Disease-related miRNAs Research Articles

Minimally Invasive Real-Time Monitoring for Rapid and Sensitive Diagnosis of Spinal Cord Injury.

Spinal cord injury (SCI) is a serious neurological injury that is currently extremely difficult to cure clinically. SCI involves numerous pathophysiological processes, and microRNAs (miRNAs) play an important role in these processes. Meanwhile, miRNAs have received a lot of attention for their role in other diseases as well. Therefore, the detection of disease-related miRNAs is important for the study of disease development, treatment, and prognosis. With the rapid development of molecular biology, the traditional detection methods of miRNA can no longer meet the needs of experiments. Electrochemical detection methods are widely used because of their excellent detection performance. Here, we designed an electrochemical sensor prepared using borosilicate glass microneedle electrodes for real-time monitoring of miR-21-5p expression in vivo after SCI. The sensor showed a good linear relationship between the oxidation peak current value and the concentration of miR-21-5p in the concentration range 0-2 fM (Y = 12.025X + 90.396, R2 = 0.98). The limit of detection (LOD) of the sensor was 0.3667 fM. The experimental results showed that the borosilicate glass microneedle electrochemical sensor achieved fast, accurate, highly sensitive, highly specific, highly stable, and reproducible monitoring of miR-21-5p. More importantly, the electrochemical sensor has a better clinical translation prospect, which is important for the research of clinical diseases.

High circulating microRNA-197 levels are associated with an increased risk of incident stroke among elderly survivors of the Great East Japan Earthquake

Background Incidence of ischemic stroke increased after natural disasters. Therefore, it is important to establish a means of identifying high-risk populations for incident stroke. We performed a prospective cohort study to examine whether these three cardiovascular disease-related miRNAs (miR-126, miR-197, and miR-223) are associated with incident stroke among elderly survivors of the Great East Japan Earthquake. Method This cohort study was conducted using the data of 1192 survivors of the Great East Japan Earthquake over 60-years old who underwent a health check-up in December 2011. We followed up participants to record stroke cases until the end of 2016. We measured serum miRNAs by quantitative real-time polymerase chain reaction. HRs for incident stroke were estimated by Cox proportional hazard regression analyses. Result The serum miR-197 level was significantly associated with the incident stroke; the HR per one standard deviation change in the miR-197 level was 1.65 (95% confidence interval: 1.19 − 2.30). In contrast, the levels of miR-126 and miR-223 were not associated with the incident stroke. Conclusion We found that a higher miR-197 level is associated with an increased risk of incident stroke; thus, miR-197 is expected to be useful as a predictive biomarker.

RSANMDA: Resampling based subview attention network for miRNA-disease association prediction

Many studies have demonstrated the importance of accurately identifying miRNA-disease associations (MDAs) for understanding disease mechanisms. However, the number of known MDAs is significantly fewer than the unknown pairs. Here, we propose RSANMDA, a subview attention network for predicting MDAs. We first extract miRNA and disease features from multiple similarity matrices. Next, using resampling techniques, we generate different subviews from known MDAs. Each subview undergoes multi-head graph attention to capture its features, followed by semantic attention to integrate features across subviews. Finally, combining raw and training features, we use a multilayer scoring perceptron for prediction. In the experimental section, we conducted comparative experiments with other advanced models on both HMDD v2.0 and HMDD v3.2 datasets. We also performed a series of ablation studies and parameter tuning exercises. Comprehensive experiments conclusively demonstrate the superiority of our model. Case studies on lung, breast, and esophageal cancers further validate our method's predictive capability for identifying disease-related miRNAs.

