Lessons from the Hamster: Cricetulus griseus Tissue and CHO Cell Line Proteome Comparison.
Kelley M HeffnerDeniz Baycin HizalGeorge S YerganianAmit KumarÖzge CanRobert O'MeallyRobert ColeRaghothama ChaerkadyHerren WuMichael A BowenMichael J BetenbaughPublished in: Journal of proteome research (2017)
Chinese hamster ovary cells represent the dominant host for therapeutic recombinant protein production. However, few large-scale data sets have been generated to characterize this host organism and derived CHO cell lines at the proteomics level. Consequently, an extensive label-free quantitative proteomics analysis of two cell lines (CHO-S and CHO DG44) and two Chinese hamster tissues (liver and ovary) was used to identify a total of 11 801 unique proteins containing at least two unique peptides. 9359 unique proteins were identified specifically in the cell lines, representing a 56% increase over previous work. Additionally, 6663 unique proteins were identified across liver and ovary tissues, providing the first Chinese hamster tissue proteome. Protein expression was more conserved within cell lines during both growth phases than across cell lines, suggesting large genetic differences across cell lines. Overall, both gene ontology and KEGG pathway analysis revealed enrichment of cell-cycle activity in cells. In contrast, upregulated molecular functions in tissue include glycosylation and lipid transporter activity. Furthermore, cellular components including Golgi apparatus are upregulated in both tissues. In conclusion, this large-scale proteomics analysis enables us to delineate specific changes between tissues and cells derived from these tissues, which can help explain specific tissue function and the adaptations cells incur for applications in biopharmaceutical productions.
Keyphrases
- induced apoptosis
- cell cycle
- cell cycle arrest
- gene expression
- mass spectrometry
- cell proliferation
- genome wide
- endoplasmic reticulum stress
- signaling pathway
- cell death
- magnetic resonance
- oxidative stress
- magnetic resonance imaging
- computed tomography
- dna methylation
- high resolution
- machine learning
- deep learning
- artificial intelligence
- big data