Long Noncoding RNA TYKRIL Plays a Role in Pulmonary Hypertension via the p53-mediated Regulation of PDGFRß.

Rationale: Long noncoding RNAs (lncRNAs) are emerging as important regulators of diverse biological functions. Their role in pulmonary arterial hypertension (PAH) remains to be explored.Objectives: To elucidate the role of TYKRIL (tyrosine kinase receptor-inducing lncRNA) as a regulator of p53/ PDGFRß (platelet-derived growth factor receptor ß) signaling pathway and to investigate its role in PAH.Methods: Pericytes and pulmonary arterial smooth muscle cells exposed to hypoxia and derived from patients with idiopathic PAH were analyzed with RNA sequencing. TYKRIL knockdown was performed in above-mentioned human primary cells and in precision-cut lung slices derived from patients with PAH.Measurements and Main Results: Using RNA sequencing data, TYKRIL was identified to be consistently upregulated in pericytes and pulmonary arterial smooth muscles cells exposed to hypoxia and derived from patients with idiopathic PAH. TYKRIL knockdown reversed the proproliferative (n = 3) and antiapoptotic (n = 3) phenotype induced under hypoxic and idiopathic PAH conditions. Owing to the poor species conservation of TYKRIL, ex vivo studies were performed in precision-cut lung slices from patients with PAH. Knockdown of TYKRIL in precision-cut lung slices decreased the vascular remodeling (n = 5). The number of proliferating cell nuclear antigen-positive cells in the vessels was decreased and the number of terminal deoxynucleotide transferase-mediated dUTP nick end label-positive cells in the vessels was increased in the LNA (locked nucleic acid)-treated group compared with control. Expression of PDGFRß, a key player in PAH, was found to strongly correlate with TYKRIL expression in the patient samples (n = 12), and TYKRIL knockdown decreased PDGFRß expression (n = 3). From the transcription factor-screening array, it was observed that TYKRIL knockdown increased the p53 activity, a known repressor of PDGFRß. RNA immunoprecipitation using various p53 mutants demonstrated that TYKRIL binds to the N-terminal of p53 (an important region for p300 interaction with p53). The proximity ligation assay revealed that TYKRIL interferes with the p53-p300 interaction (n = 3) and regulates p53 nuclear translocation.Conclusions: TYKRIL plays an important role in PAH by regulating the p53/PDGFRß axis.

Cardiac mesenchymal cells from failing and nonfailing hearts limit ventricular dilation when administered late after infarction.

Although cell therapy-mediated cardiac repair offers promise for treatment/management of heart failure, lack of fundamental understanding of how cell therapy works limits its translational potential. In particular, whether reparative cells from failing hearts differ from cells derived from nonfailing hearts remains unexplored. Here, we assessed differences between cardiac mesenchymal cells (CMC) derived from failing (HF) versus nonfailing (Sham) hearts and whether the source of donor cells (i.e., from HF vs. Sham) limits reparative capacity, particularly when administered late after infarction. To determine the impact of the donor source of CMCs, we characterized the transcriptional profile of CMCs isolated from sham (Sham-CMC) and failing (HF-CMC) hearts. RNA-seq analysis revealed unique transcriptional signatures in Sham-CMC and HF-CMC, suggesting that the donor source impacts CMC. To determine whether the donor source affects reparative potential, C57BL6/J female mice were subjected to 60 min of regional myocardial ischemia and then reperfused for 35 days. In a randomized, controlled, and blinded fashion, vehicle, HF-CMC, or Sham-CMC were injected into the lumen of the left ventricle at 35 days post-MI. An additional 5 weeks later, cardiac function was assessed by echocardiography, which indicated that delayed administration of Sham-CMC and HF-CMC attenuated ventricular dilation. We also determined whether Sham-CMC and HF-CMC treatments affected ventricular histopathology. Our data indicate that the donor source (nonfailing vs. failing hearts) affects certain aspects of CMC, and these insights may have implications for future studies. Our data indicate that delayed administration of CMC limits ventricular dilation and that the source of CMC may influence their reparative actions.NEW & NOTEWORTHY Most preclinical studies have used only cells from healthy, nonfailing hearts. Whether donor condition (i.e., heart failure) impacts cells used for cell therapy is not known. We directly tested whether donor condition impacted the reparative effects of cardiac mesenchymal cells in a chronic model of myocardial infarction. Although cells from failing hearts differed in multiple aspects, they retained the potential to limit ventricular remodeling.