Identification of key genes and functions in lung metastasis of osteosarcoma based on bioinformatics

To screen the differentially expressed genes of lung metastasis of osteosarcoma by bioinformatics, and explore their functions and regulatory networks. The data set of GSE14359 was screened from GEO database(http://www.ncbi.nlm.nih.gov/gds) and the differentially expressed gene(DEG) was identified using GEO2R online tool. Download osteosarcoma disease related miRNAs from the online HMMD database(http://www.cuilab.cn/hmdd) and then FunRich software was used to predict the target gene, intersects with DEG to obtains the target gene. The miRNA-mRNA relationship pairs were formed according to the targeted joints, then the data was imported into Cytoscape for visualization, DAVID was used to performe GO and KEGG analysis on target genes, STRING was used to construct PPI network, Cytoscape visualization, CytoHubba plug-in screening central genes and online website for expression and survival analysis. Total 704 DEGs were identified, consisting of 477 up-regulated genes and 227 down regulated genes. FunRich predicted 7 888 mRNAs and 343 target genes were obtained through intersection of the two. KEGG analysis showed that it was mainly involved in focal adhesion, ECM receptor interaction, TNF signal pathway, PI3K-Akt signal pathway, IL-17 signal pathway and MAPK signal pathway. Ten central genes (CCNB1, CHEK1, AURKA, DTL, RRM2, MELK, CEP55, FEN1, KPNA2, TYMS) were identified as potential key genes. Among them, CCNB1, DTL, MELK were highly correlated with poor prognosis. The key genes and functional pathways identified in this study may be helpful to understand the molecular mechanism of the occurrence and progression of lung metastases from osteosarcoma, and provide potential therapeutic targets.

Kernel Bayesian logistic tensor decomposition with automatic rank determination for predicting multiple types of miRNA-disease associations.

Identifying the association and corresponding types of miRNAs and diseases is crucial for studying the molecular mechanisms of disease-related miRNAs. Compared to traditional biological experiments, computational models can not only save time and reduce costs, but also discover potential associations on a large scale. Although some computational models based on tensor decomposition have been proposed, these models usually require manual specification of numerous hyperparameters, leading to a decrease in computational efficiency and generalization ability. Additionally, these linear models struggle to analyze complex, higher-order nonlinear relationships. Based on this, we propose a novel framework, KBLTDARD, to identify potential multiple types of miRNA-disease associations. Firstly, KBLTDARD extracts information from biological networks and high-order association network, and then fuses them to obtain more precise similarities of miRNAs (diseases). Secondly, we combine logistic tensor decomposition and Bayesian methods to achieve automatic hyperparameter search by introducing sparse-induced priors of multiple latent variables, and incorporate auxiliary information to improve prediction capabilities. Finally, an efficient deterministic Bayesian inference algorithm is developed to ensure computational efficiency. Experimental results on two benchmark datasets show that KBLTDARD has better Top-1 precision, Top-1 recall, and Top-1 F1 for new type predictions, and higher AUPR, AUC, and F1 values for new triplet predictions, compared to other state-of-the-art methods. Furthermore, case studies demonstrate the efficiency of KBLTDARD in predicting multiple types of miRNA-disease associations.

Meta-Path Semantic and Global-Local Representation Learning Enhanced Graph Convolutional Model for Disease-Related miRNA Prediction.

Dysregulation of miRNAs is closely related to the progression of various diseases, so identifying disease-related miRNAs is crucial. Most recently proposed methods are based on graph reasoning, while they did not completely exploit the topological structure composed of the higher-order neighbor nodes and the global and local features of miRNA and disease nodes. We proposed a prediction method, MDAP, to learn semantic features of miRNA and disease nodes based on various meta-paths, as well as node features from the entire heterogeneous network perspective, and node pair attributes. Firstly, for both the miRNA and disease nodes, node category-wise meta-paths were constructed to integrate the similarity and association connection relationships. Each target node has its specific neighbor nodes for each meta-path, and the neighbors of longer meta-paths constitute its higher-order neighbor topological structure. Secondly, we constructed a meta-path specific graph convolutional network module to integrate the features of higher-order neighbors and their topology, and then learned the semantic representations of nodes. Thirdly, for the entire miRNA-disease heterogeneous network, a global-aware graph convolutional autoencoder was built to learn the network-view feature representations of nodes. We also designed semantic-level and representation-level attentions to obtain informative semantic features and node representations. Finally, the strategy based on the parallel convolutional-deconvolutional neural networks were designed to enhance the local feature learning for a pair of miRNA and disease nodes. The experiment results showed that MDAP outperformed other state-of-the-art methods, and the ablation experiments demonstrated the effectiveness of MDAP's major innovations. MDAP's ability in discovering potential disease-related miRNAs was further analyzed by the case studies over three diseases.

