Abstract: BACKGROUND: RNA-seq is widely used for transcriptomic profiling, but the bioinformatics analysis of resultant data can be time-consuming and challenging, especially for biologists. We aim to streamline the bioinformatic analyses of gene-level data by developing a user-friendly, interactive web application for exploratory data analysis, differential expression, and pathway analysis. RESULTS: iDEP (integrated Differential Expression and Pathway analysis) seamlessly connects 63 R/Bioconductor packages, 2 web services, and comprehensive annotation and pathway databases for 220 plant and animal species. The workflow can be reproduced by downloading customized R code and related pathway files. As an example, we analyzed an RNA-Seq dataset of lung fibroblasts with Hoxa1 knockdown and revealed the possible roles of SP1 and E2F1 and their target genes, including microRNAs, in blocking G1/S transition. In another example, our analysis shows that in mouse B cells without functional p53, ionizing radiation activates the MYC pathway and its downstream genes involved in cell proliferation, ribosome biogenesis, and non-coding RNA metabolism. In wildtype B cells, radiation induces p53-mediated apoptosis and DNA repair while suppressing the target genes of MYC and E2F1, and leads to growth and cell cycle arrest. iDEP helps unveil the multifaceted functions of p53 and the possible involvement of several microRNAs such as miR-92a, miR-504, and miR-30a. In both examples, we validated known molecular pathways and generated novel, testable hypotheses. CONCLUSIONS: Combining comprehensive analytic functionalities with massive annotation databases, iDEP ( http://ge-lab.org/idep/ ) enables biologists to easily translate transcriptomic and proteomic data into actionable insights.

Abstract: Japanese encephalitis is a zoonotic disease caused by the Japanese encephalitis virus (JEV). It is mainly epidemic in Asia with an estimated 69,000 cases occurring per year. However, no approved agents are available for the treatment of JEV infection, and existing vaccines cannot control various types of JEV strains. Drug repurposing is a new concept for finding new indication of existing drugs, and, recently, the concept has been used to discover new antiviral agents. Identifying host proteins involved in the progress of JEV infection and using these proteins as targets are the center of drug repurposing for JEV infection. In this study, based on the gene expression data of JEV infection and the phenome-wide association study (PheWAS) data, we identified 286 genes that participate in the progress of JEV infection using systems biology methods. The enrichment analysis of these genes suggested that the genes identified by our methods were predominantly related to viral infection pathways and immune response-related pathways. We found that bortezomib, which can target these genes, may have an effect on the treatment of JEV infection. Subsequently, we evaluated the antiviral activity of bortezomib using a JEV-infected mouse model. The results showed that bortezomib can lower JEV-induced lethality in mice, alleviate suffering in JEV-infected mice and reduce the damage in brains caused by JEV infection. This work provides an agent with new indication to treat JEV infection.

Abstract: BACKGROUND: Among the various molecular fingerprints available to describe small organic molecules, extended connectivity fingerprint, up to four bonds (ECFP4) performs best in benchmarking drug analog recovery studies as it encodes substructures with a high level of detail. Unfortunately, ECFP4 requires high dimensional representations (>/= 1024D) to perform well, resulting in ECFP4 nearest neighbor searches in very large databases such as GDB, PubChem or ZINC to perform very slowly due to the curse of dimensionality. RESULTS: Herein we report a new fingerprint, called MinHash fingerprint, up to six bonds (MHFP6), which encodes detailed substructures using the extended connectivity principle of ECFP in a fundamentally different manner, increasing the performance of exact nearest neighbor searches in benchmarking studies and enabling the application of locality sensitive hashing (LSH) approximate nearest neighbor search algorithms. To describe a molecule, MHFP6 extracts the SMILES of all circular substructures around each atom up to a diameter of six bonds and applies the MinHash method to the resulting set. MHFP6 outperforms ECFP4 in benchmarking analog recovery studies. By leveraging locality sensitive hashing, LSH approximate nearest neighbor search methods perform as well on unfolded MHFP6 as comparable methods do on folded ECFP4 fingerprints in terms of speed and relative recovery rate, while operating in very sparse and high-dimensional binary chemical space. CONCLUSION: MHFP6 is a new molecular fingerprint, encoding circular substructures, which outperforms ECFP4 for analog searches while allowing the direct application of locality sensitive hashing algorithms. It should be well suited for the analysis of large databases. The source code for MHFP6 is available on GitHub ( https://github.com/reymond-group/mhfp ).

