The most striking characteristic of CHO cells is their adaptability, which enables efficient production of proteins as well as growth under a variety of culture conditions, but also results in genomic and phenotypic instability. To investigate the relative contribution of genomic and epigenetic modifications towards phenotype evolution, comprehensive genome and epigenome data are presented for six related CHO cell lines, both in response to perturbations (different culture conditions and media as well as selection of a specific phenotype with increased transient productivity) and in steady state (prolonged time in culture under constant conditions). Clear transitions were observed in DNA‐methylation patterns upon each perturbation, while few changes occurred over time under constant conditions. Only minor DNA‐methylation changes were observed between exponential and stationary growth phase; however, throughout a batch culture the histone modification pattern underwent continuous adaptation. Variation in genome sequence between the six cell lines on the level of SNPs, InDels, and structural variants is high, both upon perturbation and under constant conditions over time. The here presented comprehensive resource may open the door to improved control and manipulation of gene expression during industrial bioprocesses based on epigenetic mechanisms. Biotechnol. Bioeng. 2016;113: 2241–2253. © 2016 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.

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Chinese hamster ovary (CHO) cells are the number one production system for therapeutic proteins. A pre-requirement for their use in industrial production of biopharmaceuticals is to be clonal, thus originating from a single cell in order to be phenotypically and genomically identical. In the present study it was evaluated whether standard procedures, such as the generation of a recombinant cell line in combination with selection for a specific and stable phenotype (expression of the recombinant product) or subcloning have any impact on karyotype stability or homogeneity in CHO cells. Analyses used were the distribution of chromosome counts per cell as well as chromosome painting to identify specific karyotype patterns within a population. Results indicate that subclones both of the host and the recombinant cell line are of comparable heterogeneity and (in)stability as the original pool. In contrast, the rigorous selection for a stably expressing phenotype generated cell lines with fewer variation and more stable karyotypes, both at the level of the sorted pool and derivative subclones. We conclude that the process of subcloning itself does not contribute to an improved karyotypic homogeneity of a population, while the selection for a specific cell property inherently can provide evolutionary pressure that may lead to improved chromosomal stability as well as to a more homogenous population.

The existence of dynamic cellular phenotypes in changing environmental conditions is of major interest for cell biologists who aim to understand the mechanism and sequence of regulation of gene expression. In the context of therapeutic protein production by Chinese Hamster Ovary (CHO) cells, a detailed temporal understanding of cell‐line behavior and control is necessary to achieve a more predictable and reliable process performance. Of particular interest are data on dynamic, temporally resolved transcriptional regulation of genes in response to altered substrate availability and culture conditions. In this study, the gene transcription dynamics throughout a 9‐day batch culture of CHO cells was examined by analyzing histone modifications and gene expression profiles in regular 12‐ and 24‐hr intervals, respectively. Three levels of regulation were observed: (a) the presence or absence of DNA methylation in the promoter region provides an ON/OFF switch; (b) a temporally resolved correlation is observed between the presence of active transcription‐ and promoter‐specific histone marks and the expression level of the respective genes; and (c) a major mechanism of gene regulation is identified by interaction of coding genes with long non‐coding RNA (lncRNA), as observed in the regulation of the expression level of both neighboring coding/lnc gene pairs and of gene pairs where the lncRNA is able to form RNA–DNA–DNA triplexes. Such triplex‐forming regions were predominantly found in the promoter or enhancer region of the targeted coding gene. Significantly, the coding genes with the highest degree of variation in expression during the batch culture are characterized by a larger number of possible triplex‐forming interactions with differentially expressed lncRNAs. This indicates a specific role of lncRNA‐triplexes in enabling rapid and large changes in transcription. A more comprehensive understanding of these regulatory mechanisms will provide an opportunity for new tools to control cellular behavior and to engineer enhanced phenotypes.

Chinese Hamster Ovary (CHO) cells are the preferred cell line for production of biopharmaceuticals. These cells are capable to grow without serum supplementation, but drastic changes in their phenotype occur during adaptation to protein-free growth, which typically include the change to a suspension phenotype with reduced growth rate. A possible approach to understand this transformation, with the intention to counteract the reduction in growth by targeted supplementation of protein-free media, is gene expression profiling. The increasing availability of genome-scale data for CHO now facilitates quests for a better understanding of metabolic pathways and gene networks. So far, systematic large-scale expression profiling in CHO cells by microarray was limited due to lack of publicly available array designs and limitations of alternative approaches. Based on the recent release of CHO and Chinese Hamster genome sequences, including an annotated RefSeq genome, we have constructed a publicly available microarray design for effective genome-scale expression profiling. The design employed microarray probes optimized for uniformity, sensitivity, and specificity, with probe properties computed using the latest thermodynamic models. We validated the platform in an analysis of gene expression changes in response to serum-free adaptation. The observed effects on the lipid metabolism as well as on nucleotide synthesis were used to successfully select media supplements that were able to increase growth rate.

Over the last three decades, product yields from CHO cells have increased dramatically, yet specific productivity (qP) remains a limiting factor. In a previous study, using repeated cell-sorting, we have established different host cell subclones that show superior transient qP over their respective parental cell lines (CHO-K1, CHO-S). The transcriptome of the resulting six cell lines in different biological states (untransfected, mock transfected, plasmid transfected) was first explored by hierarchical clustering and indicated that gene activity associated with increased qP did not stem from a certain cellular state but seemed to be inherent for a high qP host line. We then performed a novel gene regression analysis identifying drivers for an increase in qP. Genes significantly implicated were first systematically tested for enrichment of GO terms using a Bayesian approach incorporating the hierarchical structure of the GO term tree. Results indicated that specific cellular components such as nucleus, ER, and Golgi are relevant for cellular productivity. This was complemented by targeted GSA that tested functionally homogeneous, manually curated subsets of KEGG pathways known to be involved in transcription, translation, and protein processing. Significantly implicated pathways included mRNA surveillance, proteasome, protein processing in the ER and SNARE interactions in vesicular transport.

Differential 2-DE (DIGE) is a widely applied tool for the quantitative analysis of differentially represented proteins. The method involves covalent minimal labeling of proteins prior to their electrophoretic separation using CyDye DIGE Fluor minimal dyes. This methodology creates two different species per protein, the labeled (approx. 1-2%) and unlabeled (approx. 98-99%) ones, which differ in their molecular masses by 434-464 Da, depending on the attached dye. DIGE followed by automated spot picking according to the CyDye coordinates misses in many instances the exact positions where the maximum amounts of the considered proteins are located. This fact leads to a loss in sensitivity of the subsequent MALDI-MS analyses and results in a reduced reliability of protein identification and sequence coverage. In this paper, the migration differences of labeled and unlabeled species are quantified together with the impact of this effect on the certainty of protein identification and sequence coverage investigating proteins up to 90 kDa.

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