A cell-based in vitro alternative to identify skin sensitizers by gene expression

Toxicology and Applied Pharmacology 231 (2008) 103–111

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Toxicology and Applied Pharmacology j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / y t a a p

A cell-based in vitro alternative to identify skin sensitizers by gene expression Jef Hooyberghs ⁎, Elke Schoeters, Nathalie Lambrechts, Inge Nelissen, Hilda Witters, Greet Schoeters, Rosette Van Den Heuvel Flemish Institute for Technological Research (VITO), Environmental Toxicology, Boeretang 200, 2400 Mol, Belgium

A R T I C L E

I N F O

Article history: Received 22 January 2008 Revised 14 March 2008 Accepted 17 March 2008 Available online 4 April 2008 Keywords: In vitro Skin sensitization Dendritic cells Gene expression VITOSENS

A B S T R A C T The ethical and economic burden associated with animal testing for assessment of skin sensitization has triggered intensive research effort towards development and validation of alternative methods. In addition, new legislation on the registration and use of cosmetics and chemicals promote the use of suitable alternatives for hazard assessment. Our previous studies demonstrated that human CD34+ progenitorderived dendritic cells from cord blood express specific gene profiles upon exposure to low molecular weight sensitizing chemicals. This paper presents a classification model based on this cell type which is successful in discriminating sensitizing chemicals from non-sensitizing chemicals based on transcriptome analysis of 13 genes. Expression profiles of a set of 10 sensitizers and 11 non-sensitizers were analyzed by RT-PCR using 9 different exposure conditions and a total of 73 donor samples. Based on these data a predictive dichotomous classifier for skin sensitizers has been constructed, which is referred to as VITOSENS®. In a first step the dimensionality of the input data was reduced by selectively rejecting a number of exposure conditions and genes. Next, the generalization of a linear classifier was evaluated by a cross-validation which resulted in a prediction performance with a concordance of 89%, a specificity of 97% and a sensitivity of 82%. These results show that the present model may be a useful human in vitro alternative for further use in a test strategy towards the reduction of animal use for skin sensitization. © 2008 Elsevier Inc. All rights reserved.

Introduction Many low molecular weight (MW) compounds can cause allergic contact dermatitis. Therefore the identification of this toxicological hazard is of great importance to prevent occupational and consumer health problems. The OECD guidelines (406, 429) originally use guinea pigs to assess skin sensitization, later on the local lymph node assay (LLNA) in mouse was accepted as a new assay that has the potential to reduce the number of animals required (Kimber et al., 1986). More recently the refined local lymph node assay was developed, which further reduced the number of animals needed (Kimber et al., 2006). However, these methods still require in vivo experiments. The 7th amendment of the Council Directive relating to cosmetic products requests a ban on animal testing for finished cosmetic products and cosmetic ingredients from 2009 on for all human health effects except for repeated-dose toxicity, reproductive toxicity and toxicokinetics for which animal tests should be banned after 2013. This implies that development of alternatives to animal testing, the validation and international adoption of harmonised methods is under high pressure. Non-animal skin sensitization tests rank high on the priority list. Moreover, in the context of an expected increase of testing demand due to the European regulation of REACH, the need for alternative testing methods is high. ⁎ Corresponding author. Fax: +32 14 58 26 57. E-mail address: [email protected] (J. Hooyberghs). 0041-008X/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.taap.2008.03.014

Several approaches have been attempted for developing an in vitro or an in silico skin sensitization test. For the in vitro approach different cellular-based method have been suggested (Ryan et al., 2001) and several cell lines have been tested for this purpose: THP1 (Ashikaga et al., 2002; Yoshida et al., 2003), KG-1 (Hulette et al., 2002; Yoshida et al., 2003), MUTZ-3 (Azam et al., 2006) and U937 (Python et al., 2007). Besides cell lines, primary human dendritic cells (DC) are of particular interest because of their direct immunological relevance (Ryan et al., 2007). The DC, including Langerhans cells (LC), play a critical role in the induction phase of allergic contact hypersensitivity. Following exposure to chemical allergens, LC are capable of internalizing and processing allergens. Upon antigen capture, LC differentiate, mature, and migrate to the draining lymph nodes where they present the allergens to naive T cells and trigger their proliferation (Hart, 1997; Banchereau and Steinman, 1998). Human epidermal LC have been isolated from the skin and used to develop in vitro alternatives for skin sensitization based on alteration of their phenotype (HLA-DR and E-cadherin) and function (Verrier et al., 1999; Moulon et al., 1993; Krasteva et al., 1996; Rizova et al., 1999). To date however, the use of in vivo-derived LC has been restricted for the development of an in vitro skin sensitization test due to the difficulty of obtaining a sufficient number of human LC from the epidermis, the weak viability of LC, the shortage of available human skin, and the spontaneous maturation of LC once cultured. Therefore, human LC-like DC have been derived from peripheral blood monocytes (Mo-DC) (Sallusto and Lanzavecchia, 1994) and cord blood or bone marrow-derived CD34+

