The Comparative Toxicogenomics Database facilitates identification and understanding of chemical-gene-disease associations: arsenic as a case study

CTD tetramers: a new online tool that computationally links curated chemicals, genes, phenotypes, and diseases to inform molecular mechanisms for environmental health

The molecular mechanisms connecting environmental exposures to adverse endpoints are often unknown, reflecting knowledge gaps. At the Comparative Toxicogenomics Database (CTD), we developed a bioinformatics approach that integrates manually curated, literature-based interactions from CTD to generate a "CGPD-tetramer": a 4-unit block of information organized as a step-wise molecular mechanism linking an initiating Chemical, an interacting Gene, a Phenotype, and a Disease outcome. Here, we describe a novel, user-friendly tool called CTD Tetramers that generates these evidence-based CGPD-tetramers for any curated chemical, gene, phenotype, or disease of interest. Tetramers offer potential solutions for the unknown underlying mechanisms and intermediary phenotypes connecting a chemical exposure to a disease. Additionally, multiple tetramers can be assembled to construct detailed modes-of-action for chemical-induced disease pathways. As well, tetramers can help inform environmental influences on adverse outcome pathways (AOPs). We demonstrate the tool's utility with relevant use cases for a variety of environmental chemicals (eg, perfluoroalkyl substances, bisphenol A), phenotypes (eg, apoptosis, spermatogenesis, inflammatory response), and diseases (eg, asthma, obesity, male infertility). Finally, we map AOP adverse outcome terms to corresponding CTD terms, allowing users to query for tetramers that can help augment AOP pathways with additional stressors, genes, and phenotypes, as well as formulate potential AOP disease networks (eg, liver cirrhosis and prostate cancer). This novel tool, as part of the complete suite of tools offered at CTD, provides users with computational datasets and their supporting evidence to potentially fill exposure knowledge gaps and develop testable hypotheses about environmental health.

Testing sulforaphane as a strategy against toxic chemicals of public health concern by toxicogenomic data analysis: Friend or foe at the gene level - Colorectal carcinoma case study

Toxic metals (cadmium (Cd), lead (Pb), mercury (Hg) and arsenic (As)) and plastificators (bis (2 - ethylhexyl) phthalate (DEHP), dibutyl phthalate (DBP)) and bisphenol A (BPA)) have been suggested to aid in colorectal carcinoma (CRC) advancement. Sulforaphane (SFN), isothiocyanate from cruciferous vegetables, diminishes chemical carcinogenesis susceptibility, but has been shown to act as a friend or a foe depending on various factors. By conducting the mechanistic toxicogenomic data mining approach, this research aimed to determine if SFN can alleviate toxic-metal and/or phthalate/BPA mixture-induced CRC at the gene level. Comparative Toxicogenomics Database, ToppGene Suite portal, Cytoscape software, InteractiVenn and Gene Expression Omnibus (GEO) database (GEO2R tool) was used. Among the mutual genes for all the investigated substances, SFN had a protective impact only through PTGS2. Other proposed protective SFN-targets included ABCA1, ALDH2, BMP2, DPYD, MYC, SLCO2A1, and SOD2, only in the case of phthalates/BPA exposure. The only additional gene relevant for SFN protection against the toxic metal mixture-induced CRC was ABCB1. Additionally, the majority of the top 15 molecular pathways extracted for SFN impact on phthalate and BPA mixture-linked CRC development were directly linked with cancer development, which was not the case with the toxic metal mixture. The current research has indicated that SFN is a more effective chemoprotective agent against CRC induced by phthalates/BPA mixture than by toxic-metal mixture. It has also presented the value of computational methods as a simple tool for directing further research, selecting appropriate biomarkers and exploring the mechanisms of toxicity.

Integrative Data Mining Approach: Case Study with Adverse Outcome Pathway Network Leading to Pulmonary Fibrosis

An adverse outcome pathway (AOP) framework can be applied as an efficient tool for the rapid screening of environmental chemicals. For the development of an AOP, a database mining approach can support an expert derivation approach by gathering a wider range of evidence than in a literature review. In this study, data from various databases were integrated and analyzed to supplement the AOP leading to pulmonary fibrosis by analyzing additional evidence using a data mining approach and establishing an application domain for chemicals. First, we collected chemicals, genes, and phenotypes that were studied and related to pulmonary fibrosis through the Comparative Toxicogenomics Database (CTD). CGPD-tetramers constructed by linking each related chemical, gene, phenotype, and disease can provide the basic components for the assembly of putative AOPs. Next, an AOP network was established by connecting eight existing AOPs for pulmonary fibrosis developed by expert derivation from the AOP Wiki. Finally, the pulmonary fibrosis AOP network was proposed by integrating the AOP network from AOP Wiki and the CGPD-tetramers from the CTD. To prioritize potential chemical stressors in the AOP network, 61 chemicals were ranked using the relevance of the chemical to the AOP and chemical exposure information from the CompTox Chemicals Dashboard. The approach proposed in this study can guide the utilization of available evidence from various databases as well as the literature in constructing AOP networks related to specific diseases.

Epidemiology meets toxicogenomics: Mining toxicologic evidence in support of an untargeted analysis of pesticides exposure and Parkinson's disease

BACKGROUND

Pesticides have been widely used in agriculture for more than half a century. However, with thousands currently in use, most have not been adequately assessed for influence Parkinson's disease (PD).

OBJECTIVES

Here we aimed to assess biologic plausibility of 70 pesticides implicated with PD through an agnostic pesticide-wide association study using a data mining approach linking toxicology and toxicogenomics databases.

METHODS

We linked the 70 targeted pesticides to quantitative high-throughput screening assay findings from the Toxicology in the 21st Century (Tox21) program and pesticide-related genetic/disease information with the Comparative Toxicogenomics Database (CTD). We used the CTD to determine networks of genes each pesticide has been linked to and assess enrichment of relevant gene ontology (GO) annotations. With Tox21, we evaluated pesticide induced activity on a series of 43 nuclear receptor and stress response assays and two cytotoxicity assays.

RESULTS

Overall, 59 % of the 70 pesticides had chemical-gene networks including at least one PD gene/gene product. In total, 41 % of the pesticides had chemical-gene networks enriched for ≥ 1 high-priority PD GO terms. For instance, 23 pesticides had chemical-gene networks enriched for response to oxidative stress, 21 for regulation of neuron death, and twelve for autophagy, including copper sulfate, endosulfan and chlorpyrifos. Of the pesticides tested against the Tox21 assays, 79 % showed activity on ≥ 1 assay and 11 were toxic to the two human cell lines. The set of PD-associated pesticides showed more activity than expected on assays testing for xenobiotic homeostasis, mitochondrial membrane permeability, and genotoxic stress.

CONCLUSIONS

Overall, cross-database queries allowed us to connect a targeted set of pesticides implicated in PD via epidemiology to specific biologic targets relevant to PD etiology. This knowledge can be used to help prioritize targets for future experimental studies and improve our understanding of the role of pesticides in PD etiology.

Systematic molecular profiling of acute leukemia cancer stem cells allows identification of druggable targets

Acute myeloid leukemia (AML) is one of the most prevalent and acute blood cancers with a poor prognosis and low overall survival rate, especially in the elderly. Although several new AML markers and drug targets have been recently identified, the rate of long-term cancer eradication has not improved significantly due to the presence and drug resistance of AML cancer stem cells (CSCs). Here we develop a novel computational pipeline to analyze the transcriptomic profiles of AML cancer (stem) cells and identify novel candidate AML CSC markers and drug targets. In our novel pipeline we apply a top-down meta-analysis strategy to integrate The Cancer Genome Atlas data with CSC datasets to infer cell stemness features. As a result, a set of genes termed the "AML key CSC genes" along with all the available drugs/compounds that could target them were identified. Overall, our novel computational pipeline could retrieve known cancer drugs (Carfilzomib) and predicted novel drugs such as Zonisamide, Amitriptyline, and their targets amongst the top ranked drugs and drug targets for targeting AML. Additionally, the pipeline applied in this study could be used for the identification of CSC-specific markers, drivers and their respective targeting drugs in other cancer types.

