Systems Biology Approach for Evaluating the Biological Impact of

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Systems Biology Approach for Evaluating the Biological Impact of Environmental Toxicants in Vitro Ignacio Gonzalez-Suarez,*,† Alain Sewer,† Paul Walker,‡ Carole Mathis,† Samantha Ellis,‡ Heather Woodhouse,‡ Emmanuel Guedj,† Remi Dulize,† Diego Marescotti,† Stefano Acali,† Florian Martin,† Nikolai V. Ivanov,† Julia Hoeng,† and Manuel C. Peitsch† †

Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland Cyprotex, 15 Beech Lane, SK10 2DR Macclesfield, United Kingdom

‡

S Supporting Information *

ABSTRACT: Exposure to cigarette smoke is a leading cause of lung diseases including chronic obstructive pulmonary disease and cancer. Cigarette smoke is a complex aerosol containing over 6000 chemicals and thus it is difficult to determine individual contributions to overall toxicity as well as the molecular mechanisms by which smoke constituents exert their effects. We selected three well-known harmful and potentially harmful constituents (HPHCs) in tobacco smoke, acrolein, formaldehyde and catechol, and established a highcontent screening method using normal human bronchial epithelial cells, which are the first bronchial cells in contact with cigarette smoke. The impact of each HPHC was investigated using 13 indicators of cellular toxicity complemented with a microarray-based whole-transcriptome analysis followed by a computational approach leveraging mechanistic network models to identify and quantify perturbed molecular pathways. HPHCs were evaluated over a wide range of concentrations and at different exposure time points (4, 8, and 24 h). By high-content screening, the toxic effects of the three HPHCs could be observed only at the highest doses. Whole-genome transcriptomics unraveled toxicity mechanisms at lower doses and earlier time points. The most prevalent toxicity mechanisms observed were DNA damage/growth arrest, oxidative stress, mitochondrial stress, and apoptosis/necrosis. A combination of multiple toxicological end points with a systems-based impact assessment allows for a more robust scientific basis for the toxicological assessment of HPHCs, allowing insight into timeand dose-dependent molecular perturbations of specific biological pathways. This approach allowed us to establish an in vitro systems toxicology platform that can be applied to a broader selection of HPHCs and their mixtures and can serve more generally as the basis for testing the impact of other environmental toxicants on normal bronchial epithelial cells.

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INTRODUCTION

and their contributions to the overall effects should be quantified and analyzed in an integrative manner. This approach requires the use of modern tools and technologies including medium- and high-throughput in vitro screening assays, computational toxicology, systems biology, and pharmacokinetic modeling.1,2,5 The new testing paradigm is also a direct consequence of the need to find alternatives to animal testing.5 Although conducted in vitro, the new approach presented here is based on human cell lines and thus better represents certain aspects of human biology (particularly when primary cell models are used). In vitro models have limitations in terms of exposure duration and translatability to in vivo exposure scenarios. Nevertheless, the possibility to test simultaneously a high number of experimental conditions,

Toxicological risk assessment relies heavily on the use of in vivo animal studies, which are costly, time-consuming, and unable to provide high-throughput information. Furthermore, these types of studies are based on the development of phenotypic responses in the animals that are usually observed only at high doses that do not represent typical exposures in the human population.1,2 Toxicological studies also utilize validated in vitro assays, but they too are limited by low throughput and effective doses, which again are far from relevant exposure scenarios. In addition, the experimental systems and the exposure conditions vary in each case, which hinders data integration. In recent years, a clear shift to innovative testing strategies for toxicological assessment has been set in motion using evolving technologies, a concept known as toxicity testing in the 21st century.1,3,4 Relevant molecular pathways specifically perturbed upon exposure to toxic agents need to be identified, © XXXX American Chemical Society

Special Issue: Systems Toxicology Received: November 5, 2013

A

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Table 1 μg/L acrolein formaldehyde catechol

dose 1

dose 2

dose 3

dose 4

dose 5

dose 6

dose 7

dose 8

dose 9

2800 4500 3300

1400 450 1500

350 150 175

150 75 100

100 30 50

50 20 20

20 10 10

10 5 5

5 2 2

sources of exposure include vehicle emissions, food, and cooking as well as the use of formaldehyde as a disinfectant.19 Exposure to high levels of formaldehyde (4 mg/m3 and higher) have been reported for embalmers, pathologists, and paper workers.19,20 Catechol is categorized as possibly carcinogenic to humans (IARC class 2B) because of the increased incidence of adenocarcinomas in exposed rodents.19 In addition, it is considered toxic via the oral and dermal routes and harmful by inhalation and is suspected of causing genetic defects.19,22 In vitro, catechol induces growth arrest and apoptosis as well as DNA damage and genomic instability at doses between 1−3 mg/L.13,23,24 Catechol is present in both the particulate and the gas/vapor phase of CS at concentrations ranging between 20 and 150 μg/L depending on the brand and smoking profile.21 Another significant source of exposure is the industrial production of pesticides and plastics, the burning of wood or fuel, and dermal contact with contaminated water.22 Catechol can also be found in some plants, such as onions, apples, and oak trees, although at lower doses.22 To provide mechanistic insight into the mode of action of acrolein, formaldehyde, and catechol, we performed highcontent screening (HCS) analysis and measured 13 different indicators of cellular toxicity. The study was complemented with a microarray-based transcriptomics analysis followed by a quantitative systems biology-based approach leveraging mechanistic network models.25−27 This approach allowed us to identify and characterize perturbed molecular pathways impacted upon exposure to the toxicants at different doses and time points.