In silico determination of potential miRNAs encoded in wheat genome that target AvrSr35 in Puccinia graminis f. sp. tritici

Wheat (Triticum aestivum L.) belonging to Poaceae family is a valuable staple food all over the world. Puccinia graminis f. sp. tritici is one of the major limiting factor in wheat production, causing stem rust disease. RNA interference (RNAi) is an effective technique for plant production against abiotic stresses. One of the most investigated RNAi molecules is microRNAs (miRNAs). It is well established that miRNAs play important role for the regulation of gene expression at post-transcriptional and pos-translational level. In the present study, wheat miRNAs-stem rust interactions were investigated by in silico methods. For this purpose, reference mature wheat miRNAs were retrieved from miRBase, and the sequences of avirulence gene (AvrSr35) in Puccinia graminis f. sp. tritici were obtained from NCBI. RNAhybrid algorithm was used to predict the relationships between miRNAs and avirulence gene targets. Moreover, a phylogenetic tree was constructed by using miRNAs via MEGA X. We determined that AvrSr35 gene was targeted by a total of 12 miRNAs including tae-miR9657a-3p, tae-miR9671-5p, tae-miR1122c-3p, tae-miR1130b-3p, tae-miR9678-3p, tae-miR9781, tae-miR9666b-3p, tae-miR531, tae-miR9773, tae-miR9778, tae-miR9677b and tae-miR10516. All miRNAs were separated into three major groups. All miRNAs showed close relationship. The first main group consisted of only tae-miR9778. Obtaining findings are expected to contribute roles of miRNAs in the interaction between disease-related miRNAs in plants and pathogens.

EMCMDA: predicting miRNA-disease associations via efficient matrix completion

Abundant researches have consistently illustrated the crucial role of microRNAs (miRNAs) in a wide array of essential biological processes. Furthermore, miRNAs have been validated as promising therapeutic targets for addressing complex diseases. Given the costly and time-consuming nature of traditional biological experimental validation methods, it is imperative to develop computational methods. In the work, we developed a novel approach named efficient matrix completion (EMCMDA) for predicting miRNA-disease associations. First, we calculated the similarities across multiple sources for miRNA/disease pairs and combined this information to create a holistic miRNA/disease similarity measure. Second, we utilized this biological information to create a heterogeneous network and established a target matrix derived from this network. Lastly, we framed the miRNA-disease association prediction issue as a low-rank matrix-complete issue that was addressed via minimizing matrix truncated schatten p-norm. Notably, we improved the conventional singular value contraction algorithm through using a weighted singular value contraction technique. This technique dynamically adjusts the degree of contraction based on the significance of each singular value, ensuring that the physical meaning of these singular values is fully considered. We evaluated the performance of EMCMDA by applying two distinct cross-validation experiments on two diverse databases, and the outcomes were statistically significant. In addition, we executed comprehensive case studies on two prevalent human diseases, namely lung cancer and breast cancer. Following prediction and multiple validations, it was evident that EMCMDA proficiently forecasts previously undisclosed disease-related miRNAs. These results underscore the robustness and efficacy of EMCMDA in miRNA-disease association prediction.

An Exploratory Review on Recent Computational Approaches Devised for MiRNA Disease Association Prediction

Abstract: Recent evidence demonstrated the fundamental role of miRNAs as disease biomarkers and their role in disease progression and pathology. Identifying disease related miRNAs using computational approaches has become one of the trending topics in health informatics. Many biological databases and online tools were developed for uncovering novel disease-related miRNAs. Hence, a brief overview regarding the disease biomarkers, miRNAs as disease biomarkers and their role in complex disorders is given here. Various methods for calculating miRNA and disease similarities are included and the existing machine learning and network based computational approaches for detecting disease associated miRNAs are reviewed along with the benchmark dataset used. Finally, the performance matrices, validation measures and online tools used for miRNA Disease Association (MDA) predictions are also outlined.

HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations.

Existing studies have shown that the abnormal expression of microRNAs (miRNAs) usually leads to the occurrence and development of human diseases. Identifying disease-related miRNAs contributes to studying the pathogenesis of diseases at the molecular level. As traditional biological experiments are time-consuming and expensive, computational methods have been used as an effective complement to infer the potential associations between miRNAs and diseases. However, most of the existing computational methods still face three main challenges: (i) learning of high-order relations; (ii) insufficient representation learning ability; (iii) importance learning and integration of multi-view embedding representation. To this end, we developed a HyperGraph Contrastive Learning with view-aware Attention Mechanism and Integrated multi-view Representation (HGCLAMIR) model to discover potential miRNA-disease associations. First, hypergraph convolutional network (HGCN) was utilized to capture high-order complex relations from hypergraphs related to miRNAs and diseases. Then, we combined HGCN with contrastive learning to improve and enhance the embedded representation learning ability of HGCN. Moreover, we introduced view-aware attention mechanism to adaptively weight the embedded representations of different views, thereby obtaining the importance of multi-view latent representations. Next, we innovatively proposed integrated representation learning to integrate the embedded representation information of multiple views for obtaining more reasonable embedding information. Finally, the integrated representation information was fed into a neural network-based matrix completion method to perform miRNA-disease association prediction. Experimental results on the cross-validation set and independent test set indicated that HGCLAMIR can achieve better prediction performance than other baseline models. Furthermore, the results of case studies and enrichment analysis further demonstrated the accuracy of HGCLAMIR and unconfirmed potential associations had biological significance.

Experimental capture of miRNA targetomes: disease-specific 3′UTR library-based miRNA targetomics for Parkinson’s disease

The identification of targetomes remains a challenge given the pleiotropic effect of miRNAs, the limited effects of miRNAs on individual targets, and the sheer number of estimated miRNA–target gene interactions (MTIs), which is around 44,571,700. Currently, targetome identification for single miRNAs relies on computational evidence and functional studies covering smaller numbers of targets. To ensure that the targetome analysis could be experimentally verified by functional assays, we employed a systematic approach and explored the targetomes of four miRNAs (miR-129-5p, miR-129-1-3p, miR-133b, and miR-873-5p) by analyzing 410 predicted target genes, both of which were previously associated with Parkinson’s disease (PD). After performing 13,536 transfections, we validated 442 of the 705 putative MTIs (62,7%) through dual luciferase reporter assays. These analyses increased the number of validated MTIs by at least 2.1-fold for miR-133b and by a maximum of 24.3-fold for miR-873-5p. Our study contributes to the experimental capture of miRNA targetomes by addressing i) the ratio of experimentally verified MTIs to predicted MTIs, ii) the sizes of disease-related miRNA targetomes, and iii) the density of MTI networks. A web service to support the analyses on the MTI level is available online (https://ccb-web.cs.uni-saarland.de/utr-seremato), and all the data have been added to the miRATBase database (https://ccb-web.cs.uni-saarland.de/miratbase).

A Multi-Relational Graph Encoder Network for Fine-Grained Prediction of MiRNA-Disease Associations.

MicroRNAs (miRNAs) are critical in diagnosing and treating various diseases. Automatically demystifying the interdependent relationships between miRNAs and diseases has recently made remarkable progress, but their fine-grained interactive relationships still need to be explored. We propose a multi-relational graph encoder network for fine-grained prediction of miRNA-disease associations (MRFGMDA), which uses practical and current datasets to construct a multi-relational graph encoder network to predict disease-related miRNAs and their specific relationship types (upregulation, downregulation, or dysregulation). We evaluated MRFGMDA and found that it accurately predicted miRNA-disease associations, which could have far-reaching implications for clinical medical analysis, early diagnosis, prevention, and treatment. Case analyses, Kaplan-Meier survival analysis, expression difference analysis, and immune infiltration analysis further demonstrated the effectiveness and feasibility of MRFGMDA in uncovering potential disease-related miRNAs. Overall, our work represents a significant step toward improving the prediction of miRNA-disease associations using a fine-grained approach could lead to more accurate diagnosis and treatment of diseases.

