MEFFGRN: Matrix enhancement and feature fusion-based method for reconstructing the gene regulatory network of epithelioma papulosum cyprini cells by spring viremia of carp virus infection.

Gene regulatory networks (GRNs) are crucial for understanding organismal molecular mechanisms and processes. Construction of GRN in the epithelioma papulosum cyprini (EPC) cells of cyprinid fish by spring viremia of carp virus (SVCV) infection helps understand the immune regulatory mechanisms that enhance the survival capabilities of cyprinid fish. Although many computational methods have been used to infer GRNs, specialized approaches for predicting the GRN of EPC cells following SVCV infection are lacking. In addition, most existing methods focus primarily on gene expression features, neglecting the valuable network structural information in known GRNs. In this study, we propose a novel supervised deep neural network, named MEFFGRN (Matrix Enhancement- and Feature Fusion-based method for Gene Regulatory Network inference), to accurately predict the GRN of EPC cells following SVCV infection. MEFFGRN considers both gene expression data and network structure information of known GRN and introduces a matrix enhancement method to address the sparsity issue of known GRN, extracting richer network structure information. To optimize the benefits of CNN (Convolutional Neural Network) in image processing, gene expression and enhanced GRN data were transformed into histogram images for each gene pair respectively. Subsequently, these histograms were separately fed into CNNs for training to obtain the corresponding gene expression and network structural features. Furthermore, a feature fusion mechanism was introduced to comprehensively integrate the gene expression and network structural features. This integration considers the specificity of each feature and their interactive information, resulting in a more comprehensive and precise feature representation during the fusion process. Experimental results from both real-world and benchmark datasets demonstrate that MEFFGRN achieves competitive performance compared with state-of-the-art computational methods. Furthermore, study findings from SVCV-infected EPC cells suggest that MEFFGRN can predict novel gene regulatory relationships.

Inference of gene regulatory networks based on directed graph convolutional networks.

Inferring gene regulatory network (GRN) is one of the important challenges in systems biology, and many outstanding computational methods have been proposed; however there remains some challenges especially in real datasets. In this study, we propose Directed Graph Convolutional neural network-based method for GRN inference (DGCGRN). To better understand and process the directed graph structure data of GRN, a directed graph convolutional neural network is conducted which retains the structural information of the directed graph while also making full use of neighbor node features. The local augmentation strategy is adopted in graph neural network to solve the problem of poor prediction accuracy caused by a large number of low-degree nodes in GRN. In addition, for real data such as E.coli, sequence features are obtained by extracting hidden features using Bi-GRU and calculating the statistical physicochemical characteristics of gene sequence. At the training stage, a dynamic update strategy is used to convert the obtained edge prediction scores into edge weights to guide the subsequent training process of the model. The results on synthetic benchmark datasets and real datasets show that the prediction performance of DGCGRN is significantly better than existing models. Furthermore, the case studies on bladder uroepithelial carcinoma and lung cancer cells also illustrate the performance of the proposed model.

DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding.

The inference of gene regulatory networks (GRNs) from gene expression profiles has been a key issue in systems biology, prompting many researchers to develop diverse computational methods. However, most of these methods do not reconstruct directed GRNs with regulatory types because of the lack of benchmark datasets or defects in the computational methods. Here, we collect benchmark datasets and propose a deep learning-based model, DeepFGRN, for reconstructing fine gene regulatory networks (FGRNs) with both regulation types and directions. In addition, the GRNs of real species are always large graphs with direction and high sparsity, which impede the advancement of GRN inference. Therefore, DeepFGRN builds a node bidirectional representation module to capture the directed graph embedding representation of the GRN. Specifically, the source and target generators are designed to learn the low-dimensional dense embedding of the source and target neighbors of a gene, respectively. An adversarial learning strategy is applied to iteratively learn the real neighbors of each gene. In addition, because the expression profiles of genes with regulatory associations are correlative, a correlation analysis module is designed. Specifically, this module not only fully extracts gene expression features, but also captures the correlation between regulators and target genes. Experimental results show that DeepFGRN has a competitive capability for both GRN and FGRN inference. Potential biomarkers and therapeutic drugs for breast cancer, liver cancer, lung cancer and coronavirus disease 2019 are identified based on the candidate FGRNs, providing a possible opportunity to advance our knowledge of disease treatments.

