bioRxivBioinformaticsPreprint
Perfect 21-nucleotide matches to beneficial fungi are common in canonical antifungal dsRNA targets: an in-silico off-target hazard screen for spray-induced gene silencing
Muhammad WasifiD, Gábor TarcaliiD, Moman KhaniD, Huma SaleemiD, Muhammad Abubakkar Azmat
Abstract
Double-stranded RNA (dsRNA) biopesticides that silence essential fungal genes by spray-induced gene silencing (SIGS) are advancing towards registration, underpinned by the claim that silencing is confined to sequence-matched target organisms, a claim that has never been tested systematically against beneficial fungi. We screened twelve dsRNA constructs (the whole transcripts of eleven canonical antifungal target genes of Fusarium graminearum: three CYP51 sterol 14-demethylases, six chitin synthases and two {beta}-tubulins, plus a concatenation of the three CYP51 transcripts modelling the flagship CYP3RNA design) for perfect 21-nucleotide (21-nt) identity, the primary match criterion of recent regulatory bioinformatics frameworks, against the transcriptomes of seven non-target fungi, including commercial biocontrol agents (Trichoderma harzianum, T. virens, Beauveria bassiana, Metarhizium anisopliae, M. brunneum), the arbuscular mycorrhizal fungus Rhizophagus irregularis and Saccharomyces cerevisiae. Eleven of the twelve constructs carried off-target hazard; only CYP51C was completely clean. TUB2b carried 580 perfect 21-nt matches and contained no hazard-free window of 250 consecutive 21-mer start positions, whereas 26.6-60.0% of screened windows were hazard-free in the designable genes. Hits landed overwhelmingly on orthologues of the targeted gene and concentrated in the Hypocreales; R. irregularis and S. cerevisiae were nearly clean. These are hazard findings, not risk findings: perfect siRNA-length matches to beneficial fungi are common in canonical antifungal dsRNA targets, and current regulatory screening panels contain no fungus that would detect them.
- organism: fungi (other)
- assay: DNA methylation
- research focus: benchmarking
- research focus: variant calling
- approach: statistical model
- Citations
- 0
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