The Comparative Toxicogenomics Database: update 2017

Leptin receptor gene polymorphisms and sex modify the association between acetaminophen use and asthma among young adults: results from two observational studies

Background

Epidemiologic studies have demonstrated associations between acetaminophen use and asthma. This investigation sought to determine whether sex modifies the acetaminophen-asthma association and whether leptin (LEP) and leptin receptor (LEPR) gene polymorphisms modulate the sex-specific associations.

Methods

Data from the Isle of Wight birth cohort (IOW; n = 1456, aged 18 years) and Kuwait University Allergy (KUA; n = 1154, aged 18–26 years) studies were analyzed. Acetaminophen use and current asthma were self-reported. Genotype information for eighteen polymorphisms in LEP and LEPR genes were available in the IOW study. Associations between acetaminophen use and asthma were stratified by sex and genotype. Poisson regression models with robust variance estimation were evaluated to estimate adjusted prevalence ratios (aPR) and 95% confidence intervals (CI).

Results

Acetaminophen use was dose-dependently associated with an increased prevalence of current asthma in the IOW and KUA studies. In both studies, sex-stratified analysis showed that acetaminophen use was associated with asthma among males, but not in females (P_interaction <  0.05). Moreover, a sex- and genotype-stratified analysis of the IOW data indicated that acetaminophen was associated with asthma to a similar extent among males and females carrying two common alleles of LEPR polymorphisms. In contrast, among those carrying at least one copy of the minor allele of LEPR polymorphisms, the magnitude of association between acetaminophen use and asthma was pronounced among males (aPR = 6.83, 95% CI: 2.87–16.24), but not among females (aPR = 1.22, 95% CI: 0.61–2.45).

Conclusions

The identified sex-related effect modification of the acetaminophen-asthma association varied across LEPR genotypes, indicating that the sex-specific association was confined to individuals with certain genetic susceptibility. If the acetaminophen-asthma association is causal, then our findings will aid susceptibility-based stratification of at-risk individuals and augment preventive public health efforts.

Electronic supplementary material

The online version of this article (10.1186/s12931-018-0892-y) contains supplementary material, which is available to authorized users.

Chemical-Induced Phenotypes at CTD Help Inform the Predisease State and Construct Adverse Outcome Pathways

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org) is a public resource that manually curates the scientific literature to provide content that illuminates the molecular mechanisms by which environmental exposures affect human health. We introduce our new chemical-phenotype module that describes how chemicals can affect molecular, cellular, and physiological phenotypes. At CTD, we operationally distinguish between phenotypes and diseases, wherein a phenotype refers to a nondisease biological event: eg, decreased cell cycle arrest (phenotype) versus liver cancer (disease), increased fat cell proliferation (phenotype) versus morbid obesity (disease), etc. Chemical-phenotype interactions are expressed in a formal structured notation using controlled terms for chemicals, phenotypes, taxon, and anatomical descriptors. Combining this information with CTD's chemical-disease module allows inferences to be made between phenotypes and diseases, yielding potential insight into the predisease state. Integration of all 4 CTD modules furnishes unique opportunities for toxicologists to generate computationally predictive adverse outcome pathways, linking chemical-gene molecular initiating events with phenotypic key events, adverse diseases, and population-level health outcomes. As examples, we present 3 diverse case studies discerning the effect of vehicle emissions on altered leukocyte migration, the role of cadmium in influencing phenotypes preceding Alzheimer disease, and the connection of arsenic-induced glucose metabolic phenotypes with diabetes. To date, CTD contains over 165 000 interactions that connect more than 6400 chemicals to 3900 phenotypes for 760 anatomical terms in 215 species, from over 19 000 scientific articles. To our knowledge, this is the first comprehensive set of manually curated, literature-based, contextualized, chemical-induced, nondisease phenotype data provided to the public.

Combined Biomarkers Composed of Environment and Genetic Factors in Stroke

It was widely accepted that stroke onset was the result of interactions between environment and genetic factors. However, the combined biomarkers covering environment and genetic factors and their interplay information in stroke were still lacking. In this study, we proposed a framework to identify the targeting or indicating role each factor played in the combined stroke biomarkers. A combined set of 36 biomarkers were identified based on evaluation and importance scores. Validations on three independent microarray data sets justified that the obtained markers were pervasively effective in discriminating stroke patients of different stages from healthy people on genetic levels. 8 and 3 genetic factors were identified as biomarkers in the acute and recovery phases of stroke, respectively. For example, the expression changing of SERPINH1 only appeared in the acute phase of stroke showing its targeting role in the combined biomarker. Compared with this, 11 genetic factors such as MMP9 were found to be differentially expressed in both acute and recovery phases of stroke showing their indicating roles in stroke. Functional analyses further revealed that the biomarkers could be grouped into 4 closely related processes of stroke including prevention, occurrence, processing, and recovery, respectively. These results indicated that the adoption of interactions between environment and genetic factors would be helpful in selecting robust and biologically relevant biomarkers, which cast a new insight for stroke biomarker identification.

A Cloud-Based Metabolite and Chemical Prioritization System for the Biology/Disease-Driven Human Proteome Project

Targeted metabolomics and biochemical studies complement the ongoing investigations led by the Human Proteome Organization (HUPO) Biology/Disease-Driven Human Proteome Project (B/D-HPP). However, it is challenging to identify and prioritize metabolite and chemical targets. Literature-mining-based approaches have been proposed for target proteomics studies, but text mining methods for metabolite and chemical prioritization are hindered by a large number of synonyms and nonstandardized names of each entity. In this study, we developed a cloud-based literature mining and summarization platform that maps metabolites and chemicals in the literature to unique identifiers and summarizes the copublication trends of metabolites/chemicals and B/D-HPP topics using Protein Universal Reference Publication-Originated Search Engine (PURPOSE) scores. We successfully prioritized metabolites and chemicals associated with the B/D-HPP targeted fields and validated the results by checking against expert-curated associations and enrichment analyses. Compared with existing algorithms, our system achieved better precision and recall in retrieving chemicals related to B/D-HPP focused areas. Our cloud-based platform enables queries on all biological terms in multiple species, which will contribute to B/D-HPP and targeted metabolomics/chemical studies.

