CHO gene expression profiling in biopharmaceutical process analysis and design

Combined multivariate statistical and flux balance analyses uncover media bottlenecks to the growth and productivity of CHO cell cultures

Chinese hamster ovary (CHO) cells are widely used for producing recombinant proteins. To enhance their productivity and product quality, media reformulation has been a key strategy, albeit with several technical challenges, due to the myriad of complex molecular mechanisms underlying media effects on culture performance. Thus, it is imperative to characterize metabolic bottlenecks under various media conditions systematically. To do so, we combined partial least square regression (PLS-R) with the flux balance analysis of a genome-scale metabolic model to elucidate the physiological states and metabolic behaviors of human alpha-1 antitrypsin producing CHO-DG44 cells grown in one commercial and another two in-house media under development. At the onset, PLS-R was used to identify metabolite exchanges that were correlated to specific growth and productivity. Then, by comparing metabolic states described by resultant flux distributions under two of the media conditions, we found sub-optimal level of four nutrients and two metabolic wastes, which plausibly hindered cellular growth and productivity; mechanistically, lactate and ammonia recycling were modulated by glutamine and asparagine metabolisms in the media conditions, and also by hitherto unsuspected folate and choline supplements. Our work demonstrated how multivariate statistical analysis can be synergistically combined with metabolic modelling to uncover the mechanistic elements underlying differing media performance. It thus paved the way for the systematic identification of nutrient targets for medium reformulation to enhance recombinant protein production in CHO cells. This article is protected by copyright. All rights reserved.

Incorporating RNA-Seq transcriptomics into glycosylation-integrating metabolic network modelling kinetics: Multiomic Chinese hamster ovary (CHO) cell bioreactors

In this work, the kinetic model based on the previously developed metabolic and glycan reaction networks of the ovarian cells of the Chinese hamster ovary (CHO) cell line was improved by the inclusion of transcriptomic data that took into account the values of the RPKM gene (Reads per Kilobase of Exon per Million Reads Mapped). The transcriptomic (RNASeq) data were obtained together with metabolic and glycan data from the literature, and the concentrations with RPKM values were collected at several points in time from two fed-batch processes. First, the fluxes were determined by regression analysis of the metabolic data, then these fluxes were corrected by using the fold change in gene expression as a measure of enzyme concentrations. Next, the corrected fluxes in the kinetic model were used to calculate the concentration profiles of the metabolites, and literature data were used to evaluate the predicted results of the model. Compared to other studies where the concentration profiles of CHO cell metabolites were described using a kinetic model without consideration of RNA-Seq data to correct the fluxes, this model is unique. The additional integration of transcriptomic data led to better predictions of metabolic concentrations in the fed-batch process, which is a significant improvement of the modelling technique used.

Methylthioadenosine (MTA) boosts cell-specific productivities of Chinese hamster ovary cultures: dosage effects on proliferation, cell cycle and gene expression

A major goal for process and cell engineering in the biopharmaceutical industry is enhancing production through increasing volumetric and cell‐specific productivities (CSP). Here, we present 5′‐deoxy‐5′‐(methylthio)adenosine (MTA), the degradation product of S‐(5′‐adenosyl)‐L‐methionine (SAM), as a highly attractive native additive which can boost CSP by 79% when added to exponentially growing cells at a concentration of 250–300 μm. Notably, cell viability and cell size remain higher than in non‐treated cultures. In addition, cell cycle arrests first in S‐, then in G2‐phase before levelling out compared to non‐treated cultivations. Intensive differential gene analysis reveals that expression of genes for cytoskeleton mediated proteins and vesicle transport is amplified by treatment. Furthermore, the interaction of MTA with cell proliferation additionally stimulated recombinant protein formation. The results may serve as a promising starting point for further developments in process and cell engineering to boost productivity.

S-adenosylmethionine and methylthioadenosine boost cellular productivities of antibody forming Chinese hamster ovary cells

The improvement of cell specific productivities for the formation of therapeutic proteins is an important step towards intensified production processes. Among others, the induction of the desired production phenotype via proper media additives is a feasible solution provided that said compounds adequately trigger metabolic and regulatory programs inside the cells. In this study, S-(5'-adenosyl)- l-methionine (SAM) and 5'-deoxy-5'-(methylthio)adenosine (MTA) were found to stimulate cell specific productivities up to approx. 50% while keeping viable cell densities transiently high and partially arresting the cell cycle in an anti-IL-8-producing CHO-DP12 cell line. Noteworthy, MTA turned out to be the chemical degradation product of the methyl group donor SAM and is consumed by the cells.