Logic programming to infer complex RNA expression patterns from RNA-seq data.

To meet the increasing demand in the field, numerous long noncoding RNA (lncRNA) databases are available. Given many lncRNAs are specifically expressed in certain cell types and/or time-dependent manners, most lncRNA databases fall short of providing such profiles. We developed a strategy using logic programming to handle the complex organization of organs, their tissues and cell types as well as gender and developmental time points. To showcase this strategy, we introduce 'RenalDB' (http://renaldb.uni-frankfurt.de), a database providing expression profiles of RNAs in major organs focusing on kidney tissues and cells. RenalDB uses logic programming to describe complex anatomy, sample metadata and logical relationships defining expression, enrichment or specificity. We validated the content of RenalDB with biological experiments and functionally characterized two long intergenic noncoding RNAs: LOC440173 is important for cell growth or cell survival, whereas PAXIP1-AS1 is a regulator of cell death. We anticipate RenalDB will be used as a first step toward functional studies of lncRNAs in the kidney.

Airn Regulates Igf2bp2 Translation in Cardiomyocytes.

RATIONALE: Increasing evidence indicates the presence of lncRNAs in various cell types. Airn is an imprinting gene transcribed from the paternal chromosome. It is in antisense orientation to the imprinted, but maternally derived, Igf2r gene, on which Airn exerts its regulation in cis. Although Airn is highly expressed in the heart, functions aside from imprinting remain unknown. OBJECTIVE: Here, we studied the functions of Airn in the heart, especially cardiomyocytes. METHODS AND RESULTS: Silencing of Airn via siRNAs augmented cell death, vulnerability to cellular stress, and reduced cell migration. To find the cause of such phenotypes, the potential binding partners of Airn were identified via RNA pull-down followed by mass spectrometry, which indicated Igf2bp2 (insulin-like growth factor 2 mRNA-binding protein 2) and Rpa1 (replication protein A1) as potential binding partners. Further experiments showed that Airn binds to Igf2bp2 to control the translation of several genes. Moreover, silencing of Airn caused less binding of Igf2bp2 to other mRNAs and reduced translation of Igf2bp2 protein. CONCLUSIONS: Our study uncovers a new function of Airn and demonstrates that Airn is important for the physiology of cardiomyocytes.

Resolving the problem of multiple accessions of the same transcript deposited across various public databases.

Maintaining the consistency of genomic annotations is an increasingly complex task because of the iterative and dynamic nature of assembly and annotation, growing numbers of biological databases and insufficient integration of annotations across databases. As information exchange among databases is poor, a 'novel' sequence from one reference annotation could be annotated in another. Furthermore, relationships to nearby or overlapping annotated transcripts are even more complicated when using different genome assemblies. To better understand these problems, we surveyed current and previous versions of genomic assemblies and annotations across a number of public databases containing long noncoding RNA. We identified numerous discrepancies of transcripts regarding their genomic locations, transcript lengths and identifiers. Further investigation showed that the positional differences between reference annotations of essentially the same transcript could lead to differences in its measured expression at the RNA level. To aid in resolving these problems, we present the algorithm 'Universal Genomic Accession Hash (UGAHash)' and created an open source web tool to encourage the usage of the UGAHash algorithm. The UGAHash web tool (http://ugahash.uni-frankfurt.de) can be accessed freely without registration. The web tool allows researchers to generate Universal Genomic Accessions for genomic features or to explore annotations deposited in the public databases of the past and present versions. We anticipate that the UGAHash web tool will be a valuable tool to check for the existence of transcripts before judging the newly discovered transcripts as novel.

Screening and validation of lncRNAs and circRNAs as miRNA sponges.