Association of DFNA5, SYK, and NELL1 variants along with HPV infection in oral cancer among the prolonged tobacco-chewers

Southeast Asia, especially India, is well known for the highest use of smokeless tobacco. These products are known to induce oral squamous cell carcinoma. However, not all long-term tobacco-chewers develop oral squamous cell carcinoma. In addition, germline variants play a crucial role in susceptibility, prognosis, development, and progression of the disease. These prompted us to study the genetic susceptibility to oral squamous cell carcinoma among the long-term tobacco-chewers. Here, we presented a retrospective study on prolonged tobacco-chewers of Northeast India to identify the potential protective or risk-associated germline variants in tobacco-related oral squamous cell carcinoma along with HPV infection. Targeted re-sequencing (n = 60) of 170 genetic regions from 75 genes was carried out in Ion-PGM™ and validation (n = 116) of the observed variants was done using Sequenom iPLEX MassARRAY™ platform followed by polymerase chain reaction-based HPV genotyping and p16-immunohistochemistry study. Subsequently, estimation of population structure, different statistical and in silico approaches were undertaken. We identified one nonsense-mediated mRNA decay transcript variant in the DFNA5 region (rs2237306), associated with Benzo(a)pyrene, as a protective factor (odds ratio = 0.33; p = 0.009) and four harmful (odds ratio > 2.5; p < 0.05) intronic variants, rs182361, rs290974, and rs169724 in SYK and rs1670661 in NELL1 region, involved in genetic susceptibility to tobacco- and HPV-mediated oral oncogenesis. Among the oral squamous cell carcinoma patients, 12.6% (11/87) were HPV positive, out of which 45.5% (5/11) were HPV16-infected, 27.3% (3/11) were HPV18-infected, and 27.3% (3/11) had an infection of both subtypes. Multifactor dimensionality reduction analysis showed that the interactions among HPV and NELL1 variant rs1670661 with age and gender augmented the risk of both non-tobacco- and tobacco-related oral squamous cell carcinoma, respectively. These suggest that HPV infection may be one of the important risk factors for oral squamous cell carcinoma in this population. Finally, we newly report a DFNA5 variant probably conferring protection via nonsense-mediated mRNA decay pathway against tobacco-related oral squamous cell carcinoma. Thus, the analytical approach used here can be useful in predicting the population-specific significant variants associated with oral squamous cell carcinoma in any heterogeneous population.

Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis

Toxicity prediction is very important to public health. Among its many applications, toxicity prediction is essential to reduce the cost and labor of a drug's preclinical and clinical trials, because a lot of drug evaluations (cellular, animal, and clinical) can be spared due to the predicted toxicity. In the era of Big Data and artificial intelligence, toxicity prediction can benefit from machine learning, which has been widely used in many fields such as natural language processing, speech recognition, image recognition, computational chemistry, and bioinformatics, with excellent performance. In this article, we review machine learning methods that have been applied to toxicity prediction, including deep learning, random forests, k-nearest neighbors, and support vector machines. We also discuss the input parameter to the machine learning algorithm, especially its shift from chemical structural description only to that combined with human transcriptome data analysis, which can greatly enhance prediction accuracy.

Review and comparative assessment of similarity-based methods for prediction of drug–protein interactions in the druggable human proteome

Drug–protein interactions (DPIs) underlie the desired therapeutic actions and the adverse side effects of a significant majority of drugs. Computational prediction of DPIs facilitates research in drug discovery, characterization and repurposing. Similarity-based methods that do not require knowledge of protein structures are particularly suitable for druggable genome-wide predictions of DPIs. We review 35 high-impact similarity-based predictors that were published in the past decade. We group them based on three types of similarities and their combinations that they use. We discuss and compare key aspects of these methods including source databases, internal databases and their predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually and all possible combinations of similarities. We assess predictive quality at the database-wide DPI level and we are the first to also include evaluation over individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures area under the receiver operating characteristic curve of 0.93. We offer a comprehensive analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets. The benchmark database and a webserver for the seven predictors are freely available at http://biomine.cs.vcu.edu/servers/CONNECTOR/.

Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data

The toxicogenomics field aims to understand and predict toxicity by using 'omics' data in order to study systems-level responses to compound treatments. In recent years there has been a rapid increase in publicly available toxicological and 'omics' data, particularly gene expression data, and a corresponding development of methods for its analysis. In this review, we summarize recent progress relating to the analysis of RNA-Seq and microarray data, review relevant databases, and highlight recent applications of toxicogenomics data for understanding and predicting compound toxicity. These include the analysis of differentially expressed genes and their enrichment, signature matching, methods based on interaction networks, and the analysis of co-expression networks. In the future, these state-of-the-art methods will likely be combined with new technologies, such as whole human body models, to produce a comprehensive systems-level understanding of toxicity that reduces the necessity of in vivo toxicity assessment in animal models.

Contribution of Inhibitor of Differentiation and Estrogenic Endocrine Disruptors to Neurocognitive Disorders

The devastating growth in the worldwide frequency of neurocognitive disorders and its allied difficulties, such as decline in memory, spatial competency, and ability to focus, poses a significant psychological public health problem. Inhibitor of differentiation (ID) proteins are members of a family of helix-loop-helix (HLH) transcription factors. ID proteins have been demonstrated to be involved in neurodevelopmental and depressive diseases and, thus, may influence neurocognitive deficiencies due to environmental exposure. Previously, it has been demonstrated that environmental factors, such as estrogenic endocrine disruptors (EEDs), have played an essential role in the influence of various neurocognitive disorders such as Alzheimer’s, dementia, and Parkinson’s disease. Based on this increasing number of reports, we consider the impact of these environmental pollutants on ID proteins. Better understanding of how these ID proteins by which EED exposure can affect neurocognitive disorders in populations will prospectively deliver valuable information in the impediment and regulation of these diseases linked with environmental factor exposure.

Identification of genes and signaling pathways associated with the pathogenesis of juvenile spondyloarthritis

The aim of the present study was to identify key genes and signaling pathways associated with the pathogenesis of juvenile spondyloarthritis (JSA). The gene expression profile dataset GSE58667, including data from 15 human whole blood samples collected from 11 patients with JSA and four healthy controls, was analyzed to identify differentially expressed genes (DEGs) associated with disease characteristics. Additionally, Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed. Protein‑protein, microRNA‑transcription factor and chemical‑gene interaction networks were constructed. A total of 326 DEGs, 196 upregulated and 130 downregulated, were identified. DEGs, including C‑X‑C motif chemokine ligand 5 (CXCL5), BCL2 interacting protein 3 like (BNIP3L), dual specificity phosphatase 5 (DUSP5) and tumor protein p53 (TP53) were enriched in functions associated with apoptosis, the cell cycle and immune responses. KEGG pathway enrichment analysis revealed that pathways associated with inflammation and the mitogen‑activated protein kinase 1 (MAPK) signaling pathway were the most enriched by DEGs. The results of the present study indicated that the MAPK signaling pathway and four genes, including CXCL5, BNIP3L, DUSP5 and TP53, may be implicated in the pathogenesis of JSA.

