Circular RNA expression profiles during the differentiation of mouse neural stem cells

Background

Circular RNAs (circRNAs) have recently been found to be expressed in human brain tissue, and many lines ofevidence indicate that circRNAs play regulatory roles in neurodevelopment. Proliferation and differentiation of neural stem cells (NSCs) are critical parts during development of central nervous system (CNS).To date, there have been no reports ofcircRNA expression profiles during the differentiation of mouse NSCs. We hypothesizethat circRNAs mayregulate gene expression in the proliferation anddifferentiation of NSCs.

Results

In this study, we obtained NSCs from the wild-type C57BL/6 J mouse fetal cerebral cortex. We extracted total RNA from NSCs in different differentiation stagesand then performed RNA-seq. By analyzing the RNA-Seq data, we found 37circRNAs and 4182 mRNAs differentially expressedduringthe NSC differentiation. Gene Ontology (GO) enrichment analysis of thecognate linear genes of these circRNAsrevealed that some enriched GO terms were related to neural activity. Furthermore, we performed a co-expression network analysis of these differentially expressed circRNAs and mRNAs. The result suggested a stronger GO enrichmentin neural features for both the cognate linear genes of circRNAs and differentially expressed mRNAs.

Conclusion

We performed the first circRNA investigation during the differentiation of mouse NSCs. Wefound that12 circRNAs might have regulatory roles duringthe NSC differentiation, indicating that circRNAs might be modulated during NSC differentiation.Our network analysis suggested the possible complex circRNA-mRNA mechanisms during differentiation, and future experimental workis need to validate these possible mechanisms.

     

Sampled ensemble neutrality as a feature to classify potential structured RNAs

Background

Structured RNAs have many biological functions ranging from catalysis of chemical reactions to gene regulation. Yet, many homologous structured RNAs display most of their conservation at the secondary or tertiary structure level. As a result, strategies for structured RNA discovery rely heavily on identification of sequences sharing a common stable secondary structure. However, correctly distinguishing structured RNAs from surrounding genomic sequence remains challenging, especially during de novo discovery. RNA also has a long history as a computational model for evolution due to the direct link between genotype (sequence) and phenotype (structure). From these studies it is clear that evolved RNA structures, like protein structures, can be considered robust to point mutations. In this context, an RNA sequence is considered robust if its neutrality (extent to which single mutant neighbors maintain the same secondary structure) is greater than that expected for an artificial sequence with the same minimum free energy structure.

Results

In this work, we bring concepts from evolutionary biology to bear on the structured RNA de novo discovery process. We hypothesize that alignments corresponding to structured RNAs should consist of neutral sequences. We evaluate several measures of neutrality for their ability to distinguish between alignments of structured RNA sequences drawn from Rfam and various decoy alignments. We also introduce a new measure of RNA structural neutrality, the structure ensemble neutrality (SEN). SEN seeks to increase the biological relevance of existing neutrality measures in two ways. First, it uses information from an alignment of homologous sequences to identify a conserved biologically relevant structure for comparison. Second, it only counts base-pairs of the original structure that are absent in the comparison structure and does not penalize the formation of additional base-pairs.

Conclusion

We find that several measures of neutrality are effective at separating structured RNAs from decoy sequences, including both shuffled alignments and flanking genomic sequence. Furthermore, as an independent feature classifier to identify structured RNAs, SEN yields comparable performance to current approaches that consider a variety of features including stability and sequence identity. Finally, SEN outperforms other measures of neutrality at detecting mutational robustness in bacterial regulatory RNA structures.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-014-1203-8) contains supplementary material, which is available to authorized users.     

Circ_0027599/PHDLA1 suppresses gastric cancer progression by sponging miR-101-3p.1

Background

Pleckstrin homology-like domain family A member 1 (PHLDA1) is a tumor suppressor gene in gastric cancer, but its role regulated by circular RNAs (circRNAs) is not known. CircRNAs are important regulators in cancer growth and progression, however, the molecular roles of circRNAs in gastric cancer are rarely known. The study was aimed to investigate the role of circRNAs in regulating PHLDA1 expression in gastric cancer.

Results

The circRNA expression profile in the gastric cancer tissues by circRNA microarray showed that hsa_circ_0027599 (circ_0027599) was significantly down-regulated in gastric cancer patients and cells when comparing with the controls. Circ_0027599 overexpression suppressed gastric cancer cell proliferation and metastasis. By using bioinformatics tools and luciferase reporter assays, circ_0027599 was verified as a sponge of miR-101-3p.1 (miR-101) and suppressed cancer cell survival and metastasis. It was also verified that PHLDA1 was regulated by circ_0027599 in gastric cancer cells.

Conclusions

The study uncovered that PHLDA1 was regulated by circ_0027599/miR-101, which suppressed gastric cancer survival and metastasis in gastric cancer.

     

ExpaRNA-P: simultaneous exact pattern matching and folding of RNAs

Background

Identifying sequence-structure motifs common to two RNAs can speed up the comparison of structural RNAs substantially. The core algorithm of the existent approach ExpaRNA solves this problem for a priori known input structures. However, such structures are rarely known; moreover, predicting them computationally is no rescue, since single sequence structure prediction is highly unreliable.