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Table 1 Set of 21 chemical compounds used in the present study Chemical

Cas-no.

Abbr.

Effect

LLNA

Dinitrobenzenesulfonic acid Dinitrofluorobenzene Dinitrochlorobenzene p-phenylenediamine 2-mercaptobenzothiazole Cinnamaldehyde Tetramethylthiuram disulfide Ammonium hexachloroplatinate IV Eugenol Nickel sulfate

885-62-1 70-34-8 97-00-7 106-50-3 149-30-4 104-55-2 137-26-8 16919-58-7 97-53-0 10101-97-0

DNBS DNFB DNCB PPD 2MBT CA TMTD HCPt eugenol NiSO4

+ + + + + + + + + +

+ + + + + + + + + −

Benzalkonium chloride Dimethylsulfoxide L-Ascorbic acid L-Glutamic acid Methyl salicylate p-Aminobenzoic acid Phenol Sodium lauryl/Dodecyl sulfate Tributyltin chloride Triton X-100 Zinc sulphatesulfate

8001-54-5 67-68-5 50-81-7 56-86-0 119-36-8 150-13-0 108-95-2 151-21-3 1461-22-9 9002-93-1 7733-02-0

BC DMSO L-AA L-GA MeSA PABA phenol SDS TBT Triton ZnSO4

− − − − − − − − − − −

− −

Modified

GPMT/BT

Human

+

+ + + + +

+ + + + +

+ +

+ +

− −

+

+ + + +

+

− − − +

−

− −

−

+ +

− −

− − −

− − − − − − − −

Legend: +, sensitizing; −, non-sensitizing; empty cell, not available. Effect: a priori sensitizing character, LLNA: local lymph node assay, Modified: a modified protocol of LLNA, GPMT/ BT: Guinea Pig Maximisation Test/Buehler Patch Test, Human: conclusions as reported in Technical report No. 77 of ECETOC 1999.

progenitor cells (CD34-DC) (Caux et al.,1992) by culturing these cells in the presence of specific cytokines (e.g. granulocyte macrophage-colony stimulating factor (GM-CSF) and tumor necrosis factor (TNF)-α). In the development of cellular DC-based assays, endpoints relating to the induction process of skin sensitization, such as DC maturation marker (e.g. CD86, HLA-DR) and cytokine expression (e.g. IL-1β, IL-8), are mostly studied as candidate prediction markers for purposes of hazard identification (Aiba et al., 1997; Aiba et al., 2003; Coutant et al., 1999; De Smedt et al., 2001; De Smedt et al., 2002; Staquet et al., 2004; Rougier et al., 2000). In recent years a growing interest is seen for alternative experimental strategies, including microarray transcript profiling, to search for novel markers and pathways that influence the ability of DC to initiate adaptive immune responses to chemical allergens (Ryan et al., 2004; Gildea et al., 2006; Schoeters et al., 2007). In our lab an extended microarray study has been performed on the transcriptional response of human CD34-DC to a set of 4 skin sensitizers and 2 skin irritants (Schoeters et al., 2007). From the resulting gene expression data and literature search, 13 genes were selected for their discriminating behavior. These genes were the starting point of the present research. Their response to 10 sensitizers and 11 nonsensitizers was measured in in vitro cultured CD34-DC by real-time RTPCR. In total, nine different exposure conditions were studied: three exposure times (6 h, 11 h, and 24 h) and three compound-specific exposure concentrations (IC5, IC10, and IC20 defined as cell growth inhibition concentration in % at 24 h). Since each of the selected genes was known to show individually a degree of discriminative response to (non-) sensitizers, the focus of this paper is on a strategy to find an optimal combination of genes and exposure conditions. Materials and methods Chemical compounds. The selection of chemical compounds was based on a number of criteria. First, the in vivo sensitizing character of the compound had to be known from other sources with a reasonable degree of certainty. It is a prerequisite in the early design of a prediction model to have a solid reference. More practical criteria included solubility, stability and commercial availability of the compounds. Further, the set of chemicals was designed to cover the whole range from non-sensitizing to strong sensitizing compounds while keeping an equilibrium in the number of non-sensitizers versus sensitizers. All of the selected chemicals have low MWs and log Kow values greater than 1, which are thought to favor penetration across the lipid-rich stratum corneum (Smith-Pease et al., 2003). Finally, attention was paid to include organic and inorganic compounds from different chemical classes. This resulted in a set of 21 chemicals shown in Table 1. The last four columns of this table show information from different in vivo sources and the column “effect” is the a priori sensitizing character used in the present study (sources: Technical report No. 77 of ECETOC 1999, NIH