Mammalian Systems Biotechnology Reveals Global Cellular Adaptations in a Recombinant CHO Cell Line

Effective development of host cells for therapeutic protein production is hampered by the poor characterization of cellular transfection. Here, we employed a multi-omics-based systems biotechnology approach to elucidate the genotypic and phenotypic differences between a wild-type and recombinant antibody-producing Chinese hamster ovary (CHO) cell line. At the genomic level, we observed extensive rearrangements in specific targeted loci linked to transgene integration sites. Transcriptional re-wiring of DNA damage repair and cellular metabolism in the antibody producer, via changes in gene copy numbers, was also detected. Subsequent integration of transcriptomic data with a genome-scale metabolic model showed a substantial increase in energy metabolism in the antibody producer. Metabolomics, lipidomics, and glycomics analyses revealed an elevation in long-chain lipid species, potentially associated with protein transport and secretion requirements, and a surprising stability of N-glycosylation profiles between both cell lines. Overall, the proposed knowledge-based systems biotechnology framework can further accelerate mammalian cell-line engineering in a targeted manner.

Accelerating Biologics Manufacturing by Modeling or: Is Approval under the QbD and PAT Approaches Demanded by Authorities Acceptable Without a Digital-Twin?

Innovative biologics, including cell therapeutics, virus-like particles, exosomes,recombinant proteins, and peptides, seem likely to substitute monoclonal antibodies as the maintherapeutic entities in manufacturing over the next decades. This molecular variety causes agrowing need for a general change of methods as well as mindset in the process development stage,as there are no platform processes available such as those for monoclonal antibodies. Moreover,market competitiveness demands hyper-intensified processes, including accelerated decisionstoward batch or continuous operation of dedicated modular plant concepts. This indicates gaps inprocess comprehension, when operation windows need to be run at the edges of optimization. Inthis editorial, the authors review and assess potential methods and begin discussing possiblesolutions throughout the workflow, from process development through piloting to manufacturingoperation from their point of view and experience. Especially, the state-of-the-art for modeling inred biotechnology is assessed, clarifying differences and applications of statistical, rigorousphysical-chemical based models as well as cost modeling. “Digital-twins” are described and effortsvs. benefits for new applications exemplified, including the regulation-demanded QbD (quality bydesign) and PAT (process analytical technology) approaches towards digitalization or industry 4.0based on advanced process control strategies. Finally, an analysis of the obstacles and possiblesolutions for any successful and efficient industrialization of innovative methods from processdevelopment, through piloting to manufacturing, results in some recommendations. A centralquestion therefore requires attention: Considering that QbD and PAT have been required byauthorities since 2004, can any biologic manufacturing process be approved by the regulatoryagencies without being modeled by a “digital-twin” as part of the filing documentation?

Efficient development of a stable cell pool for antibody production using a single plasmid

Therapeutic antibodies are the fastest growing group of biopharmaceuticals. Evaluation of drug candidates requires a sufficient amount of antibodies. Production of antibodies with stable cell pools is an efficient strategy to produce grams of proteins for drug candidate selection. Many methods have been described for developing stable cell pools for antibody expression. However, most of the reported methods are laborious due to the low frequency of high producers. In this study, we determined optimal vectors and screening parameters to develop a strategy for efficient construction of stable antibody expressing cell pools. The cell pool constructed using the optimized strategy consistently yielded a higher expression titer, up to 10-fold improvement. Further, this method resulted in a higher ratio of the cell pools with the main product peak above 95% as assessed by size-exclusion chromatography. High producers could be obtained by means of screening five 96-well plates. This strategy will greatly reduce clone-screening size during Clinical Lead Selection. This study provides a platform with efficient design of plasmids and screening strategies for significant cost and labour savings in high expression of two-subunit proteins such as antibodies.

