Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk

Background

With the development of sequencing technology, more and more long non-coding RNAs (lncRNAs) have been identified. Some lncRNAs have been confirmed that they play an important role in the process of development through the dosage compensation effect, epigenetic regulation, cell differentiation regulation and other aspects. However, the majority of the lncRNAs have not been functionally characterized. Explore the function of lncRNAs and the regulatory network has become a hot research topic currently.

Methods

In the work, a network-based model named BiRWLGO is developed. The ultimate goal is to predict the probable functions for lncRNAs at large scale. The new model starts with building a global network composed of three networks: lncRNA similarity network, lncRNA-protein association network and protein-protein interaction (PPI) network. After that, it utilizes bi-random walk algorithm to explore the similarities between lncRNAs and proteins. Finally, we can annotate an lncRNA with the Gene Ontology (GO) terms according to its neighboring proteins.

Results

We compare the performance of BiRWLGO with the state-of-the-art models on a manually annotated lncRNA benchmark with known GO terms. The experimental results assert that BiRWLGO outperforms other methods in terms of both maximum F-measure (F_max) and coverage.

Conclusions

BiRWLGO is a relatively efficient method to predict the functions of lncRNA. When protein interaction data is integrated, the predictive performance of BiRWLGO gains a great improvement.

     

CRlncRC: a machine learning-based method for cancer-related long noncoding RNA identification using integrated features

Background

Long noncoding RNAs (lncRNAs) are widely involved in the initiation and development of cancer. Although some computational methods have been proposed to identify cancer-related lncRNAs, there is still a demanding to improve the prediction accuracy and efficiency. In addition, the quick-update data of cancer, as well as the discovery of new mechanism, also underlay the possibility of improvement of cancer-related lncRNA prediction algorithm. In this study, we introduced CRlncRC, a novel Cancer-Related lncRNA Classifier by integrating manifold features with five machine-learning techniques.

Results

CRlncRC was built on the integration of genomic, expression, epigenetic and network, totally in four categories of features. Five learning techniques were exploited to develop the effective classification model including Random Forest (RF), Naïve bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR) and K-Nearest Neighbors (KNN). Using ten-fold cross-validation, we showed that RF is the best model for classifying cancer-related lncRNAs (AUC?=?0.82). The feature importance analysis indicated that epigenetic and network features play key roles in the classification. In addition, compared with other existing classifiers, CRlncRC exhibited a better performance both in sensitivity and specificity. We further applied CRlncRC to lncRNAs from the TANRIC (The Atlas of non-coding RNA in Cancer) dataset, and identified 121 cancer-related lncRNA candidates. These potential cancer-related lncRNAs showed a certain kind of cancer-related indications, and many of them could find convincing literature supports.

Conclusions

Our results indicate that CRlncRC is a powerful method for identifying cancer-related lncRNAs. Machine-learning-based integration of multiple features, especially epigenetic and network features, had a great contribution to the cancer-related lncRNA prediction. RF outperforms other learning techniques on measurement of model sensitivity and specificity. In addition, using CRlncRC method, we predicted a set of cancer-related lncRNAs, all of which displayed a strong relevance to cancer as a valuable conception for the further cancer-related lncRNA function studies.

     

Microarray expression profiling in the denervated hippocampus identifies long noncoding RNAs functionally involved in neurogenesis

Background

The denervated hippocampus provides a proper microenvironment for the survival and neuronal differentiation of neural progenitors. While thousands of lncRNAs were identified, only a few lncRNAs that regulate neurogenesis in the hippocampus are reported. The present study aimed to perform microarray expression profiling to identify long noncoding RNAs (lncRNAs) that might participate in the hippocampal neurogenesis, and investigate the potential roles of identified lncRNAs in the hippocampal neurogenesis.

Results

In this study, the profiling suggested that 74 activated and 29 repressed (|log fold-change|>1.5) lncRNAs were differentially expressed between the denervated and the normal hippocampi. Furthermore, differentially expressed lncRNAs associated with neurogenesis were found. According to the tissue-specific expression profiles, and a novel lncRNA (lncRNA2393) was identified as a neural regulator in the hippocampus in this study. The expression of lncRNA2393 was activated in the denervated hippocampus. FISH showed lncRNA2393 specially existed in the subgranular zone of the dentate gyrus in the hippocampus and in the cytoplasm of neural stem cells (NSCs). The knockdown of lncRNA2393 depletes the EdU-positive NSCs. Besides, the increased expression of lncRNA2393 was found to be triggered by the change in the microenvironment.

