Gene and metabolite expression dependence on body mass index in human myocardium.

We hypothesized that body mass index (BMI) dependent changes in myocardial gene expression and energy-related metabolites underlie the biphasic association between BMI and mortality (the obesity paradox) in cardiac surgery. We performed transcriptome profiling and measured a panel of 144 metabolites in 53 and 55, respectively, myocardial biopsies from a cohort of sixty-six adult patients undergoing coronary artery bypass grafting (registration: NCT02908009). The initial analysis identified 239 transcripts with biphasic BMI dependence. 120 displayed u-shape and 119 n-shape expression patterns. The identified local minima or maxima peaked at BMI 28-29. Based on these results and to best fit the WHO classification, we grouped the patients into three groups: BMI < 25, 25 ≤ BMI ≤ 32, and BMI > 32. The analysis indicated that protein translation-related pathways were downregulated in 25 ≤ BMI ≤ 32 compared with BMI < 25 patients. Muscle contraction transcripts were upregulated in 25 ≤ BMI ≤ 32 patients, and cholesterol synthesis and innate immunity transcripts were upregulated in the BMI > 32 group. Transcripts involved in translation, muscle contraction and lipid metabolism also formed distinct correlation networks with biphasic dependence on BMI. Metabolite analysis identified acylcarnitines and ribose-5-phosphate increasing in the BMI > 32 group and α-ketoglutarate increasing in the BMI < 25 group. Molecular differences in the myocardium mirror the biphasic relationship between BMI and mortality.

scAPAdb: a comprehensive database of alternative polyadenylation at single-cell resolution.

Alternative polyadenylation (APA) is a widespread regulatory mechanism of transcript diversification in eukaryotes, which is increasingly recognized as an important layer for eukaryotic gene expression. Recent studies based on single-cell RNA-seq (scRNA-seq) have revealed cell-to-cell heterogeneity in APA usage and APA dynamics across different cell types in various tissues, biological processes and diseases. However, currently available APA databases were all collected from bulk 3'-seq and/or RNA-seq data, and no existing database has provided APA information at single-cell resolution. Here, we present a user-friendly database called scAPAdb (http://www.bmibig.cn/scAPAdb), which provides a comprehensive and manually curated atlas of poly(A) sites, APA events and poly(A) signals at the single-cell level. Currently, scAPAdb collects APA information from > 360 scRNA-seq experiments, covering six species including human, mouse and several other plant species. scAPAdb also provides batch download of data, and users can query the database through a variety of keywords such as gene identifier, gene function and accession number. scAPAdb would be a valuable and extendable resource for the study of cell-to-cell heterogeneity in APA isoform usages and APA-mediated gene regulation at the single-cell level under diverse cell types, tissues and species.

tsRFun: a comprehensive platform for decoding human tsRNA expression, functions and prognostic value by high-throughput small RNA-Seq and CLIP-Seq data.

tRNA-derived small RNA (tsRNA), a novel type of regulatory small noncoding RNA, plays an important role in physiological and pathological processes. However, the understanding of the functional mechanism of tsRNAs in cells and their role in the occurrence and development of diseases is limited. Here, we integrated multiomics data such as transcriptome, epitranscriptome, and targetome data, and developed novel computer tools to establish tsRFun, a comprehensive platform to facilitate tsRNA research (http://rna.sysu.edu.cn/tsRFun/ or http://biomed.nscc-gz.cn/DB/tsRFun/). tsRFun evaluated tsRNA expression profiles and the prognostic value of tsRNAs across 32 types of cancers, identified tsRNA target molecules utilizing high-throughput CLASH/CLEAR or CLIP sequencing data, and constructed the interaction networks among tsRNAs, microRNAs, and mRNAs. In addition to its data presentation capabilities, tsRFun offers multiple real-time online tools for tsRNA identification, target prediction, and functional enrichment analysis. In summary, tsRFun provides a valuable data resource and multiple analysis tools for tsRNA investigation.

exoRBase 2.0: an atlas of mRNA, lncRNA and circRNA in extracellular vesicles from human biofluids.

