ToxSci Advance Access originally published online on April 17, 2007
Toxicological Sciences 2007 99(1):26-34; doi:10.1093/toxsci/kfm090
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Published by Oxford University Press 2007.
Toward a Checklist for Exchange and Interpretation of Data from a Toxicology Study
* NIEHS, LMIT ITSS Contract, Research Triangle Park, North Carolina 27709-2233
Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824
Lilly Research Laboratory, Greenfield, Indiana 46140
Johnson and Johnson PRD, Raritan, New Jersey 08869
¶ National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711
| NIEHS, Research Triangle Park, North Carolina 27709-2233
|| National Toxicology Program, Research Triangle Park, North Carolina 27709
||| Ontology Workshop, LLC, Columbia, Maryland 21045-9998
# National Center for Toxicological Research, Jefferson, Arkansas 72079
** Integrated Life Sciences, Research Triangle Park, North Carolina 27709
Leadscope, Columbus, Ohio 43212
1 To whom correspondence should be addressed at NIEHS, MD F1-05, PO Box 12233, 111 Alexander Drive, Research Triangle Park, NC, 27709-2233. E-mail: fostel{at}niehs.nih.gov.
Received February 13, 2007; accepted April 3, 2007
 |
ABSTRACT |
|---|
 TOP ABSTRACT BACKGROUND AND RATIONALETHE CHECKLISTUTILITY OF THIS CHECKLISTEXAMPLES OF META-ANALYSIS...SUMMARYSUPPLEMENTARY DATAREFERENCES |
|---|
Data from toxicology and toxicogenomics studies are valuable, and can be combined for meta-analysis using public data repositories such as Chemical Effects in Biological Systems Knowledgebase, ArrayExpress, and Gene Expression Omnibus. In order to fully utilize the data for secondary analysis, it is necessary to have a description of the study and good annotation of the accompanying data. This study annotation permits sophisticated cross-study comparison and analysis, and allows data from comparable subjects to be identified and fully understood. The Minimal Information About a Microarray Experiment Standard was proposed to permit deposition and sharing of microarray data. We propose the first step toward an analogous standard for a toxicogenomics/toxicology study, by describing a checklist of information that best practices would suggest be included with the study data. When the information in this checklist is deposited together with the study data, the checklist information helps the public explore the study data in context of time, or identify data from similarly treated subjects, and also explore/identify potential sources of experimental variability. The proposed checklist summarizes useful information to include when sharing study data for publication, deposition into a database, or electronic exchange with collaborators. It is not a description of how to carry out an experiment, but a definition of how to describe an experiment. It is anticipated that once a toxicology checklist is accepted and put into use, then toxicology databases can be configured to require and output these fields, making it straightforward to annotate data for interpretation by others.
Key Words: Toxicogenomics; MIAME; Data integration; Database.
|
BACKGROUND AND RATIONALE |
|---|
|
TOPABSTRACTBACKGROUND AND RATIONALETHE CHECKLISTUTILITY OF THIS CHECKLISTEXAMPLES OF META-ANALYSIS...SUMMARYSUPPLEMENTARY DATAREFERENCES |
|---|
This article arises from the authors' experience with databases, data exchange, and interpretation of cross-study data. It does not describe how to do a study, but rather defines useful information to include in describing the data from the study when it is published or exchanged with collaborators. The checklist herein reflects best practice, i.e., the ideal annotation to include with data to permit interpretation in the context of the study. This checklist focuses on the biological information needed to interpret data from a study, and thus is a logical complement to the technology-focused checklists under development now. With community use, we anticipate that the minimum information required for interpretation of a study will emerge from this checklist.
A biological or biomedical investigation is viewed as a self-contained unit of scientific enquiry. Investigations often include studies of biological subjects, which are examined in situ or in a laboratory, in observational or perturbational studies. This proposal is to expand the data exchange checklist for toxicology/toxicogenomics to reflect the fact that a toxicological study can focus on any one of a number of different subject types. Therefore, it makes more sense to create a checklist tailored to the particular subject type and study design used rather than to aim for a single "toxicology study data checklist." This is not intended to be a closed, complete list, but rather to be an illustrative proposal and a living document, so that as additional aspects of study and subject are found to be critical to understanding a study, these pieces will be included in this checklist, and in the data exchange associated with it.
