Abstract：
:N-linked glycosylation of proteins has both functional and structural significance. Importantly, the glycan structure of a therapeutic protein influences its efficacy, pharmacokinetics, pharmacodynamics and immunogenicity. In this work, we developed glycosylation flux analysis (GFA) for predicting intracellular production and consumption rates (fluxes) of glycoforms, and applied this analysis to CHO fed-batch immunoglobulin G (IgG) production using two different media compositions, with and without additional manganese feeding. The GFA is based on a constraint-based modeling of the glycosylation network, employing a pseudo steady state assumption. While the glycosylation fluxes in the network are balanced at each time point, the GFA allows the fluxes to vary with time by way of two scaling factors: (1) an enzyme-specific factor that captures the temporal changes among glycosylation reactions catalysed by the same enzyme, and (2) the cell specific productivity factor that accounts for the dynamic changes in the IgG production rate. The GFA of the CHO fed-batch cultivations showed that regardless of the media composition, galactosylation fluxes decreased with the cultivation time more significantly than the other glycosylation reactions. Furthermore, the GFA showed that the addition of Mn, a cofactor of galactosyltransferase, has the effect of increasing the galactosylation fluxes but only during the beginning of the cultivation period. The results thus demonstrated the power of the GFA in delineating the dynamic alterations of the glycosylation fluxes by local (enzyme-specific) and global (cell specific productivity) factors.
journal_name
Metab Engjournal_title
Metabolic engineeringauthors
Hutter S,Villiger TK,Brühlmann D,Stettler M,Broly H,Soos M,Gunawan Rdoi
10.1016/j.ymben.2017.07.005subject
Has Abstractpub_date
2017-09-01 00:00:00pages
9-20issue
Pt Aeissn
1096-7176issn
1096-7184pii
S1096-7176(17)30096-4journal_volume
43pub_type
杂志文章abstract：:Genome-wide or large-scale methodologies employed in functional genomics such as DNA sequencing, transcription profiling, proteomics, and metabolite profiling have become important tools in many metabolic engineering strategies. These techniques allow the identification of genetic differences and insight into their ce...
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