Genetic toxicology: web resources

Molecular docking and metagenomics assisted mitigation of microplastic pollution

Microplastics, tiny, flimsy, and direct progenitors of principal and subsidiary plastics, cause environmental degradation in aquatic and terrestrial entities. Contamination concerns include irrevocable impacts, potential cytotoxicity, and negative health effects on mortals. The detection, recovery, and degradation strategies of these pollutants in various biota and ecosystems, as well as their impact on plants, animals, and humans, have been a topic of significant interest. But the natural environment is infested with several types of plastics, all having different chemical makeup, structure, shape, and origin. Plastic trash acts as a substrate for microbial growth, creating biofilms on the plastisphere surface. This colonizing microbial diversity can be glimpsed with meta-genomics, a culture-independent approach. Owing to its comprehensive description of microbial communities, genealogical evidence on unconventional biocatalysts or enzymes, genomic correlations, evolutionary profile, and function, it is being touted as one of the promising tools in identifying novel enzymes for the degradation of polymers. Additionally, computational tools such as molecular docking can predict the binding of these novel enzymes to the polymer substrate, which can be validated through in vitro conditions for its environmentally feasible applications. This review mainly deals with the exploration of metagenomics along with computational tools to provide a clearer perspective into the microbial potential in the biodegradation of microplastics. The computational tools due to their polymathic nature will be quintessential in identifying the enzyme structure, binding affinities of the prospective enzymes to the substrates, and foretelling of degradation pathways involved which can be quite instrumental in the furtherance of the plastic degradation studies.

In Silico Approaches in Predictive Genetic Toxicology

Genetic toxicology testing is a weight-of-evidence approach to identify and characterize chemical substances that can cause genetic modifications in somatic and/or germ cells. Prediction of genetic toxicology using computational tools is gaining more attention and preferred by regulatory authorities as an alternate safety assessment for in vivo or in vitro approaches. Due to the cost and time associated with experimental genetic toxicity tests, it is essential to develop more robust in silico methods to predict chemical genetic toxicity. A number of in silico genotoxicity predictive tools/models are developed based on the experimental data gathered over the years. These in silico tools are divided into statistical quantitative structure-activity relationships (QSAR)-based approaches and expert-based systems. This chapter covers the state of the art in silico toxicology approaches and standardized protocols, essential for conducting genetic toxicity predictions of chemicals. This chapter also highlights various parameters for the validation of the prediction results obtained from QSAR models.

In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR

A plethora of databases exist online that can assist in in silico chemical or drug safety assessment. However, a systematic review and grouping of databases, based on purpose and information content, consolidated in a single source, has been lacking. To resolve this issue, this review provides a comprehensive listing of the key in silico data resources relevant to: chemical identity and properties, drug action, toxicology (including nano-material toxicity), exposure, omics, pathways, Absorption, Distribution, Metabolism and Elimination (ADME) properties, clinical trials, pharmacovigilance, patents-related databases, biological (genes, enzymes, proteins, other macromolecules etc.) databases, protein-protein interactions (PPIs), environmental exposure related, and finally databases relating to animal alternatives in support of 3Rs policies. More than nine hundred databases were identified and reviewed against criteria relating to accessibility, data coverage, interoperability or application programming interface (API), appropriate identifiers, types of in vitro, in vivo,-clinical or other data recorded and suitability for modelling, read-across, or similarity searching. This review also specifically addresses the need for solutions for mapping and integration of databases into a common platform for better translatability of preclinical data to clinical data.

The genomic effects of cell phone exposure on the reproductive system

Humans are exposed to increasing levels of electromagnetic fields (EMF) at various frequencies as technology advances. In this context, improving understanding of the biological effects of EMF remains an important, high priority issue. Although a number of studies in this issue and elsewhere have focused on the mechanisms of the oxidative stress caused by EMF, the precise understanding of the processes involved remains to be elucidated. Due to unclear results among the studies, the issue of EMF exposure in the literature should be evaluated at the genomic level on the reproductive system. Based on this requirement, a detail review of recently published studies is necessary. The main objectives of this study are to show differences between negative and positive effect of EMF on the reproductive system of animal and human. Extensive review of literature has been made based on well known data bases like Web of Science, PubMed, MEDLINE, Google Scholar, Science Direct, Scopus. This paper reviews the current literature and is intended to contribute to a better understanding of the genotoxic effects of EMF emitted from mobile phones and wireless systems on the human reproductive system, especially on fertility. The current literature reveals that mobile phones can affect cellular functions via non-thermal effects. Although the cellular targets of global system for mobile communications (GSM)-modulated EMF are associated with the cell membrane, the subject is still controversial. Studies regarding the genotoxic effects of EMF have generally focused on DNA damage. Possible mechanisms are related to ROS formation due to oxidative stress. EMF increases ROS production by enhancing the activity of nicotinamide adenine dinucleotide (NADH) oxidase in the cell membrane. Further detailed studies are needed to elucidate DNA damage mechanisms and apoptotic pathways during oogenesis and spermatogenesis in germ cells exposed to EMF.

