Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network.

Emerging evidence has proved that circular RNAs (circRNAs) are implicated in pathogenic processes. They are regarded as promising biomarkers for diagnosis due to covalently closed loop structures. As opposed to traditional experiments, computational approaches can identify circRNA-disease associations at a lower cost. Aggregating multi-source pathogenesis data helps to alleviate data sparsity and infer potential associations at the system level. The majority of computational approaches construct a homologous network using multi-source data, but they lose the heterogeneity of the data. Effective methods that use the features of multi-source data are considered as a matter of urgency. In this paper, we propose a model (CDHGNN) based on edge-weighted graph attention and heterogeneous graph neural networks for potential circRNA-disease association prediction. The circRNA network, micro RNA network, disease network and heterogeneous network are constructed based on multi-source data. To reflect association probabilities between nodes, an edge-weighted graph attention network model is designed for node features. To assign attention weights to different types of edges and learn contextual meta-path, CDHGNN infers potential circRNA-disease association based on heterogeneous neural networks. CDHGNN outperforms state-of-the-art algorithms in terms of accuracy. Edge-weighted graph attention networks and heterogeneous graph networks have both improved performance significantly. Furthermore, case studies suggest that CDHGNN is capable of identifying specific molecular associations and investigating biomolecular regulatory relationships in pathogenesis. The code of CDHGNN is freely available at https://github.com/BioinformaticsCSU/CDHGNN.

PGCNMDA: Learning node representations along paths with graph convolutional network for predicting miRNA-disease associations.

Identifying miRNA-disease associations (MDAs) is crucial for improving the diagnosis and treatment of various diseases. However, biological experiments can be time-consuming and expensive. To overcome these challenges, computational approaches have been developed, with Graph Convolutional Network (GCN) showing promising results in MDA prediction. The success of GCN-based methods relies on learning a meaningful spatial operator to extract effective node feature representations. To enhance the inference of MDAs, we propose a novel method called PGCNMDA, which employs graph convolutional networks with a learning graph spatial operator from paths. This approach enables the generation of meaningful spatial convolutions from paths in GCN, leading to improved prediction performance. On HMDD v2.0, PGCNMDA obtains a mean AUC of 0.9229 and an AUPRC of 0.9206 under 5-fold cross-validation (5-CV), and a mean AUC of 0.9235 and an AUPRC of 0.9212 under 10-fold cross-validation (10-CV), respectively. Additionally, the AUC of PGCNMDA also reaches 0.9238 under global leave-one-out cross-validation (GLOOCV). On HMDD v3.2, PGCNMDA obtains a mean AUC of 0.9413 and an AUPRC of 0.9417 under 5-CV, and a mean AUC of 0.9419 and an AUPRC of 0.9425 under 10-CV, respectively. Furthermore, the AUC of PGCNMDA also reaches 0.9415 under GLOOCV. The results show that PGCNMDA is superior to other compared methods. In addition, the case studies on pancreatic neoplasms, thyroid neoplasms and leukemia show that 50, 50 and 48 of the top 50 predicted miRNAs linked to these diseases are confirmed, respectively. It further validates the effectiveness and feasibility of PGCNMDA in practical applications.

DTKGIN: Predicting drug-target interactions based on knowledge graph and intent graph.

Knowledge graph intent graph attention mechanism Predicting drug-target interactions (DTIs) plays a crucial role in drug discovery and drug development. Considering the high cost and risk of biological experiments, developing computational approaches to explore the interactions between drugs and targets can effectively reduce the time and cost of drug development. Recently, many methods have made significant progress in predicting DTIs. However, existing approaches still suffer from the high sparsity of DTI datasets and the cold start problem. In this paper, we develop a new model to predict drug-target interactions via a knowledge graph and intent graph named DTKGIN. Our method can effectively capture biological environment information for targets and drugs by mining their associated relations in the knowledge graph and considering drug-target interactions at a fine-grained level in the intent graph. DTKGIN learns the representation of drugs and targets from the knowledge graph and the intent graph. Then the probabilities of interactions between drugs and targets are obtained through the inner product of the representation of drugs and targets. Experimental results show that our proposed method outperforms other state-of-the-art methods in 10-fold cross-validation, especially in cold-start experimental settings. Furthermore, the case studies demonstrate the effectiveness of DTKGIN in predicting potential drug-target interactions. The code is available on GitHub: https://github.com/Royluoyi123/DTKGIN.

