EDGE: A Centralized Resource for the Comparison, Analysis, and Distribution of Toxicogenomic Information

AhR and CYP1B1 Control Oxygen Effects on Bone Marrow Progenitor Cells: The Enrichment of Multiple Olfactory Receptors as Potential Microbiome Sensors

Polycyclic aromatic hydrocarbon (PAH) pollutants and microbiome products converge on the aryl hydrocarbon receptor (AhR) to redirect selective rapid adherence of isolated bone marrow (BM) cells. In young adult mice, Cyp1b1-deficiency and AhR activation by PAH, particularly when prolonged by Cyp1a1 deletion, produce matching gene stimulations in these BM cells. Vascular expression of Cyp1b1 lowers reactive oxygen species (ROS), suppressing NF-κB/RelA signaling. PAH and allelic selectivity support a non-canonical AhR participation, possibly through RelA. Genes stimulated by Cyp1b1 deficiency were further resolved according to the effects of Cyp1b1 and Cyp1a1 dual deletions (DKO). The adherent BM cells show a cluster of novel stimulations, including select developmental markers; multiple re-purposed olfactory receptors (OLFR); and α-Defensin, a microbial disruptor. Each one connects to an enhanced specific expression of the catalytic RNA Pol2 A subunit, among 12 different subunits. Mesenchymal progenitor BMS2 cells retain these features. Cyp1b1-deficiency removes lymphocytes from adherent assemblies as BM-derived mesenchymal stromal cells (BM-MSC) expand. Cyp1b1 effects were cell-type specific. In vivo, BM-MSC Cyp1b1 expression mediated PAH suppression of lymphocyte progenitors. In vitro, OP9-MSC sustained these progenitors, while Csf1 induced monocyte progenitor expansion to macrophages. Targeted Cyp1b1 deletion (Cdh5-Cre; Cyp1b1fl/fl) established endothelium control of ROS that directs AhR-mediated suppression of B cell progenitors. Monocyte Cyp1b1 deletion (Lyz2-Cre; Cyp1b1fl/fl) selectively attenuated M1 polarization of expanded macrophages, but did not enhance effects on basal M2 polarization. Thus, specific sources of Cyp1b1 link to AhR and to an OLFR network to provide BM inflammatory modulation via diverse microbiome products.

A Retrospective Analysis of Toxicogenomics in the Safety Assessment of Drug Candidates

Toxicogenomics is considered a valuable tool for reducing pharmaceutical candidate attrition by facilitating earlier identification, prediction and understanding of toxicities. A retrospective evaluation of 3 years of routine transcriptional profiling in non-clinical safety studies was undertaken to assess the utility of toxicogenomics in drug safety assessment. Based on the analysis of studies with 33 compounds, marked global transcriptional changes (>4% transcripts at p < 0.01) were shown to be a robust biomarker for dosages considered to be toxic. In general, there was an inconsistent correlation between transcription and histopathology, most likely due to differences in sensitivity to focal microscopic lesions, to secondary effects, and to events that precede structural tissue changes. For 60% of toxicities investigated with multiple time-point data, transcriptional changes were observed prior to changes in traditional study endpoints. Candidate transcriptional markers of pharmacologic effects were detected in 40% of targets profiled. Mechanistic classification of toxicity was obtained for 30% of targets. Furthermore, data comparison to compendia of transcriptional changes provided assessments of the specificity of transcriptional responses. Overall, our experience suggests that toxicogenomics has contributed to a greater understanding of mechanisms of toxicity and to reducing drug attrition by empiric analysis where safety assessment combines toxicogenomic and traditional evaluations.

Cyp1b1 affects external control of mouse hepatocytes, fatty acid homeostasis and signaling involving HNF4α and PPARα

Cytochrome P450 1b1 (Cyp1b1) is expressed in endothelia, stellate cells and pre-adipocytes, but not hepatocytes. Deletion alters liver fatty acid metabolism and prevents obesity and hepatic steatosis. This suggests a novel extra-hepatocyte regulation directed from cells that express Cyp1b1. To characterize these mechanisms, microarray gene expression was analyzed in livers of normal and congenic Cyp1b1-ko C57BL/6 J mice fed either low or high fat diets. Cyp1b1-ko gene responses indicate suppression of endogenous PPARα activity, a switch from triglyceride storage to mitochondrial fatty acid oxidation and decreased oxidative stress. Many gene responses in Cyp1b1-ko are sexually dimorphic and correspond to increased activity of growth hormone mediated by HNF4α. Male responses stimulated by GH pulses are enhanced, whereas responses that decline exhibit further suppression, including Cyp regulation by PPARα, CAR and PXR. These effects of Cyp1b1 deletion overlap with effects caused by deletion of the small heterodimeric partner, a suppressor of these nuclear factors. Redirection of gene expression associated with liver fat homeostasis in Cyp1b1-ko mice that directs hypothalamic control of GH and leptin. Cyp1b1-ko suppresses neonatal Scd1 and delays adult maturation of dimorphic GH/HNF4α signaling. Alternatively, deletion may diminish hypothalamic metabolism of estradiol, which establishes adult GH regulation.

Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products

It is widely accepted that drug discovery often requires a systems-level polypharmacology approach to tackle problems such as lack of efficacy and emerging resistance of single-targeted compounds. Network pharmacology approaches are increasingly being developed and applied to find new therapeutic opportunities and to re-purpose approved drugs. However, these recent advances have been relatively slow to be translated into the field of natural products. Here, we argue that a network pharmacology approach would enable an effective mapping of the yet unexplored target space of natural products, hence providing a systematic means to extend the druggable space of proteins implicated in various complex diseases. We give an overview of the key network pharmacology concepts and recent experimental-computational approaches that have been successfully applied to natural product research, including unbiased elucidation of mechanisms of action as well as systematic prediction of effective therapeutic combinations. We focus specifically on anticancer applications that use in vivo and in vitro functional phenotypic measurements, such as genome-wide transcriptomic response profiles, which enable a global modelling of the multi-target activity at the level of the biological pathways and interaction networks. We also provide representative examples of other disease applications, databases and tools as well as existing and emerging resources, which may prove useful for future natural product research. Finally, we offer our personal view of the current limitations, prospective developments and open questions in this exciting field.

Loss of Grainy head-like 1 is associated with disruption of the epidermal barrier and squamous cell carcinoma of the skin

The Grainyhead-like 1 (GRHL1) transcription factor regulates the expression of desmosomal cadherin desmoglein 1 (Dsg1) in suprabasal layers of the epidermis. As a consequence, the epidermis of Grhl1-null mice displays fewer desmosomes that are abnormal in structure. These mice also exhibit mild chronic skin barrier defects as evidenced by altered keratinocyte terminal differentiation, increased expression of inflammatory markers and infiltration of the skin by immune cells. Exposure of Grhl1^−/− mice to a standard chemical skin carcinogenesis protocol results in development of fewer papillomas than in wild type control animals, but with a rate of conversion to squamous cell carcinoma (SCC) that is strikingly higher than in normal littermates. The underlying molecular mechanism differs from mice with conditional ablation of a closely related Grhl family member, Grhl3, in the skin, which develop SCC due to the loss of expression of phosphatase and tensin homolog (PTEN) and activation of the phosphatidylinositol 3-kinase (PI3K)/AKT/mechanistic target of rapamycin (mTOR) signaling pathway.

