Blood gene expression signatures predict
P. R. Bushel*, A. N. Heinloth†, J. Li*, L. Huang*, J. W. Chou*, G. A. Boorman‡, D. E. Malarkey‡, C. D. Houle§, S. M. Ward‡,
R. E. Wilson‡, R. D. Fannin¶, M. W. Russo?, P. B. Watkins?, R. W. Tennant**, and R. S. Paules†¶††
*Biostatistics Branch,†Environmental Stress and Cancer Group,‡Environmental Toxicology Program,¶Microarray Group, **Cancer Biology Group, National
Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC 27709;§Experimental Pathology Laboratories, Inc., Research
Triangle Park, NC 27709; and?Department of Medicine, University of North Carolina, Chapel Hill, NC 27599
Edited by Mark T. Groudine, Fred Hutchinson Cancer Research Center, Seattle, WA, and approved September 21, 2007 (received for review July 25, 2007)
To respond to potential adverse exposures properly, health care
providers need accurate indicators of exposure levels. The indica-
tors are particularly important in the case of acetaminophen
(APAP) intoxication, the leading cause of liver failure in the U.S. We
hypothesized that gene expression patterns derived from blood
cells would provide useful indicators of acute exposure levels. To
test this hypothesis, we used a blood gene expression data set
from rats exposed to APAP to train classifiers in two prediction
algorithms and to extract patterns for prediction using a profiling
algorithm. Prediction accuracy was tested on a blinded, indepen-
dent rat blood test data set and ranged from 88.9% to 95.8%.
Genomic markers outperformed predictions based on traditional
clinical parameters. The expression profiles of the predictor genes
from the patterns extracted from the blood exhibited remarkable
(97% accuracy) transtissue APAP exposure prediction when liver
gene expression data were used as a test set. Analysis of human
samples revealed separation of APAP-intoxicated patients from
control individuals based on blood expression levels of human
orthologs of the rat discriminatory genes. The major biological
signal in the discriminating genes was activation of an inflamma-
tory response after exposure to toxic doses of APAP. These results
support the hypothesis that gene expression data from peripheral
blood cells can provide valuable information about exposure
It also supports the potential use of genomic markers in the blood
as surrogates for clinical markers of potential acute liver damage.
acetaminophen ? hepatotoxicity ? microarray ? prediction ? genomics
xenobiotic-induced hepatic injury is estimated to be ?14/100,000
inhabitants of Western countries (1). Among these individuals,
acetaminophen (APAP) is responsible for the majority of clinical
cases that present with acute liver failure (1). Recently, it has been
shown that treatment with recommended doses of APAP fre-
quently produces liver injury in healthy adults (2). There is an
effective antidote for APAP intoxication, N-acetyl cysteine, that
can minimize liver injury (3). However, 50% of overdoses are
unintentional, and patients may present with undetectable levels of
of prognosis at presentation are critical to clinicians but can be
challenging. Serum markers are not very sensitive and are poor
predictors of outcome (4). Liver biopsies to obtain material for
histopathological evaluations are invasive and are connected with a
or undetectable once liver injury occurs (2). Thus, there is a need
can be obtained with minimal invasion.
Gene expression technology using microarrays allows analysis of
thousands of genes in parallel. A challenge in these studies is to
identify signature patterns of genes that allow prediction of classes
of samples with a high degree of accuracy. In this study, we tested
the hypothesis that it is possible to predict acute exposure to
iver injury is the most commonly observed adverse effect in
response to many environmental exposures. The incidence of
harmful levels of an agent based solely on gene expression data
obtained from blood cells. Prediction algorithms used classifiers
and a pattern-based method (Fig. 1). Emphasis was given to the
utilization of genomic markers for times after exposure that pre-
ceded clinical signs of injury. We used a rat model to generate a
training data set consisting of genomic, clinical chemistry, histopa-
thology, and hematology data. These measurements were analyzed
not be injurious to the liver, i.e., ‘‘nontoxic’’ or ‘‘subtoxic,’’ from
levels that would be expected to result in serious liver injury, i.e.,
‘‘toxic.’’ Subsequently, those criteria were used to predict the
exposure level of independent, blinded test samples. The accuracy
of prediction was compared between the various measurements.
