TOXICOLOGICAL SCIENCES 126(2), 578–588 (2012)
Advance Access publication January 19, 2012
Quantitative High-Throughput Screening for Chemical Toxicity in
a Population-Based In Vitro Model
Eric F. Lock,* Nour Abdo,* Ruili Huang,† Menghang Xia,† Oksana Kosyk,* Shannon H. O’Shea,* Yi-Hui Zhou,*
Alexander Sedykh,* Alexander Tropsha,* Christopher P. Austin,† Raymond R. Tice,‡ Fred A. Wright,*,1and Ivan Rusyn*,1,2
*University of North Carolina, Chapel Hill, North Carolina 27599; †NIH Chemical Genomics Center, National Human Genome Research Institute, National
Institutes of Health, Bethesda, Maryland 20892; and ‡Division of the National Toxicology Program, National Institute of Environmental Health Sciences,
Research Triangle Park, North Carolina 27711
1These authors contributed equally to the work.
2To whom correspondence should be addressed at Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel
Hill, NC 27599. Fax: (919) 843-2596. E-mail: firstname.lastname@example.org.
Received December 23, 2011; accepted January 15, 2012
A shift in toxicity testing from in vivo to in vitro may
efficiently prioritize compounds, reveal new mechanisms, and
screening (qHTS) is a major source of data for computational
toxicology, and our goal in this study was to aid in the
development of predictive in vitro models of chemical-induced
toxicity, anchored on interindividual genetic variability. Eighty-
one human lymphoblast cell lines from 27 Centre d’Etude du
Polymorphisme Humain trios were exposed to 240 chemical
substances (12 concentrations, 0.26nM–46.0mM) and evaluated
for cytotoxicity and apoptosis. qHTS screening in the genetically
defined population produced robust and reproducible results,
which allowed for cross-compound, cross-assay, and cross-
individual comparisons. Some compounds were cytotoxic to all
cell types at similar concentrations, whereas others exhibited
interindividual differences in cytotoxicity. Specifically, the qHTS
in a population-based human in vitro model system has several
unique aspects that are of utility for toxicity testing, chemical
prioritization, and high-throughput risk assessment. First, stan-
dardized and high-quality concentration-response profiling, with
reproducibility confirmed by comparison with previous experi-
ments, enables prioritization of chemicals for variability in
interindividual range in cytotoxicity. Second, genome-wide
association analysis of cytotoxicity phenotypes allows exploration
of the potential genetic determinants of interindividual variability
in toxicity. Furthermore, highly significant associations identified
through the analysis of population-level correlations between
basal gene expression variability and chemical-induced toxicity
suggest plausible mode of action hypotheses for follow-up
analyses. We conclude that as the improved resolution of genetic
profiling can now be matched with high-quality in vitro screening
data, the evaluation of the toxicity pathways and the effects of
genetic diversity are now feasible through the use of human
lymphoblast cell lines.
Key Words: chemical cytotoxicity; apoptosis; HapMap;
The ‘‘Registration, Evaluation, Authorisation and Restriction
of Chemicals’’ regulations in Europe and Toxic Substances
Control Act reform activities in the United States are creating
substantial pressure to develop improved methods for evalu-
ating potential chemical hazards (Plunkett et al., 2010). Current
chemical safety evaluation (National Research Council, 2007)
relies on in vivo animal testing. In Europe alone, it is expected
that 100,000þ chemicals will require new safety data; yet the
worldwide capacity to evaluate chemicals for the most animal-
intensive in vivo tests is 200–300 chemicals each year (Hartung
and Rovida, 2009).
In the United States, the Tox21 program (Collins et al., 2008) is
a collaborative initiative of four government agencies. This effort
leads the field in its use of a broad spectrum of in vitro assays,
many in quantitative high-throughput screening (qHTS) format
(Inglese et al., 2006), to screen thousands of environmental
chemicals for their potential to affect biological pathways that may
result in human disease (Xia et al., 2008). Such data on
toxicologically relevant in vitro endpoints can assist in decision
making (Reif et al., 2010), serve as predictive surrogates for
in vivo toxicity (Martin et al., 2010; Zhu et al., 2008), and
generate testable hypotheses on the mechanisms (Xia et al., 2009).
Another important consideration in assessing the potential
human health hazard is the degree of interindividual biological
variability in the human population (National Research Council,
2008). A comprehensive characterization of human genome
sequence variation is important for understanding observed
inherited variation in toxicity phenotypes. Indeed, genetic
polymorphisms can have a profound influence on disease risk
after drug or toxicant exposure (Harrill et al., 2009); yet, these
factors are difficult to quantitatively evaluate using current
in vivo animal test systems or established cell lines (Rusyn et al.,
2010). The availability of genetically diverse genetically defined
renewable sources of human cells, such as lymphoblasts from
the International HapMap (International HapMap Consortium,
? The Author 2012. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved.
