KGANSynergy: knowledge graph attention network for drug synergy prediction.

Combination therapy is widely used to treat complex diseases, particularly in patients who respond poorly to monotherapy. For example, compared with the use of a single drug, drug combinations can reduce drug resistance and improve the efficacy of cancer treatment. Thus, it is vital for researchers and society to help develop effective combination therapies through clinical trials. However, high-throughput synergistic drug combination screening remains challenging and expensive in the large combinational space, where an array of compounds are used. To solve this problem, various computational approaches have been proposed to effectively identify drug combinations by utilizing drug-related biomedical information. In this study, considering the implications of various types of neighbor information of drug entities, we propose a novel end-to-end Knowledge Graph Attention Network to predict drug synergy (KGANSynergy), which utilizes neighbor information of known drugs/cell lines effectively. KGANSynergy uses knowledge graph (KG) hierarchical propagation to find multi-source neighbor nodes for drugs and cell lines. The knowledge graph attention network is designed to distinguish the importance of neighbors in a KG through a multi-attention mechanism and then aggregate the entity's neighbor node information to enrich the entity. Finally, the learned drug and cell line embeddings can be utilized to predict the synergy of drug combinations. Experiments demonstrated that our method outperformed several other competing methods, indicating that our method is effective in identifying drug combinations.

SLHSD: hybrid scaffolding method based on short and long reads.

In genome assembly, scaffolding can obtain more complete and continuous scaffolds. Current scaffolding methods usually adopt one type of read to construct a scaffold graph and then orient and order contigs. However, scaffolding with the strengths of two or more types of reads seems to be a better solution to some tricky problems. Combining the advantages of different types of data is significant for scaffolding. Here, a hybrid scaffolding method (SLHSD) is present that simultaneously leverages the precision of short reads and the length advantage of long reads. Building an optimal scaffold graph is an important foundation for getting scaffolds. SLHSD uses a new algorithm that combines long and short read alignment information to determine whether to add an edge and how to calculate the edge weight in a scaffold graph. In addition, SLHSD develops a strategy to ensure that edges with high confidence can be added to the graph with priority. Then, a linear programming model is used to detect and remove remaining false edges in the graph. We compared SLHSD with other scaffolding methods on five datasets. Experimental results show that SLHSD outperforms other methods. The open-source code of SLHSD is available at https://github.com/luojunwei/SLHSD.

Biomedical data and computational models for drug repositioning: a comprehensive review.

Drug repositioning can drastically decrease the cost and duration taken by traditional drug research and development while avoiding the occurrence of unforeseen adverse events. With the rapid advancement of high-throughput technologies and the explosion of various biological data and medical data, computational drug repositioning methods have been appealing and powerful techniques to systematically identify potential drug-target interactions and drug-disease interactions. In this review, we first summarize the available biomedical data and public databases related to drugs, diseases and targets. Then, we discuss existing drug repositioning approaches and group them based on their underlying computational models consisting of classical machine learning, network propagation, matrix factorization and completion, and deep learning based models. We also comprehensively analyze common standard data sets and evaluation metrics used in drug repositioning, and give a brief comparison of various prediction methods on the gold standard data sets. Finally, we conclude our review with a brief discussion on challenges in computational drug repositioning, which includes the problem of reducing the noise and incompleteness of biomedical data, the ensemble of various computation drug repositioning methods, the importance of designing reliable negative samples selection methods, new techniques dealing with the data sparseness problem, the construction of large-scale and comprehensive benchmark data sets and the analysis and explanation of the underlying mechanisms of predicted interactions.

Drug-drug similarity measure and its applications.

Drug similarities play an important role in modern biology and medicine, as they help scientists gain deep insights into drugs' therapeutic mechanisms and conduct wet labs that may significantly improve the efficiency of drug research and development. Nowadays, a number of drug-related databases have been constructed, with which many methods have been developed for computing similarities between drugs for studying associations between drugs, human diseases, proteins (drug targets) and more. In this review, firstly, we briefly introduce the publicly available drug-related databases. Secondly, based on different drug features, interaction relationships and multimodal data, we summarize similarity calculation methods in details. Then, we discuss the applications of drug similarities in various biological and medical areas. Finally, we evaluate drug similarity calculation methods with common evaluation metrics to illustrate the important roles of drug similarity measures on different applications.

