Inverse gene expression patterns for macrophage activating hepatotoxicants and peroxisome proliferators in rat liver

Per- and polyfluoroalkyl substances alter innate immune function: evidence and data gaps

Per- and polyfluoroalkyl substances (PFASs) are a large class of compounds used in a variety of processes and consumer products. Their unique chemical properties make them ubiquitous and persistent environmental contaminants while also making them economically viable and socially convenient. To date, several reviews have been published to synthesize information regarding the immunotoxic effects of PFASs on the adaptive immune system. However, these reviews often do not include data on the impact of these compounds on innate immunity. Here, current literature is reviewed to identify and incorporate data regarding the effects of PFASs on innate immunity in humans, experimental models, and wildlife. Known mechanisms by which PFASs modulate innate immune function are also reviewed, including disruption of cell signaling, metabolism, and tissue-level effects. For PFASs where innate immune data are available, results are equivocal, raising additional questions about common mechanisms or pathways of toxicity, but highlighting that the innate immune system within several species can be perturbed by exposure to PFASs. Recommendations are provided for future research to inform hazard identification, risk assessment, and risk management practices for PFASs to protect the immune systems of exposed organisms as well as environmental health.

Effect of perfluorooctane sulphonate-induced Kupffer cell activation on hepatocyte proliferation through the NF-κB/TNF-α/IL-6-dependent pathway

Perfluorooctane sulfonate (PFOS), one member of polyfluoroalkyl chemicals (PFASs), persist in the environment and are found in relatively high concentrations in animal livers. PFOS has been shown to induce tumour of the liver in rats following chronic dietary administration. However, the molecular mechanisms involved in PFOS-induced hepatocellular hypertrophy are still not well characterized. In this study, male Sprague-Dawley rats were daily gavaged with PFOS (1 or 10 mg/kg body weight) for 28 days. Rat primary cultured Kupffer cells or hepatocytes were exposed to 100 μM PFOS for 0-48 h. Our results showed that PFOS exposure caused serious hepatocellular damage and obvious inflammatory cell infiltration and increased serum tumour necrosis factor-ɑ (TNF-α) and interleukin-6 (IL-6) levels. Particularly, PFOS exposure triggered Kupffer cell activation and significantly upregulated the expression of proliferating cell nuclear antigen (PCNA), c-Jun, c-MYC and Cyclin D1 (CyD1) in liver. In vitro, PFOS significantly induced production of TNF-α and IL-6 in Kupffer cells and increased PCNA, c-Jun, c-MYC and CyD1 expression in the primary hepatocytes co-cultured with Kupffer cells. However, Kupffer cell activation was mostly abolished by anti-TNF-α or anti-IL6 treatment. Furthermore, blockage of TNF-α and IL-6 significantly inhibited hepatocyte proliferation by gadolinium chloride (GdCl₃) pre-treatment in PFOS-treated mice and primary cultured Kupffer cells. On the other hand, NF-κB inhibitor (PDTC) and c-Jun amino-terminal kinase (JNK) inhibitor (SP600125) significantly inhibited production of PFOS-induced TNF-α and IL-6. Taken together, these data suggest that PFOS induces Kupffer cell activation, leading to hepatocyte proliferation by through the NF-κB/TNF-ɑ/IL-6-dependent pathway.

CEBS: a comprehensive annotated database of toxicological data

The Chemical Effects in Biological Systems database (CEBS) is a comprehensive and unique toxicology resource that compiles individual and summary animal data from the National Toxicology Program (NTP) testing program and other depositors into a single electronic repository. CEBS has undergone significant updates in recent years and currently contains over 11 000 test articles (exposure agents) and over 8000 studies including all available NTP carcinogenicity, short-term toxicity and genetic toxicity studies. Study data provided to CEBS are manually curated, accessioned and subject to quality assurance review prior to release to ensure high quality. The CEBS database has two main components: data collection and data delivery. To accommodate the breadth of data produced by NTP, the CEBS data collection component is an integrated relational design that allows the flexibility to capture any type of electronic data (to date). The data delivery component of the database comprises a series of dedicated user interface tables containing pre-processed data that support each component of the user interface. The user interface has been updated to include a series of nine Guided Search tools that allow access to NTP summary and conclusion data and larger non-NTP datasets. The CEBS database can be accessed online at http://www.niehs.nih.gov/research/resources/databases/cebs/.

