OncomiR miR-182-5p Enhances Radiosensitivity by Inhibiting the Radiation-Induced Antioxidant Effect through SESN2 in Head and Neck Cancer

Radiotherapy is routinely used for the treatment of head and neck squamous cell carcinoma (HNSCC). However, the therapeutic efficacy is usually reduced by acquired radioresistance and locoregional recurrence. In this study, The Cancer Genome Atlas (TCGA) analysis showed that radiotherapy upregulated the miR-182/96/183 cluster and that miR-182 was the most significantly upregulated. Overexpression of miR-182-5p enhanced the radiosensitivity of HNSCC cells by increasing intracellular reactive oxygen species (ROS) levels, suggesting that expression of the miR-182 family is beneficial for radiotherapy. By intersecting the gene targeting results from three microRNA target prediction databases, we noticed that sestrin2 (SESN2), a molecule resistant to oxidative stress, was involved in 91 genes predicted in all three databases to be directly recognized by miR-182-5p. Knockdown of SESN2 enhanced radiation-induced ROS and cytotoxicity in HNSCC cells. In addition, the radiation-induced expression of SESN2 was repressed by overexpression of miR-182-5p. Reciprocal expression of the miR-182-5p and SESN2 genes was also analyzed in the TCGA database, and a high expression of miR-182-5p combined with a low expression of SESN2 was associated with a better survival rate in patients receiving radiotherapy. Taken together, the current data suggest that miR-182-5p may regulate radiation-induced antioxidant effects and mediate the efficacy of radiotherapy.

An improvement of ComiR algorithm for microRNA target prediction by exploiting coding region sequences of mRNAs

MicroRNA are small non-coding RNAs that post-transcriptionally regulate the expression levels of messenger RNAs. MicroRNA regulation activity depends on the recognition of binding sites located on mRNA molecules. ComiR is a web tool realized to predict the targets of a set of microRNAs, starting from their expression profile. ComiR was trained with the information regarding binding sites in the 3’utr region, by using a reliable dataset containing the targets of endogenously expressed microRNA in D. melanogaster S2 cells. This dataset was obtained by comparing the results from two different experimental approaches, i.e., inhibition, and immunoprecipitation of the AGO1 protein--a component of the microRNA induced silencing complex. In this work, we tested whether including coding region binding sites in ComiR algorithm improves the performance of the tool in predicting microRNA targets. We focused the analysis on the D. melanogaster species and updated the ComiR underlying database with the currently available releases of mRNA and microRNA sequences. As a result, we find that ComiR algorithm trained with the information related to the coding regions is more efficient in predicting the microRNA targets, with respect to the algorithm trained with 3’utr information. On the other hand, we show that 3’utr based predictions can be seen as complementary to the coding region based predictions, which suggests that both predictions, from 3’utr and coding regions, should be considered in comprehensive analysis. Furthermore, we observed that the lists of targets obtained by analyzing data from one experimental approach only, that is, inhibition or immunoprecipitation of AGO1, are not reliable enough to test the performance of our microRNA target prediction algorithm. Further analysis will be conducted to investigate the effectiveness of the tool with data from other species, provided that validated datasets, as obtained from the comparison of RISC proteins inhibition and immunoprecipitation experiments, will be available for the same samples. Finally, we propose to upgrade the existing ComiR web-tool by including the coding region based trained model, available together with the 3’utr based one.

The Impact of Population Variation in the Analysis of microRNA Target Sites

The impact of population variation in the analysis of regulatory interactions is an underdeveloped area. MicroRNA target recognition occurs via pairwise complementarity. Consequently, a number of computational prediction tools have been developed to identify potential target sites that can be further validated experimentally. However, as microRNA target predictions are done mostly considering a reference genome sequence, target sites showing variation among populations are neglected. Here, we studied the variation at microRNA target sites in human populations and quantified their impact in microRNA target prediction. We found that African populations carry a significant number of potential microRNA target sites that are not detectable in the current human reference genome sequence. Some of these targets are conserved in primates and only lost in Out-of-Africa populations. Indeed, we identified experimentally validated microRNA/transcript interactions that are not detected in standard microRNA target prediction programs, yet they have segregating target alleles abundant in non-European populations. In conclusion, we show that ignoring population diversity may leave out regulatory elements essential to understand disease and gene expression, particularly neglecting populations of African origin.

The impact of population variation in the analysis of microRNA target sites

The impact of population variation in the analysis of regulatory interactions is an underdeveloped area. MicroRNA target recognition occurs via pairwise complementarity. Consequently, a number of computational prediction tools have been developed to identify potential target sites, that can be further validated experimentally. However, as microRNA target predictions are done mostly considering a reference genome sequence, target sites showing variation among populations are neglected. Here we study variation at microRNA target sites in human populations and quantify their impact in microRNA target prediction. We found that African populations carry a significant number of potential microRNA target sites that are not detectable in the current human reference genome sequence. Some of these targets are conserved in primates and only lost in Out-of-Africa populations. Indeed, we identified experimentally validated microRNA/transcript interactions that are not detected in standard microRNA target prediction programs, yet they have segregating target alleles abundant in non-European populations. In conclusion, here we show that ignoring population diversity may leave out regulatory elements essential to understand disease and gene expression, particularly neglecting populations of African origin.

FilTar: Using RNA-Seq data to improve microRNA target prediction accuracy in animals

MicroRNAs (miRNAs) are a class of small non-coding RNA molecule, approximately 22nt in length, which guide the repression of mRNA transcripts. A number of tools have been developed to predict miRNA targets in animals which do not account for the effects of a specific cellular context on miRNA targeting. We present FilTar (Filtering of predicted miRNATargets), a method which utilises available RNA-Seq information to filter non- or lowly expressed transcripts and refine existing 3’UTR annotations for a given cellular context, to increase miRNA target prediction accuracy in animals.The FilTar tool is available athttps://github.com/TBradley27/FilTar.