Personalized Driver Gene Prediction Using Graph Convolutional Networks with Conditional Random Fields.

Cancer is a complex and evolutionary disease mainly driven by the accumulation of genetic variations in genes. Identifying cancer driver genes is important. However, most related studies have focused on the population level. Cancer is a disease with high heterogeneity. Thus, the discovery of driver genes at the individual level is becoming more valuable but is a great challenge. Although there have been some computational methods proposed to tackle this challenge, few can cover all patient samples well, and there is still room for performance improvement. In this study, to identify individual-level driver genes more efficiently, we propose the PDGCN method. PDGCN integrates multiple types of data features, including mutation, expression, methylation, copy number data, and system-level gene features, along with network structural features extracted using Node2vec in order to construct a sample-gene interaction network. Prediction is performed using a graphical convolutional neural network model with a conditional random field layer, which is able to better combine the network structural features with biological attribute features. Experiments on the ACC (Adrenocortical Cancer) and KICH (Kidney Chromophobe) datasets from TCGA (The Cancer Genome Atlas) demonstrated that the method performs better compared to other similar methods. It can identify not only frequently mutated driver genes, but also rare candidate driver genes and novel biomarker genes. The results of the survival and enrichment analyses of these detected genes demonstrate that the method can identify important driver genes at the individual level.

CNNGRN: A Convolutional Neural Network-Based Method for Gene Regulatory Network Inference From Bulk Time-Series Expression Data.

Gene regulatory networks (GRNs) participate in many biological processes, and reconstructing them plays an important role in systems biology. Although many advanced methods have been proposed for GRN reconstruction, their predictive performance is far from the ideal standard, so it is urgent to design a more effective method to reconstruct GRN. Moreover, most methods only consider the gene expression data, ignoring the network structure information contained in GRN. In this study, we propose a supervised model named CNNGRN, which infers GRN from bulk time-series expression data via convolutional neural network (CNN) model, with a more informative feature. Bulk time series gene expression data imply the intricate regulatory associations between genes, and the network structure feature of ground-truth GRN contains rich neighbor information. Hence, CNNGRN integrates the above two features as model inputs. In addition, CNN is adopted to extract intricate features of genes and infer the potential associations between regulators and target genes. Moreover, feature importance visualization experiments are implemented to seek the key features. Experimental results show that CNNGRN achieved competitive performance on benchmark datasets compared to the state-of-the-art computational methods. Finally, hub genes identified based on CNNGRN have been confirmed to be involved in biological processes through literature.

Disease biomarker identification based on sample network optimization.

A large amount of evidence shows that biomarkers are discriminant features related to disease development. Thus, the identification of disease biomarkers has become a basic problem in the analysis of complex diseases in the medical fields, such as disease stage judgment, disease diagnosis and treatment. Research based on networks have become one of the most popular methods. Several algorithms based on networks have been proposed to identify biomarkers, however the networks of genes or molecules ignored the similarities and associations among the samples. It is essential to further understand how to construct and optimize the networks to make the identified biomarkers more accurate. On this basis, more effective strategies can be developed to improve the performance of biomarkers identification. In this study, a multi-objective evolution algorithm based on sample similarity networks has been proposed for disease biomarker identification. Specifically, we design the sample similarity networks to extract the structural characteristic information among samples, which used to calculate the influence of the sample to each class. Besides, based on the networks and the group of biomarkers we choose in every iteration, we can divide samples into different classes by the importance for each class. Then, in the process of evolution algorithm population iteration, we develop the elite guidance strategy and fusion selection strategy to select the biomarkers which make the sample classification more accurate. The experiment results on the five gene expression datasets suggests that the algorithm we proposed is superior over some state-of-the-art disease biomarker identification methods.

iPromoter-CLA: Identifying promoters and their strength by deep capsule networks with bidirectional long short-term memory.