ABC gene-ranking for prediction of drug-induced cholestasis in rats

As legacy toxicogenomics databases have become available, improved data mining approaches are now key to extracting and visualizing subtle relationships between toxicants and gene expression. In the present study, a novel “aggregating bundles of clusters” (ABC) procedure was applied to separate cholestatic from non-cholestatic drugs and model toxicants in the Johnson & Johnson (Janssen) rat liver toxicogenomics database [3]. Drug-induced cholestasis is an important issue, particularly when a new compound enters the market with this liability, with standard preclinical models often mispredicting this toxicity. Three well-characterized cholestasis-responsive genes (Cyp7a1, Mrp3 and Bsep) were chosen from a previous in-house Janssen gene expression signature; these three genes show differing, non-redundant responses across the 90+ paradigm compounds in our database. Using the ABC procedure, extraneous contributions were minimized in comparisons of compound gene responses. All genes were assigned weights proportional to their correlations with Cyp7a1, Mrp3 and Bsep, and a resampling technique was used to derive a stable measure of compound similarity. The compounds that were known to be associated with rat cholestasis generally had small values of this measure relative to each other but also had large values of this measure relative to non-cholestatic compounds. Visualization of the data with the ABC-derived signature showed a very tight, essentially identically behaving cluster of robust human cholestatic drugs and experimental cholestatic toxicants (ethinyl estradiol, LPS, ANIT and methylene dianiline, disulfiram, naltrexone, methapyrilene, phenacetin, alpha-methyl dopa, flutamide, the NSAIDs–—indomethacin, flurbiprofen, diclofenac, flufenamic acid, sulindac, and nimesulide, butylated hydroxytoluene, piperonyl butoxide, and bromobenzene), some slightly less active compounds (3′-acetamidofluorene, amsacrine, hydralazine, tannic acid), some drugs that behaved very differently, and were distinct from both non-cholestatic and cholestatic drugs (ketoconazole, dipyridamole, cyproheptadine and aniline), and many postulated human cholestatic drugs that in rat showed no evidence of cholestasis (chlorpromazine, erythromycin, niacin, captopril, dapsone, rifampicin, glibenclamide, simvastatin, furosemide, tamoxifen, and sulfamethoxazole). Most of these latter drugs were noted previously by other groups as showing cholestasis only in humans. The results of this work suggest that the ABC procedure and similar statistical approaches can be instrumental in combining data to compare toxicants across toxicogenomics databases, extract similarities among responses and reduce unexplained data varation.

Release of (and lessons learned from mining) a pioneering large toxicogenomics database

AIM

We release the Janssen Toxicogenomics database. This rat liver gene-expression database was generated using Codelink microarrays, and has been used over the past years within Janssen to derive signatures for multiple end points and to classify proprietary compounds.

MATERIALS & METHODS

The release consists of gene-expression responses to 124 compounds, selected to give a broad coverage of liver-active compounds. A selection of the compounds were also analyzed on Affymetrix microarrays.

RESULTS

The release includes results of an in-house reannotation pipeline to Entrez gene annotations, to classify probes into different confidence classes. High confidence unambiguously annotated probes were used to create gene-level data which served as starting point for cross-platform comparisons. Connectivity map-based similarity methods show excellent agreement between Codelink and Affymetrix runs of the same samples. We also compared our dataset with the Japanese Toxicogenomics Project and observed reasonable agreement, especially for compounds with stronger gene signatures. We describe an R-package containing the gene-level data and show how it can be used for expression-based similarity searches.

CONCLUSION

Comparing the same biological samples run on the Affymetrix and the Codelink platform, good correspondence is observed using connectivity mapping approaches. As expected, this correspondence is smaller when the data are compared with an independent dataset such as TG-GATE. We hope that this collection of gene-expression profiles will be incorporated in toxicogenomics pipelines of users.

Oxidative stress/reactive metabolite gene expression signature in rat liver detects idiosyncratic hepatotoxicants

Previously we reported a gene expression signature in rat liver for detecting a specific type of oxidative stress (OS) related to reactive metabolites (RM). High doses of the drugs disulfiram, ethinyl estradiol and nimesulide were used with another dozen paradigm OS/RM compounds, and three other drugs flutamide, phenacetin and sulindac were identified by this signature. In a second study, antiepileptic drugs were compared for covalent binding and their effects on OS/RM; felbamate, carbamazepine, and phenobarbital produced robust OS/RM gene expression. In the present study, liver RNA samples from drug-treated rats from more recent experiments were examined for statistical fit to the OS/RM signature. Of all 97 drugs examined, in addition to the nine drugs noted above, 19 more were identified as OS/RM-producing compounds-chlorpromazine, clozapine, cyproterone acetate, dantrolene, dipyridamole, glibenclamide, isoniazid, ketoconazole, methapyrilene, naltrexone, nifedipine, sulfamethoxazole, tamoxifen, coumarin, ritonavir, amitriptyline, valproic acid, enalapril, and chloramphenicol. Importantly, all of the OS/RM drugs listed above have been linked to idiosyncratic hepatotoxicity, excepting chloramphenicol, which does not have a package label for hepatotoxicity, but does have a black box warning for idiosyncratic bone marrow suppression. Most of these drugs are not acutely toxic in the rat. The OS/RM signature should be useful to avoid idiosyncratic hepatotoxicity of drug candidates.

