Long noncoding RNAs (lncRNAs) are emerging as regulators of gene expression in pathogenesis, including cancer. Recently, lncRNAs have been implicated in progression of specific subtypes of breast cancer. One aggressive, basal-like subtype associates with increased EGFR signaling, while another, the HER2-enriched subtype, engages a kin of EGFR. Based on the premise that EGFR-regulated lncRNAs might control the aggressiveness of basal-like tumors, we identified multiple EGFR-inducible lncRNAs in basal-like normal cells and overlaid them with the transcriptomes of over 3,000 breast cancer patients. This led to the identification of 11 prognostic lncRNAs. Functional analyses of this group uncovered LINC01089 (here renamed LncRNA Inhibiting Metastasis; LIMT), a highly conserved lncRNA, which is depleted in basal-like and in HER2-positive tumors, and the low expression of which predicts poor patient prognosis. Interestingly, EGF rapidly downregulates LIMT expression by enhancing histone deacetylation at the respective promoter. We also find that LIMT inhibits extracellular matrix invasion of mammary cells in vitro and tumor metastasis in vivo. In conclusion, lncRNAs dynamically regulated by growth factors might act as novel drivers of cancer progression and serve as prognostic biomarkers.

Cellular regulation mechanisms that involve proteins and other active molecules interacting with specific targets often involve the recognition of sequence patterns. Short sequence elements on DNA, RNA and proteins play a central role in mediating such molecular recognition events. Studies that focus on measuring and investigating sequence-based recognition processes make use of statistical and computational tools that support the identification and understanding of sequence motifs. We present a new web application, named DRIMust, freely accessible through the website http://drimust.technion.ac.il for de novo motif discovery services. The DRIMust algorithm is based on the minimum hypergeometric statistical framework and uses suffix trees for an efficient enumeration of motif candidates. DRIMust takes as input ranked lists of sequences in FASTA format and returns motifs that are over-represented at the top of the list, where the determination of the threshold that defines top is data driven. The resulting motifs are presented individually with an accurate P-value indication and as a Position Specific Scoring Matrix. Comparing DRIMust with other state-of-the-art tools demonstrated significant advantage to DRIMust, both in result accuracy and in short running times. Overall, DRIMust is unique in combining efficient search on large ranked lists with rigorous P-value assessment for the detected motifs.

PUF proteins bind mRNAs and regulate their translation, stability, and localization. Each PUF protein binds a selective group of mRNAs, enabling their coordinate control. We focus here on the specificity of Puf2p and Puf1p of Saccharomyces cerevisiae, which copurify with overlapping groups of mRNAs. We applied an RNA-adapted version of the DRIM algorithm to identify putative binding sequences for both proteins. We first identified a novel motif in the 39 UTRs of mRNAs previously shown to associate with Puf2p. This motif consisted of two UAAU tetranucleotides separated by a 3-nt linker sequence, which we refer to as the dual UAAU motif. The dual UAAU motif was necessary for binding to Puf2p, as judged by gel shift, yeast three-hybrid, and coimmunoprecipitation from yeast lysates. The UAAU tetranucleotides are required for optimal binding, while the identity and length of the linker sequences are less critical. Puf1p also binds the dual UAAU sequence, consistent with the prior observation that it associates with similar populations of mRNAs. In contrast, three other canonical yeast PUF proteins fail to bind the Puf2p recognition site. The dual UAAU motif is distinct from previously known PUF protein binding sites, which invariably possess a UGU trinucleotide. This study expands the repertoire of cis elements bound by PUF proteins and suggests new modes by which PUF proteins recognize their mRNA targets.

Sequence elements, at all levels—DNA, RNA and protein, play a central role in mediating molecular recognition and thereby molecular regulation and signaling. Studies that focus on measuring and investigating sequence-based recognition make use of statistical and computational tools, including approaches to searching sequence motifs. State-of-the-art motif searching tools are limited in their coverage and ability to address large motif spaces. We develop and present statistical and algorithmic approaches that take as input ranked lists of sequences and return significant motifs. The efficiency of our approach, based on suffix trees, allows searches over motif spaces that are not covered by existing tools. This includes searching variable gap motifs—two half sites with a flexible length gap in between—and searching long motifs over large alphabets. We used our approach to analyze several high-throughput measurement data sets and report some validation results as well as novel suggested motifs and motif refinements. We suggest a refinement of the known estrogen receptor 1 motif in humans, where we observe gaps other than three nucleotides that also serve as significant recognition sites, as well as a variable length motif related to potential tyrosine phosphorylation.

BackgroundMicro (mi)RNAs comprise a large family of small non-coding RNAs that are thought to regulate a large fraction of protein-coding genes. Generally, miRNAs downregulate messenger (m)RNA expression by binding to the 3' untranslated regions (UTRs) of the RNA molecules. An important factor for binding specificity is the matching in the seed region. In addition, target site accessibility is thought to be crucial for efficient repression of miRNA targets. Several recent studies indicated that miRNA repression can be facilitated by RNA-binding proteins. In this study, we examine the conjecture that RNA-binding proteins are involved in ushering miRNAs to bind targets that are initially less accessible.ResultsWe analyzed human 3'-UTR sequences containing potential binding sites of 153 conserved miRNA families, and ranked sequences around the sites according to their miRNA accessibility. By applying a rank-based motif search tool to these miRNA targets, we found motifs that are enriched among less accessible targets. As expected from our ranking method, most of the significant motifs were GC-rich. However, one AU-rich motif was found to be enriched among miR-410 less accessible targets. This motif resembles the Pumilio homolog 1 (PUM1) consensus binding site. We observed a stronger enrichment of the PUM1 motif in conserved targets than in non-conserved targets; moreover, the enrichment of this motif was found to be conserved in a subset of placental mammals. Further, we analyzed publicly available gene expression data, and found that the mutual expression of PUM1 and miR-410 has a greater negative influence on the expression of low accessibility targets than on other targets, an effect that was stronger than when considering both miR-410 and PUM1 separately.ConclusionsTaken together, our findings suggest a cooperative relationship between miR-410 and PUM1 in regulating human highly structured 3'-UTRs. This kind of cooperation can allow a second level of regulation of such targets. Considering cases in which miRNAs bind low accessibility targets may help to improve current miRNA prediction tools and to obtain a better understanding of the mechanisms underlying miRNA regulation activity.