Arsenic and cancer: Evidence and mechanisms

Arsenic is a potent carcinogen and poses a significant health concern worldwide. Exposure occurs through ingestion of drinking water and contaminated foods and through inhalation due to pollution. Epidemiological evidence shows arsenic induces cancers of the skin, lung, liver, and bladder among other tissues. While studies in animal and cell culture models support arsenic as a carcinogen, the mechanisms of arsenic carcinogenesis are not fully understood. Arsenic carcinogenesis is a complex process due its ability to be metabolized and because of the many cellular pathways it targets in the cell. Arsenic metabolism and the multiple forms of arsenic play distinct roles in its toxicity and contribute differently to carcinogenic endpoints, and thus must be considered. Arsenic generates reactive oxygen species increasing oxidative stress and damaging DNA and other macromolecules. Concurrently, arsenic inhibits DNA repair, modifies epigenetic regulation of gene expression, and targets protein function due its ability to replace zinc in select proteins. While these mechanisms contribute to arsenic carcinogenesis, there remain significant gaps in understanding the complex nature of arsenic cancers. In the future improving models available for arsenic cancer research and the use of arsenic induced human tumors will bridge some of these gaps in understanding arsenic driven cancers.

Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning

The ability to estimate the probability of a drug to receive approval in clinical trials provides natural advantages to optimizing pharmaceutical research workflows. Success rates of clinical trials have deep implications for costs, duration of development, and under pressure due to stringent regulatory approval processes. We propose a machine learning approach that can predict the outcome of the trial with reliable accuracies, using biological activities, physicochemical properties of the compounds, target-related features, and NLP-based compound representation. In the above list, biological activities have never been used as an independent variable towards the prediction of clinical trial outcomes. We have extracted the drug-disease pair from clinical trials and mapped target(s) to that pair using multiple data sources. Empirical results demonstrate that ensemble learning outperforms independently trained, small-data ML models. We report results and inferences derived from a Random forest classifier with an average accuracy of 93%, and an F1 score of 0.96 for the "Pass" class. "Pass" refers to one of the two classes (Pass/Fail) of all clinical trials, and the model performed well in predicting the "Pass" category. Through the analysis of feature contributions to predictive capability, we have demonstrated that bioactivity plays a statistically significant role in predicting clinical trial outcome. A significant effort has gone into the production of the dataset that, for the first time, integrates clinical trial information with protein targets. Cleaned, organized, integrated data and code to map these entities, created as a part of this work, are available open-source. This reproducibility and the freely available code ensure that researchers with access to deep curated and proprietary clinical trial databases (we only use open-source data in this study) can further expand the scope of the results.

Potential Small Molecules for Therapy of Lupus Nephritis Based on Genetic Effect and Immune Infiltration

Lupus nephritis (LN) is the most common and significant complication of systemic lupus erythematosus (SLE) due to its poor prognosis and mortality rates in SLE patients. There is a critical need for new drugs as the pathogenesis of LN remains to be elucidated and immunosuppressive therapy comes with many deficiencies. In this study, 23 hub genes (IFI6, PLSCR1, XAF1, IFI16, IFI44, MX1, IFI44L, IFIT3, IFIT2, IFI27, DDX58, EIF2AK2, IFITM1, RTP4, IFITM3, TRIM22, PARP12, IFIH1, OAS1, HERC6, RSAD2, DDX60, and MX2) were identified through bioinformatics and network analysis and are closely related to interferon production and function. Interestingly, immune cell infiltration analysis and correlation analysis demonstrate a positive correlation between the expression of 23 hub genes and monocyte infiltration in glomeruli and M2 macrophage infiltration in the tubulointerstitium of LN patients. Additionally, the CTD database, DsigDB database, and DREIMT database were used to explore the bridging role of genes in chemicals and LN as well as the potential influence of these chemicals on immune cells. After comparison and discussion, six small molecules (Acetohexamide, Suloctidil, Terfenadine, Prochlorperazine, Mefloquine, and Triprolidine) were selected for their potential ability in treating lupus nephritis.

The possible role of arsenic and gene-arsenic interactions in susceptibility to breast cancer: a systematic review

The roles of many environmental contaminants in increasing breast cancer risk remain controversial. Arsenic (As) is a major global environmental contaminant and carcinogen. We conducted a systematic review of the role of As and gene-arsenic interactions in susceptibility to breast cancer. Following a systematic literature search using well-defined inclusion/exclusion criteria, a total of 15 epidemiologic studies (two meta-analyses, three systematic reviews, three cohort studies, two case-control studies, and five cross-sectional studies) were reviewed. In addition, several animal, in vitro, in vivo, and in silico (i.e., computer modeling) studies provided mechanistic insights into the association between As and breast cancer. Our review suggests a possible overall main effect of As on breast cancer risk. The evidence for an effect of gene-As interactions on breast cancer risk is strong. Studies that measured levels of As metabolites among participants and/or evaluated interactions between As exposure and genetic or epigenetic factors generally reported positive associations with breast cancer risk. Our analysis of the Comparative Toxicogenomics and the Ingenuity Pathway Analysis Databases provided further evidence for As-gene interactions and their effects on breast cancer-related biologic pathways. Our findings provide potential leads for future epidemiologic studies of As-associated cancer risks and interventions to reduce population exposure.

Integrating in silico with in vivo approach to investigate phthalate and bisphenol A mixture-linked asthma development: Positive probiotic intervention

The aim of this study was to explore the mechanisms of bis(2- ethylhexyl) phthalate (DEHP), dibutyl phthalate (DBP) and bisphenol A (BPA) mixture-induced asthma development and test probiotic as a potential positive intervention. Comparative Toxicogenomics Database (CTD) and ToppGene Suite were used as the main tools for in silico analysis. In vivo 28-day experiment was conducted on rats - seven groups (n = 6): (1) Control: corn oil, (2) P: probiotic (8.78 * 10⁸ CFU/kg/day); (3) DEHP: 50 mg/kg b.w./day, (4) DBP: 50 mg/kg b.w./day, (5) BPA: 25 mg/kg b.w./day; (6) MIX: DEHP + DBP + BPA; (7) MIX + P. Lungs, thymus and kidneys were extracted and prepared for redox status and essential metals analysis. By conducting additional in vitro experiment, probiotic phthalate and BPA binding ability was explored. There were 24 DEHP, DBP and BPA asthma-related genes, indicating the three most probable mechanisms - apoptosis, inflammation and oxidative stress. In vivo experiment confirmed that significant changes in redox status/essential metal parameters were either prominent, or only present in the MIX group, indicating possible additive effects. In vitro experiment confirmed the ability of the multy-strain probiotic to bind DEHP/DBP/BPA mixture, while probiotic administration ameliorated mixture-induced changes in rat tissue.

Probiotic reduced the impact of phthalates and bisphenol A mixture on type 2 diabetes mellitus development: Merging bioinformatics with in vivo analysis

Linkage between bis(2-ethylhexyl) phthalate (DEHP), dibutyl phthalate (DBP), and bisphenol A (BPA) co-exposure and type 2 diabetes mellitus (T2DM), as well as ability of multi-strained probiotic to reduce DEHP, DBP and BPA mixture-induced oxidative damage in rat pancreas were investigated. The Comparative Toxicogenomics Database, Cytoscape software and ToppGene Suite were used for data-mining. Animals were sorted into seven groups (n = 6): (1) Control group: corn oil, (2) P: probiotic: Saccharomyces boulardii + Lactobacillus rhamnosus + Lactobacillus plantarum LP 6595 + Lactobacillus plantarum HEAL9; (3) DEHP: 50 mg/kg b.w./day, (4) DBP: 50 mg/kg b.w./day, (5) BPA: 25 mg/kg b.w./day, and (6) MIX: 50 mg/kg b.w./day DEHP + 50 mg/kg b.w/day DBP + 25 mg/kg b.w./day BPA; (7) MIX + P. Rats were sacrificed after 28 days of oral exposure. In silico investigation highlighted 44 DEHP, DBP and BPA mutual genes linked to the T2DM, while apoptosis and oxidative stress were highlighted as the main mechanisms of DEHP, DBP and BPA mixture-linked T2DM. In vivo experiment confirmed the presence of significant changes in redox status parameters (TOS, SOD and SH groups) only in the MIX group, indicating possible additive effects, while probiotic ameliorated mixture-induced redox status changes in rat pancreatic tissue.