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2005) and 1000 Genomes (Durbin et al., 2010) projects, enables
in vitro testing at the population scale. As the risk assessment
process shifts toward in vitro data, the quantitative assessment of
interindividual variability in responses to chemicals as well as an
understanding of the underlying genetic causes are needed so
that regulatory decisions can be based on data rather than default
To demonstrate the feasibility of an in vitro model system
to assess interindividual and population-wide variability of
chemical-induced toxicity phenotypes, we exposed cells from
over 80 Centre d’Etude du Polymorphisme Humain (CEPH) cell
chemicals and assessed induction of caspase-3/7, indicative of
of adenosine triphosphate (ATP) as a surrogate for cell number.
This study showed that an in vitro genetics–anchored human
model system can be utilized in a population-level screen for
chemical toxicity, with the potential to identify candidate genetic
susceptibility factors for further study. As a next step, we report
here on a larger scale population-based qHTS using hundreds
of compounds and covering a more comprehensive range of
concentrations. The quantitative assessment of interindividual
variability in response at this scale demonstrates the potential of
this methodology for toxicity screening, hazard evaluation, and
exploration of genetic determinants of susceptibility.
MATERIALS AND METHODS
Chemicals. A subset (240 compounds) of the National Toxicology
Program’s 1408 chemical library (Xia et al., 2008) was used in these experiments.
See Supplementary table 1 for a complete list of chemicals used in these
experiments. Chemicals were dissolved with dimethyl sulfoxide (DMSO) into 12
different stock concentrations ranging from 56.5nM to 10mM and were aliquoted
to 1536-well plate format via pin tool (Kalypsys, San Diego, CA). The final
concentration ranges from 0.26nM to 46.08lM in the assay plates. The negative
control was DMSO at 0.5% vol/vol; the positive control was staurosporine at the
tested concentration range.
Cell lines. A set of 81 immortalized lymphoblastoid cell lines was acquired
from Coriell Cell Repositories (Camden, NJ). The 81 cell lines were from
HapMap Consortium’s CEPH panel and consisted of 27 trios (father, mother, and
a child). Screening was conducted in three batches, and cell lines were randomly
divided into batches without regard to family structure. Cells were cultured at
37?C with 5% CO2in suspension in flasks with upright position in RPMI 1640
media (Gibco, Carlsbad, CA) supplemented with 15% fetal bovine serum
(HyClone, South Logan, UT) and 0.1% penicillin-streptomycin (Gibco). Media
were changed every 3 days. Cell counts and viability were assessed prior to
chemical treatment using Cellometer Auto T4 Plus (Nexcelem Bioscience,
Lawrence, MA). Cells were grown to a concentration up to 106cells/ml, volume
of at least 100 ml, and viability of > 85% before treatment. After centrifugation,
the cells were resuspended in fresh media. The cell suspension was filtered
through a 40-lm nylon cell strainer (BD Biosciences, Durham, NC). Cell stock
was diluted with fresh media to final concentrations of 3–4 3 105cells/ml and
plated into a tissue culture–treated 1536-well white/solid bottom assay plates
(Greiner Bio-One North America, Monroe, NC) at 2000 cells per 5 ll per well
using a flying reagent dispenser (Aurora Discovery, Carlsbad, CA). To increase
the robustness of the data and evaluate reproducibility, each cell line was seeded
on multiple plates (six plates except for two cell lines where five plates were
seeded) so that each compound was screened in each cell line on 2–3 plates
(chemicals were randomly divided in half to enable screening of 120 compound
3 12 concentrations on each plate).
Cytotoxicity and caspase-3/7 assays. Two assays were chosen to evaluate
cytotoxicity according to the manufacturer’s protocols. CellTiter-Glo Luminescent
Cell Viability (Promega Corporation, Madison, WI) assay was used to assess
intracellular ATP concentration, a marker for cytotoxicity, 40 h posttreatment.
Caspase-Glo 3/7 (Promega) was used to assess activity of caspase-3/7, a marker of
apoptosis, 16 h posttreatment. These assays were selected based on their utility for
in vitro screening of cytotoxicity in cell type– (Xia et al., 2008) and individual-
independent (Choy et al., 2008) manner. Time points were selected based on
previous experiments at the National Institutes of Health Chemical Genomics
Center (NCGC) (Xia et al., 2008). A ViewLux plate reader (PerkinElmer, Shelton,
CT) was used to detect luminescent intensity in each well for both assays. Data are
publicly available from PubChem (AIDs: 588812 and 588813).