Abstract: The large number of chemical and pharmaceutical patents has attracted researchers doing biomedical text mining to extract valuable information such as chemicals, genes and gene products. To facilitate gene and gene product annotations in patents, BioCreative V.5 organized a gene- and protein-related object (GPRO) recognition task, in which participants were assigned to identify GPRO mentions and determine whether they could be linked to their unique biological database records. In this paper, we describe the system constructed for this task. Our system is based on two different NER approaches: the statistical-principle-based approach (SPBA) and conditional random fields (CRF). Therefore, we call our system SPBA-CRF. SPBA is an interpretable machine-learning framework for gene mention recognition. The predictions of SPBA are used as features for our CRF-based GPRO recognizer. The recognizer was developed for identifying chemical mentions in patents, and we adapted it for GPRO recognition. In the BioCreative V.5 GPRO recognition task, SPBA-CRF obtained an F-score of 73.73% on the evaluation metric of GPRO type 1 and an F-score of 78.66% on the evaluation metric of combining GPRO types 1 and 2. Our results show that SPBA trained on an external NER dataset can perform reasonably well on the partial match evaluation metric. Furthermore, SPBA can significantly improve performance of the CRF-based recognizer trained on the GPRO dataset.

Abstract: BACKGROUND: Well-designed clinical trials are of great importance in validating novel treatments and ensuring an evidence-based approach for sarcoma. This study aimed to provide a comprehensive landscape of the characteristics of metastatic or advanced sarcoma clinical trials using the substantial resource of the ClincialTrials.gov database. METHODS: We identified 260,755 trials registered with ClinicalTrials.gov in the last 20 years, and 277 of them were eligible for inclusion. The baseline characteristics were ascertained for each trial. The trials were systematically reviewed to validate their classification into 96 trials registered before 2008 and 181 trials registered between 2008 and 2017. RESULTS: We found that in the last decade, metastatic and advanced sarcoma trials were predominantly phase II-III studies (p = 0.048), were more likely to be >/=2 arms (17.7% vs 35.3%, respectively; p = 0.007), and were more likely to use randomized (13.5% vs 30.4%; p = 0.002) and double-blinded (2.1% vs 9.4%; p = 0.024) assignment than trials registered before 2008. Furthermore, in the last 10-year period, metastatic sarcoma trials were more likely to be conducted in Asia. Treatment involving target therapy and immunotherapy were more common (71.8% vs 37.5%; p < 0.001) than in previous years. CONCLUSIONS: Our data showed provocative changes in the sarcoma landscape and demonstrated that the incidence of clinical trials with target therapy and immunotherapy is increasing. These findings emphasize the desperate need for novel strategies, including target therapy and immunotherapy, to improve the outcomes for patients with advanced sarcoma.

Abstract: Isoniazid is a drug that is widely used against tuberculosis. However, it shows high interpatient variability in metabolism kinetics and clinical effect, which complicates the prescription of the medication and jeopardizes the success of the therapy. Therefore, in a specific patient, the pharmacokinetics of the drug must be elucidated to decide the proper dosage and intake frequency to make the drug suitable for therapeutic drug monitoring. This can be performed by the quantification of the drug in urine as this process is non-invasive and allows the effects of long-time exposure to be inferred. The paper describes the development of a micellar liquid chromatographic method to quantify isoniazid in urine samples. Extraction steps were avoided, making the procedure easy to handle and reducing the waste of toxic organic solvents. Isoniazid was eluted in less than 5 min without interference from other compounds of the urine using a mobile phase containing 0.15 SDS(-)12.5% 1-propanol (v/v)(-)Na(2)HPO(4) 0.01 M buffered at pH 7, running at 1 mL/min under isocratic mode through a C18 column with the detection wavelength at 265 nm. The method was validated by following the requirements of the Guidelines on Bioanalytical Method Validation issued by the European Medicines Agency (EMA) in terms of selectivity, calibration curve (r(2) = 0.9998 in the calibration range (0.03(-)10.0 mug/mL), limit of detection and quantification (10 and 30 ng/mL respectively), precision (<16.0%), accuracy (-0.9 to +8.5%), carry-over, matrix effect, and robustness. The developed method was applied to quantify isoniazid in urine samples of patients of an Indian hospital with good results. The method was found to be useful for routine analysis to check the amount of isoniazid in these patients and could be used in its therapeutic monitoring.

Abstract: The quality of data used for QSAR model derivation is extremely important as it strongly affects the final robustness and predictive power of the model. Ambiguous or wrong structures need to be carefully checked, because they lead to errors in calculation of descriptors, hence leading to meaningless results. The increasing amounts of data, however, have often made it hard to check of very large databases manually. In the light of this, we designed and implemented a semi-automated workflow integrating structural data retrieval from several web-based databases, automated comparison of these data, chemical structure cleaning, selection and standardization of data into a consistent, ready-to-use format that can be employed for modeling. The workflow integrates best practices for data curation that have been suggested in the recent literature. The workflow has been implemented with the freely available KNIME software and is freely available to the cheminformatics community for improvement and application to a broad range of chemical datasets.