including low doses with test systems that are more sensitive than traditional methods, is seen as an important advantage over in vivo studies. In the present study, we used an in vitro model of normal primary human bronchial epithelial (NHBE) cells to investigate the biological impact of environmental toxicants over a wide range of concentrations and at different exposure durations. Lung epithelial cells constitute the first biological barrier against toxic gases and particles in inhaled air. Thus, they are of interest for the assessment of environmental air pollutants, including those usually found in cigarette smoke (CS). Moreover, the use of human primary cells provides an important advantage because it minimizes the risk of genetic modifications (which are always present in immortalized cell lines) that may act as a confounding factor when trying to identify perturbations in specific molecular pathways using whole-genome transcriptomics.6 CS is a highly complex mixture of more than 6100 constituents.7 Here, we focused on three highly reactive compounds, acrolein, formaldehyde, and catechol, which are part of the harmful or potentially harmful constituents (HPHCs) of tobacco products and tobacco smoke listed by the Food and Drug Administration (FDA).8 Acrolein is one of the nine CS constituents suggested by the WHO Working Group on Tobacco Regulation for mandated lowering.9 It is a very reactive molecule and will directly form adducts with proteins and DNA.10 Acrolein is cytotoxic to all cells tested and induces both necrosis and apoptosis in lung and other cell types at concentrations ranging from 0.5 to 10 mg/ mL.11−14 It is also genotoxic in some cell lines but only at very high doses, which has likely prevented the demonstration of carcinogenicity in animal bioassays and epidemiological studies.15 Acrolein is present in the gas/vapor phase of CS at doses ranging between 50 and 300 μg/L. Another relevant source of exposure for the general population is the combustion of organic materials, including diesel fuels and food.16−18 Finally, acrolein is also released into the environment as a product of fermentation or ripening processes. Formaldehyde has recently been classified as carcinogenic to humans by the inhalation route of exposure by the International Agency for Research on Cancer (IARC)19 and the U.S. Environmental Protection Agency (EPA).20 In addition to a clear association with cancers of the nose, pharynx, and lymphohematopoietic system, seven different noncancer health effects caused by formaldehyde were identified: sensory irritation of the eyes, nose, and throat; upper respiratory tract pathology; pulmonary function; asthma and atopy; neurologic and behavioral toxicity; reproductive and developmental toxicity; and immunological toxicity.19,20 Formaldehyde has only moderate cytotoxicity at the cellular level, with EC50 values ranging between 0.6 and 2 mg/L depending on the cell type,5,19,20 although it is genotoxic at lower doses. Exposure to formaldehyde arises from multiple sources, both natural, via cell metabolism, and artificial. Formaldehyde is present in the gas/ vapor phase of CS at doses ranging between 50 and 300 μg/L depending on the brand and smoking profile.18,21 Other

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EXPERIMENTAL PROCEDURES

Cell Culture. Primary normal human bronchial epithelial (NHBE) cells were purchased from Lonza (Allendale, NJ, USA). The donor was a 60 year old Caucasian male with no history of smoking. The cells were maintained in a humidified incubator at 37 °C and 5% CO2. The cells were cultured in bronchial epithelial cell medium (Bullet Kit CC 3170, Lonza) according to the vendor’s recommendations. Toxicants and Doses. Acrolein, formaldehyde, and catechol were purchased from Sigma-Aldrich (St. Louis, MO, USA) at the highest available grade. The doses tested are indicated in Table 1. Doses 1 and 2 are within the range of concentrations previously reported to induce toxicity in lung epithelial and other cell types. The doses were selected on the basis of literature review of in vitro toxicity studies of acrolein,11−13,15,28 formaldehyde,19,20,29,30 and catechol13,23 in lung epithelial cells and other cell types. Doses 3−7 are concentrations of acrolein, formaldehyde, and catechol in the mainstream smoke of one conventional cigarette (CC) and simulate different levels of exposure in smokers. For dose selection, we used internal data as well as available literature to calculate the quantities (in micrograms) of acrolein, formaldehyde, and catechol in mainstream smoke from one single cigarette of different commercial brands as well as the reference cigarette 3R4F under different smoking regimens.21,31 The concentrations were calculated by dividing these amounts by the total volume of smoke generated (number of puffs × puff volume in milliliters). Two additional doses below the level of exposure to CC were also tested (doses 8 and 9). Ethanol was used as vehicle at a final B

