The Comparative Toxicogenomics Database's 10th year anniversary: update 2015

In Silico Investigation of the Pharmacological Mechanisms of Beneficial Effects of Ginkgo biloba L. on Alzheimer's Disease

Based on compelling experimental and clinical evidence, Ginkgo biloba L. exerts a beneficial effect in ameliorating mild to moderate dementia in patients with Alzheimer’s disease (AD) and other neurological disorders, although the pharmacological mechanisms remain unknown. In the present study, compounds, their putative target proteins identified using an inverse docking approach, and clinically tested AD-related target proteins were systematically integrated together with applicable bioinformatics methods in silico. The results suggested that the beneficial effects of G. biloba on AD may be contributed by the regulation of hormone sensitivity, improvements in endocrine homeostasis, maintenance of endothelial microvascular integrity, and proteolysis of tau proteins, particularly prior to amyloid β-protein (Aβ) plaque formation. Moreover, we identified six putative protein targets that are significantly related to AD, but have not been researched or have had only preliminary studies conducted on the anti-AD effects of G. biloba. These mechanisms and protein targets are very significant for future scientific research. In addition, the existing mechanisms were also verified, such as the reduction of oxidative stress, anti-apoptotic effects, and protective effects against amyloidogenesis and Aβ aggregation. The discoveries summarized here may provide a macroscopic perspective that will improve our understanding of the molecular mechanism of medicinal plants or dietary supplements, as well as new clues for the future development of therapeutic strategies for AD.

Computational repositioning and preclinical validation of mifepristone for human vestibular schwannoma

The computational repositioning of existing drugs represents an appealing avenue for identifying effective compounds to treat diseases with no FDA-approved pharmacotherapies. Here we present the largest meta-analysis to date of differential gene expression in human vestibular schwannoma (VS), a debilitating intracranial tumor, and use these data to inform the first application of algorithm-based drug repositioning for this tumor class. We apply an open-source computational drug repositioning platform to gene expression data from 80 patient tumors and identify eight promising FDA-approved drugs with potential for repurposing in VS. Of these eight, mifepristone, a progesterone and glucocorticoid receptor antagonist, consistently and adversely affects the morphology, metabolic activity, and proliferation of primary human VS cells and HEI-193 human schwannoma cells. Mifepristone treatment reduces VS cell viability more significantly than cells derived from patient meningiomas, while healthy human Schwann cells remain unaffected. Our data recommend a Phase II clinical trial of mifepristone in VS.

Literature-based prediction of novel drug indications considering relationships between entities

There have been many attempts to identify and develop new uses for existing drugs, which is known as drug repositioning. Among these efforts, text mining is an effective means of discovering novel knowledge from a large amount of literature data. We identify a gene regulation by a drug and a phenotype based on the biomedical literature. Drugs or phenotypes can activate or inhibit gene regulation. We calculate the therapeutic possibility that a drug acts on a phenotype by means of these two types of regulation. We assume that a drug treats a phenotype if the genes regulated by the phenotype are inversely correlated with the genes regulated by the drug. Based on this hypothesis, we identify drug-phenotype associations with therapeutic possibility. To validate the drug-phenotype associations predicted by our method, we make an enrichment comparison with known drug-phenotype associations. We also identify candidate drugs for drug repositioning from novel associations and thus reveal that our method is a novel approach to drug repositioning.

Mapping genes for calcium signaling and their associated human genetic disorders

Motivation

Signal transduction via calcium ions (Ca²⁺) represents a fundamental signaling pathway in all eukaryotic cells. A large portion of the human genome encodes proteins used to assemble signaling systems that can transduce signals with diverse spatial and temporal dynamics.

Results

Here, we provide a map of all of the genes involved in Ca²⁺ signaling and link these genes to human genetic disorders. Using Gene Ontology terms and genome databases, 1805 genes were identified as regulators or targets of intracellular Ca²⁺ signals. Associating these 1805 genes with human genetic disorders uncovered 1470 diseases with mutated ‘Ca²⁺ genes’. A network with scale-free properties appeared when the Ca²⁺ genes were mapped to their associated genetic disorders.

Availability and Implementation

The Ca²⁺ genome database is freely available at http://cagedb.uhlenlab.org and will foster studies of gene functions and genetic disorders associated with Ca²⁺ signaling.

Supplementary information

Supplementary data are available at Bioinformatics online.

A transition-based joint model for disease named entity recognition and normalization

Motivation

Disease named entities play a central role in many areas of biomedical research, and automatic recognition and normalization of such entities have received increasing attention in biomedical research communities. Existing methods typically used pipeline models with two independent phases: (i) a disease named entity recognition (DER) system is used to find the boundaries of mentions in text and (ii) a disease named entity normalization (DEN) system is used to connect the mentions recognized to concepts in a controlled vocabulary. The main problems of such models are: (i) there is error propagation from DER to DEN and (ii) DEN is useful for DER, but pipeline models cannot utilize this.

Methods

We propose a transition-based model to jointly perform disease named entity recognition and normalization, casting the output construction process into an incremental state transition process, learning sequences of transition actions globally, which correspond to joint structural outputs. Beam search and online structured learning are used, with learning being designed to guide search. Compared with the only existing method for joint DEN and DER, our method allows non-local features to be used, which significantly improves the accuracies.

Results

We evaluate our model on two corpora: the BioCreative V Chemical Disease Relation (CDR) corpus and the NCBI disease corpus. Experiments show that our joint framework achieves significantly higher performances compared to competitive pipeline baselines. Our method compares favourably to other state-of-the-art approaches.

Availability and Implementation

Data and code are available at https://github.com/louyinxia/jointRN.

Contact

dhji@whu.edu.cn.

Network pharmacology-based and clinically relevant prediction of the active ingredients and potential targets of Chinese herbs in metastatic breast cancer patients

Chinese Herbal Medicine (CHM) plays a significant role in breast cancer treatment. We conduct the study to ascertain the relative molecular targets of effective Chinese herbs in treating stage IV breast cancer.Survival benefit of CHM was verified by Kaplan-Meier method and Cox regression analysis. A bivariate correlation analysis was used to find and establish the effect of herbs in complex CHM formulas. A network pharmacological approach was adopted to explore the potential mechanisms of CHM.Patients in the CHM group had a median survival time of 55 months, which was longer than the 23 months of patients in the non-CHM group. Cox regression analysis indicated that CHM was an independent protective factor. Correlation analysis showed that 10 herbs were strongly correlated with favorable survival outcomes (P<0.01). Bioinformatics analyses suggested that the 10 herbs might achieve anti-breast cancer activity primarily through inhibiting HSP90, ERα and TOP-II related pathways.