Publication No. 99-4494 on the LLNA 1999, and Gerberick et al., 2005). All chemicals were purchased from Sigma-Aldrich Chemie Gmbh, except for DMSO (LabScan Ltd). Cell culture. CD34+-cell isolation and culture procedures have been described before (Schoeters et al., 2007). Briefly, human cord blood samples were collected from the umbilical blood vessels of placentas of normal, full-term infants. Informed consent was given by the mothers and the study was approved by the ethical commission of the Heilig Hart hospital at Mol, Belgium. Mononuclear cells were separated from the cord blood by density gradient centrifugation and subsequently CD34+ progenitor cells were extracted by positive immunomagnetic selection. These cells were cultured in Iscove's modified Dulbecco's medium (IMDM) in the presence of GM-CSF (Gentaur, Brussels, Belgium), SCF (Biosource, Nivelles, Belgium) and IL-4 (Biosource) to induce proliferation and differentiation towards immature CD34-DC according to the method described by Lardon et al. (1997). Chemical exposure. Immature DC (4 × 106 cells/4 ml) from the same donor were exposed in 6-well plates to a (non-) contact sensitizer during 6, 11 and 24 h (at 37 °C and 5% CO2) at a concentration of IC20, IC10 and IC5. The cell growth inhibition concentrations (IC in % at 24 h) were previously determined using at least three donor samples by alamarBlue™, WST-1 metabolisation or Propidium Iodide staining experiments (data not shown). No cell growth inhibition was found for MeSA, L-GA and PABA. For these chemicals the highest soluble concentration was used instead of IC20 (as explained further in the paper, the IC10 and IC5 are not used in the final prediction model). Negative control samples were obtained by treating DC from the same donor with the respective solvent for the same time periods. For each chemical and its corresponding solvent, DC of at least three independent donors were exposed (except for DNCB only two). The chemicals CA, TBT, MeSA, DNCB, TMTD, PPD, eugenol, phenol, DNFB were dissolved in DMSO (LabScan Ltd). The final concentration of DMSO in the culture medium was 0.05%. BC, PABA, SDS, HCPt, NiSO4, 2MBT, DNBS, DMSO, Triton, ZnSO4, L-AA, and L-GA were dissolved in IMDM (Invitrogen).

Table 2 Set of 13 genes used in the present study ABCA6 AQP3 CCR2 CCR7 CREM CXCR4 ENC MAD NINJ PBEF1 PSCDBP PTGS2 SLC2A3

ATP-binding cassette, sub-family A (ABC1), member 6 Aquaporin 3 Chemokine (C–C motif) receptor 2 Chemokine (C–C motif) receptor 7 CAMP responsive element modulator Chemokine (C–X–C motif) receptor 4 Ectodermal–neural cortex (with BTB-like domain) MAX dimerization protein 1 Ninjurin 1 Pre-B-cell colony-enhancing factor 1 Pleckstrin homology, Sec7 and coiled–coil domains, binding protein Prostaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase) Solute carrier family 2 (facilitated glucose transporter), member 3

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Fig. 1. The expression profile of one donor sample after exposure to DNFB. For each gene, per exposure concentration, successive time points are drawn connected.

