Classification of and Detection Techniques for RNAi-induced Effects in GM plants

Cecilia Andrea Diaz ¹ Steve U Ayobahan ¹ Samson Simon ² Luise Zühl ² Andreas Schiermeyer ¹ Elke Eilebrecht ¹ Sebastian Eilebrecht ^1*

• ¹ Fraunhofer Institute for Molecular Biology and Applied Ecology (IME), Schmallenberg, Germany
• ² Bundesamt für Naturschutz (BfN), Bonn, North Rhine-Westphalia, Germany

RNA interference (RNAi) is a biotechnological tool used for gene silencing in plants, with both endogenous and exogenous applications. Endogenous approaches, such as host-induced gene silencing (HIGS), involve genetically modified (GM) plants, while exogenous methods include sprayinduced gene silencing (SIGS). The RNAi mechanism hinges on the introduction of double-stranded RNA (dsRNA), which is processed into short interfering RNAs (siRNAs) that degrade specific messenger RNAs (mRNAs). However, unintended effects on non-target organisms and GM plants are a concern due to sequence homologies or siRNA-induced epigenetic changes. Regulatory bodies such as the EPA and EFSA emphasize the need for comprehensive risk assessments. Detecting unintended effects is complex, often relying on bioinformatic tools and untargeted analyses like transcriptomics and metabolomics, though these methods require extensive genomic data. This review aims to classify mechanisms of RNAi effects induced by short interfering RNA from different sources in plants and to identify technologies that can be used to detect these effects. In addition, practical case studies are summarized and discussed in which previously unintended RNAi effects in genetically modified plants have been investigated. Current literature is limited but suggests RNAi is relatively specific, with few unintended effects observed in GM crops. However, further studies are needed to fully understand and mitigate potential risks, particularly those related to transcriptional gene silencing (TGS) mechanisms, which are less predictable than post-transcriptional gene silencing (PTGS). Particularly the application of untargeted approaches such as small RNA sequencing and transcriptomics is recommended for thorough and comprehensive risk assessments.