MicroRNAs (miRNAs) are endogenous 22-nt long non-coding RNAs that regulate gene expression post-transcriptionally in a diversity of organisms (Bartel, 2004). Although the understanding of miRNA biological functions is progressing remarkably, the exact mechanisms by which miRNAs regulate gene expression are still not unambiguously defined. However, it is commonly believed that miRNAs trigger target mRNA regulation by binding to 3’UTRs (Chekulaeva & Filipowicz, 2009). The discovery of the first miRNA (lin-4) revealed sequence complementarity to multiple conserved sites in the 3’UTR of the lin-14 mRNA (Lee et al., 1993; Wightman et al., 1993). Exact principles of gene expression knockdown mediated by miRNA are still in debate (Eulalio et al., 2008). Binding site properties have an essential impact on miRNA-mRNA interaction. Rational design of binding sites could thereby help to achieve a narrow range of knockdown. The applicability is still limited by redundant target sites and various miRNA expression patterns within the cells which are hard to measure. The experimental identification and/or validation of miRNA targets by means of proteomic or transcriptomic studies, reporter gene assays or over-expression/knockdown analysis is necessary to understand the biological relevance of the miRNA-mRNA interaction. Although progressively improving, even the reliability of in silico target prediction tools is still far away from being precise.
With our synthetic miRNA Kit we cannot only benefit by using the miRNA properties mentioned above; additionally, a big part of our project aims at the characterization of binding site sequences. Therefore we also wrote a programm from constructing synthetic miRNA binding sites (miBS designer). Some functionally important parts of miRNAs have been described in literature, such as the seed region. The seed region is defined as a region of seven nucleotides that show perfect pairing between the miRNA and its target sequence. The seed usually consists of the nucleotides 2-8 of a miRNA binding sites (Grimson et al., 2007; Bartel, 2009). Based on this simple principle, we randomized our miRNA binding sites between nucleotide 9 and 12 or 9 and 22 or tried rational exchanges of nucleotides to see how they would effect binding of the miRNA, e. g. replacing one purin base with another versus replacing it with a pyrimidin base. A more detail description of the different binding sites we characterized can be found in our measurements page. After creating a binding site library and testing the miRNA-binding site interaction in vitro, we were able to compute an in silico model based on a machine learning approach.
The basic set-up of our fine tuning construct, miTuner, allows on the simultaneous expression of a synthetic miRNA and a gene of interest (GOI) that is fused with a binding site for this miRNA. Our kit comes with different parts that can be combined by choice, e. g. different mammalian promoters and characterized binding sites of specific properties. By chosing a certain binding site to tag the GOI, one can adjust the level of expression of this gene. In a proof of principle approach, we show the fine tuning capability of our set up using a Dual Luciferase Assay. Here, firefly luciferase acts as the GOI targeted by a synthetic miRNA, while Renilla is used to normalize measurements. FIGURES
Another application of our Synthetic miRNA Kit makes use of tissue specific expression of endogenous miRNAs. These can be exploited for On and Off-Targeting. On targeting in this case would mean that the presence of a certain miRNA in a cell switches on expression of the GOI. This can be accomplished by using a repressor that is targeted by an endogenously expressed miRNA. We exemplified this scenario by using a Tet Repressor fused with a perfect binding site for miRNA 122, a miRNA specific for liver cells (REF!). At the same time, the promoter expressing the GOI would be under control of a Tet Operator. Upon presence of the miRNA 122, the Tet Repressor would be knocked down, release the promoter and expression of the GOI could be established.
(YOUC AN CHANGE THIS INTO PAST TENSE IF IT WORKED. AND ADD THE OFF SWITCHE; I AM NOT CERTAIN OF WHAT WE DID THERE, AND FIGURES!)
We further tested our kit using a gene that is an interesting candidate for gene therapy, human alpha-1-antitrypsin (haat) (ref, description). In this approach, we tag haat, that we used as our GOI, with binding sites that we measured and characterized with our miMeasure construct beforehand and wanted to test in a gene therapeutic background.
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