expression data is significantly better than prediction based on
clinical chemistry, hematology, or histopathology. Our results also
demonstrate that blood gene expression data are sufficient to
predict exposure to harmful levels of APAP. In addition, analysis
of human samples revealed separation of APAP-intoxicated pa-
tients from control individuals based on blood expression levels of
that such a gene expression signature could be useful and supports
further testing to determine the extent that such indicators can be
translated into the clinical setting for surveying individuals present-
ing with APAP intoxication.
We developed training and test data sets to test the hypothesis that
genomic analysis of whole blood RNA could allow prediction of
of prediction algorithms. The training set included male Fisher rats
treated with 0, 150, 1,500, or 2,500 mg/kg APAP by oral gavage,
killed 6, 12, or 24 h after exposure. The test data consisted of rats
treated with 0, 150, 1,500, or 2,000 mg/kg APAP by oral gavage,
killed 3, 6 or 24 h after treatment. Both sets included gene
Author contributions: P.R.B. and A.N.H. contributed equally to this work; P.R.B., A.N.H.,
P.B.W., R.W.T., and R.S.P. designed research; P.R.B., A.N.H., G.A.B., D.E.M., R.D.F., and
M.W.R. performed research; P.R.B., A.N.H., J.L., L.H., J.W.C., D.E.M., C.D.H., S.M.W., R.E.W.,
and R.D.F. analyzed data; and P.R.B., A.N.H., J.L., J.W.C., and R.S.P. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access option.
Abbreviations: APAP, acetaminophen; DME, dose main effect; DCE, dose confounded
effect; MC-SVM, multicategory support vector machines; EPIG, extracting patterns and
identifying genes; k-NN, k-nearest neighbors; LOOCV, leave-one-out cross validation; PCA,
principal component analysis; GO, Gene Ontology; Fuzzy ARTMAP, fuzzy adaptive reso-
nance theory map; ALT, alanine aminotransferase; SDH, sorbitol dehydrogenase.
(GEO) Database, www.ncbi.nlm.nih.gov/geo (accession no. GSE5652).
P.O. Box 12233, Research Triangle Park, NC 27709-2233. E-mail: email@example.com.
This article contains supporting information online at www.pnas.org/cgi/content/full/
© 2007 by The National Academy of Sciences of the USA
November 13, 2007 ?
vol. 104 ?
no. 46 ?
expression data from animals exposed to subtoxic (150 mg/kg) and
toxic (1,500 and 2,000 or 2,500 mg/kg) doses of APAP hybridized
against a time-matched vehicle control. Additionally, the test data
set contained genomic data from animals that had received treat-
ment with vehicle-only (nontoxic) hybridized against the vehicle-
only controls. The test data set was evaluated in a blinded fashion.
Clinical Chemistry Parameters Lack Discriminating Sensitivity. Based
on alanine aminotransferase (ALT) and sorbitol dehydrogenase
(SDH) data obtained from the training set, we set threshold values
that would indicate exposure to a toxic versus ‘‘subtoxic/nontoxic’’
dose of APAP. Because clinical chemistry measurements after 6
and 12 h were indistinguishable between the dose groups, we could
use only the 24 h values for this exercise. We determined the range
of activity levels for ALT and SDH in the training set [Table 1 and
supporting information (SI) Fig. 3]. Based on those ranges of
activity levels, we assigned the animals of the test group using the
following criteria: (i) if at least one of the two clinical chemistry
measurements fell within the range of activity for either subtoxic/
respective group; (ii) if one measurement fell within the subtoxic/
nontoxic dose range and the other into the toxic dose range, the
animal was predicted toxic; (iii) if neither parameter fell into a
predefined range of activity, the animal was classified as not
45 of 72 animals were predicted correctly, resulting in an accuracy
of prediction of 62.5% [Table 2 and SI Table 4].
