Nandita Vishwanathan

Global Insights Into the Chinese Hamster and CHO Cell Transcriptomes

Transcriptomics is increasingly being used on Chinese hamster ovary (CHO) cells to unveil physiological insights related to their performance during production processes. The rich transcriptome data can be exploited to provide impetus for systems investigation such as modeling the central carbon metabolism or glycosylation pathways, or even building genome‐scale models. To harness the power of transcriptome assays, we assembled and annotated a set of RNA‐Seq data from multiple CHO cell lines and Chinese hamster tissues, and constructed a DNA microarray. The identity of genes involved in major functional pathways and their transcript levels generated in this study will serve as a reference for future studies employing kinetic models. In particular, the data on glycolysis and glycosylation pathways indicate that the variability of gene expression level among different cell lines and tissues may contribute to their differences in metabolism and glycosylation patterns. Thereby, these insights can potentially lead to opportunities for cell engineering. This repertoire of transcriptome data also enables the identification of potential sequence variants in cell lines and allows tracing of cell lineages. Overall the study is an illustration of the potential benefit of RNA‐Seq that is yet to be exploited. Biotechnol. Bioeng. 2015;112: 965–976.

Transcriptome dynamics of transgene amplification in Chinese hamster ovary cells

Dihydrofolate reductase (DHFR) system is used to amplify the product gene to multiple copies in Chinese Hamster Ovary (CHO) cells for generating cell lines which produce the recombinant protein at high levels. The physiological changes accompanying the transformation of the non‐protein secreting host cells to a high producing cell line is not well characterized. We performed transcriptome analysis on CHO cells undergoing the selection and amplification processes. A host CHO cell line was transfected with a vector containing genes encoding the mouse DHFR (mDHFR) and a recombinant human IgG (hIgG). Clones were isolated following selection and subcloned following amplification. Control cells were transfected with a control plasmid which did not have the hIgG genes. Although methotrexate (MTX) amplification increased the transcript level of the mDHFR gene significantly, its effect on both hIgG heavy and light chain genes was more modest. The subclones appeared to retain the transcriptome signatures of their parental clones, however, their productivity varied among those derived from the same clone. The transcript levels of hIgG transgenes of all subclones fall in a narrower range than the product titer, alluding to the role of many functional attributes, other than transgene transcript, on productivity. We cross examined functional class enrichment during selection and amplification as well as between high and low producers and discerned common features among them. We hypothesize that the role of amplification is not merely increasing transcript levels, but also enriching survivors which have developed the cellular machinery for secreting proteins, leading to an increased frequency of isolating high‐producing clones. We put forward the possibility of assembling a hyper‐productivity gene set through comparative transcriptome analysis of a wide range of samples. Biotechnol. Bioeng. 2014;111: 518–528.

Dynamic gene expression for metabolic engineering of mammalian cells in culture.

Recombinant mammalian cells are the major hosts for the production of protein therapeutics. In addition to high expression of the product gene, a hyper-producer must also harbor superior phenotypic traits related to metabolism, protein secretion, and growth control. Introduction of genes endowing the relevant hyper-productivity traits is a strategy frequently used to enhance the productivity. Most of such cell engineering efforts have been performed using constitutive expression systems. However, cells respond to various environmental cues and cellular events dynamically according to cellular needs. The use of inducible systems allows for time dependent expression, but requires external manipulation. Ideally, a transgenes expression should be synchronous to the host cells own rhythm, and at levels appropriate for the objective. To that end, we identified genes with different expression dynamics and intensity ranges using pooled transcriptome data. Their promoters may be used to drive the expression of the transgenes following the desired dynamics. We isolated the promoter of the Thioredoxin-interacting protein (Txnip) gene and demonstrated its capability to drive transgene expression in concert with cell growth. We further employed this Chinese hamster promoter to engineer dynamic expression of the mouse GLUT5 fructose transporter in Chinese hamster ovary (CHO) cells, enabling them to utilize sugar according to cellular needs rather than in excess as typically seen in culture. Thus, less lactate was produced, resulting in a better growth rate, prolonged culture duration, and higher product titer. This approach illustrates a novel concept in metabolic engineering which can potentially be used to achieve dynamic control of cellular behaviors for enhanced process characteristics.

Advancing biopharmaceutical process science through transcriptome analysis

Global survey of transcriptome dynamics can provide molecular insights into cell physiology. In the past few years, DNA microarray for transcriptome analysis has been augmented by high-throughput sequencing methods; extending the reach of transcriptome analysis to the rodent species of biotechnological importance, for which the development of genomic tools has been lagging. The rapid accumulation of sequencing data for these species highlighted the need for more evidence-based annotation. Recent findings in the epigenetic regulation in human and mouse will inspire similar research in CHO and BHK cells. Transcriptome studies in these recombinant cells will likely lay the foundation for a systems-based genome engineering which can be used to develop superior producing cell lines. Herein, we summarized the recent findings and advances in transcriptome studies of cell culture bioprocesses. The potential impact of transcriptomics on biopharmaceutical process technology is also discussed.

