Multi-cell-line learning for the data-driven construction of mechanistic metabolic models.

Mammalian cells are commonly used as hosts in cell culture for biologics production in the pharmaceutical industry. Structured mechanistic models of metabolism have been used to capture complex cellular mechanisms that contribute to varying metabolic shifts in different cell lines. However, little research has focused on the impact of temporal changes in enzyme abundance and activity on the modeling of cell metabolism. In this work, we present a framework for constructing mechanistic models of metabolism that integrate growth-signaling control of enzyme activity and transcript dynamics. The proposed approach is applied to build models for three Chinese hamster ovary (CHO) cell lines using fed-batch culture data and time-series transcript profiles. Leveraging information from the transcriptome data, we develop a parameter estimation approach based on multi-cell-line (MCL) learning, which combines data sets from different cell lines and trains the individual cell-line models jointly to improve model accuracy. The computational results demonstrate the important role of growth signaling and transcript variability in metabolic models as well as the virtue of the MCL approach for constructing cell-line models with a limited amount of data. The resulting models exhibit a high level of accuracy in predicting distinct metabolic behaviors in the different cell lines; these models can potentially be used to accelerate the process and cell-line development for the biomanufacturing of new protein therapeutics.

Model-Driven Engineering of N-Linked Glycosylation in Chinese Hamster Ovary Cells.

Chinese hamster ovary (CHO) cells are used for industrial production of protein-based therapeutics (i.e., "biologics"). Here we describe a method for combining systems-level kinetic models with a synthetic biology platform for multigene overexpression to rationally perturb N-linked glycosylation. Specifically, we sought to increase galactose incorporation on a secreted Immunoglobulin G (IgG) protein. We rationally design, build, and test a total of 23 transgenic cell pools that express single or three-gene glycoengineering cassettes comprising a total of 100 kilobases of engineered DNA sequence. Through iterative engineering and model refinement, we rationally increase the fraction of bigalactosylated glycans five-fold from 11.9% to 61.9% and simultaneously decrease the glycan heterogeneity on the secreted IgG. Our approach allows for rapid hypothesis testing and identification of synergistic behavior from genetic perturbations by bridging systems and synthetic biology.

Single Copy Transgene Integration in a Transcriptionally Active Site for Recombinant Protein Synthesis.

For the biomanufacturing of protein biologics, establishing stable cell lines with high transgene transcription is critical for high productivity. Modern genome engineering tools can direct transgene insertion to a specified genomic locus and can potentially become a valuable tool for cell line generation. In this study, the authors survey transgene integration sites and their transcriptional activity to identify characteristics of desirable regions. A lentivirus containing destabilized Green Fluorescent Protein (dGFP) is used to infect Chinese hamster ovary cells at a low multiplicity of infection, and cells with high or low GFP fluorescence are isolated. RNA sequencing and Assay for Transposase Accessible Chromatin using sequencing data shows integration sites with high GFP expression are in larger regions of high transcriptional activity and accessibility, but not necessarily within highly transcribed genes. This method is used to obtain high Immunoglobulin G (IgG) expressing cell lines with a single copy of the transgene integrated into transcriptionally active and accessible genomic regions. Dual recombinase-mediated cassette exchange is then employed to swap the IgG transgene for erythropoietin or tumor necrosis factor receptor-Fc. This work thus highlights a strategy to identify desirable sites for transgene integration and to streamline the development of new product producing cell lines.