A Workflow for Transcriptome-Wide Assessment of Antisense Oligonucleotide Selectivity.

Area of Science:

• Molecular Biology
• Genomics
• Drug Discovery

Background:

• Antisense oligonucleotides (ASOs), specifically gapmer ASOs, are effective in downregulating therapeutic target gene expression by recruiting RNase H1.
• Identifying selective ASOs that target specific transcripts without affecting unintended ones is crucial but challenging due to suboptimal in silico off-target prediction.
• Nonselective ASOs can lead to unintended gene silencing, highlighting the need for robust methods to assess ASO selectivity.

Purpose of the Study:

• To develop and validate an experimental workflow for assessing the selectivity of gapmer antisense oligonucleotides (ASOs).
• To improve the prediction of off-target effects and enhance the identification of selective ASOs for therapeutic applications.

Main Methods:

• Developed a novel experimental workflow named concentration-response digital gene expression (CR-DGE).
• CR-DGE involves treating cells with ASOs at increasing concentrations and measuring transcriptome-wide expression changes using 3'Tag-Seq.
• Analyzed expression data to identify genes exhibiting concentration-responsive knockdown, indicating ASO activity.

Main Results:

• CR-DGE demonstrated high reproducibility and greater sensitivity in identifying concentration-responsive genes compared to conventional single-concentration assays.
• Application of CR-DGE to a panel of gapmer ASOs revealed a spectrum of ASO selectivity.
• The method successfully identified genes responsive to ASO treatment in a concentration-dependent manner.

Conclusions:

• CR-DGE is an effective tool for evaluating the selectivity of gapmer ASOs.
• This workflow provides a valuable approach for researchers and developers in optimizing ASO-based therapeutics.
• Enhanced assessment of ASO selectivity using CR-DGE can lead to safer and more effective gene-silencing strategies.