Identification of Hyperuricemia Alleviating Peptides from Yellow Tuna Thunnus albacares.

The development of food-derived antihyperuricemic substances is important for alleviating hyperuricemia (HUA) and associated inflammation. Here, novel peptides fromThunnus albacares (TAP) with strong antihyperuricemic activity were prepared. TAP was prepared by alkaline protease (molecular weight <1000 Da), with an IC50 value of xanthine oxidase inhibitory activity of 2.498 mg/mL, and 5 mg/mL TAP could reduce uric acid (UA) by 33.62% in human kidney-2 (HK-2) cells (P < 0.01). Mice were fed a high-purine diet and injected with potassium oxonate to induce HUA. Oral administration of TAP (600 mg/kg/d) reduced serum UA significantly by 42.22% and increased urine UA by 79.02% (P < 0.01) via regulating urate transporters GLUT9, organic anion transporter 1, and ATP-binding cassette subfamily G2. Meantime, TAP exhibited hepatoprotective and nephroprotective effects, according to histological analysis. Besides, HUA mice treated with TAP showed anti-inflammatory activity by decreasing the levels of toll-like receptor 4, nuclear factors-κB p65, NLRP3, ASC, and Caspase-1 in the kidneys (P < 0.01). According to serum non-targeted metabolomics, 91 differential metabolites between the MC and TAP groups were identified, and purine metabolism was considered to be the main pathway for TAP alleviating HUA. In a word, TAP exhibited strong antihyperuricemic activity both in vitro and in vivo.

hnRNP A1 dysfunction alters RNA splicing and drives neurodegeneration in multiple sclerosis (MS).

Neurodegeneration is the primary driver of disease progression in multiple sclerosis (MS) resulting in permanent disability, creating an urgent need to discover its underlying mechanisms. Herein, we establish that dysfunction of the RNA binding protein heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) results in differential of binding to RNA targets causing alternative RNA splicing, which contributes to neurodegeneration in MS and its models. Using RNAseq of MS brains, we discovered differential expression and aberrant splicing of hnRNP A1 target RNAs involved in neuronal function and RNA homeostasis. We confirmed this in vivo in experimental autoimmune encephalomyelitis employing CLIPseq specific for hnRNP A1, where hnRNP A1 differentially binds and regulates RNA, including aberrantly spliced targets identified in human samples. Additionally, dysfunctional hnRNP A1 expression in neurons caused neurite loss and identical changes in splicing, corroborating hnRNP A1 dysfunction as a cause of neurodegeneration. Collectively, these data indicate hnRNP A1 dysfunction causes altered neuronal RNA splicing, resulting in neurodegeneration in MS.

Biomarker Identification via a Factorization Machine-Based Neural Network With Binary Pairwise Encoding.

Biomolecules, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), play critical roles in diverse fundamental and vital biological processes. They can serve as disease biomarkers as their dysregulations could cause complex human diseases. Identifying those biomarkers is helpful with the diagnosis, treatment, prognosis, and prevention of diseases. In this study, we propose a factorization machine-based deep neural network with binary pairwise encoding, DFMbpe, to identify the disease-related biomarkers. First, to comprehensively consider the interdependence of features, a binary pairwise encoding method is designed to obtain the raw feature representations for each biomarker-disease pair. Second, the raw features are mapped into their corresponding embedding vectors. Then, the factorization machine is conducted to get the wide low-order feature interdependence, while the deep neural network is applied to obtain the deep high-order feature interdependence. Finally, two kinds of features are combined to get the final prediction results. Unlike other biomarker identification models, the binary pairwise encoding considers the interdependence of features even though they never appear in the same sample, and the DFMbpe architecture emphasizes both low-order and high-order feature interactions simultaneously. The experimental results show that DFMbpe greatly outperforms the state-of-the-art identification models on both cross-validation and independent dataset evaluation. Besides, three types of case studies further demonstrate the effectiveness of this model.

