Iridoid glycosides from Morinda officinalis induce lysosomal biogenesis and promote autophagic flux to attenuate oxidative stress.

Morinda officinalis is a traditional Chinese tonic herb, and have been used in the treatment of multiple diseases. Here, three iridoid glycosides isolated from M. officinalis were evaluated for their roles in the autophagy-lysosomal pathway. All three iridoid glycosides could induce TFEB/TFE3-mediated lysosomal biogenesis and trigger autophagy. Interestingly, they promoted the nuclear import of TFEB/TFE3 without affecting their nuclear export, suggesting their role in the maintenance of lysosomal homeostasis. The results from this study shed light on the identification of autophagy activators from M. officinalis and provide a basis for developing them in the treatment of oxidative stress-involved diseases.

scGCC: Graph Contrastive Clustering With Neighborhood Augmentations for scRNA-Seq Data Analysis.

Single-cell RNA sequencing (scRNA-seq) has rapidly emerged as a powerful technique for analyzing cellular heterogeneity at the individual cell level. In the analysis of scRNA-seq data, cell clustering is a critical step in downstream analysis, as it enables the identification of cell types and the discovery of novel cell subtypes. However, the characteristics of scRNA-seq data, such as high dimensionality and sparsity, dropout events and batch effects, present significant computational challenges for clustering analysis. In this study, we propose scGCC, a novel graph self-supervised contrastive learning model, to address the challenges faced in scRNA-seq data analysis. scGCC comprises two main components: a representation learning module and a clustering module. The scRNA-seq data is first fed into a representation learning module for training, which is then used for data classification through a clustering module. scGCC can learn low-dimensional denoised embeddings, which is advantageous for our clustering task. We introduce Graph Attention Networks (GAT) for cell representation learning, which enables better feature extraction and improved clustering accuracy. Additionally, we propose five data augmentation methods to improve clustering performance by increasing data diversity and reducing overfitting. These methods enhance the robustness of clustering results. Our experimental study on 14 real-world datasets has demonstrated that our model achieves extraordinary accuracy and robustness. We also perform downstream tasks, including batch effect removal, trajectory inference, and marker genes analysis, to verify the biological effectiveness of our model.

Convolution Neural Networks Using Deep Matrix Factorization for Predicting Circrna-Disease Association.

CircRNAs have a stable structure, which gives them a higher tolerance to nucleases. Therefore, the properties of circular RNAs are beneficial in disease diagnosis. However, there are few known associations between circRNAs and disease. Biological experiments identify new associations is time-consuming and high-cost. As a result, there is a need of building efficient and achievable computation models to predict potential circRNA-disease associations. In this paper, we design a novel convolution neural networks framework(DMFCNNCD) to learn features from deep matrix factorization to predict circRNA-disease associations. Firstly, we decompose the circRNA-disease association matrix to obtain the original features of the disease and circRNA, and use the mapping module to extract potential nonlinear features. Then, we integrate it with the similarity information to form a training set. Finally, we apply convolution neural networks to predict the unknown association between circRNAs and diseases. The five-fold cross-validation on various experiments shows that our method can predict circRNA-disease association and outperforms state of the art methods.

A New Method Based on Matrix Completion and Non-Negative Matrix Factorization for Predicting Disease-Associated miRNAs.

Numerous studies have shown that microRNAs are associated with the occurrence and development of human diseases. Thus, studying disease-associated miRNAs is significantly valuable to the prevention, diagnosis and treatment of diseases. In this paper, we proposed a novel method based on matrix completion and non-negative matrix factorization (MCNMF)for predicting disease-associated miRNAs. Due to the information inadequacy on miRNA similarities and disease similarities, we calculated the latter via two models, and introduced the Gaussian interaction profile kernel similarity. In addition, the matrix completion (MC)was employed to further replenish the miRNA and disease similarities to improve the prediction performance. And to reduce the sparsity of miRNA-disease association matrix, the method of weighted K nearest neighbor (WKNKN)was used, which is a pre-processing step. We also utilized non-negative matrix factorization (NMF)using dual L2,1-norm, graph Laplacian regularization, and Tikhonov regularization to effectively avoid the overfitting during the prediction. Finally, several experiments and a case study were implemented to evaluate the effectiveness and performance of the proposed MCNMF model. The results indicated that our method could reliably and effectively predict disease-associated miRNAs.