Drug repositioning based on multi-view learning with matrix completion.

Determining drug indications is a critical part of the drug development process. However, traditional drug discovery is expensive and time-consuming. Drug repositioning aims to find potential indications for existing drugs, which is considered as an important alternative to the traditional drug discovery. In this article, we propose a multi-view learning with matrix completion (MLMC) method to predict the potential associations between drugs and diseases. Specifically, MLMC first learns the comprehensive similarity matrices from five drug similarity matrices and two disease similarity matrices based on the multi-view learning (ML) with Laplacian graph regularization, and updates the drug-disease association matrix simultaneously. Then, we introduce matrix completion (MC) to add some positive entries in original association matrix based on low-rank structure, and re-execute the multi-view learning algorithm for association prediction. At last, the prediction results of the above two operations are integrated as the final output. Evaluated by 10-fold cross-validation and de novo tests, MLMC achieves higher prediction accuracy than the current state-of-the-art methods. Moreover, case studies confirm the ability of our method in novel drug-disease association discovery. The codes of MLMC are available at https://github.com/BioinformaticsCSU/MLMC. Contact: jxwang@mail.csu.edu.cn.

A survey on predicting microbe-disease associations: biological data and computational methods.

Various microbes have proved to be closely related to the pathogenesis of human diseases. While many computational methods for predicting human microbe-disease associations (MDAs) have been developed, few systematic reviews on these methods have been reported. In this study, we provide a comprehensive overview of the existing methods. Firstly, we introduce the data used in existing MDA prediction methods. Secondly, we classify those methods into different categories by their nature and describe their algorithms and strategies in detail. Next, experimental evaluations are conducted on representative methods using different similarity data and calculation methods to compare their prediction performances. Based on the principles of computational methods and experimental results, we discuss the advantages and disadvantages of those methods and propose suggestions for the improvement of prediction performances. Considering the problems of the MDA prediction at present stage, we discuss future work from three perspectives including data, methods and formulations at the end.

PDMDA: predicting deep-level miRNA-disease associations with graph neural networks and sequence features.

MOTIVATION: Many studies have shown that microRNAs (miRNAs) play a key role in human diseases. Meanwhile, traditional experimental methods for miRNA-disease association identification are extremely costly, time-consuming and challenging. Therefore, many computational methods have been developed to predict potential associations between miRNAs and diseases. However, those methods mainly predict the existence of miRNA-disease associations, and they cannot predict the deep-level miRNA-disease association types. RESULTS: In this study, we propose a new end-to-end deep learning method (called PDMDA) to predict deep-level miRNA-disease associations with graph neural networks (GNNs) and miRNA sequence features. Based on the sequence and structural features of miRNAs, PDMDA extracts the miRNA feature representations by a fully connected network (FCN). The disease feature representations are extracted from the disease-gene network and gene-gene interaction network by GNN model. Finally, a multilayer with three fully connected layers and a softmax layer is designed to predict the final miRNA-disease association scores based on the concatenated feature representations of miRNAs and diseases. Note that PDMDA does not take the miRNA-disease association matrix as input to compute the Gaussian interaction profile similarity. We conduct three experiments based on six association type samples (including circulations, epigenetics, target, genetics, known association of which their types are unknown and unknown association samples). We conduct fivefold cross-validation validation to assess the prediction performance of PDMDA. The area under the receiver operating characteristic curve scores is used as metric. The experiment results show that PDMDA can accurately predict the deep-level miRNA-disease associations. AVAILABILITY AND IMPLEMENTATION: Data and source codes are available at https://github.com/27167199/PDMDA.

Characterization of infected process and primary mechanism in rice Acuce defense against rice blast fungus, Magnaporthe oryzae.

