A detailed investigation of accessibilities around target sites of siRNAs and miRNAs

A tRNA modification with aminovaleramide facilitates AUA decoding in protein synthesis

Modified tRNA anticodons are critical for proper mRNA translation during protein synthesis. It is generally thought that almost all bacterial tRNAsIle use a modified cytidine-lysidine (L)-at the first position (34) of the anticodon to decipher the AUA codon as isoleucine (Ile). Here we report that tRNAsIle from plant organelles and a subset of bacteria contain a new cytidine derivative, designated 2-aminovaleramididine (ava2C). Like L34, ava2C34 governs both Ile-charging ability and AUA decoding. Cryo-electron microscopy structural analyses revealed molecular details of codon recognition by ava2C34 with a specific interaction between its terminal amide group and an mRNA residue 3'-adjacent to the AUA codon. These findings reveal the evolutionary variation of an essential tRNA modification and demonstrate the molecular basis of AUA decoding mediated by a unique tRNA modification.

Intra-tumoral administration of CHST15 siRNA remodels tumor microenvironment and augments tumor-infiltrating T cells in pancreatic cancer

The dense stroma is one cause of poor efficacy of T cell-mediated immunotherapy in pancreatic ductal adenocarcinoma (PDAC). Carbohydrate sulfotransferase 15 (CHST15) is a proteoglycan-synthetic enzyme responsible for remodeling tumor stroma. Intra-tumoral injection of CHST15 small interfering RNA (siRNA) has been shown to increase the tumor-infiltrating T cells (TILs) in patients with unresectable PDAC. However, the mechanism underlying the enhanced accumulation of TILs is not fully explored. Here, we demonstrate that intra-tumoral injection of CHST15 siRNA locally and remotely diminishes myeloid-derived suppressor cells (MDSCs) and enhances TILs in mice. CHST15 was expressed by tumor cells and MDSCs in both tumor and tumor-draining lymph nodes (TDLNs), and CHST15 siRNA repressed stromal density, neutrophil extracellular traps, and Ly6C/G+ MDSCs in vivo. Remarkably, tumor growth inhibition was only observed in the immunocompetent KPC model, which is associated with enhanced TILs. In vitro, CHST15 siRNA significantly downregulated the levels of CHST15 and indoleamine 2,3-dioxygenase mRNA in CD33+ MDSCs derived from human peripheral blood mononuclear cells. These results suggest a dual role for intra-tumorally injected CHST15 siRNA on modulating the tumor immune microenvironment for T cell entry and remotely diminishing CHST15+ MDSCs, decreasing T cell suppression and expanding T cells in the TDLN, ultimately leading to an enhanced accumulation of TILs.

Add-on multiple submucosal injections of the RNA oligonucleotide GUT-1 to anti-TNF antibody treatment in patients with moderate-to-severe ulcerative colitis: an open-label, proof-of concept study

BACKGROUND

Carbohydrate sulfotransferase 15 (CHST15) is an enzyme biosynthesizing matrix glycosaminoglycan that modulates tissue remodeling. We evaluated the efficacy of add-on submucosal injections of GUT-1, the RNA oligonucleotide inhibitor of CHST15, to ongoing anti-tumor necrosis factor (TNF) antibody treatment in patients with moderate-to-severe ulcerative colitis (UC).

METHODS

This was an open-label study of 250 nM of GUT-1 by endoscopic submucosal injections at weeks 0, 2, 4 in five UC patients who lost response during maintenance treatment to anti-TNF antibodies. The primary endpoint was the rate of endoscopic improvement at week 6 and secondary endpoints included the rates of clinical remission by modified Mayo Score (mMS). Patients received follow-up observation with continuous maintenance treatment by the same anti-TNF antibody till the time of clinical recurrence or for overall 52 weeks.

RESULTS

At week 6, rates of endoscopic improvement and clinical remission were 80% (n = 4/5) and 60% (n = 3/5), respectively. The mean Endoscopy Subscore was reduced from 2.4 (95%CI: 1.7 to 3.1) at baseline, to 1.0 (95%CI: 0.1 to 1.9) at week 6. The mean mMS was reduced from 7.8 (95%CI: 6.2 to 9.4) to 1.3 (95%CI: 2.9 to 4.3). GUT-1 was well tolerated. Three patients did not show clinical recurrence for 52 weeks. All three corticosteroid-dependent patients showed no corticosteroid exposure for at least 24 weeks after achieving clinical remission. Multiple dosing was also well tolerated.

CONCLUSIONS

Add-on multiple injections of GUT-1 to ongoing anti-TNF antibody was able to induce rapid and durable clinical responses in UC patients who lost response to anti-TNF therapy.

TRIAL REGISTRATION

Clinical trial Registration Number (Japan): UMIN000020900.

Submucosal Injection of the RNA Oligonucleotide GUT-1 in Active Ulcerative Colitis Patients: A Randomized, Double-Blind, Placebo-Controlled Phase 2a Induction Trial

BACKGROUND AND AIMS

Carbohydrate sulfotransferase 15 [CHST15] biosynthesizes sulphated matrix glycosaminoglycans and is implicated in intestinal inflammation and fibrosis. Here, we evaluate the efficacy and safety of the double-stranded RNA oligonucleotide GUT-1, a specific blocker of CHST15, as induction therapy in patients with ulcerative colitis [UC].

METHODS

In this randomized, double-blind, placebo-controlled, phase 2a study, we enrolled endoscopically active UC patients, refractory to conventional therapy, in five hospital centres across Germany. Patients were randomized 1:1:1 using a block randomized technique to receive a single dosing of 25 nM GUT-1, 250 nM GUT-1, or placebo by endoscopic submucosal injections. The primary outcome measure was improvement of endoscopic lesions at weeks 2 or 4. The secondary outcome measures included clinical and histological responses. Safety was assessed in all patients who received treatment.

RESULTS

Twenty-eight patients were screened, 24 were randomized, and 21 were evaluated. Endoscopic improvement at weeks 2 or 4 was achieved by 71.4% in the GUT-1 250 nM, 0% in the GUT-1 25 nM, and 28.6% in the placebo group. Clinical remission was shown by 57.1% in the GUT-1 250 nM, 0% in the GUT-1 25 nM, and 14.3% in the placebo groups. Histological improvement was shown by 42.9% in the GUT-1 250 nM, 0% in the GUT-1 25 nM, and 0% in the placebo groups. GUT-1 250 nM reduced CHST15 expression significantly and suppressed mucosal inflammation and fibrosis. GUT-1 application was well tolerated.

CONCLUSION

Single dosing by submucosal injection of GUT-1 repressed CHST15 mucosal expression and may represent a novel induction therapy by modulating tissue remodelling in UC.

DeepRaccess: high-speed RNA accessibility prediction using deep learning

RNA accessibility is a useful RNA secondary structural feature for predicting RNA-RNA interactions and translation efficiency in prokaryotes. However, conventional accessibility calculation tools, such as Raccess, are computationally expensive and require considerable computational time to perform transcriptome-scale analysis. In this study, we developed DeepRaccess, which predicts RNA accessibility based on deep learning methods. DeepRaccess was trained to take artificial RNA sequences as input and to predict the accessibility of these sequences as calculated by Raccess. Simulation and empirical dataset analyses showed that the accessibility predicted by DeepRaccess was highly correlated with the accessibility calculated by Raccess. In addition, we confirmed that DeepRaccess could predict protein abundance in E.coli with moderate accuracy from the sequences around the start codon. We also demonstrated that DeepRaccess achieved tens to hundreds of times software speed-up in a GPU environment. The source codes and the trained models of DeepRaccess are freely available at https://github.com/hmdlab/DeepRaccess.

