Model-based analysis of N-glycosylation in Chinese hamster ovary cells

The Chinese hamster ovary (CHO) cell is the gold standard for manufacturing of glycosylated recombinant proteins for production of biotherapeutics. The similarity of its glycosylation patterns to the human versions enable the products of this cell line favorable pharmacokinetic properties and lower likelihood of causing immunogenic responses. Because glycan structures are the product of the concerted action of intracellular enzymes, it is difficult to predict a priori how the effects of genetic manipulations alter glycan structures of cells and therapeutic properties. For that reason, quantitative models able to predict glycosylation have emerged as promising tools to deal with the complexity of glycosylation processing. For example, an earlier version of the same model used in this study was used by others to successfully predict changes in enzyme activities that could produce a desired change in glycan structure. In this study we utilize an updated version of this model to provide a comprehensive analysis of N-glycosylation in ten Chinese hamster ovary (CHO) cell lines that include a wild type parent and nine mutants of CHO, through interpretation of previously published mass spectrometry data. The updated N-glycosylation mathematical model contains up to 50,605 glycan structures. Adjusting the enzyme activities in this model to match N-glycan mass spectra produces detailed predictions of the glycosylation process, enzyme activity profiles and complete glycosylation profiles of each of the cell lines. These profiles are consistent with biochemical and genetic data reported previously. The model-based results also predict glycosylation features of the cell lines not previously published, indicating more complex changes in glycosylation enzyme activities than just those resulting directly from gene mutations. The model predicts that the CHO cell lines possess regulatory mechanisms that allow them to adjust glycosylation enzyme activities to mitigate side effects of the primary loss or gain of glycosylation function known to exist in these mutant cell lines. Quantitative models of CHO cell glycosylation have the potential for predicting how glycoengineering manipulations might affect glycoform distributions to improve the therapeutic performance of glycoprotein products

Characterizing the effect of glutamine supplementation on asparagine and glutamine metabolism using 13C metabolic flux analysis

Upstream development efforts often focus on improved productivity. Among those efforts, improvements in medium formulations have translated into greater titers. To continue this historical trend, a better understanding of the cell metabolism is warranted for guiding efficient utilization of medium components to improve titer while minimizing byproducts. 13C Metabolic Flux Analysis (13C MFA) offers opportunities to study metabolic phenotypes by applying isotope tracers to estimate the intracellular fluxes through metabolic pathways. In this work, 13C MFA was applied to study the effects of glutamine supplementation by 13C parallel labelling of cultures with [U-13C]asparagine, [U-13C]glutamine and an a mixture of [U-13C]glucose with [1,2-13C]glucose. The study was focused on two metabolic states characterized by glutamine consumption in the early exponential phase and glutamine production in the late exponential phase of a fed-batch culture. To quantify individual metabolic pathway activity, metabolic flux maps were generated for the glutamine supplemented feeds compared to a control case with glutamine in the initial medium. The glutamine supplementation condition resulted in redistribution of the fluxes in the TCA cycle. Furthermore, measurements of the enrichment of cell protein indicate different allocations of the fed nutrients into generated biomass for the glutamine supplemented condition. Comparison between the early and the late exponential phases provided novel insights on how glutamine modulates CHO central carbon metabolism and supports the important role of glutamine as a major source of energy for cell proliferation. These findings contribute towards an improved characterization of the metabolism of industrial cells with useful implications for optimizing medium and feed development

Integration of the Transcriptome and Glycome for Identification of Glycan Cell Signatures

<div><p>Abnormalities in glycan biosynthesis have been conclusively linked to many diseases but the complexity of glycosylation has hindered the analysis of glycan data in order to identify glycoforms contributing to disease. To overcome this limitation, we developed a quantitative N-glycosylation model that interprets and integrates mass spectral and transcriptomic data by incorporating key glycosylation enzyme activities. Using the cancer progression model of androgen-dependent to androgen-independent Lymph Node Carcinoma of the Prostate (LNCaP) cells, the N-glycosylation model identified and quantified glycan structural details not typically derived from single-stage mass spectral or gene expression data. Differences between the cell types uncovered include increases in H(II) and Le^y epitopes, corresponding to greater activity of α2-Fuc-transferase (FUT1) in the androgen-independent cells. The model further elucidated limitations in the two analytical platforms including a defect in the microarray for detecting the GnTV (MGAT5) enzyme. Our results demonstrate the potential of systems glycobiology tools for elucidating key glycan biomarkers and potential therapeutic targets. The integration of multiple data sets represents an important application of systems biology for understanding complex cellular processes.</p> </div

Schematic N-glycosylation pathway representation characteristic of high and low passage LNCaP cells.

<p>The steps to elaborate the glycan structures corresponding to both LNCaP cells lines are represented in a simplified N-glycosylation pathway according to the mass spectral structural data as well as the transcription expression data. A main feature for this pathway is the lower levels of Type I glycans (light blue filled rectangles) compared to type II glycans (light orange filled rectangles) in both cell lines, implying that glycans characteristic of both cell lines are principally type II glycans. Where indicated, genes in the pathways are listed in parenthesis and located below their corresponding enzymes. For example, the enzyme b4GalT, associated with type II glycans, is mainly represented by expression of B4GALT1 and B4GALT3 genes among other genes. The main difference between low and high passage LNCaP cell lines is the increased expression of FucTH (FUTI) in high passage LNCaP cells as noted in both microarray data and mass spectra based model predicted enzyme levels. This is also translated in increases of H(II) and Le^y epitopes (indicated by the glycan structures within the dark orange border rectangles). The dashed arrows point to glycan structures that are absent or marginally present. Quantitative detail of the corresponding type I and type II glycan abundances for the structures in this figure are depicted in both <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002813#pcbi-1002813-g005" target="_blank">Figures 5</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002813#pcbi-1002813-g006" target="_blank">6</a>. Initial steps of glycan formation as well as sialylation processing are omitted for simplicity.</p

Current reaction rules.

<p>Current reaction rules.</p

Adjustment rules and factors corresponding to the rule indices in Table 2.

<p>Adjustment rules and factors corresponding to the rule indices in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002813#pcbi-1002813-t002" target="_blank">Table 2</a>.</p