Intensive research in past two decades has uncovered the presence and importance of noncoding RNAs (ncRNAs), which includes microRNAs (miRs) and long ncRNAs (lncRNAs). These two classes of ncRNAs interact to a certain extent, as some lncRNAs bind to miRs to sequester them. Such lncRNAs are collectively called 'competing endogenous RNAs' or 'miRNA sponges'. In this study, we screened for lncRNAs that may act as miRNA sponges using the publicly available data sets and databases. To uncover the roles of miRNA sponges, loss-of-function experiments were conducted, which revealed the biological roles as miRNA sponges. LINC00324 is important for the cell survival by binding to miR-615-5p leading to the de-repression of its target BTG2. LOC400043 controls several biological functions via sequestering miR-28-3p and miR-96-5p, thereby changing the expressions of transcriptional regulators. Finally, we also screened for circular RNAs (circRNAs) that may function as miRNA sponges. The results were negative at least for the selected circRNAs in this study. In conclusion, miRNA sponges can be identified by applying a series of bioinformatics techniques and validated with biological experiments.

RNAEditor: easy detection of RNA editing events and the introduction of editing islands.

RNA editing of adenosine residues to inosine ('A-to-I editing') is the most common RNA modification event detectible with RNA sequencing (RNA-seq). While not directly detectable, inosine is read by next-generation sequencers as guanine. Therefore, mapping RNA-seq reads to their corresponding reference genome can detect potential editing events by identifying 'A-to-G' conversions. However, one must exercise caution when searching for editing sites, as A-to-G conversions also arise from sequencing errors as well as mutations. To address these complexities, several algorithms and software products have been developed to accurately identify editing events. Here, we survey currently available methods to analyze RNA editing events and introduce a new easy-to-use bioinformatics tool 'RNAEditor' for the detection of RNA editing events. During the development of RNAEditor, we noticed editing often happened in clusters, which we named 'editing islands'. We developed a clustering algorithm to find editing islands and included it in RNAEditor. RNAEditor is freely available at http://rnaeditor.uni-frankfurt.de. We anticipate that RNAEditor will provide biologists with an easy-to-use tool for studying RNA editing events and the newly defined editing islands.

The identification and characterization of novel transcripts from RNA-seq data.

Owing greatly to the advancement of next-generation sequencing (NGS), the amount of NGS data is increasing rapidly. Although there are many NGS applications, one of the most commonly used techniques 'RNA sequencing (RNA-seq)' is rapidly replacing microarray-based techniques in laboratories around the world. As more and more of such techniques are standardized, allowing technicians to perform these experiments with minimal hands-on time and reduced experimental/operator-dependent biases, the bottleneck of such techniques is clearly visible; that is, data analysis. Further complicating the matter, increasing evidence suggests most of the genome is transcribed into RNA; however, the majority of these RNAs are not translated into proteins. These RNAs that do not become proteins are called 'noncoding RNAs (ncRNAs)'. Although some time has passed since the discovery of ncRNAs, their annotations remain poor, making analysis of RNA-seq data challenging. Here, we examine the current limitations of RNA-seq analysis using case studies focused on the detection of novel transcripts and examination of their characteristics. Finally, we validate the presence of novel transcripts using biological experiments, showing novel transcripts can be accurately identified when a series of filters is applied. In conclusion, novel transcripts that are identified from RNA-seq must be examined carefully before proceeding to biological experiments.

C-It-Loci: a knowledge database for tissue-enriched loci.