Conclusion

We concluded that expression changes of lncRNAs exists in the microenvironment of denervated hippocampus, of which promotes hippocampal neurogenesis. The identified lncRNA lncRNA2393 expressed in neural stem cells, located in the subgranular zone of the dentate gyrus, which can promote NSCs proliferation in vitro. Therefore, the question is exactly which part of the denervated hippocampus induced the expression of lncRNA2393. Further studies should aim to explore the exact molecular mechanism behind the expression of lncRNA2393 in the hippocampus, to lay the foundation for the clinical application of NSCs in treating diseases of the central nervous system.

     

Autophagy regulated by lncRNA HOTAIR contributes to the cisplatin-induced resistance in endometrial cancer cells

Objectives

To identify whether lncRNAs (long non-coding RNA) participate in the regulation of cisplatin-resistant induced autophagy in endometrial cancer cells.

Results

Autophagy activity was significantly boosted in cisplatin-resistant Ishikawa cells, a human endometrial cancer cell line, compared with that in parental Ishikawa cells. After analyzing the overall long noncoding RNA (lncRNA) profiling, a meaningful lncRNA, HOTAIR, was identified. It was down-regulated simultaneously in cisplatin-resistant Ishikawa cells and parental Ishikawa cells treated with cisplatin. RNA interference of HOTAIR reduced the proliferation of cisplatin-resistant Ishikawa cells and enhanced the autophagy activity of cisplatin-resistant Ishikawa cells with or without cisplatin treatment, in addition, beclin-1, multidrug resistance (MDR), and P-glycoprotein (P-gp) were mediated by lncRNA HOTAIR.

Conclusions

It is clear that lncRNAs, specifically HOTAIR, can regulate the cisplatin-resistance ability of human endometrial cancer cells through the regulation of autophagy by influencing Beclin-1, MDR, and P-gp expression.

     

Hepatitis B virus x protein induces epithelial-mesenchymal transition of hepatocellular carcinoma cells by regulating long non-coding RNA

Background

It has been widely accepted that hepatitis B virus X protein (HBx) plays an important role in hepatocellular carcinoma (HCC). This study aimed to explore the function of long non-coding RNAs (lncRNAs) in the epithelial-mesenchymal transition (EMT) induced by HBx.

Methods

The association between HBx and EMT markers was detected using immunohistochemistry in HCC tissues. The effect of HBx on HCC EMT was assessed through morphological analysis, transwell assay, metastatic in vivo study and detection of EMT markers. LncRNA microarray was used to screen the differently expressed lncRNAs. Small interfering RNA and Western blot were used to analyse the function and mechanism of the locked lncRNA.

Results

HBx was negatively correlated with the epithelial marker E-cadherin but positively correlated with the mesenchymal marker vimentin in HCC tissues. HBx induced the mesenchymal phenotype and improved the metastatic ability of HCC cells. Meanwhile, HBx down-regulated E-cadherin, whereas it up-regulated vimentin. In HCC cells, HBx altered the expression of 2002 lncRNAs by more than 2-fold. One of them was ZEB2-AS1. Inhibition of ZEB2-AS1 can compensate for the EMT phenotype and reverse the expression of EMT markers regulated by HBx. Additionally, HBx affected the Wnt signalling pathway.

Conclusions

HBx promotes HCC cell metastasis by inducing EMT, which is at least partly mediated by lncRNAs.

     

Novel link prediction for large-scale miRNA-lncRNA interaction network in a bipartite graph

Background

Current knowledge and data on miRNA-lncRNA interactions is still limited and little effort has been made to predict target lncRNAs of miRNAs. Accumulating evidences suggest that the interaction patterns between lncRNAs and miRNAs are closely related to relative expression level, forming a titration mechanism. It could provide an effective approach for characteristic feature extraction. In addition, using the coding non-coding co-expression network and sequence data could also help to measure the similarities among miRNAs and lncRNAs. By mathematically analyzing these types of similarities, we come up with two findings that (i) lncRNAs/miRNAs tend to collaboratively interact with miRNAs/lncRNAs of similar expression profiles, and vice versa, and (ii) those miRNAs interacting with a cluster of common target genes tend to jointly target at the common lncRNAs.

Methods

In this work, we developed a novel group preference Bayesian collaborative filtering model called GBCF for picking up a top-k probability ranking list for an individual miRNA or lncRNA based on the known miRNA-lncRNA interaction network.