CTD Anatomy: analyzing chemical-induced phenotypes and exposures from an anatomical perspective, with implications for environmental health studies

The Comparative Toxicogenomics Database (CTD) is a freely available public resource that curates and interrelates chemical, gene/protein, phenotype, disease, organism, and exposure data. CTD can be used to address toxicological mechanisms for environmental chemicals and facilitate the generation of testable hypotheses about how exposures affect human health. At CTD, manually curated interactions for chemical-induced phenotypes are enhanced with anatomy terms (tissues, fluids, and cell types) to describe the physiological system of the reported event. These same anatomy terms are used to annotate the human media (e.g., urine, hair, nail, blood, etc.) in which an environmental chemical was assayed for exposure. Currently, CTD uses more than 880 unique anatomy terms to contextualize over 255,000 chemical-phenotype interactions and 167,000 exposure statements. These annotations allow chemical-phenotype interactions and exposure data to be explored from a novel, anatomical perspective. Here, we describe CTD's anatomy curation process (including the construction of a controlled, interoperable vocabulary) and new anatomy webpages (that coalesce and organize the curated chemical-phenotype and exposure data sets). We also provide examples that demonstrate how this feature can be used to identify system- and cell-specific chemical-induced toxicities, help inform exposure data, prioritize phenotypes for environmental diseases, survey tissue and pregnancy exposomes, and facilitate data connections with external resources. Anatomy annotations advance understanding of environmental health by providing new ways to explore and survey chemical-induced events and exposure studies in the CTD framework.

Combining in vivo pathohistological and redox status analysis with in silico toxicogenomic study to explore the phthalates and bisphenol A mixture-induced testicular toxicity

The aim of this study was to: (i) determine and compare the capacity of bis (2 -ethylhexyl) phthalate (DEHP), dibutyl phthalate (DBP), bisphenol A (BPA), and their mixture to produce testicular toxicity after the subacute exposure; (ii) explore the mechanisms behind the observed changes using in silico toxicogenomic approach. Male rats were randomly split into groups (n = 6): (1) Control (corn oil); (2) DEHP (50 mg/kg b.w./day); (3) DBP (50 mg/kg b.w./day); (4) BPA (25 mg/kg b.w./day); and (5) MIX (50 mg/kg b.w./day DEHP + 50 mg/kg b.w/day DBP + 25 mg/kg b.w./day BPA). Animals were sacrificed after 28 days of oral exposure, testes were extracted and prepared for histological assessments under the light microscope (haematoxylin and eosin staining) and redox status analysis. The Comparative Toxicogenomics Database (CTD; http://CTD.mdibl.org), Cytoscape software (https://cytoscape.org) and ToppGene Suite (https://toppgene.cchmc.org) were used for data-mining. Present pathohistological study has demonstrated more pronounced testicular toxicity of the MIX group (desquamated germinal epithelium cells, enlarged cells with hyperchromatic nuclei, multinucleated cell forms and intracytoplasmic vacuoles) in comparison with the single substances, while effects on redox status parameters were either more prominent, or present only in the MIX group. In silico investigation revealed 20 genes linked to male reproductive disorders, affected by all three investigated substances. Effects on metabolism, AhR pathway, apoptosis and oxidative stress could be singled out as the most probable mechanisms involved in the subacute DEHP, DBP and BPA mixture testicular toxicity, while the effect on oxidative stress parameters was confirmed by in vivo experiment.

Elucidating the influence of environmentally relevant toxic metal mixture on molecular mechanisms involved in the development of neurodegenerative diseases: In silico toxicogenomic data-mining

This in silico toxicogenomic analysis aims to: (i) testify the hypothesis about the influence of the environmentally relevant toxic metals (lead, methylmercury (organic form of mercury), cadmium and arsenic) on molecular mechanisms involved in amyotrophic lateral sclerosis (ALS), Parkinson's Disease (PD) and Alzheimer's disease (AD) development; and (ii) demonstrate the capability of in silico toxicogenomic data-mining for distinguishing the probable mechanisms of mixture-induced toxic effects. The Comparative Toxicogenomics Database (CTD; http://ctd. mdibl.org) and Cytoscape software were used as the main data-mining tools in this analysis. The results have shown that there were 7, 13 and 14 common genes for all the metals present in the mixture for each of the selected neurodegenerative disease (ND), respectively: ALS, PD and AD. Physical interactions (68.18%) were the most prominent interactions between the genes extracted for ALS, co-expression (60.85%) for PD and interactions predicted by the server (44.30%) for AD. SOD2 gene was noted as the mutual gene for all the selected ND. Oxidative stress, folate metabolism, vitamin B12, AGE-RAGE, apoptosis were noted as the key disrupted molecular pathways that contribute to the neurodegenerative disease's development. Gene ontology analysis revealed biological processes affected by the investigated mixture (glutathione metabolic process was listed as the most important for ALS, cellular response to toxic substance for PD, and neuron death for AD). Our results emphasize the role of oxidative stress, particularly SOD2, in neurodegeneration triggered by environmental toxic metal mixture and give a new insight into common molecular mechanisms involved in ALS, PD and AD pathology.

Gene expression patterns associated with tumor-infiltrating CD4+ and CD8+ T cells in invasive breast carcinomas

BACKGROUND

Breast carcinoma is one of the most common tumors in women. The immune microenvironment, especially T cell infiltration, is related to the occurrence and prognosis of breast carcinoma.

OBJECTIVE

This study investigated the gene expression patterns associated with tumor-infiltrating CD4+ and CD8+ T cells in invasive breast carcinomas.

METHODS

The gene expression data and corresponding clinical phenotype data from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) were downloaded. The stromal and immune score were calculated using ESTIMATE. The differentially expressed genes (DEGs) with a high vs. low stromal score and a high vs. low immune score were screened and then functionally enriched. The tumor-infiltrating immune cells were investigated using the Cibersort algorithm, and the CD4+ and CD8+ T cell-related genes were identified using a Spearman correlation test of infiltrating abundance with the DEGs. Moreover, the miRNA-mRNA pairs and lncRNA-miRNA pairs were predicted to construct the competing endogenous RNAs (ceRNA) network. Kaplan-Meier (K-M) survival curves were also plotted.

RESULTS

In total, 478 DEGs with a high vs. low stromal score and 796 DEGs with a high vs. low immune score were identified. In addition, 39 CD4+ T cell-related genes and 78 CD8+ T cell-related genes were identified; of these, 14 genes were significantly associated with the prognosis of BRCA patients. Moreover, for CD4+ T cell-related genes, the chr22-38_28785274-29006793.1-miR-34a/c-5p-CAPN6 axis was identified from the ceRNA network, whereas the chr22-38_28785274-29006793.1-miR-494-3p-SLC9A7 axis was identified for CD8+ T cell-related genes.

CONCLUSIONS

The chr22-38_28785274-29006793.1-miR-34a/c-5p-CAPN6 axis and the chr22-38_28785274-29006793.1-miR-494-3p-SLC9A7 axis might regulate cellular activities associated with CD4+ and CD8+ T cell infiltration, respectively, in BRCA.

Safety assessment of drug combinations used in COVID-19 treatment: in silico toxicogenomic data-mining approach

Improvement of COVID-19 clinical condition was seen in studies where combination of antiretroviral drugs, lopinavir and ritonavir, as well as immunomodulant antimalaric, chloroquine/hydroxychloroquine together with the macrolide-type antibiotic, azithromycin, was used for patient's treatment. Although these drugs are "old", their pharmacological and toxicological profile in SARS-CoV-2 - infected patients are still unknown. Thus, by using in silico toxicogenomic data-mining approach, we aimed to assess both risks and benefits of the COVID-19 treatment with the most promising candidate drugs combinations: lopinavir/ritonavir and chloroquine/hydroxychloroquine + azithromycin. The Comparative Toxicogenomics Database (CTD; http://CTD.mdibl.org), Cytoscape software (https://cytoscape.org) and ToppGene Suite portal (https://toppgene.cchmc.org) served as a foundation in our research. Our results have demonstrated that lopinavir/ritonavir increased the expression of the genes involved in immune response and lipid metabolism (IL6, ICAM1, CCL2, TNF, APOA1, etc.). Chloroquine/hydroxychloroquine + azithromycin interacted with 6 genes (CCL2, CTSB, CXCL8, IL1B, IL6 and TNF), whereas chloroquine and azithromycin affected two additional genes (BCL2L1 and CYP3A4), which might be a reason behind a greater number of consequential diseases. In contrast to lopinavir/ritonavir, chloroquine/hydroxychloroquine + azithromycin downregulated the expression of TNF and IL6. As expected, inflammation, cardiotoxicity, and dyslipidaemias were revealed as the main risks of lopinavir/ritonavir treatment, while chloroquine/hydroxychloroquine + azithromycin therapy was additionally linked to gastrointestinal and skin diseases. According to our results, these drug combinations should be administrated with caution to patients suffering from cardiovascular problems, autoimmune diseases, or acquired and hereditary lipid disorders.