Cell line development for biomanufacturing processes: recent advances and an outlook

At the core of a biomanufacturing process for recombinant proteins is the production cell line. It influences the productivity and product quality. Its characteristics also dictate process development, as the process is optimized to complement the producing cell to achieve the target productivity and quality. Advances in the past decade, from vector design to cell line screening, have greatly expanded our capability to attain producing cell lines with certain desired traits. Increasing availability of genomic and transcriptomic resources for industrially important cell lines coupled with advances in genome editing technology have opened new avenues for cell line development. These developments are poised to help biosimilar manufacturing, which requires targeting pre-defined product quality attributes, e.g., glycoform, to match the innovator’s range. This review summarizes recent advances and discusses future possibilities in this area.

Unveiling gene trait relationship by cross-platform meta-analysis on Chinese hamster ovary cell transcriptome

In the past few years, transcriptome analysis has been increasingly employed to better understand the physiology of Chinese hamster ovary (CHO) cells at a global level. As more transcriptome data accumulated, meta‐analysis on data sets collected from various sources can potentially provide better insights on common properties of those cells. Here, we performed meta‐analysis on transcriptome data of different CHO cell lines obtained using NimbleGen or Affymetrix microarray platforms. Hierarchical clustering, non‐negative matrix factorization (NMF) analysis, and principal component analysis (PCA) accordantly showed the samples were clustered into two groups: one consists of adherent cells in serum‐containing medium, and the other suspension cells in serum‐free medium. Genes that were differentially expressed between the two clusters were enriched in a few functional classes by Database for Annotation, Visualization, and Integrated Discovery (DAVID) of which many were common with the enriched gene sets identified by Gene Set Enrichment Analysis (GSEA), including extracellular matrix (ECM) receptor interaction, cell adhesion molecules (CAMs), and lipid related metabolism pathways. Despite the heterogeneous sources of the cell samples, the adherent and suspension growth characteristics and serum‐supplementation appear to be a dominant feature in the transcriptome. The results demonstrated that meta‐analysis of transcriptome could uncover features in combined data sets that individual data set might not reveal. As transcriptome data sets accumulate over time, meta‐analysis will become even more revealing. Biotechnol. Bioeng. 2017;114: 1583–1592.

A comparative genomic hybridization approach to study gene copy number variations among chinese hamster cell lines

Chinese Hamster Ovary (CHO) cells are aneuploid in nature. The genome of recombinant protein producing CHO cell lines continuously undergoes changes in its structure and organization. We analyzed nine cell lines, including parental cell lines, using a comparative genomic hybridization (CGH) array focused on gene‐containing regions. The comparison of CGH with copy‐number estimates from sequencing data showed good correlation. Hierarchical clustering of the gene copy number variation data from CGH data revealed the lineage relationships between the cell lines. On analyzing the clones of a clonal population, some regions with altered genomic copy number status were identified indicating genomic changes during passaging. A CGH array is thus an effective tool in quantifying genomic alterations in industrial cell lines and can provide insights into the changes in the genomic structure during cell line derivation and long term culture. Biotechnol. Bioeng. 2017;114: 1903–1908.

Recurring genomic structural variation leads to clonal instability and loss of productivity: BANDYOPADHYAY et al.

Chinese hamster ovary cells, commonly used in the production of therapeutic proteins, are aneuploid. Their chromosomes bear structural abnormality and undergo changes in structure and number during cell proliferation. Some production cell lines are unstable and lose their productivity over time in the manufacturing process and during the product’s life cycle. To better understand the link between genomic structural changes and productivity stability, an immunoglobulin G producing cell line was successively single‐cell cloned to obtain subclones that retained or lost productivity, and their genomic features were compared. Although each subclone started with a single karyotype, the progeny quickly diversified to a population with a distribution of chromosome numbers that is not distinctive from the parent and among subclones. The comparative genomic hybridization (CGH) analysis showed that the extent of copy variation of gene coding regions among different subclones stayed at levels of a few percent. Genome regions that were prone to loss of copies, including one with a product transgene integration site, were identified in CGH. The loss of the transgene copy was accompanied by loss of transgene transcript level. Sequence analysis of the host cell and parental producing cell showed prominent structural variations within the regions prone to loss of copies. Taken together, we demonstrated the transient nature of clonal homogeneity in cell line development and the retention of a population distribution of chromosome numbers; we further demonstrated that structural variation in the transgene integration region caused cell line instability. Future cell line development may target the transgene into structurally stable regions.

Transcriptome Meta Data Compilation for Chinese hamster tissues and CHO cell lines

The data is organized in the speadsheet file named Transcriptome Meta Data.xlsx and has been color coded for easy visualization. Gene information contains gene description, symbol and orthologous mouse ENSEMBL identifiers. Columns in pink pertaining to 26 different functional pathways can be sorted for the value 1 to quickly assess expression of genes in that pathway. This is followed by RNA sequencing and microarray expression data for tissues and cell lines. The minimum, mean and maximum expression values of each gene for all the cell lines in both microarray and sequencing datasets have also been included.