Histopathological Evaluation Shows Limited Accuracy of Prediction at
Early Time Points After Treatment. Board-certified veterinary pa-
thologists (n ? 3) extensively evaluated liver histologic slides from
necrosis and degeneration after toxic, subtoxic, and nontoxic
APAP exposure. The same pathologists each evaluated the liver of
power of histopathological evaluation, they were to determine,
based solely on histologic evidence, which animals had received a
subtoxic/nontoxic dose or a toxic dose of APAP. The overall
accuracy of prediction varied from 66.7% to 75% between the
pathologists (Table 2). The majority of missed calls were due to
false negative results at early time points (3 and 6 h) after toxic
exposure (SI Table 5).
Blood Cell Counts Allow Limited Prediction of Toxic Treatment. Based
on lymphocyte and neutrophil counts retrieved from the training
set, threshold values were set that would allow prediction of
unknown samples from the blood test set as either subtoxic/
nontoxic or toxic. Because of the strong diurnal variation of the
baseline counts (SI Table 6), it was necessary to determine a
neutrophil to lymphocyte ratio for each animal and establish
thresholds for those ratios (Table 3). Thus, animals in this set were
grouped into either subtoxic/nontoxic or toxic depending on their
neutrophil-to-lymphocyte ratio or not-determinable if they fell
outside those thresholds. By using these criteria, 56 of 72 animals
were predicted correctly: an accuracy of 77.8% (Table 2).
Selection of the Predictor Genes. To identify the genes with expres-
sion values in the blood training samples that vary significantly
between subtoxic- and toxic-dosed samples, two ANOVA models
were constructed. In one case, a main effect for the dose exposure
was modeled, and, in the other case, dose and time main effects
were confounded. By using the dose main effect (DME) ANOVA
with Bonferroni correction (P value ? 0.05) for multiple compar-
isons, 152 genes were identified as significantly different between
comparisons of the dose administered to the samples (SI Table 7).
(DCE) ANOVA without a correction for multiple comparisons
of subtoxic-dosed and toxic-dosed samples (SI Table 7). Applying
a Bonferroni correction for multiple comparisons with the DCE
ANOVA model analysis yielded few (n ? 9) significant genes.
The gene expression ratio values for the 152 genes and from the
training samples were analyzed by a k-nearest neighbors (k-NN)
classifier with 10-fold cross validation and a multicategory support
vector machine (MC-SVM) using leave-one-out cross validation
(LOOCV) to select genes which had a high accuracy of prediction.
A ?95% accuracy of prediction was obtained by using the top 35
significant genes from the DME ANOVA model and k-NN clas-
sifier (SI Table 7). A 97.1% accuracy prediction rate was achieved
by using 20 genes selected as optimal for prediction via the
by using 20 genes selected as optimal for prediction via the
MC-SVM on the 264 genes from the DCE ANOVA (SI Table 7).
In the MC-SVM cases, a single rat (no. 3338), analyzed 12 h after
being dosed with 2,500 mg/kg APAP, was predicted incorrectly as
a subtoxic-dosed sample. Principal component analysis (PCA) of
the training samples using the 152 genes selected by the DME
ANOVA and plotting the first three principal components shows
that rat no. 3338 situated in dimensional space very close to the
subtoxic dose samples (SI Fig. 4a).
35 predictor genes from the DME ANOVA model and k-NN
classifier, a 95.8% accuracy of prediction of the blood test samples
correctly independent of time (SI Table 8). However, the predicted
subtoxic/nontoxic group contained three toxic-dosed samples (two
the training data using a fuzzy adaptive resonance theory map
(Fuzzy ARTMAP) neural network (7, 8). LOOCV of the training
the classifier-based and pattern-based approaches are shown.
