br Glycoengineering br The Omics perspective The

Glycoengineering
The « Omics » perspective The publication of the CHO-K1 genome sequence in 2011, followed by the publication of two Chinese hamster and six CHO cell line genomes in 2013, bring new opportunities in developing and engineering CHO Docetaxel synthesis for improved glycoprotein production (Xu et al., 2011, Brinkrolf et al., 2013, Lewis et al., 2013). The biopharmaceutical industry has entered the “omics” era, where systems biology is a central concept (Kildegaard et al., 2013). Genomics, transcriptomics, proteomics and metabolomics are now some examples of the available tools to enhance protein production in CHO cell factories. Sequencing of the CHO-K1 genome allowed the identification and positioning of their genes and of most of human glycosylation-associated transcripts (Xu et al., 2011). This information, combined with advances of in deep-sequencing and genome-editing technologies such as ZFN or CRISP/Cas9, now facilitate genome manipulations for cell engineering strategies. As an example, CHO cells, where simultaneous disruption of FUT8, BAX and BAK gene by CRISP/Cas9 was achieved and confirmed by deep sequencing, showed increased resistance to apoptosis (Grav et al., 2015). (For a detailed review of engineering strategies using the CRISP/Cas9 technology for genome edition in CHO or human cell lines see (Lee et al., 2015a). Moreover, these omics technologies will certainly help for cGMP compliant processes and biomanufacturing, as they will facilitate extensive characterization of engineered cell lines. For example, deep-sequencing of knock out cell lines can now be achieved to determine their exact sequence and to ensure their stability in terms of genomic rearrangements (Kremkow and Lee, 2013). In a next generation sequencing (NGS) study regrouping diverse cDNA libraries from CHO cell lines, 29 000 transcripts were assembled, which identified 13 187 mRNA transcripts (Becker et al., 2011). Combined with previous NGS studies in various CHO cell lines, this study was determinant in establishing the transcriptome profile of these cells (Birzele et al., 2010, Jacob et al., 2010). So far, the analysis of transcriptomic data has already helped finding chromosomal regions or genes influencing cell productivity. Comparison of gene expression profiles between high and low producing CHO clones allowed the identification of a deletion in chromosome 8 telomeric region of the cells correlating with higher productivity (Ritter et al., 2016b). The C12orf35 gene comprised within this region seems to be responsible for the phenotype (Ritter et al., 2016a). Moreover, with the help of transcriptomic tools such as RNA-seq, microRNA expression profiles of CHO cells are now available (Hackl et al., 2011, Johnson et al., 2011, Hammond et al., 2012, Kozomara and Griffiths-Jones, 2014). With growing evidence for their role in diverse biological processes, some microRNA genes serve as novel targets for cell engineering strategies (Muller et al., 2008, Jadhav et al., 2013). Higher recombinant protein production was obtained when overexpressing the miR-7 gene in CHO cells (Barron et al., 2011). CHO cell lines producing recombinant IgG were also found to have decreased expression of miR-221 and miR-222 compared to their parental cell line DG44 (Lin et al., 2011). Also, as discussed earlier, inhibition of the mmu-miR–466h microRNA was shown to increase apoptosis resistance of these cells (Druz et al., 2011, Druz et al., 2013). The CHO genomic sequence also contributed to a surge in proteomic studies. A first large-scale proteomic analysis identified 6164 proteins from both the proteome and glycoproteome of CHO‐K1 cells (Baycin-Hizal et al., 2012). This multidimensional study also resolved the codon frequency in these cells, which largely contributed to sequence optimization of genes encoding recombinant proteins for this expression system. Moreover, combination of these proteomic data with available transcriptomic data could elucidate the relative levels of the different biological pathways found within CHO-K1 cells (Kildegaard et al., 2013). Proteome analysis using SILAC (stable isotope labeling) and iTRAQ (isobaric tags for relative and absolute quantification) techniques are now widely used for biomarker identification in antibody-producing cells (Kildegaard et al., 2013). Such biomarkers contribute in targeting specific proteins for cell line engineering. Using iTRAQ, MCM2 and MCM5 helicases were identified as cell growth markers in CHO cells (Carlage et al., 2012). Besides, chaperones BiP and PDI were differentially expressed in stationary compared to exponential phase in CHO cells overexpressing Bcl-xl (Carlage et al., 2012). Furthermore, two-dimensional gel electrophoresis and mass spectrometry techniques are both proteomic tools widely used to identify changes in protein levels under precise experimental conditions (see (Kim et al., 2012) for a detailed review). Together, these two techniques will definitely contribute to the elaboration of cell engineering strategies. Indeed, comparison of proteomic profiles of fast vs slow growing CHO clones has already identified the valosin-containing protein (VCP) as a potential candidate for enhancement of cell growth by cell engineering (Doolan et al., 2010). Other candidates will likely come out of these analyses in the near future.