Emerging functions for snornas and snorna‐derived fragments

The widespread implementation of mass sequencing has revealed a diverse landscape of small RNAs derived from larger precursors. Whilst many of these are likely to be byproducts of degradation, there are nevertheless metabolically stable fragments derived from tRNAs, rRNAs, snoRNAs, and other non-coding RNA, with a number of examples of the production of such fragments being conserved across species. Coupled with specific interactions to RNA-binding proteins and a growing number of experimentally reported examples suggesting function, a case is emerging whereby the biological significance of small non-coding RNAs extends far beyond miRNAs and piRNAs. Related to this, a similarly complex picture is emerging of non-canonical roles for the non-coding precursors, such as for snoRNAs that are also implicated in such areas as the silencing of gene expression and the regulation of alternative splicing. This is in addition to a body of literature describing snoRNAs as an additional source of miRNA-like regulators. This review seeks to highlight emerging roles for such non-coding RNA, focusing specifically on “new” roles for snoRNAs and the small fragments derived from them.Maliha Wajahat, Cameron Peter Bracken and Ayla Oran

Dangerous liaisons: Interplay between SWI/SNF, NURD, and polycomb in chromatin regulation and cancer

Changes in chromatin structure mediated by ATP-dependent nucleosome remodelers and histone modifying enzymes are integral to the process of gene regulation. Here, we review the roles of the SWI/SNF (switch/sucrose nonfermenting) and NuRD (nucleosome remodeling and deacetylase) and the Polycomb system in chromatin regulation and cancer. First, we discuss the basic molecular mechanism of nucleosome remodeling, and how this controls gene transcription. Next, we provide an overview of the functional organization and biochemical activities of SWI/SNF, NuRD, and Polycomb complexes. We describe how, in metazoans, the balance of these activities is central to the proper regulation of gene expression and cellular identity during development. Whereas SWI/SNF counteracts Polycomb, NuRD facilitates Polycomb repression on chromatin. Finally, we discuss how disruptions of this regulatory equilibrium contribute to oncogenesis, and how new insights into the biological functions of remodelers and Polycombs are opening avenues for therapeutic interventions on a broad range of cancer types

Making use of transcription factor enrichment to identify functional microRNA-regulons

microRNAs (miRNAs) are important modulators of messenger RNA stability and translation, controlling wide gene networks. Albeit generally modest on individual targets, the regulatory effect of miRNAs translates into meaningful pathway modulation through concurrent targeting of regulons with functional convergence. Identification of miRNA-regulons is therefore essential to understand the function of miRNAs and to help realise their therapeutic potential, but it remains challenging due to the large number of false positive target sites predicted per miRNA. In the current work, we investigated whether genes regulated by a given miRNA were under the transcriptional control of a predominant transcription factor (TF). Strikingly we found that for ~50% of the miRNAs analysed, their targets were significantly enriched in at least one common TF. We leveraged such miRNA-TF co-regulatory networks to identify pathways under miRNA control, and demonstrated that filtering predicted miRNA-target interactions (MTIs) relying on such pathways significantly enriched the proportion of predicted true MTIs. To our knowledge, this is the first description of an in- silico pipeline facilitating the identification of miRNA-regulons, to help understand miRNA function.Pacôme B. Prompsy, John Toubia, Linden J. Gearing, Randle L. Knight, Samuel C. Forster, Cameron P. Bracken, Michael P. Gantie

CBNA: a control theory based method for identifying coding and non-coding cancer drivers

A key task in cancer genomics research is to identify cancer driver genes. As these genes initialise and progress cancer, understanding them is critical in designing effective cancer interventions. Although there are several methods developed to discover cancer drivers, most of them only identify coding drivers. However, non-coding RNAs can regulate driver mutations to develop cancer. Hence, novel methods are required to reveal both coding and non-coding cancer drivers. In this paper, we develop a novel framework named Controllability based Biological Network Analysis (CBNA) to uncover coding and non-coding cancer drivers (i.e. miRNA cancer drivers). CBNA integrates different genomic data types, including gene expression, gene network, mutation data, and contains a two-stage process: (1) Building a network for a condition (e.g. cancer condition) and (2) Identifying drivers. The application of CBNA to the BRCA dataset demonstrates that it is more effective than the existing methods in detecting coding cancer drivers. In addition, CBNA also predicts 17 miRNA drivers for breast cancer. Some of these predicted miRNA drivers have been validated by literature and the rest can be good candidates for wet-lab validation. We further use CBNA to detect subtype-specific cancer drivers and several predicted drivers have been confirmed to be related to breast cancer subtypes. Another application of CBNA is to discover epithelial-mesenchymal transition (EMT) drivers. Of the predicted EMT drivers, 7 coding and 6 miRNA drivers are in the known EMT gene lists.Vu V. H. Pham, Lin Liu, Cameron P. Bracken, Gregory J. Goodall, Qi Long, Jiuyong Li, Thuc D. L

"Crowd-control" by RNA: a pervasive theme in biology

As we continue to find new regulatory roles for RNAs, a theme is emerging in which regulation may not be mediated through the actions of a specific RNA, as one typically thinks of a regulator and target, but rather through the collective nature of many RNAs, each contributing a small degree of the regulatory load. This mechanism has been termed "crowd-control" and may apply broadly to miRNAs and to RNAs that bind and regulate protein activity. This provides an alternative way of thinking about how RNAs can act as biological regulators and has repercussions, both for the understanding of biological systems, and for the interpretation of results in which individual members of the "crowd" can replicate the effects of the crowd when overexpressed, but are not individually significant biological regulators.Cameron P. Bracke

Transcriptional and post-transcriptional control of epithelial-mesenchymal plasticity: why so many regulators?

