Towards next generation CHO cell biology: Bioinformatics methods for RNA‐Seq‐based expression profiling

Monger, Craig; Kelly, Paul S.; Gallagher, Clair; Clynes, Martin; Barron, Niall; Clarke, Colin

doi:10.1002/biot.201500107

Cited by 16 publications

(9 citation statements)

References 109 publications

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“…We can now begin to understand the molecular basis of protein expression thanks to recent efforts to sequence the genomes of cell lines utilized for recombinant expression123. Specifically, RNA sequencing (RNA-Seq)24567891011 and proteomics1213 have provided detailed insights into transcription and post-translational processes; however, the translation of endogenous and recombinant mRNA has remained poorly characterized. This is of particular concern since RNA-Seq analyses neglect the fact that translation control of the mRNA is an essential and highly regulated step in determining levels of an expressed protein14.…”

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confidence: 99%

Ribosome profiling-guided depletion of an mRNA increases cell growth rate and protein secretion

Kallehauge

Pedersen

et al. 2017

Sci Rep

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Recombinant protein production coopts the host cell machinery to provide high protein yields of industrial enzymes or biotherapeutics. However, since protein translation is energetically expensive and tightly controlled, it is unclear if highly expressed recombinant genes are translated as efficiently as host genes. Furthermore, it is unclear how the high expression impacts global translation. Here, we present the first genome-wide view of protein translation in an IgG-producing CHO cell line, measured with ribosome profiling. Through this we found that our recombinant mRNAs were translated as efficiently as the host cell transcriptome, and sequestered up to 15% of the total ribosome occupancy. During cell culture, changes in recombinant mRNA translation were consistent with changes in transcription, demonstrating that transcript levels influence specific productivity. Using this information, we identified the unnecessary resistance marker NeoR to be a highly transcribed and translated gene. Through siRNA knock-down of NeoR, we improved the production- and growth capacity of the host cell. Thus, ribosomal profiling provides valuable insights into translation in CHO cells and can guide efforts to enhance protein production.

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confidence: 99%

Ribosome profiling-guided depletion of an mRNA increases cell growth rate and protein secretion

Kallehauge

Pedersen

et al. 2017

Sci Rep

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“…It should be noted that RNA-Seq based gene expression analysis is also influenced by many other factors. Substantial efforts have been focused on other key factors, such as the experimental design (e.g., number of biological replicates), sequencing parameters (e.g., sequencing depth and length), and the choices of tools at various analysis steps (e.g., read alignment, gene expression quantification, and differential gene expression calling) (Benjamin et al, 2014;Chhangawala et al, 2015;Conesa et al, 2016;Emmert-Streib and Glazko, 2011;Li et al, 2015;Liu et al, 2014;Monger et al, 2015;Oshlack et al, 2010;Seyednasrollah et al, 2015;Teng et al, 2016). Obviously, the quality of the genome reference is a key determinant of RNA-Seq data analysis results.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of two public genome references for chinese hamster ovary cells in the context of rna‐seq based gene expression analysis

Chen

Lê

Goudar

2017

Biotech & Bioengineering

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RNA-Seq is a powerful transcriptomics tool for mammalian cell culture process development. Successful RNA-Seq data analysis requires a high quality reference for read mapping and gene expression quantification. Currently, there are two public genome references for Chinese hamster ovary (CHO) cells, the predominant mammalian cell line in the biopharmaceutical industry. In this study, we compared these two references by analyzing 60 RNA-Seq samples from a variety of CHO cell culture conditions. Among the 20,891 common genes in both references, we observed that 31.5% have more than 7.1% quantification differences, implying gene definition differences in the two references. We propose a framework to quantify this difference using two metrics, Consistency and Stringency, which account for the average quantification difference between the two references over all samples, and the sample-specific effect on the quantification result, respectively. These two metrics can be used to identify potential genes for future gene model improvement and to understand the reliability of differentially expressed genes identified by RNA-Seq data analysis. Before a more comprehensive genome reference for CHO cells emerges, the strategy proposed in this study can enable more robust transcriptome analysis from CHO cell RNA-Seq data. Biotechnol. Bioeng. 2017;114: 1603-1613. © 2017 Wiley Periodicals, Inc.

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“…Data generated with microarrays can be uploaded to the Gene Expression Omnibus database under MIAME standards (GEO; http://www.ncbi.nlm.nih.gov/geo/; Table ). In addition, warehouse solutions, InterMine, and BioMart, have been created for integrating diverse biological datasets and to support analysis …”

Section: Transcriptomicsmentioning

confidence: 99%

CHO‐Omics Review: The Impact of Current and Emerging Technologies on Chinese Hamster Ovary Based Bioproduction

Stolfa

Smonskey

Boniface

et al. 2017

Biotechnology Journal

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CHO cells are the most prevalent platform for modern bio-therapeutic production. Currently, there are several CHO cell lines used in bioproduction with distinct characteristics and unique genotypes and phenotypes. These differences limit advances in productivity and quality that can be achieved by the most common approaches to bioprocess optimization and cell line engineering. Incorporating omics-based approaches into current bioproduction processes will complement traditional methodologies to maximize gains from CHO engineering and bioprocess improvements. In order to highlight the utility of omics technologies in CHO bioproduction, the authors discuss current applications as well as limitations of genomics, transcriptomics, proteomics, metabolomics, lipidomics, fluxomics, glycomics, and multi-omics approaches and the potential they hold for the future of bioproduction. Multiple omics approaches are currently being used to improve CHO bioprocesses; however, the application of these technologies is still limited. As more CHO-omic datasets become available and integrated into systems models, the authors expect significant gains in product yield and quality. While individual omics technologies provide incremental improvements in bioproduction, the authors will likely see the most significant gains by applying multi-omics and systems biology approaches to individual CHO cell lines.

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Towards next generation CHO cell biology: Bioinformatics methods for RNA‐Seq‐based expression profiling

Cited by 16 publications

References 109 publications

Ribosome profiling-guided depletion of an mRNA increases cell growth rate and protein secretion

Ribosome profiling-guided depletion of an mRNA increases cell growth rate and protein secretion

Evaluation of two public genome references for chinese hamster ovary cells in the context of rna‐seq based gene expression analysis

CHO‐Omics Review: The Impact of Current and Emerging Technologies on Chinese Hamster Ovary Based Bioproduction

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