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Table 2 μM HCS end point mitochondrial membrane potential mitochondrial mass cytochrome C release reactive oxigen species mitosis apoptosis membrane permeability GSH content DNA damage MAPK signaling

positive control

dose 1

dose 2

dose 3

CCCP

500

200

50

20

tacrine nocodazole staurosporine

1000 1 300

400 0.4 120

100 0.1 30

ethacrynic acid mytomicin c colchicine

1000 200 10

400 50 4

100 20 1

concentration of 2% v/v. Preliminary experiments showed that this concentration of vehicle ensures >80% cell viability after 24 h of exposure. High-Content Screening. Exposure. NHBE cells were seeded in black, clear-bottom 96-well tissue culture plates at a density of 12 000 cells for all end points except the phosphorylated histone 3 (pH3) end-point assay, where 3000 cells where seeded. The cells were incubated for 24 h in the culture medium and then exposed (in three replicates) to increasing doses of the three toxicants or to the empty vehicle (2% ethanol). The cells were exposed for 4, 8, or 24 h before running the HCS assays. In parallel, appropriate positive controls were used for each assay: carbonyl cyanide m-chlorophenyl hydrazone (CCCP), mitochondrial membrane potential and mitochondrial mass (MitoTracker, Life Technologies); cytochrome C release (antibody, Abcam); tacrine (reactive oxygen species); dihydroethidium (Sigma); nocodazole (mitosis; pH3 antibody, Millipore); staurosporine (apoptosis; caspase 3/7 activity CellEvent, Life Technologies and membrane permeability, YO-PRO-1, Life Technologies); ethacrynic acid (gluthatione content; monochlorobimane, Sigma); mitomicyn C (DNA damage; pH2AX antibody, Millipore); colchicine (MAPK signaling; p-cJun antibody, Millipore). The doses tested are detailed in Table 2. DMSO (0.5%) was used as the vehicle control for all controls treatments. The experiments were performed once, with three biological repeats per dose and time point. Cell count, nuclear size, and DNA structure were measured in all assays using Hoechst 33342 (Life Technologies). Following staining of the NHBE cells, fluorescence was analyzed by image acquisition with a Thermo Fisher Cellomics ArrayScanVTI high-content screening reader (ThermoFisher Scientific Inc., Waltham, MA, USA) and vHCSTMview software (ThermoFisher Scientific Inc.). Twenty fields were imaged per well using a 10× wide-field objective. Staining and Data Analysis. The image acquisition data were normalized to vehicle control values. Dose−response curves were defined and evaluated with the following equations

ξ(C ; c ; ω) ≡ (ln(C) − c)/ω

(1)

t(ξ) ≡ (1 + tanh(ξ))/2

(2)

R(t ; R 0; R∞) ≡ R 0(1 − t ) + R∞t

(3)

dose 4

dose 5

dose 6

dose 7

dose 8

dose 9

5

2

0.5

0.2

0.05

40 0.04 12

10 0.01 3

4 0.004 1.2

1 0.001 0.3

0.4 0.0004 0.12

0.1 0.0001 0.03

40 5 0.4

10 2 0.1

4 0.5 0.04

1 0.2 0.01

0.4 0.05 0.004

0.2 0.02 0.001

radio cytotoxicity kit (Promega, Fitchburg, WI, USA) according to the manufacturer’s instructions. RNA Extraction and Microarray Hybridization. Exposed cells were lysed at the different post exposure time points using RLT, which contains beta mercaptoethanol 1% (Qiagen), and extraction was performed with the RNeasy mini kit (Qiagen). The quality of the total RNAs was verified by an Agilent 2100 bioanalyzer profile: an RNA integrity number (RIN) of greater than 8 was required. For mRNA analysis, 50 ng of total RNA was processed as described in the GeneChip 3′ IVT Express user manual (Affymetrix, Santa Clara, CA, USA), and Genechip Human Genome U133 Plus 2.0 arrays were used for hybridization, which simultaneously probe the expression of thousands of genes. Hybridization was performed on RNA samples from one exposure, with three biological replicates per dose and time point. The total number of RNA samples analyzed was 72. The gene expression data used in this publication has been deposited in ArrayExpress (http://www.ebi.ac.uk/arrayexpress/) and is accessible through accession number E-MTAB-2080. Quantification of Network Perturbation Amplitudes (NPA). A recently published collection of hierarchically structured network models describing the molecular mechanisms underlying the essential biological processes in healthy lung tissues was used for mechanistic analysis (cell proliferation,32 cellular stress,33 inflammatory processes,34 DNA damage, autophagy, apoptosis, necroptosis, and senescence).35 They were combined with the differential gene-expression profiles induced by exposure to the three toxicants, which were computed from the microarray hybridization data using standard Bioconductor bioinformatics packages.36−38 The NPA values represent the differences, compared with control vehicle, of the activity of the biological process described by a network induced by the toxicant exposure and which are observable through the transcriptome of the exposed cells. They are constructed from the differential expression profiles of the individual genes underlying the network models using both reversecausal reasoning and an aggregation procedure that takes into account the network structure.25 In the present study, the NPA values and their associated uncertainty and specificity statistics were calculated using the geometric perturbation index metric and the downweighting promiscuous hypothesis (DPH) method to account for overlaps in subnetwork nodes.25 The uncertainty statistic quantifies the variability of a NPA value resulting from the individual variability of the differential gene-expression profiles across the experimental replicates. The specificity statistic tests whether a NPA value is really a consequence of the perturbation of the biological process described by the network model. It compares it to the distribution of alternative NPA values obtained by randomly replacing the genes underlying the network model with genes unrelated to it.25 These distributions of alternative NPA values were also used to standardize the actual NPA values and make them comparable across networks. These standardized DPH-based NPA values are intended to highlight better the specific distributions of the perturbations induced by each toxicant across the various networks models, which enables a meaningful comparative characterization of their biological impact.25 It is important to note that a network model with a significant specificity