Leveraging the Comparative Toxicogenomics Database to Fill in Knowledge Gaps for Environmental Health: A Test Case for Air Pollution-induced Cardiovascular Disease

Environmental health studies relate how exposures (eg, chemicals) affect human health and disease; however, in most cases, the molecular and biological mechanisms connecting an exposure with a disease remain unknown. To help fill in these knowledge gaps, we sought to leverage content from the public Comparative Toxicogenomics Database (CTD) to identify potential intermediary steps. In a proof-of-concept study, we systematically compute the genes, molecular mechanisms, and biological events for the environmental health association linking air pollution toxicants with 2 cardiovascular diseases (myocardial infarction and hypertension) as a test case. Our approach integrates 5 types of curated interactions in CTD to build sets of "CGPD-tetramers," computationally constructed information blocks relating a Chemical- Gene interaction with a Phenotype and Disease. This bioinformatics strategy generates 653 CGPD-tetramers for air pollution-associated myocardial infarction (involving 5 pollutants, 58 genes, and 117 phenotypes) and 701 CGPD-tetramers for air pollution-associated hypertension (involving 3 pollutants, 96 genes, and 142 phenotypes). Collectively, we identify 19 genes and 96 phenotypes shared between these 2 air pollutant-induced outcomes, and suggest important roles for oxidative stress, inflammation, immune responses, cell death, and circulatory system processes. Moreover, CGPD-tetramers can be assembled into extensive chemical-induced disease pathways involving multiple gene products and sequential biological events, and many of these computed intermediary steps are validated in the literature. Our method does not require a priori knowledge of the toxicant, interacting gene, or biological system, and can be used to analyze any environmental chemical-induced disease curated within the public CTD framework. This bioinformatics strategy links and interrelates chemicals, genes, phenotypes, and diseases to fill in knowledge gaps for environmental health studies, as demonstrated for air pollution-associated cardiovascular disease, but can be adapted by researchers for any environmentally influenced disease-of-interest.

The Comparative Toxicogenomics Database: update 2019

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is a premier public resource for literature-based, manually curated associations between chemicals, gene products, phenotypes, diseases, and environmental exposures. In this biennial update, we present our new chemical–phenotype module that codes chemical-induced effects on phenotypes, curated using controlled vocabularies for chemicals, phenotypes, taxa, and anatomical descriptors; this module provides unique opportunities to explore cellular and system-level phenotypes of the pre-disease state and allows users to construct predictive adverse outcome pathways (linking chemical–gene molecular initiating events with phenotypic key events, diseases, and population-level health outcomes). We also report a 46% increase in CTD manually curated content, which when integrated with other datasets yields more than 38 million toxicogenomic relationships. We describe new querying and display features for our enhanced chemical–exposure science module, providing greater scope of content and utility. As well, we discuss an updated MEDIC disease vocabulary with over 1700 new terms and accession identifiers. To accommodate these increases in data content and functionality, CTD has upgraded its computational infrastructure. These updates continue to improve CTD and help inform new testable hypotheses about the etiology and mechanisms underlying environmentally influenced diseases.

Integration of curated and high-throughput screening data to elucidate environmental influences on disease pathways

Addressing the complex relationship between public health and environmental exposure requires multiple types and sources of data. An important source of chemical data derives from high-throughput screening (HTS) efforts, such as the Tox21/ToxCast program, which aim to identify chemical hazard using primarily in vitro assays to probe toxicity. While most of these assays target specific genes, assessing the disease-relevance of these assays remains challenging. Integration with additional data sets may help to resolve these questions by providing broader context for individual assay results. The Comparative Toxicogenomics Database (CTD), a publicly available database that builds networks of chemical, gene, and disease information from manually curated literature sources, offers a promising solution for contextual integration with HTS data. Here, we tested the value of integrating data across Tox21/ToxCast and CTD by linking elements common to both databases (i.e., assays, genes, and chemicals). Using polymarcine and Parkinson's disease as a case study, we found that their union significantly increased chemical-gene associations and disease-pathway coverage. Integration also enabled new disease associations to be made with HTS assays, expanding coverage of chemical-gene data associated with diseases. We demonstrate how integration enables development of predictive adverse outcome pathways using 4-nonylphenol, branched as an example. Thus, we demonstrate enhancements to each data source through database integration, including scenarios where HTS data can efficiently probe chemical space that may be understudied in the literature, as well as how CTD can add biological context to those results.

Network Pharmacology Reveals the Molecular Mechanism of Cuyuxunxi Prescription in Promoting Wound Healing in Patients with Anal Fistula

Background

The healing process of the surgical wound of anal fistulotomy is much slower because of the presence of stool within the wound. Cuyuxunxi (CYXX) prescription is a Chinese herbal fumigant that is being used to wash surgical wound after anal fistulotomy. This study aimed at investigating the molecular mechanism of CYXX prescription using a network pharmacology-based strategy.

Materials and Methods

The active compounds in each herbal medicine were retrieved from the traditional Chinese medicine systems pharmacology (TCMSP) database and in Traditional Chinese Medicine Integrated Database (TCMID) analysis platform based on the criteria of oral bioavailability ≥40% and drug-likeness ≥0.2. The disease-related target genes were extracted from the Comparative Toxicogenomics Database. Protein-protein interaction network was built for the overlapped genes as well as functional enrichment analysis. Finally, an ingredient-target genes-pathway network was built by integrating all information.

Results

A total of 375 chemical ingredients of the 5 main herbal medicines in CYXX prescription were retrieved from TCMSP database and TCMID. Among the 375 chemical ingredients, 59 were active compounds. Besides, 325 target genes for 16 active compounds in 3 herbal medicines were obtained. Functional enrichment analysis revealed that these overlapped genes were significantly related with immune response, biosynthesis of antibiotics, and complement and coagulation cascades. A comprehensive network which contains 133 nodes (8 disease nodes, 3 drug nodes, 8 ingredients, 103 target gene nodes, 7 GO nodes, and 4 pathway nodes) was built.

Conclusion

The network built in this study might aid in understanding the action mechanism of CYXX prescription at molecular level to pathway level.

Metallothionein 3 Is a Hypoxia-Upregulated Oncogene Enhancing Cell Invasion and Tumorigenesis in Human Bladder Carcinoma Cells

Metallothioneins have been viewed as modulators in a number of biological regulations regarding cancerous development; however, the function of metallothionein 3 (MT3) in bladder cancer is unexplored. We determined the regulatory mechanisms and potential function of MT3 in bladder carcinoma cells. Real-Time Reverse Transcriptase-Polymerase Chain Reaction (RT-qPCR) assays revealed that TSGH-8301 cells expressed more MT3 levels than RT-4, HT1376, and T24 cells. Immunoblot and RT-qPCR assays showed that arsenic (AS₂O₃) treatments enhanced the gene expression of MT3. Hypoxia induced HIF-1α, HIF-2α, and MT3 expression; furthermore, HIF-2α-knockdown attenuated hypoxic activation on MT3 expression. Ectopic overexpression of MT3 increased cell proliferation, invasion, and tumorigenesis significantly in T24 and HT1376 cells in vitro and in vivo; however, MT3-knockdown in TSGH-8301 cells had the reverse effect. Moreover, knockdown of MT3 enhanced arsenic-induced apoptosis determined by the Annexin V-FITC apoptosis assay. MT3-overexpression downregulated the gene expressions of N-myc downstream regulated gene 1 (NDRG1), N-myc downstream regulated gene 2 (NDRG2), and the mammary serine protease inhibitor (MASPIN) in HT1376 and T24 cells, whereas MT3-knockdown in TSGH-8301 cells had the opposite effect. The experiments indicated that MT3 is an arsenic- and hypoxia-upregulated oncogene that promotes cell growth and invasion of bladder carcinoma cells via downregulation of NDRG1, NDRG2, and MASPIN expressions.