The dynamic transition between epithelial-like and mesenchymal-like cell states has been a focus for extensive investigation for decades, reflective of the importance of Epithelial-Mesenchymal Transition (EMT) through development, in the adult, and the contributing role EMT has to pathologies including metastasis and fibrosis. Not surprisingly, regulation of the complex genetic networks that underlie EMT have been attributed to multiple transcription factors and microRNAs. What is surprising, however, are the sheer number of different regulators (hundreds of transcription factors and microRNAs) for which critical roles have been described. This review seeks not to collate these studies, but to provide a perspective on the fundamental question of whether it is really feasible that so many regulators play important roles and if so, what does this tell us about EMT and more generally, the genetic machinery that controls complex biological processes.Melodie Migault, Sunil Sapkota, Cameron P. Bracke

On the rules of engagement for microRNAs targeting protein coding regions

OnlinePublMiRNAs post-transcriptionally repress gene expression by binding to mRNA 3 UTRs, but the extent to which they act through protein coding regions (CDS regions) is less well established. MiRNA interaction studies show a substantial proportion of binding occurs in CDS regions, however sequencing studies show much weaker effects on mRNA levels than from 3 UTR interactions, presumably due to competition from the translating ribosome. Consequently, most target prediction algorithms consider only 3 UTR interactions. However, the consequences of CDS interactions may have been underestimated, with the reporting of a novel mode of miRNA-CDS interaction requiring base pairing of the miRNA 3 end, but not the canonical seed site, leading to repression of translation with little effect on mRNA turnover. Using extensive reporter, western blotting and bioinformatic analyses, we confirm that miRNAs can indeed suppress genes through CDS-interaction in special circumstances. However, in contrast to that previously reported, we find repression requires extensive base-pairing, including of the canonical seed, but does not strictly require base pairing of the 3 miRNA terminus and is mediated through reducing mRNA levels. We conclude that suppression of endogenous genes can occur through miRNAs binding to CDS, but the requirement for extensive basepairing likely limits the regulatory impacts to modest effects on a small subset of targets.Sunil Sapkota, Katherine A. Pillman, B. Kate Dredge, Dawei Liu, Julie M. Bracken, Saba Ataei Kachooei, Bradley Chereda, Philip A. Gregory, Cameron P. Bracken, and Gregory J. Goodal

Regulation of cyclin D1 RNA stability by SNIP1

Cyclin D1 expression represents one of the key mitogen-regulated events during the G1 phase of the cell cycle, whereas Cyclin D1 overexpression is frequently associated with human malignancy. Here, we describe a novel mechanism regulating Cyclin D1 levels. We find that SNIP1, previously identified as a regulator of Cyclin D1 expression, does not, as previously thought, primarily function as a transcriptional coactivator for this gene. Rather, SNIP1 plays a critical role in cotranscriptional or posttranscriptional Cyclin D1 mRNA stability. Moreover, we show that the majority of nucleoplasmic SNIP1 is present within a previously undescribed complex containing SkIP, THRAP3, BCLAF1, and Pinin, all proteins with reported roles in RNA processing and transcriptional regulation. We find that this complex, which we have termed the SNIP1/SkIP–associated RNA-processing complex, is coordinately recruited to both the 3' end of the Cyclin D1 gene and Cyclin D1 RNA. Significantly, SNIP1 is required for the further recruitment of the RNA processing factor U2AF65 to both the Cyclin D1 gene and RNA. This study shows a novel mechanism regulating Cyclin D1 expression and offers new insight into the role of SNIP1 and associated proteins as regulators of proliferation and cancer. [Cancer Res 2008;68(18):7621–8]Cameron P. Bracken, Steven J. Wall, Benjamin Barré, Kostya I. Panov, Paul M. Ajuh and Neil D. Perkin

DriverGroup: a novel method for identifying driver gene groups

Motivation Identifying cancer driver genes is a key task in cancer informatics. Most existing methods are focused on individual cancer drivers which regulate biological processes leading to cancer. However, the effect of a single gene may not be sufficient to drive cancer progression. Here, we hypothesize that there are driver gene groups that work in concert to regulate cancer, and we develop a novel computational method to detect those driver gene groups.ResultsWe develop a novel method named DriverGroup to detect driver gene groups by using gene expression and gene interaction data. The proposed method has three stages: (i) constructing the gene network, (ii) discovering critical nodes of the constructed network and (iii) identifying driver gene groups based on the discovered critical nodes. Before evaluating the performance of DriverGroup in detecting cancer driver groups, we firstly assess its performance in detecting the influence of gene groups, a key step of DriverGroup. The application of DriverGroup to DREAM4 data demonstrates that it is more effective than other methods in detecting the regulation of gene groups. We then apply DriverGroup to the BRCA dataset to identify driver groups for breast cancer. The identified driver groups are promising as several group members are confirmed to be related to cancer in literature. We further use the predicted driver groups in survival analysis and the results show that the survival curves of patient subpopulations classified using the predicted driver groups are significantly differentiated, indicating the usefulness of DriverGroup. Availability and implementation DriverGroup is available at https://github.com/pvvhoang/DriverGroup. Supplementary information Supplementary data are available at Bioinformatics online.Vu V H Pham, Lin Liu, Cameron P Bracken, Gregory J Goodall, Jiuyong Li, Thuc D L