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論文掲載:Boehringer Ingelheim社、新たに発見されたマイクロRNAがCHOモノクローナル抗体へ与える影響
Loss of a Newly Discovered microRNA in Chinese Hamster Ovary Cells Leads to Upregulation of N-glycolylneuraminic Acid Sialylation on Monoclonal Antibodies

Biotechnology and Bioengineering
December 21, 2021

Successful biotherapeutic development relies upon specific cell lines and their generation of products with consistent characteristics. Heterogeneity can lead to low product quality and result in unexpected patient adverse reactions. It is, therefore, crucial to confirm the identity of the cell line used and the products generated. Next-Generation Sequencing (NGS) allows performing in-depth cell line characterization to identify and better understand the behavior of your expression host.

Fischer et al. identified an unusual post-translational sialylation of monoclonal antibodies produced by Chinese Hamster Ovary (CHO) cell lines. It was important to investigate this aberrant glycosylation, as this is believed to result in an increased immunogenic response in patients. Knowing the breadth of applications provided by NGS in cell line characterization, the team decided to leverage this technique to understand the root cause of this anomalous sialylation. Fischer et al. performed a multi-omics study that involved whole-genome sequencing as well as transcriptomic analysis through RNA sequencing. The resulting findings were validated through a series of functional knock-out and CRISPR experiments as well as flow cytometry experiments. With NGS analyses, it was demonstrated that genomic point mutations caused the downregulation of a newly discovered miRNA-111, leading to an upregulation of CMAH, which resulted in the uncommon sialylation of the expressed protein product.

In this work, Genedata’s scientific consultants provided support for the NGS data analysis. Genedata Selector® was used to process and analyze the various DNA and RNA-seq data of the different clones. The platform enabled proprietary genome annotation, mutation profiling of the clones, deep statistical analysis of expression data, and many more. Also, the software solution was used to support the CRISPR approach, as well as data analysis following the gene editing.