HiRAND: A novel GCN semi-supervised deep learning-based framework for classification and feature selection in drug research and development.

The prediction of response to drugs before initiating therapy based on transcriptome data is a major challenge. However, identifying effective drug response label data costs time and resources. Methods available often predict poorly and fail to identify robust biomarkers due to the curse of dimensionality: high dimensionality and low sample size. Therefore, this necessitates the development of predictive models to effectively predict the response to drugs using limited labeled data while being interpretable. In this study, we report a novel Hierarchical Graph Random Neural Networks (HiRAND) framework to predict the drug response using transcriptome data of few labeled data and additional unlabeled data. HiRAND completes the information integration of the gene graph and sample graph by graph convolutional network (GCN). The innovation of our model is leveraging data augmentation strategy to solve the dilemma of limited labeled data and using consistency regularization to optimize the prediction consistency of unlabeled data across different data augmentations. The results showed that HiRAND achieved better performance than competitive methods in various prediction scenarios, including both simulation data and multiple drug response data. We found that the prediction ability of HiRAND in the drug vorinostat showed the best results across all 62 drugs. In addition, HiRAND was interpreted to identify the key genes most important to vorinostat response, highlighting critical roles for ribosomal protein-related genes in the response to histone deacetylase inhibition. Our HiRAND could be utilized as an efficient framework for improving the drug response prediction performance using few labeled data.

Use of Basalt Fiber-Reinforced Tailings for Improving the Stability of Tailings Dam.

As one of the largest artificial geotechnical structures on earth, the tailings dams are classified as one of the high-risk sources in China's industry. How to improve the stability and safety of tailings dams remains a challenge for mine operators currently. In this paper, an innovative method is presented for improving the stability of tailings dams, in which the basalt fiber is used to reinforce tailings. The mechanical properties of tailings used for dam-construction have a great influence on the stability of tailings dam. In order to investigate the mechanical performance of basalt fiber-reinforced tailings (BFRT), a series of laboratory triaxial tests were conducted. The effects of five parameters (fiber length, fiber content, particle size, dry density and confining pressure) on the mechanical properties of BFRT were studied. The microstructure and the behavior of interfaces between basalt fibers and tailings particles were analyzed by using scanning electron microscopy (SEM). The triaxial experimental test results show that the mechanical properties of BFRT increase with the increases of fiber length and content, particle size, dry density and confining pressure. The SEM results indicate that the interfacial interaction between fibers and tailings particles is mainly affected by particle shape.

LncACTdb 2.0: an updated database of experimentally supported ceRNA interactions curated from low- and high-throughput experiments.

We describe LncACTdb 2.0 (http://www.bio-bigdata.net/LncACTdb/), an updated and significantly expanded database which provides comprehensive information of competing endogenous RNAs (ceRNAs) in different species and diseases. We have updated LncACTdb 2.0 with more data and several new features, including (i) manually curating 2663 experimentally supported ceRNA interactions from >5000 published literatures; (ii) expanding the scope of the database up to 23 species and 213 diseases/phenotypes; (iii) curating more ceRNA types such as circular RNAs and pseudogenes; (iv) identifying and scoring candidate lncRNA-associated ceRNA interactions across 33 cancer types from TCGA data; (v) providing illustration of survival, network and cancer hallmark information for ceRNAs. Furthermore, several flexible online tools including LncACT-Get, LncACT-Function, LncACT-Survival, LncACT-Network and LncACTBrowser have been developed to perform customized analysis, functional analysis, survival analysis, network illustration and genomic visualization. LncACTdb 2.0 also provides newly designed, user-friendly web interfaces to search, browse and download all the data. The BLAST interface is convenient for users to query dataset by inputting custom sequences. The Hot points interface provides users the most studied items by others. LncACTdb 2.0 is a continually updated database and will serve as an important resource to explore ceRNAs in physiological and pathological processes.

Inhibitor analysis revealed that clathrin-mediated endocytosis is involed in cellular entry of type III grass carp reovirus.