Quantitative Proteomics of Synaptosomal Fractions in a Rat Overexpressing Human DISC1 Gene Indicates Profound Synaptic Dysregulation in the Dorsal Striatum

Disrupted-in-schizophrenia 1 (DISC1) is a key protein involved in behavioral processes and various mental disorders, including schizophrenia and major depression. A transgenic rat overexpressing non-mutant human DISC1, modeling aberrant proteostasis of the DISC1 protein, displays behavioral, biochemical and anatomical deficits consistent with aspects of mental disorders, including changes in the dorsal striatum, an anatomical region critical in the development of behavioral disorders. Herein, dorsal striatum of 10 transgenic DISC1 (tgDISC1) and 10 wild type (WT) littermate control rats was used for synaptosomal preparations and for performing liquid chromatography-tandem mass spectrometry (LC-MS)-based quantitative proteomics, using isobaric labeling (TMT10plex). Functional enrichment analysis was generated from proteins with level changes. The increase in DISC1 expression leads to changes in proteins and synaptic-associated processes including membrane trafficking, ion transport, synaptic organization and neurodevelopment. Canonical pathway analysis assigned proteins with level changes to actin cytoskeleton, Gαq, Rho family GTPase and Rho GDI, axonal guidance, ephrin receptor and dopamine-DARPP32 feedback in cAMP signaling. DISC1-regulated proteins proposed in the current study are also highly associated with neurodevelopmental and mental disorders. Bioinformatics analyses from the current study predicted that the following biological processes may be activated by overexpression of DISC1, i.e., regulation of cell quantities, neuronal and axonal extension and long term potentiation. Our findings demonstrate that the effects of overexpression of non-mutant DISC1 or its misassembly has profound consequences on protein networks essential for behavioral control. These results are also relevant for the interpretation of previous as well as for the design of future studies on DISC1.

NRDTD: a database for clinically or experimentally supported non-coding RNAs and drug targets associations

In recent years, more and more non-coding RNAs (ncRNAs) have been identified and increasing evidences have shown that ncRNAs may affect gene expression and disease progression, making them a new class of targets for drug discovery. It thus becomes important to understand the relationship between ncRNAs and drug targets. For this purpose, an ncRNAs and drug targets association database would be extremely beneficial. Here, we developed ncRNA Drug Targets Database (NRDTD) that collected 165 entries of clinically or experimentally supported ncRNAs as drug targets, including 97 ncRNAs and 96 drugs. Moreover, we annotated ncRNA-drug target associations with drug information from KEGG, PubChem, DrugBank, CTD or Wikipedia, GenBank sequence links, OMIM disease ID, pathway and function annotation for ncRNAs, detailed description of associations between ncRNAs and diseases from HMDD or LncRNADisease and the publication PubMed ID. Additionally, we provided users a link to submit novel disease-ncRNA-drug associations and corresponding supporting evidences into the database. We hope NRDTD will be a useful resource for investigating the roles of ncRNAs in drug target identification, drug discovery and disease treatment.

Database URL:http://chengroup.cumt.edu.cn/NRDTD

Gene annotation bias impedes biomedical research

We found tremendous inequality across gene and protein annotation resources. We observed that this bias leads biomedical researchers to focus on richly annotated genes instead of those with the strongest molecular data. We advocate that researchers reduce these biases by pursuing data-driven hypotheses.

Accessing an Expanded Exposure Science Module at the Comparative Toxicogenomics Database

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org) is a free resource that provides manually curated information on chemical, gene, phenotype, and disease relationships to advance understanding of the effect of environmental exposures on human health. Four core content areas are independently curated: chemical-gene interactions, chemical-disease and gene-disease associations, chemical-phenotype interactions, and environmental exposure data (e.g., effects of chemical stressors on humans). Since releasing exposure data in 2015, we have vastly increased our coverage of chemicals and disease/phenotype outcomes; greatly expanded access to exposure content; added search capability by stressors, cohorts, population demographics, and measured outcomes; and created user-specified displays of content. These enhancements aim to facilitate human studies by allowing comparisons among experimental parameters and across studies involving specified chemicals, populations, or outcomes. Integration of data among CTD's four content areas and external data sets, such as Gene Ontology annotations and pathway information, links exposure data with over 1.8 million chemical-gene, chemical-disease and gene-disease interactions. Our analysis tools reveal direct and inferred relationships among the data and provide opportunities to generate predictive connections between environmental exposures and population-level health outcomes. https://doi.org/10.1289/EHP2873.

LPTK: a linguistic pattern-aware dependency tree kernel approach for the BioCreative VI CHEMPROT task

Identifying the interactions between chemical compounds and genes from biomedical literatures is one of the frequently discussed topics of text mining in the life science field. In this paper, we describe Linguistic Pattern-Aware Dependency Tree Kernel, a linguistic interaction pattern learning method developed for CHEMPROT task-BioCreative VI, to capture chemical-protein interaction (CPI) patterns within biomedical literatures. We also introduce a framework to integrate these linguistic patterns with smooth partial tree kernel to extract the CPIs. This new method of feature representation models aspects of linguistic probability in geometric representation, which not only optimizes the sufficiency of feature dimension for classification, but also defines features as interpretable contexts rather than long vectors of numbers. In order to test the robustness and efficiency of our system in identifying different kinds of biological interactions, we evaluated our framework on three separate data sets, i.e. CHEMPROT corpus, Chemical-Disease Relation corpus and Protein-Protein Interaction corpus. Corresponding experiment results demonstrate that our method is effective and outperforms several compared systems for each data set.

Investigation of candidate genes and mechanisms underlying postmenopausal osteoporosis using bioinformatics analysis

The present study aimed to determine candidate genes, chemicals and mechanisms underlying postmenopausal osteoporosis (PMOP). A gene expression profile (accession no. GSE68303), which included 12 tissue samples from ovariectomized mice (OVX group) and 11 normal tissue samples from sham surgery mice (control group), was downloaded from the Gene Expression Omnibus database. The identification of differentially expressed genes (DEGs), and Gene Ontology functional enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses, was performed, followed by an investigation of protein‑protein interactions (PPI), PPI modules, transcription factors (TFs) and chemicals. A total of 784 upregulated and 729 downregulated DEGs between the two groups were identified. Furthermore, 2 upregulated modules and 6 downregulated modules were determined. The upregulated DEGs in modules were enriched in 'sensory perception of smell' function and 'olfactory transduction' pathway, and a number of genes belonging to the olfactory receptor (OLFR) family were identified in upregulated modules. The downregulated DEGs in modules were enriched in 'DNA replication initiation' function and 'cell cycle' pathway. A total of 8 TFs, including SP1 TF (SP1) and protein C‑ets‑1 (ETS1), were associated with PMOP. Furthermore, estradiol and resveratrol were identified as key chemicals in the chemical‑gene interaction network. Therefore, TFs, including SP1 and ETS1, in addition to members of the OLFR gene family, may be employed as novel targets for treatment of PMOP. Furthermore, functions including 'sensory perception of smell' and 'replication initiation', and 'olfactory transduction' and 'cell cycle' pathways, may serve roles in PMOP. In addition, based on the chemical‑gene interaction network, estradiol and resveratrol may also be considered for the treatment PMOP.