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.

Environmental arsenic exposure: From genetic susceptibility to pathogenesis

More than 200 million people in 70 countries are exposed to arsenic through drinking water. Chronic exposure to this metalloid has been associated with the onset of many diseases, including cancer. Epidemiological evidence supports its carcinogenic potential, however, detailed molecular mechanisms remain to be elucidated. Despite the global magnitude of this problem, not all individuals face the same risk. Susceptibility to the toxic effects of arsenic is influenced by alterations in genes involved in arsenic metabolism, as well as biological factors, such as age, gender and nutrition. Moreover, chronic arsenic exposure results in several genotoxic and epigenetic alterations tightly associated with the arsenic biotransformation process, resulting in an increased cancer risk. In this review, we: 1) review the roles of inter-individual DNA-level variations influencing the susceptibility to arsenic-induced carcinogenesis; 2) discuss the contribution of arsenic biotransformation to cancer initiation; 3) provide insights into emerging research areas and the challenges in the field; and 4) compile a resource of publicly available arsenic-related DNA-level variations, transcriptome and methylation data. Understanding the molecular mechanisms of arsenic exposure and its subsequent health effects will support efforts to reduce the worldwide health burden and encourage the development of strategies for managing arsenic-related diseases in the era of personalized medicine.

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.

Hepatic transcriptomic responses in mice exposed to arsenic and different fat diet

Chronic exposure to inorganic arsenic (iAs) or a high-fat diet (HFD) can produce liver injury. However, effects of HFD on risk assessment of iAs in drinking water are unclear. In this study, we examined how HFD and iAs interact to alter iAs-induced liver injury in C57BL/6 mice. Mice fed low-fat diet (LFD) or HFD were exposed to 3 mg/L iAs or deionized water for 10 weeks. Results showed that HFD changed intake and excretion of iAs by mice. Then, HFD increased the amount of iAs-induced hepatic DNA damage and amplified changes in pathways related to cell death and growth, signal transduction, lipid metabolism, and insulin signaling. Compared to gene expression profiles caused by iAs alone or HFD alone, insulin signaling pathway might play important roles in the interactive effects of iAs and HFD. Our data suggest that HFD increases sensitivity of mice to iAs in drinking water, resulting in increased hepatotoxicity. This study highlight that HFD might enhance the risk of iAs hepatotoxicity in iAs-polluted regions. The diet should be considered during risk assessment of iAs in drinking water.

The Comparative Toxicogenomics Database: update 2017

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) provides information about interactions between chemicals and gene products, and their relationships to diseases. Core CTD content (chemical-gene, chemical-disease and gene-disease interactions manually curated from the literature) are integrated with each other as well as with select external datasets to generate expanded networks and predict novel associations. Today, core CTD includes more than 30.5 million toxicogenomic connections relating chemicals/drugs, genes/proteins, diseases, taxa, Gene Ontology (GO) annotations, pathways, and gene interaction modules. In this update, we report a 33% increase in our core data content since 2015, describe our new exposure module (that harmonizes exposure science information with core toxicogenomic data) and introduce a novel dataset of GO-disease inferences (that identify common molecular underpinnings for seemingly unrelated pathologies). These advancements centralize and contextualize real-world chemical exposures with molecular pathways to help scientists generate testable hypotheses in an effort to understand the etiology and mechanisms underlying environmentally influenced diseases.

Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database

Strategies for discovering common molecular events among disparate diseases hold promise for improving understanding of disease etiology and expanding treatment options. One technique is to leverage curated datasets found in the public domain. The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) manually curates chemical-gene, chemical-disease, and gene-disease interactions from the scientific literature. The use of official gene symbols in CTD interactions enables this information to be combined with the Gene Ontology (GO) file from NCBI Gene. By integrating these GO-gene annotations with CTD’s gene-disease dataset, we produce 753,000 inferences between 15,700 GO terms and 4,200 diseases, providing opportunities to explore presumptive molecular underpinnings of diseases and identify biological similarities. Through a variety of applications, we demonstrate the utility of this novel resource. As a proof-of-concept, we first analyze known repositioned drugs (e.g., raloxifene and sildenafil) and see that their target diseases have a greater degree of similarity when comparing GO terms vs. genes. Next, a computational analysis predicts seemingly non-intuitive diseases (e.g., stomach ulcers and atherosclerosis) as being similar to bipolar disorder, and these are validated in the literature as reported co-diseases. Additionally, we leverage other CTD content to develop testable hypotheses about thalidomide-gene networks to treat seemingly disparate diseases. Finally, we illustrate how CTD tools can rank a series of drugs as potential candidates for repositioning against B-cell chronic lymphocytic leukemia and predict cisplatin and the small molecule inhibitor JQ1 as lead compounds. The CTD dataset is freely available for users to navigate pathologies within the context of extensive biological processes, molecular functions, and cellular components conferred by GO. This inference set should aid researchers, bioinformaticists, and pharmaceutical drug makers in finding commonalities in disease mechanisms, which in turn could help identify new therapeutics, new indications for existing pharmaceuticals, potential disease comorbidities, and alerts for side effects.

Analysis of the ToxCast Chemical-Assay Space Using the Comparative Toxicogenomics Database

Many studies have attempted to predict in vivo hazards based on the ToxCast in vitro assay results with the goal of using these predictions to prioritize compounds for conventional toxicity testing. Most of these conventional studies rely on in vivo end points observed using preclinical species (e.g., mice and rats). Although the preclinical animal studies provide valuable insights, there can often be significant disconnects between these studies and safety concerns in humans. One way to address these concerns, for an admittedly more limited set of compounds, is to explore relationships between the in vitro data from human cell lines and observations from human related studies. The Comparative Toxicogenomics Database (CTD; http://ctdbase.org ) is a rich source of data linking chemicals to human diseases/adverse events and pathways. In this study we explored the relationships between ToxCast chemicals, their ToxCast in vitro test results, and their annotations of human disease/adverse event end points as captured in the CTD database. We mined these associations to identify potentially interesting, statistically significant in vitro assay and in vivo toxicity correlations. To the best of our knowledge, this is one of the first studies analyzing the relationships between the ToxCast in vitro assays results and the CTD disease/adverse event end point annotations. The in vitro profiles identified in this analysis may prove useful for prioritizing compounds for toxicity testing, suggesting mechanisms of toxicity, and forecasting potential in vivo human drug induced injury.

Using quantitative systems pharmacology for novel drug discovery

INTRODUCTION

Over the past three decades, the predominant paradigm in drug discovery was designing selective ligands for a specific target to avoid unwanted side effects. However, in the last 5 years, the aim has shifted to take into account the biological network in which they interact. Quantitative and Systems Pharmacology (QSP) is a new paradigm that aims to understand how drugs modulate cellular networks in space and time, in order to predict drug targets and their role in human pathophysiology.

AREAS COVERED

This review discusses existing computational and experimental QSP approaches such as polypharmacology techniques combined with systems biology information and considers the use of new tools and ideas in a wider 'systems-level' context in order to design new drugs with improved efficacy and fewer unwanted off-target effects.

EXPERT OPINION

The use of network biology produces valuable information such as new indications for approved drugs, drug-drug interactions, proteins-drug side effects and pathways-gene associations. However, we are still far from the aim of QSP, both because of the huge effort needed to model precisely biological network models and the limited accuracy that we are able to reach with those. Hence, moving from 'one molecule for one target to give one therapeutic effect' to the 'big systems-based picture' seems obvious moving forward although whether our current tools are sufficient for such a step is still under debate.