MLRDFM: a multi-view Laplacian regularized DeepFM model for predicting miRNA-disease associations.

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 v3.2 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.

Identifying Gene Signatures for Cancer Drug Repositioning Based on Sample Clustering.

Drug repositioning is an important approach for drug discovery. Computational drug repositioning approaches typically use a gene signature to represent a particular disease and connect the gene signature with drug perturbation profiles. Although disease samples, especially from cancer, may be heterogeneous, most existing methods consider them as a homogeneous set to identify differentially expressed genes (DEGs)for further determining a gene signature. As a result, some genes that should be in a gene signature may be averaged off. In this study, we propose a new framework to identify gene signatures for cancer drug repositioning based on sample clustering (GS4CDRSC). GS4CDRSC first groups samples into several clusters based on their gene expression profiles. Second, an existing method is applied to the samples in each cluster for generating a list of DEGs. Then a weighting approach is used to identify an intergrated gene signature from all the lists of DEGs. The integrated gene signature is used to connect with drug perturbation profiles in the Connectivity Map (CMap)database to generate a list of drug candidates. GS4CDRSC has been tested with several cancer datasets and existing methods. The computational results show that GS4CDRSC outperforms those methods without the sample clustering and weighting approaches in terms of both number and rate of predicted known drugs for specific cancers.

Predicting miRNA-Disease Associations Based On Multi-View Variational Graph Auto-Encoder With Matrix Factorization.

MicroRNAs (miRNAs) have been proved to play critical roles in diverse biological processes, including the human disease development process. Exploring the potential associations between miRNAs and diseases can help us better understand complex disease mechanisms. Given that traditional biological experiments are expensive and time-consuming, computational models can serve as efficient means to uncover potential miRNA-disease associations. This study presents a new computational model based on variational graph auto-encoder with matrix factorization (VGAMF) for miRNA-disease association prediction. More specifically, VGAMF first integrates four different types of information about miRNAs into an miRNA comprehensive similarity network and two types of information about diseases into a disease comprehensive similarity network, respectively. Then, VGAMF gets the non-linear representations of miRNAs and diseases, respectively, from those two comprehensive similarity networks with variational graph auto-encoders. Simultaneously, a non-negative matrix factorization is conducted on the miRNA-disease association matrix to get the linear representations of miRNAs and diseases. Finally, a fully connected neural network combines linear and non-linear representations of miRNAs and diseases to get the final predicted association score for all miRNA-disease pairs. In the 10-fold cross-validation experiments, VGAMF achieves an average AUC of 0.9280 on HMDD v2.0 and 0.9470 on HMDD v3.2, which outperforms other competing methods. Besides, the case studies on colon cancer and esophageal cancer further demonstrate the effectiveness of VGAMF in predicting novel miRNA-disease associations.

Human Protein Complex-Based Drug Signatures for Personalized Cancer Medicine.

Disease signature-based drug repositioning approaches typically first identify a disease signature from gene expression profiles of disease samples to represent a particular disease. Then such a disease signature is connected with the drug-induced gene expression profiles to find potential drugs for the particular disease. In order to obtain reliable disease signatures, the size of disease samples should be large enough, which is not always a single case in practice, especially for personalized medicine. On the other hand, the sample sizes of drug-induced gene expression profiles are generally large. In this study, we propose a new drug repositioning approach (HDgS), in which the drug signature is first identified from drug-induced gene expression profiles, and then connected to the gene expression profiles of disease samples to find the potential drugs for patients. In order to take the dependencies among genes into account, the human protein complexes (HPC) are used to define the drug signature. The proposed HDgS is applied to the drug-induced gene expression profiles in LINCS and several types of cancer samples. The results indicate that the HPC-based drug signature can effectively find drug candidates for patients and that the proposed HDgS can be applied for personalized medicine with even one patient sample.

[Content determination of six flavonoids in Dendrobium officinale stems from different producing areas,cultivation and processing methods by QAMS combined with dual-wavelength method].