Results

To evaluate the effectiveness of GBCF, leave-one-out and k-fold cross validations as well as a series of comparison experiments were carried out. GBCF achieved the values of area under ROC curve of 0.9193, 0.8354+/??0.0079, 0.8615+/??0.0078, and 0.8928+/??0.0082 based on leave-one-out, 2-fold, 5-fold, and 10-fold cross validations respectively, demonstrating its reliability and robustness.

Conclusions

GBCF could be used to select potential lncRNA targets of specific miRNAs and offer great insights for further researches on ceRNA regulation network.

     

Measures of co-expression for improved function prediction of long non-coding RNAs

Background

Almost 16,000 human long non-coding RNA (lncRNA) genes have been identified in the GENCODE project. However, the function of most of them remains to be discovered. The function of lncRNAs and other novel genes can be predicted by identifying significantly enriched annotation terms in already annotated genes that are co-expressed with the lncRNAs. However, such approaches are sensitive to the methods that are used to estimate the level of co-expression.

Results

We have tested and compared two well-known statistical metrics (Pearson and Spearman) and two geometrical metrics (Sobolev and Fisher) for identification of the co-expressed genes, using experimental expression data across 19 normal human tissues. We have also used a benchmarking approach based on semantic similarity to evaluate how well these methods are able to predict annotation terms, using a well-annotated set of protein-coding genes.

Conclusion

This work shows that geometrical metrics, in particular in combination with the statistical metrics, will predict annotation terms more efficiently than traditional approaches. Tests on selected lncRNAs confirm that it is possible to predict the function of these genes given a reliable set of expression data. The software used for this investigation is freely available.

     

The lncRNA MEG3 downregulation leads to osteoarthritis progression via miR-16/SMAD7 axis

Background

Osteoarthritis (OA) is a chronic joint disease and there is no a definitive cure at present. Long non-coding RNAs (lncRNAs) have been confirmed to play important roles in the development of OA. However, the underlying mechanism of lncRNA maternally expressed gene 3 (MEG3) in OA has not been well elucidated.

Methods

The rat OA model and interleukin-1β (IL-1β)-induced rat chondrocytes were constructed. The expression pattern of lncRNA MEG3 and miR-16 was detected by RT-qPCR assay in cartilage tissues of rat OA model. The effect of MEG3 and miR-16 on IL-1β-induced chondrocytes was evaluated on the basis of cell viability and apoptosis. Then, the interaction among MEG3, miR-16 SMAD7 was explored by dual-luciferase reporter assay and RIP assay.

Results

It is found that lncRNA MEG3 was down-regulated and miR-16 was up-regulated in rat OA cartilage tissues. MEG3 knockdown promoted proliferation and inhibited apoptosis, while miR-16 knockdown suppressed proliferation and promoted apoptosis in IL-1β-induced rat chondrocytes. Moreover, MEG3 was involved in miR-16 pathway and MEG3 suppressed miR-16 expression. Additionally, SMAD7 was a target gene of miR-16 and miR-16 suppressed SMAD7 expression in IL-1β-induced chondrocytes. Moreover, the expression of SMAD7 induced by MEG3 or si-MEG3 was markedly reversed by the introduction of miR-16 or anti-miR-16. Furthermore, MEG3 exerted its anti-proliferation and pro-apoptosis by regulating miR-16 and SMAD7.

Conclusion

MEG3 was down-regulated and miR-16 was up-regulated in cartilage tissues of rat OA model. MEG3 knockdown might lead to the progression of OA through miR-16/SMAD7 axis.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

Laplacian normalization and bi-random walks on heterogeneous networks for predicting lncRNA-disease associations

Background

Evidences have increasingly indicated that lncRNAs (long non-coding RNAs) are deeply involved in important biological regulation processes leading to various human complex diseases. Experimental investigations of these disease associated lncRNAs are slow with high costs. Computational methods to infer potential associations between lncRNAs and diseases have become an effective prior-pinpointing approach to the experimental verification.

Results

In this study, we develop a novel method for the prediction of lncRNA-disease associations using bi-random walks on a network merging the similarities of lncRNAs and diseases. Particularly, this method applies a Laplacian technique to normalize the lncRNA similarity matrix and the disease similarity matrix before the construction of the lncRNA similarity network and disease similarity network. The two networks are then connected via existing lncRNA-disease associations. After that, bi-random walks are applied on the heterogeneous network to predict the potential associations between the lncRNAs and the diseases. Experimental results demonstrate that the performance of our method is highly comparable to or better than the state-of-the-art methods for predicting lncRNA-disease associations. Our analyses on three cancer data sets (breast cancer, lung cancer, and liver cancer) also indicate the usefulness of our method in practical applications.