A comprehensive review of scaffolding methods in genome assembly.

In the field of genome assembly, scaffolding methods make it possible to obtain a more complete and contiguous reference genome, which is the cornerstone of genomic research. Scaffolding methods typically utilize the alignments between contigs and sequencing data (reads) to determine the orientation and order among contigs and to produce longer scaffolds, which are helpful for genomic downstream analysis. With the rapid development of high-throughput sequencing technologies, diverse types of reads have emerged over the past decade, especially in long-range sequencing, which have greatly enhanced the assembly quality of scaffolding methods. As the number of scaffolding methods increases, biology and bioinformatics researchers need to perform in-depth analyses of state-of-the-art scaffolding methods. In this article, we focus on the difficulties in scaffolding, the differences in characteristics among various kinds of reads, the methods by which current scaffolding methods address these difficulties, and future research opportunities. We hope this work will benefit the design of new scaffolding methods and the selection of appropriate scaffolding methods for specific biological studies.

SPOP inhibits HBV transcription and replication by ubiquitination and degradation of HNF1α.

Hepatitis B virus (HBV) infection remains a significant public health burden worldwide. The persistence of covalently closed circular DNA (cccDNA) within the nucleus of infected hepatocytes is responsible for the failure of antiviral treatments. The ubiquitin proteasome system (UPS) has emerged as a promising antiviral target, as it can regulate HBV replication by promoting critical protein degradation in steps of viral life cycle. Speckle-type POZ protein (SPOP) is a critical adaptor for Cul3-RBX1 E3 ubiquitin ligase complex, but the effect of SPOP on HBV replication is less known. Here, we identified SPOP as a novel host antiviral factor against HBV infection. SPOP overexpression significantly inhibited the transcriptional activity of HBV cccDNA without affecting cccDNA level in HBV-infected HepG2-NTCP and primary human hepatocyte cells. Mechanism studies showed that SPOP interacted with hepatocyte nuclear factor 1α (HNF1α), and induced HNF1α degradation through host UPS pathway. Moreover, the antiviral role of SPOP was also confirmed in vivo. Together, our findings reveal that SPOP is a novel host factor which inhibits HBV transcription and replication by ubiquitination and degradation of HNF1α, providing a potential therapeutic strategy for the treatment of HBV infection.

Root-secreted (-)-loliolide modulates both belowground defense and aboveground flowering in Arabidopsis and tobacco.

Plant defense, growth, and reproduction can be modulated by chemicals emitted from neighboring plants, mainly via volatile aboveground signals. However, belowground signals and their underlying control mechanisms are largely unknown. Here, we experimentally demonstrate that the root-secreted carotenoid (-)-loliolide mediates both defensive and reproductive responses in wild-type Arabidopsis, a carotenoid-deficient Arabidopsis mutant (szl1-1), and tobacco (Nicotiana benthamiana). Wild-type Arabidopsis plants flower later than szl1-1, and they secrete (-)-loliolide into the soil, whereas szl1-1 roots do not. When Arabidopsis and tobacco occur together, wild-type Arabidopsis induces nicotine production and defense-related gene expression in tobacco, whereas szl1-1 impairs this induction but accelerates tobacco flowering. Furthermore, nicotine production and the expression of the key genes involved in nicotine biosynthesis (QPT, PMT1), plant defense (CAT1, SOD1, PR-2a, PI-II, TPI), and flowering (AP1, LFY, SOC1, FT3, FLC) are differently regulated by incubation with wild-type Arabidopsis and szl1-1 root exudates or (-)-loliolide. In particular, (-)-loliolide up-regulated flowering suppressors (FT3 and FLC) and transiently down-regulated flowering stimulators (AP1 and SOC1), delaying tobacco flowering. Therefore, root-secreted (-)-loliolide modulates plant belowground defense and aboveground flowering, yielding critical insights into plant-plant signaling interactions.