BACKGROUND AND OBJECTIVE: The promoter is a fragment of DNA and a specific sequence with transcriptional regulation function in DNA. Promoters are located upstream at the transcription start site, which is used to initiate downstream gene expression. So far, promoter identification is mainly achieved by biological methods, which often require more effort. It has become a more effective classification and prediction method to identify promoter types through computational methods. METHODS: In this study, we proposed a new capsule network and recurrent neural network hybrid model to identify promoters and predict their strength. Firstly, we used one-hot to encode DNA sequence. Secondly, we used three one-dimensional convolutional layers, a one-dimensional convolutional capsule layer and digit capsule layer to learn local features. Thirdly, a bidirectional long short-time memory was utilized to extract global features. Finally, we adopted the self-attention mechanism to improve the contribution of relatively important features, which further enhances the performance of the model. RESULTS: Our model attains a cross-validation accuracy of 86% and 73.46% in prokaryotic promoter recognition and their strength prediction, which showcases a better performance compared with the existing approaches in both the first layer promoter identification and the second layer promoter's strength prediction. CONCLUSIONS: our model not only combines convolutional neural network and capsule layer but also uses a self-attention mechanism to better capture hidden information features from the perspective of sequence. Thus, we hope that our model can be widely applied to other components.

Promoter prediction in nannochloropsis based on densely connected convolutional neural networks.

Promoter is a key DNA element located near the transcription start site, which regulates gene transcription by binding RNA polymerase. Thus, the identification of promoters is an important research field in synthetic biology. Nannochloropsis is an important unicellular industrial oleaginous microalgae, and at present, some studies have identified some promoters with specific functions by biological methods in Nannochloropsis, whereas few studies used computational methods. Here, we propose a method called DNPPro (DenseNet-Predict-Promoter) based on densely connected convolutional neural networks to predict the promoter of Nannochloropsis. First, we collected promoter sequences from six Nannochloropsis strains and removed 80% similarity using CD-HIT for each strain to yield a reliable set of positive datasets. Then, in order to construct a robust classifier, within-group scrambling method was used to generate negative dataset which overcomes the limitation of randomly selecting a non-promoter region from the same genome as a negative sample. Finally, we constructed a densely connected convolutional neural network, with the sequence one-hot encoding as the input. Compared with commonly used sequence processing methods, DNPPro can extract long sequence features to a greater extent. The cross-strain experiment on independent dataset verifies the generalization of our method. At the same time, T-SNE visualization analysis shows that our method can effectively distinguish promoters from non-promoters.

Identification of driver genes based on gene mutational effects and network centrality.

BACKGROUND: As one of the deadliest diseases in the world, cancer is driven by a few somatic mutations that disrupt the normal growth of cells, and leads to abnormal proliferation and tumor development. The vast majority of somatic mutations did not affect the occurrence and development of cancer; thus, identifying the mutations responsible for tumor occurrence and development is one of the main targets of current cancer treatments. RESULTS: To effectively identify driver genes, we adopted a semi-local centrality measure and gene mutation effect function to assess the effect of gene mutations on changes in gene expression patterns. Firstly, we calculated the mutation score for each gene. Secondly, we identified differentially expressed genes (DEGs) in the cohort by comparing the expression profiles of tumor samples and normal samples, and then constructed a local network for each mutation gene using DEGs and mutant genes according to the protein-protein interaction network. Finally, we calculated the score of each mutant gene according to the objective function. The top-ranking mutant genes were selected as driver genes. We name the proposed method as mutations effect and network centrality. CONCLUSIONS: Four types of cancer data in The Cancer Genome Atlas were tested. The experimental data proved that our method was superior to the existing network-centric method, as it was able to quickly and easily identify driver genes and rare driver factors.

Caffeic Acid in Tobacco Root Exudate Defends Tobacco Plants From Infection by Ralstonia solanacearum.