Databases such as the CEBS Chemical Effects in Biological Systems Knowledgebase (Waters et al., 2003
), ArrayTrack (Tong et al., 2003, 2004), and dbZach (Burgoon et al., 2006) all collect and store study data. At the moment only CEBS is a public data repository, but all three database initiatives will benefit from consensus around the minimal information needed to interpret a study, and therefore from a checklist, such as in this proposal, of the information important to include in a study description. It is important to keep in mind that the data fields included in the following tables are intended to be close to the minimum data required for exchange and interpretation of a biomedical study. The CEBS Data Dictionary (CEBS-DD; Fostel et al., 2005) includes a longer checklist of additional data which enriches interpretation of the study data, and supports meta-analysis of data from multiple studies. The aim of the CEBS-DD was to define the maximal set of data elements that could be used to describe a study; this set is growing as additional studies are deposited in CEBS. The current effort is to identify the minimal set of data elements without which it is difficult or impossible to interpret data from a study.
A number of standards and data exchange checklists initiatives are currently underway. At the moment, these initiatives are each focused on a specific technology, but do not fully represent the accompanying biology. Examples include microarray/transcriptomics (the Minimal Information About a Microarray Experiment [MIAME]; Brazma et al., 2001; Microarray Gene Expression Data [MGED] Society Transcriptomics Working Group, http://fugo.sourceforge.net/community/community.php), proteomics (Protein Standards Initiative [PSI], http://psidev.sourceforge.net/), metabolomics/metabonomics (Metabolomics Standards Initiative, http://msi-workgroups.sourceforge.net/), in situ hybridization (Minimum Information Specification For In Situ Hybridization and Immunohistochemistry Experiments, http://scgap.systemsbiology.net/standards/misfishie/), etc., or on a particular scientific discipline such as nutrigenomics (MIAME-Nut; see http://www.mged.org/Workgroups/rsbi/rsbi.html) or environmental work (MIAME-Env; http://nebc.nox.ac.uk/miame/miame_env.html). An early effort also created a MIAME-Tox (http://www.ebi.ac.uk/microarray/doc/standards.html).
The Minimum Information Checklist (MICheck) project (http://micheck.sourceforge.net/) has just been initiated, and is in the process of collecting and organizing these checklists. We aim to deposit this checklist in MICheck after publication. Additional efforts such as the MGED Reporting Structure for Biomedical Investigation (http://www.mged.org/Workgroups/rsbi/rsbi.htm) and the OBI (Ontology for Biomedical Investigations; http://obi.sourceforge.net/) project aim to support data exchange of a biomedical investigation, and this checklist is aligned with these efforts.
|
THE CHECKLIST |
|---|
|
TOPABSTRACTBACKGROUND AND RATIONALETHE CHECKLISTUTILITY OF THIS CHECKLISTEXAMPLES OF META-ANALYSIS...SUMMARYSUPPLEMENTARY DATAREFERENCES |
|---|
There are (at least) four components of the information about a biomedical investigation. (1) Characteristics of the subjects; (2) the study design and execution (procedures and timeline); (3) description of specimens/biomaterials produced and preserved; and (4) description of the assay(s) performed on subjects or specimens plus data from the assay(s). These are not always addressed uniformly in current standards initiatives. As relating to ‘omics studies, components 3 and 4 have been well covered in MIAME, PSI, and other ‘omics-driven standards. The OBI project is developing an ontology to use to exchange data relating to a biomedical investigation.
What follows is a proposal for component 1 (characteristics of subjects and procedures), applying to many different types of biological subjects rather than to a specific scientific area, and for component 2 (study design, execution, and timeline), applying to several different study design types. Additionally, we suggest an approach to defining additional aspects of the biomaterial (component 3) and offer a controlled vocabulary for specimens derived from laboratory animals. This checklist can then feed directly into "any" technology or assay domain. Finally we include two new assay domains (component 4): clinical pathology and histopathology, which are related to toxicology, preclinical, and clinical investigations. Our final recommendation is that for best results, each study includes data from two or more assay domains for each study subject, thus providing an internal reference for the interpretation of the data from the subject.
