miR-217 is an oncogene that enhances the germinal center reaction.

microRNAs are a class of regulators of gene expression that have been shown critical for a great number of biological processes; however, little is known of their role in germinal center (GC) B cells. Although the GC reaction is crucial to ensure a competent immune response, GC B cells are also the origin of most human lymphomas, presumably due to bystander effects of the immunoglobulin gene remodeling that takes place at these sites. Here we report that miR-217 is specifically upregulated in GC B cells. Gain- and loss-of-function mouse models reveal that miR-217 is a positive modulator of the GC response that increases the generation of class-switched antibodies and the frequency of somatic hypermutation. We find that miR-217 down-regulates the expression of a DNA damage response and repair gene network and in turn stabilizes Bcl-6 expression in GC B cells. Importantly, miR-217 overexpression also promotes mature B-cell lymphomagenesis; this is physiologically relevant as we find that miR-217 is overexpressed in aggressive human B-cell lymphomas. Therefore, miR-217 provides a novel molecular link between the normal GC response and B-cell transformation.S

Quantification of miRNA-mRNA Interactions

miRNAs are small RNA molecules (′ 22nt) that interact with their corresponding target mRNAs inhibiting the translation of the mRNA into proteins and cleaving the target mRNA. This second effect diminishes the overall expression of the target mRNA. Several miRNA-mRNA relationship databases have been deployed, most of them based on sequence complementarities. However, the number of false positives in these databases is large and they do not overlap completely. Recently, it has been proposed to combine expression measurement from both miRNA and mRNA and sequence based predictions to achieve more accurate relationships. In our work, we use LASSO regression with non-positive constraints to integrate both sources of information. LASSO enforces the sparseness of the solution and the non-positive constraints restrict the search of miRNA targets to those with down-regulation effects on the mRNA expression. We named this method TaLasso (miRNA-Target LASSO)

Quantification of miRNA-mRNA interactions

miRNAs are small RNA molecules (' 22nt) that interact with their corresponding target mRNAs inhibiting the translation of the mRNA into proteins and cleaving the target mRNA. This second effect diminishes the overall expression of the target mRNA. Several miRNA-mRNA relationship databases have been deployed, most of them based on sequence complementarities. However, the number of false positives in these databases is large and they do not overlap completely. Recently, it has been proposed to combine expression measurement from both miRNA and mRNA and sequence based predictions to achieve more accurate relationships. In our work, we use LASSO regression with non-positive constraints to integrate both sources of information. LASSO enforces the sparseness of the solution and the non-positive constraints restrict the search of miRNA targets to those with down-regulation effects on the mRNA expression. We named this method TaLasso (miRNA-Target LASSO).We used TaLasso on two public datasets that have paired expression levels of human miRNAs and mRNAs. The top ranked interactions recovered by TaLasso are especially enriched (more than using any other algorithm) in experimentally validated targets. The functions of the genes with mRNA transcripts in the top-ranked interactions are meaningful. This is not the case using other algorithms.TaLasso is available as Matlab or R code. There is also a web-based tool for human miRNAs at http://talasso.cnb.csic.es/