MicroRNAs (miRNAs) are small non-coding RNA molecules mediating the translational repression and degradation of target mRNAs in the cell. Mature miRNAs are used as a template by the RNA-induced silencing complex (RISC) to recognize the complementary mRNAs to be regulated. Up to 60% of human genes are putative targets of one or more miRNAs. Several prediction tools are available to suggest putative miRNA targets, however, only a small part of them has been validated by experimental approaches. In addition, none of these tools does take into account the network structure of miRNA-mRNA interactions, which involve competition effects crucial to efficiently predict the miRNA regulation effects in a specific cellular context. We aim to model the miRNA-mRNA interaction network (interactome), by considering all the miRNAs and mRNAs endogenously expressed in any specific cellular condition. Out test bed has been breast cancer MCF-7 cells. We collected several miRNA and mRNA expression profiles, by using the Agilent microarray platforms. We analyzed samples derived from the immunoprecipitation (IP) of two RISC proteins, AGO2 and GW182, and correspondent input and flow-through as well. The expression level of the top expressed miRNAs has been validated by real time PCR. Due to the singularity of our dataset, we used non-standard bioinformatics techniques to preprocess and analyze the obtained expression profiles. As result, we validated the sample extraction technique, by obtaining expression profile clustering and regression results consistent with the experimental design. The compiled dataset will be useful to further investigate on miRNAmRNA interactions.
Compilation of a gene/miRNA expression profile dataset for miRNA:mRNA interactome analysis
Giovanni Perconti;Patrizia Rubino;Salvatore Feo;Agata Giallongo
2015
Abstract
MicroRNAs (miRNAs) are small non-coding RNA molecules mediating the translational repression and degradation of target mRNAs in the cell. Mature miRNAs are used as a template by the RNA-induced silencing complex (RISC) to recognize the complementary mRNAs to be regulated. Up to 60% of human genes are putative targets of one or more miRNAs. Several prediction tools are available to suggest putative miRNA targets, however, only a small part of them has been validated by experimental approaches. In addition, none of these tools does take into account the network structure of miRNA-mRNA interactions, which involve competition effects crucial to efficiently predict the miRNA regulation effects in a specific cellular context. We aim to model the miRNA-mRNA interaction network (interactome), by considering all the miRNAs and mRNAs endogenously expressed in any specific cellular condition. Out test bed has been breast cancer MCF-7 cells. We collected several miRNA and mRNA expression profiles, by using the Agilent microarray platforms. We analyzed samples derived from the immunoprecipitation (IP) of two RISC proteins, AGO2 and GW182, and correspondent input and flow-through as well. The expression level of the top expressed miRNAs has been validated by real time PCR. Due to the singularity of our dataset, we used non-standard bioinformatics techniques to preprocess and analyze the obtained expression profiles. As result, we validated the sample extraction technique, by obtaining expression profile clustering and regression results consistent with the experimental design. The compiled dataset will be useful to further investigate on miRNAmRNA interactions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