In vitro and in vivo studies of the toxic effects of perfluorononanoic acid on rat hepatocytes and Kupffer cells

This study investigated the toxic effects of perfluorononanoic acid (PFNA), a persistent organic pollutant, on rat hepatocytes and Kupffer cells in vitro and in vivo. The results showed that administration of 5μM PFNA increased the viabilities of the hepatocytes and the Kupffer cells. An exposure of 50μM PFNA did not alter the viabilities of both cells, as well as the release of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) from the primary cultured hepatocytes or the hepatocytes co-cultured with Kupffer cells. An exposure of 100μM PFNA only decreased the viability of the hepatocytes. The administration of PFNA changed the hepatocyte expression of several genes related to lipid metabolism in vitro and in vivo. Oil Red O Staining revealed that 5mg PFNA/kg/D treatment lead to dramatic accumulation of lipids in rat liver. At the same dose PFNA damaged hepatocytes histopathologically. Up-regulated expressions of the inflammatory cytokines occurred in the Kupffer cells treated with 50μM PFNA and in the livers of the rat receiving a 5mg PFNA/kg/D treatment. In addition, these cytokines also increased in serum of the rat receiving higher dose of PFNA. In summary, on the one hand, PFNA exposure affected the viability of the hepatocytes, hepatic lipid metabolism and lead to lipid accumulation in liver. On the other hand, for the first time, PFNA exposure was demonstrated to affect the viability of the Kupffer cells as well as their expression of cytokines, which involved in regulation of various liver functions. Therefore, we conclude that both the hepatocyte and the Kupffer cell contribute to the observed hepatotoxicity of PFNA.

Organotypic liver culture models: meeting current challenges in toxicity testing

Prediction of chemical-induced hepatotoxicity in humans from in vitro data continues to be a significant challenge for the pharmaceutical and chemical industries. Generally, conventional in vitro hepatic model systems (i.e. 2-D static monocultures of primary or immortalized hepatocytes) are limited by their inability to maintain histotypic and phenotypic characteristics over time in culture, including stable expression of clearance and bioactivation pathways, as well as complex adaptive responses to chemical exposure. These systems are less than ideal for longer-term toxicity evaluations and elucidation of key cellular and molecular events involved in primary and secondary adaptation to chemical exposure, or for identification of important mediators of inflammation, proliferation and apoptosis. Progress in implementing a more effective strategy for in vitro-in vivo extrapolation and human risk assessment depends on significant advances in tissue culture technology and increasing their level of biological complexity. This article describes the current and ongoing need for more relevant, organotypic in vitro surrogate systems of human liver and recent efforts to recreate the multicellular architecture and hemodynamic properties of the liver using novel culture platforms. As these systems become more widely used for chemical and drug toxicity testing, there will be a corresponding need to establish standardized testing conditions, endpoint analyses and acceptance criteria. In the future, a balanced approach between sample throughput and biological relevance should provide better in vitro tools that are complementary with animal testing and assist in conducting more predictive human risk assessment.

Kupffer cells suppress perfluorononanoic acid-induced hepatic peroxisome proliferator-activated receptor α expression by releasing cytokines

Kupffer cells (KCs) have been demonstrated to play a role in the regulation of intra-hepatic lipid metabolism through the synthesis and secretion of biologically active products. The involvement of KCs in the disturbance of lipid metabolism that induced by perfluorononanoic acid (PFNA), a known agonist of the peroxisome proliferator-activated receptor alpha (PPARα), was investigated in this study. Rats were exposed to PFNA or PFNA combined with gadolinium chloride, an inhibitor of KCs, for 14 days. PFNA exposure dose-dependently increased absolute and relative liver weights, induced triglyceride accumulation, up-regulated the expression of both SERBP-1c and PPARα, and stimulated the release of TNFα and IL-1β. Inactivation of KCs markedly lowered TNFα and IL-1β level, enhanced PFNA-induced expression of PPARα and its target genes, and reduced liver triglyceride levels. In vitro, PFNA-induced expression of PPARα in primary cultured hepatocytes was suppressed by recombinant rat TNFα and IL-1β. However, inhibition of the NF-κB pathway prevented this. Transient transfection and promoter analysis further revealed that these two cytokines and NF-κB were coordinately involved in the suppression of PPARα promoter activity. Our data demonstrate that TNFα and IL-1β released from KCs following PFNA exposure can suppress the expression of PPARα via NF-κB pathway, which partially contribute to the evident accumulation of triglycerides in rat liver.