Response normalization and curve fitting. Data were normalized relative
to the positive/negative controls and corrected as detailed elsewhere (Xia et al.,
2008). Concentration-response titration points were fitted to a Hill equation for
each chemical. Chemicals were classified into three categories based on their
concentration-response curves: active, nonactive, and inconclusive (Huang
et al., 2008; Xia et al., 2008). Specifically, in data from cytotoxicity assay, the
curve classes ?1.1, ?1.2, and ?2.1 were classified as ‘‘active,’’ any positive
curve class as ‘‘nonactive,’’ and others as ‘‘inconclusive.’’ For data from
caspase-3/7 assay, curve classes 1.1, 1.2, and 2.1 were classified as active, any
negative curve class as nonactive, and others as inconclusive.
Curve P. To evaluate the cytotoxic potency of each compound, we
calculated a ‘‘curve P’’ value for each compound-cell line pair. Curve P is
defined as the lowest concentration, which showed a consistent deviation from
the baseline response and derived as detailed in Sedykh et al. (2011). It can be
regarded as a close approximation for the point of departure. Curve P was
derived for all compounds even if little or no toxicity was observed. For the
latter compounds, to enable the follow-up statistical analyses, the curve P was
assigned to a concentration of 50lM. Batch effects were adjusted using the
ComBat method (Johnson et al., 2007).
Assessing variability across individual, chemical, and assay. The
Pearson correlation coefficient (r) between pairs of replicate plates was used
to assess experimental reproducibility. For this analysis, two replicate plates
were randomly selected for each chemical and cell line pair (240 chemicals 3 81
cell lines ¼ 19,440 total replicate pairs sampled).
Kruskal-Wallis ANOVA (Kruskal and Wallis, 1952) was used to assess the
significance of a cell line effect (vs. experimental effect) in curve P for each
chemical. The Benjamini-Hochberg false discovery rate (FDR) (Benjamini and
Yekutieli, 2001) was used to correct for multiple comparisons. To measure
potential confounding with basal metabolic rate, the Spearman (rank) cor-
relation coefficient between curve P and the average ATP level in DMSO-treated
cells was computed for each chemical. The Spearman correlation between the
average curve P value for the cytotoxicity assay and the average curve P value for
the apoptosis assay for each chemical was computed to measure an overall
relationship between the two assays. Furthermore, within each chemical, the
correlation between the two assays across cell lines (averaged over replications)
was computed separately. For both assays, chemical-by-chemical correlation
heatmaps were used to identify clusters of chemicals with similar response across
cell lines. The order of the chemicals in these heatmaps was determined by
complete-linkage distance clustering.
All computations, graphs, and heatmaps used the R programming
environment for statistical computing and graphics (2.10.0; R Development
Core Team, Vienna, Austria).
Concentration response for populations and individuals. For the ATP
assay data for progesterone, a four-parameter logistic model was fit to the assay
IN VITRO SCREENING IN A POPULATION MODEL
versus concentration data for each cell line, using maximum likelihood and the
optim routine in R. The model can be written assay ¼ fðconcentrationÞ þ e;
where fðconcentrationÞ ¼ min þ ðmax ? minÞðexpðb0þ b1concentrationÞ=
ð1 þ expðb0þ b1concentrationÞÞÞ; e~Nð0;r2Þ; where (min, max, b0, b1, r2)
are cell line-specific parameter vectors. For a negative concentration-response
relationship, EC10is the concentration for which expðb0þ b1concentrationÞ=
ð1 þ expðb0þ b1concentrationÞÞ ¼ 0:9: The variation in the EC10estimates
was used as illustrative of population variation in true EC10values, although
additional sampling variation underlies each EC10estimate. An overall logistic
concentration-response curve was fit to the aggregated data across all
Assessing heritability and genetic associations. Heritability calculations
were used to determine overall familial effects among the 27 CEPH trios for
each chemical, on both assays. Calculations were motivated by the mid-parent
regression model y ¼ b0þ b1
apis the father’s response, amis the mother’s response, and e is an error term.
A likelihood ratio significance test is then based on the heritability h2: the
variability in response due to shared genetics as a proportion of total variability
in response. For this analysis, curve P values for each chemical were quantile
normalized to the standard Gaussian distribution.
