Transcripts with in silico predicted RNA structure are enriched everywhere in the mouse brain

Clusters of mammalian conserved RNA structures in UTRs associate with RBP binding sites

RNA secondary structures play essential roles in the formation of the tertiary structure and function of a transcript. Recent genome-wide studies highlight significant potential for RNA structures in the mammalian genome. However, a major challenge is assigning functional roles to these structured RNAs. In this study, we conduct a guilt-by-association analysis of clusters of computationally predicted conserved RNA structure (CRSs) in human untranslated regions (UTRs) to associate them with gene functions. We filtered a broad pool of ∼500 000 human CRSs for UTR overlap, resulting in 4734 and 24 754 CRSs from the 5' and 3' UTR of protein-coding genes, respectively. We separately clustered these CRSs for both sets using RNAscClust, obtaining 793 and 2403 clusters, each containing an average of five CRSs per cluster. We identified overrepresented binding sites for 60 and 43 RNA-binding proteins co-localizing with the clustered CRSs. Furthermore, 104 and 441 clusters from the 5' and 3' UTRs, respectively, showed enrichment for various Gene Ontologies, including biological processes such as 'signal transduction', 'nervous system development', molecular functions like 'transferase activity' and the cellular components such as 'synapse' among others. Our study shows that significant functional insights can be gained by clustering RNA structures based on their structural characteristics.

Expansion of the RNAStructuromeDB to include secondary structural data spanning the human protein-coding transcriptome

RNA plays vital functional roles in almost every component of biology, and these functional roles are often influenced by its folding into secondary and tertiary structures. An important role of RNA secondary structure is in maintaining proper gene regulation; therefore, making accurate predictions of the structures involved in these processes is important. In this study, we have expanded on our previous work that led to the creation of the RNAStructuromeDB. Unlike this previous study that analyzed the human genome at low resolution, we have now scanned the protein-coding human transcriptome at high (single nt) resolution. This provides more robust structure predictions for over 100,000 isoforms of known protein-coding genes. Notably, we also utilize the motif identification tool, ScanFold, to model structures with high propensity for ordered/evolved stability. All data have been uploaded to the RNAStructuromeDB, allowing for easy searching of transcripts, visualization of data tracks (via the Integrative Genomics Viewer or IGV), and download of ScanFold data—including unique highly-ordered motifs. Herein, we provide an example analysis of MAT2A to demonstrate the utility of ScanFold at finding known and novel secondary structures, highlighting regions of potential functionality, and guiding generation of functional hypotheses through use of the data.

From Junk to Function: LncRNAs in CNS Health and Disease

Recent advances in RNA sequencing technologies helped to uncover the existence of tens of thousands of long non-coding RNAs (lncRNAs) that arise from the dark matter of the genome. These lncRNAs were originally thought to be transcriptional noise but an increasing number of studies demonstrate that these transcripts can modulate protein-coding gene expression by a wide variety of transcriptional and post-transcriptional mechanisms. The spatiotemporal regulation of lncRNA expression is particularly evident in the central nervous system, suggesting that they may directly contribute to specific brain processes, including neurogenesis and cellular homeostasis. Not surprisingly, lncRNAs are therefore gaining attention as putative novel therapeutic targets for disorders of the brain. In this review, we summarize the recent insights into the functions of lncRNAs in the brain, their role in neuronal maintenance, and their potential contribution to disease. We conclude this review by postulating how these RNA molecules can be targeted for the treatment of yet incurable neurological disorders.

A preliminary study on the differential expression of long noncoding RNAs and messenger RNAs in obese and control mice