Blood transcriptional and microRNA responses to short-term exposure to disinfection by-products in a swimming pool

BACKGROUND

Swimming in a chlorinated pool results in high exposure levels to disinfection by-products (DBPs), which have been associated with an increased risk of bladder cancer.

OBJECTIVES

By studying molecular responses at the blood transcriptome level we examined the biological processes associated with exposure to these compounds.

METHODS

Whole-genome gene expression and microRNA analysis was performed on blood samples collected from 43 volunteers before and 2h after 40min swimming in an indoor chlorinated pool (PISCINAII study). Exposure to THMs was measured in exhaled breath. Heart rate and kcal expenditure were measured as proxies for physical activity. Associations between exposure levels and gene expression were assessed using multivariate normal models (MVN), correcting for age, body mass index and sex. A Bonferroni threshold at 5% was applied.

RESULTS

MVN-models for the individual exposures identified 1778 genes and 23 microRNAs that were significantly associated with exposure to at least one DBP. Due to co-linearity it was not possible to statistically disentangle responses to DBP exposure from those related to physical activity. However, after eliminating previously reported transcripts associated with physical activity a large number of hits remained associated with DBP exposure. Among those, 9 were linked with bladder and 31 with colon cancer. Concordant microRNA/mRNA expressions were identified in association with DBP exposure for hsa-mir-22-3p and hsa-miR-146a-5p and their targets RCOR1 and TLR4, both related to colon cancer in association with DBP exposure.

CONCLUSIONS

Short-term exposure to low levels of DBPs shows genomics responses that may be indicative of increased cancer risk.

Integrating genome-wide association study summaries and element-gene interaction datasets identified multiple associations between elements and complex diseases

Chemical elements are closely related to human health. Extensive genomic profile data of complex diseases offer us a good opportunity to systemically investigate the relationships between elements and complex diseases/traits. In this study, we applied gene set enrichment analysis (GSEA) approach to detect the associations between elements and complex diseases/traits though integrating element-gene interaction datasets and genome-wide association study (GWAS) data of complex diseases/traits. To illustrate the performance of GSEA, the element-gene interaction datasets of 24 elements were extracted from the comparative toxicogenomics database (CTD). GWAS summary datasets of 24 complex diseases or traits were downloaded from the dbGaP or GEFOS websites. We observed significant associations between 7 elements and 13 complex diseases or traits (all false discovery rate (FDR) < 0.05), including reported relationships such as aluminum vs. Alzheimer's disease (FDR = 0.042), calcium vs. bone mineral density (FDR = 0.031), magnesium vs. systemic lupus erythematosus (FDR = 0.012) as well as novel associations, such as nickel vs. hypertriglyceridemia (FDR = 0.002) and bipolar disorder (FDR = 0.027). Our study results are consistent with previous biological studies, supporting the good performance of GSEA. Our analyzing results based on GSEA framework provide novel clues for discovering causal relationships between elements and complex diseases.

Exploring human disease using the Rat Genome Database

Rattus norvegicus, the laboratory rat, has been a crucial model for studies of the environmental and genetic factors associated with human diseases for over 150 years. It is the primary model organism for toxicology and pharmacology studies, and has features that make it the model of choice in many complex-disease studies. Since 1999, the Rat Genome Database (RGD; http://rgd.mcw.edu) has been the premier resource for genomic, genetic, phenotype and strain data for the laboratory rat. The primary role of RGD is to curate rat data and validate orthologous relationships with human and mouse genes, and make these data available for incorporation into other major databases such as NCBI, Ensembl and UniProt. RGD also provides official nomenclature for rat genes, quantitative trait loci, strains and genetic markers, as well as unique identifiers. The RGD team adds enormous value to these basic data elements through functional and disease annotations, the analysis and visual presentation of pathways, and the integration of phenotype measurement data for strains used as disease models. Because much of the rat research community focuses on understanding human diseases, RGD provides a number of datasets and software tools that allow users to easily explore and make disease-related connections among these datasets. RGD also provides comprehensive human and mouse data for comparative purposes, illustrating the value of the rat in translational research. This article introduces RGD and its suite of tools and datasets to researchers - within and beyond the rat community - who are particularly interested in leveraging rat-based insights to understand human diseases.

Identification of genes involved in the four stages of colorectal cancer: Gene expression profiling

BACKGROUND

Colorectal cancer (CRC) is a common cancer with high morbidity and mortality. However, its molecular mechanism is not clear, nor the genes related to CRC stages.

METHODS

Gene expression data in CRC and healthy colorectal tissues were obtained from gene expression omnibus. Limma package was used to identify the differentially expressed genes (DEGs) between control and CRC (stage I, II, III, and IV), obtaining 4 DEG sets. VennPlex was utilized to find all DEGs and intersection DEGs. Functional interactions between all DEGs and protein-protein interactions (PPIs) between intersection DEGs were analyzed using ReactomeFIViz and STRING, respectively, and networks were visualized. Known CRC-related genes were down-loaded from Comparative Toxicogenomics Database and mapped to PPI network.

RESULTS

Totally, 851, 760, 729, and 878 DEGs were found between control and CRC stage I, II, III, and IV, respectively. Taken together, 1235 DEGs were found, as well as 128 up-regulated intersection DEGs, 365 down-regulated intersection DEGs, and 0 contra-regulated DEG. A functional interaction network of all DEGs and a PPI network of intersection DEGs were constructed, in which CDC20, PTTG1, and MAD2L1 interacted with BUB1B; UGT2B17 interacted with ADH1B; MCM7 interacted with MCM2. BUB1B, ADH1B, and MCM2 were known CRC-related genes. Gradually upregulated expressions of CDC20, PTTG1, MAD2L1, UGT2B17, and MCM7 in stage I, II, III, and IV CRC were confirmed by using quantitative PCR. Besides, up-regulated intersection DEGs enriched in pathways about Cell cycle, DNA replication, and p53 signaling.

CONCLUSION

CDC20, PTTG1, MAD2L1, UGT2B17, and MCM7 might be CRC stage-related genes.