Biomarkers for drug-induced liver injury

Of the estimated 10,000 documented human drugs, more than 1000 have been associated with drug-induced liver injury (DILI), although causality has not always been established clearly. Numerous biomarkers for DILI have been explored, but less than ten are adopted or qualified as valid by the US FDA. The biomarkers for DILI are individual or a panel of proteins, nucleic acids or metabolites from various sources, such as the liver, blood and urine. While most DILI biomarkers are drug independent, some possibly 'drug-specific' DILIs have been explored, but specificity and sensitivity of both types need to be improved for the diagnosis of DILI during drug development and in clinical practice. Novel approaches for DILI biomarkers have been actively investigated recently, but produced mainly animal-based biomarkers, which are possibly useful for drug development, but are not suitable or have not been validated for clinical applications. This review summarizes the current practice and future perspectives for DILI biomarkers.

In vivo effect of selective macrophage suppression on the development of intrahepatic cholestasis in mice

We studied the role of selective suppression of liver Kupffer cells (gadolinium chloride, 14 mg/kg intravenously) in the development of intrahepatic cholestasis in CBA/C57B1/6 mice after intraperitoneal injection of alpha-naphthylisothiocyanate in a single dose of 200 mg/kg. Pretreatment with gadolinium chloride increased the severity of cholestasis and signs of liver damage. Gadolinium accumulation in the liver peaked after 24 h and was accompanied by a decrease in activities of cathepsin D and cathepsin B and concentration of matrix metalloprotease-2. Our results confirm the hypothesis that normal function of Kupffer cells and extracellular matrix plays an important role in cholestasis. Administration of gadolinium chloride serves as a convenient model to study the side effects, toxicity, and safety of lanthanides as nanoparticles.

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.

Systems biology and functional genomics approaches for the identification of cellular responses to drug toxicity

Extensive growth in the field of molecular biology in recent decades has led to the development of new and powerful experimental and computational tools that enable the analysis of complex biological responses to chemical exposure on both a functional and structural genetic level. The ability to profile global responses on a transcriptional level has become a valuable resource in the science of toxicology and attempts are now being made to further understand toxicity mechanisms by incorporating metabolomics and proteomics approaches. In addition, recent progress in understanding the extent of the genetic diversity within and between species allows us to take a fresh look at research on genetic polymorphisms that may influence an individual's susceptibility to toxicity. Whereas new technologies have the potential to make a sizeable impact on our understanding of the mechanisms of toxicity, considerable challenges remain to be addressed, especially with regard to the regulatory acceptance and successful integration of omics data. This review highlights recent advancements in the application of functional and structural genomics techniques to chemical hazard identification and characterization, and to the understanding of the interindividual differences in susceptibility to adverse drug reactions.

Filling and mining the reactive metabolite target protein database

The post-translational modification of proteins is a well-known endogenous mechanism for regulating protein function and activity. Cellular proteins are also susceptible to post-translational modification by xenobiotic agents that possess, or whose metabolites possess, significant electrophilic character. Such non-physiological modifications to endogenous proteins are sometimes benign, but in other cases they are strongly associated with, and are presumed to cause, lethal cytotoxic consequences via necrosis and/or apoptosis. The Reactive Metabolite Target Protein Database (TPDB) is a searchable, freely web-accessible (http://tpdb.medchem.ku.edu:8080/protein_database/) resource that attempts to provide a comprehensive, up-to-date listing of known reactive metabolite target proteins. In this report we characterize the TPDB by reviewing briefly how the information it contains came to be known. We also compare its information to that provided by other types of "-omics" studies relevant to toxicology, and we illustrate how bioinformatic analysis of target proteins may help to elucidate mechanisms of cytotoxic responses to reactive metabolites.

A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

BACKGROUND

Bioactivity profiling using high-throughput in vitro assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex in vitro/in vivo datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods.

RESULTS

The classification performance of Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM) in the presence and absence of filter-based feature selection was analyzed using K-way cross-validation testing and independent validation on simulated in vitro assay data sets with varying levels of model complexity, number of irrelevant features and measurement noise. While the prediction accuracy of all ML methods decreased as non-causal (irrelevant) features were added, some ML methods performed better than others. In the limit of using a large number of features, ANN and SVM were always in the top performing set of methods while RPART and KNN (k = 5) were always in the poorest performing set. The addition of measurement noise and irrelevant features decreased the classification accuracy of all ML methods, with LDA suffering the greatest performance degradation. LDA performance is especially sensitive to the use of feature selection. Filter-based feature selection generally improved performance, most strikingly for LDA.

