Fendrr synergizes with Wnt signalling to regulate fibrosis related genes during lung development

Long non-coding RNAs are a very versatile class of molecules that can have important roles in regulating a cells function, including regulating other genes on the transcriptional level. One of these mechanisms is that RNA can directly interact with DNA thereby recruiting additional components such as proteins to these sites via a RNA:dsDNA triplex formation. We genetically deleted the triplex forming sequence (FendrrBox) from the lncRNA Fendrr in mice and find that this FendrrBox is partially required for Fendrr function in vivo. We find that the loss of the triplex forming site in developing lungs causes a dysregulation of gene programs, associated with lung fibrosis. A set of these genes contain a triplex site directly at their promoter and are expressed in fibroblasts. We find that Fendrr with the Wnt signaling pathway regulates these genes, implicating that Fendrr synergizes with Wnt signaling in lung fibrosis.

HIF1α-AS1 is a DNA:DNA:RNA triplex-forming lncRNA interacting with the HUSH complex

DNA:DNA:RNA triplexes that are formed through Hoogsteen base-pairing have been observed in vitro, but the extent to which these interactions occur in cells and how they impact cellular functions remains elusive. Using a combination of bioinformatic techniques, RNA/DNA pulldown and biophysical studies, we set out to identify functionally important DNA:DNA:RNA triplex-forming long non-coding RNAs (lncRNA) in human endothelial cells. The lncRNA HIF1α-AS1 was retrieved as a top hit. Endogenous HIF1α-AS1 reduced the expression of numerous genes, including EPH Receptor A2 and Adrenomedullin through DNA:DNA:RNA triplex formation by acting as an adapter for the repressive human silencing hub complex (HUSH). Moreover, the oxygen-sensitive HIF1α-AS1 was down-regulated in pulmonary hypertension and loss-of-function approaches not only resulted in gene de-repression but also enhanced angiogenic capacity. As exemplified here with HIF1α-AS1, DNA:DNA:RNA triplex formation is a functionally important mechanism of trans-acting gene expression control.

Deep learning based DNA:RNA triplex forming potential prediction

Background Long non-coding RNAs (lncRNAs) can exert functions via forming triplex with DNA. The current methods in predicting the triplex formation mainly rely on mathematic statistic according to the base paring rules. However, these methods have two main limitations: (1) they identify a large number of triplex-forming lncRNAs, but the limited number of experimentally verified triplex-forming lncRNA indicates that maybe not all of them can form triplex in practice, and (2) their predictions only consider the theoretical relationship while lacking the features from the experimentally verified data. Results In this work, we develop an integrated program named TriplexFPP (Triplex Forming Potential Prediction), which is the first machine learning model in DNA:RNA triplex prediction. TriplexFPP predicts the most likely triplex-forming lncRNAs and DNA sites based on the experimentally verified data, where the high-level features are learned by the convolutional neural networks. In the fivefold cross validation, the average values of Area Under the ROC curves and PRC curves for removed redundancy triplex-forming lncRNA dataset with threshold 0.8 are 0.9649 and 0.9996, and these two values for triplex DNA sites prediction are 0.8705 and 0.9671, respectively. Besides, we also briefly summarize the cis and trans targeting of triplexes lncRNAs. Conclusions The TriplexFPP is able to predict the most likely triplex-forming lncRNAs from all the lncRNAs with computationally defined triplex forming capacities and the potential of a DNA site to become a triplex. It may provide insights to the exploration of lncRNA functions.

Deep learning based DNA:RNA triplex forming potential prediction

Background: Long non-coding RNAs (lncRNAs) can exert functions via forming triplex with DNA. The current methods in predicting the triplex formation mainly rely on mathematic statistic according to the base paring rules. However, these methods have two main limitations: i) they identify a large number of triplex-forming lncRNAs, but the limited number of experimentally verified triplex-forming lncRNA indicates that maybe not all of them can form triplex in practice, and ii) their predictions only consider the theoretical relationship while lacking the features from the experimentally verified data. Results: In this work, we develop an integrated program named TriplexFPP (Triplex Forming Potential Prediction), which is the first machine learning model in DNA:RNA triplex prediction. TriplexFPP predicts the most likely triplex-forming lncRNAs and DNA sites based on the experimentally verified data, where the high-level features are learned by the convolutional neural networks. In the 5-fold cross validation, the average values of Area Under the ROC curves and PRC curves for removed redundancy triplex-forming lncRNA dataset with threshold 0.8 are 0.9649 and 0.9996, and these two values for triplex DNA sites prediction are 0.8705 and 0.9671, respectively. Besides, we also briefly summarise the cis and trans targeting of triplexes lncRNAs. Conclusions: The TriplexFPP is able to predict the most likely triplex-forming lncRNAs from all the lncRNAs with computationally defined triplex forming capacities and the potential of a DNA site to become a triplex. It may provide insights to the exploration of lncRNA functions.