Silencing of tumoral carbohydrate sulfotransferase 15 reactivates lymph node pancreatic cancer T cells in mice

Limited intratumoral T-cell infiltration in pancreatic ductal adenocarcinoma (PDAC) is an obstacle to immunotherapy, yet the efficient approach to enhance tumor-infiltrating T cells is not fully established. Here, we show that tumor-specific knockdown of carbohydrate sulfotransferase 15 (CHST15), a tumor stromal proteoglycan-synthetic enzyme, suppresses tumor growth in a T-cell-dependent manner in a murine model of PDAC. Silencing of tumoral CHST15 unexpectedly expanded CD4+ and CD8+ T cells in tumor draining LN (TDLN), leading to accelerated accumulation of EdU+ proliferating CD4+ and CD8+ T cells and granzyme B+ CD8+ T cells in the tumor. RNA expression analysis indicated that tumoral CHST15 knockdown (KD) downregulated matrix remodeling-related genes, while upregulated anti-tumor T-cell activity-related genes in both tumor and TDLN. CHST15 KD significantly diminished intratumoral and TDLN Ly6C/G+ myeloid-derived suppressor cells prior to TDLN T-cell expansion, suggesting that tumoral CHST15 remotely regulated myeloid-derived suppressor cell mediated T-cell suppression in the TDLN. Our findings illustrate a novel immunotherapeutic potential of tumoral CHST15 blockage by reactivating T cells in immune suppressive TDLN of PDAC.

Ultradeep characterisation of translational sequence determinants refutes rare-codon hypothesis and unveils quadruplet base pairing of initiator tRNA and transcript

Translation is a key determinant of gene expression and an important biotechnological engineering target. In bacteria, 5'-untranslated region (5'-UTR) and coding sequence (CDS) are well-known mRNA parts controlling translation and thus cellular protein levels. However, the complex interaction of 5'-UTR and CDS has so far only been studied for few sequences leading to non-generalisable and partly contradictory conclusions. Herein, we systematically assess the dynamic translation from over 1.2 million 5'-UTR-CDS pairs in Escherichia coli to investigate their collective effect using a new method for ultradeep sequence-function mapping. This allows us to disentangle and precisely quantify effects of various sequence determinants of translation. We find that 5'-UTR and CDS individually account for 53% and 20% of variance in translation, respectively, and show conclusively that, contrary to a common hypothesis, tRNA abundance does not explain expression changes between CDSs with different synonymous codons. Moreover, the obtained large-scale data provide clear experimental evidence for a base-pairing interaction between initiator tRNA and mRNA beyond the anticodon-codon interaction, an effect that is often masked for individual sequences and therefore inaccessible to low-throughput approaches. Our study highlights the indispensability of ultradeep sequence-function mapping to accurately determine the contribution of parts and phenomena involved in gene regulation.

Fast RNA-RNA Interaction Prediction Methods for Interaction Analysis of Transcriptome-Scale Large Datasets

The computational prediction of RNA-RNA interactions has long been studied in RNA informatics. Most of the existing approaches focused on the interaction prediction of short RNAs in small datasets. However, in recent years, two fast prediction methods, RIsearch2 and RIblast, have been developed to predict transcriptome-scale interactions or long RNA interactions. The key idea of the software acceleration of these tools was the integration of a seed-and-extend method, which is used in fast sequence alignment tools, into RNA-RNA interaction prediction. As a result, the two software programs were ten to a thousand times faster than the existing tools; because of this acceleration, detection of genome-wide microRNA target sites or interaction partners of function-unknown long noncoding RNAs has become possible. In this review, we describe the basic concept of the algorithm, its applications, and the future perspectives of the fast RNA-RNA interaction prediction tools.

Genome-Wide RNA Secondary Structure Prediction

The information of RNA secondary structure has been widely applied to the inference of RNA function. However, a classical prediction method is not feasible to long RNAs such as mRNA due to the problems of computational time and numerical errors. To overcome those problems, sliding window methods have been applied while their results are not directly comparable to global RNA structure prediction. In this chapter, we introduce ParasoR, a method designed for parallel computation of genome-wide RNA secondary structures. To enable genome-wide prediction, ParasoR distributes dynamic programming (DP) matrices required for structure prediction to multiple computational nodes. Using the database of not the original DP variable but the ratio of variables, ParasoR can locally compute the structure scores such as stem probability or accessibility on demand. A comprehensive analysis of local secondary structures by ParasoR is expected to be a promising way to detect the statistical constraints on long RNAs.

Rtools: A Web Server for Various Secondary Structural Analyses on Single RNA Sequences

Predicting the secondary structures of RNA molecules is an essential step to characterize their functions, but the thermodynamic probability of any prediction is generally small. On the other hand, there are a few tools for calculating and visualizing various secondary structural information from RNA sequences. We implemented a web server that calculates in parallel various features of secondary structures: different types of secondary structure predictions, the marginal probabilities for local structural contexts, accessibilities of the subsequences, the energy changes by arbitrary base mutations, and the measures for validations of the predicted secondary structures. The web server is available at http://rtools.cbrc.jp , which integrates software tools, CentroidFold, CentroidHomfold, IPknot, CapR, Raccess, Rchange, RintD, and RintW.

STNM01, the RNA oligonucleotide targeting carbohydrate sulfotransferase 15, as second-line therapy for chemotherapy-refractory patients with unresectable pancreatic cancer: An open label, phase I/IIa trial

BACKGROUND

The impact of stroma-targeting therapy on tumor immune suppression is largely unexplored. An RNA oligonucleotide, STNM01, has been shown to repress carbohydrate sulfotransferase 15 (CHST15) responsible for tumor proteoglycan synthesis and matrix remodeling. This phase I/IIa study aimed to evaluate the safety and efficacy of STNM01 in patients with unresectable pancreatic ductal adenocarcinoma (PDAC).

METHODS

This was an open-label, dose-escalation study of STNM01 as second-line therapy in gemcitabine plus nab-paclitaxel-refractory PDAC. A cycle comprised three 2-weekly endoscopic ultrasound-guided locoregional injections of STNM01 at doses of 250, 1,000, 2,500, or 10,000 nM in combination with S-1 (80-120 mg twice a day for 14 days every 3 weeks). The primary outcome was the incidence of dose-liming toxicity (DLT). The secondary outcomes included overall survival (OS), tumor response, changes in tumor microenvironment on immunohistopathology, and safety (jRCT2031190055).

FINDINGS

A total of 22 patients were enrolled, and 3 cycles were repeated at maximum; no DLT was observed. The median OS was 7.8 months. The disease control rate was 77.3%; 1 patient showed complete disappearance of visible lesions in the pancreas and tumor-draining lymph nodes. Higher tumoral CHST15 expression was associated with poor CD3⁺ and CD8⁺ T cell infiltration at baseline. STNM01 led to a significant reduction in CHST15, and increased tumor-infiltrating CD3⁺ and CD8⁺ T cells in combination with S-1 at the end of cycle 1. Higher fold increase in CD3⁺ T cells correlated with longer OS. There were 8 grade 3 adverse events.

INTERPRETATION

Locoregional injection of STNM01 was well tolerated in patients with unresectable PDAC as combined second-line therapy. It prolonged survival by enhancing T cell infiltration in tumor microenvironment.