CONCLUSION

We have developed a novel simulation model to evaluate machine learning methods for the analysis of data sets in which in vitro bioassay data is being used to predict in vivo chemical toxicology. From our analysis, we can recommend that several ML methods, most notably SVM and ANN, are good candidates for use in real world applications in this area.

Prediction of a carcinogenic potential of rat hepatocarcinogens using toxicogenomics analysis of short-term in vivo studies

The carcinogenic potential of chemicals is currently evaluated with rodent life-time bioassays, which are time consuming, and expensive with respect to cost, number of animals and amount of compound required. Since the results of these 2-year bioassays are not known until quite late during development of new chemical entities, and since the short-term test battery to test for genotoxicity, a characteristic of genotoxic carcinogens, is hampered by low specificity, the identification of early biomarkers for carcinogenicity would be a big step forward. Using gene expression profiles from the livers of rats treated up to 14 days with genotoxic and non-genotoxic carcinogens we previously identified characteristic gene expression profiles for these two groups of carcinogens. We have now added expression profiles from further hepatocarcinogens and from non-carcinogens the latter serving as control profiles. We used these profiles to extract biomarkers discriminating genotoxic from non-genotoxic carcinogens and to calculate classifiers based on the support vector machine (SVM) algorithm. These classifiers then predicted a set of independent validation compound profiles with up to 88% accuracy, depending on the marker gene set. We would like to present this study as proof of the concept that a classification of carcinogens based on short-term studies may be feasible.

Gene expression profiles in mouse liver cells after exposure to different types of radiation

The liver is one of the target organs of radiation-induced cancers by internal exposures. In order to elucidate radiation-induced liver cancers including Thorotrast, we present a new approach to investigate in vivo effects of internal exposure to alpha-particles. Adopting boron neutron capture, we separately irradiated Kupffer cells and endothelial cells in mouse liver in vivo and analyzed the changes in gene transcriptions by an oligonucleotide microarray. Differential expression was defined as more than 3-fold for up-regulation and less than 1/3 for under-regulation, compared with non-irradiated controls. Of 6,050 genes examined, 68 showed differential expression compared with non-irradiated mice. Real-time polymerase chain reaction validated the results of the microarray analysis. Exposure to alpha-particles and gamma-rays produced different patterns of altered gene expression. Gene expression profiles revealed that the liver was in an inflammatory state characterized by up-regulation of positive acute phase protein genes, irrespective of the target cells exposed to radiation. In comparison with chemical and biological hepatotoxicants, inductions of Metallothionein 1 and Hemopexin, and suppressions of cytochrome P450s are characteristic of radiation exposure. Anti-inflammatory treatment could be helpful for the prevention and protection of radiation-induced hepatic injury.

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/.

Prediction and classification of drug toxicity using probabilistic modeling of temporal metabolic data: the consortium on metabonomic toxicology screening approach

Detection and classification of in vivo drug toxicity is an expensive and time-consuming process. Metabolic profiling is becoming a key enabling tool in this area as it provides a unique perspective on the characterization and mechanisms of response to toxic insult. As part of the Consortium on Metabonomic Toxicology (COMET) project, a substantial metabolic and pathological database was constructed. We chose a set of 80 treatments to build a modeling system for toxicity prediction using NMR spectroscopy of urine samples (n=12935) from laboratory rats (n=1652). The compound structures and activities were diverse but there was an emphasis on the selection of hepato and nephrotoxins. We developed a two-stage strategy based on the assumptions that (a) adverse effects would produce metabolic profiles deviating from those of normal animals and (b) such deviations would be similar for treatments having similar physiological effects. To address the first stage, we developed a multivariate model of normal urine, using principal components analysis of specially preprocessed 1H NMR spectra. The model demonstrated a high correspondence between the occurrence of toxicity and abnormal metabolic profiles. In the second stage, we extended a density estimation method, "CLOUDS", to compute multidimensional similarities between treatments. Crucially, the technique allowed a distribution-free estimate of similarity across multiple animals and time points for each treatment and the resulting matrix of similarities showed segregation between liver toxins and other treatments. Using the similarity matrix, we were able to correctly identify the target organ of two "blind" treatments, even at sub-toxic levels. To further validate the approach, we then applied a leave-one-out approach to predict the main organ of toxicity (liver or kidney) showing significant responses using the three most similar matches in the matrix. Where predictions could be made, there was an error rate of 8%. The sensitivities to liver and kidney toxicity were 67 and 41%, respectively, whereas the corresponding specificities were 77 and 100%. In some cases, it was not possible to make predictions because of interference by drug-related metabolite signals (18%), an inconsistent histopathological or urinary response (11%), genuine class overlap (8%), or lack of similarity to any other treatment (2%). This study constitutes the largest validation to date of the metabonomic approach to preclinical toxicology assessment, confirming that the methodology offers practical utility for rapid in vivo drug toxicity screening.