On the selection of appropriate distances for gene expression data clustering

BACKGROUND

Clustering is crucial for gene expression data analysis. As an unsupervised exploratory procedure its results can help researchers to gain insights and formulate new hypothesis about biological data from microarrays. Given different settings of microarray experiments, clustering proves itself as a versatile exploratory tool. It can help to unveil new cancer subtypes or to identify groups of genes that respond similarly to a specific experimental condition. In order to obtain useful clustering results, however, different parameters of the clustering procedure must be properly tuned. Besides the selection of the clustering method itself, determining which distance is going to be employed between data objects is probably one of the most difficult decisions.

RESULTS AND CONCLUSIONS

We analyze how different distances and clustering methods interact regarding their ability to cluster gene expression, i.e., microarray data. We study 15 distances along with four common clustering methods from the literature on a total of 52 gene expression microarray datasets. Distances are evaluated on a number of different scenarios including clustering of cancer tissues and genes from short time-series expression data, the two main clustering applications in gene expression. Our results support that the selection of an appropriate distance depends on the scenario in hand. Moreover, in each scenario, given the very same clustering method, significant differences in quality may arise from the selection of distinct distance measures. In fact, the selection of an appropriate distance measure can make the difference between meaningful and poor clustering outcomes, even for a suitable clustering method.

Identifying genes relevant to specific biological conditions in time course microarray experiments

Microarrays have been useful in understanding various biological processes by allowing the simultaneous study of the expression of thousands of genes. However, the analysis of microarray data is a challenging task. One of the key problems in microarray analysis is the classification of unknown expression profiles. Specifically, the often large number of non-informative genes on the microarray adversely affects the performance and efficiency of classification algorithms. Furthermore, the skewed ratio of sample to variable poses a risk of overfitting. Thus, in this context, feature selection methods become crucial to select relevant genes and, hence, improve classification accuracy. In this study, we investigated feature selection methods based on gene expression profiles and protein interactions. We found that in our setup, the addition of protein interaction information did not contribute to any significant improvement of the classification results. Furthermore, we developed a novel feature selection method that relies exclusively on observed gene expression changes in microarray experiments, which we call "relative Signal-to-Noise ratio" (rSNR). More precisely, the rSNR ranks genes based on their specificity to an experimental condition, by comparing intrinsic variation, i.e. variation in gene expression within an experimental condition, with extrinsic variation, i.e. variation in gene expression across experimental conditions. Genes with low variation within an experimental condition of interest and high variation across experimental conditions are ranked higher, and help in improving classification accuracy. We compared different feature selection methods on two time-series microarray datasets and one static microarray dataset. We found that the rSNR performed generally better than the other methods.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Genome-wide gene expression profiles in antioxidant pathways and their potential sex differences and connections to vitamin C in mice

Vitamin C (VC) is well known as an antioxidant in humans, primates and guinea pigs. Studies have suggested gender differences in VC requirements in humans, and gender differences in oxidant injury vulnerability in early life may represent a biological mechanism contributing to gender disparity in later life. Using spontaneous bone fracture (sfx) mice, which lack the gene for L-Gulonolactone oxidase (Gulo), we studied the potential sex difference in expression profiles of oxidative genes at the whole-genome level. Then, we analyzed data of gene expressions in a mouse population of recombinant inbred (RI) strains originally derived by crossing C57BL/6J (B6) and DBA/2J (D2) mice. Our data indicated that there were sex differences in the regulation of pre- and pro-oxidative genes in sfx mice. The associations of expression levels among Gulo, its partner genes and oxidative genes in the BXD (B6 × D2) RI strains showed a sex difference. Transcriptome mapping suggests that Gulo was regulated differently between female and male mice in BXD RI strains. Our study indicates the importance of investigating sex differences in Gulo and its oxidative function by using available mouse models.

Activator protein-1 regulation of murine aldehyde dehydrogenase 1a1

Previously we demonstrated that aldehyde dehydrogenase (ALDH) 1a1 is the major ALDH expressed in mouse liver and is an effective catalyst in metabolism of lipid aldehydes. Quantitative real-time polymerase chain reaction analysis revealed a ≈2.5- to 3-fold induction of the hepatic ALDH1A1 mRNA in mice administered either acrolein (5 mg/kg acrolein p.o.) or butylated hydroxylanisole (BHA) (0.45% in the diet) and of cytosolic NAD⁺-dependent ALDH activity. We observed ≈2-fold increases in ALDH1A1 mRNA levels in both Nrf2⁺/⁺ and Nrf2⁻/⁻ mice treated with BHA compared with controls, suggesting that BHA-induced expression is independent of nuclear factor E2-related factor 2 (Nrf2). The levels of activator protein-1 (AP-1) mRNA and protein, as well as the amount of phosphorylated c-Jun were significantly increased in mouse liver or Hepa1c1c7 cells treated with either BHA or acrolein. With use of luciferase reporters containing the 5'-flanking sequence of Aldh1a1 (-1963/+27), overexpression of c-Jun resulted in an ≈4-fold induction in luciferase activity, suggesting that c-Jun transactivates the Aldh1a1 promoter as a homodimer and not as a c-Jun/c-Fos heterodimer. Promoter deletion and mutagenesis analyses demonstrated that the AP-1 site at position -758 and possibly -1069 relative to the transcription start site was responsible for c-Jun-mediated transactivation. Electrophoretic mobility shift assay analysis with antibodies against c-Jun and c-Fos showed that c-Jun binds to the proximal AP-1 site at position -758 but not at -1069. Recruitment of c-Jun to this proximal AP-1 site by BHA was confirmed by chromatin immunoprecipitation analysis, indicating that recruitment of c-Jun to the mouse Aldh1a1 gene promoter results in increased transcription. This mode of regulation of an ALDH has not been described before.

In-silico studies in Chinese herbal medicines' research: evaluation of in-silico methodologies and phytochemical data sources, and a review of research to date

The available databases that catalogue information on traditional Chinese medicines are reviewed in terms of their content and utility for in-silico research on Chinese herbal medicines, as too are the various protein database resources, and the software available for use in such studies. The software available for bioinformatics and 'omics studies of Chinese herbal medicines are summarised, and a critical evaluation given of the various in-silico methods applied in screening Chinese herbal medicines, including classification trees, neural networks, support vector machines, docking and inverse docking algorithms. Recommendations are made regarding any future in-silico studies of Chinese herbal medicines.

Omic techniques in systems biology approaches to traditional Chinese medicine research: present and future

Omic techniques have become key tools in the development of systems biology. As the holistic approaches underlying the practice of traditional Chinese medicine (TCM) and new tendencies in Western medicine towards personalised medicine require in-depth knowledge of mechanisms of action and active compounds, the use of omic techniques is crucial for understanding and interpretation of TCM development, especially in view of its expansion in Western countries. In this short review, omic applications in TCM research are reviewed which has allowed some speculation regarding future perspectives for these approaches in TCM modernisation and standardisation. Guidelines for good practice for the application of omics in TCM research are also proposed.

