Incorporating RNA‐Seq transcriptomics into glycosylation‐integrating metabolic network modelling kinetics: Multiomic Chinese hamster ovary (CHO) cell bioreactors. Issue 4 (21st January 2021) - Bezjak, Lara; Erklavec Zajec, Vivian; Baebler, Špela; Stare, Tjaša; Gruden, Kristina; Pohar, Andrej; Novak, Uroš; Likozar, Blaž
Abstract: In this work, the kinetic model based on the previously developed metabolic and glycan reaction networks of the ovarian cells of the Chinese hamster ovary (CHO) cell line was improved by the inclusion of transcriptomic data that took into account the values of the RPKM gene (Reads per Kilobase of Exon per Million Reads Mapped). The transcriptomic (RNASeq) data were obtained together with metabolic and glycan data from the literature, and the concentrations with RPKM values were collected at several points in time from two fed‐batch processes. First, the fluxes were determined by regression analysis of the metabolic data, then these fluxes were corrected by using the fold change in gene expression as a measure of enzyme concentrations. Next, the corrected fluxes in the kinetic model were used to calculate the concentration profiles of the metabolites, and literature data were used to evaluate the predicted results of the model. Compared to other studies where the concentration profiles of CHO cell metabolites were described using a kinetic model without consideration of RNA‐Seq data to correct the fluxes, this model is unique. The additional integration of transcriptomic data led to better predictions of metabolic concentrations in the fed‐batch process, which is a significant improvement of the modelling technique used. Graphical Abstract: The developed kinetic model of metabolic and glycan reaction networks of Chinese hamster ovary cell line was improved by incorporating transcriptomic data that took into account the values of the RPKM gene in the fed‐batch process. The metabolic fluxes were corrected by using the fold change in gene expression as a measure of enzyme concentration, which led to better predictions of metabolic concentrations.