According to the current view, each microRNA regulates hundreds of genes. Computational tools aim at identifying microRNA targets, usually selecting evolutionarily conserved microRNA binding sites. While the false positive rates have been evaluated for some prediction programs, that information is rarely put forward in studies making use of their predictions. Here, we provide evidence that such predictions are often biologically irrelevant. Focusing on miR-223-guided repression, we observed that it is often smaller than inter-individual variability in gene expression among wild-type mice, suggesting that most predicted targets are functionally insensitive to that microRNA. Furthermore, we found that human haplo-insufficient genes tend to bear the most highly conserved microRNA binding sites. It thus appears that biological functionality of microRNA binding sites depends on the dose-sensitivity of their host gene and that, conversely, it is unlikely that every predicted microRNA target is dose-sensitive enough to be functionally regulated by microRNAs. We also observed that some mRNAs can efficiently titrate microRNAs, providing a reason for microRNA binding site conservation for inefficiently repressed targets. Finally, many conserved microRNA binding sites are conserved in a microRNA-independent fashion: Sequence elements may be conserved for other reasons, while being fortuitously complementary to microRNAs. Collectively, our data suggest that the role of microRNAs in normal and pathological conditions has been overestimated due to the frequent overlooking of false positive rates.

Comprehensive analysis of molecular pathology requires a collection of reference samples representing normal tissues from healthy donors. For the available limited collections of normal tissues from postmortal donors, there is a problem of data incompatibility, as different datasets generated using different experimental platforms often cannot be merged in a single panel. Here, we constructed and deposited the gene expression database of normal human tissues based on uniformly screened original sequencing data. In total, 142 solid tissue samples representing 20 organs were taken from post-mortal human healthy donors of different age killed in road accidents no later than 36 hours after death. Blood samples were taken from 17 healthy volunteers. We then compared them with the 758 transcriptomic profiles taken from the other databases. We found that overall 463 biosamples showed tissue-specific rather than platform- or database-specific clustering and could be aggregated in a single database termed Oncobox Atlas of Normal Tissue Expression (ANTE) . Our data will be useful to all those working with the analysis of human gene expression.

In the Drosophila germline, retrotransposons are silenced by the PIWI-interacting RNA (piRNA) pathway. Telomeric retroelements HeT-A, TART and TAHRE, which are involved in telomere maintenance in Drosophila, are also the targets of piRNA-mediated silencing. We have demonstrated that expression of reporter genes driven by the HeT-A promoter is under the control of the piRNA silencing pathway independent of the transgene location. In order to test directly whether piRNAs affect the transcriptional state of retrotransposons we performed a nuclear run-on (NRO) assay and revealed increased density of the active RNA polymerase complexes at the sequences of endogenous HeT-A and TART telomeric retroelements as well as HeT-A-containing constructs in the ovaries of spn-E mutants and in flies with piwi knockdown. This strongly correlates with enrichment of two histone H3 modifications (dimethylation of lysine 79 and dimethylation of lysine 4), which mark transcriptionally active chromatin, on the same sequences in the piRNA pathway mutants. spn-E mutation and piwi knockdown results in transcriptional activation of some other non-telomeric retrotransposons in the ovaries, such as I-element and HMS Beagle. Therefore piRNA-mediated transcriptional mode of silencing is involved in the control of retrotransposon expression in the Drosophila germline.

Multiple myeloma (MM) affects ~500,000 people and results in ~100,000 deaths annually, being currently considered treatable but incurable. There are several MM chemotherapy treatment regimens, among which eleven include bortezomib, a proteasome-targeted drug. MM patients respond differently to bortezomib, and new prognostic biomarkers are needed to personalize treatments. However, there is a shortage of clinically annotated MM molecular data that could be used to establish novel molecular diagnostics. We report new RNA sequencing profiles for 53 MM patients annotated with responses on two similar chemotherapy regimens: bortezomib, doxorubicin, dexamethasone (PAD), and bortezomib, cyclophosphamide, dexamethasone (VCD), or with responses to their combinations. Fourteen patients received both PAD and VCD; six received only PAD, and 33 received only VCD. We compared profiles for the good and poor responders and found five genes commonly regulated here and in the previous datasets for other bortezomib regimens (all upregulated in the good responders): FGFR3, MAF, IGHA2, IGHV1-69, and GRB14. Four of these genes are linked with known immunoglobulin locus rearrangements. We then used five machine learning (ML) methods to build a classifier distinguishing good and poor responders for two cohorts: PAD + VCD (53 patients), and separately VCD (47 patients). We showed that the application of FloWPS dynamic data trimming was beneficial for all ML methods tested in both cohorts, and also in the previous MM bortezomib datasets. However, the ML models build for the different datasets did not allow cross-transferring, which can be due to different treatment regimens, experimental profiling methods, and MM heterogeneity.