Article  PubMed  PubMed Central  Google Scholar 

  6. 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

    Article  PubMed  Google Scholar 

  7. Farrell A, McLoughlin N, Milne JJ, Marison IW, Bones J (2014) Application of multi-omics techniques for bioprocess design and optimization in Chinese hamster ovary cells. J Proteome Res 13(7):3144–3159

    Article  CAS  PubMed  Google Scholar 

  8. Gao Y, Ray S, Dai S, Ivanov AR, Abu-Absi NR, Lewis AM, Huang Z, Xing Z, Borys MC, Li ZJ (2016) Combined metabolomics and proteomics reveals hypoxia as a cause of lower productivity on scale-up to a 5000-liter CHO bioprocess. Biotechnol J 11(9):1190–1200

    Article  CAS  PubMed  Google Scholar 

  9. Fischer S, Handrick R, Otte K (2015) The art of CHO cell engineering: a comprehensive retrospect and future perspectives. Biotechnol Adv 33(8):1878–1896

    Article  CAS  PubMed  Google Scholar 

  10. Baik JY, Dahodwala H, Oduah E, Talman L, Gemmill TR, Gasimli L, Datta P, Yang B, Li G, Zhang F (2015) Optimization of bioprocess conditions improves production of a CHO cell-derived, bioengineered heparin. Biotechnol J 10(7):1067–1081

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Zhang H, Wang H, Liu M, Zhang T, Zhang J, Wang X, Xiang W (2013) Rational development of a serum-free medium and fed-batch process for a GS-CHO cell line expressing recombinant antibody. Cytotechnology 65(3):363–378

    Article  PubMed  Google Scholar 

  12. Mellahi K, Brochu D, Gilbert M, Perrier M, Ansorge S, Durocher Y, Henry O (2019) Process intensification for the production of rituximab by an inducible CHO cell line. Bioprocess Biosyst Eng 42(5):711–725

    Article  CAS  PubMed  Google Scholar 

  13. Pereira S, Kildegaard HF, Andersen MR (2018) Impact of CHO metabolism on cell growth and protein production: an overview of toxic and inhibiting metabolites and nutrients. Biotechnol J 13(3):1700499

    Article  Google Scholar 

  14. Bordbar A, Monk JM, King ZA, Palsson BØ (2014) Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet 15(2):107–120

    Article  CAS  PubMed  Google Scholar 

  15. Gu C, Kim GB, Kim WJ, Kim HU, Lee SY (2019) Current status and applications of genome-scale metabolic models. Genome Biol 20(1):121

    Article  PubMed  PubMed Central  Google Scholar 

  16. Hefzi H, Ang KS, Hanscho M, Bordbar A, Ruckerbauer D, Lakshmanan M, Orellana CA, Baycin-Hizal D, Huang Y, Ley D (2016) A consensus genome-scale reconstruction of Chinese hamster ovary cell metabolism. Cell Syst 3(5):434–443

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Long MR, Ong WK, Reed JL (2015) Computational methods in metabolic engineering for strain design. Curr Opin Biotechnol 34:135–141

    Article  CAS  PubMed  Google Scholar 

  18. Park HM, Kim HU, Park JM, Lee SY, Kim TY, Kim WJ (2012) Flux variability scanning based on enforced objective flux for identifying gene amplification targets. BMC Syst Biol 6(1):106

    Article  PubMed  PubMed Central  Google Scholar 

  19. Schellenberger J, Que R, Fleming RM, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2. 0. Nat Protoc 6(9):1290

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Nocon J, Steiger MG, Pfeffer M, Sohn SB, Kim TY, Maurer M, Rußmayer H, Pflügl S, Ask M, Haberhauer-Troyer C (2014) Model based engineering of Pichia pastoris central metabolism enhances recombinant protein production. Metab Eng 24:129–138

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Torkashvand F, Mahboudi F, Vossoughi M, Fatemi E, Moosavi Basri SM, Vaziri B (2018) Quantitative proteomic analysis of cellular responses to a designed amino acid feed in a monoclonal antibody producing Chinese hamster ovary cell line. Iran Biomed J 22(6):385–393

    Article  PubMed  PubMed Central  Google Scholar 

  22. Barrentine LB (1999) An introduction to design of experiments: a simplified approach. ASQ Quality Press, Milwaukee

    Google Scholar 

  23. Plackett RL, Burman JP (1946) The design of optimum multifactorial experiments. Biometrika 33(4):305–325

    Article  Google Scholar 

  24. Vanaja K, Shobha Rani R (2007) Design of experiments: concept and applications of Plackett Burman design. Clin Res Regul Aff 24(1):1–23

    Article  Google Scholar 

  25. Guha M, Ali SZ, Bhattacharya S (2003) Screening of variables for extrusion of rice flour employing a Plackett-Burman design. J Food Eng 57(2):135–144

    Article  Google Scholar 

  26. Cornell JA (2011) Experiments with mixtures: designs, models, and the analysis of mixture data, vol 895. John Wiley & Sons, New York

