# Utargetome: A targetome prediction tool for modified U1-snRNAs to identify distal-target positions with improved selectivity

## TL;DR

Utargetome is a computational tool that predicts where modified U1-snRNAs bind in the transcriptome, helping design more selective gene therapies.

## Contribution

Utargetome introduces a novel computational pipeline to predict U1-snRNA targetomes with improved selectivity for therapeutic applications.

## Key findings

- Modified U1s targeting positions 3 nucleotides downstream of canonical sites have minimal off-target effects.
- Utargetome analysis of 30,204 U1s revealed optimal distal-targeting positions for improved selectivity.
- The tool accurately predicts on- and off-target sites by considering mismatches and alternative binding registers.

## Abstract

The endogenous U1 small nuclear RNA (U1-snRNA) plays a crucial role in splicing initiation through base-pairing to donor splice sites (5′-SSs). Likewise, modified U1s that carry a mutation-adapted 5′-terminal sequence have been demonstrated to rescue exon splicing when this is disrupted by genetic mutations within the 5′-SS. Given the base-pairing flexibility of the endogenous U1, the selectivity of modified U1s requires investigation. We developed a computational pipeline (Utargetome) that considers combinations of mismatches and alternative annealing registers to predict the transcriptome-wide binding sites (or targetome) of a U1. The pipeline accuracy was tested by recapitulating well-established alternative annealing registers and specificity for 5′-SSs in the predicted targetome of the human endogenous U1. It was then applied to analyse the targetome of 54 modified U1s that have been demonstrated to restore exon inclusion when affected by 5′-SS pathogenic mutations. While the targetome size was found to be wide-ranging, the off-target load appeared to be reduced for U1s targeting distal sites from the canonical U1-binding position. This feature was predicted also for a large set of 30,204 newly designed U1s targeting 839 5′-SS pathogenic mutations that were expected to affect exon inclusion. Targetome analysis indeed revealed an optimal distal-targeting position at 3 nucleotides downstream from the canonical 5′-SS, for which a modified U1 is likely to have minimal off-targets at 5′-SSs and acceptor splice sites (3′-SSs). Based on these insights, we propose to implement targetome prediction in the design and optimization of therapeutic U1s with improved selectivity.

In the context of evolving gene therapy technologies that demand higher precision and safety, we present Utargetome, a computational tool designed to predict the binding sites of modified U1-snRNAs across the transcriptome. U1 plays a key role in splicing by binding to specific sites on RNA, and modified U1s have been used to restore normal splicing in cases where splice sites are lost due to mutations. However, ensuring the selectivity of these modified U1s, particularly avoiding unintended off-target effects, is critical for their therapeutic application. Utargetome predicts both the intended (on-target) and unintended (off-target) binding sites of U1s, accounting for mismatches and alternative binding registers. Our findings from analysing more than 30,000 modified U1s show that U1s targeting positions slightly downstream of their typical binding site have fewer off-target events. The insight enables the design of precise U1-based therapies for genetic disorders caused by splicing defects, and towards the advancement of safer gene therapies.