As the title of our project states, “DNA is not enough”. There are several upper-level regulation systems in superior organisms. Our main idea was using miRNA to tune down the expression of genes, having tissue-specific, exactly tuned gene therapy as objective.
miRNA are non-coding regulatory RNAs functioning as post-transcriptional gene silencers. After they are processed, they are usually 22 nucleotides long and they usually bind to the 3’UTR region of the mRNA (although they can also bind to the ORF or to the 5'UTR), forcing the mRNA into degradation or just repressing translation.
In vegetal organisms, miRNA usually bind to the mRNA with extensive complementarity. In animals, interactions are more inexact, creating a lot of uncertainty in the in silico prediction of targets.
The seed of the miRNA is usually defined as the region centered in the nucleotides 2-7 in the 5’ end of the miRNA, and it usually requires extensive pairing. (For the sake of simplicity, we extended slightly the term seed to include the nucleotides 1-8.)
Outside the seed, the existence of supplemental pairing (at least 3 contiguous nucleotides and centered in nucleotides 13-16 of the miRNA) stabilizes the bound complex and increases the efficacy of the binding site.
Binding sites with a high local AU density around the binding site have proven to be more effective (possibly because of the destabilization of the mRNA secondary structure around the site).
The position of the binding site not in the middle of the 3’UTR but either at the end or 15 nt after the stop codon increases the efficacy of repression.
IS THE FOLLOWING REALLY NECESSARY? I DON'T THINK I CAN MANAGE TO EVER FIT THE TOPIC HERE...
About the mechanistics of the repression, it has been shown that the repressive effect is much higher when the binding site for the miRNA is in the first 15 nt of the 3'UTR. This would match the hypothesis.... BLA BLA BLA
Targetscan Scores Jan ????
Figure 1: Interactions between two miRNAs and their binding sites. Examples to show different types of seeds.
miRockdown on miBEAT
Right from the beginning of our modeling project, we knew we would have to integrate our trained models into an online GUI. We realized it in the most user friendly way we could think of: The user only needs to input the desired knockdown percentage (kd%) and choose an sh/miRNA sequence, to get a binding site that satisfies the users needs.
Overview of the miRockdown script flow. The knockdown percentage (kd%) input invokes the selection of the right experimental and model binding site or binding site parameters respectively. The binding site (BS) sequence input starts the generation of on the fly generated BS sequences, which are characterized by a modified targetscan_scores algorithm. The parameters of the selected model BS are correlated with the generated BS parameters and the most similar of the generated BS is the output.
The results of both of our models and the experimentally verified binding sites are integrated in [miRockdown] (see Figure: miRockdown) on the [miBEAT] GUI. For every binding site request of a user there are the results of the three different concepts displayed. Thus the users can always choose which of the three differently generated binding to use. The binding site with the most similar experimentally observed knockdown percentage is given out, together with its properties and oligos ready to clone into the miTuner-construct.
The binding sites generated from the model results come into play, when the user wants to use his or her own sh/miRNA, or when the experimentally verified binding sites have a knockdown, that is not sufficiently similar to the desired knockdown.
A script integrated into miRockdown will correlate the desired kd% with a database file for every model. The content of the database files consists of a set of binding site parameters objects spanning the complete range of the model input binding site parameters. Additionally the database files contain the models kd% result calculated for the whole set of objects.
With the user-chosen sh/miRNA sequence as input a binding site generator script is invoked, which varies the seed-type, 3'-pairing, AU-content and bulge-size of on the fly generated binding sites. The 3'-pairing and the AU-content score of the generated BS are characterized by a modified version of the targetscan_50_context_scores – Algorithm [http://2010.igem.org/Team:Heidelberg/Modeling#References (Rodriguez et al., 2007)]. The input and output functions were adapted to the mode of operation of miRockdown, thus no files have to be generated while running miRockdown.
Now, that the generated binding sites are completely characterized, they can be compared with the parameters of the suitable model BS. The generated BS that fits the parameters of the suitable model BS best is selected as the output BS of miRockdown.
[http://www.targetscan.org/cgi-bin/targetscan/data_download.cgi?db=vert_50 targetscan_50_context_scores.pl] Copyright(c) 2007,2008 Whitehead Institute for Biomedical Research. All Rights Reserved Joe Rodriguez, Robin Ge, Kim Walker, and George Bell
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