FUNDING

The present study was supported by the Japan Agency for Medical Research and Development (AMED).

Analysis of 11,430 recombinant protein production experiments reveals that protein yield is tunable by synonymous codon changes of translation initiation sites

Recombinant protein production is a key process in generating proteins of interest in the pharmaceutical industry and biomedical research. However, about 50% of recombinant proteins fail to be expressed in a variety of host cells. Here we show that the accessibility of translation initiation sites modelled using the mRNA base-unpairing across the Boltzmann's ensemble significantly outperforms alternative features. This approach accurately predicts the successes or failures of expression experiments, which utilised Escherichia coli cells to express 11,430 recombinant proteins from over 189 diverse species. On this basis, we develop TIsigner that uses simulated annealing to modify up to the first nine codons of mRNAs with synonymous substitutions. We show that accessibility captures the key propensity beyond the target region (initiation sites in this case), as a modest number of synonymous changes is sufficient to tune the recombinant protein expression levels. We build a stochastic simulation model and show that higher accessibility leads to higher protein production and slower cell growth, supporting the idea of protein cost, where cell growth is constrained by protein circuits during overexpression.

A natural single-nucleotide polymorphism variant in sulfite reductase influences sulfur assimilation in maize

Plants absorb sulfur from the environment and assimilate it into suitable forms for the biosynthesis of a broad range of molecules. Although the biochemical pathway of sulfur assimilation is known, how genetic differences contribute to natural variation in sulfur assimilation remains poorly understood. Here, using a genome-wide association study, we uncovered a single-nucleotide polymorphism (SNP) variant in the sulfite reductase (SiR) gene that was significantly associated with SiR protein abundance in a maize natural association population. We also demonstrated that the synonymous C to G base change at SNP69 may repress translational activity by altering messenger RNA secondary structure, which leads to reduction in ZmSiR protein abundance and sulfur assimilation activity. Population genetic analyses showed that the SNP69C allele was likely a variant occurring after the initial maize domestication and accumulated with the spread of maize cultivation from tropical to temperate regions. This study provides the first evidence that genetic polymorphisms in the exon of ZmSiR could influence the protein abundance through a posttranscriptional mechanism and in part contribute to natural variation in sulfur assimilation. These findings provide a prospective target to improve maize varieties with proper sulfur nutrient levels assisted by molecular breeding and engineering.

Computational analysis of sense-antisense chimeric transcripts reveals their potential regulatory features and the landscape of expression in human cells

Many human genes are transcribed from both strands and produce sense-antisense gene pairs. Sense-antisense (SAS) chimeric transcripts are produced upon the coalescing of exons/introns from both sense and antisense transcripts of the same gene. SAS chimera was first reported in prostate cancer cells. Subsequently, numerous SAS chimeras have been reported in the ChiTaRS-2.1 database. However, the landscape of their expression in human cells and functional aspects are still unknown. We found that longer palindromic sequences are a unique feature of SAS chimeras. Structural analysis indicates that a long hairpin-like structure formed by many consecutive Watson-Crick base pairs appears because of these long palindromic sequences, which possibly play a similar role as double-stranded RNA (dsRNA), interfering with gene expression. RNA-RNA interaction analysis suggested that SAS chimeras could significantly interact with their parental mRNAs, indicating their potential regulatory features. Here, 267 SAS chimeras were mapped in RNA-seq data from 16 healthy human tissues, revealing their expression in normal cells. Evolutionary analysis suggested the positive selection favoring sense-antisense fusions that significantly impacted the evolution of their function and structure. Overall, our study provides detailed insight into the expression landscape of SAS chimeras in human cells and identifies potential regulatory features.

Long Non-coding RNAs: Mechanisms, Experimental, and Computational Approaches in Identification, Characterization, and Their Biomarker Potential in Cancer

Long non-coding RNAs are diverse class of non-coding RNA molecules >200 base pairs of length having various functions like gene regulation, dosage compensation, epigenetic regulation. Dysregulation and genomic variations of several lncRNAs have been implicated in several diseases. Their tissue and developmental specific expression are contributing factors for them to be viable indicators of physiological states of the cells. Here we present an comprehensive review the molecular mechanisms and functions, state of the art experimental and computational pipelines and challenges involved in the identification and functional annotation of lncRNAs and their prospects as biomarkers. We also illustrate the application of co-expression networks on the TCGA-LIHC dataset for putative functional predictions of lncRNAs having a therapeutic potential in Hepatocellular carcinoma (HCC).

In Vitro Inhibitory Analysis of Rationally Designed siRNAs against MERS-CoV Replication in Huh7 Cells

MERS-CoV was identified for the first time in Jeddah, Saudi Arabia in 2012 in a hospitalized patient. This virus subsequently spread to 27 countries with a total of 939 deaths and 2586 confirmed cases and now has become a serious concern globally. Camels are well known for the transmission of the virus to the human population. In this report, we have discussed the prediction, designing, and evaluation of potential siRNA targeting the ORF1ab gene for the inhibition of MERS-CoV replication. The online software, siDirect 2.0 was used to predict and design the siRNAs, their secondary structure and their target accessibility. ORF1ab gene folding was performed by RNAxs and RNAfold software. A total of twenty-one siRNAs were selected from 462 siRNAs according to their scoring and specificity. siRNAs were evaluated in vitro for their cytotoxicity and antiviral efficacy in Huh7 cell line. No significant cytotoxicity was observed for all siRNAs in Huh7 cells. The in vitro study showed the inhibition of viral replication by three siRNAs. The data generated in this study provide preliminary and encouraging information to evaluate the siRNAs separately as well as in combination against MERS-CoV replication in other cell lines. The prediction of siRNAs using online software resulted in the filtration and selection of potential siRNAs with high accuracy and strength. This computational approach resulted in three effective siRNAs that can be taken further to in vivo animal studies and can be used to develop safe and effective antiviral therapies for other prevalent disease-causing viruses.

Evolutionarily Conserved Long Non-coding RNA Regulates Gene Expression in Cytokine Storm During COVID-19

Coronavirus is a family of viruses including alpha-, beta-, gamma-, delta-coronaviruses. Only alpha- and betacoronaviruses have been observed to infect humans. Past outbreaks of SARS-CoV and MERS-CoV, both betacoronavirus, are the result of a spillover from animals. Recently, a new strain termed SARS-CoV-2 emerged in December 2019 in Wuhan, China. Severe cases of COVID-19, the disease caused by SARS-CoV-2, lead to acute respiratory distress syndrome (ARDS). One contributor to the development of ARDS is cytokine storm, an overwhelming inflammatory immune response. Long non-coding RNAs (lncRNAs) are genetic regulatory elements that, among many functions, alter gene expression and cellular processes. lncRNAs identified to be pertinent in COVID-19 cytokine storm have the potential to serve as disease markers or drug targets. This project aims to computationally identify conserved lncRNAs potentially regulating gene expression in cytokine storm during COVID-19. We found 22 lncRNAs that can target 10 cytokines overexpressed in COVID-19 cytokine storm, 8 of which targeted two or more cytokine storm cytokines. In particular, the lncRNA non-coding RNA activated by DNA damage (NORAD), targeted five out of the ten identified cytokine storm cytokines, and is evolutionarily conserved across multiple species. These lncRNAs are ideal candidates for further in vitro and in vivo analysis.