Classifying short gene expression time-courses with Bayesian estimation of piecewise constant functions

MOTIVATION

Analyzing short time-courses is a frequent and relevant problem in molecular biology, as, for example, 90% of gene expression time-course experiments span at most nine time-points. The biological or clinical questions addressed are elucidating gene regulation by identification of co-expressed genes, predicting response to treatment in clinical, trial-like settings or classifying novel toxic compounds based on similarity of gene expression time-courses to those of known toxic compounds. The latter problem is characterized by irregular and infrequent sample times and a total lack of prior assumptions about the incoming query, which comes in stark contrast to clinical settings and requires to implicitly perform a local, gapped alignment of time series. The current state-of-the-art method (SCOW) uses a variant of dynamic time warping and models time series as higher order polynomials (splines).

RESULTS

We suggest to model time-courses monitoring response to toxins by piecewise constant functions, which are modeled as left-right Hidden Markov Models. A Bayesian approach to parameter estimation and inference helps to cope with the short, but highly multivariate time-courses. We improve prediction accuracy by 7% and 4%, respectively, when classifying toxicology and stress response data. We also reduce running times by at least a factor of 140; note that reasonable running times are crucial when classifying response to toxins. In conclusion, we have demonstrated that appropriate reduction of model complexity can result in substantial improvements both in classification performance and running time.

AVAILABILITY

A Python package implementing the methods described is freely available under the GPL from http://bioinformatics.rutgers.edu/Software/MVQueries/.

The evolution of bioinformatics in toxicology: advancing toxicogenomics

As one reflects back through the past 50 years of scientific research, a significant accomplishment was the advance into the genomic era. Basic research scientists have uncovered the genetic code and the foundation of the most fundamental building blocks for the molecular activity that supports biological structure and function. Accompanying these structural and functional discoveries is the advance of techniques and technologies to probe molecular events, in time, across environmental and chemical exposures, within individuals, and across species. The field of toxicology has kept pace with advances in molecular study, and the past 50 years recognizes significant growth and explosive understanding of the impact of the compounds and environment to basic cellular and molecular machinery. The advancement of molecular techniques applied in a whole-genomic capacity to the study of toxicant effects, toxicogenomics, is no doubt a significant milestone for toxicological research. Toxicogenomics has also provided an avenue for advancing a joining of multidisciplinary sciences including engineering and informatics in traditional toxicological research. This review will cover the evolution of the field of toxicogenomics in the context of informatics integration its current promise, and limitations.

Application of bioactivity database of Chinese herbal medicine on the therapeutic prediction, drug development, and safety evaluation

AIM OF THE STUDY

Chinese herbal medicine has been used for the treatments of various diseases for years. However, it is often difficult to analyze their biological activities and molecule mechanisms because of their complex nature. In this study, we applied DNA microarray to analyze the biological events induced by herbal formulae, predict the therapeutic potentials of formulae, and evaluate the safety of formulae.

MATERIALS AND METHODS

Mice were administrated orally with 15 formulae for 7 consecutive days, and the gene expression profiles in liver or kidney were further analyzed by transcriptomic tools.

RESULTS

Our data showed that most formulae altered the metabolic pathways, such as glutathione metabolism and oxidative phosphorylation, and regulatory pathways, such as antigen processing and presentation and insulin-like growth factor signaling pathway. By comparing the gene expression signatures of formulae with those of disease states or drugs, we found that mice responsive to formula treatments might be related to disease states, especially metabolic and cardiovascular diseases, and drugs, which exhibit anti-cancer, anti-inflammatory, and anti-oxidative effects. Moreover, most formulae altered the expression levels of cytochrome p450, glutathione S-transferase, and UDP glycosyltransferase genes, suggesting that caution should be paid to possible drug interaction of these formulae. Furthermore, the similarities of gene expression profiles between formulae and toxic chemicals were low in kidney, suggesting that these formulae might not induce nephrotoxicities in mice.

CONCLUSIONS

This report applied transcriptomic tools as a novel platform of translational medicine for Chinese herbal medicine. This platform will not only for understanding the therapeutic mechanisms involving herbal formulae and gene interactions, but also for the new theories in drug discovery.

Toxicity and recovery in the pregnant mouse after gestational exposure to the cyanobacterial toxin, cylindrospermopsin

Cylindrospermopsin (CYN) is a tricyclic alkaloid toxin produced by fresh water cyanobacterial species worldwide. CYN has been responsible for both livestock and human poisoning after oral exposure. This study investigated the toxicity of CYN to pregnant mice exposed during different segments of gestation. The course of recovery and individual responses to the toxin were evaluated. Adverse effects of CYN were monitored up to 7 weeks post-dosing by clinical examination, histopathology, biochemistry and gene expression. Exposure on gestational days (GD) 8-12 induced significantly more lethality than GD13-17 exposure. Periorbital, gastrointestinal and distal tail hemorrhages were seen in both groups. Serum markers indicative of hepatic injury (alanine amino transferase, aspartate amino transferase and sorbitol dehydrogenase) were increased in both groups; markers of renal dysfunction (blood urea nitrogen and creatinine) were elevated in the GD8-12 animals. Histopathology was observed in the liver (centrilobular necrosis) and kidney (interstitial inflammation) in groups exhibiting abnormal serum markers. The expression profiles of genes involved in ribosomal biogenesis, xenobiotic and lipid metabolism, inflammatory response and oxidative stress were altered 24 h after the final dose. One week after dosing, gross, histological and serum parameters had returned to normal, although increased liver/body weight ratio and one instance of gastrointestinal bleeding was found in the GD13-17 group. Gene expression changes persisted up to 2 weeks post-dosing and returned to normal by 4 weeks. Responses of individual animals to CYN exposure indicated highly significant inter-animal variability within the treated groups.

Practical application of toxicogenomics for profiling toxicant-induced biological perturbations

A systems-level understanding of molecular perturbations is crucial for evaluating chemical-induced toxicity risks appropriately, and for this purpose comprehensive gene expression analysis or toxicogenomics investigation is highly advantageous. The recent accumulation of toxicity-associated gene sets (toxicogenomic biomarkers), enrichment in public or commercial large-scale microarray database and availability of open-source software resources facilitate our utilization of the toxicogenomic data. However, toxicologists, who are usually not experts in computational sciences, tend to be overwhelmed by the gigantic amount of data. In this paper we present practical applications of toxicogenomics by utilizing biomarker gene sets and a simple scoring method by which overall gene set-level expression changes can be evaluated efficiently. Results from the gene set-level analysis are not only an easy interpretation of toxicological significance compared with individual gene-level profiling, but also are thought to be suitable for cross-platform or cross-institutional toxicogenomics data analysis. Enrichment in toxicogenomics databases, refinements of biomarker gene sets and scoring algorithms and the development of user-friendly integrative software will lead to better evaluation of toxicant-elicited biological perturbations.

