DOI: 10.6084/m9.figshare.30228535
title: Raw Data for the Thesis: "<i>Enhancing RNAi-Based Pest Control through Effective Target Gene Selection and Optimal dsRNA Design</i>"

description: The growing global population demand new strategies for sustainable pest management. Chemical insecticides, while historically successful in controlling pest populations to protect agricultural yields and suppress disease vectors, face critical challenges, including environmental harm and resistance development. RNA interference (RNAi) has emerged as a promising alternative, offering species-specific and environmentally friendly pest control through the delivery of double-stranded RNA (dsRNA) targeting essential pest genes. Despite advances in delivery technologies and commercial progress, two major gaps remain: standardized approaches for effective target gene selection and optimization of dsRNA sequences, which fundamentally determine RNAi efficacy.<br>The main aims of this thesis are to establish standardized approaches for effective target gene identification in insect pests and optimize dsRNA sequences for improved insecticidal efficacy. A further overarching goal is to develop data-driven and user-friendly computational tools that allow researchers to translate these insights into RNAi-based pest control applications. These aims have practical implications, as they have the potential to improve insecticidal efficacy in field applications, thereby reducing the cost of RNAi-based pest control applications.<br>Chapter 2 investigated the transferability of effective target genes identified in a genome-wide RNAi screen in the red flour beetle <i>Tribolium castaneum</i> to a major oilseed rape pest, the cabbage stem flea beetle (<i>Psylliodes chrysocephala</i>). The results revealed moderate transferability (~50%) of highly effective targets from <i>T. castaneum</i>, which increased to approximately 80% when considering genes already validated in other leaf beetles. These findings are both conceptually important, in demonstrating partial but significant cross-species transferability of RNAi targets, and practically valuable for guiding the development of RNAi-based solutions against this important pest.<br>Chapter 3 synthesizes progress in identifying effective RNAi target genes across insect pests. Drawing on genome-wide data from <i>T. castaneum</i>, experimental results from Chapter 2, and other transfer studies, it highlights that effective RNAi targets are largely distinct from the targets of conventional chemical insecticides. The chapter argues that unbiased, screen-based approaches outperform hypothesis-driven gene selection due to the unpredictable influence of cellular processes on RNAi efficacy. Importantly, it compiles a curated list of highly effective RNAi targets validated across multiple pest species, providing a critical resource for future discovery efforts.<br>Chapter 4 introduces the dsRIP web platform (https://dsrip.uni-goettingen.de/) for designing sequence-optimized dsRNA for RNAi-based pest control. In the experimental part, small interfering RNA (siRNA) features that were associated with RNAi efficacy in human cells were tested in <i>T. castaneum </i>by targeting an essential gene and measuring insecticidal efficacy. Next, an algorithm was developed for designing dsRNA that maximizes effective siRNA features identified in <i>T. castaneum</i>. This algorithm, in most cases, improved the insecticidal efficacy of dsRNA treatments in <i>T. castaneum</i> and two other coleopteran pests. RISC-bound sRNA-seq showed that the improvement was associated with an increase in the antisense (target gene complementary) to sense siRNA strand ratio generated from the delivered dsRNA. The algorithm was made available as a dsRNA designer tool in dsRIP. In addition, dsRIP uses the curated list described in Chapter 3 and conducts orthology inference to suggest highly promising effective target gene candidates in pests of interest. Also, dsRIP is able to predict off-targets in selected non-target organisms, such as honey bees, to minimize the potential harm of dsRNA on these organisms. dsRIP web platform is one of the major contributions of this thesis to the RNAi-based pest control field and is actively being used by multiple research groups and companies. Chapter 5 applies the dsRIP pipeline to the hop flea beetle <i>Psylliodes attenuata</i>, a key pest of hop plants. Three dsRNAs with lethal and feeding-inhibitory effects were identified, demonstrating both the feasibility of RNAi in managing this pest and the utility of dsRIP as a comprehensive design pipeline in previously unestablished pest targets.<br>Chapters 6 and 7 adopt an exploratory approach and use high-throughput sequencing following insecticidal dsRNA delivery to gain novel insights into the mode of action of RNAi-based pest control at the molecular level. Chapter 6 combines RNA degradomics, RISC-bound small RNA-seq, and proteomics to characterize the mode of action of a proteasome-targeting insecticidal dsRNA at the molecular level. Interestingly, the study identified discrepancies between siRNA abundance and target mRNA cleavage patterns. This suggests that improving the abundance of siRNAs with the highest cleavage efficacies might be an additional strategy to increase dsRNA efficacy. Chapter 7 investigates the processing of insecticidal dsRNA into siRNAs using small RNA-seq from <i>T. castaneum</i> injected with many different dsRNAs. The key advantage of this chapter compared to the literature is the extensive number of different dsRNAs that were included in the small RNA-seq experiments, which provided an opportunity to understand general dsRNA processing patterns.