Distinct Regulation of the Expression of Satellite DNAs in the Beetle Tribolium castaneum

In the flour beetle, Tribolium castaneum (peri)centromeric heterochromatin is mainly composed of a major satellite DNA TCAST1 interspersed with minor satellites. With the exception of heterochromatin, clustered satellite repeats are found dispersed within euchromatin. In order to uncover a possible satellite DNA function within the beetle genome, we analysed the expression of the major TCAST1 and a minor TCAST2 satellite during the development and upon heat stress. The results reveal that TCAST1 transcription was strongly induced at specific embryonic stages and upon heat stress, while TCAST2 transcription is stable during both processes. TCAST1 transcripts are processed preferentially into piRNAs during embryogenesis and into siRNAs during later development, contrary to TCAST2 transcripts, which are processed exclusively into piRNAs. In addition, increased TCAST1 expression upon heat stress is accompanied by the enrichment of the silent histone mark H3K9me3 on the major satellite, while the H3K9me3 level at TCAST2 remains unchanged. The transcription of the two satellites is proposed to be affected by the chromatin state: heterochromatin and euchromatin, which are assumed to be the prevalent sources of TCAST1 and TCAST2 transcripts, respectively. In addition, distinct regulation of the expression might be related to diverse roles that major and minor satellite RNAs play during the development and stress response.

Improving the prediction accuracy of protein abundance in Escherichia coli using mRNA accessibility

RNA secondary structure around translation initiation sites strongly affects the abundance of expressed proteins in Escherichia coli. However, detailed secondary structural features governing protein abundance remain elusive. Recent advances in high-throughput DNA synthesis and experimental systems enable us to obtain large amounts of data. Here, we evaluated six types of structural features using two large-scale datasets. We found that accessibility, which is the probability that a given region around the start codon has no base-paired nucleotides, showed the highest correlation with protein abundance in both datasets. Accessibility showed a significantly higher correlation (Spearman's ρ = 0.709) than the widely used minimum free energy (0.554) in one of the datasets. Interestingly, accessibility showed the highest correlation only when it was calculated by a log-linear model, indicating that the RNA structural model and how to utilize it are important. Furthermore, by combining the accessibility and activity of the Shine-Dalgarno sequence, we devised a method for predicting protein abundance more accurately than existing methods. We inferred that the log-linear model has a broader probabilistic distribution than the widely used Turner energy model, which contributed to more accurate quantification of ribosome accessibility to translation initiation sites.

Practical Guidance in Genome-Wide RNA:DNA Triple Helix Prediction

Long noncoding RNAs (lncRNAs) play a key role in many cellular processes including chromatin regulation. To modify chromatin, lncRNAs often interact with DNA in a sequence-specific manner forming RNA:DNA triple helices. Computational tools for triple helix search do not always provide genome-wide predictions of sufficient quality. Here, we used four human lncRNAs (MEG3, DACOR1, TERC and HOTAIR) and their experimentally determined binding regions for evaluating triplex parameters that provide the highest prediction accuracy. Additionally, we combined triplex prediction with the lncRNA secondary structure and demonstrated that considering only single-stranded fragments of lncRNA can further improve DNA-RNA triplexes prediction.

Multiple Sequence Alignments Enhance Boundary Definition of RNA Structures

Self-contained structured domains of RNA sequences have often distinct molecular functions. Determining the boundaries of structured domains of a non-coding RNA (ncRNA) is needed for many ncRNA gene finder programs that predict RNA secondary structures in aligned genomes because these methods do not necessarily provide precise information about the boundaries or the location of the RNA structure inside the predicted ncRNA. Even without having a structure prediction, it is of interest to search for structured domains, such as for finding common RNA motifs in RNA-protein binding assays. The precise definition of the boundaries are essential for downstream analyses such as RNA structure modelling, e.g., through covariance models, and RNA structure clustering for the search of common motifs. Such efforts have so far been focused on single sequences, thus here we present a comparison for boundary definition between single sequence and multiple sequence alignments. We also present a novel approach, named RNAbound, for finding the boundaries that are based on probabilities of evolutionarily conserved base pairings. We tested the performance of two different methods on a limited number of Rfam families using the annotated structured RNA regions in the human genome and their multiple sequence alignments created from 14 species. The results show that multiple sequence alignments improve the boundary prediction for branched structures compared to single sequences independent of the chosen method. The actual performance of the two methods differs on single hairpin structures and branched structures. For the RNA families with branched structures, including transfer RNA (tRNA) and small nucleolar RNAs (snoRNAs), RNAbound improves the boundary predictions using multiple sequence alignments to median differences of −6 and −11.5 nucleotides (nts) for left and right boundary, respectively (window size of 200 nts).

The long noncoding RNA landscape of neuroendocrine prostate cancer and its clinical implications

Background

Treatment-induced neuroendocrine prostate cancer (tNEPC) is an aggressive variant of late-stage metastatic castrate-resistant prostate cancer that commonly arises through neuroendocrine transdifferentiation (NEtD). Treatment options are limited, ineffective, and, for most patients, result in death in less than a year. We previously developed a first-in-field patient-derived xenograft (PDX) model of NEtD. Longitudinal deep transcriptome profiling of this model enabled monitoring of dynamic transcriptional changes during NEtD and in the context of androgen deprivation. Long non-coding RNA (lncRNA) are implicated in cancer where they can control gene regulation. Until now, the expression of lncRNAs during NEtD and their clinical associations were unexplored.

Results

We implemented a next-generation sequence analysis pipeline that can detect transcripts at low expression levels and built a genome-wide catalogue (n = 37,749) of lncRNAs. We applied this pipeline to 927 clinical samples and our high-fidelity NEtD model LTL331 and identified 821 lncRNAs in NEPC. Among these are 122 lncRNAs that robustly distinguish NEPC from prostate adenocarcinoma (AD) patient tumours. The highest expressed lncRNAs within this signature are H19, LINC00617, and SSTR5-AS1. Another 742 are associated with the NEtD process and fall into four distinct patterns of expression (NEtD lncRNA Class I, II, III, and IV) in our PDX model and clinical samples. Each class has significant (z-scores >2) and unique enrichment for transcription factor binding site (TFBS) motifs in their sequences. Enriched TFBS include (1) TP53 and BRN1 in Class I, (2) ELF5, SPIC, and HOXD1 in Class II, (3) SPDEF in Class III, (4) HSF1 and FOXA1 in Class IV, and (5) TWIST1 when merging Class III with IV. Common TFBS in all NEtD lncRNA were also identified and include E2F, REST, PAX5, PAX9, and STAF. Interrogation of the top deregulated candidates (n = 100) in radical prostatectomy adenocarcinoma samples with long-term follow-up (median 18 years) revealed significant clinicopathological associations. Specifically, we identified 25 that are associated with rapid metastasis following androgen deprivation therapy (ADT). Two of these lncRNAs (SSTR5-AS1 and LINC00514) stratified patients undergoing ADT based on patient outcome.