Genes. In our previous research an extended transcriptional microarray setup was used in CD34-DC to identify genes with a discriminative potential for skin sensitization (Schoeters et al., 2007). From the analysis of these experiments and literature search, genes were extracted based on experimental evidence for their discriminative response to (non-) contact sensitizers and on their expression level in DC. In the present study 13 of these marker genes were selected to build up a classification model (Table 2). Real-time RT-PCR. After the appropriate exposure time, the cell culture medium was removed and the dendritic cells were lysed. Total RNA was isolated and cDNA synthesis was performed. Real-time reverse transcriptase polymerase chain reaction (RT-PCR) was performed in triplicate for each sample on a BioRad iCycler (BioRad, Nazareth, Belgium). A detailed description can be found in Schoeters et al., 2007. On each RT-PCR plate there was a non-template control for each primer analyzed. Amplification reactions were monitored using a SYBRGreen assay. After amplification, a threshold was set for each primer and Ct-values were calculated for all samples. Primers were designed using Primer Express® Software v2.0 from Applied Biosystems and thoroughly tested. Gene expression changes were analyzed using the qBase software (Hellemans et al., 2007). The results were analyzed with the ΔΔCt method corrected for genespecific efficiencies and gene expression changes were determined as fold changes: ratios of gene expression levels of exposed samples over corresponding solvent control samples. To achieve accurate fold changes, it was set as a criterion that the normalization should be performed to a set of at least three stable housekeeping genes. The stability of these genes can depend on the applied exposure chemical. Therefore, five housekeeping genes were measured (GAPDH, HPRT, SDHA, RPLI3A, YWHAZ), and their stability was assessed as described in Vandesompele et al., 2002. For each chemical, a set of at least three stable housekeeping was effectively found. The fold changes of all 13 genes, induced by a given chemical, were subsequently normalized to the corresponding housekeeping set. Data set. After the real-time RT-PCR measurements, all experimental data was collected in a data set of fold changes. Since these fold changes are known to have a non-Gaussian distribution, a two-base logarithm of them was used. These logarithmic fold changes (LFC) follow a more symmetric and more Gaussian distribution. Note that a strictly positive or negative LFC means that the chemical exposure induces respectively an up- or down-regulation of the corresponding gene expression level. For each of the 21 chemical compounds, the DC from at least three different donors1 were exposed and analyzed, which resulted in a final set of 73 donor samples. In the

1

The compound DNCB is an exception, only two donors were fully analyzed.

initial stage of our research, 13 genes were considered in 9 different exposure conditions which resulted in 117 LFC per exposed cell culture: • 3 exposure concentrations (IC5, IC10, and IC20) • 3 exposure times per concentration (6 h, 11 h, and 24 h) • 13 genes per exposure condition. See Fig. 1 for an example of all LFC for one donor sample. This full set of gene expression levels was measured for the following 9 compounds: 2MBT, DMSO, DNBS, DNCB, DNFB, Nickel, PPD, TBT, Triton. After the analysis of these measurements it was decided to put priority onto IC20 exposure data (see Results section). Consequently, for the 12 other compounds not all samples contained measurements at IC10 and IC5. Supervised learning. The goal of the present research was the design of an in vitro model for predictive dichotomous classification of possible contact sensitizers based on gene expression measurements. Hereto the above described transcriptional reference dataset of 21 chemicals was build built and used to optimize a parametric classification model. In such a supervised learning approach the risk of overfitting has to be controlled. The true goal is a prediction model with a good generalization to new data, not merely a good performance on the reference set. Therefore, a dimensional reduction of the input space was first applied. Further, a classification model of low complexity was chosen and its generalization to new chemicals was assessed through a cross-validation. These topics are elaborated in the Results section.

Results Dimensionality reduction From a mathematical point of view, the design of a classification model consists of constructing a mapping from a set of measurements (input variables or explanatory variables) to a categorical output (the variable to predict). Optimization of the model consist of tuning the mapping parameters by reference examples. As discussed below, a simple linear mapping was chosen in our application, and the optimization is only meaningful if the number of training examples is much higher than the number of input variables. If not, the model overfits the training examples and will perform poorly in predicting new samples. In our case the output was a dichotomous class membership and the

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Fig. 2. Expression profile over time of gene CCR7 after exposure of multiple donor samples to compounds at IC10 concentrations (left panel) and IC20 (right panel). Plotted are averages (± standard deviation) over samples, grouped by sensitizing character of exposure chemical. Based on data from exposure to 2MBT, DMSO, DNBS, DNCB, DNFB, NiSO4, PPD, TBT, Triton.