KEY MESSAGE: Identification of infection process and defense response during M. oryzae infecting Acuce. Magnaporthe oryzae is a destructive rice pathogen. Recent studies have focused on the initial infectious stage, with a few studies conducted to elucidate the characteristics of the late infectious stages. This study aims to decipher the characteristics at different stages (biotrophic, biotrophy-necrotrophy switch (BNS), and necrotrophic) between the interaction of two M. oryzae-rice combinations and investigate the resistance mechanisms of rice to M. oryzae using cytological and molecular methods. The biotrophic phase of M. oryzae-LTH compatible interaction was found to be longer than that of M. oryzae-Acuce incompatible interaction. We also found that jasmonic acid (JA) signaling plays an important role in defense by regulating antimicrobial compound accumulation in infected Acuce via a synergistic interaction of JA-salicylic acid (SA) and JA-ethylene (ET). In infected LTH, JA-ET/JA-SA showed antagonistic interaction. Ibuprofen (IBU) is a JA inhibitor. Despite the above findings, we found that exogenous JA-Ile and IBU significantly alleviated blast symptoms in infected LTH at 36 hpi (biotrophic) and 72 hpi (BNS), indicating these two-time points may be critical for managing blast disease in the compatible interaction. Conversely, IBU significantly increased blast symptoms on the infected Acuce at 36 hpi, confirming that the JA signal plays a central role in the defense response in infected Acuce. According to transcriptional analysis, the number of genes enriched in the plant hormone signal pathway was significantly higher than in other pathways. Our findings suggested that JA-mediated defense mechanism is essential in regulating Acuce resistance, particularly during the biotrophic and BNS phases.

MoSDT1 triggers defense response through modulating phosphorylated proteins in rice.

KEY MESSAGE: MoSDT1, a rice blast fungus transcription factor, is as an inducer to activate defense response through mainly mediating phosphorylated proteins in rice. Pathogen effector proteins play a dual role in infecting the host or triggering a defense response. Our previous research found a Magnaporthe oryzae effector, MoSDT1, which could activate the rice defense response when it was overexpressed in rice. However, we still know little about the mechanisms on how MoSDT1 in vivo or in vitro influences the resistance ability of rice. Our results showed that decreased ROS and increased lignin contents appeared along with significant upregulation of defense-related genes, raffinose synthesis gene, and phenylalanine ammonialyase gene. Moreover, we revealed that the contents of lignin were increased, which was in accordance with the upregulation of its precursor phenylalanine gene despite the fact that the glutamate-/thiamine-responsive genes were inhibited in MoSDT1 transgenic rice, and these indicated that MoSDT1 triggered the defense system of rice in vivo. Interestingly, in vitro studies, we further found that MoSDT1 induced the defense system by ROS synthesis, callose deposition, PR gene expression and SA/JA synthesis/signal genes using the purified prokaryotic expression system in rice plants. In addition, this defense response was confirmed to be activated by the zinc finger domain of MoSDT1 via prokaryotic expression of MoSDT1 truncated mutants in rice plants. To elucidate the regulative effects of MoSDT1 on protein phosphorylation in rice, phosphoproteome analysis was performed in both MoSDT1-transgenic and wild type  rice. We found that MoSDT1 specifically up-regulated the expression levels of a few phosphorylated proteins, which were involved in multiple functions, such as biotic/abiotic stress and growth. In addition, the motifs in these specific proteins ranked the top among the top-five conserved motifs in the MoSDT1-transgenic rice. MoSDT1 played a crucial role in enhancing rice resistance by modulating several genes and signaling pathways.

DeepBLI: A Transferable Multichannel Model for Detecting ß-Lactamase-Inhibitor Interaction.