CLASS PREDICTION IN TOXICOGENOMICS

The intent of this article is to discuss some of the complexities of toxicogenomics data and the statistical design and analysis issues that arise in the course of conducting a toxicogenomics study. We also describe a procedure for classifying compounds into various hepatotoxicity classes based on gene expression data. The methodology involves first classifying a compound as toxic or nontoxic and subsequently classifying the toxic compounds into the hepatotoxicity classes, based on votes by binary classifiers. The binary classifiers are constructed by using genes selected to best elicit differences between the two classes. We show that the gene selection strategy improves the misclassification error rates and also delivers gene pathways that exhibit biological relevance.

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.

Application of genomics in preclinical drug safety evaluation

Understanding the response of biological systems to xenobiotics is fundamental to the evaluation of drug safety. Toxicologists have traditionally gathered pathological, morphological, chemical and biochemical information from in vivo studies of preclinical species in order to assess drug safety and to determine how new drugs can be safely administered to the human patient population. In recent years the emerging "-omics" technologies have been developed and integrated into preclinical studies in order to better assess drug safety by gaining information on the cellular and molecular events underlying adverse drug reactions. Genomics approaches in particular have become readily available and are being applied in several stages of drug development. The burgeoning literature on what has become known as "toxicogenomics" has for the most part highlighted successful applications of gene expression profiling in predictive toxicology, enabling decisions to be made on the developability of a compound early in the drug development process. It is also becoming apparent that toxicogenomic approaches are good starting points to develop experiments designed to gain a mechanistic insight into drug toxicities within and across species. Gene expression arrays permit the measurement of responses of essentially all the genes in the entire genome to be monitored, and knowledge of the function of the genes affected can identify the potential mechanisms to then be confirmed using conventional biochemical, toxicological and pathological approaches. As toxicologists put these technologies into practice they build up a knowledge base to better characterize toxicities at the molecular level and to make the search for much needed, novel biomarkers of toxicity more achievable.

Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix® database

A range of genomics technologies are increasingly becoming integrated with existing scientific disciplines to broaden and strengthen existing capabilities and open new avenues of research in drug discovery and development. Examples of these new research fields are proteomics, pharmacogenomics, metabolomics and toxicogenomics. Here we review the application of toxicogenomics to improve the evaluation of drug safety, mechanism of action and toxicity in the drug discovery and development process.

Predictive toxicogenomics approaches reveal underlying molecular mechanisms of nongenotoxic carcinogenicity

Toxicogenomics technology defines toxicity gene expression signatures for early predictions and hypotheses generation for mechanistic studies, which are important approaches for evaluating toxicity of drug candidate compounds. A large gene expression database built using cDNA microarrays and liver samples treated with over one hundred paradigm compounds was mined to determine gene expression signatures for nongenotoxic carcinogens (NGTCs). Data were obtained from male rats treated for 24 h. Training/testing sets of 24 NGTCs and 28 noncarcinogens were used to select genes. A semiexhaustive, nonredundant gene selection algorithm yielded six genes (nuclear transport factor 2, NUTF2; progesterone receptor membrane component 1, Pgrmc1; liver uridine diphosphate glucuronyltransferase, phenobarbital-inducible form, UDPGTr2; metallothionein 1A, MT1A; suppressor of lin-12 homolog, Sel1h; and methionine adenosyltransferase 1, alpha, Mat1a), which identified NGTCs with 88.5% prediction accuracy estimated by cross-validation. This six genes signature set also predicted NGTCs with 84% accuracy when samples were hybridized to commercially available CodeLink oligo-based microarrays. To unveil molecular mechanisms of nongenotoxic carcinogenesis, 125 differentially expressed genes (P<0.01) were selected by Student's t-test. These genes appear biologically relevant, of 71 well-annotated genes from these 125 genes, 62 were overrepresented in five biochemical pathway networks (most linked to cancer), and all of these networks were linked by one gene, c-myc. Gene expression profiling at early time points accurately predicts NGTC potential of compounds, and the same data can be mined effectively for other toxicity signatures. Predictive genes confirm prior work and suggest pathways critical for early stages of carcinogenesis.