Novel application of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene sets associated with disease: use case in breast carcinogenesis

Advances in technology within biomedical sciences have led to an inundation of data across many fields, raising new challenges in how best to integrate and analyze these resources. For example, rapid chemical screening programs like the US Environmental Protection Agency's ToxCast and the collaborative effort, Tox21, have produced massive amounts of information on putative chemical mechanisms where assay targets are identified as genes; however, systematically linking these hypothesized mechanisms with in vivo toxicity endpoints like disease outcomes remains problematic. Herein we present a novel use of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene associations with biological concepts as represented by Medical Subject Headings (MeSH terms) in PubMed. Resources that tag genes to articles were integrated, then cross-species orthologs were identified using UniRef50 clusters. MeSH term frequency was normalized to reflect the MeSH tree structure, and then the resulting GeneID-MeSH associations were ranked using NPMI. The resulting network, called Entity MeSH Co-occurrence Network (EMCON), is a scalable resource for the identification and ranking of genes for a given topic of interest. The utility of EMCON was evaluated with the use case of breast carcinogenesis. Topics relevant to breast carcinogenesis were used to query EMCON and retrieve genes important to each topic. A breast cancer gene set was compiled through expert literature review (ELR) to assess performance of the search results. We found that the results from EMCON ranked the breast cancer genes from ELR higher than randomly selected genes with a recall of 0.98. Precision of the top five genes for selected topics was calculated as 0.87. This work demonstrates that EMCON can be used to link in vitro results to possible biological outcomes, thus aiding in generation of testable hypotheses for furthering understanding of biological function and the contribution of chemical exposures to disease.

Automatic extraction of gene-disease associations from literature using joint ensemble learning

A wealth of knowledge concerning relations between genes and its associated diseases is present in biomedical literature. Mining these biological associations from literature can provide immense support to research ranging from drug-targetable pathways to biomarker discovery. However, time and cost of manual curation heavily slows it down. In this current scenario one of the crucial technologies is biomedical text mining, and relation extraction shows the promising result to explore the research of genes associated with diseases. By developing automatic extraction of gene-disease associations from the literature using joint ensemble learning we addressed this problem from a text mining perspective. In the proposed work, we employ a supervised machine learning approach in which a rich feature set covering conceptual, syntax and semantic properties jointly learned with word embedding are trained using ensemble support vector machine for extracting gene-disease relations from four gold standard corpora. Upon evaluating the machine learning approach shows promised results of 85.34%, 83.93%,87.39% and 85.57% of F-measure on EUADR, GAD, CoMAGC and PolySearch corpora respectively. We strongly believe that the presented novel approach combining rich syntax and semantic feature set with domain-specific word embedding through ensemble support vector machines evaluated on four gold standard corpora can act as a new baseline for future works in gene-disease relation extraction from literature.

Chemical-induced disease extraction via recurrent piecewise convolutional neural networks

BACKGROUND

Extracting relationships between chemicals and diseases from unstructured literature have attracted plenty of attention since the relationships are very useful for a large number of biomedical applications such as drug repositioning and pharmacovigilance. A number of machine learning methods have been proposed for chemical-induced disease (CID) extraction due to some publicly available annotated corpora. Most of them suffer from time-consuming feature engineering except deep learning methods. In this paper, we propose a novel document-level deep learning method, called recurrent piecewise convolutional neural networks (RPCNN), for CID extraction.

RESULTS

Experimental results on a benchmark dataset, the CDR (Chemical-induced Disease Relation) dataset of the BioCreative V challenge for CID extraction show that the highest precision, recall and F-score of our RPCNN-based CID extraction system are 65.24, 77.21 and 70.77%, which is competitive with other state-of-the-art systems.

CONCLUSIONS

A novel deep learning method is proposed for document-level CID extraction, where domain knowledge, piecewise strategy, attention mechanism, and multi-instance learning are combined together. The effectiveness of the method is proved by experiments conducted on a benchmark dataset.

An integrative network-based approach to identify novel disease genes and pathways: a case study in the context of inflammatory bowel disease

BACKGROUND

There are different and complicated associations between genes and diseases. Finding the causal associations between genes and specific diseases is still challenging. In this work we present a method to predict novel associations of genes and pathways with inflammatory bowel disease (IBD) by integrating information of differential gene expression, protein-protein interaction and known disease genes related to IBD.

RESULTS

We downloaded IBD gene expression data from NCBI's Gene Expression Omnibus, performed statistical analysis to determine differentially expressed genes, collected known IBD genes from DisGeNet database, which were used to construct a IBD related PPI network with HIPPIE database. We adapted our graph-based clustering algorithm DPClusO to cluster the disease PPI network. We evaluated the statistical significance of the identified clusters in the context of determining the richness of IBD genes using Fisher's exact test and predicted novel genes related to IBD. We showed 93.8% of our predictions are correct in the context of other databases and published literatures related to IBD.

CONCLUSIONS

Finding disease-causing genes is necessary for developing drugs with synergistic effect targeting many genes simultaneously. Here we present an approach to identify novel disease genes and pathways and discuss our approach in the context of IBD. The approach can be generalized to find disease-associated genes for other diseases.

Identifying a panel of genes/proteins/miRNAs modulated by arsenicals in bladder, prostate, kidney cancers

Arsenic and arsenic-derivative compounds, named as arsenicals, represent a worldwide problem for their effect on the human health and, in particular, for their capability to increase the risk of developing cancer such as kidney, bladder and prostate cancer. The main source of arsenical exposure is drinking water. Nowadays, it is well known that the chronic exposure to arsenicals leads to a series of epigenetic alterations that have a role in arsenic-induced effects on human health including cancer. Based on these observations, the aim of our study was to select by network analysis the genes/proteins/miRNAs implicated in kidney, bladder and prostate cancer development upon arsenical exposure. From this analysis we identified: (i) the nodes linking the three molecular networks specific for kidney, bladder and prostate cancer; (ii) the relative HUB nodes (RXRA, MAP3K7, NR3C1, PABPC1, NDRG1, RELA and CTNNB1) that link the three cancer networks; (iii) the miRNAs able to target these HUB nodes. In conclusion, we highlighted a panel of potential molecules related to the molecular mechanisms of arsenical-induced cancerogenesis and suggest their utility as biomarkers or therapeutic targets.