Extracellular vesicles (EVs) are small membranous vesicles that contain an abundant cargo of different RNA species with specialized functions and clinical implications. Here, we introduce an updated online database (http://www.exoRBase.org), exoRBase 2.0, which is a repository of EV long RNAs (termed exLRs) derived from RNA-seq data analyses of diverse human body fluids. In exoRBase 2.0, the number of exLRs has increased to 19 643 messenger RNAs (mRNAs), 15 645 long non-coding RNAs (lncRNAs) and 79 084 circular RNAs (circRNAs) obtained from ∼1000 human blood, urine, cerebrospinal fluid (CSF) and bile samples. Importantly, exoRBase 2.0 not only integrates and compares exLR expression profiles but also visualizes the pathway-level functional changes and the heterogeneity of origins of circulating EVs in the context of different physiological and pathological conditions. Our database provides an attractive platform for the identification of novel exLR signatures from human biofluids that will aid in the discovery of new circulating biomarkers to improve disease diagnosis and therapy.

scAPAatlas: an atlas of alternative polyadenylation across cell types in human and mouse.

Alternative polyadenylation (APA) has been widely recognized as a crucial step during the post-transcriptional regulation of eukaryotic genes. Recent studies have demonstrated that APA exerts key regulatory roles in many biological processes and often occurs in a tissue- and cell-type-specific manner. However, to our knowledge, there is no database incorporating information about APA at the cell-type level. Single-cell RNA-seq is a rapidly evolving and powerful tool that enable APA analysis at the cell-type level. Here, we present a comprehensive resource, scAPAatlas (http://www.bioailab.com:3838/scAPAatlas), for exploring APA across different cell types, and interpreting potential biological functions. Based on the curated scRNA-seq data from 24 human and 25 mouse normal tissues, we systematically identified cell-type-specific APA events for different cell types and examined the correlations between APA and gene expression level. We also estimated the crosstalk between cell-type-specific APA events and microRNAs or RNA-binding proteins. A user-friendly web interface has been constructed to support browsing, searching and visualizing multi-layer information of cell-type-specific APA events. Overall, scAPAatlas, incorporating a rich resource for exploration of APA at the cell-type level, will greatly help researchers chart cell type with APA and elucidate the biological functions of APA.

Identification of Potential Long Non-Coding RNA Candidates that Contribute to Triple-Negative Breast Cancer in Humans through Computational Approach.

Breast cancer (BC) is the most frequent malignancy identified in adult females, resulting in enormous financial losses worldwide. Owing to the heterogeneity as well as various molecular subtypes, the molecular pathways underlying carcinogenesis in various forms of BC are distinct. Therefore, the advancement of alternative therapy is required to combat the ailment. Recent analyses propose that long non-coding RNAs (lncRNAs) perform an essential function in controlling immune response, and therefore, may provide essential information about the disorder. However, their function in patients with triple-negative BC (TNBC) has not been explored in detail. Here, we analyzed the changes in the genomic expression of messenger RNA (mRNA) and lncRNA in standard control in response to cancer metastasis using publicly available single-cell RNA-Seq data. We identified a total of 197 potentially novel lncRNAs in TNBC patients of which 86 were differentially upregulated and 111 were differentially downregulated. In addition, among the 909 candidate lncRNA transcripts, 19 were significantly differentially expressed (DE) of which three were upregulated and 16 were downregulated. On the other hand, 1901 mRNA transcripts were significantly DE of which 1110 were upregulated and 791 were downregulated by TNBCs subtypes. The Gene Ontology (GO) analyses showed that some of the host genes were enriched in various biological, molecular, and cellular functions. The Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis showed that some of the genes were involved in only one pathway of prostate cancer. The lncRNA-miRNA-gene network analysis showed that the lncRNAs TCONS_00076394 and TCONS_00051377 interacted with breast cancer-related micro RNAs (miRNAs) and the host genes of these lncRNAs were also functionally related to breast cancer. Thus, this study provides novel lncRNAs as potential biomarkers for the therapeutic intervention of this cancer subtype.

