Mark Leonard

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.

Integrated miRNA, mRNA and protein expression analysis reveals the role of post-transcriptional regulation in controlling CHO cell growth rate

BackgroundTo study the role of microRNA (miRNA) in the regulation of Chinese hamster ovary (CHO) cell growth, qPCR, microarray and quantitative LC-MS/MS analysis were utilised for simultaneous expression profiling of miRNA, mRNA and protein. The sample set under investigation consisted of clones with variable cellular growth rates derived from the same population. In addition to providing a systems level perspective on cell growth, the integration of multiple profiling datasets can facilitate the identification of non-seed miRNA targets, complement computational prediction tools and reduce false positive and false negative rates.Results51 miRNAs were associated with increased growth rate (35 miRNAs upregulated and 16 miRNAs downregulated). Gene ontology (GO) analysis of genes (n=432) and proteins (n=285) found to be differentially expressed (DE) identified biological processes driving proliferation including mRNA processing and translation. To investigate the influence of miRNA on these processes we combined the proteomic and transcriptomic data into two groups. The first set contained candidates where evidence of translational repression was observed (n=158). The second group was a mixture of proteins and mRNAs where evidence of translational repression was less clear (n=515). The TargetScan algorithm was utilised to predict potential targets within these two groups for anti-correlated DE miRNAs.ConclusionsThe evidence presented in this study indicates that biological processes such as mRNA processing and protein synthesis are correlated with growth rate in CHO cells. Through the integration of expression data from multiple levels of the biological system a number of proteins central to these processes including several hnRNPs and components of the ribosome were found to be post-transcriptionally regulated. We utilised the expression data in conjunction with in-silico tools to identify potential miRNA-mediated regulation of mRNA/proteins involved in CHO cell growth rate. These data have allowed us to prioritise candidates for cell engineering and/or biomarkers relevant to industrial cell culture. We also expect the knowledge gained from this study to be applicable to other fields investigating the role of miRNAs in mammalian cell growth.

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.

Transcriptional Profiling of Gene Expression Changes in a PACE-Transfected CHO DUKX Cell Line Secreting High Levels of rhBMP-2

Chinese hamster ovary (CHO) cells are widely used in the biopharmaceutical industry for the production of recombinant human proteins including complex polypeptides such as recombinant human bone morphogenic protein 2 (rhBMP-2). Large-scale manufacture of rhBMP-2 has associated production difficulties resulting from incomplete processing of the recombinant human protein due to insufficient endogenous levels of the paired basic amino acid cleaving enzyme (PACE) in CHO. In order to resolve this issue, CHO DUKX cells expressing rhBMP-2 were transfected with the soluble version of human PACE (PACEsol) resulting in improved amino-terminal homogeneity and a fourfold increase in rhBMP-2 productivity. In this article, we present a microarray expression profile analysis comparing the parental lineage to the higher producing subclone co-expressing PACEsol using a proprietary CHO-specific microarray. Using this technology we observed 1,076 significantly different genes in the high-productivity cells co-expressing PACEsol. Following further analysis of the differentially expressed genes, the Unfolded Protein Response (UPR) component of the endoplasmic reticulum stress response pathway was identified as a key candidate for effecting increased productivity in this cell system. Several additional ER- and Golgi-localised proteins were identified which may also contribute to this effect. The results presented here support the use of large-scale microarray expression profiling as a viable and valuable route towards understanding the behaviour of bioprocess cultures inxa0vitro.

Transcriptomic analysis of clonal growth rate variation during CHO cell line development.

The selection of clones displaying a high rate of cell growth is an essential component of Chinese hamster ovary (CHO) cell line development. In recent years various omics technologies have been utilised to understand the mechanisms underlying bioprocess phenotypes. In this study, gene expression analysis using a CHO-specific microarray was conducted for a panel of CHO-K1 MAb-secreting cell lines spanning a range of growth rates that were derived from a single cell line development project. In-silico functional analysis of the resulting transcriptomic data revealed the overrepresentation of biological processes such as cell cycle and translation within those genes upregulated during fast growth, while genes associated with cellular homeostasis were downregulated. Using differential expression and correlation analysis we identified a high priority group of 416 transcripts (190 upregulated; 226 downregulated) associated with growth rate. Expression changes of eight of these genes were independently confirmed by qPCR. Finally, we demonstrate the enrichment of predicted mRNA targets of miR17-92, a microRNA (miRNA) cluster known to be upregulated during rapid proliferation, within downregulated transcripts.

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.

CGCDB: A web-based resource for the investigation of gene coexpression in CHO cell culture

UNLABELLEDnCoexpression analysis is a powerful, widely used methodology for the investigation of underlying patterns in gene expression data. This guilt-by-association approach aims to find groups of genes with closely correlated expression profiles. Observation of consistent correlations across phenotypically diverse samples indicates that these genes have a shared function. We have recently described the application of weighted gene coexpression network analysis (WGCNA) to a 295 sample production CHO cell line microarray dataset and elucidated groups of genes related to growth rate and cell-specific productivity (Qp). In this study, we present the CHO gene coexpression database (CGCDB), a web-based system, designed specifically for researchers in the CHO community to provide user-friendly access to these gene-gene coexpression patterns. In addition to correlation between genes, the direct correlations between probesets and either growth rate or Qp are provided. Results are presented to the user via an interactive network diagram and in a downloadable tabular format. It is hoped that this resource will allow researchers to prioritize cell line engineering and/or biomarker candidates to enhance CHO-based cell culture for the production of biotherapeutics.nnnAVAILABILITYnwww.cgcdb.org.