To measure genotype-toxicity relationships, genome-wide association studies
(GWAS) were performed in R using the GenABEL package (Aulchenko et al.,
2007). Phase III genotype data, on approximately 1.4 3 106single-nucleotide
polymorphisms (SNPs), were obtained for each cell line from the International
HapMap Project (International HapMap Consortium, 2005). GWAS were
performed for each chemical on both assays, with quantile-normalized curve
P values as the response phenotype. The significance of an association between
a given SNP and the response was measured using a likelihood-based score test
(Schaid et al., 2002) (qtscore in GenABEL). For our initial screen, the familial
trio relationships were not used for the analysis, due to the low evidence for
overall heritability, on the grounds that methods such as transmission
disequilibrium testing would reduce power and with the intent to follow any
significant findings with further testing. LocusZoom (Pruim et al., 2010) was
used to visualize the genomic context for suggestive loci determined by GWAS.
?þ e; where y is the child’s response,
RNA-Seq expression versus toxicity assays. The 42 cell lines in common
between Montgomery et al. (2010) and the present study were matched with
HapMap IDs, using RNA-Seq tag counts mapped to the genome as previously
described for 20,000 genes (Zhou et al., 2011). For computational efficiency,
simple read proportions consisting of number of tag counts per gene divided by the
mapped library size (Zhou et al., 2011) were used in linear regression as predictors
for the cytotoxicity assays. FDR q values were then obtained for the entire set of
genes and chemicals, using p.adjust() in R. For the caspase assay, ~5000 genes
were determined to have at least one chemical with q < 0.01, and these genes were
retained for clustering. Hierarchical clustering with average linkage was performed
directly on the FDR q values using the heatmap function in R.
qHTS in a Population of Human Lymphoblasts Yields Robust
and Reproducible Data
Screening was conducted in a 1536-well plate format using
a robotic system. The 81 cell lines were randomly subdivided
into three batches, and each line was screened against 240
chemical substances (see Supplementary table 1 for a complete
list) at 12 concentrations (0.26nM–46.0lM). Each 1536-well
plate contained one cell line exposed to 120 chemicals
accompanied by concurrent vehicle (DMSO) and positive
controls. To increase the robustness of the data, duplicates or
triplicates of each plate were run. Assays for intracellular ATP
content and caspase-3/7 activity were used based on their
utility for in vitro screening of cytotoxicity and apoptosis,
respectively, in cell type– and individual-independent manner
(Choy et al., 2008; Xia et al., 2008). A combination of the two
assays allows for the role of apoptosis in the cytotoxicity
response to be evaluated (Shi et al., 2010).
Several metrics were used to evaluate the reproducibility of
the toxicity phenotypes. First, the concentration-response curve
class (Parham et al., 2009) was identical across replicate plates
95.2% of the time for cytotoxicity and 94.1% for apoptosis.
Second, the pair-wise Pearson correlation among replicate plate
pairs using log (AC50) values for the compounds with active
curve classes for the cytotoxicity and apoptosis assays was
r ¼ 0.99 and r ¼ 0.98, respectively. Third, to evaluate the
effects correlation for all compounds, we calculated a
‘‘curve P’’ value, the lowest concentration that showed
a consistent deviation from the baseline response (Sedykh
et al., 2011), which can be regarded as a close approximation
for the lowest observed adverse effect level. For chemicals
exhibiting no effect across the concentrations tested, the curve
P was assigned to 50lM to enable straightforward statistical
analyses. The pair-wise correlation among replicate plates of
the log (curve P) values was equally high (r[cytotoxicity] ¼
0.91, r[apoptosis] ¼ 0.95) when all compounds were included
(Figs. 1a and b). Finally, there were eight duplicates among the
compounds screened. High concordance in median and range
of responses for these was observed (Figs. 1c and d).
Range in Cytotoxicity Across the Chemicals
The chemicals selected for screening were a subset of
1408 compounds previously tested in one or more traditional
toxicological assays and had been profiled for cytotoxicity
and caspase-3/7 induction by the National Toxicology Program
and NCGC using qHTS (Xia et al., 2008) in (i) 13 human and
rodent cells derived from liver, blood, kidney, nerve, lung, and
skin; and in (ii) 26 human lymphoblast cells (data available from
PubChem AIDs: 963–989). Of these, 240 compounds that were
clearly active in those experiments were selected for the
current study (iii).