A novel HPLC method with the quantitative analysis of multi-components by single marker( QAMS) combined with the dual-wavelength method was developed for simultaneous determination of six flavonoids in Dendrobium officinale stems from different producing areas,cultivation and processing methods to clarify the main factors contributing to the different composition of flavonoids.The separation of six flavonoids was performed on a Shiseido Capcell PAK MGⅡ C18 column( 4. 6 mm×250 mm,5 µm) using a linear gradient elution system of acetonitrile-0. 1% formic acid aqueous solution. Schaftoside,isoschaftoside,vicenin-2,and glucosylvitexin were simultaneously analyzed using rutin as a reference standard at detection wavelength of 340 nm,and naringenin was determined at290 nm. The credibility and feasibility of QAMS method were validated and the results demonstrated that no significant differences were observed as compared with the external standard method. Finally,a total of 82 batches of D. officinale samples were analyzed and principal component analysis( PCA) and discriminant analysis were applied to distinguish and compare D. officinale samples from different producing areas,cultivation and processing methods. The results showed that the total flavonoid content of D. officinale stems cultivated in the simulated wild( attached tree cultivation or attached stone cultivation) was significantly higher than that in greenhouse bed cultivation. The content of flavonoids in simulated-wild D. officinale stems was higher in Jiangxi,Guizhou,Zhejiang,and Fujian provinces,while that in greenhouse bed cultivation was higher in Fujian and Zhejiang provinces. The content of naringenin was positively correlated with processing temperature,and that of the other five flavonoids was negatively correlated with processing temperature. PCA showed that wild-simulated D. officinale and greenhouse bed-cultivated D. officinale could be roughly divided into two clusters. The samples cultivated in the greenhouse bed were divided into four categories according to the geographical habitats. Wild-simulated D. officinale samples from Guizhou gathered together,and there was no obvious rule in samples from other producing areas. The established method simplified the determination method of flavonoids in D. officinale,and could provide the basis for effective quality control,cultivation and processing of D. officinale.

The Impact of Dietary Knowledge on Health: Evidence from the China Health and Nutrition Survey.

Promoting a healthy diet through education is part of the Healthy China 2030 action plan. However, studies examining how dietary knowledge affects public health in China are sparse. This study employs multiple waves of the China Health and Nutrition Survey (CHNS) data to examine the impacts of dietary knowledge on Chinese adults' health, with a particular emphasis on how the impacts of dietary knowledge vary across different demographic groups. Moreover, we contribute to the literature by incorporating the spouse's dietary knowledge into the analysis framework to inspect the relationship between a spouse's dietary knowledge and an individual's health. Our results indicate that dietary knowledge significantly improves an individual's health status. However, there is no evidence that an individual's health is influenced by his/her spouse's dietary knowledge. Moreover, we find that individuals with a lower level of education and rural residents benefit more from increasing dietary knowledge. Policy implications of this study are also discussed.

Machine learning approaches for predicting biomolecule-disease associations.

Biomolecules, such as microRNAs, circRNAs, lncRNAs and genes, are functionally interdependent in human cells, and all play critical roles in diverse fundamental and vital biological processes. The dysregulations of such biomolecules can cause diseases. Identifying the associations between biomolecules and diseases can uncover the mechanisms of complex diseases, which is conducive to their diagnosis, treatment, prognosis and prevention. Due to the time consumption and cost of biologically experimental methods, many computational association prediction methods have been proposed in the past few years. In this study, we provide a comprehensive review of machine learning-based approaches for predicting disease-biomolecule associations with multi-view data sources. Firstly, we introduce some databases and general strategies for integrating multi-view data sources in the prediction models. Then we discuss several feature representation methods for machine learning-based prediction models. Thirdly, we comprehensively review machine learning-based prediction approaches in three categories: basic machine learning methods, matrix completion-based methods and deep learning-based methods, while discussing their advantages and disadvantages. Finally, we provide some perspectives for further improving biomolecule-disease prediction methods.