Component 1. Subject Characteristics and Applied Procedures
Information about the study subject includes (1) characteristics of the subject, and (2) details of the procedures applied during a perturbational study or details of the observation time and place in an observational study. Importantly, the minimal characteristics needed about a given subject depend on the type of subject.
Subject characteristics.
At the moment, we envision eight different subject types, and will address characteristics of the first seven (Table 1).
|
Several of the subject types can be further categorized, and so we define subject nature to describe major groups within each subject type. Each subject type/nature class has specific characteristics for each subject type/nature that constitute the checklist for data exchange for studies involving that subject type.
Characteristics needed to describe each subject type (see Table 2).
|
Procedures applied to subjects.
Subjects (1) are observed/collected (in an observational study), (2) are acclimated and handled, (3) are treated (in a perturbational study), (4) exit from the study, and (5) are used to prepare specimens. There will be different procedural information for each subject type (see below).
Observational studies.
For observation/collection, irrespective of subject type, the checklist includes the following:
- (1) Location of collection (latitude and longitude and altitude)
- (2) Season/time of day/time of tide (if applicable)
- (3) Temperature/weather conditions
- (4) Specific details of site pertinent to study
- (5) Method of capture/collection/euthanasia
- (2) Season/time of day/time of tide (if applicable)
Additional work in this area has been done by MIAME-Env (see http://nebc.nox.ac.uk/miame/miame_env.html).
Procedures associated with perturbational studies.
Checklist for acclimation and handling procedures: (see Table 3)
|
In a perturbational study, a stressor is applied to the subjects. The checklist for stressor characteristics depends on the stressor type, and at the moment we are aware of several types of stressors: Chemical, Genetic, Disease, Environmental/Physical, and Surgical. If the Stressor is a chemical, the checklist includes the CAS number, formula, a SMILE string, and InChI code (see DSSTox guidelines at http://www.epa.gov/ncct/dsstox/index.html). If the Stressor is a genetic alteration, then the targeted locus + reference coordinates and a description of the alteration (mutation, RNAi, Knock-In) and expected effects are needed. We have not had direct experience with disease or surgical stressors, and will follow the guidelines developed by the Clinical Data Interchange Standards Consortium for these Stressor types.
Checklist for treatment (as in perturbational study) procedures (see Table 4).
|
Observations (also called adverse events, in a study with human subjects) have an observation name, associated event, and value + unit. Observations are made during the study timeline, and thus are associated with timeline events. The format of observation descriptions is similar to the format of assay descriptions.
Checklist for describing study exit procedure (see Table 5).
|
Checklist for describing specimen preparation (see Table 6).
|
Component 2. Information about a Study Design and Organization (plus Timeline)
Three descriptors assist in defining the study class:
- (1) Study classification: observational or perturbational.
- (2) Study time type: clock-time/study-time/not-time-dependent.
- (3) Study design: Study designs for perturbational, time-dependent studies include parallel, and cross-over; study designs for perturbational, not-time-dependent studies includes Latin square, and assay.
- (2) Study time type: clock-time/study-time/not-time-dependent.
Study organization.
How the subjects are organized; which are biological replicates, which are comparators, and (if a perturbational study) what experimental factors were applied. We envision a list of study subject IDs with a field for each factor giving the value of that factor. Subjects with the same factor values are necessarily biological replicates. An example is provided below, for a study with two factors, dose, and time. (see Table 7).
|
Study timeline (only applies to time-dependent studies).
Study events are points in time at which a particular protocol is applied to one or more groups of study subjects. There are currently six event types: acclimation phase, treatment (in perturbational study), care or handling, observation, exit, specimen preparation. What follows is an example of the study timeline for a 2-day study with a 2-week acclimation phase. Details of procedures are defined in Table 8.
|
The biomaterial.