in which C represents the test compound concentration and R0, R∞, c, and ω are fitting parameters. The final response at a given concentration C is expressed as R(t(ξ(C; c; ω)); R0; R∞). It was restricted such that ω > 0, which implies R → R0 as C → 0 and R → R∞ as C → ∞. The coefficient of determination (r2) was calculated for each compound and dose−response curve. An r2 value of greater than 0.65 was used as QC criteria and was required in all response curves. Lactate Dehydrogenase Release Assay. Lactate dehydrogenase (LDH) release was measured in the supernatant fluid of the treated cells after 24 h of exposure to the toxicants using the CytoTox nonC

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Figure 1. Systems biology approach for the assessment of environmental toxicants. One or more experimental systems (e.g., NHBE cells) (1) are exposed to one or more stimuli (e.g., acrolein, formaldehyde, and catechol) (2) at different doses and time points (3). The biological impact of the stimuli on the cells is initially evaluated by 13 indicators of toxicity using a high-content screening platform (4). In addition, relevant doses and time points are selected for further analysis by high-throughput profiling techniques (metabolomics, proteomics, or, as in this case, whole-genome transcriptomics) (5). A randomization plan at the time of sample extraction and processing is used to minimize batch effects that may later affect data evaluation. The raw data is processed, and replicate samples are grouped and compared to the vehicle control to identify differences in expression (6). Generated omics data is applied to our network models describing the essential biological processes in healthy lung tissues (cell proliferation, cellular stress, inflammatory processes, DNA damage, autophagy, apoptosis, necroptosis, and senescence). This step allows for the identification of specific molecular mechanisms impacted by the different stimuli (7). The NPA scoring algorithm is then used to quantify the level of perturbation in each network under the different experimental conditions (8). Finally, the different NPA scores are computed and plotted together into a biological impact factor, which provides a straightforward yet comprehensive overview of the impact on the biological system. The data generated by the NPA scores are further supported by the results previously obtained with the HCS (9). Images provided by and used with permission from Philip Morris International. statistic (p value < 0.05, or approximately equivalently a standardized NPA value > 2) usually implies that some but not necessarily all of its subnetwork models also display significant specificity statistics. This enables a top−down investigation of perturbed biological pathways. Inversely, a subnetwork model with significant specificity statistic does not always imply that its overarching network model also displays a significant specificity statistic. This results from the fact that a perturbation that is specific to the biological mechanisms described by a subnetwork usually becomes less specific when evaluated with respect to the wider assembly of (not necessarily perturbed) biological processes described by the overarching network model.

Gene-expression profiling generates an overwhelmingly vast amount of information, which makes data interpretation difficult and does not provide comprehensive mechanistic understanding. To integrate omics data in a relevant biologic context, we used a systems biology-based approach built on causal biological network models.26,32−35 The models can be modified to represent specific species and/or tissue contexts (e.g., human lung). When applied to the transcriptomics data sets, the network models provide a qualitative overview on the biological perturbations caused by a given stimulus (i.e., acrolein, catechol, or formaldehyde). In addition, we developed a network perturbation amplitude (NPA) scoring algorithm that enables quantification of the level of perturbation of the biological processes in those networks.25 The combination of NPA scores and HCS end points provides a robust scientific basis for toxicological assessment of environmental toxicants while at the same time gaining insight into the molecular mechanisms activated upon exposure. High-Content Screening Analysis. The toxic effects of acrolein, formaldehyde, and catechol were initially evaluated using a HCS platform in a dose range covering approximately 4 orders of magnitude. To ensure that any observed effects in the cell were a direct consequence of exposure to a specific toxicant and not the result of unspecific events secondary to cytotoxicity, the highest dose was selected such that no more than a 20% decrease in cell count was observed after 24 h of exposure (Supporting Information Figure 1A). The absence of cell loss was confirmed by the quantitative measurement of LDH released into the media from damaged cells as a biomarker for cellular cytotoxicity and cytolysis. (Supporting Information Figure 1B). The two additional exposure times, 4 and 8 h, were selected to identify early responses in the cells.

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RESULTS The integrated systems biology approach used for the assessment of toxicants is detailed in Figure 1. In this study, we focused on one in vitro system (NHBE cells) and three individual toxicants; however, the same approach can be used to investigate multiple stimuli in different in vitro systems. NHBE cells were exposed to acrolein, formaldehyde, or catechol at different doses and time points. Initial assessment was performed using a HCS platform and measuring the following 13 toxicity end points: cell number, nuclear size, DNA structure, cytochrome C release, cell membrane permeability, mitochondrial mass, mitochondrial membrane potential, cell proliferation (phosphorylation of histone H3, pH3), oxidative stress (dihydroethidium, DHE), DNA damage (phospho-histone H2AX, pH2AX), cell stress (phosphorylation of c-Jun), glutathione (GSH) content, and apoptosis (caspase 3/7 activity). The results generated, which are further discussed later, provide a first mechanistic insight into the mode of action of each toxicant. Moreover, on the basis of these results, we selected three doses for further analysis using whole-genome transcriptomics. D

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Table 3. Summary of the HCS Results for Acrolein, Formaldehyde, and Catechola

a Green cells indicate a positive response compared with vehicle control. The direction of the response is indicated by the arrow (↑, increase; ↓, decrease). The values indicate the minimum dose of each toxicant at which a significant response (p < 0.05) was observed. × indicates the absence of a statistically significant response compared to the vehicle control.