BACKGROUND: Grass carp (Ctenopharyngodon idella) hemorrhagic disease is caused by an acute infection with grass carp reovirus (GCRV). The frequent outbreaks of this disease have suppressed development of the grass carp farming industry. GCRV104, the representative strain of genotype III grass carp (Ctenopharyngodon idella) reovirus, belongs to the Spinareovirinae subfamily and serves as a model for studying the strain of GCRV which encodes an outer-fiber protein. There is no commercially available vaccine for this genotype of GCRV. Therefore, the discovery of new inhibitors for genotype III of GCRV will be clinically beneficial. In addition, the mechanism of GCRV with fiber entry into cells remains poorly understood. METHODS: Viral entry was determined by a combination of specific pharmacological inhibitors, transmission electron microscopy, and real-time quantitative PCR. RESULTS: Our results demonstrate that both GCRV-JX01 (genotype I) and GCRV104 (genotype III) of GCRV propagated in the grass carp kidney cell line (CIK) with a typical cytopathic effect (CPE). However, GCRV104 replicated slower than GCRV-JX01 in CIK cells. The titer of GCRV-JX01 was 1000 times higher than GCRV104 at 24 h post-infection. We reveal that ammonium chloride, dynasore, pistop2, chlorpromazine, and rottlerin inhibit viral entrance and infection, but not nystatin, methyl-ß-cyclodextrin, IPA-3, amiloride, bafilomycin A1, nocodazole, and latrunculin B. Furthermore, GCRV104 and GCRV-JX01 infection of CIK cells depended on dynamin and the acidification of the endosome. This was evident by the significant inhibition following prophylactic treatment with the lysosomotropic drug ammonium chloride or dynasore. CONCLUSIONS: Taken together, our data have suggested that GCRV104 enters CIK cells through clathrin-mediated endocytosis in a pH-dependent manner. We also suggest that dynamin is critical for efficient viral entry. Additionally, the phosphatidylinositol 3-kinase inhibitor wortmannin and the protein kinase C inhibitor rottlerin block GCRV104 cell entry and replication.

A systems biology approach to identify microRNAs contributing to cisplatin resistance in human ovarian cancer cells.

Cisplatin (CDDP)-based chemotherapy is a standard first-line therapy for ovarian cancer. However, drug resistance remains a major obstacle to its efficacy. Recently, increasing evidence suggested that the aberrant expression of microRNAs (miRNAs) may contribute to drug resistance. Here, we proposed a systems biology analysis strategy to identify the novel miRNAs potentially involved in CDDP resistance in human ovarian cancer cells. Firstly, we identified the candidate miRNAs associated with CDDP resistance using NCI-60 data. Next, the differentially expressed genes (DEGs) in the CDDP-resistant ovarian cancer cell line OVCAR-8R were obtained. After mapping the DEGs to a human protein-protein interaction network, a CDDP resistance-related sub-network for ovarian cancer was constructed, and subsequently the functional gene modules were identified. Then, based on the experimentally validated miRNA regulations to target genes, 4 candidate miRNAs (miR-24-3p, miR-192-5p, miR-139-5p and miR-155-5p) were identified to potentially contribute to ovarian cancer cell chemoresistance to CDDP through mediating OVCAR-8R cell CDDP resistance-related gene modules, which participated in functions that were closely related to "apoptosis", "cell cycle" and "adhesion". In addition, we predicted the therapeutic drugs that might reduce or reverse CDDP resistance by targeting these 4 identified miRNAs. This study revealed the underlying mechanism of CDDP resistance, and provided novel potential drug targets and therapeutics for CDDP-resistant ovarian cancer patients.

Grass carp reovirus outer capsid proteins VP5 and VP7 interact in vitro.

Grass carp reovirus (GCRV) causes viral hemorrhagic disease in cultured grass carp. However, there is no effective means of controlling GCRV. Previous studies have shown, by cryoelectron microscopy, that the outer capsid of GCRV is composed of 200 trimers of VP5-VP7 heterodimers. However, confirmation of this interaction between VP5 and VP7 through molecular biochemistry is still lacking. This study characterized the interactions between VP5 and VP7 in vitro. VP5 was shown to interact with VP7 in a commercial yeast-two-hybrid screen. A dot-blot overlay assay was used to show that VP7 binding to VP5 was dose-dependent. Finally, a yeast-two-hybrid approach confirmed interactions between full-length and truncated forms of VP5 and VP7. In conclusion, our results indicate that VP5 and VP7 interact directly in vitro.

Disruption of clathrin-dependent trafficking results in the failure of grass carp reovirus cellular entry.

BACKGROUND: Grass carp reovirus (GCRV) is responsible for viral hemorrhagic disease in cultured grass carp (Ctenopharyngon idellus). GCRV is a non-enveloped, double-stranded RNA virus in the genus Aquareovirus, of the family Reoviridae, which encodes seven structural proteins (VP1-VP7) and five nonstructural proteins (NS80, NS38, NS31, NS26, and NS16). To date, the mechanism of GCRV entry into CIK Ctenopharyngon idellus kidney (CIK) cells remains poorly understood. RESULTS: Here, we present a study of the GCRV internalization mechanism in CIK cells. Our results indicated that GCRV infection was inhibited by chlorpromazine, the specific inhibitor for clathrin-mediated endocytosis. Colocalization of GCRV virions with endogenous clathrin was observed during early infection by confocal microscopy. Moreover, GCRV infection of CIK cells depended on acidification of the endosome. This was indicated by significant inhibition of viral infection following prophylactic treatment with the lysosomotropic drugs chloroquine or ammonium chloride. In addition, the disturbance of dynamin activity blocked GCRV entry, which confirmed the dynamin-dependent nature of clathrin-mediated endocytosis. CONCLUSION: Our findings suggest that GCRV might enter CIK cells via clathrin-mediated endocytosis in a pH-dependent manner. Additionally, dynamin is critical for efficient viral entry.