A Biomaterial, or experiment sample, was identified in the Microarray Gene Expression (MAGE) Ontology (http://mged.sourceforge.net/ontologies/MGEDontology.php) as it is a useful term to describe the entity that is the transition point between the Study and Assay domains. In order to avoid use of ambiguous terms, we prefer "specimen" to "experimental sample." The method used to prepare specimens of tissue, plus time within the study of such a preparation event is described in "Components 1 and 2."
We recommend the use of a common vocabulary, based on the work of the SEND Consortium (Standard for Exchange of Nonclinical Data), for organs and organ sections; see http://www.cdisc.org/models/send/v2.3/index.html.
Since the response of an organ once removed from the animal is very different if the organ is removed intact or as a section, this information is requested in the Specimen preparation method.
Specimens can be further divided using methods such as laser capture microdissection or fluorescent activated cell sorting, to produce a subspecimen. If this was done, information about the method used and cell type targeted is included in the checklist. Additionally, specimens can be pooled to produce a mixture from multiple subjects. An example would be to pool blood specimens from several animals to have sufficient material to test for the presence of a test article. The subjects contributing specimens to the pool should be identified. The biomaterial is a specimen, subspecimen or pool. Additionally, the method of preservation and the time of storage are important parameters, which are therefore included in the checklist.
Assays and data.
A biomaterial is the starting point for an assay. Assay domains include microarray, proteomics, other ‘omics technologies, as well as clinical pathology and histopathology. These latter two are pertinent to both toxicology and clinical studies, and have relatively well-defined terminologies (http://www.ascp.org/). Clinical Pathology includes measures made of chemicals and enzymes in blood (clinical chemistry), urine (urinalysis), and tissue. Hematology assays, counts of cell populations in blood, are often considered to be part of clinical pathology, and we also include gross observations of organs and tissues. Since many clinical pathology measures are standardized, we recommend the list of standard assay names created by the SEND Consortium.
From the view of the checklist, information about an Assay includes (1) the method name (e.g., ALT, alanine aminotransferase level); if not a standard assay, a definition of the method should be included (2) the unit of measure unless it is a ratio; and (3) the value obtained. Data from Assays that produce a ratio should include the name of the reference and comparator, and if possible, the raw data for each plus the appropriate unit. In addition to quantitative measures, such as result from Clinical Pathology Assays, qualitative results, such as Observations made on the Subjects during the study or on Specimens prepared after Study exit are also important types of Assay data. Use of a common lexicon, such as the Pathology Code Tables used by the National Toxicology Program (http://ntp-server.niehs.nih.gov/), improves the consistency and clarity of histopathology data shared among different institutions. Data from assays such as transcriptomics or proteomics should follow the checklist developed for these technologies.
|
UTILITY OF THIS CHECKLIST |
|---|
|
TOPABSTRACTBACKGROUND AND RATIONALETHE CHECKLISTUTILITY OF THIS CHECKLISTEXAMPLES OF META-ANALYSIS...SUMMARYSUPPLEMENTARY DATAREFERENCES |
|---|
The checklist will be implemented in a series of tags around tabular text, similar to Simple Omnibus Format in Text (http://www.ncbi.nlm.nih.gov/projects/geo/info/soft2.html) and MAGE-Tab (Rayner et al., 2006
). This will facilitate data entry into CEBS via BID (Biomedical Investigation Database). The BID database, developed at the National Institute of Environmental Health Sciences (NIEHS, in preparation), serves as a curation and loading tool for CEBS, as well as a prototyping vehicle for additional data types. All data loaded into CEBS pass through BID. We are in the process of improving the efficiency of data entry by implementing a means to load tagged data, using the checklist of data elements described herein. By using this data exchange medium, we can accomplish data exchange between toxicology databases including ArrayTrack, dbZach, and CEBS. Additionally, the data included in this checklist will facilitate meta-analysis of data in microarray databases such as Gene Expression Omnibus and ArrayExpress. |
EXAMPLES OF META-ANALYSIS FACILITATED BY INFORMATION COVERED IN THE CHECKLIST |
|---|
|
TOPABSTRACTBACKGROUND AND RATIONALETHE CHECKLISTUTILITY OF THIS CHECKLISTEXAMPLES OF META-ANALYSIS...SUMMARYSUPPLEMENTARY DATAREFERENCES |
|---|
Inclusion of Assay Results from more than One Domain
As one example of the utility of including information from the checklist with published study data, consider the Investigation "2004-dose and time responses to acetaminophen in rats," which contains Study "APAP_Oral_F344" found in CEBS (cebs.niehs.nih.gov). The study timeline from CEBS is given in Figure 1B, showing a 48-h study with a single acute exposure. The CEBS timeline shows five types of events—Treatment, Disposition, Care, In-life observations, and Preparation of specimens. The timing of each event type is shown on the timeline; protocols for each event type are linked via the "(protocol)" link at the left. The display uses checklist information provided when the study data was deposited.