Pathogens producing ß-lactamase pose a great challenge to antibiotic-resistant infection treatment; thus, it is urgent to discover novel ß-lactamase inhibitors for drug development. Conventional high-throughput screening is very costly, and structure-based virtual screening is limited with mechanisms. In this study, we construct a novel multichannel deep neural network (DeepBLI) for ß-lactamase inhibitor screening, pretrained with a label reversal KIBA data set and fine-tuned on ß-lactamase-inhibitor pairs from BindingDB. First, the pairs of encoders (Conv and Att) fuse the information spatially and sequentially for both enzymes and inhibitors. Then, a co-attention module creates the connection between the inhibitor and enzyme embeddings. Finally, multichannel outputs fuse with an element-wise product and then are fed into 3-layer fully connected networks to predict interactions. Comparing the state-of-the-art methods, DeepBLI yields an AUROC of 0.9240 and an AUPRC of 0.9715, which indicates that it can identify new ß-lactamase-inhibitor interactions. To demonstrate its prediction ability, an application of DeepBLI is described to screen potential inhibitor compounds for metallo-ß-lactamase AIM-1 and repurpose rottlerin for four classes of ß-lactamase targets, showing the possibility of being a broad-spectrum inhibitor. DeepBLI provides an effective way for antibacterial drug development, contributing to antibiotic-resistant therapeutics.

Magnaporthe oryzae systemic defense trigger 1 (MoSDT1)-mediated metabolites regulate defense response in Rice.

BACKGROUND: Some of the pathogenic effector proteins play an active role in stimulating the plant defense system to strengthen plant resistance. RESULTS: In this study, ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC/Q-TOF-MS) was implemented to identify altered metabolites in transgenic rice containing over-expressed M. oryzae Systemic Defense Trigger 1 (MoSDT1) that was infected at three-time points. The characterized dominating metabolites were organic acids and their derivatives, organic oxygen compounds, lipids, and lipid-like molecules. Among the identified metabolites, shikimate, galactinol, trehalose, D-mannose, linolenic acid, dopamine, tyramine, and L-glutamine are precursors for the synthesis of many secondary defense metabolites Carbohydrate, as well as amino acid metabolic, pathways were revealed to be involved in plant defense responses and resistance strengthening. CONCLUSION: The increasing salicylic acid (SA) and jasmonic acid (JA) content enhanced interactions between JA synthesis/signaling gene, SA synthesis/receptor gene, raffinose/fructose/sucrose synthase gene, and cell wall-related genes all contribute to defense response in rice. The symptoms of rice after M. oryzae infection were significantly alleviated when treated with six identified metabolites, i.e., galactol, tyramine, L-glutamine, L-tryptophan, α-terpinene, and dopamine for 72 h exogenously. Therefore, these metabolites could be utilized as an optimal metabolic marker for M. oryzae defense.

miR-320a/SP1 negative reciprocal interaction contributes to cell growth and invasion in colorectal cancer.

BACKGROUND: Transcription factors (TFs) may be engaged in reciprocal regulatory circuits with certain miRNAs to maintain cellular homeostasis. Disequilibrium of the reciprocities by certain tumor-related stimuli may give rise to deregulation of downstream cellular signaling pathways, thus promoting malignant tumor phenotypes. Specificity Protein 1 (SP1) is the most representative member of the tumor-related transcription factors. Previous studies disclosed that SP1 can transcriptionally regulate miRNAs and coding genes to facilitate tumor progression. In our study, we used bioinformatic analysis to predict several SP1-binding sites within the miR-320a promoter and found that SP1 is a predicted target gene of miR-320a. Therefore, we hypothesize a reciprocal regulatory link between SP1 and miR-320a that participates in colorectal cancer (CRC) development METHODS: We performed bioinformatic analysis, quantitative polymerase chain reaction (qPCR), immunoblotting, dual-luciferase reporter assays, and a series of in vitro and in vivo functional assays to describe a novel SP1/miR-320a reciprocal interaction in CRC RESULTS: First, we found that miR-320a was significantly downregulated in CRC tissues and cell lines. Consistent with findings in other cancers, miR-320a exhibited inhibitory effects on cell growth and invasion of CRC in vitro and in vivo. Moreover, we identified SP1 as a target gene of miR-320a, and ectopic SP1 expression partly abolished miR-320a-induced inhibitory effects. Conversely, we confirmed that SP1 interacts with the miR-320a promoter, leading to depression of miR-320a. This illustrates a double-negative feedback loop between miR-320a and SP1. Additionally, based on the fact that SP1 promotes MACC1 transcription, we determined via immunoblotting that the oncogenic MACC1/MET signaling pathway was inactivated in the context of miR-320a-induced SP1 downregulation CONCLUSION: Taken together, our study is the first to describe a miR-320a/SP1 negative reciprocal interaction, which contributes to cell growth and invasion in CRC through modulation of the MACC1/MET signaling pathway.