Variational graph auto-encoders for miRNA-disease association prediction.

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.

Deep belief network-Based Matrix Factorization Model for MicroRNA-Disease Associations Prediction.

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.

Predicting novel CircRNA-disease associations based on random walk and logistic regression model.

Circular RNAs (circRNAs), a large group of small endogenous noncoding RNA molecules, have been proved to modulate protein-coding genes in the human genome. In recent years, many experimental studies have demonstrated that circRNAs are dysregulated in a number of diseases, and they can serve as biomarkers for disease diagnosis and prognosis. However, it is expensive and time-consuming to identify circRNA-disease associations by biological experiments and few computational models have been proposed for novel circRNA-disease association prediction. In this study, we develop a computational model based on the random walk and the logistic regression (RWLR) to predict circRNA-disease associations. Firstly, a circRNA-circRNA similarity network is constructed by calculating their functional similarity of circRNA based on circRNA-related gene ontology. Then, a random walk with restart is implemented on the circRNA similarity network, and the features of each pair of circRNA-disease are extracted based on the results of the random walk and the circRNA-disease association matrix. Finally, a logistic regression model is used to predict novel circRNA-disease associations. Leave one out validation (LOOCV), five-fold cross validation (5CV) and ten-fold cross validation (10CV) are adopted to evaluate the prediction performance of RWLR, by comparing with the latest two methods PWCDA and DWNN-RLS. The experiment results show that our RWLR has higher AUC values of LOOCV, 5CV and 10CV than the other two latest methods, which demonstrates that RWLR has a better performance than other computational methods. What's more, case studies also illustrate the reliability and effectiveness of RWLR for circRNA-disease association prediction.

A new nucleoside and two new pyrrole alkaloid derivatives from Cordyceps militaris.

A new nucleoside, a new natural product nucleoside, and two new pyrrole alkaloids derivatives with eight known compounds were isolated from the fruiting body of Cordyceps militaris. The structures of the new compounds were elucidated through extensive analysis of spectroscopic data including 1D and 2D NMR, HRESIMS, IR and UV. All the isolated compounds were detected for their bioactivities against LPS-induced NO production in RAW 264.7 cells. Unfortunately, all the isolates have shown no obvious activity.

deepDriver: Predicting Cancer Driver Genes Based on Somatic Mutations Using Deep Convolutional Neural Networks.

With the advances in high-throughput technologies, millions of somatic mutations have been reported in the past decade. Identifying driver genes with oncogenic mutations from these data is a critical and challenging problem. Many computational methods have been proposed to predict driver genes. Among them, machine learning-based methods usually train a classifier with representations that concatenate various types of features extracted from different kinds of data. Although successful, simply concatenating different types of features may not be the best way to fuse these data. We notice that a few types of data characterize the similarities of genes, to better integrate them with other data and improve the accuracy of driver gene prediction, in this study, a deep learning-based method (deepDriver) is proposed by performing convolution on mutation-based features of genes and their neighbors in the similarity networks. The method allows the convolutional neural network to learn information within mutation data and similarity networks simultaneously, which enhances the prediction of driver genes. deepDriver achieves AUC scores of 0.984 and 0.976 on breast cancer and colorectal cancer, which are superior to the competing algorithms. Further evaluations of the top 10 predictions also demonstrate that deepDriver is valuable for predicting new driver genes.

Four new compounds from Dendrobium devonianum.

Two new phenanthrenes dendrodevonin A and B, two new aliphatic acids dendrodevonic acid A and B, along with ten known compounds, were isolated from the stems of Dendrobium devonianum. The structures of new compounds were elucidated on the basis of MS and NMR spectroscopic data, and single-crystal X-ray diffraction analysis. The cytotoxicities of isolates towards HT-29 cell were discussed, and 4-methoxy-2,7-phenanthrenediol exhibited inhibitory activity compared with other studied compounds.