Therapeutic Targeting of the Premetastatic Stage in Human Lung-to-Brain Metastasis

Brain metastases (BM) result from the spread of primary tumors to the brain and are a leading cause of cancer mortality in adults. Secondary tissue colonization remains the main bottleneck in metastatic development, yet this "premetastatic" stage of the metastatic cascade, when primary tumor cells cross the blood-brain barrier and seed the brain before initiating a secondary tumor, remains poorly characterized. Current studies rely on specimens from fully developed macrometastases to identify therapeutic options in cancer treatment, overlooking the potentially more treatable "premetastatic" phase when colonizing cancer cells could be targeted before they initiate the secondary brain tumor. Here we use our established brain metastasis initiating cell (BMIC) models and gene expression analyses to characterize premetastasis in human lung-to-BM. Premetastatic BMIC engaged invasive and epithelial developmental mechanisms while simultaneously impeding proliferation and apoptosis. We identified the dopamine agonist apomorphine to be a potential premetastasis-targeting drug. In vivo treatment with apomorphine prevented BM formation, potentially by targeting premetastasis-associated genes KIF16B, SEPW1, and TESK2 Low expression of these genes was associated with poor survival of patients with lung adenocarcinoma. These results illuminate the cellular and molecular dynamics of premetastasis, which is subclinical and currently impossible to identify or interrogate in human patients with BM. These data present several novel therapeutic targets and associated pathways to prevent BM initiation.Significance: These findings unveil molecular features of the premetastatic stage of lung-to-brain metastases and offer a potential therapeutic strategy to prevent brain metastases. Cancer Res; 78(17); 5124-34. ©2018 AACR.

ChemDIS-Mixture: an online tool for analyzing potential interaction effects of chemical mixtures

The assessment of bioactivity and toxicity for mixtures remains a challenging work. Although several computational models have been developed to accelerate the evaluation of chemical-chemical interaction, a specific biological endpoint should be defined before applying the models that usually relies on clinical and experimental data. The development of computational methods is desirable for identifying potential biological endpoints of mixture interactions. To facilitate the identification of potential effects of mixture interactions, a novel online system named ChemDIS-Mixture is proposed to analyze the shared target proteins, and common enriched functions, pathways, and diseases affected by multiple chemicals. Venn diagram tools have been implemented for easy analysis and visualization of interaction targets and effects. Case studies have been provided to demonstrate the capability of ChemDIS-Mixture for identifying potential effects of mixture interactions in clinical studies. ChemDIS-Mixture provides useful functions for the identification of potential effects of coexposure to multiple chemicals. ChemDIS-Mixture is freely accessible at http://cwtung.kmu.edu.tw/chemdis/mixture .

Systems Pharmacological Approach to Investigate the Mechanism of Acori Tatarinowii Rhizoma for Alzheimer's Disease

In traditional Chinese medicine (TCM), Acori Tatarinowii Rhizoma (ATR) is widely used to treat memory and cognition dysfunction. This study aimed to confirm evidence regarding the potential therapeutic effect of ATR on Alzheimer's disease (AD) using a system network level based in silico approach. Study results showed that the compounds in ATR are highly connected to AD-related signaling pathways, biological processes, and organs. These findings were confirmed by compound-target network, target-organ location network, gene ontology analysis, and KEGG pathway enrichment analysis. Most compounds in ATR have been reported to have antifibrillar amyloid plaques, anti-tau phosphorylation, and anti-inflammatory effects. Our results indicated that compounds in ATR interact with multiple targets in a synergetic way. Furthermore, the mRNA expressions of genes targeted by ATR are elevated significantly in heart, brain, and liver. Our results suggest that the anti-inflammatory and immune system enhancing effects of ATR might contribute to its major therapeutic effects on Alzheimer's disease.

A Computational Systems Pharmacology Approach to Investigate Molecular Mechanisms of Herbal Formula Tian-Ma-Gou-Teng-Yin for Treatment of Alzheimer's Disease

Traditional Chinese medicine (TCM) is typically prescribed as formula to treat certain symptoms. A TCM formula contains hundreds of chemical components, which makes it complicated to elucidate the molecular mechanisms of TCM. Here, we proposed a computational systems pharmacology approach consisting of network link prediction, statistical analysis, and bioinformatics tools to investigate the molecular mechanisms of TCM formulae. Taking formula Tian-Ma-Gou-Teng-Yin as an example, which shows pharmacological effects on Alzheimer’s disease (AD) and its mechanism is unclear, we first identified 494 formula components together with corresponding 178 known targets, and then predicted 364 potential targets for these components with our balanced substructure-drug–target network-based inference method. With Fisher’s exact test and statistical analysis we identified 12 compounds to be most significantly related to AD. The target genes of these compounds were further enriched onto pathways involved in AD, such as neuroactive ligand–receptor interaction, serotonergic synapse, inflammatory mediator regulation of transient receptor potential channel and calcium signaling pathway. By regulating key target genes, such as ACHE, HTR2A, NOS2, and TRPA1, the formula could have neuroprotective and anti-neuroinflammatory effects against the progression of AD. Our approach provided a holistic perspective to study the relevance between TCM formulae and diseases, and implied possible pharmacological effects of TCM components.

Predicting drug-disease associations by using similarity constrained matrix factorization

Background

Drug-disease associations provide important information for the drug discovery. Wet experiments that identify drug-disease associations are time-consuming and expensive. However, many drug-disease associations are still unobserved or unknown. The development of computational methods for predicting unobserved drug-disease associations is an important and urgent task.

Results

In this paper, we proposed a similarity constrained matrix factorization method for the drug-disease association prediction (SCMFDD), which makes use of known drug-disease associations, drug features and disease semantic information. SCMFDD projects the drug-disease association relationship into two low-rank spaces, which uncover latent features for drugs and diseases, and then introduces drug feature-based similarities and disease semantic similarity as constraints for drugs and diseases in low-rank spaces. Different from the classic matrix factorization technique, SCMFDD takes the biological context of the problem into account. In computational experiments, the proposed method can produce high-accuracy performances on benchmark datasets, and outperform existing state-of-the-art prediction methods when evaluated by five-fold cross validation and independent testing.

Conclusion

We developed a user-friendly web server by using known associations collected from the CTD database, available at http://www.bioinfotech.cn/SCMFDD/. The case studies show that the server can find out novel associations, which are not included in the CTD database.

From gene networks to drugs: systems pharmacology approaches for AUD

The alcohol research field has amassed an impressive number of gene expression datasets spanning key brain areas for addiction, species (humans as well as multiple animal models), and stages in the addiction cycle (binge/intoxication, withdrawal/negative effect, and preoccupation/anticipation). These data have improved our understanding of the molecular adaptations that eventually lead to dysregulation of brain function and the chronic, relapsing disorder of addiction. Identification of new medications to treat alcohol use disorder (AUD) will likely benefit from the integration of genetic, genomic, and behavioral information included in these important datasets. Systems pharmacology considers drug effects as the outcome of the complex network of interactions a drug has rather than a single drug-molecule interaction. Computational strategies based on this principle that integrate gene expression signatures of pharmaceuticals and disease states have shown promise for identifying treatments that ameliorate disease symptoms (called in silico gene mapping or connectivity mapping). In this review, we suggest that gene expression profiling for in silico mapping is critical to improve drug repurposing and discovery for AUD and other psychiatric illnesses. We highlight studies that successfully apply gene mapping computational approaches to identify or repurpose pharmaceutical treatments for psychiatric illnesses. Furthermore, we address important challenges that must be overcome to maximize the potential of these strategies to translate to the clinic and improve healthcare outcomes.

