Recently, RNA-based therapy has started to attract attention in the medicine industry. The first ever RNAi drug was approved by the FDA in 2018, the second in 2020 and the two first-to-market corona vaccinas was mRNA-based, supporting we may be at the dawn of a new treatment era.
Whereas most classic drug molecules elicit their effect by affecting single proteins directly, RNA act in finetuning complex cellular mechanisms at different levels. Hence, RNA-based therapy is a promising approach to solve currently dead-ends afflictions, especially in cases with multifactual pathologies.
Before any molecule can be approved and go into the clinic, it has to go through several steps of analysis for both positive (on-target) and negative (off-target) effects.
In omiics, we have several established work-flows for early assessment of a specific ASO sequence, by
To assess the on-target activity of anti-miRs, e.g. ASOs, we can evaluate both direct knockdown of mRNA transcripts and inhibition of miRNA function.
- mRNA knockdown can be visualized by volcano plots, highlighting targeted transcripts with significant downregulation.
- miRNA inhibition can be examined using cumulative distribution function (CDF) analyses of predicted targets versus non-targets, providing evidence for relief of natural miRNA repression upon ASO binding.
The volcano plot illustrates the global transcriptional response following ASO-mediated knockdown.
The transcript targeted by the ASO is significantly downregulated, highlighted by their strong fold change and statistical significance.
This pattern confirms the specificity and efficacy of the ASO in silencing its intended targets while allowing visualization of potential off-target effects in the broader transcriptome.
The cumulative distribution function (CDF) plot compares predicted miRNA targets (identified by TargetScan) with non-targets after ASO-mediated miRNA blockade.
Since direct binding of the ASO to the miRNA cannot be observed in sequencing data, the analysis instead focuses on whether the natural repression of predicted targets is relieved.
A rightward shift of target curves relative to non-targets indicates reduced miRNA activity, consistent with effective ASO inhibition.
Having established a potent on-target effect of your ASO is the first step towards validating a potential as a therapeutic agent. A second parameter to evaluate is the degree of potential off-target and secondary effects.
Off-target effects occur when an ASO (or any targeted molecule) binds unintended sequences that share partial complementarity, leading to silencing or modulation of transcripts that were not the intended therapeutic target. These are driven by unintended molecular recognition.
Secondary effects, on the other hand, arise indirectly as a downstream consequence of the desired knockdown or blockade. When the primary target is silenced, it can disrupt regulatory networks, alter signaling pathways, or change cellular states, which in turn affects other transcripts or processes not directly bound by the ASO.
👉In short: off-target = unintended direct binding; secondary = downstream biological consequences of intended targeting.
The analysis begins with an in silico prediction of potential off-targets, based on imperfect pairing of the ASO sequence with the transcriptome of the organism of interest (e.g., human). Mismatches of one, two, or three bases are systematically assessed, with higher mismatch tolerance resulting in an increasing number of predicted off-target sites.
This is performed against both pre-spliced and spliced transcriptomes.
Sequence match | In silico predicted targets | +/- ASO Expression in vitro | Up-regulated | Down-regulated | Tissue specific e.g. neuron (database) |
Perfect | 1 | 1 | 0 | 1 | 1 |
1 Mismatch* | 42 | 12 | 0 (0) | 3 (12) | 0 (0) |
2 Mismatch* | 311 | 107 | 4 (11) | 14 (47) | 0 (0) |
3 Mismatch* | 1532 | 941 | 19 (62) | 36 (102) | 1 (3) |
* Mismatch: Insertion, deletion or substitution
Next, cell culture experiments are carried out with appropriate controls (with and without ASO treatment), followed by RNA-seq to obtain transcriptome-wide expression data. Transcripts significantly altered by ASO treatment are identified and compared to the in silico predictions. Transcripts found in both datasets are considered likely off-targets, whereas significant changes not overlapping with predictions are classified as secondary effects, arising as downstream consequences of either on-target or off-target activity.
Finally, predicted off-targets with significant expression changes are cross-referenced against validated tissue- and cell-specific expression databases for the target organ. Only transcripts expressed in the target organ are expected to contribute to physiologically relevant off-target effects, whereas transcripts not expressed in the target organ are unlikely to have an impact.
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