Lipidomics of CHO Cell Bioprocessing: Relation to Cell Growth and Specific Productivity of a Monoclonal Antibody

As the demand for biological therapeutic proteins rises, there is an increasing need for robust and highly efficient bioprocesses, specifically, maximizing protein production by controlling the cellular nutritional and metabolic needs. A comprehensive lipidomics analysis has been performed, for the first time, over the time course of CHO cells producing an IgG1 monoclonal antibody (mAb) with fed batch 5 L bioreactors. The dynamic nature and importance of the CHO lipidome, especially on cellular growth and specific productivity, is demonstrated. A robust LC-MS method using positive and negative mode ESI was developed for lipid identification and quantitation of 377 unique lipids. The analysis revealed large changes in lipid features between the different days in bioprocessing including accumulation of triacylglycerol (TG) and lysophospholipid species with depletion of diacylglycerol (DG) species. Exploring pathway analysis where the lipid data was combined with polar metabolites and transcriptomics (RNA sequencing) revealed differences in lipid metabolism between the various stages of cellular growth and highlighted the role of key features of lipid metabolism on cell growth and specific productivity. The study demonstrates the importance of lipidomics in the expanding role of 'Omics methodologies in gaining insight into cellular behavior during protein production in a fed batch bioprocess.

Process Analytical Technology for Advanced Process Control in Biologics Manufacturing with the Aid of Macroscopic Kinetic Modeling

Productivity improvements of mammalian cell culture in the production of recombinant proteins have been made by optimizing cell lines, media, and process operation. This led to enhanced titers and process robustness without increasing the cost of the upstream processing (USP); however, a downstream bottleneck remains. In terms of process control improvement, the process analytical technology (PAT) initiative, initiated by the American Food and Drug Administration (FDA), aims to measure, analyze, monitor, and ultimately control all important attributes of a bioprocess. Especially, spectroscopic methods such as Raman or near-infrared spectroscopy enable one to meet these analytical requirements, preferably in-situ. In combination with chemometric techniques like partial least square (PLS) or principal component analysis (PCA), it is possible to generate soft sensors, which estimate process variables based on process and measurement models for the enhanced control of bioprocesses. Macroscopic kinetic models can be used to simulate cell metabolism. These models are able to enhance the process understanding by predicting the dynamic of cells during cultivation. In this article, in-situ turbidity (transmission, 880 nm) and ex-situ Raman spectroscopy (785 nm) measurements are combined with an offline macroscopic Monod kinetic model in order to predict substrate concentrations. Experimental data of Chinese hamster ovary cultivations in bioreactors show a sufficiently linear correlation (R² ≥ 0.97) between turbidity and total cell concentration. PLS regression of Raman spectra generates a prediction model, which was validated via offline viable cell concentration measurement (RMSE ≤ 13.82, R² ≥ 0.92). Based on these measurements, the macroscopic Monod model can be used to determine different process attributes, e.g., glucose concentration. In consequence, it is possible to approximately calculate (R² ≥ 0.96) glucose concentration based on online cell concentration measurements using turbidity or Raman spectroscopy. Future approaches will use these online substrate concentration measurements with turbidity and Raman measurements, in combination with the kinetic model, in order to control the bioprocess in terms of feeding strategies, by employing an open platform communication (OPC) network—either in fed-batch or perfusion mode, integrated into a continuous operation of upstream and downstream.

A Comparative Transcriptomics Workflow for Analyzing Microarray Data From CHO Cell Cultures

Microarray-based comparative transcriptomics analysis is a powerful tool to understand therapeutic protein producing mammalian cell lines at the gene expression level. However, an integrated analysis workflow specifically designed for end-to-end analysis of microarray data for CHO cells, the most prevalent host for commercial recombinant protein production, is lacking. To address this gap, an automated data analysis workflow in R that leverages public domain analysis modules is developed to analyze microarray based gene expression data. In addition to testing the global transcriptome differences of CHO cells at different conditions, the workflow identifies differentially expressed genes and pathways with intuitive visualizations as the outputs. The utility of this automated workflow is demonstrated by comparing the transcriptomic profiles of recombinant protein expressing CHO cells with and without a temperature shift. Statistically significant differential expression at the gene, pathway, and global transcriptome levels are identified and visualized. An automated workflow like the one developed in this study will enable rapid translation of CHO culture microarray data into biologically relevant information for mechanism-driven cell line optimization and bioprocess development.

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. © 2017 Wiley Periodicals, Inc.