Figure 2. Network perturbation amplitude (NPA) values for acrolein, formaldehyde, and catechol. Radar plots of the standardized NPA values show the main mechanistic components for all doses and exposure time points compared with the vehicle control. Dark green outlines represent the high dose, light green outlines represent the medium dose, and yellow outlines represent the low dose. The gray grid indicates integer values of z = 0, 1, 2, 3, ... from the center outward, and the gray area inside the z = 2 polygon delimits (approximately) the region of specificity statistics above the confidence threshold (p > 0.05).

E

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Table 4. Summary of Positive NPA Results for Acrolein, Formaldehyde, and Catechola

a

Green cells indicate a network perturbation with a statistically significant specificity statistic at the network level (p < 0.05). Stars indicate a positive result in one of more specific subnetworks, although the network is not significantly perturbed as a whole. The values indicate the minimum dose of each toxicant at which a significant response (compared to the vehicle control) was observed.

Exposure to acrolein at concentrations above 1400 μg/L significantly decreased the levels of GSH in NHBE cells after only 4 h (Table 3 andSupporting Information Figure 2A). A similar effect was observed at 8 h and, to a lesser extent, at 24 h, where GSH content decreased only at the highest dose. This time dependency may indicate a partial regeneration of GSH stores. Acrolein also caused a dose-dependent increase in pH3positive cells at concentrations above 1400 μg/L after 8 h of exposure. The same effect was observed at even lower doses after 24 h of exposure (Table 3 and Supporting Information Figure 2B). The phosphorylation of histone 3 protein is a tightly regulated process that takes place during mitosis and is commonly used to monitor cell proliferation. However, accumulation of pH3-positive cells is also observed during cell cycle arrest. The fact that that the increase in pH3-positive cells is not accompanied by an increase in cell number suggest that acrolein induces cell cycle arrest; however, additional information is required to confirm this possibility. No effects were observed for the other HCS end points. The toxic effects of formaldehyde were observed only at the highest dose (4500 μg/L) and included a decrease in GSH levels, increased DNA damage, and activation of the stress kinase pathway. Exposure to formaldehyde induced a decrease in the mitochondrial mass. Interestingly, we also observed an increase in mitochondrial membrane potential. This may suggest an adaptive response to cellular energy demand (Table 3 and Supporting Information Figure 3). No effects were observed for the other HCS end points. The only effect observed for catechol was an increase in DNA damage at doses above 1500 μg/L and after 24 h of exposure (Table 3 and Supporting Information Figure 4). Systems Biology-Based Toxicological Assessment. To gain better insight into the toxicity mechanisms of the three toxicants, we performed a systems-wide analysis of transcriptomics measurements. On the basis of the results from the HCS assays, we selected three doses for each toxicant (acrolein: 2800, 150, and 10 μg/L; formaldehyde: 4500, 75, and 10 μg/L; and catechol: 3300, 100, and 20 μg/L). In all cases,

the highest dose selected was also the highest concentration used in the HCS analysis, the medium dose represented the level of exposure to one CC, and the low dose represented a level of exposure below one CC. We first computed the NPA values using the cell proliferation network,32 cellular stress network,33 inflammatory processes network,34 and the DNA damage, autophagy, cell death (apoptosis and necroptosis), and senescence networks.35 Figure 2, Table 4, and Supporting Information Table 1 show the biological impact of acrolein, formaldehyde, and catechol on the different networks at the different doses and time points. The radar plots provide a general overview of the biological processes that most specifically contribute to the toxicity of each toxicant. NPA values can be further decomposed into more specific subnetworks, providing more detailed mechanistic information. In the case of acrolein, the main mechanistic components were stress, proliferation, and senescence. The effects were observed at both high and medium doses but not at low doses. When these processes were decomposed into subnetworks, we observed that acrolein at high doses caused cell cycle inhibition and growth arrest at all exposure time points (Figure 2 and Supporting Information Figure 5). On the basis of this observation, the aforementioned increase in pH3-positive cells observed with HCS would not indicate increased cell proliferation but rather a cell cycle arrest at G2/M. Acrolein also impacted a number of networks associated with the defense against oxidative stress, including drug metabolism response and the nuclear factor (erythroid-derived 2)-like 2 (NFE2L2 or Nrf2) pathway. These effects were observed at the different exposure time points for high and medium doses, but no effects were observed at the lower dose. Finally, we observed the activation of oncogene-regulated senescence networks (Supporting Information Figure 5). This effect is consistent with the presence of oxidative stress and the cell cycle inhibition. The most perturbed networks upon exposure to formaldehyde were senescence, proliferation, DNA damage, F

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Figure 3. Systems toxicology-based profiles of acrolein, formaldehyde, and catechol. The plots show the relative contributions of the different mechanistic components to overall toxicity. They have been obtained by counting the NPA values with significant specificity statistics across all doses and exposure time points. This minimal representation allows for a systems biology-based classification of the toxicants that integrates the contributions of all of the specifically perturbed biological pathways.