Comparison of the cytotoxicity average log (curve P) from the
current study showed high concordance with that in panels (i) and
across three data sets was highly significant (p < 0.0001). High
correlation (r ¼ 0.87; rank correlation ¼ 0.83) was observed
between lymphoblast panels (ii) and (iii), whereas the correla-
tions with the diverse panel (i) were moderately high (r ¼ 0.74
or 0.75; rank correlation ¼ 0.72 or 0.75 with (ii) and (iii),
respectively). Together, the results indicate high external
reproducibility for this measurement of cytotoxicity and,
importantly, the potential utility of lymphoblast cell lines as
a toll for population-based toxicity screening.
Interindividual Variability in Response Across Cell Lines
In contrast to the highly invariant reproducible results found
within individual cell lines, the chemicals induced a wide range
LOCK ET AL.
of responses among the lymphoblast lines. The percentage of
compounds classified as active in the cytotoxicity assay varied
from 28 to 56% (Fig. 2a); an equally broad range of activity
(i.e., 24–45%) was seen in the caspase-3/7 assay (Fig. 2b).
Among actives, a wide range of potency, assessed from
the curve P, was observed for each cell line in both assays
(Figs. 2c and d).
Some chemicals were classified as active for cytotoxicity and
caspase-3/7 induction in all of the lymphoblast lines, whereas
others were not active for either endpoint (Figs. 3a and b).
In both assays, most chemicals were active in some cell lines,
whereas not active in others, indicative of interindividual
(cell line) variability in response. The significant correlation
(rank correlation ¼ 0.77; p ¼ 2.2 3 10?16; all compounds
tested) between the chemical’s average curve P for cytotoxicity
and caspase-3/7 (Fig. 3c) indicates the primary cause of cell
death for these compounds is most likely via apoptosis.
A heatmap shows correlations between average log (curve P)
for all chemicals in both assays (Fig. 3d). Clusters of chemicals
with highly concordant responses across cell lines were evident
for cytotoxicity, apoptosis, or both phenotypes. A significant
(FDR < 5%) correlation between responses in cytotoxicity and
apoptosis assays was observed for most of the compounds
Interindividual variability in cytotoxicity was visualized
using boxplots of log (curve P) for each chemical (Figs. 4a
and b). Although median cytotoxicity differed between chemicals
tested, interindividual variability was observed even for the most
active chemicals. Variance components heritability testing for
each chemical/assay showed that none of the derived h2statistics
was significant after adjusting for multiple comparisons, an
observation which was confirmed using mid-parent assays’
values compared with those of the offspring (data not shown).
Interindividual (between cell lines) versus experimental
(between replicates) variability for each chemical was
evaluated using Kruskal-Wallis ANOVA (Kruskal and Wallis,
1952). Most chemicals show a significant (FDR < 5%) cell line
effect (Figs. 4c and d). It has been suggested that differences in
chemical’s toxicity among lymphoblast lines could be partly
attributed to differences in baseline growth rate and metabolic
status (Choy et al., 2008). Correcting for these measurements
reduces effect correlation that would otherwise make responses
across chemicals appear more similar. We therefore normalized
for control levels of intracellular ATP (e.g., metabolic activity)
randomly selected pairs of replicate plates within each chemical and cell line (240 chemicals 3 81 cell lines ¼ 19,440 replicate pairs displayed). Panels c and d show
side-by-side boxplots for eight duplicate compounds that were tested in two independent wells on each plate.
Intraexperimental reproducibility for cytotoxicity (panels a and c) and caspase-3/7 (panels b and d) assays. Panels a and b show log (curve P) values for
IN VITRO SCREENING IN A POPULATION MODEL
and basal activity of caspase-3/7 as well as for the response of
the positive control cytotoxicant. In addition, we directly
assessed for each chemical whether the basal metabolic rate,
an endpoint which correlates closely with the growth rate
(Choy et al., 2008), significantly correlated with cytotoxicity.
Approximately 80% and 90% of chemicals (Figs. 4c and d;
black dots) exhibited no correlation (FDR > 0.05) between
basal metabolic rate (ATP level in vehicle-treated cells) and
cytotoxicity or apoptosis, respectively, across the cell panel.
Assessing Relationships Between Cytotoxicity and Genotype
With variability among cells from different individuals
demonstrated, we then asked if we could identify genetic
loci responsible, utilizing toxicity phenotypes as quantitative
traits and publicly available genotypes (International HapMap
Consortium, 2005) (Fig. 5). The top two plots in Figure 5 show
p values for the most significant SNP associated with cyto-
toxicity (Fig. 5a) or induction of caspase-3/7 (Fig. 5b) for each
chemical. The inset shows a plot of ?log10 (p values) for SNP
endpoint associations for the selected chemicals. Progesterone
had the lowest p value SNPs on chromosome 9, whereas
guggulsterones Z (4,17(20)-pregnadiene-3,16-dione, z-isoform)
exhibited many suggestive associations on chromosome 6p.
