The use of genomic approaches to better understand the adverse effects of environmental and xenobiotic exposures on human injury and disease processes engendered a great deal of early enthusiasm and excitement. This research initially focused on using gene expression alterations as measured by microarray analyses and is often referred to as 'toxicogenomics' [1
]. Quite early on, investigators were able to demonstrate that exposure to different toxicants could be discriminated or classified in rodent model systems by microarray profiling of gene expression alterations in the target tissues, that is, tissues that display visible adverse effects in response to toxicant exposure [2
Gene expression microarrays have developed over the past decade into a powerful tool for investigating biological, mechanistic, and disease processes in addition to developing genomic classifiers. Recent standardization efforts by the Microarray Quality Control Consortium, the Toxicogenomics Resource Consortium as well as other groups have clearly demonstrated the reproducibility of transcript level data generated using these approaches [6
]. However, in most instances these studies have understandably been based on reference samples with little or no biological significance. The Microarray Quality Control Consortium did substantiate their findings by performing a cross-platform study using samples from a multi-agent rat toxicogenomics study at a single dose and time point and the Toxicogenomics Resource Consortium did perform a cross-laboratory, time course assessment using samples from a single toxic agent [10
]. However, there are still open questions regarding the utility and applicability of the microarray technology in biological research and in particular with respect to understanding and classifying injury processes that arise as a consequence of exposures to various agents. For example, can gene expression data distinguish similar biological responses that occur in different physiological regions within an organ (for example, necrosis within different zones of the liver lobule) or similar lesions that are the result of exposure to different compounds?
Linking gene expression changes with more traditional toxicological measurements of adverse biological responses to toxicants (for example, histopathology and clinical chemistry), referred to as 'phenotypic anchoring', allowed investigators to gain new insight into the processes involved in the adverse effects on target tissues [12
]. In addition to analysis of target tissues, the use of whole blood as a tissue source for gene expression profiling is extremely appealing and already has been demonstrated for a variety of diseases and exposures [16
]. This has tremendous potential in a therapeutic setting - the use of blood as a surrogate for the primary tissue of interest greatly facilitates sample collection and analysis. The benefits would be realized in basic research studies as well. If transcript data in whole blood can function as a surrogate for the target organ, a researcher would be able to collect serial time points from an animal as opposed to harvesting tissue at a single time point after sacrifice. This would not only decrease the number of animals being used in a study, but would increase the amount and value of the data generated from a single animal as early transcriptional events could be phenotypically anchored to histopathological observations or clinical chemistry data that were not observed until later time points within the same animal. The amount of total RNA required to perform microarray-based gene expression profiling from whole blood continues to decrease, thereby increasing the potential for practical applications. Thus, one question of interest is whether expression data from whole blood can serve as a surrogate for a target organ through either an ability to detect the same transcript changes or an ability to identify different transcript biomarkers with similar or enhanced classification utility.
While much progress has been made in the application of toxicogenomics to the classification of toxicants and the investigation of mechanisms of toxicity, a full realization of its potential in a systems biology context, sometimes referred to as 'systems toxicology' [23
], has yet to be accomplished. A primary obstacle has been the lack of truly robust data sets that capture not only genome-wide gene expression measurements but also traditional biological and toxicological information associated with exposures that vary over dose and time. This need was recently highlighted in the National Research Council's report on toxicogenomics [24
]. Here we present a comprehensive, public dataset of gene expression and accompanying data (histopathology, clinical chemistry, hematology) from a standardized study to serve as a resource to further advance the development of systems toxicology. The present report details the experimental design and the different data that were collected, and provides examples of how these data can be used to address important biological questions. This investigation of eight known hepatotoxicants was designed to evoke acute liver injury with similarities as well as differences in the type and location of injury that result.
The eight compounds are all acute hepatotoxins that cause hepatocellular necrosis following a single administration at a suitable dose. Most of the compounds target hepatocytes; however, monocrotaline targets endothelial cells leading to hemorrhage and activation of the coagulation system. The toxicity of bromobenzene, 1,2- and 1,4-dichlorobenzene, N-nitrosomorpholine, monocrotaline, and thioacetamide require metabolic activation by various cytochrome P450s to reactive intermediates [25
]. Since cytochrome P450 expression occurs primarily in the perivenous to centrilobular region of the hepatic plate, these agents generally damage the centrilobular region of the liver. The toxicity of diquat is associated with a one electron reduction/oxidation reaction that is catalyzed by NADPH cytochrome c reductase and leads to the production of high levels of reactive oxygen species [30
]. The mechanism responsible for the toxicity of galactosamine is not as well understood but is thought to be associated with depletion of UTP caused by the conversion of galactosamine to UDP-hexosamines and UDP-N-acetylhexosamines [31
The work was performed in its entirety using standardized procedures for the in-life work and for the generation of gene expression microarray data (n = 1,704 hybridizations). Doses that ranged from 'sub-toxic' to 'toxic' exposures were selected. Additionally, gene expression profiling was performed on two commercially available platforms (Agilent and Affymetrix) for one of the tissues (liver), thereby providing an opportunity to corroborate findings across platforms. In-life observations were recorded and clinical chemistry, hematology, and liver histopathology were also assessed for all 426 animals. These additional data facilitate the phenotypic anchoring of the gene expression data and confirm that the goal of the study to evoke different types of liver injury was achieved. Gene expression levels in whole blood were also assayed to evaluate whether biomarkers of different types of liver damage could be identified in this readily accessible bio-fluid.
The analyses presented here further demonstrate the utility of using microarrays as a tool for gene expression profiling to address biological questions. Specifically, this experiment was designed to provide a means to generate mechanistic and predictive measures of toxicity by integrating multiple data streams that were recorded within this standardized study. These model chemical exposure studies also provide a comprehensive data set with a well defined phenotypic anchor (liver injury) that is needed to assess the utility of gene expression profiling from whole blood samples. These data can now serve as a rich resource to the scientific community to test and validate these as well as other hypotheses, and have been made freely available in a public repository [32
]. While the experimental design is based on a common model for toxicogenomics studies, the findings and/or the results of mining these data can be applied to other fields using microarrays as a research tool.