RNA sequencing and immunofluorescence of the myotendinous junction of mature horses and humans.

The myotendinous junction (MTJ) is a specialized interface for transmitting high forces between the muscle and tendon and yet the MTJ is a common site of strain injury with a high recurrence rate. The aim of this study was to identify previously unknown MTJ components in mature animals and humans. Samples were obtained from the superficial digital flexor (SDF) muscle-tendon interface of 20 horses, and the tissue was separated through a sequential cryosectioning approach into muscle, MTJ (muscle tissue enriched in myofiber tips attached to the tendon), and tendon fractions. RT-PCR was performed for genes known to be expressed in the three tissue fractions and t-distributed stochastic neighbor embedding (t-SNE) plots were used to select the muscle, MTJ, and tendon samples from five horses for RNA sequencing. The expression of previously known and unknown genes identified through RNA sequencing was studied by immunofluorescence on human hamstring MTJ tissue. The main finding was that RNA sequencing identified the expression of a panel of 61 genes enriched at the MTJ. Of these, 48 genes were novel for the MTJ and 13 genes had been reported to be associated with the MTJ in earlier studies. The expression of known [COL22A1 (collagen XXII), NCAM (neural cell adhesion molecule), POSTN (periostin), NES (nestin), OSTN (musclin/osteocrin)] and previously undescribed [MNS1 (meiosis-specific nuclear structural protein 1), and LCT (lactase)] MTJ genes was confirmed at the protein level by immunofluorescence on tissue sections of human MTJ. In conclusion, in muscle-tendon interface tissue enriched with myofiber tips, we identified the expression of previously unknown MTJ genes representing diverse biological processes, which may be important in the maintenance of the specialized MTJ.

Essential shared and species-specific features of mammalian oocyte maturation-associated transcriptome changes impacting oocyte physiology.

Oogenesis is a complex process resulting in the production of a truly remarkable cell-the oocyte. Oocytes execute many unique processes and functions such as meiotic segregation of maternal genetic material, and essential life-generating functions after fertilization including posttranscriptional support of essential homeostatic and metabolic processes, and activation and reprogramming of the embryonic genome. An essential goal for understanding female fertility and infertility in mammals is to discover critical features driving the production of quality oocytes, particularly the complex regulation of oocyte maternal mRNAs. We report here the first in-depth meta-analysis of oocyte maturation-associated transcriptome changes, using eight datasets encompassing 94 RNAseq libraries for human, rhesus monkey, mouse, and cow. A majority of maternal mRNAs are regulated in a species-restricted manner, highlighting considerable divergence in oocyte transcriptome handling during maturation. We identified 121 mRNAs changing in relative abundance similarly across all four species (92 of high homology), and 993 (670 high homology) mRNAs regulated similarly in at least three of the four species, corresponding to just 0.84% and 6.9% of mRNAs analyzed. Ingenuity Pathway Analysis (IPA) revealed an association of these shared mRNAs with many shared pathways and functions, most prominently oxidative phosphorylation and mitochondrial function. These shared functions were reinforced further by primate-specific and species-specific differentially expressed genes (DEGs). Thus, correct downregulation of mRNAs related to oxidative phosphorylation and mitochondrial function is a major shared feature of mammalian oocyte maturation.

Feature extraction approaches for biological sequences: a comparative study of mathematical features.