Self-Supervised Contrastive Learning on Attribute and Topology Graphs for Predicting Relationships Among lncRNAs, miRNAs and Diseases.

Exploring potential association between long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and diseases is an essential part of prevention, diagnosis and treatment of diseases. Since determining these relationships experimentally is resource-intensive and time-consuming, therefore computational methods have emerged as an attractive way to address this issue. However, existing computational approaches for inferring lncRNA-disease associations (LDA), miRNA-disease associations (MDA) and lncRNA-miRNA interactions (LMI) tend to focus on single task, neglecting the benefits of leveraging multiple biomolecular interactions and domain-specific knowledge for multi-task prediction. Furthermore, labeled data for LDA, MDA and LMI is scarce and costly in real-word applications, making it challenging for models to learn comprehensive node embedding patterns. Building on our previous work, this paper proposes a multi-task prediction model (called SSCLMD) that employs self-supervised contrastive learning on attribute and topology graphs to identify potential LDAs, MDAs and LMIs. Specifically, firstly, domain knowledge of lncRNAs, miRNAs and diseases as well as their interactions are exploited to construct attribute graph and topology graph, respectively. Then, the nodes are encoded in the attribute and topology spaces to extract the specific and common feature. Meanwhile, the attention mechanism is performed to adaptively fuse the embedding from different views. SSCLMD incorporates a contrastive self-supervised learning task as a regularize to guide the learning of node embeddings in both attribute and topology space without relying on labels. Severing as a regularize in multi-task learning paradigm, it to improves the model's generalization capabilities. Extensive experiments on 2 manually curated datasets demonstrate that SSCLMD significantly outperforms other baseline methods in LDA, MDA and LMI prediction tasks. Additionally, case studies on both new and old datasets further supported the ability of SSCLMD to uncover novel disease-related lncRNAs and miRNAs. The source codes and supplementary file of this work are publicly available on \url{https://github.com/sheng-n/SSCLMD}.

Hierarchical hypergraph learning in association-weighted heterogeneous network for miRNA-disease association identification.

MicroRNAs (miRNAs) play a significant role in cell differentiation, biological development as well as the occurrence and growth of diseases. Although many computational methods contribute to predicting the association between miRNAs and diseases, they do not fully explore the attribute information contained in associated edges between miRNAs and diseases. In this study, we propose a new method, Hierarchical Hypergraph learning in Association-Weighted heterogeneous network for MiRNA-Disease association identification (HHAWMD). HHAWMD first adaptively fuses multi-view similarities based on channel attention and distinguishes the relevance of different associated relationships according to changes in expression levels of disease-related miRNAs, miRNA similarity information, and disease similarity information. Then, HHAWMD assigns edge weights and attribute features according to the association level to construct an association-weighted heterogeneous graph. Next, HHAWMD extracts the subgraph of the miRNA-disease node pair from the heterogeneous graph and builds the hyperedge (a kind of virtual edge) between the node pair to generate the hypergraph. Finally, HHAWMD proposes a hierarchical hypergraph learning approach, including node-aware attention and hyperedge-aware attention, which aggregates the abundant semantic information contained in deep and shallow neighborhoods to the hyperedge in the hypergraph. Our experiment results suggest that HHAWMD has better performance and can be used as a powerful tool for miRNA-disease association identification. The source code and data of HHAWMD are available at https://github.com/ningq669/HHAWMD/.

MGCNRF: Prediction of Disease-Related miRNAs Based on Multiple Graph Convolutional Networks and Random Forest.