methods based on causal biological networks allowed us to identify and quantify molecular mechanisms specifically perturbed upon exposure to acrolein, formaldehyde, and catechol under different experimental conditions. One of the advantages of our approach is that the biological information is structured into levels of increasing granularity, thus allowing for a stepwise interpretation of the biological data. The first level (Figure 3) provides a global overview of the biological impact of acrolein, formaldehyde, and catechol. This representation is also useful to classify environmental toxicants based on systems toxicology profiles. The toxic effects of acrolein on NHBE cells are dominated by the induction of cellular stress followed by proliferation and to a lesser extent senescence networks. The cellular response profile of formaldehyde is quite different, with a lower impact on cellular stress and a higher impact on DNA damage and apoptosis networks. The cellular response profile of catechol is somewhere in between the other two, with a more equal distribution between all networks. The second and third levels (Figure 2 and Supporting Information Figure 5, respectively) provide quantitative information on the specific biological networks and subnetworks perturbed at the different doses and exposure times and allow for a more comprehensive interpretation of the data. Acrolein is highly electrophilic and rapidly reacts with its biological targets, which mainly comprise sulfhydryl or amino functional groups, such as those in GSH.10,16 GSH is a major endogenous antioxidant produced by the cells that participates directly in the neutralization of free radicals and reactive oxygen compounds. We observed by HCS a rapid decrease in the GSH content in NHBE cells exposed to high doses of acrolein, which is consistent with previous reports in other cell types, such as alveolar macrophages and the adenocarcinoma alveolar basal epithelia cell line A549.10 However, we did not detect an increase in ROS. This could be due to the fact that we used lower concentrations than other studies10−13 and/or low sensitivity of the assay. Nevertheless, we observed the activation of defense mechanisms against oxidative stress via Nrf2 activation by sulfhydryl modification of Keap1 at different doses and time points. NEFL2 (Nrf 2 gene) is the major regulator of cytoprotective responses to oxidative stress and drives the expression of different detoxifying enzymes, including heme-oxigenases and glutathione S-transferases, which in turn restore GSH levels.42 Interestingly, the activation of stress responses also occurred at lower doses, suggesting that some of the toxic effects of acrolein are independent of its ability to deplete GSH levels. The maintenance of adequate levels of GSH is essential in many cellular processes, including proliferation. In fact, low GSH levels are associated with

apoptosis, and stress (Figure 2 and Supporting Information Figure 5). At the high dose, formaldehyde activated DNA repair processes after only 4 h of exposure, and a more intense perturbation was observed at later exposure time points. Consistent with the activation of a DNA damage response, there was also an activation of subnetworks regulating cell proliferation, including the G2/M and the G1/S checkpoints. Finally, we observed activation of prosurvival and pro-apoptotic signals in the mitochondria after 8 h. At the 24 h exposure time point, however, the prosurvival responses were inhibited in favor of p53-regulated apoptotic processes. Upon exposure to catechol, the most perturbed networks were proliferation, stress, senescence, apoptosis, and DNA damage (Figure 2 and Supporting Information Figure 5). Catechol causes a time-dependent activation of xenobiotic metabolism and NFE2L2-regulated signaling pathways, indicating the presence of oxidative stress in the cells. These effects were observed across the different exposure time points but only at medium and high doses. Catechol also caused the activation of DNA repair pathways and a time-dependent inhibition of cell proliferation at high dose, consistent with the development of a DNA damage response. Finally, we integrated all of the NPA values with significant specificity statistics across all doses and exposure time points to generate systems toxicology profiles for acrolein, formaldehyde, and catechol (Figure 3). This type of representation provides an overview of the contribution of all the specifically perturbed biological pathways upon exposure to the toxicants.

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DISCUSSION The ability to predict the consequences of exposure to environmental toxicants has always been a major challenge in the field of toxicology. However and despite recent technological advances, toxicological risk assessment has remained essentially unchanged over the past decades. The need for a new framework for adequate toxicological assessment has forced both the scientific community and regulatory bodies to investigate the use of systems biology approaches as an alternative to toxicity testing.1,4,39,40 The strength of these technologies is that they do not necessarily focus on the toxic phenotype per se but rather on a limited number of molecular pathways regulating cell homeostasis and that ultimately determine cell fate upon exposure to a given toxicant.1,41 In this study, we used a systems toxicology approach for the assessment of well-established environmental toxicants. The combination of HCS using array scans of 13 toxicological end points, whole-genome transcriptomics, and computational G