The Japanese toxicogenomics project: application of toxicogenomics

Biotechnology advances have provided novel methods for the risk assessment of chemicals. The application of microarray technologies to toxicology, known as toxicogenomics, is becoming an accepted approach for identifying chemicals with potential safety problems. Gene expression profiling is expected to identify the mechanisms that underlie the potential toxicity of chemicals. This technology has also been applied to identify biomarkers of toxicity to predict potential hazardous chemicals. Ultimately, toxicogenomics is expected to aid in risk assessment. The following discussion explores potential applications and features of the Japanese Toxicogenomics Project.

Genomics Portals: integrative web-platform for mining genomics data

Background

A large amount of experimental data generated by modern high-throughput technologies is available through various public repositories. Our knowledge about molecular interaction networks, functional biological pathways and transcriptional regulatory modules is rapidly expanding, and is being organized in lists of functionally related genes. Jointly, these two sources of information hold a tremendous potential for gaining new insights into functioning of living systems.

Results

Genomics Portals platform integrates access to an extensive knowledge base and a large database of human, mouse, and rat genomics data with basic analytical visualization tools. It provides the context for analyzing and interpreting new experimental data and the tool for effective mining of a large number of publicly available genomics datasets stored in the back-end databases. The uniqueness of this platform lies in the volume and the diversity of genomics data that can be accessed and analyzed (gene expression, ChIP-chip, ChIP-seq, epigenomics, computationally predicted binding sites, etc), and the integration with an extensive knowledge base that can be used in such analysis.

Conclusion

The integrated access to primary genomics data, functional knowledge and analytical tools makes Genomics Portals platform a unique tool for interpreting results of new genomics experiments and for mining the vast amount of data stored in the Genomics Portals backend databases. Genomics Portals can be accessed and used freely at http://GenomicsPortals.org.

EDGE(3): a web-based solution for management and analysis of Agilent two color microarray experiments

Background

The ability to generate transcriptional data on the scale of entire genomes has been a boon both in the improvement of biological understanding and in the amount of data generated. The latter, the amount of data generated, has implications when it comes to effective storage, analysis and sharing of these data. A number of software tools have been developed to store, analyze, and share microarray data. However, a majority of these tools do not offer all of these features nor do they specifically target the commonly used two color Agilent DNA microarray platform. Thus, the motivating factor for the development of EDGE³ was to incorporate the storage, analysis and sharing of microarray data in a manner that would provide a means for research groups to collaborate on Agilent-based microarray experiments without a large investment in software-related expenditures or extensive training of end-users.

Results

EDGE³ has been developed with two major functions in mind. The first function is to provide a workflow process for the generation of microarray data by a research laboratory or a microarray facility. The second is to store, analyze, and share microarray data in a manner that doesn't require complicated software. To satisfy the first function, EDGE³ has been developed as a means to establish a well defined experimental workflow and information system for microarray generation. To satisfy the second function, the software application utilized as the user interface of EDGE³ is a web browser. Within the web browser, a user is able to access the entire functionality, including, but not limited to, the ability to perform a number of bioinformatics based analyses, collaborate between research groups through a user-based security model, and access to the raw data files and quality control files generated by the software used to extract the signals from an array image.

Conclusion

Here, we present EDGE³, an open-source, web-based application that allows for the storage, analysis, and controlled sharing of transcription-based microarray data generated on the Agilent DNA platform. In addition, EDGE³ provides a means for managing RNA samples and arrays during the hybridization process. EDGE³ is freely available for download at .

Clustered alignments of gene-expression time series data

Motivation: Characterizing and comparing temporal gene-expression responses is an important computational task for answering a variety of questions in biological studies. Algorithms for aligning time series represent a valuable approach for such analyses. However, previous approaches to aligning gene-expression time series have assumed that all genes should share the same alignment. Our work is motivated by the need for methods that identify sets of genes that differ in similar ways between two time series, even when their expression profiles are quite different.

Results: We present a novel algorithm that calculates clustered alignments; the method finds clusters of genes such that the genes within a cluster share a common alignment, but each cluster is aligned independently of the others. We also present an efficient new segment-based alignment algorithm for time series called SCOW (shorting correlation-optimized warping). We evaluate our methods by assessing the accuracy of alignments computed with sparse time series from a toxicogenomics dataset. The results of our evaluation indicate that our clustered alignment approach and SCOW provide more accurate alignments than previous approaches. Additionally, we apply our clustered alignment approach to characterize the effects of a conditional Mop3 knockout in mouse liver.

Availability: Source code is available at http://www.biostat.wisc.edu/∼aasmith/catcode.

Contact:aasmith@cs.wisc.edu

Genomic and proteomic approaches for predicting toxicity and adverse drug reactions

BACKGROUND

In the toxicology field, it remains a major challenge to predict and understand drug toxicity and adverse drug reactions (ADRs) in man.

OBJECTIVE

Recent progress in genomics and proteomics technologies and their application in predicting drug toxicity and ADRs.

METHODS

The key genomic and proteomic approaches are outlined, their applications in predicting toxicity and ADRs are described and their future developments in this field are discussed.

CONCLUSION

These technologies, used to measure expression at the transcript and protein levels, each convey different information and have different technical capabilities that can complement each other. The fields of genomics and proteomics continue to develop rapidly and it is already evident that genomic and proteomic approaches have much to contribute to the early prediction of drug toxicity and ADRs.

Toxicogenomic biomarkers for liver toxicity

Toxicogenomics (TGx) is a widely used technique in the preclinical stage of drug development to investigate the molecular mechanisms of toxicity. A number of candidate TGx biomarkers have now been identified and are utilized for both assessing and predicting toxicities. Further accumulation of novel TGx biomarkers will lead to more efficient, appropriate and cost effective drug risk assessment, reinforcing the paradigm of the conventional toxicology system with a more profound understanding of the molecular mechanisms of drug-induced toxicity. In this paper, we overview some practical strategies as well as obstacles for identifying and utilizing TGx biomarkers based on microarray analysis. Since clinical hepatotoxicity is one of the major causes of drug development attrition, the liver has been the best documented target organ for TGx studies to date, and we therefore focused on information from liver TGx studies. In this review, we summarize the current resources in the literature in regard to TGx studies of the liver, from which toxicologists could extract potential TGx biomarker gene sets for better hepatotoxicity risk assessment.