MOTIVATION: Increasing evidences suggest that most of the genome is transcribed into RNAs, but many of them are not translated into proteins. All those RNAs that do not become proteins are called 'non-coding RNAs (ncRNAs)', which outnumbers protein-coding genes. Interestingly, these ncRNAs are shown to be more tissue specifically expressed than protein-coding genes. Given that tissue-specific expressions of transcripts suggest their importance in the expressed tissue, researchers are conducting biological experiments to elucidate the function of such ncRNAs. Owing greatly to the advancement of next-generation techniques, especially RNA-seq, the amount of high-throughput data are increasing rapidly. However, due to the complexity of the data as well as its high volume, it is not easy to re-analyze such data to extract tissue-specific expressions of ncRNAs from published datasets. RESULTS: Here, we introduce a new knowledge database called 'C-It-Loci', which allows a user to screen for tissue-specific transcripts across three organisms: human, mouse and zebrafish. C-It-Loci is intuitive and easy to use to identify not only protein-coding genes but also ncRNAs from various tissues. C-It-Loci defines homology through sequence and positional conservation to allow for the extraction of species-conserved loci. C-It-Loci can be used as a starting point for further biological experiments. AVAILABILITY AND IMPLEMENTATION: C-It-Loci is freely available online without registration at http://c-it-loci.uni-frankfurt.de. CONTACT: uchida@med.uni-frankfurt.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predicting genome-scale Arabidopsis-Pseudomonas syringae interactome using domain and interolog-based approaches.

BACKGROUND: Every year pathogenic organisms cause billions of dollars' worth damage to crops and livestock. In agriculture, study of plant-microbe interactions is demanding a special attention to develop management strategies for the destructive pathogen induced diseases that cause huge crop losses every year worldwide. Pseudomonas syringae is a major bacterial leaf pathogen that causes diseases in a wide range of plant species. Among its various strains, pathovar tomato strain DC3000 (PstDC3000) is asserted to infect the plant host Arabidopsis thaliana and thus, has been accepted as a model system for experimental characterization of the molecular dynamics of plant-pathogen interactions. Protein-protein interactions (PPIs) play a critical role in initiating pathogenesis and maintaining infection. Understanding the PPI network between a host and pathogen is a critical step for studying the molecular basis of pathogenesis. The experimental study of PPIs at a large scale is very scarce and also the high throughput experimental results show high false positive rate. Hence, there is a need for developing efficient computational models to predict the interaction between host and pathogen in a genome scale, and find novel candidate effectors and/or their targets. RESULTS: In this study, we used two computational approaches, the interolog and the domain-based to predict the interactions between Arabidopsis and PstDC3000 in genome scale. The interolog method relies on protein sequence similarity to conduct the PPI prediction. A Pseudomonas protein and an Arabidopsis protein are predicted to interact with each other if an experimentally verified interaction exists between their respective homologous proteins in another organism. The domain-based method uses domain interaction information, which is derived from known protein 3D structures, to infer the potential PPIs. If a Pseudomonas and an Arabidopsis protein contain an interacting domain pair, one can expect the two proteins to interact with each other. The interolog-based method predicts ~0.79M PPIs involving around 7700 Arabidopsis and 1068 Pseudomonas proteins in the full genome. The domain-based method predicts 85650 PPIs comprising 11432 Arabidopsis and 887 Pseudomonas proteins. Further, around 11000 PPIs have been identified as interacting from both the methods as a consensus. CONCLUSION: The present work predicts the protein-protein interaction network between Arabidopsis thaliana and Pseudomonas syringae pv. tomato DC3000 in a genome wide scale with a high confidence. Although the predicted PPIs may contain some false positives, the computational methods provide reasonable amount of interactions which can be further validated by high throughput experiments. This can be a useful resource to the plant community to characterize the host-pathogen interaction in Arabidopsis and Pseudomonas system. Further, these prediction models can be applied to the agriculturally relevant crops.

LacSubPred: predicting subtypes of Laccases, an important lignin metabolism-related enzyme class, using in silico approaches.