In Silico Pharmacoepidemiologic Evaluation of Drug-Induced Cardiovascular Complications Using Combined Classifiers

Drug-induced cardiovascular complications are the most common adverse drug events and account for the withdrawal or severe restrictions on the use of multitudinous postmarketed drugs. In this study, we developed new in silico models for systematic identification of drug-induced cardiovascular complications in drug discovery and postmarketing surveillance. Specifically, we collected drug-induced cardiovascular complications covering the five most common types of cardiovascular outcomes (hypertension, heart block, arrhythmia, cardiac failure, and myocardial infarction) from four publicly available data resources: Comparative Toxicogenomics Database, SIDER, Offsides, and MetaADEDB. Using these databases, we developed a combined classifier framework through integration of five machine-learning algorithms: logistic regression, random forest, k-nearest neighbors, support vector machine, and neural network. The totality of models included 180 single classifiers with area under receiver operating characteristic curves (AUC) ranging from 0.647 to 0.809 on 5-fold cross-validations. To develop the combined classifiers, we then utilized a neural network algorithm to integrate the best four single classifiers for each cardiovascular outcome. The combined classifiers had higher performance with an AUC range from 0.784 to 0.842 compared to single classifiers. Furthermore, we validated our predicted cardiovascular complications for 63 anticancer agents using experimental data from clinical studies, human pluripotent stem cell-derived cardiomyocyte assays, and literature. The success rate of our combined classifiers reached 87%. In conclusion, this study presents powerful in silico tools for systematic risk assessment of drug-induced cardiovascular complications. This tool is relevant not only in early stages of drug discovery but also throughout the life of a drug including clinical trials and postmarketing surveillance.

Data-driven analysis of biomedical literature suggests broad-spectrum benefits of culinary herbs and spices

Spices and herbs are key dietary ingredients used across cultures worldwide. Beyond their use as flavoring and coloring agents, the popularity of these aromatic plant products in culinary preparations has been attributed to their antimicrobial properties. Last few decades have witnessed an exponential growth of biomedical literature investigating the impact of spices and herbs on health, presenting an opportunity to mine for patterns from empirical evidence. Systematic investigation of empirical evidence to enumerate the health consequences of culinary herbs and spices can provide valuable insights into their therapeutic utility. We implemented a text mining protocol to assess the health impact of spices by assimilating, both, their positive and negative effects. We conclude that spices show broad-spectrum benevolence across a range of disease categories in contrast to negative effects that are comparatively narrow-spectrum. We also implement a strategy for disease-specific culinary recommendations of spices based on their therapeutic tradeoff against adverse effects. Further by integrating spice-phytochemical-disease associations, we identify bioactive spice phytochemicals potentially involved in their therapeutic effects. Our study provides a systems perspective on health effects of culinary spices and herbs with applications for dietary recommendations as well as identification of phytochemicals potentially involved in underlying molecular mechanisms.

The relative resistance of children to sepsis mortality: from pathways to drug candidates

Attempts to develop drugs that address sepsis based on leads developed in animal models have failed. We sought to identify leads based on human data by exploiting a natural experiment: the relative resistance of children to mortality from severe infections and sepsis. Using public datasets, we identified key differences in pathway activity (Pathprint) in blood transcriptome profiles of septic adults and children. To find drugs that could promote beneficial (child) pathways or inhibit harmful (adult) ones, we built an in silico pathway drug network (PDN) using expression correlation between drug, disease, and pathway gene signatures across 58,475 microarrays. Specific pathway clusters from children or adults were assessed for correlation with drug‐based signatures. Validation by literature curation and by direct testing in an endotoxemia model of murine sepsis of the most correlated drug candidates demonstrated that the Pathprint‐PDN methodology is more effective at generating positive drug leads than gene‐level methods (e.g., CMap). Pathway‐centric Pathprint‐PDN is a powerful new way to identify drug candidates for intervention against sepsis and provides direct insight into pathways that may determine survival.

Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.

Network Analysis of Se-and Zn-related Proteins in the Serum Proteomics Expression Profile of the Endemic Dilated Cardiomyopathy Keshan Disease

Keshan disease (KD) is an endemic cardiomyopathy with high mortality. Selenium (Se) and zinc (Zn) deficiencies are closely related to KD. The molecular mechanism of KD pathogenesis is still unclear. There are only few studies on the interaction of trace elements and proteins associated with the pathogenesis of KD. In this study, isobaric tags for relative and absolute quantitation (iTRAQ)-coupled two-dimensional liquid chromatography tandem mass spectrometry (2DLC-MS/MS) technique analysis was used to analyze the differential expression of proteins from serum samples. Comparative Toxicogenomics Database (CTD) was used to screen Se- and Zn-associated proteins. Then, pathway and network analyses of Se- and Zn-associated proteins were constituted by Cytoscape ClueGO and GeneMANIA plugins. One hundred and five differentially expressed proteins were obtained by 2DLC-MS/MS, among them 19 Se- and 3 Zn-associated proteins. Fifty-two pathways were identified from ClueGO and 1 network from GeneMANIA analyses. The results showed that Se-associated proteins STAT3 and MAPK1 and Zn-associated proteins HIF1A and PARP1, the proteins involved in HIF-1 signaling pathway and apoptosis pathway, may play significant roles in the pathogenesis of KD. The approach of this study would be also beneficial for further dissecting molecular mechanism of other trace element-associated disease.

Identifying the common genetic networks of ADR (adverse drug reaction) clusters and developing an ADR classification model

Adverse drug reactions (ADRs) are one of the major concerns threatening public health and have resulted in failures in drug development. Thus, predicting ADRs and discovering the mechanisms underlying ADRs have become important tasks in pharmacovigilance. Identification of potential ADRs by computational approaches in the early stages would be advantageous in drug development. Here we propose a computational method that elucidates the action mechanisms of ADRs and predicts potential ADRs by utilizing ADR genes, drug features, and protein-protein interaction (PPI) networks. If some ADRs share similar features, there is a high possibility that they may appear together in a drug and share analogous mechanisms. Proceeding from this assumption, we clustered ADRs according to interactions of ADR genes in the PPI networks and the frequency of co-occurrence of ADRs in drugs. ADR clusters were verified based on a side effect database and literature data regarding whether ADRs have relevance to other ADRs in the same cluster. Gene networks shared by ADRs in each cluster were constructed by cumulating the shortest paths between drug target genes and ADR genes in the PPI network. We developed a classification model to predict potential ADRs using these gene networks shared by ADRs and calculated cross-validation AUC (area under the curve) values for each ADR cluster. In addition, in order to demonstrate correlations between gene networks shared by ADRs and ADRs in a cluster, we applied the Wilcoxon rank sum statistical test to the literature data and results of a Google query search. We attained statistically meaningful p-values (<0.05) for every ADR cluster. The results suggest that our approach provides insights into discovering the action mechanisms of ADRs and is a novel attempt to predict ADRs in a biological aspect.