Increasing microRNAs (miRNAs) have been confirmed to be inextricably linked to various diseases, and the discovery of their associations has become a routine way of treating diseases. To overcome the time-consuming and laborious shortcoming of traditional experiments in verifying the associations of miRNAs and diseases (MDAs), a variety of computational methods have emerged. However, these methods still have many shortcomings in terms of predictive performance and accuracy. In this study, a model based on multiple graph convolutional networks and random forest (MGCNRF) was proposed for the prediction MDAs. Specifically, MGCNRF first mapped miRNA functional similarity and sequence similarity, disease semantic similarity and target similarity, and the known MDAs into four different two-layer heterogeneous networks. Second, MGCNRF applied four heterogeneous networks into four different layered attention graph convolutional networks (GCNs), respectively, to extract MDA embeddings. Finally, MGCNRF integrated the embeddings of every MDA into the features of the miRNA-disease pair and predicted potential MDAs through the random forest (RF). Fivefold cross-validation was applied to verify the prediction performance of MGCNRF, which outperforms the other seven state-of-the-art methods by area under curve. Furthermore, the accuracy and the case studies of different diseases further demonstrate the scientific rationale of MGCNRF. In conclusion, MGCNRF can serve as a scientific tool for predicting potential MDAs.

Complementary feature learning across multiple heterogeneous networks and multimodal attribute learning for predicting disease-related miRNAs

Complementary feature learning across multiple heterogeneous networks and multimodal attribute learning for predicting disease-related miRNAs

Bioinformatics and system biology analysis revealed the crosstalk between COVID-19 and osteoarthritis.

The global coronavirus disease 2019 (COVID-19) outbreak has significantly impacted public health. Moreover, there has been an association between the incidence and severity of osteoarthritis (OA) and the onset of COVID-19. However, the optimal diagnosis and treatment strategies for patients with both diseases remain uncertain. Bioinformatics is a novel approach that may help find the common pathology between COVID-19 and OA. Differentially expressed genes (DEGs) were screened by R package "limma."Functional enrichment analyses were performed to find key biological functions. Protein-protein interaction (PPI)network was constructed by STRING database and then Cytoscape was used to select hub genes. External data sets and OA mouse model validated and identified the hub genes in both mRNA and protein levels. Related transcriptional factors (TF) and microRNAs (miRNAs) were predicted with miRTarBase and JASPR database. Candidate drugs were obtained from Drug Signatures database. The immune infiltration levels of COVID-19 and OA were evaluated by CIBERSORT and scRNA-seq. A total of 74 common DEGs were identified between COVID-19 and OA. Receiver operating characteristiccurves validated the effective diagnostic values (area under curve > 0.7) of four hub genes (matrix metalloproteinases9, ATF3, CCL4, and RELA) in both the training and validation data sets of COVID-19 and OA. Quantitative polymerase chain reaction and Western Blot showed significantly higher hub gene expression in OA mice than in healthy controls. A total of 84 miRNAs and 28 TFs were identified to regulate the process of hub gene expression. The top 10 potential drugs were screened including "Simvastatin,""Hydrocortisone,"and "Troglitazone" which have been proven by Food and Drug Administration. Correlated with hub gene expression, Macrophage M0 was highly expressed while Natural killer cells and Mast cells were low in both COVID-19 and OA. Four hub genes, disease-related miRNAs, TFs, drugs, and immune infiltration help to understand the pathogenesis and perform further studies, providing a potential therapy target for COVID-19 and OA.

Predicting miRNA-Disease Associations From miRNA-Gene-Disease Heterogeneous Network With Multi-Relational Graph Convolutional Network Model.

MiRNAs are reported to be linked to the pathogenesis of human complex diseases. Disease-related miRNAs may serve as novel bio-marks and drug targets. This work focuses on designing a multi-relational Graph Convolutional Network model to predict miRNA-disease associations (HGCNMDA) from a Heterogeneous network. HGCNMDA introduces a gene layer to construct a miRNA-gene-disease heterogeneous network. We refine the features of nodes into initial and inductive features so that the direct and indirect associations between diseases and miRNA can be considered simultaneously. Then HGCNMDA learns feature embeddings for miRNAs and disease through a multi-relational graph convolutional network model that can assign appropriate weights to different types of edges in the heterogeneous network. Finally, the miRNA-disease associations were decoded by the inner product between miRNA and disease feature embeddings. We apply our model to predict human miRNA-disease associations. The HGCNMDA is superior to the other state-of-the-art models in identifying missing miRNA-disease associations and also performs well on recommending related miRNAs/diseases to new diseases/ miRNAs. The codes are available at https://github.com/weiba/HGCNMDA.