BACKGROUND: Laccases (E.C. 1.10.3.2) are multi-copper oxidases that have gained importance in many industries such as biofuels, pulp production, textile dye bleaching, bioremediation, and food production. Their usefulness stems from the ability to act on a diverse range of phenolic compounds such as o-/p-quinols, aminophenols, polyphenols, polyamines, aryl diamines, and aromatic thiols. Despite acting on a wide range of compounds as a family, individual Laccases often exhibit distinctive and varied substrate ranges. This is likely due to Laccases involvement in many metabolic roles across diverse taxa. Classification systems for multi-copper oxidases have been developed using multiple sequence alignments, however, these systems seem to largely follow species taxonomy rather than substrate ranges, enzyme properties, or specific function. It has been suggested that the roles and substrates of various Laccases are related to their optimal pH. This is consistent with the observation that fungal Laccases usually prefer acidic conditions, whereas plant and bacterial Laccases prefer basic conditions. Based on these observations, we hypothesize that a descriptor-based unsupervised learning system could generate homology independent classification system for better describing the functional properties of Laccases. RESULTS: In this study, we first utilized unsupervised learning approach to develop a novel homology independent Laccase classification system. From the descriptors considered, physicochemical properties showed the best performance. Physicochemical properties divided the Laccases into twelve subtypes. Analysis of the clusters using a t-test revealed that the majority of the physicochemical descriptors had statistically significant differences between the classes. Feature selection identified the most important features as negatively charges residues, the peptide isoelectric point, and acidic or amidic residues. Secondly, to allow for classification of new Laccases, a supervised learning system was developed from the clusters. The models showed high performance with an overall accuracy of 99.03%, error of 0.49%, MCC of 0.9367, precision of 94.20%, sensitivity of 94.20%, and specificity of 99.47% in a 5-fold cross-validation test. In an independent test, our models still provide a high accuracy of 97.98%, error rate of 1.02%, MCC of 0.8678, precision of 87.88%, sensitivity of 87.88% and specificity of 98.90%. CONCLUSION: This study provides a useful classification system for better understanding of Laccases from their physicochemical properties perspective. We also developed a publically available web tool for the characterization of Laccase protein sequences (http://lacsubpred.bioinfo.ucr.edu/). Finally, the programs used in the study are made available for researchers interested in applying the system to other enzyme classes (https://github.com/tweirick/SubClPred).

Identification and characterization of plastid-type proteins from sequence-attributed features using machine learning.

BACKGROUND: Plastids are an important component of plant cells, being the site of manufacture and storage of chemical compounds used by the cell, and contain pigments such as those used in photosynthesis, starch synthesis/storage, cell color etc. They are essential organelles of the plant cell, also present in algae. Recent advances in genomic technology and sequencing efforts is generating a huge amount of DNA sequence data every day. The predicted proteome of these genomes needs annotation at a faster pace. In view of this, one such annotation need is to develop an automated system that can distinguish between plastid and non-plastid proteins accurately, and further classify plastid-types based on their functionality. We compared the amino acid compositions of plastid proteins with those of non-plastid ones and found significant differences, which were used as a basis to develop various feature-based prediction models using similarity-search and machine learning. RESULTS: In this study, we developed separate Support Vector Machine (SVM) trained classifiers for characterizing the plastids in two steps: first distinguishing the plastid vs. non-plastid proteins, and then classifying the identified plastids into their various types based on their function (chloroplast, chromoplast, etioplast, and amyloplast). Five diverse protein features: amino acid composition, dipeptide composition, the pseudo amino acid composition, N(terminal)-Center-C(terminal) composition and the protein physicochemical properties are used to develop SVM models. Overall, the dipeptide composition-based module shows the best performance with an accuracy of 86.80% and Matthews Correlation Coefficient (MCC) of 0.74 in phase-I and 78.60% with a MCC of 0.44 in phase-II. On independent test data, this model also performs better with an overall accuracy of 76.58% and 74.97% in phase-I and phase-II, respectively. The similarity-based PSI-BLAST module shows very low performance with about 50% prediction accuracy for distinguishing plastid vs. non-plastids and only 20% in classifying various plastid-types, indicating the need and importance of machine learning algorithms. CONCLUSION: The current work is a first attempt to develop a methodology for classifying various plastid-type proteins. The prediction modules have also been made available as a web tool, PLpred available at http://bioinfo.okstate.edu/PLpred/ for real time identification/characterization. We believe this tool will be very useful in the functional annotation of various genomes.

Administration of cardiac mesenchymal cells modulates innate immunity in the acute phase of myocardial infarction in mice.