Abstract: Inferring potential adverse drug reactions is an important and challenging task for the drug discovery and healthcare industry. Many previous studies in computational pharmacology have proposed utilizing multi-source drug information to predict drug side effects have and achieved initial success. However, most of the prediction methods mainly rely on direct similarities inferred from drug information and cannot fully utilize the drug information about the impact of protein(-)protein interactions (PPI) on potential drug targets. Moreover, most of the methods are designed for specific tasks. In this work, we propose a novel heterogeneous network embedding approach for learning drug representations called SDHINE, which integrates PPI information into drug embeddings and is generic for different adverse drug reaction (ADR) prediction tasks. To integrate heterogeneous drug information and learn drug representations, we first design different meta-path-based proximities to calculate drug similarities, especially target propagation meta-path-based proximity based on PPI network, and then construct a semi-supervised stacking deep neural network model that is jointly optimized by the defined meta-path proximities. Extensive experiments with three state-of-the-art network embedding methods on three ADR prediction tasks demonstrate the effectiveness of the SDHINE model. Furthermore, we compare the drug representations in terms of drug differentiation by mapping the representations into 2D space; the results show that the performance of our approach is superior to that of the comparison methods.

Abstract: BACKGROUND: Cardiac hypertrophy and acute myocardial infarction (AMI) are two common heart diseases worldwide. However, research is needed into the exact pathogenesis and effective treatment strategies for these diseases. Recently, microRNAs (miRNAs) have been suggested to regulate the pathological pathways of heart disease, indicating a potential role in novel treatments. RESULTS: In our study, we constructed a miRNA-gene-drug network and analyzed its topological features. We also identified some significantly dysregulated miRNA-gene-drug triplets (MGDTs) in cardiac hypertrophy and AMI using a computational method. Then, we characterized the activity score profile features for MGDTs in cardiac hypertrophy and AMI. The functional analyses suggested that the genes in the network held special functions. We extracted an insulin-like growth factor 1 receptor-related subnetwork in cardiac hypertrophy and a vascular endothelial growth factor A-related subnetwork in AMI. Finally, we considered insulin-like growth factor 1 receptor and vascular endothelial growth factor A as two candidate drug targets by utilizing the cardiac hypertrophy and AMI pathways. CONCLUSION: These results provide novel insights into the mechanisms and treatment of cardiac hypertrophy and AMI.

Abstract: Importance: Prenatal exposure to certain medications has been hypothesized to influence the risk of autism spectrum disorders (ASD). However, the underlying effects on the neurotransmitter systems have not been comprehensively assessed. Objective: To investigate the association of early-life interference with different neurotransmitter systems by prenatal medication exposure on the risk of ASD in offspring. Design, Setting, and Participants: This case-control study included children born from January 1, 1997, through December 31, 2007, and followed up for ASD until January 26, 2015, within a single Israeli health maintenance organization. Using publicly available data, 55 groups of medications affecting neurotransmitter systems and prescribed to pregnant women in this sample were identified. Children prenatally exposed to medications were compared with nonexposed children. Data were analyzed from March 1, 2017, through June 20, 2018. Main Outcome and Measures: Hazard ratios (HRs) and 95% CIs of ASD risk associated with exposure to medication groups using Cox proportional hazards regression, adjusted for the relevant confounders (eg, birth year, maternal age, maternal history of psychiatric and neurologic disorders, or maternal number of all medical diagnoses 1 year before pregnancy). Results: The analytic sample consisted of 96249 individuals (1405 cases; 94844 controls; mean [SD] age at the end of follow-up, 11.6 [3.1] years; 48.8% female), including 1405 with ASD and 94844 controls. Of 34 groups of medications, 5 showed nominally statistically significant association with ASD in fully adjusted models. Evidence of confounding effects of the number of maternal diagnoses on the association between offspring exposure to medication and ASD was found. Adjusting for this factor, lower estimates of ASD risk among children exposed to cannabinoid receptor agonists (HR, 0.72; 95% CI, 0.55-0.95; P = .02), muscarinic receptor 2 agonists (HR, 0.49; 95% CI, 0.24-0.98; P = .04), opioid receptor kappa and epsilon agonists (HR, 0.67; 95% CI, 0.45-0.99; P = .045), or alpha2C-adrenergic receptor agonists (HR, 0.43; 95% CI, 0.19-0.96; P = .04) were observed. Exposure to antagonists of neuronal nicotinic acetylcholine receptor alpha was associated with higher estimates of ASD risk (HR, 12.94; 95% CI, 1.35-124.25; P = .03). Conclusions and Relevance: Most of the medications affecting neurotransmitter systems in this sample had no association with the estimates of ASD risk. Replication and/or validation using experimental techniques are required.