Context-dependent compensation among phosphatidylserine-recognition receptors

Phagocytes express multiple phosphatidylserine (PtdSer) receptors that recognize apoptotic cells. It is unknown whether these receptors are interchangeable or if they play unique roles during cell clearance. Loss of the PtdSer receptor Mertk is associated with apoptotic corpse accumulation in the testes and degeneration of photoreceptors in the eye. Both phenotypes are linked to impaired phagocytosis by specialized phagocytes: Sertoli cells and the retinal pigmented epithelium (RPE). Here, we overexpressed the PtdSer receptor BAI1 in mice lacking MerTK (Mertk -/- Bai1 Tg ) to evaluate PtdSer receptor compensation in vivo. While Bai1 overexpression rescues clearance of apoptotic germ cells in the testes of Mertk -/- mice it fails to enhance RPE phagocytosis or prevent photoreceptor degeneration. To determine why MerTK is critical to RPE function, we examined visual cycle intermediates and performed unbiased RNAseq analysis of RPE from Mertk +/+ and Mertk -/- mice. Prior to the onset of photoreceptor degeneration, Mertk -/- mice had less accumulation of retinyl esters and dysregulation of a striking array of genes, including genes related to phagocytosis, metabolism, and retinal disease in humans. Collectively, these experiments establish that not all phagocytic receptors are functionally equal, and that compensation among specific engulfment receptors is context and tissue dependent.

Bioinformatic Analysis Identifies Three Potentially Key Differentially Expressed Genes in Peripheral Blood Mononuclear Cells of Patients with Takayasu's Arteritis

Objective

This study aimed to identify several potentially key genes associated with the pathogenesis of Takayasu’s arteritis (TA). This identification may lead to a deeper mechanistic understanding of TA etiology and pave the way for potential therapeutic approaches.

Materials and Methods

In this experimental study, the microarray dataset GSE33910, which includes expression data for peripheral blood mononuclear cell (PBMC) samples isolated from TA patients and normal volunteers, was downloaded from the publicly accessible Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified in PBMCs of TA patients compared with normal controls. Gene ontology (GO) enrichment analysis of DEGs and analysis of protein-protein interaction (PPI) network were carried out. Several hub proteins were extracted from the PPI network based on node degrees and random walk algorithm. Additionally, transcription factors (TFs) were predicted and the corresponding regulatory network was constructed.

Results

A total of 932 DEGs (372 up- and 560 down-regulated genes) were identified in PBMCs from TA patients. Interestingly, up-regulated and down-regulated genes were involved in different GO terms and pathways. A PPI network of proteins encoded by DEGs was constructed and RHOA, FOS, EGR1, and GNB1 were considered to be hub proteins with both higher random walk score and node degree. A total of 13 TFs were predicted to be differentially expressed. A total of 49 DEGs had been reported to be associated with TA in the Comparative Toxicogenomics Database (CTD). The only TA marker gene in the CTD database was NOS2, confirmed by three studies. However, NOS2 was not significantly altered in the analyzed microarray dataset. Nevertheless,NOS3 was a significantly down-regulated gene and was involved in the platelet activation pathway.

Conclusion

RHOA, FOS, and EGR1 are potential candidate genes for the diagnosis and therapy of TA.

Endothelial cell-derived GABA signaling modulates neuronal migration and postnatal behavior

The cerebral cortex is essential for integration and processing of information that is required for most behaviors. The exquisitely precise laminar organization of the cerebral cortex arises during embryonic development when neurons migrate successively from ventricular zones to coalesce into specific cortical layers. While radial glia act as guide rails for projection neuron migration, pre-formed vascular networks provide support and guidance cues for GABAergic interneuron migration. This study provides novel conceptual and mechanistic insights into this paradigm of vascular-neuronal interactions, revealing new mechanisms of GABA and its receptor-mediated signaling via embryonic forebrain endothelial cells. With the use of two new endothelial cell specific conditional mouse models of the GABA pathway (Gabrb3^ΔTie2-Cre and Vgat^ΔTie2-Cre), we show that partial or complete loss of GABA release from endothelial cells during embryogenesis results in vascular defects and impairs long-distance migration and positioning of cortical interneurons. The downstream effects of perturbed endothelial cell-derived GABA signaling are critical, leading to lasting changes to cortical circuits and persistent behavioral deficits. Furthermore, we illustrate new mechanisms of activation of GABA signaling in forebrain endothelial cells that promotes their migration, angiogenesis and acquisition of blood-brain barrier properties. Our findings uncover and elucidate a novel endothelial GABA signaling pathway in the CNS that is distinct from the classical neuronal GABA signaling pathway and shed new light on the etiology and pathophysiology of neuropsychiatric diseases, such as autism spectrum disorders, epilepsy, anxiety, depression and schizophrenia.

Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases

Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (G×Es) in large-scale studies of human populations. There are opportunities in this field, particularly with respect to the incorporation of -omics and next-generation sequencing data and continual improvement in measures of environmental exposures implicated in complex disease outcomes. In a workshop called "Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases," held October 17-18, 2014, by the National Institute of Environmental Health Sciences and the National Cancer Institute in conjunction with the annual American Society of Human Genetics meeting, participants explored new approaches and tools that have been developed in recent years for G×E discovery. This paper highlights current and critical issues and themes in G×E research that need additional consideration, including the improved data analytical methods, environmental exposure assessment, and incorporation of functional data and annotations.

Towards Optimal Connectivity on Multi-layered Networks

Networks are prevalent in many high impact domains. Moreover, cross-domain interactions are frequently observed in many applications, which naturally form the dependencies between different networks. Such kind of highly coupled network systems are referred to as multi-layered networks, and have been used to characterize various complex systems, including critical infrastructure networks, cyber-physical systems, collaboration platforms, biological systems and many more. Different from single-layered networks where the functionality of their nodes is mainly affected by within-layer connections, multi-layered networks are more vulnerable to disturbance as the impact can be amplified through cross-layer dependencies, leading to the cascade failure to the entire system. To manipulate the connectivity in multi-layered networks, some recent methods have been proposed based on two-layered networks with specific types of connectivity measures. In this paper, we address the above challenges in multiple dimensions. First, we propose a family of connectivity measures (SUBLINE) that unifies a wide range of classic network connectivity measures. Third, we reveal that the connectivity measures in SUBLINE family enjoy diminishing returns property, which guarantees a near-optimal solution with linear complexity for the connectivity optimization problem. Finally, we evaluate our proposed algorithm on real data sets to demonstrate its effectiveness and efficiency.

Predicting new indications of compounds with a network pharmacology approach: Liuwei Dihuang Wan as a case study

With the ever increasing cost and time required for drug development, new strategies for drug development are highly demanded, whereas repurposing old drugs has attracted much attention in drug discovery. In this paper, we introduce a new network pharmacology approach, namely PINA, to predict potential novel indications of old drugs based on the molecular networks affected by drugs and associated with diseases. Benchmark results on FDA approved drugs have shown the superiority of PINA over traditional computational approaches in identifying new indications of old drugs. We further extend PINA to predict the novel indications of Traditional Chinese Medicines (TCMs) with Liuwei Dihuang Wan (LDW) as a case study. The predicted indications, including immune system disorders and tumor, are validated by expert knowledge and evidences from literature, demonstrating the effectiveness of our proposed computational approach.