Gene-expression profiling using suppression-subtractive hybridization and cDNA microarray in rat mononuclear cells in response to welding-fume exposure

Welders with radiographic pneumoconiosis abnormalities have shown a gradual clearing of the X-ray identified effects following removal from exposure. In some cases, the pulmonary fibrosis associated with welding fumes appears in a more severe form in welders. Accordingly, for the early detection of welding-fume-exposure-induced pulmonary fibrosis, the gene expression profiles of peripheral mononuclear cells from rats exposed to welding fumes were studied using suppression-subtractive hybridization (SSH) and a cDNA microarray. As such, Sprague-Dawley rats were exposed to a stainless steel arc welding fume for 2 h/day in an inhalation chamber with a 107.59 / 2.6 mg/m3 concentration of total suspended particulate (TSP) for 30 days. Thereafter, the total RNA was extracted from the peripheral blood mononuclear cells, the cDNA synthesized from the total RNA using the SMARTTM PCR cDNA method, and SSH performed to select the welding-fume-exposure-regulated genes. The cDNAs identified by the SSH were then cloned into a plasmid miniprep, sequenced and the sequences analysed using the NCBI BLAST programme. In the SSH cloned cDNA microarray analysis, five genes were found to increase their expression by 1.9-fold or more, including Rgs 14, which plays an important function in cellular signal transduction pathways; meanwhile 36 genes remained the same and 30 genes decreased their expression by more than 59%, including genes associated with the immune response, transcription factors and tyrosine kinases. Among the 5200 genes analysed, 256 genes (5.1%) were found to increase their gene expression, while 742 genes (15%) decreased their gene expression in response to the welding-fume exposure when tested using a commercial 5.0k DNA microarray. Therefore, unlike exposure to other toxic substances, prolonged welding-fume exposure was found to substantially downregulate many genes.

MutAIT: an online genetic toxicology data portal and analysis tools

Assessment of genetic toxicity and/or carcinogenic activity is an essential element of chemical screening programs employed to protect human health. Dose-response and gene mutation data are frequently analysed by industry, academia and governmental agencies for regulatory evaluations and decision making. Over the years, a number of efforts at different institutions have led to the creation and curation of databases to house genetic toxicology data, largely, with the aim of providing public access to facilitate research and regulatory assessments. This article provides a brief introduction to a new genetic toxicology portal called Mutation Analysis Informatics Tools (MutAIT) (www.mutait.org) that provides easy access to two of the largest genetic toxicology databases, the Mammalian Gene Mutation Database (MGMD) and TransgenicDB. TransgenicDB is a comprehensive collection of transgenic rodent mutation data initially compiled and collated by Health Canada. The updated MGMD contains approximately 50 000 individual mutation spectral records from the published literature. The portal not only gives access to an enormous quantity of genetic toxicology data, but also provides statistical tools for dose-response analysis and calculation of benchmark dose. Two important R packages for dose-response analysis are provided as web-distributed applications with user-friendly graphical interfaces. The 'drsmooth' package performs dose-response shape analysis and determines various points of departure (PoD) metrics and the 'PROAST' package provides algorithms for dose-response modelling. The MutAIT statistical tools, which are currently being enhanced, provide users with an efficient and comprehensive platform to conduct quantitative dose-response analyses and determine PoD values that can then be used to calculate human exposure limits or margins of exposure.

