We have shown that the expression of genes predicted to be under endogenous miRNA regulation is affected by small RNA transfection; that the effect is observable both at the mRNA and protein levels; and that it occurs following transfection of siRNAs designed to inhibit particular genes, as well as miRNA mimics and miRNA inhibitors introduced to test the biological effects of miRNAs. In a quantitative approach, we built a regression model that can to a large extent recover the endogenous miRNA profile simply from the changes in gene expression following small RNA transfections. The purpose of this approach is not to infer the miRNA profiles per se but to provide independent strong evidence of the indirect perturbation of miRNA function. Finally, we used a series of published data to show that the dynamics and dose response of the genes affected by the proposed competition effect follow the same patterns as that of the genes directly targeted by the transfection.
The most plausible model for these observations is saturation of the RISC complex (or other necessary small RNA processing or transport machinery) and competition between the transfected small RNA and endogenous miRNA for binding (). Other models cannot be ruled out by this analysis and may be consistent with the observed effect. While the precise mechanism of this competition effect remains to be established, the statistical significance of the observed shifts in transcript levels is clear, and the results of this analysis strongly support the thesis that small RNA transfections unexpectedly and unintentionally (from the point of view of the investigators) disturb gene regulation by endogenous miRNA.
Our results have potentially important practical consequences for the use of siRNAs, as well as shRNAs, in functional genomics experiments. While it is already known that siRNAs can produce unwanted off-target effects, i.e. unintended downregulation of mRNAs via a partial sequence match between the siRNA and target, the effects observed here are distinct and involve the de-repression of miRNA-regulated genes.
Our findings also have consequences for the development of miRNA target prediction methods, in two ways. First, as measuring mRNA expression changes after si/miRNA perturbations is a standard way to validate miRNA target prediction methods [23
], one should take the saturation effect into consideration. Despite concerted efforts, bioinformatic si/miRNA target prediction methods still significantly over-predict the number of targets by at least 7 fold [24
]. Elegant work showing the dynamic (condition and cell-type dependent) regulation of UTR lengths [37
] may explain some of these false positives, since shortening of UTRs may lead to loss of target sites, but is unlikely to explain all. The proposed competition effect may offer an explanation for false positive target prediction in cases where UTRs have target sites for both the transfected and endogenous miRNAs (). Second, as miRNAs may compete with each other, for target sites in mRNAs, the very idea of a ‘target’ mRNA should be re-assessed.
Further, our results have consequences for the development of small RNA therapeutics, considered to hold substantial promise [38
]. miRNA inhibitors, e.g., anti-miR-122, have been used to target cholesterol synthesis [39
] as well as HCV (hepatitis C virus) [39
] and HSV2 (herpes simplex virus) [41
]. Therapeutic siRNAs have also been designed for potential treatment of cancer, including in melanoma, against VEGF-A/-C [42
], and through anti-miR-21 in glioma [38
]. Our work illustrates the potentially broad consequences of the perturbation of the cell's miRNA activity profile after introduction of si/miRNA inhibitors and suggests that these effects be considered quantitatively during development of small RNA therapies. Experiments that quantify the relative concentrations of protein machinery and small RNAs in a particular cellular context, as well as a fuller exploration of the kinetics of the various binding events involved in small RNA biogenesis and function, are clearly required. Our quantitative model implies a procedure for calibrating and potentially avoiding unwanted effects of the designed small RNA therapeutics.
Our work tests the hypothesis that transfections of small RNAs can perturb endogenous miRNA function, subject to some limitations. In particular, this report does not attempt to resolve details of the mechanism behind the competition effect. The calculations of the effect, though carefully evaluated in statistical terms, are subject to the inaccuracies of miRNA target prediction, which entails both false positives and false negatives at the level of particular target genes. We therefore argue in terms of overall distributions, rather than attempting to quantify the involvement of individual target sites in transfection-mediated expression changes. In future work, a number of quantitative criteria will determine the extent of the competition between exogenous and endogenous miRNAs and their effects on gene targeting. Quantitative detail will depend on knowing the concentration of the RISC complex and of other components of the small RNA machinery in the cell, the concentration of the transfected and endogenous miRNAs, the concentrations of the target mRNAs, and the number of actual targets in the cell for a specific small RNA, as well as kinetic parameters such as the on and off rates of small RNAs in the RNA-protein complexes. Models that posit different concentration-dependent and kinetic scenarios could help focus the range of experiments needed to quantify these effects.
Finally, our results may have an important biological correlate, as plausibly the competition effect may have a role in normal biological or disease-related cellular processes, e.g., in affecting miRNA-dependent regulatory programs. For example, during both differentiation and disease processes such as cancer, miRNA profiles can change dramatically both in the identity of the dominant miRNAs and in total cellular miRNA concentration. Such changes, via competition for limited resources, may orchestrate observable changes in cellular regulatory programs with potential physiological consequences.
In summary, the proposed and statistically supported competition effect for small RNAs may point to new biological mechanisms and likely has important practical consequences for the use of small RNAs in functional genomics experiments, for the development of miRNA target and siRNA off-target prediction methods, and for the development of small RNA therapeutics.