Screening critical genes associated with malignant glioma using bioinformatics analysis

Malignant gliomas are high‑grade gliomas, which are derived from glial cells in the spine or brain. To examine the mechanisms underlying malignant gliomas in the present study, the expression profile of GSE54004, which included 12 grade II astrocytomas, 33 grade III astrocytomas and 98 grade IV astrocytomas, was downloaded from the Gene Expression Omnibus. Using the Limma package in R, the differentially expressed genes (DEGs) in grade III, vs. grade II astrocytoma, grade IV, vs. grade II astrocytoma, and grade IV, vs. grade III astrocytoma were analyzed. Venn diagram analysis and enrichment analyses were performed separately for the DEGs using VennPlex software and the Database for Annotation, Visualization and Integrated Discovery. Protein‑protein interaction (PPI) networks were visualized using Cytoscape software, and subsequent module analysis of the PPI networks was performed using the ClusterONE tool. Finally, glioma‑associated genes and glioma marker genes among the DEGs were identified using the CTD database. A total of 27, 1,446 and 776 DEGs were screened for the grade III, vs. grade II, grade IV, vs. grade II, and grade IV, vs. grade III astrocytoma comparison groups, respectively. Functional enrichment analyses showed that matrix metalloproteinase 9 (MMP9) and chitinase 3‑like 1 (CHI3L1) were enriched in the extracellular matrix and extracellular matrix structural constituent, respectively. In the PPI networks, annexin A1 (ANXA1) had a higher degree and MMP9 had interactions with vascular endothelial growth factor A (VEGFA). There were 10 common glioma marker genes between the grade IV, vs. grade II and the grade IV, vs. grade III comparison groups, including MMP9, CHI3L1, VEGFA and S100 calcium binding protein A4 (S100A4). This suggested that MMP9, CHI3L1, VEGFA, S100A4 and ANXA1 may be involved in the progression of malignant gliomas.

Jumping across biomedical contexts using compressive data fusion

Motivation: The rapid growth of diverse biological data allows us to consider interactions between a variety of objects, such as genes, chemicals, molecular signatures, diseases, pathways and environmental exposures. Often, any pair of objects—such as a gene and a disease—can be related in different ways, for example, directly via gene–disease associations or indirectly via functional annotations, chemicals and pathways. Different ways of relating these objects carry different semantic meanings. However, traditional methods disregard these semantics and thus cannot fully exploit their value in data modeling.

Results: We present Medusa, an approach to detect size-k modules of objects that, taken together, appear most significant to another set of objects. Medusa operates on large-scale collections of heterogeneous datasets and explicitly distinguishes between diverse data semantics. It advances research along two dimensions: it builds on collective matrix factorization to derive different semantics, and it formulates the growing of the modules as a submodular optimization program. Medusa is flexible in choosing or combining semantic meanings and provides theoretical guarantees about detection quality. In a systematic study on 310 complex diseases, we show the effectiveness of Medusa in associating genes with diseases and detecting disease modules. We demonstrate that in predicting gene–disease associations Medusa compares favorably to methods that ignore diverse semantic meanings. We find that the utility of different semantics depends on disease categories and that, overall, Medusa recovers disease modules more accurately when combining different semantics.

Availability and implementation: Source code is at http://github.com/marinkaz/medusa

Contact:marinka@cs.stanford.edu, blaz.zupan@fri.uni-lj.si

Identification of informative features for predicting proinflammatory potentials of engine exhausts

BACKGROUND

The immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. However, the evaluation of immunotoxicity of engine exhausts is currently based on expensive and time-consuming experiments. It is desirable to develop efficient methods for immunotoxicity assessment.

METHODS

To accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proinflammatory potentials of engine exhausts. A principal component regression (PCR) algorithm was applied to develop prediction models. The informative features were identified by a sequential backward feature elimination (SBFE) algorithm.

RESULTS

A total of 19 informative chemical and biological features were successfully identified by SBFE algorithm. The informative features were utilized to develop a computational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. FS-CBM model achieved a high performance with correlation coefficient values of 0.997 and 0.943 obtained from training and independent test sets, respectively.

CONCLUSIONS

The FS-CBM model was developed for predicting proinflammatory potentials of engine exhausts with a large improvement on prediction performance compared with our previous CBM model. The proposed method could be further applied to construct models for bioactivities of mixtures.

Assessment of the DNA damaging potential of environmental chemicals using a quantitative high-throughput screening approach to measure p53 activation

Genotoxicity potential is a critical component of any comprehensive toxicological profile. Compounds that induce DNA or chromosomal damage often activate p53, a transcription factor essential to cell cycle regulation. Thus, within the US Tox21 Program, we screened a library of ∼10,000 (∼8,300 unique) environmental compounds and drugs for activation of the p53-signaling pathway using a quantitative high-throughput screening assay employing HCT-116 cells (p53^+/+ ) containing a stably integrated β-lactamase reporter gene under control of the p53 response element (p53RE). Cells were exposed (-S9) for 16 hr at 15 concentrations (generally 1.2 nM to 92 μM) three times, independently. Excluding compounds that failed analytical chemistry analysis or were suspected of inducing assay interference, 365 (4.7%) of 7,849 unique compounds were concluded to activate p53. As part of an in-depth characterization of our results, we first compared them with results from traditional in vitro genotoxicity assays (bacterial mutation, chromosomal aberration); ∼15% of known, direct-acting genotoxicants in our library activated the p53RE. Mining the Comparative Toxicogenomics Database revealed that these p53 actives were significantly associated with increased expression of p53 downstream genes involved in DNA damage responses. Furthermore, 53 chemical substructures associated with genotoxicity were enriched in certain classes of p53 actives, for example, anthracyclines (antineoplastics) and vinca alkaloids (tubulin disruptors). Interestingly, the tubulin disruptors manifested unusual nonmonotonic concentration response curves suggesting activity through a unique p53 regulatory mechanism. Through the analysis of our results, we aim to define a role for this assay as one component of a comprehensive toxicological characterization of large compound libraries. Environ. Mol. Mutagen. 58:494-507, 2017. © 2017 Wiley Periodicals, Inc.