Building Porous Ni(Salen)-Based Catalysts from Waste Styrofoam via Autocatalytic Coupling Chemistry for Heterogeneous Oxidation with Molecular Oxygen.

The development of robust and industrially viable catalysts from plastic waste is of great significance, and the facile construction of high performance heterogeneous catalyst systems for phenol-quinone conversions remains a grand challenge. Herein, a feasible strategy is demonstrated to reclaim Styrofoam into hierarchically porous nickel-salen-loaded hypercrosslinked polystyrene (PS@Ni-salen) catalysts with high activities through an unusual autocatalytic coupling route. The salen is immobilized onto PS chain by Friedel-Crafts alkylation of benzyl chloride derivatives, and the generated hydrogen chloride coordinately promotes the simultaneous crosslinking and bridge formation between aromatic rings via a Scholl coupling route, leading to hierarchically porous networks. After the metallization with Ni, the resultant networks exhibit high catalytic activity for the oxidation of 2,3,6-trimethylphenol to 2,3,5-trimethyl-1,4-benzoquinone under mild conditions (303 K, 1 bar of O2 ). This catalyst also demonstrates attractive recycling performance without an obvious loss of catalytic efficiency over five consecutive cycles. This methodology might provide a potential sustainable alternative to construct environmentally benign and cost-effective catalysts for specific organic transformation.

Sub-chronic ammonia exposure induces hepatopancreatic damage, oxidative stress, and immune dysfunction in red swamp crayfish (Procambarus clarkii).

Ammonia, as one of the primary water pollutants in aquaculture, has been shown to induce a wide range of ecotoxicological effects on aquatic animals. In order to investigate the antioxidant and innate immune responses in crustaceans disrupted by ammonia, red swamp crayfish (Procambarus clarkii) were exposed to 0, 15, 30, and 50 mg/L total ammonia nitrogen for 30 d, the alterations of antioxidant responses as well as innate immunity were studied. The results showed that the severity of hepatopancreatic injury were aggravated by the increasing ammonia levels, which were mainly characterized by tubule lumen dilatation and vacuolization. The swollen mitochondria and disappeared mitochondria ridges suggested that oxidative stress induced by ammonia targets the mitochondria. Concurrently, enhanced MDA levels, and decreased GSH levels as well as the decreased transcription and activity of antioxidant enzymes, including SOD, CAT, and GPx were noticed, which suggested that high concentrations of ammonia exposure induce oxidative stress in P. clarkii. Furthermore, a significant decrease of the hemolymph ACP, AKP, and PO along with the significant downregulation of immune-related genes (ppo, hsp70, hsp90, alf1, ctl) jointly indicated that ammonia stress inhibited the innate immune function. Our findings demonstrated that sub-chronic ammonia stress induced hepatopancreatic injury and exert suppressive effects on the antioxidant capacity as well as innate immunity of P. clarkii. Our results provide a fundamental basis for the deleterious effects of ammonia stress on aquatic crustaceans.

Deep learning approach for cancer subtype classification using high-dimensional gene expression data.

MOTIVATION: Studies have shown that classifying cancer subtypes can provide valuable information for a range of cancer research, from aetiology and tumour biology to prognosis and personalized treatment. Current methods usually adopt gene expression data to perform cancer subtype classification. However, cancer samples are scarce, and the high-dimensional features of their gene expression data are too sparse to allow most methods to achieve desirable classification results. RESULTS: In this paper, we propose a deep learning approach by combining a convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU): our approach, DCGN, aims to achieve nonlinear dimensionality reduction and learn features to eliminate irrelevant factors in gene expression data. Specifically, DCGN first uses the synthetic minority oversampling technique algorithm to equalize data. The CNN can handle high-dimensional data without stress and extract important local features, and the BiGRU can analyse deep features and retain their important information; the DCGN captures key features by combining both neural networks to overcome the challenges of small sample sizes and sparse, high-dimensional features. In the experiments, we compared the DCGN to seven other cancer subtype classification methods using breast and bladder cancer gene expression datasets. The experimental results show that the DCGN performs better than the other seven methods and can provide more satisfactory classification results.