As consequence of the various genomic sequencing projects, an increasing volume of biological sequence data is being produced. Although machine learning algorithms have been successfully applied to a large number of genomic sequence-related problems, the results are largely affected by the type and number of features extracted. This effect has motivated new algorithms and pipeline proposals, mainly involving feature extraction problems, in which extracting significant discriminatory information from a biological set is challenging. Considering this, our work proposes a new study of feature extraction approaches based on mathematical features (numerical mapping with Fourier, entropy and complex networks). As a case study, we analyze long non-coding RNA sequences. Moreover, we separated this work into three studies. First, we assessed our proposal with the most addressed problem in our review, e.g. lncRNA and mRNA; second, we also validate the mathematical features in different classification problems, to predict the class of lncRNA, e.g. circular RNAs sequences; third, we analyze its robustness in scenarios with imbalanced data. The experimental results demonstrated three main contributions: first, an in-depth study of several mathematical features; second, a new feature extraction pipeline; and third, its high performance and robustness for distinct RNA sequence classification. Availability:https://github.com/Bonidia/FeatureExtraction_BiologicalSequences.

Synthetase of the methyl donor S-adenosylmethionine from nitrogen-fixing α-rhizobia can bind functionally diverse RNA species.

Function of bacterial small non-coding RNAs (sRNAs) and overall RNA metabolism is largely shaped by a vast diversity of RNA-protein interactions. However, in non-model bacteria with defined non-coding transcriptomes the sRNA interactome remains almost unexplored. We used affinity chromatography to capture proteins associated in vivo with MS2-tagged trans-sRNAs that regulate nutrient uptake (AbcR2 and NfeR1) and cell cycle (EcpR1) mRNAs by antisense-based translational inhibition in the nitrogen-fixing α-rhizobia Sinorhizobium meliloti. The three proteomes were rather distinct, with that of EcpR1 particularly enriched in cell cycle-related enzymes, whilst sharing several transcription/translation-related proteins recurrently identified associated with sRNAs. Strikingly, MetK, the synthetase of the major methyl donor S-adenosylmethionine, was reliably recovered as a binding partner of the three sRNAs, which reciprocally co-immunoprecipitated with a FLAG-tagged MetK variant. Induced (over)expression of the trans-sRNAs and MetK depletion did not influence canonical riboregulatory traits, `for example, protein titration or sRNA stability, respectively. An in vitro filter assay confirmed binding of AbcR2, NfeR1 and EcpR1 to MetK and further revealed interaction of the protein with other non-coding and coding transcripts but not with the 5S rRNA. These findings uncover a broad specificity for RNA binding as an unprecedented feature of this housekeeping prokaryotic enzyme.

PAR-TERRA is the main contributor to telomeric repeat-containing RNA transcripts in normal and cancer mouse cells.

Telomeric repeat-containing RNA (TERRA) molecules play important roles at telomeres, from heterochromatin regulation to telomerase activity control. In human cells, TERRA is transcribed from subtelomeric promoters located on most chromosome ends and associates with telomeres. The origin of mouse TERRA molecules is, however, unclear, as transcription from the pseudoautosomal PAR locus was recently suggested to account for the vast majority of TERRA in embryonic stem cells (ESC). Here, we confirm the production of TERRA from both the chromosome 18q telomere and the PAR locus in mouse embryonic fibroblasts, ESC, and various mouse cancer and immortalized cell lines, and we identify two novel sources of TERRA on mouse chromosome 2 and X. Using various approaches, we show that PAR-TERRA molecules account for the majority of TERRA transcripts, displaying an increase of two to four orders of magnitude compared to the telomeric 18q transcript. Finally, we present a SILAC-based pull-down screen revealing a large overlap between TERRA-interacting proteins in human and mouse cells, including PRC2 complex subunits, chromatin remodeling factors, DNA replication proteins, Aurora kinases, shelterin complex subunits, Bloom helicase, Coilin, and paraspeckle proteins. Hence, despite originating from distinct genomic regions, mouse and human TERRA are likely to play similar functions in cells.

PresRAT: a server for identification of bacterial small-RNA sequences and their targets with probable binding region.