Workflow to predict the exposure level of the samples. The steps in
Table 1. Shown are the lowest and highest values of the
indicated clinical chemistry parameters in either the 0 and 150
mg/kg APAP groups at 24 h (sub/nontoxic dose) or the 1,500
and 2,500 mg/kg APAP groups at 24 h (toxic dose)
We defined the range these values span as predictive areas for toxicity of
the given parameter. The numbers in parenthesis indicate the median.
www.pnas.org?cgi?doi?10.1073?pnas.0706987104Bushel et al.
data at 0.01 increments over the range of the vigilance parameter
(from 0 to 1), indicated that a vigilance parameter value of 0.2 was
sufficient for maximal accuracy (data not shown). To predict the
class of dose administered to the rats, the sets of 20 genes selected
as predictors were used to compile the gene expression ratio values
from the test set samples. The gene expression data were analyzed
by using the Fuzzy ARTMAP neural network with the vigilance
parameter set at 0.2, and the gene expression ratio values from the
training data were used to construct the classifier. The accuracy of
dose classification, the 20 predictor genes from the DME ANOVA
had a higher accuracy of prediction (95.8%) than the 20 predictor
genes from the DCE ANOVA (88.9%) (SI Table 8).
Extracting Gene Expression Patterns in the Blood Data for Prediction
of Exposure. By using the extracting patterns and identifying genes
(EPIG) approach (9), eight distinct patterns of gene expression
were obtained from the training data (SI Fig. 4b). Pattern 1 has a
clear separation of the subtoxic/nontoxic and the toxic-dosed
samples based on the expression of the genes above or below a log
base 2 ratio value of ??0.3. From the eight patterns, 248 genes
were selected by EPIG and included in the signature list for
prediction (SI Table 7).
248 genes in the signature list was performed to predict the classes
of the samples. When test samples were projected into 3D PCA
was judged, and membership to a class was predicted (SI Fig. 5a),
resulting in a 91.6% accuracy (Table 2 and SI Table 8).
Inflammatory Processes as the Most Significant Biological Discrimi-
nator Between Subtoxic/Nontoxic and Toxic Exposures. The gene
selection methods used in this work yielded from 20 (DME and
DCE ANOVAs with SVM), 35 (ANOVA/k-NN), and 248 (EPIG)
predictor genes. Ten genes were common to the four methods (SI
Table 7). The union of all predictor genes resulted in a total of 270
genes involve the activation of immune or inflammatory responses
against an external stimulus. Examples of overrepresented catego-
ries are defense response, immune response, response to stress,
regulation of phagocytosis, regulation of endocytosis, response to
bacteria, and inflammatory response. Analyzing the GO categories
impacted by the individual predictor gene sets resulted in the
following. EPIG genes indicated similar immune response activa-
tion as with the analysis of the gene union. The highest scores for
the k-NN genes were obtained for specific immune response GO
categories (MHC protein complex, immunological synapse), in
agreement with the major theme of immune or inflammatory
responses. The most significant GO categories affected in both
1-mediated inflammatory response, as did the 10 genes shared by
all of the predictive gene lists.
GO analysis of all lists identified genes involved in immune/
inflammation response processes as the most prominent discrimi-
nators between subtoxic/nontoxic exposure and toxic exposure to
immune response to more specific mechanisms of response with
decreasing members of a given gene list (Fig. 2).
Pathway analysis of the predictor genes revealed a down-
regulation of energy consuming pathways (gluconeogenesis and
propionate metabolism) and up-regulation of energy producing
pathways (glycogen phosphorylase) after exposure to a toxic dose.
were down-regulated, whereas anti-apoptotic I?-B was down-
regulated after 6 h and up-regulated at later time points.
We tested the hypothesis that blood gene expression data can carry
information that allows discrimination of levels of certain adverse
of APAP. We used three different prediction strategies to deter-
mine gene sets as indicators that allow discrimination of subtoxic/
nontoxic and toxic dose levels and extracted one to two signature
gene sets with each method from the training data set. Those gene
to predict exposure to subtoxic/nontoxic versus toxic doses with
very high accuracy (88.9–95.8%). Prediction of APAP-induced
liver injury based on blood gene expression data outperformed
predictions based on clinical chemistry, histopathology, and hema-
tology (Table 2). These traditional clinical parameters were par-
ticularly inferior (compared with gene expression analysis) at the
prediction of exposure levels of animals, which were analyzed at
set, at which point peak injury had developed, the prediction
did not reach the prediction accuracy of three of the four gene
The analytical methods used for prediction of the blinded
samples based on gene expression data used similar data-mining
to narrow down the genes to a candidate set. To ensure the
reliability of the results from the analytical procedures used in this
in the gene-selection step, a false discovery rate (FDR) was
determined to control possible family-wise errors and to balance
between type I and type II errors in the statistical models.