Although cardiac mesenchymal cell (CMC) therapy mitigates post-infarct cardiac dysfunction, the underlying mechanisms remain unidentified. It is acknowledged that donor cells are neither appreciably retained nor meaningfully contribute to tissue regeneration-suggesting a paracrine-mediated mechanism of action. As the immune system is inextricably linked to wound healing/remodeling in the ischemically injured heart, the reparative actions of CMCs may be attributed to their immunoregulatory properties. The current study evaluated the consequences of CMC administration on post myocardial infarction (MI) immune responses in vivo and paracrine-mediated immune cell function in vitro. CMC administration preferentially elicited the recruitment of cell types associated with innate immunity (e.g., monocytes/macrophages and neutrophils). CMC paracrine signaling assays revealed enhancement in innate immune cell chemoattraction, survival, and phagocytosis, and diminished pro-inflammatory immune cell activation; data that identifies and catalogues fundamental immunomodulatory properties of CMCs, which have broad implications regarding the mechanism of action of CMCs in cardiac repair.

Investigation of RNA Editing Sites within Bound Regions of RNA-Binding Proteins.

Studies in epitranscriptomics indicate that RNA is modified by a variety of enzymes. Among these RNA modifications, adenosine to inosine (A-to-I) RNA editing occurs frequently in the mammalian transcriptome. These RNA editing sites can be detected directly from RNA sequencing (RNA-seq) data by examining nucleotide changes from adenosine (A) to guanine (G), which substitutes for inosine (I). However, a careful investigation of such nucleotide changes must be conducted to distinguish sequencing errors and genomic mutations from the genuine editing sites. Building upon our recent introduction of an easy-to-use bioinformatics tool, RNA Editor, to detect RNA editing events from RNA-seq data, we examined the extent by which RNA editing events affect the binding of RNA-binding proteins (RBP). Through employing bioinformatic techniques, we uncovered that RNA editing sites occur frequently in RBP-bound regions. Moreover, the presence of RNA editing sites are more frequent when RNA editing islands were examined, which are regions in which RNA editing sites are present in clusters. When the binding of one RBP, human antigen R [HuR; encoded by ELAV-like protein 1 (ELAV1)], was quantified experimentally, its binding was reduced upon silencing of the RNA editing enzyme adenosine deaminases acting on RNA (ADAR) compared to the control-suggesting that the presence of RNA editing islands influence HuR binding to its target regions. These data indicate RNA editing as an important mediator of RBP-RNA interactions-a mechanism which likely constitutes an additional mode of post-transcription gene regulation in biological systems.

Increasing workflow development speed and reproducibility with Vectools.

Despite advances in bioinformatics, custom scripts remain a source of difficulty, slowing workflow development and hampering reproducibility. Here, we introduce Vectools, a command-line tool-suite to reduce reliance on custom scripts and improve reproducibility by offering a wide range of common easy-to-use functions for table and vector manipulation. Vectools also offers a number of vector related functions to speed up workflow development, such as simple machine learning and common statistics functions.

Long Non-coding RNAs in Endothelial Biology.

In recent years, the role of RNA has expanded to the extent that protein-coding RNAs are now the minority with a variety of non-coding RNAs (ncRNAs) now comprising the majority of RNAs in higher organisms. A major contributor to this shift in understanding is RNA sequencing (RNA-seq), which allows a largely unconstrained method for monitoring the status of RNA from whole organisms down to a single cell. This observational power presents both challenges and new opportunities, which require specialized bioinformatics tools to extract knowledge from the data and the ability to reuse data for multiple studies. In this review, we summarize the current status of long non-coding RNA (lncRNA) research in endothelial biology. Then, we will cover computational methods for identifying, annotating, and characterizing lncRNAs in the heart, especially endothelial cells.

The Genome of a Thermo Tolerant, Pathogenic Albino Aspergillus fumigatus.