available inputs were logarithmic fold changes (LFC). These LFC were measured for 13 genes at 9 exposure conditions, which resulted in 117 LFC per exposed donor sample (see Fig. 1). To optimize the classification model 73 donor samples were available, therefore the number of inputs needed to be reduced from 117 to a number significantly below 73. In classification design such a reduction is often achieved by either combining inputs in an unsupervised manner (such as principal component analysis) or by discarding inputs which are expected to contain less explanatory information. The latter technique was applied here, since this offers the additional technical advantage that fewer measurements are needed in the operational phase of the model. Selection of exposure concentration Fig. 1 presents the data on the sensitizing compound DNFB for one donor sample. The plot shows that the expression profiles of all considered genes have the same relation with exposure concentration: the LFC values move towards zero when the concentration moves towards IC5. This observation was made after the measurements of the first 9 compounds (see description of the data set of measurements), and turned out to be general for this data set, provided that IC20 induced non-zero LFC (the non-senitizers often show LFC close to zero even at IC20). It showed that IC5 was not a useful concentration

since at least some fold change is needed to make a classification. IC5 was consequently discarded for further study. A second selection between IC10 and IC20 was less trivial: a higher fold change does not necessarily implicate a more discriminative one. However, a closer analysis of this data showed that IC20 induced higher fold changes for the sensitizers, while the effect on gene expression of the non-sensitizers remained low. As an example consider Fig. 2 which displays the expression of CCR7 averaged over a set of donor samples which were grouped by the sensitizing character of the applied exposure. After exposure to a sensitizer this gene showed up-regulation, especially at 11 h and 24 h, at both concentrations, but more pronounced for IC20. The fold change difference between the compound groups of (non-) sensitizers was larger for IC20. To quantify the significance of this compound group separation at IC10 and IC20, a Fisher's linear discriminant analysis was performed (the method is described below in the section on gene-selection). This analysis provided a p-value per gene at IC10 and IC20. The p-values turned out to be systematically lower at IC20, which made us decide to restrict to this IC for the construction of the classification model. In the real-time RT-PCR measurements that followed this decision, priority was given to IC20 and not all donor samples were measured at IC10 and IC5. Henceforth in this paper only gene expressions at IC20 are considered. As expected and in contrast with the exposure concentrations, response to

Fig. 3. Expression profile over time of gene AQP3 and CXCR4 at IC20. Plotted are averages (±standard deviation) of multiple donor samples, grouped by the sensitizing character of the exposure chemical. Based on data from all 21 chemicals and all 73 donor samples.

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Fig. 4. Feature value after a Fisher's discriminant analysis: time-weighted average per gene, per donor sample. The p-values come from a Wilcoxon ranksum test.

different exposure times was gene-dependent as can be seen in Fig. 1. Therefore no time points were removed a priori. Selection of genes In a further effort to reduce the number of inputs, genes were univariately scored. Fig. 3 shows the expression profile of two genes using all 73 donor samples which were grouped by the sensitizing character of the applied exposure. The discriminating response of the genes can be estimated from this: AQP3 more clearly discriminates the two groups of chemicals than CXCR4 does. A full separation does not occur, as for these genes some sensitizers induce a similar expression as some non-sensitizers. To quantify this for all genes, a Fisher's linear discriminant analysis (LDA) was performed. Per gene all measurements can be depicted as a set of points in a three-dimensional space: each point represents the gene expression (LFC) at three time points for one donor sample exposed to one compound at IC20. Fisher's LDA determines the direction in this space that optimizes the discrimination between classes. After projection on this direction, the ratio of between-class scatter to within-class scatter is larger than for any other direction. This analysis resulted for each gene in one feature value representing a time-weighted average of the three LFC's. Finally, to score each gene a Wilcoxon ranksum test was performed on this feature value. The resulting p-value is smaller for more discriminating genes. This procedure was performed for all genes, which provided us a gene ranking. A summary is presented in Fig. 4. The discriminative power is strongly gene-dependent, the genes at the top of the list have very small p-values, but no single gene can make a perfect separation. For the construction of a prediction model, a combination of genes was used.

samples), while aiming a minimal loss of information. By using the top N genes from the list in Fig. 4, the number of inputs was restricted to 3N LFC values (3 time points, 1 concentration, N genes) per exposed donor sample. Different numbers of input genes (N) were evaluated. For the construction of the prediction model a linear classifier was used: the 3N input variables were combined to one predictor variable by a weighted average, which was optimized by Fisher's LDA. To avoid overfitting, the flexibility of the model was reduced by carrying out the weighting in two steps: first the three time points per gene, next the resulting feature values of the N genes. As will be discussed below, the model gave best generalization towards new compounds if the top N = 2 genes were used; the discussion will focus on this case. The result for all donor samples is given in Fig. 5: the predictor variable clearly separated the great majority of donor samples according to the sensitizing character of the compound to which they were exposed. Finally, a threshold value was set to conclude the construction of the predictive classifier: a predictor variable above or below the threshold corresponds respectively to a predicted sensitizer or non-sensitizer. Since the variance of the predictor variable was unequal in the two classes, a quadratic discriminant analysis was used to set the threshold. Note that the value of this threshold is arbitrary to some degree,