Discussion

To date, a comprehensive characterization of the dynamic landscape of lncRNAs during the NEtD process has not been performed. A temporal analysis of the PDX-based NEtD model has for the first time provided this dynamic landscape. TFBS analysis identified NEPC-related TF motifs present within the NEtD lncRNA sequences, suggesting functional roles for these lncRNAs in NEPC pathogenesis. Furthermore, select NEtD lncRNAs appear to be associated with metastasis and patients receiving ADT. Treatment-related metastasis is a clinical consequence of NEPC tumours. Top candidate lncRNAs FENDRR, H19, LINC00514, LINC00617, and SSTR5-AS1 identified in this study are implicated in the development of NEPC. We present here for the first time a genome-wide catalogue of NEtD lncRNAs that characterize the transdifferentiation process and a robust NEPC lncRNA patient expression signature. To accomplish this, we carried out the largest integrative study that applied a PDX NEtD model to clinical samples. These NEtD and NEPC lncRNAs are strong candidates for clinical biomarkers and therapeutic targets and warrant further investigation.

MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions

Motivation

Long non-coding RNAs (lncRNAs) are defined as transcripts longer than 200 nt that do not get translated into proteins. Often these transcripts are processed (spliced, capped and polyadenylated) and some are known to have important biological functions. However, most lncRNAs have unknown or poorly understood functions. Nevertheless, because of their potential role in cancer, lncRNAs are receiving a lot of attention, and the need for computational tools to predict their possible mechanisms of action is more than ever. Fundamentally, most of the known lncRNA mechanisms involve RNA-RNA and/or RNA-protein interactions. Through accurate predictions of each kind of interaction and integration of these predictions, it is possible to elucidate potential mechanisms for a given lncRNA.

Results

Here, we introduce MechRNA, a pipeline for corroborating RNA-RNA interaction prediction and protein binding prediction for identifying possible lncRNA mechanisms involving specific targets or on a transcriptome-wide scale. The first stage uses a version of IntaRNA2 with added functionality for efficient prediction of RNA-RNA interactions with very long input sequences, allowing for large-scale analysis of lncRNA interactions with little or no loss of optimality. The second stage integrates protein binding information pre-computed by GraphProt, for both the lncRNA and the target. The final stage involves inferring the most likely mechanism for each lncRNA/target pair. This is achieved by generating candidate mechanisms from the predicted interactions, the relative locations of these interactions and correlation data, followed by selection of the most likely mechanistic explanation using a combined P-value. We applied MechRNA on a number of recently identified cancer-related lncRNAs (PCAT1, PCAT29 and ARLnc1) and also on two well-studied lncRNAs (PCA3 and 7SL). This led to the identification of hundreds of high confidence potential targets for each lncRNA and corresponding mechanisms. These predictions include the known competitive mechanism of 7SL with HuR for binding on the tumor suppressor TP53, as well as mechanisms expanding what is known about PCAT1 and ARLn1 and their targets BRCA2 and AR, respectively. For PCAT1-BRCA2, the mechanism involves competitive binding with HuR, which we confirmed using HuR immunoprecipitation assays.

Availability and implementation

MechRNA is available for download at https://bitbucket.org/compbio/mechrna.

Supplementary information

Supplementary data are available at Bioinformatics online.

A Novel Method for Assessing the Statistical Significance of RNA-RNA Interactions Between Two Long RNAs

RNA-RNA interactions are key mechanisms through which noncoding RNA (ncRNA) regions exert biological functions. Computational prediction of RNA-RNA interactions is an essential method for detecting novel RNA-RNA interactions because their comprehensive detection by biological experimentation is still quite difficult. Many RNA-RNA interaction prediction tools have been developed, but they tend to produce many false positives. Accordingly, assessment of the statistical significance of computationally predicted interactions is an important task. However, there is no method to evaluate the statistical significance of RNA-RNA interactions that is applicable to interactions between two long RNA sequences. We developed a method to calculate the p-value for the minimal interaction energy between two long RNA sequences. The developed method depends on the fact that minimum interaction energies of RNA-RNA interactions between long RNAs follow a Gumbel distribution when repeat sequences in RNAs are masked. To show the usefulness of the developed method, we applied it to whole human 5'-untranslated region (UTR) and 3'-UTR sequences to detect novel 5'-UTR-3'-UTR interactions. We thus identified two significant 5'-UTR-3'-UTR interactions. Specifically, the human small proline-rich repeat protein 3 shows conserved 5'-UTR-3'-UTR interactions with some nucleotide variations preserving base pairings among primates. Our developed method enables us to detect statistically significant RNA-RNA interactions between long RNAs such as long ncRNAs. Statistical significance estimates help in identification of interactions for experimental validation and provide novel insights into the function of ncRNA regions.

RIblast: an ultrafast RNA-RNA interaction prediction system based on a seed-and-extension approach

Motivation

LncRNAs play important roles in various biological processes. Although more than 58 000 human lncRNA genes have been discovered, most known lncRNAs are still poorly characterized. One approach to understanding the functions of lncRNAs is the detection of the interacting RNA target of each lncRNA. Because experimental detections of comprehensive lncRNA–RNA interactions are difficult, computational prediction of lncRNA–RNA interactions is an indispensable technique. However, the high computational costs of existing RNA–RNA interaction prediction tools prevent their application to large-scale lncRNA datasets.

Results

Here, we present ‘RIblast’, an ultrafast RNA–RNA interaction prediction method based on the seed-and-extension approach. RIblast discovers seed regions using suffix arrays and subsequently extends seed regions based on an RNA secondary structure energy model. Computational experiments indicate that RIblast achieves a level of prediction accuracy similar to those of existing programs, but at speeds over 64 times faster than existing programs.

Availability and implementation

The source code of RIblast is freely available at https://github.com/fukunagatsu/RIblast.

Supplementary information

Supplementary data are available at Bioinformatics online.

Effects of mRNA secondary structure on the expression of HEV ORF2 proteins in Escherichia coli

Background

Viral protein expression in Escherichia coli (E. coli) is a powerful tool for structural/functional studies as well as for vaccine and diagnostics development. However, numerous factors such as codon bias, mRNA secondary structure and nucleotides distribution, have been indentified to hamper this heterologous expression.

Results

In this study, we combined computational and biochemical methods to analyze the influence of these factors on the expression of different segments of hepatitis E virus (HEV) ORF 2 protein and hepatitis B virus surface antigen (HBsAg). Three out of five HEV antigens were expressed while all three HBsAg fragments were not. The computational analysis revealed a significant difference in nucleotide distribution between expressed and non-expressed genes; and all these non-expressing constructs shared similar stable 5′-end mRNA secondary structures that affected the accessibility of both Shine-Dalgarno (SD) sequence and start codon AUG. By modifying the 5′-end of HEV and HBV non-expressed genes, there was a significant increase in the total free energy of the mRNA secondary structures that permitted the exposure of the SD sequence and the start codon, which in turn, led to the successful expression of these genes in E. coli.

Conclusions

This study demonstrates that the mRNA secondary structure near the start codon is the key limiting factor for an efficient expression of HEV ORF2 proteins in E. coli. It describes also a simple and effective strategy for the production of viral proteins of different lengths for immunogenicity/antigenicity comparative studies during vaccine and diagnostics development.

Electronic supplementary material

The online version of this article (10.1186/s12934-017-0812-8) contains supplementary material, which is available to authorized users.