Extra Trees Method for Predicting LncRNA-Disease Association Based On Multi-Layer Graph Embedding Aggregation.

Lots of experimental studies have revealed the significant associations between lncRNAs and diseases. Identifying accurate associations will provide a new perspective for disease therapy. Calculation-based methods have been developed to solve these problems, but these methods have some limitations. In this paper, we proposed an accurate method, named MLGCNET, to discover potential lncRNA-disease associations. Firstly, we reconstructed similarity networks for both lncRNAs and diseases using top k similar information, and constructed a lncRNA-disease heterogeneous network (LDN). Then, we applied Multi-Layer Graph Convolutional Network on LDN to obtain latent feature representations of nodes. Finally, the Extra Trees was used to calculate the probability of association between disease and lncRNA. The results of extensive 5-fold cross-validation experiments show that MLGCNET has superior prediction performance compared to the state-of-the-art methods. Case studies confirm the performance of our model on specific diseases. All the experiment results prove the effectiveness and practicality of MLGCNET in predicting potential lncRNA-disease associations.

Predicting miRNA-Disease Association Based on Improved Graph Regression.

Recently, as a growing number of associations between microRNAs (miRNAs) and diseases are discovered, researchers gradually realize that miRNAs are closely related to several complicated biological processes and human diseases. Hence, it is especially important to construct availably models to infer associations between miRNAs and diseases. In this study, we presented Improved Graph Regression for miRNA-Disease Association Prediction (IGRMDA) to observe potential relationship between miRNAs and diseases. In order to reduce the inherent noise existing in the acquired biological datasets, we utilized matrix decomposition algorithm to process miRNA functional similarity and disease semantic similarity and then combining them with existing similarity information to obtain final miRNA similarity data and disease similarity data. Then, we applied miRNA-disease association data, miRNA similarity data and disease similarity data to form corresponding latent spaces. Furthermore, we performed improved graph regression algorithm in latent spaces, which included miRNA-disease association space, miRNA similarity space and disease similarity space. Non-negative matrix factorization and partial least squares were used in the graph regression process to obtain important related attributes. The cross validation experiments and case studies were also implemented to prove the effectiveness of IGRMDA, which showed that IGRMDA could predict potential associations between miRNAs and diseases.

GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder.

microRNAs (miRNAs) are small non-coding RNAs related to a number of complicated biological processes. A growing body of studies have suggested that miRNAs are closely associated with many human diseases. It is meaningful to consider disease-related miRNAs as potential biomarkers, which could greatly contribute to understanding the mechanisms of complex diseases and benefit the prevention, detection, diagnosis and treatment of extraordinary diseases. In this study, we presented a novel model named Graph Convolutional Autoencoder for miRNA-Disease Association Prediction (GCAEMDA). In the proposed model, we utilized miRNA-miRNA similarities, disease-disease similarities and verified miRNA-disease associations to construct a heterogeneous network, which is applied to learn the embeddings of miRNAs and diseases. In addition, we separately constructed miRNA-based and disease-based sub-networks. Combining the embeddings of miRNAs and diseases, graph convolutional autoencoder (GCAE) was utilized to calculate association scores of miRNA-disease on two sub-networks, respectively. Furthermore, we obtained final prediction scores between miRNAs and diseases by adopting an average ensemble way to integrate the prediction scores from two types of subnetworks. To indicate the accuracy of GCAEMDA, we applied different cross validation methods to evaluate our model whose performances were better than the state-of-the-art models. Case studies on a common human diseases were also implemented to prove the effectiveness of GCAEMDA. The results demonstrated that GCAEMDA was beneficial to infer potential associations of miRNA-disease.