Construction of the classifier The ranking of genes and the rejection of IC5 and IC10 provided us a way to reduce the dimensionality significantly below 73 (number of

Fig. 5. Predictor variable for the top N = 2 genes after a Fisher's discriminant analysis: time- and gene-weighted average per donor sample.

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Fig. 6. Predictions of the VITOSENS® model (based on the top N = 2 genes) in the cross-validation. Per test compound, each circle corresponds to a donor sample and is represented by its predictor value: positive = sensitized, negative = non-sensitized. The right panel gives the prediction per compound by applying a majority rule on the representing test samples: + = sensitizer, − = non-sensitizer.

its value can be adjusted to shift the model between high sensitivity or high specificity. An actual prediction for an unknown compound then involves the following steps: - expose CD34-DC to the compound at an IC20 for 6, 11 and 24 h, - measure the expression values for the N genes, - calculate the predictor variable by applying the weights (which were previously determined by the set of training compounds), - predict the sensitizing character according to the predictor variable threshold (which was previously determined by the set of training compounds). The procedure described here will further be addressed to as the VITOSENS® model (patent pending, see reference). Evaluation of the model by cross-validation To validate the predictive ability of an optimized model, a new independent data set is needed. A standard way to achieve this without the need for new measurements is a cross-validation exercise. All data from one compound (on average three donor samples) were removed from the data set and put aside as a test set. The remaining data were used to optimize the classification model as described above. The threshold value was determined by a quadratic discriminant analysis. For ease of visualization this threshold was subtracted from the predictor variable, resulting in a new predictor variable: above zero = sensitizer, below zero = non-sensitizer. Once the model was set, it was used to predict the sensitizing potential of the compound in the test set. Note that this is an actual prediction, since the test set was not used to tune the classifier. This whole procedure was repeated cyclically for the 21 compounds. The result of the VITOSENS® model in this cross-validation by ‘leaving out one compound’ is shown in Fig. 6. Each circle corresponds to the predictor variable of a donor sample that was used as a test set. The majority of the compounds were

correctly and unanimously classified by their representing donor samples. Some compounds contained erroneously classified samples: eugenol, HCPt, NiSO4 and PPD showed false negative(s) and SDS one false positive. With a simple majority rule applied on the sample numbers (right panel of Fig. 6), only PPD was not correctly categorized compared to the assigned a priori effect (Table 1) since three out of five samples were false negatives. A further quantification of the predictive ability is given in Table 3, which shows a cross-tabulation of the sample predictions from the cross-validation and the derived concordance, specificity and sensitivity of the VITOSENS® model to identify the sensitizing character. The same tabulation could be created for the predictions on the level of compounds instead of samples (by applying a majority rule) and would yield a higher prediction accuracy (only PPD as false negative). However since the number of tested compounds is limited, these figures would contain a higher degree of uncertainty, therefore the more conservative estimation was preferred here. The data in Fig. 6 came from the classification model with N = 2 input genes, and with the predictor value threshold determined by a quadratic discriminant analysis. While the number of input genes has a general effect on the models accuracy, the threshold value mainly determines the choice between higher sensitivity or higher specificity. It is interesting to see the impact of these choices on the classifier. First, Fig. 7 shows the concordance resulting from the same cross-

Table 3 Contingency table of the VITOSENS® model

Sensitizing Nonsensitizing Total

Predicted sensitizing

Predicted non-sensitizing

Total

32 1

7 33

39 34

Sensitivity = 32/39 Specificity = 33/34

82% 97%

33

40

73

Concordance = (32+33)/73

89%

The predictions are from a cross-validation (leaving out one compound, based on the top N = 2 genes). The contingency is presented on the level of number of donor samples.