A Systematic Approach to Time-series Metabolite Profiling and RNA-seq Analysis of Chinese Hamster Ovary Cell Culture

Chinese hamster ovary (CHO) cells are the primary host used for biopharmaceutical protein production. The engineering of CHO cells to produce higher amounts of biopharmaceuticals has been highly dependent on empirical approaches, but recent high-throughput "omics" methods are changing the situation in a rational manner. Omics data analyses using gene expression or metabolite profiling make it possible to identify key genes and metabolites in antibody production. Systematic omics approaches using different types of time-series data are expected to further enhance understanding of cellular behaviours and molecular networks for rational design of CHO cells. This study developed a systematic method for obtaining and analysing time-dependent intracellular and extracellular metabolite profiles, RNA-seq data (enzymatic mRNA levels) and cell counts from CHO cell cultures to capture an overall view of the CHO central metabolic pathway (CMP). We then calculated correlation coefficients among all the profiles and visualised the whole CMP by heatmap analysis and metabolic pathway mapping, to classify genes and metabolites together. This approach provides an efficient platform to identify key genes and metabolites in CHO cell culture.

The 'Omics Revolution in CHO Biology: Roadmap to Improved CHO Productivity

Increased understanding of Chinese hamster ovary (CHO) cell physiology has been ushered in upon availability of the parental CHO-K1 cell line genome. Free and openly accessible sequence information has complemented transcriptomic and proteomic studies. The previous decade has also seen an increase in sensitivity and accuracy of proteomic methods due to technology development. In this genomic era, high-throughput screening methods, sophisticated informatic tools, and models continually drive major innovations in cell line development and process engineering. This review describes the various achievements in 'omics techniques and their application to improve recombinant protein expression from CHO cell lines.

Hyperosmotic stimulus study discloses benefits in ATP supply and reveals miRNA/mRNA targets to improve recombinant protein production of CHO cells

Biopharmaceuticals are predominantly produced by Chinese hamster ovary (CHO) cells cultivated in fed-batch mode. Hyperosmotic culture conditions (≥ 350 mOsmol kg(∑1) ) resulting from feeding of nutrients may enhance specific product formation rates (qp ). As an improved ATP supply was anticipated to enhance qp this study focused on the identification of suitable miRNA/mRNA targets to increase ATP levels. Therefor next generation sequencing and a compartment specific metabolomics approach were applied to analyze the response of an antibody (mAB) producing CHO cell line upon osmotic shift (280 → 430 mOsmol kg(-1) ). Hyperosmotic culture conditions caused a ∼2.6-fold increase of specific ATP formation rates together with a ∼1.7-fold rise in cytosolic and mitochondrial ATP-pools, thus showing increased ATP supply. mRNA expression analysis identified several genes encoding glycosylated proteins with strictly tissue related function. In addition, hyperosmotic culture conditions induced an upregulation of miR-132-3p, miR-132-5p, miR-182, miR-183, miR-194, miR-215-3p, miR-215-5p which have all been related to cell cycle arrest/proliferation in cancer studies. In relation to a previous independent CHO study miR-183 may be the most promising target to enhance qp by stable overexpression. Furthermore, deletion of genes with presumably dispensable function in suspension growing CHO cells may enhance mAB formation by increased ATP levels.

Biological insights into the expression of translation initiation factors from recombinant CHOK1SV cell lines and their relationship to enhanced productivity

We show for translation initiation factors involved in formation of the closed loop mRNA, their expression is associated with recombinant antibody productivity in Chinese hamster ovary cells and maintaining these is important in determining the cells capacity for antibody productivity.

The use of 'Omics technology to rationally improve industrial mammalian cell line performance

Biologics represent an increasingly important class of therapeutics, with 7 of the 10 top selling drugs from 2013 being in this class. Furthermore, health authority approval of biologics in the immuno-oncology space is expected to transform treatment of patients with debilitating and deadly diseases. The growing importance of biologics in the healthcare field has also resulted in the recent approvals of several biosimilars. These recent developments, combined with pressure to provide treatments at lower costs to payers, are resulting in increasing need for the industry to quickly and efficiently develop high yielding, robust processes for the manufacture of biologics with the ability to control quality attributes within narrow distributions. Achieving this level of manufacturing efficiency and the ability to design processes capable of regulating growth, death and other cellular pathways through manipulation of media, feeding strategies, and other process parameters will undoubtedly be facilitated through systems biology tools generated in academic and public research communities. Here we discuss the intersection of systems biology, 'Omics technologies, and mammalian bioprocess sciences. Specifically, we address how these methods in conjunction with traditional monitoring techniques represent a unique opportunity to better characterize and understand host cell culture state, shift from an empirical to rational approach to process development and optimization of bioreactor cultivation processes. We summarize the following six key areas: (i) research applied to parental, non-recombinant cell lines; (ii) systems level datasets generated with recombinant cell lines; (iii) datasets linking phenotypic traits to relevant biomarkers; (iv) data depositories and bioinformatics tools; (v) in silico model development, and (vi) examples where these approaches have been used to rationally improve cellular processes. We critically assess relevant and state of the art research being conducted in academic, government and industrial laboratories. Furthermore, we apply our expertise in bioprocess to define a potential model for integration of these systems biology approaches into biologics development.