Table 2. Summary of prediction accuracies of the test data set with different predictive methods trained on the training set
Histopathology Microarray bioinformatics analysis
Pathologist 1 Pathologist 2Pathologist 3EPIG
k-NN DME ANOVADCE ANOVA
Accuracy of prediction, %62.5
In parentheses are displayed the prediction accuracies for the 24 h time point only of the test data set; in brackets are displayed the prediction accuracies for
the liver gene expression data of the test set based on blood classifiers.
Table 3. The lowest and highest values of the neutrophil/
lymphocyte cell count ratio in either the 0 and 150 mg/kg APAP
group at 6, 12, or 24 h (subtoxic/nontoxic) or the 1,500 and
2,500 mg/kg APAP group at 6, 12, or 24 h (toxic)
cell count ratio
The range these values span we defined as predictive areas for toxicity of
the given parameter.
Bushel et al. PNAS ?
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vol. 104 ?
no. 46 ?
Microarray gene expression analysis lends the ability to profile
the overall response of thousands of genes simultaneously across
several experimental conditions. This approach presents a well
known large n (number of genes) and small p (number of samples)
obtained and used to build the classifiers for the prediction models.
Furthermore, cross-validation procedures were used in the predic-
tions to minimize the overfitting of the classifiers on the training
EPIG method, yielding the most genes, was also able to predict the
toxic dose of exposure when testing expression values from liver
samples of the test set animals (after training on blood gene
expression data of the training set) with 97.2% accuracy (Table 2
and SI Fig. 5b). The classifier-based methods are more useful for
identifying a small, focused set of gene indicators of toxic exposure,
whereas pattern-based methods such as EPIG are promising when
attempting to identify groups of genes to discern biological pro-
cesses perturbed by environmental pressures, toxic exposure, or
To test whether our predictive gene sets would be useful on
human samples, we retrieved human orthologous genes for the
union set of the 270 predictor genes and found 66 of them to be
present on both the human and rat Agilent chips. Interestingly,
cluster analysis using the expression values of these genes from the
blood of five human APAP overdose victims and three control
individuals (SI Table 9) allowed clear separation dependent on
APAP exposure status (SI Fig. 6a). The cosine correlation values
from the individual clusters of overdosed victims are high (??0.9)
and moderately high (??0.8) among them as a whole cluster (SI
Fig. 6b). To test the significance of this finding, 10,000 random
selections of 66 genes for k-means clustering into two partitions
were performed and yielded a probability of 0.0061 that the
clustering of the samples would be as stable (10) as the 66
orthologous genes by chance. More data and further analysis are
needed to completely understand the significance of these findings
for patient samples. However, it is encouraging to see a separation
of overdosed and normal individuals based on genes that were
retrieved from a rat blood training set.
Analysis of gene expression data, in conjunction with other
histopathological and serum parameters, revealed several animals
at the 24 h time point in the test set that presented an altered
response to the exposure to toxic levels of APAP. In particular,
those animals presented no significant elevations of ALT and/or
SDH activities in the serum and no or minimal histopathological
changes. For example, one animal (no. 3338) in the training set was
consistently predicted as a subtoxic-dosed animal and was found to
have only minimal necrosis and no ALT or SDH elevation. In the
test set, six animals showed this characteristic of having received a
toxic dose but showed this altered response (nos. 61, 64, 65, 67, 69,
and 70). Of these animals, four were predicted by two or more
algorithms as having received subtoxic or nontoxic treatment (SI
Table 8). Interestingly, the EPIG analysis grouped five of those six
animals in the subtoxic/nontoxic group. This result raises the issue
whether those animals would have developed a more pronounced
toxic phenotype at a later time point after treatment or whether
the severe response seen in the majority of animals. If the latter,
then EPIG would qualify as the analysis method that discriminates
most accurately based on the actual pathobiology associated with
exposure. Because of the study design (endpoint and not longitu-
dinal), we cannot know what the ultimate fate of animals killed at
earlier times after exposure would have been, because peak injury
time point, even after highly toxic exposure levels.