Prioritization of Contaminants of Emerging Concern in Wastewater Treatment Plant Discharges Using Chemical:Gene Interactions in Caged Fish

We examined whether contaminants present in surface waters could be prioritized for further assessment by linking the presence of specific chemicals to gene expression changes in exposed fish. Fathead minnows were deployed in cages for 2, 4, or 8 days at three locations near two different wastewater treatment plant discharge sites in the Saint Louis Bay, Duluth, MN and one upstream reference site. The biological impact of 51 chemicals detected in the surface water of 133 targeted chemicals was determined using biochemical endpoints, exposure activity ratios for biological and estrogenic responses, known chemical:gene interactions from biological pathways and knowledge bases, and analysis of the covariance of ovary gene expression with surface water chemistry. Thirty-two chemicals were significantly linked by covariance with expressed genes. No estrogenic impact on biochemical endpoints was observed in male or female minnows. However, bisphenol A (BPA) was identified by chemical:gene covariation as the most impactful estrogenic chemical across all exposure sites. This was consistent with identification of estrogenic effects on gene expression, high BPA exposure activity ratios across all test sites, and historical analysis of the study area. Gene expression analysis also indicated the presence of nontargeted chemicals including chemotherapeutics consistent with a local hospital waste stream. Overall impacts on gene expression appeared to be related to changes in treatment plant function during rain events. This approach appears useful in examining the impacts of complex mixtures on fish and offers a potential route in linking chemical exposure to adverse outcomes that may reduce population sustainability.

Cross-Dependency Inference in Multi-Layered Networks: A Collaborative Filtering Perspective

The increasingly connected world has catalyzed the fusion of networks from different domains, which facilitates the emergence of a new network model-multi-layered networks. Examples of such kind of network systems include critical infrastructure networks, biological systems, organization-level collaborations, cross-platform e-commerce, and so forth. One crucial structure that distances multi-layered network from other network models is its cross-layer dependency, which describes the associations between the nodes from different layers. Needless to say, the cross-layer dependency in the network plays an essential role in many data mining applications like system robustness analysis and complex network control. However, it remains a daunting task to know the exact dependency relationships due to noise, limited accessibility, and so forth. In this article, we tackle the cross-layer dependency inference problem by modeling it as a collective collaborative filtering problem. Based on this idea, we propose an effective algorithm Fascinate that can reveal unobserved dependencies with linear complexity. Moreover, we derive Fascinate-ZERO, an online variant of Fascinate that can respond to a newly added node timely by checking its neighborhood dependencies. We perform extensive evaluations on real datasets to substantiate the superiority of our proposed approaches.

Towards a more molecular taxonomy of disease

Background

Disease taxonomies have been designed for many applications, but they tend not to fully incorporate the growing amount of molecular-level knowledge of disease processes, inhibiting research efforts. Understanding the degree to which we can infer disease relationships from molecular data alone may yield insights into how to ultimately construct more modern taxonomies that integrate both physiological and molecular information.

Results

We introduce a new technique we call Parent Promotion to infer hierarchical relationships between disease terms using disease-gene data. We compare this technique with both an established ontology inference method (CliXO) and a minimum weight spanning tree approach. Because there is no gold standard molecular disease taxonomy available, we compare our inferred hierarchies to both the Medical Subject Headings (MeSH) category C forest of diseases and to subnetworks of the Disease Ontology (DO). This comparison provides insights about the inference algorithms, choices of evaluation metrics, and the existing molecular content of various subnetworks of MeSH and the DO. Our results suggest that the Parent Promotion method performs well in most cases. Performance across MeSH trees is also correlated between inference methods. Specifically, inferred relationships are more consistent with those in smaller MeSH disease trees than larger ones, but there are some notable exceptions that may correlate with higher molecular content in MeSH.

Conclusions

Our experiments provide insights about learning relationships between diseases from disease genes alone. Future work should explore the prospect of disease term discovery from molecular data and how best to integrate molecular data with anatomical and clinical knowledge. This study nonetheless suggests that disease gene information has the potential to form an important part of the foundation for future representations of the disease landscape.

Electronic supplementary material

The online version of this article (doi:10.1186/s13326-017-0134-0) contains supplementary material, which is available to authorized users.

High-Throughput Screening Data Interpretation in the Context of In Vivo Transcriptomic Responses to Oral Cr(VI) Exposure

The toxicity of hexavalent chromium [Cr(VI)] in drinking water has been studied extensively, and available in vivo and in vitro studies provide a robust dataset for application of advanced toxicological tools to inform the mode of action (MOA). This study aimed to contribute to the understanding of Cr(VI) MOA by evaluating high-throughput screening (HTS) data and other in vitro data relevant to Cr(VI), and comparing these findings to robust in vivo data, including transcriptomic profiles in target tissues. Evaluation of Tox21 HTS data for Cr(VI) identified 11 active assay endpoints relevant to the Ten Key Characteristics of Carcinogens (TKCCs) that have been proposed by other investigators. Four of these endpoints were related to TP53 (tumor protein 53) activation mapping to genotoxicity (KCC#2), and four were related to cell death/proliferation (KCC#10). HTS results were consistent with other in vitro data from the Comparative Toxicogenomics Database. In vitro responses were compared to in vivo transcriptomic responses in the most sensitive target tissue, the duodenum, of mice exposed to ≤ 180 ppm Cr(VI) for 7 and 90 days. Pathways that were altered both in vitro and in vivo included those relevant to cell death/proliferation. In contrast, pathways relevant to p53/DNA damage were identified in vitro but not in vivo. Benchmark dose modeling and phenotypic anchoring of in vivo transcriptomic responses strengthened the finding that Cr(VI) causes cell stress/injury followed by proliferation in the mouse duodenum at high doses. These findings contribute to the body of evidence supporting a non-mutagenic MOA for Cr(VI)-induced intestinal cancer.

RGIFE: a ranked guided iterative feature elimination heuristic for the identification of biomarkers

BACKGROUND

Current -omics technologies are able to sense the state of a biological sample in a very wide variety of ways. Given the high dimensionality that typically characterises these data, relevant knowledge is often hidden and hard to identify. Machine learning methods, and particularly feature selection algorithms, have proven very effective over the years at identifying small but relevant subsets of variables from a variety of application domains, including -omics data. Many methods exist with varying trade-off between the size of the identified variable subsets and the predictive power of such subsets. In this paper we focus on an heuristic for the identification of biomarkers called RGIFE: Rank Guided Iterative Feature Elimination. RGIFE is guided in its biomarker identification process by the information extracted from machine learning models and incorporates several mechanisms to ensure that it creates minimal and highly predictive features sets.

RESULTS

We compare RGIFE against five well-known feature selection algorithms using both synthetic and real (cancer-related transcriptomics) datasets. First, we assess the ability of the methods to identify relevant and highly predictive features. Then, using a prostate cancer dataset as a case study, we look at the biological relevance of the identified biomarkers.

CONCLUSIONS

We propose RGIFE, a heuristic for the inference of reduced panels of biomarkers that obtains similar predictive performance to widely adopted feature selection methods while selecting significantly fewer feature. Furthermore, focusing on the case study, we show the higher biological relevance of the biomarkers selected by our approach. The RGIFE source code is available at: http://ico2s.org/software/rgife.html .

Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases

Adverse drug reactions (ADRs) pose critical public health issues, affecting over 6% of hospitalized patients. While knowledge of potential drug-drug interactions (DDI) is necessary to prevent ADR, the rapid pace of drug discovery makes it challenging to maintain a strong insight into DDIs. In this study, we present a novel literature-mining framework for enhancing the predictions of DDIs and ADR types by integrating drug-gene interactions (DGIs). The ADR types were adapted from a DDI corpus, including i) adverse effect; ii) effect at molecular level; iii) effect related to pharmacokinetics; and iv) DDIs without known ADRs. By using random forest classifier our approach achieves an F-score of 0.87 across the ADRs classification using only the DDI features. We then enhanced the performance of the classifier by including DGIs (F-score = 0.90), and applied the classification model trained with the DDI corpus to identify the drugs that might interact with the drugs for cutaneous diseases. We successfully predict previously known ADRs for drugs prescribed to cutaneous diseases, and are also able to identify promising new ADRs.

Identification of key genes and pathways associated with obesity in children

The present study aimed to identify potential key genes and pathways in obese children in order to explore possible molecular mechanisms associated with child obesity. The array dataset GSE29718 was downloaded from the Gene Expression Omnibus database. Subcutaneous adipose tissue samples derived from 7 obese children and 8 lean children were selected for the analysis. Differentially expressed genes (DEGs) in samples from obese children compared with those from lean children were analyzed by the limma package. Gene ontology (GO) annotation, Kyoto Encyclopedia of Genes and Genomes and Reactome pathway enrichment analyses for up and downregulated genes were performed. A protein-protein interaction (PPI) network was constructed with Cytoscape software and important genes associated with obesity were determined using IRegulon. A total of 199 DEGs (79 up and 120 downregulated genes) were identified in the samples of obese children compared with those from lean children. The PPI network was established with 103 nodes and 147 protein pairs. Matrix metalloproteinase 9 (MMP9) and acetyl-CoA carboxylase β (ACACB) were identified as hub genes in the PPI network and may therefore be marker genes for child obesity. In addition, upregulated DEGs were enriched in Reactome pathways associated with the immune system. Besides, MMP9 was upregulated in immune system processes as a GO term in the category Biological Processes. The results of the present study indicated that MMP9, ACACB and immune system pathways may have a significant role in child obesity.

Genetic background influences susceptibility to chemotherapy-induced hematotoxicity

Hematotoxicity is a life-threatening side effect of many chemotherapy regimens. While clinical factors influence patient responses, genetic factors may also play an important role. We sought to identify genomic loci that influence chemotherapy-induced hematotoxicity by dosing Diversity Outbred mice with one of three chemotherapy drugs; doxorubicin, cyclophosphamide or docetaxel. We observed that each drug had a distinct effect on both the changes in blood cell sub-populations and the underlying genetic architecture of hematotoxicity. For doxorubicin, we mapped the change in cell counts before and after dosing and found that alleles of ATP-binding cassette B1B (Abcb1b) on chromosome 5 influence all cell populations. For cyclophosphamide and docetaxel, we found that each cell population was influenced by distinct loci, none of which overlapped between drugs. These results suggest that susceptibility to chemotherapy-induced hematotoxicity is influenced by different genes for different chemotherapy drugs.

DrugSig: A resource for computational drug repositioning utilizing gene expression signatures

Computational drug repositioning has been proved as an effective approach to develop new drug uses. However, currently existing strategies strongly rely on drug response gene signatures which scattered in separated or individual experimental data, and resulted in low efficient outputs. So, a fully drug response gene signatures database will be very helpful to these methods. We collected drug response microarray data and annotated related drug and targets information from public databases and scientific literature. By selecting top 500 up-regulated and down-regulated genes as drug signatures, we manually established the DrugSig database. Currently DrugSig contains more than 1300 drugs, 7000 microarray and 800 targets. Moreover, we developed the signature based and target based functions to aid drug repositioning. The constructed database can serve as a resource to quicken computational drug repositioning. Database URL: http://biotechlab.fudan.edu.cn/database/drugsig/.

Comprehensive strategy for the design of precision drugs and identification of genetic signature behind proneness of the disease-a pharmacogenomic approach

The proneness of diseases and susceptibility towards drugs vary from person to person. At present, there is a strong demand for the personalization of drugs. The genetic signature behind proneness of the disease has been studied through a comprehensive 'octopodial approach'. All the genetic variants included in the approach have been introduced. The breast cancer associated with BRCA1 mutation has been taken as the illustrative example to introduce all these factors. The genetic variants associated with the drug action of tamoxifen have been fully illustrated in the manuscript. The design of a new personalized anti-breast cancer drug has been explained in the third phase. For the design of new personalized drugs, a metabolite of anti-cancer drug chlorambucil has been taken as the template. The design of drug has been made with respect to the protein 1T15 of BRCA1 gene corresponding to the genetic signature of rs28897696.

Linking sources to early effects by profiling urine metabolome of residents living near oil refineries and coal-fired power plants

BACKGROUND

This study aims at identifying metabolic changes linking external exposure to industrial air toxics with oxidative stress biomarkers.

METHODS

We classified 252 study subjects as 111 high vs. 141 low exposure subjects by the distance from their homes to the two main emission sources, oil refineries and coal-fired power plants. We estimated individual's external exposure to heavy metals and polycyclic aromatic hydrocarbons (PAHs) by dispersion and kriging models, respectively. We measured urinary levels of heavy metals and 1-hydroxypyrene (1-OHP) as biomarkers of internal exposure, and 8-OHdG, HNE-MA, 8-isoPGF_2α, and 8-NO₂Gua as biomarkers of early health effects. We used two-dimensional gas chromatography time-of-flight mass spectrometry to identify urine metabolomics. We applied "meet-in-the-middle" approach to identify potential metabolites as putative intermediate biomarkers linking multiple air toxics exposures to oxidative stress with plausible exposures-related pathways.

RESULTS

High exposure subjects showed elevated ambient concentrations of vanadium and PAHs, increased urine concentrations of 1-OHP, vanadium, nickel, copper, arsenic, strontium, cadmium, mercury, and thallium, and higher urine concentrations of all four urine oxidative stress biomarkers compared to low exposure subjects. We identified a profile of putative intermediate biomarkers that were associated with both exposures and oxidative stress biomarkers in participants. Urine metabolomics identified age-dependent biological pathways, including tryptophan metabolism and phenylalanine metabolism in children subjects (aged 9-11), and glycine, serine, and threonine metabolism in elderly subjects (aged>55), that could associate multiple exposures with oxidative stress.

CONCLUSION

By profiling urine biomarkers and metabolomics in children and elderly residents living near a petrochemical complex, we can link their internal exposure to oxidative stress biomarkers through biological pathways associated with common complex chronic diseases and allergic respiratory diseases. The internal exposure may possibly be traced to multiple air toxics emitted from specific sources of oil refineries and coal-fired power plants.