Zebrafish as a systems toxicology model for developmental neurotoxicity testing

The developing brain is extremely sensitive to many chemicals. Exposure to neurotoxicants during development has been implicated in various neuropsychiatric and neurological disorders, including autism spectrum disorder, attention deficit hyperactive disorder, schizophrenia, Parkinson's disease, and Alzheimer's disease. Although rodents have been widely used for developmental neurotoxicity testing, experiments using large numbers of rodents are time-consuming, expensive, and raise ethical concerns. Using alternative non-mammalian animal models may relieve some of these pressures by allowing testing of large numbers of subjects while reducing expenses and minimizing the use of mammalian subjects. In this review, we discuss some of the advantages of using zebrafish in developmental neurotoxicity testing, focusing on central nervous system development, neurobehavior, toxicokinetics, and toxicodynamics in this species. We also describe some important examples of developmental neurotoxicity testing using zebrafish combined with gene expression profiling, neuroimaging, or neurobehavioral assessment. Zebrafish may be a systems toxicology model that has the potential to reveal the pathways of developmental neurotoxicity and to provide a sound basis for human risk assessments.

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

Ten years ago, the Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) was developed out of a need to formalize, harmonize and centralize the information on numerous genes and proteins responding to environmental toxic agents across diverse species. CTD's initial approach was to facilitate comparisons of nucleotide and protein sequences of toxicologically significant genes by curating these sequences and electronically annotating them with chemical terms from their associated references. Since then, however, CTD has vastly expanded its scope to robustly represent a triad of chemical–gene, chemical–disease and gene–disease interactions that are manually curated from the scientific literature by professional biocurators using controlled vocabularies, ontologies and structured notation. Today, CTD includes 24 million toxicogenomic connections relating chemicals/drugs, genes/proteins, diseases, taxa, phenotypes, Gene Ontology annotations, pathways and interaction modules. In this 10th year anniversary update, we outline the evolution of CTD, including our increased data content, new ‘Pathway View’ visualization tool, enhanced curation practices, pilot chemical–phenotype results and impending exposure data set. The prototype database originally described in our first report has transformed into a sophisticated resource used actively today to help scientists develop and test hypotheses about the etiologies of environmentally influenced diseases.

Curation and analysis of multitargeting agents for polypharmacological modeling

In drug discovery and development, the conventional “single drug, single target” concept has been shifted to “single drug, multiple targets” – a concept coined as polypharmacology. For studies in this emerging field, dedicated and high-quality databases of multitargeting ligands would be exceedingly beneficial. To this end, we conducted a comprehensive analysis of the structural and chemical/biological profiles of polypharmacological agents and present a Web-based database (Polypharma). All of these compounds curated herein have been cocrystallized with more than one unique protein with intensive reports of their multitargeting activities. The present study provides more insight of drug multitargeting and is particularly useful for polypharmacology modeling. This specialized curation has been made publically available at http:/imdlab.org/polypharma/

Pharmacomicrobiomics: the impact of human microbiome variations on systems pharmacology and personalized therapeutics

The Human Microbiome Project (HMP) is a global initiative undertaken to identify and characterize the collection of human-associated microorganisms at multiple anatomic sites (skin, mouth, nose, colon, vagina), and to determine how intra-individual and inter-individual alterations in the microbiome influence human health, immunity, and different disease states. In this review article, we summarize the key findings and applications of the HMP that may impact pharmacology and personalized therapeutics. We propose a microbiome cloud model, reflecting the temporal and spatial uncertainty of defining an individual's microbiome composition, with examples of how intra-individual variations (such as age and mode of delivery) shape the microbiome structure. Additionally, we discuss how this microbiome cloud concept explains the difficulty to define a core human microbiome and to classify individuals according to their biome types. Detailed examples are presented on microbiome changes related to colorectal cancer, antibiotic administration, and pharmacomicrobiomics, or drug-microbiome interactions, highlighting how an improved understanding of the human microbiome, and alterations thereof, may lead to the development of novel therapeutic agents, the modification of antibiotic policies and implementation, and improved health outcomes. Finally, the prospects of a collaborative computational microbiome research initiative in Africa are discussed.

Development of a screening system for the detection of chemically induced DNA methylation alterations in a zebrafish liver cell line

Early molecular events with correlation to disease, such as aberrant DNA methylation, emphasize the importance of DNA methylation as a potential environmental biomarker. Currently, little is known regarding how various environmental contaminants and mixtures alter DNA methylation in aquatic organisms, and testing is both time- and labor-consuming. Therefore, the potential of an in vitro screening method was evaluated by exposing zebrafish liver cells (ZF-L) for 96 h to the nonmutagenic model substance 5'-azacytidine (AZA), as well as a selection of environmental pollutants such as sodium arsenite (NAS), 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), 17α-ethinylestradiol (EE2), and diethylstilbestrol (DES). Six single genes with reported and anticipated importance in cancer were selected for analysis. Methylation of gene promoter areas was monitored by bisulfite conversion and high-resolution melt (HRM) analysis after exposure to sublethal concentrations of the test compounds. Subsequently, results were validated with direct bisulfite sequencing. Exposure of ZF-L cells to 0.5 μM AZA for 96 h led to hypomethylation of genes with both low and high basal methylation indicating similarity to mechanism of action in mammals. Further, NAS, EE2, and DES were shown to induce significant alterations in methylation, whereas TCDD did not. It was concluded that cell line exposure in combination with HRM may provide an initial contaminant screening assay by quantifying DNA methylation alterations with high throughput capacity. In addition, the rapid determination of effects following contaminant exposure with this in vitro system points to the possibility for new in vivo applications to be useful for environmental monitoring.

A CTD-Pfizer collaboration: manual curation of 88,000 scientific articles text mined for drug-disease and drug-phenotype interactions

Improving the prediction of chemical toxicity is a goal common to both environmental health research and pharmaceutical drug development. To improve safety detection assays, it is critical to have a reference set of molecules with well-defined toxicity annotations for training and validation purposes. Here, we describe a collaboration between safety researchers at Pfizer and the research team at the Comparative Toxicogenomics Database (CTD) to text mine and manually review a collection of 88 629 articles relating over 1 200 pharmaceutical drugs to their potential involvement in cardiovascular, neurological, renal and hepatic toxicity. In 1 year, CTD biocurators curated 2 54 173 toxicogenomic interactions (1 52 173 chemical–disease, 58 572 chemical–gene, 5 345 gene–disease and 38 083 phenotype interactions). All chemical–gene–disease interactions are fully integrated with public CTD, and phenotype interactions can be downloaded. We describe Pfizer’s text-mining process to collate the articles, and CTD’s curation strategy, performance metrics, enhanced data content and new module to curate phenotype information. As well, we show how data integration can connect phenotypes to diseases. This curation can be leveraged for information about toxic endpoints important to drug safety and help develop testable hypotheses for drug–disease events. The availability of these detailed, contextualized, high-quality annotations curated from seven decades’ worth of the scientific literature should help facilitate new mechanistic screening assays for pharmaceutical compound survival. This unique partnership demonstrates the importance of resource sharing and collaboration between public and private entities and underscores the complementary needs of the environmental health science and pharmaceutical communities.

Database URL: http://ctdbase.org/

Polypharmacology: drug discovery for the future

In recent years, even with remarkable scientific advancements and a significant increase of global research and development spending, drugs are frequently withdrawn from markets. This is primarily due to their side effects or toxicities. Drug molecules often interact with multiple targets, coined as polypharmacology, and the unintended drug-target interactions could cause side effects. Polypharmacology remains one of the major challenges in drug development, and it opens novel avenues to rationally design the next generation of more effective, but less toxic, therapeutic agents. This review outlines the latest progress and challenges in polypharmacology studies.