Comparative genomic hybridization (CGH) in genotoxicology

In the past two decades comparative genomic hybridization (CGH) and array CGH have become crucial and indispensable tools in clinical diagnostics. Initially developed for the genome-wide screening of chromosomal imbalances in tumor cells, CGH as well as array CGH have also been employed in genotoxicology and most recently in toxicogenomics. The latter methodology allows a multi-endpoint analysis of how genes and proteins react to toxic agents revealing molecular mechanisms of toxicology. This chapter provides a background on the use of CGH and array CGH in the context of genotoxicology as well as a protocol for conventional CGH to understand the basic principles of CGH. Array CGH is still cost intensive and requires suitable analytical algorithms but might become the dominating assay in the future when more companies provide a large variety of different commercial DNA arrays/chips leading to lower costs for array CGH equipment as well as consumables such as DNA chips. As the amount of data generated with microarrays exponentially grows, the demand for powerful adaptive algorithms for analysis, competent databases, as well as a sound regulatory framework will also increase. Nevertheless, chromosomal and array CGH are being demonstrated to be effective tools for investigating copy number changes/variations in the whole genome, DNA expression patterns, as well as loss of heterozygosity after a genotoxic impact. This will lead to new insights into affected genes and the underlying structures of regulatory and signaling pathways in genotoxicology and could conclusively identify yet unknown harmful toxicants.

Gene expression profiling in the lung tissue of cynomolgus monkeys in response to repeated exposure to welding fumes

Many in the welding industry suffer from bronchitis, lung function changes, metal fume fever, and diseases related to respiratory damage. These phenomena are associated with welding fumes; however, the mechanism behind these findings remains to be elucidated. In this study, the lungs of cynomolgus monkeys were exposed to MMA-SS welding fumes for 229 days and allowed to recover for 153 days. After the exposure and recovery period, gene expression profiles were investigated using the Affymetrix GeneChip Human U133 plus 2.0. In total, it was confirmed that 1,116 genes were up-or downregulated (over 2-fold changes, P\0.01) for the T1 (31.4 ± 2.8 mg/m3) and T2 (62.5 ± 2.7 mg/m3) dose groups. Differentially expressed genes in the exposure and recovery groups were analyzed, based on hierarchical clustering, and were imported into Ingenuity Pathways Analysis to analyze the biological and toxicological functions. Functional analysis identified genes involved in immunological disease in both groups. Additionally, differentially expressed genes in common between monkeys and rats following welding fume exposure were compared using microarray data, and the gene expression of selected genes was verified by real-time PCR. Genes such as CHI3L1, RARRES1, and CTSB were up-regulated and genes such as CYP26B1, ID4, and NRGN were down-regulated in both monkeys and rats following welding fume exposure. This is the first comprehensive gene expression profiling conducted for welding fume exposure in monkeys, and these expressed genes are expected to be useful in helping to understand transcriptional changes in monkey lungs after welding fume exposure.

The EDKB: an established knowledge base for endocrine disrupting chemicals

BACKGROUND

Endocrine disruptors (EDs) and their broad range of potential adverse effects in humans and other animals have been a concern for nearly two decades. Many putative EDs are widely used in commercial products regulated by the Food and Drug Administration (FDA) such as food packaging materials, ingredients of cosmetics, medical and dental devices, and drugs. The Endocrine Disruptor Knowledge Base (EDKB) project was initiated in the mid 1990's by the FDA as a resource for the study of EDs. The EDKB database, a component of the project, contains data across multiple assay types for chemicals across a broad structural diversity. This paper demonstrates the utility of EDKB database, an integral part of the EDKB project, for understanding and prioritizing EDs for testing.

RESULTS

The EDKB database currently contains 3,257 records of over 1,800 EDs from different assays including estrogen receptor binding, androgen receptor binding, uterotropic activity, cell proliferation, and reporter gene assays. Information for each compound such as chemical structure, assay type, potency, etc. is organized to enable efficient searching. A user-friendly interface provides rapid navigation, Boolean searches on EDs, and both spreadsheet and graphical displays for viewing results. The search engine implemented in the EDKB database enables searching by one or more of the following fields: chemical structure (including exact search and similarity search), name, molecular formula, CAS registration number, experiment source, molecular weight, etc. The data can be cross-linked to other publicly available and related databases including TOXNET, Cactus, ChemIDplus, ChemACX, Chem Finder, and NCI DTP.

CONCLUSION

The EDKB database enables scientists and regulatory reviewers to quickly access ED data from multiple assays for specific or similar compounds. The data have been used to categorize chemicals according to potential risks for endocrine activity, thus providing a basis for prioritizing chemicals for more definitive but expensive testing. The EDKB database is publicly available and can be found online at http://edkb.fda.gov/webstart/edkb/index.html.