Figures 5c and d provide a zoomed-in view of the genomic
context for these suggestive regions.
Progesterone was not highly cytotoxic, yet showed an
appreciable degree of interindividual variability in curve
P values (Fig. 5c inset). A characteristic pattern of SNPs with
low p values in linkage disequilibrium is evident in a ~300 kb
region containing two genes, structural maintenance of chro-
mosomes protein 5 (SMC5) and MAM domain containing
2 (MAMDC2). Guggulsterones Z, a bioactive constituent of
resinous sap from Commiphora mukul, is a farnesoid X
receptor antagonist and is used widely as a nutraceutical. It is
known to suppress expression of antiapoptotic genes, promote
apoptosis, and inhibit nuclear factor-kappa B (NF-jB)
(Shishodia and Aggarwal, 2004). In our study, it was
moderately active in inducing caspase-3/7 (Fig. 5d inset) and
exhibited interindividual variability. A narrow 100 kb region on
chromosome 6p, containing the gene human immunodeficiency
virus type I enhancer binding protein 1 (HIVEP1), shows
association with the apoptosis phenotype.
Concentration Response for Populations and Individuals
The availability of cytotoxicity screens on 80þ individuals,
with the assays performed under controlled conditions, enables
percentage of chemicals classified as ‘‘active,’’ ‘‘nonactive,’’ or ‘‘inconclusive’’ for each cell line. Panels c and d give the range of potency (curve P) for active
chemicals in each cell line.
Distribution of cytotoxicity across chemicals for cytotoxicity (panels a and c) and caspase-3/7 (panels b and d) assays. Panels a and b give the
LOCK ET AL.
sensitive investigation of variation in individual dose-response
profiles (National Research Council, 2008). This concept is
illustrated in Figure 6a, in which the ATP assay values for
cycloheximide are shown in gray for each concentration for
all individuals. Separate logistic curve fits were performed,
providing for each individual cell line an ‘‘effective concen-
tration 10%’’ (EC10) the estimated concentration at which the
response deviates by at least 10% from the control baseline,
and these are shown as a histogram. The mean of these EC10
values offers a population-wide summary of the activity (e.g.,
cytotoxicity, caspase-3/7) of a chemical and is very similar
to the EC10produced when the data are first pooled for all
individuals and then fit using a single concentration-response
curve (red-dashed curve in Fig. 6a). However, aggregation
across the population ignores the variability in toxic suscep-
tibility, and the EC10estimated fifth percentile may be used to
illustrate the concept of a ‘‘vulnerable’’ subpopulation.
Defining Mode of Action Chemical-Perturbed Pathways
Gene expression data form another rich source of publicly
available data, which can be matched with cytotoxicity profiles
to provide further evidence of toxicity pathway activity. Many
of the HapMap cell lines have been profiled for expression in
a number of studies, including highly sensitive RNA-Seq
profiling (Montgomery et al., 2010). For the 42 cell lines for
which RNA-Seq data are publicly available, expression values
for each of ~20,000 genes were compared with the caspase-3/7
and cytotoxicity assay results, with a number of highly
significant associations. A heatmap of clustering performed
on FDR q values (Fig. 6b) shows striking patterns of gene-
chemical relationships, with much of the structure resolving
into distinct sets of genes associated with sets of chemicals.
The results for progesterone are shown as a highly specific
subgroup, with lymphoblast cytotoxicity for several chemicals
being significantly associated with background RNA levels for
six transcripts and several microRNAs.
New paradigms for the rapid and accurate evaluation of
the potential health hazard from environmental chemicals are
needed, given the large number of environmental chemicals
to be evaluated, and the high cost and low throughput of
the mean ATP curve P value versus the mean caspase curve P value for each chemical. Panel d shows a heatmap of the correlations between log (curve P) values
for all chemical-assay combinations.
The percent of cell lines exhibiting activity for each chemical for cytotoxicity (panel a) and caspase-3/7 (panel b) assays. Panel c displays the rank of
IN VITRO SCREENING IN A POPULATION MODEL
traditional toxicity testing approaches (Collins et al., 2008).
Development of in vitro toxicity tests that can be utilized
in a tiered framework is necessary, feasible, and consistent
with the needs of scientifically rigorous high-throughput risk
assessment (Kavlock et al., 2009). A particular challenge in
developing such next generation toxicity testing schemata is
the assessment of differential susceptibility among individu-
als. The results presented here provide proof of principle of
such a testing system, demonstrating the feasibility and utility
of screening a panel of cells from genetically diverse
individuals, whereby both population-wide and individual
responses can be evaluated.