Reprint of: Environmental toxicology and omics: A question of sex

Molecular initiating events and downstream transcriptional/proteomic responses provide valuable information for adverse outcome pathways, which can be used predict the effects of chemicals on physiological systems. There has been a paucity of research that addresses sex-specific expression profiling in toxicology and due to cost, time, and logistic considerations, sex as a variable has not been widely considered. In response to this deficiency, federal agencies in the United States, Canada, and Europe have highlighted the importance of including sex as a variable in scientific investigations. Using case studies from both aquatic and mammalian toxicology, we report that there can be less than ~20-25% consensus in how the transcriptome and proteome of each sex responds to chemicals. Chemicals that have been shown to elicit sex-specific responses in the transcriptome or proteome include pharmaceuticals, anti-fouling agents, anticorrosive agents, and fungicides, among others. Sex-specific responses in the transcriptome and proteome are not isolated to whole animals, as investigations demonstrate that primary cell cultures isolated from each sex responds differently to toxicants. This signifies that sex is important, even in cell lines. Sex has significant implications for predictive toxicology, and both male and female data are required to improve robustness of adverse outcome pathways.

BIOLOGICAL SIGNIFICANCE

Clinical toxicology recognizes that sex is an important variable, as pharmacokinetics (ADME; absorption, distribution, metabolism, and excretion) can differ between females and males. However, few studies in toxicology have explored the implication of sex in relation to the transcriptome and proteome of whole organisms. High-throughput molecular approaches are becoming more frequently applied in toxicity screens (e.g. pre-clinical experiments, fish embryos, cell lines, synthetic tissues) and such data are expected to build upon reporter-based cell assays (e.g. receptor activation, enzyme inhibition) used in toxicant screening programs (i.e. Tox21, ToxCast, REACH). Thus, computational models can more accurately predict the diversity of adverse effects that can occur from chemical exposure within the biological system. Our studies and those synthesized from the literature suggest that the transcriptome and proteome of females and males respond quite differentially to chemicals. This has significant implications for predicting adverse effects in one sex when using molecular data generated in the other sex. While molecular initiating events are not expected to differ dramatically between females and males (i.e. an estrogen binds estrogen receptors in both sexes), it is important to acknowledge that the downstream transcriptomic and proteomic responses can differ based upon the presence/absence of co-regulators and inherent sex-specific variability in regulation of transcriptional and translational machinery. Transcriptomic and proteomic studies also reveal that cell processes affected by chemicals can differ due to sex, and this can undoubtedly lead to sex-specific physiological responses.

Microarray analysis of obese women with polycystic ovary syndrome for key gene screening, key pathway identification and drug prediction

PURPOSE

This study aimed to screen key genes and pathways involved in obese polycystic ovary syndrome (PCOS), and predict drugs for treatment of obese PCOS via bioinformatics approaches.

METHODS

Microarray dataset GSE10946 were downloaded from the Gene Expression Omnibus database, including 7 cumulus cell samples from obese PCOS patients and 6 lean control samples. Differentially expressed genes (DEGs) between obese PCOS and controls were obtained using Bayesian test after data preprocessing, followed by functional enrichment analyses for DEGs. Besides, protein-protein interaction (PPI) network and sub-network analyses were performed. Furthermore, drug prediction was carried out based on the DEGs.

RESULTS

A total of 793 DEGs were identified in PCOS compared with control, including 352 up-regulated and 441 down-regulated DEGs. Specifically, upregulated RNA polymerase I subunit B (POLR1B), DNA polymerase epsilon 3, accessory subunit (POLE3), and DNA polymerase delta 3, accessory subunit (POLD3) were enriched in pathway of pyrimidine metabolism associated with obesity and PCOS, and 5-hydroxytryptamine receptor 2C (HTR2C) was enriched calcium signaling pathway. Additionally, 10 significant potential drugs, such as spironolactone targeting androgen receptor (AR), trimipramine targeting adrenoceptor beta 2 (ADRB2), and L-ornithine targeting ornithine decarboxylase antizyme 3 (OAZ3), were obtained.

CONCLUSIONS

In conclusion, POLR1B, POLE3, POLD3, and HTR2C might play important roles in obese PCOS via involvement of pyrimidine metabolism and calcium signaling pathway. Moreover, AR, ADRB2, and OAZ3 might be targets of spironolactone, trimipramine, and L-ornithine in the treatment of obese PCOS.

Transcriptional Responses Reveal Similarities Between Preclinical Rat Liver Testing Systems

Toxicogenomics (TGx) is an important tool to gain an enhanced understanding of toxicity at the molecular level. Previously, we developed a pair ranking (PRank) method to assess in vitro to in vivo extrapolation (IVIVE) using toxicogenomic datasets from the Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System (TG-GATEs) database. With this method, we investiagted three important questions that were not addressed in our previous study: (1) is a 1-day in vivo short-term assay able to replace the 28-day standard and expensive toxicological assay? (2) are some biological processes more conservative across different preclinical testing systems than others? and (3) do these preclinical testing systems have the similar resolution in differentiating drugs by their therapeutic uses? For question 1, a high similarity was noted (PRank score = 0.90), indicating the potential utility of shorter term in vivo studies to predict outcome in longer term and more expensive in vivo model systems. There was a moderate similarity between rat primary hepatocytes and in vivo repeat-dose studies (PRank score = 0.71) but a low similarity (PRank score = 0.56) between rat primary hepatocytes and in vivo single dose studies. To address question 2, we limited the analysis to gene sets relevant to specific toxicogenomic pathways and we found that pathways such as lipid metabolism were consistently over-represented in all three assay systems. For question 3, all three preclinical assay systems could distinguish compounds from different therapeutic categories. This suggests that any noted differences in assay systems was biological process-dependent and furthermore that all three systems have utility in assessing drug responses within a certain drug class. In conclusion, this comparison of three commonly used rat TGx systems provides useful information in utility and application of TGx assays.