Particulate matter of air pollution may increase risk of kidney failure in IgA nephropathy.

IgA nephropathy (IgAN) is characterized by deposition of galactose-deficient IgA1 (Gd-IgA1) in glomerular mesangium associated with mucosal immune disorders. Since environmental pollution has been associated with the progression of chronic kidney disease in the general population, we specifically investigated the influence of exposure to fine particulate matter less than 2.5 µm in diameter (PM2.5) on IgAN progression. Patients with biopsy-proven primary IgAN were recruited from seven Chinese kidney centers. PM2.5 exposure from 1998 to 2016 was derived from satellite aerosol optical depth data and a total of 1,979 patients with IgAN, including 994 males were enrolled. The PM2.5 exposure levels for patients from different provinces varied but, in general, the PM2.5 exposure levels among patients from the north were higher than those among patients from the south. The severity of PM2.5 exposure in different regions was correlated with regional kidney failure burden. In addition, each 10 µg/m3 increase in annual average concentration of PM2.5 exposure before study entry (Hazard Ratio, 1.14; 95% confidence interval, 1.06-1.22) or time-varying PM2.5 exposure after study entry (1.10; 1.01-1.18) were associated with increased kidney failure risk after adjustment for age, gender, estimated glomerular filtration rate, urine protein, uric acid, hemoglobin, mean arterial pressure, Oxford classification, glucocorticoid and renin-angiotensin system blocker therapy. The associations were robust when the time period, risk factors of cardiovascular diseases or city size were further adjusted on the basis of the above model. Thus, our results suggest that PM2.5 is an independent risk factor for kidney failure in patients with IgAN, but these findings will require validation in more diverse populations and other geographic regions.

Distinct vulnerability to oxidative stress determines the ammonia sensitivity of crayfish (Procambarus clarkii) at different developmental stages.

Red swamp crayfish (Procambarus clarkii) has increasingly become a high-value freshwater product in China. During the intensive cultivation, excessive ammonia exposure is an important lethal factor of crayfish. We investigated the toxic effects and mechanisms of ammonia on crayfish at two different developmental stages. A preliminary ammonia stress test showed a 96-h LC50 of 135.10 mg/L and 299.61 mg/L for Stage_1 crayfish (8.47 ± 1.68 g) and Stage_2 crayfish (18.33 ± 2.41 g), respectively. During a prolonged ammonia exposure (up to 96 h), serum acid phosphatase and alkaline phosphatase showed a time-dependent decrease. Histological assessment indicated the degree of hepatopancreatic injury, which was mainly characterized as tubule lumen dilatation, degenerated tubule, vacuolization and dissolved hepatic epithelial cell, increased with exposure time. Enhanced malondialdehyde level and reduced antioxidant capacity of hepatopancreas were also observed. The mRNA expression and activity of catalase and superoxide dismutase showed an initial up-regulation within 24 h, and then gradually down-regulated with the exposure time. In the post-treatment recovery period, the Stage_2 crayfish exerted a stronger antioxidant and detoxification capacity than that of the Stage_1 crayfish, and thus quickly recovered from the ammonia exposure. Our findings provide a further understanding of the adverse effects of ammonia stress and suggest guidelines for water quality management during crayfish farming.

BreakNet: detecting deletions using long reads and a deep learning approach.

BACKGROUND: Structural variations (SVs) occupy a prominent position in human genetic diversity, and deletions form an important type of SV that has been suggested to be associated with genetic diseases. Although various deletion calling methods based on long reads have been proposed, a new approach is still needed to mine features in long-read alignment information. Recently, deep learning has attracted much attention in genome analysis, and it is a promising technique for calling SVs. RESULTS: In this paper, we propose BreakNet, a deep learning method that detects deletions by using long reads. BreakNet first extracts feature matrices from long-read alignments. Second, it uses a time-distributed convolutional neural network (CNN) to integrate and map the feature matrices to feature vectors. Third, BreakNet employs a bidirectional long short-term memory (BLSTM) model to analyse the produced set of continuous feature vectors in both the forward and backward directions. Finally, a classification module determines whether a region refers to a deletion. On real long-read sequencing datasets, we demonstrate that BreakNet outperforms Sniffles, SVIM and cuteSV in terms of their F1 scores. The source code for the proposed method is available from GitHub at https://github.com/luojunwei/BreakNet . CONCLUSIONS: Our work shows that deep learning can be combined with long reads to call deletions more effectively than existing methods.