Temperature-responsive miRNAs in Drosophila orchestrate adaptation to different ambient temperatures

The majority of Drosophila genes are expressed in a temperature-dependent manner, but the way in which small RNAs may contribute to this effect is completely unknown as we currently lack an idea of how small RNA transcriptomes change as a function of temperature. Applying high-throughput sequencing techniques complemented by quantitative real-time PCR experiments, we demonstrate that altered ambient temperature induces drastic but reversible changes in sequence composition and total abundance of both miRNA and piRNA populations. Further, mRNA sequencing reveals that the expression of miRNAs and their predicted target transcripts correlates inversely, suggesting that temperature-responsive miRNAs drive adaptation to different ambient temperatures on the transcriptome level. Finally, we demonstrate that shifts in temperature affect both primary and secondary piRNA pools, and the observed aberrations are consistent with altered expression levels of the involved Piwi-pathway factors. We further reason that enhanced ping-pong processing at 29°C is driven by dissolved RNA secondary structures at higher temperatures, uncovering target sites that are not accessible at low temperatures. Together, our results show that small RNAs are an important part of epigenetic regulatory mechanisms that ensure homeostasis and adaptation under fluctuating environmental conditions.

Hydroxylation of a conserved tRNA modification establishes non-universal genetic code in echinoderm mitochondria

The genetic code is not frozen but still evolving, which can result in the acquisition of 'dialectal' codons that deviate from the universal genetic code. RNA modifications in the anticodon region of tRNAs play a critical role in establishing such non-universal genetic codes. In echinoderm mitochondria, the AAA codon specifies asparagine instead of lysine. By analyzing mitochondrial (mt-) tRNA^Lys isolated from the sea urchin (Mesocentrotus nudus), we discovered a novel modified nucleoside, hydroxy-N⁶-threonylcarbamoyladenosine (ht⁶A), 3' adjacent to the anticodon (position 37). Biochemical analysis revealed that ht⁶A37 has the ability to prevent mt-tRNA^Lys from misreading AAA as lysine, thereby indicating that hydroxylation of N⁶-threonylcarbamoyladenosine (t⁶A) contributes to the establishment of the non-universal genetic code in echinoderm mitochondria.

Computational prediction of lncRNA-mRNA interactionsby integrating tissue specificity in human transcriptome

Long noncoding RNAs (lncRNAs) play a key role in normal tissue differentiation and cancer development through their tissue-specific expression in the human transcriptome. Recent investigations of macromolecular interactions have shown that tissue-specific lncRNAs form base-pairing interactions with various mRNAs associated with tissue-differentiation, suggesting that tissue specificity is an important factor controlling human lncRNA-mRNA interactions.Here, we report investigations of the tissue specificities of lncRNAs and mRNAs by using RNA-seq data across various human tissues as well as computational predictions of tissue-specific lncRNA-mRNA interactions inferred by integrating the tissue specificity of lncRNAs and mRNAs into our comprehensive prediction of human lncRNA-RNA interactions. Our predicted lncRNA-mRNA interactions were evaluated by comparisons with experimentally validated lncRNA-mRNA interactions (between the TINCR lncRNA and mRNAs), showing the improvement of prediction accuracy over previous prediction methods that did not account for tissue specificities of lncRNAs and mRNAs. In addition, our predictions suggest that the potential functions of TINCR lncRNA not only for epidermal differentiation but also for esophageal development through lncRNA-mRNA interactions.

REVIEWERS

This article was reviewed by Dr. Weixiong Zhang and Dr. Bojan Zagrovic.

Phase 1 Clinical Study of siRNA Targeting Carbohydrate Sulphotransferase 15 in Crohn's Disease Patients with Active Mucosal Lesions

BACKGROUND AND AIMS

Carbohydrate sulphotransferase 15 [CHST15] is a specific enzyme biosynthesizing chondroitin sulphate E that binds various pathogenic mediators and is known to create local fibrotic lesions. We evaluated the safety of STNM01, a synthetic double-stranded RNA oligonucleotide directed against CHST15, in Crohn's disease [CD] patients whose mucosal lesions were refractory to conventional therapy.

METHODS

This was a randomized, double-blind, placebo-controlled, concentration-escalation study of STNM01 by a single-dose endoscopic submucosal injection in 18 CD patients. Cohorts of increasing concentration of STNM01 were enrolled sequentially as 2.5nM [n = 3], 25nM [n = 3], and 250nM [n = 3] were applied. A cohort of placebo [n = 3] was included in each concentration. Safety was monitored for 30 days. Pharmacokinetics was monitored for 24h. The changes from baseline in the segmental Simple Endoscopic Score for CD [SES-CD] as well as the histological fibrosis score were evaluated.

RESULTS

STNM01 was well tolerated and showed no drug-related adverse effects in any cohort of treated patients. There were no detectable plasma concentrations of STNM01 at all measured time points in all treatment groups. Seven of nine subjects who received STNM01 showed reduction in segmental SES-CD at Day 30, when compared with those who received placebo. Histological analyses of biopsy specimens revealed that STNM01 reduced the extent of fibrosis.

CONCLUSION

Local application of STNM01 is safe and well tolerated in CD patients with active mucosal lesions.

Pivotal Role of Carbohydrate Sulfotransferase 15 in Fibrosis and Mucosal Healing in Mouse Colitis

Induction of mucosal healing (MH) is an important treatment goal in inflammatory bowel disease (IBD). Although the molecular mechanisms underlying MH in IBD is not fully explored, local fibrosis would contribute to interfere mucosal repair. Carbohydrate sulfotransferase 15 (CHST15), which catalyzes sulfation of chondroitin sulfate to produce rare E-disaccharide units, is a novel mediator to create local fibrosis. Here we have used siRNA-based approach of silencing CHST15 in dextran sulfate sodium (DSS) induced colitis in mice, human colon fibroblasts and cancer cell lines. In a DSS-induced acute colitis model, CHST15 siRNA reduced CHST15 mRNA in the colon, serum IL-6, disease activity index (DAI) and accumulation of F4/80+ macrophages and ER-TR7+ fibroblasts, while increased Ki-67+ epithelial cells. In DSS-induced chronic colitis models, CHST15 siRNA reduced CHST15 mRNA in the colon, DAI, alpha-smooth muscle actin+ fibroblasts and collagen deposition, while enhanced MH as evidenced by reduced histological and endoscopic scores. We also found that endoscopic submucosal injection achieved effective pancolonic delivery of CHST15 siRNA in mice. In human CCD-18 Co cells, CHST15 siRNA inhibited the expression of CHST15 mRNA and selectively reduced E-units, a specific product biosynthesized by CHST15, in the culture supernatant. CHST15 siRNA significantly suppressed vimentin in both TGF-ß-stimulated CCD18-Co cells and HCT116 cells while up-regulated BMP7 and E-cadherin in HCT116 cells. The present study demonstrated that blockade CHST15 represses colonic fibrosis and enhances MH partly though reversing EMT pathway, illustrating a novel therapeutic opportunity to refractory and fibrotic lesions in IBD.

Parallel computation of genome-scale RNA secondary structure to detect structural constraints on human genome

Background

RNA secondary structure around splice sites is known to assist normal splicing by promoting spliceosome recognition. However, analyzing the structural properties of entire intronic regions or pre-mRNA sequences has been difficult hitherto, owing to serious experimental and computational limitations, such as low read coverage and numerical problems.

Results

Our novel software, “ParasoR”, is designed to run on a computer cluster and enables the exact computation of various structural features of long RNA sequences under the constraint of maximal base-pairing distance. ParasoR divides dynamic programming (DP) matrices into smaller pieces, such that each piece can be computed by a separate computer node without losing the connectivity information between the pieces. ParasoR directly computes the ratios of DP variables to avoid the reduction of numerical precision caused by the cancellation of a large number of Boltzmann factors. The structural preferences of mRNAs computed by ParasoR shows a high concordance with those determined by high-throughput sequencing analyses.

