RNA Interference Strategies for Future Management of Plant Pathogenic Fungi: Prospects and Challenges.
Daniel Endale GebremichaelZeraye Mehari HaileFrancesca NegriniSabbadini SilviaLuca CapriottiMezzetti BrunoElena BaraldiPublished in: Plants (Basel, Switzerland) (2021)
Plant pathogenic fungi are the largest group of disease-causing agents on crop plants and represent a persistent and significant threat to agriculture worldwide. Conventional approaches based on the use of pesticides raise social concern for the impact on the environment and human health and alternative control methods are urgently needed. The rapid improvement and extensive implementation of RNA interference (RNAi) technology for various model and non-model organisms has provided the initial framework to adapt this post-transcriptional gene silencing technology for the management of fungal pathogens. Recent studies showed that the exogenous application of double-stranded RNA (dsRNA) molecules on plants targeting fungal growth and virulence-related genes provided disease attenuation of pathogens like Botrytis cinerea, Sclerotinia sclerotiorum and Fusarium graminearum in different hosts. Such results highlight that the exogenous RNAi holds great potential for RNAi-mediated plant pathogenic fungal disease control. Production of dsRNA can be possible by using either in-vitro or in-vivo synthesis. In this review, we describe exogenous RNAi involved in plant pathogenic fungi and discuss dsRNA production, formulation, and RNAi delivery methods. Potential challenges that are faced while developing a RNAi strategy for fungal pathogens, such as off-target and epigenetic effects, with their possible solutions are also discussed.
Keyphrases
- human health
- cell wall
- risk assessment
- climate change
- gram negative
- antimicrobial resistance
- healthcare
- gene expression
- primary care
- escherichia coli
- current status
- mental health
- dna methylation
- pseudomonas aeruginosa
- staphylococcus aureus
- multidrug resistant
- high resolution
- binding protein
- transcription factor
- quality improvement
- quantum dots
- sensitive detection