Toward a public toxicogenomics capability for supporting predictive toxicology: survey of current resources and chemical indexing of experiments in GEO and ArrayExpress

A publicly available toxicogenomics capability for supporting predictive toxicology and meta-analysis depends on availability of gene expression data for chemical treatment scenarios, the ability to locate and aggregate such information by chemical, and broad data coverage within chemical, genomics, and toxicological information domains. This capability also depends on common genomics standards, protocol description, and functional linkages of diverse public Internet data resources. We present a survey of public genomics resources from these vantage points and conclude that, despite progress in many areas, the current state of the majority of public microarray databases is inadequate for supporting these objectives, particularly with regard to chemical indexing. To begin to address these inadequacies, we focus chemical annotation efforts on experimental content contained in the two primary public genomic resources: ArrayExpress and Gene Expression Omnibus. Automated scripts and extensive manual review were employed to transform free-text experiment descriptions into a standardized, chemically indexed inventory of experiments in both resources. These files, which include top-level summary annotations, allow for identification of current chemical-associated experimental content, as well as chemical-exposure-related (or "Treatment") content of greatest potential value to toxicogenomics investigation. With these chemical-index files, it is possible for the first time to assess the breadth and overlap of chemical study space represented in these databases, and to begin to assess the sufficiency of data with shared protocols for chemical similarity inferences. Chemical indexing of public genomics databases is a first important step toward integrating chemical, toxicological and genomics data into predictive toxicology.

Scoring multiple toxicological endpoints using a toxicogenomic database

As information regarding microarray data sets and toxicogenomic biomarkers grows rapidly, the process of analyzing data and interpreting the results is increasingly complicated. To facilitate data analysis, a simple expression ratio-based scoring method called the TGP1 score was previously proposed [Kiyosawa, N., Shiwaku, K., Hirode, M., Omura, K., Uehara, T., Shimizu, T., Mizukawa, Y., Miyagishima, T., Ono, A., Nagao, T., Urushidani, T., 2006. Utilization of a one-dimensional score for surveying chemical-induced changes in expression levels of multiple biomarker gene sets using a large-scale toxicogenomics database. J. Toxicol. Sci. 31, 433-448]. Although the TGP1 score has demonstrated its efficacy for rapid comprehension of large-scale toxicogenomic data sets, inclusion of low quality gene expression data in the biomarker gene set produced flaws in the calculated score. To overcome this shortcoming, we tested a new scoring method called the differentially expressed gene score (D-score), where Detection Call as well as signal log ratios generated by MAS5 algorithm on Affymetrix GeneChip data were considered for the calculation. Four prototypical toxicants, namely acetaminophen, phenobarbital, clofibrate and acetamidofluorene, were used for detailed analysis. A toxicogenomics database (TG-GATEs) was utilized as a reference data set. The D-score successfully alleviated the effects of low quality data on the score calculation, and captured the overall direction of expression changes as well as the magnitude of expression change level of a set of genes, highlighting the affected toxicological endpoints elicited by chemical treatment. The D-score will be useful for high-throughput toxicity screening using a toxicogenomic database and biomarkers.

The role of the dioxin-responsive element cluster between the Cyp1a1 and Cyp1a2 loci in aryl hydrocarbon receptor biology

The aryl hydrocarbon receptor (AHR) plays a central role in 2,3,7,8-tetrachlorodibenzo-p-dioxin (dioxin) hepatotoxicity, regulation of xenobiotic metabolism, and hepatovascular development. Each of these processes appears to be dependent on binding of the AHR to dioxin- responsive elements (DREs) within the genome. The Cyp1a1 and Cyp1a2 loci represent linked genes thought to play important roles in AHR biology. In the mouse, 8 DREs are located in the 14-kb intergenic region between the Cyp1a1 and Cyp1a2 genes. Seven of these DREs, collectively known as the DRE cluster (DREC), are located 1.4 kb upstream of the Cyp1a1 transcriptional start site and 12.6 kb upstream of the Cyp1a2 start site. To investigate the role of the DREC in each aspect of AHR biology, we generated a DREC-deficient mouse model through homologous recombination. Using this mouse model, we demonstrate that the DREC controls the adaptive up-regulation of both Cyp1a1 and Cyp1a2 genes in vivo. Using selected aspects of acute hepatic injury as endpoints, we also demonstrate that DREC null mice are more sensitive to dioxin-induced hepatotoxicity than WT mice. The results of parallel toxicologic studies using individual Cyp1a1 and Cyp1a2 null mice support the observation that up-regulation of these P450s is not the cause of many aspects of dioxin hepatotoxicity. Finally, we observed normal closure of the ductus venosus (DV) in DREC null mice. Given the 100% penetrance of patent DV in Ahr null mice, these results indicate that Cyp1a1 and Cyp1a2 do not play a dominant role in AHR-mediated vascular development.

Toxicogenomics: transcription profiling for toxicology assessment

Toxicogenomics, the application of transcription profiling to toxicology, has been widely used for elucidating the molecular and cellular actions of chemicals and other environmental stressors on biological systems, predicting toxicity before any functional damages, and classification of known or new toxicants based on signatures of gene expression. The success of a toxicogenomics study depends upon close collaboration among experts in different fields, including a toxicologist or biologist, a bioinformatician, statistician, physician and, sometimes, mathematician. This review is focused on toxicogenomics studies, including transcription profiling technology, experimental design, significant gene extraction, toxicological results interpretation, potential pathway identification, database input and the applications of toxicogenomics in various fields of toxicological study.

Comparative Toxicogenomics Database: a knowledgebase and discovery tool for chemical-gene-disease networks

The Comparative Toxicogenomics Database (CTD) is a curated database that promotes understanding about the effects of environmental chemicals on human health. Biocurators at CTD manually curate chemical–gene interactions, chemical–disease relationships and gene–disease relationships from the literature. This strategy allows data to be integrated to construct chemical–gene–disease networks. CTD is unique in numerous respects: curation focuses on environmental chemicals; interactions are manually curated; interactions are constructed using controlled vocabularies and hierarchies; additional gene attributes (such as Gene Ontology, taxonomy and KEGG pathways) are integrated; data can be viewed from the perspective of a chemical, gene or disease; results and batch queries can be downloaded and saved; and most importantly, CTD acts as both a knowledgebase (by reporting data) and a discovery tool (by generating novel inferences). Over 116 000 interactions between 3900 chemicals and 13 300 genes have been curated from 270 species, and 5900 gene–disease and 2500 chemical–disease direct relationships have been captured. By integrating these data, 350 000 gene–disease relationships and 77 000 chemical–disease relationships can be inferred. This wealth of chemical–gene–disease information yields testable hypotheses for understanding the effects of environmental chemicals on human health. CTD is freely available at http://ctd.mdibl.org.

Similarity queries for temporal toxicogenomic expression profiles

We present an approach for answering similarity queries about gene expression time series that is motivated by the task of characterizing the potential toxicity of various chemicals. Our approach involves two key aspects. First, our method employs a novel alignment algorithm based on time warping. Our time warping algorithm has several advantages over previous approaches. It allows the user to impose fairly strong biases on the form that the alignments can take, and it permits a type of local alignment in which the entirety of only one series has to be aligned. Second, our method employs a relaxed spline interpolation to predict expression responses for unmeasured time points, such that the spline does not necessarily exactly fit every observed point. We evaluate our approach using expression time series from the Edge toxicology database. Our experiments show the value of using spline representations for sparse time series. More significantly, they show that our time warping method provides more accurate alignments and classifications than previous standard alignment methods for time series.