A disease inference method based on symptom extraction and bidirectional Long Short Term Memory networks.

The wide applications of automatic disease inference in many medical fields improve the efficiency of medical treatments. Many efforts have been made to predict patients' future health conditions according to their full clinical texts, clinical measurements or medical codes. Symptoms reflect the onset of diseases and can provide credible information for disease diagnosis. In this study, we propose a new disease inference method by extracting symptoms and integrating two symptom representation approaches. To reduce the uncertainty and irregularity of symptom descriptions in Electronic Medical Records (EMR), a comprehensive clinical knowledge database consisting of massive amount of data about diseases, symptoms, and their relationships, we extract symptoms with existing nature language process tool Metamap which is designed for biomedical texts. To take advantages of the complex relationship between symptoms and diseases to enhance the accuracy of disease inference, we present two symptom representation models: term frequency-inverse document frequency (TF-IDF) model for the representation of the relationship between symptoms and diseases and Word2Vec for the expression of the semantic relationship between symptoms. Based on these two symptom representations, we employ the bidirectional Long Short Term Memory networks (BiLSTMs) to model symptom sequences in EMR. Our proposed model shows a significant improvement in term of AUC (0.895) and F1 (0.572) for 50 diseases in MIMIC-III dataset. The results illustrate that the model with the combination of the two symptom representations perform better than the one with only one of them.

PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences.

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.

IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning.

BACKGROUND: Viral infectious diseases are the serious threat for human health. The receptor-binding is the first step for the viral infection of hosts. To more effectively treat human viral infectious diseases, the hidden virus-receptor interactions must be discovered. However, current computational methods for predicting virus-receptor interactions are limited. RESULT: In this study, we propose a new computational method (IILLS) to predict virus-receptor interactions based on Initial Interaction scores method via the neighbors and the Laplacian regularized Least Square algorithm. IILLS integrates the known virus-receptor interactions and amino acid sequences of receptors. The similarity of viruses is calculated by the Gaussian Interaction Profile (GIP) kernel. On the other hand, we also compute the receptor GIP similarity and the receptor sequence similarity. Then the sequence similarity is used as the final similarity of receptors according to the prediction results. The 10-fold cross validation (10CV) and leave one out cross validation (LOOCV) are used to assess the prediction performance of our method. We also compare our method with other three competing methods (BRWH, LapRLS, CMF). CONLUSION: The experiment results show that IILLS achieves the AUC values of 0.8675 and 0.9061 with the 10-fold cross validation and leave-one-out cross validation (LOOCV), respectively, which illustrates that IILLS is superior to the competing methods. In addition, the case studies also further indicate that the IILLS method is effective for the virus-receptor interaction prediction.

DDIGIP: predicting drug-drug interactions based on Gaussian interaction profile kernels.