Biotechnologists are interested in thermo tolerant fungi to manufacture enzymes active and stable at high temperatures, because they provide improved catalytic efficiency, strengthen enzyme substrate interactions, accelerate substrate enzyme conversion rates, enhance mass transfer, lower substrate viscosity, lessen contamination risk and offer the potential for enzyme recycling. Members of the genus Aspergillus live a wide variety of lifestyles, some embrace GRAS status routinely employed in food processing while others such as Aspergillus fumigatus are human pathogens. A. fumigatus produces melanins, pyomelanin protects the fungus against reactive oxygen species and DHN melanin produced by the pksP gene cluster confers the gray-greenish color. pksP mutants are attenuated in virulence. Here we report on the genomic DNA sequence of a thermo tolerant albino Aspergillus isolated from rain forest composted floors. Unexpectedly, the nucleotide sequence was 95.7% identical to the reported by Aspergillus fumigatus Af293. Genome size and predicted gene models were also highly similar, however differences in DNA content and conservation were observed. The albino strain, classified as Aspergillus fumigatus var. niveus, had 160 gene models not present in A. fumigatus Af293 and A. fumigatus Af293 had 647 not found in the albino strain. Furthermore, the major pigment generating gene cluster pksP appeared to have undergone genomic rearrangements and a key tyrosinase present in many aspergilli was missing from the genome. Remarkably however, despite the lack of pigmentation A. fumigatus var. niveus killed neutropenic mice and survived macrophage engulfment at similar rates as A. fumigatus Af293.

A novel long non-coding RNA Myolinc regulates myogenesis through TDP-43 and Filip1.

Myogenesis is a complex process required for skeletal muscle formation during embryonic development and for regeneration and growth of myofibers in adults. Accumulating evidence suggests that long non-coding RNAs (lncRNAs) play key roles in regulating cell fate decision and function in various tissues. However, the role of lncRNAs in the regulation of myogenesis remains poorly understood. In this study, we identified a novel muscle-enriched lncRNA called 'Myolinc (AK142388)', which we functionally characterized in the C2C12 myoblast cell line. Myolinc is predominately localized in the nucleus, and its levels increase upon induction of the differentiation. Knockdown of Myolinc impairs the expression of myogenic regulatory factors and formation of multi-nucleated myotubes in cultured myoblasts. Myolinc also regulates the expression of Filip1 in a cis-manner. Similar to Myolinc, knockdown of Filip1 inhibits myogenic differentiation. Furthermore, Myolinc binds to TAR DNA-binding protein 43 (TDP-43), a DNA/RNA-binding protein that regulates the expression of muscle genes (e.g. Acta1 and MyoD). Knockdown of TDP-43 inhibits myogenic differentiation. We also show that Myolinc-TDP-43 interaction is essential for the binding of TDP-43 to the promoter regions of muscle marker genes. Finally, we show that silencing of Myolinc inhibits skeletal muscle regeneration in adult mice. Altogether, our study identifies a novel lncRNA that controls key regulatory networks of myogenesis.

ANGIOGENES: knowledge database for protein-coding and noncoding RNA genes in endothelial cells.

Increasing evidence indicates the presence of long noncoding RNAs (lncRNAs) is specific to various cell types. Although lncRNAs are speculated to be more numerous than protein-coding genes, the annotations of lncRNAs remain primitive due to the lack of well-structured schemes for their identification and description. Here, we introduce a new knowledge database "ANGIOGENES" (http://angiogenes.uni-frankfurt.de) to allow for in silico screening of protein-coding genes and lncRNAs expressed in various types of endothelial cells, which are present in all tissues. Using the latest annotations of protein-coding genes and lncRNAs, publicly-available RNA-seq data was analyzed to identify transcripts that are expressed in endothelial cells of human, mouse and zebrafish. The analyzed data were incorporated into ANGIOGENES to provide a one-stop-shop for transcriptomics data to facilitate further biological validation. ANGIOGENES is an intuitive and easy-to-use database to allow in silico screening of expressed, enriched and/or specific endothelial transcripts under various conditions. We anticipate that ANGIOGENES serves as a starting point for functional studies to elucidate the roles of protein-coding genes and lncRNAs in angiogenesis.