|dc.description.abstract||Chinese hamster ovary (CHO) cells were isolated in the late 1950’s and have been the workhorse of biotherapeutics production for decades. While previous efforts compared CHO cell lines by proteomics, research into the original Chinese hamster (Cricetulus griseus) host has not been conducted. Thus, we sought to understand proteomic differences across CHO-S and CHO DG44 cell lines in relation to brain, heart, kidney, liver, lung, ovary, and spleen tissues. As glycosylation is critical for recombinant protein quality, we additionally performed a glycoproteomics and sialoproteomics analysis of wild-type and mutant CHO cell lines that differ in glycosylation capacity.
First, wild-type CHO was compared with tunicamycin-treated CHO and Lec9.4a cells, a mutant CHO cell line which shows 50% of wild-type glycosylation levels. A total of 381 glycoproteins were identified, including heavily-glycosylated membrane proteins and transporters. Proteins related to glycosylation downregulated in Lec9.4a include alpha-(1,3)-fucosyltransferase and dolichyl-diphosphooligosaccharide-protein glycosyltransferase subunit 1. Next, wild-type Pro-5 CHO was compared with Lec2 cells, which have a mutation in CMP-sialic acid transporter that reduces sialylation. A total of 272 sialylated proteins were identified. Downregulated sialoproteins, including dolichyl-diphosphooligosaccharide-protein glycosyltransferase subunit STT3A and beta-1,4-galactosyltransferase 3, detect glycosylation defects.
Next, a label-free quantitative proteomics analysis of CHO-S and CHO DG44 cell lines and liver and ovary tissue identified 11801 proteins, including 9359 proteins specifically in the cell lines, representing a 56% increase over previous work. Additionally, 6663 proteins were identified across liver and ovary tissues providing the first Chinese hamster tissue proteome. 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 transport.
Finally, we used labeled proteomics to compare CHO-S and CHO DG44 cell lines with brain, heart, kidney, liver, lung, ovary, and spleen tissues to identify 8464 proteins. After protein expression and functional analyses, we combined the proteomics with transcriptomic data obtained by RNAseq in order to correlate mRNA and protein expression. Over 65% of genes show agreement between transcriptome and proteome. The remaining genes were categorized as stable or unstable and related to metabolic pathways. In conclusion, this large-scale proteomics analysis delineates specific changes across cell lines and tissues, which can help explain tissue function and the adaptations cells incur as biotherapeutics production hosts.||