Mark Melville

Microarray and proteomics expression profiling identifies several candidates, including the valosin-containing protein (VCP), involved in regulating high cellular growth rate in production CHO cell lines.

A high rate of cell growth (µ) leading to rapid accumulation of viable biomass is a desirable phenotype during scale up operations and the early stages of production cultures. In order to identify genes and proteins that contribute to higher growth rates in Chinese hamster ovary (CHO) cells, a combined approach using microarray and proteomic expression profiling analysis was carried out on two matched pairs of CHO production cell lines that displayed either fast or slow growth rates. Statistical analysis of the microarray and proteomic data separately resulted in the identification of 118 gene transcripts and 58 proteins that were differentially expressed between the fast‐ and slow‐growing cells. Overlap comparison of both datasets identified a priority list of 21 candidates associated with a high growth rate phenotype in CHO. Functional analysis (by siRNA) of five of these candidates identified the valosin‐containing protein (VCP) as having a substantial impact on CHO cell growth and viability. Knockdown of HSPB1 and ENO1 also had an effect on cell growth (negative and positive, respectively). Further functional validation in CHO using both gene knockdown (siRNA) and overexpression (cDNA) confirmed that altered VCP expression impacted CHO cell proliferation, indicating that VCP and other genes and proteins identified here may play an important role in the regulation of CHO cell growth during log phase culture and are potential candidates for CHO cell line engineering strategies. Biotechnol. Bioeng. 2010; 106: 42–56.

Large scale microarray profiling and coexpression network analysis of CHO cells identifies transcriptional modules associated with growth and productivity

Weighted gene coexpression network analysis (WGCNA) was utilised to explore Chinese hamster ovary (CHO) cell transcriptome patterns associated with bioprocess relevant phenotypes. The dataset set used in this study consisted of 295 microarrays from 121 individual CHO cultures producing a range of biologics including monoclonal antibodies, fusion proteins and therapeutic factors; non-producing cell lines were also included. Samples were taken from a wide range of process scales and formats that varied in terms of seeding density, temperature, medium, feed medium, culture duration and product type. Cells were sampled for gene expression analysis at various stages of the culture and bioprocess-relevant characteristics including cell density, growth rate, viability, lactate, ammonium and cell specific productivity (Qp) were determined. WGCNA identified six distinct clusters of co-expressed genes, five of which were found to have associations with bioprocess variables. Two coexpression clusters were found to be associated with culture growth rate (1 positive and 1 negative). In addition, associations between a further three coexpression modules and Qp were observed (1 positive and 2 negative). Gene set enrichment analysis (GSEA) identified a number of significant biological processes within coexpressed gene clusters including cell cycle, protein secretion and vesicle transport. In summary, the approach presented in this study provides a novel perspective on the CHO cell transcriptome.

Predicting cell-specific productivity from CHO gene expression.

Improving the rate of recombinant protein production in Chinese hamster ovary (CHO) cells is an important consideration in controlling the cost of biopharmaceuticals. We present the first predictive model of productivity in CHO bioprocess culture based on gene expression profiles. The dataset used to construct the model consisted of transcriptomic data from 70 stationary phase, temperature-shifted CHO production cell line samples, for which the cell-specific productivity had been determined. These samples were utilised to investigate gene expression over a range of high to low monoclonal antibody and fc-fusion-producing CHO cell lines. We utilised a supervised regression algorithm, partial least squares (PLS) incorporating jackknife gene selection, to produce a model of cell-specific productivity (Qp) capable of predicting Qp to within 4.44 pg/cell/day root mean squared error in cross model validation (RMSE(CMV)). The final model, consisting of 287 genes, was capable of accurately predicting Qp in a further panel of 10 additional samples which were incorporated as an independent validation. Several of the genes constituting the model are linked with biological processes relevant to protein metabolism.

Sustained productivity in recombinant Chinese Hamster Ovary (CHO) cell lines: proteome analysis of the molecular basis for a process-related phenotype

BackgroundThe ability of mammalian cell lines to sustain cell specific productivity (Qp) over the full duration of bioprocess culture is a highly desirable phenotype, but the molecular basis for sustainable productivity has not been previously investigated in detail. In order to identify proteins that may be associated with a sustained productivity phenotype, we have conducted a proteomic profiling analysis of two matched pairs of monoclonal antibody-producing Chinese hamster ovary (CHO) cell lines that differ in their ability to sustain productivity over a 10 day fed-batch culture.ResultsProteomic profiling of inherent differences between the two sets of comparators using 2D-DIGE (Difference Gel Electrophoresis) and LC-MS/MS resulted in the identification of 89 distinct differentially expressed proteins. Overlap comparisons between the two sets of cell line pairs identified 12 proteins (AKRIB8, ANXA1, ANXA4, EIF3I, G6PD, HSPA8, HSP90B1, HSPD1, NUDC, PGAM1, RUVBL1 and CNN3) that were differentially expressed in the same direction.ConclusionThese proteins may have an important role in sustaining high productivity of recombinant protein over the duration of a fed-batch bioprocess culture. It is possible that many of these proteins could be useful for future approaches to successfully manipulate or engineer CHO cells in order to sustain productivity of recombinant protein.