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Fig. 7. Concordance as determined by a cross-validation (leaving out one compound), based on the top N genes.

validation performed with a different number of input genes: N varying from one to eight. In general, the optimal number of inputs is a trade-off between extra information and extra degrees of freedom that need to be optimized by the training data. This is reflected by the increase of concordance from N = 1 to 2, and the subsequent slow decrease when more genes were added. Since the number of samples was 73, each erroneously classified sample corresponds to a concordance reduction of 1/73 ≈ 1.4%. Next, a Receiver Operating Characteristic (ROC) curve was constructed in Fig. 8 to asses the effect of shifting the predictor variable threshold away from zero (see Fig. 6). In an ROC curve the true positive rate (sensitivity) is plotted as a function of the false positive rate (1-specificity) for different threshold values. Every point on the curve represents a (sensitivity, specificity) pair corresponding to a particular threshold value. A classifier that lacks predictive power yields an ROC curve close to the line of identity, while a perfect classifier is presented by a line passing through the upper left corner of the plot. The ROC curve for N = 2 is located closely to the upper left corner and shows the freedom to choose between high sensitivity or high specificity, without sacrificing too much of the other criterion. Further, Fig. 8 shows that the VITOSENS® model improved when the number of input genes was increased from 1 to 2, while the prediction accuracy slowly decreased when more genes were added; this is in agreement with Fig. 7. When more data will become available, the optimal set of genes may change, but currently the top-two performs best. As a final evaluation the cross-validation for the VITOSENS® model was repeated, but with leaving out four compounds instead of one, which roughly corresponds to 20% of the samples. Every combination of two sensitizers and two non-sensitizers was used once as an independent test set. This test was performed for N = 2 and with the prediction value threshold set to zero; the results need to be compared with those in Table 3. The resulting prediction performance was hardly affected when compared to the leaving out one approach, it produced a sensitivity = 81%, a specificity = 96% and a concordance = 88%. This confirmed the robustness of the model which is not suffering from overfitting.

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recorded by real-time RT-PCR. The evaluated exposure concentrations were IC5, IC10 and IC20 and a total 73 donor samples were used. In the data analysis, first the relation of concentration and time versus discriminative response was evaluated. It was found that IC20 induced the highest and most specific response. At IC20 the genes were more influenced (up- or down-regulated) by the sensitizing than by the non-sensitizing compounds. Note that the rejection of IC5 and the use of an IC20 is in agreement with other experiments which showed that slight cytotoxicity is a prerequisite to clearly observe sensitization effects in DC or LC (Hulette et al., 2005; Jacobs et al., 2006). The optimal exposure time for discrimination was gene-dependent and less clear. For further analysis all three time points at IC20 were retained. Subsequently the 13 genes were ranked for their individual ability to separate the two compound classes, hereto a combined response of their expressions at 6 h, 11 h and 24 h was used. Next a linear classifier was constructed with as input the data of the top N ranked genes. To asses the predictive classification performance a cross-validation was performed. For this the classifier was optimized by all data except those of one compound, which were used as an independent test set. A cyclic iteration of this procedure provided an estimate of the prediction ability which was found to be best for N = 2 genes. When the predictions were considered on the compound level, most chemicals were correctly and unanimously classified; only 5 compounds contained non-unanimous sample results: eugenol, HCPt, NiSO4, PPD and SDS. Both eugenol and HCPt showed one false negative out of respectively 4 and 5 assessed samples. For HCPt the negative sample was a clear outlier, which supported the conclusion that it is a sensitizer. For eugenol the result was less pronounced, but still correctly pointing in the direction of sensitization, which is a nontrivial result for an in vitro model since it is known that eugenol needs metabolization to activate the sensitizing potential (Lepoittevin and Mutterer, 1998). NiSO4 induced 2 false negatives out of 6 samples, which might be attributed to its moderate allergenic character. For SDS one false positive sample was found out of 3 assessments, with the weight of the results in the non-sensitizing group. As a strong irritant it is known to give a false positive in some in vivo tests, e.g. the LLNA (Gerberick et al., 2005, Basketter et al., 2006). Only for PPD the results are less straightforward: three negative versus two positive samples, all with a prediction variable close to zero. When a majority rule is applied, PPD is classified in our system as a non-sensitizer while in vivo studies clearly show a strong sensitizing potential. A possible explanation is that in vitro PPD is acetylated in cytosol with consequent loss of sensitization potency: after acetylation the metabolites

Discussion In the present study gene expression responses in human CD34-DC to (non-) contact allergens were measured with the aim to construct a predictive dichotomous classifier for skin sensitization. The cells were exposed to 10 sensitizers and 11 non-sensitizers, for 6 h, 11 h and 24 h, after which the changes in expression profile for 13 genes were

Fig. 8. Receiver Operating Characteristic (ROC) curve determined by cross-validation using the top N genes as input for the classification model.

