Defining the landscape of circular RNAs in neuroblastoma unveils a global suppressive function of MYCN

Circular RNAs (circRNAs) are a regulatory RNA class. While cancer-driving functions have been identified for single circRNAs, how they modulate gene expression in cancer is not well understood. We investigate circRNA expression in the pediatric malignancy, neuroblastoma, through deep whole-transcriptome sequencing in 104 primary neuroblastomas covering all risk groups. We demonstrate that MYCN amplification, which defines a subset of high-risk cases, causes globally suppressed circRNA biogenesis directly dependent on the DHX9 RNA helicase. We detect similar mechanisms in shaping circRNA expression in the pediatric cancer medulloblastoma implying a general MYCN effect. Comparisons to other cancers identify 25 circRNAs that are specifically upregulated in neuroblastoma, including circARID1A. Transcribed from the ARID1A tumor suppressor gene, circARID1A promotes cell growth and survival, mediated by direct interaction with the KHSRP RNA-binding protein. Our study highlights the importance of MYCN regulating circRNAs in cancer and identifies molecular mechanisms, which explain their contribution to neuroblastoma pathogenesis

Intronic tRNAs of mitochondrial origin regulate constitutive and alternative splicing.

Background: The presence of nuclear mitochondrial DNA (numtDNA) has been reported within several nuclear genomes. Next to mitochondrial protein-coding genes, numtDNA sequences also encode for mitochondrial tRNA genes. However, the biological roles of numtDNA remain elusive. Results: Employing in silico analysis, we identify 281 mitochondrial tRNA homologs in the human genome, which we term nimtRNAs (nuclear intronic mitochondrial-derived tRNAs), being contained within introns of 76 nuclear host genes. Despite base changes in nimtRNAs when compared to their mtRNA homologs, a canonical tRNA cloverleaf structure is maintained. To address potential functions of intronic nimtRNAs, we insert them into introns of constitutive and alternative splicing reporters and demonstrate that nimtRNAs promote pre-mRNA splicing, dependent on the number and positioning of nimtRNA genes and splice site recognition efficiency. A mutational analysis reveals that the nimtRNA cloverleaf structure is required for the observed splicing increase. Utilizing a CRISPR/Cas9 approach, we show that a partial deletion of a single endogenous nimtRNALys within intron 28 of the PPFIBP1 gene decreases inclusion of the downstream-located exon 29 of the PPFIBP1 mRNA. By employing a pull-down approach followed by mass spectrometry, a 3′-splice site-associated protein network is identified, including KHDRBS1, which we show directly interacts with nimtRNATyr by an electrophoretic mobility shift assay. Conclusions: We propose that nimtRNAs, along with associated protein factors, can act as a novel class of intronic splicing regulatory elements in the human genome by participating in the regulation of splicing

Predicting RNA Secondary Structures: One-grammar-fits-all Solution

LNCS v. 9096 entitled: Bioinformatics Research and Applications: 11th International Symposium, ISBRA 2015 Norfolk, USA, June 7-10, 2015 ProceedingsRNA secondary structures are known to be important in many biological processes. Many available programs have been developed for RNA secondary structure prediction. Based on our knowledge, however, there still exist secondary structures of known RNA sequences which cannot be covered by these algorithms. In this paper, we provide an efficient algorithm that can handle all RNA secondary structures found in Rfam database. We designed a new stochastic context-free grammar named Rectangle Tree Grammar (RTG) which significantly expands the classes of structures that can be modelled. Our algorithm runs in O(n 6) time and the accuracy is reasonably high, with average PPV and sensitivity over 75%. In addition, the structures that RTG predicts are very similar to the real ones

Multiomics reveal unique signatures of human epiploic adipose tissue related to systemic insulin resistance.

OBJECTIVE: Human white adipose tissue (AT) is a metabolically active organ with distinct depot-specific functions. Despite their locations close to the gastrointestinal tract, mesenteric AT and epiploic AT (epiAT) have only scarcely been investigated. Here, we aim to characterise these ATs in-depth and estimate their contribution to alterations in whole-body metabolism. DESIGN: Mesenteric, epiploic, omental and abdominal subcutaneous ATs were collected from 70 patients with obesity undergoing Roux-en-Y gastric bypass surgery. The metabolically well-characterised cohort included nine subjects with insulin sensitive (IS) obesity, whose AT samples were analysed in a multiomics approach, including methylome, transcriptome and proteome along with samples from subjects with insulin resistance (IR) matched for age, sex and body mass index (n=9). Findings implying differences between AT depots in these subgroups were validated in the entire cohort (n=70) by quantitative real-time PCR. RESULTS: While mesenteric AT exhibited signatures similar to those found in the omental depot, epiAT was distinct from all other studied fat depots. Multiomics allowed clear discrimination between the IS and IR states in all tissues. The highest discriminatory power between IS and IR was seen in epiAT, where profound differences in the regulation of developmental, metabolic and inflammatory pathways were observed. Gene expression levels of key molecules involved in AT function, metabolic homeostasis and inflammation revealed significant depot-specific differences with epiAT showing the highest expression levels. CONCLUSION: Multi-omics epiAT signatures reflect systemic IR and obesity subphenotypes distinct from other fat depots. Our data suggest a previously unrecognised role of human epiploic fat in the context of obesity, impaired insulin sensitivity and related diseases

Time and space efficient RNA-RNA interaction prediction via sparse folding

In the past few years, a large set of new regulatory ncRNAs have been identified, but the number of experimentally verified targets is considerably low. Thus, computational target prediction methods are on high demand. Whereas all previous approaches for predicting a general joint structure have a complexity of O(n 6) running time and O(n 4) space, a more time and space efficient interaction prediction that is able to handle complex joint structures is necessary for genome-wide target prediction problems. In this paper we show how to reduce both the time and space complexity of RNA-RNA interaction prediction problem as described by Alkan et al. [1] by a linear factor via dynamic programming sparsification- which allows to safely discard large portions of DP tables. Applying sparsification techniques reduces the complexity of the original algorithm to O(n 4 ψ(n)) in time and O(n 2 ψ(n) + n 3) in space for some function ψ(n), which turns out to have small values for the range of n that we encounter in practice. By the use of polymer-zeta property for RNA-structures, we demonstrate that ψ(n) = O(n) on average. We evaluate our sparsified algorithm for RNA-RNA interaction prediction through total free energy minimization, based on the energy model of Chitsaz et al. [11], on a set of known interactions. Our results confirm the significant reduction of time and space requirements in practice