Utilization of a toxicogenomic biomarker for evaluation of chemical-induced glutathione deficiency in rat livers across the GeneChip data of different generations

Previously, we reported 69 probe sets (GSH probe sets) of RG U34A GeneChip that were useful for the evaluation of chemical-induced glutathione depletion in rat livers. The aim of the present study was to investigate whether these probe sets could be applied to the analysis of RAE 230A GeneChip data. Since a straightforward data comparison of RG U34A and RAE 230A GeneChips could not overcome the generation-dependent discrepancy in signal profiles, we tried two methods to improve the data compatibility between the two GeneChips. First, we re-calculated the signal values by excluding the probes with poor-overlapping sequences between the two GeneChips, but the data compatibility did not improve from the view point of Spearman's and Pearson's correlation coefficients. On the other hand, the PCA result demonstrated that an adjustment of the baseline signal level between the RG U34A and RAE 230A GeneChip data on vehicle-treated rats dramatically improved the data compatibility, suggesting that the GSH probe sets identified from RG U34A GeneChip data can be utilized in RAE 230A GeneChip data as well. Such a baseline adjustment of signal data is an easy and practical way to utilize biomarkers across GeneChip data of different generations.

On Methods for Gene Function Scoring as a Means of Facilitating the Interpretation of Microarray Results

As gene annotation databases continue to evolve and improve, it has become feasible to incorporate the functional and pathway information about genes, available in these databases into the analysis of gene expression data, for a better understanding of the underlying mechanisms. A few methods have been proposed in the literature to formally convert individual gene results into gene function results. In this paper, we will compare the various methods, propose and examine some new ones, and offer a structured approach to incorporating gene function or pathway information into the analysis of expression data. We study the performance of the various methods and also compare them on real data, using a case study from the toxicogenomics area. Our results show that the approaches based on gene function scores yield a different, and functionally more interpretable, array of genes than methods that rely solely on individual gene scores. They also suggest that functional class scoring methods appear to perform better and more consistently than overrepresentation analysis and distributional score methods.

Leukotrienes modulate cytokine release from dendritic cells

Leukotriene B(4) (LTB(4)) and cysteinyl leukotrienes (CysLTs) are known as potent mediators of inflammation, whereas their role in the regulation of adaptive immunity remains poorly characterized. Dendritic cells (DCs) are specialized antigen-presenting cells, uniquely capable to initiate primary immune responses. We have found that zymosan, but not lipopolysaccharide (LPS) stimulates murine bone marrow-derived dendritic cells (BM-DCs) to produce large amounts of CysLTs and LTB(4) from endogenous substrates. A selective inhibitor of leukotriene synthesis MK886 as well as an antagonist of the high affinity LTB(4) receptor (BLT(1)) U-75302 slightly inhibited zymosan-, but not LPS-stimulated interleukin (IL)-10 release from BM-DCs. In contrast, U-75302 increased zymosan-stimulated release of IL-12 p40 by approximately 23%. Pre-treatment with transforming growth factor-beta1 enhanced both stimulated leukotriene synthesis and the inhibitory effect of U-75302 and MK886 on IL-10 release from DCs. Consistent with the effects of leukotriene antagonists, exogenous LTB(4) enhanced LPS-stimulated IL-10 release by approximately 39% and inhibited IL-12 p40 release by approximately 22%. Both effects were mediated by the BLT(1) receptor. Ligands of the high affinity CysLTs receptor (CysLT(1)), MK-571 and LTD(4) had little or no effect on cytokine release. Agonists of the nuclear LTB(4) receptor peroxisome proliferator-activated receptor-alpha, 8(S)-hydroxyeicosatetraenoic acid and 5,8,11,14-eicosatetraynoic acid, inhibited release of both IL-12 p40 and IL-10. Our results indicate that both autocrine and paracrine leukotrienes may modulate cytokine release from DCs, in a manner that is consistent with previously reported T helper 2-polarizing effects of leukotrienes.

Applications of DNA microarrays in biology

DNA microarrays have enabled biology researchers to conduct large-scale quantitative experiments. This capacity has produced qualitative changes in the breadth of hypotheses that can be explored. In what has become the dominant mode of use, changes in the transcription rate of nearly all the genes in a genome, taking place in a particular tissue or cell type, can be measured in disease states, during development, and in response to intentional experimental perturbations, such as gene disruptions and drug treatments. The response patterns have helped illuminate mechanisms of disease and identify disease subphenotypes, predict disease progression, assign function to previously unannotated genes, group genes into functional pathways, and predict activities of new compounds. Directed at the genome sequence itself, microarrays have been used to identify novel genes, binding sites of transcription factors, changes in DNA copy number, and variations from a baseline sequence, such as in emerging strains of pathogens or complex mutations in disease-causing human genes. They also serve as a general demultiplexing tool to sort spatially the sequence-tagged products of highly parallel reactions performed in solution. A brief review of microarray platform technology options, and of the process steps involved in complete experiment workflows, is included.