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Chemical Profile

micronuclei in human lymphocytes,45 the formation of 8-oxo7,8-dihydro-2′-deoxyguanosine in the HL-60 human leukemia cell line,46 and the induction of aneuploidy, sister chromatid exchanges, and chromosome aberrations in Syrian hamster embryo cells.47 We also observed activation of DNA repair mechanisms at lower concentrations, indicating the genotoxic potential of catechol at doses well below those described in the literature. Catechol exhibits ambivalent redox properties and can act as both an antioxidant that prevents lipid peroxidation and a pro-oxidant of DNA and proteins.48 Indeed, induction of DNA damage by catechol has been attributed to both alkylation of DNA and oxidative stress.49,50 Fittingly, we observed activation of defense responses against oxidative stress in NHBE cells, including NEF2L2-regulated processes and a drug metabolism response. Some of these perturbations were observed at different time points and doses, again indicating that exposure to even small amounts of catechol can have a biological impact on NHBE cells. Taken together, our data demonstrate that our systems biology approach provides robust insight into the molecular mechanisms of toxicity of known environmental toxicants. The results observed at high doses are in agreement with previously published work on the effects of acrolein, formaldehyde, and catechol but also allow for a better understanding of the modes of action. The integration of this knowledge allowed us to classify the three tested toxicants based on their perturbation profiles. We believe that this type of representation could prove very useful for the improved classification of environmental toxicants.

increased senescence and apoptosis in different cell types, including alveolar macrophages, adenocarcinoma cell line A549, and leukemia cell line K562.10 Accordingly, we observed the activation of oncogene-regulated senescence and the inhibition of the cell proliferation networks. Moreover, acrolein caused an increase in pH3 levels that was not accompanied by changes in cell number, indicating the presence of cell cycle arrest at G2/ M. This cellular scenario is consistent with the induction of senescence and further supports the link between oxidative stress and the inhibition of cell proliferation. Formaldehyde is also reactive toward nucleophiles, and it can bind amine, sulfhydryl, and hydroxyl groups in proteins and DNA to form adducts and cross-links. Endogenous levels of formaldehyde are efficiently neutralized by GSH and detoxification pathways; however, environmental and occupational exposures can saturate the metabolic capacity of the cell and lead to genotoxicity.43 Exposure to high doses of formaldehyde induced the activation of DNA damage responses, included p53-regulated pathways after 24 h of exposure. This result was further confirmed by an increase in pH2AX levels. DNA damage responses usually include the activation of cell cycle checkpoints so that cell proliferation is inhibited until the DNA damage is repaired. Consistently, we found inhibition of cell cycle subnetworks in response to formaldehyde. The fact that we did not observe and increase in pH3 levels in the exposed cells suggests that growth arrest occurred outside G2/M checkpoint. We observed a significant decrease in GSH levels. This result is consistent with previous reports showing a decrease in GSH content in the liver of formaldehyde exposed rats19,20 and would indicate that formaldehyde causes oxidative stress. Formaldehyde has also been reported to induce mitochondrial toxicity in rat hepatocytes, inhibiting mitochondrial respiration and potentially causing oxidative stress and apoptosis.19,20 We did not detect an increase in ROS formation by HCS. However, on the basis of the activation of the molecular networks, we observed a balance between prosurvival and pro-apoptotic signals in the mitochondria of NHBE cells after 8 h of exposure. Moreover, the activation of p53-regulated pathways was also predicted in the context of apoptosis (Supporting Information Figure 5B). Taken together, these findings suggest the existence of a toxicity threshold in the mitochondria beyond which cellular integrity is compromised. This hypothesis is further supported by the fact that no significant perturbations were observed at lower doses of formaldehyde, whereas the prolonged exposure to high levels of formaldehyde leads to inhibition of the prosurvival networks and further activation of apoptotic signals. The induction of mitochondrial toxicity is supported by HCS, where we observed a decrease in mitochondrial mass at 24 h. Interestingly, we also observed an increase in mitochondrial membrane potential. Mitochondrial hyperpolarization prevents the formation of the mitochondrial transition pore and prevents the activation of the apoptosis cascade.44 This protective mechanism may explain why we did not observe caspase activation by HCS. The toxic effects of catechol are caused by its ability to induce both DNA damage and oxidative stress. We observed an increase in the number of pH2AX-positive cells after 24 h of exposure at doses above 1500 μg/mL. Also, NPA shows activation of DNA repair pathways and cell cycle arrest at high doses. Our results are supported by previous reports showing the genotoxic effects of catechol in different cellular models at comparable doses. Such effects include the induction of

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ASSOCIATED CONTENT

S Supporting Information *

Cell count, LDH release, and selected HCS results for acrolein, formaldehyde, and catechol as well as NPA scores for the most relevant perturbed networks in response to acrolein, formaldehyde, and catechol. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Funding

The research described in this article and the collaboration with Cyprotex was funded by Philip Morris International. Notes

I.G.-S., A.S., C.M., E.G., R.D., D.M., S.A., F.M., N.I., J.H., and M.C.P. were employed by Philip Morris International at the time of the study. P.W., S.E., and H.W. were employed by Cyprotex at the time of the study. The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank Stefan Frentzel and Mathias Schorp for their critical review of the manuscript. ABREVIATIONS CC, conventional cigarette; CCCP, carbonyl cyanide mchlorophenyl hydrazone; CS, cigarettte smoke; DHE, dihydroethidium; EPA, Environmental Protection Agency; FDA, Food and Drug Administration; HCS, high-content screening; HPHC, harmful or potentially harmful constituent; IARC, International Agency for Research on Cancer; LDH, lactate H

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Chemical Research in Toxicology dehydrogenase; like 2; NPA, phosphorylated H3; RIN, RNA