The in vitro toxicity–screening paradigm detailed here has
focused on a population-based cell culture model, an approach
that affords several key benefits compared with collections of
unrelated cell lines from different species and tissues (Xia
caspase-3/7 (panel b) assays. For cytotoxicity (panel c) and caspase-3/7 (panel d) assays, ?log (p values, Kruskal-Wallis test) were plotted against mean curve
P (micromolar). The blue line gives a FDR-adjusted significance threshold (FDR ¼ 0.05). Chemicals colored in red had a significant correlation between activity
and basal metabolic rate (ATP level in vehicle-treated cells) across the panel of cell lines (Spearman rank correlation; FDR < 0.05).
Boxplots of curve P values for each of the 240 chemicals (arranged by mean activity) across the 81 cell lines are shown for cytotoxicity (panel a) and
LOCK ET AL.
et al., 2008). Our results show that many chemicals exhibit
interindividual variation in induction of toxicity, and this
information is crucial for chemical-testing prioritization. This
screening paradigm also provides quantitative data on population-
wide variability in toxicity, which may be used to establish data-
driven uncertainty estimates when extrapolating from in vitro
data to potential in vivo toxicity (Judson et al., 2011). Even
though the data collected herein are on a limited population
(81 individuals), it is immediately interpretable for ranking and
prioritizing chemicals. For example, a population-based view
of dose- or concentration-response is an important concept that
directly addresses the issue of subpopulations (National
Research Council, 2008); however, actual experimental data-
driven implementation has been limited. We reason that the
population-based concentration response in vitro qHTS data
allows for the development of models to estimate in vitro point
of departure and safety/uncertainty factors (Crump et al., 2010)
because variation between genetically defined/ diverse cell
lines may be treated as reflective of that among individuals.
The recognition of underlying genetic causes may further
enhance extrapolation and understanding of the shape of the
dose-response relationships. In addition, the data may be used
to explore potential differences/similarities in modes of action
between chemicals on the population-wide level.
By combining toxicity data with publicly available genetic
information, such as that provided by the HapMap (International
HapMap Consortium, 2005), 1000 Genomes (Durbin et al.,
2010), and public RNA–sequencing projects (Montgomery
et al., 2011), it is possible to probe the contribution of genomics
to toxicity phenotypes. Such an approach represents a substantial
savings of cost and time, capitalizing on the extensive prior
characterization of these samples. Accordingly, we have begun
to explore variation in toxicity susceptibility as a function of
genotype as well as the relationship between toxic response and
basal expression profiles.
Genotype-phenotype relationships are likely to reflect causal
action of underlying physiological variation and are thus of
great interest to epidemiologists for understanding the ultimate
sources of population variation. However, the effect sizes are
typically small, as has been the source of considerable discussion
in the genomics community (Manolio et al., 2009). Variation
in basal messenger RNA (mRNA) expression, in contrast,
b and d) assays. Panels a and b give p values (-log10 scale) for the most significant SNP associated with toxicity for each chemical. The inset in the diagram gives
?log10 (p values) for SNP-toxicity associations across the entire genome, for progesterone (cytotoxicity assay, inset in panel a) and Guggulsterones Z (caspase-3/7
assay, inset in panel b). Panels c and d provide a zoomed-in look at the locus with the most significant p value for each of the two compounds, respectively. Correlation
between SNPs is identified with colors. SNP and gene tracks are also shown. Inset: box and whisker plots for each compound’s curve P.
Toxicity-genotype relationships were assessed using GWAS analysis for the 240 chemicals on both cytotoxicity (panels a and c) and caspase-3/7 (panels
IN VITRO SCREENING IN A POPULATION MODEL
may reflect cascades of responses controlled by the underlying
genotype and typically involves a smaller multiple testing
penalty. Thus, we likely have more power to detect association
of expression with toxicity response phenotypes, even though
the underlying causality relationships may remain elusive. The
highly significant associations identified through the analysis of
population-level correlations between basal gene expression
variability and chemical-induced toxicity have revealed several
reasonable mode of action hypotheses. For example, the in vitro
toxicity of 1,3-indandione-containing rodenticides has been
shown to occur through the inhibition of the pyrimidine
synthetic pathway (Hall et al., 1994), and thioredoxin reductase
(e.g., TXNRD3IT1) is required for deoxynucleotide triphosphate
pool maintenance during S phase (Koc et al., 2006). Expression
of somatostatin receptor 4 correlates with progesterone receptor
levels in human breast tumors (Kumar et al., 2005). Thioredoxin
reductase affects expression of progesterone receptor–controlled
genes in MCF-7 cells (Rao et al., 2009).