Results

The novel algorithm ExpaRNA-P computes exactly matching sequence-structure motifs in entire Boltzmann-distributed structure ensembles of two RNAs; thereby we match and fold RNAs simultaneously, analogous to the well-known “simultaneous alignment and folding” of RNAs. While this implies much higher flexibility compared to ExpaRNA, ExpaRNA-P has the same very low complexity (quadratic in time and space), which is enabled by its novel structure ensemble-based sparsification. Furthermore, we devise a generalized chaining algorithm to compute compatible subsets of ExpaRNA-P’s sequence-structure motifs. Resulting in the very fast RNA alignment approach ExpLoc-P, we utilize the best chain as anchor constraints for the sequence-structure alignment tool LocARNA. ExpLoc-P is benchmarked in several variants and versus state-of-the-art approaches. In particular, we formally introduce and evaluate strict and relaxed variants of the problem; the latter makes the approach sensitive to compensatory mutations. Across a benchmark set of typical non-coding RNAs, ExpLoc-P has similar accuracy to LocARNA but is four times faster (in both variants), while it achieves a speed-up over 30-fold for the longest benchmark sequences (≈400nt). Finally, different ExpLoc-P variants enable tailoring of the method to specific application scenarios. ExpaRNA-P and ExpLoc-P are distributed as part of the LocARNA package. The source code is freely available at http://www.bioinf.uni-freiburg.de/Software/ExpaRNA-P.

Conclusions

ExpaRNA-P’s novel ensemble-based sparsification reduces its complexity to quadratic time and space. Thereby, ExpaRNA-P significantly speeds up sequence-structure alignment while maintaining the alignment quality. Different ExpaRNA-P variants support a wide range of applications.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0404-0) contains supplementary material, which is available to authorized users.     

Diversity,expression and mRNA targeting abilities of Argonaute-targeting miRNAs among selected vascular plants

Background

Micro (mi)RNAs are important regulators of plant development. Across plant lineages, Dicer-like 1 (DCL1) proteins process long ds-like structures to produce micro (mi) RNA duplexes in a stepwise manner. These miRNAs are incorporated into Argonaute (AGO) proteins and influence expression of RNAs that have sequence complementarity with miRNAs. Expression levels of AGOs are greatly regulated by plants in order to minimize unwarranted perturbations using miRNAs to target mRNAs coding for AGOs. AGOs may also have high promoter specificity-sometimes expression of AGO can be limited to just a few cells in a plant. Viral pathogens utilize various means to counter antiviral roles of AGOs including hijacking the host encoded miRNAs to target AGOs. Two host encoded miRNAs namely miR168 and miR403 that target AGOs have been described in the model plant Arabidopsis and such a mechanism is thought to be well conserved across plants because AGO sequences are well conserved.

Results

We show that the interaction between AGO mRNAs and miRNAs is species-specific due to the diversity in sequences of two miRNAs that target AGOs, sequence diversity among corresponding target regions in AGO mRNAs and variable expression levels of these miRNAs among vascular plants. We used miRNA sequences from 68 plant species representing 31 plant families for this analysis. Sequences of miR168 and miR403 are not conserved among plant lineages, but surprisingly they differ drastically in their sequence diversity and expression levels even among closely related plants. Variation in miR168 expression among plants correlates well with secondary structures/length of loop sequences of their precursors.

Conclusions

Our data indicates a complex AGO targeting interaction among plant lineages due to miRNA sequence diversity and sequences of miRNA targeting regions among AGO mRNAs, thus leading to the assumption that the perturbations by viruses that use host miRNAs to target antiviral AGOs can only be species-specific. We also show that rapid evolution and likely loss of expression of miR168 isoforms in tobacco is related to the insertion of MITE-like transposons between miRNA and miRNA* sequences, a possible mechanism showing how miRNAs are lost in few plant lineages even though other close relatives have abundantly expressing miRNAs.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1049) contains supplementary material, which is available to authorized users.     

Identification of potential proteins translated from circular RNA splice variants

Circular RNAs (circRNAs) are covalently closed RNA molecules generated from precursor RNAs by the head-to-tail backsplicing of exons. Hundreds of studies demonstrated that circRNAs are ubiquitously expressed and regulate cellular events by modulating microRNA (miRNA) and RNA-binding protein (RBP) activities. A few circRNAs are also known to translate into functional polypeptides regulating cellular physiology. All these functions primarily depend on the full-length sequence of the circRNAs. CircRNA backsplice junction sequence is the key to identifying circRNAs and their full-length mature sequence. However, some multi-exonic circRNAs exist in different isoforms sharing identical backsplice junction sequences and are termed circRNA splice variants. Here, we analyzed the previously published HeLa cell RNA-seq datasets to identify circRNA splice variants using the de novo module of the CIRCexplorer2 circRNA annotation pipeline. A subset of circRNAs with splice variants was validated by the circRNA-rolling circle amplification (circRNA-RCA) method. Interestingly, several validated circRNAs were predicted to translate into proteins by the riboCIRC database. Furthermore, polyribosome fractionation followed by quantitative PCR confirmed the association of a subset of circRNAs with polyribosome supporting their protein-coding potential. Finally, bioinformatics analysis of proteins derived from splice variants of circCORO1C and circASPH suggested altered protein sequences and structures that could affect their physiological functions. Together, our study identified novel circRNA splice variants and their potential translation into protein isoforms which may regulate various physiological processes.