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Based on the same technological principle as HTPathwaySeq, HTTargetSeq is Biogazelle’s proprietary low-cost high-throughput molecular characterization service of RNA targeting medicines such as short interfering RNAs (siRNA) and antisense oligonucleotides (ASO). A typical experiment assesses 96 conditions (in quadruplicate, 384 samples in total) based on 3’ end sequencing of cell lysates.
Oligonucleotide drugs like ASOs or siRNAs are designed to specifically target a single RNA molecule in order to reduce its expression (or modulate splicing). Despite sequence specificity for the target RNA through rational design, non-specific regulation of so-called off-target genes is frequently observed for both ASOs and siRNAs. These off-target effects may result in toxicity of the oligonucleotide and failure in clinical trials.
Oligonucleotide drug specificity is often assessed based on computational sequence analyses. For example, in the case of siRNAs, off-target effects are mediated by microRNA-like binding of the siRNA seed to the off-target mRNA. Identifying mRNAs that harbor one or multiple siRNA seed binding sites can enrich for potential off-target genes. However, these approaches are prone to false positive predictions, and do not provide any information on the magnitude of the effect. For instance, an off-target gene that is repressed 20% may be less relevant than one that is repressed 50%.
Additionally, accurate identification of off-target mRNAs harbors an enormous untapped potential to identify novel candidate therapeutic targets. RNAi-based library screens target hundreds of candidate genes using multiple siRNAs (or short hairpin RNAs) per gene. siRNAs/shRNAs targeted against a specific gene are considered hits when all (or the overwhelming majority) of them produce both a knockdown effect on the target gene as well as the desired phenotype. If several siRNAs/shRNAs cause a knockdown of a the target gene but only some of those produce the desired phenotype, the phenotype is considered to have resulted from off-target effects.
In several published studies, further characterization of these off-target effects has provided novel insights in disease pathways and ultimately novel therapeutic targets, a process called target deconvolution.
To enable the objective study of off-target effects, Biogazelle has developed a high-throughput sequencing-based methodology, HTTargetSeq. Based on the same technology as HTPathwaySeq, HTTargetSeq is performed directly on crude cell lysates from 96-well culture plates, typically at 4 replicates per condition. As each experiment includes 8 internal controls, up to 94 conditions of interest can be analyzed simultaneously. HTTargetSeq relies on a 3’ end-sequencing library prep workflow with shallow sequencing (5 million reads per sample), resulting in reproducible detection of 7,000 to 10,000 genes per sample.
HTTargetSeq integrates differential gene expression results with seed based sequence analysis to provide a more accurate identification of the siRNA off-target repertoire. The figure below shows data from a study of a library of 86 siRNAs displaying off-target effects. To verify that genes with seed matches for these siRNAs are indeed preferentially downregulated, we compared fold changes for genes without seed (black) and genes with different canonical seed types. As expected, we observed stronger downregulation for seed sites with increasing potency (see plot on the right). More specifically, genes with 8mer seed sites showed stronger downregulation compared to genes with 7mer or 6mer seed sites. Note that almost half of the genes that have a seed site are not downregulated, underscoring the problem of false positive predictions when relying on seed analysis alone. In addition, only a fraction of genes with seed sites (5%) show a 2-fold or higher downregulation and are likely the most biologically relevant off-target genes.
In the above example, to prioritize off-target genes that cause the desired phenotype, we also looked at additional features, most importantly recurrence. Genes that are identified as off-targets across multiple of the 86 siRNAs that were screened are more likely to play an important role in the pathway.
HTTargetSeq can be used to: