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Objective. Inflammatory responses play important roles in the pathogenesis of atherosclerosis. The purpose of this study was to investigate the relationship between microRNA-146b-3p (miR-146b-3p) and inflammatory factors in thrombosis. Method. THP-1 cells were cultured in vitro, Western blot was used to determine the protein levels of COX-2 and p38MAPK in the cells, and real-time PCR was used to detect the mRNA expression of miRNA-146b-3p and COX-2. A lentiviral expression vector of miRNA-146b-3p and its inhibitor were constructed to transfect THP-1 cells. COX-2 and p38MAPK expression in transfected cells was detected by Western blot and real-time PCR, respectively. Results. Ang II and TNF-α could elevate the expression of COX-2 in monocytes. The expression of COX-2 was upregulated by p38MAPK, which could be phosphorylated by Ang II, while there was an increasing tendency of p38MAPK phosphorylation after TNF-α stimulation. In addition, COX-2 expression and P38MAPK phosphorylation could be downregulated by miRNA-146b-3p and upregulated by the miRNA-146b-3p inhibitor. Ang II could increase miR-146b-3p expression, although there was no significant difference; however, the expression of miR-146b-3p was enhanced significantly by TNF-α. Conclusion. Our data implied that altered expression of miR-146b-3p was closely related to the progression of inflammation mediating the P38MAPK/COX-2 pathway. We suggest that the miR-146b-3p/p38MAPK/COX-2 pathway plays a key role in inflammation and arterial thrombosis. [ABSTRACT FROM AUTHOR]

Background: MicroRNAs (miRNAs) are a kind of small noncoding RNA molecules that are direct posttranscriptional regulations of mRNA targets. Studies have indicated that miRNAs play key roles in complex diseases by taking part in many biological processes, such as cell growth, cell death and so on. Therefore, in order to improve the effectiveness of disease diagnosis and treatment, it is appealing to develop advanced computational methods for predicting the essentiality of miRNAs. Result: In this study, we propose a method (PESM) to predict the miRNA essentiality based on gradient boosting machines and miRNA sequences. First, PESM extracts the sequence and structural features of miRNAs. Then it uses gradient boosting machines to predict the essentiality of miRNAs. We conduct the 5-fold cross-validation to assess the prediction performance of our method. The area under the receiver operating characteristic curve (AUC), F-measure and accuracy (ACC) are used as the metrics to evaluate the prediction performance. We also compare PESM with other three competing methods which include miES, Gaussian Naive Bayes and Support Vector Machine. Conclusion: The results of experiments show that PESM achieves the better prediction performance (AUC: 0.9117, F-measure: 0.8572, ACC: 0.8516) than other three computing methods. In addition, the relative importance of all features also further shows that newly added features can be helpful to improve the prediction performance of methods. [ABSTRACT FROM AUTHOR]

AMP-activated protein kinase (AMPK) is a metabolic regulator that acts to limit the growth of cancer cells. AMPK is downregulated by melanoma antigens A3/6 (MAGEA3/6), which are cancer-specific proteins that enhance the activity of specific E3 ubiquitin ligases to ubiquitinate and degrade AMP-activated protein kinase α1 (AMPKα1). Here, using a bioinformatic approach, we identified a microRNA, miR-1273g-3p, that is predicted to target the 3' untranslated region (UTR) of MAGEA3/6. Analyzing miR-1273g-3p expression in human colon cancer tissues, we found a reduction in miR-1273g-3p expression correlating with increased MAGEA3/6 expression and AMPKα1 downregulation. Expression of miR-1273g in HT-29 cells and primary human colon cancer cells down-regulated MAGEA3/6, leading to AMPKα1 upregulation, inhibition of proliferation and cell apoptosis. The anti-CRC activity of miR-1273g was blocked by AMPKα1 knockout. MAGEA3/6 shRNA silencing mimicked and abolished miR-1273g-induced actions in HT-29 cells. In vivo, miR-1273g- or MAGEA3/6 shRNA-expressing HT-29 tumors grew significantly slower than control tumors. We propose a novel miRNA-based mechanism, whereby miR-1273g represses MAGEA3/6 expression in human CRC cells and tissues, which may provide a novel cancer-specific therapeutic. [ABSTRACT FROM AUTHOR]

Background Increasing evidence indicates that the dysregulation of microRNAs (miRNAs) play critical roles tumor progression and metastasis, but very few papers had reported the function of miR-9-5p in lung cancer, especially in NSCLCs. Methods In this study, we investigated the role of miR-9-5p in non-small cell lung cancers (NSCLCs). MiR-9-5p level were analyzed in 62 clinical NSCLC lung tissue samples and adjacent normal lung tissues by RT-PCR. The target of miR-9-5p was predicted by TargetScan and luciferase reporter assay was used to verify the binding site of miR-9-5p on TGFBR2 mRNA. MTT assay, wound healing assay and invasion assay were performed in both miR-5p inhibitor transfected A549 and miR-5p mimic transfected SK-MES-1 cells. To further investigate whether TGFBR2 is the major target of miR-9-5p, we used TGFBR2 siRNA to transfect A549 and SK-MES-1 cells with miR-9-5p inhibitor or miR-9-5p mimic transfection. Western blot were then used to analyze TGFBR2, p-smad2 and p-smad3 protein expressions after transfection. Results Results indicated that NSCLC patients’ tissues had a significantly higher expression of miR-9-5p compared to adjacent normal lung tissues. MiR-9-5p mimic transfection promoted proliferation, metastasis and invasion abilities in both A549 and SK-MES-1 cells. Conversely, miR-9-5p inhibitor transfection showed the decreased abilities of these cells. Luciferase reporter assay indicated that TGFBR2 is a direct target of miR-9-5p and the up-regulation of TGFBR2 suppressed cell proliferation, metastasis and invasion. The knock down of TGFBR2 abrogated the effect of miR-9-5p in down-regulating p-smad2 and p-smad3 expressions, which indicated that TGFBR2 is the major target of miR-9-5p in NSCLC cells. Conclusions Our finding indicated that miR-9-5p promotes the proliferation, metastasis and invasion of NSCLC cells by down-regulating TGFBR2 expression. [ABSTRACT FROM AUTHOR]

Abstract: MicroRNAs (miRNAs) are a group of short (∼22nt) noncoding RNAs that specifically regulate cellular gene expression at the post-transcriptional level. miRNA precursors (pre-miRNAs), which are imperfect stem loop structures of ∼70nt, are processed into mature miRNAs by cellular RNases III. To date, hundreds of miRNAs and their corresponding targets have been reported in kinds of species. Although only a few of these miRNA/target pairs have been functionally verified, some do play important roles in regulating normal development and physiology. Several viruses (e.g. the Epstein-Barr virus and human herpesvirus Kaposi''s sarcoma-associated herpesvirus) has been reported to encode miRNAs. Here, we extend the analysis of miRNA-encoding potential to the Hepatitis B virus (HBV). Using computational approaches, we found that HBV putatively encodes only one candidate pre-miRNA. We then matched deduced mature miRNA sequence from this pre-miRNA against a database of 3′ untranslated sequences (UTR) from the human genome. Surprisingly, none of cellular transcripts could potentially be targeted by the viral miRNA (vmiRNA) sequence. However, one viral mRNA was found to be targeted by the vmiRNA when we searched the target from viral mRNAs. We propose that HBV has evolved to use vmiRNAs as a means to regulate its own gene expression for its benefit. [Copyright &y& Elsevier]

Background: Many evidences have demonstrated that circRNAs (circular RNA) play important roles in controlling gene expression of human, mouse and nematode. More importantly, circRNAs are also involved in many diseases through fine tuning of post-transcriptional gene expression by sequestering the miRNAs which associate with diseases. Therefore, identifying the circRNA-disease associations is very appealing to comprehensively understand the mechanism, treatment and diagnose of diseases, yet challenging. As the complex mechanism between circRNAs and diseases, wet-lab experiments are expensive and time-consuming to discover novel circRNA-disease associations. Therefore, it is of dire need to employ the computational methods to discover novel circRNA-disease associations. Result: In this study, we develop a method (DWNN-RLS) to predict circRNA-disease associations based on Regularized Least Squares of Kronecker product kernel. The similarity of circRNAs is computed from the Gaussian Interaction Profile(GIP) based on known circRNA-disease associations. In addition, the similarity of diseases is integrated by the mean of GIP similarity and sematic similarity which is computed by the direct acyclic graph (DAG) representation of diseases. The kernels of circRNA-disease pairs are constructed from the Kronecker product of the kernels of circRNAs and diseases. DWNN (decreasing weight k-nearest neighbor) method is adopted to calculate the initial relational score for new circRNAs and diseases. The Kronecker product kernel based regularised least squares approach is used to predict new circRNA-disease associations. We adopt 5-fold cross validation (5CV), 10-fold cross validation (10CV) and leave one out cross validation (LOOCV) to assess the prediction performance of our method, and compare it with other six competing methods (RLS-avg, RLS-Kron, NetLapRLS, KATZ, NBI, WP). Conlusion: The experiment results show that DWNN-RLS reaches the AUC values of 0.8854, 0.9205 and 0.9701 in 5CV, 10CV and LOOCV, respectively, which illustrates that DWNN-RLS is superior to the competing methods RLS-avg, RLS-Kron, NetLapRLS, KATZ, NBI, WP. In addition, case studies also show that DWNN-RLS is an effective method to predict new circRNA-disease associations. [ABSTRACT FROM AUTHOR]

Motivation MicroRNAs (miRNAs), as critical regulators, are involved in various fundamental and vital biological processes, and their abnormalities are closely related to human diseases. Predicting disease-related miRNAs is beneficial to uncovering new biomarkers for the prevention, detection, prognosis, diagnosis and treatment of complex diseases. Results In this study, we propose a multi-view Laplacian regularized deep factorization machine (DeepFM) model, MLRDFM, to predict novel miRNA-disease associations while improving the standard DeepFM. Specifically, MLRDFM improves DeepFM from two aspects: first, MLRDFM takes the relationships among items into consideration by regularizing their embedding features via their similarity-based Laplacians. In this study, miRNA Laplacian regularization integrates four types of miRNA similarity, while disease Laplacian regularization integrates two types of disease similarity. Second, to judiciously train our model, Laplacian eigenmaps are utilized to initialize the weights in the dense embedding layer. The experimental results on the latest HMDD v 3.2 dataset show that MLRDFM improves the performance and reduces the overfitting phenomenon of DeepFM. Besides, MLRDFM is greatly superior to the state-of-the-art models in miRNA-disease association prediction in terms of different evaluation metrics with the 5-fold cross-validation. Furthermore, case studies further demonstrate the effectiveness of MLRDFM. [ABSTRACT FROM AUTHOR]

• Variational graph auto-encoders are excellent for predicting miRNA-disease associations. • Graph convolutional networks obtain good representations for miRNAs and diseases. • Variational auto-encoders can deal with missing data in the miRNA-disease network. • Integrating different databases helps predict novel miRNA-disease associations. Cumulative experimental studies have demonstrated the critical roles of microRNAs (miRNAs) in the diverse fundamental and important biological processes, and in the development of numerous complex human diseases. Thus, exploring the relationships between miRNAs and diseases is helpful with understanding the mechanisms, the detection, diagnosis, and treatment of complex diseases. As the identification of miRNA-disease associations via traditional biological experiments is time-consuming and expensive, an effective computational prediction method is appealing. In this study, we present a deep learning framework with variational graph auto-encoder for miRNA-disease association prediction (VGAE-MDA). VGAE-MDA first gets the representations of miRNAs and diseases from the heterogeneous networks constructed by miRNA-miRNA similarity, disease-disease similarity, and known miRNA-disease associations. Then, VGAE-MDA constructs two sub-networks: miRNA-based network and disease-based network. Combining the representations based on the heterogeneous network, two variational graph auto-encoders (VGAE) are deployed for calculating the miRNA-disease association scores from two sub-networks, respectively. Lastly, VGAE-MDA obtains the final predicted association score for a miRNA-disease pair by integrating the scores from these two trained networks. Unlike the previous model, the VGAE-MDA can mitigate the effect of noises from random selection of negative samples. Besides, the use of graph convolutional neural (GCN) network can naturally incorporate the node features from the graph structure while the variational autoencoder (VAE) makes use of latent variables to predict associations from the perspective of data distribution. The experimental results show that VGAE-MDA outperforms the state-of-the-art approaches in miRNA-disease association prediction. Besides, the effectiveness of our model has been further demonstrated by case studies. [ABSTRACT FROM AUTHOR]

Simple Summary: CircRNAs (circular RNAs), a novel kind of non-coding RNAs, play a regulatory role in cellular processes. A growing number of biological experiments has proved that circRNAs can be used as biomarkers and therapeutic targets of some cancers. As the time and financial costs of biological experiments are high, computational methods have become a better way to predict the associations between circRNAs and diseases. Graph attention network was first applied to predict circRNA-disease associations with multiple similarities of data in this study. The circRNA–miRNA interactions and disease-mRNA interactions were adopted to construct features. The computational method proposed in this study has improved the prediction performance. CircRNAs (circular RNAs) are a class of non-coding RNA molecules with a closed circular structure. CircRNAs are closely related to the occurrence and development of diseases. Due to the time-consuming nature of biological experiments, computational methods have become a better way to predict the interactions between circRNAs and diseases. In this study, we developed a novel computational method called GATCDA utilizing a graph attention network (GAT) to predict circRNA–disease associations with disease symptom similarity, network similarity, and information entropy similarity for both circRNAs and diseases. GAT learns representations for nodes on a graph by an attention mechanism, which assigns different weights to different nodes in a neighborhood. Considering that the circRNA–miRNA–mRNA axis plays an important role in the generation and development of diseases, circRNA–miRNA interactions and disease–mRNA interactions were adopted to construct features, in which mRNAs were related to 88% of miRNAs. As demonstrated by five-fold cross-validation, GATCDA yielded an AUC value of 0.9011. In addition, case studies showed that GATCDA can predict unknown circRNA–disease associations. In conclusion, GATCDA is a useful method for exploring associations between circRNAs and diseases. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are small single-stranded noncoding RNAs that have shown to play a critical role in regulating gene expression. In past decades, cumulative experimental studies have verified that miRNAs are implicated in many complex human diseases and might be potential biomarkers for various types of diseases. With the increase of miRNA-related data and the development of analysis methodologies, some computational methods have been developed for predicting miRNA-disease associations, which are more economical and time-saving than traditional biological experimental approaches. In this study, a novel computational model, deep belief network (DBN)-based matrix factorization (DBN-MF), is proposed for miRNA-disease association prediction. First, the raw interaction features of miRNAs and diseases were obtained from the miRNA-disease adjacent matrix. Second, 2 DBNs were used for unsupervised learning of the features of miRNAs and diseases, respectively, based on the raw interaction features. Finally, a classifier consisting of 2 DBNs and a cosine score function was trained with the initial weights of DBN from the last step. During the training, the miRNA-disease adjacent matrix was factorized into 2 feature matrices for the representation of miRNAs and diseases, and the final prediction label was obtained according to the feature matrices. The experimental results show that the proposed model outperforms the state-of-the-art approaches in miRNA-disease association prediction based on the 10-fold cross-validation. Besides, the effectiveness of our model was further demonstrated by case studies. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are small single-stranded noncoding RNAs that have shown to play a critical role in regulating gene expression. In past decades, cumulative experimental studies have verified that miRNAs are implicated in many complex human diseases and might be potential biomarkers for various types of diseases. With the increase of miRNA-related data and the development of analysis methodologies, some computational methods have been developed for predicting miRNA-disease associations, which are more economical and time-saving than traditional biological experimental approaches. In this study, a novel computational model, deep belief network (DBN)-based matrix factorization (DBN-MF), is proposed for miRNA-disease association prediction. First, the raw interaction features of miRNAs and diseases were obtained from the miRNA-disease adjacent matrix. Second, 2 DBNs were used for unsupervised learning of the features of miRNAs and diseases, respectively, based on the raw interaction features. Finally, a classifier consisting of 2 DBNs and a cosine score function was trained with the initial weights of DBN from the last step. During the training, the miRNA-disease adjacent matrix was factorized into 2 feature matrices for the representation of miRNAs and diseases, and the final prediction label was obtained according to the feature matrices. The experimental results show that the proposed model outperforms the state-of-the-art approaches in miRNA-disease association prediction based on the 10-fold cross-validation. Besides, the effectiveness of our model was further demonstrated by case studies. [ABSTRACT FROM AUTHOR]

Plant-derived exosome-like nanoparticles (ELNs) have been proposed as a novel therapeutic tool for preventing human diseases. However, the number of well-verified plant ELNs remains limited. In this study, the microRNAs in ELNs derived from fresh Rehmanniae Radix , a well-known traditional Chinese herb for treating inflammatory and metabolic diseases, were determined by using microRNA sequencing to investigate the active components in the ELNs and the protection against lipopolysaccharide (LPS)-induced acute lung inflammation in vivo and in vitro. The results showed that rgl-miR-7972 (miR-7972) was the main ingredient in ELNs. It exerted stronger protective activities against LPS-induced acute lung inflammation than catalpol and acteoside, which are two well-known chemical markers in this herb. Moreover, miR-7972 decreased the production of pro-inflammatory cytokines (IL-1β, IL-6, and TNF-α), reactive oxygen species (ROS) and nitric oxide (NO) in LPS-exposed RAW264.7 cells, thereby facilitating M2 macrophage polarization. Mechanically, miR-7972 downregulated the expression of G protein-coupled receptor 161 (GPR161), activating the Hedgehog pathway, and inhibited the biofilm form of Escherichia coli via targeting virulence gene sxt2. Therefore, miR-7972 derived from fresh R. Radix alleviated LPS-induced lung inflammation by targeting the GPR161-mediated Hedgehog pathway, recovering gut microbiota dysbiosis. It also provided a new direction for gaining novel bioactivity nucleic acid drugs and broadening the knowledge on cross-kingdom physiological regulation through miRNAs. [Display omitted] • Rgl-miR-7972 was the main ingredient in ELNs derived from fresh Rehmanniae Radix. • Rgl-miR-7972 improved LPS-induced inflammation via inhibiting GPR161. • Rgl-miR-7972 inhibited the biofilm form of Escherichia coli via inhibiting sxt2. • ELNs act as novel drug deliver for cross-kingdom physiological regulation. [ABSTRACT FROM AUTHOR]

An immobilization-free electrochemical impedance biosensor for microRNA detection was developed in this work, which was based on both the duplex-specific nuclease assisted target recycling (DSNATR) and capture probes (Cps) enriched from the solution to electrode surface via magnetic beads (MBs). In the absence of miR-21, Cps cannot be hydrolyzed due to the low activity of duplex-specific nuclease (DSN) against ssDNA. Therefore, the intact Cps could be attached to the surface of magnetic glass carbon electrode (MGCE), resulting in a compact negatively charged layer as well as a large charge-transfer resistance. While in the presence of miR-21, it hybridized with Cp to form a DNA–RNA heteroduplex. Due to the considerable cleavage preference for DNA in DNA–RNA hybrids, DSN hydrolyzed the target-binding part of the Cp while liberating the intact miR-21 to hybridize with a new Cp and initiate the second cycle of hydrolysis. In this way, a single miR-21 was able to trigger the permanent hydrolysis of multiple Cps. Finally, all Cps were digested. Thus, the negatively charged layer could not be formed, resulting in a small charge-transfer resistance. By employing the above strategy, the proposed biosensor achieved ultrahigh sensitivity toward miR-21 with a detection limit of 60 aM. Meanwhile, the method showed little cross-hybridization among the closely related miRNA family members even at the single-base-mismatched level. Successful attempts were made in applying the approach to detect miR-21 in human serum samples of breast cancer patients. [ABSTRACT FROM AUTHOR]

The new acridone derivative 5, 7-dinitro-2-sulfo-acridone (DSA) with excellent electrochemical activity was synthesized and reported for the first time in this paper. Then an electrochemical biosensor was fabricated for the signal amplified detection of microRNA (miRNA) via applying home-made DSA as signal unit. The p19 protein-functionalized magnetic beads (PFMBs) for specific recognition and enrichment of miRNA. Then DSA is combined with the long DNA concatamers, which functions as a signal enhancement platform to facilitate the high selectivity and sensitivity determination of miRNA. The usage of this novel electrochemical activity made a contribution to the performance of the approach, such as achieving a detection limit of 6 aM. To the best of our knowledge, this is the first attempt to apply DSA, PFMBs and long DNA concatamers for the fabrication of the electrochemical biosensors, which may represent a promising path toward early diagnosis of cancer at the point of care. [ABSTRACT FROM AUTHOR]

In this paper, we describe a novel label-free fluorescence method for microRNA-21 (miR-21) detection based on terbium (Tb3+) and duplex-specific nuclease (DSN) assisted target recycling. Capture probes (Cps), containing a target-binding part and a signal-output part, are immobilized on magnetic beads (MBs). In the presence of the target miR-21, it hybridizes with the target-binding part of a Cp to form a DNA–RNA heteroduplex. Due to the considerable cleavage preference for DNA in DNA–RNA hybrids, DSN hydrolyzes the target-binding part of the Cp while liberating the intact target miR-21 to hybridize with a new Cp and initiate the second cycle of hydrolysis. Eventually, through magnetic separation, only the signal-output part of the Cp could remain in solution and function as a signalling flare to increase the fluorescence intensity of Tb3+ dramatically. By employing the above strategy, this approach can gain an amplified fluorescent signal and detect as low as 8 fM miR-21 under the optimized conditions. Moreover, due to the high selectivity of DSN, the method shows little cross-hybridization among the closely related miRNA family members even at the single-base-mismatched level. Successful attempts were made in applying the approach to detect miR-21 in human cell lysate samples of breast cancer patients. [ABSTRACT FROM AUTHOR]