An Online Compendium of CHO RNA‐Seq Data Allows Identification of CHO Cell Line‐Specific Transcriptomic Signatures. Issue 10 (5th July 2018)

• Record Type: Journal Article
• Authors: Singh, Ankita; Kildegaard, Helene F.; Andersen, Mikael R.
• Is Part Of: Biotechnology journal. Volume 13:Issue 10(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-07-05
• Subjects: bioinformatics -- CHO cells -- CHO gene expression visualization application -- differential expression analysis -- gene expression -- omics -- transcriptomics; Biotechnology -- Periodicals; 660.605
• DOI: 10.1002/biot.201800070
• Languages: English
• ISSNs: 1860-6768

Abstract

Abstract : Chinese hamster ovary (CHO) cell lines can fold, assemble, and modify proteins post‐translationally to produce human‐like proteins; as a consequence, it is the single most common expression systems for industrial production of recombinant therapeutic proteins. A thorough knowledge of cultivation conditions of different CHO cell lines has been developed over the last decade, but comprehending gene or pathway‐specific distinctions between CHO cell lines at transcriptome level remains a challenge. To address these challenges, a compendium of 23 RNA‐Seq studies from public and in‐house data on CHO cell lines, i.e., CHO‐S, CHO‐K1, and DG44 is compiled. Significantly differentially expressed (DE) genes particularly related to subcellular structure and macromolecular categories are used to identify differences between the cell lines. A R‐based web application is developed specifically for CHO cell lines to further visualize expression values across different cell lines, and make available the normalized full CHO data set graphically as a CHO research community resource. This study quantitatively categorizes CHO cell lines based on patterns at transcriptomic level and detects gene and pathway specific key distinctions among sibling cell lines. Studies such as this can be used to select desired characteristics across various CHO cell lines. Furthermore, the availability of the data as an internet‐based application can be applied to broad range of CHO engineering applications. Abstract : A R‐based web application CGEVA (CHO gene expression visualization application) has been developed, specifically for CHO cell lines from RNA‐Seq transcriptome analysis with a user‐friendly, graphical visualization website of publicly available CHO RNA‐Seq data. Further, gene set enrichment analysis (GSEA) has been performed to finally profile transcriptomic footprints from them. Combining the strength of CGEVA and GSEA, this study will allow us to add knowledge existing in the field and deciphering the distinction between various CHO cell line on the basis of their own unique and distinct behavior. Also, such database is valuable to the readers of Biotechnology Journal .