In rhizospheres, chemical barrier-forming natural compounds play a key role in preventing pathogenic bacteria from infecting plant roots. Here, we sought to identify specific phenolic exudates in tobacco (Nicotiana tobaccum) plants infected by the soil-borne pathogen Ralstonia solanacearum that may exhibit antibacterial activity and promote plant resistance against pathogens. Among detected phenolic acids, only caffeic acid was significantly induced in infected plants by R. solanacearum relative to healthy plants, and the concentration of caffeic acid reached 1.95 µg/mL. In vivo, caffeic acid at 200 µg/mL was highly active against R. solanacearum and obviously damaged the membrane structure of the R. solanacearum cells, resulting in the thinning of the cell membrane and irregular cavities in cells. Moreover, caffeic acid significantly inhibited biofilm formation by repressing the expression of the lecM and epsE genes. In vitro, caffeic acid could effectively activate phenylalanine ammonia-lyase (PAL) and peroxidase (POD) and promote the accumulation of lignin and hydroxyproline. In pot and field experiments, exogenous applications of caffeic acid significantly reduced and delayed the incidence of tobacco bacterial wilt. Taken together, all these results suggest that caffeic acid played a crucial role in defending against R. solanacearum infection and was a potential and effective antibacterial agent for controlling bacterial wilt.

Double matrix completion for circRNA-disease association prediction.

BACKGROUND: Circular RNAs (circRNAs) are a class of single-stranded RNA molecules with a closed-loop structure. A growing body of research has shown that circRNAs are closely related to the development of diseases. Because biological experiments to verify circRNA-disease associations are time-consuming and wasteful of resources, it is necessary to propose a reliable computational method to predict the potential candidate circRNA-disease associations for biological experiments to make them more efficient. RESULTS: In this paper, we propose a double matrix completion method (DMCCDA) for predicting potential circRNA-disease associations. First, we constructed a similarity matrix of circRNA and disease according to circRNA sequence information and semantic disease information. We also built a Gauss interaction profile similarity matrix for circRNA and disease based on experimentally verified circRNA-disease associations. Then, the corresponding circRNA sequence similarity and semantic similarity of disease are used to update the association matrix from the perspective of circRNA and disease, respectively, by matrix multiplication. Finally, from the perspective of circRNA and disease, matrix completion is used to update the matrix block, which is formed by splicing the association matrix obtained in the previous step with the corresponding Gaussian similarity matrix. Compared with other approaches, the model of DMCCDA has a relatively good result in leave-one-out cross-validation and five-fold cross-validation. Additionally, the results of the case studies illustrate the effectiveness of the DMCCDA model. CONCLUSION: The results show that our method works well for recommending the potential circRNAs for a disease for biological experiments.

Exploring the Potential Mechanism of Costunolide-Induced MCF-7 Cells Apoptosis by Multi-Spectroscopy, Molecular Docking and Cell Experiments.

Breast cancer is one of the most common cancer with high morbidity and mortality in women. This study aimed to explore the potential mechanism of costunolide inducing MCF-7 cells apoptosis by multi-spectroscopy, molecular docking, and cell experiments. The results manifested that costunolide interacted with calf thymus DNA (ct-DNA) in a spontaneous manner, and the minor groove as the preferential binding mode. Furthermore, costunolide inhibited cell proliferation and colony formation. Hoechst 33258 staining showed that cell apoptosis induced by costunolide might be related to DNA damage. The apoptosis mechanism relied on regulating the protein expression of Bax, Bcl-2, p53, Caspase-3 and the activation of p38MAPK and nuclear factor κB (NF-κB) pathways. This study will provide some experimental basis and potential therapeutic strategy for breast cancer treatment.

Estimation of spatial distribution and health risk by arsenic and heavy metals in shallow groundwater around Dongting Lake plain using GIS mapping.

Potable groundwater has become the primary water source for the local population because of the serious pollution of As and heavy metals in the surface water around the Dongting Lake Plain. A comprehensive research on the shallow groundwater was performed in this study via geographical information system (GIS) and geochemical method to evaluate groundwater quality and health risks of shallow groundwater in Dongting Lake Plain. Eighty-seven samples were collected and the content of As and twelve other heavy metals (e.g., Al, Fe, Zn, Cu, Mo, Ni, Mn, Co, Ba, Pb, Cd, and Cr) in the samples were detected by inductively coupled plasma-mass spectrum (ICP-MS) technology. The water pollution situation was assessed using heavy metal contents and evaluation indices, and human health risks were evaluated on the basis of both carcinogenic and noncarcinogenic aspects. Results showed that the shallow groundwater quality is moderately to heavily contaminated and should be considered in some areas of the Li and Xiangjiang River coasts. Several regions have the potential of carcinogenic risks induced by As and the groundwater in some regions may have the risk of Cr carcinogenesis in the wet season. These findings suggested that the potential harm caused by Fe, Zn, Mn, Cr, and As pollution of groundwater, especially As and Cr in wet season, must be considered. The spatio-temporal study on the groundwater quality evaluation may be beneficial to the protection and sustainable development of groundwater resources in Dongting Lake Plain.Summary: Although the overall noncarcinogenic health risk by metals in shallow groundwater of Dongting Lake is low, noncarcinogenic health risks caused by Fe, Zn, Mn, and As exist in some areas.