Interestingly, although the three different algorithms used pro-
duced signature gene sets of very different numbers and extracted
those lists from the data by different approaches, the main biolog-
ical response captured by each was shared between all of them. An
alteration of inflammatory pathways involving interleukin-1 and
NF-?B was the main biological difference between exposure to
toxic or subtoxic/nontoxic doses of APAP. The role of inflamma-
identification of this inflammatory response in a genomic signature
blood that discriminate between expo-
sure to subtoxic/nontoxic or toxic dose of
APAP. Pictured is a subgroup of genes
mation. Gray filling of circles beside
genes indicates identification of genes in
EPIG; middle circle, k-NN, bottom circle,
ANOVA DME and DCE), and coloring of
squares signals direction of change (red,
up-regulation; green, down-regulation
Differentially expressed genes in
www.pnas.org?cgi?doi?10.1073?pnas.0706987104 Bushel et al.
in the blood is a previously undescribed finding of our study. This
finding might provide one of the missing links for the phenomenon
of organ-to-organ communication seen after APAP-induced tox-
icity as described by Neff et al. (12). These authors described
alterations of cytokine/chemokine expression in the liver in re-
three interpretations. First, the blood might react in the same
manner as the lung to the release of cytokines and chemokines by
the liver, with the inflammatory patterns we observed in response
to those stimulants. Second, the inflammatory response we ob-
served might be a direct result of blood cells being exposed to
APAP and its toxic metabolites and occurring parallel to a similar
response in the liver. Third, APAP could be producing a general
response in the blood common to other agents that induce liver
injury or a nonliver inflammatory (immune) response. These
possibilities are not mutually exclusive and several might occur
simultaneously. By using gene expression data acquired from the
blood of rats exposed to a compendium of hepatotoxicants (13),
some of which elicit liver injury (centrilobular necrosis) similar to
APAP, as well as data from a study in which rats were treated with
the inflamogen LPS (14), our 270 genes clearly show a different
pattern of expression of APAP-toxic exposure in comparison with
the compendium of hepatotoxicants, and they also distinguish the
LPS-treated animals from those exposed to APAP (SI Fig. 7). For
example, N-nitrosomorpholine at 300 mg/kg exposure to the rat
liver for 48 h causes elevations in liver injury enzyme markers
aspartate aminotransferase (AST) and ALT, and centrilobular
necrosis is manifested to a marked severity level (see SI Table 10).
the APAP-treated samples and other hepatotoxicants shows that
APAP-toxic samples, but there are clear differences in the expres-
sion profiles of some of the genes. Furthermore, the pattern of our
270 genes did not group the LPS animals with either the toxic or
subtoxic APAP doses.
We conclude that blood gene expression data can provide
signatures that are good predictors of exposure to toxic doses of
APAP that are superior to traditional toxicological parameters,
especially at early time points after exposure. A diagnostic test that
would help in the identification and prognosis of individuals with
APAP-induced hepatotoxicity would be clinically useful. It will be
intriguing to further test to what extent our result can be translated
into the clinical setting with individuals presenting with APAP
Materials and Methods
Animals and Animal Care. Male F344/N rats, 10–12 weeks old, were
obtained from Taconic Laboratories (Germantown, NY) and pro-
vided with NIH-07 diet and tap water ad libitum.