Broadening the Gas Separation Utility of Monolayer Nanoporous Graphene Membranes by an Ionic Liquid Gating.

Ultrathin two-dimensional (2D) monolayer atomic crystal materials offer great potential for extending the field of novel separation technology due to their infinitesimal thickness and mechanical strength. One difficult and ongoing challenge is to perforate the 2D monolayer material with subnanometer pores with atomic precision for sieving similarly sized molecules. Here, we demonstrate the exceptional separation performance of ionic liquid (IL)/graphene hybrid membranes for challenging separation of CO2 and N2. Notably, the ultrathin ILs afford dynamic tuning of the size and chemical affinity of nanopores while preserving the high permeance of the monolayer nanoporous graphene membranes. The hybrid membrane yields a high CO2 permeance of 4000 GPU and an outstanding CO2/N2 selectivity up to 32. This rational hybrid design provides a universal direction for broadening gas separation capability of atomically thin nanoporous membranes.

NEDD: a network embedding based method for predicting drug-disease associations.

BACKGROUND: Drug discovery is known for the large amount of money and time it consumes and the high risk it takes. Drug repositioning has, therefore, become a popular approach to save time and cost by finding novel indications for approved drugs. In order to distinguish these novel indications accurately in a great many of latent associations between drugs and diseases, it is necessary to exploit abundant heterogeneous information about drugs and diseases. RESULTS: In this article, we propose a meta-path-based computational method called NEDD to predict novel associations between drugs and diseases using heterogeneous information. First, we construct a heterogeneous network as an undirected graph by integrating drug-drug similarity, disease-disease similarity, and known drug-disease associations. NEDD uses meta paths of different lengths to explicitly capture the indirect relationships, or high order proximity, within drugs and diseases, by which the low dimensional representation vectors of drugs and diseases are obtained. NEDD then uses a random forest classifier to predict novel associations between drugs and diseases. CONCLUSIONS: The experiments on a gold standard dataset which contains 1933 validated drug-disease associations show that NEDD produces superior prediction results compared with the state-of-the-art approaches.

Correction to: SLR: a scaffolding algorithm based on long reads and contig classification.

Following publication of the original article [1], the author reported that there is an error in the original article.

Drug repositioning based on bounded nuclear norm regularization.

MOTIVATION: Computational drug repositioning is a cost-effective strategy to identify novel indications for existing drugs. Drug repositioning is often modeled as a recommendation system problem. Taking advantage of the known drug-disease associations, the objective of the recommendation system is to identify new treatments by filling out the unknown entries in the drug-disease association matrix, which is known as matrix completion. Underpinned by the fact that common molecular pathways contribute to many different diseases, the recommendation system assumes that the underlying latent factors determining drug-disease associations are highly correlated. In other words, the drug-disease matrix to be completed is low-rank. Accordingly, matrix completion algorithms efficiently constructing low-rank drug-disease matrix approximations consistent with known associations can be of immense help in discovering the novel drug-disease associations. RESULTS: In this article, we propose to use a bounded nuclear norm regularization (BNNR) method to complete the drug-disease matrix under the low-rank assumption. Instead of strictly fitting the known elements, BNNR is designed to tolerate the noisy drug-drug and disease-disease similarities by incorporating a regularization term to balance the approximation error and the rank properties. Moreover, additional constraints are incorporated into BNNR to ensure that all predicted matrix entry values are within the specific interval. BNNR is carried out on an adjacency matrix of a heterogeneous drug-disease network, which integrates the drug-drug, drug-disease and disease-disease networks. It not only makes full use of available drugs, diseases and their association information, but also is capable of dealing with cold start naturally. Our computational results show that BNNR yields higher drug-disease association prediction accuracy than the current state-of-the-art methods. The most significant gain is in prediction precision measured as the fraction of the positive predictions that are truly positive, which is particularly useful in drug design practice. Cases studies also confirm the accuracy and reliability of BNNR. AVAILABILITY AND IMPLEMENTATION: The code of BNNR is freely available at https://github.com/BioinformaticsCSU/BNNR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Competitive Adsorption of Lubricant Base Oil and Ionic Liquid Additives at Air/Liquid and Solid/Liquid Interfaces.