Transcriptomic characterization of MRI contrast with focus on the T1-w/T2-w ratio in the cerebral cortex

Magnetic resonance (MR) images of the brain are of immense clinical and research utility. At the atomic and subatomic levels, the sources of MR signals are well understood. However, we lack a comprehensive understanding of the macromolecular correlates of MR signal contrast. To address this gap, we used genome-wide measurements to correlate gene expression with MR signal intensity across the cerebral cortex in the Allen Human Brain Atlas (AHBA). We focused on the ratio of T1-weighted and T2-weighted intensities (T1-w/T2-w ratio image), which is considered to be a useful proxy for myelin content. As expected, we found enrichment of positive correlations between myelin-associated genes and the ratio image, supporting its use as a myelin marker. Genome-wide, there was an association with protein mass, with genes coding for heavier proteins expressed in regions with high T1-w/T2-w values. Oligodendrocyte gene markers were strongly correlated with the T1-w/T2-w ratio, but this was not driven by myelin-associated genes. Mitochondrial genes exhibit the strongest relationship, showing higher expression in regions with low T1-w/T2-w ratio. This may be due to the pH gradient in mitochondria as genes up-regulated by pH in the brain were also highly correlated with the ratio. While we corroborate associations with myelin and synaptic plasticity, differences in the T1-w/T2-w ratio across the cortex are more strongly linked to molecule size, oligodendrocyte markers, mitochondria, and pH. We evaluate correlations between AHBA transcriptomic measurements and a group averaged T1-w/T2-w ratio image, showing agreement with in-sample results. Expanding our analysis to the whole brain results in strong positive T1-w/T2-w correlations for immune system, inflammatory disease, and microglia marker genes. Genes with negative correlations were enriched for neuron markers and synaptic plasticity genes. Lastly, our findings are similar when performed on T1-w or inverted T2-w intensities alone. These results provide a molecular characterization of MR contrast that will aid interpretation of future MR studies of the brain.

Polysaccharide from Angelica sinensis protects H9c2 cells against oxidative injury and endoplasmic reticulum stress by activating the ATF6 pathway

Objectives

Angelica sinensis exerts various pharmacological effects, such as antioxidant and anti-apoptotic activity. This study aimed to investigate the active ingredients in A. sinensis with antioxidant properties and whether A. sinensis polysaccharide (ASP) protects H9c2 cells against oxidative and endoplasmic reticulum (ER) stress.

Methods

The ingredients of A. sinensis and their targets and related pathways were determined using web-based databases. Markers of oxidative stress, cell viability, apoptosis, and ER stress-related signalling pathways were measured in H9c2 cells treated with hydrogen peroxide (H₂O₂) and ASP.

Results

The ingredient–pathway–disease network showed that A. sinensis exerted protective effects against oxidative injury through its various active ingredients on regulation of multiple pathways. Subsequent experiments showed that ASP pretreatment significantly decreased H₂O₂-induced cytotoxicity and apoptosis in H9c2 cells. ASP pretreatment inhibited H₂O₂-induced reactive oxygen species generation, lactic dehydrogenase release, and malondialdehyde production. ASP exerted beneficial effects by inducing activating transcription factor 6 (ATF6) and increasing ATF6 target protein levels, which in turn attenuated ER stress and increased antioxidant activity.

Conclusions

Our findings indicate that ASP, a major water-soluble component of A. sinensis, exerts protective effects against H₂O₂-induced injury in H9c2 cells by activating the ATF6 pathway, thus ameliorating ER and oxidative stress.

Ensemble of rankers for efficient gene signature extraction in smoke exposure classification

Background

System toxicology aims at understanding the mechanisms used by biological systems to respond to toxicants. Such understanding can be leveraged to assess the risk of chemicals, drugs, and consumer products in living organisms. In system toxicology, machine learning techniques and methodologies are applied to develop prediction models for classification of toxicant exposure of biological systems. Gene expression data (RNA/DNA microarray) are often used to develop such prediction models.

Results

The outcome of the present work is an experimental methodology to develop prediction models, based on robust gene signatures, for the classification of cigarette smoke exposure and cessation in humans. It is a result of the participation in the recent sbv IMPROVER SysTox Computational Challenge. By merging different gene selection techniques, we obtain robust gene signatures and we investigate prediction capabilities of different off-the-shelf machine learning techniques, such as artificial neural networks, linear models and support vector machines. We also predict six novel genes in our signature, and firmly believe these genes have to be further investigated as biomarkers for tobacco smoking exposure.

Conclusions

The proposed methodology provides gene signatures with top-ranked performances in the prediction of the investigated classification methods, as well as new discoveries in genetic signatures for bio-markers of the smoke exposure of humans.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2035-3) contains supplementary material, which is available to authorized users.

Systematic Protein Prioritization for Targeted Proteomics Studies through Literature Mining

There are more than 3.7 million published articles on the biological functions or disease implications of proteins, constituting an important resource of proteomics knowledge. However, it is difficult to summarize the millions of proteomics findings in the literature manually and quantify their relevance to the biology and diseases of interest. We developed a fully automated bioinformatics framework to identify and prioritize proteins associated with any biological entity. We used the 22 targeted areas of the Biology/Disease-driven (B/D)-Human Proteome Project (HPP) as examples, prioritized the relevant proteins through their Protein Universal Reference Publication-Originated Search Engine (PURPOSE) scores, validated the relevance of the score by comparing the protein prioritization results with a curated database, computed the scores of proteins across the topics of B/D-HPP, and characterized the top proteins in the common model organisms. We further extended the bioinformatics workflow to identify the relevant proteins in all organ systems and human diseases and deployed a cloud-based tool to prioritize proteins related to any custom search terms in real time. Our tool can facilitate the prioritization of proteins for any organ system or disease of interest and can contribute to the development of targeted proteomic studies for precision medicine.

Petri net-based prediction of therapeutic targets that recover abnormally phosphorylated proteins in muscle atrophy

Background

Muscle atrophy, an involuntary loss of muscle mass, is involved in various diseases and sometimes leads to mortality. However, therapeutics for muscle atrophy thus far have had limited effects. Here, we present a new approach for therapeutic target prediction using Petri net simulation of the status of phosphorylation, with a reasonable assumption that the recovery of abnormally phosphorylated proteins can be a treatment for muscle atrophy.