Text mining effectively scores and ranks the literature for improving chemical-gene-disease curation at the comparative toxicogenomics database

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is a public resource that curates interactions between environmental chemicals and gene products, and their relationships to diseases, as a means of understanding the effects of environmental chemicals on human health. CTD provides a triad of core information in the form of chemical-gene, chemical-disease, and gene-disease interactions that are manually curated from scientific articles. To increase the efficiency, productivity, and data coverage of manual curation, we have leveraged text mining to help rank and prioritize the triaged literature. Here, we describe our text-mining process that computes and assigns each article a document relevancy score (DRS), wherein a high DRS suggests that an article is more likely to be relevant for curation at CTD. We evaluated our process by first text mining a corpus of 14,904 articles triaged for seven heavy metals (cadmium, cobalt, copper, lead, manganese, mercury, and nickel). Based upon initial analysis, a representative subset corpus of 3,583 articles was then selected from the 14,094 articles and sent to five CTD biocurators for review. The resulting curation of these 3,583 articles was analyzed for a variety of parameters, including article relevancy, novel data content, interaction yield rate, mean average precision, and biological and toxicological interpretability. We show that for all measured parameters, the DRS is an effective indicator for scoring and improving the ranking of literature for the curation of chemical-gene-disease information at CTD. Here, we demonstrate how fully incorporating text mining-based DRS scoring into our curation pipeline enhances manual curation by prioritizing more relevant articles, thereby increasing data content, productivity, and efficiency.

Bisphenol A-associated epigenomic changes in prepubescent girls: a cross-sectional study in Gharbiah, Egypt

BACKGROUND

There is now compelling evidence that epigenetic modifications link adult disease susceptibility to environmental exposures during specific life stages, including pre-pubertal development. Animal studies indicate that bisphenol A (BPA), the monomer used in epoxy resins and polycarbonate plastics, may impact health through epigenetic mechanisms, and epidemiological data associate BPA levels with metabolic disorders, behavior changes, and reproductive effects. Thus, we conducted an environmental epidemiology study of BPA exposure and CpG methylation in pre-adolescent girls from Gharbiah, Egypt hypothesizing that methylation profiles exhibit exposure-dependent trends.

METHODS

Urinary concentrations of total (free plus conjugated) species of BPA in spot samples were quantified for 60 girls aged 10 to 13. Genome-wide CpG methylation was concurrently measured in bisulfite-converted saliva DNA using the Infinium HumanMethylation27 BeadChip (N = 46). CpG sites from four candidate genes were validated via quantitative bisulfite pyrosequencing.

RESULTS

CpG methylation varied widely among girls, and higher urinary BPA concentrations were generally associated with less genomic methylation. Based on pathway analyses, genes exhibiting reduced methylation with increasing urinary BPA were involved in immune function, transport activity, metabolism, and caspase activity. In particular, hypomethylation of CpG targets on chromosome X was associated with higher urinary BPA. Using the Comparative Toxicogenomics Database, we identified a number of candidate genes in our sample that previously have been associated with BPA-related expression change.

CONCLUSIONS

These data indicate that BPA may affect human health through specific epigenomic modification of genes in relevant pathways. Thus, epigenetic epidemiology holds promise for the identification of biomarkers from previous exposures and the development of epigenetic-based diagnostic strategies.

Systems biology and birth defects prevention: blockade of the glucocorticoid receptor prevents arsenic-induced birth defects

BACKGROUND

The biological mechanisms by which environmental metals are associated with birth defects are largely unknown. Systems biology-based approaches may help to identify key pathways that mediate metal-induced birth defects as well as potential targets for prevention.

OBJECTIVES

First, we applied a novel computational approach to identify a prioritized biological pathway that associates metals with birth defects. Second, in a laboratory setting, we sought to determine whether inhibition of the identified pathway prevents developmental defects.

METHODS

Seven environmental metals were selected for inclusion in the computational analysis: arsenic, cadmium, chromium, lead, mercury, nickel, and selenium. We used an in silico strategy to predict genes and pathways associated with both metal exposure and developmental defects. The most significant pathway was identified and tested using an in ovo whole chick embryo culture assay. We further evaluated the role of the pathway as a mediator of metal-induced toxicity using the in vitro midbrain micromass culture assay.

RESULTS

The glucocorticoid receptor pathway was computationally predicted to be a key mediator of multiple metal-induced birth defects. In the chick embryo model, structural malformations induced by inorganic arsenic (iAs) were prevented when signaling of the glucocorticoid receptor pathway was inhibited. Further, glucocorticoid receptor inhibition demonstrated partial to complete protection from both iAs- and cadmium-induced neurodevelopmental toxicity in vitro.

CONCLUSIONS

Our findings highlight a novel approach to computationally identify a targeted biological pathway for examining birth defects prevention.

Targeted journal curation as a method to improve data currency at the Comparative Toxicogenomics Database

The Comparative Toxicogenomics Database (CTD) is a public resource that promotes understanding about the effects of environmental chemicals on human health. CTD biocurators read the scientific literature and manually curate a triad of chemical–gene, chemical–disease and gene–disease interactions. Typically, articles for CTD are selected using a chemical-centric approach by querying PubMed to retrieve a corpus containing the chemical of interest. Although this technique ensures adequate coverage of knowledge about the chemical (i.e. data completeness), it does not necessarily reflect the most current state of all toxicological research in the community at large (i.e. data currency). Keeping databases current with the most recent scientific results, as well as providing a rich historical background from legacy articles, is a challenging process. To address this issue of data currency, CTD designed and tested a journal-centric approach of curation to complement our chemical-centric method. We first identified priority journals based on defined criteria. Next, over 7 weeks, three biocurators reviewed 2425 articles from three consecutive years (2009–2011) of three targeted journals. From this corpus, 1252 articles contained relevant data for CTD and 52 752 interactions were manually curated. Here, we describe our journal selection process, two methods of document delivery for the biocurators and the analysis of the resulting curation metrics, including data currency, and both intra-journal and inter-journal comparisons of research topics. Based on our results, we expect that curation by select journals can (i) be easily incorporated into the curation pipeline to complement our chemical-centric approach; (ii) build content more evenly for chemicals, genes and diseases in CTD (rather than biasing data by chemicals-of-interest); (iii) reflect developing areas in environmental health and (iv) improve overall data currency for chemicals, genes and diseases.

Database URL: http://ctdbase.org/

Gut Pharmacomicrobiomics: the tip of an iceberg of complex interactions between drugs and gut-associated microbes

The influence of resident gut microbes on xenobiotic metabolism has been investigated at different levels throughout the past five decades. However, with the advance in sequencing and pyrotagging technologies, addressing the influence of microbes on xenobiotics had to evolve from assessing direct metabolic effects on toxins and botanicals by conventional culture-based techniques to elucidating the role of community composition on drugs metabolic profiles through DNA sequence-based phylogeny and metagenomics. Following the completion of the Human Genome Project, the rapid, substantial growth of the Human Microbiome Project (HMP) opens new horizons for studying how microbiome compositional and functional variations affect drug action, fate, and toxicity (pharmacomicrobiomics), notably in the human gut. The HMP continues to characterize the microbial communities associated with the human gut, determine whether there is a common gut microbiome profile shared among healthy humans, and investigate the effect of its alterations on health. Here, we offer a glimpse into the known effects of the gut microbiota on xenobiotic metabolism, with emphasis on cases where microbiome variations lead to different therapeutic outcomes. We discuss a few examples representing how the microbiome interacts with human metabolic enzymes in the liver and intestine. In addition, we attempt to envisage a roadmap for the future implications of the HMP on therapeutics and personalized medicine.

The Comparative Toxicogenomics Database: update 2013

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) provides information about interactions between environmental chemicals and gene products and their relationships to diseases. Chemical-gene, chemical-disease and gene-disease interactions manually curated from the literature are integrated to generate expanded networks and predict many novel associations between different data types. CTD now contains over 15 million toxicogenomic relationships. To navigate this sea of data, we added several new features, including DiseaseComps (which finds comparable diseases that share toxicogenomic profiles), statistical scoring for inferred gene-disease and pathway-chemical relationships, filtering options for several tools to refine user analysis and our new Gene Set Enricher (which provides biological annotations that are enriched for gene sets). To improve data visualization, we added a Cytoscape Web view to our ChemComps feature, included color-coded interactions and created a 'slim list' for our MEDIC disease vocabulary (allowing diseases to be grouped for meta-analysis, visualization and better data management). CTD continues to promote interoperability with external databases by providing content and cross-links to their sites. Together, this wealth of expanded chemical-gene-disease data, combined with novel ways to analyze and view content, continues to help users generate testable hypotheses about the molecular mechanisms of environmental diseases.