Identification of (-)-epigallocatechin-3-gallate as a potential agent for blocking infection by grass carp reovirus.

Grass carp reovirus (GCRV), the representative strain of the species Aquareovirus C, serves as a model for studying the pathogenesis of aquareoviruses. Previously, epigallocatechin gallate (EGCG) was shown to inhibit orthoreovirus infection. The aim of this study was to test its potential in blocking infection by GCRV. We show that adhesion to the CIK (Ctenopharyngodon idellus kidney) cell surface by GCRV particles is inhibited in a dose-dependent manner by EGCG, as well as by a crude extract of green tea. We also evaluated the safety of EGCG and green tea extract using CIK cells, and the results suggest that EGCG is a promising compound that may be developed as a plant-derived small molecular therapeutic agent against grass carp hemorrhagic disease caused by GCRV infection. As the ligand for the 37/67-kDa laminin receptor (LamR), EGCG's blocking effect on GCRV attachment was associated with the binding potential of GCRV particles to LamR, which was inferred from a VOPBA assay.

Grass carp Ctenopharyngodon idella Fibulin-4 as a potential interacting partner for grass carp reovirus outer capsid proteins.

Mammalian EGF containing fibulin-like extracellular matrix protein 2 (Fibulin-4/EFEMP2), an extracellular matrix(ECM) protein and a member of the fibulin family, is involved in elastic fiber formation, connective tissue development and some human diseases. In a yeast-two hybrid screening of host proteins interacting with outer capsid protein of grass carp reovirus (GCRV), a grass carp homologue of Fibulin-4 (designated as GcFibulin-4) is suggested to hold the potential to bind VP7, VP56 and VP55, the outer capsid protein encoded by type I, II, III GCRV, respectively. GcFibulin-4 gene of grass carp was cloned and sequenced from the cDNA library constructed for the yeast two-hybrid screening. Full-length cDNA of GcFibulin-4 contains an open reading frame (ORF) of 1323 bp encoding a putative protein of 440 amino acids. Phylogenetic analysis of GcFibulin-4 indicated that it shared a high homology with zebra fish Fibulin-4 protein. Transcriptional distribution analysis of GcFibulin-4 in various tissues of healthy grass carp showed that GcFibulin-4 was highly expressed in muscle, moderately expressed in the intestine and brain, and slightly expressed in other examined tissues; the expression pattern is consistent with tissue tropism of GCRV resulting in hemorrhage symptom in the corresponding tissues. Our results suggested that Fibulin-4 might enable free GCRV particles, the pathogen for grass carp hemorrhagic disease, to target fish tissues more efficiently by interacting with viral outer capsid proteins.

Community of protein complexes impacts disease association.

One important challenge in the post-genomic era is uncovering the relationships among distinct pathophenotypes by using molecular signatures. Given the complex functional interdependencies between cellular components, a disease is seldom the consequence of a defect in a single gene product, instead reflecting the perturbations of a group of closely related gene products that carry out specific functions together. Therefore, it is meaningful to explore how the community of protein complexes impacts disease associations. Here, by integrating a large amount of information from protein complexes and the cellular basis of diseases, we built a human disease network in which two diseases are linked if they share common disease-related protein complex. A systemic analysis revealed that linked disease pairs exhibit higher comorbidity than those that have no links, and that the stronger association two diseases have based on protein complexes, the higher comorbidity they are prone to display. Moreover, more connected diseases tend to be malignant, which have high prevalence. We provide novel disease associations that cannot be identified through previous analysis. These findings will potentially provide biologists and clinicians new insights into the etiology, classification and treatment of diseases.

A novel method to quantify gene set functional association based on gene ontology.

Numerous gene sets have been used as molecular signatures for exploring the genetic basis of complex disorders. These gene sets are distinct but related to each other in many cases; therefore, efforts have been made to compare gene sets for studies such as those evaluating the reproducibility of different experiments. Comparison in terms of biological function has been demonstrated to be helpful to biologists. We improved the measurement of semantic similarity to quantify the functional association between gene sets in the context of gene ontology and developed a web toolkit named Gene Set Functional Similarity (GSFS; http://bioinfo.hrbmu.edu.cn/GSFS). Validation based on protein complexes for which the functional associations are known demonstrated that the GSFS scores tend to be correlated with sequence similarity scores and that complexes with high GSFS scores tend to be involved in the same functional catalogue. Compared with the pairwise method and the annotation method, the GSFS shows better discrimination and more accurately reflects the known functional catalogues shared between complexes. Case studies comparing differentially expressed genes of prostate tumour samples from different microarray platforms and identifying coronary heart disease susceptibility pathways revealed that the method could contribute to future studies exploring the molecular basis of complex disorders.