ILPMDA: Predicting miRNA-Disease Association Based on Improved Label Propagation.

MicroRNAs (miRNAs) are small non-coding RNAs that have been demonstrated to be related to numerous complex human diseases. Considerable studies have suggested that miRNAs affect many complicated bioprocesses. Hence, the investigation of disease-related miRNAs by utilizing computational methods is warranted. In this study, we presented an improved label propagation for miRNA-disease association prediction (ILPMDA) method to observe disease-related miRNAs. First, we utilized similarity kernel fusion to integrate different types of biological information for generating miRNA and disease similarity networks. Second, we applied the weighted k-nearest known neighbor algorithm to update verified miRNA-disease association data. Third, we utilized improved label propagation in disease and miRNA similarity networks to make association prediction. Furthermore, we obtained final prediction scores by adopting an average ensemble method to integrate the two kinds of prediction results. To evaluate the prediction performance of ILPMDA, two types of cross-validation methods and case studies on three significant human diseases were implemented to determine the accuracy and effectiveness of ILPMDA. All results demonstrated that ILPMDA had the ability to discover potential miRNA-disease associations.

SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.

miRNAs belong to small non-coding RNAs that are related to a number of complicated biological processes. Considerable studies have suggested that miRNAs are closely associated with many human diseases. In this study, we proposed a computational model based on Similarity Constrained Matrix Factorization for miRNA-Disease Association Prediction (SCMFMDA). In order to effectively combine different disease and miRNA similarity data, we applied similarity network fusion algorithm to obtain integrated disease similarity (composed of disease functional similarity, disease semantic similarity and disease Gaussian interaction profile kernel similarity) and integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity and miRNA Gaussian interaction profile kernel similarity). In addition, the L2 regularization terms and similarity constraint terms were added to traditional Nonnegative Matrix Factorization algorithm to predict disease-related miRNAs. SCMFMDA achieved AUCs of 0.9675 and 0.9447 based on global Leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, the case studies on two common human diseases were also implemented to demonstrate the prediction accuracy of SCMFMDA. The out of top 50 predicted miRNAs confirmed by experimental reports that indicated SCMFMDA was effective for prediction of relationship between miRNAs and diseases.

MiRNA-disease association prediction via hypergraph learning based on high-dimensionality features.

BACKGROUND: MicroRNAs (miRNAs) have been confirmed to have close relationship with various human complex diseases. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases. However, it is still a big challenge to identify which miRNAs are related to diseases. As experimental methods are in general expensive and time-consuming, it is important to develop efficient computational models to discover potential miRNA-disease associations. METHODS: This study presents a novel prediction method called HFHLMDA, which is based on high-dimensionality features and hypergraph learning, to reveal the association between diseases and miRNAs. Firstly, the miRNA functional similarity and the disease semantic similarity are integrated to form an informative high-dimensionality feature vector. Then, a hypergraph is constructed by the K-Nearest-Neighbor (KNN) method, in which each miRNA-disease pair and its k most relevant neighbors are linked as one hyperedge to represent the complex relationships among miRNA-disease pairs. Finally, the hypergraph learning model is designed to learn the projection matrix which is used to calculate uncertain miRNA-disease association score. RESULT: Compared with four state-of-the-art computational models, HFHLMDA achieved best results of 92.09% and 91.87% in leave-one-out cross validation and fivefold cross validation, respectively. Moreover, in case studies on Esophageal neoplasms, Hepatocellular Carcinoma, Breast Neoplasms, 90%, 98%, and 96% of the top 50 predictions have been manually confirmed by previous experimental studies. CONCLUSION: MiRNAs have complex connections with many human diseases. In this study, we proposed a novel computational model to predict the underlying miRNA-disease associations. All results show that the proposed method is effective for miRNA-disease association predication.

SNFIMCMDA: Similarity Network Fusion and Inductive Matrix Completion for miRNA-Disease Association Prediction.