Prioritizing Cancer Genes Based on an Improved Random Walk Method.

Identifying driver genes that contribute to cancer progression from numerous passenger genes, although a central goal, is a major challenge. The protein-protein interaction network provides convenient and reasonable assistance for driver gene discovery. Random walk-based methods have been widely used to prioritize nodes in social or biological networks. However, most studies select the next arriving node uniformly from the random walker's neighbors. Few consider transiting preference according to the degree of random walker's neighbors. In this study, based on the random walk method, we propose a novel approach named Driver_IRW (Driver genes discovery with Improved Random Walk method), to prioritize cancer genes in cancer-related network. The key idea of Driver_IRW is to assign different transition probabilities for different edges of a constructed cancer-related network in accordance with the degree of the nodes' neighbors. Furthermore, the global centrality (here is betweenness centrality) and Katz feedback centrality are incorporated into the framework to evaluate the probability to walk to the seed nodes. Experimental results on four cancer types indicate that Driver_IRW performs more efficiently than some previously published methods for uncovering known cancer-related genes. In conclusion, our method can aid in prioritizing cancer-related genes and complement traditional frequency and network-based methods.

Erratum: Anti-inflammatory effect of Resolvin D1 on LPS-treated MG-63 cells.

[This corrects the article DOI: 10.3892/etm.2018.6721.].

Highly-efficient separation of oil and water enabled by a silica nanoparticle coating with pH-triggered tunable surface wettability.

Environmentally switched superwetting surfaces that can be used for separating various oil/water mixtures are of particular interest due to the increasing difficulty and complexity in oily wastewater treatment. Here, a novel fluorine-free pH-responsive coating is prepared by surface modification of SiO2 nanoparticles with dimethyloctadecyl [3-(trimethoxysilyl) propyl] ammonium chloride and (N, N-dimethyl-3-aminopropyl) trimethoxysilane. With the assistance of polyethylene imine as a binder, such coating can be used for different porous substrates, e. g. cotton fabric and filter paper, to develop separation materials having tunable superhydrophilicity/superhydrophobicity and high antibacterial property. Due to the well-controlled surface wettability upon the pH variation, the as-prepared materials can effectively separate various types of oil/water mixtures with efficiency higher than 99.9%, including the layered oil/water mixture, water-in-oil emulsions and oil-in-water emulsions stabilized by different types of surfactants. Additionally, the materials can resist strong acid/base solutions and various organic solvents as well as 50-cycle mechanical abrasion and 120-cycle tape-peeling without losing anti-wetting performance. Featuring the tunable surface wettability, chemical/mechanical robustness, and antibacterial activity, such coating holds promising applications for treating various oil/water mixtures in harsh and biological-contamination conditions.

Identifying Disease-Gene Associations With Graph-Regularized Manifold Learning.

Complex diseases are known to be associated with disease genes. Uncovering disease-gene associations is critical for diagnosis, treatment, and prevention of diseases. Computational algorithms which effectively predict candidate disease-gene associations prior to experimental proof can greatly reduce the associated cost and time. Most existing methods are disease-specific which can only predict genes associated with a specific disease at a time. Similarities among diseases are not used during the prediction. Meanwhile, most methods predict new disease genes based on known associations, making them unable to predict disease genes for diseases without known associated genes.In this study, a manifold learning-based method is proposed for predicting disease-gene associations by assuming that the geodesic distance between any disease and its associated genes should be shorter than that of other non-associated disease-gene pairs. The model maps the diseases and genes into a lower dimensional manifold based on the known disease-gene associations, disease similarity and gene similarity to predict new associations in terms of the geodesic distance between disease-gene pairs. In the 3-fold cross-validation experiments, our method achieves scores of 0.882 and 0.854 in terms of the area under of the receiver operating characteristic (ROC) curve (AUC) for diseases with more than one known associated genes and diseases with only one known associated gene, respectively. Further de novo studies on Lung Cancer and Bladder Cancer also show that our model is capable of identifying new disease-gene associations.