We are developing an approach to characterize chemicals and environmental conditions by comparing their effects on gene expression with those of well characterized treatments. We evaluate our approach in the context of the Edge (Environment, Drugs, and Gene Expression) database, which contains microarray observations collected from mouse liver tissue over the days following exposure to a variety of treatments. Our approach takes as input an unknown query series, consisting of several gene-expression measurements over time. It then picks out treatments from a database of known treatments that exhibit the most similar expression responses. This task is difficult because the data tends to be noisy, sparse in time, and measured at irregular intervals. We start by reconstructing the unobserved parts of the series using splines. We then align the given query to each database series so that the similarities in their expression responses are maximized. Our approach uses dynamic programming to find the best alignment of each pair of series. Unlike other methods, our approach allows alignments in which the end of one of the two series remains unaligned, if it appears that one series shows more of the expression response than the other. We finally return the best match(es) and alignment(s), in the hope that they will help with the query's eventual characterization and addition to the database.

Patterns of dioxin-altered mRNA expression in livers of dioxin-sensitive versus dioxin-resistant rats

Dioxins exert their major toxicologic effects by binding to the aryl hydrocarbon receptor (AHR) and altering gene transcription. Numerous dioxin-responsive genes previously were identified both by conventional biochemical and molecular techniques and by recent mRNA expression microarray studies. However, of the large set of dioxin-responsive genes the specific genes whose dysregulation leads to death remain unknown. To identify specific genes that may be involved in dioxin lethality we compared changes in liver mRNA levels following exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in three strains/lines of dioxin-sensitive rats with changes in three dioxin-resistant rat strains/lines. The three dioxin-resistant strains/lines all harbor a large deletion in the transactivation domain of the aryl hydrocarbon receptor (AHR). Despite this deletion, many genes exhibited a "Type-I" response-that is, their responses were similar in dioxin-sensitive and dioxin-resistant rats. Several genes that previously were well established as being dioxin-responsive or under AHR regulation emerged as Type-I responses (e.g. CYP1A1, CYP1A2, CYP1B1 and Gsta3). In contrast, a relatively small number of genes exhibited a Type-II response-defined as a difference in responsiveness between dioxin-sensitive and dioxin-resistant rat strains. Type-II genes include: malic enzyme 1, ubiquitin C, cathepsin L, S-adenosylhomocysteine hydrolase and ferritin light chain 1. In silico searches revealed that AH response elements are conserved in the 5'-flanking regions of several genes that respond to TCDD in both the Type-I and Type-II categories. The vast majority of changes in mRNA levels in response to 100 microg/kg TCDD were strain-specific; over 75% of the dioxin-responsive clones were affected in only one of the six strains/lines. Selected genes were assessed by quantitative RT-PCR in dose-response and time-course experiments and responses of some genes were assessed in Ahr-null mice to determine if their response was AHR-dependent. Type-II genes may lie in pathways that are central to the difference in susceptibility to TCDD lethality in this animal model.

Cytotoxic doses of ketoconazole affect expression of a subset of hepatic genes

Ketoconazole is a widely prescribed antifungal drug, which has also been investigated as an anticancer therapy in both clinical and pre-clinical settings. However, severe hepatic injuries were reported to be associated with the use of ketoconazole, even in patients routinely monitored for their liver functions. Several questions concerning ketoconazole-induced hepatic injury remain unanswered, including (1) does ketoconazole alter cytochrome P450 expression at the transcriptional level?, (2) what types of gene products responsible for cytotoxicity are induced by ketoconazole?, and (3) what role do the major metabolites of ketoconazole play in this pathophysiologic process? A mouse model was employed to investigate hepatic gene expression following hepatotoxic doses of ketoconazole. Hepatic gene expression was analyzed using a toxicogenomic microarray platform, which is comprised of cDNA probes generated from livers exposed to various hepatoxicants. These hepatoxicants fall into five well-studied toxicological categories: peroxisome proliferators, aryl hydrocarbon receptor agonists, noncoplanar polychlorinated biphenyls, inflammatory agents, and hypoxia-inducing agents. Nine genes encoding enzymes involved in Phase I metabolism and one Phase II enzyme (glutathione S-transferase) were found to be upregulated. Serum amyloid A (SAA1/2) and hepcidin were the only genes that were downregulated among the 2364 genes assessed. In vitro cytotoxicity and transcription analyses revealed that SAA and hepcidin are associated with the general toxicity of ketoconazole, and might be usefully explored as generalized surrogate markers of xenobiotic-induced hepatic injury. Finally, it was shown that the primary metabolite of ketoconazole (de-N-acetyl ketoconazole) is largely responsible for the hepatoxicity and the downregulation of SAA and hepcidin.

CEBS--Chemical Effects in Biological Systems: a public data repository integrating study design and toxicity data with microarray and proteomics data

CEBS (Chemical Effects in Biological Systems) is an integrated public repository for toxicogenomics data, including the study design and timeline, clinical chemistry and histopathology findings and microarray and proteomics data. CEBS contains data derived from studies of chemicals and of genetic alterations, and is compatible with clinical and environmental studies. CEBS is designed to permit the user to query the data using the study conditions, the subject responses and then, having identified an appropriate set of subjects, to move to the microarray module of CEBS to carry out gene signature and pathway analysis. Scope of CEBS: CEBS currently holds 22 studies of rats, four studies of mice and one study of Caenorhabditis elegans. CEBS can also accommodate data from studies of human subjects. Toxicogenomics studies currently in CEBS comprise over 4000 microarray hybridizations, and 75 2D gel images annotated with protein identification performed by MALDI and MS/MS. CEBS contains raw microarray data collected in accordance with MIAME guidelines and provides tools for data selection, pre-processing and analysis resulting in annotated lists of genes of interest. Additionally, clinical chemistry and histopathology findings from over 1500 animals are included in CEBS. CEBS/BID: The BID (Biomedical Investigation Database) is another component of the CEBS system. BID is a relational database used to load and curate study data prior to export to CEBS, in addition to capturing and displaying novel data types such as PCR data, or additional fields of interest, including those defined by the HESI Toxicogenomics Committee (in preparation). BID has been shared with Health Canada and the US Environmental Protection Agency. CEBS is available at http://cebs.niehs.nih.gov. BID can be accessed via the user interface from https://dir-apps.niehs.nih.gov/arc/. Requests for a copy of BID and for depositing data into CEBS or BID are available at http://www.niehs.nih.gov/cebs-df/.

Hepatic transcriptional networks induced by exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin

The environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) serves as a prototype for a range of environmental toxicants and as a pharmacologic probe to study signal transduction by the aryl hydrocarbon receptor (AHR). Despite a detailed understanding of how TCDD exposure leads to the transcriptional up-regulation of cytochrome P450-dependent monooxygenases, we know little about how compounds like TCDD lead to a variety of AHR-dependent toxic end points such as liver pathology, terata, thymic involution, and cancer. Using an acute exposure protocol and the toxic response of the mouse liver as a model system, we have begun a detailed microarray analysis to describe the transcriptional changes that occur after various TCDD doses and treatment times. Through correlation analysis of time- and dose-dependent toxicological end points, we are able to identify coordinately responsive transcriptional events that can be defined as primary transcriptional events and downstream events that may represent mechanistically linked sequelae or that have potential as biomarkers of toxicity.