E-SovTox: An Online Database of the Main Publicly-available Sources of Toxicity Data concerning REACH-relevant Chemicals Published in the Russian Language

A new open-access online database, E-SovTox, is presented. E-SovTox provides toxicological data for substances relevant to the EU Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) system, from publicly-available Russian language data sources. The database contains information selected mainly from scientific journals published during the Soviet Union era. The main information source for this database — the journal, Gigiena Truda i Professional'nye Zabolevania [ Industrial Hygiene and Occupational Diseases], published between 1957 and 1992 — features acute, but also chronic, toxicity data for numerous industrial chemicals, e.g. for rats, mice, guinea-pigs and rabbits. The main goal of the above-mentioned toxicity studies was to derive the maximum allowable concentration limits for industrial chemicals in the occupational health settings of the former Soviet Union. Thus, articles featured in the database include mostly data on LD50 values, skin and eye irritation, skin sensitisation and cumulative properties. Currently, the E-SovTox database contains toxicity data selected from more than 500 papers covering more than 600 chemicals. The user is provided with the main toxicity information, as well as abstracts of these papers in Russian and in English (given as provided in the original publication). The search engine allows cross-searching of the database by the name or CAS number of the compound, and the author of the paper. The E-SovTox database can be used as a decision-support tool by researchers and regulators for the hazard assessment of chemical substances.

The toxicity data landscape for environmental chemicals

OBJECTIVE

Thousands of chemicals are in common use, but only a portion of them have undergone significant toxicologic evaluation, leading to the need to prioritize the remainder for targeted testing. To address this issue, the U.S. Environmental Protection Agency (EPA) and other organizations are developing chemical screening and prioritization programs. As part of these efforts, it is important to catalog, from widely dispersed sources, the toxicology information that is available. The main objective of this analysis is to define a list of environmental chemicals that are candidates for the U.S. EPA screening and prioritization process, and to catalog the available toxicology information.

DATA SOURCES

We are developing ACToR (Aggregated Computational Toxicology Resource), which combines information for hundreds of thousands of chemicals from > 200 public sources, including the U.S. EPA, National Institutes of Health, Food and Drug Administration, corresponding agencies in Canada, Europe, and Japan, and academic sources.

DATA EXTRACTION

ACToR contains chemical structure information; physical-chemical properties; in vitro assay data; tabular in vivo data; summary toxicology calls (e.g., a statement that a chemical is considered to be a human carcinogen); and links to online toxicology summaries. Here, we use data from ACToR to assess the toxicity data landscape for environmental chemicals.

DATA SYNTHESIS

We show results for a set of 9,912 environmental chemicals being considered for analysis as part of the U.S. EPA ToxCast screening and prioritization program. These include high-and medium-production-volume chemicals, pesticide active and inert ingredients, and drinking water contaminants.

CONCLUSIONS

Approximately two-thirds of these chemicals have at least limited toxicity summaries available. About one-quarter have been assessed in at least one highly curated toxicology evaluation database such as the U.S. EPA Toxicology Reference Database, U.S. EPA Integrated Risk Information System, and the National Toxicology Program.

ACToR--Aggregated Computational Toxicology Resource

ACToR (Aggregated Computational Toxicology Resource) is a database and set of software applications that bring into one central location many types and sources of data on environmental chemicals. Currently, the ACToR chemical database contains information on chemical structure, in vitro bioassays and in vivo toxicology assays derived from more than 150 sources including the U.S. Environmental Protection Agency (EPA), Centers for Disease Control (CDC), U.S. Food and Drug Administration (FDA), National Institutes of Health (NIH), state agencies, corresponding government agencies in Canada, Europe and Japan, universities, the World Health Organization (WHO) and non-governmental organizations (NGOs). At the EPA National Center for Computational Toxicology, ACToR helps manage large data sets being used in a high-throughput environmental chemical screening and prioritization program called ToxCast.

Gene-expression profiling of human mononuclear cells from welders using cDNA microarray

A toxicogenomic chip developed to detect welding-related diseases was tested and validated for field trials. To verify the suitability of the microarray, white blood cells (WBC) or whole blood was purified and characterized from 20 subjects in the control group (average work experience of 7 yr) and 20 welders in the welding-fume exposed group (welders with an average work experience of 23 yr). Two hundred and fifty-three rat genes homologous to human genes were obtained and spotted on the chip slide. Meanwhile, a human cDNA chip spotted with 8600 human genes was also used to detect any increased or decreased levels of gene expression among the welders. After comparing the levels of gene expression between the control and welder groups using the toxicogenomic chips, 103 genes were identified as likely to be specifically changed by welding-fume exposure. Eighteen of the 253 rat genes were specifically changed in the welders, while 103 genes from the human cDNA chip were specifically changed. The genes specifically expressed by the welders were associated with inflammatory responses, toxic chemical metabolism, stress proteins, transcription factors, and signal transduction. In contrast, there was no significant change in the genes related to short-term welding-fume exposure, such as tumor necrosis factor (TNF)-alpha and interleukin. In conclusion, if further validation studies are conducted, the present toxicogenomic gene chips could be used for the effective monitoring of welding-fume-exposure-related diseases among welders.