Using ParasoR, we investigated the global structural preferences of transcribed regions in the human genome. A genome-wide folding simulation indicated that transcribed regions are significantly more structural than intergenic regions after removing repeat sequences and k-mer frequency bias. In particular, we observed a highly significant preference for base pairing over entire intronic regions as compared to their antisense sequences, as well as to intergenic regions. A comparison between pre-mRNAs and mRNAs showed that coding regions become more accessible after splicing, indicating constraints for translational efficiency. Such changes are correlated with gene expression levels, as well as GC content, and are enriched among genes associated with cytoskeleton and kinase functions.

Conclusions

We have shown that ParasoR is very useful for analyzing the structural properties of long RNA sequences such as mRNAs, pre-mRNAs, and long non-coding RNAs whose lengths can be more than a million bases in the human genome. In our analyses, transcribed regions including introns are indicated to be subject to various types of structural constraints that cannot be explained from simple sequence composition biases. ParasoR is freely available at https://github.com/carushi/ParasoR.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1067-9) contains supplementary material, which is available to authorized users.

Rtools: a web server for various secondary structural analyses on single RNA sequences

The secondary structures, as well as the nucleotide sequences, are the important features of RNA molecules to characterize their functions. According to the thermodynamic model, however, the probability of any secondary structure is very small. As a consequence, any tool to predict the secondary structures of RNAs has limited accuracy. On the other hand, there are a few tools to compensate the imperfect predictions by calculating and visualizing the secondary structural information from RNA sequences. It is desirable to obtain the rich information from those tools through a friendly interface. We implemented a web server of the tools to predict secondary structures and to calculate various structural features based on the energy models of secondary structures. By just giving an RNA sequence to the web server, the user can get the different types of solutions of the secondary structures, the marginal probabilities such as base-paring probabilities, loop probabilities and accessibilities of the local bases, the energy changes by arbitrary base mutations as well as the measures for validations of the predicted secondary structures. The web server is available at http://rtools.cbrc.jp, which integrates software tools, CentroidFold, CentroidHomfold, IPKnot, CapR, Raccess, Rchange and RintD.

Comprehensive prediction of lncRNA-RNA interactions in human transcriptome

Motivation

Recent studies have revealed that large numbers of non-coding RNAs are transcribed in humans, but only a few of them have been identified with their functions. Identification of the interaction target RNAs of the non-coding RNAs is an important step in predicting their functions. The current experimental methods to identify RNA–RNA interactions, however, are not fast enough to apply to a whole human transcriptome. Therefore, computational predictions of RNA–RNA interactions are desirable, but this is a challenging task due to the huge computational costs involved.

Results

Here, we report comprehensive predictions of the interaction targets of lncRNAs in a whole human transcriptome for the first time. To achieve this, we developed an integrated pipeline for predicting RNA–RNA interactions on the K computer, which is one of the fastest super-computers in the world. Comparisons with experimentally-validated lncRNA–RNA interactions support the quality of the predictions. Additionally, we have developed a database that catalogs the predicted lncRNA–RNA interactions to provide fundamental information about the targets of lncRNAs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2307-5) contains supplementary material, which is available to authorized users.

Small interfering RNA therapy against carbohydrate sulfotransferase 15 inhibits cardiac remodeling in rats with dilated cardiomyopathy

Carbohydrate sulfotransferase 15 (CHST15) is a sulfotransferase responsible for biosynthesis of chondroitin sulfate E (CS-E), which plays important roles in numerous biological events such as biosynthesis of proinflammatory cytokines. However, the effects of CHST15 siRNA in rats with chronic heart failure (CHF) after experimental autoimmune myocarditis (EAM) have not yet been investigated. CHF was elicited in Lewis rats by immunization with cardiac myosin, and after immunization, the rats were divided into two groups and treated with either CHST15 siRNA (2μg/week) or vehicle. Age matched normal rats without immunizations were also included in this study. After 7weeks of treatment, we investigated the effects of CHST15 siRNA on cardiac function, proinflammatory cytokines, and cardiac remodeling in EAM rats. Myocardial functional parameters measured by hemodynamic and echocardiographic studies were significantly improved by CHST15 siRNA treatment in rats with CHF compared with that of vehicle-treated CHF rats. CHST15 siRNA significantly reduced cardiac fibrosis, and hypertrophy and its marker molecules (left ventricular (LV) mRNA expressions of transforming growth factor beta1, collagens I and III, and atrial natriuretic peptide) compared with vehicle-treated CHF rats. CHF-induced increased myocardial mRNA expressions of proinflammatory cytokines [interleukin (IL)-6, IL-1β], monocyte chemoattractant protein-1, and matrix metalloproteinases (MMP-2 and -9), and CHST15 were also suppressed by the treatment with CHST15 siRNA. Western blotting study has confirmed the results obtained from mRNA analysis as CHST15 siRNA treated rats expressed reduced levels of inflammatory and cardiac remodeling marker proteins. Our results demonstrate for the first time, that CHST15 siRNA treatment significantly improved LV function and ameliorated the progression of cardiac remodeling in rats with CHF after EAM.

miRNAs in pancreatic cancer: therapeutic potential, delivery challenges and strategies

Pancreatic ductal adenocarcinoma (PDAC) is a severe pancreatic malignancy and is predicted to victimize 1.5% of men and women during their lifetime (Cancer statistics: SEER stat fact sheet, National Cancer Institute, 2014). miRNAs have emerged as a promising prognostic, diagnostic and therapeutic tool to fight against pancreatic cancer. miRNAs could modulate gene expression by imperfect base-pairing with target mRNA and hence provide means to fine-tune multiple genes simultaneously and alter various signaling pathways associated with the disease. This exceptional miRNA feature has provided a paradigm shift from the conventional one drug one target concept to one drug multiple target theory. However, in vivo miRNA delivery is not fully realized due to challenges posed by this special class of therapeutic molecules, which involves thorough understanding of the biogenesis and physicochemical properties of miRNA and delivery carriers along with the pathophysiology of the PDAC. This review highlights the delivery strategies of miRNA modulators (mimic/inhibitor) in cancer with special emphasis on PDAC since successful delivery of miRNA in vivo constitutes the major challenge in clinical translation of this promising class of therapeutics.

Improved asymmetry prediction for short interfering RNAs

In the development of RNA interference therapeutics, merely selecting short interfering RNA (siRNA) sequences that are complementary to the mRNA target does not guarantee target silencing. Current algorithms for selecting siRNAs rely on many parameters, one of which is asymmetry, often predicted through calculation of the relative thermodynamic stabilities of the two ends of the siRNA. However, we have previously shown that highly active siRNA sequences are likely to have particular nucleotides at each 5'-end, independently of their thermodynamic asymmetry. Here, we describe an algorithm for predicting highly active siRNA sequences based only on these two asymmetry parameters. The algorithm uses end-sequence nucleotide preferences and predicted thermodynamic stabilities, each weighted on the basis of training data from the literature, to rank the probability that an siRNA sequence will have high or low activity. The algorithm successfully predicts weakly and highly active sequences for enhanced green fluorescent protein and protein kinase R. Use of these two parameters in combination improves the prediction of siRNA activity over current approaches for predicting asymmetry. Going forward, we anticipate that this approach to siRNA asymmetry prediction will be incorporated into the next generation of siRNA selection algorithms.