|
Experimental groups to examine can be selected on the "Display All Studies" page in CEBS. The two groups selected for the example (24 h_150 mg/kg and 24 h_1500 mg/kg) are indicated in Figure 1B. The clinical pathology results for the rats in these experimental groups are shown in Figure 2, parts B and D; clinical pathology results for control rats are in parts A and C of Figure 2. Examining part B of Figure 2, it is clear that in this group Rat 3074 has elevated serum levels of alanine aminotransferase (ALT level) compared to the other rats in the group. This is also seen in part D of Figure 2, where there is a 100-fold difference in ALT levels among the rats in the group. Checking the pathology data in CEBS for the same rats shows the Rat 3020 also did not exhibit necrosis, whereas others in the group did. This information permits the user to identify rats showing a consistent response to the treatment before beginning microarray analysis.
|
Comparison of Similar Treatments
It is of interest that the rats in the previous example showed very different responses to similar treatment with acetaminophen. The literature reports several factors which can influence response to acetaminophen in rodents, including age, sex, genetic background, exposure to ethanol, position within circadian rhythm, lobe of liver sampled, and gastric content at the time of exposure (Dai et al., 2006
; Irwin et al., 2005; Liang et al., 1996; Morishita et al., 2006; Price et al., 1987; Sato et al., 1981; Schnell et al., 1984; Stillings et al., 2000; Walker et al., 1982; Zaher et al., 1998). Within this single study, age, strain, circadian rhythm, and liver lobe are held constant, and the rats were given feed ad libitum (and no ethanol). The depositors did not include exception comments which might indicate a problem with the exposure of a particular rat, so technical issues with exposure are not likely. It is possible that the rats had different eating patterns related to social standing among cagemates, and thus that the animals may have had differing gastric content when the acetaminophen was administered. This hypothesis would provide a biological explanation for the observations which is consistent with the literature. When all the male rats in CEBS with ALT levels above 200 u/l are selected using the CEBS Search Subjects screen, the list of studies shown in Figure 4 is returned. The checklist information associated with these studies can be reviewed in CEBS, and groups with similar treatment selected, as described above. This example will focus on the experimental group in each study that was treated with a bolus dose of 1500 mg/kg acetaminophen, and sacrificed 24 h following dose. The histopathology associated with these animals is summarized in Figure 4A, where the height of each bar is equal to the number of animals with the corresponding severity of each finding. Rats from one study (with yellow bars) were consistently found to have more severe symptoms than rats from the other two. The ALT and aspartate aminotransferase (AST) levels for the same animals are given in Figure 4B, with studies indicated by the same colors. The AST and ALT levels are more overlapping than the histopathology might have suggested. There are several potential explanations. First, ALT/AST levels are highly elevated, and may be beyond the quantitative range. Another explanation for the difference in pathology might be a difference in scoring range used by the pathologists in the three studies. Another explanation may lie in the study parameters for the studies, which are given in Table 9. In the study with elevated pathology, the rats were fasted prior to dosing, thus their gastric contents are likely different from the rats in the other two studies, which were not fasted. There were other differences in experimental parameters, which may or may not contribute to the differences in pathology observed. In any case, using the checklist to identify important experimental details to include with study data permits the public to form their own judgments about which data to select for further analysis.
|
|
|
SUMMARY |
|---|
|
TOPABSTRACTBACKGROUND AND RATIONALETHE CHECKLISTUTILITY OF THIS CHECKLISTEXAMPLES OF META-ANALYSIS...SUMMARYSUPPLEMENTARY DATAREFERENCES |
|---|
We have described information that should be included with data derived from a biological study. The exact details of the checklist information depend on the study design and the nature of the subjects used in the study. Example checklists for a lab animal study using a chemical stressor, and an in vitro assay using a genetic stressor are included as Supplemental Materials to this article.
