Predicting Potential miRNA Targets for Antibody Glycosylation in CHO Cells Using a Simplified Evaluation Workflow

Abstract

Introduction: In the past decade, microRNAs (miRNAs), which are small, noncoding RNAs of ?22 nucleotides length, have gained attention as novel engineering tools for biopharmaceutical cell line development. As miRNAs provide the ability to fine-tune the regulation of their targets, they offer attractive options for the development of differentially glycosylated monoclonal antibodies (mAbs) in production hosts like Chinese hamster ovary (CHO) cells. However, as one miRNA can potentially interact with several hundred gene transcripts, targeted miRNA mediated glycosylation regulation is complex to implement. This circumstance raises the need for computational aid in the prediction of miRNA targets. Method: In our study, we present a workflow using the target prediction tool RNA22 in combination with a comprehensive dataset of sequences for CHO cells, followed by curation of prediction results. Results: We created a knowledge-based database consisting of biochemically relevant genes for mAb N-glycosylation, to rationally process relevant results. Comparison to experimental data of target regulation unraveled the potential of our method to correctly predict 55 of 69 (79%) regulations caused by 16 different miRNAs known to affect mAb glycosylation. Conclusion: This work could potentially serve as a starting point for the development of new bioinformatics-assisted workflows to select miRNAs serving the user's exact needs.

Keywords

• CHO
• microRNA
• target prediction
• glycosylation
• engineering