MicroRNAs (miRNAs) that belong to non-coding RNAs are verified to be closely associated with several complicated biological processes and human diseases. In this study, we proposed a novel model that was Similarity Network Fusion and Inductive Matrix Completion for miRNA-Disease Association Prediction (SNFIMCMDA). We applied inductive matrix completion (IMC) method to acquire possible associations between miRNAs and diseases, which also could obtain corresponding correlation scores. IMC was performed based on the verified connections of miRNA-disease, miRNA similarity, and disease similarity. In addition, miRNA similarity and disease similarity were calculated by similarity network fusion, which could masterly integrate multiple data types to obtain target data. We integrated miRNA functional similarity and Gaussian interaction profile kernel similarity by similarity network fusion to obtain miRNA similarity. Similarly, disease similarity was integrated in this way. To indicate the utility and effectiveness of SNFIMCMDA, we both applied global leave-one-out cross-validation and five-fold cross-validation to validate our model. Furthermore, case studies on three significant human diseases were also implemented to prove the effectiveness of SNFIMCMDA. The results demonstrated that SNFIMCMDA was effective for prediction of possible associations of miRNA-disease.

GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest.

Predicting disease-related long non-coding RNAs (lncRNAs) is beneficial to finding of new biomarkers for prevention, diagnosis and treatment of complex human diseases. In this paper, we proposed a machine learning techniques-based classification approach to identify disease-related lncRNAs by graph auto-encoder (GAE) and random forest (RF) (GAERF). First, we combined the relationship of lncRNA, miRNA and disease into a heterogeneous network. Then, low-dimensional representation vectors of nodes were learned from the network by GAE, which reduce the dimension and heterogeneity of biological data. Taking these feature vectors as input, we trained a RF classifier to predict new lncRNA-disease associations (LDAs). Related experiment results show that the proposed method for the representation of lncRNA-disease characterizes them accurately. GAERF achieves superior performance owing to the ensemble learning method, outperforming other methods significantly. Moreover, case studies further demonstrated that GAERF is an effective method to predict LDAs.

Two Novel Quassinoid Glycosides with Antiviral Activity from the Samara of Ailanthus altissima.

Phytochemistry investigations on Ailanthus altissima (Mill.) Swingle, a Simaroubaceae plant that is recognized as a traditional herbal medicine, have afforded various natural products, among which C20 quassinoid is the most attractive for their significant and diverse pharmacological and biological activities. Our continuous study has led to the isolation of two novel quassinoid glycosides, named chuglycosides J and K, together with fourteen known lignans from the samara of A. altissima. The new structures were elucidated based on comprehensive spectra data analysis. All of the compounds were evaluated for their anti-tobacco mosaic virus activity, among which chuglycosides J and K exhibited inhibitory effects against the virus multiplication with half maximal inhibitory concentration (IC50) values of 56.21 ± 1.86 and 137.74 ± 3.57 µM, respectively.

Graph regularized L2,1-nonnegative matrix factorization for miRNA-disease association prediction.

BACKGROUND: The aberrant expression of microRNAs is closely connected to the occurrence and development of a great deal of human diseases. To study human diseases, numerous effective computational models that are valuable and meaningful have been presented by researchers. RESULTS: Here, we present a computational framework based on graph Laplacian regularized L2, 1-nonnegative matrix factorization (GRL2, 1-NMF) for inferring possible human disease-connected miRNAs. First, manually validated disease-connected microRNAs were integrated, and microRNA functional similarity information along with two kinds of disease semantic similarities were calculated. Next, we measured Gaussian interaction profile (GIP) kernel similarities for both diseases and microRNAs. Then, we adopted a preprocessing step, namely, weighted K nearest known neighbours (WKNKN), to decrease the sparsity of the miRNA-disease association matrix network. Finally, the GRL2,1-NMF framework was used to predict links between microRNAs and diseases. CONCLUSIONS: The new method (GRL2, 1-NMF) achieved AUC values of 0.9280 and 0.9276 in global leave-one-out cross validation (global LOOCV) and five-fold cross validation (5-CV), respectively, showing that GRL2, 1-NMF can powerfully discover potential disease-related miRNAs, even if there is no known associated disease.

