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. 2010 Nov 16:6:433.
doi: 10.1038/msb.2010.89.

mRNA turnover rate limits siRNA and microRNA efficacy

Erik Larsson  1 , Chris Sander, Debora Marks
Affiliations

mRNA turnover rate limits siRNA and microRNA efficacy

Erik Larsson et al. Mol Syst Biol. .

Abstract

The microRNA pathway participates in basic cellular processes and its discovery has enabled the development of si/shRNAs as powerful investigational tools and potential therapeutics. Based on a simple kinetic model of the mRNA life cycle, we hypothesized that mRNAs with high turnover rates may be more resistant to RNAi-mediated silencing. The results of a simple reporter experiment strongly supported this hypothesis. We followed this with a genome-wide scale analysis of a rich corpus of experiments, including RT-qPCR validation data for thousands of siRNAs, siRNA/microRNA overexpression data and mRNA stability data. We find that short-lived transcripts are less affected by microRNA overexpression, suggesting that microRNA target prediction would be improved if mRNA turnover rates were considered. Similarly, short-lived transcripts are more difficult to silence using siRNAs, and our results may explain why certain transcripts are inherently recalcitrant to perturbation by small RNAs.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
Schematic illustration. The relative change in degradation rate after perturbation of an mRNA by a small RNA will be different depending on the pre-existing degrading effect.
Figure 2
Figure 2
Theoretical variability in targeting efficacy as a function the pre-existing decay rate. (A) Various degrees of synergism and antagonism between pre-existing mRNA decay rate and the added contribution of an introduced degrading factor (e.g., an siRNA) were explored. The curves represent q=0 (additive model), q=−1 (antagonistic), q=−2 (suppressive) and q=1 and 5 (synergistic) (the interaction coefficient q is described in Materials and methods). (B) The exogenous component was randomized between 0 and 1 to describe variation in efficacy between different small RNAs (additive model).
Figure 3
Figure 3
Reporter experiments. (A) Schematic overview of luciferase reporter constructs harboring variable numbers of destabilizing (AUUU)n multimers (AREs) in their 3′ UTRs. n varied from 1 (non-functional ARE, ‘ΔARE’) to 7 (potent ARE). To keep UTR length constant, (AUUU) elements were never removed but rather replaced by non-functional (GUUU) elements (Zubiaga et al, 1995). (B) Relative luciferase signals from reporter constructs after transfection in HEK293 cells (normalized to Renilla luciferase). (C) Co-transfections with a luciferase siRNA. Bars show reporter activities in siLuc-transfected cells relative to a control siRNA (siCtrl), and error bars indicate s.e.m. (n=3). b=2.5% per (AUUU) repeat; 95% CI 1.2–3.7% using linear regression. White and gray bars show theoretically expected results (least-squares fitting) based on an additive (q=0) and a weakly synergistic (q=0.42) model (sum of squared errors=0.021 and 0.018, respectively).
Figure 4
Figure 4
Turnover rates influence siRNA efficacy. (A) Distribution of turnover rates for mRNAs in HeLa as determined using Act D chase. (B) RT–qPCR validation data for 1778 siRNAs. The scatter plot shows the obtained repression, indicated by the log2-transformed mRNA expression ratio, as a function of the mRNA decay slope β1. siRNAs were further grouped into three bins based on the half-life of their targets. The bars indicate the fraction of siRNAs in each bin that reached 10% remaining expression (90% silencing, −3.32 log2) or better. The cumulative distribution function (CDF) of log2-fold changes is indicated for each group. (C) Similar analysis on a per-gene basis. The plot only shows genes for which at least four siRNAs were evaluated. Source data is available for this figure at www.nature.com/msb.
Figure 5
Figure 5
High-turnover mRNAs are less influenced by microRNA overexpression. (A) Genes were divided into three bins based on their half-lives (t1/2). The plot shows the cumulative distribution function (CDF) of relative mRNA expression levels after transfection of synthetic microRNAs. Z-normalized log2-transformed mRNA expression ratios from 20 microRNA overexpression experiments were pooled. The bar graphs indicate, for each half-life bin, the fraction of predicted targets or non-targets that were strongly repressed (z-score <−3). (B) Similar analysis based on a time series of miR-124 overexpression in HepG2 cells. Result shown is for 24 h after transfection. Source data is available for this figure at www.nature.com/msb.
Figure 6
Figure 6
High-turnover mRNAs are recalcitrant to repression, regardless of their steady-state levels. The color code indicates, for each individual siRNA, whether the achieved silencing was >90%. Multiple siRNAs targeting the same transcript are plotted in groups close to each other. siRNAs were divided into bins based on abundance and turnover rate, and the pie charts indicate the fraction of siRNA that reached 90% silencing or better in each bin. Source data is available for this figure at www.nature.com/msb.
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