Transcriptome study and identification of potential marker genes related to the stable expression of recombinant proteins in CHO clones

BACKGROUND

Chinese hamster ovary (CHO) cells have become the host of choice for the production of recombinant proteins, due to their capacity for correct protein folding, assembly, and posttranslational modifications. The most widely used system for recombinant proteins is the gene amplification procedure that uses the CHO-Dhfr expression system. However, CHO cells are known to have a very unstable karyotype. This is due to chromosome rearrangements that can arise from translocations and homologous recombination, especially when cells with the CHO-Dhfr expression system are treated with methotrexate hydrate. The present method used in the industry for testing clones for their long-term stability of recombinant protein production is empirical, and it involves their cultivation over extended periods of time prior to the selection of the most suitable clone for further bioprocess development. The aim of the present study was the identification of marker genes that can predict stable expression of recombinant genes in particular clones early in the development stage.

RESULTS

The transcriptome profiles of CHO clones with stable and unstable recombinant protein production were investigated over 10-weeks of cultivation, using a DNA microarray. We identified 14 genes that were differentially expressed between the stable and unstable clones already at 2 weeks from the beginning of the cultivation. Their expression was validated by reverse-transcription quantitative real-time PCR (RT-qPCR). Furthermore, the k-nearest neighbour algorithm approach shows that the combination of the gene expression patterns of only five of these 14 genes is sufficient to predict stable recombinant protein production in clones in the early phases of cell-line development.

CONCLUSIONS

The exact molecular mechanisms that cause unstable recombinant protein production are not fully understood. However, the expression profiles of some genes in clones with stable and unstable recombinant protein production allow prediction of such instability early in the cell-line development stage. We have thus developed a proof-of-concept for a novel approach to eliminate unstable clones in the CHO-Dhfr expression system, which saves time and labour-intensive work in cell-line development.

The endoplasmic reticulum and unfolded protein response in the control of mammalian recombinant protein production

The endoplasmic reticulum (ER) of eukaryotic cells is involved in the synthesis and processing of proteins and lipids in the secretory pathway. These processing events that proteins undergo in the ER may present major limiting steps for recombinant protein production. Increased protein synthesis, accumulation of improperly processed or mis-folded protein can induce ER stress. To cope with ER stress, the ER has quality control mechanisms, such as the unfolded protein response (UPR) and ER-associated degradation to restore homeostasis. ER stress and UPR activation trigger multiple physiological cellular changes. Here we review cellular mechanisms that cope with ER stress and illustrate how this knowledge can be applied to increase the efficiency of recombinant protein expression.

Transcriptome data analysis for cell culture processes

In the past decade, DNA microarrays have fundamentally changed the way we study complex biological systems. By measuring the expression levels of thousands of transcripts, the paradigm of studying organisms has shifted from focusing on the local phenomena of a few genes to surveying the whole genome. DNA microarrays are used in a variety of ways, from simple comparisons between two samples to more intricate time-series studies. With the large number of genes being studied, the dimensionality of the problem is inevitably high. The analysis of microarray data thus requires specific approaches. In the case of time-series microarray studies, data analysis is further complicated by the correlation between successive time points in a series.In this review, we survey the methodologies used in the analysis of static and time-series microarray data, covering data pre-processing, identification of differentially expressed genes, profile pattern recognition, pathway analysis, and network reconstruction. When available, examples of their use in mammalian cell cultures are presented.