Genomic profiling in nuclear receptor-mediated toxicity

Nuclear receptors (NRs) are attractive drug targets due to their role in regulation of a wide range of physiologic responses. In addition to providing therapeutic value, many pharmaceutical agents along with environmental chemicals are ligands for NRs and can cause adverse health effects that are directly related to activation of NRs. Identifying the molecular events that produce a toxic response may be confounded by the fact that there is a significant overlap in the biological processes that NRs regulate. Microarrays and other methods for gene expression profiling have served as useful, sensitive tools for discerning the mechanisms by which therapeutics and environmental chemicals invoke toxic effects. The capability to probe thousands of genes simultaneously has made genomics a prime technology for identifying drug targets, biomarkers of exposure/toxicity and key players in the mechanisms of disease. The complex intertwining networks regulated by NRs are hard to probe comprehensively without global approaches and genomics has become a key technology that facilitates our understanding of NR-dependent and -independent events. The future of drug discovery, design and optimization, and risk assessment of chemical toxicants that activate NRs will inevitably involve genomic profiling. This review will focus on genomics studies related to PPAR, CAR, PXR, RXR, LXR, FXR, and AHR.

Molecular portrait of cisplatin induced response in human testis cancer cell lines based on gene expression profiles

BACKGROUND

Testicular germ cell tumors (TGCTs) respond well to cisplatin-based chemotherapy and show a low incidence of acquired resistance compared to most somatic tumors. The reasons for these specific characteristics are not known in detail but seem to be multifactorial. We have studied gene expression profiles of testicular and colon cancer derived cell lines treated with cisplatin. The main goal of this study was to identify novel gene expression profiles with their functional categories and the biochemical pathways that are associated with TGCT cells' response to cisplatin.

RESULTS

Genes that were differentially expressed between the TGCT cell lines vs the (somatic) HCT116 cell line, after cisplatin treatment, were identified using the significance analysis of microarrays (SAM) method. The response of TGCT cells was strikingly different from that of HCT116, and we identified 1794 genes that were differentially expressed. Functional classification of these genes showed that they participate in a variety of different and widely distributed functional categories and biochemical pathways. Database mining showed significant association of genes (n = 41) induced by cisplatin in our study, and genes previously reported to by expressed in differentiated TGCT cells. We identified 37 p53-responsive genes that were altered after cisplatin exposure. We also identified 40 target genes for two microRNAs, hsa-mir-372 and 373 that may interfere with p53 signaling in TGCTs. The tumor suppressor genes NEO1 and LATS2, and the estrogen receptor gene ESR1, all have binding sites for p53 and hsa-mir-372/373. NEO1 and LATS2 were down-regulated in TGCT cells following cisplatin exposure, while ESR1 was up-regulated in TGCT cells. Cisplatin-induced genes associated with terminal growth arrest through senescence were identified, indicating associations which were not previously described for TGCT cells.

CONCLUSION

By linking our gene expression data to publicly available databases and literature, we provide a global pattern of cisplatin induced cellular response that is specific for testicular cancer cell lines. We have identified cisplatin-responsive functional classes and pathways, such as the angiogenesis, Wnt, integrin, and cadherin signaling pathways. The identification of differentially expressed genes in this study may contribute to a better understanding of the unusual sensitivity of TGCT to some DNA-damaging agents.

Toxicogenomics: a pivotal piece in the puzzle of toxicological research

Toxicogenomics, resulting from the merge of conventional toxicology with functional genomics, being the scientific field studying the complex interactions between the cellular genome, toxic agents in the environment, organ dysfunction and disease state. When an organism is exposed to a toxic agent the cells respond by altering the pattern of gene expression. Genes are transcribed into mRNA, which in turn is translated into proteins that serve in a variety of cellular functions. Toxicogenomics through microarray technology, offers large-scale detection and quantification of mRNA transcripts, related to alterations in mRNA stability or gene regulation. This may prove advantageous in toxicological research. In the present review, the applications of toxicogenomics, especially to mechanistic and predictive toxicology are reported. The limitations arising from the use of this technology are also discussed. Additionally, a brief report of other approaches, using other -omic technologies (proteomics and metabonomics) that overcome limitations and give global information related to toxicity, is included.

Toxicogenomics applied to predictive and exploratory toxicology for the safety assessment of new chemical entities: a long road with deep potholes

Toxicology is the perturbation of metabolism by external factors such as xenobiotics, environmental factors or drugs. As such, toxicology covers a broad range of fields from studies of the whole organism responses to minute biochemical events. Mechanistic toxicogenomics is an attempt to harness genomic tools to understand the physiological basis for a toxic event based on an analysis of transcriptional, translational or metabolomic profiles. These studies are complicated by non-toxic adaptive responses in transcript, protein or metabolite expression levels that have to be distinguished from those that are proximally related to the toxic event. Substantial progress has been made on the identification of biomarkers and the establishment of screens derived from such toxicogenomics studies. The ultimate goal, of course, is predictive toxicogenomics, which is an attempt to infer the likelihood of occurrence of a toxic event with exposure to a new agent based upon comparative responses with large databases of gene, protein or metabolite expression data. Gene expression databases are currently limited by the fact that measurable toxic phenotypes generally precede or at best coincide with the earliest observable changes in transcriptional profiles. Unfortunately, predictive protein databases have been limited by technical difficulties. Metabonomics-based databases, which would probably have the highest predictive value, are limited in turn by the inability to perform high dose studies in humans. This chapter will conclude by reviewing those elements of toxicogenomics that apply specifically to the development of anti-infectives and the potential for accurately modelling the toxicity of future drugs.

The comparative toxicogenomics database: a cross-species resource for building chemical-gene interaction networks

Chemicals in the environment play a critical role in the etiology of many human diseases. Despite their prevalence, the molecular mechanisms of action and the effects of chemicals on susceptibility to disease are not well understood. To promote understanding of these mechanisms, the Comparative Toxicogenomics Database (CTD; http://ctd.mdibl.org/) presents scientifically reviewed and curated information on chemicals, relevant genes and proteins, and their interactions in vertebrates and invertebrates. CTD integrates sequence, reference, species, microarray, and general toxicology information to provide a unique centralized resource for toxicogenomic research. The database also provides visualization capabilities that enable cross-species comparisons of gene and protein sequences. These comparisons will facilitate understanding of structure-function correlations and the genetic basis of susceptibility. Manual curation and integration of cross-species chemical-gene and chemical-protein interactions from the literature are now underway. These data will provide information for building complex interaction networks. New CTD features include (1) cross-species gene, rather than sequence, query and visualization capabilities; (2) integrated cross-links to microarray data from chemicals, genes, and sequences in CTD; (3) a reference set related to chemical-gene and protein interactions identified by an information retrieval system; and (4) a "Chemicals in the News" initiative that provides links from CTD chemicals to environmental health articles from the popular press. Here we describe these new features and our novel cross-species curation of chemical-gene and chemical-protein interactions.

