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. 2021 Nov;18(11):1747-1759.
doi: 10.1080/15476286.2020.1868680. Epub 2021 Jan 4.

Off-target effects of RNAi correlate with the mismatch rate between dsRNA and non-target mRNA

Affiliations

Off-target effects of RNAi correlate with the mismatch rate between dsRNA and non-target mRNA

Jiasheng Chen et al. RNA Biol. 2021 Nov.

Abstract

RNAi is a potent technique for the knockdown of target genes. However, its potential off-target effects limit the widespread applications in both reverse genetic analysis and genetic manipulation. Previous efforts have uncovered rules underlying specificity of siRNA-based silencing, which has broad applications in humans, but the basis for specificity of dsRNAs, which are better suited for use as insecticides, is poorly understood. Here, we investigated the rules governing dsRNA specificity. Mutational analyses showed that dsRNAs with >80% sequence identity with target genes triggered RNAi efficiently. dsRNAs with ≥16 bp segments of perfectly matched sequence or >26 bp segments of almost perfectly matched sequence with one or two mismatches scarcely distributed (single mismatches inserted between ≥5 bp matching segments or mismatched couplets inserted between ≥8 bp matching segments) also able to trigger RNAi. Using these parameters to predict off-target risk, dsRNAs can be designed to optimize specificity and efficiency, paving the way to the widespread, rational application of RNAi in pest control.

Keywords: RNA interference; RNAi efficiency; dsRNA specificity; off-target effect; risk assessment; sequence identity.

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Figures

Figure 1.
Figure 1.
Knockdown efficiency of genes in T. castaneum triggered by 100 bp dsCYP6BQ6. (A) The number connected with a dash to the gene name represents the identity between the gene fragment and dsCYP6BQ6. The dsEGFP of the same length was used as control. Values are presented as mean±SE, n = 4 (*, p < 0.05; **, p < 0.01; ***, p < 0.001). The ‘n = 4’ represents four biological repeats, each biological repeat contains 10 larval individuals for RNAi experiments. (B) Alignments showing the complementarity between dsCYP6BQ6 and the corresponding area of the extremely sensitive or insensitive genes, CYP6BQ12, CYP6BK13, CYP6A1 and CYP6BK7, with the longest fragments of perfectly or almost perfectly matching sequence are indicated
Figure 2.
Figure 2.
Knockdown efficiency of five genes in fifth instar T. castaneum larvae triggered by a series dsRNAs with varied identity. (A) CYP4G7; (B) Drip: D. melanogaster integral protein homologous; (C) AANAT1: Arylalkylamine N-acetyltransferase 1; (D) CYP6BK13; and (E) CYP6BQ6. The expression levels of these genes were 600, 272, 246, 189 and 54 times that of CPR18, respectively (Chen, et al., under review). The per cent depletions are presented as mean±SE, n = 4 (*, p < 0.05; **, p < 0.01; ***, p < 0.001). Bold grey dots (dsRNA identity <77%) were excluded from the curve modulations. (F) Sketch map used to compare the five index curves obtained
Figure 3.
Figure 3.
The scatter diagram showing knockdown efficiencies in T. castaneum triggered by a series of chimeric dsRNA containing a varied length fragment of contiguous matching bases. (A) Design of model of chimeric dsRNA. The short bars on the line represents EGFP sequence, and the tall bars on the line represent the target sequence. Numbers in the brackets represent the length of the target sequence. (B) Knockdown efficiency of CYP4Q7 and CYP6BK13 triggered by 100 bp chimeric dsRNA. Expression levels of these two genes were 3142.8 and 188.7 times that of CPR18, respectively. The circle represents the location of coordinate points with the largest slope change. Coordinate points (bp length, per cent depletion) for CYP4Q7 were (15.2, 19.8) and (16.3, 72.0), which were calculated using the formula deriv(derivn(0.5456 + 90.6244/(1 + 1015.73-x), x, 2), x) = 0. For CYP6BK13, they were (15.7, 14.2) and (16.8, 50.6) and the formula was deriv(derivn(0.7375 + 63.2525/(1 + 1016.25-x), x, 2), x) = 0
Figure 4.
Figure 4.
Knockdown induced by dsCYP4Q7 containing evenly distributed mismatching base couples in T. castaneum. (A) The distribution model of the mismatching base couples in the sequence. The tall bars standing on the line represent matching bases between the target gene and dsRNA, and the adjoin short bars standing on the line represent mismatching base couples. (B) Knockdown of CYP4Q7 gene triggered by dsRNA containing evenly distributed mismating base couples at varying intervals. The circle represents the location of coordinate points with the largest slope change. Coordinate points (bp length, per cent depletion) was (7.4, 12.2) and (8.6, 66.2), calculated using the formula deriv(derivn(−7.497 + 93.357/(1 + 107.982-x), x, 2), x) = 0
Figure 5.
Figure 5.
The non-target effects in T. castaneum induced by dsRNA synthesized using Diabrotica virgifera virgifera SNF7 gene fragment as a template (dsDvSNF7). (A) Alignment of sequences of SNF7 homologs from T. castaneum and D. virgifera. (B) The expression depletion of T. castaneum SNF7 triggered by dsDvSNF7 and dsTcSNF7. (C) Mortality of T. castaneum induced by dsDvSNF7 and dsTcSNF7 (Tc, T. castaneum; Dv, D. virgifera). (D) Mortality of T. castaneum induced by dsCsEF1 and dsTcEF1. Mean±SE (n = 4) are presented. *, p < 0.05; **, p < 0.01; ***, p < 0.001)

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