Chemical. APAP (99% pure) was purchased from Sigma (St. Louis,
MO), and suspension formulations were prepared by mixing with
Study Design. For the training set, groups of four male rats, 12–14
weeks old, not fasted before dosing, each received 0 (vehicle only),
150, 1,500 or 2,500 mg/kg APAP in 0.5% ethyl cellulose by oral
after 6, 12, or 24 h. For the test set, groups of six male rats each
received 0 (vehicle only), 150, 1,500, or 2,000 mg/kg APAP in 0.5%
ethyl cellulose by oral gavage in two doses. These animals were
were incorporated in the test data set to ensure that the predictive
power is not limited to the exact conditions represented by the
training set. Experiments were performed according to the guide-
lines established in the National Institutes of Health Guide for the
Care and Use of Laboratory Animals (15), and an approved
Animal Study Protocol was on file before initiation of the study.
Histopathology. A study pathologist initially evaluated two H&E-
stained sections of left liver lobes. A second pathologist reviewed
by a pathology working group review (16).
For the blinded histopathology evaluation of the test set, three
pathologists evaluated the training set. They received the left liver
lobe histopathology slides of the test set in a blinded fashion and
were charged to group the slides based on the criteria that (i) no
insult (hepatocyte degeneration and necrosis) to the animal.
Clinical Pathology. Blood was collected at euthanization into serum
separation tubes (BD Microtainer tubes; BD, Franklin Lakes, NJ),
and serum was separated. Clinical chemistry analyses (albumin,
concentrations, triglycerides, and activities of ALT, alkaline phos-
phatase, aspartate aminotransferase, lactate dehydrogenase, and
SDH) were performed on all rats at study termination.
Hematology. Blood was collected in EDTA tubes (BD Microtainer
tubes). Complete blood counts (white blood cells, red blood cells,
hemoglobin, hematocrit, and platelets), reticulocyte counts and
differential white blood cell counts (neutrophils, lymphocytes,
monocytes, eosinophils, and basophils) were performed on all rats
at study termination.
were isolated for histopathology evaluation and fixed in 10%
frozen, and pulverized as described (17). Total hepatic RNA was
isolated from individual rat livers with Qiagen RNeasy Maxi Kits
(Qiagen, Valencia, CA) as described (18). Blood was collected by
was isolated as described (14).
For the training set, equal amounts of blood RNA from each of
four vehicle-only-treated control animals at the 6 and 12 h time
points and from each of six vehicle-only-treated control animals at
the 24 h time point were pooled for control gene expression. These
pools were compared with individual treated animals at each dose
and time period. For the test set, equal amounts of blood or liver
RNA from each of six vehicle-only-treated control animals were
treated animals at each dose and time point. The samples were
hybridized in duplicate (fluor-flips) for each individual rat. Thus,
for each dose and time period, 8 arrays were performed for the
training set (with the exception of only 6 for both the 150 mg/kg
APAP and 1,500 mg/kg dose groups) and 12 arrays for the test set.
Microarray Analysis. RNA samples were labeled with Cy3 and Cy5
with the Agilent Fluorescent Linear Amplification Kit (Agilent,
Palo Alto, CA) and hybridized to Agilent Rat Oligonucleotide
Microarrays (Agilent G4130A) according to the manufacturer’s
instructions. Fluorescent intensities were measured with an Agilent
DNA Microarray Scanner (Agilent G2565AA) and processed with
the Agilent G2565AA Feature Extraction Software. Detailed pro-
tocols are available at www.niehs.nih.gov/research/atniehs/core/
Expression Omnibus (GEO) Database under accession no.
Identification of Significantly Expressed Genes. The gene expression
data were loaded into the Rosetta Resolver database (build
184.108.40.206.23, Rosetta Inpharmatics; Agilent Technologies, Palo Alto,
CA) and merged according to fluor-flip hybridization pairs to
generate weighted-averaged ratio values (computed from the nor-
malized and background-subtracted pixel intensity values). An
Bushel et al. PNAS ?
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vol. 104 ?
no. 46 ?
error-weighted (19, 20) two-way ANOVA model was constructed Download full-text
with the gene expression data to capture both dose and time main
doses (150, 1,500, and 2,500 mg/kg) of the training samples
yijk? ? ? Ti? Dj? ?TD?ij? ?ijk,
where y is the log base 10 of the ratio value for the kthgene, ?
represents the grand mean, T is the main effect for time, D is the
and ? is the term for stochastic error. An error-weighted one-way
ANOVA was also constructed with the gene expression data to
capture the dose effect confounded by time of exposure.
yjk? ? ? Dj? ?jk.