Identification of potential biomarkers of genotoxicity and carcinogenicity in L5178Y mouse lymphoma cells by cDNA microarray analysis

In the present study, cDNA microarray analyses were performed with mouse cDNA chips in order to evaluate similarities and differences in the gene expression profiles for compounds differing in their genotoxic and carcinogenic potential. Eight test substances were evaluated, two each from four classes of compounds: genotoxic carcinogens (1,2-dibromoethane and glycidol), genotoxic noncarcinogens (8-hydroxyquinoline and emodin), nongenotoxic carcinogens (methyl carbamate and o-nitrotoluene), and nongenotoxic noncarcinogens (D-mannitol and 1,2-dichlorobenzene). Quadruplicate hybridization experiments were performed in order to identify a set of genes with significant expression changes for these four classes of substances. Twelve genes were consistently altered more than twofold by the genotoxic noncarcinogens while four genes were consistently regulated by the nongenotoxic carcinogens. One gene (Trp63) was identified whose expression was upregulated by all four genotoxic substances regardless of the presence or absence of carcinogenicity; this finding, however, was not confirmed by quantitative real-time RT-PCR. RT-PCR did confirm the change in expression of 9 of 15 genes (60%) identified by microarray analysis. Interestingly, the downregulated genes were least likely to be validated by real-time RT-PCR. Those genes showing more than a twofold change in expression level in response to at least one substance were further analyzed with hierarchical clustering after category assignment of each gene according to its main cellular function. Clustering revealed differences in the gene expression profiles between the genotoxic and nongenotoxic substances for genes involved in cell cycle control, the stress response, and the immune response. However, no clustering specific to all four carcinogenic substances was observed in any of the functional categories. Taken together, these results suggest that gene expression profiling in mouse lymphoma cells can provide valuable information for the evaluation of potential genotoxicity but may have limitations in predicting carcinogenicity.

The Intelligent Data Management System for Toxicogenomics

Toxicogenomics is now emerging as one of the most important genomic application because the toxicity test based on gene expression profiles is expected to be more precise and efficient than current histopathological approaches in a pre-clinical phase. One of the challenging issues in toxicogenomics is the construction of intelligent database management system which can deal with heterogeneous and complex data from many different experimental and information sources. TEST(Toxicogenomics for Efficient Safety Test) database is especially focused on the connectivity of heterogeneous data and the intelligent query system which enable users to obtain relevant useful information from the complex data sets. The database deals with four kinds of information; compound, histopathology, gene expression, and annotation information. Currently, TEST database maintains toxicogenomics information for 16 compounds, 45 microarrays, 190 animal experiments, and customized 4.8 K rat clone set. Our presented system is expected to be a good information source for studying of toxicology mechanism in the genome-wide level and can also be applied to the designing toxicity test chip.

cDNA microarray gene expression profiling of hydroxyurea, paclitaxel, and p-anisidine, genotoxic compounds with differing tumorigenicity results

The potential application of toxicogenomics to predictive toxicology has been discussed widely, but the utility of the approach remains largely unproven. Using cDNA microarrays, we compared the gene expression profiles produced in mouse lymphoma cells by three genotoxic compounds, hydroxyurea (a carcinogen), p-anisidine (a noncarcinogen), and paclitaxel (carcinogenicity unknown). To minimize the effect of biological variability and technological limitations, quadruplicate observations were made for each compound and a subset of genes yielding reproducible induction/repression was selected for comparison. A method was applied to attach normalized expression data to genes with a low false-discovery rate (<0.1) to yield more confidence regarding differential expression. This analysis identified genotoxicity-specific gene expression. Seven genes were consistently upregulated and 12 downregulated more than 2-fold by the three genotoxic compounds. Using additional genes, the expression pattern induced by the genotoxic noncarcinogen, p-anisidine, was readily distinguished from that associated with the genotoxic carcinogen, hydroxyurea. Comparison of paclitaxel-induced expression data to data for p-anisidine and hydroxyurea suggested that paclitaxel's profile is more similar to the genotoxic noncarcinogen. With further supporting evidence it may be possible to perform large-scale monitoring of gene expression during drug and chemical development that can provide an early warning of potential toxicological responses.