Results

The Petri net model was employed to simulate phosphorylation status in three states, i.e. reference, atrophic and each gene-inhibited state based on the myocyte-specific phosphorylation network. Here, we newly devised a phosphorylation specific Petri net that involves two types of transitions (phosphorylation or de-phosphorylation) and two types of places (activation with or without phosphorylation). Before predicting therapeutic targets, the simulation results in reference and atrophic states were validated by Western blotting experiments detecting five marker proteins, i.e. RELA, SMAD2, SMAD3, FOXO1 and FOXO3. Finally, we determined 37 potential therapeutic targets whose inhibition recovers the phosphorylation status from an atrophic state as indicated by the five validated marker proteins. In the evaluation, we confirmed that the 37 potential targets were enriched for muscle atrophy-related terms such as actin and muscle contraction processes, and they were also significantly overlapping with the genes associated with muscle atrophy reported in the Comparative Toxicogenomics Database (p-value < 0.05). Furthermore, we noticed that they included several proteins that could not be characterized by the shortest path analysis. The three potential targets, i.e. BMPR1B, ROCK, and LEPR, were manually validated with the literature.

Conclusions

In this study, we suggest a new approach to predict potential therapeutic targets of muscle atrophy with an analysis of phosphorylation status simulated by Petri net. We generated a list of the potential therapeutic targets whose inhibition recovers abnormally phosphorylated proteins in an atrophic state. They were evaluated by various approaches, such as Western blotting, GO terms, literature, known muscle atrophy-related genes and shortest path analysis. We expect the new proposed strategy to provide an understanding of phosphorylation status in muscle atrophy and to provide assistance towards identifying new therapies.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0555-0) contains supplementary material, which is available to authorized users.

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

AOP-DB: A database resource for the exploration of Adverse Outcome Pathways through integrated association networks

The Adverse Outcome Pathway (AOP) framework describes the progression of a toxicity pathway from molecular perturbation to population-level outcome in a series of measurable, mechanistic responses. The controlled, computer-readable vocabulary that defines an AOP has the ability to, automatically and on a large scale, integrate AOP knowledge with publically available sources of biological high-throughput data and its derived associations. To support the discovery and development of putative (existing) and potential AOPs, we introduce the AOP-DB, an exploratory database resource that aggregates association relationships between genes and their related chemicals, diseases, pathways, species orthology information, ontologies, and gene interactions. These associations are mined from publically available annotation databases and are integrated with the AOP information centralized in the AOP-Wiki, allowing for the automatic characterization of both putative and potential AOPs in the context of multiple areas of biological information, referred to here as "biological entities". The AOP-DB acts as a hypothesis-generation tool for the expansion of putative AOPs, as well as the characterization of potential AOPs, through the creation of association networks across these biological entities. Finally, the AOP-DB provides a useful interface between the AOP framework and existing chemical screening and prioritization efforts by the US Environmental Protection Agency.

BCScreen: A gene panel to test for breast carcinogenesis in chemical safety screening

Targeted gene lists have been used in clinical settings to specify breast tumor type, and to predict breast cancer prognosis and response to treatment. Separately, panels have been curated to predict systemic toxicity and xenoestrogen activity as a part of chemical screening strategies. However, currently available panels do not specifically target biological processes relevant to breast development and carcinogenesis. We have developed a gene panel called the Breast Carcinogen Screen (BCScreen) as a tool to identify potential breast carcinogens and characterize mechanisms of toxicity. First, we used four seminal reviews to identify 14 key characteristics of breast carcinogenesis, such as apoptosis, immunomodulation, and genotoxicity. Then, using a hybrid data and knowledge-driven framework, we systematically combined information from whole transcriptome data from genomic databases, biomedical literature, the CTD chemical-gene interaction database, and primary literature review to generate a panel of 500 genes relevant to breast carcinogenesis. We used normalized pointwise mutual information (NPMI) to rank genes that frequently co-occurred with key characteristics in biomedical literature. We found that many genes identified for BCScreen were not included in prognostic breast cancer or systemic toxicity panels. For example, more than half of BCScreen genes were not included in the Tox21 S1500+ general toxicity gene list. Of the 230 that did overlap between the two panels, representation varied across characteristics of carcinogenesis ranging from 21% for genes associated with epigenetics to 82% for genes associated with xenobiotic metabolism. Enrichment analysis of BCScreen identified pathways and processes including response to steroid hormones, cancer, cell cycle, apoptosis, DNA damage and breast cancer. The biologically-based systematic approach to gene prioritization demonstrated here provides a flexible framework for creating disease-focused gene panels to support discovery related to etiology. With validation, BCScreen may also be useful for toxicological screening relevant to breast carcinogenesis.

Tox21 Enricher: Web-based Chemical/Biological Functional Annotation Analysis Tool Based on Tox21 Toxicity Screening Platform

The US Toxicology Testing in the 21st Century (Tox21) program was established to develop more efficient and human-relevant toxicity assessment methods. The Tox21 program screens >10,000 chemicals using quantitative high-throughput screening (qHTS) of assays that measure effects on toxicity pathways. To date, more than 70 assays have yielded >12 million concentration-response curves. The patterns of activity across assays can be used to define similarity between chemicals. Assuming chemicals with similar activity profiles have similar toxicological properties, we may infer toxicological properties based on its neighbourhood. One approach to inference is chemical/biological annotation enrichment analysis. Here, we present Tox21 Enricher, a web-based chemical annotation enrichment tool for the Tox21 toxicity screening platform. Tox21 Enricher identifies over-represented chemical/biological annotations among lists of chemicals (neighbourhoods), facilitating the identification of the toxicological properties and mechanisms in the chemical set.

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.

MicroRNA regulatory networks reflective of polyhexamethylene guanidine phosphate-induced fibrosis in A549 human alveolar adenocarcinoma cells

Polyhexamethylene guanidine phosphate (PHMG-phosphate), an active component of humidifier disinfectant, is suspected to be a major cause of pulmonary fibrosis. Fibrosis, induced by recurrent epithelial damage, is significantly affected by epigenetic regulation, including microRNAs (miRNAs). The aim of this study was to investigate the fibrogenic mechanisms of PHMG-phosphate through the profiling of miRNAs and their target genes. A549 cells were treated with 0.75 μg/mL PHMG-phosphate for 24 and 48 h and miRNA microarray expression analysis was conducted. The putative mRNA targets of the miRNAs were identified and subjected to Gene Ontology analysis. After exposure to PHMG-phosphate for 24 and 48 h, 46 and 33 miRNAs, respectively, showed a significant change in expression over 1.5-fold compared with the control. The integrated analysis of miRNA and mRNA microarray results revealed the putative targets that were prominently enriched were associated with the epithelial-mesenchymal transition (EMT), cell cycle changes, and apoptosis. The dose-dependent induction of EMT by PHMG-phosphate exposure was confirmed by western blot. We identified 13 putative EMT-related targets that may play a role in PHMG-phosphate-induced fibrosis according to the Comparative Toxicogenomic Database. Our findings contribute to the comprehension of the fibrogenic mechanism of PHMG-phosphate and will aid further study on PHMG-phosphate-induced toxicity.