In broad terms, this checklist recognizes that study data can be best interpreted in context of the subject characteristics, the conditions used to treat or collect the subjects, the conditions of care and disposition, and the timeline of study execution. The data from the study are described by assay name and unit of measure in the case of quantitative data such as measure of clinical chemistry, or description from a standardized lexicon as in the case of histopathology. Assay data from a technology covered by a current standard, such as the MIAME standard for microarray data, should adhere to that standard. We recommend that data from two or more assay types be included with the data to be exchanged so that the interpretation of the data can be cross-checked to identify possible biological heterogeneity of response.
With the large volume of toxicogenomics data being released currently, the possibilities for comparative analyses that both confirm and extend the initial results are limitless. However, to realize the full potential for these studies, minimum standards must be established and followed (Burgoon, 2007). We hope that this checklist will serve as the initial step in this process.
|
SUPPLEMENTARY DATA |
|---|
|
TOPABSTRACTBACKGROUND AND RATIONALETHE CHECKLISTUTILITY OF THIS CHECKLISTEXAMPLES OF META-ANALYSIS...SUMMARYSUPPLEMENTARY DATAREFERENCES |
|---|
Supplementary data are available online at http://toxsci.oxfordjournals.org/.
|
|
NOTES |
|---|
This manuscript has been reviewed and approved for publication by the Environmental Protection Agency but does not necessarily reflect the views of the Agency. Mention of trade names or commercial products does not constitute endorsement or recommendations for use.
|
ACKNOWLEDGMENTS |
|---|
This research was supported by the Intramural Research Program of the National Institutes of Health, and NIEHS. We thank Julian Preston, Richard Judson, Jiang Li, and Kevin Gerrish for critical review of this manuscript. We thank an anonymous reviewer for helpful comments that have improved the manuscript.
|
REFERENCES |
|---|
|
TOPABSTRACTBACKGROUND AND RATIONALETHE CHECKLISTUTILITY OF THIS CHECKLISTEXAMPLES OF META-ANALYSIS...SUMMARYSUPPLEMENTARY DATAREFERENCES |
|---|
Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat. Genet. (2001) 29:365–371.[CrossRef][Web of Science][Medline]
Burgoon LD, Boutros PC, Dere E, Zacharewski TR. dbZach: A MIAME-compliant toxicogenomic supportive relational database. Toxicol. Sci. (2006) 90:558–568.
Burgoon LD. Clearing the standards landscape: The semantics of terminology and their impact on toxicogenomics. Tox. Sci. (2007) doi:10.1093/toxsci/kfm108.
Dai G, He L, Chou N, Wan YJ. Acetaminophen metabolism does not contribute to gender difference in its hepatotoxicity in mouse. Toxicol. Sci. (2006) 92:33–41.
Fostel J, Choi D, Zwickl C, Morrison N, Rashid A, Hasan A, Bao W, Richard A, Tong W, Bushel PR, et al. Chemical effects in biological systems—Data dictionary (CEBS-DD): A compendium of terms for the capture and integration of biological study design description, conventional phenotypes, and 'omics data. Toxicol. Sci. (2005) 88:585–601.
Irwin RD, Parker JS, Lobenhofer EK, Burka LT, Blackshear PE, Vallant MK, Lebetkin EH, Gerken DF, Boorman GA. Transcriptional profiling of the left and median liver lobes of male f344/n rats following exposure to acetaminophen. Toxicol. Pathol. (2005) 33:111–117.