Two new compounds from the fruit of Ailanthus altissima.

A new phenolic derivative, 4-hydroxyphenol-1-O-[6-O-(E)-feruloyl-ß-d-glucopyranosyl]-(1→6)-ß-d-glucopyranoside (1), and a new terpenylated coumarin, named altissimacoumarin H (2) were identified from the fruit of Ailanthus altissima (Mill.) Swingle (Simaroubaceae), together with ten known compounds (3-12), including two coumarins and eight phenylpropanoids. Their structures were determined on the basis of chemical method and spectroscopic data. Antiviral effect against Tobacco mosaic virus (TMV) of all the compounds obtained were evaluated using leaf-disc method.

Anti-Tobacco Mosaic Virus Quassinoids from Ailanthus altissima (Mill.) Swingle.

Quassinoids are bitter constituents characteristic of the family Simaroubaceae. A total of 18 C20 quassinoids, including nine new quassinoid glycosides, named chuglycosides A-I (1-6 and 8-10), were identified from the samara of Ailanthus altissima (Mill.) Swingle. All of the quassinoids showed potent anti-tobacco mosaic virus (TMV) activity. A preliminary structure-anti-TMV activity relationship of quassinoids was discussed. The effects of three quassinoids, including chaparrinone (12), glaucarubinone (15), and ailanthone (16), on the accumulation of TMV coat protein (CP) were studied by western blot analysis. Ailanthone (16) was further investigated for its influence on TMV spread in the Nicotiana benthamiana plant.

Phenylpropionamides, Piperidine, and Phenolic Derivatives from the Fruit of Ailanthus altissima.

Four novel compounds-two phenylpropionamides, one piperidine, and one phenolic derivatives-were isolated and identified from the fruit of a medicinal plant, Ailanthus altissima (Mill.) Swingle (Simaroubaceae), together with one known phenylpropionamide, 13 known phenols, and 10 flavonoids. The structures of the new compounds were elucidated as 2-hydroxy-N-[(2-O-ß-d-glucopyranosyl)phenyl]propionamide (1), 2-hydroxy-N-[(2-O-ß-d-glucopyranosyl-(1→6)-ß-d-glucopyranosyl)phenyl]propionamide (2), 2ß-carboxyl-piperidine-4ß-acetic acid methyl ester (4), and 4-hydroxyphenyl-1-O-[6-(hydrogen-3-hydroxy-3-methylpentanedioate)]-ß-d-glucopyranoside (5) based on spectroscopic analysis. All the isolated compounds were evaluated for their inhibitory activity against Tobacco mosaic virus (TMV) using the leaf-disc method. Among the compounds isolated, arbutin (6), ß-d-glucopyranosyl-(1→6)-arbutin (7), 4-methoxyphenylacetic acid (10), and corilagin (18) showed moderate inhibition against TMV with IC50 values of 0.49, 0.51, 0.27, and 0.45 mM, respectively.

Metabolites Produced by an Endophytic Phomopsis sp. and Their Anti-TMV Activity.

The fermentation and isolation of metabolites produced by an endophytic fungus, which was identified as Phomopsis sp. FJBR-11, based on phylogenetic analysis, led to the identification of six compounds, including dothiorelones A-C, and H, and cytosporones C and U. Among these compounds, cytosporone U exhibited potent inhibitory activity against Tobacco mosaic virus (TMV). Moreover, the crude and a purified exopolysaccharide were proved to possess strong inhibitory effects against the virus infection.

A New Erythrinan Alkaloid Glycoside from the Seeds of Erythrina crista-galli.

A new Erythrina alkaloid glycoside, named erythraline-11ß-O-glucopyranoside, was isolated from the seeds of Erythrina crista-galli L., together with five known Erythrina alkaloids and an indole alkaloid. The structure of the new alkaloid glycoside was elucidated by spectroscopic methods, and all of the compounds were evaluated for their antiviral activity against tobacco mosaic virus.