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Chemical Profile

(16) U.S. Environmental Protection Agency (2003) Toxicological Review of Acrolein, U.S. Environmental Protection Agency, Washington, DC. http://www.epa.gov/iris/toxreviews/0364tr.pdf. (17) Health Effects Institute Air Toxics Review Panel (2007) Acrolein. In Mobile-Source Air Toxics: A Critical Review of the Literature on Exposure and Health Effects, Special Report 16 from the Health Effects Institute Air Toxics Review Panel, Boston, MA. http://pubs. healtheffects.org/getfile.php?u=389. (18) Roemer, E., Schorp, M. K., Piade, J. J., Seeman, J. I., Leyden, D. E., and Haussmann, H. J. (2012) Scientific assessment of the use of sugars as cigarette tobaco ingredients: a review of published and other publicly available studies. Crit. Rev. Toxicol. 42, 244−278. (19) International Agency for Research on Cancer (2006) Formaldehyde Monograph, International Agency for Research on Cancer, Lyon, France. http://monographs.iarc.fr/ENG/Monographs/vol88/ mono88-6A.pdf. (20) National Toxicology Program (2010) Final Report on Carcinogens: Background Document for Formaldehyde, U.S. Department of Health and Human Services, Washington, DC. http://ntp.niehs.nih. gov/ntp/roc/twelfth/2009/November/Formaldehyde_BD_Final.pdf. (21) Bodnar, J. A., Morgan, W. T., Murphy, P. A., and Ogden, M. W. (2012) Mainstream smoke chemistry analysis of samples from the 2009 US cigarette market. Regul. Toxicol. Pharmacol. 64, 35−42. (22) Hazarous Substances Data Bank (1993) Catechol, U.S. Environmental Protection Agency, Washington, DC. http://toxnet. nlm.nih.gov/cgi-bin/sis/search/r?dbs+hsdb:@term+@ DOCNO+1436. (23) Moran, J. L., Siegel, D., Sun, X. M., and Ross, D. (1996) Induction of apoptosis by benzene metabolites in HL60 and CD34+ human bone marrow progenitor cells. Mol. Pharmacol. 50, 610−615. (24) Neun, D. J., Penn, A., and Snyder, C. A. (1992) Evidence for strain-specific differences in benzene toxicity as a function of host target cell susceptibility. Arch. Toxicol. 66, 11−17. (25) Thomson, T. M., Sewer, A., Martin, F., Belcastro, V., Frushour, B., Gebel, S., Park, J., Schlage, W. K., Talikka, M., Vasilyev, D., Westra, J. W., Hoeng, J., and Peitsch, M. C. (2013) Quantitative assessment of biological impact using transcriptomic data and mechanistic network models. Toxicol. Appl. Pharmacol. 272, 863−878. (26) Hoeng, J., Deehan, R., Pratt, D., Martin, F., Sewer, A., Thomson, T. M., Drubin, D. A., Waters, C. A., de Graaf, D., and Peitsch, M. C. (2012) A network-based approach to quantifying the impact of biologically active substances. Drug Discovery Today 17, 413−418. (27) Hoeng, J., Talikka, M., Martin, F., Sewer, A., Yang, A., Iskandar, A., Schlage, W. K., and Peitsch, M. C. (2013) Case study: The role of mechanistic network models in systems toxicology. Drug Discovery Today [Online early access], DOI: 10.1016/j.drudis.2013.07.023, Published Online: August 9, 2013. (28) Tewes, F. J., Meisgen, T. J., Veltel, D. J., Roemer, E., and Patskan, G. (2003) Toxicological evaluation of an electrically heated cigarette. Part 3: Genotoxicity and cytotoxicity of mainstream smoke. J. Appl. Toxicol. 23, 341−348. (29) Grafstrom, R. C., Formace, A. J., Autrup, H., Lechner, J. F., and Harris, C. C. (1983) Formaldehyde damage to DNA and inhibition of DNA repair in human bronchial cells. Science 220, 216−218. (30) Ho, Y. C., Huang, F. M., and Chang, Y. C. (2007) Cytotoxicity of formaldehyde on human osteoblastic cells is related to intracellular glutathione levels. J. Biomed. Mater. Res., Part B 83B, 340−344. (31) Fujioka, K., and Shibamoto, T. (2006) Determination of toxic carbonyl compounds in cigarette smoke. Environ. Toxicol. 21, 47−54. (32) Westra, J. W., Schlage, W. K., Frushour, B. P., Gebel, S., Catlett, N. L., Han, W., Eddy, S. F., Hengstermann, A., Matthews, A. L., Mathis, C., Lichtner, R. B., Poussin, C., Talikka, M., Veljkovic, E., Van Hooser, A. A., Wong, B., Maria, M. J., Peitsch, M. C., Deehan, R., and Hoeng, J. (2011) Construction of a computable cell proliferation network focused on non-diseased lung cells. BMC Syst. Biol. 5, 105-1− 105-16. (33) Schlage, W. K., Westra, J. W., Gebel, S., Catlett, N. L., Mathis, C., Frushour, B. P., Hengstermann, A., Van Hooser, A., Poussin, C., Wong, B., Lietz, M., Park, J., Drubin, D., Veljkovic, E., Peitsch, M. C.,

NFEL2, nuclear factor (erythroid-derived 2)network perturbation amplitude; pH2AX, histone H2AX; pH3, phosphorylated histone integrity number

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