BACKGROUND: A drug-drug interaction (DDI) is defined as a drug effect modified by another drug, which is very common in treating complex diseases such as cancer. Many studies have evidenced that some DDIs could be an increase or a decrease of the drug effect. However, the adverse DDIs maybe result in severe morbidity and even morality of patients, which also cause some drugs to withdraw from the market. As the multi-drug treatment becomes more and more common, identifying the potential DDIs has become the key issue in drug development and disease treatment. However, traditional biological experimental methods, including in vitro and vivo, are very time-consuming and expensive to validate new DDIs. With the development of high-throughput sequencing technology, many pharmaceutical studies and various bioinformatics data provide unprecedented opportunities to study DDIs. RESULT: In this study, we propose a method to predict new DDIs, namely DDIGIP, which is based on Gaussian Interaction Profile (GIP) kernel on the drug-drug interaction profiles and the Regularized Least Squares (RLS) classifier. In addition, we also use the k-nearest neighbors (KNN) to calculate the initial relational score in the presence of new drugs via the chemical, biological, phenotypic data of drugs. We compare the prediction performance of DDIGIP with other competing methods via the 5-fold cross validation, 10-cross validation and de novo drug validation. CONLUSION: In 5-fold cross validation and 10-cross validation, DDRGIP method achieves the area under the ROC curve (AUC) of 0.9600 and 0.9636 which are better than state-of-the-art method (L1 Classifier ensemble method) of 0.9570 and 0.9599. Furthermore, for new drugs, the AUC value of DDIGIP in de novo drug validation reaches 0.9262 which also outperforms the other state-of-the-art method (Weighted average ensemble method) of 0.9073. Case studies and these results demonstrate that DDRGIP is an effective method to predict DDIs while being beneficial to drug development and disease treatment.

Overexpression of Magnaporthe Oryzae Systemic Defense Trigger 1 (MoSDT1) Confers Improved Rice Blast Resistance in Rice.

The effector proteins secreted by a pathogen not only promote virulence and infection of the pathogen, but also trigger plant defense response. Therefore, these proteins could be used as important genetic resources for transgenic improvement of plant disease resistance. Magnaporthe oryzae systemic defense trigger 1 (MoSDT1) is an effector protein. In this study, we compared the agronomic traits and blast disease resistance between wild type (WT) and MoSDT1 overexpressing lines in rice. Under control conditions, MoSDT1 transgenic lines increased the number of tillers without affecting kernel morphology. In addition, MoSDT1 transgenic lines conferred improved blast resistance, with significant effects on the activation of callose deposition, reactive oxygen species (ROS) accumulation and cell death. On the one hand, overexpression of MoSDT1 could delay biotrophy-necrotrophy switch through regulating the expression of biotrophy-associated secreted protein 4 (BAS4) and Magnaporthe oryzaecell death inducing protein 1 (MoCDIP1), and activate plant defense response by regulating the expression of Bsr-d1, MYBS1, WRKY45, peroxidase (POD), heat shock protein 90 (HSP90), allenoxide synthase 2 (AOS2), phenylalanine ammonia lyase (PAL), pathogenesis-related protein 1a (PR1a) in rice. On the other hand, overexpression of MoSDT1 could increase the accumulation of some defense-related primary metabolites such as two aromatic amino acids (L-tyrosine and L-tryptohan), 1-aminocyclopropane carboxylic acid, which could be converted to ethylene, vanillic acid and L-saccharopine. Taken together, overexpression of MoSDT1 confers improved rice blast resistance in rice, through modulation of callose deposition, ROS accumulation, the expression of defense-related genes, and the accumulation of some primary metabolites.

Joint Mobile Data Collection and Wireless Energy Transfer in Wireless Rechargeable Sensor Networks.

In wireless rechargeable sensor networks (WRSNs), there is a way to use mobile vehicles to charge node and collect data. It is a rational pattern to use two types of vehicles, one is for energy charging, and the other is for data collecting. These two types of vehicles, data collection vehicles (DCVs) and wireless charging vehicles (WCVs), are employed to achieve high efficiency in both data gathering and energy consumption. To handle the complex scheduling problem of multiple vehicles in large-scale networks, a twice-partition algorithm based on center points is proposed to divide the network into several parts. In addition, an anchor selection algorithm based on the tradeoff between neighbor amount and residual energy, named AS-NAE, is proposed to collect the zonal data. It can reduce the data transmission delay and the energy consumption for DCVs' movement in the zonal. Besides, we design an optimization function to achieve maximum data throughput by adjusting data rate and link rate of each node. Finally, the effectiveness of proposed algorithm is validated by numerical simulation results in WRSNs.

Piperlongumine induces gastric cancer cell apoptosis and G2/M cell cycle arrest both in vitro and in vivo.