Obesity has become one of the public health problems that threatens children's health, but its specific etiology and pathogenesis are still unclear. Recently, many long noncoding RNAs (lncRNAs) have been shown to be involved in the occurrence of obesity. However, their roles are still poorly understood. Thus, the aim of this study was to discover the profiles of the lncRNAs and messenger RNAs (mRNAs) altered in obesity. Epididymal fat samples were collected from mice fed with control and high-fat diets (HFD) for 16 weeks to investigate the differentially expressed lncRNAs and mRNAs by lncRNA microarray, after which seven lncRNAs and nine mRNAs were validated using reverse-transcription polymerase chain reaction (RT-PCR). Bioinformatics analysis and predictions were used to determine the potential biofunctions of these differentially expressed lncRNAs. Then a coexpression network was constructed to determine the transcriptional regulatory relationship of the differentially expressed lncRNAs and mRNAs between the control and HFD groups. The body weight of the HFD group was much higher than that of the control group, as a result of the increased energy intake. In total, 8421 differentially expressed lncRNAs and 6840 mRNAs were profiled using the lncRNAs microarray. Bioinformatics predictions and the coexpression network all indicated that the occurrence of obesity was attributed to those differentially expressed lncRNAs and mRNAs associated with energy metabolism, cell differentiation, and oxidative phosphorylation. The expression levels of Cyp2e1, Atp5b, Hibch, Cnbp, Frmd6, Ptchd3, ENSMUST00000155948, AK140152, ENSMUST00000135194, and ENSMUST00000180861 were significantly different between the control and HFD groups. All these Results suggested that obesity was partially attributed to those lncRNAs associated with energy metabolism, cell differentiation, and oxidative phosphorylation.

A Polymer Physics Framework for the Entropy of Arbitrary Pseudoknots

The accurate prediction of RNA secondary structure from primary sequence has had enormous impact on research from the past 40 years. Although many algorithms are available to make these predictions, the inclusion of non-nested loops, termed pseudoknots, still poses challenges arising from two main factors: 1) no physical model exists to estimate the loop entropies of complex intramolecular pseudoknots, and 2) their NP-complete enumeration has impeded their study. Here, we address both challenges. First, we develop a polymer physics model that can address arbitrarily complex pseudoknots using only two parameters corresponding to concrete physical quantities-over an order of magnitude fewer than the sparsest state-of-the-art phenomenological methods. Second, by coupling this model to exhaustive enumeration of the set of possible structures, we compute the entire free energy landscape of secondary structures resulting from a primary RNA sequence. We demonstrate that for RNA structures of ∼80 nucleotides, with minimal heuristics, the complete enumeration of possible secondary structures can be accomplished quickly despite the NP-complete nature of the problem. We further show that despite our loop entropy model's parametric sparsity, it performs better than or on par with previously published methods in predicting both pseudoknotted and non-pseudoknotted structures on a benchmark data set of RNA structures of ≤80 nucleotides. We suggest ways in which the accuracy of the model can be further improved.

Associating transcription factors and conserved RNA structures with gene regulation in the human brain

Anatomical subdivisions of the human brain can be associated with different neuronal functions. This functional diversification is reflected by differences in gene expression. By analyzing post-mortem gene expression data from the Allen Brain Atlas, we investigated the impact of transcription factors (TF) and RNA secondary structures on the regulation of gene expression in the human brain. First, we modeled the expression of a gene as a linear combination of the expression of TFs. We devised an approach to select robust TF-gene interactions and to determine localized contributions to gene expression of TFs. Among the TFs with the most localized contributions, we identified EZH2 in the cerebellum, NR3C1 in the cerebral cortex and SRF in the basal forebrain. Our results suggest that EZH2 is involved in regulating ZIC2 and SHANK1 which have been linked to neurological diseases such as autism spectrum disorder. Second, we associated enriched regulatory elements inside differentially expressed mRNAs with RNA secondary structure motifs. We found a group of purine-uracil repeat RNA secondary structure motifs plus other motifs in neuron related genes such as ACSL4 and ERLIN2.

A molecular conundrum involving hypothalamic responses to and roles of long non-coding RNAs following food deprivation

Long non-coding RNAs (lncRNAs) are one of most poorly understood RNA classes in the mammalian transcriptome. However, they are emerging as important players in transcriptional regulation, especially within the complexity of the nervous system. This review summarizes the known information about lncRNAs, and their roles in endocrine processes, as well as the lesser-known information about lncRNAs in the brain, and in the neuroendocrine hypothalamus. A "call-to-action" is presented for researchers to use archival transcriptome data to characterize differentially expressed lncRNA species within the hypothalamus. In accordance, we analyze for differential-expression of lncRNA between normal mice and mice with a targeted deletion of the nescient helix-loop-helix 2 gene, and between C57Bl/6 and 129Sv/J mice. Finally, strategies and approaches for researchers to analyze their own datasets or those on the NCBI GEO datasets repository are provided, in hopes that future studies will reveal many new roles for lncRNAs in hypothalamic physiological responses, solving this so-called "molecular conundrum" once and for all.