Genes identified as significantly expressed between subtoxic-
base 10 value replaced with 0.
ANOVA Gene Selection and k-NN Classifier Building. For gene selec-
tion, a one-way ANOVA on the two classes of the training data
(subtoxic dose, 150 mg/kg; toxic dose, 1,500 and 2,500 mg/kg) was
constructed to identify the genes that had the highest significance
k-NN approach was implemented by using Euclidean distance and
k ? 3. The k-NN classifier model was constructed by using the
training data, and its accuracy was validated with a 10-fold cross-
validation scheme. This type of validation procedure has been
proven to have a low mean square error and small bias of the
classifier (21). A 10-fold cross-validation approach was taken to
render the highest assessment of classification precision without
extra computational cost or loss of classifier generalization. The
classifier was built by using Partek Pro 6.0 (build 6.04.1112; Partek,
St. Louis, MO).
SVM for Gene Selection. The Gene Expression Model Selector
software (22) was used to construct an MC-SVM from a linear
kernel for gene selection. Two classes from the training data were
generated: a subtoxic-dosed class containing the samples treated
with 150 mg/kg APAP and a toxic-dosed class containing samples
treated with 1,500 mg/kg or 2,500 mg/kg APAP. Briefly, using the
subtoxic class versus the toxic class. A minimum of 20 genes for
genes was performed by using LOOCV. Accuracy was determined
by the proportion of correct classifications over the total number of
Simplified Fuzzy ARTMAP Prediction. The genes selected as highly
the MC-SVM were used to compile the ratio values from the test
data sets. The simplified Fuzzy ARTMAP tracking neural network
samples in the test sets. Methodology and the software for per-
during prediction of the APAP test sets.
Pattern Extraction and Gene Selection for Prediction. The extraction
of gene expression patterns and identification of genes (via EPIG)
was performed. Briefly, ratio intensity values from all of the genes
on the arrays were log base 2 transformed, adjusted by systematic
variation normalization (23) and corrected for dye bias. An outlier
animal (no. 3338) was not included in the analysis. A set of nine
intra-groups (samples at the three time points for each of the three
extracted based on the expression profiles correlation values, the
minimum cluster size for the patterns, and the cluster-partitioning
resolution (9). From the patterns and using signal-to-noise ratio ?
3, magnitude ? 1.5, and a correlation r value of 0.64 of the gene
profiles, genes were selected and included in the signature list for
was performed to predict the classes of the samples by visualizing
the closeness of the test samples to either class of the training
Biological Pathway Analysis.Foreachpredictivegenelistandforthe
union of all four lists, GO analysis was performed in Rosetta
Resolver. Genes involved in the top categories were selected and
Redwood City, CA). Additionally, the complete predictive gene
were analyzed with the Ingenuity Pathway Analysis tool and with
Metacore (GeneGo, St. Joseph, MI).
Human Blood Analysis. Blood was drawn from normal healthy
volunteers or APAP-overdose patients admitted to the University
of North Carolina Emergency Room under UNC IRB protocol
04-MED-416 (SI Table 11). RNA was purified by using the PAX-
gene system as above and analyzed by using Agilent Human
Oligonucleotide 1Av2 microarrays.
We thank Pamela Blackshear for excellent support with histopathological
evaluations; Edward Lobenhofer, Todd Auman, and Gail Carpenter for
stimulating discussions that supported the preparation of this manuscript;
Ben Van Houten, Steven Kleeberger, and Douglas Bell for critical review
of the manuscript, and Boston University for granting the authors permis-
sion to use Fuzzy ARTMAP. This research was supported in part by the
Intramural Research Program of the National Institutes of Health (NIH)
work also was funded in part with Federal funds from NIEHS, NIH, under
Contracts N01-ES-25497, N01-ES-95442, and NO1-ES-35513.
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