Back

Cross-kingdom degradomics identifies natural plant small RNAs with disease protecting activity

Werner, B. T.; Nasfi, S.; Procida, T.; Schmidt, R.; Wilhelm, J.; Schaefer, P.

2025-10-06 plant biology
10.1101/2025.10.06.680647 bioRxiv
Show abstract

RNA interference (RNAi) is a highly specific process regulating genes transcriptionally or post-transcriptionally via silencing. small regulatory RNAs (sRNAs) provide the specificity of post-transcriptional silencing by binding to complementary mRNAs leading to their degradation or translational repression. sRNAs are promising non-chemical control agents against plant pests and diseases. Their application requires, however, a deep understanding of the determinants of functional specificity and efficiency. In nature, interacting plants and pathogens follow the manipulative strategy of cross-kingdom RNAi (ckRNAi) based on the targeted transfer of sRNAs. In this study, we employed a genome-wide approach to explore the diversity of natural ck-sRNAs exchanged in the pathogenic interaction of Brachypodium distachyon with the fungal pathogen Fusarium graminearum. By deep-sequencing of the cross-kingdom sRNAome, transcriptome and degradome 258 ckRNAi-mediating sRNAs were discovered. In a simulation-based approach, specific sequence characteristics allowed categorizing sRNAs into functionality classes. Subsequent genome annotation analyses revealed the organization of plant ck-sRNAs in long non-coding RNAs. Moreover, comparative sequence analyses revealed an evolution of ck-sRNAs towards invariant mRNA target regions. Functional analyses with sRNA candidates confirmed an antipathogenic activity at exceptionally low application doses. It indicates natural sRNA as untapped resource and potential blueprint for the development of highly specific and effective plant-derived bioprotectants.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.