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Biology subjects

Mistry, R. K.

Publications and source records attributed to Mistry, R. K..

2 recordsLinked to original sources

Identification of Cellular Signatures Associated with Chinese Hamster Ovary (CHO) Cell Adaptation for Secretion of Antibodies

The secretory capacity of Chinese hamster ovary (CHO) cells remains a fundamental bottleneck in the manufacturing of protein-based therapeutics. Unconventional biological drugs with complex structures and processing requirements are particularly problematic. Although engineered vector DNA elements can achieve rapid and high-level therapeutic protein production, a high metabolic and protein folding burden is imposed on the host cell. Cellular adaptations to these conditions include differential gene expression profiles that can in turn influence the productivity and quality control of recombinant proteins. In this study, we used quantitative transcriptomics and proteomics analyses to investigate how biological pathways change with antibody titre. Gene and protein expression profiles of CHO pools and clones producing a panel of different monoclonal and bispecific antibodies were analysed during fed-batch production. Antibody-expressing CHO pools were heterogeneous, resulting in few discernible genetic signatures. Clonal lines derived from these pools, selected for high and low production, yielded a small number of differentially expressed proteins that correlated with productivity and were shared across biotherapeutics. However, the dominant feature associated with higher protein production was transgene copy number and resulting mRNA expression level. Moreover, variability between clones suggested that the process of cellular adaptation is variable with diverse cellular changes associated with individual adaptation events.

bioengineering↗

Protein-specific signal peptides for mammalian vector engineering

Expression of recombinant proteins in mammalian cell factories relies on synthetic assemblies of genetic parts to optimally control flux through the product biosynthetic pathway. In comparison to other genetic part-types, there is a relative paucity of characterized signal peptide components, particularly for mammalian cell contexts. In this study, we describe a toolkit of signal peptide elements, created using bioinformatics-led and synthetic design approaches, that can be utilized to enhance production of biopharmaceutical proteins in Chinese Hamster Ovary cell factories. We demonstrate, for the first time in a mammalian cell context, that machine learning can be used to predict how discrete signal peptide elements will perform when utilized to drive ER translocation of specific single chain protein products. For more complex molecular formats, such as multichain monoclonal antibodies, we describe how a combination of in silico and targeted design rule-based in vitro testing can be employed to rapidly identify product-specific signal peptide solutions from minimal screening spaces. The utility of this technology is validated by deriving vector designs that increase product titers [≥] 1.8x, compared to standard industry systems, for a range of products, including a difficult-to-express monoclonal antibody. The availability of a vastly expanded toolbox of characterized signal peptide parts, combined with streamlined in silico/in vitro testing processes, will permit efficient expression vector re-design to maximize titers of both simple and complex protein products. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/532380v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@b0d655org.highwire.dtl.DTLVardef@1c76ae8org.highwire.dtl.DTLVardef@13bae76org.highwire.dtl.DTLVardef@13ae35b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