    Book  Google Scholar 

  27. Montgomery DC (2017) Design and analysis of experiments. John wiley & sons, New York

    Google Scholar 

  28. Box GE, Wilson KB (1992) On the experimental attainment of optimum conditions. Breakthroughs in statistics. Springer, New York, pp 270–310

    Chapter  Google Scholar 

  29. Vining GG, Kowalski S (2010) Statistical methods for engineers. Cengage Learning

  30. Torkashvand F, Vaziri B, Maleknia S, Heydari A, Vossoughi M, Davami F, Mahboudi F (2015) Designed amino acid feed in improvement of production and quality targets of a therapeutic monoclonal antibody. PLoS ONE 10(10):e0140597

    Article  PubMed  PubMed Central  Google Scholar 

  31. Rosenthal MD (1987) Fatty acid metabolism of isolated mammalian cells. Prog Lipid Res 26(2):87–124

    Article  CAS  PubMed  Google Scholar 

  32. Kelley DS, Taylor PC, Nelson GJ, Mackey BE (1998) Arachidonic acid supplementation enhances synthesis of eicosanoids without suppressing immune functions in young healthy men. Lipids 33(2):125–130

    Article  CAS  PubMed  Google Scholar 

  33. Hammarström S (1983) Leukotrienes. Annu Rev Biochem 52(1):355–377

    Article  PubMed  Google Scholar 

  34. Needleman P, Truk J, Jakschik BA, Morrison AR, Lefkowith JB (1986) Arachidonic acid metabolism. Annu Rev Biochem 55(1):69–102

    Article  CAS  PubMed  Google Scholar 

  35. Cabral M, Martín-Venegas R, Moreno JJ (2013) Role of arachidonic acid metabolites on the control of non-differentiated intestinal epithelial cell growth. Int J Biochem Cell Biol 45(8):1620–1628

    Article  CAS  PubMed  Google Scholar 

  36. Bourre J, Faivre A, Dumont O, Nouvelot A, Loudes C, Puymirat J, Tixier-Vidal A (1983) Effect of polyunsaturated fatty acids on fetal mouse brain cells in culture in a chemically defined medium. J Neurochem 41(5):1234–1242

    Article  CAS  PubMed  Google Scholar 

  37. Habbel P, Weylandt KH, Lichopoj K, Nowak J, Purschke M, Wang J-D, He C-W, Baumgart DC, Kang JX (2009) Docosahexaenoic acid suppresses arachidonic acid-induced proliferation of LS-174T human colon carcinoma cells. World J Gastroenterol 15(9):1079–1084

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Chang N-W, Wu C-T, Chen D-R, Yeh C-Y, Lin C (2013) High levels of arachidonic acid and peroxisome proliferator-activated receptor-alpha in breast cancer tissues are associated with promoting cancer cell proliferation. J Nutr Biochem 24(1):274–281

    Article  CAS  PubMed  Google Scholar 

  39. Lambremont EN, Lee T-c, Blank ML, Snyder F (1978) Δ5 desaturation of fatty acids in LM cells. Biochem Biophys Res Commun 80(4):813–818

    Article  CAS  PubMed  Google Scholar 

  40. Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, Bolival B, Assad-Garcia N, Glass JI, Covert MW (2012) A whole-cell computational model predicts phenotype from genotype. Cell 150(2):389–401

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ranganathan S, Suthers PF, Maranas CD (2010) OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions. PLoS Comput Biol 6(4):e1000744

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We have to kindly thank Samira Ahmadi and Radin Biotech Company, Iran, for gifting the CHO cell line to be used in our study.

Funding

No funding available.

Author information

Authors and Affiliations

  1. Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran

    Hamideh Fouladiha & Sayed-Amir Marashi

  2. Protein Chemistry and Proteomics Laboratory, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran

    Fatemeh Torkashvand, Fereidoun Mahboudi & Behrouz Vaziri

  3. Department of Bioengineering, University of California, San Diego, USA

    Nathan E. Lewis

  4. Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego, USA

    Nathan E. Lewis

  5. Department of Pediatrics, University of California, San Diego, USA

    Nathan E. Lewis

Authors
  1. Hamideh Fouladiha
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  2. Sayed-Amir Marashi
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  3. Fatemeh Torkashvand
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  4. Fereidoun Mahboudi
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  5. Nathan E. Lewis
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  6. Behrouz Vaziri
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Contributions

H.F. and S.-A.M. designed the computational studies. N.E.L. was involved in computational modeling of CHO cells metabolism. H.F., B.V., F.T., and F.M. designed the lab experiments. H.F. wrote the main manuscript. N.E.L., B.V., and S.-A.M. reviewed the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Sayed-Amir Marashi or Behrouz Vaziri.

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Fouladiha, H., Marashi, SA., Torkashvand, F. et al. A metabolic network-based approach for developing feeding strategies for CHO cells to increase monoclonal antibody production. Bioprocess Biosyst Eng 43, 1381–1389 (2020). https://doi.org/10.1007/s00449-020-02332-6

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  • DOI: https://doi.org/10.1007/s00449-020-02332-6

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