Nickel-catalyzed allylic defluorinative alkylation of trifluoromethyl alkenes with reductive decarboxylation of redox-active esters.

Herein, we report a nickel-catalyzed allylic defluorinative alkylation of trifluoromethyl alkenes through reductive decarboxylation of redox-active esters. The present reaction enables the preparation of functionalized gem-difluoroalkenes with the formation of sterically hindered C(sp3)-C(sp3) bonds under very mild reaction conditions, while tolerating many sensitive functional groups and requiring minimal substrate protection. Therefore, this method provides an efficient and convenient approach for late-stage modification of biologically interesting molecules.

MECoRank: cancer driver genes discovery simultaneously evaluating the impact of SNVs and differential expression on transcriptional networks.

BACKGROUND: Although there are huge volumes of genomic data, how to decipher them and identify driver events is still a challenge. The current methods based on network typically use the relationship between genomic events and consequent changes in gene expression to nominate putative driver genes. But there may exist some relationships within the transcriptional network. METHODS: We developed MECoRank, a novel method that improves the recognition accuracy of driver genes. MECoRank is based on bipartite graph to propagates the scores via an iterative process. After iteration, we will obtain a ranked gene list for each patient sample. Then, we applied the Condorcet voting method to determine the most impactful drivers in a population. RESULTS: We applied MECoRank to three cancer datasets to reveal candidate driver genes which have a greater impact on gene expression. Experimental results show that our method not only can identify more driver genes that have been validated than other methods, but also can recognize some impactful novel genes which have been proved to be more important in literature. CONCLUSIONS: We propose a novel approach named MECoRank to prioritize driver genes based on their impact on the expression in the molecular interaction network. This method not only assesses mutation's effect on the transcriptional network, but also assesses the differential expression's effect within the transcriptional network. And the results demonstrated that MECoRank has better performance than the other competing approaches in identifying driver genes.

Anti-inflammatory effect of Resolvin D1 on LPS-treated MG-63 cells.

Inflammation reaction plays an important role in the pathogenesis of ankle fracture. The aim of the present study was to investigate the effect of RvD1 on the inflammatory response and underlying molecular mechanisms in MG-63 cells. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and ELISA were used to determine the mRNA and protein expression of cytokines extracted from peripheral blood in children with or without ankle fracture, respectively. MG-63 cells were pre-treated with/without RvD1 and stimulated with 1 µg/ml LPS. The cell viability was detected by MTT assay. The production of cytokines from MG-63 cells was assessed by RT-qPCR and western blot, respectively. The expression of p-p38, NF-κB (p50) and cyclooxygenase-2 (COX-2) mRNA and protein were detected by western blot and/or RT-qPCR. The levels of NLRP3, associated recruitment domain (ASC), cleaved caspase1, caspase-1 were measured by RT-qPCR and/or western blot. The levels of interleukin (IL)-1ß, IL-6 and tumor necrosis factor (TNF-α) mRNA and protein were up-regulated in children with ankle fracture compared with healthy children. RvD1 treatment did not induce cytotoxicity in MG-63 cells, but it can inhibit LPS induced MG-63 cell proliferation inhibition. RvD1 was able to dose-dependently reverse LPS induced up-regulation of TNF-α, IL-1ß, IL-6 mRNA and protein expression. Furthermore, the LPS induced up-regulation of p-p38, NF-κB (p50), and NLRP3, ASC, cleaved caspase-1/caspase-1, and COX-2 was dose-dependently reversed by RvD1. In conclusion, The present study demonstrated that RvD1 inhibited inflammation though inhibiting MAPKp38/NF-κB pathway and NLRP3 inflammasome expression in MG-63 cells, indicating that it may be an effective drug for the treatment of ankle fracture.