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of PPD have been shown not to activate dendritic cells (Aeby et al., 2004). In vivo, when PPD is exposed to air, atmospheric oxidation prevents further acetylation and the sensitising sensitizing potency is retained (Goebel et al., 2007). In the data from our previous microarray experiments (Schoeters et al., 2007) we found that CD34-DC express the N-acetyltransferase gene NAT1, which is involved in this reaction. To further quantify the accuracy of the VITOSENS® model, a crosstabulation of the predictions on sample level was made. The sensitivity of the classification model was 82%, the specificity 97% and the concordance 89%. We took several precautions to make these numbers a reliable estimate of the generalization of the VITOSENS® model: the set of compounds was balanced between sensitizers and non-sensitizers, the compound set contains a variety of chemical classes, the dimension of the input space is limited, a model of low complexity is used, a considerable number of training examples was available (73 donor samples) and a cross-validation was applied to avoid the pitfall of overfitting. The good prediction performance of VITOSENS® appears similar to other in vitro alternatives which are currently investigated. It e.g. stands a comparison with that of in vitro tests for acute skin irritation (Spielmann et al., 2007) in which comparable values for sensitivity and specificity are achieved. This shows the potential of the VITOSENS® model as a human in vitro alternative for use in a strategy towards the reduction of animal testing for skin sensitization. Further validation of the model is the next step to take. This will require the involvement of multiple laboratories and an extended set of chemicals. Obviously, concerning the replacement of in vivo tests, the pros and contras of a cell-based assay need to be kept in mind. Our model only monitors the reaction of DC to chemicals and can assess at most this single, though crucial, step of induction of the immunological cascade that involves allergic contact dermatitis. It can give no information on what precedes or follows, and should therefore be only one key element in an integrated alternative test strategy for skin sensitization. On the other hand, the CD34-DC are cultured from human primary cells, and are therefore a preferential cell type for human hazard assessment. Acknowledgments Lambrechts Nathalie and Schoeters Elke were supported by a VITO PhD fellowship. For expert technical assistance and discussions the authors are grateful to Hollanders Karen, Leppens Hilde, Nuijten JeanMarie, Ooms Daniëlla and Dr. Van Rompay An. The authors thank Dr. Cochet L., Dr. Muyldermans K., Dr. Van Ballaer P., Dr. Vanderhoydonck R. and the delivery nursing staff of the Heilig Hart hospital at Mol, Belgium for their help in collecting the cord blood samples. References Aeby, P., Wyss, C., Beck, H., Griem, P., Scheffler, H., Goebel, C., 2004. Characterization of the sensitizing potential of chemicals by in vitro analysis of dendritic cell activation and skin penetration. J. Invest. Dermatol. 122 (5), 1154–1164. Aiba, S., Terunuma, A., Manome, H., Tagami, H., 1997. Dendritic cells differently respond to haptens and irritants by their production of cytokines and expression of costimulatory molecules. Eur. J. Immunol. 27 (11), 3031–3038. Aiba, S., Manome, H., Nakagawa, S., Mollah, Z.U., Mizuashi, M., Ohtani, T., Yoshino, Y., Tagami, H., 2003. p38 Mitogen-activated protein kinase and extracellular signalregulated kinases play distinct roles in the activation of dendritic cells by two representative haptens, NiCl2 and 2,4-dinitrochlorobenzene. J. Invest. Dermatol. 120 (3), 390–399. Ashikaga, T., Hoya, M., Itagaki, H., Katsumura, Y., Aiba, S., 2002. Evaluation of CD86 expression and MHC class II molecule internalization in THP-1 human monocyte cells as predictive endpoints for contact sensitizers. Toxicol. In Vitro 16, 711–716. Azam, P., Peiffer, J.L., Chamousset, D., Tissier, M.H., Bonnet, P.A., Vian, L., Fabre, I., Ourlin, J.C., 2006. The cytokine-dependent MUTZ-3 cell line as an in vitro model for the screening of contact sensitizers. Toxicol. Appl. Pharmacol. 212, 14–23. Banchereau, J., Steinman, R.M., 1998. Dendritic cells and the control of immunity. Nature 392 (6673), 245–252. Basketter, D.A., McFadden, J., Evans, P., Andersen, K.E., Jowsey, I., 2006. Identification and classification of skin sensitizers: identifying false positives and false negatives. Contact Dermatitis 55 (5), 268–273.

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