Proteomic profiling of CHO cells with enhanced rhBMP-2 productivity following co-expression of PACEsol

Chinese hamster ovary (CHO) cells are widely used for the production of recombinant protein biopharmaceuticals. The purpose of this study was to investigate differences in the proteome of CHO DUKX cells expressing recombinant human bone morphogenetic protein‐2 (rhBMP‐2) (G5 cells) compared to cells also expressing soluble exogenous paired basic amino acid cleaving enzyme soluble paired basic amino acid cleaving enzyme (PACEsol) (3C9 cells), which has been previously found to improve the post‐translational processing of the mature rhBMP‐2 dimer. PACEsol co‐expression was also associated with a significant increase (almost four‐fold) in cellular productivity of rhBMP‐2 protein. Differential proteomic expression profiling using 2‐D DIGE and MALDI‐TOF MS was performed to compare 3C9 and G5 cells, and revealed a list of 60 proteins that showed differential expression (up/downregulated), with a variety of different cellular functions. A substantial number of these altered proteins were found to have chaperone activity, involved with protein folding, assembly and secretion, as well as a number of proteins involved in protein translation. These results support the use of proteomic profiling as a valuable tool towards understanding the biology of bioprocess cultures.

Development and characterization of a Chinese hamster ovary cell-specific oligonucleotide microarray.

The Chinese hamster ovary (CHO) cell line is one of the most widely used mammalian cell lines for biopharmaceutical production. We have developed and characterized a gene expression microarray (WyeHamster2a) specific for CHO cells that has enabled the study of ~3,500 sequences. Analysis of multiple sets of replicate scans showed that data derived from the WyeHamster2a array is highly reproducible confirming it as a robust tool for profiling. Twelve gene sequences were selected for follow-up RT-qPCR to confirm the accuracy and precision of the microarray results. In all but the most subtle gene expression differences, the microarray proved to be a reliable measure of differential gene expression. Finally, we were able to quantify the difference between using a bona fide CHO-specific microarray for profiling CHO cells versus an alternate, commercially available, rodent microarray such as a mouse or rat-specific format.

Microarray expression profiling identifies genes regulating sustained cell specific productivity (S‐Qp) in CHO K1 production cell lines

Fed batch culture processes are often characterized by decreasing cell culture performance as the process continues, presumably through the depletion of vital nutrients and the accumulation of toxic byproducts. We have similarly observed that cellular productivity (Qp) often declines during the course of a fed batch process; however, it is not clear why some cell lines elicit this behavior, while others do not. We here present a transcriptomic profiling analysis of a phenotype of sustained Qp (S‐Qp) in production Chinese hamster ovary (CHO) culture, in which a marked drop in Qp levels (“non‐sustained” (NS) phenotype) in two cell lines irrespective of viability levels was compared to two cell lines that consistently displayed high Qp throughout the culture (“sustained” (S) phenotype). Statistical analysis of the microarray data resulted in the identification of 22 gene transcripts whose expression patterns were either significantly negatively or positively correlated with long‐term maintenance of Qp over the culture lifespan. qPCR analysis of four of these genes on one of each (NS2, S2) of the cell lines examined by microarray analysis confirmed that two genes (CRYAB and MGST1) both replicated the microarray results and were differentially regulated between the NS and S phenotypes.

Differential Expression Profiling of Industrially Relevant CHO Cell Phenotypes Using a Proprietary CHO-Specific Microarray and Proteomics Technology Platforms

We have previously reported on the development and use of a proprietary CHO-specific microarray and proteomics technology platforms to interrogate large and diverse sets of CHO cell samples, which embodied several industrially relevant phenotypes. In a collaboration between Wyeth BioPharma and NICB/Dublin City University, we have completed the analysis of 375 individual samples representing 29 different recombinant CHO cell lines expressing monoclonal antibody, receptor-Fc fusion molecules, coagulation factors or growth factors. In this paper, we will present key learnings and insights that we have made upon analysis of the large dataset generated by the collaboration. Of note is that, despite sometimes large differences in observed phenotypes, the changes that we measured in the genomics and proteomics experiments were relatively subtle. Lastly, functional validation is important to confirming the relevance of any particular target. The development of a robust, comprehensive, and high-throughput validation workflow will be discussed.

Expression Profiling Analysis of Sodium Butyrate-Induced Chinese Hamster Ovary Cells in Defined Medium

The production of recombinant protein for biopharmaceutical application typically requires vast numbers of cells, and agents that induce expression of the desired product to even higher levels. A typical strategy employed by the biotech industry is to express the protein of interest in Chinese Hamster Ovary (CHO) cells stably transfected with the gene. Where even higher levels of expression are desired, inducing agents such as sodium butyrate or valeric acid are added to the media. Current research suggests that these chemicals increase expression by blocking deacetylation of histone proteins bound to DNA. This has the effect of relaxing DNA structure, and allowing greater access to the transcription machinery. Thus, higher levels of mRNA are achieved. However, these agents have the ultimate effect of blocking cell proliferation, and eventually inducing apoptosis in treated cells. We have undertaken an expression profiling approach to identify the genes up and down-regulated in induction conditions.