Bacterial small-RNA (sRNA) sequences are functional RNAs, which play an important role in regulating the expression of a diverse class of genes. It is thus critical to identify such sRNA sequences and their probable mRNA targets. Here, we discuss new procedures to identify and characterize sRNA and their targets via the introduction of an integrated online platform 'PresRAT'. PresRAT uses the primary and secondary structural attributes of sRNA sequences to predict sRNA from a given sequence or bacterial genome. PresRAT also finds probable target mRNAs of sRNA sequences from a given bacterial chromosome and further concentrates on the identification of the probable sRNA-mRNA binding regions. Using PresRAT, we have identified a total of 66,209 potential sRNA sequences from 292 bacterial genomes and 2247 potential targets from 13 bacterial genomes. We have also implemented a protocol to build and refine 3D models of sRNA and sRNA-mRNA duplex regions and generated 3D models of 50 known sRNAs and 81 sRNA-mRNA duplexes using this platform. Along with the server part, PresRAT also contains a database section, which enlists the predicted sRNA sequences, sRNA targets, and their corresponding 3D models with structural dynamics information.

Rpb4 and Puf3 imprint and post-transcriptionally control the stability of a common set of mRNAs in yeast.

Gene expression involving RNA polymerase II is regulated by the concerted interplay between mRNA synthesis and degradation, crosstalk in which mRNA decay machinery and transcription machinery respectively impact transcription and mRNA stability. Rpb4, and likely dimer Rpb4/7, seem the central components of the RNA pol II governing these processes. In this work we unravel the molecular mechanisms participated by Rpb4 that mediate the posttranscriptional events regulating mRNA imprinting and stability. By RIP-Seq, we analysed genome-wide the association of Rpb4 with mRNAs and demonstrated that it targeted a large population of more than 1400 transcripts. A group of these mRNAs was also the target of the RNA binding protein, Puf3. We demonstrated that Rpb4 and Puf3 physically, genetically, and functionally interact and also affect mRNA stability, and likely the imprinting, of a common group of mRNAs. Furthermore, the Rpb4 and Puf3 association with mRNAs depends on one another. We also demonstrated, for the first time, that Puf3 associates with chromatin in an Rpb4-dependent manner. Our data also suggest that Rpb4 could be a key element of the RNA pol II that coordinates mRNA synthesis, imprinting and stability in cooperation with RBPs.

Identification of Dominant Transcripts in Oxidative Stress Response by a Full-Length Transcriptome Analysis.

Our body responds to environmental stress by changing the expression levels of a series of cytoprotective enzymes/proteins through multilayered regulatory mechanisms, including the KEAP1-NRF2 system. While NRF2 upregulates the expression of many cytoprotective genes, there are fundamental limitations in short-read RNA sequencing (RNA-Seq), resulting in confusion regarding interpreting the effectiveness of cytoprotective gene induction at the transcript level. To precisely delineate isoform usage in the stress response, we conducted independent full-length transcriptome profiling (isoform sequencing; Iso-Seq) analyses of lymphoblastoid cells from three volunteers under normal and electrophilic stress-induced conditions. We first determined the first exon usage in KEAP1 and NFE2L2 (encoding NRF2) and found the presence of transcript diversity. We then examined changes in isoform usage of NRF2 target genes under stress conditions and identified a few isoforms dominantly expressed in the majority of NRF2 target genes. The expression levels of isoforms determined by Iso-Seq analyses showed striking differences from those determined by short-read RNA-Seq; the latter could be misleading concerning the abundance of transcripts. These results support that transcript usage is tightly regulated to produce functional proteins under electrophilic stress. Our present study strongly argues that there are important benefits that can be achieved by long-read transcriptome sequencing.

NONCODEV6: an updated database dedicated to long non-coding RNA annotation in both animals and plants.