Improving the efficiency of CHO cell line generation using glutamine synthetase gene knockout cells

Although Chinese hamster ovary (CHO) cells, with their unique characteristics, have become a major workhorse for the manufacture of therapeutic recombinant proteins, one of the major challenges in CHO cell line generation (CLG) is how to efficiently identify those rare, high-producing clones among a large population of low- and non-productive clones. It is not unusual that several hundred individual clones need to be screened for the identification of a commercial clonal cell line with acceptable productivity and growth profile making the cell line appropriate for commercial application. This inefficiency makes the process of CLG both time consuming and laborious. Currently, there are two main CHO expression systems, dihydrofolate reductase (DHFR)-based methotrexate (MTX) selection and glutamine synthetase (GS)-based methionine sulfoximine (MSX) selection, that have been in wide industrial use. Since selection of recombinant cell lines in the GS-CHO system is based on the balance between the expression of the GS gene introduced by the expression plasmid and the addition of the GS inhibitor, L-MSX, the expression of GS from the endogenous GS gene in parental CHOK1SV cells will likely interfere with the selection process. To study endogenous GS expression's potential impact on selection efficiency, GS-knockout CHOK1SV cell lines were generated using the zinc finger nuclease (ZFN) technology designed to specifically target the endogenous CHO GS gene. The high efficiency (∼2%) of bi-allelic modification on the CHO GS gene supports the unique advantages of the ZFN technology, especially in CHO cells. GS enzyme function disruption was confirmed by the observation of glutamine-dependent growth of all GS-knockout cell lines. Full evaluation of the GS-knockout cell lines in a standard industrial cell culture process was performed. Bulk culture productivity improved two- to three-fold through the use of GS-knockout cells as parent cells. The selection stringency was significantly increased, as indicated by the large reduction of non-producing and low-producing cells after 25 µM L-MSX selection, and resulted in a six-fold efficiency improvement in identifying similar numbers of high-productive cell lines for a given recombinant monoclonal antibody. The potential impact of GS-knockout cells on recombinant protein quality is also discussed.

Advancing biopharmaceutical process development by system-level data analysis and integration of omics data

Development of efficient bioprocesses is essential for cost-effective manufacturing of recombinant therapeutic proteins. To achieve further process improvement and process rationalization comprehensive data analysis of both process data and phenotypic cell-level data is essential. Here, we present a framework for advanced bioprocess data analysis consisting of multivariate data analysis (MVDA), metabolic flux analysis (MFA), and pathway analysis for mapping of large-scale gene expression data sets. This data analysis platform was applied in a process development project with an IgG-producing Chinese hamster ovary (CHO) cell line in which the maximal product titer could be increased from about 5 to 8 g/L.Principal component analysis (PCA), k-means clustering, and partial least-squares (PLS) models were applied to analyze the macroscopic bioprocess data. MFA and gene expression analysis revealed intracellular information on the characteristics of high-performance cell cultivations. By MVDA, for example, correlations between several essential amino acids and the product concentration were observed. Also, a grouping into rather cell specific productivity-driven and process control-driven processes could be unraveled. By MFA, phenotypic characteristics in glycolysis, glutaminolysis, pentose phosphate pathway, citrate cycle, coupling of amino acid metabolism to citrate cycle, and in the energy yield could be identified. By gene expression analysis 247 deregulated metabolic genes were identified which are involved, inter alia, in amino acid metabolism, transport, and protein synthesis.

Into the unknown: expression profiling without genome sequence information in CHO by next generation sequencing

The arrival of next-generation sequencing (NGS) technologies has led to novel opportunities for expression profiling and genome analysis by utilizing vast amounts of short read sequence data. Here, we demonstrate that expression profiling in organisms lacking any genome or transcriptome sequence information is feasible by combining Illumina’s mRNA-seq technology with a novel bioinformatics pipeline that integrates assembled and annotated Chinese hamster ovary (CHO) sequences with information derived from related organisms. We applied this pipeline to the analysis of CHO cells which were chosen as a model system owing to its relevance in the production of therapeutic proteins. Specifically, we analysed CHO cells undergoing butyrate treatment which is known to affect cell cycle regulation and to increase the specific productivity of recombinant proteins. By this means, we identified sequences for >13 000 CHO genes which added sequence information of ∼5000 novel genes to the CHO model. More than 6000 transcript sequences are predicted to be complete, as they covered >95% of the corresponding mouse orthologs. Detailed analysis of selected biological functions such as DNA replication and cell cycle control, demonstrated the potential of NGS expression profiling in organisms without extended genome sequence to improve both data quantity and quality.