Recently, several studies have shown that piperlongumine (PL) can selectively kill cancer cells by targeting reactive oxygen species (ROS). However, the potential therapeutic effects and detailed mechanism of PL in gastric cancer are still not clear. In the current report, we found that PL significantly suppressed gastric cancer both in vitro and in vivo. PL obviously increased ROS generation in gastric cancer cells. Anti-oxidant glutathione (GSH) and N-acetyl-L-cysteine (NAC) can abrogate PL-induced gastric cancer cell death and proliferation inhibition. GADD45α was induced in PL-treated cancer cells and led to G2/M phase arrest, whereas genetic depletion of GADD45α by small interfering RNAs (siRNAs) could partly reverse PL-induced cell cycle arrest in gastric cancer cells. Interestingly, we also found that PL treatment decreased the expression of telomerase reverse transcriptase (TERT) gene, which plays an essential role in cancer initiation and progression. Our findings thus revealed a potential anti-tumor effect of PL on gastric cancer cells and may have therapeutic implications.

PMDAGS: Predicting miRNA-Disease Associations With Graph Nonlinear Diffusion Convolution Network and Similarities.

Many studies have proven that microRNAs (miRNAs) can participate in a wide range of biological processes and can be considered as potential noninvasive biomarkers for disease diagnosis and prognosis. Therefore, many computational methods have been developed to identifying miRNA-disease associations, ultimately enhancing the efficiency of disease diagnosis and treatment. In this study, we also introduced a new computational method called PMDAGS, which predicts miRNA-disease associations by utilizing graph nonlinear diffusion convolution network and similarities. PMDAGS first calculates miRNA similarity and disease similarity based on miRNA-target interactions, disease-gene associations and known miRNA-disease associations, respectively. Next, we construct the initial feature of each miRNA (disease) by concatenating its final similarity vector with its known association vector. Based on the known miRNA-disease association network and the initial feature vector of each node, we further apply nonlinear diffusion graph convolution network model to extract the feature embedding vectors. Finally, we concatenate the feature embedding vectors of miRNA and disease and input them into a multi-layer perceptron to identify potential miRNA-disease associations. We conduct 5-fold cross validation (5CV), 10-fold cross validation (10CV), and global leave-one-out cross validation (GLOOCV) on HMDD v2.0 and HMDD v3.2. PMDAGS achieves AUCs of 0.9222, 0.9228, and 0.9221 under 5CV, 10CV and GLOOCV on HMDD v2.0, respectively. In addition, PMDAGS also achieves AUC values of 0.9366, 0.9377, and 0.9376 under 5CV, 10CV and GLOOCV on HMDD v3.2, respectively. According to the experimental results, we can conclude that PMDAGS outperforms other compared methods and can effectively predict miRNA-disease associations.

DLP: Duplex Link Prediction Via Subspace Segmentation for Predicting drug-MiRNA Associations.

The arduous and costly journey of drug discovery is increasingly intersecting with computational approaches, which promise to accelerate the analysis of bioassays and biomedical literature. The critical role of microRNAs (miRNAs) in disease progression has been underscored in recent studies, elevating them as potential therapeutic targets. This emphasizes the need for the development of sophisticated computational models that can effectively identify promising drug targets, such as miRNAs. Herein, we present a novel method, termed Duplex Link Prediction (DLP), rooted in subspace segmentation, to pinpoint potential miRNA targets. Our approach initiates with the application of the Network Enhancement (NE) algorithm to refine the similarity metric between miRNAs. Thereafter, we construct two matrices by pre-loading the association matrix from both the drug and miRNA perspectives, employing the K Nearest Neighbors (KNN) technique. The DLSR algorithm is then applied to predict potential associations. The final predicted association scores are ascertained through the weighted mean of the two matrices. Our empirical findings suggest that the DLP algorithm outperforms current methodologies in the realm of identifying potential miRNA drug targets. Case study validations further reinforce the real-world applicability and effectiveness of our proposed method. The code of DLP is freely available at https://github.com/kaizheng-academic/DLP.