Similarly, the quantitative assessment of interindividual
genetic variability in responses to environmental agents in vitro
demonstrates the potential of this approach to explore the
genetic basis for susceptibility through genome-wide associa-
tion analysis. The genes SMC5 and MAMDC2 implicated in
this study as associated with progesterone-induced toxicity
are highly plausible and belong to pathways critical for
development. The same locus was reported as associated with
developmental abnormalities cleft palate and Kabuki syndrome
(Kuniba et al., 2009; Marazita et al., 2004), and exposure to
progesterone during gestation is known to cause cleft palate in
rabbits (Andrew and Staples, 1977). Likewise, the association
between guggulsterones Z and polymorphisms in HIVEP1 is
highly credible, given the known effects of guggulsterones Z
on apoptosis through NF-jB–related signaling (Shishodia and
Aggarwal, 2004). HIVEP1 belongs to a family of large zinc
finger–containing transcription factors that bind specifically to
the NF-jB motif and related sequences (Yu et al., 2009). The
alternative splice variant of HIVEP1, the gatekeeper of
apoptosis activating proteins (GAAP)-1 protein, can regulate
p53 and IRF-1-dependent cell proliferation and apoptosis
(Lallemand et al., 2002).
Important limitations to in vitro toxicity profiling using
lymphoblasts, as compared with primary cells that may be
obtained from other tissues of interest, include inability to
assess target organ adverse effects or a potential role of other
environmental factors such as lifestyle, diet, or coexposures. In
addition, the challenge of assessing the potential toxicity of
chemical’s metabolites or the potential lack of the receptor-
mediated signaling that may be critical for the downstream
adverse molecular events, in lymphoblast cell lines also should
be taken into consideration when interpreting the data. Still,
whereas lymphocytes do not have the metabolic capacity of the
liver or even that of freshly isolated hepatocytes, they do
express a number of nuclear receptors, as well as most genes of
the phase I and II metabolism, and transporters (Siest et al.,
2008). A comparison of the population-wide (250þ individuals
of various races, ages, and gender) variability in mRNA levels
for several dozen liver-specific thyroid hormone–related genes
between human liver (Schadt et al., 2008) and lymphoblast cell
lines (Stranger et al., 2007) shows that most of the nuclear
receptors and metabolism genes are expressed in lymphoblasts,
albeit at 10 to 100 times lower quantity. Importantly, the
between subject variability in expression of these genes in either
human liver or lymphoblasts is also of appreciable magnitude
(4- to 10-fold). To overcome these limitations, both higher
concentrations and known metabolites can be tested in vitro
because of high throughput. Correcting for the cell growth rate
and baseline metabolic rate also reduces effect correlation that
may make responses across chemicals appear more similar
(Choy et al., 2008).
Based on these results, we reason that a full and sensitive
analysis of genomic predictors of toxicity response will be
feasible through the joint use of toxicity phenotypes, genotype,
in vitro qHTS data using cycloheximide data (cytotoxicity assay) as an example.
Logistic dose-response modeling was performed for each individual to the values
shown in gray, providing individual 10% effect concentration values (EC10). The
EC10obtained by performing the modeling on average assay values for each
concentration (see frequency distribution) are shown in the inset. Panel b,
a heatmap of clustered FDRs (q values, see color bar) for association of the data
from caspase-3/7 assay with publicly available RNA-Seq expression data on
a subset of cell lines. A sample subcluster is shown.
Panel a, a population concentration response was modeled using
LOCK ET AL.
and expression information, though considerably larger sample
sizes—likely on the order of several hundred or thousands of
individual cell lines—will be necessary. Such a population-
based in vitro survey would greatly advance our understanding
of the genetic underpinnings of susceptibility-related regulatory
networks and is ongoing in our laboratories.
Supplementary data are available online at http://toxsci.
This research was supported, in part, by the Intramural
Research Programs of the National Toxicology Program,
National Institute of Environmental Health Sciences inter-
agency agreement Y2-ES-7020-01 and by grants from the
National Institutes of Health (NIH) (R01 ES015241) and U.S.
Environmental Protection Agency (U.S. EPA) (RD83382501).
We thank Srilatha Sakamuru for technical support. The
research described in this article has not been subjected to each
funding agency’s peer review and policy review and therefore
does not necessarily reflect their views and no official endorse-
ment should be inferred. The authors declare no competing
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