Biology subjects

Mihaela Zavolan

Publications and source records attributed to Mihaela Zavolan.

4 recordsLinked to original sources

bioRxiv2016

High-throughput identification of C/D box snoRNA targets with CLIP and RiboMeth-seq.

Identification of long and short RNAs, their processing and expression patterns have been greatly facilitated by high-throughput sequencing. Frequently, these RNAs act as guides for ribonucleoprotein complexes that regulate the expression or processing of target RNAs. However, to determine the targets of the many newly discovered regulatory RNAs in high-throughput remains a challenge. To globally assign guide small nucleolar RNAs to site of 2-O-ribose methylation in human cells, we here developed novel computational methods for the analysis of data that was generated with protocols designed to capture direct small RNA-target interactions and to identify the sites of 2-O-ribose methylation genome-wide. We thereby determined that many \"orphan\" snoRNAs appear to guide 2-O-ribose methylation at sites that are targeted by other snoRNAs and that snoRNAs can be reliably captured in interaction with many mRNAs, in which a subsequent 2-O-methylation cannot be detected. Our study provides a reliable approach to the comprehensive characterization of snoRNA-target interactions in species beyond those in which these interactions have been traditionally studied and contribute to the rapidly developing field of \"epitranscriptomics\".

Bioinformatics↗

bioRxiv2015

Exploiting variability of single cells to uncover the in vivo hierarchy of miRNA targets

MiRNAs are post-transcriptional repressors of gene expression that may additionally reduce the cell-to-cell variability in protein expression, induce correlations between target expression levels and provide a layer through which targets can influence each others expression as competing RNAs (ceRNAs). Here we combined single cell sequencing of human embryonic kidney cells in which the expression of two distinct miRNAs was induced over a wide range, with mathematical modeling, to estimate Michaelis-Menten (KM)-type constants for hundreds of evolutionarily conserved miRNA targets. These parameters, which we inferred here for the first time in the context of the entire network of endogenous miRNA targets, vary over ~2 orders of magnitude. They reveal an in vivo hierarchy of miRNA targets, defined by the concentration of miRNA-Argonaute complexes at which the targets are most sensitively down-regulated. The data further reveals miRNA-induced correlations in target expression at the single cell level, as well as the response of target noise to the miRNA concentration. The approach is generalizable to other miRNAs and post-transcriptional regulators and provides a deeper understanding of gene expression dynamics.

Systems Biology↗

bioRxiv2015

Ewing sarcoma breakpoint region 1 prevents transcription-associated genome instability

Ewing Sarcoma break point region 1 (EWSR1) is a multi-functional RNA-binding protein that is involved in many cellular processes, from gene expression to RNA processing and transport. Translocations into its locus lead to chimeric proteins with tumorigenic activity, that lack the RNA binding domain. With crosslinking and immunoprecipitation we have found that EWSR1 binds to intronic regions that are present in polyadenylated nuclear RNAs, which include the translocation-prone region of its own locus. Reduced EWSR1 expression leads to gene expression changes that indicate reduced proliferation. By fluorescence in situ hybridization (FISH) with break-apart probes that flanked the translocation-prone region within the EWSR1 locus we found that reduced EWSR1 expression increases the frequency of split signals, indicative of DNA double strand breaks (DSB). The response in phosphorylated histone H2AX and p53-binding protein 1 (53BP1) double-stained foci to the topoisomerase poison camptothecin in cells treated with a control shRNA and with sh-EWSR1 further suggests that EWSR1 functions in the prevention of DNA DSBs. Our data reveal a new function of the EWSR1 member of the FET family and suggest a connection between the RNA-binding activity of EWSR1 and the instability of its own locus that may play a role in malignancy-associated translocations.

Systems Biology↗

bioRxiv2015

A comprehensive analysis of 3′ end sequencing data sets reveals novel polyadenylation signals and the repressive role of heterogenous ribonucleoprotein C on cleavage and polyadenylation

Alternative polyadenylation (APA) is a general mechanism of transcript diversification in mammals, which has been recently linked to proliferative states and cancer. Different 3 untranslated region (3 UTR) isoforms interact with different RNA binding proteins (RBPs), which modify the stability, translation, and subcellular localization of the corresponding transcripts. Although the heterogeneity of pre-mRNA 3 end processing has been established with high-throughput approaches, the mechanisms that underlie systematic changes in 3 UTR lengths remain to be characterized. Through a uniform analysis of a large number of 3 end sequencing data sets we have uncovered 18 signals, 6 of which novel, whose positioning with respect to pre-mRNA cleavage sites indicates a role in pre-mRNA 3 end processing in both mouse and human. With 3 end sequencing we have demonstrated that the heterogeneous ribonucleoprotein C (HNRNPC), which binds the poly(U) motif whose frequency also peaks in the vicinity of polyadenylation (poly(A)) sites, has a genome-wide effect on poly(A) site usage. HNRNPC-regulated 3 UTRs are enriched in ELAV-like RNA binding protein 1 (ELAVL1) binding sites and include those of the CD47 molecule (CD47) gene, which participate in the recently discovered mechanism of 3 UTR-dependent protein localization (UDPL). Our study thus establishes an up-to-date, high-confidence catalog of 3 end processing sites and poly(A) signals and it uncovers an important role of HNRNPC in regulating 3 end processing. It further suggests that U-rich elements mediate interactions with multiple RBPs that regulate different stages in a transcripts life cycle.

Genomics↗