Oil-soluble ionic liquids (ILs) have been proved as effective additives in lubricant oils through tribological experiments and post-test analytical analyses. In this study, surface structures of lubricant base oil, oil-soluble ILs, and their mixtures at the air/liquid and solid/liquid interfaces have been studied using sum frequency generation (SFG) vibrational spectroscopy. At the air/base oil and air/IL interfaces, the alkyl chains of the studied compounds were shown to be conformationally disordered and their terminal methyl groups point outward at the liquid surface. The base oil dominates the air/(base oil + IL) interface due to its higher surface excess propensity and larger bulk concentration. At the solid (silica) surface, ILs adopt a structure with their charged headgroups in contact with the silica surface, while their alkyl chains are more conformationally ordered or packed compared to the air/IL interface. At the interface between silica and (base oil + IL) mixtures, ILs also preferentially adsorb to the silica surface with their layer structures somewhat different from those of ILs alone. These results showed that ILs can adsorb onto the solid surface even before tribological contacts are made. The insights obtained from this SFG study provide a better understanding of the role of ionic liquids in lubrication.

Overlap matrix completion for predicting drug-associated indications.

Identification of potential drug-associated indications is critical for either approved or novel drugs in drug repositioning. Current computational methods based on drug similarity and disease similarity have been developed to predict drug-disease associations. When more reliable drug- or disease-related information becomes available and is integrated, the prediction precision can be continuously improved. However, it is a challenging problem to effectively incorporate multiple types of prior information, representing different characteristics of drugs and diseases, to identify promising drug-disease associations. In this study, we propose an overlap matrix completion (OMC) for bilayer networks (OMC2) and tri-layer networks (OMC3) to predict potential drug-associated indications, respectively. OMC is able to efficiently exploit the underlying low-rank structures of the drug-disease association matrices. In OMC2, first of all, we construct one bilayer network from drug-side aspect and one from disease-side aspect, and then obtain their corresponding block adjacency matrices. We then propose the OMC2 algorithm to fill out the values of the missing entries in these two adjacency matrices, and predict the scores of unknown drug-disease pairs. Moreover, we further extend OMC2 to OMC3 to handle tri-layer networks. Computational experiments on various datasets indicate that our OMC methods can effectively predict the potential drug-disease associations. Compared with the other state-of-the-art approaches, our methods yield higher prediction accuracy in 10-fold cross-validation and de novo experiments. In addition, case studies also confirm the effectiveness of our methods in identifying promising indications for existing drugs in practical applications.

X-ray photoelectron spectroscopy of piperidinium ionic liquids: a comparison to the charge delocalised pyridinium analogues.

In this study, nine piperidinium-based ionic liquids are analysed by X-ray photoelectron spectroscopy. The effect of alkyl substituent length and the nature of the anion on the electronic environment of the cation are investigated. The electronic environment of the hetero carbon and the cationic nitrogen is compared between two structurally similar cations, 1-octyl-1-methylpiperidinium ([C8C1Pip]+) versus 1-octylpyridinium ([C8Py]+). Due to the charge delocalisation, the hetero carbon component within [C8Py]+ is more positively charged, which exhibits much higher binding energy; whilst the cationic nitrogen component is in the similar electronic environment. The impact of the charge delocalisation on the electronic environment of the anion is also compared between [C8C1Pip]+ and [C8Py]+. It is found that for the more basic anion, the cation can significantly affect the electronic environment of the anion; for the less basic anion, such an effect concentrates on the component bearing more negative point charges.