Relationship between Hepatic Gene Expression Profiles and Hepatotoxicity in Five Typical Hepatotoxicant-Administered Rats

In the field of gene expression analysis, DNA microarray technology has a major impact on many different areas including toxicogenomics, such as in predicting the adverse effects of new drug candidates and improving the process of risk assessment and safety evaluation. In this study, we investigated whether there is relationship between the hepatotoxic phenotypes and gene expression profiles of hepatotoxic chemicals measured by DNA microarray analyses. Sprague-Dawley rats (6 weeks old) were administered five hepatotoxicants: acetaminophen (APAP), bromobenzene, carbon tetrachloride, dimethylnitrosamine, and thioacetamide. Serum biochemical markers for liver toxicity were measured to estimate the maximal toxic time of each chemical. Hepatic mRNA was isolated, and the gene expression profiles were analyzed by DNA microarray containing 1,097 drug response genes, such as cytochrome P450s, other phase I and phase II enzymes, nuclear receptors, signal transducers, and transporters. All the chemicals tested generated specific gene expression patterns. APAP was sorted to a different cluster from the other four chemicals. From the gene expression profiles and maximal toxic time estimated by serum biochemical markers, we identified 10 up-regulated genes and 10 down-regulated genes as potential markers of hepatotoxicity. By Quality-Threshold (QT) clustering analysis, we identified major up- and down-regulated expression patterns in each group. Interestingly, the average gene expression patterns from the QT clustering were correlated with the mean value profiles from the biochemical markers. Furthermore, this correlation was observed at any extent of hepatotoxicity. In this study, we identified 17 potential toxicity markers, and those expression profiles could estimate the maximal toxic time independently of the hepatotoxicity levels. This expression profile analysis could be one of the useful tools for evaluating a potential hepatotoxicant in the drug development process.

A gene expression signature for oxidant stress/reactive metabolites in rat liver

Formation of free radicals and other reactive molecules is responsible for the adverse effects produced by a number of hepatotoxic compounds. cDNA microarray technology was used to compare transcriptional profiles elicited by training and testing sets of 15 oxidant stressors/reactive metabolite treatments to those produced by approximately 85 other paradigm compounds (mostly hepatotoxicants) to determine a shared signature profile for oxidant stress-associated hepatotoxicity. Initially, 100 genes were chosen that responded significantly different to oxidant stressors/reactive metabolites (OS/RM) compared to other samples in the database, then a 25-gene subset was selected by multivariate analysis. Many of the selected genes (e.g., aflatoxin aldehyde reductase, diaphorase, epoxide hydrolase, heme oxgenase and several glutathione transferases) are well-characterized oxidant stress/Nrf-2-responsive genes. Less than 10 other compounds co-cluster with our training and testing set compounds and these are known to generate OS/RMs as part of their mechanisms of toxicity. Using OS/RM signature gene sets, compounds previously associated with macrophage activation formed a distinct cluster separate from OS/RM and other compounds. A 69-gene set was chosen to maximally separate compounds in control, macrophage activator, peroxisome proliferator and OS/RM classes. The ease with which these 'oxidative stressor' classes can be separated indicates a role for microarray technology in early prediction and classification of hepatotoxicants. The ability to rapidly screen the oxidant stress potential of compounds may aid in avoidance of some idiosyncratic drug reactions as well as overtly toxic compounds.

Preclinical Drug Safety Analysis by Chemogenomic Profiling in the Liver

The economic hurdles of drug development and the emergence of genomic technologies such as chemogenomics are combining to shift the existing paradigms in preclinical drug development. Today, the information gleaned from high content molecular data has begun to augment traditional approaches to the assessment of drug safety. The optimal approach is a hybrid strategy employing chemogenomic data and gene expression-based biomarkers of drug efficacy and toxicity to supplement low content and insensitive methods for risk assessment and mechanistic evaluation of drug candidates. Large reference databases of chemogenomic data are essential to the derivation and validation of accurate and predictive gene expression biomarkers. An example of the development of a predictive biomarker for hepatic bile duct hyperplasia is described herein. As gene expression technologies improve, biomarkers will achieve higher throughput, and become more cost effective and increasingly accurate. This will elevate the value of chemogenomics in drug development, shift attrition to earlier in the process, and reduce the overall cost of drug development. Over the past 2 to 3 years, the transition of chemogenomics from a research tool to a decision-making tool has begun and regulatory agencies are anxiously awaiting implementation of this technology to make faster and more informed evaluations of potential drugs.