Identification of Stage-Specific Gene Expression Signatures in Response to Retinoic Acid during the Neural Differentiation of Mouse Embryonic Stem Cells

We have previously established a protocol for the neural differentiation of mouse embryonic stem cells (mESCs) as an efficient tool to evaluate the neurodevelopmental toxicity of environmental chemicals. Here, we described a multivariate bioinformatic approach to identify the stage-specific gene sets associated with neural differentiation of mESCs. We exposed mESCs (B6G-2 cells) to 10⁻⁸ or 10⁻⁷ M of retinoic acid (RA) for 4 days during embryoid body formation and then performed morphological analysis on day of differentiation (DoD) 8 and 36, or genomic microarray analysis on DoD 0, 2, 8, and 36. Three gene sets, namely a literature-based gene set (set 1), an analysis-based gene set (set 2) using self-organizing map and principal component analysis, and an enrichment gene set (set 3), were selected by the combined use of knowledge from literatures and gene information selected from the microarray data. A gene network analysis for each gene set was then performed using Bayesian statistics to identify stage-specific gene expression signatures in response to RA during mESC neural differentiation. Our results showed that RA significantly increased the size of neurosphere, neuronal cells, and glial cells on DoD 36. In addition, the gene network analysis showed that glial fibrillary acidic protein, a neural marker, remarkably up-regulates the other genes in gene set 1 and 3, and Gbx2, a neural development marker, significantly up-regulates the other genes in gene set 2 on DoD 36 in the presence of RA. These findings suggest that our protocol for identification of developmental stage-specific gene expression and interaction is a useful method for the screening of environmental chemical toxicity during neurodevelopmental periods.

Benzo[a]pyrene exposure influences the cardiac development and the expression of cardiovascular relative genes in zebrafish (Danio rerio) embryos

It is reported that the most abundant polycyclic aromatic hydrocarbons (PAHs) in weathered crude oils are cardiotoxic. However, the action mechanism of PAHs on vertebrate cardiovascular development and disease is unclear. In the present study, the cardiac morphology and functioning of zebrafish embryos exposed to benzo[a]pyrene [B(a)P], as a high-ring PAHs, for 72 h were observed and determined. The results showed that B(a)P exposure resulted in cardiac developmental defects in zebrafish embryos. Significant changes in expression level of multiple genes potentially critical for regulating the B(a)P-induced cardiovascular developmental defects were also found. A gene network regulating cardiac development perturbed by B(a)P exposure was identified and established by computational analysis and employment of some databases. The information from the network could provide a clue for further mechanistic studies explaining molecular events regulating B(a)P-mediated cardiovascular defects and consequences.

MEDIC: a practical disease vocabulary used at the Comparative Toxicogenomics Database

The Comparative Toxicogenomics Database (CTD) is a public resource that promotes understanding about the effects of environmental chemicals on human health. CTD biocurators manually curate a triad of chemical–gene, chemical–disease and gene–disease relationships from the scientific literature. The CTD curation paradigm uses controlled vocabularies for chemicals, genes and diseases. To curate disease information, CTD first had to identify a source of controlled terms. Two resources seemed to be good candidates: the Online Mendelian Inheritance in Man (OMIM) and the ‘Diseases’ branch of the National Library of Medicine's Medical Subject Headers (MeSH). To maximize the advantages of both, CTD biocurators undertook a novel initiative to map the flat list of OMIM disease terms into the hierarchical nature of the MeSH vocabulary. The result is CTD’s ‘merged disease vocabulary’ (MEDIC), a unique resource that integrates OMIM terms, synonyms and identifiers with MeSH terms, synonyms, definitions, identifiers and hierarchical relationships. MEDIC is both a deep and broad vocabulary, composed of 9700 unique diseases described by more than 67 000 terms (including synonyms). It is freely available to download in various formats from CTD. While neither a true ontology nor a perfect solution, this vocabulary has nonetheless proved to be extremely successful and practical for our biocurators in generating over 2.5 million disease-associated toxicogenomic relationships in CTD. Other external databases have also begun to adopt MEDIC for their disease vocabulary. Here, we describe the construction, implementation, maintenance and use of MEDIC to raise awareness of this resource and to offer it as a putative scaffold in the formal construction of an official disease ontology.

Database URL:http://ctd.mdibl.org/voc.go?type=disease

Biological databases for behavioral neurobiology

Databases are, at their core, abstractions of data and their intentionally derived relationships. They serve as a central organizing metaphor and repository, supporting or augmenting nearly all bioinformatics. Behavioral domains provide a unique stage for contemporary databases, as research in this area spans diverse data types, locations, and data relationships. This chapter provides foundational information on the diversity and prevalence of databases, how data structures support the various needs of behavioral neuroscience analysis and interpretation. The focus is on the classes of databases, data curation, and advanced applications in bioinformatics using examples largely drawn from research efforts in behavioral neuroscience.

Bisphenol A and phthalates exhibit similar toxicogenomics and health effects

Plastics are widely used in modern life, and their unbound chemicals bisphenol A and phthalates can leach out into the surrounding environment. BPA and PAEs have recently attracted the special attention of the scientific community, regulatory agencies and the general public because of their high production volume, widespread use of plastics, and endocrine-disrupting effects. In The Comparative Toxicogenomics Database, BPA and five most frequently curated PAEs (DEHP/MEHP and DBP/BBP/MBP) were found to have 1932 and 484 interactions with genes/proteins, respectively. Five of their top ten toxicity networks were found to be involved in inflammation, and their top ten diseases included genital, prostatic, endomentrial, ovarian and breast diseases. BPA and PAEs were found to exhibit similar toxicogenomics and adverse effects on human health owning to their 89 common interacting genes/proteins. These 89 genes/proteins may serve as biomarkers to assay the toxicities of different chemicals leached out from the widely used plastics.

The curation paradigm and application tool used for manual curation of the scientific literature at the Comparative Toxicogenomics Database

The Comparative Toxicogenomics Database (CTD) is a public resource that promotes understanding about the effects of environmental chemicals on human health. CTD biocurators read the scientific literature and convert free-text information into a structured format using official nomenclature, integrating third party controlled vocabularies for chemicals, genes, diseases and organisms, and a novel controlled vocabulary for molecular interactions. Manual curation produces a robust, richly annotated dataset of highly accurate and detailed information. Currently, CTD describes over 349 000 molecular interactions between 6800 chemicals, 20 900 genes (for 330 organisms) and 4300 diseases that have been manually curated from over 25 400 peer-reviewed articles. This manually curated data are further integrated with other third party data (e.g. Gene Ontology, KEGG and Reactome annotations) to generate a wealth of toxicogenomic relationships. Here, we describe our approach to manual curation that uses a powerful and efficient paradigm involving mnemonic codes. This strategy allows biocurators to quickly capture detailed information from articles by generating simple statements using codes to represent the relationships between data types. The paradigm is versatile, expandable, and able to accommodate new data challenges that arise. We have incorporated this strategy into a web-based curation tool to further increase efficiency and productivity, implement quality control in real-time and accommodate biocurators working remotely.

Database URL:http://ctd.mdibl.org

The role of molecular biology in the biomonitoring of human exposure to chemicals

Exposure to different substances in an occupational environment is of utmost concern to global agencies such as the World Health Organization and the International Labour Organization. Interest in improving work health conditions, particularly of those employees exposed to noxious chemicals, has increased considerably and has stimulated the search for new, more specific and selective tests. Recently, the field of molecular biology has been indicated as an alternative technique for monitoring personnel while evaluating work-related pathologies. Originally, occupational exposure to environmental toxicants was assessed using biochemical techniques to determine the presence of higher concentrations of toxic compounds in blood, urine, or other fluids or tissues; results were used to evaluate potential health risk. However, this approach only estimates the presence of a noxious chemical and its effects, but does not prevent or diminish the risk. Molecular biology methods have become very useful in occupational medicine to provide more accurate and opportune diagnostics. In this review, we discuss the role of the following common techniques: (1) Use of cell cultures; (2) evaluation of gene expression; (3) the “omic” sciences (genomics, transcriptomics, proteomics and metabolomics) and (4) bioinformatics. We suggest that molecular biology has many applications in occupational health where the data can be applied to general environmental conditions.