Analysis of spatial-temporal gene expression patterns reveals dynamics and regionalization in developing mouse brain

Allen Brain Atlas (ABA) provides a valuable resource of spatial/temporal gene expressions in mammalian brains. Despite rich information extracted from this database, current analyses suffer from several limitations. First, most studies are either gene-centric or region-centric, thus are inadequate to capture the superposition of multiple spatial-temporal patterns. Second, standard tools of expression analysis such as matrix factorization can capture those patterns but do not explicitly incorporate spatial dependency. To overcome those limitations, we proposed a computational method to detect recurrent patterns in the spatial-temporal gene expression data of developing mouse brains. We demonstrated that regional distinction in brain development could be revealed by localized gene expression patterns. The patterns expressed in the forebrain, medullary and pontomedullary, and basal ganglia are enriched with genes involved in forebrain development, locomotory behavior, and dopamine metabolism respectively. In addition, the timing of global gene expression patterns reflects the general trends of molecular events in mouse brain development. Furthermore, we validated functional implications of the inferred patterns by showing genes sharing similar spatial-temporal expression patterns with Lhx2 exhibited differential expression in the embryonic forebrains of Lhx2 mutant mice. These analysis outcomes confirm the utility of recurrent expression patterns in studying brain development.

Long non-coding RNAs in corticogenesis: deciphering the non-coding code of the brain

Evidence on the role of long non-coding (lnc) RNAs has been accumulating over decades, but it has been only recently that advances in sequencing technologies have allowed the field to fully appreciate their abundance and diversity. Despite this, only a handful of lncRNAs have been phenotypically or mechanistically studied. Moreover, novel lncRNAs and new classes of RNAs are being discovered at growing pace, suggesting that this class of molecules may have functions as diverse as protein-coding genes. Interestingly, the brain is the organ where lncRNAs have the most peculiar features including the highest number of lncRNAs that are expressed, proportion of tissue-specific lncRNAs and highest signals of evolutionary conservation. In this work, we critically review the current knowledge about the steps that have led to the identification of the non-coding transcriptome including the general features of lncRNAs in different contexts in terms of both their genomic organisation, evolutionary origin, patterns of expression, and function in the developing and adult mammalian brain.

A novel approach to represent and compare RNA secondary structures

Structural information is crucial in ribonucleic acid (RNA) analysis and functional annotation; nevertheless, how to include such structural data is still a debated problem. Dot-bracket notation is the most common and simple representation for RNA secondary structures but its simplicity leads also to ambiguity requiring further processing steps to dissolve. Here we present BEAR (Brand nEw Alphabet for RNA), a new context-aware structural encoding represented by a string of characters. Each character in BEAR encodes for a specific secondary structure element (loop, stem, bulge and internal loop) with specific length. Furthermore, exploiting this informative and yet simple encoding in multiple alignments of related RNAs, we captured how much structural variation is tolerated in RNA families and convert it into transition rates among secondary structure elements. This allowed us to compute a substitution matrix for secondary structure elements called MBR (Matrix of BEAR-encoded RNA secondary structures), of which we tested the ability in aligning RNA secondary structures. We propose BEAR and the MBR as powerful resources for the RNA secondary structure analysis, comparison and classification, motif finding and phylogeny.

Computational analysis of conserved RNA secondary structure in transcriptomes and genomes

Transcriptomics experiments and computational predictions both enable systematic discovery of new functional RNAs. However, many putative noncoding transcripts arise instead from artifacts and biological noise, and current computational prediction methods have high false positive rates. I discuss prospects for improving computational methods for analyzing and identifying functional RNAs, with a focus on detecting signatures of conserved RNA secondary structure. An interesting new front is the application of chemical and enzymatic experiments that probe RNA structure on a transcriptome-wide scale. I review several proposed approaches for incorporating structure probing data into the computational prediction of RNA secondary structure. Using probabilistic inference formalisms, I show how all these approaches can be unified in a well-principled framework, which in turn allows RNA probing data to be easily integrated into a wide range of analyses that depend on RNA secondary structure inference. Such analyses include homology search and genome-wide detection of new structural RNAs.