NONCODE (http://www.noncode.org/) is a comprehensive database of collection and annotation of noncoding RNAs, especially long non-coding RNAs (lncRNAs) in animals. NONCODEV6 is dedicated to providing the full scope of lncRNAs across plants and animals. The number of lncRNAs in NONCODEV6 has increased from 548 640 to 644 510 since the last update in 2017. The number of human lncRNAs has increased from 172 216 to 173 112. The number of mouse lncRNAs increased from 131 697 to 131 974. The number of plant lncRNAs is 94 697. The relationship between lncRNAs in human and cancer were updated with transcriptome sequencing profiles. Three important new features were also introduced in NONCODEV6: (i) updated human lncRNA-disease relationships, especially cancer; (ii) lncRNA annotations with tissue expression profiles and predicted function in five common plants; iii) lncRNAs conservation annotation at transcript level for 23 plant species. NONCODEV6 is accessible through http://www.noncode.org/.

A systematic evaluation of bioinformatics tools for identification of long noncoding RNAs.

High-throughput RNA sequencing unveiled the complexity of transcriptome and significantly increased the records of long noncoding RNAs (lncRNAs), which were reported to participate in a variety of biological processes. Identification of lncRNAs is a key step in lncRNA analysis, and a bunch of bioinformatics tools have been developed for this purpose in recent years. While these tools allow us to identify lncRNA more efficiently and accurately, they may produce inconsistent results, making selection a confusing issue. We compared the performance of 41 analysis models based on 14 software packages and different data sets, including high-quality data and low-quality data from 33 species. In addition, computational efficiency, robustness, and joint prediction of the models were explored. As a practical guidance, key points for lncRNA identification under different situations were summarized. In this investigation, no one of these models could be superior to others under all test conditions. The performance of a model relied to a great extent on the source of transcripts and the quality of assemblies. As general references, FEELnc_all_cl, CPC, and CPAT_mouse work well in most species while COME, CNCI, and lncScore are good choices for model organisms. Since these tools are sensitive to different factors such as the species involved and the quality of assembly, researchers must carefully select the appropriate tool based on the actual data. Alternatively, our test suggests that joint prediction could behave better than any single model if proper models were chosen. All scripts/data used in this research can be accessed at http://bioinfo.ihb.ac.cn/elit.

cncRNAdb: a manually curated resource of experimentally supported RNAs with both protein-coding and noncoding function.

RNA endowed with both protein-coding and noncoding functions is referred to as 'dual-function RNA', 'binary functional RNA (bifunctional RNA)' or 'cncRNA (coding and noncoding RNA)'. Recently, an increasing number of cncRNAs have been identified, including both translated ncRNAs (ncRNAs with coding functions) and untranslated mRNAs (mRNAs with noncoding functions). However, an appropriate database for storing and organizing cncRNAs is still lacking. Here, we developed cncRNAdb, a manually curated database of experimentally supported cncRNAs, which aims to provide a resource for efficient manipulation, browsing and analysis of cncRNAs. The current version of cncRNAdb documents about 2600 manually curated entries of cncRNA functions with experimental evidence, involving more than 2,000 RNAs (including over 1300 translated ncRNAs and over 600 untranslated mRNAs) across over 20 species. In summary, we believe that cncRNAdb will help elucidate the functions and mechanisms of cncRNAs and develop new prediction methods. The database is available at http://www.rna-society.org/cncrnadb/.

Development of an immunogenomic landscape for the competing endogenous RNAs network of peri-implantitis.

