Applying genome scale metabolic models integrated with OMICs technologies for improvemwent of commercial CHO cell culture process

Although metabolic flux analysis has been established in microbial fermentation, their application in CHO cell culture is sparse. In general CHO cell culture process development is highly rely on empirical experience with limited cell and metabolite data without good mechanism understanding. The purpose of this research is to apply genome scale metabolic modeling for CHO cell culture process improvement. Recently we found that several medium components had significant impact on mAb production by BMSCHO1, a proprietary cell line (Fig. 1). Some of medium components at a low concentration, though within normal ranges for CHO cell culture, caused the BMSCHO1 crashed. Meanwhile some of the other medium components at a low concentration did not cause cell crash, but significantly decreased productivity. The preliminary genetic test results indicated no change in DNA copy number and southern blot integration profile under different medium conditions. Currently we are investigating both supernatant and cell pellets for metabolomics analysis using NMR and LCMS, and assessing epigenetic characteristics. In addition, transcriptomics data have been analyzed by RNA sequence and RT-PCR. Genome-scale modeling integrated with these OMICS datasets have been built and analyzed. In the presentation, we plan to share the investigation details of commercial cell-line and manufacturing process based on the application of genome scale modeling integrated with OMICS technology. Please click Additional Files below to see the full abstract

Upstream control strategy development for afucosylated species in mAb biomanufacturing

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Insights into the Impact of Rosmarinic Acid on CHO Cell Culture Improvement through Transcriptomics Analysis

The use of antioxidants in Chinese hamster ovary (CHO) cell cultures to improve monoclonal antibody production has been a topic of great interest. Nevertheless, the antioxidants do not have consistent benefits of production improvement, which might be cell line specific and/or process specific. In this work, we investigated how treatment with the antioxidant rosmarinic acid (RA) improved cell growth and titer in CHO cell cultures using transcriptomics. In particular, transcriptomics analysis indicated that RA treatment modified gene expression and strongly affected the MAPK and PI3K/Akt signaling pathways, which regulate cell survival and cell death. Moreover, it was observed that these signaling pathways, which had been identified to be up-regulated on day 2 and day 6 by RA, were also up-regulated over time (from initial growth phase day 2 to slow growth or protein production phase day 6) in both conditions. In summary, this transcriptomics analysis provides insights into the role of the antioxidant RA in industrial cell culture processes. The current study also represents an example in the industry of how omics can be applied to gain an in-depth understanding of CHO cell biology and to identify critical pathways that can contribute to cell culture process improvement and cell line engineering

Prediction of N-linked Glycoform Profiles of Monoclonal Antibody with Extracellular Metabolites and Two-Step Intracellular Models

Monoclonal antibodies (mAbs) are commonly glycosylated and show varying levels of galactose attachment. A set of experiments in our work showed that the galactosylation level of mAbs was altered by the culture conditions of hybridoma cells. The uridine diphosphate galactose (UDP-Gal) is one of the substrates of galactosylation. Based on that, we proposed a two-step model to predict N-linked glycoform profiles by solely using extracellular metabolites from cell culture. At the first step, the flux level of UDP-Gal in each culture was estimated based on a computational flux balance analysis (FBA); its level was found to be linear with the galactosylation degree on mAbs. At the second step, the glycoform profiles especially for G0F (agalactosylated), G1F (monogalactosylated) and G2F (digalactosylated) were predicted by a kinetic model. The model outputs well matched with the experimental data. Our study demonstrated that the integrated mathematical approach combining FBA and kinetic model is a promising strategy to predict glycoform profiles for mAbs during cell culture processes

QTL mapping based on different genetic systems for essential amino acid contents in cottonseeds in different environments.

Cottonseeds are rich in various essential amino acids. However, the inheritance of them at molecular level are still not defined across various genetic systems. In the present study, using a newly developed mapping model that can analyze the embryo and maternal main effects as well as QTL × environment interaction effects on quantitative quality trait loci (QTLs) in cottonseeds, a study on QTL located in the tetraploid embryo and tetraploid maternal plant genomes for essential amino acid contents in cottonseeds under different environments was carried out, using the immortal F2 (IF2) populations from a set of 188 recombinant inbred lines derived from an intraspecific hybrid cross of two upland cotton germplasms HS46 and MARKCBUCAG8US-1-88 as experimental materials. The results showed a total of 35 QTLs associated with these quality traits in cottonseeds. Nineteen QTLs were subsequently mapped on chromosome 5, 6 and 8 in sub-A genome and chromosome 15, 18, 22 and 23 in sub-D genome. Eighteen QTLs were also found having QTL × environment (QE) interaction effects. The genetic main effects from QTLs located on chromosomes in the embryo and maternal plant genomes and their QE effects in different environments were all important for these essential amino acids in cottonseeds. The results suggested that the influence of environmental factors on the expression of some QTLs located in different genetic systems should be considered when improving for these amino acids. This study can serve as the foundation for the improvement of these essential amino acids in cottonseeds

Identification of Cell Culture Factors Influencing Afucosylation Levels in Monoclonal Antibodies by Partial Least-Squares Regression and Variable Importance Metrics

Retrospective analysis of historic data for cell culture processes is a powerful tool to develop further process understanding. In particular, deploying retrospective analyses can identify important cell culture process parameters for controlling critical quality attributes, e.g., afucosylation, for the production of monoclonal antibodies (mAbs). However, a challenge of analyzing large cell culture data is the high correlation between regressors (particularly media composition), which makes traditional analyses, such as analysis of variance and multivariate linear regression, inappropriate. Instead, partial least-squares regression (PLSR) models, in combination with machine learning techniques such as variable importance metrics, are an orthogonal or alternative approach to identifying important regressors and overcoming the challenge of a highly covariant data structure. A specific workflow for the retrospective analysis of cell culture data is proposed that covers data curation, PLS regression, model analysis, and further steps. In this study, the proposed workflow was applied to data from four mAb products in an industrial cell culture process to identify significant process parameters that influence the afucosylation levels. The PLSR workflow successfully identified several significant parameters, such as temperature and media composition, to enhance process understanding of the relationship between cell culture processes and afucosylation levels

Genetic main effects and QE interaction effects from the QTLs of embryo and maternal plant for essential amino acid contents in cottonseeds.

<p>Notes: <i>a^e</i>, embryo additive main effect; <i>d^e</i>, embryo dominance main effect; <i>a^m</i>, Maternal additive main effect; <i>a^e</i>E₁ and <i>a^e</i>E₂, embryo additive interaction effects in environment 1 and environment 2; <i>d^e</i>E₁ and <i>d^m</i>E₂, embryo dominance interaction effects in environment 1 and environment 2, <i>a^m</i>E₁ and <i>a^m</i>E₂ maternal additive interaction effects in environment 1 and environment 2, respectively. *<i>P</i> = 0.05; **<i>P</i> = 0.01.</p

Contributions, positions of the QTLs for essential amino acid content in cottonseeds.

<p>R² (<i>a^e</i>), R² (<i>d^e</i>) and R² (<i>a^m</i>), represents the phenotypic variations explained by the <i>a^e</i>, <i>d^e</i> and <i>a^m</i>, respectively.</p><p>R², Phenotypic variation explained by a single QTL.</p

The seven essential amino acid contents (%) of parents and the IF₂ population in 2009 and 2010.

<p>a, b significance <i>P</i> = 0.05; A, B significance <i>P</i> = 0.0.</p