The Consortium for Metabonomic Toxicology (COMET): aims, activities and achievements

The utility of metabonomics in the evaluation of xenobiotic toxicity has been comprehensively assessed by the Consortium for Metabonomic Toxicology (COMET), formed between five major pharmaceutical companies and Imperial College London, UK. The main objectives were to assess methodologies, to generate a metabonomic database using (1)H nuclear magnetic resonance (NMR) spectroscopy of rodent urine and blood serum and to build a predictive expert system for target organ toxicity. The analytic and biologic variation that might arise through the use of metabonomics was evaluated and a high degree of robustness demonstrated. With the completion of 147 studies, the chief deliverables of a curated database of rodent biofluid NMR spectra and computer-based expert systems for the prediction of kidney or liver toxicity in rat and mouse based on the spectral data have been generated, and delivered to the sponsoring companies. The project, with its relatively modest resources, has met and exceeded all of its targets and was judged a resounding success by the sponsoring companies who are, in many cases, already enhancing and making use of the data in their in-house studies.

Microarray analysis of thioacetamide-treated type 1 diabetic rats

It is well known that diabetes imparts high sensitivity to numerous hepatotoxicants. Previously, we have shown that a normally non-lethal dose of thioacetamide (TA, 300 mg/kg) causes 90% mortality in type 1 diabetic (DB) rats due to inhibited tissue repair allowing progression of liver injury. On the other hand, DB rats exposed to 30 mg TA/kg exhibit delayed tissue repair and delayed recovery from injury. The objective of this study was to investigate the mechanism of impaired tissue repair and progression of liver injury in TA-treated DB rats by using cDNA microarray. Gene expression pattern was examined at 0, 6, and 12 h after TA challenge, and selected mechanistic leads from microarray experiments were confirmed by real-time RT-PCR and further investigated at protein level over the time course of 0 to 36 h after TA treatment. Diabetic condition itself increased gene expression of proteases and decreased gene expression of protease inhibitors. Administration of 300 mg TA/kg to DB rats further elevated gene expression of proteases and suppressed gene expression of protease inhibitors, explaining progression of liver injury in DB rats after TA treatment. Inhibited expression of genes involved in cell division cycle (cyclin D1, IGFBP-1, ras, E2F) was observed after exposure of DB rats to 300 mg TA/kg, explaining inhibited tissue repair in these rats. On the other hand, DB rats receiving 30 mg TA/kg exhibit delayed expression of genes involved in cell division cycle, explaining delayed tissue repair in these rats. In conclusion, impaired cyclin D1 signaling along with increased proteases and decreased protease inhibitors may explain impaired tissue repair that leads to progression of liver injury initiated by TA in DB rats.

dbZach: A MIAME-compliant toxicogenomic supportive relational database

Quantitative risk assessment and the elucidation of mechanisms of toxicity requires computational infrastructure and innovative analysis approaches that systematically consider available data at all levels of biological organization. dbZach (http://dbzach.fst.msu.edu) is a modular relational database with associated data insertion, retrieval, and mining tools that manages traditional toxicology and complementary toxicogenomic data to facilitate comprehensive data integration, analysis, and sharing. It consists of four Core Subsystems (i.e., Clones, Genes, Sample Annotation, and Protocols), four Experimental Subsystems (i.e., Microarray, Affymetrix, Real-Time PCR, and Toxicology), and three Computational Subsystems (i.e., Gene Regulation, Pathways, Orthology) that comply with the Minimum Information About a Microarray Experiment (MIAME) standard. It is capable of including emerging technologies and other model systems, including ecologically relevant species. dbZach represents an enterprise toxicogenomic data management system which facilitates data integration and analysis, and reduces uncertainties in the continuum from initial exposure to toxicity while facilitating more comprehensive elucidations of mechanisms of toxicity and supporting mechanistically-based quantitative risk assessment.

Toxicogenomic analysis methods for predictive toxicology

Toxicogenomics, the application of genomic data to elucidate or predict an organism's response to a toxicant, can inform the drug development process in important ways. It is apparent that standardized approaches to many types of toxicogenomic questions are still being formulated. Specifically, a significant body of proof of principle studies has emerged that demonstrates a range of statistical methodologies applied to predictive toxicology. These studies rely on class prediction methods--mathematical models generated using the gene expression profiles of known toxins from representative toxicological classes--to predict the toxicological effect of a compound based on the similarities between its gene expression profile and the profiles of a given toxicological class. Class prediction methods hold promise for increasing the rate at which compounds can be evaluated for toxicity early in the drug discovery process, while at the same time reducing the length of toxicological studies and their associated costs. Class prediction methods are informed by class comparison and class discovery steps, which inform, respectively, the selection of genes whose response can be used to distinguish among the toxicological classes and the number of classes distinguishable using the response of these genes. Together these steps use a variety of complementary statistical techniques to achieve a successful class prediction model. This report attempts to review some of the themes that appear to be emerging in the application of these techniques to predictive toxicology methods over toxicogenomics' short history.

Aryl hydrocarbon receptor regulates distinct dioxin-dependent and dioxin-independent gene batteries

Conventional biochemical and molecular techniques identified previously several genes whose expression is regulated by the aryl hydrocarbon receptor (AHR). We sought to map the complete spectrum of AHR-dependent genes in male adult liver using expression arrays to contrast mRNA profiles in Ahr-null mice (Ahr(-/-)) with those in mice with wild-type AHR (Ahr(+)(/)(+)). Transcript profiles were determined both in untreated mice and in mice treated 19 h earlier with 1000 microg/kg 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Expression of 456 ProbeSets was significantly altered by TCDD in an AHR-dependent manner, including members of the classic AHRE-I gene battery, such as Cyp1a1, Cyp1a2, Cyp1b1, and Nqo1. In the absence of exogenous ligand, AHR status alone affected expression of 392 ProbeSets, suggesting that the AHR has multiple functions in normal physiology. In Ahr(-/-) mice, only 32 ProbeSets exhibited responses to TCDD, indicating that the AHR is required for virtually all transcriptional responses to dioxin exposure in liver. The flavin-containing monooxygenases, Fmo2 and Fmo3, considered previously to be uninducible, were highly induced by TCDD in an AHR-dependent manner. The estrogen receptor alpha as well as two estrogen-receptor-related genes (alpha and gamma) exhibit AHR-dependent expression, thereby extending cross-talk opportunities between the intensively studied AHR and estrogen receptor pathways. p53 binding sites are over-represented in genes down-regulated by TCDD, suggesting that TCDD inhibits p53 transcriptional activity. Overall, our study identifies a wide range of genes that depend on the AHR, either for constitutive expression or for response to TCDD.

Large-scale databases in toxicogenomics

The use of genomics to improve molecular strategies in safety assessment has immense promise, with increased mechanistic understanding and improved prediction of unknown compounds possible. Several public initiatives in toxicogenomics are now underway, and mechanistic findings are clearly emerging. A number of databases and standards are emerging to support these initiatives. Significant attention to standardization, both for biologic and technical issues, will be necessary for effective community database(s) to be fully operational.