CapR: revealing structural specificities of RNA-binding protein target recognition using CLIP-seq data

RNA-binding proteins (RBPs) bind to their target RNA molecules by recognizing specific RNA sequences and structural contexts. The development of CLIP-seq and related protocols has made it possible to exhaustively identify RNA fragments that bind to RBPs. However, no efficient bioinformatics method exists to reveal the structural specificities of RBP–RNA interactions using these data. We present CapR, an efficient algorithm that calculates the probability that each RNA base position is located within each secondary structural context. Using CapR, we demonstrate that several RBPs bind to their target RNA molecules under specific structural contexts. CapR is available at https://sites.google.com/site/fukunagatsu/software/capr.

Decoding mechanism of non-universal genetic codes in Loligo bleekeri mitochondria

Non-universal genetic codes are frequently found in animal mitochondrial decoding systems. In squid mitochondria, four codons deviate from the universal genetic code, namely AUA, UGA, and AGA/AGG (AGR) for Met, Trp, and Ser, respectively. To understand the molecular basis for establishing the non-universal genetic code, we isolated and analyzed five mitochondrial tRNAs from a squid, Loligo bleekeri. Primary structures of the isolated tRNAs, including their post-transcriptional modifications, were analyzed by mass spectrometry. tRNA(Met)(AUR) possessed an unmodified cytidine at the first position of the anticodon, suggesting that the AUA codon is deciphered by CAU anticodon via non-canonical A-C pairing. We identified 5-taurinomethyluridine (τm(5)U) at the first position of the anticodon in tRNA(Trp)(UGR). τm(5)U enables tRNA(Trp) to decipher UGR codons as Trp. In addition, 5-taurinomethyl-2-thiouridine (τm(5)s(2)U) was found in mitochondrial tRNAs for Leu(UUR) and Lys in L. bleekeri. This is the first discovery of τm(5)U and τm(5)s(2)U in molluscan mitochondrial tRNAs.

Finding microRNA targets in plants: current status and perspectives

MicroRNAs (miRNAs), a class of ∼20–24 nt long non-coding RNAs, have critical roles in diverse biological processes including development, proliferation, stress response, etc. With the development and availability of experimental technologies and computational approaches, the field of miRNA biology has advanced tremendously over the last decade. By sequence complementarity, miRNAs have been estimated to regulate certain mRNA transcripts. Although it was once thought to be simple and straightforward to find plant miRNA targets, this viewpoint is being challenged by genetic and biochemical studies. In this review, we summarize recent progress in plant miRNA target recognition mechanisms, principles of target prediction, and introduce current experimental and computational tools for plant miRNA target prediction. At the end, we also present our thinking on the outlook for future directions in the development of plant miRNA target finding methods.

Genome-wide search for miRNA-target interactions in Arabidopsis thaliana with an integrated approach

Background

MiRNA are about 22nt long small noncoding RNAs that post transcriptionally regulate gene expression in animals, plants and protozoa. Confident identification of MiRNA-Target Interactions (MTI) is vital to understand their function. Currently, several integrated computational programs and databases are available for animal miRNAs, the mechanisms of which are significantly different from plant miRNAs.

Methods

Here we present an integrated MTI prediction and analysis toolkit (imiRTP) for Arabidopsis thaliana. It features two important functions: (i) combination of several effective plant miRNA target prediction methods provides a sufficiently large MTI candidate set, and (ii) different filters allow for an efficient selection of potential targets. The modularity of imiRTP enables the prediction of high quality targets on genome-wide scale. Moreover, predicted MTIs can be presented in various ways, which allows for browsing through the putative target sites as well as conducting simple and advanced analyses.

Results

Results show that imiRTP could always find high quality candidates compared with single method by choosing appropriate filter and parameter. And we also reveal that a portion of plant miRNA could bind target genes out of coding region. Based on our results, imiRTP could facilitate the further study of Arabidopsis miRNAs in real use. All materials of imiRTP are freely available under a GNU license at (http://admis.fudan.edu.cn/projects/imiRTP.htm).

Rchange: algorithms for computing energy changes of RNA secondary structures in response to base mutations

MOTIVATION

Measuring the effects of base mutations is a powerful tool for functional and evolutionary analyses of RNA structures. To date, only a few methods have been developed for systematically computing the thermodynamic changes of RNA secondary structures in response to base mutations.

RESULTS

We have developed algorithms for computing the changes of the ensemble free energy, mean energy and the thermodynamic entropy of RNA secondary structures for exhaustive patterns of single and double mutations. The computational complexities are O(NW(2)) (where N is sequence length and W is maximal base pair span) for single mutations and O(N(2)W(2)) for double mutations with large constant factors. We show that the changes are relatively insensitive to GC composition and the maximal span constraint. The mean free energy changes are bounded ~7-9 kcal/mol and depend only weakly on position if sequence lengths are sufficiently large. For tRNA sequences, the most stabilizing mutations come from the change of the 5(')-most base of the anticodon loop. We also show that most of the base changes in the acceptor stem destabilize the structures, indicating that the nucleotide sequence in the acceptor stem is highly optimized for secondary structure stability. We investigate the 22 tRNA genes in the human mitochondrial genome and show that non-pathogenic polymorphisms tend to cause smaller changes in thermodynamic variables than generic mutations, suggesting that a mutation which largely increases thermodynamic variables has higher possibility to be a pathogenic or lethal mutation.

AVAILABILITY AND IMPLEMENTATION

The C++ source code of the Rchange software is available at http://www.ncrna.org/software/rchange/.

Global or local? Predicting secondary structure and accessibility in mRNAs

Determining the structural properties of mRNA is key to understanding vital post-transcriptional processes. As experimental data on mRNA structure are scarce, accurate structure prediction is required to characterize RNA regulatory mechanisms. Although various structure prediction approaches are available, it is often unclear which to choose and how to set their parameters. Furthermore, no standard measure to compare predictions of local structure exists. We assessed the performance of different methods using two types of data: transcriptome-wide enzymatic probing information and a large, curated set of cis-regulatory elements. To compare the approaches, we introduced structure accuracy, a measure that is applicable to both global and local methods. Our results showed that local folding was more accurate than the classic global approach. We investigated how the locality parameters, maximum base pair span and window size, influenced the prediction performance. A span of 150 provided a reasonable balance between maximizing the number of accurately predicted base pairs, while minimizing effects of incorrect long-range predictions. We characterized the error at artificial sequence ends, which we reduced by setting the window size sufficiently greater than the maximum span. Our method, LocalFold, diminished all border effects and produced the most robust performance.

Taurine-containing uridine modifications in tRNA anticodons are required to decipher non-universal genetic codes in ascidian mitochondria

Variations in the genetic code are found frequently in mitochondrial decoding systems. Four non-universal genetic codes are employed in ascidian mitochondria: AUA for Met, UGA for Trp, and AGA/AGG(AGR) for Gly. To clarify the decoding mechanism for the non-universal genetic codes, we isolated and analyzed mitochondrial tRNAs for Trp, Met, and Gly from an ascidian, Halocynthia roretzi. Mass spectrometric analysis identified 5-taurinomethyluridine (τm(5)U) at the anticodon wobble positions of tRNA(Met)(AUR), tRNA(Trp)(UGR), and tRNA(Gly)(AGR), suggesting that τm(5)U plays a critical role in the accurate deciphering of all four non-universal codes by preventing the misreading of pyrimidine-ending near-cognate codons (NNY) in their respective family boxes. Acquisition of the wobble modification appears to be a prerequisite for the genetic code alteration.