Deep learning based DNA:RNA triplex forming potential prediction

Background: Long non-coding RNAs (lncRNAs) can exert functions via forming triplex with DNA. The current methods in predicting the triplex formation mainly rely on mathematic statistic according to the base paring rules. However, these methods have two main limitations: i) they identify a large number of triplex-forming lncRNAs, but the limited number of experimentally verified triplex-forming lncRNA indicates that maybe not all of them can form triplex in practice, and ii) their predictions only consider the theoretical relationship while lacking the features from the experimentally verified data.Results: In this work, we develop an integrated program named TriplexFPP (Triplex Forming Potential Prediction), which is the first machine learning model in DNA:RNA triplex prediction. TriplexFPP predicts the most likely triplex-forming lncRNAs and DNA sites based on the experimentally verified data, where the high-level features are learned by the convolutional neural networks. In the 5-fold cross validation, the average values of Area Under the ROC curves and PRC curves for removed redundancy triplex-forming lncRNA dataset with threshold 0.8 are 0.9649 and 0.9996, and these two values for triplex DNA sites prediction are 0.8705 and 0.9671, respectively. Besides, we also briefly summarize the cis and trans targeting of triplexes lncRNAs. Conclusions: The TriplexFPP is able to predict the most likely triplex-forming lncRNAs from all the lncRNAs with computationally defined triplex forming capacities and the potential of a DNA site to become a triplex. It may provide insights to the exploration of lncRNA functions.

LncRNA Hmrhl regulates expression of cancer related genes in Chronic Myelogenous Leukemia through chromatin association

ABSTRACTLong non-coding RNA has emerged as a key regulator of myriad gene functions. One such lncRNA mrhl, reported by our group, was found to be a regulator of SOX8, Wnt-signalling along with an important role in embryonic development in mouse. Recently, its human homolog, human mrhl (Hmrhl) was uncovered and study revealed its differential expression in several type of cancers, notably leukemia. In the present study, we further characterize molecular features of lncRNA Hmrhl and gain insight into its functional role in leukemia by gene silencing and transcriptome-based studies. Results indicate its high expression in CML patient samples as well as in K562 cell line. Silencing experiments suggest role of Hmrhl in cell proliferation, migration & invasion in K562 cells. RNA-seq and ChiRP-seq data analysis further revealed its association with important biological processes, including perturbed expression of crucial TFs and cancer-related genes. Among them ZIC1, PDGRFβ and TP53 were identified as regulatory targets, with high possibility of triplex formation by Hmrhl at their promoter site. In addition, we also found TAL-1 to be a potential regulator of Hmrhl expression in K562 cells. Thus, we hypothesize that Hmrhl lncRNA may play a significant role in the pathobiology of CML.

Deep learning based DNA:RNA triplex forming potential prediction

Background Long non-coding RNAs (lncRNAs) can exert functions via forming triplex with DNA. The current methods in predicting the triplex formation mainly rely on mathematic statistic according to the base paring rules. However, these methods have two main limitations: i) they identify a large number of triplex forming lncRNAs, but the limited number of experimental verified triplex forming lncRNA indicate that maybe not all of them can from triplex in practice, and ii) their prediction only consider the theoretical relationship while lacking the features from the experimentally verified data. Results In this work, we develop an integrated program named TriplexFPP (Triplex Forming Potential Prediction), which is the first machine learning model in DNA:RNA triplex prediction. TriplexFPP predicts the most likely triplex forming lncRNAs and DNA sites based on the experimentally verified data, where their high-level features are learned by the deep neural networks. In the 5-fold cross validation, its average values of Area Under the ROC curves and PRC curves for triplex forming lncRNA and DNA sites predictions are 0.9949 and 0.9999, 0.8775 and 0.9692, respectively. Besides, we also briefly summarized the cis and trans targeting of triplexes lncRNAs. Conclusions The TriplexFPP can predict the most likely triplex forming lncRNAs from all the lncRNAs with computationally defined triplex forming capacities, and predict the potential of a DNA site to become a triplex. It may provide insights to the exploration of lncRNA functions.