Applying evolutionary genetics to developmental toxicology and risk assessment

Evolutionary thinking continues to challenge our views on health and disease. Yet, there is a communication gap between evolutionary biologists and toxicologists in recognizing the connections among developmental pathways, high-throughput screening, and birth defects in humans. To increase our capability in identifying potential developmental toxicants in humans, we propose to apply evolutionary genetics to improve the experimental design and data interpretation with various in vitro and whole-organism models. We review five molecular systems of stress response and update 18 consensual cell-cell signaling pathways that are the hallmark for early development, organogenesis, and differentiation; and revisit the principles of teratology in light of recent advances in high-throughput screening, big data techniques, and systems toxicology. Multiscale systems modeling plays an integral role in the evolutionary approach to cross-species extrapolation. Phylogenetic analysis and comparative bioinformatics are both valuable tools in identifying and validating the molecular initiating events that account for adverse developmental outcomes in humans. The discordance of susceptibility between test species and humans (ontogeny) reflects their differences in evolutionary history (phylogeny). This synthesis not only can lead to novel applications in developmental toxicity and risk assessment, but also can pave the way for applying an evo-devo perspective to the study of developmental origins of health and disease.

Biological responses to phenylurea herbicides in fish and amphibians: New directions for characterizing mechanisms of toxicity

Urea-based herbicides are applied in agriculture to control broadleaf and grassy weeds, acting to either inhibit photosynthesis at photosystem II (phenylureas) or to inhibit acetolactate synthase acetohydroxyacid synthase (sulfonylureas). While there are different chemical formulas for urea-based herbicides, the phenylureas are a widely used class in North America and have been detected in aquatic environments due to agricultural run-off. Here, we summarize the current state of the literature, synthesizing data on phenylureas and their biological effects in two non-target animals, fish and amphibians, with a primary focus on diuron and linuron. In fish, although the acutely lethal effects of diuron in early life stages appear to be >1mg/L, recent studies measuring sub-lethal behavioural and developmental endpoints suggest that diuron causes adverse effects at lower concentrations (i.e. <0.1mg/L). Considerably less toxicity data exist for amphibians, and this is a knowledge gap in the literature. In terms of sub-lethal effects and mode of action (MOA), linuron is well documented to have anti-androgenic effects in vertebrates, including fish. However, there are other MOAs that are not adequately assessed in toxicology studies. In order to identify additional potential MOAs, we conducted in silico analyses for linuron and diuron that were based upon transcriptome studies and chemical structure-function relationships (i.e. ToxCast™, Prediction of Activity Spectra of Substances). Based upon these analyses, we suggest that steroid biosynthesis, cholesterol metabolism and pregnane X receptor activation are common targets, and offer some new endpoints for future investigations of phenylurea herbicides in non-target animals.

Derivation and Evaluation of Putative Adverse Outcome Pathways for the Effects of Cyclooxygenase Inhibitors on Reproductive Processes in Female Fish

Cyclooxygenase (COX) inhibitors are ubiquitous in aquatic systems and have been detected in fish tissues. The exposure of fish to these pharmaceuticals is concerning because COX inhibitors disrupt the synthesis of prostaglandins (PGs), which modulate a variety of essential biological functions, including reproduction. In this study, we investigated the effects of well-characterized mammalian COX inhibitors on female fathead minnow reproductive health. Fish (n = 8) were exposed for 96 h to water containing indomethacin (IN; 100 µg/l), ibuprofen (IB; 200 µg/l) or celecoxib (CX; 20 µg/l), and evaluated for effects on liver metabolome and ovarian gene expression. Metabolomic profiles of IN, IB and CX were not significantly different from control or one another. Exposure to IB and CX resulted in differential expression of comparable numbers of genes (IB = 433, CX = 545). In contrast, 2558 genes were differentially expressed in IN-treated fish. Functional analyses (canonical pathway and gene set enrichment) indicated extensive effects of IN on PG synthesis pathway, oocyte meiosis, and several other processes consistent with physiological roles of PGs. Transcriptomic data were congruent with PG data; IN-reduced plasma PG F2α concentration, whereas IB and CX did not. Five putative AOPs were developed linking the assumed molecular initiating event of COX inhibition, with PG reduction and the adverse outcome of reproductive failure via reduction of: (1) ovulation, (2) reproductive behaviors mediated by exogenous or endogenous PGs, and (3) oocyte maturation in fish. These pathways were developed using, in part, empirical data from the present study and other publicly available data.

A neural joint model for entity and relation extraction from biomedical text

BACKGROUND

Extracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature engineering when feature-based models are employed. Moreover, pipeline models may suffer error propagation and are not able to utilize the interactions between subtasks. Therefore, we propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above.

RESULTS

Our model was evaluated on two tasks, i.e., the task of extracting adverse drug events between drug and disease entities, and the task of extracting resident relations between bacteria and location entities. Compared with the state-of-the-art systems in these tasks, our model improved the F1 scores of the first task by 5.1% in entity recognition and 8.0% in relation extraction, and that of the second task by 9.2% in relation extraction.

CONCLUSIONS

The proposed model achieves competitive performances with less work on feature engineering. We demonstrate that the model based on neural networks is effective for biomedical entity and relation extraction. In addition, parameter sharing is an alternative method for neural models to jointly process this task. Our work can facilitate the research on biomedical text mining.

Candidate miRNAs and pathogenesis investigation for hepatocellular carcinoma based on bioinformatics analysis

The present study aimed to explore the mechanisms behind the development and progression of hepatocellular carcinoma (HCC) and identify information regarding HCC-related microRNAs (miRNAs) or marker genes for the gene therapy of HCC. Gene expression profile of GSE67882, generated from 4 hepatitis B virus infected HCC tissue samples (HCC group) and 8 chronic hepatitis B tissue samples with no fibrosis (control group) were downloaded from the Gene Expression Omnibus database. The differentially expressed miRNAs functional enrichment and pathway analyses of HCC were revealed, followed by transcription factor-miRNA interaction network construction and analyses. A total of 14 upregulated miRNAs and 16 downregulated miRNAs between HCC and control samples were obtained. Differentially expressed miRNAs were mainly involved in biological processes like the regulation of histone H3-K9 methylation, and the KEGG pathways in cancer map05200 demonstrates their involvement in cancer. A total of 3 outstanding regulatory networks of miRNAs: hsa-miR-15a, hsa-miR-125b and hsa-miR-122 were revealed. A total of 11 differentially expressed miRNAs including hsa-miR-146p-5b that regulated the marker genes of HCC were explored. miRNAs such as hsa-miR-15a, hsa-miR-125b, hsa-miR-122 and hsa-miR-146b-5p may be new biomarkers for the gene therapy of HCC. Furthermore, histone H3-K9 methylation and other pathways in cancer observed in the KEGG map05200 may be closely related with the development of HCC.