Conclusions

Our proposed method, including the construction of the lncRNA similarity network and disease similarity network and the bi-random walks algorithm on the heterogeneous network, could be used for prediction of potential associations between the lncRNAs and the diseases.

     

Microarray analysis reveals a potential role of LncRNAs expression in cardiac cell proliferation

Background

Long non-coding RNAs (LncRNAs) have been identified to play important roles in epigenetic processes that underpin organogenesis. However, the role of LncRNAs in the regulation of transition from fetal to adult life of human heart has not been evaluated.

Methods

Immunofiuorescent staining was used to determine the extent of cardiac cell proliferation. Human LncRNA microarrays were applied to define gene expression signatures of the fetal (13–17 weeks of gestation, n?=?4) and adult hearts (30–40 years old, n?=?4). Pathway analysis was performed to predict the function of differentially expressed mRNAs (DEM). DEM related to cell proliferation were selected to construct a lncRNA-mRNA co-expression network. Eight lncRNAs were confirmed by quantificational real-time polymerase chain reaction (n?=?6).

Results

Cardiac cell proliferation was significant in the fetal heart. Two thousand six hundred six lncRNAs and 3079 mRNAs were found to be differentially expressed. Cell cycle was the most enriched pathway in down-regulated genes in the adult heart. Eight lncRNAs (RP11-119 F7.5, AX747860, HBBP1, LINC00304, TPTE2P6, AC034193.5, XLOC_006934 and AL833346) were predicted to play a central role in cardiac cell proliferation.

Conclusions

We discovered a profile of lncRNAs differentially expressed between the human fetal and adult heart. Several meaningful lncRNAs involved in cardiac cell proliferation were disclosed.

     

Identifying sequence features that drive ribosomal association for lncRNA

Background

With the increasing number of annotated long noncoding RNAs (lncRNAs) from the genome, researchers are continually updating their understanding of lncRNAs. Recently, thousands of lncRNAs have been reported to be associated with ribosomes in mammals. However, their biological functions or mechanisms are still unclear.

Results

In this study, we tried to investigate the sequence features involved in the ribosomal association of lncRNA. We have extracted ninety-nine sequence features corresponding to different biological mechanisms (i.e., RNA splicing, putative ORF, k-mer frequency, RNA modification, RNA secondary structure, and repeat element). An \(\mathcal {L}1\)-regularized logistic regression model was applied to screen these features. Finally, we obtained fifteen and nine important features for the ribosomal association of human and mouse lncRNAs, respectively.

Conclusion

To our knowledge, this is the first study to characterize ribosome-associated lncRNAs and ribosome-free lncRNAs from the perspective of sequence features. These sequence features that were identified in this study may shed light on the biological mechanism of the ribosomal association and provide important clues for functional analysis of lncRNAs.

     

The diagnostic value and pathogenetic role of lncRNA-ATB in patients with osteoarthritis

Background

In view of the roles of long non-coding RNAs (lncRNAs) in human diseases and the high incidence of osteoarthritis, we investigated the role of long non-coding RNA activated by transforming growth factor-β (lncRNA-ATB) in osteoarthritis and explored its diagnostic value for this disease.

Methods

The study involved 98 patients with osteoarthritis and 76 healthy subjects. Blood was extracted from each participant and the expression of lncRNA-ATB in the serum was detected using quantitative Real Time -PCR. ROC curve analysis was performed to evaluate the diagnostic value of lncRNA-ATB for osteoarthritis. Based on the median serum level of lncRNA-ATB, patients were divided into a high-level group and a low-level group. Correlations between the serum levels of lncRNA-ATB and basic information about the patients were analyzed using the chi-square test. LncRNA-ATB overexpression in human chondrocyte cell line CHON-001 (ATCC CRL-2846) was established to study the effects on chondrocyte proliferation (using the CCK-8 assay) and viability.

Results

LncRNA-ATB expression was significantly downregulated in the serum of osteoarthritis patients compared with the healthy controls, meaning this downregulation effectively distinguished osteoarthritis patients from healthy subjects. LncRNA-ATB expression in the serum was not significantly affected by the patients’ gender, age or habits, including smoking and alcohol consumption. LncRNA-ATB overexpression activated Akt signaling, promoted proliferation and increased the viability of the chondrocytes.

Conclusion

We conclude that downregulation of lncRNA-ATB in serum is a reliable diagnostic marker for osteoarthritis and that this lncRNA participates in the pathogenesis of osteoarthritis by regulating the proliferation and viability of chondrocytes through the activation of Akt signaling.