BACKGROUND: Peri-implantitis is an inflammation that occurs around the implant, resulting in varying degrees of inflammatory damage to the soft and hard tissues. The characteristic criterion is the loss of the supporting bone in an inflammatory environment. However, the specific mechanisms and biomarkers involved in peri-implantitis remain to be further studied. Recently, competing endogenous RNAs (ceRNA) and immune microenvironment have been found to play a more important role in the inflammatory process. In our study, we analyzed the expression of immune related microRNAs (miRNAs), long noncoding RNAs (lncRNAs) and message RNAs (mRNAs) in peri-implantitis by analyzing GSE33774 and GSE57631. METHODS: In this study, we explored the expression profile data of immune-related lncRNAs, miRNAs and mRNAs, and constructed immune-related ceRNA network involved in the pathogenesis of peri-implantitis. In addition, the CIBERSORT was used to evaluate the content of immune cells in normal tissues and peri-implantitis to detect the immune microenvironment of peri-implantitis. RESULTS: In the analysis, 14 DElncRNAs, 16 DEmiRNAs, and 18 DEmRNAs were used to establish an immune related ceRNA network and the immune infiltration patterns associated with peri-implantitis was discovered. Through the mutual verification of the two datasets, we found that GSK3B and miR-1297 may have important significance in the immune microenvironment and pathogenesis of peri-implantitis and GSK3B was closely related to four types of immune cells, especially with the highest correlation with resting mast cells (P = 0.0003). CONCLUSIONS: Through immune-related ceRNA network, immune-related genes (IRGs) and immune cell infiltration can further comprehensively understand the pathogenesis of peri-implantitis, which built up an immunogenomic landscape with clinical significance for peri-implantitis.

Identification of protein-protected mRNA fragments and structured excised intron RNAs in human plasma by TGIRT-seq peak calling.

Human plasma contains > 40,000 different coding and non-coding RNAs that are potential biomarkers for human diseases. Here, we used thermostable group II intron reverse transcriptase sequencing (TGIRT-seq) combined with peak calling to simultaneously profile all RNA biotypes in apheresis-prepared human plasma pooled from healthy individuals. Extending previous TGIRT-seq analysis, we found that human plasma contains largely fragmented mRNAs from > 19,000 protein-coding genes, abundant full-length, mature tRNAs and other structured small non-coding RNAs, and less abundant tRNA fragments and mature and pre-miRNAs. Many of the mRNA fragments identified by peak calling correspond to annotated protein-binding sites and/or have stable predicted secondary structures that could afford protection from plasma nucleases. Peak calling also identified novel repeat RNAs, miRNA-sized RNAs, and putatively structured intron RNAs of potential biological, evolutionary, and biomarker significance, including a family of full-length excised intron RNAs, subsets of which correspond to mirtron pre-miRNAs or agotrons.

Predicting miRNA-disease association from heterogeneous information network with GraRep embedding model.

In recent years, accumulating evidences have shown that microRNA (miRNA) plays an important role in the exploration and treatment of diseases, so detection of the associations between miRNA and disease has been drawn more and more attentions. However, traditional experimental methods have the limitations of high cost and time- consuming, a computational method can help us more systematically and effectively predict the potential miRNA-disease associations. In this work, we proposed a novel network embedding-based heterogeneous information integration method to predict miRNA-disease associations. More specifically, a heterogeneous information network is constructed by combining the known associations among lncRNA, drug, protein, disease, and miRNA. After that, the network embedding method Learning Graph Representations with Global Structural Information (GraRep) is employed to learn embeddings of nodes in heterogeneous information network. In this way, the embedding representations of miRNA and disease are integrated with the attribute information of miRNA and disease (e.g. miRNA sequence information and disease semantic similarity) to represent miRNA-disease association pairs. Finally, the Random Forest (RF) classifier is used for predicting potential miRNA-disease associations. Under the 5-fold cross validation, our method obtained 85.11% prediction accuracy with 80.41% sensitivity at the AUC of 91.25%. In addition, in case studies of three major Human diseases, 45 (Colon Neoplasms), 42 (Breast Neoplasms) and 44 (Esophageal Neoplasms) of top-50 predicted miRNAs are respectively verified by other miRNA-disease association databases. In conclusion, the experimental results suggest that our method can be a powerful and useful tool for predicting potential miRNA-disease associations.