Diana Széliová1, David E Ruckerbauer1, Sarah N Galleguillos1, Lars B Petersen2, Klaus Natter3, Michael Hanscho4, Christina Troyer5, Tim Causon5, Harald Schoeny6, Hanne B Christensen2, Dong-Yup Lee7, Nathan E Lewis8, Gunda Koellensperger6, Stephan Hann9, Lars K Nielsen10, Nicole Borth11, Jürgen Zanghellini12. 1. Acib - Austrian Centre of Industrial Biotechnology, 1190, Vienna, Austria; Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, 1190, Vienna, Austria. 2. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, 2800, Lyngby, Denmark. 3. Institute of Molecular Biosciences, NAWI Graz, University of Graz, 8010, Graz, Austria. 4. Acib - Austrian Centre of Industrial Biotechnology, 1190, Vienna, Austria. 5. Department of Chemistry, University of Natural Resources and Life Sciences, Vienna, 1190, Vienna, Austria. 6. Department of Analytical Chemistry, University of Vienna, 1010, Vienna, Austria. 7. Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore; School of Chemical Engineering, Sungkyunkwan University, Suwon, Republic of Korea. 8. Dept. of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA. 9. Acib - Austrian Centre of Industrial Biotechnology, 1190, Vienna, Austria; Department of Chemistry, University of Natural Resources and Life Sciences, Vienna, 1190, Vienna, Austria. 10. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, 2800, Lyngby, Denmark; Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, Queensland, 4072, Australia. 11. Acib - Austrian Centre of Industrial Biotechnology, 1190, Vienna, Austria; Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, 1190, Vienna, Austria. Electronic address: nicole.borth@boku.ac.at. 12. Acib - Austrian Centre of Industrial Biotechnology, 1190, Vienna, Austria; Department of Analytical Chemistry, University of Vienna, 1010, Vienna, Austria. Electronic address: juergen.zanghellini@univie.ac.at.
Abstract
BACKGROUND: Cell line-specific, genome-scale metabolic models enable rigorous and systematic in silico investigation of cellular metabolism. Such models have recently become available for Chinese hamster ovary (CHO) cells. However, a key ingredient, namely an experimentally validated biomass function that summarizes the cellular composition, was so far missing. Here, we close this gap by providing extensive experimental data on the biomass composition of 13 parental and producer CHO cell lines under various conditions. RESULTS: We report total protein, lipid, DNA, RNA and carbohydrate content, cell dry mass, and detailed protein and lipid composition. Furthermore, we present meticulous data on exchange rates between cells and environment and provide detailed experimental protocols on how to determine all of the above. The biomass composition is converted into cell line- and condition-specific biomass functions for use in cell line-specific, genome-scale metabolic models of CHO. Finally, flux balance analysis (FBA) is used to demonstrate consistency between in silico predictions and experimental analysis. CONCLUSIONS: Our study reveals a strong variability of the total protein content and cell dry mass across cell lines. However, the relative amino acid composition is independent of the cell line and condition and thus needs not be explicitly measured for each new cell line. In contrast, the lipid composition is strongly influenced by the growth media and thus will have to be determined in each case. These cell line-specific variations in biomass composition have a small impact on growth rate predictions with FBA, as inaccuracies in the predictions are rather dominated by inaccuracies in the exchange rate spectra. Cell-specific biomass variations only become important if the experimental errors in the exchange rate spectra drop below twenty percent.
BACKGROUND: Cell line-specific, genome-scale metabolic models enable rigorous and systematic in silico investigation of cellular metabolism. Such models have recently become available for Chinese hamster ovary (CHO) cells. However, a key ingredient, namely an experimentally validated biomass function that summarizes the cellular composition, was so far missing. Here, we close this gap by providing extensive experimental data on the biomass composition of 13 parental and producer CHO cell lines under various conditions. RESULTS: We report total protein, lipid, DNA, RNA and carbohydrate content, cell dry mass, and detailed protein and lipid composition. Furthermore, we present meticulous data on exchange rates between cells and environment and provide detailed experimental protocols on how to determine all of the above. The biomass composition is converted into cell line- and condition-specific biomass functions for use in cell line-specific, genome-scale metabolic models of CHO. Finally, flux balance analysis (FBA) is used to demonstrate consistency between in silico predictions and experimental analysis. CONCLUSIONS: Our study reveals a strong variability of the total protein content and cell dry mass across cell lines. However, the relative amino acid composition is independent of the cell line and condition and thus needs not be explicitly measured for each new cell line. In contrast, the lipid composition is strongly influenced by the growth media and thus will have to be determined in each case. These cell line-specific variations in biomass composition have a small impact on growth rate predictions with FBA, as inaccuracies in the predictions are rather dominated by inaccuracies in the exchange rate spectra. Cell-specific biomass variations only become important if the experimental errors in the exchange rate spectra drop below twenty percent.
Authors: Brian J Kirsch; Sandra V Bennun; Adam Mendez; Amy S Johnson; Hongxia Wang; Haibo Qiu; Ning Li; Shawn M Lawrence; Hanne Bak; Michael J Betenbaugh Journal: Biotechnol Bioeng Date: 2022-01-06 Impact factor: 4.395
Authors: Andreas Dräger; Tomáš Helikar; Matteo Barberis; Marc Birtwistle; Laurence Calzone; Claudine Chaouiya; Jan Hasenauer; Jonathan R Karr; Anna Niarakis; María Rodríguez Martínez; Julio Saez-Rodriguez; Juilee Thakar Journal: Bioinformatics Date: 2021-06-24 Impact factor: 6.937