Tae-miR397 Negatively Regulates Wheat Resistance to Blumeria graminis.

MicroRNA (miRNA) plays a crucial role in the interactions between plants and pathogens, and identifying disease-related miRNAs could help us understand the mechanisms underlying plant disease pathogenesis and breed resistant varieties. However, the role of miRNA in wheat defense responses remains largely unexplored. The miR397 family is highly conserved in plants and involved in plant development and defense response. Therefore, the purpose of this study was to investigate the function of tae-miR397 in wheat resistance to powdery mildew. The expression pattern analysis revealed that tae-miR397 expression was higher in young leaves than in other tissues and was significantly decreased in wheat Bainong207 leaves after Blumeria graminis (Bgt) infection and chitin treatment. Additionally, the expression of tae-miR397 was significantly down-regulated by salicylic acid and induced under jasmonate treatment. The overexpression of tae-miR397 in common wheat Bainong207 enhanced the wheat's susceptibility to powdery mildew in the seedling and adult stages. The rate of Bgt spore germination and mycelial growth in transgenic wheat plants overexpressing tae-miR397 was faster than in the untransformed wild-type plants. The target gene of tae-miR397 was predicted to be a wound-induced protein (Tae-WIP), and the function was investigated. We demonstrated that silencing of Tae-WIP via barley-stripe-mosaic-virus-induced gene silencing enhanced wheat's susceptibility to powdery mildew. qRT-PCR indicated that tae-miR397 regulated wheat immunity by controlling pathogenesis-related gene expressions. Moreover, the transgenic plants overexpressing tae-miR397 exhibited more tillers than the wild-type plants. This work suggests that tae-miR397 is a negative regulator of resistance against powdery mildew and has great potential for breeding disease-resistant cultivars.

HOPEXGB: A Consensual Model for Predicting miRNA/lncRNA-Disease Associations Using a Heterogeneous Disease-miRNA-lncRNA Information Network.

Predicting disease-related microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) is crucial to find new biomarkers for the prevention, diagnosis, and treatment of complex human diseases. Computational predictions for miRNA/lncRNA-disease associations are of great practical significance, since traditional experimental detection is expensive and time-consuming. In this paper, we proposed a consensual machine-learning technique-based prediction approach to identify disease-related miRNAs and lncRNAs by high-order proximity preserved embedding (HOPE) and eXtreme Gradient Boosting (XGB), named HOPEXGB. By connecting lncRNA, miRNA, and disease nodes based on their correlations and relationships, we first created a heterogeneous disease-miRNA-lncRNA (DML) information network to achieve an effective fusion of information on similarities, correlations, and interactions among miRNAs, lncRNAs, and diseases. In addition, a more rational negative data set was generated based on the similarities of unknown associations with the known ones, so as to effectively reduce the false negative rate in the data set for model construction. By 10-fold cross-validation, HOPE shows better performance than other graph embedding methods. The final consensual HOPEXGB model yields robust performance with a mean prediction accuracy of 0.9569 and also demonstrates high sensitivity and specificity advantages compared to lncRNA/miRNA-specific predictions. Moreover, it is superior to other existing methods and gives promising performance on the external testing data, indicating that integrating the information on lncRNA-miRNA interactions and the similarities of lncRNAs/miRNAs is beneficial for improving the prediction performance of the model. Finally, case studies on lung, stomach, and breast cancers indicate that HOPEXGB could be a powerful tool for preclinical biomarker detection and bioexperiment preliminary screening for the diagnosis and prognosis of cancers. HOPEXGB is publicly available at https://github.com/airpamper/HOPEXGB.