Liang HC, Li H, McKinnon RA, Duffy JJ, Potter SS, Puga A, Nebert DW. Cyp1a2(–/–) null mutant mice develop normally but show deficient drug metabolism. Proc. Natl. Acad. Sci. U. S. A. (1996) 93:1671–1676.
Morishita K, Mizukawa Y, Kasahara T, Okuyama M, Takashima K, Toritsuka N, Miyagishima T, Nagao T, Urushidani T. Gene expression profile in liver of differing ages of rats after single oral administration of acetaminophen. J. Toxicol. Sci. (2006) 31:491–507.[CrossRef][Medline]
Price VF, Miller MG, Jollow DJ. Mechanisms of fasting-induced potentiation of acetaminophen hepatotoxicity in the rat. Biochem. Pharmacol. (1987) 36:427–433.[CrossRef][Web of Science][Medline]
Rayner TF, Rocca-Serra P, Spellman PT, Causton HC, Farne A, Holloway E, Irizarry RA, Liu J, Maier DS, Miller M, et al. A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB. BMC Bioinformatics (2006) 7:489.[CrossRef][Medline]
Sato C, Matsuda Y, Lieber CS. Increased hepatotoxicity of acetaminophen after chronic ethanol consumption in the rat. Gastroenterology (1981) 80:140–148.[Web of Science][Medline]
Schnell RC, Bozigian HP, Davies MH, Merrick BA, Park KS, McMillan DA. Factors influencing circadian rhythms in acetaminophen lethality. Pharmacology (1984) 29:149–157.[CrossRef][Web of Science][Medline]
Stillings M, Havlik I, Chetty M, Clinton C, Schall R, Moodley I, Muir N, Little S. Comparison of the pharmacokinetic profiles of soluble aspirin and solid paracetamol tablets in fed and fasted volunteers. Curr. Med. Res. Opin. (2000) 16:115–124.[CrossRef][Web of Science][Medline]
Tong W, Cao X, Harris S, Sun H, Fang H, Fuscoe J, Harris A, Hong H, Xie Q, Perkins R, et al. ArrayTrack—Supporting toxicogenomic research at the U.S. Food and Drug Administration National Center for Toxicological Research. Environ. Health Perspect. (2003) 111:1819–1826.[Web of Science][Medline]
Tong W, Harris S, Cao X, Fang H, Shi L, Sun H, Fuscoe J, Harris A, Hong H, Xie Q, et al. Development of public toxicogenomics software for microarray data management and analysis. Mutat. Res. (2004) 549:241–253.[Web of Science][Medline]
Walker RM, Massey TE, McElligott TF, Racz WJ. Acetaminophen toxicity in fed and fasted mice. Can. J. Physiol. Pharmacol. (1982) 60:399–404.[Web of Science][Medline]
Waters M, Boorman G, Bushel P, Cunningham M, Irwin R, Merrick A, Olden K, Paules R, Selkirk J, Stasiewicz S, et al. Systems toxicology and the Chemical Effects in Biological Systems (CEBS) knowledge base. EHP Toxicogenomics (2003) 111:15–28.[Medline]
Zaher H, Buters JT, Ward JM, Bruno MK, Lucas AM, Stern ST, Cohen SD, Gonzalez FJ. Protection against acetaminophen toxicity in CYP1A2 and CYP2E1 double-null mice. Toxicol. Appl. Pharmacol. (1998) 152:193–199.[CrossRef][Web of Science][Medline]
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  C. R. Williams-Devane, M. A. Wolf, and A. M. Richard Toward a Public Toxicogenomics Capability for Supporting Predictive Toxicology: Survey of Current Resources and Chemical Indexing of Experiments in GEO and ArrayExpress Toxicol. Sci., June 1, 2009; 109(2): 358 - 371. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Waters, S. Stasiewicz, B. Alex Merrick, K. Tomer, P. Bushel, R. Paules, N. Stegman, G. Nehls, K. J. Yost, C. H. Johnson, et al. CEBS Chemical Effects in Biological Systems: a public data repository integrating study design and toxicity data with microarray and proteomics data Nucleic Acids Res., January 11, 2008; 36(suppl_1): D892 - D900. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||